From 1924aa07af3b803ef535de37f7ce665f2e7710c2 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 29 Jul 2024 15:25:15 +0100
Subject: [PATCH 001/153] Adding PRS-CSx functinoality and testing in opensnp

---
 Scripts/PRS-CSx/PRS_CSx_plink2.R              | 352 -----------
 Scripts/PRS-CSx/PRS_CSx_plink2_slurm.R        | 392 ------------
 Scripts/pgs_methods/prscsx.R                  | 228 +++++++
 functions/constants.R                         |   7 +
 functions/pgs.R                               |  19 +-
 functions/pipeline.R                          | 583 ++++++++----------
 pipeline/config.yaml                          |   9 +-
 .../misc/cross_pop_test/config_cross_pop.yaml |  11 +
 .../misc/cross_pop_test/cross_pop_test.Rmd    |  48 ++
 pipeline/misc/cross_pop_test/gwas_groups.txt  |   2 +
 pipeline/misc/cross_pop_test/gwas_list.txt    |   3 +
 .../gwas/yengo_2022_height_eas.txt.gz         | Bin 0 -> 21815 bytes
 .../gwas/yengo_2022_height_eur.txt.gz         | Bin 0 -> 22893 bytes
 pipeline/misc/dev/test_setup.Rmd              |  15 +
 pipeline/misc/opensnp/config_cross_pop.yaml   |   7 +
 pipeline/rules/dependencies.smk               |  82 ++-
 pipeline/rules/pgs_methods.smk                |  83 ++-
 17 files changed, 745 insertions(+), 1096 deletions(-)
 delete mode 100644 Scripts/PRS-CSx/PRS_CSx_plink2.R
 delete mode 100644 Scripts/PRS-CSx/PRS_CSx_plink2_slurm.R
 create mode 100644 Scripts/pgs_methods/prscsx.R
 create mode 100644 pipeline/misc/cross_pop_test/config_cross_pop.yaml
 create mode 100644 pipeline/misc/cross_pop_test/cross_pop_test.Rmd
 create mode 100644 pipeline/misc/cross_pop_test/gwas_groups.txt
 create mode 100644 pipeline/misc/cross_pop_test/gwas_list.txt
 create mode 100644 pipeline/misc/dev/test_data/gwas/yengo_2022_height_eas.txt.gz
 create mode 100644 pipeline/misc/dev/test_data/gwas/yengo_2022_height_eur.txt.gz
 create mode 100644 pipeline/misc/opensnp/config_cross_pop.yaml

diff --git a/Scripts/PRS-CSx/PRS_CSx_plink2.R b/Scripts/PRS-CSx/PRS_CSx_plink2.R
deleted file mode 100644
index cfafc9b5..00000000
--- a/Scripts/PRS-CSx/PRS_CSx_plink2.R
+++ /dev/null
@@ -1,352 +0,0 @@
-#!/usr/bin/Rscript
-# This script was written by Oliver Pain whilst at King's College London University.
-start.time <- Sys.time()
-suppressMessages(library("optparse"))
-
-option_list = list(
-make_option("--ref_plink_chr", action="store", default=NA, type='character',
-		help="Path to per chromosome reference PLINK files [required]"),
-make_option("--ref_pop_scale", action="store", default=NA, type='character',
-		help="File containing the population code and location of the keep file [required]"),
-make_option("--plink2", action="store", default='plink', type='character',
-		help="Path PLINK v2 software binary [required]"),
-make_option("--output", action="store", default='NA', type='character',
-		help="Path for output files [required]"),
-make_option("--memory", action="store", default=5000, type='numeric',
-		help="Memory limit [optional]"),
-make_option("--sumstats1", action="store", default=NA, type='character',
-    help="GWAS summary statistics 1 [required]"),
-make_option("--sumstats2", action="store", default=NA, type='character',
-    help="GWAS summary statistics 2 [required]"),
-make_option("--pop1", action="store", default=NA, type='character',
-    help="Super population or  sumstats1 [required]"),
-make_option("--pop2", action="store", default=NA, type='character',
-    help="Super population for sumstats2 [required]"),
-make_option("--PRS_CSx_path", action="store", default=NA, type='character',
-    help="Path to PRScs executable [required]"),
-make_option("--PRS_CSx_ref_path", action="store", default=NA, type='character',
-    help="Path to PRScs ld reference data [required]"),
-make_option("--n_cores", action="store", default=1, type='numeric',
-		help="Number of cores for parallel computing [optional]"),
-make_option("--test", action="store", default=NA, type='character',
-    help="Specify number of SNPs to include [optional]"),
-make_option("--phi_param", action="store", default='auto', type='character',
-    help="Path to PRScs reference [optional]"),
-make_option("--seed", action="store", default=NA, type='numeric',
-    help="Seed number for PRScs [optional]")
-)
-
-opt = parse_args(OptionParser(option_list=option_list))
-
-library(data.table)
-library(foreach)
-library(doMC)
-registerDoMC(opt$n_cores)
-
-opt$output_dir<-paste0(dirname(opt$output),'/')
-opt$output_name<-basename(opt$output)
-system(paste0('mkdir -p ',opt$output_dir))
-
-phi_param<-unlist(strsplit(opt$phi_param,','))
-
-CHROMS<-1:22
-
-if(!is.na(opt$test)){
-  if(grepl('chr', opt$test)){
-    single_chr_test<-T
-    CHROMS<-as.numeric(gsub('chr','',opt$test))
-  } else {
-    single_chr_test<-F
-    opt$test<-as.numeric(opt$test)
-  }
-}
-
-if(any(grepl('auto',phi_param))){
-	phi_param<-c(sprintf("%1.00e", as.numeric(phi_param[!grepl('auto',phi_param)])),'auto')
-} else {
-	phi_param<-sprintf("%1.00e", as.numeric(phi_param))
-}
-
-sink(file = paste(opt$output,'.log',sep=''), append = F)
-cat(
-'#################################################################
-# polygenic_score_file_creator_PRScs.R V1.0
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-Analysis started at',as.character(start.time),'
-Options are:\n')
-
-cat('Options are:\n')
-print(opt)
-cat('Analysis started at',as.character(start.time),'\n')
-sink()
-
-#####
-# Read in sumstats and insert p-values
-#####
-
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Reading in GWAS and harmonising with reference.\n')
-sink()
-
-###
-# sumstats1
-###
-
-GWAS1<-fread(cmd=paste0('zcat ',opt$sumstats1), nThread=1)
-GWAS1<-GWAS1[complete.cases(GWAS1),]
-
-# Extract subset if testing
-if(!is.na(opt$test)){
-  if(single_chr_test == F){
-    sink(file = paste(opt$output,'.log',sep=''), append = T)
-    cat('Testing mode enabled. Extracted ',opt$test,' variants per chromsome.\n', sep='')
-    sink()
-    
-    GWAS1_test<-NULL
-    for(i in 1:22){
-      GWAS1_tmp<-GWAS1[GWAS1$CHR == i,]
-      GWAS1_tmp<-GWAS1_tmp[order(GWAS1_tmp$BP),]
-      GWAS1_tmp<-GWAS1_tmp[1:opt$test,]
-      GWAS1_test<-rbind(GWAS1_test,GWAS1_tmp)
-    }
-    
-    GWAS1<-GWAS1_test
-    GWAS1<-GWAS1[complete.cases(GWAS1),]
-    rm(GWAS1_test)
-    print(table(GWAS1$CHR))
-    
-  } else {
-    sink(file = paste(opt$output,'.log',sep=''), append = T)
-    cat('Testing mode enabled. Extracted chromosome ',opt$test,' variants per chromsome.\n', sep='')
-    sink()
-    
-    GWAS1<-GWAS1[GWAS1$CHR == CHROMS,]
-    print(table(GWAS1$CHR))
-  }
-}
-
-GWAS1_N<-mean(GWAS1$N)
-
-if(('BETA' %in% names(GWAS1))){
-  GWAS1<-GWAS1[,c('SNP','A1','A2','BETA','P')]
-} else {
-  GWAS1<-GWAS1[,c('SNP','A1','A2','OR','P')]
-}
-
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('sumstats1 contains',dim(GWAS1)[1],'variants.\n')
-sink()
-
-fwrite(GWAS1, paste0(opt$output_dir,'GWAS1_sumstats_temp.txt'), sep=' ')
-
-rm(GWAS1)
-gc()
-
-###
-# sumstats2
-###
-
-GWAS2<-fread(cmd=paste0('zcat ',opt$sumstats2), nThread=1)
-GWAS2<-GWAS2[complete.cases(GWAS2),]
-
-# Extract subset if testing
-if(!is.na(opt$test)){
-  if(single_chr_test == F){
-    sink(file = paste(opt$output,'.log',sep=''), append = T)
-    cat('Testing mode enabled. Extracted ',opt$test,' variants per chromsome.\n', sep='')
-    sink()
-    
-    GWAS2_test<-NULL
-    for(i in 1:22){
-      GWAS2_tmp<-GWAS2[GWAS2$CHR == i,]
-      GWAS2_tmp<-GWAS2_tmp[order(GWAS2_tmp$BP),]
-      GWAS2_tmp<-GWAS2_tmp[1:opt$test,]
-      GWAS2_test<-rbind(GWAS2_test,GWAS2_tmp)
-    }
-    
-    GWAS2<-GWAS2_test
-    GWAS2<-GWAS2[complete.cases(GWAS2),]
-    rm(GWAS2_test)
-    print(table(GWAS2$CHR))
-    
-  } else {
-    sink(file = paste(opt$output,'.log',sep=''), append = T)
-    cat('Testing mode enabled. Extracted chromosome ',opt$test,' variants per chromsome.\n', sep='')
-    sink()
-    
-    GWAS2<-GWAS2[GWAS2$CHR == CHROMS,]
-    print(table(GWAS2$CHR))
-  }
-}
-
-GWAS2_N<-mean(GWAS2$N)
-
-if(('BETA' %in% names(GWAS2))){
-  GWAS2<-GWAS2[,c('SNP','A1','A2','BETA','P')]
-} else {
-  GWAS2<-GWAS2[,c('SNP','A1','A2','OR','P')]
-}
-
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('sumstats1 contains',dim(GWAS2)[1],'variants.\n')
-sink()
-
-fwrite(GWAS2, paste0(opt$output_dir,'GWAS2_sumstats_temp.txt'), sep=' ')
-
-rm(GWAS2)
-gc()
-
-if(!is.na(opt$test)){
-  sink(file = paste(opt$output,'.log',sep=''), append = T)
-  test_start.time <- Sys.time()
-  cat('Test started at',as.character(test_start.time),'\n')
-  sink()
-}
-
-#####
-# Process sumstats using PRScsx
-#####
-
-# Make a data.frame listing chromosome and phi combinations
-jobs<-NULL
-for(i in CHROMS){
-  jobs<-rbind(jobs,data.frame(CHR=i,
-                       phi=phi_param))
-}
-
-# Run using PRScs auto, and specifying a range of global shrinkage parameters (1e-6, 1e-4, 1e-2, 1)
-foreach(i=1:dim(jobs)[1], .combine=c, .options.multicore=list(preschedule=FALSE)) %dopar% {
-  if(is.na(opt$seed)){
-    if(jobs$phi[i] == 'auto'){
-      system(paste0(opt$PRS_CSx_path,' --meta=True --ref_dir=',opt$PRS_CSx_ref_path,' --bim_prefix=',opt$ref_plink_chr,jobs$CHR[i],' --sst_file=',opt$output_dir,'GWAS1_sumstats_temp.txt,',opt$output_dir,'GWAS2_sumstats_temp.txt --pop=',opt$pop1,',',opt$pop2,' --n_gwas=',round(GWAS1_N,0),',',round(GWAS2_N,0),' --out_dir=',opt$output_dir,' --out_name=',opt$output_name,' --chrom=',jobs$CHR[i]))
-    } else {
-      system(paste0(opt$PRS_CSx_path,' --meta=True --ref_dir=',opt$PRS_CSx_ref_path,' --bim_prefix=',opt$ref_plink_chr,jobs$CHR[i],' --phi=',jobs$phi[i],' --sst_file=',opt$output_dir,'GWAS1_sumstats_temp.txt,',opt$output_dir,'GWAS2_sumstats_temp.txt --pop=',opt$pop1,',',opt$pop2,' --n_gwas=',round(GWAS1_N,0),',',round(GWAS2_N,0),' --out_dir=',opt$output_dir,' --out_name=',opt$output_name,' --chrom=',jobs$CHR[i]))
-    }
-  } else {
-    if(jobs$phi[i] == 'auto'){
-      system(paste0(opt$PRS_CSx_path,' --meta=True --ref_dir=',opt$PRS_CSx_ref_path,' --bim_prefix=',opt$ref_plink_chr,jobs$CHR[i],' --sst_file=',opt$output_dir,'GWAS1_sumstats_temp.txt,',opt$output_dir,'GWAS2_sumstats_temp.txt --pop=',opt$pop1,',',opt$pop2,' --n_gwas=',round(GWAS1_N,0),',',round(GWAS2_N,0),' --out_dir=',opt$output_dir,' --out_name=',opt$output_name,' --chrom=',jobs$CHR[i],' --seed=',opt$seed))
-    } else {
-      system(paste0(opt$PRS_CSx_path,' --meta=True --ref_dir=',opt$PRS_CSx_ref_path,' --bim_prefix=',opt$ref_plink_chr,jobs$CHR[i],' --phi=',jobs$phi[i],' --sst_file=',opt$output_dir,'GWAS1_sumstats_temp.txt,',opt$output_dir,'GWAS2_sumstats_temp.txt --pop=',opt$pop1,',',opt$pop2,' --n_gwas=',round(GWAS1_N,0),',',round(GWAS2_N,0),' --out_dir=',opt$output_dir,' --out_name=',opt$output_name,' --chrom=',jobs$CHR[i],' --seed=',opt$seed))
-    }
-  }
-}
-
-system(paste0('rm ',opt$output_dir,'GWAS1_sumstats_temp.txt'))
-system(paste0('rm ',opt$output_dir,'GWAS2_sumstats_temp.txt'))
-
-if(!is.na(opt$test)){
-  end.time <- Sys.time()
-  time.taken <- end.time - test_start.time
-  sink(file = paste(opt$output,'.log',sep=''), append = T)
-  cat('Test run finished at',as.character(end.time),'\n')
-  cat('Test duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
-  sink()
-  system(paste0('rm ',opt$output,'*.txt'))
-  q()
-}
-
-####
-# Combine score files 
-####
-for(pop_disc in c(opt$pop1, opt$pop2, 'META')){
-  score_all<-NULL
-  for(phi_i in phi_param){
-    score_phi<-NULL
-    for(i in CHROMS){
-      score_phi_i<-fread(paste0(opt$output,'_',pop_disc,'_pst_eff_a1_b0.5_phi',phi_i,'_chr',i,'.txt'))
-      score_phi<-rbind(score_phi, score_phi_i)
-    }
-    if(phi_i == phi_param[1]){
-      score_phi<-score_phi[,c('V2','V4','V6'), with=F]
-      names(score_phi)<-c('SNP','A1',paste0('SCORE_phi_',phi_i))
-    } else {
-      score_phi<-score_phi[,'V6', with=F]
-      names(score_phi)<-paste0('SCORE_phi_',phi_i)
-    }
-
-    score_all<-cbind(score_all, score_phi)
-  
-  }
-  
-  fwrite(score_all, paste0(opt$output,'.',pop_disc,'.score'), col.names=T, sep=' ', quote=F)
-  
-  if(file.exists(paste0(opt$output,'.',pop_disc,'.score.gz'))){
-    system(paste0('rm ',opt$output,'.',pop_disc,'.score.gz'))
-  }
-  
-  system(paste0('gzip ',opt$output,'.',pop_disc,'.score'))
-}
-  
-####
-# Calculate mean and sd of polygenic scores
-####
-
-# Calculate polygenic scores for reference individuals
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Calculating polygenic scores in reference...')
-sink()
-
-for(pop_disc in c(opt$pop1, opt$pop2, 'META')){
-  if(length(phi_param) == 1){
-    for(i in CHROMS){
-      system(paste0(opt$plink2, ' --bfile ',opt$ref_plink_chr,i,' --score ',opt$output,'.',pop_disc,'.score.gz header-read --out ',opt$output,'.',pop_disc,'.profiles.chr',i,' --memory ',floor(opt$memory*0.7)))
-    }
-  } else {
-    for(i in CHROMS){
-      system(paste0(opt$plink2, ' --bfile ',opt$ref_plink_chr,i,' --score ',opt$output,'.',pop_disc,'.score.gz header-read --score-col-nums 3-',2+length(phi_param),' --out ',opt$output,'.',pop_disc,'.profiles.chr',i,' --memory ',floor(opt$memory*0.7)))
-    }
-  }
-}
-
-# Add up the scores across chromosomes
-fam<-fread(paste0(opt$ref_plink_chr,'22.fam'))
-
-for(pop_disc in c(opt$pop1, opt$pop2, 'META')){
-  scores<-list()
-  for(i in as.character(CHROMS)){
-    sscore<-fread(paste0(opt$output,'.',pop_disc,'.profiles.chr',i,'.sscore'))
-    scores[[i]]<-sscore[,grepl('SCORE_', names(sscore)),with=F]
-    scores[[i]]<-as.matrix(scores[[i]]*sscore$NMISS_ALLELE_CT)
-  }
-  
-  scores<-Reduce(`+`, scores)
-  scores<-data.table(FID=fam$V1,
-                     IID=fam$V2,
-                     scores)
-  
-  names(scores)<-c('FID','IID',names(score_all)[-1:-2])
-  
-  # Calculate the mean and sd of scores for each population specified in pop_scale
-  pop_keep_files<-read.table(opt$ref_pop_scale, header=F, stringsAsFactors=F)
-  
-  for(k in 1:dim(pop_keep_files)[1]){
-  	pop<-pop_keep_files$V1[k]
-  	keep<-fread(pop_keep_files$V2[k], header=F)
-  	scores_keep<-scores[(scores$FID %in% keep$V1),]
-  
-  	ref_scale<-data.frame(	Param=names(scores_keep[,-1:-2]),
-  													Mean=round(sapply(scores_keep[,-1:-2], function(x) mean(x)),3),
-  													SD=round(sapply(scores_keep[,-1:-2], function(x) sd(x)),3))
-  
-  	fwrite(ref_scale, paste0(opt$output,'.',pop_disc,'.',pop,'.scale'), sep=' ')
-  }
-}
-
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Done!\n')
-sink()
-
-###
-# Clean up temporary files
-###
-
-system(paste0('rm ',opt$output,'*.profiles.*'))
-system(paste0('rm ',opt$output,'*_pst_eff_a1_b0.5_*'))
-
-end.time <- Sys.time()
-time.taken <- end.time - start.time
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Analysis finished at',as.character(end.time),'\n')
-cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
-sink()
diff --git a/Scripts/PRS-CSx/PRS_CSx_plink2_slurm.R b/Scripts/PRS-CSx/PRS_CSx_plink2_slurm.R
deleted file mode 100644
index 6ab637aa..00000000
--- a/Scripts/PRS-CSx/PRS_CSx_plink2_slurm.R
+++ /dev/null
@@ -1,392 +0,0 @@
-#!/usr/bin/Rscript
-# This script was written by Oliver Pain whilst at King's College London University.
-start.time <- Sys.time()
-suppressMessages(library("optparse"))
-
-option_list = list(
-make_option("--ref_plink_chr", action="store", default=NA, type='character',
-		help="Path to per chromosome reference PLINK files [required]"),
-make_option("--ref_pop_scale", action="store", default=NA, type='character',
-		help="File containing the population code and location of the keep file [required]"),
-make_option("--plink2", action="store", default='plink', type='character',
-		help="Path PLINK v2 software binary [required]"),
-make_option("--output", action="store", default='NA', type='character',
-		help="Path for output files [required]"),
-make_option("--memory", action="store", default=5000, type='numeric',
-		help="Memory limit [optional]"),
-make_option("--sumstats1", action="store", default=NA, type='character',
-    help="GWAS summary statistics 1 [required]"),
-make_option("--sumstats2", action="store", default=NA, type='character',
-    help="GWAS summary statistics 2 [required]"),
-make_option("--pop1", action="store", default=NA, type='character',
-    help="Super population or  sumstats1 [required]"),
-make_option("--pop2", action="store", default=NA, type='character',
-    help="Super population for sumstats2 [required]"),
-make_option("--PRS_CSx_path", action="store", default=NA, type='character',
-    help="Path to PRScs executable [required]"),
-make_option("--PRS_CSx_ref_path", action="store", default=NA, type='character',
-    help="Path to PRScs ld reference data [required]"),
-make_option("--n_cores", action="store", default=1, type='numeric',
-		help="Number of cores for parallel computing [optional]"),
-make_option("--test", action="store", default=NA, type='character',
-    help="Specify number of SNPs to include [optional]"),
-make_option("--phi_param", action="store", default='auto', type='character',
-    help="Path to PRScs reference [optional]")
-)
-
-opt = parse_args(OptionParser(option_list=option_list))
-
-library(data.table)
-
-opt$output_dir<-paste0(dirname(opt$output),'/')
-opt$output_name<-basename(opt$output)
-system(paste0('mkdir -p ',opt$output_dir))
-
-phi_param<-unlist(strsplit(opt$phi_param,','))
-
-CHROMS<-1:22
-
-if(!is.na(opt$test)){
-  if(grepl('chr', opt$test)){
-    single_chr_test<-T
-    CHROMS<-as.numeric(gsub('chr','',opt$test))
-  } else {
-    single_chr_test<-F
-    opt$test<-as.numeric(opt$test)
-  }
-}
-
-if(any(grepl('auto',phi_param))){
-	phi_param<-c(sprintf("%1.00e", as.numeric(phi_param[!grepl('auto',phi_param)])),'auto')
-} else {
-	phi_param<-sprintf("%1.00e", as.numeric(phi_param))
-}
-
-sink(file = paste(opt$output,'.log',sep=''), append = F)
-cat(
-'#################################################################
-# polygenic_score_file_creator_PRS-CSx.R V1.0
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-Analysis started at',as.character(start.time),'
-Options are:\n')
-
-cat('Options are:\n')
-print(opt)
-cat('Analysis started at',as.character(start.time),'\n')
-sink()
-
-#####
-# Read in sumstats and insert p-values
-#####
-
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Reading in GWAS and harmonising with reference.\n')
-sink()
-
-###
-# sumstats1
-###
-
-GWAS1<-fread(cmd=paste0('zcat ',opt$sumstats1), nThread=1)
-GWAS1<-GWAS1[complete.cases(GWAS1),]
-
-# Extract subset if testing
-if(!is.na(opt$test)){
-  if(single_chr_test == F){
-    sink(file = paste(opt$output,'.log',sep=''), append = T)
-    cat('Testing mode enabled. Extracted ',opt$test,' variants per chromsome.\n', sep='')
-    sink()
-    
-    GWAS1_test<-NULL
-    for(i in 1:22){
-      GWAS1_tmp<-GWAS1[GWAS1$CHR == i,]
-      GWAS1_tmp<-GWAS1_tmp[order(GWAS1_tmp$BP),]
-      GWAS1_tmp<-GWAS1_tmp[1:opt$test,]
-      GWAS1_test<-rbind(GWAS1_test,GWAS1_tmp)
-    }
-    
-    GWAS1<-GWAS1_test
-    GWAS1<-GWAS1[complete.cases(GWAS1),]
-    rm(GWAS1_test)
-    print(table(GWAS1$CHR))
-    
-  } else {
-    sink(file = paste(opt$output,'.log',sep=''), append = T)
-    cat('Testing mode enabled. Extracted chromosome ',opt$test,' variants per chromsome.\n', sep='')
-    sink()
-    
-    GWAS1<-GWAS1[GWAS1$CHR == CHROMS,]
-    print(table(GWAS1$CHR))
-  }
-}
-
-GWAS1_N<-mean(GWAS1$N)
-
-if(('BETA' %in% names(GWAS1))){
-  GWAS1<-GWAS1[,c('SNP','A1','A2','BETA','P')]
-} else {
-  GWAS1<-GWAS1[,c('SNP','A1','A2','OR','P')]
-}
-
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('sumstats1 contains',dim(GWAS1)[1],'variants.\n')
-sink()
-
-fwrite(GWAS1, paste0(opt$output_dir,'GWAS1_sumstats_temp.txt'), sep=' ')
-
-rm(GWAS1)
-gc()
-
-###
-# sumstats2
-###
-
-GWAS2<-fread(cmd=paste0('zcat ',opt$sumstats2), nThread=1)
-GWAS2<-GWAS2[complete.cases(GWAS2),]
-
-# Extract subset if testing
-if(!is.na(opt$test)){
-  if(single_chr_test == F){
-    sink(file = paste(opt$output,'.log',sep=''), append = T)
-    cat('Testing mode enabled. Extracted ',opt$test,' variants per chromsome.\n', sep='')
-    sink()
-    
-    GWAS2_test<-NULL
-    for(i in 1:22){
-      GWAS2_tmp<-GWAS2[GWAS2$CHR == i,]
-      GWAS2_tmp<-GWAS2_tmp[order(GWAS2_tmp$BP),]
-      GWAS2_tmp<-GWAS2_tmp[1:opt$test,]
-      GWAS2_test<-rbind(GWAS2_test,GWAS2_tmp)
-    }
-    
-    GWAS2<-GWAS2_test
-    GWAS2<-GWAS2[complete.cases(GWAS2),]
-    rm(GWAS2_test)
-    print(table(GWAS2$CHR))
-    
-  } else {
-    sink(file = paste(opt$output,'.log',sep=''), append = T)
-    cat('Testing mode enabled. Extracted chromosome ',opt$test,' variants per chromsome.\n', sep='')
-    sink()
-    
-    GWAS2<-GWAS2[GWAS2$CHR == CHROMS,]
-    print(table(GWAS2$CHR))
-  }
-}
-
-GWAS2_N<-mean(GWAS2$N)
-
-if(('BETA' %in% names(GWAS2))){
-  GWAS2<-GWAS2[,c('SNP','A1','A2','BETA','P')]
-} else {
-  GWAS2<-GWAS2[,c('SNP','A1','A2','OR','P')]
-}
-
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('sumstats1 contains',dim(GWAS2)[1],'variants.\n')
-sink()
-
-fwrite(GWAS2, paste0(opt$output_dir,'GWAS2_sumstats_temp.txt'), sep=' ')
-
-rm(GWAS2)
-gc()
-
-if(!is.na(opt$test)){
-  sink(file = paste(opt$output,'.log',sep=''), append = T)
-  test_start.time <- Sys.time()
-  cat('Test started at',as.character(test_start.time),'\n')
-  sink()
-}
-
-#####
-# Process sumstats using PRScsx
-#####
-
-# Make a data.frame listing chromosome and phi combinations
-jobs<-NULL
-for(i in CHROMS){
-  jobs<-rbind(jobs,data.frame(CHR=i,
-                       phi=phi_param))
-}
-
-write.table(jobs, paste0(opt$output_dir,'job_list'), col.names=F, row.names=F, quote=F)
-
-# Write batch job
-writeLines(paste0("#!/bin/sh
-
-#SBATCH -p shared,brc
-#SBATCH --mem 10G
-#SBATCH -n 1
-#SBATCH --nodes=1
-#SBATCH -t 24:00:00
-#SBATCH -J PRScs
-
-export MKL_NUM_THREADS=$SLURM_CPUS_ON_NODE
-export NUMEXPR_NUM_THREADS=$SLURM_CPUS_ON_NODE
-export OMP_NUM_THREADS=$SLURM_CPUS_ON_NODE
-
-echo $SLURM_CPUS_ON_NODE
-chr=$(awk -v var=$SLURM_ARRAY_TASK_ID 'NR == var {print $1}' ", opt$output_dir,"job_list)
-phi=$(awk -v var=$SLURM_ARRAY_TASK_ID 'NR == var {print $2}' ", opt$output_dir,"job_list)
-
-echo ${chr}
-echo ${phi}
-
-if [ ${phi} == \"auto\" ];then
-
-",opt$PRS_CSx_path," --meta=True --ref_dir=",opt$PRS_CSx_ref_path," --bim_prefix=",opt$ref_plink_chr,"${chr} --sst_file=",opt$output_dir,"/GWAS1_sumstats_temp.txt,",opt$output_dir,"/GWAS2_sumstats_temp.txt --pop=",opt$pop1,",",opt$pop2," --n_gwas=",round(GWAS1_N,0),",",round(GWAS2_N,0)," --out_dir=",opt$output_dir," --out_name=",opt$output_name," --chrom=${chr}
-
-else
-
-",opt$PRS_CSx_path," --meta=True --ref_dir=",opt$PRS_CSx_ref_path," --bim_prefix=",opt$ref_plink_chr,"${chr} --phi=${phi} --sst_file=",opt$output_dir,"/GWAS1_sumstats_temp.txt,",opt$output_dir,"/GWAS2_sumstats_temp.txt --pop=",opt$pop1,",",opt$pop2," --n_gwas=",round(GWAS1_N,0),",",round(GWAS2_N,0)," --out_dir=",opt$output_dir," --out_name=",opt$output_name," --chrom=${chr}
-
-fi
-
-"), paste0(opt$output_dir,'batch.sh'))
-
-# Run batch job
-jobID<-system(paste0("sbatch --array ",1,"-",nrow(jobs),"%",opt$n_cores," ", opt$output_dir,'batch.sh'),intern=T) 
-jobID<-gsub('.* ','', jobID)
-
-# Check whether finished
-Sys.sleep(30)
-while(i){
-  system(paste0('sacct -j ',jobID,' > ',opt$output_dir,'sacct_log.txt'))
-  sacct_log<-fread(paste0(opt$output_dir,'sacct_log.txt'), fill=T)
-  sacct_log<-sacct_log[sacct_log$JobName == 'PRScs',]
-  
-  print(sacct_log)
-  
-  if(sum(sacct_log$State == 'FAILED') > 0){
-    sink(file = paste(opt$output,'.log',sep=''), append = T)
-    cat('Job failed.\n')
-    sink()
-    q()
-  }
-  
-  if(sum(sacct_log$State != 'COMPLETED') == 0){
-    break
-  } else {
-    Sys.sleep(60)
-  }
-}
-
-system(paste0('rm ',opt$output_dir,'GWAS1_sumstats_temp.txt'))
-system(paste0('rm ',opt$output_dir,'GWAS2_sumstats_temp.txt'))
-
-if(!is.na(opt$test)){
-  end.time <- Sys.time()
-  time.taken <- end.time - test_start.time
-  sink(file = paste(opt$output,'.log',sep=''), append = T)
-  cat('Test run finished at',as.character(end.time),'\n')
-  cat('Test duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
-  sink()
-  system(paste0('rm ',opt$output,'*.txt'))
-  q()
-}
-
-####
-# Combine score files 
-####
-for(pop_disc in c(opt$pop1, opt$pop2, 'META')){
-  score_all<-NULL
-  for(phi_i in phi_param){
-    score_phi<-NULL
-    for(i in CHROMS){
-      score_phi_i<-fread(paste0(opt$output,'_',pop_disc,'_pst_eff_a1_b0.5_phi',phi_i,'_chr',i,'.txt'))
-      score_phi<-rbind(score_phi, score_phi_i)
-    }
-    if(phi_i == phi_param[1]){
-      score_phi<-score_phi[,c('V2','V4','V6'), with=F]
-      names(score_phi)<-c('SNP','A1',paste0('SCORE_phi_',phi_i))
-    } else {
-      score_phi<-score_phi[,'V6', with=F]
-      names(score_phi)<-paste0('SCORE_phi_',phi_i)
-    }
-
-    score_all<-cbind(score_all, score_phi)
-  
-  }
-  
-  fwrite(score_all, paste0(opt$output,'.',pop_disc,'.score'), col.names=T, sep=' ', quote=F)
-  
-  if(file.exists(paste0(opt$output,'.',pop_disc,'.score.gz'))){
-    system(paste0('rm ',opt$output,'.',pop_disc,'.score.gz'))
-  }
-  
-  system(paste0('gzip ',opt$output,'.',pop_disc,'.score'))
-}
-  
-####
-# Calculate mean and sd of polygenic scores
-####
-
-# Calculate polygenic scores for reference individuals
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Calculating polygenic scores in reference...')
-sink()
-
-for(pop_disc in c(opt$pop1, opt$pop2, 'META')){
-  if(length(phi_param) == 1){
-    for(i in CHROMS){
-      system(paste0(opt$plink2, ' --bfile ',opt$ref_plink_chr,i,' --score ',opt$output,'.',pop_disc,'.score.gz header-read --out ',opt$output,'.',pop_disc,'.profiles.chr',i,' --memory ',floor(opt$memory*0.7)))
-    }
-  } else {
-    for(i in CHROMS){
-      system(paste0(opt$plink2, ' --bfile ',opt$ref_plink_chr,i,' --score ',opt$output,'.',pop_disc,'.score.gz header-read --score-col-nums 3-',2+length(phi_param),' --out ',opt$output,'.',pop_disc,'.profiles.chr',i,' --memory ',floor(opt$memory*0.7)))
-    }
-  }
-}
-
-# Add up the scores across chromosomes
-fam<-fread(paste0(opt$ref_plink_chr,'22.fam'))
-
-for(pop_disc in c(opt$pop1, opt$pop2, 'META')){
-  scores<-list()
-  for(i in as.character(CHROMS)){
-    sscore<-fread(paste0(opt$output,'.',pop_disc,'.profiles.chr',i,'.sscore'))
-    scores[[i]]<-sscore[,grepl('SCORE_', names(sscore)),with=F]
-    scores[[i]]<-as.matrix(scores[[i]]*sscore$NMISS_ALLELE_CT)
-  }
-  
-  scores<-Reduce(`+`, scores)
-  scores<-data.table(FID=fam$V1,
-                     IID=fam$V2,
-                     scores)
-  
-  names(scores)<-c('FID','IID',names(score_all)[-1:-2])
-  
-  # Calculate the mean and sd of scores for each population specified in pop_scale
-  pop_keep_files<-read.table(opt$ref_pop_scale, header=F, stringsAsFactors=F)
-  
-  for(k in 1:dim(pop_keep_files)[1]){
-  	pop<-pop_keep_files$V1[k]
-  	keep<-fread(pop_keep_files$V2[k], header=F)
-  	scores_keep<-scores[(scores$FID %in% keep$V1),]
-  
-  	ref_scale<-data.frame(	Param=names(scores_keep[,-1:-2]),
-  													Mean=round(sapply(scores_keep[,-1:-2], function(x) mean(x)),3),
-  													SD=round(sapply(scores_keep[,-1:-2], function(x) sd(x)),3))
-  
-  	fwrite(ref_scale, paste0(opt$output,'.',pop_disc,'.',pop,'.scale'), sep=' ')
-  }
-}
-
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Done!\n')
-sink()
-
-###
-# Clean up temporary files
-###
-
-system(paste0('rm ',opt$output,'*.profiles.*'))
-system(paste0('rm ',opt$output,'*_pst_eff_a1_b0.5_*'))
-
-end.time <- Sys.time()
-time.taken <- end.time - start.time
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Analysis finished at',as.character(end.time),'\n')
-cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
-sink()
diff --git a/Scripts/pgs_methods/prscsx.R b/Scripts/pgs_methods/prscsx.R
new file mode 100644
index 00000000..be744b6f
--- /dev/null
+++ b/Scripts/pgs_methods/prscsx.R
@@ -0,0 +1,228 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
+      help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--pop_data", action="store", default=NULL, type='character',
+      help="File containing the population code and location of the keep file [required]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+      help="Path PLINK v2 software binary [optional]"),
+  make_option("--output", action="store", default=NULL, type='character',
+      help="Path for output files [required]"),
+  make_option("--memory", action="store", default=5000, type='numeric',
+      help="Memory limit [optional]"),
+  make_option("--sumstats", action="store", default=NULL, type='character',
+      help="Comma-seperated list of GWAS summary statistics [required]"),
+  make_option("--populations", action="store", default=NULL, type='character',
+      help="Comma-seperated list of population codes matching GWAS [required]"),
+  make_option("--prscsx_path", action="store", default=NULL, type='character',
+      help="Path to PRS-CSx executable [required]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+      help="Number of cores for parallel computing [optional]"),
+  make_option("--prscsx_ref_path", action="store", default=NULL, type='character',
+      help="Comma-seperated list of PRS-CSx reference data [required]"),
+  make_option("--test", action="store", default=NA, type='character',
+      help="Specify number of SNPs to include [optional]"),
+  make_option("--phi_param", action="store", default='auto', type='character',
+      help="Path to PRScs reference [optional]"),
+  make_option("--seed", action="store", default=1, type='numeric',
+      help="Seed number for PRScs [optional]")
+)
+
+opt = parse_args(OptionParser(option_list = option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+library(foreach)
+library(doMC)
+registerDoMC(opt$n_cores)
+
+# Format phi parameters into vector
+phi_param<-unlist(strsplit(opt$phi_param,','))
+if(any(grepl('auto',phi_param))){
+	phi_param<-c(sprintf("%1.00e", as.numeric(phi_param[!grepl('auto',phi_param)])),'auto')
+} else {
+	phi_param<-sprintf("%1.00e", as.numeric(phi_param))
+}
+
+# Check required inputs
+if(is.null(opt$ref_plink_chr)){
+  stop('--ref_plink_chr must be specified.\n')
+}
+if(is.null(opt$sumstats)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$pop_data)){
+  stop('--pop_data must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--output must be specified.\n')
+}
+if(is.null(opt$prscsx_path)){
+  stop('--prscsx_path must be specified.\n')
+}
+if(is.null(opt$prscsx_ref_path)){
+  stop('--prscsx_ref_path must be specified.\n')
+}
+if(is.null(opt$populations)){
+  stop('--populations must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
+system(paste0('mkdir -p ',opt$output_dir))
+
+# Create temp directory
+tmp_dir <- tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'prscsx.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+# Split opt$sumstats
+sumstats<-unlist(strsplit(opt$sumstats, ','))
+log_add(log_file = log_file, message = paste0(length(sumstats), ' sets of GWAS have been provided.'))
+
+gwas_N<-NULL
+for(i in 1:length(sumstats)){
+
+  #####
+  # Read in sumstats
+  #####
+
+  log_add(log_file = log_file, message = 'Reading in GWAS.')
+
+  # Read in, check and format GWAS summary statistics
+  gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('SNP','A1','A2','BETA','SE','N'))
+
+  # Store average sample size
+  gwas_N <- c(gwas_N, round(mean(gwas$N), 0))
+
+  fwrite(gwas, paste0(tmp_dir, '/GWAS_sumstats_',i,'_temp.txt'), sep=' ')
+
+  rm(gwas)
+  gc()
+
+
+}
+
+# Record start time for test
+if(!is.na(opt$test)){
+  test_start.time <- test_start(log_file = log_file)
+}
+
+#####
+# Process sumstats using PRS-CSx
+#####
+
+# Create a temporary reference bim files for PRS-CSx to match to
+pvar <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
+pvar$POS<-0
+for(i in CHROMS){
+  write.table(pvar[pvar$CHR == i, c('CHR','SNP','POS','BP','A1','A2'), with=F], paste0(tmp_dir,'/ref.chr',i,'.bim'), col.names=F, row.names=F, quote=F)
+}
+
+rm(pvar)
+gc()
+
+# Make a data.frame listing chromosome and phi combinations
+jobs<-NULL
+for(i in rev(CHROMS)){
+  jobs<-rbind(jobs, data.frame(CHR=i, phi=phi_param))
+}
+
+# Run using PRScs auto, and specifying a range of global shrinkage parameters
+log <- foreach(i = 1:nrow(jobs), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+  if(jobs$phi[i] == 'auto'){
+    system(paste0(opt$prscsx_path, ' --ref_dir=', opt$prscsx_ref_path, '/ --bim_prefix=', tmp_dir,'/ref.chr', jobs$CHR[i], ' --pop=', opt$populations, ' --sst_file=', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','),' --n_gwas=', paste(gwas_N, collapse=','), ' --out_dir=', tmp_dir, '/ --out_name=output --chrom=', jobs$CHR[i], ' --meta=True --seed=', opt$seed))
+  } else {
+    system(paste0(opt$prscsx_path, ' --ref_dir=', opt$prscsx_ref_path, '/ --bim_prefix=', tmp_dir,'/ref.chr', jobs$CHR[i], ' --pop=', opt$populations, ' --phi=', jobs$phi[i], ' --sst_file=', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','),' --n_gwas=', paste(gwas_N, collapse=','), ' --out_dir=', tmp_dir, '/ --out_name=output --chrom=', jobs$CHR[i], ' --meta=True --seed=', opt$seed))
+  }
+}
+
+####
+# Combine score files
+####
+
+score_all<-NULL
+for(pop_i in c(unlist(strsplit(opt$populations, ',')), 'META')){
+  score_pop<-NULL
+  for(phi_i in phi_param){
+    score_phi<-NULL
+    for(i in CHROMS){
+      score_phi_i<-fread(paste0(tmp_dir,'/output_',pop_i,'_pst_eff_a1_b0.5_phi',phi_i,'_chr',i,'.txt'))
+      score_phi<-rbind(score_phi, score_phi_i)
+    }
+    if(phi_i == phi_param[1]){
+      score_phi<-score_phi[,c('V2', 'V4','V5', 'V6'), with=F]
+      names(score_phi)<-c('SNP', 'A1', 'A2', paste0('SCORE_',pop_i,'_phi_',phi_i))
+    } else {
+      score_phi<-score_phi[,'V6', with=F]
+      names(score_phi)<-paste0('SCORE_',pop_i,'_phi_',phi_i)
+    }
+    score_pop<-cbind(score_pop, score_phi)
+  }
+  if(pop_i == c(unlist(strsplit(opt$populations, ',')), 'META')[1]){
+    score_all<-score_pop
+  } else {
+    score_all<-merge(score_all, score_pop[, !(names(score_pop) %in% c('A1','A2')), with=F], by='SNP', all=T)
+  }
+}
+
+# Replace NA values with 0
+score_all[is.na(score_all)] <- 0
+
+# Flip effects to match reference alleles
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
+score_new <- map_score(ref = ref, score = score_all)
+
+fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+
+if(file.exists(paste0(opt$output,'.score.gz'))){
+  system(paste0('rm ',opt$output,'.score.gz'))
+}
+
+system(paste0('gzip ',opt$output,'.score'))
+
+# Record end time of test
+if(!is.na(opt$test)){
+  test_finish(log_file = log_file, test_start.time = test_start.time)
+}
+
+####
+# Calculate mean and sd of polygenic scores
+####
+
+log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
+
+# Calculate scores in the full reference
+ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
+
+# Calculate scale within each reference population
+pop_data <- read_pop_data(opt$pop_data)
+
+for(pop_i in unique(pop_data$POP)){
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
diff --git a/functions/constants.R b/functions/constants.R
index 86568932..3ce37c16 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -41,3 +41,10 @@ pgs_method_labels <- data.frame(
   label = c('pT+clump','DBSLMM','LDpred2','SBayesR','lassosum','PRS-CS','MegaPRS','External')
 )
 pgs_method_labels[order(pgs_method_labels$method),]
+
+# Make vector indicating pgs_methods that can be applied to non-european GWAS
+pgs_methods_noneur <- c('ptclump','lassosum','megaprs','prscs','dbslmm')
+
+# Make vector indicating pgs_methods that are to be applied to gwas_groups
+pgs_group_methods <- c('prscsx')
+
diff --git a/functions/pgs.R b/functions/pgs.R
index 8c42e12f..911b0821 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -12,11 +12,13 @@ list_score_files <- function(config){
     # Identify PGS methods to be included
     pgs_methods_list <- read_param(config = config, param = 'pgs_methods', return_obj = F)
 
+    # Remove methods that are applied to groups of gwas
+    pgs_methods_list <- pgs_methods_list[!(pgs_methods_list %in% pgs_group_methods)]
+
     combos <- rbind(combos,
                     expand.grid(name = gwas_list$name[gwas_list$pop == 'EUR'], method = pgs_methods_list))
 
     # List PGS methods applied to non-EUR populations
-    pgs_methods_noneur <- c('ptclump','lassosum','megaprs','prscs','dbslmm')
     pgs_methods_noneur <- pgs_methods_noneur[pgs_methods_noneur %in% pgs_methods_list]
 
     combos <- rbind(combos,
@@ -42,6 +44,21 @@ list_score_files <- function(config){
                       method = 'external'))
   }
 
+  # Read in gwas_groups
+  gwas_groups <- read_param(config = config, param = 'gwas_groups')
+
+  if(!is.null(gwas_groups)){
+    # Identify PGS methods to be included
+    pgs_methods_list <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+
+    # Retain methods that are applied to groups of gwas
+    pgs_methods_list <- pgs_methods_list[(pgs_methods_list %in% pgs_group_methods)]
+
+    combos <- rbind(combos, expand.grid(name = gwas_groups$name, method = pgs_methods_list))
+  }
+
+  combos <- data.table(apply(combos, 2, as.character))
+
   return(combos)
 }
 
diff --git a/functions/pipeline.R b/functions/pipeline.R
index abb92c8a..b2d92119 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -1,332 +1,251 @@
-#!/usr/bin/Rscript
-
-if (!require("data.table", quietly = TRUE)) {
-  library(data.table)
-}
-
-# Read in PGS
-read_pgs <- function(config, name = NULL, pgs_methods = NULL, gwas = NULL, pop = NULL){
-
-  # Read in target_list
-  target_list <- read_param(config = config, param = 'target_list')
-  if(!is.null(name)){
-    if(any(!(name %in% target_list$name))){
-      stop('Requested target samples are not present in target_list')
-    }
-    name_i <- name
-    target_list <- target_list[target_list$name %in% name_i,]
-  }
-
-  # Read in gwas_list
-  gwas_list <- read_param(config = config, param = 'gwas_list')
-
-  # Read in score_list
-  score_list <- read_param(config = config, param = 'score_list')
-
-  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
-
-  if(!is.null(score_list)){
-    # Read in score_reporter output
-    score_reporter <- fread(paste0(outdir, "/reference/pgs_score_files/external/score_report.txt"))
-    score_list <- merge(score_list, score_reporter, by='name')
-
-    # Remove scores that did not pass ref harmonisation
-    score_list <- score_list[score_list$pass == T,]
-  }
-
-  if(!is.null(gwas)){
-    if(!is.null(score_list)){
-      full_gwas_list <- c(gwas_list$name, score_list$name)
-    } else {
-      full_gwas_list <- gwas_list$name
-    }
-
-    if(any(!(gwas %in% full_gwas_list))){
-      stop('Requested GWAS are not present in gwas_list/score_list')
-    }
-    gwas_list <- gwas_list[gwas_list$name %in% gwas,]
-
-    if(!is.null(score_list)){
-      score_list <- score_list[score_list$name %in% gwas,]
-    }
-  }
-
-  # Identify PGS methods to be included
-  pgs_methods_list <- read_param(config = config, param = 'pgs_methods', return_obj = F)
-
-  if(!is.null(pgs_methods)){
-    if(!is.null(score_list)){
-      if(any(!(pgs_methods %in% c(pgs_methods_list, 'external')))){
-        stop('Requested pgs_methods are not present in pgs_methods in config')
-      }
-    } else {
-      if(any(!(pgs_methods %in% pgs_methods_list))){
-        stop('Requested pgs_methods are not present in pgs_methods in config')
-      }
-    }
-    pgs_methods_list <- pgs_methods_list[pgs_methods_list %in% pgs_methods]
-  }
-
-  # Define PGS methods applied to non-EUR GWAS
-  pgs_methods_noneur <- c('ptclump','lassosum','megaprs','prscs','dbslmm')
-
-  # Identify outdir parameter
-  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
-
-  pgs <- list()
-  for (name_i in target_list$name) {
-    # Read in keep_list to determine populations available
-    keep_list_i <- fread(paste0(outdir,'/',name_i,'/ancestry/keep_list.txt'))
-
-    if(!is.null(pop)){
-      if(any(!(pop %in% keep_list_i$POP))){
-        stop(paste0('Requested pop are not present in ',name_i,' sample.'))
-      }
-      keep_list_i <- keep_list_i[keep_list_i$POP %in% pop,]
-    }
-
-    pgs[[name_i]] <- list()
-    for (pop_i in keep_list_i$POP) {
-      pgs[[name_i]][[pop_i]] <- list()
-      for (gwas_i in gwas_list$name) {
-        pgs[[name_i]][[pop_i]][[gwas_i]] <- list()
-
-        for (pgs_method_i in pgs_methods_list) {
-          if (gwas_list$population[gwas_list$name == gwas_i] == 'EUR' | (gwas_list$population[gwas_list$name == gwas_i] != 'EUR' & (pgs_method_i %in% pgs_methods_noneur))) {
-            pgs[[name_i]][[pop_i]][[gwas_i]][[pgs_method_i]] <-
-              fread(
-                paste0(
-                  outdir, '/', name_i, '/pgs/', pop_i, '/', pgs_method_i, '/',  gwas_i, '/', name_i, '-', gwas_i, '-', pop_i, '.profiles'
-                )
-              )
-          }
-        }
-      }
-      if(!is.null(score_list)){
-        for (score_i in score_list$name) {
-          pgs[[name_i]][[pop_i]][[score_i]] <- list()
-          pgs_method_i <- 'external'
-          pgs[[name_i]][[pop_i]][[score_i]][[pgs_method_i]] <-
-            fread(
-              paste0(
-                outdir, '/', name_i, '/pgs/', pop_i, '/', pgs_method_i, '/',  score_i, '/', name_i, '-', score_i, '-', pop_i, '.profiles'
-              )
-            )
-        }
-      }
-    }
-  }
-
-  return(pgs)
-}
-
-# Create function to read in parameters in the config file
-read_param <- function(config, param, return_obj = T){
-  library(yaml)
-  
-  # Read in the config file
-  config_file <- read_yaml(config)
-
-  if(all(names(config_file) != param)){
-    # Check default config file
-    config_file <- read_yaml('config.yaml')
-    
-    if(all(names(config_file) != param)){
-      cat('Requested parameter is not present in user specified config file or default config file.')
-      return(NULL)
-    } else {
-      cat('Parameter is not present in user specified config file, so will use value in default config file.')
-    }
-  }
-  
-  # Identify value for param
-  file <- config_file[[param]]
-  file[file == 'NA']<-NA
-  
-  # If resdir, and NA, set to 'resources'
-  if(param == 'resdir'){
-    if(is.na(file)){
-      file <- 'resources'
-    }
-  }
-  
-  # If refdir, and NA, set to '<resdir>/data/ref'
-  if(param == 'refdir'){
-    if(is.na(file)){
-      resdir <- read_param(config = config, param = 'resdir', return_obj = F)
-      file <- paste0(resdir, '/data/ref')
-    }
-  }
-  
-  if(return_obj){
-    if(!is.na(file)){
-      obj <- fread(file)
-    } else {
-      obj <- NULL
-    }
-    return(obj)
-  } else {
-    file <- file[order(file)]
-    return(file)
-  }
-}
-
-# Read in ancestry classifications
-read_ancestry <- function(config, name){
-
-  # Read in the config file
-  config_file <- readLines(config)
-
-  # Identify outdir
-  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
-
-  # Read keep_list
-  keep_list <- fread(paste0(outdir,'/',name,'/ancestry/keep_list.txt'))
-
-  # Read in keep lists
-  keep_files <- list()
-  for(pop in keep_list$POP){
-    keep_files[[pop]] <- fread(keep_list$file[keep_list$POP == pop], header = F)
-  }
-
-  # Read in model predictions
-  model_pred <- fread(paste0(outdir,'/',name,'/ancestry/',name,'.Ancestry.model_pred'))
-
-  # Read in ancestry log file
-  log <- readLines(paste0(outdir,'/',name,'/ancestry/',name,'.Ancestry.log'))
-
-  output <- list(
-    keep_list = keep_list,
-    keep_files = keep_files,
-    model_pred = model_pred,
-    log = log
-  )
-
-  return(output)
-}
-
-# Return score corresponding to pseudovalidation
-find_pseudo <- function(config, gwas, pgs_method){
-
-  if(length(pgs_method) > 1){
-    stop('Only one pgs_method can be specified at a time')
-  }
-  if(length(gwas) > 1){
-    stop('Only one gwas can be specified at a time')
-  }
-
-  # Read in gwas_list
-  gwas_list <- read_param(config = config, param = 'gwas_list')
-
-  # Read in score_list
-  score_list <- read_param(config = config, param = 'score_list')
-
-  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
-
-  if(!is.null(score_list)){
-    # Read in score_reporter output
-    score_reporter <- fread(paste0(outdir, "/reference/pgs_score_files/external/score_report.txt"))
-    score_list <- merge(score_list, score_reporter, by='name')
-
-    # Remove scores that did not pass ref harmonisation
-    score_list <- score_list[score_list$pass == T,]
-  }
-
-  # Find outdir
-  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
-
-  if(!is.null(gwas)){
-    if(!is.null(score_list)){
-      full_gwas_list <- c(gwas_list$name, score_list$name)
-    } else {
-      full_gwas_list <- gwas_list$name
-    }
-
-    if(any(!(gwas %in% full_gwas_list))){
-      stop('Requested GWAS are not present in gwas_list/score_list')
-    }
-    gwas_list <- gwas_list[gwas_list$name %in% gwas,]
-
-    if(!is.null(score_list)){
-      score_list <- score_list[score_list$name %in% gwas,]
-    }
-  }
-
-  # Identify PGS methods to be included
-  pgs_methods_list <- read_param(config = config, param = 'pgs_methods', return_obj = F)
-
-  if(!is.null(pgs_method)){
-    if(!is.null(score_list)){
-      if(any(!(pgs_method %in% c(pgs_methods_list, 'external')))){
-        stop('Requested pgs_method are not present in pgs_methods in config')
-      }
-    } else {
-      if(any(!(pgs_method %in% pgs_methods_list))){
-        stop('Requested pgs_method are not present in pgs_methods in config')
-      }
-    }
-    pgs_methods_list <- pgs_methods_list[pgs_methods_list %in% pgs_method]
-  }
-
-  # Identify outdir parameter
-  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
-
-  # Use most stringent p-value threshold of 0.05 as pseudo
-  if(pgs_method == 'ptclump'){
-    return('0_1')
-  }
-
-  # Pseudoval only methods
-  if(pgs_method == 'sbayesr'){
-    return('SBayesR')
-  }
-
-
-  # Retrieve pseudoval param
-  if(pgs_method == 'dbslmm'){
-    return('DBSLMM_1')
-  }
-  if(pgs_method == 'ldpred2'){
-    return('beta_auto')
-  }
-  if(pgs_method == 'prscs'){
-    return('phi_auto')
-  }
-  if(pgs_method == 'megaprs'){
-    # Read in megaprs log file
-    log <- readLines(paste0(outdir,'/reference/pgs_score_files/',pgs_method,'/',gwas,'/ref-',gwas,'.log'))
-    log <- log[grepl('identified as the best with correlation', log)]
-    pseudoval <- gsub(' .*','', gsub('Model ', '', log))
-    return(paste0('ldak_Model', pseudoval))
-  }
-  if(pgs_method == 'lassosum'){
-    # Read in megaprs log file
-    log <- readLines(paste0(outdir,'/reference/pgs_score_files/',pgs_method,'/',gwas,'/ref-',gwas,'.log'))
-    s_val <- gsub('.* ', '', log[grepl('^s = ', log)])
-    lambda_val <- gsub('.* ', '', log[grepl('^lambda = ', log)])
-    return(paste0('s', s_val, '_lambda', lambda_val))
-  }
-
-  # If pgs_method is external, return the only score
-  if(pgs_method == 'external'){
-    return('external')
-  }
-}
-
-# Read in lassosum pseudoval results
-read_pseudo_r <- function(config, gwas){
-
-  if(length(gwas) > 1){
-    stop('Only one gwas can be specified at a time')
-  }
-
-  # Find outdir param
-  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
-
-  # Read in lassosum log file
-  log <- readLines(paste0(outdir,'/reference/pgs_score_files/lassosum/',gwas,'/ref-',gwas,'.log'))
-  r <- as.numeric(gsub('value = ','',log[grepl('value = ', log)]))
-
-  return(r)
-}
-
+#!/usr/bin/Rscript
+
+if (!require("data.table", quietly = TRUE)) {
+  library(data.table)
+}
+
+# Read in PGS
+read_pgs <- function(config, name = NULL, pgs_methods = NULL, gwas = NULL, pop = NULL){
+
+  # Read in target_list
+  target_list <- read_param(config = config, param = 'target_list')
+  if(!is.null(name)){
+    if(any(!(name %in% target_list$name))){
+      stop('Requested target samples are not present in target_list')
+    }
+    name_i <- name
+    target_list <- target_list[target_list$name %in% name_i,]
+  }
+
+  # Identify score files
+  score_file_list <- list_score_files(config)
+
+  # Subset requested gwas
+  if(!is.null(gwas)){
+    if(any(!(gwas %in% score_file_list$name))){
+      stop('Requested GWAS are not present in gwas_list/score_list')
+    }
+    score_file_list<-score_file_list[score_file_list$name %in% gwas,]
+  }
+
+  # Subset requested pgs_methods
+  if(!is.null(pgs_methods)){
+    if(any(!(pgs_methods %in% score_file_list$method))){
+      stop('Requested PGS methods are not present in gwas_list/score_list')
+    }
+    score_file_list<-score_file_list[score_file_list$method %in% pgs_methods,]
+  }
+
+  # Identify outdir parameter
+  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+  pgs <- list()
+  for (name_i in target_list$name) {
+    # Read in keep_list to determine populations available
+    keep_list_i <- fread(paste0(outdir,'/',name_i,'/ancestry/keep_list.txt'))
+
+    if(!is.null(pop)){
+      if(any(!(pop %in% keep_list_i$POP))){
+        stop(paste0('Requested pop are not present in ',name_i,' sample.'))
+      }
+      keep_list_i <- keep_list_i[keep_list_i$POP %in% pop,]
+    }
+
+    pgs[[name_i]] <- list()
+    for (pop_i in keep_list_i$POP) {
+      pgs[[name_i]][[pop_i]] <- list()
+      for(score_i in 1:nrow(score_file_list)){
+        gwas_i <- score_file_list$name[score_i]
+        pgs_method_i <- score_file_list$method[score_i]
+        pgs[[name_i]][[pop_i]][[gwas_i]] <- list()
+        pgs[[name_i]][[pop_i]][[gwas_i]][[pgs_method_i]] <-
+            fread(
+              paste0(
+                outdir, '/', name_i, '/pgs/', pop_i, '/', pgs_method_i, '/',  gwas_i, '/', name_i, '-', gwas_i, '-', pop_i, '.profiles'
+              )
+            )
+      }
+    }
+  }
+
+  return(pgs)
+}
+
+# Create function to read in parameters in the config file
+read_param <- function(config, param, return_obj = T){
+  library(yaml)
+
+  # Read in the config file
+  config_file <- read_yaml(config)
+
+  if(all(names(config_file) != param)){
+    # Check default config file
+    config_file <- read_yaml('config.yaml')
+
+    if(all(names(config_file) != param)){
+      cat(param, 'parameter is not present in user specified config file or default config file.')
+      return(NULL)
+    } else {
+      cat(param, 'parameter is not present in user specified config file, so will use value in default config file.')
+    }
+  }
+
+  # Identify value for param
+  file <- config_file[[param]]
+  file[file == 'NA']<-NA
+
+  # If resdir, and NA, set to 'resources'
+  if(param == 'resdir'){
+    if(is.na(file)){
+      file <- 'resources'
+    }
+  }
+
+  # If refdir, and NA, set to '<resdir>/data/ref'
+  if(param == 'refdir'){
+    if(is.na(file)){
+      resdir <- read_param(config = config, param = 'resdir', return_obj = F)
+      file <- paste0(resdir, '/data/ref')
+    }
+  }
+
+  if(return_obj){
+    if(!is.na(file)){
+      obj <- fread(file)
+    } else {
+      obj <- NULL
+    }
+    return(obj)
+  } else {
+    file <- file[order(file)]
+    return(file)
+  }
+}
+
+# Read in ancestry classifications
+read_ancestry <- function(config, name){
+
+  # Read in the config file
+  config_file <- readLines(config)
+
+  # Identify outdir
+  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+  # Read keep_list
+  keep_list <- fread(paste0(outdir,'/',name,'/ancestry/keep_list.txt'))
+
+  # Read in keep lists
+  keep_files <- list()
+  for(pop in keep_list$POP){
+    keep_files[[pop]] <- fread(keep_list$file[keep_list$POP == pop], header = F)
+  }
+
+  # Read in model predictions
+  model_pred <- fread(paste0(outdir,'/',name,'/ancestry/',name,'.Ancestry.model_pred'))
+
+  # Read in ancestry log file
+  log <- readLines(paste0(outdir,'/',name,'/ancestry/',name,'.Ancestry.log'))
+
+  output <- list(
+    keep_list = keep_list,
+    keep_files = keep_files,
+    model_pred = model_pred,
+    log = log
+  )
+
+  return(output)
+}
+
+# Return score corresponding to pseudovalidation
+find_pseudo <- function(config, gwas, pgs_method){
+
+  if(length(pgs_method) > 1){
+    stop('Only one pgs_method can be specified at a time')
+  }
+  if(length(gwas) > 1){
+    stop('Only one gwas can be specified at a time')
+  }
+
+  # Identify score files
+  score_file_list <- list_score_files(config)
+
+  # Subset requested gwas
+  if(!is.null(gwas)){
+    if(any(!(gwas %in% score_file_list$name))){
+      stop('Requested GWAS are not present in gwas_list/score_list')
+    }
+    score_file_list<-score_file_list[score_file_list$name %in% gwas,]
+  }
+
+  # Subset requested pgs_methods
+  if(!is.null(pgs_method)){
+    if(any(!(pgs_method %in% score_file_list$method))){
+      stop('Requested PGS method are not present in gwas_list/score_list')
+    }
+    score_file_list<-score_file_list[score_file_list$method %in% pgs_method,]
+  }
+
+  # Find outdir
+  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+  # Use most stringent p-value threshold of 0.05 as pseudo
+  if(pgs_method == 'ptclump'){
+    return('0_1')
+  }
+
+  # Pseudoval only methods
+  if(pgs_method == 'sbayesr'){
+    return('SBayesR')
+  }
+
+  # Retrieve pseudoval param
+  if(pgs_method == 'dbslmm'){
+    return('DBSLMM_1')
+  }
+  if(pgs_method == 'ldpred2'){
+    return('beta_auto')
+  }
+  if(pgs_method == 'prscs'){
+    return('phi_auto')
+  }
+  if(pgs_method == 'prscsx'){
+    return('META_phi_auto')
+  }
+  if(pgs_method == 'megaprs'){
+    # Read in megaprs log file
+    log <- readLines(paste0(outdir,'/reference/pgs_score_files/',pgs_method,'/',gwas,'/ref-',gwas,'.log'))
+    log <- log[grepl('identified as the best with correlation', log)]
+    pseudoval <- gsub(' .*','', gsub('Model ', '', log))
+    return(paste0('ldak_Model', pseudoval))
+  }
+  if(pgs_method == 'lassosum'){
+    # Read in megaprs log file
+    log <- readLines(paste0(outdir,'/reference/pgs_score_files/',pgs_method,'/',gwas,'/ref-',gwas,'.log'))
+    s_val <- gsub('.* ', '', log[grepl('^s = ', log)])
+    lambda_val <- gsub('.* ', '', log[grepl('^lambda = ', log)])
+    return(paste0('s', s_val, '_lambda', lambda_val))
+  }
+
+  # If pgs_method is external, return the only score
+  if(pgs_method == 'external'){
+    return('external')
+  }
+}
+
+# Read in lassosum pseudoval results
+read_pseudo_r <- function(config, gwas){
+
+  if(length(gwas) > 1){
+    stop('Only one gwas can be specified at a time')
+  }
+
+  # Find outdir param
+  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+  # Read in lassosum log file
+  log <- readLines(paste0(outdir,'/reference/pgs_score_files/lassosum/',gwas,'/ref-',gwas,'.log'))
+  r <- as.numeric(gsub('value = ','',log[grepl('value = ', log)]))
+
+  return(r)
+}
+
diff --git a/pipeline/config.yaml b/pipeline/config.yaml
index 15259cab..7eda72bb 100644
--- a/pipeline/config.yaml
+++ b/pipeline/config.yaml
@@ -19,7 +19,10 @@ target_list: NA
 # Specify location of score_list file
 score_list: NA
 
-# Specify pgs_methods ('ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs')
+# Specify location of gwas_groups file
+gwas_groups: NA
+
+# Specify pgs_methods ('ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs','prscsx')
 pgs_methods: NA
 
 # Specify p-value thresholds for ptclump
@@ -28,10 +31,10 @@ ptclump_pts: ['5e-8', '1e-6', '1e-4', '1e-2', '0.1', '0.2', '0.3', '0.4', '0.5',
 # Specify SNP-h2 folds in DBSLMM (1 = corresponds to default model)
 dbslmm_h2f: ['0.8', '1', '1.2']
 
-# Specify phi parameters for PRS-CS
+# Specify phi parameters for PRS-CS and PRS-CSx
 prscs_phi: ['1e-6', '1e-4', '1e-2', '1', 'auto']
 
-# Specify 1kg or ukb ld reference for PRS-CS
+# Specify 1kg or ukb ld reference for PRS-CS and PRS-CSx
 prscs_ldref: '1kg'
 
 # Specify models for LDpred2
diff --git a/pipeline/misc/cross_pop_test/config_cross_pop.yaml b/pipeline/misc/cross_pop_test/config_cross_pop.yaml
new file mode 100644
index 00000000..235fbfa4
--- /dev/null
+++ b/pipeline/misc/cross_pop_test/config_cross_pop.yaml
@@ -0,0 +1,11 @@
+outdir: test_data/output/cross_pop
+refdir: misc/dev/test_data/ref
+config_file: misc/cross_pop_test/config_cross_pop.yaml
+gwas_list: misc/cross_pop_test/gwas_list.txt
+target_list: misc/dev/test_data/config/target_list.txt
+gwas_groups: misc/cross_pop_test/gwas_groups.txt
+pgs_methods: ['ptclump','prscs','prscsx']
+testing: chr22
+cores_target_pgs: 1
+mem_target_pgs: 2000
+prscs_phi: ['auto']
diff --git a/pipeline/misc/cross_pop_test/cross_pop_test.Rmd b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
new file mode 100644
index 00000000..efc38e95
--- /dev/null
+++ b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
@@ -0,0 +1,48 @@
+---
+title: "Testing cross-populations PGS methods"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+knitr::opts_chunk$set(eval = FALSE)
+knitr::opts_knit$set(root.dir = '/scratch/prj/oliverpainfel/Software/MyGit/GenoPred/GenoPredPipe')
+
+library(data.table)
+
+```
+
+***
+
+We will test using the mini test data to speed up testing. The configuration files are in ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/misc/cross_pop_test.
+
+```{bash}
+snakemake -j1 --use-conda --configfile=misc/cross_pop_test/config_cross_pop.yaml output_all -n
+```
+
+***
+
+Test the new set up with OpenSNP data as the target. Initially restrict to chr22 to see if it works as expected.
+
+We need to edit the config file used in the previous run on OpenSNP data.
+
+<details><summary>Show code</summary>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline')
+
+config <- readLines('misc/opensnp/config.yaml')
+
+config[grepl('^testing:', config)]<- 'testing: chr22'
+config <- config[!grepl('^score_list:', config)]
+config[grepl('^outdir:', config)]<- 'outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1'
+config[grepl('^pgs_methods:', config)]<- "pgs_methods: ['ptclump','prscs','prscsx']"
+config<-c(config, 'gwas_groups: misc/cross_pop_test/gwas_groups.txt')
+
+write.table(config, 'misc/opensnp/config_cross_pop.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+```{bash}
+snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_cross_pop.yaml output_all
+```
diff --git a/pipeline/misc/cross_pop_test/gwas_groups.txt b/pipeline/misc/cross_pop_test/gwas_groups.txt
new file mode 100644
index 00000000..213fc7ff
--- /dev/null
+++ b/pipeline/misc/cross_pop_test/gwas_groups.txt
@@ -0,0 +1,2 @@
+name gwas label
+height yengo_eur,yengo_eas "Height (EUR+EAS)"
diff --git a/pipeline/misc/cross_pop_test/gwas_list.txt b/pipeline/misc/cross_pop_test/gwas_list.txt
new file mode 100644
index 00000000..28848fc5
--- /dev/null
+++ b/pipeline/misc/cross_pop_test/gwas_list.txt
@@ -0,0 +1,3 @@
+name path population n sampling prevalence mean sd label
+yengo_eur misc/dev/test_data/gwas/yengo_2022_height_eur.txt.gz EUR NA NA NA NA NA "Yengo 2022 Height EUR"
+yengo_eas misc/dev/test_data/gwas/yengo_2022_height_eas.txt.gz EAS NA NA NA NA NA "Yengo 2022 Height EAS"
diff --git a/pipeline/misc/dev/test_data/gwas/yengo_2022_height_eas.txt.gz b/pipeline/misc/dev/test_data/gwas/yengo_2022_height_eas.txt.gz
new file mode 100644
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diff --git a/pipeline/misc/dev/test_data/gwas/yengo_2022_height_eur.txt.gz b/pipeline/misc/dev/test_data/gwas/yengo_2022_height_eur.txt.gz
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literal 0
HcmV?d00001

diff --git a/pipeline/misc/dev/test_setup.Rmd b/pipeline/misc/dev/test_setup.Rmd
index 55100d1e..ca63a012 100644
--- a/pipeline/misc/dev/test_setup.Rmd
+++ b/pipeline/misc/dev/test_setup.Rmd
@@ -195,9 +195,24 @@ cp -r misc/dev/test_data/output/* /scratch/prj/oliverpainfel/Software/MyGit/Geno
 
 This output can now be used as a reference to check whether commits to the repo effects the output.
 
+***
+
+# Files for cross-population PGS methods
+
+Make mini versions of the Yengo EUR and EAS GWAS sumstats.
+
+```{r}
+library(data.table)
 
+test_snps<-fread('misc/dev/test_data/test_snps.txt', header=F)$V1
 
+for(i in c('eur','eas')){
+  ss<-fread(paste0('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_',i,'.txt'))
+  ss<-ss[ss$variant_id %in% test_snps,]
+  fwrite(ss, paste0('misc/dev/test_data/gwas/yengo_2022_height_',i,'.txt.gz'), quote=F, sep=' ', na='NA')
+}
 
+```
 
 
 
diff --git a/pipeline/misc/opensnp/config_cross_pop.yaml b/pipeline/misc/opensnp/config_cross_pop.yaml
new file mode 100644
index 00000000..3a96354a
--- /dev/null
+++ b/pipeline/misc/opensnp/config_cross_pop.yaml
@@ -0,0 +1,7 @@
+outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1
+config_file: misc/opensnp/config.yaml
+gwas_list: misc/opensnp/gwas_list.txt
+target_list: misc/opensnp/target_list.txt
+pgs_methods: ['ptclump','prscs','prscsx']
+testing: chr22
+gwas_groups: misc/cross_pop_test/gwas_groups.txt
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 4b6f5da9..29ba602c 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -314,7 +314,7 @@ prscs_ref_ukb_dropbox = {
 
 rule download_prscs_ref_ukb:
   output:
-    f"{resdir}/data/prscs_ref/ldblk_ukbb_{{population}}/ldblk_ukbb_chr1.hdf5"
+    f"{resdir}/data/prscs_ref/ukbb/ldblk_ukbb_{{population}}/ldblk_ukbb_chr1.hdf5"
   benchmark:
     f"{resdir}/data/benchmarks/download_prscs_ref_ukb-{{population}}.txt"
   log:
@@ -324,17 +324,17 @@ rule download_prscs_ref_ukb:
   shell:
     """
     {{
-      mkdir -p {resdir}/data/prscs_ref; \
-      rm -r -f {resdir}/data/prscs_ref/ldblk_ukbb_{wildcards.population}; \
-      wget --no-check-certificate -O {resdir}/data/prscs_ref/ldblk_ukbb_{wildcards.population}.tar.gz {params.url}; \
-      tar -zxvf {resdir}/data/prscs_ref/ldblk_ukbb_{wildcards.population}.tar.gz -C {resdir}/data/prscs_ref/; \
-      rm {resdir}/data/prscs_ref/ldblk_ukbb_{wildcards.population}.tar.gz
+      mkdir -p {resdir}/data/prscs_ref/ukbb; \
+      rm -r -f {resdir}/data/prscs_ref/ukbb/ldblk_ukbb_{wildcards.population}; \
+      wget --no-check-certificate -O {resdir}/data/prscs_ref/ukbb/ldblk_ukbb_{wildcards.population}.tar.gz {params.url}; \
+      tar -zxvf {resdir}/data/prscs_ref/ukbb/ldblk_ukbb_{wildcards.population}.tar.gz -C {resdir}/data/prscs_ref/ukbb/; \
+      rm {resdir}/data/prscs_ref/ukbb/ldblk_ukbb_{wildcards.population}.tar.gz
     }} > {log} 2>&1
     """
 
 rule download_prscs_ref_ukb_all:
   input:
-    lambda w: expand(f"{resdir}/data/prscs_ref/ldblk_ukbb_{{population}}/ldblk_ukbb_chr1.hdf5", population=['eur','eas','afr','amr','sas'])
+    lambda w: expand(f"{resdir}/data/prscs_ref/ukbb/ldblk_ukbb_{{population}}/ldblk_ukbb_chr1.hdf5", population=['eur','eas','afr','amr','sas'])
 
 # Download 1KG-based PRScs reference
 prscs_ref_1kg_dropbox = {
@@ -347,7 +347,7 @@ prscs_ref_1kg_dropbox = {
 
 rule download_prscs_ref_1kg:
   output:
-    f"{resdir}/data/prscs_ref/ldblk_1kg_{{population}}/ldblk_1kg_chr1.hdf5"
+    f"{resdir}/data/prscs_ref/1kg/ldblk_1kg_{{population}}/ldblk_1kg_chr1.hdf5"
   benchmark:
     f"{resdir}/data/benchmarks/download_prscs_ref_1kg-{{population}}.txt"
   log:
@@ -357,17 +357,17 @@ rule download_prscs_ref_1kg:
   shell:
     """
     {{
-      mkdir -p {resdir}/data/prscs_ref; \
-      rm -r -f {resdir}/data/prscs_ref/ldblk_1kg_{wildcards.population}; \
-      wget --no-check-certificate -O {resdir}/data/prscs_ref/ldblk_1kg_{wildcards.population}.tar.gz {params.url}; \
-      tar -zxvf {resdir}/data/prscs_ref/ldblk_1kg_{wildcards.population}.tar.gz -C {resdir}/data/prscs_ref/; \
-      rm {resdir}/data/prscs_ref/ldblk_1kg_{wildcards.population}.tar.gz
+      mkdir -p {resdir}/data/prscs_ref/1kg; \
+      rm -r -f {resdir}/data/prscs_ref/1kg/ldblk_1kg_{wildcards.population}; \
+      wget --no-check-certificate -O {resdir}/data/prscs_ref/1kg/ldblk_1kg_{wildcards.population}.tar.gz {params.url}; \
+      tar -zxvf {resdir}/data/prscs_ref/1kg/ldblk_1kg_{wildcards.population}.tar.gz -C {resdir}/data/prscs_ref/1kg/; \
+      rm {resdir}/data/prscs_ref/1kg/ldblk_1kg_{wildcards.population}.tar.gz
     }} > {log} 2>&1
     """
 
 rule download_prscs_ref_1kg_all:
   input:
-    lambda w: expand(f"{resdir}/data/prscs_ref/ldblk_1kg_{{population}}/ldblk_1kg_chr1.hdf5", population=['eur','eas','afr','amr','sas'])
+    lambda w: expand(f"{resdir}/data/prscs_ref/1kg/ldblk_1kg_{{population}}/ldblk_1kg_chr1.hdf5", population=['eur','eas','afr','amr','sas'])
 
 # Download PRScs software
 rule download_prscs_software:
@@ -385,6 +385,60 @@ rule download_prscs_software:
       git reset --hard 621fdc80daac56c93d9528eb1a1187f7b1fc9afb
     }} > {log} 2>&1
     """
+
+# Download PRS-CSx software
+rule download_prscsx_software:
+  output:
+    directory(f"{resdir}/software/prscsx/")
+  benchmark:
+    f"{resdir}/data/benchmarks/download_prscsx_software.txt"
+  log:
+    f"{resdir}/data/logs/download_prscsx_software.log"
+  shell:
+    """
+    {{
+      git clone https://github.com/getian107/PRScsx.git {output}; \
+      cd {output}; \
+      git reset --hard 29a1148875f6ae3f2594b25579f40d4b587c5691
+    }} > {log} 2>&1
+    """
+
+####
+# Download PRS-CSx SNP data for reference
+####
+
+rule download_prscs_snp_data_ukb:
+  output:
+    f"{resdir}/data/prscs_ref/ukbb/snpinfo_mult_ukbb_hm3"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_prscs_snp_data_ukb.txt"
+  log:
+    f"{resdir}/data/logs/download_prscs_snp_data_ukb.log"
+  shell:
+    """
+    {{
+      mkdir -p {resdir}/data/prscs_ref/ukbb; \
+      rm -r -f {resdir}/data/prscs_ref/ukbb/snpinfo_mult_ukbb_hm3; \
+      wget --no-check-certificate -O {resdir}/data/prscs_ref/ukbb/snpinfo_mult_ukbb_hm3 https://www.dropbox.com/s/oyn5trwtuei27qj/snpinfo_mult_ukbb_hm3?dl=0; \
+    }} > {log} 2>&1
+    """
+
+rule download_prscs_snp_data_1kg:
+  output:
+    f"{resdir}/data/prscs_ref/1kg/snpinfo_mult_1kg_hm3"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_prscs_snp_data_ukb.txt"
+  log:
+    f"{resdir}/data/logs/download_prscs_snp_data_ukb.log"
+  shell:
+    """
+    {{
+      mkdir -p {resdir}/data/prscs_ref/1kg; \
+      rm -r -f {resdir}/data/prscs_ref/1kg/snpinfo_mult_1kg_hm3; \
+      wget --no-check-certificate -O {resdir}/data/prscs_ref/1kg/snpinfo_mult_1kg_hm3 https://www.dropbox.com/s/rhi806sstvppzzz/snpinfo_mult_1kg_hm3?dl=0; \
+    }} > {log} 2>&1
+    """
+
 # Download gctb reference
 rule download_gctb_ref:
   output:
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 4933ebac..e3b91536 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -186,7 +186,7 @@ rule prep_pgs_prscs_i:
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
     rules.download_prscs_software.output,
-    lambda w: f"{resdir}/data/prscs_ref/ldblk_" + prscs_ldref + "_" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0].lower() + "/ldblk_1kg_chr1.hdf5"
+    lambda w: f"{resdir}/data/prscs_ref/" + prscs_ldref + "/ldblk_" + prscs_ldref + "_" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0].lower() + "/ldblk_1kg_chr1.hdf5"
   output:
     f"{outdir}/reference/pgs_score_files/prscs/{{gwas}}/ref-{{gwas}}.score.gz"
   conda:
@@ -211,7 +211,7 @@ rule prep_pgs_prscs_i:
     --output {outdir}/reference/pgs_score_files/prscs/{wildcards.gwas}/ref-{wildcards.gwas} \
     --pop_data {refdir}/ref.pop.txt \
     --PRScs_path {resdir}/software/prscs/PRScs.py \
-    --PRScs_ref_path {resdir}/data/prscs_ref/ldblk_{prscs_ldref}_{params.population} \
+    --PRScs_ref_path {resdir}/data/prscs_ref/{prscs_ldref}/ldblk_{prscs_ldref}_{params.population} \
     --n_cores {threads} \
     --phi_param {params.phi} \
     --test {params.testing} > {log} 2>&1
@@ -479,6 +479,83 @@ checkpoint score_reporter:
   shell:
     "Rscript ../Scripts/pipeline_misc/score_reporter.R {params.config_file} > {log} 2>&1"
 
+###########
+# Multi-ancestry methods
+###########
+
+# Read in the gwas_groups or make an empty version
+if 'gwas_groups' in config and config["gwas_groups"] != 'NA':
+  gwas_groups_df = pd.read_table(config["gwas_groups"], sep=r'\s+')
+else:
+  gwas_groups_df = pd.DataFrame(columns = ["name", "gwas", "label"])
+
+# Function to get the list of GWAS names for a given group
+def get_gwas_names(gwas_group):
+    gwas_names_str = gwas_groups_df[gwas_groups_df['name'] == gwas_group]['gwas'].iloc[0]
+    return gwas_names_str.split(',')
+
+# Function to generate comma-separated list of populations for each name
+def get_populations(gwas_group):
+    gwas_names = get_gwas_names(gwas_group)
+    sumstats_populations = []
+    for gwas in gwas_names:
+        gwas_info = gwas_list_df[gwas_list_df['name'] == gwas].iloc[0]
+        sumstats_populations.append(gwas_info['population'])
+    return sumstats_populations
+
+####
+# PRS-CSx
+####
+
+# Note. Threads are set to 1, and phi and chr are run in parallel. Increasing number of threads shows no improvement in speed.
+
+rule prep_pgs_prscsx_i:
+  resources:
+    mem_mb=2000*config['cores_prep_pgs'],
+    time_min=800
+  threads: config['cores_prep_pgs']
+  input:
+    rules.download_prscsx_software.output,
+    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
+    lambda w: expand(f"{resdir}/data/prscs_ref/{prscs_ldref}/ldblk_{prscs_ldref}_{{population}}/ldblk_{prscs_ldref}_chr1.hdf5", population=[pop.lower() for pop in get_populations(w.gwas_group)]),
+    f"{resdir}/data/prscs_ref/{prscs_ldref}/snpinfo_mult_{prscs_ldref}_hm3"
+  output:
+    f"{outdir}/reference/pgs_score_files/prscsx/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
+  conda:
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/prep_pgs_prscsx_i-{{gwas_group}}.txt"
+  log:
+    f"{outdir}/reference/logs/prep_pgs_prscsx_i-{{gwas_group}}.log"
+  params:
+    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
+    populations= lambda w: ",".join(get_populations(w.gwas_group)),
+    phi= ",".join(map(str, config["prscs_phi"])),
+    testing=config["testing"]
+  shell:
+    """
+    export MKL_NUM_THREADS=1; \
+    export NUMEXPR_NUM_THREADS=1; \
+    export OMP_NUM_THREADS=1; \
+    export OPENBLAS_NUM_THREADS=1; \
+    Rscript ../Scripts/pgs_methods/prscsx.R \
+      --ref_plink_chr {refdir}/ref.chr \
+      --sumstats {params.sumstats} \
+      --populations {params.populations} \
+      --prscsx_ref_path {resdir}/data/prscs_ref/{prscs_ldref} \
+      --phi_param {params.phi} \
+      --pop_data {refdir}/ref.pop.txt \
+      --prscsx_path {resdir}/software/prscsx/PRScsx.py \
+      --output {outdir}/reference/pgs_score_files/prscsx/{wildcards.gwas_group}/ref-{wildcards.gwas_group} \
+      --test {params.testing} \
+      --n_cores {threads} > {log} 2>&1
+    """
+
+rule prep_pgs_prscsx:
+  input: expand(f"{outdir}/reference/pgs_score_files/prscsx/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'])
+
+###############################################
+
 ##
 # Use a rule to check requested PGS methods have been run for all GWAS
 ##
@@ -501,6 +578,8 @@ if 'megaprs' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_megaprs.input)
 if 'external' in pgs_methods_all:
   pgs_methods_input.append(rules.score_reporter.output)
+if 'prscsx' in pgs_methods_all:
+  pgs_methods_input.append(rules.prep_pgs_prscsx.input)
 
 rule prep_pgs:
   input:

From 7498351c4395b461e473500228f2af12466abc5a Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 5 Aug 2024 13:15:13 +0100
Subject: [PATCH 002/153] Completed benchmark using PRS-CSx in opensnp data,
 and added in x-wing method

---
 Scripts/pgs_methods/xwing.R                   | 335 ++++++++++++++++++
 docs/Images/OpenSNP/genopred-prscsx.png       | Bin 0 -> 211258 bytes
 docs/Images/OpenSNP/genopred-yengo_eas.png    | Bin 80279 -> 76960 bytes
 .../OpenSNP/genopred-yengo_eur-external.png   | Bin 107597 -> 103806 bytes
 docs/Images/OpenSNP/genopred-yengo_eur.png    | Bin 100887 -> 97028 bytes
 .../OpenSNP/time_cpu_bench_pgs_methods.csv    |  16 +-
 .../time_cpu_bench_pgs_methods_prscsx.csv     |   2 +
 .../time_cpu_bench_pgs_methods_prscsx.png     | Bin 0 -> 73168 bytes
 docs/opensnp_benchmark.Rmd                    | 245 +++++++++++--
 docs/opensnp_benchmark.html                   | 255 ++++++++++---
 functions/constants.R                         |   4 +-
 functions/pipeline.R                          |   6 +-
 pipeline/envs/xwing.yaml                      |  20 ++
 .../misc/cross_pop_test/cross_pop_test.Rmd    | 117 ++++++
 pipeline/misc/opensnp/config.yaml             |   8 +-
 pipeline/misc/opensnp/config_cross_pop.yaml   |   2 +-
 pipeline/misc/opensnp/gwas_groups.txt         |   2 +
 pipeline/misc/opensnp/target_list.txt         |   2 +-
 pipeline/rules/dependencies.smk               | 177 +++++++++
 pipeline/rules/pgs_methods.smk                |  56 +++
 pipeline/rules/target_qc.smk                  |   3 +
 21 files changed, 1153 insertions(+), 97 deletions(-)
 create mode 100644 Scripts/pgs_methods/xwing.R
 create mode 100644 docs/Images/OpenSNP/genopred-prscsx.png
 create mode 100644 docs/Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.csv
 create mode 100644 docs/Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.png
 create mode 100644 pipeline/envs/xwing.yaml
 create mode 100644 pipeline/misc/opensnp/gwas_groups.txt

diff --git a/Scripts/pgs_methods/xwing.R b/Scripts/pgs_methods/xwing.R
new file mode 100644
index 00000000..31892bfb
--- /dev/null
+++ b/Scripts/pgs_methods/xwing.R
@@ -0,0 +1,335 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
+      help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--xwing_repo", action="store", default=NULL, type='character',
+      help="Path to X-WING repo [required]"),
+  make_option("--logodetect_ref", action="store", default=NULL, type='character',
+      help="Path LOGODetect reference data [required]"),
+  make_option("--panther_ref", action="store", default=NULL, type='character',
+      help="Path PANTHER reference data [required]"),
+  make_option("--leopard_ref", action="store", default=NULL, type='character',
+      help="Path LEOPARD reference data [required]"),
+  make_option("--panther_leopard_ref", action="store", default=NULL, type='character',
+      help="Path subsampled reference data for PANTHER/LEOPARD [required]"),
+  make_option("--pop_data", action="store", default=NULL, type='character',
+      help="File containing the population code and location of the keep file [required]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+      help="Path PLINK v2 software binary [optional]"),
+  make_option("--output", action="store", default=NULL, type='character',
+      help="Path for output files [required]"),
+  make_option("--memory", action="store", default=5000, type='numeric',
+      help="Memory limit [optional]"),
+  make_option("--sumstats", action="store", default=NULL, type='character',
+      help="Comma-seperated list of GWAS summary statistics [required]"),
+  make_option("--populations", action="store", default=NULL, type='character',
+      help="Comma-seperated list of population codes matching GWAS [required]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+      help="Number of cores for parallel computing [optional]"),
+  make_option("--test", action="store", default=NA, type='character',
+      help="Specify number of SNPs to include [optional]"),
+  make_option("--seed", action="store", default=1, type='numeric',
+      help="Seed number for PRScs [optional]")
+)
+
+opt = parse_args(OptionParser(option_list = option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+library(foreach)
+library(doMC)
+registerDoMC(opt$n_cores)
+
+# Check required inputs
+if(is.null(opt$ref_plink_chr)){
+  stop('--ref_plink_chr must be specified.\n')
+}
+if(is.null(opt$sumstats)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$pop_data)){
+  stop('--pop_data must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--output must be specified.\n')
+}
+if(is.null(opt$xwing_repo)){
+  stop('--xwing_repo must be specified.\n')
+}
+if(is.null(opt$logodetect_ref)){
+  stop('--logodetect_ref must be specified.\n')
+}
+if(is.null(opt$panther_ref)){
+  stop('--panther_ref must be specified.\n')
+}
+if(is.null(opt$leopard_ref)){
+  stop('--leopard_ref must be specified.\n')
+}
+if(is.null(opt$panther_leopard_ref)){
+  stop('--panther_leopard_ref must be specified.\n')
+}
+if(is.null(opt$populations)){
+  stop('--populations must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
+system(paste0('mkdir -p ',opt$output_dir))
+
+# Create temp directory
+tmp_dir <- tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'prscsx.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+# Split opt$sumstats
+sumstats<-unlist(strsplit(opt$sumstats, ','))
+log_add(log_file = log_file, message = paste0(length(sumstats), ' sets of GWAS have been provided.'))
+
+# Split opt$sumstats
+populations<-unlist(strsplit(opt$populations, ','))
+
+gwas_N<-NULL
+for(i in 1:length(sumstats)){
+
+  #####
+  # Read in sumstats
+  #####
+
+  log_add(log_file = log_file, message = 'Reading in GWAS.')
+
+  # Read in, check and format GWAS summary statistics
+  gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('CHR', 'SNP', 'BP', 'A1', 'A2', 'BETA', 'P', 'N'))
+
+  # Store average sample size
+  gwas_N <- c(gwas_N, round(mean(gwas$N), 0))
+  gwas$N<-NULL
+
+  fwrite(gwas, paste0(tmp_dir, '/GWAS_sumstats_',i,'_temp.txt'), sep=' ')
+
+  rm(gwas)
+  gc()
+
+
+}
+
+# Record start time for test
+if(!is.na(opt$test)){
+  test_start.time <- test_start(log_file = log_file)
+}
+
+#####
+# Process sumstats using X-WING
+#####
+
+# Make a list of GWAS pairs to be analysed
+gwas_pairs <- combn(1:length(sumstats), 2)
+gwas_pairs <- as.matrix(data.frame(gwas_1 = gwas_pairs[1, ], gwas_2 = gwas_pairs[2, ]))
+
+# Run LOGODetect
+dir.create(paste0(tmp_dir,'/LOGODetect'))
+
+for(i in 1:nrow(gwas_pairs)){
+  system(paste0(
+    'Rscript ', opt$xwing_repo, '/LOGODetect.R --sumstats ', paste0(paste0(tmp_dir, '/GWAS_sumstats_', gwas_pairs[i,],'_temp.txt'), collapse=','),' --n_gwas ', paste0(gwas_N[gwas_pairs[i,]], collapse=','), ' --ref_dir ', opt$logodetect_ref,' --pop ', paste0(populations[gwas_pairs[i,]], collapse=',') ,' --block_partition ', opt$xwing_repo,'/block_partition.txt --gc_snp ', opt$xwing_repo,'/1kg_hm3_snp.txt --out_dir ', tmp_dir, '/LOGODetect --n_cores ', opt$n_core,' --target_pop ', opt$populations,' --n_topregion 1000'
+  ))
+}
+
+##
+# Run PANTHER
+##
+# Create a temporary reference bim files for X-WING to match
+pvar <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
+pvar$POS<-0
+for(i in CHROMS){
+  write.table(pvar[pvar$CHR == i, c('CHR','SNP','POS','BP','A1','A2'), with=F], paste0(tmp_dir,'/ref.chr',i,'.bim'), col.names=F, row.names=F, quote=F)
+}
+
+rm(pvar)
+gc()
+
+combinations <- expand.grid(targ_pop = populations, pst_pop = populations, chr = CHROMS)
+
+log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+
+  targ_pop <- combinations$targ_pop[i]
+  pst_pop <- combinations$pst_pop[i]
+  chr <- combinations$chr[i]
+
+  # Create directories
+  dir.create(paste0(tmp_dir, '/PANTHER/post_targ_', targ_pop), recursive = TRUE)
+  dir.create(paste0(tmp_dir, '/PANTHER/post_collect_targ_', targ_pop), recursive = TRUE)
+
+  system(paste0(
+    'python ', opt$xwing_repo, '/PANTHER.py --ref_dir ', opt$panther_ref, ' --bim_prefix ', tmp_dir,'/ref.chr',chr,' --sumstats ', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','), ' --n_gwas ', paste(gwas_N, collapse=','), ' --anno_file ', paste0(paste0(tmp_dir, '/LOGODetect/targ_', targ_pop, '_annot_', populations, '.txt'), collapse=','), ' --chrom ', chr, ' --pop ', opt$populations ,' --target_pop ', targ_pop,' --pst_pop ', pst_pop, ' --out_name output --seed 1 --out_dir ', tmp_dir, '/PANTHER/post_targ_', targ_pop
+  ))
+}
+
+##
+# Run LEOPARD to subsample GWAS
+##
+dir.create(paste0(tmp_dir,'/LEOPARD/sampled_sumstats'), recursive = T)
+
+for(i in 1:length(sumstats)){
+  system(paste0(
+    'Rscript ', opt$xwing_repo, '/LEOPARD_Sim.R --sumstats ', tmp_dir, '/GWAS_sumstats_', i,'_temp.txt --n_gwas ', gwas_N[i], ' --train_prop 0.75 --ref_prefix ', opt$leopard_ref,'/', populations[i], '/', populations[i], '_part1 --seed ', opt$seed, ' --rep 4 --out_prefix ', tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', i
+  ))
+}
+
+##
+# Run PANTHER on subsampled GWAS
+##
+
+combinations <- expand.grid(targ_pop = populations, pst_pop = populations, chr = CHROMS, index = 1:4)
+
+log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+  targ_pop <- combinations$targ_pop[i]
+  pst_pop <- combinations$pst_pop[i]
+  chr <- combinations$chr[i]
+  index <- combinations$index[i]
+
+  dir.create(paste0(tmp_dir,'/LEOPARD/post_targ_', targ_pop), recursive = T)
+  dir.create(paste0(tmp_dir,'/LEOPARD/post_collect_targ_', targ_pop), recursive = T)
+
+  sumstats_i <- paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt')
+  sumstats_i[populations == targ_pop] <- paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', index, '_train.txt')
+
+  targ_gwas_train_n<-fread(paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', index, '_train_valid_N.txt'))$N_train
+
+  gwas_N_i<-gwas_N
+  gwas_N_i[populations == targ_pop] <- targ_gwas_train_n
+
+  system(paste0(
+    'python ', opt$xwing_repo, '/PANTHER.py --ref_dir ', opt$panther_leopard_ref, ' --bim_prefix ', tmp_dir,'/ref.chr',chr,' --sumstats ', paste0(sumstats_i, collapse=','), ' --n_gwas ', paste(gwas_N_i, collapse=','), ' --anno_file ', paste0(paste0(tmp_dir, '/LOGODetect/targ_', targ_pop, '_annot_', populations, '.txt'), collapse=','), ' --chrom ', chr, ' --pop ', opt$populations ,' --target_pop ', targ_pop,' --pst_pop ', pst_pop, ' --out_name output_', index, ' --seed 1 --out_dir ', tmp_dir, '/LEOPARD/post_targ_', targ_pop
+  ))
+}
+
+for(targ_pop in populations){
+  for(pst_pop in populations){
+    for(i in 1:4){
+      system(paste0('cat ', tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i,'_', pst_pop, '_pst_eff_phiauto_chr*.txt > ', tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i,'_', pst_pop, '_Post.txt'))
+      system(paste0("sed  -i '1iCHR\tSNP\tBP\tA1\tA2\tBETA' ", tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i, '_', pst_pop, '_Post.txt'))
+    }
+  }
+}
+
+##
+# Run LEOPARD to to find optimal weights for GWAS from each population
+##
+
+# Estimating the linear combination weights
+for(targ_pop in populations){
+  dir.create(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop), recursive = T)
+
+  for(i in 1:4){
+
+    targ_gwas_valid_n<-fread(paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', i, '_train_valid_N.txt'))$N_valid
+
+    system(paste0(
+      'Rscript ', opt$xwing_repo, '/LEOPARD_Weights.R --beta_file ', paste0(paste0(tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i, '_', populations, '_Post.txt'), collapse=','), ' --valid_file ', tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', i, '_valid.txt --n_valid ', targ_gwas_valid_n ,' --ref_prefix ', opt$leopard_ref,'/', targ_pop, '/', targ_pop, '_part3 --out ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep', i, '.txt'
+    ))
+  }
+}
+
+# Average weights across repeats
+for(targ_pop in populations){
+  system(paste0(
+    'Rscript ', opt$xwing_repo, '/LEOPARD_Avg.R --weights_prefix ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep --rep 4 --out ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_avg.txt'
+  ))
+}
+
+####
+# Combine score files
+####
+
+# We should combine the raw PANTHER score files for each population,
+# and then combine using mixing weights for each population
+score_all<-NULL
+for(targ_pop in populations){
+  mix_weights<-fread(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_avg.txt'))
+  score_pop <- NULL
+  for(pst_pop in populations){
+    score_i<-NULL
+    for(chr in CHROMS){
+      score_i_chr<-fread(paste0(tmp_dir, '/PANTHER/post_targ_', targ_pop, '/output_', pst_pop, '_pst_eff_phiauto_chr', chr,'.txt'), header=F)
+      score_i<-rbind(score_i, score_i_chr)
+    }
+
+    score_i$V6 <- score_i$V6 * mix_weights$Weights[grepl(paste0(pst_pop,'_Post.txt'), mix_weights$Path)]
+
+    names(score_i)<-c('CHR','SNP', 'BP', 'A1', 'A2', paste0('SCORE_targ_', targ_pop, '_pst_', pst_pop))
+    score_i<-score_i[, c('SNP', 'A1', 'A2', paste0('SCORE_targ_', targ_pop, '_pst_', pst_pop)), with=F]
+
+    if(is.null(score_pop)){
+      score_pop<-score_i
+    } else {
+      score_pop<-merge(score_pop, score_i, by=c('SNP','A1','A2'), all=T)
+    }
+  }
+  # Take average of weighted scores
+  score_pop[is.na(score_pop)]<-0
+  score_pop[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <- rowSums(score_pop[, grepl('SCORE_', names(score_pop)), with = F])
+
+  if(is.null(score_all)){
+    score_all<-score_pop
+  } else {
+    score_all<-merge(score_all, score_pop, by=c('SNP','A1','A2'), all=T)
+  }
+}
+
+# Flip effects to match reference alleles
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
+score_new <- map_score(ref = ref, score = score_all)
+
+fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+
+if(file.exists(paste0(opt$output,'.score.gz'))){
+  system(paste0('rm ',opt$output,'.score.gz'))
+}
+
+system(paste0('gzip ',opt$output,'.score'))
+
+# Record end time of test
+if(!is.na(opt$test)){
+  test_finish(log_file = log_file, test_start.time = test_start.time)
+}
+
+####
+# Calculate mean and sd of polygenic scores
+####
+
+log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
+
+# Calculate scores in the full reference
+ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
+
+# Calculate scale within each reference population
+pop_data <- read_pop_data(opt$pop_data)
+
+for(pop_i in unique(pop_data$POP)){
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
diff --git a/docs/Images/OpenSNP/genopred-prscsx.png b/docs/Images/OpenSNP/genopred-prscsx.png
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diff --git a/docs/Images/OpenSNP/time_cpu_bench_pgs_methods.csv b/docs/Images/OpenSNP/time_cpu_bench_pgs_methods.csv
index d2104b00..edb5b0bb 100644
--- a/docs/Images/OpenSNP/time_cpu_bench_pgs_methods.csv
+++ b/docs/Images/OpenSNP/time_cpu_bench_pgs_methods.csv
@@ -1,8 +1,8 @@
-method,s,h:m:s,max_rss,max_vms,max_uss,max_pss,io_in,io_out,mean_load,cpu_time,file,rule,label
-dbslmm,387.1521,0:06:27,1257.02,5567.45,1242.86,1250.16,0,713.19,13.77,301.95,prep_pgs_dbslmm_i-yengo_eur.txt,prep_pgs_dbslmm_i,DBSLMM
-lassosum,228.9833,0:03:48,7167.67,8927.01,7160.91,7161.64,0,1345.18,50.11,116.3,prep_pgs_lassosum_i-yengo_eur.txt,prep_pgs_lassosum_i,lassosum
-ldpred2,5521.0664,1:32:01,29245.23,542155.61,29241.36,29243.24,0,19475.16,38.51,2170.81,prep_pgs_ldpred2_i-yengo_eur.txt,prep_pgs_ldpred2_i,LDpred2
-megaprs,3473.4907,0:57:53,12611.15,527653.99,12614.9,12615.47,0,22023.21,314.81,11280.38,prep_pgs_megaprs_i-yengo_eur.txt,prep_pgs_megaprs_i,MegaPRS
-prscs,13546.2044,3:45:46,11923.03,16897.57,7606.82,7999.95,2.88,424.04,949.94,130232.23,prep_pgs_prscs_i-yengo_eur.txt,prep_pgs_prscs_i,PRS-CS
-ptclump,56.4041,0:00:56,861.47,517032.2,847.8,849.01,0,443.54,27.71,19.04,prep_pgs_ptclump_i-yengo_eur.txt,prep_pgs_ptclump_i,pT+clump
-sbayesr,2361.1658,0:39:21,23783.7,45912.44,20857.93,21125.59,0,2818.16,893.4,21218.06,prep_pgs_sbayesr_i-yengo_eur.txt,prep_pgs_sbayesr_i,SBayesR
+method,s,h:m:s,max_rss,max_vms,max_uss,max_pss,io_in,io_out,mean_load,cpu_time,file,rule,label.x,label.y,label
+dbslmm,387.1521,0:06:27,1257.02,5567.45,1242.86,1250.16,0,713.19,13.77,301.95,prep_pgs_dbslmm_i-yengo_eur.txt,prep_pgs_dbslmm_i,DBSLMM,DBSLMM,DBSLMM
+lassosum,228.9833,0:03:48,7167.67,8927.01,7160.91,7161.64,0,1345.18,50.11,116.3,prep_pgs_lassosum_i-yengo_eur.txt,prep_pgs_lassosum_i,lassosum,lassosum,lassosum
+ldpred2,5521.0664,1:32:01,29245.23,542155.61,29241.36,29243.24,0,19475.16,38.51,2170.81,prep_pgs_ldpred2_i-yengo_eur.txt,prep_pgs_ldpred2_i,LDpred2,LDpred2,LDpred2
+megaprs,3473.4907,0:57:53,12611.15,527653.99,12614.9,12615.47,0,22023.21,314.81,11280.38,prep_pgs_megaprs_i-yengo_eur.txt,prep_pgs_megaprs_i,MegaPRS,MegaPRS,MegaPRS
+prscs,13546.2044,3:45:46,11923.03,16897.57,7606.82,7999.95,2.88,424.04,949.94,130232.23,prep_pgs_prscs_i-yengo_eur.txt,prep_pgs_prscs_i,PRS-CS,PRS-CS,PRS-CS
+ptclump,56.4041,0:00:56,861.47,517032.2,847.8,849.01,0,443.54,27.71,19.04,prep_pgs_ptclump_i-yengo_eur.txt,prep_pgs_ptclump_i,pT+clump,pT+clump,pT+clump
+sbayesr,2361.1658,0:39:21,23783.7,45912.44,20857.93,21125.59,0,2818.16,893.4,21218.06,prep_pgs_sbayesr_i-yengo_eur.txt,prep_pgs_sbayesr_i,SBayesR,SBayesR,SBayesR
diff --git a/docs/Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.csv b/docs/Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.csv
new file mode 100644
index 00000000..2b357e02
--- /dev/null
+++ b/docs/Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.csv
@@ -0,0 +1,2 @@
+method,s,h:m:s,max_rss,max_vms,max_uss,max_pss,io_in,io_out,mean_load,cpu_time,file,rule,label
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diff --git a/docs/Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.png b/docs/Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.png
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diff --git a/docs/opensnp_benchmark.Rmd b/docs/opensnp_benchmark.Rmd
index 13687c35..9fcad4e8 100644
--- a/docs/opensnp_benchmark.Rmd
+++ b/docs/opensnp_benchmark.Rmd
@@ -51,7 +51,7 @@ mv 'CrowdAI_v3.tar.gz?download=1' CrowdAI_v3.tar.gz
 tar -xvzf CrowdAI_v3.tar.gz
 rm CrowdAI_v3.tar.gz
 
-# Use fullset (training) data as the target for GenoPred, as the sample size is larger
+# Use both training and testing data to maximise sample size for testing.
 ```
 
 </details>
@@ -65,14 +65,22 @@ rm CrowdAI_v3.tar.gz
 ```{r}
 library(data.table)
 
-dat <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/training_set_details.txt')
-dat$FID <- 0
-dat$IID <- dat$id
-dat <- dat[, c('FID', 'IID', 'height'), with = F]
+# Training 
+train_dat <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/training_set_details.txt')
+train_dat$FID <- train_dat$id
+train_dat$IID <- train_dat$id
+train_dat <- train_dat[, c('FID', 'IID', 'height'), with = F]
 
+# Test 
+test_dat <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/test_set_details_SECRET.txt')
+test_dat$FID <- test_dat$id
+test_dat$IID <- test_dat$id
+test_dat <- test_dat[, c('FID', 'IID', 'height'), with = F]
+
+all_dat<-rbind(train_dat, test_dat)
 dir.create('/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno', recursive = T)
 write.table(
-  dat,
+  all_dat,
   '/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno/height.txt',
   col.names = T,
   row.names = F,
@@ -89,18 +97,27 @@ write.table(
 <details><summary>Show code</summary>
 
 ```{bash}
+module add bcftools/1.12-gcc-13.2.0-python-3.11.6
+
 # Create index
-module add bcftools/1.14-gcc-10.3.0-python3+-chk-version
 bcftools index /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_train.vcf.gz
 
+bcftools index /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_test.vcf.gz
+
+# Merge the vcfs for training and testing data
+bcftools merge \
+  /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_train.vcf.gz \
+  /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_test.vcf.gz \
+  -o /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_merged.vcf.gz
+
 # Now, split by chromosome using plink2
 # Run on the command line within pipeline conda environment
 mkdir /users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno
 for chr in $(seq 1 22);do
-    plink2 \
-        --vcf /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_train.vcf.gz \
+    /users/k1806347/oliverpainfel/Software/plink2 \
+        --vcf /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_merged.vcf.gz \
         --chr ${chr} \
-        --out /users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_train.chr${chr} \
+        --out /users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_merged.chr${chr} \
         --export vcf bgz
 done
 ```
@@ -134,12 +151,13 @@ library(data.table)
 
 # Create config file
 conf <- c(
-  'outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test5',
+  'outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test6',
   'config_file: misc/opensnp/config.yaml',
   'gwas_list: misc/opensnp/gwas_list.txt',
   'score_list: misc/opensnp/score_list.txt',
   'target_list: misc/opensnp/target_list.txt',
-  "pgs_methods: ['ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs']",
+  'gwas_groups: misc/opensnp/gwas_groups.txt',
+  "pgs_methods: ['ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs','prscsx']",
   'testing: NA'
 )
 
@@ -148,11 +166,11 @@ dir.create('/users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred', recursive = T)
 write.table(conf, 'misc/opensnp/config.yaml', col.names = F, row.names = F, quote = F)
 
 # Create target_list
-target_list <- fread('example_input/target_list_example.txt')
+target_list <- fread('example_input/target_list.txt')
 target_list <- rbind(target_list,
                      data.table(name = 'opensnp',
-                                path = '/users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_train',
-                                type = 'samp_imp_vcf',
+                                path = '/users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_merged',
+                                type = 'vcf',
                                 indiv_report = F))
 
 target_list <- target_list[target_list$name == 'opensnp', ]
@@ -160,7 +178,7 @@ target_list <- target_list[target_list$name == 'opensnp', ]
 write.table(target_list, 'misc/opensnp/target_list.txt', col.names = T, row.names = F, quote = F, sep = ' ')
 
 # Create gwas_list
-gwas_list <- fread('example_input/gwas_list_example.txt')
+gwas_list <- fread('example_input/gwas_list.txt')
 gwas_list<-rbind(gwas_list, 
                  data.table(name='yengo_eur',
                             path = '/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eur.txt',
@@ -188,6 +206,15 @@ gwas_list <- gwas_list[gwas_list$name %in% c('yengo_eur', 'yengo_eas'), ]
 
 write.table(gwas_list, 'misc/opensnp/gwas_list.txt', col.names = T, row.names = F, quote = F, sep = ' ')
 
+# Create gwas_groups
+gwas_groups <- data.frame(
+  name = 'height',
+  gwas = 'yengo_eur,yengo_eas',
+  label = "\"Yengo 2022 Height EUR+EAS\""
+)
+
+write.table(gwas_groups, 'misc/opensnp/gwas_groups.txt', col.names = T, row.names = F, quote = F, sep = ' ')
+
 # Create score_list
 score_list <- data.frame(
   name = 'PGS002804',
@@ -228,8 +255,8 @@ library(cowplot)
 # Read in configuration specific benchmark files
 bm_files_i <-
   paste0(
-    '/users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test5/reference/benchmarks/',
-    list.files('/users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test5/reference/benchmarks/')
+    '/users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test6/reference/benchmarks/',
+    list.files('/users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test6/reference/benchmarks/')
   )
 
 # Read in benchmark files
@@ -246,9 +273,8 @@ bm_dat_all$rule <- gsub('-.*','',bm_dat_all$file)
 # PGS methods
 #####
 # Look at the memory required for each PGS method using the EUR GWAS
-bm_dat_yengo_eur <-
-  bm_dat_all[grepl('prep_pgs', bm_dat_all$file) &
-               grepl('yengo_eur.txt', bm_dat_all$file), ]
+bm_dat_pgs_prep <-
+  bm_dat_all[grepl('prep_pgs', bm_dat_all$file), ]
 
 bm_dat_yengo_eur$method <- gsub('_i', '', gsub('prep_pgs_','',bm_dat_yengo_eur$rule))
 bm_dat_yengo_eur<-merge(bm_dat_yengo_eur, pgs_method_labels, by='method')
@@ -275,6 +301,36 @@ dev.off()
 
 write.csv(bm_dat_yengo_eur, 'Images/OpenSNP/time_cpu_bench_pgs_methods.csv', row.names=F, quote=F)
 
+# Look at time and memory for PRS-CSx analysis
+bm_dat_height <-
+  bm_dat_all[grepl('prep_pgs', bm_dat_all$file) &
+               grepl('height.txt', bm_dat_all$file), ]
+
+bm_dat_height$method <- gsub('_i', '', gsub('prep_pgs_','',bm_dat_height$rule))
+bm_dat_height<-merge(bm_dat_height, pgs_method_labels, by='method')
+
+pgs_method_time <-
+  ggplot(bm_dat_height, aes(x = label, y = s, fill = label)) +
+  geom_bar(stat = "identity", position = "dodge") +
+  labs(x = "PGS Method", y = "Time (seconds)") +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
+
+pgs_method_mem <-
+  ggplot(bm_dat_height, aes(x = label, y = max_rss, fill = label)) +
+  geom_bar(stat = "identity", position="dodge") +
+  labs(x = "PGS Method", y = "Max Memory (Mb)") +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
+
+png('Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.png', res = 300, width = 800, height = 1800, units = 'px')
+  plot_grid(pgs_method_time, pgs_method_mem, nrow=2)
+dev.off()
+
+write.csv(bm_dat_height, 'Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.csv', row.names=F, quote=F)
+
 ```
 </details>
 
@@ -294,6 +350,16 @@ write.csv(bm_dat_yengo_eur, 'Images/OpenSNP/time_cpu_bench_pgs_methods.csv', row
 
 </details>
 
+<details><summary>Show PRS-CSx benchmark</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 50%;">
+![Time and max. memory used by PRS-CSx](Images/OpenSNP/time_cpu_bench_pgs_methods.png)
+</div>
+</div>
+
+</details>
+
 ***
 
 ## Evaluate PGS
@@ -314,12 +380,6 @@ source_all('../functions')
 # Read in pheno data
 pheno <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno/height.txt')
 
-# Define pgs_methods used
-pgs_methods <- c('external',read_param(config = 'misc/opensnp/config.yaml', param = 'pgs_methods', return_obj = F))
-
-# Define pgs_methods applied to non-EUR GWAS
-pgs_methods_eur <- c('ptclump','lassosum','megaprs','prscs','dbslmm')
-
 # Read in PGS
 pgs <- read_pgs(config = 'misc/opensnp/config.yaml', name = 'opensnp')$opensnp
 
@@ -357,7 +417,15 @@ for(pop_i in names(pgs)){
   }
 }
 
-# The European sample is the only one large enough for interpretable results
+# Save the results
+dir.create('/scratch/prj/oliverpainfel/Data/OpenSNP/assoc')
+write.csv(
+  cor,
+  '/scratch/prj/oliverpainfel/Data/OpenSNP/assoc/genopred-yengo-assoc.csv',
+  row.names = F
+)
+
+# The European sample is the only one large enough for interpretable results. All other populations are <20 individuals
 # Subset EUR results
 cor_eur <- cor[cor$pop == 'EUR', ]
 
@@ -379,13 +447,24 @@ for(pop_i in unique(cor_eur$pop)){
         top_i$model <- 'Top'
         cor_eur_subset <- rbind(cor_eur_subset, top_i)
       }
-  
+      if(pgs_method_i %in% c('prscsx')){
+        for(targ_i in unique(gsub('.*_','', gsub('_phi.*','',cor_eur_i$name)))){
+          cor_eur_k<-cor_eur_i[grepl(paste0(targ_i,'_phi_'), cor_eur_i$name),]
+          top_i <- cor_eur_k[which(cor_eur_k$r == max(cor_eur_k$r, na.rm = T))[1],]
+          top_i$model <- 'Top'
+          top_i$pgs_method<-paste0(top_i$pgs_method, " (", targ_i, ")")
+          cor_eur_subset <- rbind(cor_eur_subset, top_i)
+        }
+      }  
+      
       # PseudoVal
-      if(pgs_method_i %in% c('ptclump','sbayesr','ldpred2','megaprs','prscs','lassosum','dbslmm')){
-        cor_eur_i$name <- gsub(paste0(gwas_i, '_'), '', cor_eur_i$name)
+      if(pgs_method_i %in% c('ptclump','sbayesr','ldpred2','megaprs','prscs','lassosum','dbslmm','prscsx')){
         pseudo_param <- find_pseudo(config = 'misc/opensnp/config.yaml', gwas = gwas_i, pgs_method = pgs_method_i)
-        pseudo_i <- cor_eur_i[cor_eur_i$name == pseudo_param,]
+        pseudo_i <- cor_eur_i[grepl(paste0(pseudo_param,'$'), cor_eur_i$name),]
         pseudo_i$model <- 'Pseudo'
+        if(pgs_method_i %in% c('prscsx')){
+          pseudo_i$pgs_method<-paste0(pseudo_i$pgs_method, " (", gsub('.*_','', gsub(paste0('_',pseudo_param), '', pseudo_i$name)), ")")
+        }
         cor_eur_subset <- rbind(cor_eur_subset, pseudo_i)
       }
       
@@ -496,14 +575,89 @@ png('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP/g
 
 dev.off()
 
+# Subset EAS results
+cor_eas <- cor[cor$pop == 'EAS', ]
+# Restrict to best and and pseudoval only
+cor_eas_subset <- NULL
+for(pop_i in unique(cor_eas$pop)){
+  for(gwas_i in unique(cor_eas$gwas[cor_eas$pop == pop_i])){
+    for(pgs_method_i in unique(cor_eas$pgs_method[cor_eas$pop == pop_i & cor_eas$gwas == gwas_i])){
+      
+      # Subset relevant results
+      cor_eas_i <- cor_eas[
+        cor_eas$pop == pop_i &
+        cor_eas$gwas == gwas_i &
+        cor_eas$pgs_method == pgs_method_i,]
+  
+      # Top R
+      if(pgs_method_i %in% c('ptclump','ldpred2','megaprs','prscs','lassosum','dbslmm')){
+        top_i <- cor_eas_i[which(cor_eas_i$r == max(cor_eas_i$r, na.rm = T))[1],]
+        top_i$model <- 'Top'
+        cor_eas_subset <- rbind(cor_eas_subset, top_i)
+      }
+      if(pgs_method_i %in% c('prscsx')){
+        for(targ_i in unique(gsub('.*_','', gsub('_phi.*','',cor_eas_i$name)))){
+          cor_eas_k<-cor_eas_i[grepl(paste0(targ_i,'_phi_'), cor_eas_i$name),]
+          top_i <- cor_eas_k[which(cor_eas_k$r == max(cor_eas_k$r, na.rm = T))[1],]
+          top_i$model <- 'Top'
+          top_i$pgs_method<-paste0(top_i$pgs_method, " (", targ_i, ")")
+          cor_eas_subset <- rbind(cor_eas_subset, top_i)
+        }
+      }
+  
+      # PseudoVal
+      if(pgs_method_i %in% c('ptclump','sbayesr','ldpred2','megaprs','prscs','lassosum','dbslmm','prscsx')){
+        pseudo_param <- find_pseudo(config = 'misc/opensnp/config.yaml', gwas = gwas_i, pgs_method = pgs_method_i)
+        pseudo_i <- cor_eas_i[grepl(paste0(pseudo_param,'$'), cor_eas_i$name),]
+        pseudo_i$model <- 'Pseudo'
+        if(pgs_method_i %in% c('prscsx')){
+          pseudo_i$pgs_method<-paste0(pseudo_i$pgs_method, " (", gsub('.*_','', gsub(paste0('_',pseudo_param), '', pseudo_i$name)), ")")
+        }
+        cor_eas_subset <- rbind(cor_eas_subset, pseudo_i)
+      }
+      
+      # External
+      external_tmp<-cor_eas_i[cor_eas_i$pgs_method == 'external',]
+      external_tmp$model <- 'External'
+      cor_eas_subset <- rbind(cor_eas_subset, external_tmp)
+      
+    }
+  }
+}
+
+# Make a plot including PRS-CSx comparing in EUR and EAS OpenSNP individuals
+cor_eur_eas_subset<-rbind(cor_eur_subset, cor_eas_subset)
+cor_eur_eas_subset$pop<-paste0(cor_eur_eas_subset$pop, " (N = ", cor_eur_eas_subset$n, ")")
 
-# Save the results
-dir.create('/scratch/prj/oliverpainfel/Data/OpenSNP/assoc')
-write.csv(
-  cor,
-  '/scratch/prj/oliverpainfel/Data/OpenSNP/assoc/genopred-yengo-assoc.csv',
-  row.names = F
-)
+plot_obj_prscsx <- 
+  ggplot(cor_eur_eas_subset[cor_eur_eas_subset$gwas %in% c('yengo_eur','yengo_eas','height'),], aes(x = pgs_method, y = r, colour = model)) +
+  geom_point(stat = "identity", size = 4, position = position_dodge(width = 0.7)) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  labs(
+    y = "Correlation (SE)",
+    x = 'PGS Method',
+    colour = 'Model',
+  ) +
+  theme_half_open() +
+  panel_border() +
+  background_grid() +
+  facet_grid(pop ~ gwas, scales = 'free', space = 'free_x') +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12))
+
+png('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP/genopred-prscsx.png',
+    units = 'px',
+    width = 3000,
+    height = 1500,
+    res = 300)
+  
+  plot_obj_prscsx
+
+dev.off()
 
 ```
 
@@ -543,6 +697,19 @@ Note. 'external' is based on a larger GWAS (incl. 23andMe)
 
 </details>
 
+<details><summary>Show results in OpenSNP when using PRS-CSx</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 60%;">
+![Yengo - EUR/EAS PGS correlation with height in OpenSNP EUR and EAS populations ](Images/OpenSNP/genopred-prscsx.png)
+</div>
+</div>
+
+<br/>
+
+Note. 'external' is based on a larger GWAS (incl. 23andMe)
+
+</details>
 ***
 
 # GenoPred V1 comparison
diff --git a/docs/opensnp_benchmark.html b/docs/opensnp_benchmark.html
index 3d0b8134..b472e768 100644
--- a/docs/opensnp_benchmark.html
+++ b/docs/opensnp_benchmark.html
@@ -468,7 +468,7 @@ <h3>Download genotypes</h3>
 tar -xvzf CrowdAI_v3.tar.gz
 rm CrowdAI_v3.tar.gz
 
-# Use fullset (training) data as the target for GenoPred, as the sample size is larger</code></pre>
+# Use both training and testing data to maximise sample size for testing.</code></pre>
 </details>
 <hr />
 </div>
@@ -480,14 +480,22 @@ <h3>Prepare phenotype</h3>
 </summary>
 <pre class="r"><code>library(data.table)
 
-dat &lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/training_set_details.txt&#39;)
-dat$FID &lt;- 0
-dat$IID &lt;- dat$id
-dat &lt;- dat[, c(&#39;FID&#39;, &#39;IID&#39;, &#39;height&#39;), with = F]
+# Training 
+train_dat &lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/training_set_details.txt&#39;)
+train_dat$FID &lt;- train_dat$id
+train_dat$IID &lt;- train_dat$id
+train_dat &lt;- train_dat[, c(&#39;FID&#39;, &#39;IID&#39;, &#39;height&#39;), with = F]
 
+# Test 
+test_dat &lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/test_set_details_SECRET.txt&#39;)
+test_dat$FID &lt;- test_dat$id
+test_dat$IID &lt;- test_dat$id
+test_dat &lt;- test_dat[, c(&#39;FID&#39;, &#39;IID&#39;, &#39;height&#39;), with = F]
+
+all_dat&lt;-rbind(train_dat, test_dat)
 dir.create(&#39;/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno&#39;, recursive = T)
 write.table(
-  dat,
+  all_dat,
   &#39;/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno/height.txt&#39;,
   col.names = T,
   row.names = F,
@@ -502,18 +510,27 @@ <h3>Split VCF by chromosome</h3>
 <summary>
 Show code
 </summary>
-<pre class="bash"><code># Create index
-module add bcftools/1.14-gcc-10.3.0-python3+-chk-version
+<pre class="bash"><code>module add bcftools/1.12-gcc-13.2.0-python-3.11.6
+
+# Create index
 bcftools index /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_train.vcf.gz
 
+bcftools index /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_test.vcf.gz
+
+# Merge the vcfs for training and testing data
+bcftools merge \
+  /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_train.vcf.gz \
+  /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_test.vcf.gz \
+  -o /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_merged.vcf.gz
+
 # Now, split by chromosome using plink2
 # Run on the command line within pipeline conda environment
 mkdir /users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno
 for chr in $(seq 1 22);do
-    plink2 \
-        --vcf /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_train.vcf.gz \
+    /users/k1806347/oliverpainfel/Software/plink2 \
+        --vcf /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_merged.vcf.gz \
         --chr ${chr} \
-        --out /users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_train.chr${chr} \
+        --out /users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_merged.chr${chr} \
         --export vcf bgz
 done</code></pre>
 </details>
@@ -544,12 +561,13 @@ <h3>Create gwas_list, target_list and config</h3>
 
 # Create config file
 conf &lt;- c(
-  &#39;outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test5&#39;,
+  &#39;outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test6&#39;,
   &#39;config_file: misc/opensnp/config.yaml&#39;,
   &#39;gwas_list: misc/opensnp/gwas_list.txt&#39;,
   &#39;score_list: misc/opensnp/score_list.txt&#39;,
   &#39;target_list: misc/opensnp/target_list.txt&#39;,
-  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;dbslmm&#39;,&#39;prscs&#39;,&#39;sbayesr&#39;,&#39;lassosum&#39;,&#39;ldpred2&#39;,&#39;megaprs&#39;]&quot;,
+  &#39;gwas_groups: misc/opensnp/gwas_groups.txt&#39;,
+  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;dbslmm&#39;,&#39;prscs&#39;,&#39;sbayesr&#39;,&#39;lassosum&#39;,&#39;ldpred2&#39;,&#39;megaprs&#39;,&#39;prscsx&#39;]&quot;,
   &#39;testing: NA&#39;
 )
 
@@ -558,11 +576,11 @@ <h3>Create gwas_list, target_list and config</h3>
 write.table(conf, &#39;misc/opensnp/config.yaml&#39;, col.names = F, row.names = F, quote = F)
 
 # Create target_list
-target_list &lt;- fread(&#39;example_input/target_list_example.txt&#39;)
+target_list &lt;- fread(&#39;example_input/target_list.txt&#39;)
 target_list &lt;- rbind(target_list,
                      data.table(name = &#39;opensnp&#39;,
-                                path = &#39;/users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_train&#39;,
-                                type = &#39;samp_imp_vcf&#39;,
+                                path = &#39;/users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_merged&#39;,
+                                type = &#39;vcf&#39;,
                                 indiv_report = F))
 
 target_list &lt;- target_list[target_list$name == &#39;opensnp&#39;, ]
@@ -570,7 +588,7 @@ <h3>Create gwas_list, target_list and config</h3>
 write.table(target_list, &#39;misc/opensnp/target_list.txt&#39;, col.names = T, row.names = F, quote = F, sep = &#39; &#39;)
 
 # Create gwas_list
-gwas_list &lt;- fread(&#39;example_input/gwas_list_example.txt&#39;)
+gwas_list &lt;- fread(&#39;example_input/gwas_list.txt&#39;)
 gwas_list&lt;-rbind(gwas_list, 
                  data.table(name=&#39;yengo_eur&#39;,
                             path = &#39;/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eur.txt&#39;,
@@ -598,6 +616,15 @@ <h3>Create gwas_list, target_list and config</h3>
 
 write.table(gwas_list, &#39;misc/opensnp/gwas_list.txt&#39;, col.names = T, row.names = F, quote = F, sep = &#39; &#39;)
 
+# Create gwas_groups
+gwas_groups &lt;- data.frame(
+  name = &#39;height&#39;,
+  gwas = &#39;yengo_eur,yengo_eas&#39;,
+  label = &quot;\&quot;Yengo 2022 Height EUR+EAS\&quot;&quot;
+)
+
+write.table(gwas_groups, &#39;misc/opensnp/gwas_groups.txt&#39;, col.names = T, row.names = F, quote = F, sep = &#39; &#39;)
+
 # Create score_list
 score_list &lt;- data.frame(
   name = &#39;PGS002804&#39;,
@@ -634,8 +661,8 @@ <h2>Check time and memory requirements</h2>
 # Read in configuration specific benchmark files
 bm_files_i &lt;-
   paste0(
-    &#39;/users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test5/reference/benchmarks/&#39;,
-    list.files(&#39;/users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test5/reference/benchmarks/&#39;)
+    &#39;/users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test6/reference/benchmarks/&#39;,
+    list.files(&#39;/users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test6/reference/benchmarks/&#39;)
   )
 
 # Read in benchmark files
@@ -652,9 +679,8 @@ <h2>Check time and memory requirements</h2>
 # PGS methods
 #####
 # Look at the memory required for each PGS method using the EUR GWAS
-bm_dat_yengo_eur &lt;-
-  bm_dat_all[grepl(&#39;prep_pgs&#39;, bm_dat_all$file) &amp;
-               grepl(&#39;yengo_eur.txt&#39;, bm_dat_all$file), ]
+bm_dat_pgs_prep &lt;-
+  bm_dat_all[grepl(&#39;prep_pgs&#39;, bm_dat_all$file), ]
 
 bm_dat_yengo_eur$method &lt;- gsub(&#39;_i&#39;, &#39;&#39;, gsub(&#39;prep_pgs_&#39;,&#39;&#39;,bm_dat_yengo_eur$rule))
 bm_dat_yengo_eur&lt;-merge(bm_dat_yengo_eur, pgs_method_labels, by=&#39;method&#39;)
@@ -679,7 +705,37 @@ <h2>Check time and memory requirements</h2>
   plot_grid(pgs_method_time, pgs_method_mem, nrow=2)
 dev.off()
 
-write.csv(bm_dat_yengo_eur, &#39;Images/OpenSNP/time_cpu_bench_pgs_methods.csv&#39;, row.names=F, quote=F)</code></pre>
+write.csv(bm_dat_yengo_eur, &#39;Images/OpenSNP/time_cpu_bench_pgs_methods.csv&#39;, row.names=F, quote=F)
+
+# Look at time and memory for PRS-CSx analysis
+bm_dat_height &lt;-
+  bm_dat_all[grepl(&#39;prep_pgs&#39;, bm_dat_all$file) &amp;
+               grepl(&#39;height.txt&#39;, bm_dat_all$file), ]
+
+bm_dat_height$method &lt;- gsub(&#39;_i&#39;, &#39;&#39;, gsub(&#39;prep_pgs_&#39;,&#39;&#39;,bm_dat_height$rule))
+bm_dat_height&lt;-merge(bm_dat_height, pgs_method_labels, by=&#39;method&#39;)
+
+pgs_method_time &lt;-
+  ggplot(bm_dat_height, aes(x = label, y = s, fill = label)) +
+  geom_bar(stat = &quot;identity&quot;, position = &quot;dodge&quot;) +
+  labs(x = &quot;PGS Method&quot;, y = &quot;Time (seconds)&quot;) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position=&quot;none&quot;)
+
+pgs_method_mem &lt;-
+  ggplot(bm_dat_height, aes(x = label, y = max_rss, fill = label)) +
+  geom_bar(stat = &quot;identity&quot;, position=&quot;dodge&quot;) +
+  labs(x = &quot;PGS Method&quot;, y = &quot;Max Memory (Mb)&quot;) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position=&quot;none&quot;)
+
+png(&#39;Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.png&#39;, res = 300, width = 800, height = 1800, units = &#39;px&#39;)
+  plot_grid(pgs_method_time, pgs_method_mem, nrow=2)
+dev.off()
+
+write.csv(bm_dat_height, &#39;Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.csv&#39;, row.names=F, quote=F)</code></pre>
 </details>
 <details>
 <summary>
@@ -698,6 +754,19 @@ <h2>Check time and memory requirements</h2>
 methods are run using 10 cores.</p>
 </div>
 </details>
+<details>
+<summary>
+Show PRS-CSx benchmark
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 50%;">
+<div class="figure">
+<img src="Images/OpenSNP/time_cpu_bench_pgs_methods.png" alt="" />
+<p class="caption">Time and max. memory used by PRS-CSx</p>
+</div>
+</div>
+</div>
+</details>
 <hr />
 </div>
 <div id="evaluate-pgs" class="section level2">
@@ -719,12 +788,6 @@ <h2>Evaluate PGS</h2>
 # Read in pheno data
 pheno &lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno/height.txt&#39;)
 
-# Define pgs_methods used
-pgs_methods &lt;- c(&#39;external&#39;,read_param(config = &#39;misc/opensnp/config.yaml&#39;, param = &#39;pgs_methods&#39;, return_obj = F))
-
-# Define pgs_methods applied to non-EUR GWAS
-pgs_methods_eur &lt;- c(&#39;ptclump&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;dbslmm&#39;)
-
 # Read in PGS
 pgs &lt;- read_pgs(config = &#39;misc/opensnp/config.yaml&#39;, name = &#39;opensnp&#39;)$opensnp
 
@@ -762,7 +825,15 @@ <h2>Evaluate PGS</h2>
   }
 }
 
-# The European sample is the only one large enough for interpretable results
+# Save the results
+dir.create(&#39;/scratch/prj/oliverpainfel/Data/OpenSNP/assoc&#39;)
+write.csv(
+  cor,
+  &#39;/scratch/prj/oliverpainfel/Data/OpenSNP/assoc/genopred-yengo-assoc.csv&#39;,
+  row.names = F
+)
+
+# The European sample is the only one large enough for interpretable results. All other populations are &lt;20 individuals
 # Subset EUR results
 cor_eur &lt;- cor[cor$pop == &#39;EUR&#39;, ]
 
@@ -784,13 +855,24 @@ <h2>Evaluate PGS</h2>
         top_i$model &lt;- &#39;Top&#39;
         cor_eur_subset &lt;- rbind(cor_eur_subset, top_i)
       }
-  
+      if(pgs_method_i %in% c(&#39;prscsx&#39;)){
+        for(targ_i in unique(gsub(&#39;.*_&#39;,&#39;&#39;, gsub(&#39;_phi.*&#39;,&#39;&#39;,cor_eur_i$name)))){
+          cor_eur_k&lt;-cor_eur_i[grepl(paste0(targ_i,&#39;_phi_&#39;), cor_eur_i$name),]
+          top_i &lt;- cor_eur_k[which(cor_eur_k$r == max(cor_eur_k$r, na.rm = T))[1],]
+          top_i$model &lt;- &#39;Top&#39;
+          top_i$pgs_method&lt;-paste0(top_i$pgs_method, &quot; (&quot;, targ_i, &quot;)&quot;)
+          cor_eur_subset &lt;- rbind(cor_eur_subset, top_i)
+        }
+      }  
+      
       # PseudoVal
-      if(pgs_method_i %in% c(&#39;ptclump&#39;,&#39;sbayesr&#39;,&#39;ldpred2&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;lassosum&#39;,&#39;dbslmm&#39;)){
-        cor_eur_i$name &lt;- gsub(paste0(gwas_i, &#39;_&#39;), &#39;&#39;, cor_eur_i$name)
+      if(pgs_method_i %in% c(&#39;ptclump&#39;,&#39;sbayesr&#39;,&#39;ldpred2&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;lassosum&#39;,&#39;dbslmm&#39;,&#39;prscsx&#39;)){
         pseudo_param &lt;- find_pseudo(config = &#39;misc/opensnp/config.yaml&#39;, gwas = gwas_i, pgs_method = pgs_method_i)
-        pseudo_i &lt;- cor_eur_i[cor_eur_i$name == pseudo_param,]
+        pseudo_i &lt;- cor_eur_i[grepl(paste0(pseudo_param,&#39;$&#39;), cor_eur_i$name),]
         pseudo_i$model &lt;- &#39;Pseudo&#39;
+        if(pgs_method_i %in% c(&#39;prscsx&#39;)){
+          pseudo_i$pgs_method&lt;-paste0(pseudo_i$pgs_method, &quot; (&quot;, gsub(&#39;.*_&#39;,&#39;&#39;, gsub(paste0(&#39;_&#39;,pseudo_param), &#39;&#39;, pseudo_i$name)), &quot;)&quot;)
+        }
         cor_eur_subset &lt;- rbind(cor_eur_subset, pseudo_i)
       }
       
@@ -901,14 +983,89 @@ <h2>Evaluate PGS</h2>
 
 dev.off()
 
+# Subset EAS results
+cor_eas &lt;- cor[cor$pop == &#39;EAS&#39;, ]
+# Restrict to best and and pseudoval only
+cor_eas_subset &lt;- NULL
+for(pop_i in unique(cor_eas$pop)){
+  for(gwas_i in unique(cor_eas$gwas[cor_eas$pop == pop_i])){
+    for(pgs_method_i in unique(cor_eas$pgs_method[cor_eas$pop == pop_i &amp; cor_eas$gwas == gwas_i])){
+      
+      # Subset relevant results
+      cor_eas_i &lt;- cor_eas[
+        cor_eas$pop == pop_i &amp;
+        cor_eas$gwas == gwas_i &amp;
+        cor_eas$pgs_method == pgs_method_i,]
+  
+      # Top R
+      if(pgs_method_i %in% c(&#39;ptclump&#39;,&#39;ldpred2&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;lassosum&#39;,&#39;dbslmm&#39;)){
+        top_i &lt;- cor_eas_i[which(cor_eas_i$r == max(cor_eas_i$r, na.rm = T))[1],]
+        top_i$model &lt;- &#39;Top&#39;
+        cor_eas_subset &lt;- rbind(cor_eas_subset, top_i)
+      }
+      if(pgs_method_i %in% c(&#39;prscsx&#39;)){
+        for(targ_i in unique(gsub(&#39;.*_&#39;,&#39;&#39;, gsub(&#39;_phi.*&#39;,&#39;&#39;,cor_eas_i$name)))){
+          cor_eas_k&lt;-cor_eas_i[grepl(paste0(targ_i,&#39;_phi_&#39;), cor_eas_i$name),]
+          top_i &lt;- cor_eas_k[which(cor_eas_k$r == max(cor_eas_k$r, na.rm = T))[1],]
+          top_i$model &lt;- &#39;Top&#39;
+          top_i$pgs_method&lt;-paste0(top_i$pgs_method, &quot; (&quot;, targ_i, &quot;)&quot;)
+          cor_eas_subset &lt;- rbind(cor_eas_subset, top_i)
+        }
+      }
+  
+      # PseudoVal
+      if(pgs_method_i %in% c(&#39;ptclump&#39;,&#39;sbayesr&#39;,&#39;ldpred2&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;lassosum&#39;,&#39;dbslmm&#39;,&#39;prscsx&#39;)){
+        pseudo_param &lt;- find_pseudo(config = &#39;misc/opensnp/config.yaml&#39;, gwas = gwas_i, pgs_method = pgs_method_i)
+        pseudo_i &lt;- cor_eas_i[grepl(paste0(pseudo_param,&#39;$&#39;), cor_eas_i$name),]
+        pseudo_i$model &lt;- &#39;Pseudo&#39;
+        if(pgs_method_i %in% c(&#39;prscsx&#39;)){
+          pseudo_i$pgs_method&lt;-paste0(pseudo_i$pgs_method, &quot; (&quot;, gsub(&#39;.*_&#39;,&#39;&#39;, gsub(paste0(&#39;_&#39;,pseudo_param), &#39;&#39;, pseudo_i$name)), &quot;)&quot;)
+        }
+        cor_eas_subset &lt;- rbind(cor_eas_subset, pseudo_i)
+      }
+      
+      # External
+      external_tmp&lt;-cor_eas_i[cor_eas_i$pgs_method == &#39;external&#39;,]
+      external_tmp$model &lt;- &#39;External&#39;
+      cor_eas_subset &lt;- rbind(cor_eas_subset, external_tmp)
+      
+    }
+  }
+}
+
+# Make a plot including PRS-CSx comparing in EUR and EAS OpenSNP individuals
+cor_eur_eas_subset&lt;-rbind(cor_eur_subset, cor_eas_subset)
+cor_eur_eas_subset$pop&lt;-paste0(cor_eur_eas_subset$pop, &quot; (N = &quot;, cor_eur_eas_subset$n, &quot;)&quot;)
 
-# Save the results
-dir.create(&#39;/scratch/prj/oliverpainfel/Data/OpenSNP/assoc&#39;)
-write.csv(
-  cor,
-  &#39;/scratch/prj/oliverpainfel/Data/OpenSNP/assoc/genopred-yengo-assoc.csv&#39;,
-  row.names = F
-)</code></pre>
+plot_obj_prscsx &lt;- 
+  ggplot(cor_eur_eas_subset[cor_eur_eas_subset$gwas %in% c(&#39;yengo_eur&#39;,&#39;yengo_eas&#39;,&#39;height&#39;),], aes(x = pgs_method, y = r, colour = model)) +
+  geom_point(stat = &quot;identity&quot;, size = 4, position = position_dodge(width = 0.7)) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  labs(
+    y = &quot;Correlation (SE)&quot;,
+    x = &#39;PGS Method&#39;,
+    colour = &#39;Model&#39;,
+  ) +
+  theme_half_open() +
+  panel_border() +
+  background_grid() +
+  facet_grid(pop ~ gwas, scales = &#39;free&#39;, space = &#39;free_x&#39;) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12))
+
+png(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP/genopred-prscsx.png&#39;,
+    units = &#39;px&#39;,
+    width = 3000,
+    height = 1500,
+    res = 300)
+  
+  plot_obj_prscsx
+
+dev.off()</code></pre>
 </details>
 <hr />
 <details>
@@ -951,6 +1108,22 @@ <h2>Evaluate PGS</h2>
 <p><br/></p>
 <p>Note. ‘external’ is based on a larger GWAS (incl. 23andMe)</p>
 </details>
+<details>
+<summary>
+Show results in OpenSNP when using PRS-CSx
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 60%;">
+<div class="figure">
+<img src="Images/OpenSNP/genopred-prscsx.png" alt="" />
+<p class="caption">Yengo - EUR/EAS PGS correlation with height in
+OpenSNP EUR and EAS populations</p>
+</div>
+</div>
+</div>
+<p><br/></p>
+<p>Note. ‘external’ is based on a larger GWAS (incl. 23andMe)</p>
+</details>
 <hr />
 </div>
 </div>
diff --git a/functions/constants.R b/functions/constants.R
index 3ce37c16..23a74f9e 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -37,8 +37,8 @@ ref_pop <- data.frame(
 
 # Make a data.frame giving labels to the 1KG reference populations
 pgs_method_labels <- data.frame(
-  method = c('ptclump','dbslmm','ldpred2','sbayesr','lassosum','prscs','megaprs','external'),
-  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','lassosum','PRS-CS','MegaPRS','External')
+  method = c('ptclump','dbslmm','ldpred2','sbayesr','lassosum','prscs','megaprs','external','prscsx'),
+  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','lassosum','PRS-CS','MegaPRS','External','PRS-CSx')
 )
 pgs_method_labels[order(pgs_method_labels$method),]
 
diff --git a/functions/pipeline.R b/functions/pipeline.R
index b2d92119..a6f84bd9 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -57,7 +57,9 @@ read_pgs <- function(config, name = NULL, pgs_methods = NULL, gwas = NULL, pop =
       for(score_i in 1:nrow(score_file_list)){
         gwas_i <- score_file_list$name[score_i]
         pgs_method_i <- score_file_list$method[score_i]
-        pgs[[name_i]][[pop_i]][[gwas_i]] <- list()
+        if (is.null(pgs[[name_i]][[pop_i]][[gwas_i]])) {
+          pgs[[name_i]][[pop_i]][[gwas_i]] <- list()
+        }
         pgs[[name_i]][[pop_i]][[gwas_i]][[pgs_method_i]] <-
             fread(
               paste0(
@@ -209,7 +211,7 @@ find_pseudo <- function(config, gwas, pgs_method){
     return('phi_auto')
   }
   if(pgs_method == 'prscsx'){
-    return('META_phi_auto')
+    return('phi_auto')
   }
   if(pgs_method == 'megaprs'){
     # Read in megaprs log file
diff --git a/pipeline/envs/xwing.yaml b/pipeline/envs/xwing.yaml
new file mode 100644
index 00000000..8a121f6a
--- /dev/null
+++ b/pipeline/envs/xwing.yaml
@@ -0,0 +1,20 @@
+name: xwing
+channels:
+  - conda-forge
+  - defaults
+dependencies:
+  - r-base>=3.5.1
+  - r-snowfall
+  - r-data.table>=1.11.8
+  - r-optparse>=1.6.6
+  - r-bedmatrix>=2.0.3
+  - r-devtools
+  - python=3.6
+  - pandas=0.24.2
+  - scipy=1.2.0
+  - h5py=2.10.0
+  - numpy=1.16.5
+  - r-foreach=1.5.2
+  - r-domc=1.3.8
+  - r-r.utils=2.12.2
+  - plink2=2.00a5
diff --git a/pipeline/misc/cross_pop_test/cross_pop_test.Rmd b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
index efc38e95..5f25d3af 100644
--- a/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
+++ b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
@@ -33,6 +33,7 @@ setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline')
 
 config <- readLines('misc/opensnp/config.yaml')
 
+config[grepl('^config_file:', config)]<- 'config_file: misc/opensnp/config_cross_pop.yaml'
 config[grepl('^testing:', config)]<- 'testing: chr22'
 config <- config[!grepl('^score_list:', config)]
 config[grepl('^outdir:', config)]<- 'outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1'
@@ -46,3 +47,119 @@ write.table(config, 'misc/opensnp/config_cross_pop.yaml', col.names = F, row.nam
 ```{bash}
 snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_cross_pop.yaml output_all
 ```
+
+## Evaluate PGS
+
+<details><summary>Show code</summary>
+
+```{r}
+# Test correlation between PGS and observed height
+
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in pheno data
+pheno <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno/height.txt')
+
+# Read in PGS
+pgs <- read_pgs(config = 'misc/opensnp/config_cross_pop.yaml', name = 'opensnp')$opensnp
+
+# Estimate correlation between pheno and pgs
+cor <- NULL
+for(pop_i in names(pgs)){
+  for(gwas_i in names(pgs[[pop_i]])){
+    for(pgs_method_i in names(pgs[[pop_i]][[gwas_i]])){
+      pgs_i <- pgs[[pop_i]][[gwas_i]][[pgs_method_i]]
+      pheno_pgs<-merge(pheno, pgs_i, by = c('FID','IID'))
+      
+      for(model_i in names(pgs_i)[-1:-2]){
+        y <- scale(pheno_pgs$height)
+        x <- scale(pheno_pgs[[model_i]])
+        
+        if(all(is.na(x))){
+          next
+        }
+        
+        coef_i <- coef(summary(mod <- lm(y ~ x)))
+        
+        tmp <- data.table(
+          pop = pop_i,
+          gwas = gwas_i,
+          pgs_method = pgs_method_i,
+          name = model_i,
+          r = coef_i[2,1],
+          se = coef_i[2,2],
+          p = coef_i[2,4],
+          n = nobs(mod))
+      cor <- rbind(cor, tmp)
+      
+      }
+    }
+  }
+}
+
+# The European sample is the only one large enough for interpretable results
+# Subset EUR results
+cor_eur <- cor[cor$pop == 'EUR', ]
+
+# Restrict to best and and pseudoval only
+cor_eur_subset <- NULL
+for(pop_i in unique(cor_eur$pop)){
+  for(gwas_i in unique(cor_eur$gwas[cor_eur$pop == pop_i])){
+    for(pgs_method_i in unique(cor_eur$pgs_method[cor_eur$pop == pop_i & cor_eur$gwas == gwas_i])){
+      
+      # Subset relevant results
+      cor_eur_i <- cor_eur[
+        cor_eur$pop == pop_i &
+        cor_eur$gwas == gwas_i &
+        cor_eur$pgs_method == pgs_method_i,]
+  
+      # Top R
+      if(pgs_method_i %in% c('ptclump','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm')){
+        top_i <- cor_eur_i[which(cor_eur_i$r == max(cor_eur_i$r, na.rm = T))[1],]
+        top_i$model <- 'Top'
+        cor_eur_subset <- rbind(cor_eur_subset, top_i)
+      }
+  
+      # PseudoVal
+      if(pgs_method_i %in% c('ptclump','sbayesr','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm')){
+        cor_eur_i$name <- gsub(paste0(gwas_i, '_'), '', cor_eur_i$name)
+        pseudo_param <- find_pseudo(config = 'misc/opensnp/config_cross_pop.yaml', gwas = gwas_i, pgs_method = pgs_method_i)
+        pseudo_i <- cor_eur_i[cor_eur_i$name == pseudo_param,]
+        pseudo_i$model <- 'Pseudo'
+        cor_eur_subset <- rbind(cor_eur_subset, pseudo_i)
+      }
+    }
+  }
+}
+
+# Plot the results
+cor_eur_subset$model <- factor(cor_eur_subset$model, levels = c('Top','Pseudo','External'))
+dir.create('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP')
+
+plot_obj_eur <- 
+  ggplot(cor_eur_subset, aes(x = pgs_method, y = r, fill = model)) +
+  geom_bar(stat = "identity", position = position_dodge2(preserve = "single"), width = 0.7) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  labs(
+    y = "Correlation (SE)",
+    x = 'PGS Method',
+    fill = 'Model',
+    title = paste0("OpenSNP - EUR\n(N = ", cor_eur_subset$n[1], ")")
+  ) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12))
+
+
+```
diff --git a/pipeline/misc/opensnp/config.yaml b/pipeline/misc/opensnp/config.yaml
index 4ed349aa..d30fa2cd 100644
--- a/pipeline/misc/opensnp/config.yaml
+++ b/pipeline/misc/opensnp/config.yaml
@@ -1,7 +1,9 @@
-outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test5
+outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test7
 config_file: misc/opensnp/config.yaml
 gwas_list: misc/opensnp/gwas_list.txt
 score_list: misc/opensnp/score_list.txt
 target_list: misc/opensnp/target_list.txt
-pgs_methods: ['ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs']
-testing: NA
+gwas_groups: misc/opensnp/gwas_groups.txt
+pgs_methods: ['xwing']
+testing: chr22
+cores_prep_pgs: 20
diff --git a/pipeline/misc/opensnp/config_cross_pop.yaml b/pipeline/misc/opensnp/config_cross_pop.yaml
index 3a96354a..af80c42c 100644
--- a/pipeline/misc/opensnp/config_cross_pop.yaml
+++ b/pipeline/misc/opensnp/config_cross_pop.yaml
@@ -1,5 +1,5 @@
 outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1
-config_file: misc/opensnp/config.yaml
+config_file: misc/opensnp/config_cross_pop.yaml
 gwas_list: misc/opensnp/gwas_list.txt
 target_list: misc/opensnp/target_list.txt
 pgs_methods: ['ptclump','prscs','prscsx']
diff --git a/pipeline/misc/opensnp/gwas_groups.txt b/pipeline/misc/opensnp/gwas_groups.txt
new file mode 100644
index 00000000..2cac132c
--- /dev/null
+++ b/pipeline/misc/opensnp/gwas_groups.txt
@@ -0,0 +1,2 @@
+name gwas label
+height yengo_eur,yengo_eas "Yengo 2022 Height EUR+EAS"
diff --git a/pipeline/misc/opensnp/target_list.txt b/pipeline/misc/opensnp/target_list.txt
index d3ae5b9d..99837870 100644
--- a/pipeline/misc/opensnp/target_list.txt
+++ b/pipeline/misc/opensnp/target_list.txt
@@ -1,2 +1,2 @@
 name path type indiv_report
-opensnp /users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_train vcf FALSE
+opensnp /users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_merged vcf FALSE
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 29ba602c..5ae6cbe0 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -61,6 +61,32 @@ def check_target_paths(df, chr):
     else :
       return []
 
+# Create function that checks the type column given in the target_list
+def check_target_type(df, column='type'):
+    valid_formats = {'plink1', 'plink2', 'vcf', 'bgen', '23andMe'}
+    invalid_formats = df[~df[column].isin(valid_formats)]
+
+    if not invalid_formats.empty:
+        raise ValueError(f"Invalid format entries found in column '{column}': {invalid_formats[column].unique()}. Must be either 'plink1', 'plink2', 'vcf', 'bgen' or '23andMe'")
+
+def check_target_type(df, column='type'):
+    valid_formats = {'plink1', 'plink2', 'vcf', 'bgen', '23andMe'}
+
+    # Check if the dataframe is empty
+    if df.empty:
+        return
+
+    # Check if the column exists
+    if column not in df.columns:
+        return
+
+    # Check for invalid formats
+    invalid_formats = df[~df[column].isin(valid_formats)]
+
+    if not invalid_formats.empty:
+        raise ValueError(f"Invalid format entries found in column '{column}': {invalid_formats[column].unique()}. Must be either 'plink1', 'plink2', 'vcf', 'bgen' or '23andMe'")
+
+
 ######
 # Check config file
 ######
@@ -678,6 +704,157 @@ rule download_pgscatalog_utils:
     }} > {log} 2>&1
     """
 
+# Download XPASS for X-wing dependencies
+rule install_xpass:
+  input:
+    "envs/xwing.yaml"
+  output:
+    touch("resources/software/install_xpass.done")
+  conda:
+    "../envs/xwing.yaml"
+  benchmark:
+    "resources/data/benchmarks/install_xpass.txt"
+  log:
+    "resources/data/logs/install_xpass.log"
+  shell:
+    """
+    {{
+      Rscript -e 'devtools::install_github(\"YangLabHKUST/XPASS@65877ffba60dce69e0a6aa31c2e61045bf36dc40\")'
+    }} > {log} 2>&1
+    """
+
+# Install GenoUtils in X-wing environment
+rule install_genoutils_xwing:
+  input:
+    rules.install_xpass.output
+  output:
+    touch("resources/software/install_genoutils_xwing.done")
+  conda:
+    "../envs/xwing.yaml"
+  benchmark:
+    "resources/data/benchmarks/install_genoutils_xwing.txt"
+  log:
+    "resources/data/logs/install_genoutils_xwing.log"
+  shell:
+    """
+    {{
+      Rscript -e 'devtools::install_github(\"opain/GenoUtils@50ac8a2078226c8c2349064f904031576fbfe606\")'
+    }} > {log} 2>&1
+    """
+
+
+# Download X-wing repo
+rule download_xwing_software:
+  input:
+    rules.install_xpass.output,
+    rules.install_genoutils_xwing.output
+  output:
+    "resources/software/xwing/block_partition.txt"
+  conda:
+    "../envs/xwing.yaml"
+  benchmark:
+    "resources/data/benchmarks/download_xwing_software.txt"
+  log:
+    "resources/data/logs/download_xwing_software.log"
+  shell:
+    """
+    {{
+      rm -r -f resources/software/xwing; \
+      git clone https://github.com/qlu-lab/X-Wing resources/software/xwing; \
+      cd resources/software/xwing; \
+      git reset --hard 01cb3f3b75cbd68b58eabc1fa28cbcf5368bfdf3
+    }} > {log} 2>&1
+    """
+
+# Download LOGODetect (X-wing) reference data
+rule download_logodetect_ref:
+  output:
+    f"{resdir}/data/LOGODetect_1kg_ref/{{population}}/1000G_{{population}}_QC.bim"
+  benchmark:
+    f"{resdir}/data/benchmarks/logodetect_ref-{{population}}.txt"
+  log:
+    f"{resdir}/data/logs/logodetect_ref-{{population}}.log"
+  shell:
+    """
+    {{
+      mkdir -p {resdir}/data; \
+      wget --no-check-certificate -O {resdir}/data/LOGODetect_1kg_{wildcards.population}.tar.gz ftp://ftp.biostat.wisc.edu/pub/lu_group/Projects/XWING/ref/LOGODetect/LOGODetect_1kg_{wildcards.population}.tar.gz; \
+      tar -zxvf {resdir}/data/LOGODetect_1kg_{wildcards.population}.tar.gz -C {resdir}/data/; \
+      rm {resdir}/data/LOGODetect_1kg_{wildcards.population}.tar.gz
+    }} > {log} 2>&1
+    """
+
+rule download_logodetect_ref_all:
+  input:
+    lambda w: expand(f"{resdir}/data/LOGODetect_1kg_ref/{{population}}/1000G_{{population}}_QC.bim", population=['EUR','EAS','AFR','SAS','AMR'])
+
+# Download PANTHER (X-wing) reference data
+# The reference data is the same as the PRS-CS reference data
+# The PRS-CS ref parameter will also affect the X-WING/PANTHER analysis
+
+# Download LEOPARD (X-wing) reference data
+rule download_leopard_ref:
+  output:
+    f"{resdir}/data/LEOPARD_1kg_ref/{{population}}/{{population}}_part1.bed"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_leopard_ref-{{population}}.txt"
+  log:
+    f"{resdir}/data/logs/download_leopard_ref-{{population}}.log"
+  shell:
+    """
+    {{
+      mkdir -p {resdir}/data; \
+      wget --no-check-certificate -O {resdir}/data/LEOPARD_1kg_hm3_{wildcards.population}.tar.gz ftp://ftp.biostat.wisc.edu/pub/lu_group/Projects/XWING/ref/LEOPARD/LEOPARD_1kg_hm3_{wildcards.population}.tar.gz; \
+      tar -zxvf {resdir}/data/LEOPARD_1kg_hm3_{wildcards.population}.tar.gz -C {resdir}/data/; \
+      rm {resdir}/data/LEOPARD_1kg_hm3_{wildcards.population}.tar.gz
+    }} > {log} 2>&1
+    """
+
+rule download_leopard_ref_all:
+  input:
+    lambda w: expand(f"{resdir}/data/LEOPARD_1kg_ref/{{population}}/{{population}}_part1.bed", population=['EUR','EAS','AFR','SAS','AMR'])
+
+# Download LEOPARD and subsampled PANTHER (X-wing) reference data
+rule download_leopard_panther_ref:
+  output:
+    f"{resdir}/data/PANTHER_LEOPARD_1kg_ref/ldblk_1kg_{{population}}/ldblk_1kg_chr13.hdf5"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_leopard_panther_ref-{{population}}.txt"
+  log:
+    f"{resdir}/data/logs/download_leopard_panther_ref-{{population}}.log"
+  params:
+    pop_upper=lambda w: w.population.upper()
+  shell:
+    """
+    {{
+      mkdir -p {resdir}/data; \
+      wget --no-check-certificate -O {resdir}/data/PANTHER_LEOPARD_1kg_{wildcards.population}.tar.gz ftp://ftp.biostat.wisc.edu/pub/lu_group/Projects/XWING/ref/LEOPARD/PANTHER_LEOPARD_1kg_{params.pop_upper}.tar.gz; \
+      tar -zxvf {resdir}/data/PANTHER_LEOPARD_1kg_{wildcards.population}.tar.gz -C {resdir}/data/; \
+      rm {resdir}/data/PANTHER_LEOPARD_1kg_{wildcards.population}.tar.gz
+    }} > {log} 2>&1
+    """
+
+rule download_leopard_panther_ref_all:
+  input:
+    lambda w: expand(f"{resdir}/data/PANTHER_LEOPARD_1kg_ref/ldblk_1kg_{{population}}/ldblk_1kg_chr13.hdf5", population=['eur','eas','afr','sas','amr'])
+
+rule download_leopard_panther_snp_data:
+  output:
+    f"{resdir}/data/PANTHER_LEOPARD_1kg_ref/snpinfo_mult_1kg_hm3"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_leopard_panther_snp_data.txt"
+  log:
+    f"{resdir}/data/logs/download_leopard_panther_snp_data.log"
+  shell:
+    """
+    {{
+      mkdir -p {resdir}/data; \
+      wget --no-check-certificate -O {resdir}/data/snpinfo_mult_1kg_hm3_PANTHER_LEOPARD.tar.gz ftp://ftp.biostat.wisc.edu/pub/lu_group/Projects/XWING/ref/LEOPARD/snpinfo_mult_1kg_hm3_PANTHER_LEOPARD.tar.gz; \
+      tar -zxvf {resdir}/data/snpinfo_mult_1kg_hm3_PANTHER_LEOPARD.tar.gz -C {resdir}/data/; \
+      rm {resdir}/data/snpinfo_mult_1kg_hm3_PANTHER_LEOPARD.tar.gz
+    }} > {log} 2>&1
+    """
+
 ############
 # Check all dependencies are available
 ############
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index e3b91536..72efb01b 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -554,6 +554,60 @@ rule prep_pgs_prscsx_i:
 rule prep_pgs_prscsx:
   input: expand(f"{outdir}/reference/pgs_score_files/prscsx/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'])
 
+####
+# X-WING
+####
+
+rule prep_pgs_xwing_i:
+  resources:
+    mem_mb=2000*config['cores_prep_pgs'],
+    time_min=800
+  threads: config['cores_prep_pgs']
+  input:
+    rules.download_xwing_software.output,
+    rules.download_leopard_panther_snp_data.output,
+    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
+    lambda w: expand(f"{resdir}/data/prscs_ref/{prscs_ldref}/ldblk_{prscs_ldref}_{{population}}/ldblk_{prscs_ldref}_chr1.hdf5", population=[pop.lower() for pop in get_populations(w.gwas_group)]),
+    f"{resdir}/data/prscs_ref/{prscs_ldref}/snpinfo_mult_{prscs_ldref}_hm3",
+    lambda w: expand(f"{resdir}/data/PANTHER_LEOPARD_1kg_ref/ldblk_1kg_{{population}}/ldblk_1kg_chr13.hdf5", population=[pop.lower() for pop in get_populations(w.gwas_group)]),
+    lambda w: expand(f"{resdir}/data/LEOPARD_1kg_ref/{{population}}/{{population}}_part1.bed", population=get_populations(w.gwas_group)),
+    lambda w: expand(f"{resdir}/data/LOGODetect_1kg_ref/{{population}}/1000G_{{population}}_QC.bim", population=get_populations(w.gwas_group))
+  output:
+    f"{outdir}/reference/pgs_score_files/xwing/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
+  conda:
+    "../envs/xwing.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/prep_pgs_xwing_i-{{gwas_group}}.txt"
+  log:
+    f"{outdir}/reference/logs/prep_pgs_xwing_i-{{gwas_group}}.log"
+  params:
+    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
+    populations= lambda w: ",".join(get_populations(w.gwas_group)),
+    testing=config["testing"]
+  shell:
+    """
+    export MKL_NUM_THREADS=1; \
+    export NUMEXPR_NUM_THREADS=1; \
+    export OMP_NUM_THREADS=1; \
+    export OPENBLAS_NUM_THREADS=1; \
+    Rscript ../Scripts/pgs_methods/xwing.R \
+      --ref_plink_chr {refdir}/ref.chr \
+      --sumstats {params.sumstats} \
+      --populations {params.populations} \
+      --logodetect_ref {resdir}/data/LOGODetect_1kg_ref \
+      --panther_ref {resdir}/data/prscs_ref/{prscs_ldref} \
+      --leopard_ref {resdir}/data/LEOPARD_1kg_ref \
+      --panther_leopard_ref {resdir}/data/PANTHER_LEOPARD_1kg_ref \
+      --xwing_repo {resdir}/software/xwing \
+      --pop_data {refdir}/ref.pop.txt \
+      --output {outdir}/reference/pgs_score_files/xwing/{wildcards.gwas_group}/ref-{wildcards.gwas_group} \
+      --test {params.testing} \
+      --n_cores {threads} > {log} 2>&1
+    """
+
+rule prep_pgs_xwing:
+  input: expand(f"{outdir}/reference/pgs_score_files/xwing/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'])
+
 ###############################################
 
 ##
@@ -580,6 +634,8 @@ if 'external' in pgs_methods_all:
   pgs_methods_input.append(rules.score_reporter.output)
 if 'prscsx' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_prscsx.input)
+if 'xwing' in pgs_methods_all:
+  pgs_methods_input.append(rules.prep_pgs_xwing.input)
 
 rule prep_pgs:
   input:
diff --git a/pipeline/rules/target_qc.smk b/pipeline/rules/target_qc.smk
index eb4eec47..a810af19 100644
--- a/pipeline/rules/target_qc.smk
+++ b/pipeline/rules/target_qc.smk
@@ -20,6 +20,9 @@ else:
 # Format target data
 ####
 
+# Check specific target paths exist
+check_target_type(df = target_list_df)
+
 # Check specific target paths exist
 check_target_paths(df = target_list_df, chr = str(get_chr_range(config['testing'])[0]))
 

From b7c4001e4fb23498c14fa132f448f10f76ec2824 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 19 Aug 2024 16:42:11 +0100
Subject: [PATCH 003/153] Added TL-PRS

---
 Scripts/pgs_methods/tlprs.R                 | 245 ++++++++++++++++++++
 functions/constants.R                       |   8 +-
 functions/pgs.R                             |   8 +
 functions/pipeline.R                        |  15 +-
 pipeline/config.yaml                        |   3 +
 pipeline/misc/opensnp/config.yaml           |   5 +-
 pipeline/misc/opensnp/config_cross_pop.yaml |   7 -
 pipeline/misc/opensnp/gwas_groups.txt       |   2 -
 pipeline/rules/dependencies.smk             |  64 ++++-
 pipeline/rules/pgs_methods.smk              |  47 +++-
 10 files changed, 381 insertions(+), 23 deletions(-)
 create mode 100644 Scripts/pgs_methods/tlprs.R
 delete mode 100644 pipeline/misc/opensnp/config_cross_pop.yaml
 delete mode 100644 pipeline/misc/opensnp/gwas_groups.txt

diff --git a/Scripts/pgs_methods/tlprs.R b/Scripts/pgs_methods/tlprs.R
new file mode 100644
index 00000000..2701de11
--- /dev/null
+++ b/Scripts/pgs_methods/tlprs.R
@@ -0,0 +1,245 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
+      help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--pop_data", action="store", default=NULL, type='character',
+      help="File containing the population code and location of the keep file [required]"),
+  make_option("--ref_keep_dir", action="store", default=NULL, type='character',
+      help="Directory continaing reference keep files [required]"),
+  make_option("--plink1", action="store", default='plink', type='character',
+      help="Path PLINK v1.9 software binary [optional]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+      help="Path PLINK v2 software binary [optional]"),
+  make_option("--output", action="store", default=NULL, type='character',
+      help="Path for output files [required]"),
+  make_option("--memory", action="store", default=5000, type='numeric',
+      help="Memory limit [optional]"),
+  make_option("--sumstats", action="store", default=NULL, type='character',
+      help="Comma-seperated list of GWAS summary statistics [required]"),
+  make_option("--scores", action="store", default=NULL, type='character',
+      help="Comma-seperated list of score files [required]"),
+  make_option("--populations", action="store", default=NULL, type='character',
+      help="Comma-seperated list of population codes matching GWAS [required]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+      help="Number of cores for parallel computing [optional]"),
+  make_option("--test", action="store", default=NA, type='character',
+      help="Specify number of SNPs to include [optional]"),
+  make_option("--seed", action="store", default=1, type='numeric',
+      help="Seed number for PRScs [optional]")
+)
+
+opt = parse_args(OptionParser(option_list = option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+library(lassosum)
+library(TLPRS)
+
+library(foreach)
+library(doMC)
+registerDoMC(opt$n_cores)
+
+# Check required inputs
+if(is.null(opt$ref_plink_chr)){
+  stop('--ref_plink_chr must be specified.\n')
+}
+if(is.null(opt$sumstats)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$scores)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$pop_data)){
+  stop('--pop_data must be specified.\n')
+}
+if(is.null(opt$ref_keep_dir)){
+  stop('--ref_keep_dir must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--output must be specified.\n')
+}
+if(is.null(opt$populations)){
+  stop('--populations must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
+system(paste0('mkdir -p ',opt$output_dir))
+
+# Create temp directory
+tmp_dir <- tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'prscsx.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+# Split opt$sumstats
+sumstats<-unlist(strsplit(opt$sumstats, ','))
+log_add(log_file = log_file, message = paste0(length(sumstats), ' sets of GWAS have been provided.'))
+
+# Split opt$scores
+scores<-unlist(strsplit(opt$scores, ','))
+log_add(log_file = log_file, message = paste0(length(scores), ' sets of scores have been provided.'))
+
+# Split opt$populations
+populations<-unlist(strsplit(opt$populations, ','))
+
+######
+# Merge reference data
+######
+
+for(i in 1:length(populations)) {
+  plink_merge(
+    pfile = opt$ref_plink_chr,
+    chr = CHROMS,
+    plink2 = opt$plink2,
+    keep = paste0(opt$ref_keep_dir, '/', populations[i], '.keep'),
+    make_bed = T,
+    out = paste0(tmp_dir, '/', populations[i], '_ref_merge')
+  )
+}
+
+# Record start time for test
+if(!is.na(opt$test)){
+  test_start.time <- test_start(log_file = log_file)
+}
+
+######
+# Run TL-PRS
+######
+# We are going to use code within the TL_PRS function to generate the BETAs across across a range of gradients, avoid the validation step
+
+# Read in reference SNP data for harmonising across GWAS
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
+
+# Run for each parameter in each score file
+tl_betas_list<-list()
+for(i in 1:length(populations)){
+
+  # Read in target sumstats
+  target_gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('SNP', 'A1', 'BETA', 'P', 'N'))
+  names(target_gwas)[names(target_gwas) == 'BETA']<-'beta'
+  names(target_gwas)[names(target_gwas) == 'P']<-'p'
+
+  # Identify LD block data to be used
+  if(populations[i] %in% c('EUR','AFR')){
+    ld_block_dat <- paste0(populations[i],'.hg19')
+  }
+  if(populations[i] %in% 'EAS'){
+    ld_block_dat <- 'ASN.hg19'
+  }
+  if(populations[i] %in% c('AMR','SAS')){
+    ld_block_dat <- 'EUR.hg19'
+    log_add(log_file = log_file, message = 'Using LD block data for EUR.')
+  }
+
+  # Read in PGS score file (set up for pairs of GWAS/populations only)
+  score_file <- fread(scores[-i])
+  names(score_file)<-gsub('^SCORE','Beta', names(score_file))
+
+  target_gwas_j=merge(score_file, target_gwas, by="SNP",sort=F)
+
+  if (sum(target_gwas_j$p <= 1E-320)>0) {
+    target_gwas_j$p[target_gwas_j$p <= 1E-320] = 1E-320
+  }
+
+  target_gwas_j$cor = lassosum::p2cor(
+    p = target_gwas_j$p,
+    n = median(target_gwas_j$N, na.rm = T),
+    sign = target_gwas_j$beta
+  )
+
+  flag = which(target_gwas_j$A1.x != target_gwas_j$A1.y)
+  if (length(flag) > 0) {
+    target_gwas_j$cor[flag] = -target_gwas_j$cor[flag]
+  }
+  target_gwas_j=target_gwas_j[,c("SNP","A1.x", names(target_gwas_j)[grepl('^Beta', names(target_gwas_j))], "cor"), with=F]
+  colnames(target_gwas_j)[2]="A1"
+  gc()
+
+  beta_list = as.data.frame(
+    TLPRS:::PRStr_calculation2(
+      sum_stats_target = target_gwas_j,
+      train_file = paste0(tmp_dir, '/', populations[i], '_ref_merge'),
+      sum_stats = score_file,
+      LDblocks = ld_block_dat,
+      plink=opt$plink1,
+      temp.file = paste0(tmp_dir, '/', populations[i], '_step1')
+    )
+  )
+
+  beta_list=as.data.frame(beta_list[,-c(5,9)])
+  colnames(beta_list)[1:2]=c("SNP","A1")
+
+  beta.info=beta_list[,1:2]
+  for (k in 8:ncol(beta_list)){
+    sdtemp=sd(beta_list[,k],na.rm=T)
+    if (sdtemp>1){
+      beta_list[,k:ncol(beta_list)]=1
+    }
+  }
+
+  beta_list=beta_list[,8:ncol(beta_list)]/beta_list$sd
+  tl_betas<-data.table(cbind(beta.info, beta_list))
+  tl_betas<-merge(score_file[, c('SNP','A1','A2'), with=F], tl_betas, by=c('SNP','A1'))
+
+  names(tl_betas)<-gsub('^Beta', paste0('SCORE_targ_', populations[i]), names(tl_betas))
+
+  # Flip effects to match reference alleles
+  tl_betas <- map_score(ref = ref, score = tl_betas)
+  tl_betas_list[[i]]<-tl_betas
+}
+
+tl_betas_all<-Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE), tl_betas_list)
+
+fwrite(tl_betas_all, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+
+if(file.exists(paste0(opt$output,'.score.gz'))){
+  system(paste0('rm ',opt$output,'.score.gz'))
+}
+
+system(paste0('gzip ',opt$output,'.score'))
+
+# Record end time of test
+if(!is.na(opt$test)){
+  test_finish(log_file = log_file, test_start.time = test_start.time)
+}
+
+####
+# Calculate mean and sd of polygenic scores
+####
+
+log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
+
+# Calculate scores in the full reference
+ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
+
+# Calculate scale within each reference population
+pop_data <- read_pop_data(opt$pop_data)
+
+for(pop_i in unique(pop_data$POP)){
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
diff --git a/functions/constants.R b/functions/constants.R
index 23a74f9e..1d7abba1 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -37,14 +37,14 @@ ref_pop <- data.frame(
 
 # Make a data.frame giving labels to the 1KG reference populations
 pgs_method_labels <- data.frame(
-  method = c('ptclump','dbslmm','ldpred2','sbayesr','lassosum','prscs','megaprs','external','prscsx'),
-  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','lassosum','PRS-CS','MegaPRS','External','PRS-CSx')
+  method = c('ptclump','dbslmm','ldpred2','sbayesr','lassosum','prscs','megaprs','external','prscsx','xwing','tlprs'),
+  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','lassosum','PRS-CS','MegaPRS','External','PRS-CSx','X-Wing','TL-PRS')
 )
-pgs_method_labels[order(pgs_method_labels$method),]
+pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),]
 
 # Make vector indicating pgs_methods that can be applied to non-european GWAS
 pgs_methods_noneur <- c('ptclump','lassosum','megaprs','prscs','dbslmm')
 
 # Make vector indicating pgs_methods that are to be applied to gwas_groups
-pgs_group_methods <- c('prscsx')
+pgs_group_methods <- c('prscsx','xwing','tlprs')
 
diff --git a/functions/pgs.R b/functions/pgs.R
index 911b0821..d151e5bd 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -54,6 +54,14 @@ list_score_files <- function(config){
     # Retain methods that are applied to groups of gwas
     pgs_methods_list <- pgs_methods_list[(pgs_methods_list %in% pgs_group_methods)]
 
+    if('tlprs' %in% pgs_methods_list){
+      # For TL-PRS, list combos for tlprs_methods
+      tlprs_methods_list <- read_param(config = config, param = 'tlprs_methods', return_obj = F)
+      combos <- rbind(combos, expand.grid(name = gwas_groups$name, method = paste0('tlprs_', tlprs_methods_list)))
+    }
+
+    # Provide combos for other methods applied to groups of gwas
+    pgs_methods_list <- pgs_methods_list[pgs_methods_list != 'tlprs']
     combos <- rbind(combos, expand.grid(name = gwas_groups$name, method = pgs_methods_list))
   }
 
diff --git a/functions/pipeline.R b/functions/pipeline.R
index a6f84bd9..e3ff1bdf 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -210,9 +210,7 @@ find_pseudo <- function(config, gwas, pgs_method){
   if(pgs_method == 'prscs'){
     return('phi_auto')
   }
-  if(pgs_method == 'prscsx'){
-    return('phi_auto')
-  }
+
   if(pgs_method == 'megaprs'){
     # Read in megaprs log file
     log <- readLines(paste0(outdir,'/reference/pgs_score_files/',pgs_method,'/',gwas,'/ref-',gwas,'.log'))
@@ -232,6 +230,17 @@ find_pseudo <- function(config, gwas, pgs_method){
   if(pgs_method == 'external'){
     return('external')
   }
+
+  # Multi-population methods
+  if(pgs_method == 'prscsx'){
+    return('phi_auto')
+  }
+  if(pgs_method == 'xwing'){
+    return('targ_EUR_weighted') # NOTE. This is inappropriate when the target is not EUR. There should be a population parameter in the find_pseudo function to find score tailored for a given population. This also doesn't allow for gwas_groups that do not contain a EUR GWAS.
+  }
+  if(grepl('^tlprs', pgs_method)){
+    return('targ_EUR_0_1_TLPRS_1') # NOTE. This is inappropriate when the target is not EUR. There should be a population parameter in the find_pseudo function to find score tailored for a given population. This also doesn't allow for gwas_groups that do not contain a EUR GWAS.
+  }
 }
 
 # Read in lassosum pseudoval results
diff --git a/pipeline/config.yaml b/pipeline/config.yaml
index 7eda72bb..dd1fecaf 100644
--- a/pipeline/config.yaml
+++ b/pipeline/config.yaml
@@ -63,3 +63,6 @@ cores_impute_23andme: 10
 
 # Specify number of cores for outlier_detection rule
 cores_outlier_detection: 5
+
+# Specify PGS methods that should be used by TL-PRS
+tlprs_methods: NA
diff --git a/pipeline/misc/opensnp/config.yaml b/pipeline/misc/opensnp/config.yaml
index d30fa2cd..9c589f8b 100644
--- a/pipeline/misc/opensnp/config.yaml
+++ b/pipeline/misc/opensnp/config.yaml
@@ -4,6 +4,7 @@ gwas_list: misc/opensnp/gwas_list.txt
 score_list: misc/opensnp/score_list.txt
 target_list: misc/opensnp/target_list.txt
 gwas_groups: misc/opensnp/gwas_groups.txt
-pgs_methods: ['xwing']
+pgs_methods: ['xwing','tlprs','ptclump']
 testing: chr22
-cores_prep_pgs: 20
+cores_prep_pgs: 10
+tlprs_methods: ['ptclump']
diff --git a/pipeline/misc/opensnp/config_cross_pop.yaml b/pipeline/misc/opensnp/config_cross_pop.yaml
deleted file mode 100644
index af80c42c..00000000
--- a/pipeline/misc/opensnp/config_cross_pop.yaml
+++ /dev/null
@@ -1,7 +0,0 @@
-outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1
-config_file: misc/opensnp/config_cross_pop.yaml
-gwas_list: misc/opensnp/gwas_list.txt
-target_list: misc/opensnp/target_list.txt
-pgs_methods: ['ptclump','prscs','prscsx']
-testing: chr22
-gwas_groups: misc/cross_pop_test/gwas_groups.txt
diff --git a/pipeline/misc/opensnp/gwas_groups.txt b/pipeline/misc/opensnp/gwas_groups.txt
deleted file mode 100644
index 2cac132c..00000000
--- a/pipeline/misc/opensnp/gwas_groups.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-name gwas label
-height yengo_eur,yengo_eas "Yengo 2022 Height EUR+EAS"
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 5ae6cbe0..cab1e447 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -86,7 +86,6 @@ def check_target_type(df, column='type'):
     if not invalid_formats.empty:
         raise ValueError(f"Invalid format entries found in column '{column}': {invalid_formats[column].unique()}. Must be either 'plink1', 'plink2', 'vcf', 'bgen' or '23andMe'")
 
-
 ######
 # Check config file
 ######
@@ -108,7 +107,7 @@ def check_config_parameters(config):
         print("Error: Missing parameters in user-specified and default config files:", missing_params)
         sys.exit(1)
 
-# Check the sample config
+# Check the config
 check_config_parameters(config)
 
 # Set outdir parameter
@@ -141,6 +140,43 @@ else:
       if not os.path.exists(full_path):
           raise FileNotFoundError(f"File not found: {full_path}. Check reference data format.")
 
+# Check valid pgs_methods are specified
+def check_pgs_methods(x):
+    valid_pgs_methods = {
+        "ptclump", "dbslmm", "prscs", "sbayesr", "lassosum", "ldpred2", "megaprs", "xwing", "prscsx", "tlprs"
+    }
+
+    invalid_methods = [method for method in x if method not in valid_pgs_methods]
+
+    if invalid_methods:
+        raise ValueError(f"Invalid pgs_methods specified: {', '.join(invalid_methods)}. "
+                         f"Valid methods are: {', '.join(valid_pgs_methods)}.")
+
+check_pgs_methods(config['pgs_methods'])
+
+# Check valid tlprs_methods are specified
+def check_tlprs_methods(config):
+    valid_tlprs_methods = {
+        "ptclump", "dbslmm", "prscs", "sbayesr", "lassosum", "ldpred2", "megaprs"
+    }
+
+    # Check if 'tlprs' is in the pgs_methods list
+    if 'tlprs' in config.get('pgs_methods', []):
+        # Check if tlprs_methods is defined and not None/NA
+        tlprs_methods = config.get('tlprs_methods')
+
+        if tlprs_methods is None or tlprs_methods == 'NA':
+            raise ValueError("tlprs_methods must be specified when 'tlprs' is included in pgs_methods.")
+
+        # Check for invalid methods
+        invalid_methods = [method for method in tlprs_methods if method not in valid_tlprs_methods]
+
+        if invalid_methods:
+            raise ValueError(f"Invalid tlprs_methods specified: {', '.join(invalid_methods)}. "
+                             f"Valid methods are: {', '.join(valid_tlprs_methods)}.")
+
+check_tlprs_methods(config)
+
 ########
 # Check for repo version updates
 ########
@@ -654,6 +690,7 @@ rule install_lassosum:
       Rscript -e 'remotes::install_github(\"tshmak/lassosum@v0.4.5\")'
     }} > {log} 2>&1
     """
+
 # Install GenoUtils
 rule install_genoutils:
   input:
@@ -760,9 +797,9 @@ rule download_xwing_software:
     """
     {{
       rm -r -f resources/software/xwing; \
-      git clone https://github.com/qlu-lab/X-Wing resources/software/xwing; \
+      git clone https://github.com/opain/X-Wing resources/software/xwing; \
       cd resources/software/xwing; \
-      git reset --hard 01cb3f3b75cbd68b58eabc1fa28cbcf5368bfdf3
+      git reset --hard e9fcc264266e0e884323311816bfe20053fd3f7a
     }} > {log} 2>&1
     """
 
@@ -855,6 +892,25 @@ rule download_leopard_panther_snp_data:
     }} > {log} 2>&1
     """
 
+############
+
+# Install TL-PRS
+rule install_tlprs:
+  output:
+    touch("resources/software/install_tlprs.done")
+  conda:
+    "../envs/analysis.yaml"
+  benchmark:
+    "resources/data/benchmarks/install_tlprs.txt"
+  log:
+    "resources/data/logs/install_tlprs.log"
+  shell:
+    """
+    {{
+      Rscript -e 'devtools::install_github(\"opain/TLPRS@660c5332b55e0a78ee4b09f4785b6a1bc8e3dfa3\")'
+    }} > {log} 2>&1
+    """
+
 ############
 # Check all dependencies are available
 ############
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 72efb01b..63863af9 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -286,7 +286,7 @@ rule prep_pgs_lassosum_i:
      --gwas_pop {params.population} \
      --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
      --output {outdir}/reference/pgs_score_files/lassosum/{wildcards.gwas}/ref-{wildcards.gwas} \
-      --n_cores {threads} \
+     --n_cores {threads} \
      --pop_data {refdir}/ref.pop.txt \
      --test {params.testing} > {log} 2>&1"
 
@@ -608,6 +608,49 @@ rule prep_pgs_xwing_i:
 rule prep_pgs_xwing:
   input: expand(f"{outdir}/reference/pgs_score_files/xwing/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'])
 
+####
+# TL-PRS
+####
+
+rule prep_pgs_tlprs_i:
+  resources:
+    mem_mb=10000,
+    time_min=800
+  threads: config['cores_prep_pgs']
+  input:
+    rules.install_tlprs.output,
+    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
+    lambda w: expand(f"{outdir}/reference/pgs_score_files/{{method}}/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group), method=w.method)
+  output:
+    f"{outdir}/reference/pgs_score_files/tlprs_{{method}}/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
+  conda:
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/prep_pgs_tlprs_i-{{gwas_group}}-{{method}}.txt"
+  log:
+    f"{outdir}/reference/logs/prep_pgs_tlprs_i-{{gwas_group}}-{{method}}.log"
+  params:
+    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
+    scores= lambda w: ",".join(expand(f"{outdir}/reference/pgs_score_files/{{method}}/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group), method=w.method)),
+    populations= lambda w: ",".join(get_populations(w.gwas_group)),
+    testing=config["testing"]
+  shell:
+    """
+    Rscript ../Scripts/pgs_methods/tlprs.R \
+      --ref_plink_chr {refdir}/ref.chr \
+      --sumstats {params.sumstats} \
+      --scores {params.scores} \
+      --populations {params.populations} \
+      --pop_data {refdir}/ref.pop.txt \
+      --ref_keep_dir {refdir}/keep_files \
+      --output {outdir}/reference/pgs_score_files/tlprs_{wildcards.method}/{wildcards.gwas_group}/ref-{wildcards.gwas_group} \
+      --test {params.testing} \
+      --n_cores {threads} > {log} 2>&1
+    """
+
+rule prep_pgs_tlprs:
+  input: expand(f"{outdir}/reference/pgs_score_files/tlprs_{{method}}/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'], method=config["tlprs_methods"])
+
 ###############################################
 
 ##
@@ -636,6 +679,8 @@ if 'prscsx' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_prscsx.input)
 if 'xwing' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_xwing.input)
+if 'tlprs' in pgs_methods_all:
+  pgs_methods_input.append(rules.prep_pgs_tlprs.input)
 
 rule prep_pgs:
   input:

From cdc9950ab778e7bfb9c9b5c5e3b4df14fc3c7d69 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 22 Aug 2024 15:35:47 +0100
Subject: [PATCH 004/153] Further edits to implement TLPRS

---
 Scripts/pgs_methods/tlprs.R                   | 26 +++++----
 functions/pipeline.R                          | 11 +++-
 .../misc/cross_pop_test/cross_pop_test.Rmd    | 53 +++++++++++++++++--
 pipeline/misc/opensnp/config.yaml             |  2 -
 pipeline/misc/opensnp/config_cross_pop.yaml   |  8 +++
 pipeline/rules/dependencies.smk               |  2 +-
 6 files changed, 83 insertions(+), 19 deletions(-)
 create mode 100644 pipeline/misc/opensnp/config_cross_pop.yaml

diff --git a/Scripts/pgs_methods/tlprs.R b/Scripts/pgs_methods/tlprs.R
index 2701de11..308b82fa 100644
--- a/Scripts/pgs_methods/tlprs.R
+++ b/Scripts/pgs_methods/tlprs.R
@@ -183,26 +183,32 @@ for(i in 1:length(populations)){
     )
   )
 
-  beta_list=as.data.frame(beta_list[,-c(5,9)])
-  colnames(beta_list)[1:2]=c("SNP","A1")
+  # Flip effects to correspond to original A1
+  beta.info<-beta_list[, 1:9]
+  beta_list<-beta_list[, -1:-9]
+  flip<-beta.info$V5 != beta.info$A1
+  beta_list[flip,]<- -beta_list[flip,]
 
-  beta.info=beta_list[,1:2]
-  for (k in 8:ncol(beta_list)){
+  for (k in 1:ncol(beta_list)){
     sdtemp=sd(beta_list[,k],na.rm=T)
     if (sdtemp>1){
       beta_list[,k:ncol(beta_list)]=1
     }
   }
 
-  beta_list=beta_list[,8:ncol(beta_list)]/beta_list$sd
-  tl_betas<-data.table(cbind(beta.info, beta_list))
-  tl_betas<-merge(score_file[, c('SNP','A1','A2'), with=F], tl_betas, by=c('SNP','A1'))
+  beta_list=beta_list/beta.info$sd
 
-  names(tl_betas)<-gsub('^Beta', paste0('SCORE_targ_', populations[i]), names(tl_betas))
+  colnames(beta.info)[1:2]=c("SNP","A1")
+  beta.info<-beta.info[, 1:2]
+
+  beta_list<-data.table(cbind(beta.info, beta_list))
+  beta_list<-merge(score_file[, c('SNP','A1','A2'), with=F], beta_list, by=c('SNP','A1'))
+
+  names(beta_list)<-gsub('^Beta', paste0('SCORE_targ_', populations[i]), names(beta_list))
 
   # Flip effects to match reference alleles
-  tl_betas <- map_score(ref = ref, score = tl_betas)
-  tl_betas_list[[i]]<-tl_betas
+  beta_list <- map_score(ref = ref, score = beta_list)
+  tl_betas_list[[i]]<-beta_list
 }
 
 tl_betas_all<-Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE), tl_betas_list)
diff --git a/functions/pipeline.R b/functions/pipeline.R
index e3ff1bdf..dc197dd3 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -239,7 +239,16 @@ find_pseudo <- function(config, gwas, pgs_method){
     return('targ_EUR_weighted') # NOTE. This is inappropriate when the target is not EUR. There should be a population parameter in the find_pseudo function to find score tailored for a given population. This also doesn't allow for gwas_groups that do not contain a EUR GWAS.
   }
   if(grepl('^tlprs', pgs_method)){
-    return('targ_EUR_0_1_TLPRS_1') # NOTE. This is inappropriate when the target is not EUR. There should be a population parameter in the find_pseudo function to find score tailored for a given population. This also doesn't allow for gwas_groups that do not contain a EUR GWAS.
+    # Use most stringent p-value threshold of 0.05 as pseudo
+    if(grepl('ptclump', pgs_method)){
+      return('targ_EUR_0_1_TLPRS_1')
+    }
+
+    # Retrieve pseudoval param
+    if(grepl('dbslmm', pgs_method)){
+      return('targ_EUR_DBSLMM_1_TLPRS_1')
+    }
+    # NOTE. This is inappropriate when the target is not EUR. There should be a population parameter in the find_pseudo function to find score tailored for a given population. This also doesn't allow for gwas_groups that do not contain a EUR GWAS.
   }
 }
 
diff --git a/pipeline/misc/cross_pop_test/cross_pop_test.Rmd b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
index 5f25d3af..2be33eb9 100644
--- a/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
+++ b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
@@ -34,18 +34,19 @@ setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline')
 config <- readLines('misc/opensnp/config.yaml')
 
 config[grepl('^config_file:', config)]<- 'config_file: misc/opensnp/config_cross_pop.yaml'
-config[grepl('^testing:', config)]<- 'testing: chr22'
+config<-config[-which(grepl('^testing:', config))]
 config <- config[!grepl('^score_list:', config)]
 config[grepl('^outdir:', config)]<- 'outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1'
-config[grepl('^pgs_methods:', config)]<- "pgs_methods: ['ptclump','prscs','prscsx']"
+config[grepl('^pgs_methods:', config)]<- "pgs_methods: ['ptclump','tlprs']"
 config<-c(config, 'gwas_groups: misc/cross_pop_test/gwas_groups.txt')
+config<-c(config, "tlprs_methods: ['ptclump']")
 
 write.table(config, 'misc/opensnp/config_cross_pop.yaml', col.names = F, row.names = F, quote = F)
 
 ```
 
 ```{bash}
-snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_cross_pop.yaml output_all
+snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_cross_pop.yaml output_all -n
 ```
 
 ## Evaluate PGS
@@ -120,7 +121,7 @@ for(pop_i in unique(cor_eur$pop)){
         cor_eur$pgs_method == pgs_method_i,]
   
       # Top R
-      if(pgs_method_i %in% c('ptclump','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm')){
+      if(pgs_method_i %in% c('ptclump','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm') | any(grepl('tlprs', pgs_method_i))){
         top_i <- cor_eur_i[which(cor_eur_i$r == max(cor_eur_i$r, na.rm = T))[1],]
         top_i$model <- 'Top'
         cor_eur_subset <- rbind(cor_eur_subset, top_i)
@@ -159,7 +160,49 @@ plot_obj_eur <-
   theme_half_open() +
   background_grid() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1),
-        plot.title = element_text(hjust = 0.5, size=12))
+        plot.title = element_text(hjust = 0.5, size=12)) +
+  facet_grid(. ~ gwas)
 
 
+cor_eur_tlprs<-cor_eur[cor_eur$pgs_method == 'tlprs_ptclump',]
+cor_eur_tlprs[grepl('EUR_0_0.01', cor_eur_tlprs$name),]
+
+
+cor_eas <- cor[cor$pop == 'EAS', ]
+# Restrict to best and and pseudoval only
+cor_eas_subset <- NULL
+for(pop_i in unique(cor_eas$pop)){
+  for(gwas_i in unique(cor_eas$gwas[cor_eas$pop == pop_i])){
+    for(pgs_method_i in unique(cor_eas$pgs_method[cor_eas$pop == pop_i & cor_eas$gwas == gwas_i])){
+      
+      # Subset relevant results
+      cor_eas_i <- cor_eas[
+        cor_eas$pop == pop_i &
+        cor_eas$gwas == gwas_i &
+        cor_eas$pgs_method == pgs_method_i,]
+  
+      # Top R
+      if(pgs_method_i %in% c('ptclump','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm') | any(grepl('tlprs', pgs_method_i))){
+        top_i <- cor_eas_i[which(cor_eas_i$r == max(cor_eas_i$r, na.rm = T))[1],]
+        top_i$model <- 'Top'
+        cor_eas_subset <- rbind(cor_eas_subset, top_i)
+      }
+  
+      # PseudoVal
+      if(pgs_method_i %in% c('ptclump','sbayesr','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm')){
+        cor_eas_i$name <- gsub(paste0(gwas_i, '_'), '', cor_eas_i$name)
+        pseudo_param <- find_pseudo(config = 'misc/opensnp/config_cross_pop.yaml', gwas = gwas_i, pgs_method = pgs_method_i)
+        pseudo_i <- cor_eas_i[cor_eas_i$name == pseudo_param,]
+        pseudo_i$model <- 'Pseudo'
+        cor_eas_subset <- rbind(cor_eas_subset, pseudo_i)
+      }
+    }
+  }
+}
+
+# Plot the results
+cor_eas_subset$model <- factor(cor_eur_subset$model, levels = c('Top','Pseudo','External'))
+dir.create('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP')
+cor_eas_tlprs<-cor_eas[cor_eas$pgs_method == 'tlprs_ptclump',]
+
 ```
diff --git a/pipeline/misc/opensnp/config.yaml b/pipeline/misc/opensnp/config.yaml
index 9c589f8b..7fb0e15e 100644
--- a/pipeline/misc/opensnp/config.yaml
+++ b/pipeline/misc/opensnp/config.yaml
@@ -3,8 +3,6 @@ config_file: misc/opensnp/config.yaml
 gwas_list: misc/opensnp/gwas_list.txt
 score_list: misc/opensnp/score_list.txt
 target_list: misc/opensnp/target_list.txt
-gwas_groups: misc/opensnp/gwas_groups.txt
 pgs_methods: ['xwing','tlprs','ptclump']
 testing: chr22
 cores_prep_pgs: 10
-tlprs_methods: ['ptclump']
diff --git a/pipeline/misc/opensnp/config_cross_pop.yaml b/pipeline/misc/opensnp/config_cross_pop.yaml
new file mode 100644
index 00000000..f79728ea
--- /dev/null
+++ b/pipeline/misc/opensnp/config_cross_pop.yaml
@@ -0,0 +1,8 @@
+outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1
+config_file: misc/opensnp/config_cross_pop.yaml
+gwas_list: misc/opensnp/gwas_list.txt
+target_list: misc/opensnp/target_list.txt
+pgs_methods: ['ptclump','dbslmm','tlprs']
+cores_prep_pgs: 20
+gwas_groups: misc/cross_pop_test/gwas_groups.txt
+tlprs_methods: ['ptclump','dbslmm']
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index cab1e447..f74d4239 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -907,7 +907,7 @@ rule install_tlprs:
   shell:
     """
     {{
-      Rscript -e 'devtools::install_github(\"opain/TLPRS@660c5332b55e0a78ee4b09f4785b6a1bc8e3dfa3\")'
+      Rscript -e 'devtools::install_github(\"opain/TLPRS@fb71076267d405d5f7df97e445ab0de73d76bc0f\")'
     }} > {log} 2>&1
     """
 

From 12c8cb2e935847d5fae0eac2fe87b387044f7cf5 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 28 Aug 2024 12:59:27 +0100
Subject: [PATCH 005/153] Updates to multi-GWAS PGS methods

---
 Scripts/pgs_methods/bridgeprs.R               | 208 ++++++++++++++++++
 Scripts/pgs_methods/tlprs.R                   |  18 +-
 docs/{incomplete => }/DiverseAncestry.html    |   0
 pipeline/envs/bridgeprs.yaml                  |  19 ++
 .../misc/cross_pop_test/cross_pop_test.Rmd    |   4 +-
 pipeline/misc/opensnp/config_cross_pop.yaml   |   4 +-
 pipeline/rules/dependencies.smk               |  39 ++++
 7 files changed, 287 insertions(+), 5 deletions(-)
 create mode 100644 Scripts/pgs_methods/bridgeprs.R
 rename docs/{incomplete => }/DiverseAncestry.html (100%)
 create mode 100644 pipeline/envs/bridgeprs.yaml

diff --git a/Scripts/pgs_methods/bridgeprs.R b/Scripts/pgs_methods/bridgeprs.R
new file mode 100644
index 00000000..d365e319
--- /dev/null
+++ b/Scripts/pgs_methods/bridgeprs.R
@@ -0,0 +1,208 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
+      help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--pop_data", action="store", default=NULL, type='character',
+      help="File containing the population code and location of the keep file [required]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+      help="Path PLINK v2 software binary [optional]"),
+  make_option("--output", action="store", default=NULL, type='character',
+      help="Path for output files [required]"),
+  make_option("--memory", action="store", default=5000, type='numeric',
+      help="Memory limit [optional]"),
+  make_option("--sumstats", action="store", default=NULL, type='character',
+      help="Comma-seperated list of GWAS summary statistics [required]"),
+  make_option("--populations", action="store", default=NULL, type='character',
+      help="Comma-seperated list of population codes matching GWAS [required]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+      help="Number of cores for parallel computing [optional]"),
+  make_option("--test", action="store", default=NA, type='character',
+      help="Specify number of SNPs to include [optional]"),
+  make_option("--seed", action="store", default=1, type='numeric',
+      help="Seed number for PRScs [optional]")
+)
+
+opt = parse_args(OptionParser(option_list = option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+library(foreach)
+library(doMC)
+registerDoMC(opt$n_cores)
+
+# Check required inputs
+if(is.null(opt$ref_plink_chr)){
+  stop('--ref_plink_chr must be specified.\n')
+}
+if(is.null(opt$sumstats)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$pop_data)){
+  stop('--pop_data must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--output must be specified.\n')
+}
+if(is.null(opt$populations)){
+  stop('--populations must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
+system(paste0('mkdir -p ',opt$output_dir))
+
+# Create temp directory
+tmp_dir <- tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'prscsx.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+# Split opt$sumstats
+sumstats<-unlist(strsplit(opt$sumstats, ','))
+log_add(log_file = log_file, message = paste0(length(sumstats), ' sets of GWAS have been provided.'))
+
+gwas_N<-NULL
+for(i in 1:length(sumstats)){
+
+  #####
+  # Read in sumstats
+  #####
+
+  log_add(log_file = log_file, message = 'Reading in GWAS.')
+
+  # Read in, check and format GWAS summary statistics
+  gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('SNP','A1','A2','BETA','SE','N'))
+
+  # Store average sample size
+  gwas_N <- c(gwas_N, round(mean(gwas$N), 0))
+
+  fwrite(gwas, paste0(tmp_dir, '/GWAS_sumstats_',i,'_temp.txt'), sep=' ')
+
+  rm(gwas)
+  gc()
+
+
+}
+
+# Record start time for test
+if(!is.na(opt$test)){
+  test_start.time <- test_start(log_file = log_file)
+}
+
+#####
+# Process sumstats using PRS-CSx
+#####
+
+# Create a temporary reference bim files for PRS-CSx to match to
+pvar <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
+pvar$POS<-0
+for(i in CHROMS){
+  write.table(pvar[pvar$CHR == i, c('CHR','SNP','POS','BP','A1','A2'), with=F], paste0(tmp_dir,'/ref.chr',i,'.bim'), col.names=F, row.names=F, quote=F)
+}
+
+rm(pvar)
+gc()
+
+# Make a data.frame listing chromosome and phi combinations
+jobs<-NULL
+for(i in rev(CHROMS)){
+  jobs<-rbind(jobs, data.frame(CHR=i, phi=phi_param))
+}
+
+# Run using PRScs auto, and specifying a range of global shrinkage parameters
+log <- foreach(i = 1:nrow(jobs), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+  if(jobs$phi[i] == 'auto'){
+    system(paste0(opt$prscsx_path, ' --ref_dir=', opt$prscsx_ref_path, '/ --bim_prefix=', tmp_dir,'/ref.chr', jobs$CHR[i], ' --pop=', opt$populations, ' --sst_file=', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','),' --n_gwas=', paste(gwas_N, collapse=','), ' --out_dir=', tmp_dir, '/ --out_name=output --chrom=', jobs$CHR[i], ' --meta=True --seed=', opt$seed))
+  } else {
+    system(paste0(opt$prscsx_path, ' --ref_dir=', opt$prscsx_ref_path, '/ --bim_prefix=', tmp_dir,'/ref.chr', jobs$CHR[i], ' --pop=', opt$populations, ' --phi=', jobs$phi[i], ' --sst_file=', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','),' --n_gwas=', paste(gwas_N, collapse=','), ' --out_dir=', tmp_dir, '/ --out_name=output --chrom=', jobs$CHR[i], ' --meta=True --seed=', opt$seed))
+  }
+}
+
+####
+# Combine score files
+####
+
+score_all<-NULL
+for(pop_i in c(unlist(strsplit(opt$populations, ',')), 'META')){
+  score_pop<-NULL
+  for(phi_i in phi_param){
+    score_phi<-NULL
+    for(i in CHROMS){
+      score_phi_i<-fread(paste0(tmp_dir,'/output_',pop_i,'_pst_eff_a1_b0.5_phi',phi_i,'_chr',i,'.txt'))
+      score_phi<-rbind(score_phi, score_phi_i)
+    }
+    if(phi_i == phi_param[1]){
+      score_phi<-score_phi[,c('V2', 'V4','V5', 'V6'), with=F]
+      names(score_phi)<-c('SNP', 'A1', 'A2', paste0('SCORE_',pop_i,'_phi_',phi_i))
+    } else {
+      score_phi<-score_phi[,'V6', with=F]
+      names(score_phi)<-paste0('SCORE_',pop_i,'_phi_',phi_i)
+    }
+    score_pop<-cbind(score_pop, score_phi)
+  }
+  if(pop_i == c(unlist(strsplit(opt$populations, ',')), 'META')[1]){
+    score_all<-score_pop
+  } else {
+    score_all<-merge(score_all, score_pop[, !(names(score_pop) %in% c('A1','A2')), with=F], by='SNP', all=T)
+  }
+}
+
+# Replace NA values with 0
+score_all[is.na(score_all)] <- 0
+
+# Flip effects to match reference alleles
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
+score_new <- map_score(ref = ref, score = score_all)
+
+fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+
+if(file.exists(paste0(opt$output,'.score.gz'))){
+  system(paste0('rm ',opt$output,'.score.gz'))
+}
+
+system(paste0('gzip ',opt$output,'.score'))
+
+# Record end time of test
+if(!is.na(opt$test)){
+  test_finish(log_file = log_file, test_start.time = test_start.time)
+}
+
+####
+# Calculate mean and sd of polygenic scores
+####
+
+log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
+
+# Calculate scores in the full reference
+ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
+
+# Calculate scale within each reference population
+pop_data <- read_pop_data(opt$pop_data)
+
+for(pop_i in unique(pop_data$POP)){
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
diff --git a/Scripts/pgs_methods/tlprs.R b/Scripts/pgs_methods/tlprs.R
index 308b82fa..bd2dd592 100644
--- a/Scripts/pgs_methods/tlprs.R
+++ b/Scripts/pgs_methods/tlprs.R
@@ -24,6 +24,8 @@ option_list = list(
       help="Comma-seperated list of score files [required]"),
   make_option("--populations", action="store", default=NULL, type='character',
       help="Comma-seperated list of population codes matching GWAS [required]"),
+  make_option("--retain_nonoverlapping", action="store", default=T, type='character',
+      help="Logical indicating whether or not to retain the original BETA if variant is missing in target GWAS [required]"),
   make_option("--n_cores", action="store", default=1, type='numeric',
       help="Number of cores for parallel computing [optional]"),
   make_option("--test", action="store", default=NA, type='character',
@@ -206,12 +208,26 @@ for(i in 1:length(populations)){
 
   names(beta_list)<-gsub('^Beta', paste0('SCORE_targ_', populations[i]), names(beta_list))
 
+  if(opt$retain_nonoverlapping){
+    # Insert original BETA if variant not present in target GWAS
+    miss_snps <- score_file[!(score_file$SNP %in% beta_list$SNP),]
+    beta_list <- merge(beta_list, miss_snps, by = c('SNP','A1','A2'), all=T)
+    for(j in gsub('Beta_', '', names(score_file)[-1:-3])){
+      beta_list[!is.na(get(paste0('Beta_', j))),
+                (which(grepl('SCORE', names(beta_list)) & grepl(j, names(beta_list))))] <-
+        beta_list[[paste0('Beta_', j)]][!is.na(beta_list[[paste0('Beta_', j)]])]
+    }
+    beta_list<-beta_list[, !grepl('Beta_', names(beta_list)), with=F]
+  }
+
   # Flip effects to match reference alleles
   beta_list <- map_score(ref = ref, score = beta_list)
+
   tl_betas_list[[i]]<-beta_list
+
 }
 
-tl_betas_all<-Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE), tl_betas_list)
+tl_betas_all<-Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE, sort = F), tl_betas_list)
 
 fwrite(tl_betas_all, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
 
diff --git a/docs/incomplete/DiverseAncestry.html b/docs/DiverseAncestry.html
similarity index 100%
rename from docs/incomplete/DiverseAncestry.html
rename to docs/DiverseAncestry.html
diff --git a/pipeline/envs/bridgeprs.yaml b/pipeline/envs/bridgeprs.yaml
new file mode 100644
index 00000000..c7f89214
--- /dev/null
+++ b/pipeline/envs/bridgeprs.yaml
@@ -0,0 +1,19 @@
+name: bridgeprs
+channels:
+  - dranew
+  - bioconda
+  - conda-forge
+  - defaults
+dependencies:
+  - r-base=4.2.3
+  - plink2=2.00a5
+  - plink=1.90b6.21
+  - r-foreach=1.5.2
+  - r-domc=1.3.8
+  - r-r.utils=2.12.2
+  - r-mass=7.3_60
+  - r-data.table=1.14.8
+  - r-optparse=1.7.3
+  - r-bedmatrix=3.0.2
+  - r-glmnet=4.1_8
+  - r-boot=1.3_28.1
diff --git a/pipeline/misc/cross_pop_test/cross_pop_test.Rmd b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
index 2be33eb9..40bef4f6 100644
--- a/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
+++ b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
@@ -161,7 +161,7 @@ plot_obj_eur <-
   background_grid() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1),
         plot.title = element_text(hjust = 0.5, size=12)) +
-  facet_grid(. ~ gwas)
+  facet_grid(. ~ gwas, scales = 'free_x', space = 'free_x')
 
 
 cor_eur_tlprs<-cor_eur[cor_eur$pgs_method == 'tlprs_ptclump',]
@@ -183,7 +183,7 @@ for(pop_i in unique(cor_eas$pop)){
   
       # Top R
       if(pgs_method_i %in% c('ptclump','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm') | any(grepl('tlprs', pgs_method_i))){
-        top_i <- cor_eas_i[which(cor_eas_i$r == max(cor_eas_i$r, na.rm = T))[1],]
+        top_i <- cor_eas_i[which(cor_eas_i$r^2 == max(cor_eas_i$r^2, na.rm = T))[1],]
         top_i$model <- 'Top'
         cor_eas_subset <- rbind(cor_eas_subset, top_i)
       }
diff --git a/pipeline/misc/opensnp/config_cross_pop.yaml b/pipeline/misc/opensnp/config_cross_pop.yaml
index f79728ea..ded36deb 100644
--- a/pipeline/misc/opensnp/config_cross_pop.yaml
+++ b/pipeline/misc/opensnp/config_cross_pop.yaml
@@ -2,7 +2,7 @@ outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1
 config_file: misc/opensnp/config_cross_pop.yaml
 gwas_list: misc/opensnp/gwas_list.txt
 target_list: misc/opensnp/target_list.txt
-pgs_methods: ['ptclump','dbslmm','tlprs']
+pgs_methods: ['dbslmm','tlprs']
 cores_prep_pgs: 20
 gwas_groups: misc/cross_pop_test/gwas_groups.txt
-tlprs_methods: ['ptclump','dbslmm']
+tlprs_methods: ['dbslmm']
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index f74d4239..91b6c7c6 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -911,6 +911,45 @@ rule install_tlprs:
     }} > {log} 2>&1
     """
 
+############
+
+# Install GenoUtils in BridgePRS environment
+rule install_genoutils_bridgeprs:
+  output:
+    touch("resources/software/install_genoutils_bridgeprs.done")
+  conda:
+    "../envs/bridgeprs.yaml"
+  benchmark:
+    "resources/data/benchmarks/install_genoutils_bridgeprs.txt"
+  log:
+    "resources/data/logs/install_genoutils_bridgeprs.log"
+  shell:
+    """
+    {{
+      Rscript -e 'devtools::install_github(\"opain/GenoUtils@50ac8a2078226c8c2349064f904031576fbfe606\")'
+    }} > {log} 2>&1
+    """
+
+# Download BridgePRS
+rule download_bridgeprs_software:
+  input:
+    rules.install_genoutils_bridgeprs.output
+  output:
+    "resources/software/bridgeprs/block_partition.txt"
+  benchmark:
+    "resources/data/benchmarks/download_bridgeprs_software.txt"
+  log:
+    "resources/data/logs/download_bridgeprs_software.log"
+  shell:
+    """
+    {{
+      rm -r -f resources/software/bridgeprs; \
+      git clone https://github.com/clivehoggart/BridgePRS.git resources/software/bridgeprs; \
+      cd resources/software/xwing; \
+      git reset --hard 1e1c9477d42d44ed312759751adbc25d146aeb9f
+    }} > {log} 2>&1
+    """
+
 ############
 # Check all dependencies are available
 ############

From 4db9fe7d51d56ac62d1aa1def5f90374ddfb52d1 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 13 Sep 2024 15:08:25 +0100
Subject: [PATCH 006/153] Implementing BridgePRS

---
 Scripts/pgs_methods/bridgeprs.R               | 85 +++++++++++++++++--
 pipeline/envs/bridgeprs.yaml                  |  4 +-
 .../misc/cross_pop_test/config_cross_pop.yaml |  3 +-
 pipeline/misc/opensnp/config_cross_pop.yaml   |  2 +-
 pipeline/rules/dependencies.smk               | 26 +++---
 pipeline/rules/pgs_methods.smk                | 42 +++++++++
 6 files changed, 136 insertions(+), 26 deletions(-)

diff --git a/Scripts/pgs_methods/bridgeprs.R b/Scripts/pgs_methods/bridgeprs.R
index d365e319..b561e34a 100644
--- a/Scripts/pgs_methods/bridgeprs.R
+++ b/Scripts/pgs_methods/bridgeprs.R
@@ -33,9 +33,6 @@ library(GenoUtils)
 library(data.table)
 source('../functions/misc.R')
 source_all('../functions')
-library(foreach)
-library(doMC)
-registerDoMC(opt$n_cores)
 
 # Check required inputs
 if(is.null(opt$ref_plink_chr)){
@@ -77,27 +74,91 @@ if(!is.na(opt$test)){
 sumstats<-unlist(strsplit(opt$sumstats, ','))
 log_add(log_file = log_file, message = paste0(length(sumstats), ' sets of GWAS have been provided.'))
 
+# Split opt$populations
+populations<-unlist(strsplit(opt$populations, ','))
+
+#####
+# Create LD reference folder
+#####
+# Bridge requires the LD reference data to be stored in a folder
+# Data should be in PLINK1 format, split by chromosome with format "chr<1-22>.ext
+# The folder should also include a files called <POP>_ids.txt, which are keep files for each reference population
+
+dir.create(paste0(tmp_dir, '/ref_ld'))
+for(i in CHROMS){
+  for(j in c('bed','bim','fam')){
+    system(paste0(opt$plink2, ' --pfile ', opt$ref_plink_chr, i, ' --make-bed --out ', tmp_dir, '/ref_ld/chr', i))
+  }
+}
+
+pop_data <- read_pop_data(opt$pop_data)
+for(i in unique(pop_data$POP)){
+  fwrite(
+    pop_data[pop_data$POP == i, c('FID', 'IID'), with = F],
+    paste0(tmp_dir, '/ref_ld/', i, '_ids.txt'),
+    col.names = F,
+    row.names = F,
+    quote = F,
+    sep = ' '
+  )
+}
+
+#####
+# Prepare sumstats
+#####
+
 gwas_N<-NULL
 for(i in 1:length(sumstats)){
 
-  #####
-  # Read in sumstats
-  #####
-
   log_add(log_file = log_file, message = 'Reading in GWAS.')
 
   # Read in, check and format GWAS summary statistics
-  gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('SNP','A1','A2','BETA','SE','N'))
+  gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('CHR','SNP','A1','A2','BETA','P','N'))
 
   # Store average sample size
   gwas_N <- c(gwas_N, round(mean(gwas$N), 0))
+  gwas$N<NULL
 
-  fwrite(gwas, paste0(tmp_dir, '/GWAS_sumstats_',i,'_temp.txt'), sep=' ')
+  # Write sumstats split by chromosome
+  for(j in CHROMS){
+    tmp<-gwas[gwas$CHR == j,]
+    tmp$CHR<-NULL
+    fwrite(tmp, paste0(tmp_dir, '/GWAS_sumstats_',i,'_temp_chr', j, '.txt'), sep=' ')
+  }
 
   rm(gwas)
   gc()
 
+}
+
+#####
+# Create fake phenotype data
+#####
+
+fam<-fread(paste0(tmp_dir, '/ref_ld/chr', CHROMS[1], '.fam'))
+names(fam)[1:2]<-c('FID','IID')
+pheno<-fam[, 1:2]
+pheno$y<-rnorm(nrow(pheno))
+write.table(pheno, paste0(tmp_dir, '/fake_pheno.txt'), row.names = F, quote = F)
+
+#####
+# Create config files
+#####
 
+for(i in 1:length(sumstats)){
+  config_i<-c(
+    paste0("POP=", populations[i]),
+    paste0("LDPOP=", populations[i]),
+    paste0("LD_PATH=", tmp_dir, '/ref_ld'),
+    paste0("SUMSTATS_PREFIX=", tmp_dir, '/GWAS_sumstats_', i, '_temp_chr'),
+    "SUMSTATS_SUFFIX=.txt",
+    "SUMSTATS_FIELDS=SNP,A2,A1,P,BETA",
+    paste0("SUMSTATS_SIZE=", gwas_N[i]),
+    paste0("GENOTYPE_PREFIX=",tmp_dir, '/ref_ld/chr'),
+    paste0("PHENOTYPE_FILE=", tmp_dir, '/fake_pheno.txt')
+  )
+
+  writeLines(config_i, paste0(tmp_dir, '/', i, '.config'))
 }
 
 # Record start time for test
@@ -105,6 +166,12 @@ if(!is.na(opt$test)){
   test_start.time <- test_start(log_file = log_file)
 }
 
+#####
+# Run BridgePRS
+#####
+
+system(paste0(opt$bridgeprs_repo, '/bridgePRS pipeline go -o ', tmp_dir,'/out --config_files ', tmp_dir, '/', 1, '.config ', tmp_dir, '/', 2, '.config --phenotype y'))
+
 #####
 # Process sumstats using PRS-CSx
 #####
diff --git a/pipeline/envs/bridgeprs.yaml b/pipeline/envs/bridgeprs.yaml
index c7f89214..3e65a394 100644
--- a/pipeline/envs/bridgeprs.yaml
+++ b/pipeline/envs/bridgeprs.yaml
@@ -14,6 +14,8 @@ dependencies:
   - r-mass=7.3_60
   - r-data.table=1.14.8
   - r-optparse=1.7.3
-  - r-bedmatrix=3.0.2
+  - r-bedmatrix=2.0.3
   - r-glmnet=4.1_8
   - r-boot=1.3_28.1
+  - r-devtools=2.4.5
+  - icu=73.2
diff --git a/pipeline/misc/cross_pop_test/config_cross_pop.yaml b/pipeline/misc/cross_pop_test/config_cross_pop.yaml
index 235fbfa4..89bd3be0 100644
--- a/pipeline/misc/cross_pop_test/config_cross_pop.yaml
+++ b/pipeline/misc/cross_pop_test/config_cross_pop.yaml
@@ -4,8 +4,7 @@ config_file: misc/cross_pop_test/config_cross_pop.yaml
 gwas_list: misc/cross_pop_test/gwas_list.txt
 target_list: misc/dev/test_data/config/target_list.txt
 gwas_groups: misc/cross_pop_test/gwas_groups.txt
-pgs_methods: ['ptclump','prscs','prscsx']
+pgs_methods: ['bridgeprs']
 testing: chr22
 cores_target_pgs: 1
 mem_target_pgs: 2000
-prscs_phi: ['auto']
diff --git a/pipeline/misc/opensnp/config_cross_pop.yaml b/pipeline/misc/opensnp/config_cross_pop.yaml
index ded36deb..6c24f335 100644
--- a/pipeline/misc/opensnp/config_cross_pop.yaml
+++ b/pipeline/misc/opensnp/config_cross_pop.yaml
@@ -2,7 +2,7 @@ outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1
 config_file: misc/opensnp/config_cross_pop.yaml
 gwas_list: misc/opensnp/gwas_list.txt
 target_list: misc/opensnp/target_list.txt
-pgs_methods: ['dbslmm','tlprs']
+pgs_methods: ['dbslmm','tlprs','bridgeprs']
 cores_prep_pgs: 20
 gwas_groups: misc/cross_pop_test/gwas_groups.txt
 tlprs_methods: ['dbslmm']
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index e0f333f9..4fc2762d 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -143,7 +143,7 @@ else:
 # Check valid pgs_methods are specified
 def check_pgs_methods(x):
     valid_pgs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesr", "lassosum", "ldpred2", "megaprs", "xwing", "prscsx", "tlprs"
+        "ptclump", "dbslmm", "prscs", "sbayesr", "lassosum", "ldpred2", "megaprs", "xwing", "prscsx", "tlprs", "bridgeprs"
     }
 
     invalid_methods = [method for method in x if method not in valid_pgs_methods]
@@ -768,7 +768,7 @@ rule install_genoutils_xwing:
   shell:
     """
     {{
-      Rscript -e 'devtools::install_github(\"opain/GenoUtils@50ac8a2078226c8c2349064f904031576fbfe606\")'
+      Rscript -e 'devtools::install_github(\"opain/GenoUtils@cd4495f554be835872e77db72b6a8500c753273e\")'
     }} > {log} 2>&1
     """
 
@@ -894,9 +894,9 @@ rule install_tlprs:
   conda:
     "../envs/analysis.yaml"
   benchmark:
-    "resources/data/benchmarks/install_tlprs.txt"
+    f"{resdir}/data/benchmarks/install_tlprs.txt"
   log:
-    "resources/data/logs/install_tlprs.log"
+    f"{resdir}/data/logs/install_tlprs.log"
   shell:
     """
     {{
@@ -913,13 +913,13 @@ rule install_genoutils_bridgeprs:
   conda:
     "../envs/bridgeprs.yaml"
   benchmark:
-    "resources/data/benchmarks/install_genoutils_bridgeprs.txt"
+    f"{resdir}/data/benchmarks/install_genoutils_bridgeprs.txt"
   log:
-    "resources/data/logs/install_genoutils_bridgeprs.log"
+    f"{resdir}/data/logs/install_genoutils_bridgeprs.log"
   shell:
     """
     {{
-      Rscript -e 'devtools::install_github(\"opain/GenoUtils@50ac8a2078226c8c2349064f904031576fbfe606\")'
+      Rscript -e 'devtools::install_github(\"opain/GenoUtils@cd4495f554be835872e77db72b6a8500c753273e\")'
     }} > {log} 2>&1
     """
 
@@ -928,17 +928,17 @@ rule download_bridgeprs_software:
   input:
     rules.install_genoutils_bridgeprs.output
   output:
-    "resources/software/bridgeprs/block_partition.txt"
+    f"{resdir}/software/bridgeprs/bridgePRS"
   benchmark:
-    "resources/data/benchmarks/download_bridgeprs_software.txt"
+    f"{resdir}/data/benchmarks/download_bridgeprs_software.txt"
   log:
-    "resources/data/logs/download_bridgeprs_software.log"
+    f"{resdir}/data/logs/download_bridgeprs_software.log"
   shell:
     """
     {{
-      rm -r -f resources/software/bridgeprs; \
-      git clone https://github.com/clivehoggart/BridgePRS.git resources/software/bridgeprs; \
-      cd resources/software/xwing; \
+      rm -r -f {resdir}/software/bridgeprs; \
+      git clone https://github.com/clivehoggart/BridgePRS.git {resdir}/software/bridgeprs; \
+      cd {resdir}/software/bridgeprs; \
       git reset --hard 1e1c9477d42d44ed312759751adbc25d146aeb9f
     }} > {log} 2>&1
     """
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 75befd7c..e25792ec 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -651,6 +651,46 @@ rule prep_pgs_tlprs_i:
 rule prep_pgs_tlprs:
   input: expand(f"{outdir}/reference/pgs_score_files/tlprs_{{method}}/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'], method=config["tlprs_methods"])
 
+####
+# BridgePRS
+####
+
+rule prep_pgs_bridgeprs_i:
+  resources:
+    mem_mb=2000*config['cores_prep_pgs'],
+    time_min=800
+  threads: config['cores_prep_pgs']
+  input:
+    rules.download_bridgeprs_software.output,
+    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))
+  output:
+    f"{outdir}/reference/pgs_score_files/bridgeprs/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
+  conda:
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/prep_pgs_bridgeprs_i-{{gwas_group}}.txt"
+  log:
+    f"{outdir}/reference/logs/prep_pgs_bridgeprs_i-{{gwas_group}}.log"
+  params:
+    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
+    populations= lambda w: ",".join(get_populations(w.gwas_group)),
+    testing=config["testing"]
+  shell:
+    """
+    Rscript ../Scripts/pgs_methods/bridgeprs.R \
+      --ref_plink_chr {refdir}/ref.chr \
+      --sumstats {params.sumstats} \
+      --populations {params.populations} \
+      --pop_data {refdir}/ref.pop.txt \
+      --output {outdir}/reference/pgs_score_files/bridgeprs/{wildcards.gwas_group}/ref-{wildcards.gwas_group} \
+      --test {params.testing} \
+      --bridgeprs_repo {resdir}/software/bridgeprs \
+      --n_cores {threads} > {log} 2>&1
+    """
+
+rule prep_pgs_bridgeprs:
+  input: expand(f"{outdir}/reference/pgs_score_files/bridgeprs/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'])
+
 ###############################################
 
 ##
@@ -681,6 +721,8 @@ if 'xwing' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_xwing.input)
 if 'tlprs' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_tlprs.input)
+if 'bridgeprs' in pgs_methods_all:
+  pgs_methods_input.append(rules.prep_pgs_bridgeprs.input)
 
 rule prep_pgs:
   input:

From 27f033cfab702271c79e7028a1bfda32dedc5544 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 13 Sep 2024 15:13:43 +0100
Subject: [PATCH 007/153] Removed BridgePRS from absilable methods until
 implementation is complete

---
 pipeline/rules/dependencies.smk | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 4fc2762d..101a7d73 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -143,7 +143,7 @@ else:
 # Check valid pgs_methods are specified
 def check_pgs_methods(x):
     valid_pgs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesr", "lassosum", "ldpred2", "megaprs", "xwing", "prscsx", "tlprs", "bridgeprs"
+        "ptclump", "dbslmm", "prscs", "sbayesr", "lassosum", "ldpred2", "megaprs", "xwing", "prscsx", "tlprs"
     }
 
     invalid_methods = [method for method in x if method not in valid_pgs_methods]

From 65ff78bc949bc066c1a920da0b02f71b5202b827 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 16 Sep 2024 12:32:14 +0100
Subject: [PATCH 008/153] Updating GenoUtils package

---
 pipeline/rules/dependencies.smk | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 101a7d73..99085758 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -706,7 +706,7 @@ rule install_genoutils:
   shell:
     """
     {{
-      Rscript -e 'devtools::install_github(\"opain/GenoUtils@cd4495f554be835872e77db72b6a8500c753273e\")'
+      Rscript -e 'devtools::install_github(\"opain/GenoUtils@b90c92fe95c67d0845e1e884c0b20c3f3ab33f98\")'
     }} > {log} 2>&1
     """
 
@@ -768,7 +768,7 @@ rule install_genoutils_xwing:
   shell:
     """
     {{
-      Rscript -e 'devtools::install_github(\"opain/GenoUtils@cd4495f554be835872e77db72b6a8500c753273e\")'
+      Rscript -e 'devtools::install_github(\"opain/GenoUtils@b90c92fe95c67d0845e1e884c0b20c3f3ab33f98\")'
     }} > {log} 2>&1
     """
 
@@ -919,7 +919,7 @@ rule install_genoutils_bridgeprs:
   shell:
     """
     {{
-      Rscript -e 'devtools::install_github(\"opain/GenoUtils@cd4495f554be835872e77db72b6a8500c753273e\")'
+      Rscript -e 'devtools::install_github(\"opain/GenoUtils@b90c92fe95c67d0845e1e884c0b20c3f3ab33f98\")'
     }} > {log} 2>&1
     """
 

From 88141bb91dc8a79dfb6d8cd0e7bc40af974d0e5f Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 16 Sep 2024 15:00:12 +0100
Subject: [PATCH 009/153] Updated GenoUtils

---
 pipeline/rules/dependencies.smk | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 99085758..6f525e02 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -706,7 +706,7 @@ rule install_genoutils:
   shell:
     """
     {{
-      Rscript -e 'devtools::install_github(\"opain/GenoUtils@b90c92fe95c67d0845e1e884c0b20c3f3ab33f98\")'
+      Rscript -e 'devtools::install_github(\"opain/GenoUtils@6334159ab5d95ce936896e6938a1031c38ed4f30\")'
     }} > {log} 2>&1
     """
 
@@ -768,7 +768,7 @@ rule install_genoutils_xwing:
   shell:
     """
     {{
-      Rscript -e 'devtools::install_github(\"opain/GenoUtils@b90c92fe95c67d0845e1e884c0b20c3f3ab33f98\")'
+      Rscript -e 'devtools::install_github(\"opain/GenoUtils@6334159ab5d95ce936896e6938a1031c38ed4f30\")'
     }} > {log} 2>&1
     """
 
@@ -919,7 +919,7 @@ rule install_genoutils_bridgeprs:
   shell:
     """
     {{
-      Rscript -e 'devtools::install_github(\"opain/GenoUtils@b90c92fe95c67d0845e1e884c0b20c3f3ab33f98\")'
+      Rscript -e 'devtools::install_github(\"opain/GenoUtils@6334159ab5d95ce936896e6938a1031c38ed4f30\")'
     }} > {log} 2>&1
     """
 

From 70ac7e2f1b26174514f68c7ab763ef1f81e35433 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 16 Sep 2024 15:04:34 +0100
Subject: [PATCH 010/153] Fixed bug in DBSLMM when not using CHR 6

---
 Scripts/pgs_methods/dbslmm.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/Scripts/pgs_methods/dbslmm.R b/Scripts/pgs_methods/dbslmm.R
index 77ca2eaf..86d81374 100644
--- a/Scripts/pgs_methods/dbslmm.R
+++ b/Scripts/pgs_methods/dbslmm.R
@@ -32,7 +32,7 @@ make_option("--ld_scores", action="store", default=NULL, type='character',
     help="Path to genome-wide ld scores [required]"),
 make_option("--hm3_snplist", action="store", default=NULL, type='character',
     help="Path to LDSC HapMap3 snplist [required]"),
-make_option("--hm3_no_mhc", action="store", default=F, type='character',
+make_option("--hm3_no_mhc", action="store", default=F, type='logical',
     help="Logical indicating whether MHC region should be removed for LDSC analysis [required]"),
 make_option("--pop_prev", action="store", default=NULL, type='numeric',
     help="Population prevelance (if binary) [optional]"),
@@ -121,7 +121,7 @@ opt$h2f <- as.numeric(unlist(strsplit(opt$h2f, ',')))
 # Estimate the SNP-heritability using LD-Score Regression
 #####
 
-if(opt$hm3_no_mhc){
+if(opt$hm3_no_mhc & 6 %in% CHROMS){
   # Remove MHC region from hapmap3 SNP-list
   hm3<-fread(opt$hm3_snplist)
 

From 80ba773a6c8acc8f48303632f51c5a5eddbd7780 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 19 Sep 2024 09:13:14 +0100
Subject: [PATCH 011/153] Making updates to model_builder script and pipeline
 for crosspop

---
 Scripts/model_builder/model_builder.R |  594 ++++++++++++++
 docs/CrossPop.Rmd                     | 1071 +++++++++++++++++++++++++
 functions/model_builder.R             |   51 ++
 functions/pipeline.R                  |    2 +-
 pipeline/envs/model_builder.yaml      |   24 +
 5 files changed, 1741 insertions(+), 1 deletion(-)
 create mode 100644 Scripts/model_builder/model_builder.R
 create mode 100644 docs/CrossPop.Rmd
 create mode 100644 functions/model_builder.R
 create mode 100644 pipeline/envs/model_builder.yaml

diff --git a/Scripts/model_builder/model_builder.R b/Scripts/model_builder/model_builder.R
new file mode 100644
index 00000000..9740b989
--- /dev/null
+++ b/Scripts/model_builder/model_builder.R
@@ -0,0 +1,594 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+suppressMessages(library("optparse"))
+
+option_list = list(
+make_option("--pheno", action="store", default=NULL, type='character',
+		help="File containing phenotypic data [required]"),
+make_option("--predictors", action="store", default=NULL, type='character',
+		help="File listing files containing predictors, with a groups column for model comparison [required]"),
+make_option("--n_outer_fold", action="store", default=5, type='numeric',
+    help="Number of folds in for outer cross-validation [optional]"),
+make_option("--n_inner_fold", action="store", default=10, type='numeric',
+    help="Number of folds for inner cross-validation [optional]"),
+make_option("--n_core", action="store", default=1, type='numeric',
+		help="Number of cores for parallel computing [optional]"),
+make_option("--keep", action="store", default=NULL, type='character',
+		help="File containing list of individuals to include in analysis [optional]"),
+make_option("--outcome_pop_prev", action="store", default=NULL, type='numeric',
+		help="Prevalence of outcome in the general population [optional]"),
+make_option("--output", action="store", default=NULL, type='character',
+		help="Prefix for output files [required]"),
+make_option("--save_group_model", action="store", default=F, type='logical',
+		help="Save group models for external validation [optional]"),
+make_option("--assoc", action="store", default=T, type='logical',
+		help="Perform association analysis between each predictor and outcome [optional]"),
+make_option("--compare_predictors", action="store", default=F, type='logical',
+    help="Option to assign each predictor to own group [optional]"),
+make_option("--interaction", action="store", default=F, type='logical',
+    help="Option to include interaction terms between predictors in each group [optional]"),
+make_option("--pred_miss", action="store", default=0.1, type='numeric',
+    help="Proportion of missing values allowed in predictor [optional]"),
+make_option("--top1", action="store", default=F, type='numeric',
+    help="Evaluate model using top predictor within each group [optional]"),
+make_option("--seed", action="store", default=1, type='numeric',
+    help="Set seed number [optional]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+suppressMessages(library(GenoUtils))
+suppressMessages(library(data.table))
+source('../functions/misc.R')
+source_all('../functions')
+suppressMessages(library(glmnet))
+suppressMessages(library(doMC))
+suppressMessages(library(caret))
+suppressMessages(library(pROC))
+suppressMessages(library(verification))
+suppressMessages(library(psych))
+registerDoMC(opt$n_core)
+
+# Check required inputs
+if(is.null(opt$pheno)){
+  stop('--pheno must be specified.\n')
+}
+if(is.null(opt$predictors)){
+  stop('--predictors must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--out must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output), '/')
+system(paste0('mkdir -p ', opt$output_dir))
+
+# Create temp directory
+tmp_dir <- tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'model_builder.R', start.time = start.time)
+
+##################
+
+# Read in the predictors file
+predictors_list <- fread(opt$predictors)
+names(predictors_list)[1] <- 'predictor'
+
+# Determine if there is a group column, and if there is more than 1 group.
+if (opt$compare_predictors == F) {
+  if (!is.null(predictors_list$group)) {
+    if (length(unique(predictors_list$group)) != 1) {
+      opt$group_info <- T
+      opt$model_comp <- T
+      predictors_list$group <-
+        gsub("[[:punct:]]", ".", predictors_list$group)
+      log_add(log_file = log_file, message = 'Predictors file contains group information so model comparisons will be performed.')
+    } else {
+      opt$group_info <- T
+      opt$model_comp <- F
+      log_add(log_file = log_file, message = 'Predictors file does not contain group information so model comparisons will not be performed.')
+    }
+  } else {
+    opt$group_info <- F
+    opt$model_comp <- F
+    log_add(log_file = log_file, message = 'Predictors file does not contain group information so model comparisons will not be performed.')
+  }
+} else {
+  if (!is.null(predictors_list$group)) {
+    opt$group_info <- T
+    if (length(unique(predictors_list$group)) != 1) {
+      predictors_list$group <-
+        gsub("[[:punct:]]", ".", predictors_list$group)
+      log_add(log_file = log_file, message = 'Predictors file contains group information so model comparisons will be performed.')
+    } else {
+      log_add(log_file = log_file, message = 'Predictors file does not contain group information.')
+    }
+  } else {
+    opt$group_info <- F
+    log_add(log_file = log_file, message = 'Predictors file does not contain group information.')
+  }
+  log_add(log_file = log_file, message = 'Each predictor will be assigned to a seperate group and model comparisons will be performed.')
+  opt$model_comp <- T
+}
+
+###########
+# Read in the phenotypic data
+###########
+
+Outcome <- fread(opt$pheno)
+Outcome <- Outcome[complete.cases(Outcome), ]
+names(Outcome)[3] <- 'Outcome_var'
+
+# Create a column that combines FID and IID
+Outcome <- combine_fid_iid(Outcome)
+
+log_add(log_file = log_file, message = paste0('Phenotype file contains ',nrow(Outcome),' individuals with complete data.'))
+
+# Determine whether outcome is binary or continuous and format accordingly
+if (length(unique(Outcome$Outcome_var)) > 2) {
+  opt$family <- 'gaussian'
+}
+if (length(unique(Outcome$Outcome_var)) == 2) {
+  opt$family <- 'binomial'
+  Outcome$Outcome_var <- factor(Outcome$Outcome_var, labels = c('CONTROL', 'CASE'))
+}
+
+log_add(log_file = log_file, message = paste0('Phenotype is ', ifelse(opt$family == 'binomial', 'binary', 'quantitative'),'.'))
+
+if(!is.null(opt$keep)){
+	############
+	# Extract individuals in the keep file
+	############
+
+  # Read in keep file
+  keep_file <- fread(opt$keep)
+  names(keep_file)[1:2] <- c('FID', 'IID')
+  # Create a column that combines FID and IID
+  keep_file <- combine_fid_iid(keep_file)
+  # Extract keep individuals from the phenotypic data
+  Outcome <- Outcome[(Outcome$IID %in% keep_file$IID), ]
+
+	log_add(log_file = log_file, message = paste0('Phenotype file contains ',nrow(Outcome),' individuals after extraction of individuals in ',opt$keep,'.'))
+}
+
+############
+# Read in the predictor variables
+############
+
+if(nrow(predictors_list) > 1) {
+  Predictors<-foreach(k = 1:dim(predictors_list)[1], .combine = predictor_merger) %dopar% {
+    # Read in predictor file k
+    tmp <- fread(predictors_list$predictor[k])
+
+    # Create a column that combines FID and IID
+    tmp <- combine_fid_iid(tmp)
+
+    # Remove variables with > opt$pred_miss missing values
+    tmp <- filter_columns_by_missing(tmp, threshold = opt$pred_miss)
+
+    # Remove individuals with any missing data
+    tmp <- tmp[complete.cases(tmp), ]
+
+    # Update column names to avoid duplicate column names between predictor files
+    names(tmp)[-1] <- paste0('PredFile', k, '.', names(tmp)[-1])
+
+    if(opt$model_comp == T){
+      # Add group name to each predictor
+      names(tmp)[-1]<-paste0('Group_',predictors_list$group[k],'.',names(tmp)[-1])
+    }
+
+    log_add(log_file = log_file, message = paste0('Predictors file ',k,' contains ',ncol(tmp)-1,' predictors with sufficient data.'))
+    log_add(log_file = log_file, message = paste0('Predictors file ',k,' contains ',nrow(tmp),' individuals with complete data for remaining predictors.'))
+
+    tmp
+  }
+
+  Predictors<-data.table(Predictors)
+
+  log_add(log_file = log_file, message = paste0('After merging the ', length(predictors_list), ' predictors files,', ncol(Predictors)-1, ' predictors remain.'))
+  log_add(log_file = log_file, message = paste0('After merging the ', length(predictors_list), ' predictors files,', nrow(Predictors), ' individuals remain.'))
+
+} else {
+
+	# Read in the predictor variables
+	Predictors<-fread(predictors_list$predictor[1])
+
+	# Create a column that combines FID and IID
+	Predictors <- combine_fid_iid(Predictors)
+
+	# Remove variables with > opt$pred_miss missing values
+	Predictors <- filter_columns_by_missing(Predictors, threshold = opt$pred_miss)
+
+	# Remove individuals with any missing data
+	Predictors<-Predictors[complete.cases(Predictors),]
+
+	if(opt$compare_predictors == T){
+		# Create the object predictors_list
+		predictors_list <- data.table(predictor=names(Predictors)[-1], group=names(Predictors)[-1])
+		predictors_list$group <- gsub("[[:punct:]]", ".",predictors_list$group)
+
+		# Add group name to each predictor
+		names(Predictors)[-1]<-paste0('Group_',names(Predictors)[-1],'.',names(Predictors)[-1])
+	}
+
+	log_add(log_file = log_file, message = paste0('Predictors file contains ', ncol(Predictors)-1, ' predictors with sufficient data.'))
+	log_add(log_file = log_file, message = paste0('Predictors file contains ', dim(Predictors)[1], ' individuals with complete data for remaining predictors.'))
+
+}
+
+predictors_list_new<-predictors_list
+
+if(opt$compare_predictors == T){
+  if(opt$group_info == T){
+    # Create the object predictors_list
+    predictors_list_new <-
+      rbind(predictors_list_new,
+            data.table(
+              predictor = names(Predictors)[-1],
+              group = names(Predictors)[-1]
+            ))
+    predictors_list_new$group <- gsub("[[:punct:]]", ".",predictors_list_new$group)
+    predictors_list_new$group <- gsub('Group.','', predictors_list_new$group)
+  }
+}
+
+###########
+# Merge the phenotype and predictor variables
+###########
+
+Outcome_Predictors <- merge(Outcome, Predictors, by='IID')
+
+rm(Outcome,Predictors)
+
+log_add(log_file = log_file, message = paste0(nrow(Outcome_Predictors),' individuals have both phenotypic and predictor data.'))
+
+# Report the size of the combined outcome and predictor data
+log_add(log_file = log_file, message = paste0('Data to be carried foward is ',format(object.size(Outcome_Predictors), units='auto'),'.'))
+
+if(opt$assoc == T){
+
+	############
+	# Test association between Outcome and each variable in Predictors
+	############
+
+  log_add(log_file = log_file, message = 'Performing association analysis with each predictor...')
+
+	Outcome_Predictors_y <- Outcome_Predictors$Outcome_var
+	Outcome_Predictors_x <- Outcome_Predictors[, -1:-2]
+
+	Assoc_res<-NULL
+
+	if(opt$family == 'binomial'){
+		Assoc_res<-foreach(i=1:ncol(Outcome_Predictors_x), .combine=rbind) %dopar% {
+			if(var(Outcome_Predictors_x[[i]]) == 0){
+				Assoc_res_temp<-data.frame(	Predictor = names(Outcome_Predictors_x)[i],
+																		BETA = NA,
+																		SE = NA,
+																		P = NA,
+																		Obs_R2 = NA)
+
+				Assoc_res_temp
+			} else {
+			  mod <- glm(Outcome_Predictors_y ~ scale(Outcome_Predictors_x[[i]]), family=opt$family)
+				obs_r2 <- cor(predict(mod), as.numeric(Outcome_Predictors_y))^2
+			  sum_mod <- summary(mod)
+				prob <- predict(mod,type=c("response"))
+				Assoc_res_temp <-
+				  data.table(
+				    Predictor = names(Outcome_Predictors_x)[i],
+				    BETA = coef(sum_mod)[2, 1],
+				    SE = coef(sum_mod)[2, 2],
+				    P = coef(sum_mod)[2, 4],
+				    Obs_R2 = obs_r2
+				  )
+			  Assoc_res_temp
+			}
+		}
+		# Convert Nagelkerke R2 to liability scale
+		Assoc_res$Liab_R2 <-
+		  h2l_R2(
+		    opt$outcome_pop_prev,
+		    Assoc_res$Obs_R2,
+		    sum(Outcome_Predictors_y == 'CASE') / length(Outcome_Predictors_y)
+		  )
+	} else {
+		Assoc_res <- foreach(i = 1:ncol(Outcome_Predictors_x), .combine=rbind) %dopar% {
+			if(var(Outcome_Predictors_x[[i]]) == 0){
+				Assoc_res_temp<-data.frame(	Predictor=names(Outcome_Predictors_x)[i],
+																		BETA=NA,
+																		SE=NA,
+																		P=NA,
+																		Obs_R2=NA)
+
+				Assoc_res_temp
+			} else {
+				mod <- glm(scale(Outcome_Predictors_y) ~ scale(Outcome_Predictors_x[[i]]), family = opt$family)
+				sum_mod <- summary(mod)
+				Assoc_res_temp <-
+				  data.table(
+				    Predictor = names(Outcome_Predictors_x)[i],
+				    BETA = coef(sum_mod)[2, 1],
+				    SE = coef(sum_mod)[2, 2],
+				    P = coef(sum_mod)[2, 4],
+				    Obs_R2 = coef(sum_mod)[2, 1] ^ 2
+				  )
+				Assoc_res_temp
+			}
+		}
+	}
+
+	# Write out the results
+	write.table(Assoc_res, paste0(opt$output,'.assoc.txt'), col.names=T, row.names=F, quote=F)
+	log_add(log_file = log_file, message = paste0('Predictor association results saved as ',opt$output,'.assoc.txt.'))
+}
+
+if(opt$model_comp == T){
+  ############
+  # Build and evaluate models using predictors together
+  ############
+  # We will use nested cross validation to evaluate models
+
+  # Split the sample into n=opt$internal_validation_prop equal parts
+  set.seed(opt$seed)
+  nr <- nrow(Outcome_Predictors)
+  d <- sample(1:nr)
+
+  train.ext = createFolds(d, k = opt$n_outer_fold, returnTrain = TRUE)
+  test.ext = lapply(train.ext, function(x) (1:nr)[-x])
+
+  # Create a variable containing seeds for internal cross validation
+  seeds <- vector(mode = "list", length = opt$n_inner_fold+1)
+  for(i in 1:(opt$n_inner_fold)){
+    seeds[[i]] <- sample.int(n=1000, 10)
+  }
+  seeds[[opt$n_inner_fold + 1]] <- sample.int(n = 1000, 1)
+
+  # Create a model for each group, and using all predictors
+  predictors_list_new <-
+    rbind(predictors_list_new,
+          data.frame(predictor = 'All',
+                     group = '.'))
+
+  log_add(log_file = log_file, message = 'Initiating nested cross-validation...')
+
+  indep_pred<-list()
+  Prediction_summary_all<-NULL
+
+  if(opt$model_comp == T){
+    if(opt$top1){
+      ##############
+      # Evaluate top1 model
+      ##############
+      for(group in unique(predictors_list_new$group[predictors_list_new$group != '.'])){
+        group_name<-paste0(group, '_top1')
+        for(outer_val in 1:opt$n_outer_fold){
+          Outcome_Predictors_train_ind <- d[train.ext[[outer_val]]]
+
+          Outcome_Predictors_train <- Outcome_Predictors[Outcome_Predictors_train_ind,]
+          Outcome_Predictors_test <- Outcome_Predictors[-Outcome_Predictors_train_ind,]
+
+          Outcome_Predictors_train_y <- Outcome_Predictors_train$Outcome_var
+          Outcome_Predictors_train_x <- Outcome_Predictors_train[, -1:-2]
+
+          Outcome_Predictors_test_y <- Outcome_Predictors_test$Outcome_var
+          Outcome_Predictors_test_x <- Outcome_Predictors_test[, -1:-2]
+
+          Outcome_Predictors_train_x_group <-
+            Outcome_Predictors_train_x[,grepl(
+              paste0('Group_', group, '\\.', '|', 'Group_', group, '$'),
+              names(Outcome_Predictors_train_x)
+            ), with=F]
+
+          # Evaluate each predictor in training data
+          top1_res<-NULL
+          for(pred_i in names(Outcome_Predictors_train_x_group)){
+            res_pred_i<-cor(Outcome_Predictors_train_y, Outcome_Predictors_train_x_group[[pred_i]], use='p')
+            top1_res<-rbind(
+              top1_res,
+              data.frame(
+                pred=pred_i,
+                cor=res_pred_i)
+            )
+          }
+
+          # Build model using best predictor
+          train_tmp<-data.frame(
+            y=Outcome_Predictors_train_y,
+            x=Outcome_Predictors_train_x_group[[top1_res$pred[which.max(abs(top1_res$cor))]]])
+          train_mod<-glm(y ~ x, family=opt$family, data=train_tmp)
+
+          # Evaluate best performing predictor in test data
+          test_tmp<-data.frame(
+            x=Outcome_Predictors_test_x[[top1_res$pred[which.max(abs(top1_res$cor))]]])
+
+          Indep_Pred<-predict(object = train_mod, newdata = test_tmp, type = "response")
+
+          rm(train_tmp)
+          rm(test_tmp)
+          Indep_Pred_tab<-data.frame(obs=Outcome_Predictors_test_y,
+                                     pred=Indep_Pred)
+
+          print(top1_res$pred[which.max(abs(top1_res$cor))])
+          print(cor(Indep_Pred_tab))
+
+          # Save test set predictions from each outer loop
+          indep_pred[[group_name]]<-rbind(indep_pred[[group_name]], Indep_Pred_tab)
+
+        }
+        Indep_mod<-summary(lm(scale(as.numeric(indep_pred[[group_name]]$obs)) ~ scale(indep_pred[[group_name]]$pred)))
+
+        if(opt$family=='binomial'){
+          Prediction_summary_all<-rbind(Prediction_summary_all, data.frame(	Model=paste0(group_name,'_group'),
+                                                                            R=coef(Indep_mod)[2,1],
+                                                                            SE=coef(Indep_mod)[2,2],
+                                                                            P=coef(Indep_mod)[2,4],
+                                                                            R2l=h2l_R2(opt$outcome_pop_prev, coef(Indep_mod)[2,1]^2, sum(Outcome_Predictors_train_y == 'CASE')/length(Outcome_Predictors_train_y)),
+                                                                            N=dim(indep_pred[[group_name]])[1],
+                                                                            Ncase=sum(Outcome_Predictors_train_y == 'CASE'),
+                                                                            Ncont=sum(Outcome_Predictors_train_y == 'CONTROL')))
+        } else {
+          Prediction_summary_all<-rbind(Prediction_summary_all, data.frame(	Model=paste0(group_name,'_group'),
+                                                                            R=coef(Indep_mod)[2,1],
+                                                                            SE=coef(Indep_mod)[2,2],
+                                                                            P=coef(Indep_mod)[2,4],
+                                                                            R2o=coef(Indep_mod)[2,1]^2,
+                                                                            N=dim(indep_pred[[group_name]])[1]))
+        }
+      }
+    }
+
+    # Build glmnet using each group of predictors at a time
+    for(group in unique(predictors_list_new$group)){
+      for(outer_val in 1:opt$n_outer_fold){
+        print(outer_val)
+
+        Outcome_Predictors_train_ind <- d[train.ext[[outer_val]]]
+
+        Outcome_Predictors_train <- Outcome_Predictors[Outcome_Predictors_train_ind,]
+        Outcome_Predictors_test <- Outcome_Predictors[-Outcome_Predictors_train_ind,]
+
+        Outcome_Predictors_train_y <- Outcome_Predictors_train$Outcome_var
+        Outcome_Predictors_train_x <- Outcome_Predictors_train[, -1:-2]
+
+        Outcome_Predictors_test_y <- Outcome_Predictors_test$Outcome_var
+        Outcome_Predictors_test_x <- Outcome_Predictors_test[, -1:-2]
+
+        # Subset predictor in the group
+        print(group)
+        Outcome_Predictors_train_x_group <-
+          Outcome_Predictors_train_x[,grepl(
+            paste0('Group_', group, '\\.', '|', 'Group_', group, '$'),
+            names(Outcome_Predictors_train_x)
+          ), with=F]
+        Outcome_Predictors_test_x_group <-
+          Outcome_Predictors_test_x[,grepl(
+            paste0('Group_', group, '\\.', '|', 'Group_', group, '$'),
+            names(Outcome_Predictors_test_x)
+          ), with=F]
+
+    		# Rename group to 'all' if == '.'
+    		if (group == '.') {
+    		  group_name <- 'All'
+    		  Outcome_Predictors_train_x_group <- Outcome_Predictors_train_x
+    		  Outcome_Predictors_test_x_group <- Outcome_Predictors_test_x
+    		} else {
+    		  group_name <- group
+    		}
+
+    		# If there is only one predictor, use glm
+    		if(dim(Outcome_Predictors_train_x_group)[2] > 1){
+    				model<- train(y=Outcome_Predictors_train_y, x=Outcome_Predictors_train_x_group, trControl=trainControl(method="cv", seeds=seeds, number=opt$n_inner_fold, classProbs=T, savePredictions = 'final'), method="glmnet", family=opt$family)
+    		} else {
+    				model<- train(y=Outcome_Predictors_train_y, x=cbind(0,Outcome_Predictors_train_x_group), trControl=trainControl(method="cv", seeds=seeds, number=opt$n_inner_fold, classProbs=T, savePredictions = 'final'), method="glm", family=opt$family)
+    		}
+
+  		  if(dim(Outcome_Predictors_train_x_group)[2] > 1){
+  				Indep_Pred<-as.numeric(predict(object = model$finalModel, newx = data.matrix(Outcome_Predictors_test_x_group), type = "response", s = model$finalModel$lambdaOpt))
+  			} else {
+  					tmp<-data.frame(cbind(0,Outcome_Predictors_test_x_group))
+  					names(tmp)[1]<-'0'
+  					Indep_Pred<-predict(object = model$finalModel, newdata = tmp, type = "response")
+  					rm(tmp)
+  			}
+
+  		  Indep_Pred_tab<-data.frame(obs=Outcome_Predictors_test_y,
+  		                             pred=Indep_Pred)
+
+  			# Save test set predictions from each outer loop
+  			indep_pred[[group_name]]<-rbind(indep_pred[[group_name]], Indep_Pred_tab)
+      }
+
+      Indep_mod<-summary(lm(scale(as.numeric(indep_pred[[group_name]]$obs)) ~ scale(indep_pred[[group_name]]$pred)))
+
+      if(opt$family=='binomial'){
+        Prediction_summary_all<-rbind(Prediction_summary_all, data.frame(	Model=paste0(group_name,'_group'),
+                                                                          R=coef(Indep_mod)[2,1],
+                                                                          SE=coef(Indep_mod)[2,2],
+                                                                          P=coef(Indep_mod)[2,4],
+                                                                          R2l=h2l_R2(opt$outcome_pop_prev, coef(Indep_mod)[2,1]^2, sum(Outcome_Predictors_train_y == 'CASE')/length(Outcome_Predictors_train_y)),
+                                                                          N=dim(indep_pred[[group_name]])[1],
+                                                                          Ncase=sum(Outcome_Predictors_train_y == 'CASE'),
+                                                                          Ncont=sum(Outcome_Predictors_train_y == 'CONTROL')))
+      } else {
+        Prediction_summary_all<-rbind(Prediction_summary_all, data.frame(	Model=paste0(group_name,'_group'),
+                                                                          R=coef(Indep_mod)[2,1],
+                                                                          SE=coef(Indep_mod)[2,2],
+                                                                          P=coef(Indep_mod)[2,4],
+                                                                          R2o=coef(Indep_mod)[2,1]^2,
+                                                                          N=dim(indep_pred[[group_name]])[1]))
+      }
+    }
+
+    # Write out the results
+    write.table(Prediction_summary_all, paste0(opt$output,'.pred_eval.txt'), col.names=T, row.names=F, quote=F)
+
+    log_add(log_file = log_file, message = paste0('Model evaluation results saved as ',opt$output,'.pred_eval.txt.'))
+
+  	###################
+  	# Compare predictive utiliy of the different models
+  	###################
+    if(opt$top1){
+      predictors_list_new_top1<-predictors_list_new[-nrow(predictors_list_new),]
+      predictors_list_new_top1$group<-paste0(predictors_list_new_top1$group, '_top1')
+      predictors_list_new<-rbind(predictors_list_new, predictors_list_new_top1)
+    }
+  	comp_res_all<-NULL
+  	for(group1 in unique(predictors_list_new$group)){
+    	for(group2 in unique(predictors_list_new$group)){
+    	    if(group1 == '.'){
+    	      group1<-'All'
+    	    }
+      	  if(group2 == '.'){
+    	      group2<-'All'
+      	  }
+    	    if(group1 == group2){
+      			group1_r<-cor(scale(as.numeric(indep_pred[[group1]]$obs)), scale(indep_pred[[group1]]$pred))[1]
+    			  group2_r<-cor(scale(as.numeric(indep_pred[[group2]]$obs)), scale(indep_pred[[group2]]$pred))[1]
+
+    	      comp_res<-data.frame(Model_1=group1,
+    	                           Model_2=group2,
+    	                           Model_1_R=group1_r,
+    	                           Model_2_R=group2_r,
+    	                           R_diff=0,
+    	                           R_diff_pval=1)
+    	      comp_res_all<-rbind(comp_res_all,comp_res)
+    	      next
+    	    } else {
+    	      group1_r<-cor(scale(as.numeric(indep_pred[[group1]]$obs)), scale(indep_pred[[group1]]$pred))[1]
+    	      group2_r<-cor(scale(as.numeric(indep_pred[[group2]]$obs)), scale(indep_pred[[group2]]$pred))[1]
+
+    	      r_diff<-group1_r-group2_r
+
+    	      group1_group2_r<-cor(scale(indep_pred[[group1]]$pred), scale(indep_pred[[group2]]$pred))
+
+    	      r_diff_p<-paired.r(xy=group1_r, xz=group2_r, yz=group1_group2_r, n=length(scale(indep_pred[[group1]]$pred)), twotailed=T)$p[1]
+
+    	      comp_res<-data.frame(Model_1=group1,
+    	                           Model_2=group2,
+    	                           Model_1_R=group1_r,
+    	                           Model_2_R=group2_r,
+    	                           R_diff=r_diff,
+    	                           R_diff_pval=r_diff_p)
+    	    }
+
+    	    comp_res_all<-rbind(comp_res_all,comp_res)
+    	 }
+  	}
+
+    # Write out the results
+    write.table(comp_res_all, paste0(opt$output,'.pred_comp.txt'), col.names=T, row.names=F, quote=F)
+
+    log_add(log_file = log_file, message = paste0('Model evaluation results saved as ',opt$output,'.pred_comp.txt.'))
+  }
+
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+log_add(log_file = log_file, message = paste0('Analysis finished at ',as.character(end.time)))
+log_add(log_file = log_file, message = paste0('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units')))
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
new file mode 100644
index 00000000..98fcd29d
--- /dev/null
+++ b/docs/CrossPop.Rmd
@@ -0,0 +1,1071 @@
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+# Introduction
+
+European (EUR) GWAS are typically the largest in sample size, or even the only GWAS for certain outcomes. Here we will evaluate approaches for calculating polygenic scores (PGS) across populations. We will include single- and multi-source PGS methods, using EUR GWAS alone, or using EUR GWAS in combination with GWAS from other populations.
+
+In first instance, we will use EUR individuals in UK Biobank (UKB), to derive GWAS summary statistics, and Biobank Japan GWAS. We will evaluate PGS across populations in UKB, using outcomes available in the majority of participants to ensure sufficient sample size in non-EUR populations.
+
+***
+
+# Derive GWAS in UKB
+
+To avoid sample overlap between the EUR GWAS and the EUR target sample for evaluation, we will split EUR individuals in UKB into training and testing subsets. The GWAS will be performed in the training subset, and the PGS evaluation will occur in the testing subset.
+
+***
+
+## Run outlier detection
+
+<details><summary>Show code</summary>
+```{bash}
+cd /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/GenoPred/pipeline
+git describe --tags
+# v2.2.5-20-gb0bf674
+
+snakemake --profile slurm --use-conda --configfile=../../usr/k1806347/configs/benchmark/config.yaml outlier_detection -n 
+
+```
+</details>
+
+***
+
+## Collect phenotype data
+
+We will use the same 33 quantitative traits that were used in the PRS-CSx paper (Supp Table 10). 
+
+<details><summary>Show code</summary>
+```{bash}
+mkdir /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx
+```
+
+```{r}
+library(ukbkings)
+library(dplyr)
+library(stringr)
+library(data.table)
+
+# create data.frame showing variables used by prscsx
+prscsx_fields<-c('30620','30600','30610','30650','30160','21001','21002','30710','30680','4079','30150','30740','30750','30760','50','30030','30020','30780','30120','30050','30060','30040','30130','30140','30080','30010','30700','4080','30690','30860','30870','30000','30730')
+prscsx_trait<-c('Alanine aminotransferase','Albumin','Alkaline phosphatase','Aspartate transaminase','Basophil','Body mass index','Body weight','C-reactive protein','Calcium','Diastolic blood pressure','Eosinophil','Glucose','HbA1c','HDL-cholesterol','Height','Hematocrit','Hemoglobin','LDL-cholesterol','Lymphocyte','Mean corpuscular hemoglobin','Mean corpuscular hemoglobin concentration','Mean corpuscular volume','Monocyte','Neutrophil','Platelet','Red blood cell','Serum creatinine','Sytolic blood pressure','Total cholesterol','Total protein','Triglycerides','White blood cell','γ-glutamyl transpeptidase')
+prscsx_labels<-c('ALT','ALB','ALP','AST','BAS','BMI','BWT','CRP','Ca','DBP','EOS','GLC','HbA1c','HDL','HT','HCT','HB','LDL','LYM','MCH','MCHC','MCV','MON','NEU','PLT','RBC','CR','SBP','TC','TP','TG','WBC','GGT')
+
+prscsx_dat<-data.frame(
+  trait=prscsx_trait,
+  labels=prscsx_labels,
+  field=prscsx_fields
+)
+
+write.csv(prscsx_dat, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv', row.names = F)
+write.table(prscsx_labels, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt', col.names=F, row.names = F, quote=F)
+
+# Extract outcomes from UKB (project ukb82087)
+project_dir <- "/datasets/ukbiobank/ukb82087"
+
+system('rm /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.txt')
+f <- bio_field(project_dir)
+f %>%
+    select(field, name) %>%
+    filter(str_detect(field, paste(paste0("^", prscsx_dat$field, '-'), collapse='|'))) %>%
+    bio_field_add("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.txt")
+
+bio_phen(
+    project_dir,
+    field = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.txt",
+    out = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset"
+)
+
+system("ls -lh /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.rds")
+df <- readRDS("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.rds")
+
+# Take the first observation of each outcome
+library(tidyr)
+df_long <- df %>%
+  pivot_longer(cols = names(df)[!grepl('eid', names(df))], names_to = "variable", values_to = "outcome") %>%
+  drop_na(outcome)
+df_long$variable<-gsub('-.*','', df_long$variable)
+df_long<-df_long[!duplicated(df_long[,c('eid','variable')]),]
+
+library(data.table)
+
+for(i in 1:nrow(prscsx_dat)){
+  tmp <- df_long[df_long$variable == prscsx_dat$field[i],]
+  tmp <- data.frame(
+    eid = tmp$eid,
+    outcome = tmp$outcome
+  )
+  
+  fwrite(
+    tmp,
+    paste0(
+      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      prscsx_dat$label[i],
+      '.txt'
+    ),
+    row.names = F,
+    quote = F,
+    na = 'NA',
+    sep = '\t'
+  )
+}
+
+# Read in ancestry inference results to determine sample size per population
+# Use ancestry information from GenoPred
+keep_files<-list.files(path = '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/pcs/within_sample/', pattern = '.keep')
+
+pop_dat<-NULL
+for(i in keep_files){
+  tmp<-fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/pcs/within_sample/', i))
+  names(tmp)<-c('FID','IID')
+  tmp$POP<-gsub('.keep','', gsub('ukb.outlier_detection.','',i))
+  pop_dat<-rbind(pop_dat, tmp)
+}
+
+# Update row number IDs to project specific IDs
+psam<-fread('/scratch/prj/ukbiobank/ukb82087/imputed/ukb82087_imp_chr1_MAF1_INFO4_v1.psam')
+psam$rn<-1:nrow(psam)
+psam<-psam[,c('IID','rn'), with = F]
+
+pop_dat$FID<-NULL
+pop_dat<-merge(pop_dat, psam, by.x='IID', by.y='rn')
+pop_dat<-data.frame(
+  eid=pop_dat$IID.y,
+  POP=pop_dat$POP
+)
+
+# Merge ancestry info with phenotype data
+df_short <- dcast(df_long, eid ~ variable, value.var = "outcome")
+df_short<-merge(df_short, pop_dat, by='eid')
+
+# Remove related individuals
+greedy_related <- "/scratch/prj/ukbiobank/KCL_Data/Software/tools/GreedyRelated-master-v1.2.1/GreedyRelated"
+rel<-bio_gen_related_remove(
+      project_dir = project_dir,
+      greedy_related = greedy_related,
+      keep = df_short$eid,
+      thresh = 0.044,
+      seed = 1
+    )$eid
+
+df_short_unrel<-df_short[!(df_short$eid %in% rel),]
+
+n_table<-NULL
+for(i in 1:nrow(prscsx_dat)){
+  for(j in unique(pop_dat$POP[!is.na(pop_dat$POP)])){
+    tmp<-data.frame(
+      trait=prscsx_dat$trait[i],
+      labels=prscsx_dat$label[i],
+      field=prscsx_dat$field[i],
+      population=j,
+      n=sum(!is.na(df_short[[prscsx_dat$field[i]]][df_short$POP == j])),
+      n_unrel=sum(!is.na(df_short_unrel[[prscsx_dat$field[i]]][df_short_unrel$POP == j]))
+    )
+    n_table<-rbind(n_table, tmp)
+  }
+}
+
+write.csv(n_table, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/n_table')
+
+# Define training subset for EUR
+df_short_unrel_eur<-df_short_unrel[df_short_unrel$POP == 'EUR',]
+set.seed(1)
+train_size <- floor(0.8 * nrow(df_short_unrel_eur))
+train_indices <- sample(seq_len(nrow(df_short_unrel_eur)), size = train_size)
+
+df_short_unrel_eur_train<-df_short_unrel_eur[train_indices,]
+df_short_unrel_eur_test<-df_short_unrel_eur[-train_indices,]
+
+n_table_eur<-NULL
+for(i in 1:nrow(prscsx_dat)){
+  tmp<-data.frame(
+    trait=prscsx_dat$trait[i],
+    labels=prscsx_dat$label[i],
+    field=prscsx_dat$field[i],
+    n_train=sum(!is.na(df_short_unrel_eur_train[[prscsx_dat$field[i]]])),
+    n_test=sum(!is.na(df_short_unrel_eur_test[[prscsx_dat$field[i]]]))
+  )
+  n_table_eur<-rbind(n_table_eur, tmp)
+}
+
+write.csv(n_table_eur, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/n_table_eur')
+
+df_short_unrel$POP[df_short_unrel$eid %in% df_short_unrel_eur_train$eid]<-'EUR_train'
+df_short_unrel$POP[df_short_unrel$eid %in% df_short_unrel_eur_test$eid]<-'EUR_test'
+
+# Output phenotype data for each population
+for(i in 1:nrow(prscsx_dat)){
+  for(j in unique(df_short_unrel$POP)){
+    tmp<-df_short_unrel[df_short_unrel$POP == j,]
+    tmp <- data.frame(
+      FID = tmp$eid,
+      IID = tmp$eid,
+      outcome = tmp[[prscsx_dat$field[i]]]
+    )
+    
+    fwrite(
+      tmp,
+      paste0(
+        '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+        prscsx_dat$label[i],
+        '.unrel.', j, '.txt'
+      ),
+      row.names = F,
+      quote = F,
+      na = 'NA',
+      sep = '\t'
+    )
+    
+    # Write out with row number based IDs
+    pheno<-merge(tmp, psam, by='IID')
+    pheno<-data.frame(
+      FID=pheno$rn,
+      IID=pheno$rn,
+      outcome=pheno$outcome
+    )
+  
+    fwrite(
+      pheno,
+      paste0(
+        '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+        prscsx_dat$label[i],
+        '.unrel.', j, '.row_number.txt'
+      ),
+      row.names = F,
+      quote = F,
+      na = 'NA',
+      sep = '\t'
+    )
+  }
+}
+
+# For the EUR training GWAS, normalise and regress covariates
+# Use age, sex and PCs as covariates
+# Read in PC data released by UKB
+qc_dat<-bio_gen_sqc(project_dir)
+qc_dat<-qc_dat[,c('eid',paste0('pc',1:20))]
+df_short_unrel<-merge(df_short_unrel, qc_dat, by='eid')
+
+# Read in sex and age information
+system('rm /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.txt')
+f <- bio_field(project_dir)
+f %>%
+    select(field, name) %>%
+    filter(str_detect(field, "^21022-0.0|^31-0.0")) %>%
+    bio_field_add("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.txt")
+
+bio_phen(
+    project_dir,
+    field = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.txt",
+    out = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset"
+)
+
+system("ls -lh /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.rds")
+df <- readRDS("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.rds")
+names(df)<-gsub('-.*','',names(df))
+names(df)[names(df) == '31']<-'sex'
+names(df)[names(df) == '21022']<-'age'
+df_short_unrel<-merge(df_short_unrel, df, by='eid')
+
+# Within each population, normalise each outcome and regress out covariates
+library(RNOmni)
+covs<-c(paste0('pc',1:20), 'sex', 'age')
+df_short_unrel_eur_train<-df_short_unrel[df_short_unrel$POP == 'EUR_train',]
+for(i in 1:nrow(prscsx_dat)){
+  tmp<-df_short_unrel_eur_train[!is.na(df_short_unrel_eur_train[[prscsx_dat$field[i]]]),]
+  tmp$pheno_norm<-RNOmni::RankNorm(tmp[[prscsx_dat$field[i]]])
+  mod<-lm(as.formula(paste0('pheno_norm ~ ', paste(covs, collapse=' + '))), data=tmp)
+  tmp$pheno_norm_resid_scale<-as.numeric(scale(resid(mod)))
+  tmp<-data.frame(
+    FID=tmp$eid,
+    IID=tmp$eid,
+    outcome=tmp$pheno_norm_resid_scale
+  )
+  
+  fwrite(
+    tmp,
+    paste0(
+      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      prscsx_dat$label[i],
+      '.unrel.EUR_train.norm_resid_scale.txt'
+    ),
+    row.names = F,
+    quote = F,
+    na = 'NA',
+    sep = '\t'
+  )
+}
+
+# Convert to row number based IDs so it will work with UKB geno data from GenoPred
+for(i in 1:nrow(prscsx_dat)){
+  pheno<-fread(paste0(
+      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      prscsx_dat$label[i],
+      '.unrel.EUR_train.norm_resid_scale.txt'
+    ))
+  
+  pheno<-merge(pheno, psam, by='IID')
+  pheno<-data.frame(
+    FID=pheno$rn,
+    IID=pheno$rn,
+    outcome=pheno$outcome
+  )
+  
+  fwrite(
+    pheno,
+    paste0(
+      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      prscsx_dat$label[i],
+      '.unrel.EUR_train.norm_resid_scale.row_number.txt'
+    ),
+    row.names = F,
+    quote = F,
+    na = 'NA',
+    sep = '\t'
+  )
+}
+
+```
+</details>
+
+***
+
+## Run GWAS
+
+<details><summary>Show code</summary>
+```{bash, eval=F, echo=T}
+for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  mkdir -p /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}
+  for chr in $(seq 1 22); do
+      sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/plink2 \
+        --pfile /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/geno/ukb.ref.chr${chr} \
+        --pheno /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.EUR_train.norm_resid_scale.row_number.txt \
+        --linear omit-ref cols=+a1freq,+ax \
+        --maf 0.01 \
+        --geno 0.05 \
+        --out /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}"
+  done
+done
+
+# Once complete, merge results across chromosomes
+for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  head -n 1 /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.chr1.outcome.glm.linear > /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+    for chr in $(seq 1 22); do
+      tail -n +2 /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}.outcome.glm.linear >> /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+    done
+    
+    # Remove REF and ALT columns and rename AX column to A2
+    cut -f 4,5 --complement /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt | awk 'BEGIN{FS=OFS="\t"} NR==1 {$5="A2"} 1' > temp.txt && mv temp.txt /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+
+    gzip /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+done
+
+```
+</details>
+
+***
+
+# Download relevant BBJ sumstats
+
+```{r}
+# Identify wget command for relevant phenotypes
+library(data.table)
+
+# Read in BBJ GWAS info from BBJ website
+bbj_gwas<-fread('~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas.csv')
+
+# Map BBJ trait names to those used for UKB
+bbj_gwas$bbj_labels <-
+  gsub("\\)", '', gsub(".*\\(", '', bbj_gwas$Phenotype))
+bbj_gwas$trait <- gsub(" \\(.*", '', bbj_gwas$Phenotype)
+
+bbj_gwas$Category<-NULL
+bbj_gwas$Phenotype<-NULL
+
+# Update trait labels to match what was used in prscsx paper
+bbj_gwas$trait<-gsub(' count','', bbj_gwas$trait)
+bbj_gwas$trait[bbj_gwas$trait == 'G-glutamyl transpeptidase']<-'γ-glutamyl transpeptidase'
+
+# Merge the bbj trait info with the prscsx trait info
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat <- merge(bbj_gwas, prscsx_dat, by='trait', all=T)
+
+write.csv(prscsx_dat, '~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_prscsx.csv', row.names = F)
+
+# Create column showing what label is used in the wget command
+prscsx_dat$wget_label <-
+  gsub('.v1.zip', '', gsub('.*hum0197.v3.BBJ.', '', prscsx_dat$wget))
+
+# Write a table showing label matching prscsx info and wget url
+write.table(prscsx_dat[, c('labels', 'wget', 'wget_label'), with=F], '~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt', col.names = F, row.names = F, quote = F)
+
+```
+
+```{bash}
+# wget and unzip sumstats
+for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  url=$(awk -v var="$pheno" '$1 == var {print $2}' ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
+  sbatch -p neurohack_cpu --wrap="wget -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip ${url}
+    unzip /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip -d /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx
+    rm /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip"
+done
+
+# Delete X chromosome sumstats and rename files to be consistent with prscsx sumstat info
+for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  wget_label=$(awk -v var="$pheno" '$1 == var {print $3}' ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
+  mv ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1/GWASsummary_${wget_label}_Japanese_SakaueKanai2020.auto.txt.gz ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz
+  rm -r ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1
+done
+
+# Format so BOLT P value is used by GenoPred
+for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/pigz -dc ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz | awk 'BEGIN {OFS=\"\t\"} {print \$2, \$3, \$4, \$6, \$7, \$8, \$9, \$12, \$13, \$15}' | sed '1s/P_BOLT_LMM_INF/P/' | /users/k1806347/oliverpainfel/Software/pigz -c > ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.reformat.txt.gz"
+done
+
+```
+
+***
+
+# Run GenoPred
+
+## Prepare configuration
+
+```{r}
+######
+# gwas_list
+######
+
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-prscsx_dat[1:5,]
+
+gwas_list_eur<-data.frame(
+  name=paste0(prscsx_dat$labels,'_UKB'),
+  path=paste0('/scratch/prj/ukbiobank/usr/ollie_pain/gwas/',prscsx_dat$labels,'/ukb.eur_train.',prscsx_dat$labels,'.GW.txt.gz'),
+  population='EUR',
+  n=NA,
+  sampling=NA,
+  prevalence=NA,
+  mean=0,
+  sd=1,
+  label=paste0('"', prscsx_dat$trait, ' (UKB)"')
+)
+
+bbj_info<-fread('~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_prscsx.csv')
+bbj_info<-bbj_info[bbj_info$labels %in% prscsx_dat$labels,]
+
+gwas_list_eas<-data.frame(
+  name=paste0(bbj_info$labels,'_BBJ'),
+  path=paste0('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.',bbj_info$labels,'.reformat.txt.gz'),
+  population='EAS',
+  n=as.numeric(gsub(',','',bbj_info$`No. samples`)),
+  sampling=NA,
+  prevalence=NA,
+  mean=0,
+  sd=1,
+  label=paste0('"', prscsx_dat$trait, ' (BBJ)"')
+)
+
+gwas_list<-rbind(gwas_list_eur, gwas_list_eas)
+
+write.table(gwas_list, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt', col.names = T, row.names = F, quote = F)
+
+######
+# gwas_groups
+######
+
+gwas_groups<-data.frame(
+  name=prscsx_dat$labels,
+  gwas=sapply(prscsx_dat$labels, function(x) paste0(x,'_UKB,',x,'_BBJ')),
+  label=paste0('"', prscsx_dat$trait, '"')
+)
+
+write.table(gwas_groups, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt', col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output_crosspop",
+  "config_file: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml",
+  "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt",
+  "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
+  "gwas_groups: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt",
+  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs']",
+  "tlprs_methods: ['dbslmm']",
+  "cores_prep_pgs: 20",
+  "cores_target_pgs: 50"
+)
+
+write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+***
+
+## Run pipeline
+
+```{bash}
+snakemake --profile slurm --use-conda --configfile=/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml output_all -n
+```
+
+***
+
+# Evaluate PGS
+
+Lets use the model builder script which implements nested 10 fold cross validation. Similar set up to previous paper, evaluating a model containing the best PGS selected by 10-fold cross validation, a model containing the PGS selected by pseudovalidation (if available), and an elastic net model containing all PGS from a given method. We will need to update the model builder script to achieve this
+
+***
+
+## Create predictor lists
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in list of outcomes 
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-prscsx_dat[1:5,]
+
+##############
+# Comparison: Single source
+##############
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+pop <- c('EUR','EAS')
+for(disc_pop_i in pop){
+  disc_samp<-ifelse(disc_pop_i == 'EUR', 'UKB', 'BBJ')
+  scores_disc_pop_i <- scores[grepl(disc_samp, scores$name), ]
+
+  for(targ_pop_i in pop){
+    for(trait_i in prscsx_dat$labels){
+      dir.create(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/single.',
+          disc_pop_i,
+          '_disc.targ_',
+          targ_pop_i,
+          '/',
+          trait_i
+        ),
+        recursive = T
+      )
+      scores_tmp <- scores_disc_pop_i[grepl(trait_i, scores_disc_pop_i$name),]
+      
+      predictors_tmp <- data.frame(
+        predictor = paste0(
+          outdir,
+          '/ukb/pgs/',
+          targ_pop_i,
+          '/',
+          scores_tmp$method,
+          '/',
+          scores_tmp$name,
+          '/ukb-',
+          scores_tmp$name,
+          '-',
+          targ_pop_i,
+          '.profiles'
+        ),
+        group=scores_tmp$method
+      )
+      
+      write.table(
+        predictors_tmp,
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/single.',
+          disc_pop_i,
+          '_disc.targ_',
+          targ_pop_i,
+          '/',
+          trait_i,
+          '/predictor_list.txt'
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}
+
+##############
+# Comparison: Multi source (EUR + EAS -> EUR, EUR + EAS -> EAS)
+##############
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+pop <- c('EUR','EAS')
+for(pop_i in pop){
+  for(trait_i in prscsx_dat$labels){
+    dir.create(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_',
+        pop_i,
+        '/',
+        trait_i
+      ),
+      recursive = T
+    )
+    scores_tmp <- scores[grepl(trait_i, scores$name),]
+    
+    predictors_tmp <- data.frame(
+      predictor = paste0(
+        outdir,
+        '/ukb/pgs/',
+        pop_i,
+        '/',
+        scores_tmp$method,
+        '/',
+        scores_tmp$name,
+        '/ukb-',
+        scores_tmp$name,
+        '-',
+        pop_i,
+        '.profiles'
+      ),
+      group=scores_tmp$method
+    )
+    
+    write.table(
+      predictors_tmp,
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_',
+        pop_i,
+        '/',
+        trait_i,
+        '/predictor_list.txt'
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}
+
+```
+
+***
+
+## Run model_builder
+
+```{bash}
+###########
+# Single source
+###########
+
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+for disc_pop in $(echo EUR EAS); do
+  for targ_pop in $(echo EUR EAS); do
+    if [ "$targ_pop" == "EUR" ]; then
+        targ_pop2="EUR_test"
+    else
+        targ_pop2=$targ_pop
+    fi
+    
+    for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+      sbatch --mem 20G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+        --pheno /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/single.${disc_pop}_disc.targ_${targ_pop}/${pheno}/predictor_list.txt \
+        --output /users/k1806347/oliverpainfel/Analyses/crosspop/single.${disc_pop}_disc.targ_${targ_pop}/${pheno}/res \
+        --n_core 5 \
+        --top1 T \
+        --compare_predictors T \
+        --assoc T"
+    done
+  done
+done
+
+# Note. Will we will need to include specify --compare_predictors T to results for every PGS (allowing us to get pseudoval results)
+
+###########
+# Multi source
+###########
+
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+for targ_pop in $(echo EUR EAS); do
+  if [ "$targ_pop" == "EUR" ]; then
+      targ_pop2="EUR_test"
+  else
+      targ_pop2=$targ_pop
+  fi
+  echo $targ_pop2
+  for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+    sbatch --mem 20G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      --pheno /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/predictor_list.txt \
+      --output /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/res \
+      --n_core 5 \
+      --top1 T \
+      --compare_predictors T \
+      --assoc T"
+  done
+done
+
+```
+
+***
+
+## Plot results
+
+### Assoc results
+
+Let's see how results vary across parameters. This is a sanity check as well as being informative of how I should structure the models for each method.
+
+```{r}
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+# Read in list of outcomes 
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-prscsx_dat[1:5,]
+
+#############
+# Single source
+#############
+
+plot_list<-list()
+pop=c('EUR','EAS')
+for(pheno_i in prscsx_dat$labels){
+  res_i<-NULL
+  for(disc_pop_i in pop){
+    for(targ_pop_i in pop){
+        assoc_i <-
+          fread(
+            paste0(
+              '/users/k1806347/oliverpainfel/Analyses/crosspop/single.',
+              disc_pop_i,
+              '_disc.targ_',
+              targ_pop_i,
+              '/',
+              pheno_i,
+              '/res.assoc.txt'
+            )
+          )
+        assoc_i$method <-
+          gsub('\\..*', '', gsub('Group_', '', assoc_i$Predictor))
+        assoc_i$Predictor<-sub("^[^.]*\\.[^.]*\\.", "", assoc_i$Predictor)
+        assoc_i$gwas<-sub("^(([^_]*_)[^_]*).*", "\\1", assoc_i$Predictor)
+        assoc_i$name<-sub("^([^_]+_){2}", "", assoc_i$Predictor)
+        assoc_i$test<-paste0(disc_pop_i, ' → ', targ_pop_i)
+        
+        res_i<-rbind(res_i, assoc_i)
+    }
+  }
+  
+  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=name, y=BETA, colour = method)) +
+    geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
+                  width = .2,
+                  position = position_dodge(width = 0.7)) +
+    geom_point() +
+    geom_line() +
+    geom_hline(yintercept = 0) +
+    facet_grid(.~ test, scales = 'free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+}
+
+png(
+  '/users/k1806347/oliverpainfel/Analyses/crosspop/single.assoc.png',
+  res = 300,
+  height = 5000,
+  width = 3000,
+  units = 'px'
+)
+plot_grid(plotlist = plot_list, labels = names(plot_list), ncol=1)
+dev.off()
+
+#############
+# Multi source
+#############
+
+single_plot_list<-list()
+prscsx_xwing_plot_list<-list()
+tlprs_plot_list<-list()
+pop=c('EUR','EAS')
+for(pheno_i in prscsx_dat$labels){
+  res_i<-NULL
+  for(targ_pop_i in pop){
+      assoc_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.',
+            'targ_',
+            targ_pop_i,
+            '/',
+            pheno_i,
+            '/res.assoc.txt'
+          )
+        )
+      assoc_i$method <-
+        gsub('\\..*', '', gsub('Group_', '', assoc_i$Predictor))
+      assoc_i$Predictor<-sub("^[^.]*\\.[^.]*\\.", "", assoc_i$Predictor)
+      assoc_i$gwas<-sub("^(([^_]*_)[^_]*).*", "\\1", assoc_i$Predictor)
+      assoc_i$name<-sub("^([^_]+_){2}", "", assoc_i$Predictor)
+      assoc_i$name[assoc_i$method == 'prscsx']<-paste0(gsub('.*_','', assoc_i$gwas[assoc_i$method == 'prscsx']),'_', assoc_i$name[assoc_i$method == 'prscsx'])
+      assoc_i$disc[grepl('tlprs|prscsx|xwing', assoc_i$method)] <-'EUR+EAS'
+      assoc_i$disc[grepl('UKB', assoc_i$gwas)] <-'EUR' 
+      assoc_i$disc[grepl('BBJ', assoc_i$gwas)] <-'EAS' 
+      assoc_i$test<-paste0(assoc_i$disc, ' → ', targ_pop_i)
+      assoc_i$tlprs_target<-NA
+      assoc_i$tlprs_target[assoc_i$method == 'tlprs' & grepl('EUR_DBSLMM', assoc_i$name)]<-'EUR-tuned'
+      assoc_i$tlprs_target[assoc_i$method == 'tlprs' & grepl('EAS_DBSLMM', assoc_i$name)]<-'EAS-tuned'
+      
+      res_i<-rbind(res_i, assoc_i)
+  }
+
+    tmp<-res_i[res_i$method %in% c('ptclump','dbslmm'),]
+    tmp$name<-factor(tmp$name, levels = unique(tmp$name))
+
+    single_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=name, y=BETA, colour = method)) +
+    geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
+                  width = .2,
+                  position = position_dodge(width = 0.7)) +
+    geom_point() +
+    geom_line() +
+    geom_hline(yintercept = 0) +
+    facet_grid(.~ test, scales = 'free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+    tmp<-res_i[(res_i$method %in% c('prscsx','xwing')),]
+    tmp$name<-factor(tmp$name, levels = unique(tmp$name))
+    
+    prscsx_xwing_plot_list[[pheno_i]] <- ggplot(tmp, aes(x=name, y=BETA, colour = method)) +
+    geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
+                  width = .2,
+                  position = position_dodge(width = 0.7)) +
+    geom_point() +
+    geom_line() +
+    geom_hline(yintercept = 0) +
+    facet_grid(.~ test, scales = 'free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+  
+    tmp<-res_i[(res_i$method %in% c('tlprs') & grepl('DBSLMM_1_', res_i$name)),]
+    tmp$name<-as.numeric(gsub('.*_','',tmp$name))
+    
+    tlprs_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=name, y=BETA, colour = method)) +
+    geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
+                  width = .2,
+                  position = position_dodge(width = 0.7)) +
+    geom_point() +
+    geom_line() +
+    geom_hline(yintercept = 0) +
+    facet_grid(test ~ tlprs_target, scales = 'free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+}
+
+png(
+  '/users/k1806347/oliverpainfel/Analyses/crosspop/single.assoc.png',
+  res = 300,
+  height = 5000,
+  width = 4000,
+  units = 'px'
+)
+plot_grid(plotlist = single_plot_list, labels = names(single_plot_list), ncol=1)
+dev.off()
+
+png(
+  '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.prscsx_xwing.assoc.png',
+  res = 300,
+  height = 5000,
+  width = 4000,
+  units = 'px'
+)
+plot_grid(plotlist = prscsx_xwing_plot_list, labels = names(prscsx_xwing_plot_list), ncol=1)
+dev.off()
+
+png(
+  '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.tlprs.assoc.png',
+  res = 300,
+  height = 6000,
+  width = 3000,
+  units = 'px'
+)
+plot_grid(plotlist = tlprs_plot_list, labels = names(tlprs_plot_list), ncol=1)
+dev.off()
+
+```
+
+- PRS-CSx say to model the EUR, EAS and META output (for each phi)
+- x-wing produce output for each target population
+- TLPRS produce output for each target population
+
+***
+
+### Prediction modelling results
+
+```{r}
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+# Read in list of outcomes 
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-prscsx_dat[1:5,]
+
+#############
+# Single source
+#############
+
+plot_list<-list()
+pop=c('EUR','EAS')
+for(pheno_i in prscsx_dat$labels){
+  res_i<-NULL
+  for(disc_pop_i in pop){
+    for(targ_pop_i in pop){
+        eval_i <-
+          fread(
+            paste0(
+              '/users/k1806347/oliverpainfel/Analyses/crosspop/single.',
+              disc_pop_i,
+              '_disc.targ_',
+              targ_pop_i,
+              '/',
+              pheno_i,
+              '/res.pred_eval.txt'
+            )
+          )
+        eval_i$Model<-gsub('_group','',eval_i$Model)
+        eval_i$test<-paste0(disc_pop_i, ' → ', targ_pop_i)
+        res_i<-rbind(res_i, eval_i)
+    }
+  }
+  
+  res_i$Method<-gsub('_.*','',res_i$Model)
+  res_i$Group[grepl('top1', res_i$Model)]<-'Top1'
+  res_i$Group[!grepl('top1', res_i$Model)]<-'Multi'
+  res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
+  res_i$Group<-factor(res_i$Group, levels=unique(res_i$Group))
+  
+  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=Method, y=R, fill = Group)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = .2,
+                  position = position_dodge(width = 0.7)) +
+    geom_point(stat="identity", position=position_dodge(0.7), size=2, shape=23) +
+    geom_hline(yintercept = 0) +
+    geom_vline(xintercept = c(1.5,2.5), linetype="dotted") +
+    facet_grid(.~ test, scales = 'free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+}
+
+png(
+  '/users/k1806347/oliverpainfel/Analyses/crosspop/single.eval.png',
+  res = 300,
+  height = 5000,
+  width = 3000,
+  units = 'px'
+)
+plot_grid(plotlist = plot_list, labels = names(plot_list), ncol=1)
+dev.off()
+
+#############
+# Multi source
+#############
+
+plot_list<-list()
+pop=c('EUR','EAS')
+for(pheno_i in prscsx_dat$labels){
+  res_i<-NULL
+  for(targ_pop_i in pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.',
+            'targ_',
+            targ_pop_i,
+            '/',
+            pheno_i,
+            '/res.pred_eval.txt'
+          )
+        )
+      eval_i$Model<-gsub('_group','',eval_i$Model)
+      eval_i$target<-targ_pop_i
+      res_i<-rbind(res_i, eval_i)
+  }
+  
+  res_i$Method<-gsub('_.*','',res_i$Model)
+  res_i$Group[grepl('top1', res_i$Model)]<-'Top1'
+  res_i$Group[!grepl('top1', res_i$Model)]<-'Multi'
+  res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
+  res_i$Group<-factor(res_i$Group, levels=unique(res_i$Group))
+  
+  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=Method, y=R, fill = Group)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = .2,
+                  position = position_dodge(width = 0.7)) +
+    geom_point(stat="identity", position=position_dodge(0.7), size=2, shape=23) +
+    geom_hline(yintercept = 0) +
+    geom_vline(xintercept = seq(1.5, 5.5, by=1), linetype="dotted") +
+    facet_grid(.~ target, scales = 'free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+}
+
+png(
+  '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.eval.png',
+  res = 300,
+  height = 5000,
+  width = 3000,
+  units = 'px'
+)
+plot_grid(plotlist = plot_list, labels = names(plot_list), ncol=1)
+dev.off()
+
+```
+
diff --git a/functions/model_builder.R b/functions/model_builder.R
new file mode 100644
index 00000000..5b8bf1ec
--- /dev/null
+++ b/functions/model_builder.R
@@ -0,0 +1,51 @@
+#!/usr/bin/Rscript
+
+# Create the function for converting R2 into liability R2
+h2l_R2 <- function(k, r2, p) {
+  # K baseline disease risk
+  # r2 from a linear regression model attributable to genomic profile risk score
+  # P proportion of sample that are cases
+  # calculates proportion of variance explained on the liability scale
+  #from ABC at http://www.complextraitgenomics.com/software/
+  #Lee SH, Goddard ME, Wray NR, Visscher PM. (2012) A better coefficient of determination for genetic profile analysis. Genet Epidemiol. 2012 Apr;36(3):214-24.
+  x= qnorm(1-k)
+  z= dnorm(x)
+  i=z/k
+  C= k*(1-k)*k*(1-k)/(z^2*p*(1-p))
+  theta= i*((p-k)/(1-k))*(i*((p-k)/(1-k))-x)
+  h2l_R2 = C*r2 / (1 + C*theta*r2)
+}
+
+# Function for merging by IID
+predictor_merger <- function(x, y) {
+  return(merge(x, y, by = 'IID'))
+}
+
+# Function to combine FID and IID, into a variable called IID
+combine_fid_iid <- function(data, fid_col = "FID", iid_col = "IID") {
+  # Check if the specified columns exist in the data
+  if (!all(c(fid_col, iid_col) %in% names(data))) {
+    stop("Both FID and IID columns must be present in the data.")
+  }
+
+  # Combine FID and IID into a new IID column
+  data[[iid_col]] <- paste0(data[[fid_col]], ':', data[[iid_col]])
+
+  # Remove the FID column
+  data[[fid_col]] <- NULL
+
+  return(data)
+}
+
+# Remove variables with > opt$pred_miss missing values
+filter_columns_by_missing <- function(data, threshold, first_col_keep = TRUE) {
+  # Identify columns to keep based on missing value threshold
+  col_keep <- c(first_col_keep, sapply(data[, -1, with = FALSE], function(col) {
+    mean(!is.finite(col) | is.na(col)) < threshold
+  }))
+
+  # Filter the data by keeping only the columns that meet the criteria
+  data_filtered <- data[, col_keep, with = FALSE]
+
+  return(data_filtered)
+}
diff --git a/functions/pipeline.R b/functions/pipeline.R
index dc197dd3..cc013ca0 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -233,7 +233,7 @@ find_pseudo <- function(config, gwas, pgs_method){
 
   # Multi-population methods
   if(pgs_method == 'prscsx'){
-    return('phi_auto')
+    return('META_phi_auto')
   }
   if(pgs_method == 'xwing'){
     return('targ_EUR_weighted') # NOTE. This is inappropriate when the target is not EUR. There should be a population parameter in the find_pseudo function to find score tailored for a given population. This also doesn't allow for gwas_groups that do not contain a EUR GWAS.
diff --git a/pipeline/envs/model_builder.yaml b/pipeline/envs/model_builder.yaml
new file mode 100644
index 00000000..0214505a
--- /dev/null
+++ b/pipeline/envs/model_builder.yaml
@@ -0,0 +1,24 @@
+name: model_builder
+channels:
+  - dranew
+  - bioconda
+  - conda-forge
+  - defaults
+dependencies:
+  - r-base=4.2.3
+  - r-data.table=1.14.8
+  - r-glmnet=4.1_8
+  - r-domc=1.3.8
+  - r-caret=6.0_94
+  - r-proc=1.18.5
+  - r-verification=1.42
+  - r-psych=2.4.3
+  - r-mass=7.3_60
+  - r-r.utils=2.12.2
+  - r-optparse=1.7.3
+  - r-devtools=2.4.5
+  - r-stringi=1.7.12
+  - icu=73.2
+
+# After activating the environment, run the following commands in an R session to install the GitHub package:
+# devtools::install_github("opain/GenoUtils@6334159ab5d95ce936896e6938a1031c38ed4f30")

From 079ceb1a92c288e699bc35241c29d052e8185fac Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 23 Sep 2024 10:18:46 +0100
Subject: [PATCH 012/153] Added check that gwas_groups contains gwas in
 gwas_list, and increased time limit for xwing

---
 pipeline/rules/pgs_methods.smk | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index e25792ec..479d4e1a 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -503,6 +503,13 @@ def get_populations(gwas_group):
         sumstats_populations.append(gwas_info['population'])
     return sumstats_populations
 
+# Check whether gwas_groups contains gwas that are not in the gwas_list
+gwas_groups_gwas = gwas_groups_df['gwas'].str.split(',', expand=True).stack().unique()
+gwas_list_names = gwas_list_df['name'].unique()
+missing_gwas = set(gwas_groups_gwas) - set(gwas_list_names)
+if missing_gwas:
+    raise ValueError(f"The following GWAS are in gwas_groups but missing in gwas_list: {', '.join(missing_gwas)}")
+
 ####
 # PRS-CSx
 ####
@@ -561,7 +568,7 @@ rule prep_pgs_prscsx:
 rule prep_pgs_xwing_i:
   resources:
     mem_mb=2000*config['cores_prep_pgs'],
-    time_min=800
+    time_min=10000
   threads: config['cores_prep_pgs']
   input:
     rules.download_xwing_software.output,

From f39d225fc4d03437a234eceba784db1c29565982 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 23 Sep 2024 14:51:56 +0100
Subject: [PATCH 013/153] Updating model builder and crosspop analysis

---
 Scripts/model_builder/model_builder.R |  22 +++---
 docs/CrossPop.Rmd                     | 106 +++++++++++++++++++++++---
 docs/pipeline_readme.Rmd              |  10 +++
 functions/model_builder.R             |   7 ++
 functions/pipeline.R                  |  27 +++++--
 5 files changed, 144 insertions(+), 28 deletions(-)

diff --git a/Scripts/model_builder/model_builder.R b/Scripts/model_builder/model_builder.R
index 9740b989..10a18af7 100644
--- a/Scripts/model_builder/model_builder.R
+++ b/Scripts/model_builder/model_builder.R
@@ -364,7 +364,7 @@ if(opt$model_comp == T){
       ##############
       # Evaluate top1 model
       ##############
-      for(group in unique(predictors_list_new$group[predictors_list_new$group != '.'])){
+      for(group in unique(predictors_list_new$group[predictors_list_new$group != '.' & !grepl('PredFile', predictors_list_new$group)])){
         group_name<-paste0(group, '_top1')
         for(outer_val in 1:opt$n_outer_fold){
           Outcome_Predictors_train_ind <- d[train.ext[[outer_val]]]
@@ -481,19 +481,17 @@ if(opt$model_comp == T){
     		}
 
     		# If there is only one predictor, use glm
-    		if(dim(Outcome_Predictors_train_x_group)[2] > 1){
+    		if(ncol(Outcome_Predictors_train_x_group) > 1){
     				model<- train(y=Outcome_Predictors_train_y, x=Outcome_Predictors_train_x_group, trControl=trainControl(method="cv", seeds=seeds, number=opt$n_inner_fold, classProbs=T, savePredictions = 'final'), method="glmnet", family=opt$family)
     		} else {
-    				model<- train(y=Outcome_Predictors_train_y, x=cbind(0,Outcome_Predictors_train_x_group), trControl=trainControl(method="cv", seeds=seeds, number=opt$n_inner_fold, classProbs=T, savePredictions = 'final'), method="glm", family=opt$family)
+    				model<- train(y=Outcome_Predictors_train_y, x=Outcome_Predictors_train_x_group, trControl=trainControl(method="cv", seeds=seeds, number=opt$n_inner_fold, classProbs=T, savePredictions = 'final'), method="glm", family=opt$family)
     		}
 
-  		  if(dim(Outcome_Predictors_train_x_group)[2] > 1){
+  		  if(ncol(Outcome_Predictors_train_x_group) > 1){
   				Indep_Pred<-as.numeric(predict(object = model$finalModel, newx = data.matrix(Outcome_Predictors_test_x_group), type = "response", s = model$finalModel$lambdaOpt))
   			} else {
-  					tmp<-data.frame(cbind(0,Outcome_Predictors_test_x_group))
-  					names(tmp)[1]<-'0'
-  					Indep_Pred<-predict(object = model$finalModel, newdata = tmp, type = "response")
-  					rm(tmp)
+					Indep_Pred<-predict(object = model$finalModel, newdata = Outcome_Predictors_test_x_group, type = "response")
+					rm(tmp)
   			}
 
   		  Indep_Pred_tab<-data.frame(obs=Outcome_Predictors_test_y,
@@ -533,13 +531,13 @@ if(opt$model_comp == T){
   	# Compare predictive utiliy of the different models
   	###################
     if(opt$top1){
-      predictors_list_new_top1<-predictors_list_new[-nrow(predictors_list_new),]
+      predictors_list_new_top1<-predictors_list_new[predictors_list_new$group != '.' & !grepl('PredFile', predictors_list_new$group),]
       predictors_list_new_top1$group<-paste0(predictors_list_new_top1$group, '_top1')
-      predictors_list_new<-rbind(predictors_list_new, predictors_list_new_top1)
+      predictors_list_new2<-rbind(predictors_list_new, predictors_list_new_top1)
     }
   	comp_res_all<-NULL
-  	for(group1 in unique(predictors_list_new$group)){
-    	for(group2 in unique(predictors_list_new$group)){
+  	for(group1 in unique(predictors_list_new2$group)){
+    	for(group2 in unique(predictors_list_new2$group)){
     	    if(group1 == '.'){
     	      group1<-'All'
     	    }
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 98fcd29d..b613e15e 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -460,7 +460,7 @@ done
 ######
 
 prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
+prscsx_dat<-prscsx_dat[4,]
 
 gwas_list_eur<-data.frame(
   name=paste0(prscsx_dat$labels,'_UKB'),
@@ -701,11 +701,11 @@ for disc_pop in $(echo EUR EAS); do
     fi
     
     for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
-      sbatch --mem 20G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      sbatch --mem 10G -n 1 -p cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
         --pheno /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
         --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/single.${disc_pop}_disc.targ_${targ_pop}/${pheno}/predictor_list.txt \
         --output /users/k1806347/oliverpainfel/Analyses/crosspop/single.${disc_pop}_disc.targ_${targ_pop}/${pheno}/res \
-        --n_core 5 \
+        --n_core 1 \
         --top1 T \
         --compare_predictors T \
         --assoc T"
@@ -728,13 +728,13 @@ for targ_pop in $(echo EUR EAS); do
   else
       targ_pop2=$targ_pop
   fi
-  echo $targ_pop2
+
   for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
-    sbatch --mem 20G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+    sbatch --mem 10G -n 1 -p cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
       --pheno /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
       --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/predictor_list.txt \
       --output /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/res \
-      --n_core 5 \
+      --n_core 1 \
       --top1 T \
       --compare_predictors T \
       --assoc T"
@@ -946,13 +946,29 @@ dev.off()
 ### Prediction modelling results
 
 ```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
 library(data.table)
 library(ggplot2)
 library(cowplot)
 
+source('../functions/misc.R')
+source_all('../functions')
+
 # Read in list of outcomes 
 prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
+prscsx_dat<-prscsx_dat[4,]
+
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+  
+# Define pgs_methods used
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+
+# Define gwas_list used
+gwas_list<-read_param(config = config, param = 'gwas_list')
+
+# Define gwas_groups used
+gwas_groups<-read_param(config = config, param = 'gwas_groups')
 
 #############
 # Single source
@@ -983,8 +999,39 @@ for(pheno_i in prscsx_dat$labels){
   }
   
   res_i$Method<-gsub('_.*','',res_i$Model)
+  res_i$Method<-sub('\\..*','',res_i$Method)
+  
   res_i$Group[grepl('top1', res_i$Model)]<-'Top1'
-  res_i$Group[!grepl('top1', res_i$Model)]<-'Multi'
+  res_i$Group[!grepl('top1|PredFile', res_i$Model)]<-'Multi'
+  
+  res_i_pseudo<-res_i[grepl('PredFile', res_i$Model),]
+  res_i<-res_i[!grepl('PredFile', res_i$Model),]
+  
+  # Identify PGS selected by pseudovalidation
+  for(pgs_method_i in pgs_methods[
+    pgs_methods %in% res_i$Method & 
+    pgs_methods %in% c('dbslmm')]) { # Restrict to methods with pseudo method
+    res_i_tmp<-res_i_pseudo[res_i_pseudo$Method == pgs_method_i,]
+      gwas_list_i<-gwas_list[grepl(pheno_i, gwas_list$name),]
+      for(gwas_i in gwas_list_i$name){
+        gwas_i_2 <- gsub('_', '.', gwas_i)
+        res_i_tmp2 <-
+          res_i_tmp[grepl(gwas_i_2, res_i_tmp$Model), ]
+        res_i_tmp2$param<-gsub(paste0('.*', gwas_i_2, '.'), '', res_i_tmp2$Model)
+        
+        pseudo_param<-find_pseudo(config = config, 
+                    gwas = gwas_i, 
+                    pgs_method = pgs_method_i, 
+                    target_pop = targ_pop_i)
+        pseudo_param <- gsub("[[:punct:]]", ".",pseudo_param)
+        
+        res_i_tmp2<-res_i_tmp2[res_i_tmp2$param == pseudo_param,]
+        res_i_tmp2$param<-NULL
+        res_i_tmp2$Group<-'Pseudo'
+        res_i<-rbind(res_i, res_i_tmp2)
+      }
+  }
+
   res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
   res_i$Group<-factor(res_i$Group, levels=unique(res_i$Group))
   
@@ -1005,7 +1052,7 @@ for(pheno_i in prscsx_dat$labels){
 png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/single.eval.png',
   res = 300,
-  height = 5000,
+  height = 1200*nrow(prscsx_dat),
   width = 3000,
   units = 'px'
 )
@@ -1038,8 +1085,45 @@ for(pheno_i in prscsx_dat$labels){
   }
   
   res_i$Method<-gsub('_.*','',res_i$Model)
+  res_i$Method<-sub('\\..*','',res_i$Method)
+  
   res_i$Group[grepl('top1', res_i$Model)]<-'Top1'
-  res_i$Group[!grepl('top1', res_i$Model)]<-'Multi'
+  res_i$Group[!grepl('top1|PredFile', res_i$Model)]<-'Multi'
+  
+  res_i_pseudo<-res_i[grepl('PredFile', res_i$Model),]
+  res_i<-res_i[!grepl('PredFile', res_i$Model),]
+
+  # Identify PGS selected by pseudovalidation
+  for(pgs_method_i in pgs_methods[
+    pgs_methods %in% res_i$Method & 
+    pgs_methods %in% c('prscsx','xwing')]) { # Restrict to methods with pseudo method
+      res_i_tmp<-res_i_pseudo[res_i_pseudo$Method == pgs_method_i,]
+      gwas_groups_i<-gwas_groups[grepl(pheno_i, gwas_groups$name),]
+      
+      for(gwas_group_i in gwas_groups_i$name){
+        gwas_group_i_2 <- gsub('_', '.', gwas_group_i)
+        res_i_tmp2 <-
+          res_i_tmp[grepl(gwas_group_i_2, res_i_tmp$Model), ]
+        res_i_tmp2$param<-gsub(paste0('.*', gwas_group_i_2, '.'), '', res_i_tmp2$Model)
+        
+        for(targ_pop_i in pop){
+          pseudo_param<-find_pseudo(config = config, 
+                      gwas = gwas_group_i, 
+                      pgs_method = pgs_method_i, 
+                      target_pop = targ_pop_i)
+          pseudo_param <- gsub("[[:punct:]]", ".",pseudo_param)
+          
+          res_i_tmp3 <-
+            res_i_tmp2[res_i_tmp2$param == pseudo_param &
+                         res_i_tmp2$target == targ_pop_i, ]
+          
+          res_i_tmp3$param<-NULL
+          res_i_tmp3$Group<-'Pseudo'
+          res_i<-rbind(res_i, res_i_tmp3)
+        }
+      }
+  }
+
   res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
   res_i$Group<-factor(res_i$Group, levels=unique(res_i$Group))
   
@@ -1060,7 +1144,7 @@ for(pheno_i in prscsx_dat$labels){
 png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.eval.png',
   res = 300,
-  height = 5000,
+  height = 1200*nrow(prscsx_dat),
   width = 3000,
   units = 'px'
 )
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 5386231f..671eaf04 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -1195,10 +1195,14 @@ Determines the pseudovalidation parameter for a given GWAS and PGS method. See [
   - `config`: Configuration file specifying paths and parameters.
   - `gwas`: A single GWAS identifier.
   - `pgs_method`: A single PGS method identifier.
+  - `target_pop`: Target population parameter required when using multi-ancestry PGS method.
 - **Returns**
   - A string representing the pseudovalidation parameter.
 - **Note**
   - `ptclump` has no pseudovalidation approach, so this function will return the PGS based on a p-value threshold of 1.
+  - `prscsx` returns meta-analysed PGS weights for each population. The target_pop parameter has no effect on the output.
+  - `tlprs` has no pseudovalidation approach, so the functionreturns the pseudovalidated score for the PGS method used as input to `tlprs`, using strongest reweighting for the target population.
+  - `xwing` and `tlprs` do not have a pseudovalidation approach when using `target_pop = 'TRANS'`, so this function returns the result for a `EUR` target population. 
 
 <details>
 <summary>See usage</summary>
@@ -1210,6 +1214,12 @@ pseudo_param <-
               gwas = 'COAD01',
               pgs_method = 'ptclump')
 
+pseudo_param <-
+  find_pseudo(config = 'example_input/config.yaml',
+              gwas = 'COAD01',
+              pgs_method = 'prscsx',
+              target_pop = 'EUR')
+
 ```
 
 </details>
diff --git a/functions/model_builder.R b/functions/model_builder.R
index 5b8bf1ec..7029090d 100644
--- a/functions/model_builder.R
+++ b/functions/model_builder.R
@@ -49,3 +49,10 @@ filter_columns_by_missing <- function(data, threshold, first_col_keep = TRUE) {
 
   return(data_filtered)
 }
+
+# Set the grid search for the elastic net
+# This is expanded from the default to allow more aggressive shrinkage
+enet_grid <- expand.grid(
+  alpha = seq(0, 1, length = 5),          # Explore alpha values: 0 (Ridge) to 1 (Lasso)
+  lambda = 10^seq(-4, 1, length = 10)     # Explore lambda values: 0.0001 to 10
+)
diff --git a/functions/pipeline.R b/functions/pipeline.R
index cc013ca0..9d7b9652 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -159,7 +159,7 @@ read_ancestry <- function(config, name){
 }
 
 # Return score corresponding to pseudovalidation
-find_pseudo <- function(config, gwas, pgs_method){
+find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
 
   if(length(pgs_method) > 1){
     stop('Only one pgs_method can be specified at a time')
@@ -167,6 +167,12 @@ find_pseudo <- function(config, gwas, pgs_method){
   if(length(gwas) > 1){
     stop('Only one gwas can be specified at a time')
   }
+  if(length(target_pop) > 1){
+    stop('Only one target_pop can be specified at a time')
+  }
+  if(pgs_method %in% pgs_group_methods & is.null(target_pop)){
+    stop('target_pop must be specified when using multi-ancestry PGS method')
+  }
 
   # Identify score files
   score_file_list <- list_score_files(config)
@@ -236,19 +242,30 @@ find_pseudo <- function(config, gwas, pgs_method){
     return('META_phi_auto')
   }
   if(pgs_method == 'xwing'){
-    return('targ_EUR_weighted') # NOTE. This is inappropriate when the target is not EUR. There should be a population parameter in the find_pseudo function to find score tailored for a given population. This also doesn't allow for gwas_groups that do not contain a EUR GWAS.
+    if(target_pop == 'TRANS'){
+      cat('No pseudovalidation for TRANS target population available for xwing.')
+      cat('Returning result for EUR target population.')
+      return('targ_EUR_weighted')
+    } else {
+      return(paste0('targ_', target_pop, '_weighted'))
+    }
   }
   if(grepl('^tlprs', pgs_method)){
+    if(target_pop == 'TRANS'){
+      cat('No pseudovalidation for TRANS target population available for xwing.')
+      cat('Returning result for EUR target population.')
+      target_pop <- 'EUR'
+    }
+
     # Use most stringent p-value threshold of 0.05 as pseudo
     if(grepl('ptclump', pgs_method)){
-      return('targ_EUR_0_1_TLPRS_1')
+      return(paste0('targ_', target_pop, '_0_1_TLPRS_61'))
     }
 
     # Retrieve pseudoval param
     if(grepl('dbslmm', pgs_method)){
-      return('targ_EUR_DBSLMM_1_TLPRS_1')
+      return(paste0('targ_', target_pop, '_DBSLMM_1_TLPRS_61'))
     }
-    # NOTE. This is inappropriate when the target is not EUR. There should be a population parameter in the find_pseudo function to find score tailored for a given population. This also doesn't allow for gwas_groups that do not contain a EUR GWAS.
   }
 }
 

From 0164cb4e4af0e543020a0baf20077fbdba807709 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 25 Sep 2024 10:50:52 +0100
Subject: [PATCH 014/153] Integrated target_pop specific findo_pseudo use in
 reports

---
 Scripts/pipeline_reports/indiv_report_creator.Rmd | 3 ++-
 Scripts/pipeline_reports/samp_report_creator.Rmd  | 2 +-
 functions/pipeline.R                              | 4 ++--
 3 files changed, 5 insertions(+), 4 deletions(-)

diff --git a/Scripts/pipeline_reports/indiv_report_creator.Rmd b/Scripts/pipeline_reports/indiv_report_creator.Rmd
index a781ee1c..d81b6681 100644
--- a/Scripts/pipeline_reports/indiv_report_creator.Rmd
+++ b/Scripts/pipeline_reports/indiv_report_creator.Rmd
@@ -387,7 +387,8 @@ for (gwas_i in names(pgs)) {
     pseudo_param <-
       find_pseudo(config = params$config,
                   gwas = gwas_i,
-                  pgs_method = pgs_method_i)
+                  pgs_method = pgs_method_i,
+                  target_pop = top_pop)
     
     tmp <- pgs[[gwas_i]][[pgs_method_i]]
     names(tmp) <- gsub(paste0(gwas_i, '_'), '', names(tmp))
diff --git a/Scripts/pipeline_reports/samp_report_creator.Rmd b/Scripts/pipeline_reports/samp_report_creator.Rmd
index 78e29d5e..468ae232 100644
--- a/Scripts/pipeline_reports/samp_report_creator.Rmd
+++ b/Scripts/pipeline_reports/samp_report_creator.Rmd
@@ -283,7 +283,7 @@ pgs <- read_pgs(config = params$config, name = params$name)[[1]]
 pgs_pseudo <- NULL
 for(gwas in names(pgs[[1]])){
   for(pgs_method in names(pgs[[1]][[gwas]])){
-    pseudo_param <- find_pseudo(config = params$config, gwas = gwas, pgs_method = pgs_method)
+    pseudo_param <- find_pseudo(config = params$config, gwas = gwas, pgs_method = pgs_method, target_pop = 'TRANS')
     for(pop in names(pgs)){
       tmp <- pgs[[pop]][[gwas]][[pgs_method]]
       names(tmp) <- gsub(paste0(gwas, '_'), '', names(tmp))
diff --git a/functions/pipeline.R b/functions/pipeline.R
index 9d7b9652..7e6dfff0 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -242,7 +242,7 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
     return('META_phi_auto')
   }
   if(pgs_method == 'xwing'){
-    if(target_pop == 'TRANS'){
+    if(!is.null(target_pop) && target_pop == 'TRANS'){
       cat('No pseudovalidation for TRANS target population available for xwing.')
       cat('Returning result for EUR target population.')
       return('targ_EUR_weighted')
@@ -251,7 +251,7 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
     }
   }
   if(grepl('^tlprs', pgs_method)){
-    if(target_pop == 'TRANS'){
+    if(!is.null(target_pop) && target_pop == 'TRANS'){
       cat('No pseudovalidation for TRANS target population available for xwing.')
       cat('Returning result for EUR target population.')
       target_pop <- 'EUR'

From 498e9f8ecfc3a4a29a90bffe7bed175e48abc6d8 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 25 Sep 2024 18:45:42 +0100
Subject: [PATCH 015/153] Updates to model builder and crosspop analysis

---
 Scripts/model_builder/model_builder.R | 794 ++++++++++----------------
 docs/CrossPop.Rmd                     | 123 ++--
 functions/model_builder.R             | 123 ++++
 pipeline/rules/pgs_methods.smk        |  12 +-
 4 files changed, 508 insertions(+), 544 deletions(-)

diff --git a/Scripts/model_builder/model_builder.R b/Scripts/model_builder/model_builder.R
index 10a18af7..a11e6387 100644
--- a/Scripts/model_builder/model_builder.R
+++ b/Scripts/model_builder/model_builder.R
@@ -4,34 +4,32 @@ start.time <- Sys.time()
 suppressMessages(library("optparse"))
 
 option_list = list(
-make_option("--pheno", action="store", default=NULL, type='character',
-		help="File containing phenotypic data [required]"),
+make_option("--outcome", action="store", default=NULL, type='character',
+    help="File containing outcome data [required]"),
 make_option("--predictors", action="store", default=NULL, type='character',
-		help="File listing files containing predictors, with a groups column for model comparison [required]"),
-make_option("--n_outer_fold", action="store", default=5, type='numeric',
+    help="File listing files containing predictors, with a groups column for model comparison [required]"),
+make_option("--n_outer_fold", action="store", default=10, type='numeric',
     help="Number of folds in for outer cross-validation [optional]"),
 make_option("--n_inner_fold", action="store", default=10, type='numeric',
     help="Number of folds for inner cross-validation [optional]"),
 make_option("--n_core", action="store", default=1, type='numeric',
-		help="Number of cores for parallel computing [optional]"),
+    help="Number of cores for parallel computing [optional]"),
 make_option("--keep", action="store", default=NULL, type='character',
-		help="File containing list of individuals to include in analysis [optional]"),
+    help="File containing list of individuals to include in analysis [optional]"),
 make_option("--outcome_pop_prev", action="store", default=NULL, type='numeric',
-		help="Prevalence of outcome in the general population [optional]"),
-make_option("--output", action="store", default=NULL, type='character',
-		help="Prefix for output files [required]"),
-make_option("--save_group_model", action="store", default=F, type='logical',
-		help="Save group models for external validation [optional]"),
+    help="Prevalence of outcome in the general population [optional]"),
+make_option("--out", action="store", default=NULL, type='character',
+    help="Prefix for output files [required]"),
 make_option("--assoc", action="store", default=T, type='logical',
-		help="Perform association analysis between each predictor and outcome [optional]"),
+    help="Perform association analysis between each predictor and outcome [optional]"),
 make_option("--compare_predictors", action="store", default=F, type='logical',
     help="Option to assign each predictor to own group [optional]"),
-make_option("--interaction", action="store", default=F, type='logical',
-    help="Option to include interaction terms between predictors in each group [optional]"),
 make_option("--pred_miss", action="store", default=0.1, type='numeric',
     help="Proportion of missing values allowed in predictor [optional]"),
-make_option("--top1", action="store", default=F, type='numeric',
+make_option("--top1", action="store", default=F, type='logical',
     help="Evaluate model using top predictor within each group [optional]"),
+make_option("--all_model", action="store", default=T, type='logical',
+    help="Evaluate model containing all predictors [optional]"),
 make_option("--seed", action="store", default=1, type='numeric',
     help="Set seed number [optional]")
 )
@@ -52,541 +50,379 @@ suppressMessages(library(psych))
 registerDoMC(opt$n_core)
 
 # Check required inputs
-if(is.null(opt$pheno)){
-  stop('--pheno must be specified.\n')
+if(is.null(opt$outcome)){
+  stop('--outcome must be specified.\n')
 }
 if(is.null(opt$predictors)){
   stop('--predictors must be specified.\n')
 }
-if(is.null(opt$output)){
+if(is.null(opt$out)){
   stop('--out must be specified.\n')
 }
 
 # Create output directory
-opt$output_dir <- paste0(dirname(opt$output), '/')
+opt$output_dir <- paste0(dirname(opt$out), '/')
 system(paste0('mkdir -p ', opt$output_dir))
 
 # Create temp directory
 tmp_dir <- tempdir()
 
 # Initiate log file
-log_file <- paste0(opt$output,'.log')
+log_file <- paste0(opt$out,'.log')
 log_header(log_file = log_file, opt = opt, script = 'model_builder.R', start.time = start.time)
 
-##################
-
-# Read in the predictors file
-predictors_list <- fread(opt$predictors)
-names(predictors_list)[1] <- 'predictor'
-
-# Determine if there is a group column, and if there is more than 1 group.
-if (opt$compare_predictors == F) {
-  if (!is.null(predictors_list$group)) {
-    if (length(unique(predictors_list$group)) != 1) {
-      opt$group_info <- T
-      opt$model_comp <- T
-      predictors_list$group <-
-        gsub("[[:punct:]]", ".", predictors_list$group)
-      log_add(log_file = log_file, message = 'Predictors file contains group information so model comparisons will be performed.')
-    } else {
-      opt$group_info <- T
-      opt$model_comp <- F
-      log_add(log_file = log_file, message = 'Predictors file does not contain group information so model comparisons will not be performed.')
-    }
-  } else {
-    opt$group_info <- F
-    opt$model_comp <- F
-    log_add(log_file = log_file, message = 'Predictors file does not contain group information so model comparisons will not be performed.')
-  }
-} else {
-  if (!is.null(predictors_list$group)) {
-    opt$group_info <- T
-    if (length(unique(predictors_list$group)) != 1) {
-      predictors_list$group <-
-        gsub("[[:punct:]]", ".", predictors_list$group)
-      log_add(log_file = log_file, message = 'Predictors file contains group information so model comparisons will be performed.')
-    } else {
-      log_add(log_file = log_file, message = 'Predictors file does not contain group information.')
-    }
-  } else {
-    opt$group_info <- F
-    log_add(log_file = log_file, message = 'Predictors file does not contain group information.')
-  }
-  log_add(log_file = log_file, message = 'Each predictor will be assigned to a seperate group and model comparisons will be performed.')
-  opt$model_comp <- T
-}
-
 ###########
-# Read in the phenotypic data
+# Read in predictors
 ###########
+# We will create a table indicating which predictors belong to each group as well.
 
-Outcome <- fread(opt$pheno)
-Outcome <- Outcome[complete.cases(Outcome), ]
-names(Outcome)[3] <- 'Outcome_var'
+predictors_file <- fread(opt$predictors)
 
-# Create a column that combines FID and IID
-Outcome <- combine_fid_iid(Outcome)
+if(nrow(predictors_file) > 1){
+  predictors <- foreach(i = 1:nrow(predictors_file)) %dopar% {
+    read_predictor(x = predictors_file$predictor[i], pred_miss = opt$pred_miss, file_index = i)
+  }
+  names(predictors) <- predictors_file$group
 
-log_add(log_file = log_file, message = paste0('Phenotype file contains ',nrow(Outcome),' individuals with complete data.'))
+  group_list <- do.call(rbind, lapply(names(predictors), function(group) {
+    data.table(group = group, predictor = names(predictors[[group]])[-1])
+  }))
 
-# Determine whether outcome is binary or continuous and format accordingly
-if (length(unique(Outcome$Outcome_var)) > 2) {
-  opt$family <- 'gaussian'
+  predictors <- Reduce(function(x, y) merge(x, y, by = "IID"), predictors)
+
+  log_add(log_file = log_file, message = paste0('After merging the ', nrow(predictors_file), ' predictors files, ', ncol(predictors)-1, ' predictors remain.'))
+  log_add(log_file = log_file, message = paste0('After merging the ', nrow(predictors_file), ' predictors files, ', nrow(predictors), ' individuals remain.'))
+} else {
+  predictors <- read_predictor(x = predictors_file$predictor[1], pred_miss = opt$pred_miss)
+  group_list <- data.table(group = predictors_file$group[1], predictor = names(predictors)[-1])
 }
-if (length(unique(Outcome$Outcome_var)) == 2) {
-  opt$family <- 'binomial'
-  Outcome$Outcome_var <- factor(Outcome$Outcome_var, labels = c('CONTROL', 'CASE'))
+
+# Remove predictors with zero variance
+nz_var <- sapply(predictors[, -1, with = FALSE], function(col) var(col) != 0)
+if(sum(!nz_var) > 1){
+  log_add(log_file = log_file, message = paste0(sum(!nz_var), ' predictors have zero variance and will be excluded from downstream analyes.'))
 }
+if(all(!(nz_var))){
+  stop('All predictors have zero variance.')
+}
+predictors <- predictors[, c(TRUE, nz_var), with = FALSE]
+group_list <- group_list[group_list$predictor %in% names(predictors),]
 
-log_add(log_file = log_file, message = paste0('Phenotype is ', ifelse(opt$family == 'binomial', 'binary', 'quantitative'),'.'))
+###########
+# Create list of groups for downstream comparison
+###########
 
-if(!is.null(opt$keep)){
-	############
-	# Extract individuals in the keep file
-	############
+if(opt$compare_predictors){
+  group_list <-
+    rbind(group_list,
+          data.table(
+            group = paste0(group_list$group, '.', group_list$predictor),
+            predictor = group_list$predictor
+          ))
+  log_add(log_file = log_file, message = 'Each predictor has been assigned to its own group.')
+}
 
-  # Read in keep file
-  keep_file <- fread(opt$keep)
-  names(keep_file)[1:2] <- c('FID', 'IID')
-  # Create a column that combines FID and IID
-  keep_file <- combine_fid_iid(keep_file)
-  # Extract keep individuals from the phenotypic data
-  Outcome <- Outcome[(Outcome$IID %in% keep_file$IID), ]
 
-	log_add(log_file = log_file, message = paste0('Phenotype file contains ',nrow(Outcome),' individuals after extraction of individuals in ',opt$keep,'.'))
+# Add a group containing all predictors
+if(nrow(group_list) > 1 & opt$all_model){
+  group_list <-
+    rbind(group_list,
+          data.table(
+            group = 'all',
+            predictor = unique(group_list$predictor)
+          ))
+  log_add(log_file = log_file, message = 'Group containing all predictors created.')
 }
 
-############
-# Read in the predictor variables
-############
+log_add(log_file = log_file, message = paste0(length(unique(group_list$group)), ' groups of predictors present.'))
 
-if(nrow(predictors_list) > 1) {
-  Predictors<-foreach(k = 1:dim(predictors_list)[1], .combine = predictor_merger) %dopar% {
-    # Read in predictor file k
-    tmp <- fread(predictors_list$predictor[k])
+# Calculate the number of predictors in each group
+for(i in unique(group_list$group)){
+  group_list$n[group_list$group == i] <- sum(group_list$group == i)
+}
 
-    # Create a column that combines FID and IID
-    tmp <- combine_fid_iid(tmp)
+###########
+# Read in the outcome data
+###########
 
-    # Remove variables with > opt$pred_miss missing values
-    tmp <- filter_columns_by_missing(tmp, threshold = opt$pred_miss)
+outcome<-read_outcome(x = opt$outcome, keep = opt$keep)
 
-    # Remove individuals with any missing data
-    tmp <- tmp[complete.cases(tmp), ]
+# Determine whether outcome is binary or continuous and format accordingly
+if (length(unique(outcome$outcome_var)) > 2) {
+  family <- 'gaussian'
+}
+if (length(unique(outcome$outcome_var)) == 2) {
+  family <- 'binomial'
+  outcome$outcome_var <- factor(outcome$outcome_var, labels = c('CONTROL', 'CASE'))
+}
 
-    # Update column names to avoid duplicate column names between predictor files
-    names(tmp)[-1] <- paste0('PredFile', k, '.', names(tmp)[-1])
+log_add(log_file = log_file, message = paste0('Phenotype is ', ifelse(family == 'binomial', 'binary', 'quantitative'),'.'))
 
-    if(opt$model_comp == T){
-      # Add group name to each predictor
-      names(tmp)[-1]<-paste0('Group_',predictors_list$group[k],'.',names(tmp)[-1])
-    }
+###########
+# Merge the outcome and predictors
+###########
 
-    log_add(log_file = log_file, message = paste0('Predictors file ',k,' contains ',ncol(tmp)-1,' predictors with sufficient data.'))
-    log_add(log_file = log_file, message = paste0('Predictors file ',k,' contains ',nrow(tmp),' individuals with complete data for remaining predictors.'))
+outcome_predictors <- merge(outcome, predictors, by='IID')
 
-    tmp
-  }
+rm(outcome, predictors)
 
-  Predictors<-data.table(Predictors)
+log_add(log_file = log_file, message = paste0(nrow(outcome_predictors),' individuals have both phenotypic and predictor data.'))
 
-  log_add(log_file = log_file, message = paste0('After merging the ', length(predictors_list), ' predictors files,', ncol(Predictors)-1, ' predictors remain.'))
-  log_add(log_file = log_file, message = paste0('After merging the ', length(predictors_list), ' predictors files,', nrow(Predictors), ' individuals remain.'))
+# Report the size of the combined outcome and predictor data
+log_add(log_file = log_file, message = paste0('Data to be carried foward is ',format(object.size(outcome_predictors), units='auto'),'.'))
 
-} else {
+############
+# Test association between outcome and each predictor
+############
 
-	# Read in the predictor variables
-	Predictors<-fread(predictors_list$predictor[1])
+if(opt$assoc){
+  log_add(log_file = log_file, message = 'Performing association analysis with each predictor...')
 
-	# Create a column that combines FID and IID
-	Predictors <- combine_fid_iid(Predictors)
+	outcome_predictors_y <- outcome_predictors$outcome_var
+	outcome_predictors_x <- outcome_predictors[, -1:-2]
 
-	# Remove variables with > opt$pred_miss missing values
-	Predictors <- filter_columns_by_missing(Predictors, threshold = opt$pred_miss)
+	assoc_res <- NULL
+	assoc_res <- foreach(i = 1:ncol(outcome_predictors_x), .combine=rbind) %dopar% {
 
-	# Remove individuals with any missing data
-	Predictors<-Predictors[complete.cases(Predictors),]
+  	if(family == 'binomial'){
+  	  mod <- glm(outcome_predictors_y ~ scale(outcome_predictors_x[[i]]), family=family)
+  		obs_r2 <- cor(predict(mod), as.numeric(outcome_predictors_y))^2
+  	  sum_mod <- summary(mod)
+  	} else {
+  	  mod <- glm(scale(outcome_predictors_y) ~ scale(outcome_predictors_x[[i]]), family = family)
+  	  obs_r2 <- cor(predict(mod), as.numeric(outcome_predictors_y))^2
+  	  sum_mod <- summary(mod)
+  	}
 
-	if(opt$compare_predictors == T){
-		# Create the object predictors_list
-		predictors_list <- data.table(predictor=names(Predictors)[-1], group=names(Predictors)[-1])
-		predictors_list$group <- gsub("[[:punct:]]", ".",predictors_list$group)
+	  data.table(
+	    Group = group_list$group[group_list$predictor == names(outcome_predictors_x)[i]][1],
+	    Predictor = names(outcome_predictors_x)[i],
+	    BETA = coef(sum_mod)[2, 1],
+	    SE = coef(sum_mod)[2, 2],
+	    P = coef(sum_mod)[2, 4],
+	    Obs_R2 = obs_r2,
+  		N = length(outcome_predictors_y)
+	  )
 
-		# Add group name to each predictor
-		names(Predictors)[-1]<-paste0('Group_',names(Predictors)[-1],'.',names(Predictors)[-1])
 	}
 
-	log_add(log_file = log_file, message = paste0('Predictors file contains ', ncol(Predictors)-1, ' predictors with sufficient data.'))
-	log_add(log_file = log_file, message = paste0('Predictors file contains ', dim(Predictors)[1], ' individuals with complete data for remaining predictors.'))
-
-}
+	if(family == 'binomial'){
+  	assoc_res$N_case <- sum(outcome_predictors_y == 'CASE')
+  	assoc_res$N_control <- sum(outcome_predictors_y == 'CONTROL')
+  	assoc_res$Liab_R2 <- h2l_R2(
+  	  opt$outcome_pop_prev,
+  	  assoc_res$obs_r2,
+  	  sum(outcome_predictors_y == 'CASE') / length(outcome_predictors_y)
+  	)
+	}
 
-predictors_list_new<-predictors_list
-
-if(opt$compare_predictors == T){
-  if(opt$group_info == T){
-    # Create the object predictors_list
-    predictors_list_new <-
-      rbind(predictors_list_new,
-            data.table(
-              predictor = names(Predictors)[-1],
-              group = names(Predictors)[-1]
-            ))
-    predictors_list_new$group <- gsub("[[:punct:]]", ".",predictors_list_new$group)
-    predictors_list_new$group <- gsub('Group.','', predictors_list_new$group)
-  }
+	# Write out the results
+	write.table(assoc_res, paste0(opt$out,'.assoc.txt'), col.names=T, row.names=F, quote=F)
+	log_add(log_file = log_file, message = paste0('Predictor association results saved as ',opt$out,'.assoc.txt.'))
 }
 
-###########
-# Merge the phenotype and predictor variables
-###########
-
-Outcome_Predictors <- merge(Outcome, Predictors, by='IID')
+############
+# Prediction modelling
+############
+# We will use elastic net model when groups contain more than one predictor, and a glm when the group contains only 1 predictor.
+# Elastic net models will be derived and evaluated using nested cross validation, but glm will be evaluated using standard cross validation.
+
+# Split the sample into opt$n_outer_fold folds
+set.seed(opt$seed)
+d <- sample(1:nrow(outcome_predictors))
+train_ind <- createFolds(d, k = opt$n_outer_fold, returnTrain=TRUE)
+
+# Set seeds for internal loop of nested CV for elastic net
+seeds <- fold_seeds(opt$n_outer_fold)
+
+# Create objects to store outputs
+indep_pred_list <- list()
+eval_pred_all <- NULL
+
+####
+# Evaluate single best predictor from each group, identifying the best predictor using training data, and then evaluating in the test data
+####
+
+if(opt$top1){
+  # Only run for groups containing more than 1 predictor
+  for(group_i in unique(group_list$group[group_list$n > 1])){
+    group_name<-paste0(group_i, '.top1')
+    for(outer_val in 1:opt$n_outer_fold){
+      # Subset training and testing data
+      cv_dat <- subset_train_test(dat = outcome_predictors, train_ind = train_ind, fold = outer_val)
+
+      # Subset variables in group
+      pred_name <- group_list$predictor[group_list$group == group_i]
+      cv_dat$train$x <- cv_dat$train$x[, pred_name, with = F]
+
+      # Evaluate each predictor in training data
+      # NOTE. Should we be using the RMSE to select the best predictor within a group.
+      top1_res<-NULL
+      for(pred_i in names(cv_dat$train$x)){
+        res_pred_i <- cor(cv_dat$train$y, cv_dat$train$x[[pred_i]], use='p')
+        top1_res <- rbind(
+          top1_res,
+          data.table(
+            pred = pred_i,
+            cor = res_pred_i)
+        )
+      }
+      top_pred <- top1_res$pred[which.max(abs(top1_res$cor))]
 
-rm(Outcome,Predictors)
+      # Build model using best predictor
+      train_tmp <- data.table(y = cv_dat$train$y, x = cv_dat$train$x[[top_pred]])
+      train_mod <- glm(y ~ x, family=family, data=train_tmp)
 
-log_add(log_file = log_file, message = paste0(nrow(Outcome_Predictors),' individuals have both phenotypic and predictor data.'))
+      # Evaluate best performing predictor in test data
+      test_tmp <- data.table(x = cv_dat$test$x[[top_pred]])
+      indep_pred <- predict(object = train_mod, newdata = test_tmp, type = "response")
+      indep_pred <- data.table(obs = cv_dat$test$y, pred = indep_pred)
 
-# Report the size of the combined outcome and predictor data
-log_add(log_file = log_file, message = paste0('Data to be carried foward is ',format(object.size(Outcome_Predictors), units='auto'),'.'))
+      # Save test set predictions from each outer loop
+      indep_pred_list[[group_name]] <- rbind(indep_pred_list[[group_name]], indep_pred)
+    }
 
-if(opt$assoc == T){
+    eval_pred_all <-
+      rbind(
+        eval_pred_all,
+        data.table(Group = group_name, eval_pred(obs = indep_pred_list[[group_name]]$obs, pred = indep_pred_list[[group_name]]$pred, family = family))
+      )
+  }
+}
 
-	############
-	# Test association between Outcome and each variable in Predictors
-	############
+###
+# Evaluate model containing groups containing only one predictor
+###
 
-  log_add(log_file = log_file, message = 'Performing association analysis with each predictor...')
+for(group_i in unique(group_list$group[group_list$n == 1])){
+  for(outer_val in 1:opt$n_outer_fold){
+    # Subset training and testing data
+    cv_dat <- subset_train_test(dat = outcome_predictors, train_ind = train_ind, fold = outer_val)
 
-	Outcome_Predictors_y <- Outcome_Predictors$Outcome_var
-	Outcome_Predictors_x <- Outcome_Predictors[, -1:-2]
-
-	Assoc_res<-NULL
-
-	if(opt$family == 'binomial'){
-		Assoc_res<-foreach(i=1:ncol(Outcome_Predictors_x), .combine=rbind) %dopar% {
-			if(var(Outcome_Predictors_x[[i]]) == 0){
-				Assoc_res_temp<-data.frame(	Predictor = names(Outcome_Predictors_x)[i],
-																		BETA = NA,
-																		SE = NA,
-																		P = NA,
-																		Obs_R2 = NA)
-
-				Assoc_res_temp
-			} else {
-			  mod <- glm(Outcome_Predictors_y ~ scale(Outcome_Predictors_x[[i]]), family=opt$family)
-				obs_r2 <- cor(predict(mod), as.numeric(Outcome_Predictors_y))^2
-			  sum_mod <- summary(mod)
-				prob <- predict(mod,type=c("response"))
-				Assoc_res_temp <-
-				  data.table(
-				    Predictor = names(Outcome_Predictors_x)[i],
-				    BETA = coef(sum_mod)[2, 1],
-				    SE = coef(sum_mod)[2, 2],
-				    P = coef(sum_mod)[2, 4],
-				    Obs_R2 = obs_r2
-				  )
-			  Assoc_res_temp
-			}
-		}
-		# Convert Nagelkerke R2 to liability scale
-		Assoc_res$Liab_R2 <-
-		  h2l_R2(
-		    opt$outcome_pop_prev,
-		    Assoc_res$Obs_R2,
-		    sum(Outcome_Predictors_y == 'CASE') / length(Outcome_Predictors_y)
-		  )
-	} else {
-		Assoc_res <- foreach(i = 1:ncol(Outcome_Predictors_x), .combine=rbind) %dopar% {
-			if(var(Outcome_Predictors_x[[i]]) == 0){
-				Assoc_res_temp<-data.frame(	Predictor=names(Outcome_Predictors_x)[i],
-																		BETA=NA,
-																		SE=NA,
-																		P=NA,
-																		Obs_R2=NA)
-
-				Assoc_res_temp
-			} else {
-				mod <- glm(scale(Outcome_Predictors_y) ~ scale(Outcome_Predictors_x[[i]]), family = opt$family)
-				sum_mod <- summary(mod)
-				Assoc_res_temp <-
-				  data.table(
-				    Predictor = names(Outcome_Predictors_x)[i],
-				    BETA = coef(sum_mod)[2, 1],
-				    SE = coef(sum_mod)[2, 2],
-				    P = coef(sum_mod)[2, 4],
-				    Obs_R2 = coef(sum_mod)[2, 1] ^ 2
-				  )
-				Assoc_res_temp
-			}
-		}
-	}
+    # Subset variables in group
+    pred_name <- group_list$predictor[group_list$group == group_i]
+    cv_dat$train$x <- cv_dat$train$x[[pred_name]]
 
-	# Write out the results
-	write.table(Assoc_res, paste0(opt$output,'.assoc.txt'), col.names=T, row.names=F, quote=F)
-	log_add(log_file = log_file, message = paste0('Predictor association results saved as ',opt$output,'.assoc.txt.'))
-}
+    # Build model using predictor
+    train_tmp<-data.table(y = cv_dat$train$y, x = cv_dat$train$x)
+    train_mod<-glm(y ~ x, family=family, data=train_tmp)
 
-if(opt$model_comp == T){
-  ############
-  # Build and evaluate models using predictors together
-  ############
-  # We will use nested cross validation to evaluate models
+    # Evaluate best performing predictor in test data
+    test_tmp <- data.table(x = cv_dat$test$x[[pred_name]])
+    indep_pred <- predict(object = train_mod, newdata = test_tmp, type = "response")
+    indep_pred <- data.table(obs = cv_dat$test$y, pred = indep_pred)
 
-  # Split the sample into n=opt$internal_validation_prop equal parts
-  set.seed(opt$seed)
-  nr <- nrow(Outcome_Predictors)
-  d <- sample(1:nr)
-
-  train.ext = createFolds(d, k = opt$n_outer_fold, returnTrain = TRUE)
-  test.ext = lapply(train.ext, function(x) (1:nr)[-x])
-
-  # Create a variable containing seeds for internal cross validation
-  seeds <- vector(mode = "list", length = opt$n_inner_fold+1)
-  for(i in 1:(opt$n_inner_fold)){
-    seeds[[i]] <- sample.int(n=1000, 10)
+    # Save test set predictions from each outer loop
+    indep_pred_list[[group_i]] <- rbind(indep_pred_list[[group_i]], indep_pred)
   }
-  seeds[[opt$n_inner_fold + 1]] <- sample.int(n = 1000, 1)
-
-  # Create a model for each group, and using all predictors
-  predictors_list_new <-
-    rbind(predictors_list_new,
-          data.frame(predictor = 'All',
-                     group = '.'))
-
-  log_add(log_file = log_file, message = 'Initiating nested cross-validation...')
-
-  indep_pred<-list()
-  Prediction_summary_all<-NULL
-
-  if(opt$model_comp == T){
-    if(opt$top1){
-      ##############
-      # Evaluate top1 model
-      ##############
-      for(group in unique(predictors_list_new$group[predictors_list_new$group != '.' & !grepl('PredFile', predictors_list_new$group)])){
-        group_name<-paste0(group, '_top1')
-        for(outer_val in 1:opt$n_outer_fold){
-          Outcome_Predictors_train_ind <- d[train.ext[[outer_val]]]
-
-          Outcome_Predictors_train <- Outcome_Predictors[Outcome_Predictors_train_ind,]
-          Outcome_Predictors_test <- Outcome_Predictors[-Outcome_Predictors_train_ind,]
-
-          Outcome_Predictors_train_y <- Outcome_Predictors_train$Outcome_var
-          Outcome_Predictors_train_x <- Outcome_Predictors_train[, -1:-2]
-
-          Outcome_Predictors_test_y <- Outcome_Predictors_test$Outcome_var
-          Outcome_Predictors_test_x <- Outcome_Predictors_test[, -1:-2]
-
-          Outcome_Predictors_train_x_group <-
-            Outcome_Predictors_train_x[,grepl(
-              paste0('Group_', group, '\\.', '|', 'Group_', group, '$'),
-              names(Outcome_Predictors_train_x)
-            ), with=F]
-
-          # Evaluate each predictor in training data
-          top1_res<-NULL
-          for(pred_i in names(Outcome_Predictors_train_x_group)){
-            res_pred_i<-cor(Outcome_Predictors_train_y, Outcome_Predictors_train_x_group[[pred_i]], use='p')
-            top1_res<-rbind(
-              top1_res,
-              data.frame(
-                pred=pred_i,
-                cor=res_pred_i)
-            )
-          }
-
-          # Build model using best predictor
-          train_tmp<-data.frame(
-            y=Outcome_Predictors_train_y,
-            x=Outcome_Predictors_train_x_group[[top1_res$pred[which.max(abs(top1_res$cor))]]])
-          train_mod<-glm(y ~ x, family=opt$family, data=train_tmp)
-
-          # Evaluate best performing predictor in test data
-          test_tmp<-data.frame(
-            x=Outcome_Predictors_test_x[[top1_res$pred[which.max(abs(top1_res$cor))]]])
-
-          Indep_Pred<-predict(object = train_mod, newdata = test_tmp, type = "response")
-
-          rm(train_tmp)
-          rm(test_tmp)
-          Indep_Pred_tab<-data.frame(obs=Outcome_Predictors_test_y,
-                                     pred=Indep_Pred)
-
-          print(top1_res$pred[which.max(abs(top1_res$cor))])
-          print(cor(Indep_Pred_tab))
-
-          # Save test set predictions from each outer loop
-          indep_pred[[group_name]]<-rbind(indep_pred[[group_name]], Indep_Pred_tab)
-
-        }
-        Indep_mod<-summary(lm(scale(as.numeric(indep_pred[[group_name]]$obs)) ~ scale(indep_pred[[group_name]]$pred)))
-
-        if(opt$family=='binomial'){
-          Prediction_summary_all<-rbind(Prediction_summary_all, data.frame(	Model=paste0(group_name,'_group'),
-                                                                            R=coef(Indep_mod)[2,1],
-                                                                            SE=coef(Indep_mod)[2,2],
-                                                                            P=coef(Indep_mod)[2,4],
-                                                                            R2l=h2l_R2(opt$outcome_pop_prev, coef(Indep_mod)[2,1]^2, sum(Outcome_Predictors_train_y == 'CASE')/length(Outcome_Predictors_train_y)),
-                                                                            N=dim(indep_pred[[group_name]])[1],
-                                                                            Ncase=sum(Outcome_Predictors_train_y == 'CASE'),
-                                                                            Ncont=sum(Outcome_Predictors_train_y == 'CONTROL')))
-        } else {
-          Prediction_summary_all<-rbind(Prediction_summary_all, data.frame(	Model=paste0(group_name,'_group'),
-                                                                            R=coef(Indep_mod)[2,1],
-                                                                            SE=coef(Indep_mod)[2,2],
-                                                                            P=coef(Indep_mod)[2,4],
-                                                                            R2o=coef(Indep_mod)[2,1]^2,
-                                                                            N=dim(indep_pred[[group_name]])[1]))
-        }
-      }
-    }
 
-    # Build glmnet using each group of predictors at a time
-    for(group in unique(predictors_list_new$group)){
-      for(outer_val in 1:opt$n_outer_fold){
-        print(outer_val)
-
-        Outcome_Predictors_train_ind <- d[train.ext[[outer_val]]]
-
-        Outcome_Predictors_train <- Outcome_Predictors[Outcome_Predictors_train_ind,]
-        Outcome_Predictors_test <- Outcome_Predictors[-Outcome_Predictors_train_ind,]
-
-        Outcome_Predictors_train_y <- Outcome_Predictors_train$Outcome_var
-        Outcome_Predictors_train_x <- Outcome_Predictors_train[, -1:-2]
-
-        Outcome_Predictors_test_y <- Outcome_Predictors_test$Outcome_var
-        Outcome_Predictors_test_x <- Outcome_Predictors_test[, -1:-2]
-
-        # Subset predictor in the group
-        print(group)
-        Outcome_Predictors_train_x_group <-
-          Outcome_Predictors_train_x[,grepl(
-            paste0('Group_', group, '\\.', '|', 'Group_', group, '$'),
-            names(Outcome_Predictors_train_x)
-          ), with=F]
-        Outcome_Predictors_test_x_group <-
-          Outcome_Predictors_test_x[,grepl(
-            paste0('Group_', group, '\\.', '|', 'Group_', group, '$'),
-            names(Outcome_Predictors_test_x)
-          ), with=F]
-
-    		# Rename group to 'all' if == '.'
-    		if (group == '.') {
-    		  group_name <- 'All'
-    		  Outcome_Predictors_train_x_group <- Outcome_Predictors_train_x
-    		  Outcome_Predictors_test_x_group <- Outcome_Predictors_test_x
-    		} else {
-    		  group_name <- group
-    		}
-
-    		# If there is only one predictor, use glm
-    		if(ncol(Outcome_Predictors_train_x_group) > 1){
-    				model<- train(y=Outcome_Predictors_train_y, x=Outcome_Predictors_train_x_group, trControl=trainControl(method="cv", seeds=seeds, number=opt$n_inner_fold, classProbs=T, savePredictions = 'final'), method="glmnet", family=opt$family)
-    		} else {
-    				model<- train(y=Outcome_Predictors_train_y, x=Outcome_Predictors_train_x_group, trControl=trainControl(method="cv", seeds=seeds, number=opt$n_inner_fold, classProbs=T, savePredictions = 'final'), method="glm", family=opt$family)
-    		}
-
-  		  if(ncol(Outcome_Predictors_train_x_group) > 1){
-  				Indep_Pred<-as.numeric(predict(object = model$finalModel, newx = data.matrix(Outcome_Predictors_test_x_group), type = "response", s = model$finalModel$lambdaOpt))
-  			} else {
-					Indep_Pred<-predict(object = model$finalModel, newdata = Outcome_Predictors_test_x_group, type = "response")
-					rm(tmp)
-  			}
-
-  		  Indep_Pred_tab<-data.frame(obs=Outcome_Predictors_test_y,
-  		                             pred=Indep_Pred)
-
-  			# Save test set predictions from each outer loop
-  			indep_pred[[group_name]]<-rbind(indep_pred[[group_name]], Indep_Pred_tab)
-      }
-
-      Indep_mod<-summary(lm(scale(as.numeric(indep_pred[[group_name]]$obs)) ~ scale(indep_pred[[group_name]]$pred)))
-
-      if(opt$family=='binomial'){
-        Prediction_summary_all<-rbind(Prediction_summary_all, data.frame(	Model=paste0(group_name,'_group'),
-                                                                          R=coef(Indep_mod)[2,1],
-                                                                          SE=coef(Indep_mod)[2,2],
-                                                                          P=coef(Indep_mod)[2,4],
-                                                                          R2l=h2l_R2(opt$outcome_pop_prev, coef(Indep_mod)[2,1]^2, sum(Outcome_Predictors_train_y == 'CASE')/length(Outcome_Predictors_train_y)),
-                                                                          N=dim(indep_pred[[group_name]])[1],
-                                                                          Ncase=sum(Outcome_Predictors_train_y == 'CASE'),
-                                                                          Ncont=sum(Outcome_Predictors_train_y == 'CONTROL')))
-      } else {
-        Prediction_summary_all<-rbind(Prediction_summary_all, data.frame(	Model=paste0(group_name,'_group'),
-                                                                          R=coef(Indep_mod)[2,1],
-                                                                          SE=coef(Indep_mod)[2,2],
-                                                                          P=coef(Indep_mod)[2,4],
-                                                                          R2o=coef(Indep_mod)[2,1]^2,
-                                                                          N=dim(indep_pred[[group_name]])[1]))
-      }
-    }
+  eval_pred_all <-
+    rbind(
+      eval_pred_all,
+      data.table(Group = group_i, eval_pred(obs = indep_pred_list[[group_i]]$obs, pred = indep_pred_list[[group_i]]$pred, family = family))
+    )
+}
 
-    # Write out the results
-    write.table(Prediction_summary_all, paste0(opt$output,'.pred_eval.txt'), col.names=T, row.names=F, quote=F)
+###
+# Evaluate elastic net model containing all predictors within each group
+###
+# Only apply to groups with more than one predictor
+for(group_i in unique(group_list$group[group_list$n > 1])){
+  for(outer_val in 1:opt$n_outer_fold){
+    # Subset training and testing data
+    cv_dat <- subset_train_test(dat = outcome_predictors, train_ind = train_ind, fold = outer_val)
+
+    # Subset variables in group
+    pred_name <- group_list$predictor[group_list$group == group_i]
+    cv_dat$train$x <- cv_dat$train$x[, pred_name, with = F]
+    cv_dat$test$x <- cv_dat$test$x[, pred_name, with = F]
+
+    # Train elastic net
+    model <-
+      train(
+        y = cv_dat$train$y,
+        x = cv_dat$train$x,
+        trControl = trainControl(
+          method = "cv",
+          seeds = seeds,
+          number = opt$n_inner_fold
+        ),
+        method = "glmnet",
+        family = family,
+        tuneGrid = enet_grid
+      )
+
+    indep_pred <- as.numeric(predict(object = model$finalModel, newx = data.matrix(cv_dat$test$x), type = "response", s = model$finalModel$lambdaOpt))
+    indep_pred <- data.table(obs = cv_dat$test$y, pred = indep_pred)
+
+    # Save test set predictions from each outer loop
+    indep_pred_list[[group_i]] <- rbind(indep_pred_list[[group_i]], indep_pred)
+  }
 
-    log_add(log_file = log_file, message = paste0('Model evaluation results saved as ',opt$output,'.pred_eval.txt.'))
+  eval_pred_all <-
+    rbind(
+      eval_pred_all,
+      data.table(Group = group_i, eval_pred(obs = indep_pred_list[[group_i]]$obs, pred = indep_pred_list[[group_i]]$pred, family = family))
+    )
+}
 
-  	###################
-  	# Compare predictive utiliy of the different models
-  	###################
-    if(opt$top1){
-      predictors_list_new_top1<-predictors_list_new[predictors_list_new$group != '.' & !grepl('PredFile', predictors_list_new$group),]
-      predictors_list_new_top1$group<-paste0(predictors_list_new_top1$group, '_top1')
-      predictors_list_new2<-rbind(predictors_list_new, predictors_list_new_top1)
-    }
-  	comp_res_all<-NULL
-  	for(group1 in unique(predictors_list_new2$group)){
-    	for(group2 in unique(predictors_list_new2$group)){
-    	    if(group1 == '.'){
-    	      group1<-'All'
-    	    }
-      	  if(group2 == '.'){
-    	      group2<-'All'
-      	  }
-    	    if(group1 == group2){
-      			group1_r<-cor(scale(as.numeric(indep_pred[[group1]]$obs)), scale(indep_pred[[group1]]$pred))[1]
-    			  group2_r<-cor(scale(as.numeric(indep_pred[[group2]]$obs)), scale(indep_pred[[group2]]$pred))[1]
-
-    	      comp_res<-data.frame(Model_1=group1,
-    	                           Model_2=group2,
-    	                           Model_1_R=group1_r,
-    	                           Model_2_R=group2_r,
-    	                           R_diff=0,
-    	                           R_diff_pval=1)
-    	      comp_res_all<-rbind(comp_res_all,comp_res)
-    	      next
-    	    } else {
-    	      group1_r<-cor(scale(as.numeric(indep_pred[[group1]]$obs)), scale(indep_pred[[group1]]$pred))[1]
-    	      group2_r<-cor(scale(as.numeric(indep_pred[[group2]]$obs)), scale(indep_pred[[group2]]$pred))[1]
-
-    	      r_diff<-group1_r-group2_r
-
-    	      group1_group2_r<-cor(scale(indep_pred[[group1]]$pred), scale(indep_pred[[group2]]$pred))
-
-    	      r_diff_p<-paired.r(xy=group1_r, xz=group2_r, yz=group1_group2_r, n=length(scale(indep_pred[[group1]]$pred)), twotailed=T)$p[1]
-
-    	      comp_res<-data.frame(Model_1=group1,
-    	                           Model_2=group2,
-    	                           Model_1_R=group1_r,
-    	                           Model_2_R=group2_r,
-    	                           R_diff=r_diff,
-    	                           R_diff_pval=r_diff_p)
-    	    }
-
-    	    comp_res_all<-rbind(comp_res_all,comp_res)
-    	 }
-  	}
 
-    # Write out the results
-    write.table(comp_res_all, paste0(opt$output,'.pred_comp.txt'), col.names=T, row.names=F, quote=F)
+# Write out the results
+write.table(eval_pred_all, paste0(opt$out,'.pred_eval.txt'), col.names=T, row.names=F, quote=F)
+log_add(log_file = log_file, message = paste0('Model evaluation results saved as ',opt$out,'.pred_eval.txt.'))
+
+###################
+# Compare predictive utiliy of the different models
+###################
+
+comp_res_all<-NULL
+for(group1 in eval_pred_all$Group){
+	for(group2 in eval_pred_all$Group){
+			group1_r <- cor(scale(as.numeric(indep_pred_list[[group1]]$obs)), scale(indep_pred_list[[group1]]$pred))[1]
+		  group2_r <- cor(scale(as.numeric(indep_pred_list[[group2]]$obs)), scale(indep_pred_list[[group2]]$pred))[1]
+
+		  if(group1 == group2){
+			  comp_res <- data.table(
+			    Model_1 = group1,
+			    Model_2 = group2,
+			    Model_1_R = group1_r,
+			    Model_2_R = group2_r,
+			    R_diff = 0,
+			    R_diff_pval = 1
+			  )
+	    } else {
+	      r_diff <- group1_r - group2_r
+
+	      group1_group2_r <- cor(scale(indep_pred_list[[group1]]$pred), scale(indep_pred_list[[group2]]$pred))
+
+	      r_diff_p <-
+	        paired.r(
+	          xy = group1_r,
+	          xz = group2_r,
+	          yz = group1_group2_r,
+	          n = length(scale(indep_pred_list[[group1]]$pred)),
+	          twotailed = T
+	        )$p[1]
+
+	      comp_res <- data.table(
+	        Model_1 = group1,
+	        Model_2 = group2,
+	        Model_1_R = group1_r,
+	        Model_2_R = group2_r,
+	        R_diff = r_diff,
+	        R_diff_pval = r_diff_p
+	      )
+	    }
+		  comp_res_all <- rbind(comp_res_all, comp_res)
+	}
+}
 
-    log_add(log_file = log_file, message = paste0('Model evaluation results saved as ',opt$output,'.pred_comp.txt.'))
-  }
+# Write out the results
+write.table(comp_res_all, paste0(opt$out,'.pred_comp.txt'), col.names=T, row.names=F, quote=F)
 
-}
+log_add(log_file = log_file, message = paste0('Model evaluation results saved as ',opt$out,'.pred_comp.txt.'))
 
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 log_add(log_file = log_file, message = paste0('Analysis finished at ',as.character(end.time)))
-log_add(log_file = log_file, message = paste0('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units')))
+log_add(log_file = log_file, message = paste0('Analysis duration was ',as.character(round(time.taken,2)),attr(time.taken, 'units')))
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index b613e15e..13244601 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -557,7 +557,7 @@ outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
 prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
+prscsx_dat<-prscsx_dat[4,]
 
 ##############
 # Comparison: Single source
@@ -700,12 +700,12 @@ for disc_pop in $(echo EUR EAS); do
         targ_pop2=$targ_pop
     fi
     
-    for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
-      sbatch --mem 10G -n 1 -p cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
-        --pheno /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+    for pheno in $(head -n 4 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt | tail -n 1); do
+      sbatch --mem 20G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+        --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
         --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/single.${disc_pop}_disc.targ_${targ_pop}/${pheno}/predictor_list.txt \
-        --output /users/k1806347/oliverpainfel/Analyses/crosspop/single.${disc_pop}_disc.targ_${targ_pop}/${pheno}/res \
-        --n_core 1 \
+        --out /users/k1806347/oliverpainfel/Analyses/crosspop/single.${disc_pop}_disc.targ_${targ_pop}/${pheno}/res \
+        --n_core 5 \
         --top1 T \
         --compare_predictors T \
         --assoc T"
@@ -729,12 +729,12 @@ for targ_pop in $(echo EUR EAS); do
       targ_pop2=$targ_pop
   fi
 
-  for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
-    sbatch --mem 10G -n 1 -p cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
-      --pheno /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+  for pheno in $(head -n 4 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt | tail -n 1); do
+    sbatch --mem 20G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
       --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/predictor_list.txt \
-      --output /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/res \
-      --n_core 1 \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/res \
+      --n_core 5 \
       --top1 T \
       --compare_predictors T \
       --assoc T"
@@ -758,7 +758,7 @@ library(cowplot)
 
 # Read in list of outcomes 
 prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
+prscsx_dat<-prscsx_dat[4,]
 
 #############
 # Single source
@@ -782,7 +782,7 @@ for(pheno_i in prscsx_dat$labels){
               '/res.assoc.txt'
             )
           )
-        assoc_i$method <-
+        assoc_i$Method <-
           gsub('\\..*', '', gsub('Group_', '', assoc_i$Predictor))
         assoc_i$Predictor<-sub("^[^.]*\\.[^.]*\\.", "", assoc_i$Predictor)
         assoc_i$gwas<-sub("^(([^_]*_)[^_]*).*", "\\1", assoc_i$Predictor)
@@ -793,7 +793,11 @@ for(pheno_i in prscsx_dat$labels){
     }
   }
   
-  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=name, y=BETA, colour = method)) +
+  res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
+  res_i$test<-factor(res_i$test, levels=unique(res_i$test))
+  res_i$name<-factor(res_i$name, levels=unique(res_i$name))
+
+  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=name, y=BETA, colour = Method)) +
     geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
                   width = .2,
                   position = position_dodge(width = 0.7)) +
@@ -810,8 +814,8 @@ for(pheno_i in prscsx_dat$labels){
 png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/single.assoc.png',
   res = 300,
-  height = 5000,
-  width = 3000,
+  height = 1200*nrow(prscsx_dat),
+  width = 4000,
   units = 'px'
 )
 plot_grid(plotlist = plot_list, labels = names(plot_list), ncol=1)
@@ -839,27 +843,33 @@ for(pheno_i in prscsx_dat$labels){
             '/res.assoc.txt'
           )
         )
-      assoc_i$method <-
+      assoc_i$Method <-
         gsub('\\..*', '', gsub('Group_', '', assoc_i$Predictor))
       assoc_i$Predictor<-sub("^[^.]*\\.[^.]*\\.", "", assoc_i$Predictor)
       assoc_i$gwas<-sub("^(([^_]*_)[^_]*).*", "\\1", assoc_i$Predictor)
       assoc_i$name<-sub("^([^_]+_){2}", "", assoc_i$Predictor)
-      assoc_i$name[assoc_i$method == 'prscsx']<-paste0(gsub('.*_','', assoc_i$gwas[assoc_i$method == 'prscsx']),'_', assoc_i$name[assoc_i$method == 'prscsx'])
-      assoc_i$disc[grepl('tlprs|prscsx|xwing', assoc_i$method)] <-'EUR+EAS'
+      assoc_i$name[assoc_i$Method == 'prscsx']<-paste0(gsub('.*_','', assoc_i$gwas[assoc_i$Method == 'prscsx']),'_', assoc_i$name[assoc_i$Method == 'prscsx'])
+      assoc_i$disc[grepl('tlprs|prscsx|xwing', assoc_i$Method)] <-'EUR+EAS'
       assoc_i$disc[grepl('UKB', assoc_i$gwas)] <-'EUR' 
       assoc_i$disc[grepl('BBJ', assoc_i$gwas)] <-'EAS' 
       assoc_i$test<-paste0(assoc_i$disc, ' → ', targ_pop_i)
       assoc_i$tlprs_target<-NA
-      assoc_i$tlprs_target[assoc_i$method == 'tlprs' & grepl('EUR_DBSLMM', assoc_i$name)]<-'EUR-tuned'
-      assoc_i$tlprs_target[assoc_i$method == 'tlprs' & grepl('EAS_DBSLMM', assoc_i$name)]<-'EAS-tuned'
+      assoc_i$tlprs_target[grepl('tlprs', assoc_i$Method) & grepl('EUR.DBSLMM', assoc_i$name)]<-'EUR-tuned'
+      assoc_i$tlprs_target[grepl('tlprs', assoc_i$Method) & grepl('EAS.DBSLMM', assoc_i$name)]<-'EAS-tuned'
       
       res_i<-rbind(res_i, assoc_i)
   }
 
-    tmp<-res_i[res_i$method %in% c('ptclump','dbslmm'),]
-    tmp$name<-factor(tmp$name, levels = unique(tmp$name))
+    res_i$name<-factor(res_i$name, levels = unique(res_i$name))
+    res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
+    res_i$test<-factor(res_i$test, levels=unique(res_i$test))
+    
+    tmp<-res_i[res_i$Method %in% c('ptclump','dbslmm'),]
+    tmp$param<-gsub('.*UKB.','',tmp$name)
+    tmp$param<-gsub('.*BBJ.','',tmp$param)
+    tmp$param<-factor(tmp$param, levels=unique(tmp$param))
 
-    single_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=name, y=BETA, colour = method)) +
+    single_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=param, y=BETA, colour = Method)) +
     geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
                   width = .2,
                   position = position_dodge(width = 0.7)) +
@@ -872,10 +882,9 @@ for(pheno_i in prscsx_dat$labels){
     panel_border() + 
     theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
 
-    tmp<-res_i[(res_i$method %in% c('prscsx','xwing')),]
-    tmp$name<-factor(tmp$name, levels = unique(tmp$name))
-    
-    prscsx_xwing_plot_list[[pheno_i]] <- ggplot(tmp, aes(x=name, y=BETA, colour = method)) +
+    tmp<-res_i[(res_i$Method %in% c('prscsx','xwing')),]
+
+    prscsx_xwing_plot_list[[pheno_i]] <- ggplot(tmp, aes(x=name, y=BETA, colour = Method)) +
     geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
                   width = .2,
                   position = position_dodge(width = 0.7)) +
@@ -888,10 +897,10 @@ for(pheno_i in prscsx_dat$labels){
     panel_border() + 
     theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
   
-    tmp<-res_i[(res_i$method %in% c('tlprs') & grepl('DBSLMM_1_', res_i$name)),]
-    tmp$name<-as.numeric(gsub('.*_','',tmp$name))
+    tmp<-res_i[(grepl('tlprs', res_i$Method) & grepl('DBSLMM.1.', res_i$name)),]
+    tmp$name<-as.numeric(gsub('_','',gsub('.*TLPRS\\.','',tmp$name)))
     
-    tlprs_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=name, y=BETA, colour = method)) +
+    tlprs_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=name, y=BETA, colour = Method)) +
     geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
                   width = .2,
                   position = position_dodge(width = 0.7)) +
@@ -908,7 +917,7 @@ for(pheno_i in prscsx_dat$labels){
 png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/single.assoc.png',
   res = 300,
-  height = 5000,
+  height = 1000*nrow(prscsx_dat),
   width = 4000,
   units = 'px'
 )
@@ -918,7 +927,7 @@ dev.off()
 png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.prscsx_xwing.assoc.png',
   res = 300,
-  height = 5000,
+  height = 1000*nrow(prscsx_dat),
   width = 4000,
   units = 'px'
 )
@@ -928,7 +937,7 @@ dev.off()
 png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.tlprs.assoc.png',
   res = 300,
-  height = 6000,
+  height = 1200*nrow(prscsx_dat),
   width = 3000,
   units = 'px'
 )
@@ -992,32 +1001,30 @@ for(pheno_i in prscsx_dat$labels){
               '/res.pred_eval.txt'
             )
           )
-        eval_i$Model<-gsub('_group','',eval_i$Model)
-        eval_i$test<-paste0(disc_pop_i, ' → ', targ_pop_i)
-        res_i<-rbind(res_i, eval_i)
+        eval_i$test <- paste0(disc_pop_i, ' → ', targ_pop_i)
+        res_i <- rbind(res_i, eval_i)
     }
   }
   
-  res_i$Method<-gsub('_.*','',res_i$Model)
   res_i$Method<-sub('\\..*','',res_i$Method)
   
-  res_i$Group[grepl('top1', res_i$Model)]<-'Top1'
-  res_i$Group[!grepl('top1|PredFile', res_i$Model)]<-'Multi'
+  res_i$Model[grepl('top1', res_i$Group)]<-'Top1'
+  res_i$Model[!grepl('top1|PredFile', res_i$Group)]<-'Multi'
   
-  res_i_pseudo<-res_i[grepl('PredFile', res_i$Model),]
-  res_i<-res_i[!grepl('PredFile', res_i$Model),]
+  res_i_pseudo<-res_i[grepl('PredFile', res_i$Group),]
+  res_i<-res_i[!grepl('PredFile', res_i$Group),]
   
   # Identify PGS selected by pseudovalidation
   for(pgs_method_i in pgs_methods[
     pgs_methods %in% res_i$Method & 
     pgs_methods %in% c('dbslmm')]) { # Restrict to methods with pseudo method
-    res_i_tmp<-res_i_pseudo[res_i_pseudo$Method == pgs_method_i,]
+      res_i_tmp<-res_i_pseudo[res_i_pseudo$Method == pgs_method_i,]
       gwas_list_i<-gwas_list[grepl(pheno_i, gwas_list$name),]
       for(gwas_i in gwas_list_i$name){
         gwas_i_2 <- gsub('_', '.', gwas_i)
         res_i_tmp2 <-
-          res_i_tmp[grepl(gwas_i_2, res_i_tmp$Model), ]
-        res_i_tmp2$param<-gsub(paste0('.*', gwas_i_2, '.'), '', res_i_tmp2$Model)
+          res_i_tmp[grepl(gwas_i_2, res_i_tmp$Group), ]
+        res_i_tmp2$param<-gsub(paste0('.*', gwas_i_2, '.'), '', res_i_tmp2$Group)
         
         pseudo_param<-find_pseudo(config = config, 
                     gwas = gwas_i, 
@@ -1027,15 +1034,15 @@ for(pheno_i in prscsx_dat$labels){
         
         res_i_tmp2<-res_i_tmp2[res_i_tmp2$param == pseudo_param,]
         res_i_tmp2$param<-NULL
-        res_i_tmp2$Group<-'Pseudo'
+        res_i_tmp2$Model<-'Pseudo'
         res_i<-rbind(res_i, res_i_tmp2)
       }
   }
 
   res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
-  res_i$Group<-factor(res_i$Group, levels=unique(res_i$Group))
+  res_i$Model<-factor(res_i$Model, levels=unique(res_i$Model))
   
-  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=Method, y=R, fill = Group)) +
+  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=Method, y=R, fill = Model)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = .2,
                   position = position_dodge(width = 0.7)) +
@@ -1079,19 +1086,17 @@ for(pheno_i in prscsx_dat$labels){
             '/res.pred_eval.txt'
           )
         )
-      eval_i$Model<-gsub('_group','',eval_i$Model)
       eval_i$target<-targ_pop_i
       res_i<-rbind(res_i, eval_i)
   }
   
-  res_i$Method<-gsub('_.*','',res_i$Model)
   res_i$Method<-sub('\\..*','',res_i$Method)
   
-  res_i$Group[grepl('top1', res_i$Model)]<-'Top1'
-  res_i$Group[!grepl('top1|PredFile', res_i$Model)]<-'Multi'
+  res_i$Model[grepl('top1', res_i$Group)]<-'Top1'
+  res_i$Model[!grepl('top1|PredFile', res_i$Group)]<-'Multi'
   
-  res_i_pseudo<-res_i[grepl('PredFile', res_i$Model),]
-  res_i<-res_i[!grepl('PredFile', res_i$Model),]
+  res_i_pseudo<-res_i[grepl('PredFile', res_i$Group),]
+  res_i<-res_i[!grepl('PredFile', res_i$Group),]
 
   # Identify PGS selected by pseudovalidation
   for(pgs_method_i in pgs_methods[
@@ -1103,8 +1108,8 @@ for(pheno_i in prscsx_dat$labels){
       for(gwas_group_i in gwas_groups_i$name){
         gwas_group_i_2 <- gsub('_', '.', gwas_group_i)
         res_i_tmp2 <-
-          res_i_tmp[grepl(gwas_group_i_2, res_i_tmp$Model), ]
-        res_i_tmp2$param<-gsub(paste0('.*', gwas_group_i_2, '.'), '', res_i_tmp2$Model)
+          res_i_tmp[grepl(gwas_group_i_2, res_i_tmp$Group), ]
+        res_i_tmp2$param<-gsub(paste0('.*', gwas_group_i_2, '.'), '', res_i_tmp2$Group)
         
         for(targ_pop_i in pop){
           pseudo_param<-find_pseudo(config = config, 
@@ -1118,16 +1123,16 @@ for(pheno_i in prscsx_dat$labels){
                          res_i_tmp2$target == targ_pop_i, ]
           
           res_i_tmp3$param<-NULL
-          res_i_tmp3$Group<-'Pseudo'
+          res_i_tmp3$Model<-'Pseudo'
           res_i<-rbind(res_i, res_i_tmp3)
         }
       }
   }
 
   res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
-  res_i$Group<-factor(res_i$Group, levels=unique(res_i$Group))
+  res_i$Model<-factor(res_i$Model, levels=unique(res_i$Model))
   
-  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=Method, y=R, fill = Group)) +
+  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=Method, y=R, fill = Model)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = .2,
                   position = position_dodge(width = 0.7)) +
diff --git a/functions/model_builder.R b/functions/model_builder.R
index 7029090d..f6c685db 100644
--- a/functions/model_builder.R
+++ b/functions/model_builder.R
@@ -16,6 +16,30 @@ h2l_R2 <- function(k, r2, p) {
   h2l_R2 = C*r2 / (1 + C*theta*r2)
 }
 
+# Functions for reading in predictor file
+read_predictor<-function(x, pred_miss, file_index = NULL){
+  # Read in predictor file
+  tmp <- fread(x)
+
+  # Create a column that combines FID and IID
+  tmp <- combine_fid_iid(tmp)
+
+  # Remove variables with > opt$pred_miss missing values
+  tmp <- filter_columns_by_missing(tmp, threshold = opt$pred_miss)
+
+  # Remove individuals with any missing data
+  tmp <- tmp[complete.cases(tmp), ]
+
+  # Update column names to avoid duplicate column names between predictor files
+  names(tmp) <- gsub("[[:punct:]]", ".", names(tmp))
+  names(tmp)[-1] <- paste0('PredFile', file_index, '.', names(tmp)[-1])
+
+  log_add(log_file = log_file, message = paste0('Predictors file ', file_index, ' contains ', ncol(tmp) - 1, ' predictors with sufficient data.'))
+  log_add(log_file = log_file, message = paste0('Predictors file ', file_index, ' contains ', nrow(tmp) ,' individuals with complete data for remaining predictors.'))
+
+  return(tmp)
+}
+
 # Function for merging by IID
 predictor_merger <- function(x, y) {
   return(merge(x, y, by = 'IID'))
@@ -56,3 +80,102 @@ enet_grid <- expand.grid(
   alpha = seq(0, 1, length = 5),          # Explore alpha values: 0 (Ridge) to 1 (Lasso)
   lambda = 10^seq(-4, 1, length = 10)     # Explore lambda values: 0.0001 to 10
 )
+
+# Read in outcome file
+read_outcome<-function(x, keep = NULL){
+  outcome <- fread(x)
+  outcome <- outcome[complete.cases(outcome), ]
+  names(outcome)[3] <- 'outcome_var'
+
+  # Create a column that combines FID and IID
+  outcome <- combine_fid_iid(outcome)
+
+  log_add(log_file = log_file, message = paste0('Outcome file contains ',nrow(outcome),' individuals with complete data.'))
+
+  if(!is.null(keep)){
+    ############
+    # Extract individuals in the keep file
+    ############
+
+    # Read in keep file
+    keep_file <- fread(keep)
+    names(keep_file)[1:2] <- c('FID', 'IID')
+    # Create a column that combines FID and IID
+    keep_file <- combine_fid_iid(keep_file)
+    # Extract keep individuals from the phenotypic data
+    outcome <- outcome[(outcome$IID %in% keep_file$IID), ]
+
+    log_add(log_file = log_file, message = paste0('Outcome file contains ',nrow(outcome),' individuals after extraction of individuals in ', keep,'.'))
+  }
+
+  return(outcome)
+}
+
+# Create a variable containing seeds for internal cross validation
+fold_seeds<-function(n_fold){
+  seeds <- vector(mode = "list", length = n_fold+1)
+  for(i in 1:(n_fold)){
+    seeds[[i]]<- sample.int(n=1000, 6)
+  }
+  seeds[[n_fold+1]]<-sample.int(n=1000, 1)
+
+  return(seeds)
+}
+
+# Subset training and testing data
+subset_train_test<-function(dat, train_ind, fold){
+
+  dat_list<-list()
+  dat_list$train<-list()
+  dat_list$test<-list()
+
+  # Subset data to training and testing sets
+  dat_list$train$IID <- dat$IID[train_ind[[fold]]]
+  dat_list$test$IID <- dat$IID[-train_ind[[fold]]]
+
+  dat_list$train$y <- dat$outcome_var[train_ind[[fold]]]
+  dat_list$test$y <- dat$outcome_var[-train_ind[[fold]]]
+
+  dat_list$train$x <- dat[train_ind[[fold]], !(names(dat) %in% c('IID','outcome_var')), with=F]
+  dat_list$test$x <- dat[-train_ind[[fold]], !(names(dat) %in% c('IID','outcome_var')), with=F]
+
+  # If there is only one predictor, insert a column of 0s so elastic net function doesn't fail
+  if(ncol(dat_list$train$x) == 1){
+    dat_list$train$x<-data.table(cbind(0, dat_list$train$x))
+    dat_list$test$x<-data.table(cbind(0, dat_list$test$x))
+  }
+
+  return(dat_list)
+}
+
+# Compare predicted and observed values
+eval_pred <- function(obs, pred, family){
+  mod <- summary(lm(scale(as.numeric(obs)) ~ scale(as.numeric(pred))))
+
+  mod_sum <- data.table(
+    R = coef(mod)[2, 1],
+    SE = coef(mod)[2, 2],
+    P = coef(mod)[2, 4]
+  )
+
+  if(family == 'binomial'){
+    mod_sum <- data.table(
+      mod_sum,
+      R2l = h2l_R2(
+        opt$outcome_pop_prev,
+        coef(Indep_mod)[2, 1] ^ 2,
+        sum(cv_dat$test$y == 'CASE') / length(cv_dat$test$y)
+      ),
+      N = length(obs),
+      Ncase = sum(obs == 'CASE'),
+      Ncont = sum(obs == 'CONTROL')
+    )
+  } else {
+    mod_sum <- data.table(
+      mod_sum,
+      R2o = coef(mod)[2, 1] ^ 2,
+      N = length(obs)
+    )
+  }
+  return(mod_sum)
+}
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 479d4e1a..535cbbfc 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -181,7 +181,7 @@ rule prep_pgs_dbslmm:
 rule prep_pgs_prscs_i:
   resources:
     mem_mb=2000*config['cores_prep_pgs'],
-    time_min=800
+    time_min=5000
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
@@ -300,7 +300,7 @@ rule prep_pgs_lassosum:
 rule prep_pgs_ldpred2_i:
   resources:
     mem_mb=30000,
-    time_min=800
+    time_min=5000
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
@@ -345,7 +345,7 @@ rule prep_pgs_ldpred2:
 rule prep_pgs_megaprs_i:
   resources:
     mem_mb=20000,
-    time_min=800
+    time_min=5000
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
@@ -519,7 +519,7 @@ if missing_gwas:
 rule prep_pgs_prscsx_i:
   resources:
     mem_mb=2000*config['cores_prep_pgs'],
-    time_min=800
+    time_min=5000
   threads: config['cores_prep_pgs']
   input:
     rules.download_prscsx_software.output,
@@ -622,7 +622,7 @@ rule prep_pgs_xwing:
 rule prep_pgs_tlprs_i:
   resources:
     mem_mb=10000,
-    time_min=800
+    time_min=5000
   threads: config['cores_prep_pgs']
   input:
     rules.install_tlprs.output,
@@ -665,7 +665,7 @@ rule prep_pgs_tlprs:
 rule prep_pgs_bridgeprs_i:
   resources:
     mem_mb=2000*config['cores_prep_pgs'],
-    time_min=800
+    time_min=5000
   threads: config['cores_prep_pgs']
   input:
     rules.download_bridgeprs_software.output,

From b6dbdaad0a43e49457b9a55eb202359eb6f7c207 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 26 Sep 2024 16:22:27 +0100
Subject: [PATCH 016/153] Updates to pipeline for TLPRS

---
 docs/CrossPop.Rmd               |  4 +--
 functions/pipeline.R            | 43 +++++++++++++++------------------
 pipeline/rules/dependencies.smk |  2 +-
 3 files changed, 23 insertions(+), 26 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 13244601..f328ec86 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -515,8 +515,8 @@ config<-c(
   "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt",
   "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
   "gwas_groups: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt",
-  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs']",
-  "tlprs_methods: ['dbslmm']",
+  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','sbayesr','lassosum','ldpred2','megaprs']",
+  "tlprs_methods: ['dbslmm','sbayesr','prscs']",
   "cores_prep_pgs: 20",
   "cores_target_pgs: 50"
 )
diff --git a/functions/pipeline.R b/functions/pipeline.R
index 7e6dfff0..e70826dd 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -196,25 +196,29 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
   # Find outdir
   outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
+  # If TLPRS, find pseudo param, and then edit value for TLPRS
+  tlprs <- ifelse(grepl('tlprs', pgs_method), T, F)
+  pgs_method <- gsub('tlprs_', '', pgs_method)
+
   # Use most stringent p-value threshold of 0.05 as pseudo
   if(pgs_method == 'ptclump'){
-    return('0_1')
+    pseudo_val <- '0_1'
   }
 
   # Pseudoval only methods
   if(pgs_method == 'sbayesr'){
-    return('SBayesR')
+    pseudo_val <- 'SBayesR'
   }
 
   # Retrieve pseudoval param
   if(pgs_method == 'dbslmm'){
-    return('DBSLMM_1')
+    pseudo_val <- 'DBSLMM_1'
   }
   if(pgs_method == 'ldpred2'){
-    return('beta_auto')
+    pseudo_val <- 'beta_auto'
   }
   if(pgs_method == 'prscs'){
-    return('phi_auto')
+    pseudo_val <- 'phi_auto'
   }
 
   if(pgs_method == 'megaprs'){
@@ -222,51 +226,44 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
     log <- readLines(paste0(outdir,'/reference/pgs_score_files/',pgs_method,'/',gwas,'/ref-',gwas,'.log'))
     log <- log[grepl('identified as the best with correlation', log)]
     pseudoval <- gsub(' .*','', gsub('Model ', '', log))
-    return(paste0('ldak_Model', pseudoval))
+    pseudo_val <- paste0('ldak_Model', pseudoval)
   }
   if(pgs_method == 'lassosum'){
     # Read in megaprs log file
     log <- readLines(paste0(outdir,'/reference/pgs_score_files/',pgs_method,'/',gwas,'/ref-',gwas,'.log'))
     s_val <- gsub('.* ', '', log[grepl('^s = ', log)])
     lambda_val <- gsub('.* ', '', log[grepl('^lambda = ', log)])
-    return(paste0('s', s_val, '_lambda', lambda_val))
+    pseudo_val <- paste0('s', s_val, '_lambda', lambda_val)
   }
 
   # If pgs_method is external, return the only score
   if(pgs_method == 'external'){
-    return('external')
+    pseudo_val <- 'external'
   }
 
   # Multi-population methods
   if(pgs_method == 'prscsx'){
-    return('META_phi_auto')
+    pseudo_val <- 'META_phi_auto'
   }
   if(pgs_method == 'xwing'){
     if(!is.null(target_pop) && target_pop == 'TRANS'){
       cat('No pseudovalidation for TRANS target population available for xwing.')
       cat('Returning result for EUR target population.')
-      return('targ_EUR_weighted')
+      pseudo_val <- 'targ_EUR_weighted'
     } else {
-      return(paste0('targ_', target_pop, '_weighted'))
+      pseudo_val <- paste0('targ_', target_pop, '_weighted')
     }
   }
-  if(grepl('^tlprs', pgs_method)){
+
+  if(tlprs){
     if(!is.null(target_pop) && target_pop == 'TRANS'){
-      cat('No pseudovalidation for TRANS target population available for xwing.')
+      cat('No pseudovalidation for TRANS target population available for tlprs')
       cat('Returning result for EUR target population.')
       target_pop <- 'EUR'
     }
-
-    # Use most stringent p-value threshold of 0.05 as pseudo
-    if(grepl('ptclump', pgs_method)){
-      return(paste0('targ_', target_pop, '_0_1_TLPRS_61'))
-    }
-
-    # Retrieve pseudoval param
-    if(grepl('dbslmm', pgs_method)){
-      return(paste0('targ_', target_pop, '_DBSLMM_1_TLPRS_61'))
-    }
+    pseudo_val <- paste0('targ_', target_pop, '_', pseudo_val, '_TLPRS_61')
   }
+  return(pseudo_val)
 }
 
 # Read in lassosum pseudoval results
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 6f525e02..20e93a57 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -900,7 +900,7 @@ rule install_tlprs:
   shell:
     """
     {{
-      Rscript -e 'devtools::install_github(\"opain/TLPRS@fb71076267d405d5f7df97e445ab0de73d76bc0f\")'
+      Rscript -e 'devtools::install_github(\"opain/TLPRS@5a5528a3f709ca7d627381a3f09ccdcb923b50f4\")'
     }} > {log} 2>&1
     """
 

From c7a22675c2bf0ed2423d2973a16daa513593f99c Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 27 Sep 2024 18:41:52 +0100
Subject: [PATCH 017/153] Updated model builder, and updated cross pop analysis

---
 Scripts/model_builder/model_builder.R | 355 ++++++++++++++++--------
 docs/CrossPop.Rmd                     | 371 +++++++++++---------------
 functions/model_builder.R             |  74 +++++
 3 files changed, 474 insertions(+), 326 deletions(-)

diff --git a/Scripts/model_builder/model_builder.R b/Scripts/model_builder/model_builder.R
index a11e6387..90898f69 100644
--- a/Scripts/model_builder/model_builder.R
+++ b/Scripts/model_builder/model_builder.R
@@ -30,6 +30,8 @@ make_option("--top1", action="store", default=F, type='logical',
     help="Evaluate model using top predictor within each group [optional]"),
 make_option("--all_model", action="store", default=T, type='logical',
     help="Evaluate model containing all predictors [optional]"),
+make_option("--export_models", action="store", default=T, type='logical',
+    help="Export model coefficients [optional]"),
 make_option("--seed", action="store", default=1, type='numeric',
     help="Set seed number [optional]")
 )
@@ -67,6 +69,11 @@ system(paste0('mkdir -p ', opt$output_dir))
 # Create temp directory
 tmp_dir <- tempdir()
 
+# Create directory for final models to be saved
+if(opt$export_models){
+  system(paste0('mkdir ', opt$output_dir, '/final_models'))
+}
+
 # Initiate log file
 log_file <- paste0(opt$out,'.log')
 log_header(log_file = log_file, opt = opt, script = 'model_builder.R', start.time = start.time)
@@ -82,10 +89,9 @@ if(nrow(predictors_file) > 1){
   predictors <- foreach(i = 1:nrow(predictors_file)) %dopar% {
     read_predictor(x = predictors_file$predictor[i], pred_miss = opt$pred_miss, file_index = i)
   }
-  names(predictors) <- predictors_file$group
 
-  group_list <- do.call(rbind, lapply(names(predictors), function(group) {
-    data.table(group = group, predictor = names(predictors[[group]])[-1])
+  group_list <- do.call(rbind, lapply(1:nrow(predictors_file), function(predfile) {
+    data.table(group = predictors_file$group[predfile], predictor = names(predictors[[predfile]])[-1])
   }))
 
   predictors <- Reduce(function(x, y) merge(x, y, by = "IID"), predictors)
@@ -226,6 +232,7 @@ if(opt$assoc){
 ############
 # We will use elastic net model when groups contain more than one predictor, and a glm when the group contains only 1 predictor.
 # Elastic net models will be derived and evaluated using nested cross validation, but glm will be evaluated using standard cross validation.
+# We will store the predicted and observed values from the outer loops for each model, and then evaluate the models at the end
 
 # Split the sample into opt$n_outer_fold folds
 set.seed(opt$seed)
@@ -237,22 +244,28 @@ seeds <- fold_seeds(opt$n_outer_fold)
 
 # Create objects to store outputs
 indep_pred_list <- list()
-eval_pred_all <- NULL
 
 ####
-# Evaluate single best predictor from each group, identifying the best predictor using training data, and then evaluating in the test data
+# Generate predictions using single best predictor from each group, identifying the best predictor using training data, and then evaluating in the test data
 ####
 
 if(opt$top1){
   # Only run for groups containing more than 1 predictor
-  for(group_i in unique(group_list$group[group_list$n > 1])){
-    group_name<-paste0(group_i, '.top1')
+  top1_groups <- unique(group_list$group[group_list$n > 1])
+
+  # Initialise progress log
+  log_message <- 'Generate predictions using top1 models for each group... '
+  progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
+
+  top1_indep_pred <- foreach(i = 1:length(top1_groups), .combine = 'c') %dopar% {
+    group_name<-paste0(top1_groups[i], '.top1')
+    indep_pred <- NULL
     for(outer_val in 1:opt$n_outer_fold){
       # Subset training and testing data
       cv_dat <- subset_train_test(dat = outcome_predictors, train_ind = train_ind, fold = outer_val)
 
       # Subset variables in group
-      pred_name <- group_list$predictor[group_list$group == group_i]
+      pred_name <- group_list$predictor[group_list$group == top1_groups[i]]
       cv_dat$train$x <- cv_dat$train$x[, pred_name, with = F]
 
       # Evaluate each predictor in training data
@@ -275,151 +288,267 @@ if(opt$top1){
 
       # Evaluate best performing predictor in test data
       test_tmp <- data.table(x = cv_dat$test$x[[top_pred]])
-      indep_pred <- predict(object = train_mod, newdata = test_tmp, type = "response")
-      indep_pred <- data.table(obs = cv_dat$test$y, pred = indep_pred)
+      indep_pred_i <- predict(object = train_mod, newdata = test_tmp, type = "response")
+      indep_pred_i <- data.table(obs = cv_dat$test$y, pred = indep_pred_i)
 
       # Save test set predictions from each outer loop
-      indep_pred_list[[group_name]] <- rbind(indep_pred_list[[group_name]], indep_pred)
+      indep_pred <- rbind(indep_pred, indep_pred_i)
     }
 
-    eval_pred_all <-
-      rbind(
-        eval_pred_all,
-        data.table(Group = group_name, eval_pred(obs = indep_pred_list[[group_name]]$obs, pred = indep_pred_list[[group_name]]$pred, family = family))
-      )
+    # Derive and export final model using all data
+    if(opt$export_models){
+      top1_res<-NULL
+      for(pred_i in names(cv_dat$train$x)){
+        res_pred_i <- cor(outcome_predictors$outcome_var, outcome_predictors[[pred_i]], use='p')
+        top1_res <- rbind(
+          top1_res,
+          data.table(
+            pred = pred_i,
+            cor = res_pred_i)
+        )
+      }
+      top_pred <- top1_res$pred[which.max(abs(top1_res$cor))]
+
+      train_mod <- glm(as.formula(paste0('outcome_var ~ ', top_pred)), family=family, data=outcome_predictors)
+
+      export_final_model(model = train_mod,
+                         group = group_name,
+                         outdir = paste0(opt$output_dir,
+                                         '/final_models'))
+    }
+
+    # Update progress log
+    progress <- update_progress_file(progress_file)
+    update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(top1_groups)*100),'%'))
+
+    # Output results
+    setNames(list(indep_pred), group_name)
   }
+
+  indep_pred_list <- c(indep_pred_list, top1_indep_pred)
+  update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
 }
 
 ###
-# Evaluate model containing groups containing only one predictor
+# Generate predictions using single predictor groups
 ###
 
-for(group_i in unique(group_list$group[group_list$n == 1])){
-  for(outer_val in 1:opt$n_outer_fold){
-    # Subset training and testing data
-    cv_dat <- subset_train_test(dat = outcome_predictors, train_ind = train_ind, fold = outer_val)
+if(any(group_list$n == 1)){
+  single_groups <- unique(group_list$group[group_list$n == 1])
+
+  # Initialise progress log
+  log_message <- 'Generate predictions using models containing single predictor... '
+  progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
+
+  single_indep_pred <- foreach(i = 1:length(single_groups), .combine=c) %dopar% {
+    indep_pred <- NULL
+    for(outer_val in 1:opt$n_outer_fold){
+      # Subset training and testing data
+      cv_dat <- subset_train_test(dat = outcome_predictors, train_ind = train_ind, fold = outer_val)
+
+      # Subset variables in group
+      pred_name <- group_list$predictor[group_list$group == single_groups[i]]
+      cv_dat$train$x <- cv_dat$train$x[[pred_name]]
+
+      # Build model using predictor
+      train_tmp<-data.table(y = cv_dat$train$y, x = cv_dat$train$x)
+      train_mod<-glm(y ~ x, family=family, data=train_tmp)
+
+      # Evaluate best performing predictor in test data
+      test_tmp <- data.table(x = cv_dat$test$x[[pred_name]])
+      indep_pred_i <- predict(object = train_mod, newdata = test_tmp, type = "response")
+      indep_pred_i <- data.table(obs = cv_dat$test$y, pred = indep_pred_i)
+
+      # Save test set predictions from each outer loop
+      indep_pred <- rbind(indep_pred, indep_pred_i)
+    }
 
-    # Subset variables in group
-    pred_name <- group_list$predictor[group_list$group == group_i]
-    cv_dat$train$x <- cv_dat$train$x[[pred_name]]
+    # Derive and export final model using all data
+    if(opt$export_models){
+      train_mod <- glm(as.formula(paste0('outcome_var ~ ', pred_name)), family=family, data=outcome_predictors)
 
-    # Build model using predictor
-    train_tmp<-data.table(y = cv_dat$train$y, x = cv_dat$train$x)
-    train_mod<-glm(y ~ x, family=family, data=train_tmp)
+      export_final_model(model = train_mod,
+                         group = single_groups[i],
+                         outdir = paste0(opt$output_dir,
+                                         '/final_models'))
+    }
 
-    # Evaluate best performing predictor in test data
-    test_tmp <- data.table(x = cv_dat$test$x[[pred_name]])
-    indep_pred <- predict(object = train_mod, newdata = test_tmp, type = "response")
-    indep_pred <- data.table(obs = cv_dat$test$y, pred = indep_pred)
+    # Update progress log
+    progress <- update_progress_file(progress_file)
+    update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(single_groups)*100),'%'))
 
-    # Save test set predictions from each outer loop
-    indep_pred_list[[group_i]] <- rbind(indep_pred_list[[group_i]], indep_pred)
+    # Output results
+    setNames(list(indep_pred), single_groups[i])
   }
 
-  eval_pred_all <-
-    rbind(
-      eval_pred_all,
-      data.table(Group = group_i, eval_pred(obs = indep_pred_list[[group_i]]$obs, pred = indep_pred_list[[group_i]]$pred, family = family))
-    )
+  indep_pred_list <- c(indep_pred_list, single_indep_pred)
+  update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
 }
 
 ###
-# Evaluate elastic net model containing all predictors within each group
+# Generate predictions using elastic net model containing all predictors within each group
 ###
 # Only apply to groups with more than one predictor
-for(group_i in unique(group_list$group[group_list$n > 1])){
-  for(outer_val in 1:opt$n_outer_fold){
-    # Subset training and testing data
-    cv_dat <- subset_train_test(dat = outcome_predictors, train_ind = train_ind, fold = outer_val)
-
-    # Subset variables in group
-    pred_name <- group_list$predictor[group_list$group == group_i]
-    cv_dat$train$x <- cv_dat$train$x[, pred_name, with = F]
-    cv_dat$test$x <- cv_dat$test$x[, pred_name, with = F]
-
-    # Train elastic net
-    model <-
-      train(
-        y = cv_dat$train$y,
-        x = cv_dat$train$x,
-        trControl = trainControl(
-          method = "cv",
-          seeds = seeds,
-          number = opt$n_inner_fold
-        ),
-        method = "glmnet",
-        family = family,
-        tuneGrid = enet_grid
-      )
+if(any(group_list$n > 1)){
+
+  # Initialise progress log
+  log_message <- 'Generate predictions using elastic net models for each group... '
+  progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
+
+  group_enet <- unique(group_list$group[group_list$n > 1])
+  enet_indep_pred <- list()
+  for(i in 1:length(group_enet)){
+    indep_pred <- NULL
+    for(outer_val in 1:opt$n_outer_fold){
+      # Subset training and testing data
+      cv_dat <- subset_train_test(dat = outcome_predictors, train_ind = train_ind, fold = outer_val)
+
+      # Subset variables in group
+      pred_name <- group_list$predictor[group_list$group == group_enet[i]]
+      cv_dat$train$x <- cv_dat$train$x[, pred_name, with = F]
+      cv_dat$test$x <- cv_dat$test$x[, pred_name, with = F]
+
+      # Train elastic net
+      model <-
+        train(
+          y = cv_dat$train$y,
+          x = cv_dat$train$x,
+          trControl = trainControl(
+            method = "cv",
+            seeds = seeds,
+            number = opt$n_inner_fold
+          ),
+          method = "glmnet",
+          family = family,
+          tuneGrid = enet_grid
+        )
+
+      indep_pred_i <- as.numeric(predict(object = model$finalModel, newx = data.matrix(cv_dat$test$x), type = "response", s = model$finalModel$lambdaOpt))
+      indep_pred_i <- data.table(obs = cv_dat$test$y, pred = indep_pred_i)
+
+      # Save test set predictions from each outer loop
+      indep_pred <- rbind(indep_pred, indep_pred_i)
+    }
+
+    # Derive and export final model using all data
+    if(opt$export_models){
+      model <-
+        train(
+          y = outcome_predictors$outcome_var,
+          x = outcome_predictors[, pred_name, with=F],
+          trControl = trainControl(
+            method = "cv",
+            seeds = seeds,
+            number = opt$n_inner_fold
+          ),
+          method = "glmnet",
+          family = family,
+          tuneGrid = enet_grid
+        )
+
+      export_final_model(model = model$finalModel,
+                         group = group_enet[i],
+                         outdir = paste0(opt$output_dir,
+                                         '/final_models'))
+    }
 
-    indep_pred <- as.numeric(predict(object = model$finalModel, newx = data.matrix(cv_dat$test$x), type = "response", s = model$finalModel$lambdaOpt))
-    indep_pred <- data.table(obs = cv_dat$test$y, pred = indep_pred)
+    # Update progress log
+    progress <- update_progress_file(progress_file)
+    update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(group_enet)*100),'%'))
 
-    # Save test set predictions from each outer loop
-    indep_pred_list[[group_i]] <- rbind(indep_pred_list[[group_i]], indep_pred)
+    # Output results
+    enet_indep_pred[[group_enet[i]]] <- indep_pred
   }
 
-  eval_pred_all <-
-    rbind(
-      eval_pred_all,
-      data.table(Group = group_i, eval_pred(obs = indep_pred_list[[group_i]]$obs, pred = indep_pred_list[[group_i]]$pred, family = family))
+  indep_pred_list <- c(indep_pred_list, enet_indep_pred)
+  update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
+}
+
+###################
+# Evaluate all models
+###################
+
+# Initialise progress log
+log_message <- 'Evaluating all models... '
+progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
+
+pred_eval_all <- foreach(i = 1:length(indep_pred_list), .combine=rbind) %dopar% {
+  # Update progress log
+  progress <- update_progress_file(progress_file)
+  update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(indep_pred_list)*100),'%'))
+
+  data.table(
+      Group = names(indep_pred_list)[i],
+      eval_pred(
+        obs = indep_pred_list[[i]]$obs,
+        pred = indep_pred_list[[i]]$pred,
+        family = family
+      )
     )
 }
 
+update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
 
 # Write out the results
-write.table(eval_pred_all, paste0(opt$out,'.pred_eval.txt'), col.names=T, row.names=F, quote=F)
+write.table(pred_eval_all, paste0(opt$out,'.pred_eval.txt'), col.names=T, row.names=F, quote=F)
 log_add(log_file = log_file, message = paste0('Model evaluation results saved as ',opt$out,'.pred_eval.txt.'))
 
 ###################
 # Compare predictive utiliy of the different models
 ###################
 
-comp_res_all<-NULL
-for(group1 in eval_pred_all$Group){
-	for(group2 in eval_pred_all$Group){
-			group1_r <- cor(scale(as.numeric(indep_pred_list[[group1]]$obs)), scale(indep_pred_list[[group1]]$pred))[1]
-		  group2_r <- cor(scale(as.numeric(indep_pred_list[[group2]]$obs)), scale(indep_pred_list[[group2]]$pred))[1]
-
-		  if(group1 == group2){
-			  comp_res <- data.table(
-			    Model_1 = group1,
-			    Model_2 = group2,
-			    Model_1_R = group1_r,
-			    Model_2_R = group2_r,
-			    R_diff = 0,
-			    R_diff_pval = 1
-			  )
-	    } else {
-	      r_diff <- group1_r - group2_r
-
-	      group1_group2_r <- cor(scale(indep_pred_list[[group1]]$pred), scale(indep_pred_list[[group2]]$pred))
-
-	      r_diff_p <-
-	        paired.r(
-	          xy = group1_r,
-	          xz = group2_r,
-	          yz = group1_group2_r,
-	          n = length(scale(indep_pred_list[[group1]]$pred)),
-	          twotailed = T
-	        )$p[1]
-
-	      comp_res <- data.table(
-	        Model_1 = group1,
-	        Model_2 = group2,
-	        Model_1_R = group1_r,
-	        Model_2_R = group2_r,
-	        R_diff = r_diff,
-	        R_diff_pval = r_diff_p
-	      )
-	    }
-		  comp_res_all <- rbind(comp_res_all, comp_res)
-	}
+# Initialise progress log
+log_message <- 'Comparing model performance... '
+progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
+
+# Identify number of pairwise comparisons
+models <- pred_eval_all$Group
+model_comps <- data.table(t(combn(models, 2)))
+
+comp_res_all <- foreach(i = 1:length(models), .combine=rbind) %dopar% {
+  group1 <- models[i]
+  comp_res_i <- NULL
+  for(group2 in model_comps$V2[model_comps$V1 == group1]){
+
+  	group1_r <- cor(scale(as.numeric(indep_pred_list[[group1]]$obs)), scale(indep_pred_list[[group1]]$pred))[1]
+    group2_r <- cor(scale(as.numeric(indep_pred_list[[group2]]$obs)), scale(indep_pred_list[[group2]]$pred))[1]
+
+    r_diff <- group1_r - group2_r
+
+    group1_group2_r <- cor(scale(indep_pred_list[[group1]]$pred), scale(indep_pred_list[[group2]]$pred))
+
+    r_diff_p <-
+      paired.r(
+        xy = group1_r,
+        xz = group2_r,
+        yz = group1_group2_r,
+        n = length(scale(indep_pred_list[[group1]]$pred)),
+        twotailed = T
+      )$p[1]
+
+    comp_res <- data.table(
+      Model_1 = group1,
+      Model_2 = group2,
+      Model_1_R = group1_r,
+      Model_2_R = group2_r,
+      R_diff = r_diff,
+      R_diff_pval = r_diff_p
+    )
+
+    comp_res_i <- rbind(comp_res_i, comp_res)
+  }
+
+  # Update progress log
+  progress <- update_progress_file(progress_file)
+  update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(models)*100),'%'))
+
+  comp_res_i
 }
 
+update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
+
 # Write out the results
 write.table(comp_res_all, paste0(opt$out,'.pred_comp.txt'), col.names=T, row.names=F, quote=F)
-
 log_add(log_file = log_file, message = paste0('Model evaluation results saved as ',opt$out,'.pred_comp.txt.'))
 
 end.time <- Sys.time()
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index f328ec86..2447e8f4 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -460,7 +460,7 @@ done
 ######
 
 prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[4,]
+prscsx_dat<-prscsx_dat[1:5,]
 
 gwas_list_eur<-data.frame(
   name=paste0(prscsx_dat$labels,'_UKB'),
@@ -566,12 +566,13 @@ prscsx_dat<-prscsx_dat[4,]
 # Get a list of score files
 scores <- list_score_files(config)
 
-pop <- c('EUR','EAS')
-for(disc_pop_i in pop){
+disc_pop <- c('EUR','EAS')
+targ_pop <- c('EUR','EAS','AFR','CSA','AMR')
+for(disc_pop_i in disc_pop){
   disc_samp<-ifelse(disc_pop_i == 'EUR', 'UKB', 'BBJ')
   scores_disc_pop_i <- scores[grepl(disc_samp, scores$name), ]
 
-  for(targ_pop_i in pop){
+  for(targ_pop_i in targ_pop){
     for(trait_i in prscsx_dat$labels){
       dir.create(
         paste0(
@@ -604,6 +605,29 @@ for(disc_pop_i in pop){
         group=scores_tmp$method
       )
       
+      # Extract the pseudo score for each method and specify as a seperate group
+      for(i in 1:nrow(scores_tmp)) {
+        param <- find_pseudo(
+          config = config,
+          gwas = scores_tmp$name[i],
+          pgs_method = scores_tmp$method[i],
+          target_pop = pop_i
+        )
+        
+        predictors <- fread(
+          predictors_tmp$predictor[i],
+          select = c('FID', 'IID', 
+                     paste0(scores_tmp$name[i], '_', param)))
+        
+        fwrite(predictors, 
+               gsub('.profiles', '.pseudo.profiles', predictors_tmp$predictor[i]))
+      }
+      
+      predictors_tmp2<-predictors_tmp
+      predictors_tmp2$group<-paste0(predictors_tmp$group,'.pseudo')
+      predictors_tmp2$predictor<-gsub('.profiles', '.pseudo.profiles', predictors_tmp$predictor)
+      predictors_tmp <- rbind(predictors_tmp, predictors_tmp2)
+      
       write.table(
         predictors_tmp,
         paste0(
@@ -630,7 +654,7 @@ for(disc_pop_i in pop){
 # Get a list of score files
 scores <- list_score_files(config)
 
-pop <- c('EUR','EAS')
+pop <- c('EUR','EAS','AFR','CSA','AMR')
 for(pop_i in pop){
   for(trait_i in prscsx_dat$labels){
     dir.create(
@@ -662,6 +686,28 @@ for(pop_i in pop){
       group=scores_tmp$method
     )
     
+    # Extract the pseudo score for each method and specify as a seperate group
+    for(i in 1:nrow(scores_tmp)) {
+      param <- find_pseudo(
+        config = config,
+        gwas = scores_tmp$name[i],
+        pgs_method = scores_tmp$method[i],
+        target_pop = pop_i
+      )
+      
+      predictors <- fread(
+        predictors_tmp$predictor[i],
+        select = c('FID', 'IID', 
+                   paste0(scores_tmp$name[i], '_', param)))
+      
+      fwrite(predictors, gsub('.profiles', '.pseudo.profiles', predictors_tmp$predictor[i]))
+    }
+    
+    predictors_tmp2<-predictors_tmp
+    predictors_tmp2$group<-paste0(predictors_tmp$group,'.pseudo')
+    predictors_tmp2$predictor<-gsub('.profiles', '.pseudo.profiles', predictors_tmp$predictor)
+    predictors_tmp <- rbind(predictors_tmp, predictors_tmp2)
+
     write.table(
       predictors_tmp,
       paste0(
@@ -693,7 +739,8 @@ cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
 for disc_pop in $(echo EUR EAS); do
-  for targ_pop in $(echo EUR EAS); do
+#  for targ_pop in $(echo EUR EAS ); do
+  for targ_pop in $(echo AFR CSA AMR); do
     if [ "$targ_pop" == "EUR" ]; then
         targ_pop2="EUR_test"
     else
@@ -701,13 +748,13 @@ for disc_pop in $(echo EUR EAS); do
     fi
     
     for pheno in $(head -n 4 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt | tail -n 1); do
-      sbatch --mem 20G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      sbatch --mem 50G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
         --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
         --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/single.${disc_pop}_disc.targ_${targ_pop}/${pheno}/predictor_list.txt \
         --out /users/k1806347/oliverpainfel/Analyses/crosspop/single.${disc_pop}_disc.targ_${targ_pop}/${pheno}/res \
-        --n_core 5 \
+        --n_core 10 \
         --top1 T \
-        --compare_predictors T \
+        --all_model F \
         --assoc T"
     done
   done
@@ -722,7 +769,8 @@ done
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-for targ_pop in $(echo EUR EAS); do
+#  for targ_pop in $(echo EUR EAS ); do
+for targ_pop in $(echo AFR CSA AMR); do
   if [ "$targ_pop" == "EUR" ]; then
       targ_pop2="EUR_test"
   else
@@ -730,13 +778,13 @@ for targ_pop in $(echo EUR EAS); do
   fi
 
   for pheno in $(head -n 4 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt | tail -n 1); do
-    sbatch --mem 20G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+    sbatch --mem 50G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
       --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
       --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/predictor_list.txt \
       --out /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/res \
-      --n_core 5 \
+      --n_core 10 \
       --top1 T \
-      --compare_predictors T \
+      --all_model F \
       --assoc T"
   done
 done
@@ -760,71 +808,6 @@ library(cowplot)
 prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
 prscsx_dat<-prscsx_dat[4,]
 
-#############
-# Single source
-#############
-
-plot_list<-list()
-pop=c('EUR','EAS')
-for(pheno_i in prscsx_dat$labels){
-  res_i<-NULL
-  for(disc_pop_i in pop){
-    for(targ_pop_i in pop){
-        assoc_i <-
-          fread(
-            paste0(
-              '/users/k1806347/oliverpainfel/Analyses/crosspop/single.',
-              disc_pop_i,
-              '_disc.targ_',
-              targ_pop_i,
-              '/',
-              pheno_i,
-              '/res.assoc.txt'
-            )
-          )
-        assoc_i$Method <-
-          gsub('\\..*', '', gsub('Group_', '', assoc_i$Predictor))
-        assoc_i$Predictor<-sub("^[^.]*\\.[^.]*\\.", "", assoc_i$Predictor)
-        assoc_i$gwas<-sub("^(([^_]*_)[^_]*).*", "\\1", assoc_i$Predictor)
-        assoc_i$name<-sub("^([^_]+_){2}", "", assoc_i$Predictor)
-        assoc_i$test<-paste0(disc_pop_i, ' → ', targ_pop_i)
-        
-        res_i<-rbind(res_i, assoc_i)
-    }
-  }
-  
-  res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
-  res_i$test<-factor(res_i$test, levels=unique(res_i$test))
-  res_i$name<-factor(res_i$name, levels=unique(res_i$name))
-
-  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=name, y=BETA, colour = Method)) +
-    geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
-                  width = .2,
-                  position = position_dodge(width = 0.7)) +
-    geom_point() +
-    geom_line() +
-    geom_hline(yintercept = 0) +
-    facet_grid(.~ test, scales = 'free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-}
-
-png(
-  '/users/k1806347/oliverpainfel/Analyses/crosspop/single.assoc.png',
-  res = 300,
-  height = 1200*nrow(prscsx_dat),
-  width = 4000,
-  units = 'px'
-)
-plot_grid(plotlist = plot_list, labels = names(plot_list), ncol=1)
-dev.off()
-
-#############
-# Multi source
-#############
-
 single_plot_list<-list()
 prscsx_xwing_plot_list<-list()
 tlprs_plot_list<-list()
@@ -843,75 +826,94 @@ for(pheno_i in prscsx_dat$labels){
             '/res.assoc.txt'
           )
         )
-      assoc_i$Method <-
-        gsub('\\..*', '', gsub('Group_', '', assoc_i$Predictor))
-      assoc_i$Predictor<-sub("^[^.]*\\.[^.]*\\.", "", assoc_i$Predictor)
-      assoc_i$gwas<-sub("^(([^_]*_)[^_]*).*", "\\1", assoc_i$Predictor)
-      assoc_i$name<-sub("^([^_]+_){2}", "", assoc_i$Predictor)
-      assoc_i$name[assoc_i$Method == 'prscsx']<-paste0(gsub('.*_','', assoc_i$gwas[assoc_i$Method == 'prscsx']),'_', assoc_i$name[assoc_i$Method == 'prscsx'])
-      assoc_i$disc[grepl('tlprs|prscsx|xwing', assoc_i$Method)] <-'EUR+EAS'
-      assoc_i$disc[grepl('UKB', assoc_i$gwas)] <-'EUR' 
-      assoc_i$disc[grepl('BBJ', assoc_i$gwas)] <-'EAS' 
+      
+      # Determine whether single or multi source
+      assoc_i$multi_source <- ifelse(!(grepl(paste(pgs_group_methods, collapse ='|'), assoc_i$Group)), F, T)
+      
+      # Identify GWAS for single source
+      assoc_i$gwas[assoc_i$multi_source == F] <-
+        sub("^(([^.]+\\.){1}[^.]+)\\..*", "\\1",
+            sub("^[^.]*\\.", "",
+                assoc_i$Predictor[assoc_i$multi_source == F]))
+      
+      # Identify discovery sample
+      assoc_i$disc[assoc_i$multi_source == F] <-
+        sub(".*\\.", "",
+            assoc_i$gwas[assoc_i$multi_source == F])
+      
+      assoc_i$disc[assoc_i$disc == 'UKB'] <- 'EUR'
+      assoc_i$disc[assoc_i$disc == 'BBJ'] <- 'EAS'
+      assoc_i$disc[is.na(assoc_i$disc)] <- 'EUR+EAS'
+      
+      assoc_i$pheno <- pheno_i
       assoc_i$test<-paste0(assoc_i$disc, ' → ', targ_pop_i)
+      
       assoc_i$tlprs_target<-NA
-      assoc_i$tlprs_target[grepl('tlprs', assoc_i$Method) & grepl('EUR.DBSLMM', assoc_i$name)]<-'EUR-tuned'
-      assoc_i$tlprs_target[grepl('tlprs', assoc_i$Method) & grepl('EAS.DBSLMM', assoc_i$name)]<-'EAS-tuned'
+      assoc_i$tlprs_target[grepl('tlprs', assoc_i$Group) & grepl('targ.EUR', assoc_i$Predictor)]<-'EUR-tuned'
+      assoc_i$tlprs_target[grepl('tlprs', assoc_i$Group) & grepl('targ.EAS', assoc_i$Predictor)]<-'EAS-tuned'
       
       res_i<-rbind(res_i, assoc_i)
   }
 
-    res_i$name<-factor(res_i$name, levels = unique(res_i$name))
-    res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
-    res_i$test<-factor(res_i$test, levels=unique(res_i$test))
-    
-    tmp<-res_i[res_i$Method %in% c('ptclump','dbslmm'),]
-    tmp$param<-gsub('.*UKB.','',tmp$name)
-    tmp$param<-gsub('.*BBJ.','',tmp$param)
-    tmp$param<-factor(tmp$param, levels=unique(tmp$param))
-
-    single_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=param, y=BETA, colour = Method)) +
-    geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
-                  width = .2,
-                  position = position_dodge(width = 0.7)) +
-    geom_point() +
-    geom_line() +
-    geom_hline(yintercept = 0) +
-    facet_grid(.~ test, scales = 'free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-
-    tmp<-res_i[(res_i$Method %in% c('prscsx','xwing')),]
-
-    prscsx_xwing_plot_list[[pheno_i]] <- ggplot(tmp, aes(x=name, y=BETA, colour = Method)) +
-    geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
-                  width = .2,
-                  position = position_dodge(width = 0.7)) +
-    geom_point() +
-    geom_line() +
-    geom_hline(yintercept = 0) +
-    facet_grid(.~ test, scales = 'free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-  
-    tmp<-res_i[(grepl('tlprs', res_i$Method) & grepl('DBSLMM.1.', res_i$name)),]
-    tmp$name<-as.numeric(gsub('_','',gsub('.*TLPRS\\.','',tmp$name)))
-    
-    tlprs_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=name, y=BETA, colour = Method)) +
-    geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
-                  width = .2,
-                  position = position_dodge(width = 0.7)) +
-    geom_point() +
-    geom_line() +
-    geom_hline(yintercept = 0) +
-    facet_grid(test ~ tlprs_target, scales = 'free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+  res_i$test<-factor(res_i$test, levels = unique(res_i$test))
+  res_i$disc<-factor(res_i$disc, levels = unique(res_i$disc))
+  res_i$pheno<-factor(res_i$pheno, levels = unique(res_i$pheno))
+  res_i$tlprs_target<-factor(res_i$tlprs_target, levels = unique(res_i$tlprs_target))
+
+  tmp<-res_i[!(res_i$multi_source),]
+  tmp<-tmp[!(grepl('pseudo', tmp$Group))]
+  tmp$param<-gsub('.*UKB.','',tmp$Predictor)
+  tmp$param<-gsub('.*BBJ.','',tmp$param)
+  tmp$param<-factor(tmp$param, levels=unique(tmp$param))
+
+  single_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=param, y=BETA, colour = Group)) +
+  geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
+                width = .2,
+                position = position_dodge(width = 0.7)) +
+  geom_point() +
+  geom_line() +
+  geom_hline(yintercept = 0) +
+  facet_grid(~ test, scales = 'free', space = 'free_x') +
+  theme_half_open() +
+  background_grid() + 
+  panel_border() + 
+  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+  tmp<-res_i[(res_i$Group %in% c('prscsx','xwing')),]
+  tmp<-tmp[!(grepl('pseudo', tmp$Group))]
+  tmp$param<-gsub(paste0('.*',pheno_i,'\\.'),'',tmp$Predictor)
+  tmp$param<-factor(tmp$param, levels=unique(tmp$param))
+
+  prscsx_xwing_plot_list[[pheno_i]] <- ggplot(tmp, aes(x=param, y=BETA, colour = Group)) +
+  geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
+                width = .2,
+                position = position_dodge(width = 0.7)) +
+  geom_point() +
+  geom_line() +
+  geom_hline(yintercept = 0) +
+  facet_grid(.~ test, scales = 'free', space = 'free_x') +
+  theme_half_open() +
+  background_grid() + 
+  panel_border() + 
+  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+  tmp<-res_i[(grepl('tlprs', res_i$Group)),]
+  tmp<-tmp[(grepl('SBayesR|phi.auto|DBSLMM.1.TLPRS', tmp$Predictor)),]
+  tmp<-tmp[!(grepl('pseudo', tmp$Group))]
+  tmp$param <- as.numeric(gsub('.*\\.','',tmp$Predictor))
+
+  tlprs_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=param, y=BETA, fill = Group, colour = Group)) +
+  geom_line() +
+  geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
+                width = .2,
+                position = position_dodge(width = 0.7)) +
+  geom_point(stat="identity", position=position_dodge(0.7), size=2, shape=23) +
+  geom_hline(yintercept = 0) +
+  facet_grid(test ~ tlprs_target, scales = 'free', space = 'free_x') +
+  theme_half_open() +
+  background_grid() + 
+  panel_border() + 
+  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
 }
 
 png(
@@ -938,7 +940,7 @@ png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.tlprs.assoc.png',
   res = 300,
   height = 1200*nrow(prscsx_dat),
-  width = 3000,
+  width = 4000,
   units = 'px'
 )
 plot_grid(plotlist = tlprs_plot_list, labels = names(tlprs_plot_list), ncol=1)
@@ -1006,41 +1008,15 @@ for(pheno_i in prscsx_dat$labels){
     }
   }
   
-  res_i$Method<-sub('\\..*','',res_i$Method)
+  res_i$Method<-sub('\\..*','',res_i$Group)
   
   res_i$Model[grepl('top1', res_i$Group)]<-'Top1'
-  res_i$Model[!grepl('top1|PredFile', res_i$Group)]<-'Multi'
+  res_i$Model[grepl('pseudo', res_i$Group)]<-'Pseudo'
+  res_i$Model[!grepl('top1|pseudo', res_i$Group)]<-'Multi'
   
-  res_i_pseudo<-res_i[grepl('PredFile', res_i$Group),]
-  res_i<-res_i[!grepl('PredFile', res_i$Group),]
-  
-  # Identify PGS selected by pseudovalidation
-  for(pgs_method_i in pgs_methods[
-    pgs_methods %in% res_i$Method & 
-    pgs_methods %in% c('dbslmm')]) { # Restrict to methods with pseudo method
-      res_i_tmp<-res_i_pseudo[res_i_pseudo$Method == pgs_method_i,]
-      gwas_list_i<-gwas_list[grepl(pheno_i, gwas_list$name),]
-      for(gwas_i in gwas_list_i$name){
-        gwas_i_2 <- gsub('_', '.', gwas_i)
-        res_i_tmp2 <-
-          res_i_tmp[grepl(gwas_i_2, res_i_tmp$Group), ]
-        res_i_tmp2$param<-gsub(paste0('.*', gwas_i_2, '.'), '', res_i_tmp2$Group)
-        
-        pseudo_param<-find_pseudo(config = config, 
-                    gwas = gwas_i, 
-                    pgs_method = pgs_method_i, 
-                    target_pop = targ_pop_i)
-        pseudo_param <- gsub("[[:punct:]]", ".",pseudo_param)
-        
-        res_i_tmp2<-res_i_tmp2[res_i_tmp2$param == pseudo_param,]
-        res_i_tmp2$param<-NULL
-        res_i_tmp2$Model<-'Pseudo'
-        res_i<-rbind(res_i, res_i_tmp2)
-      }
-  }
-
   res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
   res_i$Model<-factor(res_i$Model, levels=unique(res_i$Model))
+  res_i$test<-factor(res_i$test, levels=unique(res_i$test))
   
   plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=Method, y=R, fill = Model)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
@@ -1048,8 +1024,8 @@ for(pheno_i in prscsx_dat$labels){
                   position = position_dodge(width = 0.7)) +
     geom_point(stat="identity", position=position_dodge(0.7), size=2, shape=23) +
     geom_hline(yintercept = 0) +
-    geom_vline(xintercept = c(1.5,2.5), linetype="dotted") +
-    facet_grid(.~ test, scales = 'free', space = 'free_x') +
+    geom_vline(xintercept = seq(1.5, length(unique(res_i$Method))), linetype="dotted") +
+    facet_grid(.~ test) +
     theme_half_open() +
     background_grid() + 
     panel_border() + 
@@ -1060,7 +1036,7 @@ png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/single.eval.png',
   res = 300,
   height = 1200*nrow(prscsx_dat),
-  width = 3000,
+  width = 4000,
   units = 'px'
 )
 plot_grid(plotlist = plot_list, labels = names(plot_list), ncol=1)
@@ -1090,55 +1066,24 @@ for(pheno_i in prscsx_dat$labels){
       res_i<-rbind(res_i, eval_i)
   }
   
-  res_i$Method<-sub('\\..*','',res_i$Method)
+  res_i$Method<-sub('\\..*','',res_i$Group)
   
   res_i$Model[grepl('top1', res_i$Group)]<-'Top1'
-  res_i$Model[!grepl('top1|PredFile', res_i$Group)]<-'Multi'
+  res_i$Model[grepl('pseudo', res_i$Group)]<-'Pseudo'
+  res_i$Model[!grepl('top1|pseudo', res_i$Group)]<-'Multi'
+  
+  res_i <- res_i[!grepl('pseudo.top1', res_i$Group),]
   
-  res_i_pseudo<-res_i[grepl('PredFile', res_i$Group),]
-  res_i<-res_i[!grepl('PredFile', res_i$Group),]
-
-  # Identify PGS selected by pseudovalidation
-  for(pgs_method_i in pgs_methods[
-    pgs_methods %in% res_i$Method & 
-    pgs_methods %in% c('prscsx','xwing')]) { # Restrict to methods with pseudo method
-      res_i_tmp<-res_i_pseudo[res_i_pseudo$Method == pgs_method_i,]
-      gwas_groups_i<-gwas_groups[grepl(pheno_i, gwas_groups$name),]
-      
-      for(gwas_group_i in gwas_groups_i$name){
-        gwas_group_i_2 <- gsub('_', '.', gwas_group_i)
-        res_i_tmp2 <-
-          res_i_tmp[grepl(gwas_group_i_2, res_i_tmp$Group), ]
-        res_i_tmp2$param<-gsub(paste0('.*', gwas_group_i_2, '.'), '', res_i_tmp2$Group)
-        
-        for(targ_pop_i in pop){
-          pseudo_param<-find_pseudo(config = config, 
-                      gwas = gwas_group_i, 
-                      pgs_method = pgs_method_i, 
-                      target_pop = targ_pop_i)
-          pseudo_param <- gsub("[[:punct:]]", ".",pseudo_param)
-          
-          res_i_tmp3 <-
-            res_i_tmp2[res_i_tmp2$param == pseudo_param &
-                         res_i_tmp2$target == targ_pop_i, ]
-          
-          res_i_tmp3$param<-NULL
-          res_i_tmp3$Model<-'Pseudo'
-          res_i<-rbind(res_i, res_i_tmp3)
-        }
-      }
-  }
-
   res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
   res_i$Model<-factor(res_i$Model, levels=unique(res_i$Model))
-  
+
   plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=Method, y=R, fill = Model)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = .2,
                   position = position_dodge(width = 0.7)) +
     geom_point(stat="identity", position=position_dodge(0.7), size=2, shape=23) +
     geom_hline(yintercept = 0) +
-    geom_vline(xintercept = seq(1.5, 5.5, by=1), linetype="dotted") +
+    geom_vline(xintercept = seq(1.5, length(unique(res_i$Method))), linetype="dotted") +
     facet_grid(.~ target, scales = 'free', space = 'free_x') +
     theme_half_open() +
     background_grid() + 
diff --git a/functions/model_builder.R b/functions/model_builder.R
index f6c685db..1478a233 100644
--- a/functions/model_builder.R
+++ b/functions/model_builder.R
@@ -179,3 +179,77 @@ eval_pred <- function(obs, pred, family){
   }
   return(mod_sum)
 }
+
+#########
+# Function for progress bar
+#########
+
+initialise_progress <- function(log_message, log_file){
+  progress_file <- tempfile()
+  saveRDS(0, progress_file)
+  log_add(log_file = log_file, message = log_message)
+  return(progress_file)
+}
+
+update_progress_file <- function(progress_file) {
+  # Lock the file for writing (this ensures only one worker can modify it at a time)
+  lockfile <- paste0(progress_file, ".lock")
+  while (file.exists(lockfile)) {
+    Sys.sleep(0.01)  # Wait if another worker is updating the file
+  }
+
+  # Create the lockfile
+  file.create(lockfile)
+
+  # Read current progress
+  progress <- readRDS(progress_file)
+
+  # Update progress_file
+  progress <- progress + 1
+  saveRDS(progress, progress_file)
+
+  # Remove the lockfile
+  file.remove(lockfile)
+
+  return(progress)  # Return progress
+}
+
+update_log_file <- function(log_file, message) {
+  # Read the current content of the log file
+  log_content <- readLines(log_file)
+
+  # Update the last line with the new message
+  if (length(log_content) > 0) {
+    log_content[length(log_content)] <- message
+  } else {
+    log_content <- message
+  }
+
+  # Write the updated log content back to the file
+  writeLines(log_content, con = log_file)
+}
+
+export_final_model <- function(model, group, outdir){
+  if ("glm" %in% class(model)) {
+    model_coefficients <- as.matrix(coef(model))
+    rownames(model_coefficients)[1]<-'intercept'
+  }
+  if ("glmnet" %in% class(model)) {
+    model_coefficients <- coef(model, s = model$lambdaOpt)
+    rownames(model_coefficients)[1]<-'intercept'
+    model_coefficients <- as.matrix(model_coefficients[model_coefficients[,1] != 0, , drop=F])
+  }
+
+  write.table(
+    model_coefficients,
+    paste0(
+      outdir,
+      '/',
+      group,
+      '.final_model.txt'
+    ),
+    row.names = T,
+    col.names = F,
+    quote = F
+  )
+}

From 437e448a21f11f874a4fdadedaccaae75c646289 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sat, 28 Sep 2024 13:14:23 +0100
Subject: [PATCH 018/153] Added gwas_groups info to pipeline reports

---
 Scripts/pipeline_reports/indiv_report_creator.Rmd | 4 ++++
 Scripts/pipeline_reports/samp_report_creator.Rmd  | 4 ++++
 2 files changed, 8 insertions(+)

diff --git a/Scripts/pipeline_reports/indiv_report_creator.Rmd b/Scripts/pipeline_reports/indiv_report_creator.Rmd
index d81b6681..6f31e4b5 100644
--- a/Scripts/pipeline_reports/indiv_report_creator.Rmd
+++ b/Scripts/pipeline_reports/indiv_report_creator.Rmd
@@ -57,6 +57,9 @@ target_list <- read_param(config = params$config, param = 'target_list')
 # Read in gwas_list
 gwas_list <- read_param(config = params$config, param = 'gwas_list')
 
+# Read in gwas_list
+gwas_groups <- read_param(config = params$config, param = 'gwas_groups')
+
 # Read in score_list
 score_list <- read_param(config = params$config, param = 'score_list')
 
@@ -269,6 +272,7 @@ if(!pgs_incl){
 ```{r, results='asis', eval = pgs_incl}
 
 cat0("- ", ifelse(is.null(gwas_list), 0, nrow(gwas_list)), " GWAS summary statistics were provided for polygenic scoring.\n")
+cat0("- ", ifelse(is.null(gwas_groups), 0, nrow(gwas_groups)), " GWAS groups were specified.\n")
 cat0("- ", length(pgs_methods_list), " PGS methods were applied, including ", paste0(pgs_method_labels$label[pgs_method_labels$method %in% pgs_methods_list], collapse = ', '), ".\n")
 
 if(any(gwas_list$population != 'EUR') & any(c('ldpred2','sbayesr') %in% pgs_methods_list)){
diff --git a/Scripts/pipeline_reports/samp_report_creator.Rmd b/Scripts/pipeline_reports/samp_report_creator.Rmd
index 468ae232..d5ef38f3 100644
--- a/Scripts/pipeline_reports/samp_report_creator.Rmd
+++ b/Scripts/pipeline_reports/samp_report_creator.Rmd
@@ -53,6 +53,9 @@ target_list <- read_param(config = params$config, param = 'target_list')
 # Read in gwas_list
 gwas_list <- read_param(config = params$config, param = 'gwas_list')
 
+# Read in gwas_list
+gwas_groups <- read_param(config = params$config, param = 'gwas_groups')
+
 # Read in score_list
 score_list <- read_param(config = params$config, param = 'score_list')
 
@@ -175,6 +178,7 @@ cat0("*** \n\n")
 cat0("# Polygenic Scores \n\n")
 
 cat0("- ", ifelse(is.null(gwas_list), 0, nrow(gwas_list)), " GWAS summary statistics were provided for polygenic scoring.\n")
+cat0("- ", ifelse(is.null(gwas_groups), 0, nrow(gwas_groups)), " GWAS groups were specified.\n")
 cat0("- ", length(pgs_methods_list), " PGS methods were applied, including ", paste0(pgs_methods_list, collapse = ', '), ".\n")
 
 if(any(gwas_list$population != 'EUR') & any(c('ldpred2','sbayesr') %in% pgs_methods_list)){

From f03f454644e5a9166f74fc19a69c7fdb8806388b Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sat, 28 Sep 2024 13:15:00 +0100
Subject: [PATCH 019/153] Updated docs to describe using multi-source PGS
 methods

---
 docs/pipeline_readme.Rmd     |  71 +++++++++++-
 docs/pipeline_readme.html    | 106 +++++++++++++++--
 docs/pipeline_technical.Rmd  |  55 ++++++++-
 docs/pipeline_technical.html | 218 +++++++++++++++++++++++++++++++++--
 4 files changed, 425 insertions(+), 25 deletions(-)

diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 671eaf04..1e5dc865 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -214,6 +214,12 @@ The pipeline is configured using a configfile, which tells the pipeline what to
 </div>
 </div>
 
+<div class="note-box">
+
+**Note:** The pipeline also implements a range of multi-source polygenic scoring methods, that can combine GWAS summary statistics from multiple populations. To use these methods an additional `gwas_groups` file must be provided. See [here](#multi-source-pgs-methods) for more information.
+
+</div>
+
 ***
 
 ## `configfile`
@@ -251,7 +257,7 @@ config <- list(
     gwas_list = list(
       description = "Path to `gwas_list` file, listing GWAS sumstats",
       example = '`example_input/gwas_list.txt`',
-      note = "Set to NA if you don't want to include and GWAS sumstats"
+      note = "Set to NA if you don't want to include any GWAS sumstats"
     ),
     score_list = list(
       description = "Path to `score_list` file, listing external score files",
@@ -266,7 +272,7 @@ config <- list(
     pgs_methods = list(
       description = 'List of polygenic scoring methods to run',
       example = "`['ptclump','dbslmm']`",
-      note = "Options are: `ptclump`, `dbslmm`, `prscs`, `sbayesr`, `lassosum`, `ldpred2`, `megaprs`. **Note.** `sbayesr` and `ldpred2` are only implemented for GWAS of EUR ancestry."
+      note = "Options are: `ptclump`, `dbslmm`, `prscs`, `sbayesr`, `lassosum`, `ldpred2`, `megaprs`, `prscsx`, `xwing`, `tlprs`. **Note.** `sbayesr` and `ldpred2` are only implemented for GWAS of EUR ancestry. **Note.** when using multi-source methods (`prscsx`, `xwing`, `tlprs`), the `gwas_groups` parameter must also be provided. See [here](#multi-source-pgs-methods) for more information."
     ),
     testing = list(
       description = 'Controls testing mode',
@@ -813,7 +819,7 @@ The GenoPred pipeline has many potential outputs. Here is a detailed schematic d
 
 <div class="centered-container">
 <div class="rounded-image-container">
-<img src="Images/pipeline_readme/pipeline_schematic_lowdef.png">
+<img src="Images/pipeline_readme/pipeline_schematic_groups_lowdef.png">
 </div>
 </div>
 
@@ -1335,6 +1341,65 @@ By default, the pipeline allocates 10 cores when imputing 23andMe target dataset
 
 By default, the pipeline allocates 5 cores when running the outlier_detection rule (estimating relatedness and within sample PCs). The number of cores can be altered using the  `cores_outlier_detection` parameters in the config file.
 
+***
+
+## Multi-source PGS methods
+
+The pipeline also implements a range of multi-source polygenic scoring methods, that can combine GWAS summary statistics from multiple populations. To use these methods an additional `gwas_groups` file must be provided, indicating which GWAS are to be jointly analysed. The `gwas_groups` file must be specified in the `configfile` using the `gwas_groups` parameter. An example of a gwas_groups file can be found [here](../pipeline/example_input/gwas_groups.txt). 
+
+<details>
+
+<summary>View gwas_groups format</summary>
+
+```{r, eval = T, echo = F, results = 'asis'}
+
+gwas_groups <- list(
+  Column = list(
+    name = list(
+      example = '`height`',
+      description = "ID for the group of GWAS. Cannot contain spaces (' ') or hyphens ('-')"
+    ),
+    gwas = list(
+      example = '`yengo_eur,yengo_eas`',
+      description = "Comma-seperated list of GWAS names, corresponding to the `gwas_list`."
+    ),
+    label = list(
+      example = '`\"Height (EUR+EAS)\"`',
+      description = "A human readable name for the group of GWAS Wrap in double quotes if multiple words."
+    )
+  )
+)
+
+gwas_groups_df <- NULL
+for (column in names(gwas_groups$Column)) {
+  description <- gwas_groups$Column[[column]]$description
+  example <- gwas_groups$Column[[column]]$example
+
+  # Append each parameter's details to the data frame
+  gwas_groups_df <- rbind(
+    gwas_groups_df,
+    data.frame(
+      Column = column,
+      Example = example,
+      Description = description,
+      stringsAsFactors = FALSE
+    )
+  )
+}
+
+kable(gwas_groups_df, 'markdown')
+
+```
+
+</details>
+
+<div class="note-box">
+
+**Note:** when specifying `tlprs` in the `pgs_methods` list in the `configfile`, you must also specify an additional `tlprs_methods` parameter in the `configfile`, which indicates the PGS methods that TL-PRS will be applied to. For example: `tlprs_methods: ['ptclump','dbslmm']`.
+
+</div>
+
+
 ***
 
 # Running in offline environment
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index 55263a7d..450df7d1 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -635,6 +635,13 @@ <h1>Pipeline configuration</h1>
 <p><img src="Images/pipeline_readme/input_schematic_wide.png"></p>
 </div>
 </div>
+<div class="note-box">
+<p><strong>Note:</strong> The pipeline also implements a range of
+multi-source polygenic scoring methods, that can combine GWAS summary
+statistics from multiple populations. To use these methods an additional
+<code>gwas_groups</code> file must be provided. See <a
+href="#multi-source-pgs-methods">here</a> for more information.</p>
+</div>
 <hr />
 <div id="configfile" class="section level2">
 <h2><code>configfile</code></h2>
@@ -659,10 +666,10 @@ <h2><code>configfile</code></h2>
 </summary>
 <table>
 <colgroup>
-<col width="4%" />
-<col width="19%" />
-<col width="11%" />
-<col width="63%" />
+<col width="3%" />
+<col width="12%" />
+<col width="7%" />
+<col width="77%" />
 </colgroup>
 <thead>
 <tr class="header">
@@ -700,7 +707,7 @@ <h2><code>configfile</code></h2>
 <td align="left">Path to <code>gwas_list</code> file, listing GWAS
 sumstats</td>
 <td align="left"><code>example_input/gwas_list.txt</code></td>
-<td align="left">Set to NA if you don’t want to include and GWAS
+<td align="left">Set to NA if you don’t want to include any GWAS
 sumstats</td>
 </tr>
 <tr class="odd">
@@ -725,9 +732,13 @@ <h2><code>configfile</code></h2>
 <td align="left"><code>['ptclump','dbslmm']</code></td>
 <td align="left">Options are: <code>ptclump</code>, <code>dbslmm</code>,
 <code>prscs</code>, <code>sbayesr</code>, <code>lassosum</code>,
-<code>ldpred2</code>, <code>megaprs</code>. <strong>Note.</strong>
+<code>ldpred2</code>, <code>megaprs</code>, <code>prscsx</code>,
+<code>xwing</code>, <code>tlprs</code>. <strong>Note.</strong>
 <code>sbayesr</code> and <code>ldpred2</code> are only implemented for
-GWAS of EUR ancestry.</td>
+GWAS of EUR ancestry. <strong>Note.</strong> when using multi-source
+methods (<code>prscsx</code>, <code>xwing</code>, <code>tlprs</code>),
+the <code>gwas_groups</code> parameter must also be provided. See <a
+href="#multi-source-pgs-methods">here</a> for more information.</td>
 </tr>
 <tr class="even">
 <td align="left"><code>testing</code></td>
@@ -1493,7 +1504,7 @@ <h1>Requesting outputs</h1>
 GenoPred pipeline.</p>
 <div class="centered-container">
 <div class="rounded-image-container">
-<p><img src="Images/pipeline_readme/pipeline_schematic_lowdef.png"></p>
+<p><img src="Images/pipeline_readme/pipeline_schematic_groups_lowdef.png"></p>
 </div>
 </div>
 <div class="note-box">
@@ -1913,6 +1924,8 @@ <h2><code>find_pseudo</code></h2>
 parameters.</li>
 <li><code>gwas</code>: A single GWAS identifier.</li>
 <li><code>pgs_method</code>: A single PGS method identifier.</li>
+<li><code>target_pop</code>: Target population parameter required when
+using multi-ancestry PGS method.</li>
 </ul></li>
 <li><strong>Returns</strong>
 <ul>
@@ -1922,6 +1935,16 @@ <h2><code>find_pseudo</code></h2>
 <ul>
 <li><code>ptclump</code> has no pseudovalidation approach, so this
 function will return the PGS based on a p-value threshold of 1.</li>
+<li><code>prscsx</code> returns meta-analysed PGS weights for each
+population. The target_pop parameter has no effect on the output.</li>
+<li><code>tlprs</code> has no pseudovalidation approach, so the
+functionreturns the pseudovalidated score for the PGS method used as
+input to <code>tlprs</code>, using strongest reweighting for the target
+population.</li>
+<li><code>xwing</code> and <code>tlprs</code> do not have a
+pseudovalidation approach when using <code>target_pop = 'TRANS'</code>,
+so this function returns the result for a <code>EUR</code> target
+population.</li>
 </ul></li>
 </ul>
 <details>
@@ -1931,7 +1954,13 @@ <h2><code>find_pseudo</code></h2>
 <pre class="r"><code>pseudo_param &lt;-
   find_pseudo(config = &#39;example_input/config.yaml&#39;,
               gwas = &#39;COAD01&#39;,
-              pgs_method = &#39;ptclump&#39;)</code></pre>
+              pgs_method = &#39;ptclump&#39;)
+
+pseudo_param &lt;-
+  find_pseudo(config = &#39;example_input/config.yaml&#39;,
+              gwas = &#39;COAD01&#39;,
+              pgs_method = &#39;prscsx&#39;,
+              target_pop = &#39;EUR&#39;)</code></pre>
 </details>
 <hr />
 </div>
@@ -2096,6 +2125,65 @@ <h2>Control computational resources</h2>
 <code>cores_outlier_detection</code> parameters in the config file.</p>
 <hr />
 </div>
+<div id="multi-source-pgs-methods" class="section level2">
+<h2>Multi-source PGS methods</h2>
+<p>The pipeline also implements a range of multi-source polygenic
+scoring methods, that can combine GWAS summary statistics from multiple
+populations. To use these methods an additional <code>gwas_groups</code>
+file must be provided, indicating which GWAS are to be jointly analysed.
+The <code>gwas_groups</code> file must be specified in the
+<code>configfile</code> using the <code>gwas_groups</code> parameter. An
+example of a gwas_groups file can be found <a
+href="../pipeline/example_input/gwas_groups.txt">here</a>.</p>
+<details>
+<summary>
+View gwas_groups format
+</summary>
+<table>
+<colgroup>
+<col width="6%" />
+<col width="19%" />
+<col width="74%" />
+</colgroup>
+<thead>
+<tr class="header">
+<th align="left">Column</th>
+<th align="left">Example</th>
+<th align="left">Description</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">name</td>
+<td align="left"><code>height</code></td>
+<td align="left">ID for the group of GWAS. Cannot contain spaces (’ ‘)
+or hyphens (’-’)</td>
+</tr>
+<tr class="even">
+<td align="left">gwas</td>
+<td align="left"><code>yengo_eur,yengo_eas</code></td>
+<td align="left">Comma-seperated list of GWAS names, corresponding to
+the <code>gwas_list</code>.</td>
+</tr>
+<tr class="odd">
+<td align="left">label</td>
+<td align="left"><code>"Height (EUR+EAS)"</code></td>
+<td align="left">A human readable name for the group of GWAS Wrap in
+double quotes if multiple words.</td>
+</tr>
+</tbody>
+</table>
+</details>
+<div class="note-box">
+<p><strong>Note:</strong> when specifying <code>tlprs</code> in the
+<code>pgs_methods</code> list in the <code>configfile</code>, you must
+also specify an additional <code>tlprs_methods</code> parameter in the
+<code>configfile</code>, which indicates the PGS methods that TL-PRS
+will be applied to. For example:
+<code>tlprs_methods: ['ptclump','dbslmm']</code>.</p>
+</div>
+<hr />
+</div>
 </div>
 <div id="running-in-offline-environment" class="section level1">
 <h1>Running in offline environment</h1>
diff --git a/docs/pipeline_technical.Rmd b/docs/pipeline_technical.Rmd
index 7aca331b..146ee287 100644
--- a/docs/pipeline_technical.Rmd
+++ b/docs/pipeline_technical.Rmd
@@ -118,14 +118,14 @@ The GenoPred pipeline integrates various polygenic scoring methods for adjusting
 
 ### Single Ancestry Methods 
 
-The GenoPred pipeline currently only implements PGS methods intended for GWAS based on a single ancestral population. When using GWAS based on mixed ancestry individuals, we suggest specifying the population that matches most individuals in the GWAS. 
+The GenoPred pipeline implements a range of PGS methods intended for GWAS based on a single ancestral population. When using GWAS based on mixed ancestry individuals, we suggest specifying the population that matches most individuals in the GWAS. See [here](#multi-ancestry-methods) for methods that leverage multiple GWAS from different ancestral populations. 
 
 ```{r, eval = T, echo = F}
 
 methods_table <- data.frame(
   Method = c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "SBayesR"),
   Software = c("DBSLMM", "lassosum R package", "bigsnpr R package", "LDAK", "PRS-CS", "PLINK", "GCTB"),
-  PubMedID = c(32330416, "28480976", "33326037", "34234142", "30992449", "25722852", "31704910"),
+  PubMedID = c("32330416", "28480976", "33326037", "34234142", "30992449", "25722852", "31704910"),
   PseudoValidationOption = c("Yes (only option)", "Yes", "Yes", "Yes", "Yes", "No", "Yes (only option)"),
   Parameters = c("SNP-heritability estimated using LD Score Regression (on liability scale for binary outcomes)",
                  "s = 0.2, 0.5, 0.9, 1; lambda = exp(seq(log(0.001), log(0.1), length.out=20))",
@@ -179,7 +179,7 @@ MegaPRS uses a range of priors (lasso, ridge, bolt, BayesR) for SNP effects, run
 
 #### PRS-CS
 
-PRS-CS, a Bayesian method using a continuous shrinkage prior, specifies a range of global shrinkage parameters (phi), generating multiple sets of genetic effects for polygenic scoring. Its 'auto' model estimates the optimal parameter directly from GWAS summary statistics, negating the need for an external dataset. In GenoPred, PRS-CS is run using the script [pgs_methods/prscs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscs.R). By default, GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the auto model, but the user can modify this behaviour using the prscs_phi parameter in the configfile. By default, GenoPred uses the PRS-CS provided 1KG-derived LD matrix data, matching the population of the GWAS sample. The user can select the UKB-derived LD matrix data to be used using the `prscs_ldref` parameter in the `configfile`. 1KG is used by default as PGS based on Yengo et al. sumstats performed significantly better in the OpenSNP target sample, when using the 1KG reference data (this may differ for other GWAS).
+PRS-CS, a Bayesian method using a continuous shrinkage prior, specifies a range of global shrinkage parameters (phi), generating multiple sets of genetic effects for polygenic scoring. Its 'auto' model estimates the optimal parameter directly from GWAS summary statistics, negating the need for an external dataset. In GenoPred, PRS-CS is run using the script [pgs_methods/prscs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscs.R). GenoPred clones PRS-CS from GitHub (621fdc80daac56c93d9528eb1a1187f7b1fc9afb) and uses the provided precompiled PRS-CS binary to estimate SNP effect sizes. By default, GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the auto model, but the user can modify this behaviour using the prscs_phi parameter in the configfile. By default, GenoPred uses the PRS-CS provided 1KG-derived LD matrix data, matching the population of the GWAS sample. The user can select the UKB-derived LD matrix data to be used using the `prscs_ldref` parameter in the `configfile`. 1KG is used by default as PGS based on Yengo et al. sumstats performed significantly better in the OpenSNP target sample, when using the 1KG reference data (this may differ for other GWAS).
 
 ***
 
@@ -195,6 +195,55 @@ In GenoPred, SBayesR is run using the script [pgs_methods/sbayesr.R](https://git
 
 ***
 
+### Multi-ancestry Methods 
+
+The GenoPred pipeline implements a range of PGS methods intended for multiple GWAS from different ancestral populations. 
+
+```{r, eval = T, echo = F}
+
+methods_table <- data.frame(
+  Method = c("PRS-CSx", "X-Wing", "TL-PRS"),
+  Software = c("PRS-CSx", "LOGODetect, PANTHER, LEOPARD", "TLPRS R package"),
+  PubMedID = c("35513724", "36788230", "36240765"),
+  PseudoValidationOption = c("Yes", "Yes", "No"),
+  Parameters = c("phi = 1e-6, 1e-4, 1e-2, 1, auto",
+                 "phi = auto",
+                 "Block learning rates = 1, 10, 100, 1000. Iterations (k) = "),
+  MHCRegion = c("Not excluded", "Not excluded", "Dependant on the PGS method used to generate the score file input to TL-PRS"),
+  LDReference = c("Population-matched UKB (PRS-CS provided)", "Population-matched UKB (PRS-CS provided)", "Population-matched 1KG+HGDP"),
+  CPUUsage = c(NA),
+  MemoryUsage = c(NA)
+)
+
+names(methods_table) <- c('Method','Software','PubMed ID', 'Pseudovalidation Option','Parameters','MHC Region','LD Reference','CPU usage*','Memory Usage*')
+
+kable(methods_table, "html", caption = "Table 2 provides a summary of each approach and its implementation in GenoPred.") %>%
+  kable_styling(bootstrap_options = c("striped", "hover"),
+                full_width = F) %>%
+  scroll_box(width = "100%", height = "500px")
+
+```
+
+***
+
+### PRS-CSx
+
+PRS-CSx, an extension of PRS-CS, integrates GWAS summary statistics from multiple ancestry groups using a Bayesian framework. The method is implemented in GenoPred via the script [pgs_methods/prscsx.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscsx.R). We use summary statistics from the populations most closely matching the ancestry of the GWAS data to estimate posterior effect sizes. The LD information is derived from population-matched reference panels (e.g., 1KG). GenoPred clones PRS-CSx from GitHub (29a1148875f6ae3f2594b25579f40d4b587c5691) and uses the provided precompiled PRS-CSx binary to estimate SNP effect sizes. By default, GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the auto model, but the user can modify this behaviour using the prscs_phi parameter in the configfile. By default, GenoPred uses the PRS-CS provided 1KG-derived LD matrix data, matching the population of the GWAS sample. The user can select the UKB-derived LD matrix data to be used using the `prscs_ldref` parameter in the `configfile`. 1KG is used by default as PGS based on Yengo et al. sumstats performed significantly better in the OpenSNP target sample, when using the 1KG reference data (this may differ for other GWAS).
+
+***
+
+### X-Wing
+
+X-Wing is a polygenic scoring method designed to enhance genetic prediction accuracy across ancestrally diverse populations by leveraging a cross-population weighting framework. The method operates through a combination of several software tools: LOGODetect, PANTHER, and LEOPARD. GenoPred integrates this method using the script [pgs_methods/xwing.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/xwing.R). GenoPred downloads X-WING software from GitHub (e9fcc264266e0e884323311816bfe20053fd3f7a). The LD information derived from the 1KG dataset is provided by the developers of X-Wing. 
+
+***
+
+### TL-PRS
+
+TL-PRS (Transfer Learning Polygenic Risk Score) is implemented using the script [pgs_methods/tlprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/tlprs.R). The method combines summary statistics from target GWAS and a reference dataset via transfer learning, iteratively updating SNP effect sizes. GenoPred uses a modified version of TL-PRS to improve efficiency when being applied to many score files (https://github.com/opain/TLPRS). No modifications to the parameters used has been altered from the original version of TL-PRS (https://github.com/ZhangchenZhao/TLPRS/commit/6c8d8905f91ce902f0cca8d5465f9952b0b3e274). Multiple learning rates (factors of 1, 10, 100, 1000) are tested in an iterative approach with 15 steps per factor to update the SNP effect sizes. GenoPred uses the 1KG+HGDP reference for LD estimation, matching the population of the GWAS sample.
+
+***
+
 ### Pseudovalidation
 
 Several polygenic scoring methods use a range of 'tuning' parameters when adjusting the GWAS effect sizes for polygenic scoring, resulting in multiple polygenic scores from a given GWAS. Traditionally, the user would then evaluate the predictive utility of polygenic scores derived using these various parameters, to select the best performing polygenic score, using cross-validation or bootstrapping to estimating the effect of the polygenic score without overfitting. However, several methods offer a pseudovalidation approach, whereby the optimal tuning parameter is estimated based on the GWAS summary statistics alone, convieniently avoiding the need for a tuning sample. The performance of pseudovalidation relative to formal validation varies between methods. See our previous [polygenic scoring methods comparison](comparison_of_methods_summary.html) for more information. 
diff --git a/docs/pipeline_technical.html b/docs/pipeline_technical.html
index d78d2211..5da61b4a 100644
--- a/docs/pipeline_technical.html
+++ b/docs/pipeline_technical.html
@@ -605,10 +605,12 @@ <h2>Adjustment of GWAS effect sizes</h2>
 <hr />
 <div id="single-ancestry-methods" class="section level3">
 <h3>Single Ancestry Methods</h3>
-<p>The GenoPred pipeline currently only implements PGS methods intended
-for GWAS based on a single ancestral population. When using GWAS based
-on mixed ancestry individuals, we suggest specifying the population that
-matches most individuals in the GWAS.</p>
+<p>The GenoPred pipeline implements a range of PGS methods intended for
+GWAS based on a single ancestral population. When using GWAS based on
+mixed ancestry individuals, we suggest specifying the population that
+matches most individuals in the GWAS. See <a
+href="#multi-ancestry-methods">here</a> for methods that leverage
+multiple GWAS from different ancestral populations.</p>
 <div
 style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:500px; overflow-x: scroll; width:100%; ">
 <table class="table table-striped table-hover" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
@@ -936,12 +938,15 @@ <h4>PRS-CS</h4>
 negating the need for an external dataset. In GenoPred, PRS-CS is run
 using the script <a
 href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscs.R">pgs_methods/prscs.R</a>.
-By default, GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1)
-and the auto model, but the user can modify this behaviour using the
-prscs_phi parameter in the configfile. By default, GenoPred uses the
-PRS-CS provided 1KG-derived LD matrix data, matching the population of
-the GWAS sample. The user can select the UKB-derived LD matrix data to
-be used using the <code>prscs_ldref</code> parameter in the
+GenoPred clones PRS-CS from GitHub
+(621fdc80daac56c93d9528eb1a1187f7b1fc9afb) and uses the provided
+precompiled PRS-CS binary to estimate SNP effect sizes. By default,
+GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the
+auto model, but the user can modify this behaviour using the prscs_phi
+parameter in the configfile. By default, GenoPred uses the PRS-CS
+provided 1KG-derived LD matrix data, matching the population of the GWAS
+sample. The user can select the UKB-derived LD matrix data to be used
+using the <code>prscs_ldref</code> parameter in the
 <code>configfile</code>. 1KG is used by default as PGS based on Yengo et
 al. sumstats performed significantly better in the OpenSNP target
 sample, when using the 1KG reference data (this may differ for other
@@ -974,6 +979,199 @@ <h4>SBayesR</h4>
 <hr />
 </div>
 </div>
+<div id="multi-ancestry-methods" class="section level3">
+<h3>Multi-ancestry Methods</h3>
+<p>The GenoPred pipeline implements a range of PGS methods intended for
+multiple GWAS from different ancestral populations.</p>
+<div
+style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:500px; overflow-x: scroll; width:100%; ">
+<table class="table table-striped table-hover" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
+<caption>
+Table 2 provides a summary of each approach and its implementation in
+GenoPred.
+</caption>
+<thead>
+<tr>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Method
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Software
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+PubMed ID
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Pseudovalidation Option
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Parameters
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+MHC Region
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+LD Reference
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+CPU usage*
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Memory Usage*
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+PRS-CSx
+</td>
+<td style="text-align:left;">
+PRS-CSx
+</td>
+<td style="text-align:left;">
+35513724
+</td>
+<td style="text-align:left;">
+Yes
+</td>
+<td style="text-align:left;">
+phi = 1e-6, 1e-4, 1e-2, 1, auto
+</td>
+<td style="text-align:left;">
+Not excluded
+</td>
+<td style="text-align:left;">
+Population-matched UKB (PRS-CS provided)
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+NA
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+X-Wing
+</td>
+<td style="text-align:left;">
+LOGODetect, PANTHER, LEOPARD
+</td>
+<td style="text-align:left;">
+36788230
+</td>
+<td style="text-align:left;">
+Yes
+</td>
+<td style="text-align:left;">
+phi = auto
+</td>
+<td style="text-align:left;">
+Not excluded
+</td>
+<td style="text-align:left;">
+Population-matched UKB (PRS-CS provided)
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+NA
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+TL-PRS
+</td>
+<td style="text-align:left;">
+TLPRS R package
+</td>
+<td style="text-align:left;">
+36240765
+</td>
+<td style="text-align:left;">
+No
+</td>
+<td style="text-align:left;">
+Block learning rates = 1, 10, 100, 1000. Iterations (k) =
+</td>
+<td style="text-align:left;">
+Dependant on the PGS method used to generate the score file input to
+TL-PRS
+</td>
+<td style="text-align:left;">
+Population-matched 1KG+HGDP
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+NA
+</td>
+</tr>
+</tbody>
+</table>
+</div>
+<hr />
+</div>
+<div id="prs-csx" class="section level3">
+<h3>PRS-CSx</h3>
+<p>PRS-CSx, an extension of PRS-CS, integrates GWAS summary statistics
+from multiple ancestry groups using a Bayesian framework. The method is
+implemented in GenoPred via the script <a
+href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscsx.R">pgs_methods/prscsx.R</a>.
+We use summary statistics from the populations most closely matching the
+ancestry of the GWAS data to estimate posterior effect sizes. The LD
+information is derived from population-matched reference panels (e.g.,
+1KG). GenoPred clones PRS-CSx from GitHub
+(29a1148875f6ae3f2594b25579f40d4b587c5691) and uses the provided
+precompiled PRS-CSx binary to estimate SNP effect sizes. By default,
+GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the
+auto model, but the user can modify this behaviour using the prscs_phi
+parameter in the configfile. By default, GenoPred uses the PRS-CS
+provided 1KG-derived LD matrix data, matching the population of the GWAS
+sample. The user can select the UKB-derived LD matrix data to be used
+using the <code>prscs_ldref</code> parameter in the
+<code>configfile</code>. 1KG is used by default as PGS based on Yengo et
+al. sumstats performed significantly better in the OpenSNP target
+sample, when using the 1KG reference data (this may differ for other
+GWAS).</p>
+<hr />
+</div>
+<div id="x-wing" class="section level3">
+<h3>X-Wing</h3>
+<p>X-Wing is a polygenic scoring method designed to enhance genetic
+prediction accuracy across ancestrally diverse populations by leveraging
+a cross-population weighting framework. The method operates through a
+combination of several software tools: LOGODetect, PANTHER, and LEOPARD.
+GenoPred integrates this method using the script <a
+href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/xwing.R">pgs_methods/xwing.R</a>.
+GenoPred downloads X-WING software from GitHub
+(e9fcc264266e0e884323311816bfe20053fd3f7a). The LD information derived
+from the 1KG dataset is provided by the developers of X-Wing.</p>
+<hr />
+</div>
+<div id="tl-prs" class="section level3">
+<h3>TL-PRS</h3>
+<p>TL-PRS (Transfer Learning Polygenic Risk Score) is implemented using
+the script <a
+href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/tlprs.R">pgs_methods/tlprs.R</a>.
+The method combines summary statistics from target GWAS and a reference
+dataset via transfer learning, iteratively updating SNP effect sizes.
+GenoPred uses a modified version of TL-PRS to improve efficiency when
+being applied to many score files (<a
+href="https://github.com/opain/TLPRS"
+class="uri">https://github.com/opain/TLPRS</a>). No modifications to the
+parameters used has been altered from the original version of TL-PRS (<a
+href="https://github.com/ZhangchenZhao/TLPRS/commit/6c8d8905f91ce902f0cca8d5465f9952b0b3e274"
+class="uri">https://github.com/ZhangchenZhao/TLPRS/commit/6c8d8905f91ce902f0cca8d5465f9952b0b3e274</a>).
+Multiple learning rates (factors of 1, 10, 100, 1000) are tested in an
+iterative approach with 15 steps per factor to update the SNP effect
+sizes. GenoPred uses the 1KG+HGDP reference for LD estimation, matching
+the population of the GWAS sample.</p>
+<hr />
+</div>
 <div id="pseudovalidation" class="section level3">
 <h3>Pseudovalidation</h3>
 <p>Several polygenic scoring methods use a range of ‘tuning’ parameters

From 0a40e38d5afca14d6dd3098d12fc83f940cac884 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sat, 28 Sep 2024 13:15:34 +0100
Subject: [PATCH 020/153] Updating docs

---
 .../pipeline_schematic_groups_lowdef.png       | Bin 0 -> 320065 bytes
 pipeline/example_input/gwas_groups.txt         |   2 ++
 2 files changed, 2 insertions(+)
 create mode 100644 docs/Images/pipeline_readme/pipeline_schematic_groups_lowdef.png
 create mode 100644 pipeline/example_input/gwas_groups.txt

diff --git a/docs/Images/pipeline_readme/pipeline_schematic_groups_lowdef.png b/docs/Images/pipeline_readme/pipeline_schematic_groups_lowdef.png
new file mode 100644
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literal 0
HcmV?d00001

diff --git a/pipeline/example_input/gwas_groups.txt b/pipeline/example_input/gwas_groups.txt
new file mode 100644
index 00000000..213fc7ff
--- /dev/null
+++ b/pipeline/example_input/gwas_groups.txt
@@ -0,0 +1,2 @@
+name gwas label
+height yengo_eur,yengo_eas "Height (EUR+EAS)"

From 0cab25e1e0c566a7fa87ea8ff228ac40ac9a23ae Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 30 Sep 2024 08:02:22 +0100
Subject: [PATCH 021/153] Updates for cross pop analysis

---
 Scripts/model_builder/model_builder.R |   2 +-
 docs/CrossPop.Rmd                     | 155 ++++++++++++++++++++++++--
 docs/pipeline_readme.Rmd              |   6 +-
 docs/pipeline_readme.html             |  14 ++-
 functions/pipeline.R                  |  36 ++++--
 5 files changed, 182 insertions(+), 31 deletions(-)

diff --git a/Scripts/model_builder/model_builder.R b/Scripts/model_builder/model_builder.R
index 90898f69..3bead43b 100644
--- a/Scripts/model_builder/model_builder.R
+++ b/Scripts/model_builder/model_builder.R
@@ -71,7 +71,7 @@ tmp_dir <- tempdir()
 
 # Create directory for final models to be saved
 if(opt$export_models){
-  system(paste0('mkdir ', opt$output_dir, '/final_models'))
+  system(paste0('mkdir -p ', opt$output_dir, '/final_models'))
 }
 
 # Initiate log file
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 2447e8f4..b02075f7 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -391,6 +391,8 @@ done
 
 # Download relevant BBJ sumstats
 
+<details><summary>Show code</summary>
+
 ```{r}
 # Identify wget command for relevant phenotypes
 library(data.table)
@@ -446,14 +448,129 @@ for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx
 sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/pigz -dc ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz | awk 'BEGIN {OFS=\"\t\"} {print \$2, \$3, \$4, \$6, \$7, \$8, \$9, \$12, \$13, \$15}' | sed '1s/P_BOLT_LMM_INF/P/' | /users/k1806347/oliverpainfel/Software/pigz -c > ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.reformat.txt.gz"
 done
 
+```
+</details>
+
+***
+
+# Download relevant UGR sumstats
+
+<details><summary>Show code</summary>
+
+```{r}
+# Identify wget command for relevant phenotypes
+library(data.table)
+
+# Read in UGR GWAS info from GWAS catalogue
+ugr_gwas<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats.csv')
+
+# Map UGR trait names to those used for UKB
+ugr_gwas$trait<-gsub(' levels','', ugr_gwas$reportedTrait)
+ugr_gwas$trait<-gsub(' count','', ugr_gwas$trait)
+
+ugr_to_prscsx <- c(
+  "Aspartate aminotransferase" = "Aspartate transaminase",
+  "Bilirubin" = NA,  # No direct match
+  "Eosinophils" = "Eosinophil",
+  "Gamma glutamyl transferase" = "γ-glutamyl transpeptidase",
+  "HDL cholesterol" = "HDL-cholesterol",
+  "Hemoglobin A1c" = "HbA1c",
+  "Hip circumference" = NA,  # No direct match
+  "LDL cholesterol" = "LDL-cholesterol",
+  "Red cell distribution width" = NA,  # No direct match
+  "Serum albumin" = "Albumin",
+  "Serum alkaline phosphatase" = "Alkaline phosphatase",
+  "Systolic blood pressure" = "Sytolic blood pressure",
+  "Triglyceride" = "Triglycerides",
+  "Waist circumference" = NA,  # No direct match
+  "Waist-hip ratio" = NA,  # No direct match
+  "Weight" = "Body weight"
+)
+
+ugr_gwas$trait <- ifelse(ugr_gwas$trait %in% names(ugr_to_prscsx),
+                                   ugr_to_prscsx[ugr_gwas$trait],
+                                   ugr_gwas$trait)
+
+# Merge the ugr trait info with the prscsx trait info
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat <- merge(ugr_gwas, prscsx_dat, by='trait')
+
+write.csv(prscsx_dat, '~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv', row.names = F)
+
+# Create column indicating wget command
+for(i in 1:nrow(prscsx_dat)){
+  log<-system(paste0('curl ', gsub('http:','ftp:', prscsx_dat$summaryStatistics[i]), '/'), intern = T)
+  log<-log[grepl('.txt.gz', log)]
+  log<-gsub('.* ','', log)
+  prscsx_dat$wget[i]<-paste0(prscsx_dat$summaryStatistics[i], '/', log)
+}
+
+# Write a table showing label matching prscsx info and wget url
+write.table(prscsx_dat[, c('labels', 'wget'), with=F], '~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt', col.names = F, row.names = F, quote = F)
+
+```
+
+```{bash}
+# wget and unzip sumstats
+for pheno in $(head -n 5 ~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt | cut -d' ' -f 1); do
+  url=$(awk -v var="$pheno" '$1 == var {print $2}' ~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt)
+  sbatch -p cpu --wrap="wget -O ~/oliverpainfel/Data/GWAS_sumstats/UGR/${pheno}.txt.gz ${url}"
+done
+
+```
+
+```{r}
+library(data.table)
+ugr_data<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv')[1:5,]
+
+for(i in 1:nrow(ugr_data)){
+  sumstats <- fread(paste0("~/oliverpainfel/Data/GWAS_sumstats/UGR/", ugr_data$label[i], ".txt.gz"))
+  sumstats <- sumstats[, names(sumstats) %in% c("snpid", "pval_fe", "se_fe") | grepl('^beta_|^af_|^no_', names(sumstats)), with=F]
+
+  # Extract CHR, BP, A1, A2 from snpid
+  snp_split <- tstrsplit(sumstats$snpid, ":", fixed = TRUE)
+  sumstats[, `:=`(CHR = snp_split[[1]], BP = snp_split[[2]], A1 = snp_split[[3]], A2 = snp_split[[4]])]
+
+  # Set no_ and af_ to NA if beta is NA
+  cohorts <- gsub('^no_','', names(sumstats)[grepl('^no_', names(sumstats))])
+  for (cohort in cohorts) {
+    sumstats[[paste0('no_', cohort)]][is.na(sumstats[[paste0('beta_', cohort)]])] <- NA
+    sumstats[[paste0('af_', cohort)]][is.na(sumstats[[paste0('beta_', cohort)]])] <- NA
+  }
+
+  # Calculate sample size weighted average for allele frequency
+  for (cohort in cohorts) {
+    sumstats[[paste0('af_', cohort, '_weighted')]] <- sumstats[[paste0('af_', cohort)]] * sumstats[[paste0('no_', cohort)]]
+  }
+  
+  # Calculate total N and frequency
+  sumstats[, N := rowSums(.SD, na.rm = TRUE), .SDcols = patterns("^no_")]
+  sumstats[, FREQ := rowSums(.SD, na.rm = TRUE) / N, .SDcols = patterns("weighted$")]
+
+  # Rename columns
+  setnames(sumstats, old = c('beta_fe', 'se_fe', 'pval_fe'), new = c('BETA', 'SE', 'P'))
+
+  # Select relevant columns and remove rows with missing data
+  sumstats <- sumstats[, .(CHR, BP, A1, A2, BETA, SE, P, FREQ, N)]
+  sumstats <- sumstats[complete.cases(sumstats)]
+  
+  fwrite(sumstats, paste0("~/oliverpainfel/Data/GWAS_sumstats/UGR/", ugr_data$label[i], ".reformat.txt.gz"), sep=' ', quote=F, na='NA')
+  
+}
+
 ```
 
+</details>
+
+
 ***
 
 # Run GenoPred
 
 ## Prepare configuration
 
+<details><summary>Show code</summary>
+
 ```{r}
 ######
 # gwas_list
@@ -524,6 +641,7 @@ config<-c(
 write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
 
 ```
+</details>
 
 ***
 
@@ -543,6 +661,8 @@ Lets use the model builder script which implements nested 10 fold cross validati
 
 ## Create predictor lists
 
+<details><summary>Show code</summary>
+
 ```{r}
 
 setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -725,11 +845,14 @@ for(pop_i in pop){
 }
 
 ```
+</details>
 
 ***
 
 ## Run model_builder
 
+<details><summary>Show code</summary>
+
 ```{bash}
 ###########
 # Single source
@@ -778,11 +901,11 @@ for targ_pop in $(echo AFR CSA AMR); do
   fi
 
   for pheno in $(head -n 4 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt | tail -n 1); do
-    sbatch --mem 50G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+    sbatch --mem 20G -n 5 -p cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
       --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
       --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/predictor_list.txt \
       --out /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/res \
-      --n_core 10 \
+      --n_core 5 \
       --top1 T \
       --all_model F \
       --assoc T"
@@ -790,6 +913,7 @@ for targ_pop in $(echo AFR CSA AMR); do
 done
 
 ```
+</details>
 
 ***
 
@@ -799,6 +923,8 @@ done
 
 Let's see how results vary across parameters. This is a sanity check as well as being informative of how I should structure the models for each method.
 
+<details><summary>Show code</summary>
+
 ```{r}
 library(data.table)
 library(ggplot2)
@@ -811,7 +937,7 @@ prscsx_dat<-prscsx_dat[4,]
 single_plot_list<-list()
 prscsx_xwing_plot_list<-list()
 tlprs_plot_list<-list()
-pop=c('EUR','EAS')
+pop=c('EUR','EAS','AFR','CSA','AMR')
 for(pheno_i in prscsx_dat$labels){
   res_i<-NULL
   for(targ_pop_i in pop){
@@ -920,7 +1046,7 @@ png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/single.assoc.png',
   res = 300,
   height = 1000*nrow(prscsx_dat),
-  width = 4000,
+  width = 8000,
   units = 'px'
 )
 plot_grid(plotlist = single_plot_list, labels = names(single_plot_list), ncol=1)
@@ -930,7 +1056,7 @@ png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.prscsx_xwing.assoc.png',
   res = 300,
   height = 1000*nrow(prscsx_dat),
-  width = 4000,
+  width = 8000,
   units = 'px'
 )
 plot_grid(plotlist = prscsx_xwing_plot_list, labels = names(prscsx_xwing_plot_list), ncol=1)
@@ -939,7 +1065,7 @@ dev.off()
 png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.tlprs.assoc.png',
   res = 300,
-  height = 1200*nrow(prscsx_dat),
+  height = 3500*nrow(prscsx_dat),
   width = 4000,
   units = 'px'
 )
@@ -947,6 +1073,7 @@ plot_grid(plotlist = tlprs_plot_list, labels = names(tlprs_plot_list), ncol=1)
 dev.off()
 
 ```
+</details>
 
 - PRS-CSx say to model the EUR, EAS and META output (for each phi)
 - x-wing produce output for each target population
@@ -956,6 +1083,8 @@ dev.off()
 
 ### Prediction modelling results
 
+<details><summary>Show code</summary>
+
 ```{r}
 setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 
@@ -986,11 +1115,12 @@ gwas_groups<-read_param(config = config, param = 'gwas_groups')
 #############
 
 plot_list<-list()
-pop=c('EUR','EAS')
+disc_pop=c('EUR','EAS')
+targ_pop=c('EUR','EAS','AFR','CSA','AMR')
 for(pheno_i in prscsx_dat$labels){
   res_i<-NULL
-  for(disc_pop_i in pop){
-    for(targ_pop_i in pop){
+  for(disc_pop_i in disc_pop){
+    for(targ_pop_i in targ_pop){
         eval_i <-
           fread(
             paste0(
@@ -1036,7 +1166,7 @@ png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/single.eval.png',
   res = 300,
   height = 1200*nrow(prscsx_dat),
-  width = 4000,
+  width = 5000,
   units = 'px'
 )
 plot_grid(plotlist = plot_list, labels = names(plot_list), ncol=1)
@@ -1047,7 +1177,7 @@ dev.off()
 #############
 
 plot_list<-list()
-pop=c('EUR','EAS')
+pop=c('EUR','EAS','AFR','CSA','AMR')
 for(pheno_i in prscsx_dat$labels){
   res_i<-NULL
   for(targ_pop_i in pop){
@@ -1095,11 +1225,12 @@ png(
   '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.eval.png',
   res = 300,
   height = 1200*nrow(prscsx_dat),
-  width = 3000,
+  width = 5000,
   units = 'px'
 )
 plot_grid(plotlist = plot_list, labels = names(plot_list), ncol=1)
 dev.off()
 
 ```
+</details>
 
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 1e5dc865..8b651f0a 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -1206,9 +1206,9 @@ Determines the pseudovalidation parameter for a given GWAS and PGS method. See [
   - A string representing the pseudovalidation parameter.
 - **Note**
   - `ptclump` has no pseudovalidation approach, so this function will return the PGS based on a p-value threshold of 1.
-  - `prscsx` returns meta-analysed PGS weights for each population. The target_pop parameter has no effect on the output.
-  - `tlprs` has no pseudovalidation approach, so the functionreturns the pseudovalidated score for the PGS method used as input to `tlprs`, using strongest reweighting for the target population.
-  - `xwing` and `tlprs` do not have a pseudovalidation approach when using `target_pop = 'TRANS'`, so this function returns the result for a `EUR` target population. 
+  - `prscsx` returns meta-analysed PGS weights across populations. The target_pop parameter has no effect on the output.
+  - `tlprs` has no pseudovalidation approach, so the function returns the pseudovalidated score for the PGS method used as input to `tlprs`, using strongest reweighting for the target population.
+  - `xwing` and `tlprs` do not have a pseudovalidation approach when using `target_pop = 'TRANS'`, so this function returns the result for a `EUR` target population. The function also returns the result for a `EUR` target population when the gwas_group did not contain a GWAS from the `target_pop`.
 
 <details>
 <summary>See usage</summary>
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index 450df7d1..e7e026a5 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -1935,16 +1935,18 @@ <h2><code>find_pseudo</code></h2>
 <ul>
 <li><code>ptclump</code> has no pseudovalidation approach, so this
 function will return the PGS based on a p-value threshold of 1.</li>
-<li><code>prscsx</code> returns meta-analysed PGS weights for each
-population. The target_pop parameter has no effect on the output.</li>
-<li><code>tlprs</code> has no pseudovalidation approach, so the
-functionreturns the pseudovalidated score for the PGS method used as
-input to <code>tlprs</code>, using strongest reweighting for the target
+<li><code>prscsx</code> returns meta-analysed PGS weights across
+populations. The target_pop parameter has no effect on the output.</li>
+<li><code>tlprs</code> has no pseudovalidation approach, so the function
+returns the pseudovalidated score for the PGS method used as input to
+<code>tlprs</code>, using strongest reweighting for the target
 population.</li>
 <li><code>xwing</code> and <code>tlprs</code> do not have a
 pseudovalidation approach when using <code>target_pop = 'TRANS'</code>,
 so this function returns the result for a <code>EUR</code> target
-population.</li>
+population. The function also returns the result for a <code>EUR</code>
+target population when the gwas_group did not contain a GWAS from the
+<code>target_pop</code>.</li>
 </ul></li>
 </ul>
 <details>
diff --git a/functions/pipeline.R b/functions/pipeline.R
index e70826dd..34160ef3 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -85,10 +85,10 @@ read_param <- function(config, param, return_obj = T){
     config_file <- read_yaml('config.yaml')
 
     if(all(names(config_file) != param)){
-      cat(param, 'parameter is not present in user specified config file or default config file.')
+      cat(param, 'parameter is not present in user specified config file or default config file.\n')
       return(NULL)
     } else {
-      cat(param, 'parameter is not present in user specified config file, so will use value in default config file.')
+      cat(param, 'parameter is not present in user specified config file, so will use value in default config file.\n')
     }
   }
 
@@ -174,6 +174,17 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
     stop('target_pop must be specified when using multi-ancestry PGS method')
   }
 
+  # Read in gwas_list
+  gwas_list <- read_param(config = config, param = 'gwas_list')
+
+  # Read in gwas_groups
+  gwas_groups <- read_param(config = config, param = 'gwas_groups')
+
+  # If pgs_method is multi-source, subset gwas_list to gwas in relevant group
+  if(pgs_method %in% pgs_group_methods){
+    gwas_list <- gwas_list[gwas_list$name %in% unlist(strsplit(gwas_groups$gwas[gwas_groups$name == gwas], ','))]
+  }
+
   # Identify score files
   score_file_list <- list_score_files(config)
 
@@ -247,18 +258,25 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
   }
   if(pgs_method == 'xwing'){
     if(!is.null(target_pop) && target_pop == 'TRANS'){
-      cat('No pseudovalidation for TRANS target population available for xwing.')
-      cat('Returning result for EUR target population.')
-      pseudo_val <- 'targ_EUR_weighted'
-    } else {
-      pseudo_val <- paste0('targ_', target_pop, '_weighted')
+      cat('No pseudovalidation for TRANS target population available for xwing.\n')
+      cat('Returning result for EUR target population.\n')
+      target_pop <- 'EUR'
+    } else if(!is.null(target_pop) && !(target_pop %in% gwas_list$population)){
+      cat(paste0('target_pop ', target_pop,' is not present in gwas_group ', gwas, '.\n'))
+      cat('Returning result for EUR target population.\n')
+      target_pop <- 'EUR'
     }
+    pseudo_val <- paste0('targ_', target_pop, '_weighted')
   }
 
   if(tlprs){
     if(!is.null(target_pop) && target_pop == 'TRANS'){
-      cat('No pseudovalidation for TRANS target population available for tlprs')
-      cat('Returning result for EUR target population.')
+      cat('No pseudovalidation for TRANS target population available for xwing.\n')
+      cat('Returning result for EUR target population.\n')
+      target_pop <- 'EUR'
+    } else if(!is.null(target_pop) && !(target_pop %in% gwas_list$population)){
+      cat(paste0('target_pop ', target_pop,' is not present in gwas_group ', gwas, '.\n'))
+      cat('Returning result for EUR target population.\n')
       target_pop <- 'EUR'
     }
     pseudo_val <- paste0('targ_', target_pop, '_', pseudo_val, '_TLPRS_61')

From 896c92b5bd1f211efbeac39496b960170781f82c Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 30 Sep 2024 12:22:28 +0100
Subject: [PATCH 022/153] Updated target scoring to run sequentially for each
 chromosome to avoid potentially large intermediate files

---
 .../target_scoring/target_scoring_pipeline.R  | 138 +++++++++++-------
 functions/plink.R                             |   2 +-
 2 files changed, 85 insertions(+), 55 deletions(-)

diff --git a/Scripts/target_scoring/target_scoring_pipeline.R b/Scripts/target_scoring/target_scoring_pipeline.R
index d6a272ce..5e5bb22d 100644
--- a/Scripts/target_scoring/target_scoring_pipeline.R
+++ b/Scripts/target_scoring/target_scoring_pipeline.R
@@ -88,37 +88,96 @@ if(!is.null(opt$score)){
   score_files <- score_files[score_files$name == opt$score,]
 }
 
-#####
-# Combine score files
-#####
+# Read in target_list
+target_list <- read_param(config = opt$config, param = 'target_list', return_obj = T)
+
+# Set params for plink_score
+opt$target_plink_chr <- paste0(outdir, '/', opt$name, '/geno/', opt$name, '.ref.chr')
+opt$target_keep <- paste0(outdir, '/', opt$name, '/ancestry/keep_files/model_based/', opt$population, '.keep')
+refdir <- read_param(config = opt$config, param = 'refdir', return_obj = F)
+opt$ref_freq_chr <- paste0(refdir, '/freq_files/', opt$population,'/ref.', opt$population,'.chr')
 
-log_add(log_file = log_file, message = paste0('Processing ', nrow(score_files),' score files.'))
+# Read in reference SNP data
+ref <- read_pvar(paste0(refdir, '/ref.chr'), chr = CHROMS)[, c('CHR','SNP','A1','A2'), with=F]
+
+# We will process score files and perform target scoring for one chromosome for efficiency
+for(chr_i in CHROMS){
+  log_add(log_file = log_file, message = '########################')
+  log_add(log_file = log_file, message = paste0('Processing chromosome ', chr_i,':'))
+
+  #####
+  # Combine score files
+  #####
+  # Create row number index to subset score files by chromosome
+  row_index <- format(which(ref$CHR == chr_i) + 1, scientific = FALSE)
+  write.table(row_index, paste0(tmp_dir,'/row_index.txt'), row.names=F, quote=F, col.names = F)
+
+  # Create file containing SNP, A1, and A2 information for each chromosome
+  fwrite(ref[ref$CHR == chr_i, c('SNP','A1','A2'), with=F], paste0(tmp_dir,'/map.txt'), row.names=F, quote=F, sep=' ')
+
+  # Extract process score files for each name (gwas/score) in parallel
+  foreach(i = 1:nrow(score_files), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+    system(paste0(
+      'zcat ', outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],".score.gz | ",
+      'awk \'NR==FNR {rows[$1]; next} FNR==1 || FNR in rows\' ', paste0(tmp_dir,'/row_index.txt'), ' - | ',  # Corrected to retain the header and process indexed rows
+      "cut -d' ' --complement -f1-3 | ",  # Keep relevant columns, remove first 3
+      "sed '1 s/SCORE/", paste0(score_files$method[i], '.', score_files$name[i]), "/g' > ",  # Replace SCORE in the header
+      tmp_dir, '/tmp_score.', paste0(score_files$method[i], '.', score_files$name[i]), '.txt'
+    ))
+  }
 
-# Extract SNP A1 and A2 information
-system(paste0('zcat ', outdir, '/reference/pgs_score_files/', score_files$method[1],'/', score_files$name[1],'/ref-',score_files$name[1],".score.gz | cut -d' ' -f1-3 - > ", tmp_dir,'/map.txt'))
+  # Paste files together in batches
+  # Set number of batches according to the number of score files to combine
+  num_batches <- max(c(1, min(c(opt$n_cores, floor(nrow(score_files) / 2)))))
+  tmp_score_files <- paste0(tmp_dir,'/tmp_score.',score_files$method,'.',score_files$name,'.txt')
+  set.seed(1)
+  batches <- split(sample(tmp_score_files), rep(1:num_batches, length.out = length(tmp_score_files)))
+  log_add(log_file = log_file, message = paste0('Aggregating score files in ', num_batches,' batches.'))
+  foreach(i = 1:length(batches), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+    system(paste0("paste -d ' ' ", paste(batches[[i]], collapse = " "),' > ',tmp_dir,'/tmp_batch_',i))
+    system(paste0('rm ', paste(batches[[i]], collapse = " ")))
+  }
 
-# Extract process score files for each name (gwas/score) in parallel
-foreach(i = 1:nrow(score_files), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
-  system(paste0('zcat ', outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],".score.gz | cut -d' ' --complement -f1-3 - | sed '1 s/SCORE/",paste0(score_files$method[i],'.',score_files$name[i]),"/g' > ", tmp_dir,'/tmp_score.',paste0(score_files$method[i],'.',score_files$name[i]),'.txt'))
-}
+  # Paste batches together
+  log_add(log_file = log_file, message = paste0('Aggregating batched score files.'))
+  tmp_batch_files <- paste0(tmp_dir,'/tmp_batch_',1:length(batches))
+  system(paste0("paste -d ' ' ", tmp_dir,'/map.txt ', paste(tmp_batch_files, collapse = " "), ' > ', tmp_dir, '/all_score.txt'))
+  system(paste0('rm ', paste(tmp_batch_files, collapse = " ")))
+
+  # Perform polygenic risk scoring
+  scores_i <-
+    plink_score(
+      pfile = opt$target_plink_chr,
+      chr = chr_i,
+      plink2 = opt$plink2,
+      score = paste0(tmp_dir,'/all_score.txt'),
+      keep = opt$target_keep,
+      frq = opt$ref_freq_chr,
+      threads = opt$n_cores
+    )
+
+  # Sum scores across chromosomes
+  if(chr_i == CHROMS[1]){
+    scores_ids <- scores_i[, 1:2, with = F]
+    current_scores <- as.matrix(scores_i[, -1:-2, with = FALSE])
+    scores <- current_scores
+  } else {
+    current_scores <- as.matrix(scores_i[, -1:-2, with = FALSE])
+    scores <- scores + current_scores
+  }
 
-# Paste files together in batches
-# Set number of batches according to the number of score files to combine
-num_batches <- max(c(1, min(c(opt$n_cores, floor(nrow(score_files) / 2)))))
-tmp_score_files <- paste0(tmp_dir,'/tmp_score.',score_files$method,'.',score_files$name,'.txt')
-set.seed(1)
-batches <- split(sample(tmp_score_files), rep(1:num_batches, length.out = length(tmp_score_files)))
-log_add(log_file = log_file, message = paste0('Aggregating score files in ', num_batches,' batches.'))
-foreach(i = 1:length(batches), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
-  system(paste0("paste -d ' ' ", paste(batches[[i]], collapse = " "),' > ',tmp_dir,'/tmp_batch_',i))
-  system(paste0('rm ', paste(batches[[i]], collapse = " ")))
+  system(paste0('rm ', tmp_dir, '/all_score.txt'))
+  system(paste0('rm ', tmp_dir, '/row_index.txt'))
+  system(paste0('rm ', tmp_dir, '/map.txt'))
 }
 
-# Paste batches together
-log_add(log_file = log_file, message = paste0('Aggregating batched score files.'))
-tmp_batch_files <- paste0(tmp_dir,'/tmp_batch_',1:length(batches))
-system(paste0("paste -d ' ' ", tmp_dir,'/map.txt ', paste(tmp_batch_files, collapse = " "), ' > ', tmp_dir, '/all_score.txt'))
-system(paste0('rm ', paste(tmp_batch_files, collapse = " ")))
+# Combine score with IDs
+scores<-data.table(scores_ids,
+                   scores)
+
+###
+# Scale the polygenic scores based on the reference
+###
 
 # Read in scale file and update Param
 log_add(log_file = log_file, message = paste0('Reading in scale files.'))
@@ -131,35 +190,6 @@ for(i in 1:nrow(score_files)){
 # Concatenate scale files
 all_scale<-do.call(rbind, scale_files)
 
-#####
-# Perform polygenic risk scoring
-#####
-
-# Read in target_list
-target_list <- read_param(config = opt$config, param = 'target_list', return_obj = T)
-
-# Set params for plink_score
-opt$target_plink_chr <- paste0(outdir, '/', opt$name, '/geno/', opt$name, '.ref.chr')
-opt$target_keep <- paste0(outdir, '/', opt$name, '/ancestry/keep_files/model_based/', opt$population, '.keep')
-refdir <- read_param(config = opt$config, param = 'refdir', return_obj = F)
-opt$ref_freq_chr <- paste0(refdir, '/freq_files/', opt$population,'/ref.', opt$population,'.chr')
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in the target sample.')
-scores <-
-  plink_score(
-    pfile = opt$target_plink_chr,
-    chr = CHROMS,
-    plink2 = opt$plink2,
-    score = paste0(tmp_dir,'/all_score.txt'),
-    keep = opt$target_keep,
-    frq = opt$ref_freq_chr,
-    threads = opt$n_cores
-  )
-
-###
-# Scale the polygenic scores based on the reference
-###
-
 log_add(log_file = log_file, message = 'Scaling target polygenic scores to the reference.')
 scores<-score_scale(score=scores, ref_scale=all_scale)
 
diff --git a/functions/plink.R b/functions/plink.R
index 87057064..e41a186e 100644
--- a/functions/plink.R
+++ b/functions/plink.R
@@ -455,7 +455,7 @@ plink_score<-function(bfile=NULL, pfile=NULL, score, keep=NULL, extract=NULL, ch
     cmd<-paste0(plink_opt,'--chr ',chr_i,' --out ',tmp_folder,'/profiles.chr',chr_i,' --threads ',threads)
     cmd <- gsub('CHROMOSOME_NUMBER', chr_i, cmd)
     exit_status <- system(cmd, intern=FALSE)
-    if (exit_status == 2) {
+    if (exit_status != 0) {
       stop()
     }
 

From 91db7081ffa1d423f1e6508f7f57d383cc62224f Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 30 Sep 2024 12:23:28 +0100
Subject: [PATCH 023/153] Restricting sig fig in score files to avoid
 uneccessary precision and large file sizes

---
 Scripts/pgs_methods/lassosum.R | 3 +++
 Scripts/pgs_methods/ldpred2.R  | 3 +++
 Scripts/pgs_methods/tlprs.R    | 3 +++
 Scripts/pgs_methods/xwing.R    | 3 +++
 4 files changed, 12 insertions(+)

diff --git a/Scripts/pgs_methods/lassosum.R b/Scripts/pgs_methods/lassosum.R
index 4c3b312e..cb19e145 100644
--- a/Scripts/pgs_methods/lassosum.R
+++ b/Scripts/pgs_methods/lassosum.R
@@ -156,6 +156,9 @@ for(i in 1:length(out$s)){
 ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
 score_new <- map_score(ref = ref, score = score_file)
 
+# Reduce number of significant figures to save space
+score_new[, (4:ncol(score_new)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_new)]
+
 fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
 
 if(file.exists(paste0(opt$output,'.score.gz'))){
diff --git a/Scripts/pgs_methods/ldpred2.R b/Scripts/pgs_methods/ldpred2.R
index c5629796..fd93624a 100644
--- a/Scripts/pgs_methods/ldpred2.R
+++ b/Scripts/pgs_methods/ldpred2.R
@@ -316,6 +316,9 @@ names(betas)[-1:-3] <- paste0('SCORE_', names(betas)[-1:-3])
 ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
 score_new <- map_score(ref = ref, score = betas)
 
+# Reduce number of significant figures to save space
+score_new[, (4:ncol(score_new)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_new)]
+
 fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
 
 if(file.exists(paste0(opt$output,'.score.gz'))){
diff --git a/Scripts/pgs_methods/tlprs.R b/Scripts/pgs_methods/tlprs.R
index bd2dd592..22b0caf5 100644
--- a/Scripts/pgs_methods/tlprs.R
+++ b/Scripts/pgs_methods/tlprs.R
@@ -229,6 +229,9 @@ for(i in 1:length(populations)){
 
 tl_betas_all<-Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE, sort = F), tl_betas_list)
 
+# Reduce number of significant figures to save space
+tl_betas_all[, (4:ncol(tl_betas_all)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(tl_betas_all)]
+
 fwrite(tl_betas_all, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
 
 if(file.exists(paste0(opt$output,'.score.gz'))){
diff --git a/Scripts/pgs_methods/xwing.R b/Scripts/pgs_methods/xwing.R
index 31892bfb..ef7bb98d 100644
--- a/Scripts/pgs_methods/xwing.R
+++ b/Scripts/pgs_methods/xwing.R
@@ -297,6 +297,9 @@ for(targ_pop in populations){
 ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
 score_new <- map_score(ref = ref, score = score_all)
 
+# Reduce number of significant figures to save space
+score_new[, (4:ncol(score_new)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_new)]
+
 fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
 
 if(file.exists(paste0(opt$output,'.score.gz'))){

From 1d9953874f92a617fdddee31adb32dfe60ad8416 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 2 Oct 2024 14:22:54 +0100
Subject: [PATCH 024/153] Added code to create reference data for LDpred2.
 Added functionality to specify alternative reference data for LDpred2, so it
 can be applied to non-EUR GWAS

---
 Scripts/pgs_methods/ldpred2.R   |   7 +-
 docs/prep_ldpred2_ldmat.Rmd     | 221 ++++++++++++++++++++++++++++++++
 functions/constants.R           |   2 +-
 pipeline/config.yaml            |   3 +
 pipeline/rules/dependencies.smk |  53 ++++++--
 pipeline/rules/pgs_methods.smk  |  20 +--
 6 files changed, 276 insertions(+), 30 deletions(-)
 create mode 100644 docs/prep_ldpred2_ldmat.Rmd

diff --git a/Scripts/pgs_methods/ldpred2.R b/Scripts/pgs_methods/ldpred2.R
index fd93624a..f540864f 100644
--- a/Scripts/pgs_methods/ldpred2.R
+++ b/Scripts/pgs_methods/ldpred2.R
@@ -8,8 +8,6 @@ option_list = list(
       help="Path to per chromosome reference PLINK files [required]"),
   make_option("--ldpred2_ref_dir", action="store", default=NULL, type='character',
       help="Path to directory containing LDpred2 reference data [required]"),
-  make_option("--ref_keep", action="store", default=NULL, type='character',
-      help="Keep file to subset individuals in reference for auto model [optional]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
       help="File containing the population code and location of the keep file [required]"),
   make_option("--plink2", action="store", default='plink2', type='character',
@@ -124,7 +122,8 @@ if(!is.na(opt$test)){
 
 # Harmonise with the LDpred2 reference
 map<-readRDS(paste0(opt$ldpred2_ref_dir, '/map.rds'))
-map<-map[, c('chr', 'pos', 'a0', 'a1', 'af_UKBB', 'ld')]
+names(map)[names(map) == 'af_UKBB']<-'af'
+map<-map[, c('chr', 'pos', 'a0', 'a1', 'af', 'ld')]
 info_snp <- snp_match(sumstats, map)
 
 #####
@@ -132,7 +131,7 @@ info_snp <- snp_match(sumstats, map)
 #####
 
 # Remove SDss < 0.5 * SDval or SDss > 0.1 + SDval or SDss < 0.1 or SDval < 0.05
-sd_val <- with(info_snp, sqrt(2 * af_UKBB * (1 - af_UKBB)))
+sd_val <- with(info_snp, sqrt(2 * af * (1 - af)))
 
 if(opt$binary == F){
   sd_y_est = median(sd_val * info_snp$beta_se * sqrt(info_snp$n_eff))
diff --git a/docs/prep_ldpred2_ldmat.Rmd b/docs/prep_ldpred2_ldmat.Rmd
new file mode 100644
index 00000000..963ba682
--- /dev/null
+++ b/docs/prep_ldpred2_ldmat.Rmd
@@ -0,0 +1,221 @@
+---
+title: Preparing LDpred2 LD matrices
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+Here we will calculate LD matrices for LDpred2 using HapMap3 SNPs in the 1KG super populations. This is to allow fair comparison across methods for a cross-population polygenic scoring study. 
+
+The code will follow that used to generate the LD matrices for EUR, in this paper ([link](https://doi.org/10.1016/j.xhgg.2022.100136)).
+
+```{bash}
+# Subset 1KG into super populations and merge across chromosomes
+for pop in $(echo EUR EAS AFR); do
+  mkdir -p /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}
+
+  # Create mergelist
+  rm -f /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/mergelist.txt
+  for chr in $(seq 1 22); do
+    echo ~/oliverpainfel/Data/1KG/GenoPred/v2.0.0/ref.chr${chr} >> /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/mergelist.txt
+  done
+  
+  ~/oliverpainfel/Software/plink1.9/plink \
+    --merge-list /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/mergelist.txt \
+    --make-bed \
+    --keep /users/k1806347/oliverpainfel/Data/1KG/GenoPred/v2.0.0/keep_files/${pop}.keep \
+    --out /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/ref.${pop}.GW
+done
+```
+
+```{r}
+library(bigsnpr)
+library(bigreadr)
+
+# Create object for genetic data in ldpred2 format, and impute missing values
+for(pop in c('EUR', 'EAS', 'AFR')){
+  # Read in reference data
+  snp_readBed(
+    paste0(
+      '/users/k1806347/oliverpainfel/Data/1KG/ldpred2/',
+      pop,
+      '/ref.',
+      pop,
+      '.GW.bed'
+    )
+  )
+  
+  # Attach the ref object in R session
+  ref <-
+    snp_attach(
+      paste0(
+        '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+        pop,
+        '/ref.',
+        pop,
+        '.GW.rds'
+      )
+    )
+  G <- ref$genotypes
+  NCORES <- nb_cores()
+  
+  #### Impute missing values (bigsnpr can't handle missing data in most functions)
+  G_imp <- snp_fastImputeSimple(G, method = "mean2", ncores = NCORES)
+
+  # Save imputed reference
+  ref$genotypes<-G_imp
+  saveRDS(ref, paste0(
+        '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+        pop,
+        '/ref.',
+        pop,
+        '.GW.rds'
+      ))
+}
+
+################
+# Now calculate LD matrices following code from https://doi.org/10.1016/j.xhgg.2022.100136
+
+library(dplyr)
+library(bigreadr)
+library(bigsnpr)
+
+for(pop in c('EAS', 'AFR')){
+  obj.1000G <- snp_attach(paste0(
+          '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+          pop,
+          '/ref.',
+          pop,
+          '.GW.rds'
+        ))
+  G <- obj.1000G$genotypes
+  CHR <- obj.1000G$map$chromosome
+  POS <- obj.1000G$map$physical.pos
+  POS2 <- snp_asGeneticPos(CHR, POS, dir ='/users/k1806347/oliverpainfel/Data/Genetic_Map/CEU', ncores = NCORES) # Note this is not appropriate but I think Florian did the same as used distance from UKB
+  MAF <- snp_MAF(G, ncores = NCORES)
+  keep <- (MAF > 0.02)
+
+  all_final_grp <- NULL
+  for(chr in 1:22){
+    ind.chr2 <- which(CHR == chr & keep)
+
+    corr0 <- runonce::save_run({
+      snp_cor(
+        G,
+        ind.col = ind.chr2,
+        infos.pos = POS2[ind.chr2],
+        size = 3 / 1000,
+        ncores = NCORES
+      )
+    }, file = paste0(
+      '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+      pop,
+      '/LD_chr',
+      chr,
+      ".rds"
+    ))
+    
+    # find nearly independent LD blocks
+    m <- length(ind.chr2)
+    (SEQ <- round(seq_log(m / 30, m / 5, length.out = 20)))
+    splits <- snp_ldsplit(corr0, thr_r2 = 0.05, min_size = 50, max_size = SEQ, max_r2 = 0.15)
+    splits$cost2 <- sapply(splits$all_size, function(sizes) sum(sizes^2))
+  
+    best_split <- splits %>%
+      arrange(cost2 * sqrt(5 + cost)) %>%
+      print() %>%
+      slice(1) %>%
+      print()
+  
+    (all_size <- best_split$all_size[[1]])
+    best_grp <- rep(seq_along(all_size), all_size)
+  
+    runonce::save_run({
+      corr0T <- as(corr0, "dgTMatrix")
+      corr0T@x <-
+        ifelse(best_grp[corr0T@i + 1L] == best_grp[corr0T@j + 1L], corr0T@x, 0)
+      as(Matrix::drop0(corr0T), "symmetricMatrix")
+    }, file = paste0(
+      '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+      pop,
+      '/LD_with_blocks_chr',
+      chr,
+      ".rds"
+    ))
+  
+    # return
+    all_final_grp <- rbind(all_final_grp, tibble(best_split, ind = list(ind.chr2)))
+  }
+  saveRDS(
+    all_final_grp,
+    paste0(
+      '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+      pop,
+      '/all_final_grp.rds'
+    )
+  )
+  
+  ###
+  # Create map.rds
+  ###
+  map <- obj.1000G$map[keep,]
+
+  # Compute allele frequency
+  map$af<-big_colstats(G, ind.col = which(keep), ncores = NCORES)$sum / (2 * nrow(G))
+  
+  # Compute LD scores
+  map$ld <- do.call('c', lapply(1:22, function(chr) {
+    cat(chr, ".. ", sep = "")
+    corr_chr <- readRDS(
+      paste0(
+        '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+        pop,
+        '/LD_chr',
+        chr,
+        ".rds"
+      )
+    )
+    Matrix::colSums(corr_chr ^ 2)
+  }))
+  
+  map <- map[, c('chromosome','physical.pos','allele2','allele1','marker.ID','af','ld')]
+  names(map) <- c('chr','pos','a0','a1','rsid','af','ld')
+  
+  saveRDS(
+    map,
+    paste0(
+      '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+      pop,
+      '/map.rds'
+    )
+  )
+}
+
+
+```
+
+
+
diff --git a/functions/constants.R b/functions/constants.R
index 1d7abba1..d7c34f32 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -43,7 +43,7 @@ pgs_method_labels <- data.frame(
 pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),]
 
 # Make vector indicating pgs_methods that can be applied to non-european GWAS
-pgs_methods_noneur <- c('ptclump','lassosum','megaprs','prscs','dbslmm')
+pgs_methods_noneur <- c('ptclump','lassosum','megaprs','prscs','dbslmm','ldpred2')
 
 # Make vector indicating pgs_methods that are to be applied to gwas_groups
 pgs_group_methods <- c('prscsx','xwing','tlprs')
diff --git a/pipeline/config.yaml b/pipeline/config.yaml
index dd1fecaf..f752663b 100644
--- a/pipeline/config.yaml
+++ b/pipeline/config.yaml
@@ -37,6 +37,9 @@ prscs_phi: ['1e-6', '1e-4', '1e-2', '1', 'auto']
 # Specify 1kg or ukb ld reference for PRS-CS and PRS-CSx
 prscs_ldref: '1kg'
 
+# Specify reference for LDpred2
+ldpred2_ldref: NA
+
 # Specify models for LDpred2
 ldpred2_model: ['grid', 'auto', 'inf']
 
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 20e93a57..b7d170cf 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -113,6 +113,18 @@ check_config_parameters(config)
 # Set outdir parameter
 outdir=config['outdir']
 
+# Read in the gwas_list or make an empty version
+if 'gwas_list' in config and config["gwas_list"] != 'NA':
+  gwas_list_df = pd.read_table(config["gwas_list"], sep=r'\s+')
+else:
+  gwas_list_df = pd.DataFrame(columns = ["name", "path", "population", "n", "sampling", "prevalence", "mean", "sd", "label"])
+
+# Check whether gwas_list paths exist
+check_list_paths(gwas_list_df)
+
+# Identify gwas_list with population == 'EUR'
+gwas_list_df_eur = gwas_list_df.loc[gwas_list_df['population'] == 'EUR']
+
 # Set PRS-CS ld reference path
 if config['prscs_ldref'] == 'ukb':
     prscs_ldref='ukbb'
@@ -126,6 +138,29 @@ if config['resdir'] == 'NA':
 else:
   resdir=config['resdir']
 
+# Set ldpred2 reference path
+if config['ldpred2_ldref'] == 'NA':
+  ldpred2_ldref=f"{resdir}/data/ldpred2_ref"
+else:
+  ldpred2_ldref=config['ldpred2_ldref']
+
+# Check the ldpred2 ldref data is present for the required populations in the gwas_list
+if 'ldpred2' in config['pgs_methods']:
+  for pop in gwas_list_df['population'].unique():
+    path = f"{ldpred2_ldref}/{pop}"
+    # Check if map.rds file exists
+    map_file = os.path.join(path, "map.rds")
+    if not os.path.exists(map_file):
+      print(f"File not found: {map_file}")
+      raise FileNotFoundError(f"Required file not found: {map_file}. LDpred2 reference data must include map.rds for all populations.")
+
+    # Check if LD_with_blocks_chr${chr}.rds files exist for chr 1 to 22
+    for chr in range(1, 23):
+      ld_file = os.path.join(path, f"LD_with_blocks_chr{chr}.rds")
+      if not os.path.exists(ld_file):
+        print(f"File not found: {ld_file}")
+        raise FileNotFoundError(f"Required file not found: {ld_file}. LDpred2 reference data must include files for all chromosomes.")
+
 # Set refdir parameter
 # If refdir is NA, set refdir to '${resdir}/data/ref'
 if config['refdir'] == 'NA':
@@ -544,7 +579,7 @@ rule download_gctb_software:
 # Download LDpred2 reference
 rule download_ldpred2_ref:
   output:
-    directory(f"{resdir}/data/ldpred2_ref")
+    f"{resdir}/data/ldpred2_ref/EUR/map.rds"
   benchmark:
     f"{resdir}/data/benchmarks/download_ldpred2_ref.txt"
   log:
@@ -552,14 +587,14 @@ rule download_ldpred2_ref:
   shell:
     """
     {{
-      mkdir -p {resdir}/data/ldpred2_ref; \
-      wget --no-check-certificate -O {resdir}/data/ldpred2_ref/download.zip https://figshare.com/ndownloader/articles/19213299/versions/2; \
-      unzip {resdir}/data/ldpred2_ref/download.zip -d {resdir}/data/ldpred2_ref/; \
-      rm {resdir}/data/ldpred2_ref/download.zip; \
-      unzip {resdir}/data/ldpred2_ref/ldref_with_blocks.zip -d {resdir}/data/ldpred2_ref/; \
-      mv {resdir}/data/ldpred2_ref/ldref/* {resdir}/data/ldpred2_ref/; \
-      rm {resdir}/data/ldpred2_ref/ldref_with_blocks.zip; \
-      rm -r {resdir}/data/ldpred2_ref/ldref
+      mkdir -p {resdir}/data/ldpred2_ref/EUR; \
+      wget --no-check-certificate -O {resdir}/data/ldpred2_ref/EUR/download.zip https://figshare.com/ndownloader/articles/19213299/versions/2; \
+      unzip {resdir}/data/ldpred2_ref/EUR/download.zip -d {resdir}/data/ldpred2_ref/EUR/; \
+      rm {resdir}/data/ldpred2_ref/EUR/download.zip; \
+      unzip {resdir}/data/ldpred2_ref/EUR/ldref_with_blocks.zip -d {resdir}/data/ldpred2_ref/EUR/; \
+      mv {resdir}/data/ldpred2_ref/EUR/ldref/* {resdir}/data/ldpred2_ref/EUR/; \
+      rm {resdir}/data/ldpred2_ref/EUR/ldref_with_blocks.zip; \
+      rm -r {resdir}/data/ldpred2_ref/EUR/ldref
     }} > {log} 2>&1
     """
 # Download LDAK
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 535cbbfc..10b57631 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -31,18 +31,6 @@ rule ref_pca:
 # QC and format GWAS summary statistics
 ##
 
-# Read in the gwas_list or make an empty version
-if 'gwas_list' in config and config["gwas_list"] != 'NA':
-  gwas_list_df = pd.read_table(config["gwas_list"], sep=r'\s+')
-else:
-  gwas_list_df = pd.DataFrame(columns = ["name", "path", "population", "n", "sampling", "prevalence", "mean", "sd", "label"])
-
-# Check whether gwas_list paths exist
-check_list_paths(gwas_list_df)
-
-# Identify gwas_list with population == 'EUR'
-gwas_list_df_eur = gwas_list_df.loc[gwas_list_df['population'] == 'EUR']
-
 if 'gwas_list' in config:
   rule sumstat_prep_i:
     input:
@@ -304,7 +292,7 @@ rule prep_pgs_ldpred2_i:
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
-    rules.download_ldpred2_ref.output
+    lambda w: f"{ldpred2_ldref}/" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0] + "/map.rds"
   output:
     f"{outdir}/reference/pgs_score_files/ldpred2/{{gwas}}/ref-{{gwas}}.score.gz"
   benchmark:
@@ -315,6 +303,7 @@ rule prep_pgs_ldpred2_i:
     "../envs/analysis.yaml"
   params:
     model=",".join(map(str, config["ldpred2_model"])),
+    population= lambda w: gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0],
     inference=",".join(map(str, config["ldpred2_inference"])),
     sampling= lambda w: gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'sampling'].iloc[0],
     binary=lambda w: 'T' if not pd.isna(gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'sampling'].iloc[0]) else 'F',
@@ -323,8 +312,7 @@ rule prep_pgs_ldpred2_i:
     "export OPENBLAS_NUM_THREADS=1; \
     Rscript ../Scripts/pgs_methods/ldpred2.R \
       --ref_plink_chr {refdir}/ref.chr \
-      --ref_keep {refdir}/keep_files/EUR.keep \
-      --ldpred2_ref_dir {resdir}/data/ldpred2_ref \
+      --ldpred2_ref_dir {ldpred2_ldref}/{params.population} \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --n_cores {threads} \
       --output {outdir}/reference/pgs_score_files/ldpred2/{wildcards.gwas}/ref-{wildcards.gwas} \
@@ -336,7 +324,7 @@ rule prep_pgs_ldpred2_i:
       --test {params.testing} > {log} 2>&1"
 
 rule prep_pgs_ldpred2:
-  input: expand(f"{outdir}/reference/pgs_score_files/ldpred2/{{gwas}}/ref-{{gwas}}.score.gz", gwas=gwas_list_df_eur['name'])
+  input: expand(f"{outdir}/reference/pgs_score_files/ldpred2/{{gwas}}/ref-{{gwas}}.score.gz", gwas=gwas_list_df['name'])
 
 ##
 # LDAK MegaPRS

From 5cd9a5d59133989fbac89fc32d5c154d876f3b4b Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 2 Oct 2024 14:23:50 +0100
Subject: [PATCH 025/153] Extended timelimit for target scoring rule

---
 pipeline/rules/target_scoring.smk | 184 +++++++++++++++---------------
 1 file changed, 92 insertions(+), 92 deletions(-)

diff --git a/pipeline/rules/target_scoring.smk b/pipeline/rules/target_scoring.smk
index 67841a4f..e4f95711 100644
--- a/pipeline/rules/target_scoring.smk
+++ b/pipeline/rules/target_scoring.smk
@@ -1,92 +1,92 @@
-# Create a function summarising which populations are present in target
-def ancestry_munge(x):
-    checkpoints.ancestry_reporter.get(name=x).output[0]
-    checkpoint_output = outdir + "/" + x + "/ancestry/ancestry_report.txt"
-    ancestry_report_df = pd.read_table(checkpoint_output, sep=' ')
-    return ancestry_report_df['population'].tolist()
-
-# Define which pgs_methods are can be applied to any GWAS population
-pgs_methods_noneur = ['ptclump','lassosum','megaprs','prscs','dbslmm']
-
-####
-# Projected PCs
-####
-
-rule pc_projection_i:
-  input:
-    f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",
-    f"{resdir}/data/ref/pc_score_files/{{population}}/ref-{{population}}-pcs.EUR.scale"
-  output:
-    touch(f"{outdir}/reference/target_checks/{{name}}/pc_projection-{{population}}.done")
-  benchmark:
-    f"{outdir}/reference/benchmarks/pc_projection_i-{{name}}-{{population}}.txt"
-  log:
-    f"{outdir}/reference/logs/pc_projection_i-{{name}}-{{population}}.log"
-  conda:
-    "../envs/analysis.yaml"
-  params:
-    testing=config["testing"]
-  shell:
-    "Rscript ../Scripts/target_scoring/target_scoring.R \
-      --target_plink_chr {outdir}/{wildcards.name}/geno/{wildcards.name}.ref.chr \
-      --target_keep {outdir}/{wildcards.name}/ancestry/keep_files/model_based/{wildcards.population}.keep \
-      --ref_freq_chr {refdir}/freq_files/{wildcards.population}/ref.{wildcards.population}.chr \
-      --ref_score {resdir}/data/ref/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs.eigenvec.var.gz \
-      --ref_scale {resdir}/data/ref/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs.{wildcards.population}.scale \
-      --plink2 plink2 \
-      --test {params.testing} \
-      --output {outdir}/{wildcards.name}/pcs/projected/{wildcards.population}/{wildcards.name}-{wildcards.population} > {log} 2>&1"
-
-rule pc_projection_all:
-  input:
-    lambda w: expand(f"{outdir}/reference/target_checks/{{name}}/pc_projection-{{population}}.done", name=w.name, population=ancestry_munge("{}".format(w.name)))
-  output:
-    touch(f"{outdir}/reference/target_checks/{{name}}/pc_projection.done")
-
-rule pc_projection:
-  input:
-    expand(f"{outdir}/reference/target_checks/{{name}}/pc_projection.done", name=target_list_df['name'])
-
-####
-# Polygenic scoring
-####
-
-rule target_pgs_i:
-  resources:
-    mem_mb=config['mem_target_pgs'],
-    time_min=800
-  threads: config['cores_target_pgs']
-  input:
-    f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",
-    rules.prep_pgs.input
-  output:
-    touch(f"{outdir}/reference/target_checks/{{name}}/target_pgs-{{population}}.done")
-  benchmark:
-    f"{outdir}/reference/benchmarks/target_pgs_i-{{name}}-{{population}}.txt"
-  log:
-    f"{outdir}/reference/logs/target_pgs_i-{{name}}-{{population}}.log"
-  conda:
-    "../envs/analysis.yaml"
-  params:
-    testing=config["testing"],
-    config_file = config["config_file"]
-  shell:
-    "rm -r -f {outdir}/{wildcards.name}/pgs/{wildcards.population} && \
-     Rscript ../Scripts/target_scoring/target_scoring_pipeline.R \
-      --config {params.config_file} \
-      --name {wildcards.name} \
-      --population {wildcards.population} \
-      --plink2 plink2 \
-      --test {params.testing} \
-      --n_cores {threads} > {log} 2>&1"
-
-rule target_pgs_all:
-  input:
-    lambda w: expand(f"{outdir}/reference/target_checks/{{name}}/target_pgs-{{population}}.done", name=w.name, population = ancestry_munge(w.name))
-  output:
-    touch(f"{outdir}/reference/target_checks/{{name}}/target_pgs.done")
-
-rule target_pgs:
-  input:
-    expand(f"{outdir}/reference/target_checks/{{name}}/target_pgs.done", name=target_list_df['name'])
-
+# Create a function summarising which populations are present in target
+def ancestry_munge(x):
+    checkpoints.ancestry_reporter.get(name=x).output[0]
+    checkpoint_output = outdir + "/" + x + "/ancestry/ancestry_report.txt"
+    ancestry_report_df = pd.read_table(checkpoint_output, sep=' ')
+    return ancestry_report_df['population'].tolist()
+
+# Define which pgs_methods are can be applied to any GWAS population
+pgs_methods_noneur = ['ptclump','lassosum','megaprs','prscs','dbslmm']
+
+####
+# Projected PCs
+####
+
+rule pc_projection_i:
+  input:
+    f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",
+    f"{resdir}/data/ref/pc_score_files/{{population}}/ref-{{population}}-pcs.EUR.scale"
+  output:
+    touch(f"{outdir}/reference/target_checks/{{name}}/pc_projection-{{population}}.done")
+  benchmark:
+    f"{outdir}/reference/benchmarks/pc_projection_i-{{name}}-{{population}}.txt"
+  log:
+    f"{outdir}/reference/logs/pc_projection_i-{{name}}-{{population}}.log"
+  conda:
+    "../envs/analysis.yaml"
+  params:
+    testing=config["testing"]
+  shell:
+    "Rscript ../Scripts/target_scoring/target_scoring.R \
+      --target_plink_chr {outdir}/{wildcards.name}/geno/{wildcards.name}.ref.chr \
+      --target_keep {outdir}/{wildcards.name}/ancestry/keep_files/model_based/{wildcards.population}.keep \
+      --ref_freq_chr {refdir}/freq_files/{wildcards.population}/ref.{wildcards.population}.chr \
+      --ref_score {resdir}/data/ref/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs.eigenvec.var.gz \
+      --ref_scale {resdir}/data/ref/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs.{wildcards.population}.scale \
+      --plink2 plink2 \
+      --test {params.testing} \
+      --output {outdir}/{wildcards.name}/pcs/projected/{wildcards.population}/{wildcards.name}-{wildcards.population} > {log} 2>&1"
+
+rule pc_projection_all:
+  input:
+    lambda w: expand(f"{outdir}/reference/target_checks/{{name}}/pc_projection-{{population}}.done", name=w.name, population=ancestry_munge("{}".format(w.name)))
+  output:
+    touch(f"{outdir}/reference/target_checks/{{name}}/pc_projection.done")
+
+rule pc_projection:
+  input:
+    expand(f"{outdir}/reference/target_checks/{{name}}/pc_projection.done", name=target_list_df['name'])
+
+####
+# Polygenic scoring
+####
+
+rule target_pgs_i:
+  resources:
+    mem_mb=config['mem_target_pgs'],
+    time_min=1600
+  threads: config['cores_target_pgs']
+  input:
+    f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",
+    rules.prep_pgs.input
+  output:
+    touch(f"{outdir}/reference/target_checks/{{name}}/target_pgs-{{population}}.done")
+  benchmark:
+    f"{outdir}/reference/benchmarks/target_pgs_i-{{name}}-{{population}}.txt"
+  log:
+    f"{outdir}/reference/logs/target_pgs_i-{{name}}-{{population}}.log"
+  conda:
+    "../envs/analysis.yaml"
+  params:
+    testing=config["testing"],
+    config_file = config["config_file"]
+  shell:
+    "rm -r -f {outdir}/{wildcards.name}/pgs/{wildcards.population} && \
+     Rscript ../Scripts/target_scoring/target_scoring_pipeline.R \
+      --config {params.config_file} \
+      --name {wildcards.name} \
+      --population {wildcards.population} \
+      --plink2 plink2 \
+      --test {params.testing} \
+      --n_cores {threads} > {log} 2>&1"
+
+rule target_pgs_all:
+  input:
+    lambda w: expand(f"{outdir}/reference/target_checks/{{name}}/target_pgs-{{population}}.done", name=w.name, population = ancestry_munge(w.name))
+  output:
+    touch(f"{outdir}/reference/target_checks/{{name}}/target_pgs.done")
+
+rule target_pgs:
+  input:
+    expand(f"{outdir}/reference/target_checks/{{name}}/target_pgs.done", name=target_list_df['name'])
+

From 9964db44d68d20ba7b09fe56c2f6897cffcf47a0 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 9 Oct 2024 13:09:21 +0100
Subject: [PATCH 026/153] Added code to create LD reference data for LDpred2

---
 docs/prep_ldpred2_ldmat.html | 593 +++++++++++++++++++++++++++++++++++
 docs/prep_sbayesr_ldmat.Rmd  | 140 +++++++++
 2 files changed, 733 insertions(+)
 create mode 100644 docs/prep_ldpred2_ldmat.html
 create mode 100644 docs/prep_sbayesr_ldmat.Rmd

diff --git a/docs/prep_ldpred2_ldmat.html b/docs/prep_ldpred2_ldmat.html
new file mode 100644
index 00000000..90d9b33b
--- /dev/null
+++ b/docs/prep_ldpred2_ldmat.html
@@ -0,0 +1,593 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+
+
+
+<title>Preparing LDpred2 LD matrices</title>
+
+<script src="site_libs/header-attrs-2.26/header-attrs.js"></script>
+<script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
+<meta name="viewport" content="width=device-width, initial-scale=1" />
+<link href="site_libs/bootstrap-3.3.5/css/cosmo.min.css" rel="stylesheet" />
+<script src="site_libs/bootstrap-3.3.5/js/bootstrap.min.js"></script>
+<script src="site_libs/bootstrap-3.3.5/shim/html5shiv.min.js"></script>
+<script src="site_libs/bootstrap-3.3.5/shim/respond.min.js"></script>
+<style>h1 {font-size: 34px;}
+       h1.title {font-size: 38px;}
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+       code {color: inherit; background-color: rgba(0, 0, 0, 0.04);}
+       pre:not([class]) { background-color: white }</style>
+<script src="site_libs/navigation-1.1/tabsets.js"></script>
+<link href="site_libs/highlightjs-9.12.0/default.css" rel="stylesheet" />
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+<link href="site_libs/font-awesome-6.4.2/css/all.min.css" rel="stylesheet" />
+<link href="site_libs/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet" />
+<link rel="shortcut icon" href="Images/logo/favicon.ico">
+
+<link rel="stylesheet" href="styles/night-mode.css" id="nightModeStylesheet">
+
+<script>
+function toggleNightMode() {
+    var stylesheet = document.getElementById('nightModeStylesheet');
+    if (stylesheet.disabled) {
+        stylesheet.disabled = false;
+    } else {
+        stylesheet.disabled = true;
+    }
+}
+</script>
+
+<label class="switch">
+  <input type="checkbox" id="toggleNightMode" checked>
+  <span class="slider round"></span>
+</label>
+
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+document.getElementById('toggleNightMode').addEventListener('change', function() {
+    var stylesheet = document.getElementById('nightModeStylesheet');
+    if (this.checked) {
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+    }
+});
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+  ul.task-list{list-style: none;}
+    </style>
+
+<style type="text/css">code{white-space: pre;}</style>
+<script type="text/javascript">
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+}
+</script>
+
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+
+
+<link rel="stylesheet" href="styles/styles.css" type="text/css" />
+
+
+
+<style type = "text/css">
+.main-container {
+  max-width: 940px;
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+}
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+  max-width:100%;
+}
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+  padding-top: 12px;
+}
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+  margin-bottom: 20px;
+}
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+  outline: none;
+}
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+}
+details > summary > p:only-child {
+  display: inline;
+}
+pre code {
+  padding: 0;
+}
+</style>
+
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+<style type="text/css">
+.dropdown-submenu {
+  position: relative;
+}
+.dropdown-submenu>.dropdown-menu {
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+}
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+}
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+}
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+  border-left-color: #adb5bd;
+}
+.dropdown-submenu.pull-left {
+  float: none;
+}
+.dropdown-submenu.pull-left>.dropdown-menu {
+  left: -100%;
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+}
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+  document.head.appendChild(style);
+});
+</script>
+
+<!-- tabsets -->
+
+<style type="text/css">
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+}
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+}
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+
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+<div class="container-fluid main-container">
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+        
+      </ul>
+      <ul class="nav navbar-nav navbar-right">
+        <li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Pipeline
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li>
+      <a href="pipeline_overview.html">Overview</a>
+    </li>
+    <li>
+      <a href="pipeline_readme.html">Instructions</a>
+    </li>
+    <li>
+      <a href="pipeline_technical.html">Technical documentation</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Research
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li>
+      <a href="research_index.html">Overview</a>
+    </li>
+    <li>
+      <a href="comparison_of_methods_summary.html">Polygenic Scoring Methods Comparison</a>
+    </li>
+    <li>
+      <a href="Functionally_informed_prediction.html">Quantifying Polygenic Signal Mediated by Altered Gene Expression</a>
+    </li>
+    <li>
+      <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
+    </li>
+  </ul>
+</li>
+<li>
+  <a href="more_index.html">More</a>
+</li>
+<li>
+  <a href="https://github.com/opain/GenoPred">
+    <span class="fa fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul>
+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+<div id="header">
+
+
+
+<h1 class="title toc-ignore">Preparing LDpred2 LD matrices</h1>
+
+</div>
+
+
+<hr />
+<p>Here we will calculate LD matrices for LDpred2 using HapMap3 SNPs in
+the 1KG super populations. This is to allow fair comparison across
+methods for a cross-population polygenic scoring study.</p>
+<p>The code will follow that used to generate the LD matrices for EUR,
+in this paper (<a
+href="https://doi.org/10.1016/j.xhgg.2022.100136">link</a>).</p>
+<pre class="bash"><code># Subset 1KG into super populations and merge across chromosomes
+for pop in $(echo EUR EAS AFR); do
+  mkdir -p /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}
+
+  # Create mergelist
+  rm -f /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/mergelist.txt
+  for chr in $(seq 1 22); do
+    echo ~/oliverpainfel/Data/1KG/GenoPred/v2.0.0/ref.chr${chr} &gt;&gt; /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/mergelist.txt
+  done
+  
+  ~/oliverpainfel/Software/plink1.9/plink \
+    --merge-list /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/mergelist.txt \
+    --make-bed \
+    --keep /users/k1806347/oliverpainfel/Data/1KG/GenoPred/v2.0.0/keep_files/${pop}.keep \
+    --out /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/ref.${pop}.GW
+done</code></pre>
+<pre class="r"><code>library(bigsnpr)
+library(bigreadr)
+
+# Create object for genetic data in ldpred2 format, and impute missing values
+for(pop in c(&#39;EUR&#39;, &#39;EAS&#39;, &#39;AFR&#39;)){
+  # Read in reference data
+  snp_readBed(
+    paste0(
+      &#39;/users/k1806347/oliverpainfel/Data/1KG/ldpred2/&#39;,
+      pop,
+      &#39;/ref.&#39;,
+      pop,
+      &#39;.GW.bed&#39;
+    )
+  )
+  
+  # Attach the ref object in R session
+  ref &lt;-
+    snp_attach(
+      paste0(
+        &#39;/scratch/prj/oliverpainfel/Data/1KG/ldpred2/&#39;,
+        pop,
+        &#39;/ref.&#39;,
+        pop,
+        &#39;.GW.rds&#39;
+      )
+    )
+  G &lt;- ref$genotypes
+  NCORES &lt;- nb_cores()
+  
+  #### Impute missing values (bigsnpr can&#39;t handle missing data in most functions)
+  G_imp &lt;- snp_fastImputeSimple(G, method = &quot;mean2&quot;, ncores = NCORES)
+
+  # Save imputed reference
+  ref$genotypes&lt;-G_imp
+  saveRDS(ref, paste0(
+        &#39;/scratch/prj/oliverpainfel/Data/1KG/ldpred2/&#39;,
+        pop,
+        &#39;/ref.&#39;,
+        pop,
+        &#39;.GW.rds&#39;
+      ))
+}
+
+################
+# Now calculate LD matrices following code from https://doi.org/10.1016/j.xhgg.2022.100136
+
+library(dplyr)
+library(bigreadr)
+library(bigsnpr)
+
+for(pop in c(&#39;EUR&#39;, &#39;EAS&#39;, &#39;AFR&#39;)){
+  obj.1000G &lt;- snp_attach(paste0(
+          &#39;/scratch/prj/oliverpainfel/Data/1KG/ldpred2/&#39;,
+          pop,
+          &#39;/ref.&#39;,
+          pop,
+          &#39;.GW.rds&#39;
+        ))
+  G &lt;- obj.1000G$genotypes
+  CHR &lt;- obj.1000G$map$chromosome
+  POS &lt;- obj.1000G$map$physical.pos
+  POS2 &lt;- snp_asGeneticPos(CHR, POS, dir =&#39;/users/k1806347/oliverpainfel/Data/Genetic_Map/CEU&#39;, ncores = NCORES) # Note this is not appropriate but I think Florian did the same as used distance from UKB
+  MAF &lt;- snp_MAF(G, ncores = NCORES)
+  keep &lt;- (MAF &gt; 0.02)
+
+  all_final_grp &lt;- NULL
+  for(chr in 1:22){
+    ind.chr2 &lt;- which(CHR == chr &amp; keep)
+
+    corr0 &lt;- runonce::save_run({
+      snp_cor(
+        G,
+        ind.col = ind.chr2,
+        infos.pos = POS2[ind.chr2],
+        size = 3 / 1000,
+        ncores = NCORES
+      )
+    }, file = paste0(
+      &#39;/scratch/prj/oliverpainfel/Data/1KG/ldpred2/&#39;,
+      pop,
+      &#39;/LD_chr&#39;,
+      chr,
+      &quot;.rds&quot;
+    ))
+    
+    # find nearly independent LD blocks
+    m &lt;- length(ind.chr2)
+    (SEQ &lt;- round(seq_log(m / 30, m / 5, length.out = 20)))
+    splits &lt;- snp_ldsplit(corr0, thr_r2 = 0.05, min_size = 50, max_size = SEQ, max_r2 = 0.15)
+    splits$cost2 &lt;- sapply(splits$all_size, function(sizes) sum(sizes^2))
+  
+    best_split &lt;- splits %&gt;%
+      arrange(cost2 * sqrt(5 + cost)) %&gt;%
+      print() %&gt;%
+      slice(1) %&gt;%
+      print()
+  
+    (all_size &lt;- best_split$all_size[[1]])
+    best_grp &lt;- rep(seq_along(all_size), all_size)
+  
+    runonce::save_run({
+      corr0T &lt;- as(corr0, &quot;dgTMatrix&quot;)
+      corr0T@x &lt;-
+        ifelse(best_grp[corr0T@i + 1L] == best_grp[corr0T@j + 1L], corr0T@x, 0)
+      as(Matrix::drop0(corr0T), &quot;symmetricMatrix&quot;)
+    }, file = paste0(
+      &#39;/scratch/prj/oliverpainfel/Data/1KG/ldpred2/&#39;,
+      pop,
+      &#39;/LD_with_blocks_chr&#39;,
+      chr,
+      &quot;.rds&quot;
+    ))
+  
+    # return
+    all_final_grp &lt;- rbind(all_final_grp, tibble(best_split, ind = list(ind.chr2)))
+  }
+  saveRDS(
+    all_final_grp,
+    paste0(
+      &#39;/scratch/prj/oliverpainfel/Data/1KG/ldpred2/&#39;,
+      pop,
+      &#39;/all_final_grp.rds&#39;
+    )
+  )
+  
+  ###
+  # Create map.rds
+  ###
+  map &lt;- obj.1000G$map[keep,]
+
+  # Compute allele frequency
+  map$af&lt;-big_colstats(G, ind.col = which(keep), ncores = NCORES)$sum / (2 * nrow(G))
+  
+  # Compute LD scores
+  map$ld &lt;- do.call(&#39;c&#39;, lapply(1:22, function(chr) {
+    cat(chr, &quot;.. &quot;, sep = &quot;&quot;)
+    corr_chr &lt;- readRDS(
+      paste0(
+        &#39;/scratch/prj/oliverpainfel/Data/1KG/ldpred2/&#39;,
+        pop,
+        &#39;/LD_chr&#39;,
+        chr,
+        &quot;.rds&quot;
+      )
+    )
+    Matrix::colSums(corr_chr ^ 2)
+  }))
+  
+  map &lt;- map[, c(&#39;chromosome&#39;,&#39;physical.pos&#39;,&#39;allele2&#39;,&#39;allele1&#39;,&#39;marker.ID&#39;,&#39;af&#39;,&#39;ld&#39;)]
+  names(map) &lt;- c(&#39;chr&#39;,&#39;pos&#39;,&#39;a0&#39;,&#39;a1&#39;,&#39;rsid&#39;,&#39;af&#39;,&#39;ld&#39;)
+  
+  saveRDS(
+    map,
+    paste0(
+      &#39;/scratch/prj/oliverpainfel/Data/1KG/ldpred2/&#39;,
+      pop,
+      &#39;/map.rds&#39;
+    )
+  )
+}</code></pre>
+
+<!-- footer.html -->
+<hr/>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 500px;">
+<img src="Images/logo/sponsors.png">
+</div>
+</div>
+
+
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
+
+
+</script>
+
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
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+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
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+});
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diff --git a/docs/prep_sbayesr_ldmat.Rmd b/docs/prep_sbayesr_ldmat.Rmd
new file mode 100644
index 00000000..c1dcce36
--- /dev/null
+++ b/docs/prep_sbayesr_ldmat.Rmd
@@ -0,0 +1,140 @@
+---
+title: Preparing SBayesR LD matrices
+output: 
+  html_document:
+    theme: cosmo
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+Here we will calculate LD matrices for SBayesR using HapMap3 SNPs in the 1KG super populations. This is to allow fair comparison across methods for a cross-population polygenic scoring study. 
+
+```{r}
+# Create shrunk LD matrix in 5000 SNP pieces
+gctb<-'/users/k1806347/oliverpainfel/Software/gctb_2.02_Linux/gctb'
+options(scipen=999)
+
+library(foreach)
+library(doMC)
+registerDoMC(10)
+
+for(pop in c('EAS','AFR')){
+  system(paste0('mkdir -p /users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop))
+  log <- foreach(i = 1:22, .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+    # Restrict to SNPs with MAF > 0.001
+    freq<-fread(paste0('/users/k1806347/oliverpainfel/Data/1KG/GenoPred/v2.0.0/freq_files/', pop, '/ref.', pop, '.chr', i, '.frq'))
+    freq <- freq[freq$MAF > 0.001,]
+    write.table(freq$SNP, paste0('/users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop,'/maf_001_chr',i,'.txt'), col.names=F, row.names=F, quote=F)
+
+    nsnp <- nrow(freq)
+    nsnp_chunk <- ceiling(nsnp / 5000)
+    for (j in 1:nsnp_chunk) {
+      start <- (5000 * (j - 1)) + 1
+      end <- 5000 * j
+      print(start)
+      print(end)
+      
+      system(
+        paste0(
+          "sbatch -p neurohack_cpu --mem 10G --wrap='",
+          gctb,
+          ' --bfile /scratch/prj/oliverpainfel/Data/1KG/ldpred2/EUR/ref.EUR.GW.imp',
+          ' --keep /users/k1806347/oliverpainfel/Data/1KG/GenoPred/v2.0.0/keep_files/', pop, '.keep', 
+          ' --make-shrunk-ldm',  
+          ' --extract /users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop,'/maf_001_chr',i,'.txt',
+          ' --gen-map /users/k1806347/oliverpainfel/Data/Genetic_Map/CEU/chr', i, '.OMNI.interpolated_genetic_map', 
+          ' --snp ', start, '-', end,
+          ' --out /users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop,'/maf_001_chr', i,
+          "'"
+        )
+      )
+    }
+
+    # Merge the chunks into per chromosome LD matrices
+    while(1){
+      files<-list.files(path=paste0('/users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop), 
+                        pattern=paste0('maf_001_chr',i,'.snp'))
+      files<-files[grepl('.bin',files)]
+      if(length(files) == nsnp_chunk){
+        Sys.sleep(120)
+        break
+      }
+    }
+  
+    files<-paste0('/users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/', files)
+    files<-gsub('.bin', '', files)
+    file_num<-gsub('.*.snp', '', files)
+    file_num<-as.numeric(gsub('-.*', '', file_num))
+    # Sort files in order of genomic location otherwise SBayeR does not converge!
+    files<-files[order(file_num)]
+    write.table(
+      files,
+      paste0('/users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/shrunk_ld_chr', i, '.merge_list'),
+      col.names = F,
+      row.names = F,
+      quote = F
+    )
+    system(
+      paste0(
+      "sbatch -p neurohack_cpu --mem ", round(2.5 * nsnp_chunk),"G --wrap='",
+        gctb,
+        ' --mldm /users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/shrunk_ld_chr', i, '.merge_list',
+        ' --make-shrunk-ldm', 
+        ' --out /users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/maf_001_chr', i,
+        "'"
+      )
+    )
+    
+    # Make the LD matrices sparse
+    while(1){
+      if(file.exists(paste0('/users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/maf_001_chr', i, '.ldm.shrunk.bin'))){
+        Sys.sleep(120)
+        break
+      }
+    }
+
+    system(paste0(
+      "sbatch -p neurohack_cpu --mem 60G --wrap='",
+      gctb,
+      ' --ldm /users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/maf_001_chr', i, '.ldm.shrunk', 
+      ' --chisq 0', 
+      ' --make-sparse-ldm',
+      ' --out /users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/maf_001_chr', i,
+      "'"
+    ))
+    
+    while(1){
+      if(file.exists(paste0('/users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/maf_001_chr', i,'.ldm.sparse.bin'))){
+        Sys.sleep(120)
+        break
+      }
+    }
+    system(paste0('rm /users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/maf_001_chr', i,'.snp*'))
+    system(paste0('rm /users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/maf_001_chr', i,'.txt'))
+    system(paste0('rm /users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/shrunk_ld_chr', i, '.merge_list'))
+    system(paste0('rm /users/k1806347/oliverpainfel/Data/1KG/sbayesr/', pop, '/maf_001_chr', i,'.ldm.shrunk.*'))
+    
+    'hi'
+  }
+}
+
+```
+

From 75f18cefca137587c34fa6a536763069036ff0c7 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 9 Oct 2024 13:10:24 +0100
Subject: [PATCH 027/153] Allowed LDpred2 to try a range of LD shrinkage values

---
 Scripts/pgs_methods/ldpred2.R | 31 +++++++++++++++++++++----------
 1 file changed, 21 insertions(+), 10 deletions(-)

diff --git a/Scripts/pgs_methods/ldpred2.R b/Scripts/pgs_methods/ldpred2.R
index f540864f..db3853ac 100644
--- a/Scripts/pgs_methods/ldpred2.R
+++ b/Scripts/pgs_methods/ldpred2.R
@@ -244,13 +244,25 @@ if('grid' %in% opt$model){
 ####
 
 if('auto' %in% opt$model){
-  coef_shrink <- 0.95
 
-  # takes less than 2 min with 4 cores
-  multi_auto <- snp_ldpred2_auto(
-    corr, sumstats, h2_init = ldsc[["h2"]],
-    vec_p_init = seq_log(1e-4, 0.2, length.out = 30), ncores = opt$n_cores,
-    allow_jump_sign = FALSE, shrink_corr = coef_shrink)
+  coef_shrink <- seq(0.95, 0.4, by = -0.05) # Updated the sequence to decrement correctly
+
+  for(coef_shrink_i in coef_shrink){
+    multi_auto <- snp_ldpred2_auto(
+      corr, sumstats, h2_init = ldsc[["h2"]],
+      vec_p_init = seq_log(1e-4, 0.2, length.out = 30), ncores = opt$n_cores,
+      allow_jump_sign = FALSE, shrink_corr = coef_shrink_i)
+
+    if(!all(is.na(multi_auto[[1]]$beta_est))){
+      log_add(log_file = log_file, message = paste0('Auto model: ld matrix shrink_corr=', coef_shrink_i,' was used'))
+      break  # Break the loop if beta_est is not all NA
+    }
+
+    if(coef_shrink_i == 0.4 && all(is.na(multi_auto[[1]]$beta_est))){
+      log_add(log_file = log_file, message = 'Error: auto model did not converge even with shrink_corr=0.4.')
+      stop('auto model did not converge even with shrink_corr=0.4.')
+    }
+  }
 
   # Create convergence plot
   auto <- multi_auto[[1]] # first chain
@@ -355,13 +367,12 @@ for(pop_i in unique(pop_data$POP)){
 if(opt$inference){
 
   log_add(log_file = log_file, message = 'Performing inference...')
-  coef_shrink <- 0.95
-
+  coef_shrink_i
   multi_auto <- snp_ldpred2_auto(
     corr, sumstats, h2_init = ldsc[["h2"]],
     vec_p_init = seq_log(1e-4, 0.2, length.out = 50), ncores = opt$n_cores,
     burn_in = 500, num_iter = 500, report_step = 20,
-    allow_jump_sign = FALSE, shrink_corr = coef_shrink)
+    allow_jump_sign = FALSE, shrink_corr = coef_shrink_i)
 
   range <- sapply(multi_auto, function(auto) diff(range(auto$corr_est)))
   keep <- which(range > (0.95 * quantile(range, 0.95, na.rm = TRUE)))
@@ -389,7 +400,7 @@ if(opt$inference){
   all_r2 <- do.call("cbind", lapply(seq_along(bsamp), function(ic) {
     b1 <- bsamp[[ic]]
     Rb1 <- apply(b1, 2, function(x)
-      coef_shrink * bigsparser::sp_prodVec(corr, x) + (1 - coef_shrink) * x)
+      coef_shrink_i * bigsparser::sp_prodVec(corr, x) + (1 - coef_shrink_i) * x)
     b2 <- do.call("cbind", bsamp[-ic])
     b2Rb1 <- as.matrix(Matrix::crossprod(b2, Rb1))
   }))

From a9a5d20dbb261a301c3e1170497f924dbafeda76 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 9 Oct 2024 13:11:25 +0100
Subject: [PATCH 028/153] Included some config file checks, and updated cross
 population analysis to include AFR data

---
 docs/CrossPop.Rmd               | 761 +++++++++++++++++++++-----------
 pipeline/rules/dependencies.smk |  28 ++
 pipeline/rules/pgs_methods.smk  |  12 +-
 pipeline/rules/target_qc.smk    |   5 +
 4 files changed, 551 insertions(+), 255 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index b02075f7..a5ef9a6b 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1,3 +1,5 @@
+---
+title: Cross-population evaluation of polygenic scores
 output: 
   html_document:
     theme: cosmo
@@ -606,7 +608,21 @@ gwas_list_eas<-data.frame(
   label=paste0('"', prscsx_dat$trait, ' (BBJ)"')
 )
 
-gwas_list<-rbind(gwas_list_eur, gwas_list_eas)
+ugr_data<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv')[1:5,]
+ugr_data<-ugr_data[ugr_data$labels %in% prscsx_dat$labels,]
+
+gwas_list_afr<-data.frame(
+  name=paste0(ugr_data$labels,'_UGR'),
+  path=paste0('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/UGR/',ugr_data$labels,'.reformat.txt.gz'),
+  population='AFR',
+  n=NA,
+  sampling=NA,
+  prevalence=NA,
+  mean=0,
+  sd=1,
+  label=paste0('"', prscsx_dat$trait, ' (UGR)"')
+)
+gwas_list<-do.call(rbind, list(gwas_list_eur, gwas_list_eas, gwas_list_afr))
 
 write.table(gwas_list, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt', col.names = T, row.names = F, quote = F)
 
@@ -614,12 +630,20 @@ write.table(gwas_list, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k
 # gwas_groups
 ######
 
-gwas_groups<-data.frame(
+gwas_groups_eas<-data.frame(
   name=prscsx_dat$labels,
   gwas=sapply(prscsx_dat$labels, function(x) paste0(x,'_UKB,',x,'_BBJ')),
-  label=paste0('"', prscsx_dat$trait, '"')
+  label=paste0('"', prscsx_dat$trait, " (UKB+BBJ)", '"')
+)
+
+gwas_groups_afr<-data.frame(
+  name=paste0(prscsx_dat$labels, '_ukb_ugr'),
+  gwas=sapply(prscsx_dat$labels, function(x) paste0(x,'_UKB,',x,'_UGR')),
+  label=paste0('"', prscsx_dat$trait, " (UKB+UGR)", '"')
 )
 
+gwas_groups<-rbind(gwas_groups_eas, gwas_groups_afr)
+
 write.table(gwas_groups, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt', col.names = T, row.names = F, quote = F)
 
 ######
@@ -677,84 +701,131 @@ outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
 prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[4,]
-
-##############
-# Comparison: Single source
-##############
+prscsx_dat<-prscsx_dat[1:5,]
 
 # Get a list of score files
 scores <- list_score_files(config)
 
-disc_pop <- c('EUR','EAS')
-targ_pop <- c('EUR','EAS','AFR','CSA','AMR')
-for(disc_pop_i in disc_pop){
-  disc_samp<-ifelse(disc_pop_i == 'EUR', 'UKB', 'BBJ')
-  scores_disc_pop_i <- scores[grepl(disc_samp, scores$name), ]
+# Create files for EAS and AFR targets
+targ_pop <- c('EUR','EAS','AFR')
+for(trait_i in prscsx_dat$labels){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
+  scores_i <- scores[grepl(trait_i, scores$name),]
+  scores_i$group <- scores_i$method
 
   for(targ_pop_i in targ_pop){
-    for(trait_i in prscsx_dat$labels){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'BBJ'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'UGR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('BBJ','UGR')
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == 'BBJ'){
+        disc_pop_j_2 <- 'EAS'
+      }
+      if(disc_pop_j == 'UGR'){
+        disc_pop_j_2 <- 'AFR'
+      }
+
       dir.create(
         paste0(
-          '/users/k1806347/oliverpainfel/Analyses/crosspop/single.',
-          disc_pop_i,
-          '_disc.targ_',
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
           targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
           '/',
           trait_i
         ),
         recursive = T
       )
-      scores_tmp <- scores_disc_pop_i[grepl(trait_i, scores_disc_pop_i$name),]
       
-      predictors_tmp <- data.frame(
-        predictor = paste0(
-          outdir,
-          '/ukb/pgs/',
-          targ_pop_i,
-          '/',
-          scores_tmp$method,
-          '/',
-          scores_tmp$name,
-          '/ukb-',
-          scores_tmp$name,
-          '-',
-          targ_pop_i,
-          '.profiles'
-        ),
-        group=scores_tmp$method
+      scores_i_j <- scores_i[grepl('UKB', scores_i$name, ignore.case = F) |
+                             grepl(disc_pop_j, scores_i$nam, ignore.case = T),]
+      
+      scores_i_j_multi <- scores_i_j
+      # Insert path to score file
+      scores_i_j_multi$predictor <- paste0(
+        outdir,
+        '/ukb/pgs/',
+        targ_pop_i,
+        '/',
+        scores_i_j_multi$method,
+        '/',
+        scores_i_j_multi$name,
+        '/ukb-',
+        scores_i_j_multi$name,
+        '-',
+        targ_pop_i,
+        '.profiles'
       )
       
       # Extract the pseudo score for each method and specify as a seperate group
-      for(i in 1:nrow(scores_tmp)) {
+      for(i in 1:nrow(scores_i_j)) {
         param <- find_pseudo(
           config = config,
-          gwas = scores_tmp$name[i],
-          pgs_method = scores_tmp$method[i],
-          target_pop = pop_i
+          gwas = scores_i_j$name[i],
+          pgs_method = scores_i_j$method[i],
+          target_pop = targ_pop_i
         )
         
-        predictors <- fread(
-          predictors_tmp$predictor[i],
+        pgs <- fread(
+          scores_i_j_multi$predictor[i],
           select = c('FID', 'IID', 
-                     paste0(scores_tmp$name[i], '_', param)))
+                     paste0(scores_i_j$name[i], '_', param)))
         
-        fwrite(predictors, 
-               gsub('.profiles', '.pseudo.profiles', predictors_tmp$predictor[i]))
+        fwrite(pgs, 
+               gsub('.profiles',
+                    '.pseudo.profiles',
+                    scores_i_j_multi$predictor[i]))
+      }
+      
+      scores_i_j_pseudo <- scores_i_j_multi
+      scores_i_j_pseudo$group <- paste0(scores_i_j_multi$group, '.pseudo')
+      for(i in 1:nrow(scores_i_j_pseudo)){
+        if(grepl('UKB', scores_i_j_pseudo$name[i])){
+          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.EUR.pseudo', scores_i_j_pseudo$group[i])
+        }
+        if(grepl('BBJ', scores_i_j_pseudo$name[i])){
+          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.EAS.pseudo', scores_i_j_pseudo$group[i])
+        }
+        if(grepl('UGR', scores_i_j_pseudo$name[i])){
+          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.AFR.pseudo', scores_i_j_pseudo$group[i])
+        }
       }
+      scores_i_j_pseudo$predictor <- gsub('.profiles',
+                                            '.pseudo.profiles',
+                                            scores_i_j_pseudo$predictor)
       
-      predictors_tmp2<-predictors_tmp
-      predictors_tmp2$group<-paste0(predictors_tmp$group,'.pseudo')
-      predictors_tmp2$predictor<-gsub('.profiles', '.pseudo.profiles', predictors_tmp$predictor)
-      predictors_tmp <- rbind(predictors_tmp, predictors_tmp2)
+      # Make a group for each GWAS and single source combo
+      scores_i_j_single <- scores_i_j_multi[grepl('UKB|BBJ|UGR', scores_i_j$name), ]
+      scores_i_j_single$group <-
+        paste0(scores_i_j_single$method,
+               '.',
+               gsub('.*_', '', scores_i_j_single$name))
+      scores_i_j_single$group <- gsub('UKB', 'EUR', scores_i_j_single$group)
+      scores_i_j_single$group <- gsub('BBJ', 'EAS', scores_i_j_single$group)
+      scores_i_j_single$group <- gsub('UGR', 'AFR', scores_i_j_single$group)
+      
+      predictors_i<- do.call(rbind, list(
+        scores_i_j_multi, scores_i_j_pseudo, scores_i_j_single
+      ))
+      
+      predictors_i <- predictors_i[, c('predictor', 'group'), with=F]
       
       write.table(
-        predictors_tmp,
+        predictors_i,
         paste0(
-          '/users/k1806347/oliverpainfel/Analyses/crosspop/single.',
-          disc_pop_i,
-          '_disc.targ_',
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
           targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
           '/',
           trait_i,
           '/predictor_list.txt'
@@ -767,83 +838,6 @@ for(disc_pop_i in disc_pop){
   }
 }
 
-##############
-# Comparison: Multi source (EUR + EAS -> EUR, EUR + EAS -> EAS)
-##############
-
-# Get a list of score files
-scores <- list_score_files(config)
-
-pop <- c('EUR','EAS','AFR','CSA','AMR')
-for(pop_i in pop){
-  for(trait_i in prscsx_dat$labels){
-    dir.create(
-      paste0(
-        '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_',
-        pop_i,
-        '/',
-        trait_i
-      ),
-      recursive = T
-    )
-    scores_tmp <- scores[grepl(trait_i, scores$name),]
-    
-    predictors_tmp <- data.frame(
-      predictor = paste0(
-        outdir,
-        '/ukb/pgs/',
-        pop_i,
-        '/',
-        scores_tmp$method,
-        '/',
-        scores_tmp$name,
-        '/ukb-',
-        scores_tmp$name,
-        '-',
-        pop_i,
-        '.profiles'
-      ),
-      group=scores_tmp$method
-    )
-    
-    # Extract the pseudo score for each method and specify as a seperate group
-    for(i in 1:nrow(scores_tmp)) {
-      param <- find_pseudo(
-        config = config,
-        gwas = scores_tmp$name[i],
-        pgs_method = scores_tmp$method[i],
-        target_pop = pop_i
-      )
-      
-      predictors <- fread(
-        predictors_tmp$predictor[i],
-        select = c('FID', 'IID', 
-                   paste0(scores_tmp$name[i], '_', param)))
-      
-      fwrite(predictors, gsub('.profiles', '.pseudo.profiles', predictors_tmp$predictor[i]))
-    }
-    
-    predictors_tmp2<-predictors_tmp
-    predictors_tmp2$group<-paste0(predictors_tmp$group,'.pseudo')
-    predictors_tmp2$predictor<-gsub('.profiles', '.pseudo.profiles', predictors_tmp$predictor)
-    predictors_tmp <- rbind(predictors_tmp, predictors_tmp2)
-
-    write.table(
-      predictors_tmp,
-      paste0(
-        '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_',
-        pop_i,
-        '/',
-        trait_i,
-        '/predictor_list.txt'
-      ),
-      col.names = T,
-      row.names = F,
-      quote = F
-    )
-  }
-}
-
 ```
 </details>
 
@@ -854,27 +848,34 @@ for(pop_i in pop){
 <details><summary>Show code</summary>
 
 ```{bash}
-###########
-# Single source
-###########
-
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-for disc_pop in $(echo EUR EAS); do
-#  for targ_pop in $(echo EUR EAS ); do
-  for targ_pop in $(echo AFR CSA AMR); do
-    if [ "$targ_pop" == "EUR" ]; then
-        targ_pop2="EUR_test"
-    else
-        targ_pop2=$targ_pop
-    fi
-    
-    for pheno in $(head -n 4 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt | tail -n 1); do
-      sbatch --mem 50G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+for targ_pop in $(echo EUR EAS AFR); do
+  if [ "$targ_pop" == "EUR" ]; then
+      targ_pop2="EUR_test"
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ "$targ_pop" == "EUR" ]; then
+    disc_pop=$(echo AFR EAS)
+  fi
+  
+  if [ "$targ_pop" == "EAS" ]; then
+    disc_pop="EAS"
+  fi
+  
+  if [ "$targ_pop" == "AFR" ]; then
+    disc_pop="AFR"
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+      sbatch --mem 20G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
         --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
-        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/single.${disc_pop}_disc.targ_${targ_pop}/${pheno}/predictor_list.txt \
-        --out /users/k1806347/oliverpainfel/Analyses/crosspop/single.${disc_pop}_disc.targ_${targ_pop}/${pheno}/res \
+        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
+        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
         --n_core 10 \
         --top1 T \
         --all_model F \
@@ -883,41 +884,12 @@ for disc_pop in $(echo EUR EAS); do
   done
 done
 
-# Note. Will we will need to include specify --compare_predictors T to results for every PGS (allowing us to get pseudoval results)
-
-###########
-# Multi source
-###########
-
-cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
-conda activate model_builder
-
-#  for targ_pop in $(echo EUR EAS ); do
-for targ_pop in $(echo AFR CSA AMR); do
-  if [ "$targ_pop" == "EUR" ]; then
-      targ_pop2="EUR_test"
-  else
-      targ_pop2=$targ_pop
-  fi
-
-  for pheno in $(head -n 4 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt | tail -n 1); do
-    sbatch --mem 20G -n 5 -p cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
-      --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
-      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/predictor_list.txt \
-      --out /users/k1806347/oliverpainfel/Analyses/crosspop/multi.targ_${targ_pop}/${pheno}/res \
-      --n_core 5 \
-      --top1 T \
-      --all_model F \
-      --assoc T"
-  done
-done
-
 ```
 </details>
 
 ***
 
-## Plot results
+## Plot results for each phenotype
 
 ### Assoc results
 
@@ -932,7 +904,7 @@ library(cowplot)
 
 # Read in list of outcomes 
 prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[4,]
+prscsx_dat<-prscsx_dat[1:5,]
 
 single_plot_list<-list()
 prscsx_xwing_plot_list<-list()
@@ -1097,7 +1069,7 @@ source_all('../functions')
 
 # Read in list of outcomes 
 prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[4,]
+prscsx_dat<-prscsx_dat[1:5,]
 
 config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
   
@@ -1110,31 +1082,58 @@ gwas_list<-read_param(config = config, param = 'gwas_list')
 # Define gwas_groups used
 gwas_groups<-read_param(config = config, param = 'gwas_groups')
 
-#############
-# Single source
-#############
+# Calculate corelation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in prscsx_dat$labels){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
 
-plot_list<-list()
-disc_pop=c('EUR','EAS')
-targ_pop=c('EUR','EAS','AFR','CSA','AMR')
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop = c('EUR','EAS','AFR')
+res <- list()
 for(pheno_i in prscsx_dat$labels){
   res_i<-NULL
-  for(disc_pop_i in disc_pop){
-    for(targ_pop_i in targ_pop){
-        eval_i <-
-          fread(
-            paste0(
-              '/users/k1806347/oliverpainfel/Analyses/crosspop/single.',
-              disc_pop_i,
-              '_disc.targ_',
-              targ_pop_i,
-              '/',
-              pheno_i,
-              '/res.pred_eval.txt'
-            )
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.pred_eval.txt'
           )
-        eval_i$test <- paste0(disc_pop_i, ' → ', targ_pop_i)
-        res_i <- rbind(res_i, eval_i)
+        )
+      eval_i$target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_i<-rbind(res_i, eval_i)
     }
   }
   
@@ -1144,93 +1143,347 @@ for(pheno_i in prscsx_dat$labels){
   res_i$Model[grepl('pseudo', res_i$Group)]<-'Pseudo'
   res_i$Model[!grepl('top1|pseudo', res_i$Group)]<-'Multi'
   
+  res_i$Source[grepl('EAS|EUR|AFR', res_i$Group)] <- 'Single'
+  res_i$Source[!grepl('EAS|EUR|AFR', res_i$Group)] <- 'Multi'
+  
+  res_i$Discovery[grepl('EUR', res_i$Group)] <- 'EUR'
+  res_i$Discovery[grepl('EAS', res_i$Group)] <- 'EAS'
+  res_i$Discovery[grepl('AFR', res_i$Group)] <- 'AFR'
+  res_i$Discovery[res_i$Source == 'Multi'] <- res_i$gwas_group[res_i$Source == 'Multi']
+  
   res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
-  res_i$Model<-factor(res_i$Model, levels=unique(res_i$Model))
-  res_i$test<-factor(res_i$test, levels=unique(res_i$test))
+  res_i$Model<-factor(res_i$Model, levels=c('Top1','Pseudo','Multi'))
+  res_i$Discovery<-factor(res_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+  # Remove pseudo model for methods that don't really have one (single source combo, tlprs)
+  res_i <- res_i[!which(res_i$Model == 'Pseudo' & res_i$Method == 'ptclump'),]
+  res_i <- res_i[!which(
+    res_i$Model == 'Pseudo' & 
+    res_i$Method %in% c('tlprs_dbslmm', 'tlprs_prscs', 'tlprs_sbayesr') & 
+    res_i$Source == 'Multi'),]
   
-  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=Method, y=R, fill = Model)) +
+  res[[pheno_i]]<-res_i
+  
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res for each scenario
+    res_i <- do.call(rbind, lapply(seq_along(res), function(i) {
+      x <- res[[i]]
+      x$pheno <- names(res)[i]
+      x <- x[x$target == targ_pop_i]
+      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
+    }))
+    
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_i$Sample<-'A'
+  
+    for(group_i in unique(res_i$Group)){
+      res_group_i <- res_i[res_i$Group == group_i,]
+      cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
+      
+      meta_res_i <-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_group_i
+        )
+      
+      meta_res <- rbind(meta_res,
+                        data.table(
+                          Group = group_i,
+                          Method = res_group_i$Method[1],
+                          Model = res_group_i$Model[1],
+                          Source = res_group_i$Source[1],
+                          Discovery = res_group_i$Discovery[1],
+                          gwas_group = res_group_i$gwas_group[1],
+                          Target = targ_pop_i,
+                          R = meta_res_i$es,
+                          SE = sqrt(meta_res_i$var)
+                        ))
+    }
+  }
+}
+
+meta_res$Model<-factor(meta_res$Model, levels=c('Top1','Pseudo','Multi'))
+meta_res$Discovery<-factor(meta_res$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+####
+# Comparison of models across methods, across target and discovery populations
+####
+
+tmp <- meta_res
+
+# Set SBayesR pseudo model to top1
+ggplot(meta_res, aes(x=Method, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = .2,
-                  position = position_dodge(width = 0.7)) +
-    geom_point(stat="identity", position=position_dodge(0.7), size=2, shape=23) +
-    geom_hline(yintercept = 0) +
-    geom_vline(xintercept = seq(1.5, length(unique(res_i$Method))), linetype="dotted") +
-    facet_grid(.~ test) +
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
+    labs(y = "R (SE)") +
+    facet_grid(Target ~ Discovery, scales='free', space = 'free_x') +
     theme_half_open() +
     background_grid() + 
     panel_border() + 
     theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-}
 
-png(
-  '/users/k1806347/oliverpainfel/Analyses/crosspop/single.eval.png',
-  res = 300,
-  height = 1200*nrow(prscsx_dat),
-  width = 5000,
-  units = 'px'
-)
-plot_grid(plotlist = plot_list, labels = names(plot_list), ncol=1)
-dev.off()
 
-#############
-# Multi source
-#############
+####
+# Create heatmap showing difference between all methods and models
+####
 
-plot_list<-list()
-pop=c('EUR','EAS','AFR','CSA','AMR')
+# Read in results
+targ_pop=c('EUR','EAS','AFR')
+res <- list()
 for(pheno_i in prscsx_dat$labels){
   res_i<-NULL
-  for(targ_pop_i in pop){
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
       eval_i <-
         fread(
           paste0(
-            '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.',
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
             'targ_',
             targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
             '/',
             pheno_i,
-            '/res.pred_eval.txt'
+            '/res.pred_comp.txt'
           )
         )
       eval_i$target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
       res_i<-rbind(res_i, eval_i)
+    }
   }
   
-  res_i$Method<-sub('\\..*','',res_i$Group)
+  res[[pheno_i]]<-res_i
+}
+    
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
   
-  res_i$Model[grepl('top1', res_i$Group)]<-'Top1'
-  res_i$Model[grepl('pseudo', res_i$Group)]<-'Pseudo'
-  res_i$Model[!grepl('top1|pseudo', res_i$Group)]<-'Multi'
+    # Subset res for each scenario
+    res_i <- do.call(rbind, lapply(seq_along(res), function(i) {
+      x <- res[[i]]
+      x$pheno <- names(res)[i]
+      x <- x[x$target == targ_pop_i]
+      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
+    }))
   
-  res_i <- res_i[!grepl('pseudo.top1', res_i$Group),]
+    # Calculate diff SE based on p-value
+    res_i$R_diff_pval[res_i$R_diff == 0] <- 1-0.001
+    res_i$R_diff_pval[res_i$R_diff_pval == 1]<-1-0.001
+    res_i$R_diff_z<-qnorm(res_i$R_diff_pval/2)
+    res_i$R_diff_SE<-abs(res_i$R_diff/res_i$R_diff_z)
+        
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_i$Sample<-'A'
+    res_i$Group <- paste0(res_i$Model_1, '_vs_', res_i$Model_2)
   
-  res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
-  res_i$Model<-factor(res_i$Model, levels=unique(res_i$Model))
-
-  plot_list[[pheno_i]] <-  ggplot(res_i, aes(x=Method, y=R, fill = Model)) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = .2,
-                  position = position_dodge(width = 0.7)) +
-    geom_point(stat="identity", position=position_dodge(0.7), size=2, shape=23) +
-    geom_hline(yintercept = 0) +
-    geom_vline(xintercept = seq(1.5, length(unique(res_i$Method))), linetype="dotted") +
-    facet_grid(.~ target, scales = 'free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+    for(group_i in unique(res_i$Group)){
+      res_group_i <- res_i[res_i$Group == group_i,]
+      cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
+      
+      meta_res_i <-
+        agg(
+          id = Sample,
+          es = R_diff,
+          var = R_diff_SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_group_i
+        )
+      
+      meta_res <- rbind(meta_res,
+                        data.table(
+                          Target = targ_pop_i,
+                          Group = group_i,
+                          gwas_group = res_group_i$gwas_group[1],
+                          Model_1 = res_group_i$Model_1[1],
+                          Model_2 = res_group_i$Model_2[1],
+                          R_diff = meta_res_i$es,
+                          R_diff_SE = sqrt(meta_res_i$var)
+                        ))
+    }
+  }
+}
+meta_res$R_diff_z <- meta_res$R_diff / meta_res$R_diff_SE
+meta_res$R_diff_p <- 2*pnorm(-abs(meta_res$R_diff_z))
+
+# Mirror results to fill in gaps
+meta_res_symmetric <- meta_res
+meta_res_mirrored <- meta_res
+meta_res_mirrored$Model_1 <- meta_res$Model_2
+meta_res_mirrored$Model_2 <- meta_res$Model_1
+meta_res_mirrored$R_diff <- -meta_res_mirrored$R_diff
+meta_res <- rbind(meta_res_symmetric, meta_res_mirrored)
+
+meta_res$R_diff[meta_res$Model_1_Test == meta_res$Model_2_Test]<-NA
+meta_res$R_diff_catagory<-'NA'
+meta_res$R_diff_catagory[meta_res$R_diff < -0.002]<-'-0.025 - -0.002'
+meta_res$R_diff_catagory[meta_res$R_diff < -0.025]<-'-0.08 - -0.025'
+meta_res$R_diff_catagory[meta_res$R_diff < -0.08]<-'< -0.08'
+meta_res$R_diff_catagory[meta_res$R_diff > -0.002 & meta_res$R_diff < 0.002]<-'-0.002 - 0.002'
+meta_res$R_diff_catagory[meta_res$R_diff > 0.002]<-'0.002 - 0.025'
+meta_res$R_diff_catagory[meta_res$R_diff > 0.025]<-'0.025 - 0.08'
+meta_res$R_diff_catagory[meta_res$R_diff > 0.08]<-'> 0.08'
+meta_res$R_diff_catagory<-factor(meta_res$R_diff_catagory, level=rev(c('< -0.08','-0.08 - -0.025','-0.025 - -0.002','-0.002 - 0.002','0.002 - 0.025','0.025 - 0.08','> 0.08')))
+meta_res$indep_star<-' '
+meta_res$indep_star[meta_res$R_diff_p < 0.05]<-'*'
+meta_res$indep_star[meta_res$R_diff_p < 1e-3]<-'**'
+meta_res$indep_star[meta_res$R_diff_p < 1e-6]<-'***'
+
+# Annotate tests to get order correct
+meta_res$Method<-sub('\\..*','',meta_res$Model_1)
+
+meta_res$Model[grepl('top1', meta_res$Model_1)]<-'Top1'
+meta_res$Model[grepl('pseudo', meta_res$Model_1)]<-'Pseudo'
+meta_res$Model[!grepl('top1|pseudo', meta_res$Model_1)]<-'Multi'
+
+meta_res$Model2[grepl('top1', meta_res$Model_2)]<-'Top1'
+meta_res$Model2[grepl('pseudo', meta_res$Model_2)]<-'Pseudo'
+meta_res$Model2[!grepl('top1|pseudo', meta_res$Model_2)]<-'Multi'
+
+meta_res$Source[grepl('AFR|EAS|EUR', meta_res$Model_1)] <- 'Single'
+meta_res$Source[!grepl('AFR|EAS|EUR', meta_res$Model_1)] <- 'Multi'
+
+meta_res$Source2[grepl('AFR|EAS|EUR', meta_res$Model_2)] <- 'Single'
+meta_res$Source2[!grepl('AFR|EAS|EUR', meta_res$Model_2)] <- 'Multi'
+
+meta_res$Discovery[grepl('EUR', meta_res$Model_1)] <- 'EUR'
+meta_res$Discovery[grepl('EAS', meta_res$Model_1)] <- 'EAS'
+meta_res$Discovery[grepl('AFR', meta_res$Model_1)] <- 'AFR'
+meta_res$Discovery[meta_res$Source == 'Multi'] <- meta_res$gwas_group[meta_res$Source == 'Multi']
+
+meta_res$Discovery2[grepl('EUR', meta_res$Model_2)] <- 'EUR'
+meta_res$Discovery2[grepl('EAS', meta_res$Model_2)] <- 'EAS'
+meta_res$Discovery2[grepl('AFR', meta_res$Model_2)] <- 'AFR'
+meta_res$Discovery2[meta_res$Source2 == 'Multi'] <- meta_res$gwas_group[meta_res$Source2 == 'Multi']
+
+meta_res$Method<-factor(meta_res$Method, levels=unique(meta_res$Method))
+meta_res$Model<-factor(meta_res$Model, levels=c('Top1','Pseudo','Multi'))
+meta_res$Model2<-factor(meta_res$Model2, levels=c('Top1','Pseudo','Multi'))
+meta_res$Discovery<-factor(meta_res$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+meta_res$Discovery2<-factor(meta_res$Discovery2, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
+
+meta_res<-meta_res[order(meta_res$Discovery, meta_res$Discovery2, meta_res$Method),]
+
+heatmap_list<-NULL
+for(model_i in c('Top1','Pseudo','Multi')){
+  heatmap_list[[model_i]]<-list()
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    heatmap_list[[model_i]][[targ_pop_i]]<-list()
+    for(disc_pop_i in disc_pop){
+  
+      tmp <- meta_res[meta_res$Target == targ_pop_i, ]
+      tmp <- tmp[tmp$gwas_group == paste0('EUR+', disc_pop_i), ]
+      tmp <- tmp[tmp$Model == model_i & tmp$Model2 == model_i,]
+      tmp$Model_1<-gsub('\\..*','', tmp$Model_1)
+      tmp$Model_2<-gsub('\\..*','', tmp$Model_2)
+      
+      tmp$Model_1 <- factor(
+        tmp$Model_1, 
+        levels=unique(tmp$Model_1))
+      tmp$Model_2 <- factor(
+        tmp$Model_2, 
+        levels=unique(tmp$Model_1))
+      
+      heatmap_list[[model_i]][[targ_pop_i]][[disc_pop_i]] <-
+        ggplot(data = tmp, aes(Model_2, Model_1, fill = R_diff_catagory)) +
+        geom_tile(color = "white") +
+        labs(y = 'Test', x = 'Comparison', fill = 'R difference', title = paste0('Target: ', targ_pop_i, ', Model = ', model_i)) +
+        facet_grid(Discovery ~ Discovery2, scales = 'free', space = 'free', switch="both") +
+        geom_text(
+          data = tmp,
+          aes(Model_2, Model_1, label = indep_star),
+          color = "black",
+          size = 4,
+          angle = 0,
+          vjust = 0.8
+        ) +
+        scale_fill_brewer(
+          breaks = levels(tmp$R_diff_catagory),
+          palette = "RdBu",
+          drop = F,
+          na.value = 'grey'
+        ) +
+        theme_half_open() +
+        background_grid() +
+        panel_border() +
+        theme(axis.text.x = element_text(
+          angle = 45,
+          vjust = 1,
+          hjust = 1
+        ))
+    }
+  }
 }
 
-png(
-  '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.eval.png',
-  res = 300,
-  height = 1200*nrow(prscsx_dat),
-  width = 5000,
-  units = 'px'
-)
-plot_grid(plotlist = plot_list, labels = names(plot_list), ncol=1)
-dev.off()
 
 ```
 </details>
 
+
+
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index b7d170cf..caa7adb5 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -119,6 +119,11 @@ if 'gwas_list' in config and config["gwas_list"] != 'NA':
 else:
   gwas_list_df = pd.DataFrame(columns = ["name", "path", "population", "n", "sampling", "prevalence", "mean", "sd", "label"])
 
+# Check for duplicate values in the 'name' column
+duplicate_names = gwas_list_df[gwas_list_df['name'].duplicated(keep=False)]
+if not duplicate_names.empty:
+    raise ValueError(f"Duplicate values found in 'name' column of the gwas_list: {', '.join(duplicate_names['name'].unique())}")
+
 # Check whether gwas_list paths exist
 check_list_paths(gwas_list_df)
 
@@ -161,6 +166,29 @@ if 'ldpred2' in config['pgs_methods']:
         print(f"File not found: {ld_file}")
         raise FileNotFoundError(f"Required file not found: {ld_file}. LDpred2 reference data must include files for all chromosomes.")
 
+# Set sbayesr reference path
+if config['sbayesr_ldref'] == 'NA':
+  sbayesr_ldref=f"{resdir}/data/ldpred2_ref"
+else:
+  sbayesr_ldref=config['sbayesr_ldref']
+
+# Check the sbayesr ldref data is present for the required populations in the gwas_list
+if 'sbayesr' in config['pgs_methods']:
+  for pop in gwas_list_df['population'].unique():
+    path = f"{sbayesr_ldref}/{pop}"
+    # Check if map.rds file exists
+    map_file = os.path.join(path, "map.rds")
+    if not os.path.exists(map_file):
+      print(f"File not found: {map_file}")
+      raise FileNotFoundError(f"Required file not found: {map_file}. SBayesR reference data must include map.rds for all populations.")
+
+    # Check if LD_with_blocks_chr${chr}.rds files exist for chr 1 to 22
+    for chr in range(1, 23):
+      ld_file = os.path.join(path, f"LD_with_blocks_chr{chr}.rds")
+      if not os.path.exists(ld_file):
+        print(f"File not found: {ld_file}")
+        raise FileNotFoundError(f"Required file not found: {ld_file}. SBayesR reference data must include files for all chromosomes.")
+
 # Set refdir parameter
 # If refdir is NA, set refdir to '${resdir}/data/ref'
 if config['refdir'] == 'NA':
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 10b57631..ac6b54b9 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -387,7 +387,12 @@ else:
   pgs_methods = config['pgs_methods']
   pgs_methods_all = config['pgs_methods']
 
-# Check whether gwas_list paths exist
+# Check for duplicate values in the 'name' column
+duplicate_names = score_list_df[score_list_df['name'].duplicated(keep=False)]
+if not duplicate_names.empty:
+    raise ValueError(f"Duplicate values found in 'name' column of the score_list: {', '.join(duplicate_names['name'].unique())}")
+
+# Check whether score_list paths exist
 check_list_paths(score_list_df)
 
 # Download PGS score files for PGSC if path is NA
@@ -477,6 +482,11 @@ if 'gwas_groups' in config and config["gwas_groups"] != 'NA':
 else:
   gwas_groups_df = pd.DataFrame(columns = ["name", "gwas", "label"])
 
+# Check for duplicate values in the 'name' column
+duplicate_names = gwas_groups_df[gwas_groups_df['name'].duplicated(keep=False)]
+if not duplicate_names.empty:
+    raise ValueError(f"Duplicate values found in 'name' column of the gwas_groups file: {', '.join(duplicate_names['name'].unique())}")
+
 # Function to get the list of GWAS names for a given group
 def get_gwas_names(gwas_group):
     gwas_names_str = gwas_groups_df[gwas_groups_df['name'] == gwas_group]['gwas'].iloc[0]
diff --git a/pipeline/rules/target_qc.smk b/pipeline/rules/target_qc.smk
index a810af19..d960a440 100644
--- a/pipeline/rules/target_qc.smk
+++ b/pipeline/rules/target_qc.smk
@@ -16,6 +16,11 @@ else:
   target_list_df_samp = pd.DataFrame(columns = ["name", "path" "type", "indiv_report","unrel"])
   target_list_df_indiv_report = pd.DataFrame(columns = ["name", "path" "type", "indiv_report","unrel"])
 
+# Check for duplicate values in the 'name' column
+duplicate_names = target_list_df[target_list_df['name'].duplicated(keep=False)]
+if not duplicate_names.empty:
+    raise ValueError(f"Duplicate values found in 'name' column of the target_list: {', '.join(duplicate_names['name'].unique())}")
+
 ####
 # Format target data
 ####

From 9a405b6e60a03a66eb83f0599963317fa1a737ca Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 9 Oct 2024 13:12:16 +0100
Subject: [PATCH 029/153] Updated default config to include gwas_groups

---
 pipeline/config.yaml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pipeline/config.yaml b/pipeline/config.yaml
index f752663b..a3394493 100644
--- a/pipeline/config.yaml
+++ b/pipeline/config.yaml
@@ -37,6 +37,9 @@ prscs_phi: ['1e-6', '1e-4', '1e-2', '1', 'auto']
 # Specify 1kg or ukb ld reference for PRS-CS and PRS-CSx
 prscs_ldref: '1kg'
 
+# Specify reference for SBayesR
+sbayesr_ldref: NA
+
 # Specify reference for LDpred2
 ldpred2_ldref: NA
 

From 50b72ca5eaa99d2e58f404471bb5419eb5de498d Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 9 Oct 2024 13:12:50 +0100
Subject: [PATCH 030/153] Updated document for LDpred2 ref creation

---
 docs/prep_ldpred2_ldmat.Rmd | 41 ++++++++++++++++++++++---------------
 1 file changed, 25 insertions(+), 16 deletions(-)

diff --git a/docs/prep_ldpred2_ldmat.Rmd b/docs/prep_ldpred2_ldmat.Rmd
index 963ba682..0b43efde 100644
--- a/docs/prep_ldpred2_ldmat.Rmd
+++ b/docs/prep_ldpred2_ldmat.Rmd
@@ -3,9 +3,6 @@ title: Preparing LDpred2 LD matrices
 output: 
   html_document:
     theme: cosmo
-    toc: true
-    toc_float: true
-    toc_depth: 2
     css: styles/styles.css
     includes:
       in_header: header.html
@@ -80,10 +77,9 @@ for(pop in c('EUR', 'EAS', 'AFR')){
       )
     )
   G <- ref$genotypes
-  NCORES <- nb_cores()
-  
+
   #### Impute missing values (bigsnpr can't handle missing data in most functions)
-  G_imp <- snp_fastImputeSimple(G, method = "mean2", ncores = NCORES)
+  G_imp <- snp_fastImputeSimple(G, method = "mean2", ncores = nb_cores())
 
   # Save imputed reference
   ref$genotypes<-G_imp
@@ -102,8 +98,10 @@ for(pop in c('EUR', 'EAS', 'AFR')){
 library(dplyr)
 library(bigreadr)
 library(bigsnpr)
+library(future.batchtools)
+library(furrr)
 
-for(pop in c('EAS', 'AFR')){
+for(pop in c('EUR', 'EAS', 'AFR')){
   obj.1000G <- snp_attach(paste0(
           '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
           pop,
@@ -114,12 +112,18 @@ for(pop in c('EAS', 'AFR')){
   G <- obj.1000G$genotypes
   CHR <- obj.1000G$map$chromosome
   POS <- obj.1000G$map$physical.pos
-  POS2 <- snp_asGeneticPos(CHR, POS, dir ='/users/k1806347/oliverpainfel/Data/Genetic_Map/CEU', ncores = NCORES) # Note this is not appropriate but I think Florian did the same as used distance from UKB
-  MAF <- snp_MAF(G, ncores = NCORES)
+  POS2 <- snp_asGeneticPos(CHR, POS, dir ='/users/k1806347/oliverpainfel/Data/Genetic_Map/CEU', ncores = nb_cores()) # Note this is not appropriate but I think Florian did the same as used distance from UKB
+  MAF <- snp_MAF(G, ncores = nb_cores())
   keep <- (MAF > 0.02)
 
-  all_final_grp <- NULL
-  for(chr in 1:22){
+  plan(batchtools_slurm(resources = list(
+    time = "12:00:00",
+    ntasks = 5,
+    mem = "20g",
+    partition = "neurohack_cpu"
+  )))
+  
+  all_final_grp <- furrr::future_map_dfr(1:22, function(chr) {
     ind.chr2 <- which(CHR == chr & keep)
 
     corr0 <- runonce::save_run({
@@ -128,7 +132,7 @@ for(pop in c('EAS', 'AFR')){
         ind.col = ind.chr2,
         infos.pos = POS2[ind.chr2],
         size = 3 / 1000,
-        ncores = NCORES
+        ncores = nb_cores()
       )
     }, file = paste0(
       '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
@@ -139,9 +143,13 @@ for(pop in c('EAS', 'AFR')){
     ))
     
     # find nearly independent LD blocks
+    # Playing around with the snp_ldsplit function, the max_r2 needs to be increased to 0.2 for AFR populations.
+    # This might be because we are using an inappropriate recombination map (EUR)?
+    # Use max_r2 = 0.15 for EUR and EAS as in Florian's code, but increase to 0.2 for AFR. Note. this is still lower than the default of 0.3.
+    max_r2 <- ifelse(pop %in% c('EUR','EAS'), 0.15, 0.25)
     m <- length(ind.chr2)
     (SEQ <- round(seq_log(m / 30, m / 5, length.out = 20)))
-    splits <- snp_ldsplit(corr0, thr_r2 = 0.05, min_size = 50, max_size = SEQ, max_r2 = 0.15)
+    splits <- snp_ldsplit(corr0, thr_r2 = 0.05, min_size = 50, max_size = SEQ, max_r2 = max_r2)
     splits$cost2 <- sapply(splits$all_size, function(sizes) sum(sizes^2))
   
     best_split <- splits %>%
@@ -167,8 +175,9 @@ for(pop in c('EAS', 'AFR')){
     ))
   
     # return
-    all_final_grp <- rbind(all_final_grp, tibble(best_split, ind = list(ind.chr2)))
-  }
+    tibble(best_split, ind = list(ind.chr2))
+  })
+  
   saveRDS(
     all_final_grp,
     paste0(
@@ -184,7 +193,7 @@ for(pop in c('EAS', 'AFR')){
   map <- obj.1000G$map[keep,]
 
   # Compute allele frequency
-  map$af<-big_colstats(G, ind.col = which(keep), ncores = NCORES)$sum / (2 * nrow(G))
+  map$af<-big_colstats(G, ind.col = which(keep), ncores = nb_cores())$sum / (2 * nrow(G))
   
   # Compute LD scores
   map$ld <- do.call('c', lapply(1:22, function(chr) {

From eda006230ad8a82dbb98ced8109a1ff59768762e Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 9 Oct 2024 13:13:11 +0100
Subject: [PATCH 031/153] Updated to model_builder

---
 Scripts/model_builder/model_builder.R | 9 ++++++++-
 1 file changed, 8 insertions(+), 1 deletion(-)

diff --git a/Scripts/model_builder/model_builder.R b/Scripts/model_builder/model_builder.R
index 3bead43b..d76f71ae 100644
--- a/Scripts/model_builder/model_builder.R
+++ b/Scripts/model_builder/model_builder.R
@@ -147,6 +147,9 @@ for(i in unique(group_list$group)){
   group_list$n[group_list$group == i] <- sum(group_list$group == i)
 }
 
+write.table(group_list[!duplicated(group_list$group), c('group','n'), with = F], paste0(opt$out,'.group_list.txt'), col.names=T, row.names=F, quote=F)
+log_add(log_file = log_file, message = paste0('List of groups saved as ',opt$out,'.group_list.txt.'))
+
 ###########
 # Read in the outcome data
 ###########
@@ -182,7 +185,9 @@ log_add(log_file = log_file, message = paste0('Data to be carried foward is ',fo
 ############
 
 if(opt$assoc){
-  log_add(log_file = log_file, message = 'Performing association analysis with each predictor...')
+  # Initialise progress log
+  log_message <- 'Performing association analysis with each predictor... '
+  progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
 
 	outcome_predictors_y <- outcome_predictors$outcome_var
 	outcome_predictors_x <- outcome_predictors[, -1:-2]
@@ -212,6 +217,8 @@ if(opt$assoc){
 
 	}
 
+	update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
+
 	if(family == 'binomial'){
   	assoc_res$N_case <- sum(outcome_predictors_y == 'CASE')
   	assoc_res$N_control <- sum(outcome_predictors_y == 'CONTROL')

From a776f50a9adacb5a278ead9db837d01239f61a77 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 11 Oct 2024 08:17:45 +0100
Subject: [PATCH 032/153] Allowed for chr string in chr_name column of score
 file

---
 functions/pgs.R | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/functions/pgs.R b/functions/pgs.R
index d151e5bd..44f4349a 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -406,6 +406,8 @@ read_score <- function(score, chr = 1:22, log_file = NULL){
   }
 
 	if(any(names(score) == 'CHR')){
+	  # Remove for 'chr' string in CHR column
+	  score$CHR <- gsub('chr', '', score$CHR)
 	  score <- score[score$CHR %in% chr,]
 	}
 

From 6a0358e4de62f5cedbab2c759c429ce719be331f Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 11 Oct 2024 16:11:53 +0100
Subject: [PATCH 033/153] Update to model_builder to remove duplicate features
 within each group

---
 Scripts/model_builder/model_builder.R | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/Scripts/model_builder/model_builder.R b/Scripts/model_builder/model_builder.R
index d76f71ae..74242228 100644
--- a/Scripts/model_builder/model_builder.R
+++ b/Scripts/model_builder/model_builder.R
@@ -142,6 +142,27 @@ if(nrow(group_list) > 1 & opt$all_model){
 
 log_add(log_file = log_file, message = paste0(length(unique(group_list$group)), ' groups of predictors present.'))
 
+# Remove identical predictors within each group
+group_list_non_identical <- NULL
+for(i in unique(group_list$group)){
+  if(sum(group_list$group == i) > 1){
+    ident <- group_list$predictor[group_list$group == i][
+      duplicated(
+        as.list(
+          predictors[, group_list$predictor[group_list$group == i], with = F]))]
+
+    group_list_non_identical <- rbind(
+      group_list_non_identical,
+      group_list[group_list$group == i & !(group_list$predictor %in% ident),]
+    )
+
+    if(length(ident) > 0){
+      log_add(log_file = log_file, message = paste0(length(ident), ' duplicate predictors removed from group ', i))
+    }
+  }
+}
+group_list <- group_list_non_identical
+
 # Calculate the number of predictors in each group
 for(i in unique(group_list$group)){
   group_list$n[group_list$group == i] <- sum(group_list$group == i)

From 2283202c06c71b956d6d1fe82e93540170149391 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 11 Oct 2024 16:49:28 +0100
Subject: [PATCH 034/153] Avoided recalculating PGS for unchanged score files,
 unless target genetic data has been updated

---
 .../target_scoring/target_scoring_pipeline.R  | 31 ++++++++++++++++---
 pipeline/rules/target_scoring.smk             |  3 +-
 2 files changed, 28 insertions(+), 6 deletions(-)

diff --git a/Scripts/target_scoring/target_scoring_pipeline.R b/Scripts/target_scoring/target_scoring_pipeline.R
index 5e5bb22d..77393367 100644
--- a/Scripts/target_scoring/target_scoring_pipeline.R
+++ b/Scripts/target_scoring/target_scoring_pipeline.R
@@ -55,7 +55,7 @@ system(paste0('mkdir -p ',opt$output_dir))
 tmp_dir<-tempdir()
 
 # Initiate log file
-log_file <- paste0(opt$output,'.log')
+log_file <- paste0(opt$output, '_', format(Sys.time(), "%Y-%m-%d_%H-%M-%S"), '.log')
 log_header(log_file = log_file, opt = opt, script = 'target_scoring_pipeline.R', start.time = start.time)
 
 # If testing, change CHROMS to chr value
@@ -69,8 +69,31 @@ if(!is.na(opt$test)){
 # Identify score files to be combined
 score_files<-list_score_files(opt$config)
 
-if(is.null(score_files)){
-  log_add(log_file = log_file, message = paste0('No score files specified.'))
+# Check whether score files or target genetic data are newer than target pgs
+if(!is.null(score_files)){
+  ancestry_reporter_file<-paste0(outdir, '/reference/target_checks/', opt$name, '/ancestry_reporter.done')
+  ancestry_reporter_file_time <- file.info(ancestry_reporter_file)$mtime
+  score_files_to_do <- data.table()
+  for(i in 1:nrow(score_files)){
+    pgs_i <- paste0(outdir, '/', opt$name,'/pgs/', opt$population,'/', score_files$method[i],'/', score_files$name[i],'/', opt$name,'-', score_files$name[i],'-',opt$population,'.profiles')
+    score_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '.score.gz')
+    if(!file.exists(pgs_i)){
+      score_files_to_do <- rbind(score_files_to_do, score_files[i,])
+    } else {
+      score_i_time <- file.info(score_i)$mtime
+      pgs_i_time <- file.info(pgs_i)$mtime
+      if (score_i_time > pgs_i_time | ancestry_reporter_file_time > pgs_i_time) {
+        score_files_to_do <- rbind(score_files_to_do, score_files[i,])
+        system(paste0('rm ', pgs_i))
+      }
+    }
+  }
+  log_add(log_file = log_file, message = paste0('After checking timestamps, ', nrow(score_files_to_do), '/', nrow(score_files), ' score files will be used for target scoring.'))
+  score_files <- score_files_to_do
+}
+
+if(is.null(score_files) || nrow(score_files) == 0){
+  log_add(log_file = log_file, message = paste0('No score files to be used for target scoring.'))
   end.time <- Sys.time()
   time.taken <- end.time - start.time
   sink(file = paste(opt$output,'.log',sep=''), append = T)
@@ -208,7 +231,7 @@ log_add(log_file = log_file, message = paste0('Saved polygenic scores.'))
 
 end.time <- Sys.time()
 time.taken <- end.time - start.time
-sink(file = paste(opt$output,'.log',sep=''), append = T)
+sink(file = log_file, append = T)
 cat('Analysis finished at',as.character(end.time),'\n')
 cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
 sink()
diff --git a/pipeline/rules/target_scoring.smk b/pipeline/rules/target_scoring.smk
index e4f95711..52d2b23e 100644
--- a/pipeline/rules/target_scoring.smk
+++ b/pipeline/rules/target_scoring.smk
@@ -71,8 +71,7 @@ rule target_pgs_i:
     testing=config["testing"],
     config_file = config["config_file"]
   shell:
-    "rm -r -f {outdir}/{wildcards.name}/pgs/{wildcards.population} && \
-     Rscript ../Scripts/target_scoring/target_scoring_pipeline.R \
+    "Rscript ../Scripts/target_scoring/target_scoring_pipeline.R \
       --config {params.config_file} \
       --name {wildcards.name} \
       --population {wildcards.population} \

From 1c7dfd76348a65065b7c7854152a133bb728504d Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 11 Oct 2024 18:04:27 +0100
Subject: [PATCH 035/153] fixed bug in DBSLMM when SNP-h2 is low

---
 Scripts/pgs_methods/dbslmm.R | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/Scripts/pgs_methods/dbslmm.R b/Scripts/pgs_methods/dbslmm.R
index 86d81374..6aec5225 100644
--- a/Scripts/pgs_methods/dbslmm.R
+++ b/Scripts/pgs_methods/dbslmm.R
@@ -149,6 +149,11 @@ opt$sumstats<-paste0(tmp_dir,'/sumstats.gz')
 
 ldsc_h2 <- ldsc(sumstats = opt$sumstats, ldsc = opt$ldsc, hm3_snplist = opt$hm3_snplist, munge_sumstats = opt$munge_sumstats, ld_scores = opt$ld_scores, pop_prev = opt$pop_prev, sample_prev = opt$sample_prev, log_file = log_file)
 
+if(ldsc_h2 < 0.05){
+  ldsc_h2 <- 0.05
+  log_add(log_file = log_file, message = 'SNP-h2 was set to 0.05.')
+}
+
 if(any(ldsc_h2*opt$h2f > 1)){
   ldsc_h2 <- ldsc_h2*(1/max(opt$h2f*ldsc_h2))
   log_add(log_file = log_file, message = paste0('SNP-h2 was set to ',ldsc_h2,' to avoid SNP-h2*h2f > 1.'))

From 4b49f50afb695b3d1e5b716c6d56163dcda1dac8 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 11 Oct 2024 18:04:46 +0100
Subject: [PATCH 036/153] Improved  log when SNP-heritability is low

---
 Scripts/pgs_methods/ldpred2.R | 1 +
 1 file changed, 1 insertion(+)

diff --git a/Scripts/pgs_methods/ldpred2.R b/Scripts/pgs_methods/ldpred2.R
index db3853ac..9e3ad382 100644
--- a/Scripts/pgs_methods/ldpred2.R
+++ b/Scripts/pgs_methods/ldpred2.R
@@ -175,6 +175,7 @@ log_add(log_file = log_file, message = paste0('Estimated SNP-based heritability
 
 if(ldsc[["h2"]] < 0.05){
   ldsc[["h2"]] <- 0.05
+  log_add(log_file = log_file, message = 'SNP-based heritability was set to 0.05.')
 }
 
 log_add(log_file = log_file, message = 'Creating genome-wide sparse matrix.')

From 43ac4d056e40c0831586129648487c6773fe376b Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 11 Oct 2024 18:06:45 +0100
Subject: [PATCH 037/153] Set to use pseudo model only in tlprs

---
 Scripts/pgs_methods/tlprs.R | 21 +++++++++++++++++++++
 1 file changed, 21 insertions(+)

diff --git a/Scripts/pgs_methods/tlprs.R b/Scripts/pgs_methods/tlprs.R
index 22b0caf5..731b0557 100644
--- a/Scripts/pgs_methods/tlprs.R
+++ b/Scripts/pgs_methods/tlprs.R
@@ -4,6 +4,8 @@ start.time <- Sys.time()
 library("optparse")
 
 option_list = list(
+  make_option("--config", action="store", default=NULL, type='character',
+      help="Pipeline configuration file. Required when pseudo_only is TRUE [optional]"),
   make_option("--ref_plink_chr", action="store", default=NULL, type='character',
       help="Path to per chromosome reference PLINK files [required]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
@@ -30,6 +32,8 @@ option_list = list(
       help="Number of cores for parallel computing [optional]"),
   make_option("--test", action="store", default=NA, type='character',
       help="Specify number of SNPs to include [optional]"),
+  make_option("--pseudo_only", action="store", default=T, type='character',
+      help="Apply TLPRS to model selected by pseudovalidation only [optional]"),
   make_option("--seed", action="store", default=1, type='numeric',
       help="Seed number for PRScs [optional]")
 )
@@ -49,6 +53,9 @@ library(doMC)
 registerDoMC(opt$n_cores)
 
 # Check required inputs
+if(is.null(opt$config) && opt$pseudo_only){
+  stop('--config must be specified when --pseudo_only is TRUE.\n')
+}
 if(is.null(opt$ref_plink_chr)){
   stop('--ref_plink_chr must be specified.\n')
 }
@@ -152,6 +159,20 @@ for(i in 1:length(populations)){
 
   # Read in PGS score file (set up for pairs of GWAS/populations only)
   score_file <- fread(scores[-i])
+
+  if(opt$pseudo_only){
+    method <- sub('/.*','',gsub('.*pgs_score_files/','', scores[-i]))
+    gwas <- sub('/.*','',gsub(paste0('.*/',method,'/'),'', scores[-i]))
+    param <- find_pseudo(
+      config = opt$config,
+      gwas = gwas,
+      pgs_method = method,
+      target_pop = populations[i]
+    )
+    score_file <- score_file[, c('SNP', 'A1', 'A2', paste0('SCORE_', param)), with = F]
+    log_add(log_file = log_file, message = 'Using pseudovalidated PGS only.')
+  }
+
   names(score_file)<-gsub('^SCORE','Beta', names(score_file))
 
   target_gwas_j=merge(score_file, target_gwas, by="SNP",sort=F)

From 9e38c800f491030c75c4627ef94faceec0a2aa72 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 11 Oct 2024 18:07:04 +0100
Subject: [PATCH 038/153] Using config param for tlprs

---
 pipeline/rules/pgs_methods.smk | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index ac6b54b9..9e879466 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -638,10 +638,12 @@ rule prep_pgs_tlprs_i:
     sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
     scores= lambda w: ",".join(expand(f"{outdir}/reference/pgs_score_files/{{method}}/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group), method=w.method)),
     populations= lambda w: ",".join(get_populations(w.gwas_group)),
-    testing=config["testing"]
+    testing=config["testing"],
+    config_file = config["config_file"]
   shell:
     """
     Rscript ../Scripts/pgs_methods/tlprs.R \
+      --config {params.config_file} \
       --ref_plink_chr {refdir}/ref.chr \
       --sumstats {params.sumstats} \
       --scores {params.scores} \

From 934d52b04bce9791e6421bcbfaa5d04826caef62 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 15 Oct 2024 18:27:31 +0100
Subject: [PATCH 039/153] Added checked no duplicate names across gwas_list,
 score_list and gwas_groups

---
 pipeline/rules/dependencies.smk | 126 +++++++++++++++++++++++++++++++-
 pipeline/rules/pgs_methods.smk  |  54 --------------
 pipeline/rules/target_qc.smk    |  25 -------
 3 files changed, 122 insertions(+), 83 deletions(-)

diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index caa7adb5..15369698 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -113,6 +113,48 @@ check_config_parameters(config)
 # Set outdir parameter
 outdir=config['outdir']
 
+#######
+# Check config files
+#######
+
+# Function to check for duplicate names in a dataframe
+def check_for_duplicates(df, name_col, list_name):
+    duplicate_names = df[df[name_col].duplicated(keep=False)]
+    if not duplicate_names.empty:
+        raise ValueError(f"Duplicate values found in '{name_col}' column of {list_name}: {', '.join(duplicate_names[name_col].unique())}")
+
+
+###
+# target_list
+###
+
+if 'target_list' in config and config["target_list"] != 'NA':
+  target_list_df = pd.read_table(config["target_list"], sep=r'\s+')
+  if 'unrel' not in target_list_df.columns:
+    target_list_df['unrel'] = 'NA'  # Adding a column with string 'NA' values
+  target_list_df_23andMe = target_list_df.loc[target_list_df['type'] == '23andMe']
+  samp_types = ['plink1', 'plink2', 'bgen', 'vcf']
+  target_list_df_samp = target_list_df[target_list_df['type'].isin(samp_types)]
+  target_list_df_indiv_report = target_list_df.loc[(target_list_df['indiv_report'].isin(['T', 'TRUE', True]))]
+else:
+  target_list_df = pd.DataFrame(columns = ["name", "path" "type", "indiv_report","unrel"])
+  target_list_df_23andMe = pd.DataFrame(columns = ["name", "path" "type", "indiv_report","unrel"])
+  target_list_df_samp = pd.DataFrame(columns = ["name", "path" "type", "indiv_report","unrel"])
+  target_list_df_indiv_report = pd.DataFrame(columns = ["name", "path" "type", "indiv_report","unrel"])
+
+# Check for duplicate values in the 'name' column
+check_for_duplicates(target_list_df, 'name', 'target_list')
+
+# Check specific target paths exist
+check_target_type(df = target_list_df)
+
+# Check specific target paths exist
+check_target_paths(df = target_list_df, chr = str(get_chr_range(config['testing'])[0]))
+
+###
+# gwas_list
+###
+
 # Read in the gwas_list or make an empty version
 if 'gwas_list' in config and config["gwas_list"] != 'NA':
   gwas_list_df = pd.read_table(config["gwas_list"], sep=r'\s+')
@@ -120,9 +162,7 @@ else:
   gwas_list_df = pd.DataFrame(columns = ["name", "path", "population", "n", "sampling", "prevalence", "mean", "sd", "label"])
 
 # Check for duplicate values in the 'name' column
-duplicate_names = gwas_list_df[gwas_list_df['name'].duplicated(keep=False)]
-if not duplicate_names.empty:
-    raise ValueError(f"Duplicate values found in 'name' column of the gwas_list: {', '.join(duplicate_names['name'].unique())}")
+check_for_duplicates(gwas_list_df, 'name', 'gwas_list')
 
 # Check whether gwas_list paths exist
 check_list_paths(gwas_list_df)
@@ -130,6 +170,84 @@ check_list_paths(gwas_list_df)
 # Identify gwas_list with population == 'EUR'
 gwas_list_df_eur = gwas_list_df.loc[gwas_list_df['population'] == 'EUR']
 
+###
+# score_list
+###
+
+# Read in score_list or create empty score_list
+if 'score_list' in config and config["score_list"] != 'NA':
+  score_list_df = pd.read_table(config["score_list"], sep=r'\s+')
+  pgs_methods = config['pgs_methods']
+  pgs_methods_all = list(config['pgs_methods'])
+  pgs_methods_all.append('external')
+
+  # Check whether score_list paths exist
+  check_list_paths(score_list_df)
+else:
+  score_list_df = pd.DataFrame(columns = ["name", "path", "label"])
+  pgs_methods = config['pgs_methods']
+  pgs_methods_all = config['pgs_methods']
+
+# Check for duplicate values in the 'name' column
+check_for_duplicates(score_list_df, 'name', 'score_list')
+
+# Check whether score_list paths exist
+check_list_paths(score_list_df)
+
+###
+# gwas_groups
+###
+
+# Read in the gwas_groups or make an empty version
+if 'gwas_groups' in config and config["gwas_groups"] != 'NA':
+  gwas_groups_df = pd.read_table(config["gwas_groups"], sep=r'\s+')
+else:
+  gwas_groups_df = pd.DataFrame(columns = ["name", "gwas", "label"])
+
+# Check for duplicate values in the 'name' column
+check_for_duplicates(gwas_groups_df, 'name', 'gwas_groups')
+
+# Function to get the list of GWAS names for a given group
+def get_gwas_names(gwas_group):
+    gwas_names_str = gwas_groups_df[gwas_groups_df['name'] == gwas_group]['gwas'].iloc[0]
+    return gwas_names_str.split(',')
+
+# Function to generate comma-separated list of populations for each name
+def get_populations(gwas_group):
+    gwas_names = get_gwas_names(gwas_group)
+    sumstats_populations = []
+    for gwas in gwas_names:
+        gwas_info = gwas_list_df[gwas_list_df['name'] == gwas].iloc[0]
+        sumstats_populations.append(gwas_info['population'])
+    return sumstats_populations
+
+# Check whether gwas_groups contains gwas that are not in the gwas_list
+gwas_groups_gwas = gwas_groups_df['gwas'].str.split(',', expand=True).stack().unique()
+gwas_list_names = gwas_list_df['name'].unique()
+missing_gwas = set(gwas_groups_gwas) - set(gwas_list_names)
+if missing_gwas:
+    raise ValueError(f"The following GWAS are in gwas_groups but missing in gwas_list: {', '.join(missing_gwas)}")
+
+###
+# Check there are no duplicate values in name columns of gwas_list, score_list, gwas_groups
+###
+
+def check_for_duplicates_across_lists(df_list, name_col, list_names):
+    combined_names = pd.concat([df[name_col] for df in df_list])
+
+    # Find duplicates across all lists
+    duplicate_names = combined_names[combined_names.duplicated(keep=False)]
+
+    if not duplicate_names.empty:
+        raise ValueError(f"Duplicate values found across {', '.join(list_names)}: {', '.join(duplicate_names.unique())}")
+
+check_for_duplicates_across_lists(
+    df_list=[gwas_list_df, score_list_df, gwas_groups_df],
+    name_col='name',
+    list_names=['gwas_list', 'score_list', 'gwas_groups']
+)
+
+#########
 # Set PRS-CS ld reference path
 if config['prscs_ldref'] == 'ukb':
     prscs_ldref='ukbb'
@@ -149,7 +267,7 @@ if config['ldpred2_ldref'] == 'NA':
 else:
   ldpred2_ldref=config['ldpred2_ldref']
 
-# Check the ldpred2 ldref data is present for the required populations in the gwas_list
+# Check the ldpred2 ldref data is present for the required populations in the pgwas_list
 if 'ldpred2' in config['pgs_methods']:
   for pop in gwas_list_df['population'].unique():
     path = f"{ldpred2_ldref}/{pop}"
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 9e879466..4557a0ad 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -373,28 +373,6 @@ rule prep_pgs_megaprs:
 # Process externally created score files
 ##
 
-# Read in score_list or create empty score_list
-if 'score_list' in config and config["score_list"] != 'NA':
-  score_list_df = pd.read_table(config["score_list"], sep=r'\s+')
-  pgs_methods = config['pgs_methods']
-  pgs_methods_all = list(config['pgs_methods'])
-  pgs_methods_all.append('external')
-
-  # Check whether score_list paths exist
-  check_list_paths(score_list_df)
-else:
-  score_list_df = pd.DataFrame(columns = ["name", "path", "label"])
-  pgs_methods = config['pgs_methods']
-  pgs_methods_all = config['pgs_methods']
-
-# Check for duplicate values in the 'name' column
-duplicate_names = score_list_df[score_list_df['name'].duplicated(keep=False)]
-if not duplicate_names.empty:
-    raise ValueError(f"Duplicate values found in 'name' column of the score_list: {', '.join(duplicate_names['name'].unique())}")
-
-# Check whether score_list paths exist
-check_list_paths(score_list_df)
-
 # Download PGS score files for PGSC if path is NA
 rule download_pgs_external:
   input:
@@ -476,38 +454,6 @@ checkpoint score_reporter:
 # Multi-ancestry methods
 ###########
 
-# Read in the gwas_groups or make an empty version
-if 'gwas_groups' in config and config["gwas_groups"] != 'NA':
-  gwas_groups_df = pd.read_table(config["gwas_groups"], sep=r'\s+')
-else:
-  gwas_groups_df = pd.DataFrame(columns = ["name", "gwas", "label"])
-
-# Check for duplicate values in the 'name' column
-duplicate_names = gwas_groups_df[gwas_groups_df['name'].duplicated(keep=False)]
-if not duplicate_names.empty:
-    raise ValueError(f"Duplicate values found in 'name' column of the gwas_groups file: {', '.join(duplicate_names['name'].unique())}")
-
-# Function to get the list of GWAS names for a given group
-def get_gwas_names(gwas_group):
-    gwas_names_str = gwas_groups_df[gwas_groups_df['name'] == gwas_group]['gwas'].iloc[0]
-    return gwas_names_str.split(',')
-
-# Function to generate comma-separated list of populations for each name
-def get_populations(gwas_group):
-    gwas_names = get_gwas_names(gwas_group)
-    sumstats_populations = []
-    for gwas in gwas_names:
-        gwas_info = gwas_list_df[gwas_list_df['name'] == gwas].iloc[0]
-        sumstats_populations.append(gwas_info['population'])
-    return sumstats_populations
-
-# Check whether gwas_groups contains gwas that are not in the gwas_list
-gwas_groups_gwas = gwas_groups_df['gwas'].str.split(',', expand=True).stack().unique()
-gwas_list_names = gwas_list_df['name'].unique()
-missing_gwas = set(gwas_groups_gwas) - set(gwas_list_names)
-if missing_gwas:
-    raise ValueError(f"The following GWAS are in gwas_groups but missing in gwas_list: {', '.join(missing_gwas)}")
-
 ####
 # PRS-CSx
 ####
diff --git a/pipeline/rules/target_qc.smk b/pipeline/rules/target_qc.smk
index d960a440..dd893fbe 100644
--- a/pipeline/rules/target_qc.smk
+++ b/pipeline/rules/target_qc.smk
@@ -2,35 +2,10 @@
 # Read target_list
 #######
 
-if 'target_list' in config and config["target_list"] != 'NA':
-  target_list_df = pd.read_table(config["target_list"], sep=r'\s+')
-  if 'unrel' not in target_list_df.columns:
-    target_list_df['unrel'] = 'NA'  # Adding a column with string 'NA' values
-  target_list_df_23andMe = target_list_df.loc[target_list_df['type'] == '23andMe']
-  samp_types = ['plink1', 'plink2', 'bgen', 'vcf']
-  target_list_df_samp = target_list_df[target_list_df['type'].isin(samp_types)]
-  target_list_df_indiv_report = target_list_df.loc[(target_list_df['indiv_report'].isin(['T', 'TRUE', True]))]
-else:
-  target_list_df = pd.DataFrame(columns = ["name", "path" "type", "indiv_report","unrel"])
-  target_list_df_23andMe = pd.DataFrame(columns = ["name", "path" "type", "indiv_report","unrel"])
-  target_list_df_samp = pd.DataFrame(columns = ["name", "path" "type", "indiv_report","unrel"])
-  target_list_df_indiv_report = pd.DataFrame(columns = ["name", "path" "type", "indiv_report","unrel"])
-
-# Check for duplicate values in the 'name' column
-duplicate_names = target_list_df[target_list_df['name'].duplicated(keep=False)]
-if not duplicate_names.empty:
-    raise ValueError(f"Duplicate values found in 'name' column of the target_list: {', '.join(duplicate_names['name'].unique())}")
-
 ####
 # Format target data
 ####
 
-# Check specific target paths exist
-check_target_type(df = target_list_df)
-
-# Check specific target paths exist
-check_target_paths(df = target_list_df, chr = str(get_chr_range(config['testing'])[0]))
-
 ##
 # 23andMe
 ##

From b2b8186784b76b3f9d84832ce1d823d6bd8ce258 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 15 Oct 2024 18:28:05 +0100
Subject: [PATCH 040/153] Improved find_pseudo command when using TLPRS

---
 functions/pipeline.R | 37 +++++++++++++++++++++++++++----------
 1 file changed, 27 insertions(+), 10 deletions(-)

diff --git a/functions/pipeline.R b/functions/pipeline.R
index 34160ef3..25a1fcdf 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -181,7 +181,7 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
   gwas_groups <- read_param(config = config, param = 'gwas_groups')
 
   # If pgs_method is multi-source, subset gwas_list to gwas in relevant group
-  if(pgs_method %in% pgs_group_methods){
+  if(grepl(paste0('^', pgs_group_methods, collapse = '|'), pgs_method)){
     gwas_list <- gwas_list[gwas_list$name %in% unlist(strsplit(gwas_groups$gwas[gwas_groups$name == gwas], ','))]
   }
 
@@ -210,6 +210,23 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
   # If TLPRS, find pseudo param, and then edit value for TLPRS
   tlprs <- ifelse(grepl('tlprs', pgs_method), T, F)
   pgs_method <- gsub('tlprs_', '', pgs_method)
+  if(tlprs && pgs_method %in% c('lassosum','megaprs')){
+    if(!is.null(target_pop) && target_pop == 'TRANS'){
+      cat('No pseudovalidation for TRANS target population available for ', pgs_method, '\n')
+      cat(paste0('Returning result for ', gwas_list$population[1],' target population.\n'))
+      target_pop <- gwas_list$population[1]
+    }
+    if(!is.null(target_pop) && target_pop %in% gwas_list$population){
+      # Note. selecting pseudoval from non-target GWAS, as this the score file going into TLPRS
+      gwas <- gwas_list$name[gwas_list$population != target_pop]
+    } else {
+      cat(paste0('target_pop ', target_pop,' is not present in gwas_group ', gwas, '.\n'))
+      cat(paste0('Returning result for ', gwas_list$population[1],' target population.\n'))
+      target_pop <- gwas_list$population[1]
+      # Note. selecting pseudoval from non-target GWAS, as this the score file going into TLPRS
+      gwas <- gwas_list$name[gwas_list$population != target_pop]
+    }
+  }
 
   # Use most stringent p-value threshold of 0.05 as pseudo
   if(pgs_method == 'ptclump'){
@@ -259,25 +276,25 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
   if(pgs_method == 'xwing'){
     if(!is.null(target_pop) && target_pop == 'TRANS'){
       cat('No pseudovalidation for TRANS target population available for xwing.\n')
-      cat('Returning result for EUR target population.\n')
-      target_pop <- 'EUR'
+      cat(paste0('Returning result for ', gwas_list$population[1],' target population.\n'))
+      target_pop <- gwas_list$population[1]
     } else if(!is.null(target_pop) && !(target_pop %in% gwas_list$population)){
       cat(paste0('target_pop ', target_pop,' is not present in gwas_group ', gwas, '.\n'))
-      cat('Returning result for EUR target population.\n')
-      target_pop <- 'EUR'
+      cat(paste0('Returning result for ', gwas_list$population[1],' target population.\n'))
+      target_pop <- gwas_list$population[1]
     }
     pseudo_val <- paste0('targ_', target_pop, '_weighted')
   }
 
   if(tlprs){
     if(!is.null(target_pop) && target_pop == 'TRANS'){
-      cat('No pseudovalidation for TRANS target population available for xwing.\n')
-      cat('Returning result for EUR target population.\n')
-      target_pop <- 'EUR'
+      cat('No pseudovalidation for TRANS target population available for TLPRS\n')
+      cat(paste0('Returning result for ', gwas_list$population[1],' target population.\n'))
+      target_pop <- gwas_list$population[1]
     } else if(!is.null(target_pop) && !(target_pop %in% gwas_list$population)){
       cat(paste0('target_pop ', target_pop,' is not present in gwas_group ', gwas, '.\n'))
-      cat('Returning result for EUR target population.\n')
-      target_pop <- 'EUR'
+      cat(paste0('Returning result for ', gwas_list$population[1],' target population.\n'))
+      target_pop <- gwas_list$population[1]
     }
     pseudo_val <- paste0('targ_', target_pop, '_', pseudo_val, '_TLPRS_61')
   }

From de0734a5f0a7d5333502e088f323742840a65335 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 15 Oct 2024 18:28:36 +0100
Subject: [PATCH 041/153] Updated documentation regarding find_pseudo

---
 docs/pipeline_readme.Rmd  | 2 +-
 docs/pipeline_readme.html | 8 ++++----
 2 files changed, 5 insertions(+), 5 deletions(-)

diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 8b651f0a..d068bd29 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -1208,7 +1208,7 @@ Determines the pseudovalidation parameter for a given GWAS and PGS method. See [
   - `ptclump` has no pseudovalidation approach, so this function will return the PGS based on a p-value threshold of 1.
   - `prscsx` returns meta-analysed PGS weights across populations. The target_pop parameter has no effect on the output.
   - `tlprs` has no pseudovalidation approach, so the function returns the pseudovalidated score for the PGS method used as input to `tlprs`, using strongest reweighting for the target population.
-  - `xwing` and `tlprs` do not have a pseudovalidation approach when using `target_pop = 'TRANS'`, so this function returns the result for a `EUR` target population. The function also returns the result for a `EUR` target population when the gwas_group did not contain a GWAS from the `target_pop`.
+  - `xwing` and `tlprs` do not have a pseudovalidation approach when using `target_pop = 'TRANS'`, so this function returns the result for the first population in the `gwas_group` file. The function also returns the result for first population in the `gwas_group` file when the gwas_group did not contain a GWAS from the `target_pop`.
 
 <details>
 <summary>See usage</summary>
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index e7e026a5..7d9b7fc0 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -1943,10 +1943,10 @@ <h2><code>find_pseudo</code></h2>
 population.</li>
 <li><code>xwing</code> and <code>tlprs</code> do not have a
 pseudovalidation approach when using <code>target_pop = 'TRANS'</code>,
-so this function returns the result for a <code>EUR</code> target
-population. The function also returns the result for a <code>EUR</code>
-target population when the gwas_group did not contain a GWAS from the
-<code>target_pop</code>.</li>
+so this function returns the result for the first population in the
+<code>gwas_group</code> file. The function also returns the result for
+first population in the <code>gwas_group</code> file when the gwas_group
+did not contain a GWAS from the <code>target_pop</code>.</li>
 </ul></li>
 </ul>
 <details>

From 89c6836820c2b3d2de5de47c9b2d28a22da1c809 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 15 Oct 2024 18:29:21 +0100
Subject: [PATCH 042/153] Added more detailed comparison of TLPRS

---
 docs/CrossPop.Rmd | 897 +++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 883 insertions(+), 14 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index a5ef9a6b..807536fb 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -656,10 +656,12 @@ config<-c(
   "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt",
   "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
   "gwas_groups: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt",
-  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','sbayesr','lassosum','ldpred2','megaprs']",
-  "tlprs_methods: ['dbslmm','sbayesr','prscs']",
+  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs']",
+  "tlprs_methods: ['dbslmm','prscs','lassosum']",
   "cores_prep_pgs: 20",
-  "cores_target_pgs: 50"
+  "cores_target_pgs: 50",
+  "ldpred2_inference: F",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/1KG/ldpred2"
 )
 
 write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
@@ -683,7 +685,856 @@ Lets use the model builder script which implements nested 10 fold cross validati
 
 ***
 
-## Create predictor lists
+## Evaluate TLPRS
+### Create predictor lists
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in list of outcomes 
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-prscsx_dat[1:5,]
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Subset to TLPRS scores and pseudo scores for corresponding methods
+pgs_methods <- unique(scores$method[grepl('^tlprs', scores$method)])
+pgs_methods <- c(pgs_methods, gsub('tlprs_','',pgs_methods))
+scores <- scores[scores$method %in% pgs_methods,]
+
+# Create files for EAS and AFR targets
+targ_pop <- c('EUR','EAS','AFR')
+for(trait_i in prscsx_dat$labels){
+  scores_i <- scores[grepl(trait_i, scores$name),]
+  scores_i$group <- scores_i$method
+  
+  # Current naming issue in gwas_groups leads to difficult subsetting
+  # In the future, update gwas_groups file to include disc pop in name, e.g. ALT_UKB_BBJ
+  scores_i$name_2 <- scores_i$name
+  scores_i$name_2[!(grepl('UKB|UGR|BBJ', scores_i$name_2, ignore.case = T))] <- paste0(scores_i$name_2[!(grepl('UKB|UGR|BBJ', scores_i$name_2, ignore.case = T))], '_ukb_bbj') 
+
+  for(targ_pop_i in targ_pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'BBJ'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'UGR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('BBJ','UGR')
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == 'BBJ'){
+        disc_pop_j_2 <- 'EAS'
+      }
+      if(disc_pop_j == 'UGR'){
+        disc_pop_j_2 <- 'AFR'
+      }
+
+      dir.create(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i
+        ),
+        recursive = T
+      )
+      
+      scores_i_j <- scores_i[grepl('UKB', scores_i$name_2, ignore.case = F) |
+                             grepl(disc_pop_j, scores_i$name_2, ignore.case = T),]
+      
+      # Insert the pseudo score for the non-TLPRS methods
+      scores_i_j_pseudo <- scores_i_j[which(!grepl('^tlprs', scores_i_j$method)), ]
+      scores_i_j_pseudo$group <- paste0(scores_i_j_pseudo$group,'.pseudo')
+
+      scores_i_j_pseudo$predictor <- paste0(
+        outdir,
+        '/ukb/pgs/',
+        targ_pop_i,
+        '/',
+        scores_i_j_pseudo$method,
+        '/',
+        scores_i_j_pseudo$name,
+        '/ukb-',
+        scores_i_j_pseudo$name,
+        '-',
+        targ_pop_i,
+        '.pseudo.profiles'
+      )
+      
+      for(i in 1:nrow(scores_i_j_pseudo)) {
+        tmp <- scores_i_j_pseudo[i,]
+        param <- find_pseudo(
+          config = config,
+          gwas = tmp$name,
+          pgs_method = tmp$method,
+          target_pop = targ_pop_i
+        )
+        
+        score_header <-
+          fread(gsub('.pseudo', '', tmp$predictor), nrows = 1)
+        score_cols <-
+          which(names(score_header) %in% c('FID', 'IID', paste0(tmp$name, '_', param)))
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            gsub('.pseudo', '', tmp$predictor), 
+            " > ", tmp$predictor
+          )
+        )
+      }
+      
+      # Create disc_pop specific groups
+      scores_i_j_pseudo_disc_pop <- scores_i_j_pseudo
+      scores_i_j_pseudo_disc_pop$group[grepl('UKB', scores_i_j_pseudo_disc_pop$name)] <- paste0(scores_i_j_pseudo_disc_pop$group[grepl('UKB', scores_i_j_pseudo_disc_pop$name)], '.EUR')
+      scores_i_j_pseudo_disc_pop$group[!grepl('UKB', scores_i_j_pseudo_disc_pop$name)] <- paste0(scores_i_j_pseudo_disc_pop$group[!grepl('UKB', scores_i_j_pseudo_disc_pop$name)], '.', disc_pop_j_2)
+      
+      # Insert groups for TLPRS scores for both target populations, and target specific
+      scores_i_j_multi <- scores_i_j[which(grepl('^tlprs', scores_i_j$method)), ]
+      
+      # Insert path to score file
+      scores_i_j_multi$predictor <- paste0(
+        outdir,
+        '/ukb/pgs/',
+        targ_pop_i,
+        '/',
+        scores_i_j_multi$method,
+        '/',
+        scores_i_j_multi$name,
+        '/ukb-',
+        scores_i_j_multi$name,
+        '-',
+        targ_pop_i,
+        '.profiles'
+      )
+      
+      scores_i_j_multi_targ_pop <- scores_i_j_multi
+      scores_i_j_multi_targ_pop_both<-NULL
+      for(i in 1:nrow(scores_i_j_multi_targ_pop)){
+        score_header <-
+          fread(gsub('.pseudo', '', scores_i_j_multi_targ_pop$predictor[i]), nrows = 1)
+        score_cols_EUR <-
+          which(names(score_header) %in% c('FID', 'IID', names(score_header)[grepl('targ_EUR', names(score_header))]))
+        score_cols_targ <-
+          which(names(score_header) %in% c('FID', 'IID', names(score_header)[grepl(paste0('targ_', targ_pop_i), names(score_header))]))
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols_EUR, collapse=','),
+            " ", 
+            scores_i_j_multi_targ_pop$predictor[i], 
+            " > ", 
+            paste0(
+              outdir,
+              '/ukb/pgs/',
+              targ_pop_i,
+              '/',
+              scores_i_j_multi_targ_pop$method[i],
+              '/',
+              scores_i_j_multi_targ_pop$name[i],
+              '/ukb-',
+              scores_i_j_multi_targ_pop$name[i],
+              '-',
+              targ_pop_i,
+              '.targ_EUR.profiles'
+            )
+          )
+        )
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols_targ, collapse=','),
+            " ", 
+            scores_i_j_multi_targ_pop$predictor[i], 
+            " > ", 
+            paste0(
+              outdir,
+              '/ukb/pgs/',
+              targ_pop_i,
+              '/',
+              scores_i_j_multi_targ_pop$method[i],
+              '/',
+              scores_i_j_multi_targ_pop$name[i],
+              '/ukb-',
+              scores_i_j_multi_targ_pop$name[i],
+              '-',
+              targ_pop_i,
+              '.targ_',
+              targ_pop_i,
+              '.profiles'
+            )
+          )
+        )
+        
+        tmp<-scores_i_j_multi_targ_pop[i,]
+        tmp <- rbind(tmp, tmp)
+        tmp$predictor[1] <- paste0(
+            outdir,
+            '/ukb/pgs/',
+            targ_pop_i,
+            '/',
+            scores_i_j_multi_targ_pop$method[i],
+            '/',
+            scores_i_j_multi_targ_pop$name[i],
+            '/ukb-',
+            scores_i_j_multi_targ_pop$name[i],
+            '-',
+            targ_pop_i,
+            '.targ_EUR.profiles'
+          )
+        tmp$group[1] <- paste0(tmp$group[1], '.EUR')
+        
+        tmp$predictor[2] <- paste0(
+            outdir,
+            '/ukb/pgs/',
+            targ_pop_i,
+            '/',
+            scores_i_j_multi_targ_pop$method[i],
+            '/',
+            scores_i_j_multi_targ_pop$name[i],
+            '/ukb-',
+            scores_i_j_multi_targ_pop$name[i],
+            '-',
+            targ_pop_i,
+            '.targ_',
+            targ_pop_i,
+            '.profiles'
+          )
+        tmp$group[2] <- paste0(tmp$group[2], '.', targ_pop_i)
+
+        scores_i_j_multi_targ_pop_both <- rbind(
+          scores_i_j_multi_targ_pop_both,
+          tmp)
+      }
+      
+      predictors_i<- do.call(rbind, list(
+        scores_i_j_multi, scores_i_j_multi_targ_pop_both, scores_i_j_pseudo, scores_i_j_pseudo_disc_pop
+      ))
+      
+      predictors_i <- predictors_i[, c('predictor', 'group'), with=F]
+      
+      write.table(
+        predictors_i,
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i,
+          '/predictor_list.tlprs.txt'
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}
+
+```
+
+</details>
+
+***
+
+### Run model_builder
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+for targ_pop in $(echo EUR EAS AFR); do
+  if [ "$targ_pop" == "EUR" ]; then
+      targ_pop2="EUR_test"
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ "$targ_pop" == "EUR" ]; then
+    disc_pop=$(echo AFR EAS)
+  fi
+  
+  if [ "$targ_pop" == "EAS" ]; then
+    disc_pop="EAS"
+  fi
+  
+  if [ "$targ_pop" == "AFR" ]; then
+    disc_pop="AFR"
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+      sbatch --mem 20G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+        --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
+        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
+        --n_core 10 \
+        --top1 T \
+        --all_model F \
+        --assoc T"
+    done
+  done
+done
+
+```
+</details>
+
+***
+
+### Plot results
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-prscsx_dat[1:5,]
+
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+  
+# Define pgs_methods used
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+
+# Define gwas_list used
+gwas_list<-read_param(config = config, param = 'gwas_list')
+
+# Define gwas_groups used
+gwas_groups<-read_param(config = config, param = 'gwas_groups')
+
+# Calculate corelation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in prscsx_dat$labels){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop = c('EUR','EAS','AFR')
+res <- list()
+for(pheno_i in prscsx_dat$labels){
+  res_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.tlprs.pred_eval.txt'
+          )
+        )
+      eval_i$target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_i<-rbind(res_i, eval_i)
+    }
+  }
+  
+  res_i$Method<-sub('\\..*','',res_i$Group)
+  res_i$Method_short<-sub('.*_','',res_i$Method)
+  res_i<-res_i[order(res_i$Method_short, res_i$Method),]
+  
+  res_i$Model[grepl('pseudo', res_i$Group)]<-'Pseudo'
+  res_i$Model[grepl('top1', res_i$Group)]<-'Top1'
+  res_i$Model[!grepl('top1|pseudo', res_i$Group)]<-'Multi'
+  res_i$Model[!grepl('tlprs', res_i$Group) & !grepl('EUR|EAS|AFR', res_i$Group) & !grepl('top1', res_i$Group)]<-'Multi'
+  
+  res_i$Source[!grepl('tlprs', res_i$Group)] <- 'Single'
+  res_i$Source[grepl('tlprs', res_i$Group)] <- 'Multi'
+  res_i$Source[!grepl('tlprs', res_i$Group) & !grepl('EUR|EAS|AFR', res_i$Group)] <- 'Multi'
+  
+  res_i$Discovery <- res_i$gwas_group
+  res_i$Discovery[grepl('EUR', res_i$Group) & res_i$Source == 'Single'] <- 'EUR'
+  res_i$Discovery[grepl('EAS', res_i$Group) & res_i$Source == 'Single'] <- 'EAS'
+  res_i$Discovery[grepl('AFR', res_i$Group) & res_i$Source == 'Single'] <- 'AFR'
+
+  res_i$Method <- gsub('tlprs_','tlprs - ', res_i$Method)
+  res_i$Method[grepl('tlprs', res_i$Method) & grepl('EUR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('EUR', res_i$Group)], " (EUR)")
+  res_i$Method[grepl('tlprs', res_i$Method) & grepl('EAS', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('EAS', res_i$Group)], " (EAS)")
+  res_i$Method[grepl('tlprs', res_i$Method) & grepl('AFR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('AFR', res_i$Group)], " (AFR)")
+  res_i$Method[grepl('tlprs', res_i$Method) & !grepl('EUR|EAS|AFR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & !grepl('EUR|EAS|AFR', res_i$Group)], " (Both)")
+
+  res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
+  res_i$Model<-factor(res_i$Model, levels=c('Top1','Pseudo','Multi'))
+  res_i$Discovery<-factor(res_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+  res[[pheno_i]]<-res_i
+  
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res for each scenario
+    res_i <- do.call(rbind, lapply(seq_along(res), function(i) {
+      x <- res[[i]]
+      x$pheno <- names(res)[i]
+      x <- x[x$target == targ_pop_i]
+      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
+    }))
+    
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_i$Sample<-'A'
+  
+    for(group_i in unique(res_i$Group)){
+      res_group_i <- res_i[res_i$Group == group_i,]
+      cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
+      
+      meta_res_i <-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_group_i
+        )
+      
+      meta_res <- rbind(meta_res,
+                        data.table(
+                          Group = group_i,
+                          Method = res_group_i$Method[1],
+                          Model = res_group_i$Model[1],
+                          Source = res_group_i$Source[1],
+                          Discovery = res_group_i$Discovery[1],
+                          gwas_group = res_group_i$gwas_group[1],
+                          Target = targ_pop_i,
+                          R = meta_res_i$es,
+                          SE = sqrt(meta_res_i$var)
+                        ))
+    }
+  }
+}
+
+meta_res$Model<-factor(meta_res$Model, levels=c('Top1','Pseudo','Multi'))
+meta_res$Discovery<-factor(meta_res$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+####
+# Compare TLPRS to unadjusted PGS
+####
+
+meta_res_multi_pop <- meta_res[!(meta_res$Discovery %in% c('EUR','EAS','AFR')),]
+meta_res_multi_pop$original_method <- gsub(' .*', '', gsub('tlprs - ', '', meta_res_multi_pop$Method))
+meta_res_multi_pop$test[!grepl('tlprs', meta_res_multi_pop$Method)] <- 'Unadjusted'
+meta_res_multi_pop$test[grepl('tlprs', meta_res_multi_pop$Method)] <- gsub('.* ', 'TLPRS ', meta_res_multi_pop$Method[grepl('tlprs', meta_res_multi_pop$Method)])
+meta_res_multi_pop$test <- factor(meta_res_multi_pop$test, levels=unique(meta_res_multi_pop$test))
+meta_res_multi_pop$test <- gsub('AFR', 'target pop', meta_res_multi_pop$test)
+meta_res_multi_pop$test <- gsub('EAS', 'target pop', meta_res_multi_pop$test)
+
+tmp <- meta_res_multi_pop[meta_res_multi_pop$Target %in% c('EAS', 'AFR'),]
+
+ggplot(tmp, aes(x=test, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
+    labs(y = "R (SE)") +
+    facet_grid(Target ~ original_method, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+tmp <- meta_res_multi_pop[meta_res_multi_pop$Target %in% c('EAS', 'AFR'),]
+tmp <- tmp[grepl('Unadj|Both', tmp$test),]
+tmp$test <- gsub(' .*', '', tmp$test)
+tmp$test <- factor(tmp$test, levels=c('Unadjusted','TLPRS'))
+
+ggplot(tmp, aes(x=test, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
+    labs(y = "R (SE)", x = NULL) +
+    facet_grid(Target ~ original_method, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Read in results
+targ_pop=c('EUR','EAS','AFR')
+res <- list()
+for(pheno_i in prscsx_dat$labels){
+  res_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.tlprs.pred_comp.txt'
+          )
+        )
+      eval_i$target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_i<-rbind(res_i, eval_i)
+    }
+  }
+  
+  res[[pheno_i]]<-res_i
+}
+
+#####
+# Create a plot showing relative R from TLPRS vs Unadjusted
+#####
+
+res_all <- do.call(rbind, lapply(names(res), function(name) {
+  x <- res[[name]]
+  x$pheno <- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Mirror results to fill in gaps
+res_all_symmetric <- res_all
+res_all_mirrored <- res_all
+res_all_mirrored$Model_1 <- res_all$Model_2
+res_all_mirrored$Model_2 <- res_all$Model_1
+res_all_mirrored$Model_1_R <- res_all$Model_2_R
+res_all_mirrored$Model_2_R <- res_all$Model_1_R
+res_all_mirrored$R_diff <- -res_all_mirrored$R_diff
+res_all <- rbind(res_all_symmetric, res_all_mirrored)
+
+# Subset tests where top1 TLPRS is being compared to top1 unadjusted
+res_all$Method_1 <- gsub('.*_', '', gsub('\\..*', '', res_all$Model_1))
+res_all$Method_2 <- gsub('.*_', '', gsub('\\..*', '', res_all$Model_2))
+
+res_all$TLPRS_1 <- grepl('tlprs', res_all$Model_1)
+res_all$TLPRS_2 <- grepl('tlprs', res_all$Model_2)
+
+res_all$Test_1[grepl('pseudo', res_all$Model_1)]<-'Pseudo'
+res_all$Test_1[grepl('top1', res_all$Model_1)]<-'Top1'
+res_all$Test_1[!grepl('top1|pseudo', res_all$Model_1)]<-'Multi'
+res_all$Test_1[!grepl('tlprs', res_all$Model_1) & !grepl('EUR|EAS|AFR', res_all$Model_1) & !grepl('top1', res_all$Model_1)]<-'Multi'
+
+res_all$Test_2[grepl('pseudo', res_all$Model_2)]<-'Pseudo'
+res_all$Test_2[grepl('top1', res_all$Model_2)]<-'Top1'
+res_all$Test_2[!grepl('top1|pseudo', res_all$Model_2)]<-'Multi'
+res_all$Test_2[!grepl('tlprs', res_all$Model_2) & !grepl('EUR|EAS|AFR', res_all$Model_2) & !grepl('top1', res_all$Model_2)]<-'Multi'
+
+res_all$Source_1[!grepl('tlprs', res_all$Model_1)] <- 'Single'
+res_all$Source_1[grepl('tlprs', res_all$Model_1)] <- 'Multi'
+res_all$Source_1[!grepl('tlprs', res_all$Model_1) & !grepl('EUR|EAS|AFR', res_all$Model_1)] <- 'Multi'
+
+res_all$Source_2[!grepl('tlprs', res_all$Model_2)] <- 'Single'
+res_all$Source_2[grepl('tlprs', res_all$Model_2)] <- 'Multi'
+res_all$Source_2[!grepl('tlprs', res_all$Model_2) & !grepl('EUR|EAS|AFR', res_all$Model_2)] <- 'Multi'
+
+res_all$Discovery_1 <- res_all$gwas_group
+res_all$Discovery_1[grepl('EUR', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'EUR'
+res_all$Discovery_1[grepl('EAS', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'EAS'
+res_all$Discovery_1[grepl('AFR', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'AFR'
+
+res_all$Discovery_2 <- res_all$gwas_group
+res_all$Discovery_2[grepl('EUR', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'EUR'
+res_all$Discovery_2[grepl('EAS', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'EAS'
+res_all$Discovery_2[grepl('AFR', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'AFR'
+
+res_all$TLPRS_target_1[grepl('EUR', res_all$Model_1) & res_all$TLPRS_1] <- 'EUR'
+res_all$TLPRS_target_1[grepl('EAS', res_all$Model_1) & res_all$TLPRS_1] <- 'EAS'
+res_all$TLPRS_target_1[grepl('AFR', res_all$Model_1) & res_all$TLPRS_1] <- 'AFR'
+res_all$TLPRS_target_1[!grepl('EUR|AFR|EAS', res_all$Model_1) & res_all$TLPRS_1] <- 'Both'
+res_all$TLPRS_target_1[res_all$TLPRS_target_1 == res_all$target] <- 'Target Pop.'
+
+res_all$TLPRS_target_2[grepl('EUR', res_all$Model_2) & res_all$TLPRS_2] <- 'EUR'
+res_all$TLPRS_target_2[grepl('EAS', res_all$Model_2) & res_all$TLPRS_2] <- 'EAS'
+res_all$TLPRS_target_2[grepl('AFR', res_all$Model_2) & res_all$TLPRS_2] <- 'AFR'
+res_all$TLPRS_target_2[!grepl('EUR|AFR|EAS', res_all$Model_2) & res_all$TLPRS_2] <- 'Both'
+res_all$TLPRS_target_2[res_all$TLPRS_target_2 == res_all$target] <- 'Target Pop.'
+
+# Subset to tests comparing to the Unadjusted models
+res_all <- res_all[res_all$Method_1 == res_all$Method_2, ]
+res_all <- res_all[res_all$Source_2 == 'Multi', ]
+res_all <- res_all[res_all$Test_1  == res_all$Test_2, ]
+res_all <- res_all[res_all$TLPRS_2  == F, ]
+res_all <- res_all[res_all$target %in% c('EAS', 'AFR'),]
+
+ggplot(res_all, aes(x = Method_2, y = R_diff, colour = Test_1)) +
+  geom_point(position=position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7), alpha=0.5) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(target ~ TLPRS_target_1, scales='free', space = 'free_x')
+
+
+######
+# Average R across phenotypes
+######
+
+library(MAd)
+
+meta_res <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res for each scenario
+    res_i <- res_all[res_all$target == targ_pop_i & res_all$gwas_group == paste0('EUR+', disc_pop_i), ]
+  
+    # Calculate diff SE based on p-value
+    res_i$R_diff_pval[res_i$R_diff == 0] <- 1-0.001
+    res_i$R_diff_pval[res_i$R_diff_pval == 1]<-1-0.001
+    res_i$R_diff_z<-qnorm(res_i$R_diff_pval/2)
+    res_i$R_diff_SE<-abs(res_i$R_diff/res_i$R_diff_z)
+        
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_i$Sample<-'A'
+    res_i$Group <- paste0(res_i$Model_1, '_vs_', res_i$Model_2)
+  
+    for(group_i in unique(res_i$Group)){
+      res_group_i <- res_i[res_i$Group == group_i,]
+      cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
+      
+      meta_res_i <-
+        agg(
+          id = Sample,
+          es = R_diff,
+          var = R_diff_SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_group_i
+        )
+      
+      group_info <- res_group_i[1, !(names(res_group_i[1,]) %in% c('Model_1_R', 'Model_2_R', 'R_diff', 'R_diff_pval', 'R_diff_z', 'R_diff_SE')), with=F]
+      meta_res <- rbind(meta_res,
+                        data.table(
+                          group_info,
+                          R_diff = meta_res_i$es,
+                          R_diff_SE = sqrt(meta_res_i$var)
+                        ))
+    }
+  }
+}
+meta_res$R_diff_z <- meta_res$R_diff / meta_res$R_diff_SE
+meta_res$R_diff_p <- 2*pnorm(-abs(meta_res$R_diff_z))
+
+# Plot the results
+ggplot(meta_res, aes(x=Method_1, y=R_diff, fill=Test_1)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_point(
+    data = res_all,
+    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
+    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+    alpha = 0.3
+  ) +
+  geom_errorbar(
+    aes(
+      ymin = R_diff - R_diff_SE,
+      ymax = R_diff + R_diff_SE
+    ),
+    width = 0,
+    position = position_dodge(width = 0.7)
+  ) +
+  geom_point(
+    stat = "identity",
+    position = position_dodge(0.7),
+    size = 2,
+    shape = 23,
+    colour = 'black'
+  ) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  geom_vline(xintercept = 1.5, linetype = "dotted") +
+  geom_vline(xintercept = 2.5, linetype = "dotted") +
+  geom_vline(xintercept = 3.5, linetype = "dotted") +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(target ~ TLPRS_target_1, scales='free', space = 'free_x') +
+  panel_border()
+
+
+
+# Plot the results
+ggplot(meta_res[meta_res$TLPRS_target_1 == 'Both',], aes(x=Method_1, y=R_diff, fill=Test_1)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_point(
+    data = res_all[res_all$TLPRS_target_1 == 'Both',],
+    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
+    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+    alpha = 0.3
+  ) +
+  geom_errorbar(
+    aes(
+      ymin = R_diff - R_diff_SE,
+      ymax = R_diff + R_diff_SE
+    ),
+    width = 0,
+    position = position_dodge(width = 0.7)
+  ) +
+  geom_point(
+    stat = "identity",
+    position = position_dodge(0.7),
+    size = 2,
+    shape = 23,
+    colour = 'black'
+  ) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  geom_vline(xintercept = 1.5, linetype = "dotted") +
+  geom_vline(xintercept = 2.5, linetype = "dotted") +
+  geom_vline(xintercept = 3.5, linetype = "dotted") +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(target ~ ., scales='free', space = 'free_x') +
+  panel_border()
+
+
+# Plot the results
+ggplot(meta_res[meta_res$TLPRS_target_1 == 'Both',], aes(x=Method_1, y=R_diff, fill=Test_1)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_point(
+    data = res_all[res_all$TLPRS_target_1 == 'Both',],
+    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
+    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+    alpha = 0.3
+  ) +
+  geom_errorbar(
+    aes(
+      ymin = R_diff - R_diff_SE,
+      ymax = R_diff + R_diff_SE
+    ),
+    width = 0,
+    position = position_dodge(width = 0.7)
+  ) +
+  geom_point(
+    stat = "identity",
+    position = position_dodge(0.7),
+    size = 2,
+    shape = 23,
+    colour = 'black'
+  ) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  geom_vline(xintercept = 1.5, linetype = "dotted") +
+  geom_vline(xintercept = 2.5, linetype = "dotted") +
+  geom_vline(xintercept = 3.5, linetype = "dotted") +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(target ~ ., scales='free', space = 'free_x') +
+  panel_border()
+
+
+```
+</details>
+
+***
+
+## Compare all methods
+
+### Create predictor lists
 
 <details><summary>Show code</summary>
 
@@ -706,6 +1557,9 @@ prscsx_dat<-prscsx_dat[1:5,]
 # Get a list of score files
 scores <- list_score_files(config)
 
+# Remove tlprs
+scores<-scores[!grepl('tlprs', scores$method),]
+
 # Create files for EAS and AFR targets
 targ_pop <- c('EUR','EAS','AFR')
 for(trait_i in prscsx_dat$labels){
@@ -775,15 +1629,23 @@ for(trait_i in prscsx_dat$labels){
           target_pop = targ_pop_i
         )
         
-        pgs <- fread(
-          scores_i_j_multi$predictor[i],
-          select = c('FID', 'IID', 
-                     paste0(scores_i_j$name[i], '_', param)))
+        score_header <-
+          fread(scores_i_j_multi$predictor[i], nrows = 1)
+        score_cols <-
+          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
         
-        fwrite(pgs, 
-               gsub('.profiles',
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            scores_i_j_multi$predictor[i], 
+            " > ", 
+            gsub('.profiles',
                     '.pseudo.profiles',
-                    scores_i_j_multi$predictor[i]))
+                    scores_i_j_multi$predictor[i])
+          )
+        )
       }
       
       scores_i_j_pseudo <- scores_i_j_multi
@@ -843,7 +1705,7 @@ for(trait_i in prscsx_dat$labels){
 
 ***
 
-## Run model_builder
+### Run model_builder
 
 <details><summary>Show code</summary>
 
@@ -878,7 +1740,7 @@ for targ_pop in $(echo EUR EAS AFR); do
         --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
         --n_core 10 \
         --top1 T \
-        --all_model F \
+        --all_model T \
         --assoc T"
     done
   done
@@ -1239,7 +2101,7 @@ meta_res$Discovery<-factor(meta_res$Discovery, levels=c('AFR','EAS','EUR','EUR+A
 tmp <- meta_res
 
 # Set SBayesR pseudo model to top1
-ggplot(meta_res, aes(x=Method, y=R , fill = Model)) +
+ggplot(tmp, aes(x=Method, y=R , fill = Model)) +
     #geom_hline(yintercept = 0) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
@@ -1450,8 +2312,15 @@ for(model_i in c('Top1','Pseudo','Multi')){
         tmp$Model_2, 
         levels=unique(tmp$Model_1))
       
+      diagonal_data <- data.frame(
+        Model_1 = unique(tmp$Model_1),
+        Model_2 = unique(tmp$Model_1),
+        R_diff_catagory = NA
+      )
+      
       heatmap_list[[model_i]][[targ_pop_i]][[disc_pop_i]] <-
         ggplot(data = tmp, aes(Model_2, Model_1, fill = R_diff_catagory)) +
+        geom_tile(data = diagonal_data, aes(Model_2, Model_1), fill = "grey", color = "white") +  
         geom_tile(color = "white") +
         labs(y = 'Test', x = 'Comparison', fill = 'R difference', title = paste0('Target: ', targ_pop_i, ', Model = ', model_i)) +
         facet_grid(Discovery ~ Discovery2, scales = 'free', space = 'free', switch="both") +

From b949d49a6bcf66844a1a723e1a6df9a4d3407d1f Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 15 Oct 2024 18:30:17 +0100
Subject: [PATCH 043/153] Fixed bug in target scorer when score names are
 similar

---
 Scripts/target_scoring/target_scoring_pipeline.R | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/Scripts/target_scoring/target_scoring_pipeline.R b/Scripts/target_scoring/target_scoring_pipeline.R
index 77393367..da7089a9 100644
--- a/Scripts/target_scoring/target_scoring_pipeline.R
+++ b/Scripts/target_scoring/target_scoring_pipeline.R
@@ -144,7 +144,7 @@ for(chr_i in CHROMS){
       'zcat ', outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],".score.gz | ",
       'awk \'NR==FNR {rows[$1]; next} FNR==1 || FNR in rows\' ', paste0(tmp_dir,'/row_index.txt'), ' - | ',  # Corrected to retain the header and process indexed rows
       "cut -d' ' --complement -f1-3 | ",  # Keep relevant columns, remove first 3
-      "sed '1 s/SCORE/", paste0(score_files$method[i], '.', score_files$name[i]), "/g' > ",  # Replace SCORE in the header
+      "sed '1 s/SCORE_/", paste0('score_file_', i,'.'), "/g' > ",  # Replace SCORE in the header
       tmp_dir, '/tmp_score.', paste0(score_files$method[i], '.', score_files$name[i]), '.txt'
     ))
   }
@@ -206,8 +206,8 @@ scores<-data.table(scores_ids,
 log_add(log_file = log_file, message = paste0('Reading in scale files.'))
 scale_files<-list()
 for(i in 1:nrow(score_files)){
-  scale_files[[paste0(score_files$method[i],'-',score_files$name[i])]]<-fread(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'-', opt$population,'.scale'))
-  scale_files[[paste0(score_files$method[i],'-',score_files$name[i])]]$Param<-gsub('SCORE', paste0(score_files$method[i],'.',score_files$name[i]), scale_files[[paste0(score_files$method[i],'-',score_files$name[i])]]$Param)
+  scale_files[[paste0('score_file_', i)]]<-fread(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'-', opt$population,'.scale'))
+  scale_files[[paste0('score_file_', i)]]$Param<-gsub('SCORE_', paste0('score_file_', i, '.'), scale_files[[paste0('score_file_', i)]]$Param)
 }
 
 # Concatenate scale files
@@ -221,8 +221,8 @@ scores<-score_scale(score=scores, ref_scale=all_scale)
 ###
 
 for(i in 1:nrow(score_files)){
-  scores_i <- scores[, c('FID','IID', names(scores)[grepl(paste0(score_files$method[i],'.',score_files$name[i]), names(scores))]), with=F]
-  names(scores_i) <- gsub(paste0('^', score_files$method[i],'\\.'),'', names(scores_i))
+  scores_i <- scores[, c('FID','IID', names(scores)[grepl(paste0('^score_file_', i, '\\.'), names(scores))]), with=F]
+  names(scores_i) <- gsub(paste0('^score_file_', i, '\\.'), paste0(score_files$name[i], '_'), names(scores_i))
   dir.create(paste0(outdir, '/', opt$name,'/pgs/', opt$population,'/', score_files$method[i],'/', score_files$name[i]), recursive = T)
   fwrite(scores_i, paste0(outdir, '/', opt$name,'/pgs/', opt$population,'/', score_files$method[i],'/', score_files$name[i],'/', opt$name,'-', score_files$name[i],'-',opt$population,'.profiles'), sep=' ', na='NA', quote=F)
 }

From 028d8680317531083d6e8cd9d5475638d2de89ec Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 15 Oct 2024 18:30:40 +0100
Subject: [PATCH 044/153] Update to model_builder

---
 Scripts/model_builder/model_builder.R | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/Scripts/model_builder/model_builder.R b/Scripts/model_builder/model_builder.R
index 74242228..d55ecb9a 100644
--- a/Scripts/model_builder/model_builder.R
+++ b/Scripts/model_builder/model_builder.R
@@ -159,6 +159,11 @@ for(i in unique(group_list$group)){
     if(length(ident) > 0){
       log_add(log_file = log_file, message = paste0(length(ident), ' duplicate predictors removed from group ', i))
     }
+  } else {
+    group_list_non_identical <- rbind(
+      group_list_non_identical,
+      group_list[group_list$group == i,]
+    )
   }
 }
 group_list <- group_list_non_identical

From 562e45ca1fd0eb4afc405b229a48c1a93a1fe079 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 21 Oct 2024 17:03:14 +0100
Subject: [PATCH 045/153] Added quickprs, sbayesrc, updated ldpred2 ref
 generation, added sanity check when using sequence data

---
 Scripts/model_builder/model_builder.R |  97 ++--
 Scripts/pgs_methods/quickprs.R        | 289 ++++------
 Scripts/pgs_methods/sbayesrc.R        | 245 +++++++++
 docs/CrossPop.Rmd                     | 756 +++++++++++++++++++-------
 docs/prep_ldpred2_ldmat.Rmd           | 236 ++++++++
 docs/prep_quickprs_ref.Rmd            | 116 ++++
 docs/prep_sbayesrc_ref.Rmd            | 120 ++++
 functions/constants.R                 |   6 +-
 functions/pipeline.R                  |   6 +
 pipeline/config.yaml                  |   8 +-
 pipeline/envs/sbayesrc.yaml           |  19 +
 pipeline/misc/dev/sequence_test.Rmd   |  50 ++
 pipeline/rules/dependencies.smk       | 181 +++++-
 pipeline/rules/pgs_methods.smk        | 100 +++-
 14 files changed, 1789 insertions(+), 440 deletions(-)
 create mode 100644 Scripts/pgs_methods/sbayesrc.R
 create mode 100644 docs/prep_quickprs_ref.Rmd
 create mode 100644 docs/prep_sbayesrc_ref.Rmd
 create mode 100644 pipeline/envs/sbayesrc.yaml
 create mode 100644 pipeline/misc/dev/sequence_test.Rmd

diff --git a/Scripts/model_builder/model_builder.R b/Scripts/model_builder/model_builder.R
index d55ecb9a..f9e1cf7c 100644
--- a/Scripts/model_builder/model_builder.R
+++ b/Scripts/model_builder/model_builder.R
@@ -530,59 +530,62 @@ log_add(log_file = log_file, message = paste0('Model evaluation results saved as
 # Compare predictive utiliy of the different models
 ###################
 
-# Initialise progress log
-log_message <- 'Comparing model performance... '
-progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
+if(length(pred_eval_all$Group) > 1){
 
-# Identify number of pairwise comparisons
-models <- pred_eval_all$Group
-model_comps <- data.table(t(combn(models, 2)))
-
-comp_res_all <- foreach(i = 1:length(models), .combine=rbind) %dopar% {
-  group1 <- models[i]
-  comp_res_i <- NULL
-  for(group2 in model_comps$V2[model_comps$V1 == group1]){
-
-  	group1_r <- cor(scale(as.numeric(indep_pred_list[[group1]]$obs)), scale(indep_pred_list[[group1]]$pred))[1]
-    group2_r <- cor(scale(as.numeric(indep_pred_list[[group2]]$obs)), scale(indep_pred_list[[group2]]$pred))[1]
-
-    r_diff <- group1_r - group2_r
-
-    group1_group2_r <- cor(scale(indep_pred_list[[group1]]$pred), scale(indep_pred_list[[group2]]$pred))
-
-    r_diff_p <-
-      paired.r(
-        xy = group1_r,
-        xz = group2_r,
-        yz = group1_group2_r,
-        n = length(scale(indep_pred_list[[group1]]$pred)),
-        twotailed = T
-      )$p[1]
-
-    comp_res <- data.table(
-      Model_1 = group1,
-      Model_2 = group2,
-      Model_1_R = group1_r,
-      Model_2_R = group2_r,
-      R_diff = r_diff,
-      R_diff_pval = r_diff_p
-    )
+  # Initialise progress log
+  log_message <- 'Comparing model performance... '
+  progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
 
-    comp_res_i <- rbind(comp_res_i, comp_res)
-  }
+  # Identify number of pairwise comparisons
+  models <- pred_eval_all$Group
+  model_comps <- data.table(t(combn(models, 2)))
+
+  comp_res_all <- foreach(i = 1:length(models), .combine=rbind) %dopar% {
+    group1 <- models[i]
+    comp_res_i <- NULL
+    for(group2 in model_comps$V2[model_comps$V1 == group1]){
+
+    	group1_r <- cor(scale(as.numeric(indep_pred_list[[group1]]$obs)), scale(indep_pred_list[[group1]]$pred))[1]
+      group2_r <- cor(scale(as.numeric(indep_pred_list[[group2]]$obs)), scale(indep_pred_list[[group2]]$pred))[1]
+
+      r_diff <- group1_r - group2_r
+
+      group1_group2_r <- cor(scale(indep_pred_list[[group1]]$pred), scale(indep_pred_list[[group2]]$pred))
+
+      r_diff_p <-
+        paired.r(
+          xy = group1_r,
+          xz = group2_r,
+          yz = group1_group2_r,
+          n = length(scale(indep_pred_list[[group1]]$pred)),
+          twotailed = T
+        )$p[1]
+
+      comp_res <- data.table(
+        Model_1 = group1,
+        Model_2 = group2,
+        Model_1_R = group1_r,
+        Model_2_R = group2_r,
+        R_diff = r_diff,
+        R_diff_pval = r_diff_p
+      )
 
-  # Update progress log
-  progress <- update_progress_file(progress_file)
-  update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(models)*100),'%'))
+      comp_res_i <- rbind(comp_res_i, comp_res)
+    }
 
-  comp_res_i
-}
+    # Update progress log
+    progress <- update_progress_file(progress_file)
+    update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(models)*100),'%'))
 
-update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
+    comp_res_i
+  }
 
-# Write out the results
-write.table(comp_res_all, paste0(opt$out,'.pred_comp.txt'), col.names=T, row.names=F, quote=F)
-log_add(log_file = log_file, message = paste0('Model evaluation results saved as ',opt$out,'.pred_comp.txt.'))
+  update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
+
+  # Write out the results
+  write.table(comp_res_all, paste0(opt$out,'.pred_comp.txt'), col.names=T, row.names=F, quote=F)
+  log_add(log_file = log_file, message = paste0('Model comparison results saved as ',opt$out,'.pred_comp.txt.'))
+}
 
 end.time <- Sys.time()
 time.taken <- end.time - start.time
diff --git a/Scripts/pgs_methods/quickprs.R b/Scripts/pgs_methods/quickprs.R
index 8bf3f1e6..47c4f570 100644
--- a/Scripts/pgs_methods/quickprs.R
+++ b/Scripts/pgs_methods/quickprs.R
@@ -6,10 +6,12 @@ suppressMessages(library("optparse"))
 option_list = list(
   make_option("--ref_plink_chr", action="store", default=NA, type='character',
               help="Path to per chromosome reference PLINK files [required]"),
-  make_option("--ref_pop_scale", action="store", default=NA, type='character',
+  make_option("--pop_data", action="store", default=NULL, type='character',
               help="File containing the population code and location of the keep file [required]"),
-  make_option("--plink2", action="store", default='plink', type='character',
-              help="Path PLINK v2 software binary [required]"),
+  make_option("--plink", action="store", default='plink', type='character',
+              help="Path PLINK v1.9 software binary [optional]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+              help="Path PLINK v2 software binary [optional]"),
   make_option("--output", action="store", default='NA', type='character',
               help="Path for output files [required]"),
   make_option("--memory", action="store", default=5000, type='numeric',
@@ -17,7 +19,7 @@ option_list = list(
   make_option("--sumstats", action="store", default=NA, type='character',
               help="GWAS summary statistics [optional]"),
   make_option("--ldak", action="store", default=NA, type='character',
-              help="Path to ldak executable [required]"),
+              help="Path to ldak v5.2 executable [required]"),
   make_option("--quick_prs_ref", action="store", default=NA, type='character',
               help="Path to folder containing ldak quick prs reference [required]"),
   make_option("--n_cores", action="store", default=1, type='numeric',
@@ -32,139 +34,85 @@ option_list = list(
 
 opt = parse_args(OptionParser(option_list=option_list))
 
+# Load dependencies
+library(GenoUtils)
 library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
 
-opt$output_dir<-paste0(dirname(opt$output),'/')
+# Check required inputs
+if(is.null(opt$ref_plink_chr)){
+  stop('--ref_plink_chr must be specified.\n')
+}
+if(is.null(opt$sumstats)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$pop_data)){
+  stop('--pop_data must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--output must be specified.\n')
+}
+if(is.null(opt$ldak)){
+  stop('--ldak must be specified.\n')
+}
+if(is.null(opt$quick_prs_ref)){
+  stop('--quick_prs_ref must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
 system(paste0('mkdir -p ',opt$output_dir))
 
-CHROMS<-1:22
+# Create temp directory
+tmp_dir<-tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'quickprs.R', start.time = start.time)
 
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
 if(!is.na(opt$test)){
-  if(grepl('chr', opt$test)){
-    single_chr_test<-T
-    CHROMS<-as.numeric(gsub('chr','',opt$test))
-  } else {
-    single_chr_test<-F
-    opt$test<-as.numeric(opt$test)
-  }
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
 }
 
-sink(file = paste(opt$output,'.log',sep=''), append = F)
-cat(
-  '#################################################################
-# quickprs.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-Analysis started at',as.character(start.time),'
-Options are:\n')
-
-cat('Options are:\n')
-print(opt)
-cat('Analysis started at',as.character(start.time),'\n')
-sink()
-
 #####
 # Format the sumstats
 #####
 
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Reading in GWAS and harmonising with reference.\n')
-sink()
-
-GWAS<-fread(cmd=paste0('zcat ',opt$sumstats), nThread=opt$n_cores)
-GWAS<-GWAS[complete.cases(GWAS),]
-
-# Update BP to match the reference
-ref_bim<-NULL
-for(i in 1:22){
-  ref_bim<-rbind(ref_bim, fread(paste0(opt$ref_plink_chr,i,'.bim'), header=F))
-}
-
-GWAS$BP<-NULL
-GWAS<-merge(GWAS, ref_bim[,c('V2','V4'), with=F], by.x='SNP',by.y='V2')
-names(GWAS)[names(GWAS) == 'V4']<-'BP'
-GWAS<-GWAS[order(GWAS$CHR, GWAS$BP),]
+log_add(log_file = log_file, message = 'Reading in GWAS.')
 
-# Extract subset if testing
-if(!is.na(opt$test)){
-  if(single_chr_test == F){
-    sink(file = paste(opt$output,'.log',sep=''), append = T)
-    cat('Testing mode enabled. Extracted ',opt$test,' variants per chromsome.\n', sep='')
-    sink()
-    
-    GWAS_test<-NULL
-    for(i in 1:22){
-      GWAS_tmp<-GWAS[GWAS$CHR == i,]
-      GWAS_tmp<-GWAS_tmp[order(GWAS_tmp$BP),]
-      GWAS_tmp<-GWAS_tmp[1:opt$test,]
-      GWAS_test<-rbind(GWAS_test,GWAS_tmp)
-    }
-    
-    GWAS<-GWAS_test
-    GWAS<-GWAS[complete.cases(GWAS),]
-    rm(GWAS_test)
-    print(table(GWAS$CHR))
-    
-  } else {
-    sink(file = paste(opt$output,'.log',sep=''), append = T)
-    cat('Testing mode enabled. Extracted chromosome ',opt$test,' variants per chromsome.\n', sep='')
-    sink()
-    
-    GWAS<-GWAS[GWAS$CHR == CHROMS,]
-    print(table(GWAS$CHR))
-  }
-}
+# Read in, check and format GWAS summary statistics
+gwas <- read_sumstats(sumstats = opt$sumstats, chr = CHROMS, log_file = log_file, req_cols = c('CHR','BP','SNP','A1','A2','BETA','SE','N'))
 
-# Update RSID to CHR:BP to match reference data
-# During sumstat cleaning, these columns are replaced with those found in build hg19 which is appropriate
-GWAS$SNP<-paste0(GWAS$CHR,':',GWAS$BP)
-
-# Check overlap with LDAK HapMap3 SNP-list
-ldak_hm3_file<-list.files(opt$quick_prs_ref)
-ldak_hm3_file<-ldak_hm3_file[grepl('.cors.bim',ldak_hm3_file)]
-ldak_hm3<-fread(paste0(opt$quick_prs_ref,'/',ldak_hm3_file))
-
-if(is.na(opt$test)){
-  ref_overlap<-sum(GWAS$SNP %in% ldak_hm3$V2)/nrow(ldak_hm3)
-  GWAS<-GWAS[GWAS$SNP %in% ldak_hm3$V2,]
-  sink(file = paste(opt$output,'.log',sep=''), append = T)
-  cat('GWAS-reference overlap is ',round(ref_overlap*100,2),'%.\n', sep='')
-  sink()
-} else {
-  GWAS<-GWAS[GWAS$SNP %in% ldak_hm3$V2,]
-  sink(file = paste(opt$output,'.log',sep=''), append = T)
-  cat('GWAS-reference overlap check is skipped as this is a test run.\n', sep='')
-  sink()
-}
+# Format for LDAK
+snplist <- gwas$SNP
+gwas$Z <- gwas$BETA / gwas$SE
+gwas$Predictor<-paste0(gwas$CHR, ':', gwas$BP)
+gwas<-gwas[,c('Predictor','A1','A2','N','Z')]
+names(gwas)<-c('Predictor','A1','A2','n','Z')
 
-# Calculate Z
-if(('BETA' %in% names(GWAS))){
-  GWAS$Z<-abs(GWAS$BETA)/GWAS$SE
-  GWAS$Z[GWAS$BETA < 0]<- -GWAS$Z[GWAS$BETA < 0]
-} else {
-  GWAS$Z<-abs(log(GWAS$OR))/GWAS$SE
-  GWAS$Z[GWAS$OR < 1]<- -GWAS$Z[GWAS$OR < 1]
-}
+# Check overlap between GWAS and LDAK reference
+ldak_hm3_file <- list.files(opt$quick_prs_ref)
+ldak_hm3_file <- ldak_hm3_file[grepl('.cors.bim', ldak_hm3_file)]
+ldak_hm3 <- fread(paste0(opt$quick_prs_ref, '/', ldak_hm3_file))
+ldak_hm3 <- ldak_hm3[ldak_hm3$V1 %in% CHROMS,]
+ref_overlap <- sum(gwas$Predictor %in% ldak_hm3$V2) / nrow(ldak_hm3)
 
-GWAS$Predictor<-paste0(GWAS$CHR, ':', GWAS$BP)
+log_add(log_file = log_file, message = paste0('GWAS-reference overlap is ', round(ref_overlap * 100, 2), '%.'))
 
-GWAS<-GWAS[,c('Predictor','A1','A2','N','Z')]
-names(GWAS)<-c('Predictor','A1','A2','n','Z')
+# Subset GWAS to LDAK reference data
+gwas <- gwas[gwas$Predictor %in% ldak_hm3$V2, ]
 
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('GWAS contains',dim(GWAS)[1],'variants.\n')
-sink()
-
-fwrite(GWAS, paste0(opt$output_dir,'GWAS_sumstats_temp.txt'), sep=' ')
-
-rm(GWAS)
-gc()
+# Output formatted sumstats
+fwrite(gwas, paste0(tmp_dir,'/GWAS_sumstats_temp.txt'), sep=' ')
 
+# Record start time for test
 if(!is.na(opt$test)){
-  sink(file = paste(opt$output,'.log',sep=''), append = T)
-  test_start.time <- Sys.time()
-  cat('Test started at',as.character(test_start.time),'\n')
-  sink()
+  test_start.time <- test_start(log_file = log_file)
 }
 
 ############
@@ -178,16 +126,14 @@ tagging_file<-ref_files[grepl('quickprs.tagging',ref_files)]
 matrix_file<-ref_files[grepl('quickprs.matrix',ref_files)]
 
 if(opt$genomic_control == F){
-  system(paste0(opt$ldak,' --sum-hers ',opt$output_dir,'/bld.ldak --tagfile ',opt$quick_prs_ref,'/',tagging_file,' --summary ',opt$output_dir,'GWAS_sumstats_temp.txt --matrix ',opt$quick_prs_ref,'/',matrix_file,' --max-threads ',opt$n_cores,' --check-sums NO'))
+  system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --tagfile ', opt$quick_prs_ref, '/', tagging_file, ' --summary ', tmp_dir, '/GWAS_sumstats_temp.txt --matrix ', opt$quick_prs_ref, '/', matrix_file, ' --max-threads ', opt$n_cores, ' --check-sums NO'))
 } else{
-  system(paste0(opt$ldak,' --sum-hers ',opt$output_dir,'/bld.ldak --genomic-control YES --tagfile ',opt$quick_prs_ref,'/',tagging_file,' --summary ',opt$output_dir,'GWAS_sumstats_temp.txt --matrix ',opt$quick_prs_ref,'/',matrix_file,' --max-threads ',opt$n_cores,' --check-sums NO'))
+  system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --genomic-control YES --tagfile ', opt$quick_prs_ref, '/', tagging_file, ' --summary ', tmp_dir, '/GWAS_sumstats_temp.txt --matrix ', opt$quick_prs_ref, '/', matrix_file, ' --max-threads ', opt$n_cores, ' --check-sums NO'))
 }
 
-ldak_res_her<-fread(paste0(opt$output_dir,'/bld.ldak.hers'))
+ldak_res_her<-fread(paste0(tmp_dir,'/bld.ldak.hers'))
 
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('SNP-based heritability estimated to be ',ldak_res_her$Heritability[nrow(ldak_res_her)]," (SD=", ldak_res_her$SD[nrow(ldak_res_her)],").\n",sep='')
-sink()
+log_add(log_file = log_file, message = paste0('SNP-based heritability estimated to be ',ldak_res_her$Heritability[nrow(ldak_res_her)]," (SD=", ldak_res_her$SD[nrow(ldak_res_her)],")."))
 
 ######
 # Estimate effect sizes for training and full prediction models.
@@ -195,54 +141,41 @@ sink()
 
 cor_file_prefix<-gsub('.cors.bin','',ref_files[grepl('.cors.bin',ref_files)])
 
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Running MegaPRS: ',opt$prs_model,' model.\n', sep='')
-sink()
+log_add(log_file = log_file, message = paste0('Running MegaPRS: ',opt$prs_model,' model.'))
 
-system(paste0(opt$ldak,' --mega-prs ',opt$output_dir,'/mega_full --model ',opt$prs_model,' --cors ',opt$quick_prs_ref,'/',cor_file_prefix,' --ind-hers ',opt$output_dir,'/bld.ldak.ind.hers --summary ',opt$output_dir,'GWAS_sumstats_temp.txt --high-LD ',opt$quick_prs_ref,'/highld.snps --cv-proportion 0.1 --window-cm 1 --max-threads ',opt$n_cores,' --extract ',opt$output_dir,'GWAS_sumstats_temp.txt'))
+system(paste0(opt$ldak,' --mega-prs ',tmp_dir,'/mega_full --model ',opt$prs_model,' --cors ',opt$quick_prs_ref,'/',cor_file_prefix,' --ind-hers ',tmp_dir,'/bld.ldak.ind.hers --summary ',tmp_dir,'/GWAS_sumstats_temp.txt --high-LD ',opt$quick_prs_ref,'/highld.snps --cv-proportion 0.1 --window-cm 1 --max-threads ',opt$n_cores,' --extract ',tmp_dir,'/GWAS_sumstats_temp.txt'))
 
 # Save the parameters file
-system(paste0('cp ',opt$output_dir,'/mega_full.parameters ',opt$output,'.model_param.txt'))
+system(paste0('cp ',tmp_dir,'/mega_full.parameters ',opt$output,'.model_param.txt'))
 
 # Save the pseudosummary results
-system(paste0('cp ',opt$output_dir,'/mega_full.cors ',opt$output,'.pseudoval.txt'))
+system(paste0('cp ',tmp_dir,'/mega_full.cors ',opt$output,'.pseudoval.txt'))
 
 # Identify the best fitting model
-ldak_res_cors<-fread(paste0(opt$output_dir,'/mega_full.cors'), nThread=opt$n_cores)
-best_score<-ldak_res_cors[ldak_res_cors$Correlation == max(ldak_res_cors$Correlation),]
-
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Model/Models ',paste0(best_score$Model, collapse=', '),' was/were identified as the best with correlation of ',best_score$Correlation[1],'.\n', sep='')
-sink()
+ldak_res_cors <- fread(paste0(tmp_dir, '/mega_full.cors'), nThread = opt$n_cores)
+best_score <- ldak_res_cors[which.max(ldak_res_cors$Correlation),]
 
-if(!is.na(opt$test)){
-  end.time <- Sys.time()
-  time.taken <- end.time - test_start.time
-  sink(file = paste(opt$output,'.log',sep=''), append = T)
-  cat('Test run finished at',as.character(end.time),'\n')
-  cat('Test duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
-  sink()
-}
+log_add(log_file = log_file, message = paste0('Model ', gsub('Score_','',best_score$V1[1]),' is identified as the best with correlation of ', best_score$V2))
 
 ######
 # Format final score file
 ######
 
 # Read in the scores
-score<-fread(paste0(opt$output_dir,'/mega_full.effects'), nThread=opt$n_cores)
+score <- fread(paste0(tmp_dir,'/mega_full.effects'), nThread = opt$n_cores)
 
 # Change IDs to RSIDs
-bim<-NULL
-for(i in 1:22){
-  bim<-rbind(bim, fread(paste0(opt$ref_plink_chr,i,'.bim'), nThread=opt$n_cores, header=F))
-}
-bim$Predictor<-paste0(bim$V1,':',bim$V4)
-score<-merge(score, bim[,c('Predictor','V2'),with=F], by='Predictor')
-score<-score[,c('V2','A1',names(score)[grepl('Model', names(score))]), with=F]
-names(score)[1]<-'SNP'
-names(score)[grepl('Model', names(score))]<-paste0('SCORE_ldak_',names(score)[grepl('Model', names(score))])
+ref_pvar <- read_pvar(dat = opt$ref_plink_chr, chr = CHROMS)
+ref_pvar$Predictor<-paste0(ref_pvar$CHR,':',ref_pvar$BP)
+score<-merge(score, ref_pvar[,c('Predictor','SNP'), with=F], by='Predictor')
+score<-score[, c('SNP', 'A1', 'A2', names(score)[grepl('Model', names(score))]), with=F]
+names(score)[grepl('Model', names(score))]<-'SCORE_quickprs'
+
+# Flip effects to match reference alleles
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
+score_new <- map_score(ref = ref, score = score)
 
-fwrite(score, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+fwrite(score_new, paste0(opt$output, '.score'), col.names=T, sep=' ', quote=F)
 
 if(file.exists(paste0(opt$output,'.score.gz'))){
   system(paste0('rm ',opt$output,'.score.gz'))
@@ -250,47 +183,31 @@ if(file.exists(paste0(opt$output,'.score.gz'))){
 
 system(paste0('gzip ',opt$output,'.score'))
 
+# Record end time of test
+if(!is.na(opt$test)){
+  test_finish(log_file = log_file, test_start.time = test_start.time)
+}
+
 ####
 # Calculate mean and sd of polygenic scores
 ####
 
-# Calculate polygenic scores for reference individuals
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Calculating polygenic scores in reference...')
-sink()
-
-scores<-calc_score(
-  bfile=opt$ref_plink_chr, 
-  score=paste0(opt$output,'.score.gz')
-)
+log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
 
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Done!\n')
-sink()
+# Calculate scores in the full reference
+ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
-# Calculate the mean and sd of scores for each population specified in pop_scale
-pop_keep_files<-read.table(opt$ref_pop_scale, header=F, stringsAsFactors=F)
+# Calculate scale within each reference population
+pop_data <- read_pop_data(opt$pop_data)
 
-for(k in 1:dim(pop_keep_files)[1]){
-	pop<-pop_keep_files$V1[k]
-	keep<-fread(pop_keep_files$V2[k], header=F)
-  names(keep)<-c('FID','IID')
-  ref_scale<-score_mean_sd(scores=scores, keep=keep)
-	fwrite(ref_scale, paste0(opt$output,'.',pop,'.scale'), sep=' ')
+for(pop_i in unique(pop_data$POP)){
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
 }
 
-###
-# Clean up temporary files
-###
-
-system(paste0('rm ',opt$output_dir,'bld*'))
-system(paste0('rm ',opt$output_dir,'GWAS_sums*'))
-system(paste0('rm ',opt$output_dir,'mega*'))
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
-sink(file = paste(opt$output,'.log',sep=''), append = T)
-cat('Analysis finished at',as.character(end.time),'\n')
-cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
 sink()
-
diff --git a/Scripts/pgs_methods/sbayesrc.R b/Scripts/pgs_methods/sbayesrc.R
new file mode 100644
index 00000000..b9888b02
--- /dev/null
+++ b/Scripts/pgs_methods/sbayesrc.R
@@ -0,0 +1,245 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
+      help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--pop_data", action="store", default=NULL, type='character',
+      help="File containing the population code and location of the keep file [required]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+      help="Path PLINK v2 software binary [required]"),
+  make_option("--output", action="store", default=NULL, type='character',
+      help="Path for output files [required]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+      help="Number of cores for parallel computing [optional]"),
+  make_option("--sumstats", action="store", default=NULL, type='character',
+      help="GWAS summary statistics in LDSC format [required]"),
+  make_option("--gctb", action="store", default=NULL, type='character',
+      help="Path to GCTB binary [required]"),
+  make_option("--test", action="store", default=NA, type='character',
+      help="Specify number of SNPs to include [optional]"),
+  make_option("--seed", action="store", default=1, type='numeric',
+      help="Seed number [optional]"),
+  make_option("--sbayesrc_ldref", action="store", default=NULL, type='character',
+      help="Path to SBayesRC LD reference data [required]"),
+  make_option("--sbayesrc_annot", action="store", default=NULL, type='character',
+      help="Path to SBayesRC annotations [required]")
+)
+
+opt = parse_args(OptionParser(option_list = option_list))
+
+# Load dependencies
+library(SBayesRC)
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Check required inputs
+if(is.null(opt$ref_plink_chr)){
+  stop('--ref_plink_chr must be specified.\n')
+}
+if(is.null(opt$sumstats)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$pop_data)){
+  stop('--pop_data must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--output must be specified.\n')
+}
+if(is.null(opt$gctb)){
+  stop('--gctb must be specified.\n')
+}
+if(is.null(opt$sbayesrc_ldref)){
+  stop('--sbayesrc_ref must be specified.\n')
+}
+if(is.null(opt$sbayesrc_annot)){
+  stop('--sbayesrc_annot must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
+system(paste0('mkdir -p ',opt$output_dir))
+
+# Create temp directory
+tmp_dir <- tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'sbayesrc.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+set.seed(opt$seed)
+
+#####
+# Read in sumstats
+#####
+
+log_add(log_file = log_file, message = 'Reading in GWAS.')
+
+# Read in, check and format GWAS summary statistics
+gwas <- read_sumstats(sumstats = opt$sumstats, chr = CHROMS, log_file = log_file, req_cols = c('SNP','A1','A2','FREQ','BETA','SE','P','N'))
+
+###
+# Change to COJO format
+###
+
+gwas <- gwas[, c('SNP','A1','A2','FREQ','BETA','SE','P','N'), with=F]
+names(gwas) <- c('SNP','A1','A2','freq','b','se','p','N')
+
+# Write out cojo format sumstats
+fwrite(gwas, paste0(tmp_dir,'/GWAS_sumstats_COJO.txt'), sep=' ', na = "NA", quote=F)
+
+rm(gwas)
+gc()
+
+# Record start time for test
+if(!is.na(opt$test)){
+  test_start.time <- test_start(log_file = log_file)
+}
+
+###
+# Perform sumstat QC using SBayesRC package
+###
+
+SBayesRC::tidy(
+  mafile = paste0(tmp_dir, '/GWAS_sumstats_COJO.txt'),
+  LDdir = opt$sbayesrc_ldref,
+  output = paste0(tmp_dir, '/tidy.ma'),
+  log2file = TRUE
+)
+tidy_log <- readLines(paste0(tmp_dir, '/tidy.ma.log'))
+tidy_log <- tidy_log[grepl('SNPs remained after QC', tidy_log)]
+tidy_log <- as.numeric(gsub(' .*', '', tidy_log))
+log_add(
+  log_file = log_file,
+  message = paste0(tidy_log, ' variants remain after SBayesRC tidy step.')
+)
+
+system(paste0('cp ', tmp_dir, '/tidy.ma.log ', opt$output,'.tidy.log'))
+
+###
+# Impute the GWAS sumstats using SBayesRC
+###
+
+log_add(log_file = log_file, message = 'Running SBayesRC imputation step...')
+
+SBayesRC::impute(
+  mafile = paste0(tmp_dir, '/tidy.ma'),
+  LDdir = opt$sbayesrc_ldref,
+  output = paste0(tmp_dir, '/tidy.imp.ma'),
+  log2file = T
+)
+
+###
+# Run SBayesRC
+###
+
+log_add(log_file = log_file, message = 'Running SBayesRC main analysis...')
+
+tryCatch(
+  {
+    # First attempt to run sbayesrc
+    SBayesRC::sbayesrc(
+      mafile = paste0(tmp_dir, '/tidy.imp.ma'),
+      LDdir = opt$sbayesrc_ldref,
+      outPrefix = paste0(tmp_dir, '/sbrc'),
+      annot = opt$sbayesrc_annot,
+      log2file = FALSE
+    )
+  },
+  error = function(e) {
+    # Check if the error message matches the specific issue
+    if (grepl("All correlations are negative, this may indicate errors in summary data.", e$message)) {
+      message("Specific error encountered. Retrying with btune = FALSE...")
+      log_add(log_file = log_file, message = 'Specific error encountered. Retrying with btune = FALSE...')
+      # Retry with the btune parameter set to FALSE
+      SBayesRC::sbayesrc(
+        mafile = paste0(tmp_dir, '/tidy.imp.ma'),
+        LDdir = opt$sbayesrc_ldref,
+        outPrefix = paste0(tmp_dir, '/sbrc'),
+        annot = opt$sbayesrc_annot,
+        bTune = FALSE,
+        log2file = FALSE
+      )
+    } else {
+      # For any other error, rethrow the error and stop the script
+      stop("An unexpected error occurred: ", e$message)
+    }
+  },
+  warning = function(w) {
+    message("A warning occurred while running SBayesRC::sbayesrc:")
+    message(w)
+  }
+)
+
+###
+# Process score file
+###
+
+score <- fread(paste0(tmp_dir, '/sbrc.txt'))
+
+# Insert A2 column
+ma_imp <- fread(paste0(tmp_dir, '/tidy.imp.ma'))
+if(nrow(score) == nrow(ma_imp) & all(score$A1 == ma_imp$A1)){
+  score$A2 <- ma_imp$A2
+} else {
+  log_add(log_file = log_file, message = 'Score file not aligned with sumstats for A2 insertion.')
+  system(paste0('cp -r ', tmp_dir, ' ', opt$output_dir,'/temp'))
+  stop('Score file not aligned with sumstats for A2 insertion')
+}
+
+# Save in plink score format
+score <- score[,c('SNP', 'A1', 'A2', 'BETA'), with=F]
+names(score) <- c('SNP', 'A1', 'A2', 'SCORE_SBayesRC')
+
+# Flip effects to match reference alleles
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
+score_new <- map_score(ref = ref, score = score)
+
+fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+
+if(file.exists(paste0(opt$output,'.score.gz'))){
+  system(paste0('rm ',opt$output,'.score.gz'))
+}
+system(paste0('gzip ',opt$output,'.score'))
+
+# Record end time of test
+if(!is.na(opt$test)){
+  test_finish(log_file = log_file, test_start.time = test_start.time)
+}
+
+####
+# Calculate mean and sd of polygenic scores
+####
+
+log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
+
+# Calculate scores in the full reference
+ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
+
+# Calculate scale within each reference population
+pop_data <- read_pop_data(opt$pop_data)
+
+for(pop_i in unique(pop_data$POP)){
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
+
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 807536fb..a67a030a 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -564,7 +564,6 @@ for(i in 1:nrow(ugr_data)){
 
 </details>
 
-
 ***
 
 # Run GenoPred
@@ -656,12 +655,14 @@ config<-c(
   "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt",
   "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
   "gwas_groups: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt",
-  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs']",
+  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
   "tlprs_methods: ['dbslmm','prscs','lassosum']",
   "cores_prep_pgs: 20",
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
-  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/1KG/ldpred2"
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/1KG/ldpred2",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc"
 )
 
 write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
@@ -679,6 +680,273 @@ snakemake --profile slurm --use-conda --configfile=/scratch/prj/ukbiobank/usr/ol
 
 ***
 
+# Estimate SNP-h2 and rG across populations
+
+We will estimate SNP-h2 using LD-score regression, and the rG using POPCORN.
+POPCORN can estimate the SNP-h2, but it will vary according to the other GWAS included due to SNP overlap.
+
+***
+
+## Prepare sumstats
+
+Use the sumstats QC'd by GenoPred.
+
+<details><summary>Show code</summary>
+```{r}
+library(data.table)
+dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats', recursive = T)
+gwas_list<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt')
+
+for(i in 1:nrow(gwas_list)){
+  gwas_file <-
+    paste0(
+      "/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output_crosspop/reference/gwas_sumstat/",
+      gwas_list$name[i],
+      "/",
+      gwas_list$name[i],
+      "-cleaned.gz"
+    )
+  
+  gwas_header <- fread(gwas_file, nrows = 1)
+  cols_index <- which(names(gwas_header) %in% c('SNP','A1','A2','BETA','SE','P','N'))
+  
+  system(
+    paste0(
+      "zcat ",
+      gwas_file,
+      " | cut -f ", 
+      paste0(cols_index, collapse = ','),
+      " | sed -e '1s/BETA/beta/'",
+      " | /users/k1806347/oliverpainfel/Software/pigz -f",
+      " > /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/",
+      gwas_list$name[i], 
+      ".sumstats.gz"
+      )
+    )
+}
+```
+
+</details>
+
+***
+
+## Run LDSC
+
+<details><summary>Show code</summary>
+```{bash}
+conda activate ldsc
+
+for pop in $(echo EUR EAS AFR);do
+  if [ "$pop" == "EUR" ]; then
+      samp="UKB"
+  fi
+  if [ "$pop" == "EAS" ]; then
+      samp="BBJ"
+  fi
+  if [ "$pop" == "AFR" ]; then
+      samp="UGR"
+  fi
+  
+  for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats
+
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
+     --sumstats /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/${pheno}_${samp}.sumstats.gz \
+     --out /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats/${pheno}_${samp}"
+
+  done
+done
+
+for pop in $(echo EUR EAS AFR);do
+  if [ "$pop" == "EUR" ]; then
+      samp="UKB"
+  fi
+  if [ "$pop" == "EAS" ]; then
+      samp="BBJ"
+  fi
+  if [ "$pop" == "AFR" ]; then
+      samp="UGR"
+  fi
+  
+  for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/${pheno}/${pop}
+
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \
+     --h2 /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats/${pheno}_${samp}.sumstats.gz \
+     --ref-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.${pop}.rsid \
+     --w-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.${pop}.rsid \
+     --out /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/${pheno}/${pop}/res"
+     
+  done
+done
+
+```
+
+</details>
+
+
+***
+
+## Calculate CSCOREs
+
+```{bash}
+
+# Subset the reference data into relevant populations
+for pop in $(echo EUR EAS AFR); do
+  mkdir -p /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp
+  for chr in $(seq 1 22); do
+    /users/k1806347/oliverpainfel/Software/plink1.9/plink \
+      --bfile /users/k1806347/oliverpainfel/Data/1KG/GenoPred/v2.0.0/ref.chr${chr} \
+      --keep /users/k1806347/oliverpainfel/Data/1KG/GenoPred/v2.0.0/keep_files/${pop}.keep \
+      --make-bed \
+      --out /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.${pop}.chr${chr}
+    done
+done
+
+conda activate popcorn
+for pop in $(echo EAS AFR); do
+  mkdir -p /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES
+  for chr in $(seq 1 22); do
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="popcorn \
+      compute \
+      -v 1 \
+      --bfile1 /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.EUR.chr${chr} \
+      --bfile2 /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.${pop}.chr${chr} \
+      /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_chr${chr}.txt"
+  done
+done
+
+for pop in $(echo EAS AFR); do
+  cat /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_chr*.txt > /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_all.txt
+done
+
+rm -r /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp
+```
+
+***
+
+## Run POPCORN
+
+<details><summary>Show code</summary>
+```{bash}
+conda activate popcorn
+for pop in $(echo EAS AFR);do
+  if [ "$pop" == "EAS" ]; then
+      samp="BBJ"
+  fi
+  if [ "$pop" == "AFR" ]; then
+      samp="UGR"
+  fi
+  
+  for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/${pheno}/EUR_${pop}
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="popcorn \
+       fit -v 3 \
+       --cfile /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_all.txt \
+       --sfile1 /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/${pheno}_UKB.sumstats.gz \
+       --sfile2 /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/${pheno}_${samp}.sumstats.gz \
+       --gen_effect \
+       /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/${pheno}/EUR_${pop}/rG_gen_effect"
+  done
+done
+
+```
+
+</details>
+
+***
+
+## Plot the results
+
+<details><summary>Show code</summary>
+
+```{r}
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+# Read in list of outcomes 
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-prscsx_dat[1:5,]
+
+# Plot the heritability estimates
+h2_res <- NULL
+
+for(pop in c('AFR','EAS', 'EUR')){
+  for(pheno in prscsx_dat$labels){
+    log <-
+      readLines(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/',
+          pheno,
+          '/',
+          pop,
+          '/res.log'
+        )
+      )
+    
+    h2 <- log[grepl('Total Observed scale h2:', log)]
+    h2_est <- as.numeric(gsub(' .*','', gsub('Total Observed scale h2: ', '', h2)))
+    h2_se <- as.numeric(gsub("\\)",'', gsub(".* \\(", '', h2)))
+    int <- log[grepl('Intercept:', log)]
+    int_est <- as.numeric(gsub(' .*','', gsub('Intercept: ', '', int)))
+    int_se <- as.numeric(gsub("\\)",'', gsub(".* \\(", '', int)))
+    lambda <- log[grepl('Lambda GC:', log)]
+    lambda <- as.numeric(gsub('.* ','', lambda))
+    
+    h2_res <- rbind(
+      h2_res,
+      data.table(
+        Population = pop,
+        Phenotype = pheno,
+        h2_est = h2_est,
+        h2_se = h2_se,
+        int_est = int_est,
+        int_se = int_se,
+        lambda = lambda
+      )
+    )
+  }
+}
+
+ggplot(h2_res, aes(x = Phenotype, y = h2_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=h2_est-h2_se, ymax=h2_est+h2_se), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="SNP-based Heritability (SE)") +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+# Plot rG estimates
+rg_res <- NULL
+for(pop in c('AFR','EAS')){
+  for(pheno in prscsx_dat$labels){
+    pop_res_i<-fread(paste0('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/', pheno, '/EUR_', pop, '/rG_gen_effect'))
+    names(pop_res_i) <- c('Test','Estimate','SE','Z','P')
+    pop_res_i <- pop_res_i[pop_res_i$Test == 'pge',]
+    pop_res_i$Population_1 <- 'EUR'
+    pop_res_i$Population_2 <- pop
+    pop_res_i$Phenotype <- pheno
+    rg_res <- rbind(rg_res, pop_res_i)
+  }
+}
+
+rg_res$Comparison <- paste0(rg_res$Population_1, ' vs. ', rg_res$Population_2)
+
+ggplot(rg_res, aes(x = Phenotype, y = Estimate, fill = Comparison)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=Estimate-SE, ymax=Estimate+SE), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="SNP-based\nGenetic Correlation (SE)") +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+```
+</details>
+
+***
+
 # Evaluate PGS
 
 Lets use the model builder script which implements nested 10 fold cross validation. Similar set up to previous paper, evaluating a model containing the best PGS selected by 10-fold cross validation, a model containing the PGS selected by pseudovalidation (if available), and an elastic net model containing all PGS from a given method. We will need to update the model builder script to achieve this
@@ -1568,6 +1836,12 @@ for(trait_i in prscsx_dat$labels){
   scores_i <- scores[grepl(trait_i, scores$name),]
   scores_i$group <- scores_i$method
 
+  # Current naming issue in gwas_groups leads to difficult subsetting
+  # In the future, update gwas_groups file to include disc pop in name, e.g. ALT_UKB_BBJ
+  scores_i$name_2 <- scores_i$name
+  scores_i$name_2[!(grepl('UKB|UGR|BBJ', scores_i$name_2, ignore.case = T))] <-
+    paste0(scores_i$name_2[!(grepl('UKB|UGR|BBJ', scores_i$name_2, ignore.case = T))], '_ukb_bbj')
+  
   for(targ_pop_i in targ_pop){
     # Subset GWAS based on EUR and/or targ_pop_i
     if(targ_pop_i == 'EAS'){
@@ -1600,9 +1874,9 @@ for(trait_i in prscsx_dat$labels){
         recursive = T
       )
       
-      scores_i_j <- scores_i[grepl('UKB', scores_i$name, ignore.case = F) |
-                             grepl(disc_pop_j, scores_i$nam, ignore.case = T),]
-      
+      scores_i_j <- scores_i[grepl('UKB', scores_i$name_2, ignore.case = F) |
+                       grepl(disc_pop_j, scores_i$name_2, ignore.case = T),]
+
       scores_i_j_multi <- scores_i_j
       # Insert path to score file
       scores_i_j_multi$predictor <- paste0(
@@ -1713,7 +1987,7 @@ for(trait_i in prscsx_dat$labels){
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-for targ_pop in $(echo EUR EAS AFR); do
+for targ_pop in $(echo EAS AFR EUR); do
   if [ "$targ_pop" == "EUR" ]; then
       targ_pop2="EUR_test"
   else
@@ -1721,7 +1995,7 @@ for targ_pop in $(echo EUR EAS AFR); do
   fi
   
   if [ "$targ_pop" == "EUR" ]; then
-    disc_pop=$(echo AFR EAS)
+    disc_pop=$(echo EAS AFR)
   fi
   
   if [ "$targ_pop" == "EAS" ]; then
@@ -1751,171 +2025,7 @@ done
 
 ***
 
-## Plot results for each phenotype
-
-### Assoc results
-
-Let's see how results vary across parameters. This is a sanity check as well as being informative of how I should structure the models for each method.
-
-<details><summary>Show code</summary>
-
-```{r}
-library(data.table)
-library(ggplot2)
-library(cowplot)
-
-# Read in list of outcomes 
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
-
-single_plot_list<-list()
-prscsx_xwing_plot_list<-list()
-tlprs_plot_list<-list()
-pop=c('EUR','EAS','AFR','CSA','AMR')
-for(pheno_i in prscsx_dat$labels){
-  res_i<-NULL
-  for(targ_pop_i in pop){
-      assoc_i <-
-        fread(
-          paste0(
-            '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.',
-            'targ_',
-            targ_pop_i,
-            '/',
-            pheno_i,
-            '/res.assoc.txt'
-          )
-        )
-      
-      # Determine whether single or multi source
-      assoc_i$multi_source <- ifelse(!(grepl(paste(pgs_group_methods, collapse ='|'), assoc_i$Group)), F, T)
-      
-      # Identify GWAS for single source
-      assoc_i$gwas[assoc_i$multi_source == F] <-
-        sub("^(([^.]+\\.){1}[^.]+)\\..*", "\\1",
-            sub("^[^.]*\\.", "",
-                assoc_i$Predictor[assoc_i$multi_source == F]))
-      
-      # Identify discovery sample
-      assoc_i$disc[assoc_i$multi_source == F] <-
-        sub(".*\\.", "",
-            assoc_i$gwas[assoc_i$multi_source == F])
-      
-      assoc_i$disc[assoc_i$disc == 'UKB'] <- 'EUR'
-      assoc_i$disc[assoc_i$disc == 'BBJ'] <- 'EAS'
-      assoc_i$disc[is.na(assoc_i$disc)] <- 'EUR+EAS'
-      
-      assoc_i$pheno <- pheno_i
-      assoc_i$test<-paste0(assoc_i$disc, ' → ', targ_pop_i)
-      
-      assoc_i$tlprs_target<-NA
-      assoc_i$tlprs_target[grepl('tlprs', assoc_i$Group) & grepl('targ.EUR', assoc_i$Predictor)]<-'EUR-tuned'
-      assoc_i$tlprs_target[grepl('tlprs', assoc_i$Group) & grepl('targ.EAS', assoc_i$Predictor)]<-'EAS-tuned'
-      
-      res_i<-rbind(res_i, assoc_i)
-  }
-
-  res_i$test<-factor(res_i$test, levels = unique(res_i$test))
-  res_i$disc<-factor(res_i$disc, levels = unique(res_i$disc))
-  res_i$pheno<-factor(res_i$pheno, levels = unique(res_i$pheno))
-  res_i$tlprs_target<-factor(res_i$tlprs_target, levels = unique(res_i$tlprs_target))
-
-  tmp<-res_i[!(res_i$multi_source),]
-  tmp<-tmp[!(grepl('pseudo', tmp$Group))]
-  tmp$param<-gsub('.*UKB.','',tmp$Predictor)
-  tmp$param<-gsub('.*BBJ.','',tmp$param)
-  tmp$param<-factor(tmp$param, levels=unique(tmp$param))
-
-  single_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=param, y=BETA, colour = Group)) +
-  geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
-                width = .2,
-                position = position_dodge(width = 0.7)) +
-  geom_point() +
-  geom_line() +
-  geom_hline(yintercept = 0) +
-  facet_grid(~ test, scales = 'free', space = 'free_x') +
-  theme_half_open() +
-  background_grid() + 
-  panel_border() + 
-  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-
-  tmp<-res_i[(res_i$Group %in% c('prscsx','xwing')),]
-  tmp<-tmp[!(grepl('pseudo', tmp$Group))]
-  tmp$param<-gsub(paste0('.*',pheno_i,'\\.'),'',tmp$Predictor)
-  tmp$param<-factor(tmp$param, levels=unique(tmp$param))
-
-  prscsx_xwing_plot_list[[pheno_i]] <- ggplot(tmp, aes(x=param, y=BETA, colour = Group)) +
-  geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
-                width = .2,
-                position = position_dodge(width = 0.7)) +
-  geom_point() +
-  geom_line() +
-  geom_hline(yintercept = 0) +
-  facet_grid(.~ test, scales = 'free', space = 'free_x') +
-  theme_half_open() +
-  background_grid() + 
-  panel_border() + 
-  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-
-  tmp<-res_i[(grepl('tlprs', res_i$Group)),]
-  tmp<-tmp[(grepl('SBayesR|phi.auto|DBSLMM.1.TLPRS', tmp$Predictor)),]
-  tmp<-tmp[!(grepl('pseudo', tmp$Group))]
-  tmp$param <- as.numeric(gsub('.*\\.','',tmp$Predictor))
-
-  tlprs_plot_list[[pheno_i]] <-  ggplot(tmp, aes(x=param, y=BETA, fill = Group, colour = Group)) +
-  geom_line() +
-  geom_errorbar(aes(ymin = BETA - SE, ymax = BETA + SE),
-                width = .2,
-                position = position_dodge(width = 0.7)) +
-  geom_point(stat="identity", position=position_dodge(0.7), size=2, shape=23) +
-  geom_hline(yintercept = 0) +
-  facet_grid(test ~ tlprs_target, scales = 'free', space = 'free_x') +
-  theme_half_open() +
-  background_grid() + 
-  panel_border() + 
-  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-}
-
-png(
-  '/users/k1806347/oliverpainfel/Analyses/crosspop/single.assoc.png',
-  res = 300,
-  height = 1000*nrow(prscsx_dat),
-  width = 8000,
-  units = 'px'
-)
-plot_grid(plotlist = single_plot_list, labels = names(single_plot_list), ncol=1)
-dev.off()
-
-png(
-  '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.prscsx_xwing.assoc.png',
-  res = 300,
-  height = 1000*nrow(prscsx_dat),
-  width = 8000,
-  units = 'px'
-)
-plot_grid(plotlist = prscsx_xwing_plot_list, labels = names(prscsx_xwing_plot_list), ncol=1)
-dev.off()
-
-png(
-  '/users/k1806347/oliverpainfel/Analyses/crosspop/multi.tlprs.assoc.png',
-  res = 300,
-  height = 3500*nrow(prscsx_dat),
-  width = 4000,
-  units = 'px'
-)
-plot_grid(plotlist = tlprs_plot_list, labels = names(tlprs_plot_list), ncol=1)
-dev.off()
-
-```
-</details>
-
-- PRS-CSx say to model the EUR, EAS and META output (for each phi)
-- x-wing produce output for each target population
-- TLPRS produce output for each target population
-
-***
-
-### Prediction modelling results
+### Plot results
 
 <details><summary>Show code</summary>
 
@@ -1931,18 +2041,7 @@ source_all('../functions')
 
 # Read in list of outcomes 
 prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
-
-config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
-  
-# Define pgs_methods used
-pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
-
-# Define gwas_list used
-gwas_list<-read_param(config = config, param = 'gwas_list')
-
-# Define gwas_groups used
-gwas_groups<-read_param(config = config, param = 'gwas_groups')
+prscsx_dat<-prscsx_dat[1:4,]
 
 # Calculate corelation between all phenotypes in each target population
 cors <- list()
@@ -2017,13 +2116,9 @@ for(pheno_i in prscsx_dat$labels){
   res_i$Model<-factor(res_i$Model, levels=c('Top1','Pseudo','Multi'))
   res_i$Discovery<-factor(res_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-  # Remove pseudo model for methods that don't really have one (single source combo, tlprs)
+  # Remove pseudo model for methods that don't really have one (single source combo)
   res_i <- res_i[!which(res_i$Model == 'Pseudo' & res_i$Method == 'ptclump'),]
-  res_i <- res_i[!which(
-    res_i$Model == 'Pseudo' & 
-    res_i$Method %in% c('tlprs_dbslmm', 'tlprs_prscs', 'tlprs_sbayesr') & 
-    res_i$Source == 'Multi'),]
-  
+
   res[[pheno_i]]<-res_i
   
 }
@@ -2312,15 +2407,8 @@ for(model_i in c('Top1','Pseudo','Multi')){
         tmp$Model_2, 
         levels=unique(tmp$Model_1))
       
-      diagonal_data <- data.frame(
-        Model_1 = unique(tmp$Model_1),
-        Model_2 = unique(tmp$Model_1),
-        R_diff_catagory = NA
-      )
-      
       heatmap_list[[model_i]][[targ_pop_i]][[disc_pop_i]] <-
         ggplot(data = tmp, aes(Model_2, Model_1, fill = R_diff_catagory)) +
-        geom_tile(data = diagonal_data, aes(Model_2, Model_1), fill = "grey", color = "white") +  
         geom_tile(color = "white") +
         labs(y = 'Test', x = 'Comparison', fill = 'R difference', title = paste0('Target: ', targ_pop_i, ', Model = ', model_i)) +
         facet_grid(Discovery ~ Discovery2, scales = 'free', space = 'free', switch="both") +
@@ -2354,5 +2442,277 @@ for(model_i in c('Top1','Pseudo','Multi')){
 ```
 </details>
 
+***
+
+## Check PGS association direction
+
+This is a sanity check to see whether the PGS are correlated with the outcome in the expected direction (positive). We will do this using the ptclump PGS and within population for simplicity (i.e. EUR -> EUR, etc)
+
+### Create predictor lists
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in list of outcomes 
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-prscsx_dat[1:5,]
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Restrict to ptclump
+scores<-scores[grepl('ptclump', scores$method),]
+
+# Create files for EAS and AFR targets
+pop <- c('EUR','EAS','AFR')
+for(trait_i in prscsx_dat$labels){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
+  scores_i <- scores[grepl(trait_i, scores$name),]
+  scores_i$group <- scores_i$method
+  
+  for(pop_i in pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    if(pop_i == 'EAS'){
+      samp_i <- 'BBJ'
+    }
+    if(pop_i == 'AFR'){
+      samp_i <- 'UGR'
+    }
+    if(pop_i == 'EUR'){
+      samp_i <- c('UKB')
+    }
+
+    dir.create(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i
+      ),
+      recursive = T
+    )
+    
+    scores_i_j <- scores_i[grepl(samp_i, scores_i$name, ignore.case = T),]
+    scores_i_j$predictor <- paste0(
+      outdir,
+      '/ukb/pgs/',
+      pop_i,
+      '/',
+      scores_i_j$method,
+      '/',
+      scores_i_j$name,
+      '/ukb-',
+      scores_i_j$name,
+      '-',
+      pop_i,
+      '.profiles'
+    )
+    
+    predictors_i <- scores_i_j[, c('predictor', 'group'), with=F]
+    
+    write.table(
+      predictors_i,
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i,
+        '/predictor_list.ptclump.txt'
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}
+
+```
+</details>
+
+***
+
+### Run model_builder
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+for pop in $(echo EUR EAS AFR); do
+  if [ "$pop" == "EUR" ]; then
+      pop2="EUR_test"
+  else
+      pop2=$pop
+  fi
+  
+  for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${pop2}.row_number.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/res.ptclump \
+      --n_core 5 \
+      --all_model F \
+      --assoc T"
+  done
+done
+
+```
+</details>
+
+***
+
+### Plot results
+
+<details><summary>Show code</summary>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-prscsx_dat[1:4,]
+
+# Read in results
+pop = c('EUR','EAS','AFR')
+res_all <- NULL
+for(pheno_i in prscsx_dat$labels){
+  res_i<-NULL
+  for(pop_i in pop){
+    assoc_i <-
+      fread(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          pop_i,
+          '.disc_',
+          pop_i,
+          '/',
+          pheno_i,
+          '/res.ptclump.assoc.txt'
+        )
+      )
+      assoc_i$Population <- pop_i
+      res_i<-rbind(res_i, assoc_i)
+  }
+  
+  res_i$Phenotype <- pheno_i
+  res_all<-rbind(res_all, res_i)
+}
+
+# Extract pT variable from Predictor
+res_all$pT <- gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.', '', res_all$Predictor))
+res_all$pT <- factor(res_all$pT, levels = unique(res_all$pT))
+
+ggplot(res_all, aes(x = Phenotype, y = BETA, fill = pT)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7, colour = 'black') +
+  geom_errorbar(aes(ymin=BETA-SE, ymax=BETA+SE), width=0, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="BETA (SE)") +
+  theme_half_open() +
+  background_grid() +
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y') +
+  scale_fill_manual(values = colorRampPalette(c("lightblue", "darkblue"))(length(unique(res_all$pT)))) +
+  facet_grid(Population ~.)
+
+```
+</details>
+
+***
+
+# Computational resoures
 
+```{r}
+library(data.table)
+library(ggplot2)
+library(cowplot)
+library(ggrepel)
+library(dplyr)
+library(plotly)
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+
+# Get some key variables from config
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in configuration specific benchmark files
+bm_files_i <- list.files(paste0(outdir, '/reference/benchmarks/'), full.names = T)
+
+# Subset benchmarks for pgs_methods
+bm_files_i <- bm_files_i[grepl('prep_pgs_', bm_files_i)]
+
+# Read in benchmark files
+bm_dat_all <- do.call(rbind, lapply(bm_files_i, function(file) {
+  tmp <- fread(file)
+  tmp$file <- basename(file)
+  return(tmp)
+}))
+
+# Create rule column
+bm_dat_all$rule <- gsub('-.*','',bm_dat_all$file)
+
+# Create method column
+bm_dat_all$method <-
+  gsub('_i', '', gsub('prep_pgs_', '', bm_dat_all$rule))
+
+# Calculate average time taken for each method
+method_avg <- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg <- rbind(
+    method_avg,
+    data.frame(
+      Method = i,
+      Time = mean(bm_dat_all$s[bm_dat_all$method == i])
+    )
+  )
+}
+
+# Format the time taken nicely
+method_avg$Time_clean[method_avg$Time < 60] <-
+  paste0(round(method_avg$Time[method_avg$Time < 60], 1), ' sec')
+method_avg$Time_clean[method_avg$Time > 60] <-
+  paste0(round(method_avg$Time[method_avg$Time > 60] / 60, 1), ' min')
+method_avg$Time_clean[method_avg$Time > 3600] <-
+  paste0(round(method_avg$Time[method_avg$Time > 3600] / 60 / 60, 1), ' hr')
+
+ggplot(method_avg, aes(x=Method, y = Time, fill = Method)) +
+  geom_bar()
+
+ggplot(method_avg, aes(x = Method, y = Time, fill = Method)) +
+  geom_bar(stat = "identity", position="dodge") +
+  geom_text(aes(label = Time_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
+  labs(x = "PGS Method", y = "Time (s)") +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
+
+```
 
diff --git a/docs/prep_ldpred2_ldmat.Rmd b/docs/prep_ldpred2_ldmat.Rmd
index 0b43efde..a090473f 100644
--- a/docs/prep_ldpred2_ldmat.Rmd
+++ b/docs/prep_ldpred2_ldmat.Rmd
@@ -25,6 +25,8 @@ pre code, pre, code {
 
 ***
 
+# 1KG
+
 Here we will calculate LD matrices for LDpred2 using HapMap3 SNPs in the 1KG super populations. This is to allow fair comparison across methods for a cross-population polygenic scoring study. 
 
 The code will follow that used to generate the LD matrices for EUR, in this paper ([link](https://doi.org/10.1016/j.xhgg.2022.100136)).
@@ -226,5 +228,239 @@ for(pop in c('EUR', 'EAS', 'AFR')){
 
 ```
 
+***
+
+# 1KG+HGDP
+
+Here we will calculate LD matrices for LDpred2 using HapMap3 SNPs in the 1KG+HGDP super populations. This is to allow fair comparison across methods for a cross-population polygenic scoring study. 
+
+The code will follow that used to generate the LD matrices for EUR, in this paper ([link](https://doi.org/10.1016/j.xhgg.2022.100136)).
+
+```{bash}
+# Subset 1KG+HGDP into super populations and merge across chromosomes
+for pop in $(echo EUR EAS AFR); do
+  mkdir -p /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/${pop}
+  mkdir -p /users/k1806347/oliverpainfel/Data/hgdp_1kg/prscs/ldblk_hgdp_1kg_$(echo ${pop} | tr '[:upper:]' '[:lower:]')
+
+  # Create mergelist
+  rm -f /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/${pop}/mergelist.txt
+  for chr in $(seq 1 22); do
+    echo /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} >> /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/${pop}/mergelist.txt
+  done
+  
+  /users/k1806347/oliverpainfel/Software/plink2_linux_avx2_20241011/plink2 \
+    --pmerge-list /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/${pop}/mergelist.txt \
+    --make-bed \
+    --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/keep_files/${pop}.keep \
+    --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/${pop}/ref.${pop}.GW
+done
+```
+
+```{r}
+library(bigsnpr)
+library(bigreadr)
+
+# Create object for genetic data in ldpred2 format, and impute missing values
+for(pop in c('EUR', 'EAS', 'AFR')){
+  # Read in reference data
+  snp_readBed(
+    paste0(
+      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
+      pop,
+      '/ref.',
+      pop,
+      '.GW.bed'
+    )
+  )
+  
+  # Attach the ref object in R session
+  ref <-
+    snp_attach(
+      paste0(
+        '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
+        pop,
+        '/ref.',
+        pop,
+        '.GW.rds'
+      )
+    )
+  G <- ref$genotypes
+
+  #### Impute missing values (bigsnpr can't handle missing data in most functions)
+  G_imp <- snp_fastImputeSimple(G, method = "mean2", ncores = nb_cores())
+
+  # Save imputed reference
+  ref$genotypes<-G_imp
+  saveRDS(ref, paste0(
+        '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
+        pop,
+        '/ref.',
+        pop,
+        '.GW.rds'
+      ))
+}
+
+################
+# Now calculate LD matrices following code from https://doi.org/10.1016/j.xhgg.2022.100136
+
+library(dplyr)
+library(bigreadr)
+library(bigsnpr)
+library(future.batchtools)
+library(furrr)
+
+for(pop in c('EUR', 'EAS', 'AFR')){
+  obj.hgdp_1kg <- snp_attach(paste0(
+          '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
+          pop,
+          '/ref.',
+          pop,
+          '.GW.rds'
+        ))
+  G <- obj.hgdp_1kg$genotypes
+  CHR <- obj.hgdp_1kg$map$chromosome
+  POS <- obj.hgdp_1kg$map$physical.pos
+  POS2 <- snp_asGeneticPos(CHR, POS, dir ='/users/k1806347/oliverpainfel/Data/Genetic_Map/CEU', ncores = nb_cores()) # Note this is not appropriate but I think Florian did the same as used distance from UKB
+  MAF <- snp_MAF(G, ncores = nb_cores())
+  keep <- (MAF > 0.02)
+
+  plan(batchtools_slurm(resources = list(
+    time = "12:00:00",
+    ntasks = 5,
+    mem = "20g",
+    partition = "neurohack_cpu"
+  )))
+  
+  all_final_grp <- furrr::future_map_dfr(1:22, function(chr) {
+    ind.chr2 <- which(CHR == chr & keep)
+
+    corr0 <- runonce::save_run({
+      snp_cor(
+        G,
+        ind.col = ind.chr2,
+        infos.pos = POS2[ind.chr2],
+        size = 3 / 1000,
+        ncores = nb_cores()
+      )
+    }, file = paste0(
+      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
+      pop,
+      '/LD_chr',
+      chr,
+      ".rds"
+    ))
+    
+    # find nearly independent LD blocks
+    # Playing around with the snp_ldsplit function, the max_r2 needs to be increased to 0.2 for AFR populations.
+    # This might be because we are using an inappropriate recombination map (EUR)?
+    # Use max_r2 = 0.15 for EUR and EAS as in Florian's code, but increase to 0.2 for AFR. Note. this is still lower than the default of 0.3.
+    max_r2 <- ifelse(pop %in% c('EUR','EAS'), 0.15, 0.25)
+    m <- length(ind.chr2)
+    (SEQ <- round(seq_log(m / 30, m / 5, length.out = 20)))
+    splits <- snp_ldsplit(corr0, thr_r2 = 0.05, min_size = 50, max_size = SEQ, max_r2 = max_r2)
+    splits$cost2 <- sapply(splits$all_size, function(sizes) sum(sizes^2))
+  
+    best_split <- splits %>%
+      arrange(cost2 * sqrt(5 + cost)) %>%
+      print() %>%
+      slice(1) %>%
+      print()
+  
+    (all_size <- best_split$all_size[[1]])
+    best_grp <- rep(seq_along(all_size), all_size)
+  
+    runonce::save_run({
+      corr0T <- as(corr0, "dgTMatrix")
+      corr0T@x <-
+        ifelse(best_grp[corr0T@i + 1L] == best_grp[corr0T@j + 1L], corr0T@x, 0)
+      as(Matrix::drop0(corr0T), "symmetricMatrix")
+    }, file = paste0(
+      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
+      pop,
+      '/LD_with_blocks_chr',
+      chr,
+      ".rds"
+    ))
+  
+    # LD for PRS-CS (based on the same data and LD blocks)
+    hdf5_file <- paste0("/users/k1806347/oliverpainfel/Data/hgdp_1kg/prscs/ldblk_hgdp_1kg_", tolower(pop)  , "/ldblk_hgdp_1kg_chr", chr, ".hdf5")
+    runonce::skip_run_if({
+      ind_block <- split(ind.chr2, best_grp)
+      bigparallelr::set_blas_ncores(nb_cores())
+      for (ic in seq_along(ind_block)) {
+        ind <- ind_block[[ic]]
+        ld <- big_cor(G, ind.col = ind)[]
+        rhdf5::h5write(list(ldblk = ld, snplist = paste0("snp", ind)),
+                       file = hdf5_file, name = paste0("blk_", ic), level = 9)
+      }
+    }, files = hdf5_file)
+  
+    # return
+    tibble(best_split, ind = list(ind.chr2))
+  })
+  
+  saveRDS(
+    all_final_grp,
+    paste0(
+      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
+      pop,
+      '/all_final_grp.rds'
+    )
+  )
+  
+  ###
+  # Create map.rds
+  ###
+  map <- obj.hgdp_1kg$map[keep,]
+
+  # Compute allele frequency
+  map$af<-big_colstats(G, ind.col = which(keep), ncores = nb_cores())$sum / (2 * nrow(G))
+  
+  # Compute LD scores
+  map$ld <- do.call('c', lapply(1:22, function(chr) {
+    cat(chr, ".. ", sep = "")
+    corr_chr <- readRDS(
+      paste0(
+        '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
+        pop,
+        '/LD_chr',
+        chr,
+        ".rds"
+      )
+    )
+    Matrix::colSums(corr_chr ^ 2)
+  }))
+  
+  map <- map[, c('chromosome','physical.pos','allele2','allele1','marker.ID','af','ld')]
+  names(map) <- c('chr','pos','a0','a1','rsid','af','ld')
+  
+  saveRDS(
+    map,
+    paste0(
+      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
+      pop,
+      '/map.rds'
+    )
+  )
+  
+  # GOT to here
+  
+  # need also some .bim and some snpinfo for PRS-CS
+  ind_keep <- unlist(all_final_grp$ind)
+  snpinfo <- transmute(obj.hgdp_1kg$map, chromosome, marker.ID = paste0("snp", row_number()),
+                       genetic.dist = 0, physical.pos, allele1 = "A", allele2 = "C")[ind_keep, ]
+  bigsnpr:::write.table2(snpinfo, "/users/k1806347/oliverpainfel/Data/hgdp_1kg/prscs/ldblk_hgdp_1kg_", tolower(pop)  , "/for_prscs.bim")
+  
+  snpinfo2 <- transmute(snpinfo, CHR = chromosome, SNP = marker.ID,
+                        BP = physical.pos, A1 = allele1, A2 = allele2)
+  snpinfo2$MAF <- snp_MAF(G, ind.row = ind_EAS, ind.col = ind_keep, ncores = nb_cores())
+  bigreadr::fwrite2(snpinfo2, "/users/k1806347/oliverpainfel/Data/hgdp_1kg/prscs/ldblk_hgdp_1kg_", tolower(pop)  , "/snpinfo_hgdp_1kg_hm3", sep = "\t")
+}
+
+
+```
+
+
+
 
 
diff --git a/docs/prep_quickprs_ref.Rmd b/docs/prep_quickprs_ref.Rmd
new file mode 100644
index 00000000..93d5551b
--- /dev/null
+++ b/docs/prep_quickprs_ref.Rmd
@@ -0,0 +1,116 @@
+---
+title: Preparing LDpred2 LD matrices
+output: 
+  html_document:
+    theme: cosmo
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+# 1KG+HGDP
+
+Here we will prepare reference data for QuickPRS using HapMap3 SNPs in the 1KG+HGDP super populations. This is to allow fair comparison across methods for a cross-population polygenic scoring study. 
+
+QuickPRS only seems to work for v5.2, so let make the reference data using this version.
+
+```{r}
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+opt<-list()
+opt$ref_plink_chr<-'resources/data/ref/ref.chr'
+opt$plink<-'plink'
+opt$plink2<-'plink2'
+opt$ldak_map<-'resources/data/ldak_map/genetic_map_b37'
+opt$ldak_tag<-'resources/data/ldak_bld'
+opt$ldak_highld<-'resources/data/ldak_highld/highld.txt'
+opt$ldak<-'/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/software/ldak5.2/ldak5.2.linux'
+opt$n_cores<-20
+
+for(pop in c('EUR','EAS','AFR')){
+  opt$output <- paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/', pop, '.hm3/', pop, '.hm3')
+  opt$output_dir <- paste0(dirname(opt$output),'/')
+  system(paste0('mkdir -p ',opt$output_dir))
+
+  opt$ref_keep<-paste0('resources/data/ref/keep_files/', pop, '.keep')
+  
+  ###
+  # Merge the per chromosome reference genetic data and subset opt$ref_keep
+  ###
+  
+  # Save in plink1 format for MegaPRS
+  plink_merge(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, keep = opt$ref_keep, make_bed =T, out = paste0(tmp_dir, '/ref_merge'))
+  
+  ############
+  # Format reference for LDAK
+  ############
+  
+  # Insert CHR:BP IDs
+  system(paste0("awk < ", tmp_dir, "/ref_merge.bim '{$2=$1\":\"$4;print $0}' > ", tmp_dir, '/tmp.bim; mv ', tmp_dir, '/tmp.bim ', tmp_dir, '/ref_merge.bim'))
+  
+  # Insert genetic distances
+  system(paste0(opt$plink, ' --bfile ', tmp_dir, '/ref_merge --cm-map ', opt$ldak_map,'/genetic_map_chr@_combined_b37.txt --make-bed --out ', tmp_dir, '/map'))
+  system(paste0("cat ", tmp_dir, "/map.bim | awk '{print $2, $3}' > ", tmp_dir, '/map.all'))
+  system(paste0("awk '(NR==FNR){arr[$1]=$2;next}{print $1, $2, arr[$2], $4, $5, $6}' ", tmp_dir, '/map.all ', tmp_dir, '/ref_merge.bim > ', tmp_dir, '/tmp.bim; mv ', tmp_dir, '/tmp.bim ', tmp_dir, '/ref_merge.bim'))
+  system(paste0('rm ', tmp_dir, '/map*'))
+  
+  ############
+  # Estimate Per-Predictor Heritabilities
+  ############
+  # We will use the BLD-LDAK Model, as recommended for human SNP data
+  
+  # Calculate LDAK weights
+  system(paste0(opt$ldak, ' --cut-weights ', tmp_dir,'/sections --bfile ', tmp_dir, '/ref_merge --max-threads ', opt$n_cores))
+  system(paste0(opt$ldak, ' --calc-weights-all ', tmp_dir,'/sections --bfile ', tmp_dir, '/ref_merge --max-threads ', opt$n_cores))
+  system(paste0('mkdir ', tmp_dir, '/bld'))
+  system(paste0('cp ', opt$ldak_tag, '/* ', tmp_dir, '/bld/'))
+  system(paste0('mv ', tmp_dir, '/sections/weights.short ', tmp_dir,'/bld/bld65'))
+  
+  # Calculate taggings
+    system(paste0(opt$ldak, ' --calc-tagging ', tmp_dir, '/bld.ldak --bfile ', tmp_dir, '/ref_merge --ignore-weights YES --power -.25 --annotation-number 65 --annotation-prefix ', tmp_dir, '/bld/bld --window-cm 1 --save-matrix YES --max-threads ', opt$n_cores))
+    
+  # Calculate predictor-predictor correlations
+  system(paste0(opt$ldak,' --calc-cors ', tmp_dir, '/tmp --bfile ', tmp_dir, '/ref_merge --window-cm 3 --max-threads ', opt$n_cores))
+    
+  # Identify SNPs in high LD regions
+  system(paste0(opt$ldak, ' --cut-genes ', tmp_dir, '/highld --bfile ', tmp_dir, '/ref_merge --genefile ', opt$ldak_highld, ' --max-threads ', opt$n_cores))
+
+  # Export the files required for QuickPRS
+  for (i in c('bim', 'bin', 'noise', 'root')) {
+    system(paste0('cp ', tmp_dir, '/tmp.cors.', i, ' ', opt$output, '.cors.', i))
+  }
+  for (i in c('matrix', 'tagging')) {
+    system(paste0('cp ', tmp_dir, '/bld.ldak.', i, ' ', opt$output, '.bld.ldak.quickprs.', i))
+  }
+  system(paste0('cp ', tmp_dir, '/highld/genes.predictors.used ', opt$output_dir, '/highld.snps'))
+
+  system(paste0('rm -r ', tmp_dir, '/*'))
+}
+
+```
+
diff --git a/docs/prep_sbayesrc_ref.Rmd b/docs/prep_sbayesrc_ref.Rmd
new file mode 100644
index 00000000..91d70db6
--- /dev/null
+++ b/docs/prep_sbayesrc_ref.Rmd
@@ -0,0 +1,120 @@
+---
+title: Preparing SBayesRC LD matrices
+output: 
+  html_document:
+    theme: cosmo
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+# 1KG+HGDP
+
+Here we will prepare reference data for SBayesRC using HapMap3 SNPs in the 1KG+HGDP super populations. This is to allow fair comparison across methods for a cross-population polygenic scoring study. 
+
+```{bash}
+conda activate sbayesrc
+module add parallel/20220522-gcc-13.2.0
+
+for pop in EUR EAS AFR; do
+
+# Set up directories and variables
+mkdir -p "/users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}"
+outDir="/users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/${pop}.hm3"
+rm -f /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/mergelist.txt
+
+# Create the merge list for PLINK
+for chr in $(seq 1 22); do
+  echo /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} >> /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/mergelist.txt
+done
+
+# Run PLINK to merge chromosomes and filter for the population
+/users/k1806347/oliverpainfel/Software/plink2_linux_avx2_20241011/plink2 \
+  --pmerge-list /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/mergelist.txt \
+  --make-bed \
+  --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/keep_files/${pop}.keep \
+  --maf 0.01 \
+  --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ref.${pop}.GW
+
+# Create ma_file.txt
+echo -e "SNP\tA1\tA2" > /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ma_file.txt
+cut -f 2,5,6 /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ref.${pop}.GW.bim >> /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ma_file.txt
+
+# Define variables for R scripts
+ma_file="/users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ma_file.txt"
+genotype="/users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ref.${pop}.GW"
+threads=1
+tool="/users/k1806347/oliverpainfel/Software/gctb_2.5.2_Linux/gctb"
+
+# Step 1: Generate the LD block information
+# Note. Updated script to use the tool parameter in this command
+Rscript -e "SBayesRC::LDstep1(mafile='$ma_file', genoPrefix='$genotype', outDir='$outDir', genoCHR='', blockRef='', log2file=TRUE, tool='$tool')"
+
+# Step 2: Submit the parallel jobs for LDstep2
+# Note. I tried to submit this as batch jobs in parallel, but the job kept completing halfway through. Very odd.
+seq 1 591 | parallel -j 10 'Rscript -e "SBayesRC::LDstep2(outDir=\"'$outDir'\", blockIndex={}, log2file=TRUE)"'
+
+# Step 3: Eigen decomposition for each LD block (submit and wait for Step 2 to complete)
+jobid_step3=$(sbatch --array=1-591 --parsable <<EOT
+#!/bin/bash
+#SBATCH --job-name=sbayesrc_eigen
+#SBATCH --partition=neurohack_cpu
+#SBATCH --cpus-per-task=${threads}
+#SBATCH --mem=2G
+#SBATCH --output=sbayesrc_eigen_%A_%a.out
+#SBATCH --error=sbayesrc_eigen_%A_%a.err
+export OMP_NUM_THREADS=$threads
+Rscript -e "SBayesRC::LDstep3(outDir='$outDir', blockIndex=\${SLURM_ARRAY_TASK_ID}, log2file=TRUE)"
+EOT
+)
+
+# Step 4: Merge LD information after Step 3 finishes
+sbatch --dependency=afterok:$jobid_step3 <<EOT
+#!/bin/bash
+#SBATCH --job-name=sbayesrc_merge
+#SBATCH --partition=neurohack_cpu
+#SBATCH --cpus-per-task=1
+#SBATCH --mem=2G
+#SBATCH --output=sbayesrc_merge_%j.out
+#SBATCH --error=sbayesrc_merge_%j.err
+Rscript -e "SBayesRC::LDstep4(outDir='$outDir', log2file=TRUE)"
+EOT
+
+# Step5: clean if necessary
+# Essential for analysis: $outDir/ldm.info, $outDir/snp.info, $outDir/block*.eigen.bin 
+# Other files could be removed
+# Note: before removing, check all blocks were finished.
+
+# Add a delay or wait here if you want to ensure each population's processing finishes before starting the next.
+sleep 10
+done
+
+for pop in EUR EAS AFR; do
+  rm $outDir/b*.ldm.*
+  rm $outDir/*.log
+  rm $outDir/ld.sh
+  rm -r $outDir/snplist
+  rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ref.${pop}.GW*
+  rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/${pop}.hm3.log
+  rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ma_file.txt
+  rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/mergelist.txt
+done
+
+```
diff --git a/functions/constants.R b/functions/constants.R
index d7c34f32..02886342 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -37,13 +37,13 @@ ref_pop <- data.frame(
 
 # Make a data.frame giving labels to the 1KG reference populations
 pgs_method_labels <- data.frame(
-  method = c('ptclump','dbslmm','ldpred2','sbayesr','lassosum','prscs','megaprs','external','prscsx','xwing','tlprs'),
-  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','lassosum','PRS-CS','MegaPRS','External','PRS-CSx','X-Wing','TL-PRS')
+  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','tlprs'),
+  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','TL-PRS')
 )
 pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),]
 
 # Make vector indicating pgs_methods that can be applied to non-european GWAS
-pgs_methods_noneur <- c('ptclump','lassosum','megaprs','prscs','dbslmm','ldpred2')
+pgs_methods_noneur <- c('ptclump','lassosum','megaprs','prscs','dbslmm','ldpred2','quickprs','sbayesrc')
 
 # Make vector indicating pgs_methods that are to be applied to gwas_groups
 pgs_group_methods <- c('prscsx','xwing','tlprs')
diff --git a/functions/pipeline.R b/functions/pipeline.R
index 25a1fcdf..e4af3efd 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -237,6 +237,12 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
   if(pgs_method == 'sbayesr'){
     pseudo_val <- 'SBayesR'
   }
+  if(pgs_method == 'sbayesr'){
+    pseudo_val <- 'SBayesRC'
+  }
+  if(pgs_method == 'quickprs'){
+    pseudo_val <- 'quickprs'
+  }
 
   # Retrieve pseudoval param
   if(pgs_method == 'dbslmm'){
diff --git a/pipeline/config.yaml b/pipeline/config.yaml
index a3394493..3773f131 100644
--- a/pipeline/config.yaml
+++ b/pipeline/config.yaml
@@ -22,7 +22,7 @@ score_list: NA
 # Specify location of gwas_groups file
 gwas_groups: NA
 
-# Specify pgs_methods ('ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs','prscsx')
+# Specify pgs_methods ('ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs','prscsx','quickprs','sbayesrc')
 pgs_methods: NA
 
 # Specify p-value thresholds for ptclump
@@ -40,6 +40,12 @@ prscs_ldref: '1kg'
 # Specify reference for SBayesR
 sbayesr_ldref: NA
 
+# Specify reference for SBayesRC
+sbayesrc_ldref: NA
+
+# Specify reference for QuickPRS
+quickprs_ldref: NA
+
 # Specify reference for LDpred2
 ldpred2_ldref: NA
 
diff --git a/pipeline/envs/sbayesrc.yaml b/pipeline/envs/sbayesrc.yaml
new file mode 100644
index 00000000..34ce5aaf
--- /dev/null
+++ b/pipeline/envs/sbayesrc.yaml
@@ -0,0 +1,19 @@
+name: sbayesrc
+channels:
+  - dranew
+  - bioconda
+  - conda-forge
+  - defaults
+dependencies:
+  - r-base>=3.5.1
+  - r-rcpp=1.0.11
+  - r-data.table>=1.11.8
+  - r-stringi=1.7.12
+  - r-bh=1.84.0
+  - r-rcppeigen=0.3.3.9.4
+  - r-optparse>=1.6.6
+  - r-devtools
+  - r-foreach=1.5.2
+  - r-domc=1.3.8
+  - r-r.utils=2.12.2
+  - plink2=2.00a5
diff --git a/pipeline/misc/dev/sequence_test.Rmd b/pipeline/misc/dev/sequence_test.Rmd
new file mode 100644
index 00000000..8b6e9058
--- /dev/null
+++ b/pipeline/misc/dev/sequence_test.Rmd
@@ -0,0 +1,50 @@
+---
+title: "Sequence test"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+***
+
+# Prepare data
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Data/1KG/sequence
+
+wget https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000_genomes_project/release/20181203_biallelic_SNV/ALL.chr22.shapeit2_integrated_v1a.GRCh38.20181129.phased.vcf.gz.tbi
+
+wget https://ftp.1000genomes.ebi.ac.uk/vol1/ftp/data_collections/1000_genomes_project/release/20181203_biallelic_SNV/ALL.chr22.shapeit2_integrated_v1a.GRCh38.20181129.phased.vcf.gz
+
+ln -s ALL.chr22.shapeit2_integrated_v1a.GRCh38.20181129.phased.vcf.gz.tbi 1kg_seq.chr22.vcf.gz.tbi
+ln -s ALL.chr22.shapeit2_integrated_v1a.GRCh38.20181129.phased.vcf.gz 1kg_seq.chr22.vcf.gz
+
+```
+
+```{r}
+target_list<-data.frame(
+  name='1kg_seq',
+  path='/users/k1806347/oliverpainfel/Data/1KG/sequence/1kg_seq',
+  type='vcf',
+  indiv_report=F
+)
+
+write.table(target_list, '/users/k1806347/oliverpainfel/test/1kg_seq/target_list.txt', row.names=F, col.names=T, quote=F)
+
+config<-c(
+  'outdir: /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/misc/dev/test_data/output',
+  'config_file: /users/k1806347/oliverpainfel/test/1kg_seq/config.yaml',
+  'target_list: /users/k1806347/oliverpainfel/test/1kg_seq/target_list.txt',
+  'testing: chr22'
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/test/1kg_seq/config.yaml', row.names=F, col.names=F, quote=F)
+
+```
+
+```{bash}
+snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/test/1kg_seq/config.yaml output_all -n
+```
+
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 15369698..926ac2f9 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -286,7 +286,7 @@ if 'ldpred2' in config['pgs_methods']:
 
 # Set sbayesr reference path
 if config['sbayesr_ldref'] == 'NA':
-  sbayesr_ldref=f"{resdir}/data/ldpred2_ref"
+  sbayesr_ldref=f"{resdir}/data/gctb_ref/ukbEURu_hm3_shrunk_sparse/ukbEURu_hm3_v3_50k_chr"
 else:
   sbayesr_ldref=config['sbayesr_ldref']
 
@@ -307,6 +307,39 @@ if 'sbayesr' in config['pgs_methods']:
         print(f"File not found: {ld_file}")
         raise FileNotFoundError(f"Required file not found: {ld_file}. SBayesR reference data must include files for all chromosomes.")
 
+# Set quickprs reference path
+if config['quickprs_ldref'] == 'NA':
+  quickprs_ldref=f"{resdir}/data/quickprs_ref"
+else:
+  quickprs_ldref=config['quickprs_ldref']
+
+# Check the quickprs ldref data is present for the required populations in the gwas_list
+if 'quickprs' in config['pgs_methods']:
+  for pop in gwas_list_df['population'].unique():
+    path = f"{quickprs_ldref}/{pop}.hm3"
+    # Check if required files exists
+    cors_file = os.path.join(path, f"{pop}.hm3.cors.bin")
+    if not os.path.exists(cors_file):
+      print(f"File not found: {cors_file}")
+      raise FileNotFoundError(f"Required file not found: {cors_file}. quickprs reference data must include .cors.bin for all populations.")
+
+
+# Set sbayesrc reference path
+if config['sbayesrc_ldref'] == 'NA':
+  sbayesrc_ldref=f"{resdir}/data/sbayesrc_ref"
+else:
+  sbayesrc_ldref=config['sbayesrc_ldref']
+
+# Check the sbayesrc ldref data is present for the required populations in the gwas_list
+if 'sbayesrc' in config['pgs_methods']:
+  for pop in gwas_list_df['population'].unique():
+    path = f"{sbayesrc_ldref}/{pop}"
+    # Check if required files exists
+    cors_file = os.path.join(path, f"{pop}.hm3/ldm.info")
+    if not os.path.exists(cors_file):
+      print(f"File not found: {cors_file}")
+      raise FileNotFoundError(f"Required file not found: {cors_file}. sbayesrc reference data must include ldm.info for all populations.")
+
 # Set refdir parameter
 # If refdir is NA, set refdir to '${resdir}/data/ref'
 if config['refdir'] == 'NA':
@@ -323,8 +356,12 @@ else:
 
 # Check valid pgs_methods are specified
 def check_pgs_methods(x):
+    # If pgs_methods is NA (None) or an empty list, return early without error
+    if x is None or x == "NA" or not x:
+        return
+
     valid_pgs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesr", "lassosum", "ldpred2", "megaprs", "xwing", "prscsx", "tlprs"
+        "ptclump", "dbslmm", "prscs", "sbayesr","sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs", "xwing", "prscsx", "tlprs"
     }
 
     invalid_methods = [method for method in x if method not in valid_pgs_methods]
@@ -338,7 +375,7 @@ check_pgs_methods(config['pgs_methods'])
 # Check valid tlprs_methods are specified
 def check_tlprs_methods(config):
     valid_tlprs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesr", "lassosum", "ldpred2", "megaprs"
+        "ptclump", "dbslmm", "prscs", "sbayesr", "sbayesrc", "lassosum", "ldpred2", "megaprs","quickprs"
     }
 
     # Check if 'tlprs' is in the pgs_methods list
@@ -722,6 +759,83 @@ rule download_gctb_software:
       rm {resdir}/software/gctb/gctb_2.03beta_Linux.zip
     }} > {log} 2>&1
     """
+
+# Download GCTB v2.5.2 for SBayesRC
+rule download_gctb252_software:
+  output:
+    f"{resdir}/software/gctb_2.5.2/gctb_2.5.2_Linux/gctb"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_gctb252_software.txt"
+  log:
+    f"{resdir}/data/logs/download_gctb252_software.log"
+  shell:
+    """
+    {{
+      rm -r -f {resdir}/software/gctb_2.5.2; \
+      mkdir -p {resdir}/software/gctb_2.5.2; \
+      wget --no-check-certificate -O {resdir}/software/gctb_2.5.2/gctb_2.5.2_Linux.zip https://cnsgenomics.com/software/gctb/download/gctb_2.5.2_Linux.zip; \
+      unzip {resdir}/software/gctb_2.5.2/gctb_2.5.2_Linux.zip -d {resdir}/software/gctb_2.5.2; \
+      rm {resdir}/software/gctb_2.5.2/gctb_2.5.2_Linux.zip
+    }} > {log} 2>&1
+    """
+
+# Download annotations for SBayesRC
+rule download_sbayesrc_annot:
+  output:
+    f"{resdir}/data/sbayesrc_annot/annot_baseline2.2.txt"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_sbayesrc_annot.txt"
+  log:
+    f"{resdir}/data/logs/download_sbayesrc_annot.log"
+  shell:
+    """
+    {{
+      rm -r -f {resdir}/data/sbayesrc_annot; \
+      mkdir -p {resdir}/data/sbayesrc_annot; \
+      wget --no-check-certificate -O {resdir}/data/sbayesrc_annot/annot_baseline2.2.zip https://sbayes.pctgplots.cloud.edu.au/data/SBayesRC/resources/v2.0/Annotation/annot_baseline2.2.zip; \
+      unzip {resdir}/data/sbayesrc_annot/annot_baseline2.2.zip -d {resdir}/data/sbayesrc_annot; \
+      rm {resdir}/data/sbayesrc_annot/annot_baseline2.2.zip
+    }} > {log} 2>&1
+    """
+
+# Download SBayesRC R package
+rule install_sbayesrc:
+  input:
+    "envs/sbayesrc.yaml"
+  output:
+    touch("resources/software/install_sbayesrc.done")
+  conda:
+    "../envs/sbayesrc.yaml"
+  benchmark:
+    "resources/data/benchmarks/install_sbayesrc.txt"
+  log:
+    "resources/data/logs/install_sbayesrc.log"
+  shell:
+    """
+    {{
+      Rscript -e 'install.packages(\"https://github.com/zhilizheng/SBayesRC/releases/download/v0.2.6/SBayesRC_0.2.6.tar.gz\", repos=NULL, type=\"source\")'
+    }} > {log} 2>&1
+    """
+
+# Install GenoUtils in SBayesRC environment
+rule install_genoutils_sbayesrc:
+  input:
+    rules.install_sbayesrc.output
+  output:
+    touch("resources/software/install_genoutils_sbayesrc.done")
+  conda:
+    "../envs/sbayesrc.yaml"
+  benchmark:
+    "resources/data/benchmarks/install_genoutils_sbayesrc.txt"
+  log:
+    "resources/data/logs/install_genoutils_sbayesrc.log"
+  shell:
+    """
+    {{
+      Rscript -e 'devtools::install_github(\"opain/GenoUtils@6334159ab5d95ce936896e6938a1031c38ed4f30\")'
+    }} > {log} 2>&1
+    """
+
 # Download LDpred2 reference
 rule download_ldpred2_ref:
   output:
@@ -754,7 +868,7 @@ rule download_ldak:
   shell:
     """
     {{
-      rm -r {resdir}/software/ldak; \
+      rm -r -f {resdir}/software/ldak; \
       mkdir -p {resdir}/software/ldak; \
       wget --no-check-certificate -O {resdir}/software/ldak/ldak5.1.linux_.zip https://dougspeed.com/wp-content/uploads/ldak5.1.linux_.zip; \
       unzip {resdir}/software/ldak/ldak5.1.linux_.zip -d {resdir}/software/ldak/; \
@@ -814,6 +928,64 @@ rule download_ldak_highld:
     }} > {log} 2>&1
     """
 
+# Download LDAK V5.2 for QuickPRS
+# This is a temporary solution. Only this version works for QuickPRS
+rule download_ldak5_2:
+  output:
+    f"{resdir}/software/ldak5.2/ldak5.2.linux"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_ldak5_2.txt"
+  log:
+    f"{resdir}/data/logs/download_ldak5_2.log"
+  shell:
+    """
+    {{
+      cp /users/k1806347/oliverpainfel/Software/ldak5.2.linux {resdir}/software/ldak5.2/ldak5.2.linux
+    }} > {log} 2>&1
+    """
+
+# Download LDAK V6
+rule download_ldak6:
+  output:
+    f"{resdir}/software/ldak6/ldak6.linux"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_ldak6.txt"
+  log:
+    f"{resdir}/data/logs/download_ldak6.log"
+  shell:
+    """
+    {{
+      rm -r -f {resdir}/software/ldak6; \
+      mkdir -p {resdir}/software/ldak6; \
+      wget --no-check-certificate -O {resdir}/software/ldak6/ldak6.linux https://github.com/dougspeed/LDAK/raw/main/ldak6.linux; \
+      chmod a+x {resdir}/software/ldak6/ldak6.linux
+    }} > {log} 2>&1
+    """
+
+
+# Download LDAK QuickPRS refernce data
+rule download_quickprs_ref:
+  output:
+    f"{resdir}/data/quickprs/{{population}}.hapmap/{{population}}.hapmap.cors.root"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_quickprs_ref-{{population}}.txt"
+  log:
+    f"{resdir}/data/logs/download_quickprs_ref-{{population}}.log"
+  shell:
+    """
+    {{
+      mkdir -p {resdir}/data/quickprs; \
+      rm -r -f {resdir}/data/quickprs/{wildcards.population}.hapmap; \
+      wget --no-check-certificate -O {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz https://genetics.ghpc.au.dk/doug/{wildcards.population}.hapmap.tar.gz; \
+      tar -zxvf {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz -C {resdir}/data/quickprs/; \
+      rm {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz
+    }} > {log} 2>&1
+    """
+
+rule download_quickprs_ref_all:
+  input:
+    lambda w: expand(f"{resdir}/data/quickprs/{{population}}.hapmap.cors.root", population=['gbr','sas','eas','afr'])
+
 # Download preprocessed reference data (1KG+HGDP HapMap3)
 rule download_default_ref:
   output:
@@ -853,6 +1025,7 @@ rule install_ggchicklet:
       Rscript -e 'remotes::install_github(\"hrbrmstr/ggchicklet@64c468dd0900153be1690dbfc5cfb35710da8183\")'
     }} > {log} 2>&1
     """
+
 # install lassosum
 rule install_lassosum:
   input:
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 4557a0ad..8a498150 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -218,6 +218,7 @@ rule prep_pgs_sbayesr_i:
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
+    lambda w: f"{sbayesr_ldref}/" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0] + "/map.rds",
     rules.download_gctb_ref.output,
     rules.download_gctb_software.output
   output:
@@ -235,7 +236,7 @@ rule prep_pgs_sbayesr_i:
       --ref_plink_chr {refdir}/ref.chr \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --gctb {resdir}/software/gctb/gctb_2.03beta_Linux/gctb \
-      --ld_matrix_chr {resdir}/data/gctb_ref/ukbEURu_hm3_shrunk_sparse/ukbEURu_hm3_v3_50k_chr \
+      --ld_matrix_chr {sbayesr_ldref} \
       --robust T \
       --n_cores {threads} \
       --output {outdir}/reference/pgs_score_files/sbayesr/{wildcards.gwas}/ref-{wildcards.gwas} \
@@ -369,6 +370,99 @@ rule prep_pgs_megaprs_i:
 rule prep_pgs_megaprs:
   input: expand(f"{outdir}/reference/pgs_score_files/megaprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=gwas_list_df['name'])
 
+##
+# LDAK QuickPRS
+##
+
+def get_quickprs_reference_path(w, gwas_list_df, resdir):
+  # Get the population from the GWAS list
+  population = gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0]
+
+  # Return the full path string
+  return f"{quickprs_ldref}/{population}.hm3/{population}.hm3.cors.bin"
+
+rule prep_pgs_quickprs_i:
+  resources:
+    mem_mb=20000,
+    time_min=5000
+  threads: config['cores_prep_pgs']
+  input:
+    f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
+    lambda w: get_quickprs_reference_path(w, gwas_list_df, resdir),
+    rules.download_ldak_highld.output,
+    rules.download_ldak5_2.output,
+    rules.download_ldak_map.output,
+    rules.download_ldak_bld.output
+  output:
+    f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz"
+  benchmark:
+    f"{outdir}/reference/benchmarks/prep_pgs_quickprs_i-{{gwas}}.txt"
+  log:
+    f"{outdir}/reference/logs/prep_pgs_quickprs_i-{{gwas}}.log"
+  conda:
+    "../envs/analysis.yaml"
+  params:
+    population= lambda w: gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0],
+    quick_prs_ref= lambda w: f"{quickprs_ldref}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}.hm3",
+    testing=config["testing"]
+  shell:
+    "Rscript ../Scripts/pgs_methods/quickprs.R \
+      --ref_plink_chr {refdir}/ref.chr \
+      --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
+      --ldak {resdir}/software/ldak5.2/ldak5.2.linux \
+      --quick_prs_ref {params.quick_prs_ref} \
+      --n_cores {threads} \
+      --output {outdir}/reference/pgs_score_files/quickprs/{wildcards.gwas}/ref-{wildcards.gwas} \
+      --pop_data {refdir}/ref.pop.txt \
+      --test {params.testing} > {log} 2>&1"
+
+rule prep_pgs_quickprs:
+  input: expand(f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=gwas_list_df['name'])
+
+##
+# LDAK SBayesRC
+##
+
+rule prep_pgs_sbayesrc_i:
+  resources:
+    mem_mb=20000,
+    time_min=5000
+  threads: config['cores_prep_pgs']
+  input:
+    f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
+    lambda w: f"{sbayesrc_ldref}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}.hm3/ldm.info",
+    rules.download_gctb252_software.output,
+    rules.download_sbayesrc_annot.output,
+    rules.install_genoutils_sbayesrc.output,
+    rules.install_sbayesrc.output
+  output:
+    f"{outdir}/reference/pgs_score_files/sbayesrc/{{gwas}}/ref-{{gwas}}.score.gz"
+  benchmark:
+    f"{outdir}/reference/benchmarks/prep_pgs_sbayesrc_i-{{gwas}}.txt"
+  log:
+    f"{outdir}/reference/logs/prep_pgs_sbayesrc_i-{{gwas}}.log"
+  conda:
+    "../envs/sbayesrc.yaml"
+  params:
+    population= lambda w: gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0],
+    sbayesrc_ldref= lambda w: f"{sbayesrc_ldref}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}.hm3",
+    testing=config["testing"]
+  shell:
+    "export OMP_NUM_THREADS={threads}; \
+    Rscript ../Scripts/pgs_methods/sbayesrc.R \
+      --ref_plink_chr {refdir}/ref.chr \
+      --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
+      --gctb {resdir}/software/gctb_2.5.2/gctb_2.5.2_Linux/gctb \
+      --sbayesrc_ldref {params.sbayesrc_ldref} \
+      --sbayesrc_annot {resdir}/data/sbayesrc_annot/annot_baseline2.2.txt \
+      --n_cores {threads} \
+      --output {outdir}/reference/pgs_score_files/sbayesrc/{wildcards.gwas}/ref-{wildcards.gwas} \
+      --pop_data {refdir}/ref.pop.txt \
+      --test {params.testing} > {log} 2>&1"
+
+rule prep_pgs_sbayesrc:
+  input: expand(f"{outdir}/reference/pgs_score_files/sbayesrc/{{gwas}}/ref-{{gwas}}.score.gz", gwas=gwas_list_df['name'])
+
 ##
 # Process externally created score files
 ##
@@ -666,6 +760,10 @@ if 'ldpred2' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_ldpred2.input)
 if 'megaprs' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_megaprs.input)
+if 'quickprs' in pgs_methods_all:
+  pgs_methods_input.append(rules.prep_pgs_quickprs.input)
+if 'sbayesrc' in pgs_methods_all:
+  pgs_methods_input.append(rules.prep_pgs_sbayesrc.input)
 if 'external' in pgs_methods_all:
   pgs_methods_input.append(rules.score_reporter.output)
 if 'prscsx' in pgs_methods_all:

From 60fdf042c0108b14f2f786fc24ff054e80baee42 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 29 Oct 2024 15:51:04 +0000
Subject: [PATCH 046/153] Minor updates

---
 docs/CrossPop.Rmd                       |  24 +-
 docs/incomplete/ref_stand_rethink.Rmd   | 361 ++++++++++
 docs/incomplete/ref_stand_rethink.html  | 880 ++++++++++++++++++++++++
 functions/pipeline.R                    |   2 +-
 pipeline/misc/enet_solver/enet_solver.R |  94 +++
 5 files changed, 1356 insertions(+), 5 deletions(-)
 create mode 100644 docs/incomplete/ref_stand_rethink.Rmd
 create mode 100644 docs/incomplete/ref_stand_rethink.html
 create mode 100644 pipeline/misc/enet_solver/enet_solver.R

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index a67a030a..f143f66d 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -2116,6 +2116,12 @@ for(pheno_i in prscsx_dat$labels){
   res_i$Model<-factor(res_i$Model, levels=c('Top1','Pseudo','Multi'))
   res_i$Discovery<-factor(res_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
+  # Remove Multi model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_i <- res_i[
+    !(res_i$Method %in%  c('sbayesrc', 'quickprs') & 
+      res_i$Model == 'Multi' & 
+      res_i$Source == 'Single'),]
+  
   # Remove pseudo model for methods that don't really have one (single source combo)
   res_i <- res_i[!which(res_i$Model == 'Pseudo' & res_i$Method == 'ptclump'),]
 
@@ -2157,6 +2163,20 @@ for(targ_pop_i in targ_pop){
   
     for(group_i in unique(res_i$Group)){
       res_group_i <- res_i[res_i$Group == group_i,]
+      missing_pheno <-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_group_i$pheno))]
+      
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_group_i$pheno))) {
+        print(paste0(
+          'Results missing for ',
+          targ_pop_i,
+          ' ',
+          group_i,
+          ' ',
+          paste0(missing_pheno, collapse = ' ')
+        ))
+      }
+      
       cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
       
       meta_res_i <-
@@ -2253,7 +2273,6 @@ for(pheno_i in prscsx_dat$labels){
   res[[pheno_i]]<-res_i
 }
     
-
 library(MAd)
 
 # Average R across phenotypes
@@ -2703,9 +2722,6 @@ method_avg$Time_clean[method_avg$Time > 60] <-
 method_avg$Time_clean[method_avg$Time > 3600] <-
   paste0(round(method_avg$Time[method_avg$Time > 3600] / 60 / 60, 1), ' hr')
 
-ggplot(method_avg, aes(x=Method, y = Time, fill = Method)) +
-  geom_bar()
-
 ggplot(method_avg, aes(x = Method, y = Time, fill = Method)) +
   geom_bar(stat = "identity", position="dodge") +
   geom_text(aes(label = Time_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
diff --git a/docs/incomplete/ref_stand_rethink.Rmd b/docs/incomplete/ref_stand_rethink.Rmd
new file mode 100644
index 00000000..cfa77154
--- /dev/null
+++ b/docs/incomplete/ref_stand_rethink.Rmd
@@ -0,0 +1,361 @@
+---
+title: Rethinking the reference standardisation process
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+# Background
+
+In some scenarios, PGS produced by GenoPred are shifted on the reference-standardised distribution. This has motivated a rethink of the reference standardisation process in GenoPred.
+
+The shift appears to be caused by missing data, though I do not understand why, since we are using reference frequency imputation during scoring. I have explored whether a shift in ancestry relative to the reference population could also cause a shift in the PGS, but projected PC correction of the PGS does not resolve the issue. Missingness seems to be a part of the issue, so we will need to simulate some data to explore causes and solutions further.
+
+Though, it makes me wonder whether our current approach for reference standardisation is suboptimal. We originally thought that reference frequency imputation is good as it allows simple PGS calculation, and missingness will lead to gravitation to the mean, but perhaps a more individual specific solution is available. Here is a summary of thoughts on this:
+
+- Impact of Imputing Missing Variants Using Reference MAF: When missing variants in the target sample are imputed using reference population allele frequencies, the resulting PGS tends to gravitate toward the reference mean. This approach also narrows the score’s distribution, reducing its variance and thus diminishing the variance explained (R²) within the target sample.
+
+- Interpretable Score Generation in GenoPred: GenoPred aims to create interpretable polygenic scores that are robust and transferrable across samples. A key element is the use of reference-based standardization, allowing score calculations to be independent of target sample variations and supporting consistent interpretations across studies and populations.
+
+- Adjustment for Missing Data in Interpretation: To account for missing variants in the target, scaling and standardizing the PGS as done by tools like Impute.me can improve interpretability. This approach provides z-scores that adjust for missing data, yielding a more accurate standardization. However, individual-level R² values will vary among participants due to differences in missing SNPs and their contributions.
+
+- Estimating Individual-Level R² Using GWAS Summary Statistics: With well-calibrated SNP weights, it is feasible to estimate individual-level R² by leveraging GWAS summary statistics and accounting for missing variants. This enables a calculation of R² per individual that reflects their specific missing data profile, although these estimates are influenced by the covariates used in the GWAS.
+
+- Adjusting Reported R² for Missing Data Impact: Alternatively, a global R² adjustment can be applied by using a correction factor that reflects the variance lost due to missing variants. This could involve calculating the ratio of summary statistic-based R² with missing data to R² assuming no missing data, providing a more accurate interpretation of the score’s predictive power for each individual.
+
+# Action point
+
+Create example target data with degrees of missingness that can used to see impact of missingness in the output of GenoPred. Try to recreate shift in PGS for anorexia nervosa, as it seems to be highly correlated with PC1, which seems to highlight the issue. If there are no bugs causing the shift, we may need to consider alternative, target sample specific solutions discuss above.
+
+# Simulation
+
+```{r}
+# Load necessary libraries
+library(ggplot2)
+
+# Set up parameters for simulation
+set.seed(1)
+num_individuals <- 1000
+num_snps <- 1000
+impute <- F
+
+# 1. Generate SNP effects and reference allele frequencies
+snp_effects <- data.frame(
+  SNP = paste0("rs", 1:num_snps),
+  effect_size = rnorm(num_snps, mean = 0.1, sd = 0.02)
+)
+
+# Reference population allele frequencies
+ref_allele_freq <- data.frame(
+  SNP = paste0("rs", 1:num_snps),
+  ref_freq = runif(num_snps, 0.1, 0.9)
+)
+
+# 2. Generate Target Genotypes based on Reference Allele Frequencies
+# Each individual's genotype is sampled from a binomial distribution with probability equal to ref_freq
+# This will give genotypes with frequencies matching the reference population
+target_genotypes <- data.frame(ID = 1:num_individuals)
+for (snp in snp_effects$SNP) {
+  ref_freq <- ref_allele_freq$ref_freq[ref_allele_freq$SNP == snp]
+  target_genotypes[[snp]] <- rbinom(num_individuals, 2, ref_freq)  # Generates 0, 1, or 2 with matching frequency
+}
+
+# Introduce some missing data randomly (optional, if you want missingness to remain)
+# Let's assume a 10% missing rate
+for (snp in snp_effects$SNP) {
+  missing_indices <- sample(1:num_individuals, size = round(0.9 * num_individuals))
+  target_genotypes[missing_indices, snp] <- NA
+}
+
+if(impute){
+  # Impute Missing Genotypes Using Reference Allele Frequency
+  for (snp in snp_effects$SNP) {
+    ref_freq <- ref_allele_freq$ref_freq[ref_allele_freq$SNP == snp]
+    target_genotypes[[snp]][is.na(target_genotypes[[snp]])] <- round(2 * ref_freq)
+  }
+}
+
+# 2. Calculate Population-Score Mean for each SNP
+snp_effects$population_score <- 2 * ref_allele_freq$ref_freq * snp_effects$effect_size
+population_mean_pgs <- sum(snp_effects$population_score)
+
+# 3. Calculate Raw PGS for each individual
+# Ensure genotypes are a matrix (num_individuals x num_snps)
+# Rows are individuals, columns are SNPs
+genotype_matrix <- as.matrix(target_genotypes[, snp_effects$SNP])
+genotype_matrix[is.na(genotype_matrix)] <- 0
+
+# Multiply the genotype matrix by the effect vector
+raw_pgs_vector <- genotype_matrix %*% snp_effects$effect_size
+
+pgs_data <- data.frame(ID = target_genotypes$ID, Raw_PGS = genotype_matrix %*% snp_effects$effect_size)
+
+# 4. Adjust Population Standardization Based on Missing Variants
+# Calculate individual-specific mean and standard deviation based on observed variants only
+# Convert target genotypes to a matrix, excluding the first column (ID column)
+genotype_matrix <- as.matrix(target_genotypes[, -1])
+
+# Create a mask to identify observed SNPs (1 for observed, 0 for NA)
+observed_mask <- !is.na(genotype_matrix)
+
+# Replace NAs with 0 for easier calculation
+genotype_matrix[is.na(genotype_matrix)] <- 0
+
+# Calculate population mean for each SNP (this is a constant for each SNP across individuals)
+population_means <- snp_effects$population_score
+
+# Calculate population standard deviation per SNP based on allele frequencies and effect sizes
+snp_variances <- 2 * ref_allele_freq$ref_freq * (1 - ref_allele_freq$ref_freq) * (snp_effects$effect_size^2)
+snp_std_devs <- sqrt(snp_variances)
+
+# Calculate observed population mean and standard deviation for each individual
+# Expand population_means to match observed_mask dimensions
+population_means_matrix <- matrix(population_means, nrow = nrow(observed_mask), ncol = ncol(observed_mask), byrow = TRUE)
+# Expand snp_variances to match observed_mask dimensions
+snp_variances_matrix <- matrix(snp_variances, nrow = nrow(observed_mask), ncol = ncol(observed_mask), byrow = TRUE)
+
+observed_means <- rowSums(observed_mask * population_means_matrix)
+observed_sds <- sqrt(rowSums(observed_mask * snp_variances_matrix))
+
+# Calculate Zero-Centered Score and Z-score in a vectorized way
+pgs_data$ZeroCenteredScore <- pgs_data$Raw_PGS - observed_means
+pgs_data$Z_score <- pgs_data$ZeroCenteredScore / observed_sds
+
+# 5. Estimate Overall R^2 based on GWAS Summary Statistics
+# Sum variance explained by each SNP based on allele frequencies and effect sizes
+total_r2 <- sum(2 * ref_allele_freq$ref_freq * (1 - ref_allele_freq$ref_freq) * 
+                (snp_effects$effect_size^2))
+
+# Precompute the constant part of the R² formula for each SNP
+snp_r2_contrib <- 2 * ref_allele_freq$ref_freq * (1 - ref_allele_freq$ref_freq) * (snp_effects$effect_size^2)
+
+# Convert target genotypes to a matrix, skipping the first column (ID column)
+genotype_matrix <- as.matrix(target_genotypes[, -1])
+
+# Create a mask matrix to identify observed SNPs (1 for observed, 0 for NA)
+observed_mask <- !is.na(genotype_matrix)
+
+# Replace NA values in the genotype matrix with 0 for calculations
+genotype_matrix[is.na(genotype_matrix)] <- 0
+
+# Calculate R² for each individual by summing observed SNP contributions
+pgs_data$Individual_R2 <- rowSums(observed_mask * snp_r2_contrib)
+
+# Plotting
+
+# 1. Plot Raw PGS Distribution
+ggplot(pgs_data, aes(x = Raw_PGS)) +
+  geom_histogram(fill = "skyblue", color = "black") +
+  labs(title = "Distribution of Raw Polygenic Scores",
+       x = "Raw PGS",
+       y = "Count") +
+  theme_minimal()
+
+# 2. Plot Zscore Distribution
+ggplot(pgs_data, aes(x = Z_score)) +
+  geom_histogram(fill = "skyblue", color = "black") +
+  labs(title = "Distribution of Polygenic Z Scores",
+       x = "PGS Z_score",
+       y = "Count") +
+  theme_minimal()
+
+# 3. Plot Individual-Level R^2 Distribution
+ggplot(pgs_data, aes(x = Individual_R2)) +
+  geom_histogram(fill = "skyblue", color = "black") +
+  labs(x = "Proportion of PGS R2",
+       y = "Count") +
+  theme_minimal()
+
+```
+
+```{r}
+# Function to simulate genotypes, polygenic scores, and perform optional imputation and standardization
+simulate_genotypes_pgs <- function(num_individuals = 500, num_snps = 500, impute = FALSE, missing_rate = 0.1, seed = 1) {
+  # Set up parameters for simulation
+  set.seed(seed)
+  
+  # 1. Generate SNP effects and reference allele frequencies
+  snp_effects <- data.frame(
+    SNP = paste0("rs", 1:num_snps),
+    effect_size = rnorm(num_snps, mean = 0.1, sd = 0.02)
+  )
+  
+  # Reference population allele frequencies
+  ref_allele_freq <- data.frame(
+    SNP = paste0("rs", 1:num_snps),
+    ref_freq = runif(num_snps, 0.1, 0.9)
+  )
+  
+  # 2. Generate Target Genotypes based on Reference Allele Frequencies
+  target_genotypes <- data.frame(ID = 1:num_individuals)
+  for (snp in snp_effects$SNP) {
+    ref_freq <- ref_allele_freq$ref_freq[ref_allele_freq$SNP == snp]
+    target_genotypes[[snp]] <- rbinom(num_individuals, 2, ref_freq)
+  }
+  
+  # Introduce missing data randomly
+  for (snp in snp_effects$SNP) {
+    missing_indices <- sample(1:num_individuals, size = round(missing_rate * num_individuals))
+    target_genotypes[missing_indices, snp] <- NA
+  }
+  
+  # Impute missing genotypes if specified
+  if (impute) {
+    for (snp in snp_effects$SNP) {
+      ref_freq <- ref_allele_freq$ref_freq[ref_allele_freq$SNP == snp]
+      target_genotypes[[snp]][is.na(target_genotypes[[snp]])] <- 2 * ref_freq
+    }
+  }
+  
+  # 3. Calculate Population-Score Mean for each SNP
+  snp_effects$population_score <- 2 * ref_allele_freq$ref_freq * snp_effects$effect_size
+  population_mean_pgs <- sum(snp_effects$population_score)
+  
+  # 4. Calculate Raw PGS for each individual
+  genotype_matrix <- as.matrix(target_genotypes[, snp_effects$SNP])
+  observed_mask <- !is.na(genotype_matrix)
+  genotype_matrix[is.na(genotype_matrix)] <- 0
+  raw_pgs_vector <- genotype_matrix %*% snp_effects$effect_size
+  pgs_data <- data.frame(ID = target_genotypes$ID, Raw_PGS = raw_pgs_vector)
+  
+  # 5. Adjust Population Standardization Based on Missing Variants
+  # Expand population_means and snp_variances as matrices to match observed_mask dimensions
+  population_means <- snp_effects$population_score
+  snp_variances <- 2 * ref_allele_freq$ref_freq * (1 - ref_allele_freq$ref_freq) * (snp_effects$effect_size^2)
+  
+  population_means_matrix <- matrix(population_means, nrow = nrow(observed_mask), ncol = ncol(observed_mask), byrow = TRUE)
+  snp_variances_matrix <- matrix(snp_variances, nrow = nrow(observed_mask), ncol = ncol(observed_mask), byrow = TRUE)
+  
+  # Calculate observed means and standard deviations without unintended recycling
+  observed_means <- rowSums(observed_mask * population_means_matrix)
+  observed_sds <- sqrt(rowSums(observed_mask * snp_variances_matrix))
+  
+  # Calculate Zero-Centered Score and Z-score
+  pgs_data$ZeroCenteredScore <- pgs_data$Raw_PGS - observed_means
+  pgs_data$Z_score <- pgs_data$ZeroCenteredScore / observed_sds
+  
+  # 6. Estimate Overall R^2
+  total_r2 <- sum(snp_variances)
+  
+  # Calculate individual R² values
+  pgs_data$Individual_R2 <- rowSums(observed_mask * snp_variances_matrix)
+  
+  # Return the results
+  list(
+    snp_effects = snp_effects,
+    ref_allele_freq = ref_allele_freq,
+    target_genotypes = target_genotypes,
+    pgs_data = pgs_data,
+    total_r2 = total_r2
+  )
+}
+
+pgs_z_all <- NULL
+r2_all <- NULL
+for(n_snp in c(100, 500)){
+  for(missingness in c(0, 0.1, 0.2, 0.3, 0.4, 0.8)){
+    for(imp in c(T, F)){
+      tmp <- simulate_genotypes_pgs(
+        impute = imp,
+        num_individuals = 1000,
+        num_snps = n_snp,
+        missing_rate = missingness
+      )
+      tmp2 <- tmp$pgs_data
+      tmp2$impute<-imp
+      tmp2$missing_rate<-missingness
+      tmp2$num_snps<-n_snp
+      tmp2$total_r2<-tmp$total_r2
+      pgs_z_all <- rbind(pgs_z_all, tmp2)
+    }
+  }
+}
+
+library(ggplot2)
+library(cowplot)
+
+pgs_z_all$missing_rate_lab <- paste0('Missing = ', pgs_z_all$missing_rate)
+pgs_z_all$num_snps_lab <- paste0('N SNP = ', pgs_z_all$num_snps)
+
+pgs_z_all$missing_rate_lab <- factor(pgs_z_all$missing_rate_lab, levels = unique(pgs_z_all$missing_rate_lab))
+pgs_z_all$num_snps_lab <- factor(pgs_z_all$num_snps_lab, levels = unique(pgs_z_all$num_snps_lab))
+
+###
+# Show the mean and SD on the plot
+###
+
+# N SNP doesn't change anything, so just plot missing rate with NSNP 500
+pgs_z_all_nsnp500 <- pgs_z_all[pgs_z_all$num_snps == 500,]
+
+# Calculate mean and SD without dplyr
+mean_z <- tapply(pgs_z_all_nsnp500$Z_score, 
+                 list(pgs_z_all_nsnp500$impute, pgs_z_all_nsnp500$missing_rate_lab), 
+                 mean, na.rm = TRUE)
+sd_z <- tapply(pgs_z_all_nsnp500$Z_score, 
+               list(pgs_z_all_nsnp500$impute, pgs_z_all_nsnp500$missing_rate_lab), 
+               sd, na.rm = TRUE)
+
+# Convert the results to a data frame for ggplot
+mean_z_df <- as.data.frame(as.table(mean_z))
+sd_z_df <- as.data.frame(as.table(sd_z))
+mean_r2_df <- as.data.frame(as.table(mean_z))
+colnames(mean_z_df) <- c("impute", "missing_rate_lab", "mean_z")
+colnames(sd_z_df) <- c("impute", "missing_rate_lab", "sd_z")
+stats <- merge(mean_z_df, sd_z_df, by = c("impute", "missing_rate_lab"))
+
+# Plot with density and annotated mean/SD values
+ggplot(pgs_z_all_nsnp500, aes(x = Z_score, fill = impute)) +
+  geom_vline(xintercept = 0) +
+  geom_density(alpha = 0.5, position = "identity") +
+  labs(x = "PGS Z_score", y = "Density", fill = 'Impute', colour = 'Impute') +
+  facet_grid(. ~ missing_rate_lab) +
+  theme_half_open() +
+  panel_border() +
+  geom_text(
+    data = stats,
+    aes(
+      x = ifelse(impute == "TRUE", -Inf, Inf),  # Place one group on left, other on right
+      y = Inf, 
+      label = paste0("Mean: ", round(mean_z, 2), "\nSD: ", round(sd_z, 2)),
+      color = impute
+    ),
+    hjust = ifelse(stats$impute == "TRUE", -0.1, 1.1),  # Adjust horizontal justification
+    vjust = 1.5, size = 3, inherit.aes = FALSE, show.legend = FALSE
+  )
+
+# Plot quantiles to see normality
+ggplot(pgs_z_all_nsnp500, aes(sample = Z_score, color = impute)) +
+  geom_qq() +
+  geom_qq_line() +
+  geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "black") +  # Add y = x line
+  labs(x = "Theoretical Quantiles", y = "Observed Quantiles", color = 'Impute') +
+  facet_grid(. ~ missing_rate_lab) +
+  theme_half_open() +
+  panel_border()
+
+```
+
+
diff --git a/docs/incomplete/ref_stand_rethink.html b/docs/incomplete/ref_stand_rethink.html
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+++ b/docs/incomplete/ref_stand_rethink.html
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+
+
+<h1 class="title toc-ignore">Rethinking the reference standardisation
+process</h1>
+
+</div>
+
+
+<hr />
+<div id="background" class="section level1">
+<h1>Background</h1>
+<p>In some scenarios, PGS produced by GenoPred are shifted on the
+reference-standardised distribution. This has motivated a rethink of the
+reference standardisation process in GenoPred.</p>
+<p>The shift appears to be caused by missing data, though I do not
+understand why, since we are using reference frequency imputation during
+scoring. I have explored whether a shift in ancestry relative to the
+reference population could also cause a shift in the PGS, but projected
+PC correction of the PGS does not resolve the issue. Missingness seems
+to be a part of the issue, so we will need to simulate some data to
+explore causes and solutions further.</p>
+<p>Though, it makes me wonder whether our current approach for reference
+standardisation is suboptimal. We originally thought that reference
+frequency imputation is good as it allows simple PGS calculation, and
+missingness will lead to gravitation to the mean, but perhaps a more
+individual specific solution is available. Here is a summary of thoughts
+on this:</p>
+<ul>
+<li><p>Impact of Imputing Missing Variants Using Reference MAF: When
+missing variants in the target sample are imputed using reference
+population allele frequencies, the resulting PGS tends to gravitate
+toward the reference mean. This approach also narrows the score’s
+distribution, reducing its variance and thus diminishing the variance
+explained (R²) within the target sample.</p></li>
+<li><p>Interpretable Score Generation in GenoPred: GenoPred aims to
+create interpretable polygenic scores that are robust and transferrable
+across samples. A key element is the use of reference-based
+standardization, allowing score calculations to be independent of target
+sample variations and supporting consistent interpretations across
+studies and populations.</p></li>
+<li><p>Adjustment for Missing Data in Interpretation: To account for
+missing variants in the target, scaling and standardizing the PGS as
+done by tools like Impute.me can improve interpretability. This approach
+provides z-scores that adjust for missing data, yielding a more accurate
+standardization. However, individual-level R² values will vary among
+participants due to differences in missing SNPs and their
+contributions.</p></li>
+<li><p>Estimating Individual-Level R² Using GWAS Summary Statistics:
+With well-calibrated SNP weights, it is feasible to estimate
+individual-level R² by leveraging GWAS summary statistics and accounting
+for missing variants. This enables a calculation of R² per individual
+that reflects their specific missing data profile, although these
+estimates are influenced by the covariates used in the GWAS.</p></li>
+<li><p>Adjusting Reported R² for Missing Data Impact: Alternatively, a
+global R² adjustment can be applied by using a correction factor that
+reflects the variance lost due to missing variants. This could involve
+calculating the ratio of summary statistic-based R² with missing data to
+R² assuming no missing data, providing a more accurate interpretation of
+the score’s predictive power for each individual.</p></li>
+</ul>
+</div>
+<div id="action-point" class="section level1">
+<h1>Action point</h1>
+<p>Create example target data with degrees of missingness that can used
+to see impact of missingness in the output of GenoPred. Try to recreate
+shift in PGS for anorexia nervosa, as it seems to be highly correlated
+with PC1, which seems to highlight the issue. If there are no bugs
+causing the shift, we may need to consider alternative, target sample
+specific solutions discuss above.</p>
+</div>
+<div id="simulation" class="section level1">
+<h1>Simulation</h1>
+<pre class="r"><code># Load necessary libraries
+library(ggplot2)
+
+# Set up parameters for simulation
+set.seed(1)
+num_individuals &lt;- 1000
+num_snps &lt;- 1000
+impute &lt;- F
+
+# 1. Generate SNP effects and reference allele frequencies
+snp_effects &lt;- data.frame(
+  SNP = paste0(&quot;rs&quot;, 1:num_snps),
+  effect_size = rnorm(num_snps, mean = 0.1, sd = 0.02)
+)
+
+# Reference population allele frequencies
+ref_allele_freq &lt;- data.frame(
+  SNP = paste0(&quot;rs&quot;, 1:num_snps),
+  ref_freq = runif(num_snps, 0.1, 0.9)
+)
+
+# 2. Generate Target Genotypes based on Reference Allele Frequencies
+# Each individual&#39;s genotype is sampled from a binomial distribution with probability equal to ref_freq
+# This will give genotypes with frequencies matching the reference population
+target_genotypes &lt;- data.frame(ID = 1:num_individuals)
+for (snp in snp_effects$SNP) {
+  ref_freq &lt;- ref_allele_freq$ref_freq[ref_allele_freq$SNP == snp]
+  target_genotypes[[snp]] &lt;- rbinom(num_individuals, 2, ref_freq)  # Generates 0, 1, or 2 with matching frequency
+}
+
+# Introduce some missing data randomly (optional, if you want missingness to remain)
+# Let&#39;s assume a 10% missing rate
+for (snp in snp_effects$SNP) {
+  missing_indices &lt;- sample(1:num_individuals, size = round(0.9 * num_individuals))
+  target_genotypes[missing_indices, snp] &lt;- NA
+}
+
+if(impute){
+  # Impute Missing Genotypes Using Reference Allele Frequency
+  for (snp in snp_effects$SNP) {
+    ref_freq &lt;- ref_allele_freq$ref_freq[ref_allele_freq$SNP == snp]
+    target_genotypes[[snp]][is.na(target_genotypes[[snp]])] &lt;- round(2 * ref_freq)
+  }
+}
+
+# 2. Calculate Population-Score Mean for each SNP
+snp_effects$population_score &lt;- 2 * ref_allele_freq$ref_freq * snp_effects$effect_size
+population_mean_pgs &lt;- sum(snp_effects$population_score)
+
+# 3. Calculate Raw PGS for each individual
+# Ensure genotypes are a matrix (num_individuals x num_snps)
+# Rows are individuals, columns are SNPs
+genotype_matrix &lt;- as.matrix(target_genotypes[, snp_effects$SNP])
+genotype_matrix[is.na(genotype_matrix)] &lt;- 0
+
+# Multiply the genotype matrix by the effect vector
+raw_pgs_vector &lt;- genotype_matrix %*% snp_effects$effect_size
+
+pgs_data &lt;- data.frame(ID = target_genotypes$ID, Raw_PGS = genotype_matrix %*% snp_effects$effect_size)
+
+# 4. Adjust Population Standardization Based on Missing Variants
+# Calculate individual-specific mean and standard deviation based on observed variants only
+# Convert target genotypes to a matrix, excluding the first column (ID column)
+genotype_matrix &lt;- as.matrix(target_genotypes[, -1])
+
+# Create a mask to identify observed SNPs (1 for observed, 0 for NA)
+observed_mask &lt;- !is.na(genotype_matrix)
+
+# Replace NAs with 0 for easier calculation
+genotype_matrix[is.na(genotype_matrix)] &lt;- 0
+
+# Calculate population mean for each SNP (this is a constant for each SNP across individuals)
+population_means &lt;- snp_effects$population_score
+
+# Calculate population standard deviation per SNP based on allele frequencies and effect sizes
+snp_variances &lt;- 2 * ref_allele_freq$ref_freq * (1 - ref_allele_freq$ref_freq) * (snp_effects$effect_size^2)
+snp_std_devs &lt;- sqrt(snp_variances)
+
+# Calculate observed population mean and standard deviation for each individual
+# Expand population_means to match observed_mask dimensions
+population_means_matrix &lt;- matrix(population_means, nrow = nrow(observed_mask), ncol = ncol(observed_mask), byrow = TRUE)
+# Expand snp_variances to match observed_mask dimensions
+snp_variances_matrix &lt;- matrix(snp_variances, nrow = nrow(observed_mask), ncol = ncol(observed_mask), byrow = TRUE)
+
+observed_means &lt;- rowSums(observed_mask * population_means_matrix)
+observed_sds &lt;- sqrt(rowSums(observed_mask * snp_variances_matrix))
+
+# Calculate Zero-Centered Score and Z-score in a vectorized way
+pgs_data$ZeroCenteredScore &lt;- pgs_data$Raw_PGS - observed_means
+pgs_data$Z_score &lt;- pgs_data$ZeroCenteredScore / observed_sds
+
+# 5. Estimate Overall R^2 based on GWAS Summary Statistics
+# Sum variance explained by each SNP based on allele frequencies and effect sizes
+total_r2 &lt;- sum(2 * ref_allele_freq$ref_freq * (1 - ref_allele_freq$ref_freq) * 
+                (snp_effects$effect_size^2))
+
+# Precompute the constant part of the R² formula for each SNP
+snp_r2_contrib &lt;- 2 * ref_allele_freq$ref_freq * (1 - ref_allele_freq$ref_freq) * (snp_effects$effect_size^2)
+
+# Convert target genotypes to a matrix, skipping the first column (ID column)
+genotype_matrix &lt;- as.matrix(target_genotypes[, -1])
+
+# Create a mask matrix to identify observed SNPs (1 for observed, 0 for NA)
+observed_mask &lt;- !is.na(genotype_matrix)
+
+# Replace NA values in the genotype matrix with 0 for calculations
+genotype_matrix[is.na(genotype_matrix)] &lt;- 0
+
+# Calculate R² for each individual by summing observed SNP contributions
+pgs_data$Individual_R2 &lt;- rowSums(observed_mask * snp_r2_contrib)
+
+# Plotting
+
+# 1. Plot Raw PGS Distribution
+ggplot(pgs_data, aes(x = Raw_PGS)) +
+  geom_histogram(fill = &quot;skyblue&quot;, color = &quot;black&quot;) +
+  labs(title = &quot;Distribution of Raw Polygenic Scores&quot;,
+       x = &quot;Raw PGS&quot;,
+       y = &quot;Count&quot;) +
+  theme_minimal()
+
+# 2. Plot Zscore Distribution
+ggplot(pgs_data, aes(x = Z_score)) +
+  geom_histogram(fill = &quot;skyblue&quot;, color = &quot;black&quot;) +
+  labs(title = &quot;Distribution of Polygenic Z Scores&quot;,
+       x = &quot;PGS Z_score&quot;,
+       y = &quot;Count&quot;) +
+  theme_minimal()
+
+# 3. Plot Individual-Level R^2 Distribution
+ggplot(pgs_data, aes(x = Individual_R2)) +
+  geom_histogram(fill = &quot;skyblue&quot;, color = &quot;black&quot;) +
+  labs(x = &quot;Proportion of PGS R2&quot;,
+       y = &quot;Count&quot;) +
+  theme_minimal()</code></pre>
+<pre class="r"><code># Function to simulate genotypes, polygenic scores, and perform optional imputation and standardization
+simulate_genotypes_pgs &lt;- function(num_individuals = 500, num_snps = 500, impute = FALSE, missing_rate = 0.1, seed = 1) {
+  # Set up parameters for simulation
+  set.seed(seed)
+  
+  # 1. Generate SNP effects and reference allele frequencies
+  snp_effects &lt;- data.frame(
+    SNP = paste0(&quot;rs&quot;, 1:num_snps),
+    effect_size = rnorm(num_snps, mean = 0.1, sd = 0.02)
+  )
+  
+  # Reference population allele frequencies
+  ref_allele_freq &lt;- data.frame(
+    SNP = paste0(&quot;rs&quot;, 1:num_snps),
+    ref_freq = runif(num_snps, 0.1, 0.9)
+  )
+  
+  # 2. Generate Target Genotypes based on Reference Allele Frequencies
+  target_genotypes &lt;- data.frame(ID = 1:num_individuals)
+  for (snp in snp_effects$SNP) {
+    ref_freq &lt;- ref_allele_freq$ref_freq[ref_allele_freq$SNP == snp]
+    target_genotypes[[snp]] &lt;- rbinom(num_individuals, 2, ref_freq)
+  }
+  
+  # Introduce missing data randomly
+  for (snp in snp_effects$SNP) {
+    missing_indices &lt;- sample(1:num_individuals, size = round(missing_rate * num_individuals))
+    target_genotypes[missing_indices, snp] &lt;- NA
+  }
+  
+  # Impute missing genotypes if specified
+  if (impute) {
+    for (snp in snp_effects$SNP) {
+      ref_freq &lt;- ref_allele_freq$ref_freq[ref_allele_freq$SNP == snp]
+      target_genotypes[[snp]][is.na(target_genotypes[[snp]])] &lt;- 2 * ref_freq
+    }
+  }
+  
+  # 3. Calculate Population-Score Mean for each SNP
+  snp_effects$population_score &lt;- 2 * ref_allele_freq$ref_freq * snp_effects$effect_size
+  population_mean_pgs &lt;- sum(snp_effects$population_score)
+  
+  # 4. Calculate Raw PGS for each individual
+  genotype_matrix &lt;- as.matrix(target_genotypes[, snp_effects$SNP])
+  observed_mask &lt;- !is.na(genotype_matrix)
+  genotype_matrix[is.na(genotype_matrix)] &lt;- 0
+  raw_pgs_vector &lt;- genotype_matrix %*% snp_effects$effect_size
+  pgs_data &lt;- data.frame(ID = target_genotypes$ID, Raw_PGS = raw_pgs_vector)
+  
+  # 5. Adjust Population Standardization Based on Missing Variants
+  # Expand population_means and snp_variances as matrices to match observed_mask dimensions
+  population_means &lt;- snp_effects$population_score
+  snp_variances &lt;- 2 * ref_allele_freq$ref_freq * (1 - ref_allele_freq$ref_freq) * (snp_effects$effect_size^2)
+  
+  population_means_matrix &lt;- matrix(population_means, nrow = nrow(observed_mask), ncol = ncol(observed_mask), byrow = TRUE)
+  snp_variances_matrix &lt;- matrix(snp_variances, nrow = nrow(observed_mask), ncol = ncol(observed_mask), byrow = TRUE)
+  
+  # Calculate observed means and standard deviations without unintended recycling
+  observed_means &lt;- rowSums(observed_mask * population_means_matrix)
+  observed_sds &lt;- sqrt(rowSums(observed_mask * snp_variances_matrix))
+  
+  # Calculate Zero-Centered Score and Z-score
+  pgs_data$ZeroCenteredScore &lt;- pgs_data$Raw_PGS - observed_means
+  pgs_data$Z_score &lt;- pgs_data$ZeroCenteredScore / observed_sds
+  
+  # 6. Estimate Overall R^2
+  total_r2 &lt;- sum(snp_variances)
+  
+  # Calculate individual R² values
+  pgs_data$Individual_R2 &lt;- rowSums(observed_mask * snp_variances_matrix)
+  
+  # Return the results
+  list(
+    snp_effects = snp_effects,
+    ref_allele_freq = ref_allele_freq,
+    target_genotypes = target_genotypes,
+    pgs_data = pgs_data,
+    total_r2 = total_r2
+  )
+}
+
+pgs_z_all &lt;- NULL
+r2_all &lt;- NULL
+for(n_snp in c(100, 500)){
+  for(missingness in c(0, 0.1, 0.2, 0.3, 0.4, 0.8)){
+    for(imp in c(T, F)){
+      tmp &lt;- simulate_genotypes_pgs(
+        impute = imp,
+        num_individuals = 1000,
+        num_snps = n_snp,
+        missing_rate = missingness
+      )
+      tmp2 &lt;- tmp$pgs_data
+      tmp2$impute&lt;-imp
+      tmp2$missing_rate&lt;-missingness
+      tmp2$num_snps&lt;-n_snp
+      tmp2$total_r2&lt;-tmp$total_r2
+      pgs_z_all &lt;- rbind(pgs_z_all, tmp2)
+    }
+  }
+}
+
+library(ggplot2)
+library(cowplot)
+
+pgs_z_all$missing_rate_lab &lt;- paste0(&#39;Missing = &#39;, pgs_z_all$missing_rate)
+pgs_z_all$num_snps_lab &lt;- paste0(&#39;N SNP = &#39;, pgs_z_all$num_snps)
+
+pgs_z_all$missing_rate_lab &lt;- factor(pgs_z_all$missing_rate_lab, levels = unique(pgs_z_all$missing_rate_lab))
+pgs_z_all$num_snps_lab &lt;- factor(pgs_z_all$num_snps_lab, levels = unique(pgs_z_all$num_snps_lab))
+
+###
+# Show the mean and SD on the plot
+###
+
+# N SNP doesn&#39;t change anything, so just plot missing rate with NSNP 500
+pgs_z_all_nsnp500 &lt;- pgs_z_all[pgs_z_all$num_snps == 500,]
+
+# Calculate mean and SD without dplyr
+mean_z &lt;- tapply(pgs_z_all_nsnp500$Z_score, 
+                 list(pgs_z_all_nsnp500$impute, pgs_z_all_nsnp500$missing_rate_lab), 
+                 mean, na.rm = TRUE)
+sd_z &lt;- tapply(pgs_z_all_nsnp500$Z_score, 
+               list(pgs_z_all_nsnp500$impute, pgs_z_all_nsnp500$missing_rate_lab), 
+               sd, na.rm = TRUE)
+
+# Convert the results to a data frame for ggplot
+mean_z_df &lt;- as.data.frame(as.table(mean_z))
+sd_z_df &lt;- as.data.frame(as.table(sd_z))
+mean_r2_df &lt;- as.data.frame(as.table(mean_z))
+colnames(mean_z_df) &lt;- c(&quot;impute&quot;, &quot;missing_rate_lab&quot;, &quot;mean_z&quot;)
+colnames(sd_z_df) &lt;- c(&quot;impute&quot;, &quot;missing_rate_lab&quot;, &quot;sd_z&quot;)
+stats &lt;- merge(mean_z_df, sd_z_df, by = c(&quot;impute&quot;, &quot;missing_rate_lab&quot;))
+
+# Plot with density and annotated mean/SD values
+ggplot(pgs_z_all_nsnp500, aes(x = Z_score, fill = impute)) +
+  geom_vline(xintercept = 0) +
+  geom_density(alpha = 0.5, position = &quot;identity&quot;) +
+  labs(x = &quot;PGS Z_score&quot;, y = &quot;Density&quot;, fill = &#39;Impute&#39;, colour = &#39;Impute&#39;) +
+  facet_grid(. ~ missing_rate_lab) +
+  theme_half_open() +
+  panel_border() +
+  geom_text(
+    data = stats,
+    aes(
+      x = ifelse(impute == &quot;TRUE&quot;, -Inf, Inf),  # Place one group on left, other on right
+      y = Inf, 
+      label = paste0(&quot;Mean: &quot;, round(mean_z, 2), &quot;\nSD: &quot;, round(sd_z, 2)),
+      color = impute
+    ),
+    hjust = ifelse(stats$impute == &quot;TRUE&quot;, -0.1, 1.1),  # Adjust horizontal justification
+    vjust = 1.5, size = 3, inherit.aes = FALSE, show.legend = FALSE
+  )
+
+# Plot quantiles to see normality
+ggplot(pgs_z_all_nsnp500, aes(sample = Z_score, color = impute)) +
+  geom_qq() +
+  geom_qq_line() +
+  geom_abline(slope = 1, intercept = 0, linetype = &quot;dashed&quot;, color = &quot;black&quot;) +  # Add y = x line
+  labs(x = &quot;Theoretical Quantiles&quot;, y = &quot;Observed Quantiles&quot;, color = &#39;Impute&#39;) +
+  facet_grid(. ~ missing_rate_lab) +
+  theme_half_open() +
+  panel_border()</code></pre>
+</div>
+
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+<hr/>
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diff --git a/functions/pipeline.R b/functions/pipeline.R
index e4af3efd..229d8efd 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -237,7 +237,7 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
   if(pgs_method == 'sbayesr'){
     pseudo_val <- 'SBayesR'
   }
-  if(pgs_method == 'sbayesr'){
+  if(pgs_method == 'sbayesrc'){
     pseudo_val <- 'SBayesRC'
   }
   if(pgs_method == 'quickprs'){
diff --git a/pipeline/misc/enet_solver/enet_solver.R b/pipeline/misc/enet_solver/enet_solver.R
new file mode 100644
index 00000000..638d93c8
--- /dev/null
+++ b/pipeline/misc/enet_solver/enet_solver.R
@@ -0,0 +1,94 @@
+# Load required package
+library(glmnet)
+
+# Function to compute elastic net weights using correlation information with soft thresholding
+compute_elastic_net_weights_with_thresholding <- function(correlations, predictor_corr_matrix, alpha, lambda, max_iter = 100, tol = 1e-6) {
+  # Check that dimensions match
+  n <- length(correlations)
+  if (ncol(predictor_corr_matrix) != n || nrow(predictor_corr_matrix) != n) {
+    stop("Dimensions of predictor correlation matrix must match the length of the correlation vector.")
+  }
+
+  # Define the Ridge penalty matrix
+  ridge_penalty <- (1 - alpha) * lambda * diag(n)
+
+  # Initialize weights
+  beta <- rep(0, n)
+
+  # Iterate for coordinate descent-like updates with soft-thresholding
+  for (iter in 1:max_iter) {
+    beta_old <- beta
+
+    # Update each coefficient using soft-thresholding
+    for (j in 1:n) {
+      # Calculate the adjusted correlation with residuals for variable j
+      residual_correlation <- correlations[j] - sum(predictor_corr_matrix[j, -j] * beta[-j])
+
+      # Apply the soft-thresholding step
+      beta[j] <- soft_threshold(residual_correlation / (1 + ridge_penalty[j, j]), alpha * lambda)
+    }
+
+    # Check for convergence
+    if (max(abs(beta - beta_old)) < tol) {
+      break
+    }
+  }
+
+  return(as.vector(beta))
+}
+
+# Soft thresholding function for Lasso-like behavior
+soft_threshold <- function(x, threshold) {
+  sign(x) * pmax(abs(x) - threshold, 0)
+}
+
+# Example usage:
+# Simulate data for comparison
+set.seed(123)
+n_samples <- 10000
+n_predictors <- 100
+
+# Generate random predictors
+X <- matrix(rnorm(n_samples * n_predictors), ncol = n_predictors)
+# Generate a random outcome with some noise
+y <- X %*% rnorm(n_predictors) + rnorm(n_samples)
+
+# Compute correlations between predictors and outcome
+correlations <- as.vector(cor(X, y))
+
+# Compute the correlation matrix of predictors
+predictor_corr_matrix <- cor(X)
+
+# Define a range of alphas and lambdas to test
+alphas <- c(0.5)  # Use a fixed alpha value for simplicity in this comparison
+lambdas <- c(0.4)  # Different regularization strengths
+
+# Compute custom elastic net weights
+custom_weights <- compute_elastic_net_weights_with_thresholding(correlations, predictor_corr_matrix, alphas, lambdas)
+
+# Compare with glmnet
+# Use a single alpha and a range of lambda values
+glmnet_fit <- glmnet(X, y, alpha = alphas, lambda = lambdas, standardize = TRUE)
+
+# Extract glmnet coefficients for the selected lambda values
+glmnet_weights <- as.matrix(coef(glmnet_fit))[-1, ]  # Drop the intercept
+
+plot(custom_weights, glmnet_weights)
+cor(cbind(custom_weights, glmnet_weights))
+
+# Convert custom weights data frame to match glmnet output structure for comparison
+custom_weights_glmnet_format <- custom_weights[, paste0("alpha_0.5_lambda_", lambdas)]
+
+# Combine into a single data frame for comparison
+comparison <- data.frame(
+  Predictor = paste0("X", 1:n_predictors),
+  Custom_Lambda_0.1 = custom_weights_glmnet_format[, 1],
+  Custom_Lambda_0.2 = custom_weights_glmnet_format[, 2],
+  Custom_Lambda_0.5 = custom_weights_glmnet_format[, 3],
+  GLMNet_Lambda_0.1 = glmnet_weights[, 1],
+  GLMNet_Lambda_0.2 = glmnet_weights[, 2],
+  GLMNet_Lambda_0.5 = glmnet_weights[, 3]
+)
+
+# Display the comparison
+print(comparison)

From ed44edb13fbc1949ee6bbb779cd538f5036e6872 Mon Sep 17 00:00:00 2001
From: Oliver Pain <43761992+opain@users.noreply.github.com>
Date: Wed, 30 Oct 2024 07:18:05 +0000
Subject: [PATCH 047/153] Update dependencies.smk

---
 pipeline/rules/dependencies.smk | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 8e7217f7..83bd1dda 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -161,6 +161,9 @@ if 'gwas_list' in config and config["gwas_list"] != 'NA':
 else:
   gwas_list_df = pd.DataFrame(columns = ["name", "path", "population", "n", "sampling", "prevalence", "mean", "sd", "label"])
 
+# Remove commas in the 'n' column and convert to numeric
+gwas_list_df['n'] = gwas_list_df['n'].replace({',': ''}, regex=True)
+
 # Check for duplicate values in the 'name' column
 check_for_duplicates(gwas_list_df, 'name', 'gwas_list')
 

From c32721a345c2db26c52d6dfc307e269f3df98763 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 1 Nov 2024 09:48:05 +0000
Subject: [PATCH 048/153] Typo correction in comment

---
 pipeline/rules/pgs_methods.smk | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 8a498150..0a6962ba 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -420,7 +420,7 @@ rule prep_pgs_quickprs:
   input: expand(f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=gwas_list_df['name'])
 
 ##
-# LDAK SBayesRC
+# SBayesRC
 ##
 
 rule prep_pgs_sbayesrc_i:

From 87869b3b4bce850eda79ba8d26adf234ae3fd6a9 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sun, 3 Nov 2024 07:17:02 +0000
Subject: [PATCH 049/153] Selected GWAS for crosspop analysis

---
 docs/CrossPop.Rmd | 852 ++++++++++++++++++++++++++++++----------------
 1 file changed, 555 insertions(+), 297 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index f143f66d..771d150a 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -360,7 +360,7 @@ for(i in 1:nrow(prscsx_dat)){
 
 <details><summary>Show code</summary>
 ```{bash, eval=F, echo=T}
-for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+for pheno in $(tail -n +6 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
   mkdir -p /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}
   for chr in $(seq 1 22); do
       sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/plink2 \
@@ -374,7 +374,7 @@ for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx
 done
 
 # Once complete, merge results across chromosomes
-for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+for pheno in $(tail -n +6 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
   head -n 1 /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.chr1.outcome.glm.linear > /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
     for chr in $(seq 1 22); do
       tail -n +2 /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}.outcome.glm.linear >> /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
@@ -431,7 +431,7 @@ write.table(prscsx_dat[, c('labels', 'wget', 'wget_label'), with=F], '~/oliverpa
 
 ```{bash}
 # wget and unzip sumstats
-for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
   url=$(awk -v var="$pheno" '$1 == var {print $2}' ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
   sbatch -p neurohack_cpu --wrap="wget -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip ${url}
     unzip /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip -d /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx
@@ -439,14 +439,14 @@ for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx
 done
 
 # Delete X chromosome sumstats and rename files to be consistent with prscsx sumstat info
-for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
   wget_label=$(awk -v var="$pheno" '$1 == var {print $3}' ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
   mv ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1/GWASsummary_${wget_label}_Japanese_SakaueKanai2020.auto.txt.gz ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz
   rm -r ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1
 done
 
 # Format so BOLT P value is used by GenoPred
-for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
 sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/pigz -dc ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz | awk 'BEGIN {OFS=\"\t\"} {print \$2, \$3, \$4, \$6, \$7, \$8, \$9, \$12, \$13, \$15}' | sed '1s/P_BOLT_LMM_INF/P/' | /users/k1806347/oliverpainfel/Software/pigz -c > ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.reformat.txt.gz"
 done
 
@@ -501,10 +501,14 @@ write.csv(prscsx_dat, '~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscs
 
 # Create column indicating wget command
 for(i in 1:nrow(prscsx_dat)){
-  log<-system(paste0('curl ', gsub('http:','ftp:', prscsx_dat$summaryStatistics[i]), '/'), intern = T)
-  log<-log[grepl('.txt.gz', log)]
-  log<-gsub('.* ','', log)
-  prscsx_dat$wget[i]<-paste0(prscsx_dat$summaryStatistics[i], '/', log)
+  if(!grepl('.txt', prscsx_dat$wget[i])){
+    print(i)
+    Sys.sleep(2)
+    log<-system(paste0('curl --max-time 10 ', gsub('http:','ftp:', prscsx_dat$summaryStatistics[i]), '/'), intern = T)
+    log<-log[grepl('annotated.txt.gz|annotated.txt', log)]
+    log<-gsub('.* ','', log)
+    prscsx_dat$wget[i]<-paste0(prscsx_dat$summaryStatistics[i], '/', log)
+  }
 }
 
 # Write a table showing label matching prscsx info and wget url
@@ -514,7 +518,7 @@ write.table(prscsx_dat[, c('labels', 'wget'), with=F], '~/oliverpainfel/Data/GWA
 
 ```{bash}
 # wget and unzip sumstats
-for pheno in $(head -n 5 ~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt | cut -d' ' -f 1); do
+for pheno in $(cat ~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt | cut -d' ' -f 1); do
   url=$(awk -v var="$pheno" '$1 == var {print $2}' ~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt)
   sbatch -p cpu --wrap="wget -O ~/oliverpainfel/Data/GWAS_sumstats/UGR/${pheno}.txt.gz ${url}"
 done
@@ -522,10 +526,20 @@ done
 ```
 
 ```{r}
+library(future.batchtools)
+library(furrr)
 library(data.table)
-ugr_data<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv')[1:5,]
+ugr_data<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv')
+
+plan(batchtools_slurm(resources = list(
+  time = "12:00:00",
+  ntasks = 2,
+  mem = "10g",
+  partition = "neurohack_cpu"
+)))
 
-for(i in 1:nrow(ugr_data)){
+furrr::future_map_dfr(1:nrow(ugr_data), function(i) {
+  print(i)
   sumstats <- fread(paste0("~/oliverpainfel/Data/GWAS_sumstats/UGR/", ugr_data$label[i], ".txt.gz"))
   sumstats <- sumstats[, names(sumstats) %in% c("snpid", "pval_fe", "se_fe") | grepl('^beta_|^af_|^no_', names(sumstats)), with=F]
 
@@ -558,7 +572,7 @@ for(i in 1:nrow(ugr_data)){
   
   fwrite(sumstats, paste0("~/oliverpainfel/Data/GWAS_sumstats/UGR/", ugr_data$label[i], ".reformat.txt.gz"), sep=' ', quote=F, na='NA')
   
-}
+})
 
 ```
 
@@ -566,9 +580,16 @@ for(i in 1:nrow(ugr_data)){
 
 ***
 
-# Run GenoPred
+# Estimate SNP-h2, polygenicity and rG across populations
+
+We will estimate SNP-h2 using LD-score regression, and the rG using POPCORN.
+POPCORN can estimate the SNP-h2, but it will vary according to the other GWAS included due to SNP overlap.
+Use the sumstats QC'd by GenoPred.
+To estimate polygenicity, lets use AVENGEME based on ptclump score association results. Lets generate those using GenoPred.
+
+***
 
-## Prepare configuration
+## Prepare configuration for GenoPred
 
 <details><summary>Show code</summary>
 
@@ -578,7 +599,6 @@ for(i in 1:nrow(ugr_data)){
 ######
 
 prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
 
 gwas_list_eur<-data.frame(
   name=paste0(prscsx_dat$labels,'_UKB'),
@@ -607,7 +627,7 @@ gwas_list_eas<-data.frame(
   label=paste0('"', prscsx_dat$trait, ' (BBJ)"')
 )
 
-ugr_data<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv')[1:5,]
+ugr_data<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv')
 ugr_data<-ugr_data[ugr_data$labels %in% prscsx_dat$labels,]
 
 gwas_list_afr<-data.frame(
@@ -619,31 +639,26 @@ gwas_list_afr<-data.frame(
   prevalence=NA,
   mean=0,
   sd=1,
-  label=paste0('"', prscsx_dat$trait, ' (UGR)"')
+  label=paste0('"', ugr_data$trait, ' (UGR)"')
 )
 gwas_list<-do.call(rbind, list(gwas_list_eur, gwas_list_eas, gwas_list_afr))
 
-write.table(gwas_list, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt', col.names = T, row.names = F, quote = F)
-
-######
-# gwas_groups
-######
-
-gwas_groups_eas<-data.frame(
-  name=prscsx_dat$labels,
-  gwas=sapply(prscsx_dat$labels, function(x) paste0(x,'_UKB,',x,'_BBJ')),
-  label=paste0('"', prscsx_dat$trait, " (UKB+BBJ)", '"')
-)
+# Create file listing phenotypes in common between AFR, EAS and EUR
+pheno <- gsub('_.*', '', gwas_list$name)
+pheno_intersect <- Reduce(intersect, 
+                           list(
+                             pheno[gwas_list$population == 'EUR'],
+                             pheno[gwas_list$population == 'EAS'],
+                             pheno[gwas_list$population == 'AFR']
+                             )
+                           )
 
-gwas_groups_afr<-data.frame(
-  name=paste0(prscsx_dat$labels, '_ukb_ugr'),
-  gwas=sapply(prscsx_dat$labels, function(x) paste0(x,'_UKB,',x,'_UGR')),
-  label=paste0('"', prscsx_dat$trait, " (UKB+UGR)", '"')
-)
+# Restrict gwas_list to intersecting phenotypes
+gwas_list<-gwas_list[pheno %in% pheno_intersect,]
 
-gwas_groups<-rbind(gwas_groups_eas, gwas_groups_afr)
+write.table(gwas_list, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt', col.names = T, row.names = F, quote = F)
 
-write.table(gwas_groups, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt', col.names = T, row.names = F, quote = F)
+write.table(pheno_intersect, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', col.names = F, row.names = F, quote = F)
 
 ######
 # config
@@ -651,21 +666,15 @@ write.table(gwas_groups, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr
 
 config<-c(
   "outdir: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output_crosspop",
-  "config_file: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml",
-  "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt",
+  "config_file: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml",
+  "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt",
   "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
-  "gwas_groups: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt",
-  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
-  "tlprs_methods: ['dbslmm','prscs','lassosum']",
-  "cores_prep_pgs: 20",
-  "cores_target_pgs: 50",
-  "ldpred2_inference: F",
-  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/1KG/ldpred2",
-  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs",
-  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc"
+  "pgs_methods: ['ptclump']",
+  "cores_prep_pgs: 1",
+  "cores_target_pgs: 20"
 )
 
-write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
+write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml', col.names = F, row.names = F, quote = F)
 
 ```
 </details>
@@ -675,29 +684,29 @@ write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k180
 ## Run pipeline
 
 ```{bash}
-snakemake --profile slurm --use-conda --configfile=/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml output_all -n
+snakemake --profile slurm --use-conda --configfile=/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml target_pgs -n
 ```
 
 ***
 
-# Estimate SNP-h2 and rG across populations
-
-We will estimate SNP-h2 using LD-score regression, and the rG using POPCORN.
-POPCORN can estimate the SNP-h2, but it will vary according to the other GWAS included due to SNP overlap.
-
-***
-
-## Prepare sumstats
-
-Use the sumstats QC'd by GenoPred.
+## Reformat for LDSC and POPCORN
 
 <details><summary>Show code</summary>
 ```{r}
 library(data.table)
 dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats', recursive = T)
-gwas_list<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt')
+gwas_list<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt')
 
 for(i in 1:nrow(gwas_list)){
+  if(
+    file.exists(
+      paste0(
+        "/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/",
+        gwas_list$name[i], 
+        ".sumstats.gz"))){
+    next    
+  }
+  print(i)
   gwas_file <-
     paste0(
       "/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output_crosspop/reference/gwas_sumstat/",
@@ -747,7 +756,7 @@ for pop in $(echo EUR EAS AFR);do
       samp="UGR"
   fi
   
-  for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
     mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats
 
     sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
@@ -768,7 +777,7 @@ for pop in $(echo EUR EAS AFR);do
       samp="UGR"
   fi
   
-  for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  for pheno in $(tail -n +6 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
     mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/${pheno}/${pop}
 
     sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \
@@ -784,7 +793,6 @@ done
 
 </details>
 
-
 ***
 
 ## Calculate CSCOREs
@@ -838,7 +846,7 @@ for pop in $(echo EAS AFR);do
       samp="UGR"
   fi
   
-  for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
     mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/${pheno}/EUR_${pop}
     sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="popcorn \
        fit -v 3 \
@@ -856,7 +864,7 @@ done
 
 ***
 
-## Plot the results
+## Plot the LDSC and POPCORN results
 
 <details><summary>Show code</summary>
 
@@ -866,15 +874,14 @@ library(data.table)
 library(ggplot2)
 library(cowplot)
 
-# Read in list of outcomes 
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
+# Read in phenotypes
+pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
 
 # Plot the heritability estimates
 h2_res <- NULL
 
 for(pop in c('AFR','EAS', 'EUR')){
-  for(pheno in prscsx_dat$labels){
+  for(pheno in pheno_intersect){
     log <-
       readLines(
         paste0(
@@ -910,43 +917,494 @@ for(pop in c('AFR','EAS', 'EUR')){
   }
 }
 
-ggplot(h2_res, aes(x = Phenotype, y = h2_est, fill = Population)) +
-  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
-  geom_errorbar(aes(ymin=h2_est-h2_se, ymax=h2_est+h2_se), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
-  labs(y="SNP-based Heritability (SE)") +
-  theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  background_grid(major = 'y', minor = 'y')
+write.csv(h2_res, '/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results.csv', row.names = F, quote = F)
+
+ggplot(h2_res, aes(x = Phenotype, y = h2_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=h2_est-h2_se, ymax=h2_est+h2_se), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="SNP-based Heritability (SE)") +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+# Plot rG estimates
+rg_res <- NULL
+for(pop in c('AFR','EAS')){
+  for(pheno in h2_res_sig$Phenotype){
+    pop_res_i<-fread(paste0('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/', pheno, '/EUR_', pop, '/rG_gen_effect'))
+    names(pop_res_i) <- c('Test','Estimate','SE','Z','P')
+    pop_res_i <- pop_res_i[pop_res_i$Test == 'pge',]
+    pop_res_i$Population_1 <- 'EUR'
+    pop_res_i$Population_2 <- pop
+    pop_res_i$Phenotype <- pheno
+    rg_res <- rbind(rg_res, pop_res_i)
+  }
+}
+
+rg_res$Comparison <- paste0(rg_res$Population_1, ' vs. ', rg_res$Population_2)
+
+write.csv(rg_res, '/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results.csv', row.names = F, quote = F)
+
+ggplot(rg_res, aes(x = Phenotype, y = Estimate, fill = Comparison)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=Estimate-SE, ymax=Estimate+SE), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="SNP-based\nGenetic Correlation (SE)") +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+```
+</details>
+
+## AVENGEME
+
+### Create predictor lists
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Read in phenotypes
+pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+
+# Create files for EAS and AFR targets
+pop <- c('EUR','EAS','AFR')
+for(trait_i in pheno_intersect){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
+  scores_i <- scores[grepl(paste0('^', trait_i, '_'), scores$name),]
+  scores_i$group <- scores_i$method
+  
+  for(pop_i in pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    if(pop_i == 'EAS'){
+      samp_i <- 'BBJ'
+    }
+    if(pop_i == 'AFR'){
+      samp_i <- 'UGR'
+    }
+    if(pop_i == 'EUR'){
+      samp_i <- c('UKB')
+    }
+
+    dir.create(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i
+      ),
+      recursive = T
+    )
+    
+    scores_i_j <- scores_i[grepl(samp_i, scores_i$name, ignore.case = T),]
+    scores_i_j$predictor <- paste0(
+      outdir,
+      '/ukb/pgs/',
+      pop_i,
+      '/',
+      scores_i_j$method,
+      '/',
+      scores_i_j$name,
+      '/ukb-',
+      scores_i_j$name,
+      '-',
+      pop_i,
+      '.profiles'
+    )
+    
+    predictors_i <- scores_i_j[, c('predictor', 'group'), with=F]
+    
+    write.table(
+      predictors_i,
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i,
+        '/predictor_list.ptclump.txt'
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}
+
+```
+</details>
+
+***
+
+### Run model_builder
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+for pop in $(echo EUR EAS AFR); do
+  if [ "$pop" == "EUR" ]; then
+      pop2="EUR_test"
+  else
+      pop2=$pop
+  fi
+  
+  for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
+    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${pop2}.row_number.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/res.ptclump \
+      --n_core 5 \
+      --all_model F \
+      --assoc T"
+  done
+done
+
+```
+</details>
+
+***
+
+### Plot assoc results
+
+<details><summary>Show code</summary>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in phenotypes
+pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+
+# Read in results
+pop = c('EUR','EAS','AFR')
+res_all <- NULL
+for(pheno_i in pheno_intersect){
+  res_i<-NULL
+  for(pop_i in pop){
+    assoc_i <-
+      fread(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          pop_i,
+          '.disc_',
+          pop_i,
+          '/',
+          pheno_i,
+          '/res.ptclump.assoc.txt'
+        )
+      )
+      assoc_i$Population <- pop_i
+      res_i<-rbind(res_i, assoc_i)
+  }
+  
+  res_i$Phenotype <- pheno_i
+  res_all<-rbind(res_all, res_i)
+}
+
+# Extract pT variable from Predictor
+res_all$pT <- gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.', '', res_all$Predictor))
+res_all$pT <- factor(res_all$pT, levels = unique(res_all$pT))
+
+ggplot(res_all, aes(x = Phenotype, y = BETA, fill = pT)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.8) +
+  geom_errorbar(aes(ymin=BETA-SE, ymax=BETA+SE), width=0, position=position_dodge(width = 0.8, preserve = "single")) +
+  labs(y="BETA (SE)") +
+  theme_half_open() +
+  background_grid() +
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y') +
+  scale_fill_manual(values = colorRampPalette(c("lightblue", "darkblue"))(length(unique(res_all$pT)))) +
+  facet_grid(Population ~.)
+
+```
+</details>
+
+### Run AVENGEME
+
+```{r}
+
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+library(avengeme)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Get some key variables from config
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+gwas_list <- read_param(config = config, param = 'gwas_list', return_obj = T)
+
+# Read in phenotypes
+pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+
+pop = c('EUR','EAS','AFR')
+
+mod_res_all <- NULL
+for(pop_i in pop){
+  for(pheno_i in pheno_intersect){
+    gwas_i<-gwas_list$name[gwas_list$population == pop_i & grepl(paste0('^', pheno_i, '_'),  gwas_list$name)]
+      
+    res_i <-
+      fread(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          pop_i,
+          '.disc_',
+          pop_i,
+          '/',
+          pheno_i,
+          '/res.ptclump.assoc.txt'
+        )
+      )
+    
+    res_i$Z <- res_i$BETA / res_i$SE
+    
+    res_i$pT <- as.numeric(gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.', '', res_i$Predictor)))
+
+    nsnp_log <-
+      read.table(
+        paste0(
+          outdir,
+          '/reference/pgs_score_files/ptclump/',
+          gwas_i,
+          '/ref-',
+          gwas_i,
+          '.NSNP_per_pT'
+        ),
+        header = T
+      )
+    
+    nsnp<-nsnp_log$NSNP[nrow(nsnp_log)]
+    
+    disc_N <-
+      median(
+        fread(
+          paste0(
+            outdir,
+            '/reference/gwas_sumstat/',
+            gwas_i,
+            '/',
+            gwas_i,
+            '-cleaned.gz'
+          ), nrows = 10000
+        )$N
+      )
+    
+    targ_N <- res_i$N[1]
+    
+    mod_res <- estimatePolygenicModel(
+      p = res_i$Z,
+      nsnp = nsnp,
+      n = c(disc_N, targ_N),
+      pupper = c(0, res_i$pT),
+      fixvg2pi02 = T,
+      alpha = 0.05
+    )
+    
+    mod_res_all <- rbind(
+      mod_res_all,
+      data.frame(
+        Phenotype = pheno_i,
+        Population = pop_i,
+        GWAS = gwas_i,
+        nsnp = nsnp,
+        n_disc = disc_N,
+        n_targ = targ_N,
+        vg_est = mod_res$vg[1],
+        vg_lowCI = mod_res$vg[2],
+        vg_highCI = mod_res$vg[3],
+        pi0_est = mod_res$pi0[1],
+        pi0_lowCI = mod_res$pi0[2],
+        pi0_highCI = mod_res$pi0[3]
+      )
+    )
+  }
+}
+
+dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme')
+write.csv(mod_res_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv', row.names = F, quote = F)
+
+ggplot(mod_res_all, aes(x = Phenotype, y = vg_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=vg_lowCI, ymax=vg_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="SNP-based Heritability (95%CI)") +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=1 - pi0_lowCI, ymax=1 - pi0_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="Proporition non-zero\neffects (95%CI)") +
+  theme_half_open() +
+  coord_cartesian(ylim = c(0, 0.15)) + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+```
+
+***
+
+### Select GWAS for downstream analyses
+
+```{r}
+#########
+# Select 10 GWAS for downstream analysis
+#########
+# Criteria are that SNP-h2 > 0.01 in both AVENGEME and LDSC
+# Then GWAS are selected to represent a range of polygenicity and heritability, as estimated in EUR since they are most accurate
+
+library(data.table)
+
+# Read in the AVENGEME results
+avengeme <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv')
+
+# Read in the LDSC results
+ldsc <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results.csv')
+
+# Combine results
+both <- merge(avengeme, ldsc, by = c('Population','Phenotype'))
+
+# Remove GWAS that have negative SNP-h2 from LDSC in any population
+both_h2 <- both[!(both$Phenotype %in% both$Phenotype[both$vg_est < 0.01 | both$h2_est < 0.01]),]
+
+# Select GWAS representing a range of SNP-h2 from LDSC, and a range of polygenicity from AVENGEME.
+
+both_eur<-both_h2[both_h2$Population == 'EUR',]
+traits_data <- data.frame(trait = both_eur$Phenotype, heritability = both_eur$vg_est, polygenicity = both_eur$pi0_est)
+
+# Number of bins (e.g., dividing into 5 bins each for heritability and polygenicity)
+num_bins <- 5
+
+# Create bins
+traits_data$her_bin <- cut(traits_data$heritability, breaks = num_bins)
+traits_data$poly_bin <- cut(traits_data$polygenicity, breaks = num_bins)
+
+# Split data by unique bin combinations
+split_data <- split(traits_data, list(traits_data$her_bin, traits_data$poly_bin), drop = TRUE)
+
+set.seed(1)
+# Randomly select one trait from each bin combination
+selected_traits <- do.call(rbind, lapply(split_data, function(df) df[sample(nrow(df), 1), ]))
+
+# Limit to 10 traits if more than 10 unique combinations
+if (nrow(selected_traits) > 10) {
+  selected_traits <- selected_traits[sample(nrow(selected_traits), 10), ]
+}
+
+write.table(selected_traits$trait, '/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', col.names = F, row.names = F, quote = F)
+
+```
+
+***
+
+# Run GenoPred
+
+## Prepare configuration for GenoPred
+
+<details><summary>Show code</summary>
+
+```{r}
+######
+# gwas_list
+######
+library(data.table)
+# Subset original gwas_list to include selected traits
+gwas_list<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt')
+pheno<-gsub('_.*','', gwas_list$name)
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+gwas_list<-gwas_list[pheno %in% selected_traits,]
+gwas_list$label<-paste0('"', gwas_list$label, '"')
+
+write.table(gwas_list, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt', col.names = T, row.names = F, quote = F)
+
+######
+# gwas_groups
+######
+
+gwas_groups_eas<-data.frame(
+  name=paste0(selected_traits, '_UKB_BBJ'),
+  gwas=sapply(selected_traits, function(x) paste0(x,'_UKB,',x,'_BBJ')),
+  label=paste0('"', selected_traits, " (UKB+BBJ)", '"')
+)
 
-# Plot rG estimates
-rg_res <- NULL
-for(pop in c('AFR','EAS')){
-  for(pheno in prscsx_dat$labels){
-    pop_res_i<-fread(paste0('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/', pheno, '/EUR_', pop, '/rG_gen_effect'))
-    names(pop_res_i) <- c('Test','Estimate','SE','Z','P')
-    pop_res_i <- pop_res_i[pop_res_i$Test == 'pge',]
-    pop_res_i$Population_1 <- 'EUR'
-    pop_res_i$Population_2 <- pop
-    pop_res_i$Phenotype <- pheno
-    rg_res <- rbind(rg_res, pop_res_i)
-  }
-}
+gwas_groups_afr<-data.frame(
+  name=paste0(selected_traits, '_UKB_UGR'),
+  gwas=sapply(selected_traits, function(x) paste0(x,'_UKB,',x,'_UGR')),
+  label=paste0('"', selected_traits, " (UKB+UGR)", '"')
+)
 
-rg_res$Comparison <- paste0(rg_res$Population_1, ' vs. ', rg_res$Population_2)
+gwas_groups<-rbind(gwas_groups_eas, gwas_groups_afr)
 
-ggplot(rg_res, aes(x = Phenotype, y = Estimate, fill = Comparison)) +
-  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
-  geom_errorbar(aes(ymin=Estimate-SE, ymax=Estimate+SE), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
-  labs(y="SNP-based\nGenetic Correlation (SE)") +
-  theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  background_grid(major = 'y', minor = 'y')
+write.table(gwas_groups, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt', col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output_crosspop",
+  "config_file: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml",
+  "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt",
+  "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
+  "gwas_groups: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt",
+  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
+  "tlprs_methods: ['dbslmm','prscs','lassosum']",
+  "cores_prep_pgs: 20",
+  "cores_target_pgs: 50",
+  "ldpred2_inference: F",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/1KG/ldpred2",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc"
+)
+
+write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
 
 ```
 </details>
 
 ***
 
+## Run pipeline
+
+```{bash}
+snakemake --profile slurm --use-conda --configfile=/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml output_all -n
+```
+
+***
+
 # Evaluate PGS
 
 Lets use the model builder script which implements nested 10 fold cross validation. Similar set up to previous paper, evaluating a model containing the best PGS selected by 10-fold cross validation, a model containing the PGS selected by pseudovalidation (if available), and an elastic net model containing all PGS from a given method. We will need to update the model builder script to achieve this
@@ -2458,206 +2916,6 @@ for(model_i in c('Top1','Pseudo','Multi')){
 }
 
 
-```
-</details>
-
-***
-
-## Check PGS association direction
-
-This is a sanity check to see whether the PGS are correlated with the outcome in the expected direction (positive). We will do this using the ptclump PGS and within population for simplicity (i.e. EUR -> EUR, etc)
-
-### Create predictor lists
-
-<details><summary>Show code</summary>
-
-```{r}
-
-setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
-source('../functions/misc.R')
-source_all('../functions')
-library(data.table)
-
-# Get some key variables from config
-config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
-pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
-outdir <- read_param(config = config, param = 'outdir', return_obj = F)
-
-# Read in list of outcomes 
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
-
-# Get a list of score files
-scores <- list_score_files(config)
-
-# Restrict to ptclump
-scores<-scores[grepl('ptclump', scores$method),]
-
-# Create files for EAS and AFR targets
-pop <- c('EUR','EAS','AFR')
-for(trait_i in prscsx_dat$labels){
-  # Make a group containing both GWAS for each single source method
-  # Make a group for each multisource method
-  scores_i <- scores[grepl(trait_i, scores$name),]
-  scores_i$group <- scores_i$method
-  
-  for(pop_i in pop){
-    # Subset GWAS based on EUR and/or targ_pop_i
-    if(pop_i == 'EAS'){
-      samp_i <- 'BBJ'
-    }
-    if(pop_i == 'AFR'){
-      samp_i <- 'UGR'
-    }
-    if(pop_i == 'EUR'){
-      samp_i <- c('UKB')
-    }
-
-    dir.create(
-      paste0(
-        '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
-        pop_i,
-        '.disc_',
-        pop_i,
-        '/',
-        trait_i
-      ),
-      recursive = T
-    )
-    
-    scores_i_j <- scores_i[grepl(samp_i, scores_i$name, ignore.case = T),]
-    scores_i_j$predictor <- paste0(
-      outdir,
-      '/ukb/pgs/',
-      pop_i,
-      '/',
-      scores_i_j$method,
-      '/',
-      scores_i_j$name,
-      '/ukb-',
-      scores_i_j$name,
-      '-',
-      pop_i,
-      '.profiles'
-    )
-    
-    predictors_i <- scores_i_j[, c('predictor', 'group'), with=F]
-    
-    write.table(
-      predictors_i,
-      paste0(
-        '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
-        pop_i,
-        '.disc_',
-        pop_i,
-        '/',
-        trait_i,
-        '/predictor_list.ptclump.txt'
-      ),
-      col.names = T,
-      row.names = F,
-      quote = F
-    )
-  }
-}
-
-```
-</details>
-
-***
-
-### Run model_builder
-
-<details><summary>Show code</summary>
-
-```{bash}
-cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
-conda activate model_builder
-
-for pop in $(echo EUR EAS AFR); do
-  if [ "$pop" == "EUR" ]; then
-      pop2="EUR_test"
-  else
-      pop2=$pop
-  fi
-  
-  for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
-    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
-      --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${pop2}.row_number.txt \
-      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
-      --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/res.ptclump \
-      --n_core 5 \
-      --all_model F \
-      --assoc T"
-  done
-done
-
-```
-</details>
-
-***
-
-### Plot results
-
-<details><summary>Show code</summary>
-
-```{r}
-setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
-
-library(data.table)
-library(ggplot2)
-library(cowplot)
-
-source('../functions/misc.R')
-source_all('../functions')
-
-# Read in list of outcomes 
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:4,]
-
-# Read in results
-pop = c('EUR','EAS','AFR')
-res_all <- NULL
-for(pheno_i in prscsx_dat$labels){
-  res_i<-NULL
-  for(pop_i in pop){
-    assoc_i <-
-      fread(
-        paste0(
-          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
-          pop_i,
-          '.disc_',
-          pop_i,
-          '/',
-          pheno_i,
-          '/res.ptclump.assoc.txt'
-        )
-      )
-      assoc_i$Population <- pop_i
-      res_i<-rbind(res_i, assoc_i)
-  }
-  
-  res_i$Phenotype <- pheno_i
-  res_all<-rbind(res_all, res_i)
-}
-
-# Extract pT variable from Predictor
-res_all$pT <- gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.', '', res_all$Predictor))
-res_all$pT <- factor(res_all$pT, levels = unique(res_all$pT))
-
-ggplot(res_all, aes(x = Phenotype, y = BETA, fill = pT)) +
-  geom_hline(yintercept = 0, colour = 'darkgrey') +
-  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7, colour = 'black') +
-  geom_errorbar(aes(ymin=BETA-SE, ymax=BETA+SE), width=0, position=position_dodge(width = 0.7, preserve = "single")) +
-  labs(y="BETA (SE)") +
-  theme_half_open() +
-  background_grid() +
-  panel_border() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  background_grid(major = 'y', minor = 'y') +
-  scale_fill_manual(values = colorRampPalette(c("lightblue", "darkblue"))(length(unique(res_all$pT)))) +
-  facet_grid(Population ~.)
-
 ```
 </details>
 

From 54e1a8b51802d62e691aa5fa0cd3114fc24931b0 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 4 Nov 2024 21:08:48 +0000
Subject: [PATCH 050/153] Added quickprs_multi

---
 Scripts/pgs_methods/quickprs.R       |   4 +-
 Scripts/pgs_methods/quickprs_multi.R | 335 +++++++++++++++++++++++++++
 docs/CrossPop.Rmd                    |  59 ++---
 docs/prep_quickprs_ref.Rmd           | 103 ++++++++
 functions/pgs.R                      |  50 ++++
 functions/pipeline.R                 |  12 +
 pipeline/rules/dependencies.smk      |   2 +-
 pipeline/rules/pgs_methods.smk       |  50 +++-
 8 files changed, 574 insertions(+), 41 deletions(-)
 create mode 100644 Scripts/pgs_methods/quickprs_multi.R

diff --git a/Scripts/pgs_methods/quickprs.R b/Scripts/pgs_methods/quickprs.R
index 47c4f570..45b184e8 100644
--- a/Scripts/pgs_methods/quickprs.R
+++ b/Scripts/pgs_methods/quickprs.R
@@ -97,7 +97,7 @@ names(gwas)<-c('Predictor','A1','A2','n','Z')
 
 # Check overlap between GWAS and LDAK reference
 ldak_hm3_file <- list.files(opt$quick_prs_ref)
-ldak_hm3_file <- ldak_hm3_file[grepl('.cors.bim', ldak_hm3_file)]
+ldak_hm3_file <- ldak_hm3_file[grepl('.cors.bim', ldak_hm3_file)][1]
 ldak_hm3 <- fread(paste0(opt$quick_prs_ref, '/', ldak_hm3_file))
 ldak_hm3 <- ldak_hm3[ldak_hm3$V1 %in% CHROMS,]
 ref_overlap <- sum(gwas$Predictor %in% ldak_hm3$V2) / nrow(ldak_hm3)
@@ -139,7 +139,7 @@ log_add(log_file = log_file, message = paste0('SNP-based heritability estimated
 # Estimate effect sizes for training and full prediction models.
 ######
 
-cor_file_prefix<-gsub('.cors.bin','',ref_files[grepl('.cors.bin',ref_files)])
+cor_file_prefix<-gsub('.cors.bin','',ref_files[grepl('.cors.bin',ref_files) & !grepl('subset', ref_files)])
 
 log_add(log_file = log_file, message = paste0('Running MegaPRS: ',opt$prs_model,' model.'))
 
diff --git a/Scripts/pgs_methods/quickprs_multi.R b/Scripts/pgs_methods/quickprs_multi.R
new file mode 100644
index 00000000..09082fc5
--- /dev/null
+++ b/Scripts/pgs_methods/quickprs_multi.R
@@ -0,0 +1,335 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+suppressMessages(library("optparse"))
+
+option_list = list(
+  make_option("--ref_plink_chr", action="store", default=NA, type='character',
+              help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--pop_data", action="store", default=NULL, type='character',
+              help="File containing the population code and location of the keep file [required]"),
+  make_option("--plink", action="store", default='plink', type='character',
+              help="Path PLINK v1.9 software binary [optional]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+              help="Path PLINK v2 software binary [optional]"),
+  make_option("--output", action="store", default='NA', type='character',
+              help="Path for output files [required]"),
+  make_option("--memory", action="store", default=5000, type='numeric',
+              help="Memory limit [optional]"),
+  make_option("--sumstats", action="store", default=NULL, type='character',
+              help="Comma-seperated list of GWAS summary statistics [required]"),
+  make_option("--populations", action="store", default=NULL, type='character',
+              help="Comma-seperated list of population codes matching GWAS [required]"),
+  make_option("--ldak", action="store", default=NA, type='character',
+              help="Path to ldak v5.2 executable [required]"),
+  make_option("--quick_prs_ref", action="store", default=NA, type='character',
+              help="Path to folder containing ldak quick prs reference [required]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+              help="Number of cores for parallel computing [optional]"),
+  make_option("--prs_model", action="store", default='bayesr', type='character',
+              help="Model used for deriving SNP-weights [optional]"),
+  make_option("--genomic_control", action="store", default=F, type='logical',
+              help="Logical indicating whether genomic control was applied to GWAS [optional]"),
+  make_option("--xwing_repo", action="store", default=NULL, type='character',
+              help="Path to X-WING repo [required]"),
+  make_option("--test", action="store", default=NA, type='character',
+              help="Specify number of SNPs to include [optional]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Check required inputs
+if(is.null(opt$ref_plink_chr)){
+  stop('--ref_plink_chr must be specified.\n')
+}
+if(is.null(opt$sumstats)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$pop_data)){
+  stop('--pop_data must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--output must be specified.\n')
+}
+if(is.null(opt$ldak)){
+  stop('--ldak must be specified.\n')
+}
+if(is.null(opt$quick_prs_ref)){
+  stop('--quick_prs_ref must be specified.\n')
+}
+if(is.null(opt$populations)){
+  stop('--populations must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
+system(paste0('mkdir -p ',opt$output_dir))
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'quickprs_multi.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+# Split opt$sumstats
+sumstats<-unlist(strsplit(opt$sumstats, ','))
+log_add(log_file = log_file, message = paste0(length(sumstats), ' sets of GWAS have been provided.'))
+
+# Split opt$populations
+populations<-unlist(strsplit(opt$populations, ','))
+
+#####
+# Format the sumstats
+#####
+
+gwas_N<-NULL
+for(i in 1:length(sumstats)){
+  log_add(log_file = log_file, message = paste0('Reading in GWAS ', i))
+
+  # Read in, check and format GWAS summary statistics
+  gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('CHR','BP','SNP','A1','A2','BETA','SE','N','P'))
+
+  # Format for LDAK
+  snplist <- gwas$SNP
+  gwas$Z <- gwas$BETA / gwas$SE
+  gwas$Predictor<-paste0(gwas$CHR, ':', gwas$BP)
+  gwas<-gwas[,c('Predictor','A1','A2','N','Z','CHR','BP','Predictor','BETA','P')]
+  names(gwas)<-c('Predictor','A1','A2','n','Z','CHR','BP','SNP','BETA','P')
+  gwas_N <- c(gwas_N, round(mean(gwas$n), 0))
+
+  # Check overlap between GWAS and LDAK reference
+  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', populations[i], '.hm3')
+  ldak_hm3_file <- list.files(quick_prs_ref_pop_i)
+  ldak_hm3_file <- ldak_hm3_file[grepl('.cors.bim', ldak_hm3_file)][1]
+  ldak_hm3 <- fread(paste0(quick_prs_ref_pop_i, '/', ldak_hm3_file))
+  ldak_hm3 <- ldak_hm3[ldak_hm3$V1 %in% CHROMS,]
+  ref_overlap <- sum(gwas$Predictor %in% ldak_hm3$V2) / nrow(ldak_hm3)
+
+  log_add(log_file = log_file, message = paste0('GWAS ', i,'-reference overlap is ', round(ref_overlap * 100, 2), '%.'))
+
+  # Subset GWAS to LDAK reference data
+  gwas <- gwas[gwas$Predictor %in% ldak_hm3$V2, ]
+
+  # Output formatted sumstats
+  fwrite(gwas, paste0(tmp_dir,'/GWAS_sumstats_temp', i, '.txt'), sep=' ')
+}
+
+# Record start time for test
+if(!is.na(opt$test)){
+  test_start.time <- test_start(log_file = log_file)
+}
+
+#####
+# Run QuickPRS using full sumstats
+#####
+
+log_add(log_file = log_file, message = 'Running QuickPRS using full sumstats.')
+
+score_full <- list()
+for(i in 1:length(sumstats)){
+  score_full[[populations[i]]] <- quick_prs(
+    sumstats = paste0(tmp_dir,'/GWAS_sumstats_temp', i, '.txt'),
+    ref_dir = paste0(opt$quick_prs_ref, '/', populations[i], '.hm3'),
+    genomic_control = opt$genomic_control,
+    n_cores = opt$n_cores,
+    prs_model = opt$prs_model)
+}
+
+#####
+# Subsample GWAS sumstats
+#####
+
+log_add(log_file = log_file, message = 'Subsampling sumstats.')
+
+dir.create(paste0(tmp_dir,'/LEOPARD/sampled_sumstats'), recursive = T)
+
+for(i in 1:length(sumstats)){
+  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', populations[i], '.hm3')
+  ref_files <- list.files(quick_prs_ref_pop_i)
+  ref_files <- gsub('.bed', '', ref_files[grepl('subset_1.bed', ref_files)])
+  system(
+    paste0(
+      'Rscript ',
+      opt$xwing_repo,
+      '/LEOPARD_Sim.R ',
+      '--sumstats ', tmp_dir, '/GWAS_sumstats_temp', i, '.txt ',
+      '--n_gwas ', gwas_N[i], ' ',
+      '--train_prop 0.75 ',
+      '--ref_prefix ', quick_prs_ref_pop_i, '/', ref_files, ' ',
+      '--seed 1 ',
+      '--rep 4 ',
+      '--out_prefix ', tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i
+    )
+  )
+}
+
+#####
+# Run QuickPRS using subsampled sumstats
+#####
+
+log_add(log_file = log_file, message = 'Running QuickPRS on subsampled sumstats.')
+
+score_subset <- list()
+for(i in 1:length(sumstats)){
+  dir.create(paste0(tmp_dir, '/LEOPARD/post_targ_', populations[i]))
+  score_subset[[populations[i]]] <- list()
+  for(j in 1:4){
+    sumstats_tmp <- fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.txt'))
+    n_tmp <- fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train_valid_N.txt'))
+    sumstats_tmp$n <- n_tmp$N_train
+    sumstats_tmp$Direction <-sign(sumstats_tmp$BETA)
+    sumstats_tmp <- sumstats_tmp[, c('Predictor','A1','A2','n','P','Direction'), with = F]
+    fwrite(sumstats_tmp, paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.reformat.txt'), sep=' ')
+
+    score_subset[[populations[i]]][[paste0('subset_', j)]] <- quick_prs(
+      sumstats = paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.reformat.txt'),
+      ref_dir = paste0(opt$quick_prs_ref, '/', populations[i], '.hm3'),
+      genomic_control = opt$genomic_control,
+      n_cores = opt$n_cores,
+      ref_subset = '2',
+      prs_model = opt$prs_model)
+
+    fwrite(score_subset[[populations[i]]][[paste0('subset_', j)]],
+           paste0(tmp_dir, '/LEOPARD/post_targ_', populations[i], '/output_', j, '_', populations[i], '_Post.txt'),
+           sep=' ')
+  }
+}
+
+#####
+# Estimating the linear combination weights
+#####
+
+log_add(log_file = log_file, message = 'Estimating the linear combination weights.')
+
+for(targ_pop in populations){
+  dir.create(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop), recursive = T)
+  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', targ_pop, '.hm3')
+  ref_files <- list.files(quick_prs_ref_pop_i)
+  ref_files <- gsub('.bed', '', ref_files[grepl('subset_3.bed', ref_files)])
+
+  for(j in 1:4){
+    targ_gwas_valid_n<-fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_train_valid_N.txt'))$N_valid
+
+    sumstats_tmp <- fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_valid.txt'))
+    sumstats_tmp$SNP<-sumstats_tmp$Predictor
+    sumstats_tmp <- sumstats_tmp[, c('SNP','CHR','BP','A1','A2','BETA','P'), with = F]
+    fwrite(sumstats_tmp, paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_valid.reformat.txt'), sep=' ')
+
+    system(paste0(
+      'Rscript ', opt$xwing_repo, '/LEOPARD_Weights.R ',
+      '--beta_file ', tmp_dir, '/LEOPARD/post_targ_', populations[1], '/output_', j, '_', populations[1], '_Post.txt',',',tmp_dir, '/LEOPARD/post_targ_', populations[2], '/output_', j, '_', populations[2], '_Post.txt ',
+      '--valid_file ', tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_valid.reformat.txt ',
+      '--n_valid ', targ_gwas_valid_n ,' ',
+      '--ref_prefix ', quick_prs_ref_pop_i, '/', ref_files, ' ',
+      '--out ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep', j, '.txt'
+    ))
+  }
+}
+
+# Average weights across repeats
+# Not using X-Wing due to bug when weights are 0
+avg_weights <- list()
+for(targ_pop in populations){
+
+  weights_prefix <- paste0(tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep')
+
+  cal_avg_rel_weights <- function(path){
+    weights_file <- fread(path)
+    weights_non_0 <-  ifelse(weights_file$Weights < 0, 0, weights_file$Weights)
+    rel_weights <- weights_non_0/(sum(weights_non_0))
+    return(rel_weights)
+  }
+
+  rel_weights_list <- lapply(c(1:4), function(i) cal_avg_rel_weights(paste0(weights_prefix, i, ".txt")))
+  avg_weights[[targ_pop]] <- as.numeric(colMeans(as.data.frame(do.call(rbind, rel_weights_list)), na.rm=T))
+}
+
+####
+# Combine score files
+####
+
+log_add(log_file = log_file, message = 'Creating score file.')
+
+# We should combine the raw QuickPRS score files for each population,
+# and then combine using mixing weights for each population
+score_all <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE), score_full)
+names(score_all)[grepl('BETA', names(score_all))]<-paste0('SCORE_targ_', populations)
+score_all[is.na(score_all)]<-0
+
+for(targ_pop in populations){
+  # Read in weights for target population
+  mix_weights<-avg_weights[[targ_pop]]
+  score_all_tmp<-score_all
+  for(i in populations){
+    score_all_tmp[[paste0('SCORE_targ_', i)]] <- score_all[[paste0('SCORE_targ_', i)]] * mix_weights[which(populations == targ_pop)]
+  }
+  # Take average of weighted scores
+  score_all[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <- rowSums(score_all_tmp[, grepl('SCORE_', names(score_all_tmp)), with = F])
+}
+
+names(score_all)[names(score_all) == 'SNP']<-'Predictor'
+
+# Change IDs to RSIDs
+ref_pvar <- read_pvar(dat = opt$ref_plink_chr, chr = CHROMS)
+ref_pvar$Predictor<-paste0(ref_pvar$CHR,':',ref_pvar$BP)
+score_all<-merge(score_all, ref_pvar[,c('Predictor','SNP'), with=F], by='Predictor')
+score_all<-score_all[, c('SNP', 'A1', 'A2', names(score_all)[grepl('SCORE_', names(score_all))]), with=F]
+
+# Flip effects to match reference alleles
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
+score_new <- map_score(ref = ref, score = score_all)
+
+# Reduce number of significant figures to save space
+score_new[, (4:ncol(score_new)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_new)]
+
+fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+
+if(file.exists(paste0(opt$output,'.score.gz'))){
+  system(paste0('rm ',opt$output,'.score.gz'))
+}
+
+system(paste0('gzip ',opt$output,'.score'))
+
+# Record end time of test
+if(!is.na(opt$test)){
+  test_finish(log_file = log_file, test_start.time = test_start.time)
+}
+
+####
+# Calculate mean and sd of polygenic scores
+####
+
+log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
+
+# Calculate scores in the full reference
+ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
+
+# Calculate scale within each reference population
+pop_data <- read_pop_data(opt$pop_data)
+
+for(pop_i in unique(pop_data$POP)){
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 771d150a..75cf0ffd 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1380,10 +1380,13 @@ config<-c(
   "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt",
   "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
   "gwas_groups: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt",
-  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
-  "tlprs_methods: ['dbslmm','prscs','lassosum']",
-  "cores_prep_pgs: 20",
-  "cores_target_pgs: 50",
+#  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
+  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','megaprs','sbayesrc']",
+#  "tlprs_methods: ['dbslmm','prscs','lassosum']",
+#  "cores_prep_pgs: 20",
+#  "cores_target_pgs: 50",
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 20",
   "ldpred2_inference: F",
   "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/1KG/ldpred2",
   "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs",
@@ -1428,8 +1431,7 @@ config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
 
 # Get a list of score files
 scores <- list_score_files(config)
@@ -1441,15 +1443,10 @@ scores <- scores[scores$method %in% pgs_methods,]
 
 # Create files for EAS and AFR targets
 targ_pop <- c('EUR','EAS','AFR')
-for(trait_i in prscsx_dat$labels){
+for(trait_i in selected_traits){
   scores_i <- scores[grepl(trait_i, scores$name),]
   scores_i$group <- scores_i$method
   
-  # Current naming issue in gwas_groups leads to difficult subsetting
-  # In the future, update gwas_groups file to include disc pop in name, e.g. ALT_UKB_BBJ
-  scores_i$name_2 <- scores_i$name
-  scores_i$name_2[!(grepl('UKB|UGR|BBJ', scores_i$name_2, ignore.case = T))] <- paste0(scores_i$name_2[!(grepl('UKB|UGR|BBJ', scores_i$name_2, ignore.case = T))], '_ukb_bbj') 
-
   for(targ_pop_i in targ_pop){
     # Subset GWAS based on EUR and/or targ_pop_i
     if(targ_pop_i == 'EAS'){
@@ -1482,8 +1479,8 @@ for(trait_i in prscsx_dat$labels){
         recursive = T
       )
       
-      scores_i_j <- scores_i[grepl('UKB', scores_i$name_2, ignore.case = F) |
-                             grepl(disc_pop_j, scores_i$name_2, ignore.case = T),]
+      scores_i_j <- scores_i[grepl('UKB', scores_i$name, ignore.case = F) |
+                             grepl(disc_pop_j, scores_i$name, ignore.case = T),]
       
       # Insert the pseudo score for the non-TLPRS methods
       scores_i_j_pseudo <- scores_i_j[which(!grepl('^tlprs', scores_i_j$method)), ]
@@ -2277,8 +2274,7 @@ pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
 
 # Get a list of score files
 scores <- list_score_files(config)
@@ -2288,17 +2284,11 @@ scores<-scores[!grepl('tlprs', scores$method),]
 
 # Create files for EAS and AFR targets
 targ_pop <- c('EUR','EAS','AFR')
-for(trait_i in prscsx_dat$labels){
+for(trait_i in selected_traits){
   # Make a group containing both GWAS for each single source method
   # Make a group for each multisource method
   scores_i <- scores[grepl(trait_i, scores$name),]
   scores_i$group <- scores_i$method
-
-  # Current naming issue in gwas_groups leads to difficult subsetting
-  # In the future, update gwas_groups file to include disc pop in name, e.g. ALT_UKB_BBJ
-  scores_i$name_2 <- scores_i$name
-  scores_i$name_2[!(grepl('UKB|UGR|BBJ', scores_i$name_2, ignore.case = T))] <-
-    paste0(scores_i$name_2[!(grepl('UKB|UGR|BBJ', scores_i$name_2, ignore.case = T))], '_ukb_bbj')
   
   for(targ_pop_i in targ_pop){
     # Subset GWAS based on EUR and/or targ_pop_i
@@ -2332,8 +2322,8 @@ for(trait_i in prscsx_dat$labels){
         recursive = T
       )
       
-      scores_i_j <- scores_i[grepl('UKB', scores_i$name_2, ignore.case = F) |
-                       grepl(disc_pop_j, scores_i$name_2, ignore.case = T),]
+      scores_i_j <- scores_i[grepl('UKB$', scores_i$name, ignore.case = F) |
+                       grepl(paste0(disc_pop_j, '$'), scores_i$name, ignore.case = T),]
 
       scores_i_j_multi <- scores_i_j
       # Insert path to score file
@@ -2398,7 +2388,7 @@ for(trait_i in prscsx_dat$labels){
                                             scores_i_j_pseudo$predictor)
       
       # Make a group for each GWAS and single source combo
-      scores_i_j_single <- scores_i_j_multi[grepl('UKB|BBJ|UGR', scores_i_j$name), ]
+      scores_i_j_single <- scores_i_j_multi[!(scores_i_j_multi$method %in% pgs_group_methods),]
       scores_i_j_single$group <-
         paste0(scores_i_j_single$method,
                '.',
@@ -2465,14 +2455,14 @@ for targ_pop in $(echo EAS AFR EUR); do
   fi
   
   for disc_pop_i in ${disc_pop}; do
-    for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
       sbatch --mem 20G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
         --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
         --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
         --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
         --n_core 10 \
         --top1 T \
-        --all_model T \
+        --all_model F \
         --assoc T"
     done
   done
@@ -2498,8 +2488,7 @@ source('../functions/misc.R')
 source_all('../functions')
 
 # Read in list of outcomes 
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:4,]
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
 
 # Calculate corelation between all phenotypes in each target population
 cors <- list()
@@ -2510,7 +2499,7 @@ for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
     pop_i_2 <- pop_i
   }
   pheno_pop_i <- list()
-  for(pheno_i in prscsx_dat$labels){
+  for(pheno_i in selected_traits){
     pheno_pop_i[[pheno_i]] <- fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
     names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
   }
@@ -2524,7 +2513,7 @@ for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
 # Read in results
 targ_pop = c('EUR','EAS','AFR')
 res <- list()
-for(pheno_i in prscsx_dat$labels){
+for(pheno_i in selected_traits){
   res_i<-NULL
   for(targ_pop_i in targ_pop){
     if(targ_pop_i == 'EAS'){
@@ -2562,9 +2551,8 @@ for(pheno_i in prscsx_dat$labels){
   res_i$Model[grepl('pseudo', res_i$Group)]<-'Pseudo'
   res_i$Model[!grepl('top1|pseudo', res_i$Group)]<-'Multi'
   
-  res_i$Source[grepl('EAS|EUR|AFR', res_i$Group)] <- 'Single'
-  res_i$Source[!grepl('EAS|EUR|AFR', res_i$Group)] <- 'Multi'
-  
+  res_i$Source<-ifelse(res_i$Method %in% c(pgs_group_methods, 'all') | !grepl('EUR|EAS|AFR', res_i$Group), 'Multi', 'Single')
+         
   res_i$Discovery[grepl('EUR', res_i$Group)] <- 'EUR'
   res_i$Discovery[grepl('EAS', res_i$Group)] <- 'EAS'
   res_i$Discovery[grepl('AFR', res_i$Group)] <- 'AFR'
@@ -2673,7 +2661,6 @@ meta_res$Discovery<-factor(meta_res$Discovery, levels=c('AFR','EAS','EUR','EUR+A
 
 tmp <- meta_res
 
-# Set SBayesR pseudo model to top1
 ggplot(tmp, aes(x=Method, y=R , fill = Model)) +
     #geom_hline(yintercept = 0) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
diff --git a/docs/prep_quickprs_ref.Rmd b/docs/prep_quickprs_ref.Rmd
index 93d5551b..d3a54acb 100644
--- a/docs/prep_quickprs_ref.Rmd
+++ b/docs/prep_quickprs_ref.Rmd
@@ -31,6 +31,10 @@ Here we will prepare reference data for QuickPRS using HapMap3 SNPs in the 1KG+H
 
 QuickPRS only seems to work for v5.2, so let make the reference data using this version.
 
+***
+
+## Full reference
+
 ```{r}
 
 # Load dependencies
@@ -114,3 +118,102 @@ for(pop in c('EUR','EAS','AFR')){
 
 ```
 
+***
+
+## Split reference
+
+To use GWAS subsampling techniques
+Only the correlations part needs to be derived using subset data
+Follow X-wing strategy first - Split into three parts
+
+```{r}
+
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+opt<-list()
+opt$ref_plink_chr<-'resources/data/ref/ref.chr'
+opt$plink<-'plink'
+opt$plink2<-'plink2'
+opt$ldak_map<-'resources/data/ldak_map/genetic_map_b37'
+opt$ldak_tag<-'resources/data/ldak_bld'
+opt$ldak_highld<-'resources/data/ldak_highld/highld.txt'
+opt$ldak<-'/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/software/ldak5.2/ldak5.2.linux'
+opt$n_cores<-10
+
+for(pop in c('EUR','EAS','AFR')){
+  opt$output <- paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/', pop, '.hm3/', pop, '.hm3')
+  opt$output_dir <- paste0(dirname(opt$output),'/')
+  system(paste0('mkdir -p ',opt$output_dir))
+
+  opt$ref_keep<-paste0('resources/data/ref/keep_files/', pop, '.keep')
+  
+  ###
+  # Merge the per chromosome reference genetic data and subset opt$ref_keep
+  ###
+  
+  # Save in plink1 format for MegaPRS
+  plink_merge(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, keep = opt$ref_keep, make_bed =T, out = paste0(tmp_dir, '/ref_merge'))
+  
+  ############
+  # Format reference for LDAK
+  ############
+  
+  # Insert CHR:BP IDs
+  system(paste0("awk < ", tmp_dir, "/ref_merge.bim '{$2=$1\":\"$4;print $0}' > ", tmp_dir, '/tmp.bim; mv ', tmp_dir, '/tmp.bim ', tmp_dir, '/ref_merge.bim'))
+  
+  # Insert genetic distances
+  system(paste0(opt$plink, ' --bfile ', tmp_dir, '/ref_merge --cm-map ', opt$ldak_map,'/genetic_map_chr@_combined_b37.txt --make-bed --out ', tmp_dir, '/map'))
+  system(paste0("cat ", tmp_dir, "/map.bim | awk '{print $2, $3}' > ", tmp_dir, '/map.all'))
+  system(paste0("awk '(NR==FNR){arr[$1]=$2;next}{print $1, $2, arr[$2], $4, $5, $6}' ", tmp_dir, '/map.all ', tmp_dir, '/ref_merge.bim > ', tmp_dir, '/tmp.bim; mv ', tmp_dir, '/tmp.bim ', tmp_dir, '/ref_merge.bim'))
+  system(paste0('rm ', tmp_dir, '/map*'))
+  
+  keep <- fread(opt$ref_keep)
+  
+  # Split reference into three groups
+  lines <- readLines(opt$ref_keep)
+  set.seed(1)
+  shuffled_lines <- sample(lines)
+
+  # Calculate the split points
+  n <- length(shuffled_lines)
+  split1 <- floor(n / 3)
+  split2 <- 2 * split1
+  
+  # Split the lines into three groups
+  keep1 <- shuffled_lines[1:split1]
+  keep2 <- shuffled_lines[(split1 + 1):split2]
+  keep3 <- shuffled_lines[(split2 + 1):n]
+  
+  # Write each group to a separate file
+  write.table(data.frame(0, keep1), paste0(tmp_dir, "/keep1"), col.names = F, row.names = F, quote = F)
+  write.table(data.frame(0, keep2), paste0(tmp_dir, "/keep2"), col.names = F, row.names = F, quote = F)
+  write.table(data.frame(0, keep3), paste0(tmp_dir, "/keep3"), col.names = F, row.names = F, quote = F)
+
+  for(subset_i in 1:3){
+    # Save plink format files
+    system(paste0(opt$plink, ' --bfile ', tmp_dir, '/ref_merge --keep ', tmp_dir, '/keep', subset_i, ' --make-bed --out ', opt$output, '.subset_', subset_i))
+    
+    # Save frq files
+    system(paste0(opt$plink, ' --bfile ', opt$output, '.subset_', subset_i, ' --freq --out ', opt$output, '.subset_', subset_i))
+    
+    # Calculate predictor-predictor correlations
+    system(paste0(opt$ldak,' --calc-cors ', tmp_dir, '/tmp.subset_', subset_i, ' --keep ', tmp_dir, '/keep', subset_i, ' --bfile ', tmp_dir, '/ref_merge --window-cm 3 --max-threads ', opt$n_cores))
+      
+    # Export the files required for QuickPRS
+    for (i in c('bim', 'bin', 'noise', 'root')) {
+      system(paste0('cp ', tmp_dir, '/tmp.subset_', subset_i, '.cors.', i, ' ', opt$output, '.subset_', subset_i, '.cors.', i))
+    }
+  }
+
+  system(paste0('rm -r ', tmp_dir, '/*'))
+}
+
+```
diff --git a/functions/pgs.R b/functions/pgs.R
index 44f4349a..71e2e9b5 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -414,3 +414,53 @@ read_score <- function(score, chr = 1:22, log_file = NULL){
 	return(score)
 }
 
+
+quick_prs<-function(sumstats, ref_dir, genomic_control, prs_model, n_cores = 1, ref_subset = NULL){
+  tmp_dir<-tempfile()
+  dir.create(tmp_dir)
+
+  ######
+  # Estimate Per-Predictor Heritabilities
+  ######
+
+  # Calculate Per-Predictor Heritabilities.
+  ref_files<-list.files(ref_dir)
+
+  tagging_file<-ref_files[grepl('quickprs.tagging',ref_files)]
+  matrix_file<-ref_files[grepl('quickprs.matrix',ref_files)]
+
+  if(opt$genomic_control == F){
+    system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --tagfile ', ref_dir, '/', tagging_file, ' --summary ', sumstats, ' --matrix ', ref_dir, '/', matrix_file, ' --max-threads ', n_cores, ' --check-sums NO'))
+  } else{
+    system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --genomic-control YES --tagfile ', ref_dir, '/', tagging_file, ' --summary ', sumstats, ' --matrix ', ref_dir, '/', matrix_file, ' --max-threads ', n_cores, ' --check-sums NO'))
+  }
+
+  ldak_res_her<-fread(paste0(tmp_dir,'/bld.ldak.hers'))
+
+  ######
+  # Estimate effect sizes for training and full prediction models.
+  ######
+
+  if(!is.null(ref_subset)){
+    cor_file_prefix<-gsub('.cors.bin','',ref_files[grepl(paste0('subset_', ref_subset, '.cors.bin'),ref_files)])
+  } else {
+    cor_file_prefix<-gsub('.cors.bin','',ref_files[grepl('.cors.bin',ref_files) & !grepl('subset', ref_files)])
+  }
+
+  system(paste0(opt$ldak,' --mega-prs ',tmp_dir,'/mega_full --model ', prs_model,' --cors ', ref_dir, '/', cor_file_prefix, ' --ind-hers ', tmp_dir, '/bld.ldak.ind.hers --summary ', sumstats, ' --high-LD ', ref_dir, '/highld.snps --cv-proportion 0.1 --window-cm 1 --max-threads ', n_cores,' --extract ', sumstats))
+
+  # Identify the best fitting model
+  ldak_res_cors <- fread(paste0(tmp_dir, '/mega_full.cors'), nThread = n_cores)
+  best_score <- ldak_res_cors[which.max(ldak_res_cors$Correlation),]
+
+  ######
+  # Format final score file
+  ######
+
+  # Read in the scores
+  score <- fread(paste0(tmp_dir,'/mega_full.effects'), nThread = n_cores)
+  score <- score[, c(1, 2, 3, 5), with = F]
+  names(score) <- c('SNP','A1','A2','BETA')
+
+  return(score)
+}
diff --git a/functions/pipeline.R b/functions/pipeline.R
index 229d8efd..1bbe3e93 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -291,6 +291,18 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
     }
     pseudo_val <- paste0('targ_', target_pop, '_weighted')
   }
+  if(pgs_method == 'quickprs_multi'){
+    if(!is.null(target_pop) && target_pop == 'TRANS'){
+      cat('No pseudovalidation for TRANS target population available for xwing.\n')
+      cat(paste0('Returning result for ', gwas_list$population[1],' target population.\n'))
+      target_pop <- gwas_list$population[1]
+    } else if(!is.null(target_pop) && !(target_pop %in% gwas_list$population)){
+      cat(paste0('target_pop ', target_pop,' is not present in gwas_group ', gwas, '.\n'))
+      cat(paste0('Returning result for ', gwas_list$population[1],' target population.\n'))
+      target_pop <- gwas_list$population[1]
+    }
+    pseudo_val <- paste0('targ_', target_pop, '_weighted')
+  }
 
   if(tlprs){
     if(!is.null(target_pop) && target_pop == 'TRANS'){
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 83bd1dda..86a2e7b5 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -364,7 +364,7 @@ def check_pgs_methods(x):
         return
 
     valid_pgs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesr","sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs", "xwing", "prscsx", "tlprs"
+        "ptclump", "dbslmm", "prscs", "sbayesr","sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs", "xwing", "prscsx", "tlprs","quickprs_multi"
     }
 
     invalid_methods = [method for method in x if method not in valid_pgs_methods]
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 0a6962ba..57d15f4f 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -698,6 +698,50 @@ rule prep_pgs_tlprs_i:
 rule prep_pgs_tlprs:
   input: expand(f"{outdir}/reference/pgs_score_files/tlprs_{{method}}/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'], method=config["tlprs_methods"])
 
+##
+# LDAK QuickPRS Multi
+##
+
+rule prep_pgs_quickprs_multi_i:
+  resources:
+    mem_mb=20000,
+    time_min=5000
+  threads: config['cores_prep_pgs']
+  input:
+    lambda w: expand(f"{quickprs_ldref}/{{population}}.hm3/{{population}}.hm3.cors.bin", population=[pop for pop in get_populations(w.gwas_group)]),
+    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
+    rules.download_ldak_highld.output,
+    rules.download_ldak5_2.output,
+    rules.download_ldak_map.output,
+    rules.download_ldak_bld.output
+  output:
+    f"{outdir}/reference/pgs_score_files/quickprs_multi/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
+  benchmark:
+    f"{outdir}/reference/benchmarks/prep_pgs_quickprs_multi_i-{{gwas_group}}.txt"
+  log:
+    f"{outdir}/reference/logs/prep_pgs_quickprs_multi_i-{{gwas_group}}.log"
+  conda:
+    "../envs/xwing.yaml"
+  params:
+    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
+    populations= lambda w: ",".join(get_populations(w.gwas_group)),
+    testing=config["testing"]
+  shell:
+    "Rscript ../Scripts/pgs_methods/quickprs_multi.R \
+      --ref_plink_chr {refdir}/ref.chr \
+      --sumstats {params.sumstats} \
+      --populations {params.populations} \
+      --ldak {resdir}/software/ldak5.2/ldak5.2.linux \
+      --quick_prs_ref {quickprs_ldref} \
+      --xwing_repo {resdir}/software/xwing \
+      --n_cores {threads} \
+      --output {outdir}/reference/pgs_score_files/quickprs_multi/{wildcards.gwas_group}/ref-{wildcards.gwas_group} \
+      --pop_data {refdir}/ref.pop.txt \
+      --test {params.testing} > {log} 2>&1"
+
+rule prep_pgs_quickprs_multi:
+  input: expand(f"{outdir}/reference/pgs_score_files/quickprs_multi/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'])
+
 ####
 # BridgePRS
 ####
@@ -709,7 +753,7 @@ rule prep_pgs_bridgeprs_i:
   threads: config['cores_prep_pgs']
   input:
     rules.download_bridgeprs_software.output,
-    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))
+    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas_group}}/{{gwas_group}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))
   output:
     f"{outdir}/reference/pgs_score_files/bridgeprs/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   conda:
@@ -719,7 +763,7 @@ rule prep_pgs_bridgeprs_i:
   log:
     f"{outdir}/reference/logs/prep_pgs_bridgeprs_i-{{gwas_group}}.log"
   params:
-    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
+    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas_group}}/{{gwas_group}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
     populations= lambda w: ",".join(get_populations(w.gwas_group)),
     testing=config["testing"]
   shell:
@@ -772,6 +816,8 @@ if 'xwing' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_xwing.input)
 if 'tlprs' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_tlprs.input)
+if 'quickprs_multi' in pgs_methods_all:
+  pgs_methods_input.append(rules.prep_pgs_quickprs_multi.input)
 if 'bridgeprs' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_bridgeprs.input)
 

From 727ba1148ae5f5df6d9944fcde46e22e3e507d10 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 4 Nov 2024 21:09:07 +0000
Subject: [PATCH 051/153] Added quickprs_multi

---
 functions/constants.R | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/functions/constants.R b/functions/constants.R
index 02886342..0842bef6 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -37,8 +37,8 @@ ref_pop <- data.frame(
 
 # Make a data.frame giving labels to the 1KG reference populations
 pgs_method_labels <- data.frame(
-  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','tlprs'),
-  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','TL-PRS')
+  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','tlprs','quickprs'),
+  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','TL-PRS','QuickPRS-Multi')
 )
 pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),]
 
@@ -46,5 +46,5 @@ pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),]
 pgs_methods_noneur <- c('ptclump','lassosum','megaprs','prscs','dbslmm','ldpred2','quickprs','sbayesrc')
 
 # Make vector indicating pgs_methods that are to be applied to gwas_groups
-pgs_group_methods <- c('prscsx','xwing','tlprs')
+pgs_group_methods <- c('prscsx','xwing','tlprs','quickprs_multi')
 

From d8171262e2fff60d48e9abb7db60c72624bbb9b4 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 8 Nov 2024 16:53:07 +0000
Subject: [PATCH 052/153] Edited PRS-CS, PRS-CSx, and X-Wing to stop as soon as
 an iteration of the foreach loop fails

---
 Scripts/pgs_methods/prscs.R  |  28 +++++++--
 Scripts/pgs_methods/prscsx.R |  33 ++++++++--
 Scripts/pgs_methods/xwing.R  | 116 +++++++++++++++++++++++++----------
 3 files changed, 133 insertions(+), 44 deletions(-)

diff --git a/Scripts/pgs_methods/prscs.R b/Scripts/pgs_methods/prscs.R
index 3079e2aa..fa4f21a4 100644
--- a/Scripts/pgs_methods/prscs.R
+++ b/Scripts/pgs_methods/prscs.R
@@ -126,20 +126,36 @@ gc()
 
 # Make a data.frame listing chromosome and phi combinations
 jobs<-NULL
-for(i in rev(CHROMS)){
+for(i in CHROMS){
   jobs<-rbind(jobs, data.frame(CHR=i, phi=phi_param))
 }
 
 # Run using PRScs auto, and specifying a range of global shrinkage parameters
+file.remove(paste0(tmp_dir, '/checker.txt'))
 log <- foreach(i = 1:nrow(jobs), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
-  if(jobs$phi[i] == 'auto'){
-    system(paste0(opt$PRScs_path, ' --ref_dir=', opt$PRScs_ref_path, ' --bim_prefix=', tmp_dir,'/ref.chr', jobs$CHR[i], ' --sst_file=', tmp_dir, '/GWAS_sumstats_temp.txt --n_gwas=', gwas_N, ' --out_dir=', tmp_dir, '/ --chrom=', jobs$CHR[i], ' --seed=', opt$seed))
-  } else {
-    system(paste0(opt$PRScs_path, ' --ref_dir=', opt$PRScs_ref_path, ' --bim_prefix=', tmp_dir,'/ref.chr', jobs$CHR[i], ' --phi=', jobs$phi[i], ' --sst_file=', tmp_dir, '/GWAS_sumstats_temp.txt --n_gwas=', gwas_N, ' --out_dir=', tmp_dir, '/ --chrom=', jobs$CHR[i], ' --seed=', opt$seed))
+  if(!file.exists(paste0(tmp_dir, '/checker.txt'))) {
+    # Base command
+    command <- paste0(opt$PRScs_path, ' --ref_dir=', opt$PRScs_ref_path,
+                      ' --bim_prefix=', tmp_dir, '/ref.chr', jobs$CHR[i],
+                      ' --sst_file=', tmp_dir, '/GWAS_sumstats_temp.txt --n_gwas=',
+                      gwas_N, ' --out_dir=', tmp_dir, '/ --chrom=',
+                      jobs$CHR[i], ' --seed=', opt$seed)
+
+    # Add --phi parameter if not 'auto'
+    if (jobs$phi[i] != 'auto') {
+      command <- paste0(command, ' --phi=', jobs$phi[i])
+    }
+
+    # Run command
+    log_i <- system(command)
+
+    # Check for an error
+    if(log_i != 0){
+      write("", paste0(tmp_dir, '/checker.txt'))
+    }
   }
 }
 
-
 ####
 # Combine score files
 ####
diff --git a/Scripts/pgs_methods/prscsx.R b/Scripts/pgs_methods/prscsx.R
index be744b6f..b39eaeea 100644
--- a/Scripts/pgs_methods/prscsx.R
+++ b/Scripts/pgs_methods/prscsx.R
@@ -141,16 +141,37 @@ gc()
 
 # Make a data.frame listing chromosome and phi combinations
 jobs<-NULL
-for(i in rev(CHROMS)){
+for(i in CHROMS){
   jobs<-rbind(jobs, data.frame(CHR=i, phi=phi_param))
 }
 
-# Run using PRScs auto, and specifying a range of global shrinkage parameters
+# Run using PRS-CSx auto, and specifying a range of global shrinkage parameters
+file.remove(paste0(tmp_dir, '/checker.txt'))
 log <- foreach(i = 1:nrow(jobs), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
-  if(jobs$phi[i] == 'auto'){
-    system(paste0(opt$prscsx_path, ' --ref_dir=', opt$prscsx_ref_path, '/ --bim_prefix=', tmp_dir,'/ref.chr', jobs$CHR[i], ' --pop=', opt$populations, ' --sst_file=', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','),' --n_gwas=', paste(gwas_N, collapse=','), ' --out_dir=', tmp_dir, '/ --out_name=output --chrom=', jobs$CHR[i], ' --meta=True --seed=', opt$seed))
-  } else {
-    system(paste0(opt$prscsx_path, ' --ref_dir=', opt$prscsx_ref_path, '/ --bim_prefix=', tmp_dir,'/ref.chr', jobs$CHR[i], ' --pop=', opt$populations, ' --phi=', jobs$phi[i], ' --sst_file=', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','),' --n_gwas=', paste(gwas_N, collapse=','), ' --out_dir=', tmp_dir, '/ --out_name=output --chrom=', jobs$CHR[i], ' --meta=True --seed=', opt$seed))
+  if(!file.exists(paste0(tmp_dir, '/checker.txt'))) {
+    # Base command
+    command <- paste0(opt$prscsx_path, ' ',
+    '--ref_dir=', opt$prscsx_ref_path, '/ ',
+    '--bim_prefix=', tmp_dir,'/ref.chr', jobs$CHR[i], ' ',
+    '--pop=', opt$populations, ' ',
+    '--sst_file=', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','),' ',
+    '--n_gwas=', paste(gwas_N, collapse=','), ' ',
+    '--out_dir=', tmp_dir, '/ ',
+    '--out_name=output --chrom=', jobs$CHR[i], ' ',
+    '--meta=True --seed=', opt$seed)
+
+    # Add --phi parameter if not 'auto'
+    if (jobs$phi[i] != 'auto') {
+      command <- paste0(command, ' --phi=', jobs$phi[i])
+    }
+
+    # Run command
+    log_i <- system(command)
+
+    # Check for an error
+    if(log_i != 0){
+      write("", paste0(tmp_dir, '/checker.txt'))
+    }
   }
 }
 
diff --git a/Scripts/pgs_methods/xwing.R b/Scripts/pgs_methods/xwing.R
index ef7bb98d..53815881 100644
--- a/Scripts/pgs_methods/xwing.R
+++ b/Scripts/pgs_methods/xwing.R
@@ -166,19 +166,41 @@ gc()
 
 combinations <- expand.grid(targ_pop = populations, pst_pop = populations, chr = CHROMS)
 
+file.remove(paste0(tmp_dir, '/checker.txt'))
 log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
-
-  targ_pop <- combinations$targ_pop[i]
-  pst_pop <- combinations$pst_pop[i]
-  chr <- combinations$chr[i]
-
-  # Create directories
-  dir.create(paste0(tmp_dir, '/PANTHER/post_targ_', targ_pop), recursive = TRUE)
-  dir.create(paste0(tmp_dir, '/PANTHER/post_collect_targ_', targ_pop), recursive = TRUE)
-
-  system(paste0(
-    'python ', opt$xwing_repo, '/PANTHER.py --ref_dir ', opt$panther_ref, ' --bim_prefix ', tmp_dir,'/ref.chr',chr,' --sumstats ', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','), ' --n_gwas ', paste(gwas_N, collapse=','), ' --anno_file ', paste0(paste0(tmp_dir, '/LOGODetect/targ_', targ_pop, '_annot_', populations, '.txt'), collapse=','), ' --chrom ', chr, ' --pop ', opt$populations ,' --target_pop ', targ_pop,' --pst_pop ', pst_pop, ' --out_name output --seed 1 --out_dir ', tmp_dir, '/PANTHER/post_targ_', targ_pop
-  ))
+  if(!file.exists(paste0(tmp_dir, '/checker.txt'))) {
+    targ_pop <- combinations$targ_pop[i]
+    pst_pop <- combinations$pst_pop[i]
+    chr <- combinations$chr[i]
+
+    # Create directories
+    dir.create(paste0(tmp_dir, '/PANTHER/post_targ_', targ_pop), recursive = TRUE)
+    dir.create(paste0(tmp_dir, '/PANTHER/post_collect_targ_', targ_pop), recursive = TRUE)
+
+    command <- paste0(
+      'python ', opt$xwing_repo, '/PANTHER.py ',
+      '--ref_dir ', opt$panther_ref, ' ',
+      '--bim_prefix ', tmp_dir,'/ref.chr',chr,' ',
+      '--sumstats ', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','), ' ',
+      '--n_gwas ', paste(gwas_N, collapse=','), ' ',
+      '--anno_file ', paste0(paste0(tmp_dir, '/LOGODetect/targ_', targ_pop, '_annot_', populations, '.txt'), collapse=','), ' ',
+      '--chrom ', chr, ' ',
+      '--pop ', opt$populations ,' ',
+      '--target_pop ', targ_pop,' ',
+      '--pst_pop ', pst_pop, ' ',
+      '--out_name output ',
+      '--seed 1 ',
+      '--out_dir ', tmp_dir, '/PANTHER/post_targ_', targ_pop
+    )
+
+    # Run command
+    log_i <- system(command)
+
+    # Check for an error
+    if(log_i != 0){
+      write("", paste0(tmp_dir, '/checker.txt'))
+    }
+  }
 }
 
 ##
@@ -188,7 +210,14 @@ dir.create(paste0(tmp_dir,'/LEOPARD/sampled_sumstats'), recursive = T)
 
 for(i in 1:length(sumstats)){
   system(paste0(
-    'Rscript ', opt$xwing_repo, '/LEOPARD_Sim.R --sumstats ', tmp_dir, '/GWAS_sumstats_', i,'_temp.txt --n_gwas ', gwas_N[i], ' --train_prop 0.75 --ref_prefix ', opt$leopard_ref,'/', populations[i], '/', populations[i], '_part1 --seed ', opt$seed, ' --rep 4 --out_prefix ', tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', i
+    'Rscript ', opt$xwing_repo, '/LEOPARD_Sim.R ',
+    '--sumstats ', tmp_dir, '/GWAS_sumstats_', i,'_temp.txt ',
+    '--n_gwas ', gwas_N[i], ' ',
+    '--train_prop 0.75 ',
+    '--ref_prefix ', opt$leopard_ref,'/', populations[i], '/', populations[i], '_part1 ',
+    '--seed ', opt$seed, ' ',
+    '--rep 4 ',
+    '--out_prefix ', tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', i
   ))
 }
 
@@ -198,26 +227,49 @@ for(i in 1:length(sumstats)){
 
 combinations <- expand.grid(targ_pop = populations, pst_pop = populations, chr = CHROMS, index = 1:4)
 
+file.remove(paste0(tmp_dir, '/checker.txt'))
 log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
-  targ_pop <- combinations$targ_pop[i]
-  pst_pop <- combinations$pst_pop[i]
-  chr <- combinations$chr[i]
-  index <- combinations$index[i]
-
-  dir.create(paste0(tmp_dir,'/LEOPARD/post_targ_', targ_pop), recursive = T)
-  dir.create(paste0(tmp_dir,'/LEOPARD/post_collect_targ_', targ_pop), recursive = T)
-
-  sumstats_i <- paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt')
-  sumstats_i[populations == targ_pop] <- paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', index, '_train.txt')
-
-  targ_gwas_train_n<-fread(paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', index, '_train_valid_N.txt'))$N_train
-
-  gwas_N_i<-gwas_N
-  gwas_N_i[populations == targ_pop] <- targ_gwas_train_n
-
-  system(paste0(
-    'python ', opt$xwing_repo, '/PANTHER.py --ref_dir ', opt$panther_leopard_ref, ' --bim_prefix ', tmp_dir,'/ref.chr',chr,' --sumstats ', paste0(sumstats_i, collapse=','), ' --n_gwas ', paste(gwas_N_i, collapse=','), ' --anno_file ', paste0(paste0(tmp_dir, '/LOGODetect/targ_', targ_pop, '_annot_', populations, '.txt'), collapse=','), ' --chrom ', chr, ' --pop ', opt$populations ,' --target_pop ', targ_pop,' --pst_pop ', pst_pop, ' --out_name output_', index, ' --seed 1 --out_dir ', tmp_dir, '/LEOPARD/post_targ_', targ_pop
-  ))
+  if(!file.exists(paste0(tmp_dir, '/checker.txt'))) {
+    targ_pop <- combinations$targ_pop[i]
+    pst_pop <- combinations$pst_pop[i]
+    chr <- combinations$chr[i]
+    index <- combinations$index[i]
+
+    dir.create(paste0(tmp_dir,'/LEOPARD/post_targ_', targ_pop), recursive = T)
+    dir.create(paste0(tmp_dir,'/LEOPARD/post_collect_targ_', targ_pop), recursive = T)
+
+    sumstats_i <- paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt')
+    sumstats_i[populations == targ_pop] <- paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', index, '_train.txt')
+
+    targ_gwas_train_n<-fread(paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', index, '_train_valid_N.txt'))$N_train
+
+    gwas_N_i<-gwas_N
+    gwas_N_i[populations == targ_pop] <- targ_gwas_train_n
+
+    command<-paste0(
+      'python ', opt$xwing_repo, '/PANTHER.py ',
+      '--ref_dir ', opt$panther_leopard_ref, ' ',
+      '--bim_prefix ', tmp_dir,'/ref.chr',chr,' ',
+      '--sumstats ', paste0(sumstats_i, collapse=','), ' ',
+      '--n_gwas ', paste(gwas_N_i, collapse=','), ' ',
+      '--anno_file ', paste0(paste0(tmp_dir, '/LOGODetect/targ_', targ_pop, '_annot_', populations, '.txt'), collapse=','), ' ',
+      '--chrom ', chr, ' ',
+      '--pop ', opt$populations ,' ',
+      '--target_pop ', targ_pop,' ',
+      '--pst_pop ', pst_pop, ' ',
+      '--out_name output_', index, ' ',
+      '--seed 1 ',
+      '--out_dir ', tmp_dir, '/LEOPARD/post_targ_', targ_pop
+    )
+
+    # Run command
+    log_i <- system(command)
+
+    # Check for an error
+    if(log_i != 0){
+      write("", paste0(tmp_dir, '/checker.txt'))
+    }
+  }
 }
 
 for(targ_pop in populations){

From e949997b8203a9bbedc3185db5fc7062b0b332d4 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 8 Nov 2024 16:53:39 +0000
Subject: [PATCH 053/153] Added an error catch for SBayesRC

---
 Scripts/pgs_methods/sbayesrc.R | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/Scripts/pgs_methods/sbayesrc.R b/Scripts/pgs_methods/sbayesrc.R
index b9888b02..8eb747ac 100644
--- a/Scripts/pgs_methods/sbayesrc.R
+++ b/Scripts/pgs_methods/sbayesrc.R
@@ -172,8 +172,20 @@ tryCatch(
         bTune = FALSE,
         log2file = FALSE
       )
-    } else {
-      # For any other error, rethrow the error and stop the script
+    } else if (grepl("An unexpected error occurred: Warning, the best parameter is the minimumn threshold, we suggest to expand the tuning grid by specify lower tuning value", e$message)) {
+        message("Specific error encountered. Retrying with tuneStep=c(0.995, 0.9, 0.8, 0.7, 0.6)...")
+        log_add(log_file = log_file, message = 'Specific error encountered. Retrying with btune = FALSE...')
+        # Retry with the tuneStep=c(0.995, 0.9, 0.8, 0.7, 0.6)
+        SBayesRC::sbayesrc(
+          mafile = paste0(tmp_dir, '/tidy.imp.ma'),
+          LDdir = opt$sbayesrc_ldref,
+          outPrefix = paste0(tmp_dir, '/sbrc'),
+          annot = opt$sbayesrc_annot,
+          tuneStep=c(0.995, 0.9, 0.8, 0.7, 0.6),
+          log2file = FALSE
+        )
+      } else {
+        # For any other error, rethrow the error and stop the script
       stop("An unexpected error occurred: ", e$message)
     }
   },

From 445b2ddf06e18b4c050b35061dd341538247d5c3 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 8 Nov 2024 16:54:14 +0000
Subject: [PATCH 054/153] Created LDpred2 matrices from hgdp_1kg

---
 docs/prep_ldpred2_ldmat.Rmd | 27 ---------------------------
 1 file changed, 27 deletions(-)

diff --git a/docs/prep_ldpred2_ldmat.Rmd b/docs/prep_ldpred2_ldmat.Rmd
index a090473f..d085c53e 100644
--- a/docs/prep_ldpred2_ldmat.Rmd
+++ b/docs/prep_ldpred2_ldmat.Rmd
@@ -382,19 +382,6 @@ for(pop in c('EUR', 'EAS', 'AFR')){
       ".rds"
     ))
   
-    # LD for PRS-CS (based on the same data and LD blocks)
-    hdf5_file <- paste0("/users/k1806347/oliverpainfel/Data/hgdp_1kg/prscs/ldblk_hgdp_1kg_", tolower(pop)  , "/ldblk_hgdp_1kg_chr", chr, ".hdf5")
-    runonce::skip_run_if({
-      ind_block <- split(ind.chr2, best_grp)
-      bigparallelr::set_blas_ncores(nb_cores())
-      for (ic in seq_along(ind_block)) {
-        ind <- ind_block[[ic]]
-        ld <- big_cor(G, ind.col = ind)[]
-        rhdf5::h5write(list(ldblk = ld, snplist = paste0("snp", ind)),
-                       file = hdf5_file, name = paste0("blk_", ic), level = 9)
-      }
-    }, files = hdf5_file)
-  
     # return
     tibble(best_split, ind = list(ind.chr2))
   })
@@ -442,22 +429,8 @@ for(pop in c('EUR', 'EAS', 'AFR')){
       '/map.rds'
     )
   )
-  
-  # GOT to here
-  
-  # need also some .bim and some snpinfo for PRS-CS
-  ind_keep <- unlist(all_final_grp$ind)
-  snpinfo <- transmute(obj.hgdp_1kg$map, chromosome, marker.ID = paste0("snp", row_number()),
-                       genetic.dist = 0, physical.pos, allele1 = "A", allele2 = "C")[ind_keep, ]
-  bigsnpr:::write.table2(snpinfo, "/users/k1806347/oliverpainfel/Data/hgdp_1kg/prscs/ldblk_hgdp_1kg_", tolower(pop)  , "/for_prscs.bim")
-  
-  snpinfo2 <- transmute(snpinfo, CHR = chromosome, SNP = marker.ID,
-                        BP = physical.pos, A1 = allele1, A2 = allele2)
-  snpinfo2$MAF <- snp_MAF(G, ind.row = ind_EAS, ind.col = ind_keep, ncores = nb_cores())
-  bigreadr::fwrite2(snpinfo2, "/users/k1806347/oliverpainfel/Data/hgdp_1kg/prscs/ldblk_hgdp_1kg_", tolower(pop)  , "/snpinfo_hgdp_1kg_hm3", sep = "\t")
 }
 
-
 ```
 
 

From e7cceca731245ad6b29a1d1163f8b17982357590 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 8 Nov 2024 16:54:51 +0000
Subject: [PATCH 055/153] Minor updates to crosspop analysis and addition of
 figure showing relative performance

---
 docs/CrossPop.Rmd | 1651 ++++++++++++++++++++++++---------------------
 1 file changed, 885 insertions(+), 766 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 75cf0ffd..9824e4a9 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1381,14 +1381,12 @@ config<-c(
   "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
   "gwas_groups: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt",
 #  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
-  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','megaprs','sbayesrc']",
+  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','ldpred2','megaprs','sbayesrc','prscsx','xwing','prscs']",
 #  "tlprs_methods: ['dbslmm','prscs','lassosum']",
-#  "cores_prep_pgs: 20",
-#  "cores_target_pgs: 50",
   "cores_prep_pgs: 10",
-  "cores_target_pgs: 20",
+  "cores_target_pgs: 50",
   "ldpred2_inference: F",
-  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/1KG/ldpred2",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2",
   "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs",
   "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc"
 )
@@ -1414,7 +1412,8 @@ Lets use the model builder script which implements nested 10 fold cross validati
 
 ***
 
-## Evaluate TLPRS
+## Compare all methods
+
 ### Create predictor lists
 
 <details><summary>Show code</summary>
@@ -1428,6 +1427,7 @@ library(data.table)
 
 # Get some key variables from config
 config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
@@ -1436,14 +1436,14 @@ selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_su
 # Get a list of score files
 scores <- list_score_files(config)
 
-# Subset to TLPRS scores and pseudo scores for corresponding methods
-pgs_methods <- unique(scores$method[grepl('^tlprs', scores$method)])
-pgs_methods <- c(pgs_methods, gsub('tlprs_','',pgs_methods))
-scores <- scores[scores$method %in% pgs_methods,]
+# Remove tlprs
+scores<-scores[!grepl('tlprs', scores$method),]
 
 # Create files for EAS and AFR targets
-targ_pop <- c('EUR','EAS','AFR')
+targ_pop <- c('EAS','AFR')
 for(trait_i in selected_traits){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
   scores_i <- scores[grepl(trait_i, scores$name),]
   scores_i$group <- scores_i$method
   
@@ -1479,180 +1479,83 @@ for(trait_i in selected_traits){
         recursive = T
       )
       
-      scores_i_j <- scores_i[grepl('UKB', scores_i$name, ignore.case = F) |
-                             grepl(disc_pop_j, scores_i$name, ignore.case = T),]
-      
-      # Insert the pseudo score for the non-TLPRS methods
-      scores_i_j_pseudo <- scores_i_j[which(!grepl('^tlprs', scores_i_j$method)), ]
-      scores_i_j_pseudo$group <- paste0(scores_i_j_pseudo$group,'.pseudo')
+      scores_i_j <- scores_i[grepl('UKB$', scores_i$name, ignore.case = F) |
+                       grepl(paste0(disc_pop_j, '$'), scores_i$name, ignore.case = T),]
 
-      scores_i_j_pseudo$predictor <- paste0(
+      scores_i_j_multi <- scores_i_j
+      # Insert path to score file
+      scores_i_j_multi$predictor <- paste0(
         outdir,
         '/ukb/pgs/',
         targ_pop_i,
         '/',
-        scores_i_j_pseudo$method,
+        scores_i_j_multi$method,
         '/',
-        scores_i_j_pseudo$name,
+        scores_i_j_multi$name,
         '/ukb-',
-        scores_i_j_pseudo$name,
+        scores_i_j_multi$name,
         '-',
         targ_pop_i,
-        '.pseudo.profiles'
+        '.profiles'
       )
       
-      for(i in 1:nrow(scores_i_j_pseudo)) {
-        tmp <- scores_i_j_pseudo[i,]
+      # Extract the pseudo score for each method and specify as a seperate group
+      for(i in 1:nrow(scores_i_j)) {
         param <- find_pseudo(
           config = config,
-          gwas = tmp$name,
-          pgs_method = tmp$method,
+          gwas = scores_i_j$name[i],
+          pgs_method = scores_i_j$method[i],
           target_pop = targ_pop_i
         )
         
         score_header <-
-          fread(gsub('.pseudo', '', tmp$predictor), nrows = 1)
+          fread(scores_i_j_multi$predictor[i], nrows = 1)
         score_cols <-
-          which(names(score_header) %in% c('FID', 'IID', paste0(tmp$name, '_', param)))
+          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
         
         system(
           paste0(
             "cut -d' ' -f ", 
             paste0(score_cols, collapse=','),
             " ", 
-            gsub('.pseudo', '', tmp$predictor), 
-            " > ", tmp$predictor
+            scores_i_j_multi$predictor[i], 
+            " > ", 
+            gsub('.profiles',
+                    '.pseudo.profiles',
+                    scores_i_j_multi$predictor[i])
           )
         )
       }
       
-      # Create disc_pop specific groups
-      scores_i_j_pseudo_disc_pop <- scores_i_j_pseudo
-      scores_i_j_pseudo_disc_pop$group[grepl('UKB', scores_i_j_pseudo_disc_pop$name)] <- paste0(scores_i_j_pseudo_disc_pop$group[grepl('UKB', scores_i_j_pseudo_disc_pop$name)], '.EUR')
-      scores_i_j_pseudo_disc_pop$group[!grepl('UKB', scores_i_j_pseudo_disc_pop$name)] <- paste0(scores_i_j_pseudo_disc_pop$group[!grepl('UKB', scores_i_j_pseudo_disc_pop$name)], '.', disc_pop_j_2)
-      
-      # Insert groups for TLPRS scores for both target populations, and target specific
-      scores_i_j_multi <- scores_i_j[which(grepl('^tlprs', scores_i_j$method)), ]
-      
-      # Insert path to score file
-      scores_i_j_multi$predictor <- paste0(
-        outdir,
-        '/ukb/pgs/',
-        targ_pop_i,
-        '/',
-        scores_i_j_multi$method,
-        '/',
-        scores_i_j_multi$name,
-        '/ukb-',
-        scores_i_j_multi$name,
-        '-',
-        targ_pop_i,
-        '.profiles'
-      )
-      
-      scores_i_j_multi_targ_pop <- scores_i_j_multi
-      scores_i_j_multi_targ_pop_both<-NULL
-      for(i in 1:nrow(scores_i_j_multi_targ_pop)){
-        score_header <-
-          fread(gsub('.pseudo', '', scores_i_j_multi_targ_pop$predictor[i]), nrows = 1)
-        score_cols_EUR <-
-          which(names(score_header) %in% c('FID', 'IID', names(score_header)[grepl('targ_EUR', names(score_header))]))
-        score_cols_targ <-
-          which(names(score_header) %in% c('FID', 'IID', names(score_header)[grepl(paste0('targ_', targ_pop_i), names(score_header))]))
-        
-        system(
-          paste0(
-            "cut -d' ' -f ", 
-            paste0(score_cols_EUR, collapse=','),
-            " ", 
-            scores_i_j_multi_targ_pop$predictor[i], 
-            " > ", 
-            paste0(
-              outdir,
-              '/ukb/pgs/',
-              targ_pop_i,
-              '/',
-              scores_i_j_multi_targ_pop$method[i],
-              '/',
-              scores_i_j_multi_targ_pop$name[i],
-              '/ukb-',
-              scores_i_j_multi_targ_pop$name[i],
-              '-',
-              targ_pop_i,
-              '.targ_EUR.profiles'
-            )
-          )
-        )
-        
-        system(
-          paste0(
-            "cut -d' ' -f ", 
-            paste0(score_cols_targ, collapse=','),
-            " ", 
-            scores_i_j_multi_targ_pop$predictor[i], 
-            " > ", 
-            paste0(
-              outdir,
-              '/ukb/pgs/',
-              targ_pop_i,
-              '/',
-              scores_i_j_multi_targ_pop$method[i],
-              '/',
-              scores_i_j_multi_targ_pop$name[i],
-              '/ukb-',
-              scores_i_j_multi_targ_pop$name[i],
-              '-',
-              targ_pop_i,
-              '.targ_',
-              targ_pop_i,
-              '.profiles'
-            )
-          )
-        )
-        
-        tmp<-scores_i_j_multi_targ_pop[i,]
-        tmp <- rbind(tmp, tmp)
-        tmp$predictor[1] <- paste0(
-            outdir,
-            '/ukb/pgs/',
-            targ_pop_i,
-            '/',
-            scores_i_j_multi_targ_pop$method[i],
-            '/',
-            scores_i_j_multi_targ_pop$name[i],
-            '/ukb-',
-            scores_i_j_multi_targ_pop$name[i],
-            '-',
-            targ_pop_i,
-            '.targ_EUR.profiles'
-          )
-        tmp$group[1] <- paste0(tmp$group[1], '.EUR')
-        
-        tmp$predictor[2] <- paste0(
-            outdir,
-            '/ukb/pgs/',
-            targ_pop_i,
-            '/',
-            scores_i_j_multi_targ_pop$method[i],
-            '/',
-            scores_i_j_multi_targ_pop$name[i],
-            '/ukb-',
-            scores_i_j_multi_targ_pop$name[i],
-            '-',
-            targ_pop_i,
-            '.targ_',
-            targ_pop_i,
-            '.profiles'
-          )
-        tmp$group[2] <- paste0(tmp$group[2], '.', targ_pop_i)
-
-        scores_i_j_multi_targ_pop_both <- rbind(
-          scores_i_j_multi_targ_pop_both,
-          tmp)
+      scores_i_j_pseudo <- scores_i_j_multi
+      scores_i_j_pseudo$group <- paste0(scores_i_j_multi$group, '.pseudo')
+      for(i in 1:nrow(scores_i_j_pseudo)){
+        if(grepl('UKB', scores_i_j_pseudo$name[i])){
+          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.EUR.pseudo', scores_i_j_pseudo$group[i])
+        }
+        if(grepl('BBJ', scores_i_j_pseudo$name[i])){
+          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.EAS.pseudo', scores_i_j_pseudo$group[i])
+        }
+        if(grepl('UGR', scores_i_j_pseudo$name[i])){
+          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.AFR.pseudo', scores_i_j_pseudo$group[i])
+        }
       }
+      scores_i_j_pseudo$predictor <- gsub('.profiles',
+                                            '.pseudo.profiles',
+                                            scores_i_j_pseudo$predictor)
+      
+      # Make a group for each GWAS and single source combo
+      scores_i_j_single <- scores_i_j_multi[!(scores_i_j_multi$method %in% pgs_group_methods),]
+      scores_i_j_single$group <-
+        paste0(scores_i_j_single$method,
+               '.',
+               gsub('.*_', '', scores_i_j_single$name))
+      scores_i_j_single$group <- gsub('UKB', 'EUR', scores_i_j_single$group)
+      scores_i_j_single$group <- gsub('BBJ', 'EAS', scores_i_j_single$group)
+      scores_i_j_single$group <- gsub('UGR', 'AFR', scores_i_j_single$group)
       
       predictors_i<- do.call(rbind, list(
-        scores_i_j_multi, scores_i_j_multi_targ_pop_both, scores_i_j_pseudo, scores_i_j_pseudo_disc_pop
+        scores_i_j_multi, scores_i_j_pseudo, scores_i_j_single
       ))
       
       predictors_i <- predictors_i[, c('predictor', 'group'), with=F]
@@ -1666,7 +1569,7 @@ for(trait_i in selected_traits){
           disc_pop_j_2,
           '/',
           trait_i,
-          '/predictor_list.tlprs.txt'
+          '/predictor_list.txt'
         ),
         col.names = T,
         row.names = F,
@@ -1677,7 +1580,6 @@ for(trait_i in selected_traits){
 }
 
 ```
-
 </details>
 
 ***
@@ -1698,7 +1600,7 @@ for targ_pop in $(echo EUR EAS AFR); do
   fi
   
   if [ "$targ_pop" == "EUR" ]; then
-    disc_pop=$(echo AFR EAS)
+    disc_pop=$(echo EAS AFR)
   fi
   
   if [ "$targ_pop" == "EAS" ]; then
@@ -1710,14 +1612,14 @@ for targ_pop in $(echo EUR EAS AFR); do
   fi
   
   for disc_pop_i in ${disc_pop}; do
-    for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
       sbatch --mem 20G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
         --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
-        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
-        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
+        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
+        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
         --n_core 10 \
         --top1 T \
-        --all_model F \
+        --all_model T \
         --assoc T"
     done
   done
@@ -1733,7 +1635,6 @@ done
 <details><summary>Show code</summary>
 
 ```{r}
-
 setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 
 library(data.table)
@@ -1744,19 +1645,7 @@ source('../functions/misc.R')
 source_all('../functions')
 
 # Read in list of outcomes 
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
-
-config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
-  
-# Define pgs_methods used
-pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
-
-# Define gwas_list used
-gwas_list<-read_param(config = config, param = 'gwas_list')
-
-# Define gwas_groups used
-gwas_groups<-read_param(config = config, param = 'gwas_groups')
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
 
 # Calculate corelation between all phenotypes in each target population
 cors <- list()
@@ -1767,7 +1656,7 @@ for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
     pop_i_2 <- pop_i
   }
   pheno_pop_i <- list()
-  for(pheno_i in prscsx_dat$labels){
+  for(pheno_i in selected_traits){
     pheno_pop_i[[pheno_i]] <- fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
     names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
   }
@@ -1779,10 +1668,10 @@ for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
 }
 
 # Read in results
-targ_pop = c('EUR','EAS','AFR')
-res <- list()
-for(pheno_i in prscsx_dat$labels){
-  res_i<-NULL
+targ_pop = c('EAS','AFR')
+res_eval <- list()
+for(pheno_i in selected_traits){
+  res_eval_i<-NULL
   for(targ_pop_i in targ_pop){
     if(targ_pop_i == 'EAS'){
       disc_pop <- 'EAS'
@@ -1804,44 +1693,42 @@ for(pheno_i in prscsx_dat$labels){
             disc_pop_i,
             '/',
             pheno_i,
-            '/res.tlprs.pred_eval.txt'
+            '/res.pred_eval.txt'
           )
         )
-      eval_i$target<-targ_pop_i
+      eval_i$Target<-targ_pop_i
       eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
-      res_i<-rbind(res_i, eval_i)
+      res_eval_i<-rbind(res_eval_i, eval_i)
     }
   }
   
-  res_i$Method<-sub('\\..*','',res_i$Group)
-  res_i$Method_short<-sub('.*_','',res_i$Method)
-  res_i<-res_i[order(res_i$Method_short, res_i$Method),]
+  res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
   
-  res_i$Model[grepl('pseudo', res_i$Group)]<-'Pseudo'
-  res_i$Model[grepl('top1', res_i$Group)]<-'Top1'
-  res_i$Model[!grepl('top1|pseudo', res_i$Group)]<-'Multi'
-  res_i$Model[!grepl('tlprs', res_i$Group) & !grepl('EUR|EAS|AFR', res_i$Group) & !grepl('top1', res_i$Group)]<-'Multi'
+  res_eval_i$Model[grepl('top1', res_eval_i$Group)]<-'Top1'
+  res_eval_i$Model[grepl('pseudo', res_eval_i$Group)]<-'Pseudo'
+  res_eval_i$Model[!grepl('top1|pseudo', res_eval_i$Group)]<-'Multi'
   
-  res_i$Source[!grepl('tlprs', res_i$Group)] <- 'Single'
-  res_i$Source[grepl('tlprs', res_i$Group)] <- 'Multi'
-  res_i$Source[!grepl('tlprs', res_i$Group) & !grepl('EUR|EAS|AFR', res_i$Group)] <- 'Multi'
+  res_eval_i$Source<-ifelse(res_eval_i$Method %in% c(pgs_group_methods, 'all') | !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
+         
+  res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
+  res_eval_i$Discovery[grepl('EAS', res_eval_i$Group)] <- 'EAS'
+  res_eval_i$Discovery[grepl('AFR', res_eval_i$Group)] <- 'AFR'
+  res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
   
-  res_i$Discovery <- res_i$gwas_group
-  res_i$Discovery[grepl('EUR', res_i$Group) & res_i$Source == 'Single'] <- 'EUR'
-  res_i$Discovery[grepl('EAS', res_i$Group) & res_i$Source == 'Single'] <- 'EAS'
-  res_i$Discovery[grepl('AFR', res_i$Group) & res_i$Source == 'Single'] <- 'AFR'
+  res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('Top1','Pseudo','Multi'))
+  res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-  res_i$Method <- gsub('tlprs_','tlprs - ', res_i$Method)
-  res_i$Method[grepl('tlprs', res_i$Method) & grepl('EUR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('EUR', res_i$Group)], " (EUR)")
-  res_i$Method[grepl('tlprs', res_i$Method) & grepl('EAS', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('EAS', res_i$Group)], " (EAS)")
-  res_i$Method[grepl('tlprs', res_i$Method) & grepl('AFR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('AFR', res_i$Group)], " (AFR)")
-  res_i$Method[grepl('tlprs', res_i$Method) & !grepl('EUR|EAS|AFR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & !grepl('EUR|EAS|AFR', res_i$Group)], " (Both)")
-
-  res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
-  res_i$Model<-factor(res_i$Model, levels=c('Top1','Pseudo','Multi'))
-  res_i$Discovery<-factor(res_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+  # Remove Multi model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('sbayesrc', 'quickprs') & 
+      res_eval_i$Model == 'Multi' & 
+      res_eval_i$Source == 'Single'),]
+  
+  # Remove pseudo model for methods that don't really have one (single source combo)
+  res_eval_i <- res_eval_i[!which(res_eval_i$Model == 'Pseudo' & res_eval_i$Method == 'ptclump'),]
 
-  res[[pheno_i]]<-res_i
+  res_eval[[pheno_i]]<-res_eval_i
   
 }
 
@@ -1852,7 +1739,7 @@ for(pheno_i in prscsx_dat$labels){
 library(MAd)
 
 # Average R across phenotypes
-meta_res <- NULL
+meta_res_eval <- NULL
 for(targ_pop_i in targ_pop){
   if(targ_pop_i == 'EAS'){
     disc_pop <- 'EAS'
@@ -1865,23 +1752,37 @@ for(targ_pop_i in targ_pop){
   }
   for(disc_pop_i in disc_pop){
   
-    # Subset res for each scenario
-    res_i <- do.call(rbind, lapply(seq_along(res), function(i) {
-      x <- res[[i]]
-      x$pheno <- names(res)[i]
-      x <- x[x$target == targ_pop_i]
+    # Subset res_eval for each scenario
+    res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x <- res_eval[[i]]
+      x$pheno <- names(res_eval)[i]
+      x <- x[x$Target == targ_pop_i]
       x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
     }))
     
-    # Average results for each test across phenotypes
+    # Average res_evalults for each test across phenotypes
     # Use MAd to account for correlation between them
-    res_i$Sample<-'A'
+    res_eval_i$Sample<-'A'
   
-    for(group_i in unique(res_i$Group)){
-      res_group_i <- res_i[res_i$Group == group_i,]
-      cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno <-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
       
-      meta_res_i <-
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          'res_evalults missing for ',
+          targ_pop_i,
+          ' ',
+          group_i,
+          ' ',
+          paste0(missing_pheno, collapse = ' ')
+        ))
+      }
+      
+      cors_i <- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i <-
         agg(
           id = Sample,
           es = R,
@@ -1889,43 +1790,42 @@ for(targ_pop_i in targ_pop){
           cor = cors_i,
           method = "BHHR",
           mod = NULL,
-          data = res_group_i
+          data = res_eval_group_i
         )
       
-      meta_res <- rbind(meta_res,
-                        data.table(
-                          Group = group_i,
-                          Method = res_group_i$Method[1],
-                          Model = res_group_i$Model[1],
-                          Source = res_group_i$Source[1],
-                          Discovery = res_group_i$Discovery[1],
-                          gwas_group = res_group_i$gwas_group[1],
-                          Target = targ_pop_i,
-                          R = meta_res_i$es,
-                          SE = sqrt(meta_res_i$var)
-                        ))
+      tmp <- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval <- rbind(meta_res_eval, tmp)
     }
   }
 }
 
-meta_res$Model<-factor(meta_res$Model, levels=c('Top1','Pseudo','Multi'))
-meta_res$Discovery<-factor(meta_res$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('Top1','Pseudo','Multi'))
+meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+meta_res_eval$R_diff<-NA
+meta_res_eval$reference_point<-F
+for(targ_pop_i in targ_pop){
+  # Calculate R compared to top1 model where GWAS matches target population
+  meta_res_eval$R_diff[meta_res_eval$Target == targ_pop_i] <- meta_res_eval$R[meta_res_eval$Target == targ_pop_i] - meta_res_eval$R[meta_res_eval$Target == targ_pop_i & meta_res_eval$Discovery == meta_res_eval$Target & meta_res_eval$Model == 'Top1']
+  meta_res_eval$reference_point[meta_res_eval$Target == targ_pop_i & meta_res_eval$Discovery == meta_res_eval$Target & meta_res_eval$Model == 'Top1'] <- T 
+}
 
 ####
-# Compare TLPRS to unadjusted PGS
+# Comparison of models across methods, across target and discovery populations
 ####
 
-meta_res_multi_pop <- meta_res[!(meta_res$Discovery %in% c('EUR','EAS','AFR')),]
-meta_res_multi_pop$original_method <- gsub(' .*', '', gsub('tlprs - ', '', meta_res_multi_pop$Method))
-meta_res_multi_pop$test[!grepl('tlprs', meta_res_multi_pop$Method)] <- 'Unadjusted'
-meta_res_multi_pop$test[grepl('tlprs', meta_res_multi_pop$Method)] <- gsub('.* ', 'TLPRS ', meta_res_multi_pop$Method[grepl('tlprs', meta_res_multi_pop$Method)])
-meta_res_multi_pop$test <- factor(meta_res_multi_pop$test, levels=unique(meta_res_multi_pop$test))
-meta_res_multi_pop$test <- gsub('AFR', 'target pop', meta_res_multi_pop$test)
-meta_res_multi_pop$test <- gsub('EAS', 'target pop', meta_res_multi_pop$test)
-
-tmp <- meta_res_multi_pop[meta_res_multi_pop$Target %in% c('EAS', 'AFR'),]
+tmp <- meta_res_eval
 
-ggplot(tmp, aes(x=test, y=R , fill = Model)) +
+ggplot(tmp, aes(x=Method, y=R , fill = Model)) +
     #geom_hline(yintercept = 0) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
@@ -1933,26 +1833,7 @@ ggplot(tmp, aes(x=test, y=R , fill = Model)) +
     geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
     geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
     labs(y = "R (SE)") +
-    facet_grid(Target ~ original_method, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-
-tmp <- meta_res_multi_pop[meta_res_multi_pop$Target %in% c('EAS', 'AFR'),]
-tmp <- tmp[grepl('Unadj|Both', tmp$test),]
-tmp$test <- gsub(' .*', '', tmp$test)
-tmp$test <- factor(tmp$test, levels=c('Unadjusted','TLPRS'))
-
-ggplot(tmp, aes(x=test, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
-    labs(y = "R (SE)", x = NULL) +
-    facet_grid(Target ~ original_method, scales='free', space = 'free_x') +
+    facet_grid(Target ~ Discovery, scales='free', space = 'free_x') +
     theme_half_open() +
     background_grid() + 
     panel_border() + 
@@ -1962,11 +1843,30 @@ ggplot(tmp, aes(x=test, y=R , fill = Model)) +
 # Create heatmap showing difference between all methods and models
 ####
 
+# Create a function to mirror pred_comp results
+mirror_comp<-function(x){
+  x_sym <- x
+  x_sym$Model_1 <- x$Model_2
+  x_sym$Model_2 <- x$Model_1
+  x_sym$R_diff <- -x_sym$R_diff
+  x_mirrored <- rbind(x, x_sym)
+  x_diag<-data.frame(
+      Model_1=unique(x_mirrored$Model_1),
+      Model_2=unique(x_mirrored$Model_1),
+      Model_1_R=x_mirrored$Model_1_R,
+      Model_2_R=x_mirrored$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp<-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
 # Read in results
 targ_pop=c('EUR','EAS','AFR')
-res <- list()
-for(pheno_i in prscsx_dat$labels){
-  res_i<-NULL
+res_comp <- list()
+for(pheno_i in selected_traits){
+  res_comp_i<-NULL
   for(targ_pop_i in targ_pop){
     if(targ_pop_i == 'EAS'){
       disc_pop <- 'EAS'
@@ -1978,7 +1878,7 @@ for(pheno_i in prscsx_dat$labels){
       disc_pop <- c('EAS','AFR')
     }
     for(disc_pop_i in disc_pop){
-      eval_i <-
+      comp_i <-
         fread(
           paste0(
             '/users/k1806347/oliverpainfel/Analyses/crosspop/',
@@ -1988,108 +1888,76 @@ for(pheno_i in prscsx_dat$labels){
             disc_pop_i,
             '/',
             pheno_i,
-            '/res.tlprs.pred_comp.txt'
+            '/res.pred_comp.txt'
           )
         )
-      eval_i$target<-targ_pop_i
-      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
-      res_i<-rbind(res_i, eval_i)
+      comp_i<-mirror_comp(comp_i)
+      comp_i$Target<-targ_pop_i
+      comp_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_comp_i<-rbind(res_comp_i, comp_i)
     }
   }
   
-  res[[pheno_i]]<-res_i
+  res_comp[[pheno_i]]<-res_comp_i
 }
 
-#####
-# Create a plot showing relative R from TLPRS vs Unadjusted
-#####
-
-res_all <- do.call(rbind, lapply(names(res), function(name) {
-  x <- res[[name]]
+res_comp_all <- do.call(rbind, lapply(names(res_comp), function(name) {
+  x <- res_comp[[name]]
   x$pheno <- name  # Add a new column with the name of the element
   x  # Return the updated dataframe
 }))
 
-# Mirror results to fill in gaps
-res_all_symmetric <- res_all
-res_all_mirrored <- res_all
-res_all_mirrored$Model_1 <- res_all$Model_2
-res_all_mirrored$Model_2 <- res_all$Model_1
-res_all_mirrored$Model_1_R <- res_all$Model_2_R
-res_all_mirrored$Model_2_R <- res_all$Model_1_R
-res_all_mirrored$R_diff <- -res_all_mirrored$R_diff
-res_all <- rbind(res_all_symmetric, res_all_mirrored)
-
-# Subset tests where top1 TLPRS is being compared to top1 unadjusted
-res_all$Method_1 <- gsub('.*_', '', gsub('\\..*', '', res_all$Model_1))
-res_all$Method_2 <- gsub('.*_', '', gsub('\\..*', '', res_all$Model_2))
-
-res_all$TLPRS_1 <- grepl('tlprs', res_all$Model_1)
-res_all$TLPRS_2 <- grepl('tlprs', res_all$Model_2)
-
-res_all$Test_1[grepl('pseudo', res_all$Model_1)]<-'Pseudo'
-res_all$Test_1[grepl('top1', res_all$Model_1)]<-'Top1'
-res_all$Test_1[!grepl('top1|pseudo', res_all$Model_1)]<-'Multi'
-res_all$Test_1[!grepl('tlprs', res_all$Model_1) & !grepl('EUR|EAS|AFR', res_all$Model_1) & !grepl('top1', res_all$Model_1)]<-'Multi'
-
-res_all$Test_2[grepl('pseudo', res_all$Model_2)]<-'Pseudo'
-res_all$Test_2[grepl('top1', res_all$Model_2)]<-'Top1'
-res_all$Test_2[!grepl('top1|pseudo', res_all$Model_2)]<-'Multi'
-res_all$Test_2[!grepl('tlprs', res_all$Model_2) & !grepl('EUR|EAS|AFR', res_all$Model_2) & !grepl('top1', res_all$Model_2)]<-'Multi'
-
-res_all$Source_1[!grepl('tlprs', res_all$Model_1)] <- 'Single'
-res_all$Source_1[grepl('tlprs', res_all$Model_1)] <- 'Multi'
-res_all$Source_1[!grepl('tlprs', res_all$Model_1) & !grepl('EUR|EAS|AFR', res_all$Model_1)] <- 'Multi'
-
-res_all$Source_2[!grepl('tlprs', res_all$Model_2)] <- 'Single'
-res_all$Source_2[grepl('tlprs', res_all$Model_2)] <- 'Multi'
-res_all$Source_2[!grepl('tlprs', res_all$Model_2) & !grepl('EUR|EAS|AFR', res_all$Model_2)] <- 'Multi'
-
-res_all$Discovery_1 <- res_all$gwas_group
-res_all$Discovery_1[grepl('EUR', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'EUR'
-res_all$Discovery_1[grepl('EAS', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'EAS'
-res_all$Discovery_1[grepl('AFR', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'AFR'
-
-res_all$Discovery_2 <- res_all$gwas_group
-res_all$Discovery_2[grepl('EUR', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'EUR'
-res_all$Discovery_2[grepl('EAS', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'EAS'
-res_all$Discovery_2[grepl('AFR', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'AFR'
-
-res_all$TLPRS_target_1[grepl('EUR', res_all$Model_1) & res_all$TLPRS_1] <- 'EUR'
-res_all$TLPRS_target_1[grepl('EAS', res_all$Model_1) & res_all$TLPRS_1] <- 'EAS'
-res_all$TLPRS_target_1[grepl('AFR', res_all$Model_1) & res_all$TLPRS_1] <- 'AFR'
-res_all$TLPRS_target_1[!grepl('EUR|AFR|EAS', res_all$Model_1) & res_all$TLPRS_1] <- 'Both'
-res_all$TLPRS_target_1[res_all$TLPRS_target_1 == res_all$target] <- 'Target Pop.'
-
-res_all$TLPRS_target_2[grepl('EUR', res_all$Model_2) & res_all$TLPRS_2] <- 'EUR'
-res_all$TLPRS_target_2[grepl('EAS', res_all$Model_2) & res_all$TLPRS_2] <- 'EAS'
-res_all$TLPRS_target_2[grepl('AFR', res_all$Model_2) & res_all$TLPRS_2] <- 'AFR'
-res_all$TLPRS_target_2[!grepl('EUR|AFR|EAS', res_all$Model_2) & res_all$TLPRS_2] <- 'Both'
-res_all$TLPRS_target_2[res_all$TLPRS_target_2 == res_all$target] <- 'Target Pop.'
-
-# Subset to tests comparing to the Unadjusted models
-res_all <- res_all[res_all$Method_1 == res_all$Method_2, ]
-res_all <- res_all[res_all$Source_2 == 'Multi', ]
-res_all <- res_all[res_all$Test_1  == res_all$Test_2, ]
-res_all <- res_all[res_all$TLPRS_2  == F, ]
-res_all <- res_all[res_all$target %in% c('EAS', 'AFR'),]
+# Annotate tests to get order correct
+res_comp_all$Method1<-sub('\\..*','',res_comp_all$Model_1)
+res_comp_all$Method2<-sub('\\..*','',res_comp_all$Model_2)
+
+find_model<-function(x){
+  mod <- x
+  mod[grepl('top1', x)]<-'Top1'
+  mod[grepl('pseudo', x)]<-'Pseudo'
+  mod[!grepl('top1|pseudo', x)]<-'Multi'
+  return(mod)
+}
 
-ggplot(res_all, aes(x = Method_2, y = R_diff, colour = Test_1)) +
-  geom_point(position=position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7), alpha=0.5) +
-  labs(y = "Difference in\nCorrelations (SE)", x = '') +
-  theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  background_grid(major = 'y', minor = 'y') +
-  facet_grid(target ~ TLPRS_target_1, scales='free', space = 'free_x')
+res_comp_all$Model1<-find_model(res_comp_all$Model_1)
+res_comp_all$Model2<-find_model(res_comp_all$Model_2)
 
+res_comp_all$Source1<-ifelse(res_comp_all$Method1 %in% c(pgs_group_methods, 'all') | !grepl('AFR|EAS|EUR', res_comp_all$Model_1), 'Multi', 'Single')
+res_comp_all$Source2<-ifelse(res_comp_all$Method2 %in% c(pgs_group_methods, 'all') | !grepl('AFR|EAS|EUR', res_comp_all$Model_2), 'Multi', 'Single')
 
-######
-# Average R across phenotypes
-######
+for(i in c('EUR','EAS','AFR')){
+  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] <- i
+  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] <- i
+}
+res_comp_all$Discovery1[res_comp_all$Source1 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source1 == 'Multi']
+res_comp_all$Discovery2[res_comp_all$Source2 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source2 == 'Multi']
+
+res_comp_all$Method1<-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
+res_comp_all$Method2<-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
+res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('Top1','Pseudo','Multi'))
+res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('Top1','Pseudo','Multi'))
+res_comp_all$Discovery1<-factor(res_comp_all$Discovery1, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
+res_comp_all$Discovery2<-factor(res_comp_all$Discovery2, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+# Remove Multi model for groups that contain one score (aka QuickPRS and SBayesRC)
+res_comp_all <- res_comp_all[
+  !(res_comp_all$Method1 %in%  c('sbayesrc', 'quickprs') & 
+    res_comp_all$Model1 == 'Multi' & 
+    res_comp_all$Source1 == 'Single'),]
+
+res_comp_all <- res_comp_all[
+  !(res_comp_all$Method2 %in%  c('sbayesrc', 'quickprs') & 
+    res_comp_all$Model2 == 'Multi' & 
+    res_comp_all$Source2 == 'Single'),]
+
+# Remove pseudo model for methods that don't really have one (single source combo)
+res_comp_all <- res_comp_all[!which(res_comp_all$Model1 == 'Pseudo' & res_comp_all$Method1 == 'ptclump'),]
+res_comp_all <- res_comp_all[!which(res_comp_all$Model2 == 'Pseudo' & res_comp_all$Method2 == 'ptclump'),]
 
 library(MAd)
 
-meta_res <- NULL
+# Average R across phenotypes
+meta_res_comp <- NULL
 for(targ_pop_i in targ_pop){
   if(targ_pop_i == 'EAS'){
     disc_pop <- 'EAS'
@@ -2102,161 +1970,197 @@ for(targ_pop_i in targ_pop){
   }
   for(disc_pop_i in disc_pop){
   
-    # Subset res for each scenario
-    res_i <- res_all[res_all$target == targ_pop_i & res_all$gwas_group == paste0('EUR+', disc_pop_i), ]
+    # Subset res_comp for each scenario
+    res_comp_i <- res_comp_all[res_comp_all$Target == targ_pop_i & res_comp_all$gwas_group == paste0('EUR+', disc_pop_i)]
   
     # Calculate diff SE based on p-value
-    res_i$R_diff_pval[res_i$R_diff == 0] <- 1-0.001
-    res_i$R_diff_pval[res_i$R_diff_pval == 1]<-1-0.001
-    res_i$R_diff_z<-qnorm(res_i$R_diff_pval/2)
-    res_i$R_diff_SE<-abs(res_i$R_diff/res_i$R_diff_z)
+    res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] <- 1-0.001
+    res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]<-1-0.001
+    res_comp_i$R_diff_z<-qnorm(res_comp_i$R_diff_pval/2)
+    res_comp_i$R_diff_SE<-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
         
     # Average results for each test across phenotypes
     # Use MAd to account for correlation between them
-    res_i$Sample<-'A'
-    res_i$Group <- paste0(res_i$Model_1, '_vs_', res_i$Model_2)
+    res_comp_i$Sample<-'A'
+    res_comp_i$Group <- paste0(res_comp_i$Model_1, '_vs_', res_comp_i$Model_2)
   
-    for(group_i in unique(res_i$Group)){
-      res_group_i <- res_i[res_i$Group == group_i,]
-      cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
-      
-      meta_res_i <-
-        agg(
-          id = Sample,
-          es = R_diff,
-          var = R_diff_SE ^ 2,
-          cor = cors_i,
-          method = "BHHR",
-          mod = NULL,
-          data = res_group_i
-        )
+    for(group_i in unique(res_comp_i$Group)){
+      res_comp_group_i <- res_comp_i[res_comp_i$Group == group_i,]
+      cors_i <- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
       
-      group_info <- res_group_i[1, !(names(res_group_i[1,]) %in% c('Model_1_R', 'Model_2_R', 'R_diff', 'R_diff_pval', 'R_diff_z', 'R_diff_SE')), with=F]
-      meta_res <- rbind(meta_res,
-                        data.table(
-                          group_info,
-                          R_diff = meta_res_i$es,
-                          R_diff_SE = sqrt(meta_res_i$var)
-                        ))
+      if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
+        
+        meta_res_comp_i <-
+          agg(
+            id = Sample,
+            es = R_diff,
+            var = R_diff_SE ^ 2,
+            cor = cors_i,
+            method = "BHHR",
+            mod = NULL,
+            data = res_comp_group_i
+          )
+        
+        tmp <- res_comp_group_i[1,]
+        tmp$pheno <- NULL
+        tmp$R_diff <- meta_res_comp_i$es
+        tmp$R_diff_SE <- sqrt(meta_res_comp_i$var)
+        tmp$R_diff_z <- tmp$R_diff / tmp$R_diff_SE
+        tmp$R_diff_p <- 2*pnorm(-abs(tmp$R_diff_z))
+      } else {
+        tmp <- res_comp_group_i[1,]
+        tmp$pheno <- NULL
+        tmp$R_diff <- NA
+        tmp$R_diff_SE <- NA
+        tmp$R_diff_z <- NA
+        tmp$R_diff_p <- NA
+      }
+      meta_res_comp <- rbind(meta_res_comp, tmp)
     }
   }
 }
-meta_res$R_diff_z <- meta_res$R_diff / meta_res$R_diff_SE
-meta_res$R_diff_p <- 2*pnorm(-abs(meta_res$R_diff_z))
 
-# Plot the results
-ggplot(meta_res, aes(x=Method_1, y=R_diff, fill=Test_1)) +
-  geom_hline(yintercept = 0, colour = 'darkgrey') +
-  geom_point(
-    data = res_all,
-    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
-    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
-    alpha = 0.3
-  ) +
-  geom_errorbar(
-    aes(
-      ymin = R_diff - R_diff_SE,
-      ymax = R_diff + R_diff_SE
-    ),
-    width = 0,
-    position = position_dodge(width = 0.7)
-  ) +
-  geom_point(
-    stat = "identity",
-    position = position_dodge(0.7),
-    size = 2,
-    shape = 23,
-    colour = 'black'
-  ) +
-  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
-  theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  geom_vline(xintercept = 1.5, linetype = "dotted") +
-  geom_vline(xintercept = 2.5, linetype = "dotted") +
-  geom_vline(xintercept = 3.5, linetype = "dotted") +
-  background_grid(major = 'y', minor = 'y') +
-  facet_grid(target ~ TLPRS_target_1, scales='free', space = 'free_x') +
-  panel_border()
+# Group differences
+meta_res_comp$R_diff_catagory <- cut(
+    meta_res_comp$R_diff,
+    breaks = c(-Inf, -0.08, -0.025, -0.002, 0.002, 0.025, 0.08, Inf),
+    labels = c('< -0.08', '-0.08 - -0.025', '-0.025 - -0.002', '-0.002 - 0.002', '0.002 - 0.025', '0.025 - 0.08', '> 0.08'),
+    right = FALSE
+)
+meta_res_comp$R_diff_catagory <- factor(meta_res_comp$R_diff_catagory, levels = rev(levels(meta_res_comp$R_diff_catagory)))
 
+# Assign significance stars
+meta_res_comp$indep_star<-' '
+meta_res_comp$indep_star[meta_res_comp$R_diff_p < 0.05]<-'*'
+meta_res_comp$indep_star[meta_res_comp$R_diff_p < 1e-3]<-'**'
+meta_res_comp$indep_star[meta_res_comp$R_diff_p < 1e-6]<-'***'
 
+meta_res_comp<-meta_res_comp[order(meta_res_comp$Discovery1, meta_res_comp$Discovery2, meta_res_comp$Method1),]
 
-# Plot the results
-ggplot(meta_res[meta_res$TLPRS_target_1 == 'Both',], aes(x=Method_1, y=R_diff, fill=Test_1)) +
-  geom_hline(yintercept = 0, colour = 'darkgrey') +
-  geom_point(
-    data = res_all[res_all$TLPRS_target_1 == 'Both',],
-    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
-    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
-    alpha = 0.3
-  ) +
-  geom_errorbar(
-    aes(
-      ymin = R_diff - R_diff_SE,
-      ymax = R_diff + R_diff_SE
-    ),
-    width = 0,
-    position = position_dodge(width = 0.7)
-  ) +
-  geom_point(
-    stat = "identity",
-    position = position_dodge(0.7),
-    size = 2,
-    shape = 23,
-    colour = 'black'
-  ) +
-  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
-  theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  geom_vline(xintercept = 1.5, linetype = "dotted") +
-  geom_vline(xintercept = 2.5, linetype = "dotted") +
-  geom_vline(xintercept = 3.5, linetype = "dotted") +
-  background_grid(major = 'y', minor = 'y') +
-  facet_grid(target ~ ., scales='free', space = 'free_x') +
-  panel_border()
-
-
-# Plot the results
-ggplot(meta_res[meta_res$TLPRS_target_1 == 'Both',], aes(x=Method_1, y=R_diff, fill=Test_1)) +
-  geom_hline(yintercept = 0, colour = 'darkgrey') +
-  geom_point(
-    data = res_all[res_all$TLPRS_target_1 == 'Both',],
-    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
-    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
-    alpha = 0.3
-  ) +
-  geom_errorbar(
-    aes(
-      ymin = R_diff - R_diff_SE,
-      ymax = R_diff + R_diff_SE
-    ),
-    width = 0,
-    position = position_dodge(width = 0.7)
-  ) +
-  geom_point(
-    stat = "identity",
-    position = position_dodge(0.7),
-    size = 2,
-    shape = 23,
-    colour = 'black'
-  ) +
-  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
-  theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  geom_vline(xintercept = 1.5, linetype = "dotted") +
-  geom_vline(xintercept = 2.5, linetype = "dotted") +
-  geom_vline(xintercept = 3.5, linetype = "dotted") +
-  background_grid(major = 'y', minor = 'y') +
-  facet_grid(target ~ ., scales='free', space = 'free_x') +
-  panel_border()
+heatmap_list<-NULL
+for(model_i in c('Top1','Pseudo','Multi')){
+  heatmap_list[[model_i]]<-list()
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    heatmap_list[[model_i]][[targ_pop_i]]<-list()
+    for(disc_pop_i in disc_pop){
+  
+      tmp <- meta_res_comp[meta_res_comp$Target == targ_pop_i, ]
+      tmp <- tmp[tmp$gwas_group == paste0('EUR+', disc_pop_i), ]
+      tmp <- tmp[tmp$Model1 == model_i & tmp$Model2 == model_i,]
+      tmp$Model_1<-gsub('\\..*','', tmp$Model_1)
+      tmp$Model_2<-gsub('\\..*','', tmp$Model_2)
+      
+      tmp$Model_1 <- factor(
+        tmp$Model_1, 
+        levels=unique(tmp$Model_1))
+      tmp$Model_2 <- factor(
+        tmp$Model_2, 
+        levels=unique(tmp$Model_1))
+      
+      heatmap_list[[model_i]][[targ_pop_i]][[disc_pop_i]] <-
+        ggplot(data = tmp, aes(Model_2, Model_1, fill = R_diff_catagory)) +
+        geom_tile(color = "white", show.legend = TRUE) +
+        labs(y = 'Test', x = 'Comparison', fill = 'R difference', title = paste0('Target: ', targ_pop_i, ', Model = ', model_i)) +
+        facet_grid(Discovery1 ~ Discovery2, scales = 'free', space = 'free', switch="both") +
+        geom_text(
+          data = tmp,
+          aes(Model_2, Model_1, label = indep_star),
+          color = "black",
+          size = 4,
+          angle = 0,
+          vjust = 0.8
+        ) +
+        scale_fill_brewer(
+          breaks = levels(tmp$R_diff_catagory),
+          palette = "RdBu",
+          drop = F,
+          na.value = 'grey'
+        ) +
+        theme_half_open() +
+        background_grid() +
+        panel_border() +
+        theme(axis.text.x = element_text(
+          angle = 45,
+          vjust = 1,
+          hjust = 1
+        ))
+    }
+  }
+}
 
+####
+# Plot R compared to top1 model where GWAS matches target population
+####
+meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Model2 == 'Top1' & meta_res_comp$Method2 == 'ptclump' & meta_res_comp$Discovery2 == meta_res_comp$Target,]
+meta_res_comp_ptclump_top1$reference_point<-F
+meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Model1 == 'Top1' & meta_res_comp_ptclump_top1$Method1 == 'ptclump' & meta_res_comp_ptclump_top1$Discovery1 == meta_res_comp_ptclump_top1$Target]<-T
+meta_res_comp_ptclump_top1$R_diff[is.na(meta_res_comp_ptclump_top1$R_diff)]<-0
+meta_res_comp_ptclump_top1$Discovery1 <- factor(meta_res_comp_ptclump_top1$Discovery1, levels=rev(levels(meta_res_comp_ptclump_top1$Discovery1)))
+
+res_comp_all_ptclump_top1<-res_comp_all[res_comp_all$Model2 == 'Top1' & res_comp_all$Method2 == 'ptclump' & res_comp_all$Discovery2 == res_comp_all$Target,]
+res_comp_all_ptclump_top1$Discovery1 <-  factor(res_comp_all_ptclump_top1$Discovery1, levels=levels(meta_res_comp_ptclump_top1$Discovery1))
+
+# Create data to plot reference points
+meta_res_comp_reference <- meta_res_comp_ptclump_top1
+meta_res_comp_reference$R_diff[meta_res_comp_ptclump_top1$reference_point == F] <- NA
+meta_res_comp_reference$R_diff_SE [meta_res_comp_ptclump_top1$reference_point == F] <- NA
+res_comp_all_ptclump_top1$reference_point<-F
+
+ggplot(meta_res_comp_ptclump_top1, aes(x=Method1, y=R_diff , fill = Model1)) +
+    geom_point(
+        data = res_comp_all_ptclump_top1,
+        mapping = aes(x=Method1, y=R_diff, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff - R_diff_SE,
+          ymax = R_diff + R_diff_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_res_comp_reference,
+        aes(x = Method1, y = R_diff, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method1))), linetype="dotted") +
+    labs(y = "R_diff (SE)") +
+    facet_grid(Target ~ Discovery1, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
 
 ```
 </details>
 
 ***
 
-## Compare all methods
-
+## Evaluate TLPRS
 ### Create predictor lists
 
 <details><summary>Show code</summary>
@@ -2270,7 +2174,6 @@ library(data.table)
 
 # Get some key variables from config
 config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
-pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
@@ -2279,14 +2182,14 @@ selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_su
 # Get a list of score files
 scores <- list_score_files(config)
 
-# Remove tlprs
-scores<-scores[!grepl('tlprs', scores$method),]
+# Subset to TLPRS scores and pseudo scores for corresponding methods
+pgs_methods <- unique(scores$method[grepl('^tlprs', scores$method)])
+pgs_methods <- c(pgs_methods, gsub('tlprs_','',pgs_methods))
+scores <- scores[scores$method %in% pgs_methods,]
 
 # Create files for EAS and AFR targets
 targ_pop <- c('EUR','EAS','AFR')
 for(trait_i in selected_traits){
-  # Make a group containing both GWAS for each single source method
-  # Make a group for each multisource method
   scores_i <- scores[grepl(trait_i, scores$name),]
   scores_i$group <- scores_i$method
   
@@ -2322,83 +2225,180 @@ for(trait_i in selected_traits){
         recursive = T
       )
       
-      scores_i_j <- scores_i[grepl('UKB$', scores_i$name, ignore.case = F) |
-                       grepl(paste0(disc_pop_j, '$'), scores_i$name, ignore.case = T),]
+      scores_i_j <- scores_i[grepl('UKB', scores_i$name, ignore.case = F) |
+                             grepl(disc_pop_j, scores_i$name, ignore.case = T),]
+      
+      # Insert the pseudo score for the non-TLPRS methods
+      scores_i_j_pseudo <- scores_i_j[which(!grepl('^tlprs', scores_i_j$method)), ]
+      scores_i_j_pseudo$group <- paste0(scores_i_j_pseudo$group,'.pseudo')
 
-      scores_i_j_multi <- scores_i_j
-      # Insert path to score file
-      scores_i_j_multi$predictor <- paste0(
+      scores_i_j_pseudo$predictor <- paste0(
         outdir,
         '/ukb/pgs/',
         targ_pop_i,
         '/',
-        scores_i_j_multi$method,
+        scores_i_j_pseudo$method,
         '/',
-        scores_i_j_multi$name,
+        scores_i_j_pseudo$name,
         '/ukb-',
-        scores_i_j_multi$name,
+        scores_i_j_pseudo$name,
         '-',
         targ_pop_i,
-        '.profiles'
+        '.pseudo.profiles'
       )
       
-      # Extract the pseudo score for each method and specify as a seperate group
-      for(i in 1:nrow(scores_i_j)) {
+      for(i in 1:nrow(scores_i_j_pseudo)) {
+        tmp <- scores_i_j_pseudo[i,]
         param <- find_pseudo(
           config = config,
-          gwas = scores_i_j$name[i],
-          pgs_method = scores_i_j$method[i],
+          gwas = tmp$name,
+          pgs_method = tmp$method,
           target_pop = targ_pop_i
         )
         
         score_header <-
-          fread(scores_i_j_multi$predictor[i], nrows = 1)
+          fread(gsub('.pseudo', '', tmp$predictor), nrows = 1)
         score_cols <-
-          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
+          which(names(score_header) %in% c('FID', 'IID', paste0(tmp$name, '_', param)))
         
         system(
           paste0(
             "cut -d' ' -f ", 
             paste0(score_cols, collapse=','),
             " ", 
-            scores_i_j_multi$predictor[i], 
+            gsub('.pseudo', '', tmp$predictor), 
+            " > ", tmp$predictor
+          )
+        )
+      }
+      
+      # Create disc_pop specific groups
+      scores_i_j_pseudo_disc_pop <- scores_i_j_pseudo
+      scores_i_j_pseudo_disc_pop$group[grepl('UKB', scores_i_j_pseudo_disc_pop$name)] <- paste0(scores_i_j_pseudo_disc_pop$group[grepl('UKB', scores_i_j_pseudo_disc_pop$name)], '.EUR')
+      scores_i_j_pseudo_disc_pop$group[!grepl('UKB', scores_i_j_pseudo_disc_pop$name)] <- paste0(scores_i_j_pseudo_disc_pop$group[!grepl('UKB', scores_i_j_pseudo_disc_pop$name)], '.', disc_pop_j_2)
+      
+      # Insert groups for TLPRS scores for both target populations, and target specific
+      scores_i_j_multi <- scores_i_j[which(grepl('^tlprs', scores_i_j$method)), ]
+      
+      # Insert path to score file
+      scores_i_j_multi$predictor <- paste0(
+        outdir,
+        '/ukb/pgs/',
+        targ_pop_i,
+        '/',
+        scores_i_j_multi$method,
+        '/',
+        scores_i_j_multi$name,
+        '/ukb-',
+        scores_i_j_multi$name,
+        '-',
+        targ_pop_i,
+        '.profiles'
+      )
+      
+      scores_i_j_multi_targ_pop <- scores_i_j_multi
+      scores_i_j_multi_targ_pop_both<-NULL
+      for(i in 1:nrow(scores_i_j_multi_targ_pop)){
+        score_header <-
+          fread(gsub('.pseudo', '', scores_i_j_multi_targ_pop$predictor[i]), nrows = 1)
+        score_cols_EUR <-
+          which(names(score_header) %in% c('FID', 'IID', names(score_header)[grepl('targ_EUR', names(score_header))]))
+        score_cols_targ <-
+          which(names(score_header) %in% c('FID', 'IID', names(score_header)[grepl(paste0('targ_', targ_pop_i), names(score_header))]))
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols_EUR, collapse=','),
+            " ", 
+            scores_i_j_multi_targ_pop$predictor[i], 
             " > ", 
-            gsub('.profiles',
-                    '.pseudo.profiles',
-                    scores_i_j_multi$predictor[i])
+            paste0(
+              outdir,
+              '/ukb/pgs/',
+              targ_pop_i,
+              '/',
+              scores_i_j_multi_targ_pop$method[i],
+              '/',
+              scores_i_j_multi_targ_pop$name[i],
+              '/ukb-',
+              scores_i_j_multi_targ_pop$name[i],
+              '-',
+              targ_pop_i,
+              '.targ_EUR.profiles'
+            )
+          )
+        )
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols_targ, collapse=','),
+            " ", 
+            scores_i_j_multi_targ_pop$predictor[i], 
+            " > ", 
+            paste0(
+              outdir,
+              '/ukb/pgs/',
+              targ_pop_i,
+              '/',
+              scores_i_j_multi_targ_pop$method[i],
+              '/',
+              scores_i_j_multi_targ_pop$name[i],
+              '/ukb-',
+              scores_i_j_multi_targ_pop$name[i],
+              '-',
+              targ_pop_i,
+              '.targ_',
+              targ_pop_i,
+              '.profiles'
+            )
+          )
+        )
+        
+        tmp<-scores_i_j_multi_targ_pop[i,]
+        tmp <- rbind(tmp, tmp)
+        tmp$predictor[1] <- paste0(
+            outdir,
+            '/ukb/pgs/',
+            targ_pop_i,
+            '/',
+            scores_i_j_multi_targ_pop$method[i],
+            '/',
+            scores_i_j_multi_targ_pop$name[i],
+            '/ukb-',
+            scores_i_j_multi_targ_pop$name[i],
+            '-',
+            targ_pop_i,
+            '.targ_EUR.profiles'
+          )
+        tmp$group[1] <- paste0(tmp$group[1], '.EUR')
+        
+        tmp$predictor[2] <- paste0(
+            outdir,
+            '/ukb/pgs/',
+            targ_pop_i,
+            '/',
+            scores_i_j_multi_targ_pop$method[i],
+            '/',
+            scores_i_j_multi_targ_pop$name[i],
+            '/ukb-',
+            scores_i_j_multi_targ_pop$name[i],
+            '-',
+            targ_pop_i,
+            '.targ_',
+            targ_pop_i,
+            '.profiles'
           )
-        )
-      }
-      
-      scores_i_j_pseudo <- scores_i_j_multi
-      scores_i_j_pseudo$group <- paste0(scores_i_j_multi$group, '.pseudo')
-      for(i in 1:nrow(scores_i_j_pseudo)){
-        if(grepl('UKB', scores_i_j_pseudo$name[i])){
-          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.EUR.pseudo', scores_i_j_pseudo$group[i])
-        }
-        if(grepl('BBJ', scores_i_j_pseudo$name[i])){
-          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.EAS.pseudo', scores_i_j_pseudo$group[i])
-        }
-        if(grepl('UGR', scores_i_j_pseudo$name[i])){
-          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.AFR.pseudo', scores_i_j_pseudo$group[i])
-        }
+        tmp$group[2] <- paste0(tmp$group[2], '.', targ_pop_i)
+
+        scores_i_j_multi_targ_pop_both <- rbind(
+          scores_i_j_multi_targ_pop_both,
+          tmp)
       }
-      scores_i_j_pseudo$predictor <- gsub('.profiles',
-                                            '.pseudo.profiles',
-                                            scores_i_j_pseudo$predictor)
-      
-      # Make a group for each GWAS and single source combo
-      scores_i_j_single <- scores_i_j_multi[!(scores_i_j_multi$method %in% pgs_group_methods),]
-      scores_i_j_single$group <-
-        paste0(scores_i_j_single$method,
-               '.',
-               gsub('.*_', '', scores_i_j_single$name))
-      scores_i_j_single$group <- gsub('UKB', 'EUR', scores_i_j_single$group)
-      scores_i_j_single$group <- gsub('BBJ', 'EAS', scores_i_j_single$group)
-      scores_i_j_single$group <- gsub('UGR', 'AFR', scores_i_j_single$group)
       
       predictors_i<- do.call(rbind, list(
-        scores_i_j_multi, scores_i_j_pseudo, scores_i_j_single
+        scores_i_j_multi, scores_i_j_multi_targ_pop_both, scores_i_j_pseudo, scores_i_j_pseudo_disc_pop
       ))
       
       predictors_i <- predictors_i[, c('predictor', 'group'), with=F]
@@ -2412,7 +2412,7 @@ for(trait_i in selected_traits){
           disc_pop_j_2,
           '/',
           trait_i,
-          '/predictor_list.txt'
+          '/predictor_list.tlprs.txt'
         ),
         col.names = T,
         row.names = F,
@@ -2423,6 +2423,7 @@ for(trait_i in selected_traits){
 }
 
 ```
+
 </details>
 
 ***
@@ -2435,7 +2436,7 @@ for(trait_i in selected_traits){
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-for targ_pop in $(echo EAS AFR EUR); do
+for targ_pop in $(echo EUR EAS AFR); do
   if [ "$targ_pop" == "EUR" ]; then
       targ_pop2="EUR_test"
   else
@@ -2443,7 +2444,7 @@ for targ_pop in $(echo EAS AFR EUR); do
   fi
   
   if [ "$targ_pop" == "EUR" ]; then
-    disc_pop=$(echo EAS AFR)
+    disc_pop=$(echo AFR EAS)
   fi
   
   if [ "$targ_pop" == "EAS" ]; then
@@ -2455,11 +2456,11 @@ for targ_pop in $(echo EAS AFR EUR); do
   fi
   
   for disc_pop_i in ${disc_pop}; do
-    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+    for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
       sbatch --mem 20G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
         --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
-        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
-        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
+        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
+        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
         --n_core 10 \
         --top1 T \
         --all_model F \
@@ -2478,6 +2479,7 @@ done
 <details><summary>Show code</summary>
 
 ```{r}
+
 setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 
 library(data.table)
@@ -2488,7 +2490,19 @@ source('../functions/misc.R')
 source_all('../functions')
 
 # Read in list of outcomes 
-selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-prscsx_dat[1:5,]
+
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+  
+# Define pgs_methods used
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+
+# Define gwas_list used
+gwas_list<-read_param(config = config, param = 'gwas_list')
+
+# Define gwas_groups used
+gwas_groups<-read_param(config = config, param = 'gwas_groups')
 
 # Calculate corelation between all phenotypes in each target population
 cors <- list()
@@ -2499,7 +2513,7 @@ for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
     pop_i_2 <- pop_i
   }
   pheno_pop_i <- list()
-  for(pheno_i in selected_traits){
+  for(pheno_i in prscsx_dat$labels){
     pheno_pop_i[[pheno_i]] <- fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
     names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
   }
@@ -2513,7 +2527,7 @@ for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
 # Read in results
 targ_pop = c('EUR','EAS','AFR')
 res <- list()
-for(pheno_i in selected_traits){
+for(pheno_i in prscsx_dat$labels){
   res_i<-NULL
   for(targ_pop_i in targ_pop){
     if(targ_pop_i == 'EAS'){
@@ -2536,41 +2550,43 @@ for(pheno_i in selected_traits){
             disc_pop_i,
             '/',
             pheno_i,
-            '/res.pred_eval.txt'
+            '/res.tlprs.pred_eval.txt'
           )
         )
-      eval_i$target<-targ_pop_i
+      eval_i$Target<-targ_pop_i
       eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
       res_i<-rbind(res_i, eval_i)
     }
   }
   
   res_i$Method<-sub('\\..*','',res_i$Group)
+  res_i$Method_short<-sub('.*_','',res_i$Method)
+  res_i<-res_i[order(res_i$Method_short, res_i$Method),]
   
-  res_i$Model[grepl('top1', res_i$Group)]<-'Top1'
   res_i$Model[grepl('pseudo', res_i$Group)]<-'Pseudo'
+  res_i$Model[grepl('top1', res_i$Group)]<-'Top1'
   res_i$Model[!grepl('top1|pseudo', res_i$Group)]<-'Multi'
+  res_i$Model[!grepl('tlprs', res_i$Group) & !grepl('EUR|EAS|AFR', res_i$Group) & !grepl('top1', res_i$Group)]<-'Multi'
   
-  res_i$Source<-ifelse(res_i$Method %in% c(pgs_group_methods, 'all') | !grepl('EUR|EAS|AFR', res_i$Group), 'Multi', 'Single')
-         
-  res_i$Discovery[grepl('EUR', res_i$Group)] <- 'EUR'
-  res_i$Discovery[grepl('EAS', res_i$Group)] <- 'EAS'
-  res_i$Discovery[grepl('AFR', res_i$Group)] <- 'AFR'
-  res_i$Discovery[res_i$Source == 'Multi'] <- res_i$gwas_group[res_i$Source == 'Multi']
+  res_i$Source[!grepl('tlprs', res_i$Group)] <- 'Single'
+  res_i$Source[grepl('tlprs', res_i$Group)] <- 'Multi'
+  res_i$Source[!grepl('tlprs', res_i$Group) & !grepl('EUR|EAS|AFR', res_i$Group)] <- 'Multi'
   
+  res_i$Discovery <- res_i$gwas_group
+  res_i$Discovery[grepl('EUR', res_i$Group) & res_i$Source == 'Single'] <- 'EUR'
+  res_i$Discovery[grepl('EAS', res_i$Group) & res_i$Source == 'Single'] <- 'EAS'
+  res_i$Discovery[grepl('AFR', res_i$Group) & res_i$Source == 'Single'] <- 'AFR'
+
+  res_i$Method <- gsub('tlprs_','tlprs - ', res_i$Method)
+  res_i$Method[grepl('tlprs', res_i$Method) & grepl('EUR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('EUR', res_i$Group)], " (EUR)")
+  res_i$Method[grepl('tlprs', res_i$Method) & grepl('EAS', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('EAS', res_i$Group)], " (EAS)")
+  res_i$Method[grepl('tlprs', res_i$Method) & grepl('AFR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('AFR', res_i$Group)], " (AFR)")
+  res_i$Method[grepl('tlprs', res_i$Method) & !grepl('EUR|EAS|AFR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & !grepl('EUR|EAS|AFR', res_i$Group)], " (Both)")
+
   res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
   res_i$Model<-factor(res_i$Model, levels=c('Top1','Pseudo','Multi'))
   res_i$Discovery<-factor(res_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-  # Remove Multi model for groups that contain one score (aka QuickPRS and SBayesRC)
-  res_i <- res_i[
-    !(res_i$Method %in%  c('sbayesrc', 'quickprs') & 
-      res_i$Model == 'Multi' & 
-      res_i$Source == 'Single'),]
-  
-  # Remove pseudo model for methods that don't really have one (single source combo)
-  res_i <- res_i[!which(res_i$Model == 'Pseudo' & res_i$Method == 'ptclump'),]
-
   res[[pheno_i]]<-res_i
   
 }
@@ -2599,7 +2615,7 @@ for(targ_pop_i in targ_pop){
     res_i <- do.call(rbind, lapply(seq_along(res), function(i) {
       x <- res[[i]]
       x$pheno <- names(res)[i]
-      x <- x[x$target == targ_pop_i]
+      x <- x[x$Target == targ_pop_i]
       x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
     }))
     
@@ -2609,20 +2625,6 @@ for(targ_pop_i in targ_pop){
   
     for(group_i in unique(res_i$Group)){
       res_group_i <- res_i[res_i$Group == group_i,]
-      missing_pheno <-
-        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_group_i$pheno))]
-      
-      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_group_i$pheno))) {
-        print(paste0(
-          'Results missing for ',
-          targ_pop_i,
-          ' ',
-          group_i,
-          ' ',
-          paste0(missing_pheno, collapse = ' ')
-        ))
-      }
-      
       cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
       
       meta_res_i <-
@@ -2656,71 +2658,183 @@ meta_res$Model<-factor(meta_res$Model, levels=c('Top1','Pseudo','Multi'))
 meta_res$Discovery<-factor(meta_res$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
 ####
-# Comparison of models across methods, across target and discovery populations
+# Compare TLPRS to unadjusted PGS
+####
+
+meta_res_multi_pop <- meta_res[!(meta_res$Discovery %in% c('EUR','EAS','AFR')),]
+meta_res_multi_pop$original_method <- gsub(' .*', '', gsub('tlprs - ', '', meta_res_multi_pop$Method))
+meta_res_multi_pop$test[!grepl('tlprs', meta_res_multi_pop$Method)] <- 'Unadjusted'
+meta_res_multi_pop$test[grepl('tlprs', meta_res_multi_pop$Method)] <- gsub('.* ', 'TLPRS ', meta_res_multi_pop$Method[grepl('tlprs', meta_res_multi_pop$Method)])
+meta_res_multi_pop$test <- factor(meta_res_multi_pop$test, levels=unique(meta_res_multi_pop$test))
+meta_res_multi_pop$test <- gsub('AFR', 'target pop', meta_res_multi_pop$test)
+meta_res_multi_pop$test <- gsub('EAS', 'target pop', meta_res_multi_pop$test)
+
+tmp <- meta_res_multi_pop[meta_res_multi_pop$Target %in% c('EAS', 'AFR'),]
+
+ggplot(tmp, aes(x=test, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
+    labs(y = "R (SE)") +
+    facet_grid(Target ~ original_method, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+tmp <- meta_res_multi_pop[meta_res_multi_pop$Target %in% c('EAS', 'AFR'),]
+tmp <- tmp[grepl('Unadj|Both', tmp$test),]
+tmp$test <- gsub(' .*', '', tmp$test)
+tmp$test <- factor(tmp$test, levels=c('Unadjusted','TLPRS'))
+
+ggplot(tmp, aes(x=test, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
+    labs(y = "R (SE)", x = NULL) +
+    facet_grid(Target ~ original_method, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+####
+# Create heatmap showing difference between all methods and models
 ####
 
-tmp <- meta_res
+# Read in results
+targ_pop=c('EUR','EAS','AFR')
+res <- list()
+for(pheno_i in prscsx_dat$labels){
+  res_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.tlprs.pred_comp.txt'
+          )
+        )
+      eval_i$Target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_i<-rbind(res_i, eval_i)
+    }
+  }
+  
+  res[[pheno_i]]<-res_i
+}
+
+#####
+# Create a plot showing relative R from TLPRS vs Unadjusted
+#####
+
+res_all <- do.call(rbind, lapply(names(res), function(name) {
+  x <- res[[name]]
+  x$pheno <- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Mirror results to fill in gaps
+res_all_symmetric <- res_all
+res_all_mirrored <- res_all
+res_all_mirrored$Model_1 <- res_all$Model_2
+res_all_mirrored$Model_2 <- res_all$Model_1
+res_all_mirrored$Model_1_R <- res_all$Model_2_R
+res_all_mirrored$Model_2_R <- res_all$Model_1_R
+res_all_mirrored$R_diff <- -res_all_mirrored$R_diff
+res_all <- rbind(res_all_symmetric, res_all_mirrored)
+
+# Subset tests where top1 TLPRS is being compared to top1 unadjusted
+res_all$Method_1 <- gsub('.*_', '', gsub('\\..*', '', res_all$Model_1))
+res_all$Method_2 <- gsub('.*_', '', gsub('\\..*', '', res_all$Model_2))
+
+res_all$TLPRS_1 <- grepl('tlprs', res_all$Model_1)
+res_all$TLPRS_2 <- grepl('tlprs', res_all$Model_2)
+
+res_all$Test_1[grepl('pseudo', res_all$Model_1)]<-'Pseudo'
+res_all$Test_1[grepl('top1', res_all$Model_1)]<-'Top1'
+res_all$Test_1[!grepl('top1|pseudo', res_all$Model_1)]<-'Multi'
+res_all$Test_1[!grepl('tlprs', res_all$Model_1) & !grepl('EUR|EAS|AFR', res_all$Model_1) & !grepl('top1', res_all$Model_1)]<-'Multi'
+
+res_all$Test_2[grepl('pseudo', res_all$Model_2)]<-'Pseudo'
+res_all$Test_2[grepl('top1', res_all$Model_2)]<-'Top1'
+res_all$Test_2[!grepl('top1|pseudo', res_all$Model_2)]<-'Multi'
+res_all$Test_2[!grepl('tlprs', res_all$Model_2) & !grepl('EUR|EAS|AFR', res_all$Model_2) & !grepl('top1', res_all$Model_2)]<-'Multi'
+
+res_all$Source_1[!grepl('tlprs', res_all$Model_1)] <- 'Single'
+res_all$Source_1[grepl('tlprs', res_all$Model_1)] <- 'Multi'
+res_all$Source_1[!grepl('tlprs', res_all$Model_1) & !grepl('EUR|EAS|AFR', res_all$Model_1)] <- 'Multi'
+
+res_all$Source_2[!grepl('tlprs', res_all$Model_2)] <- 'Single'
+res_all$Source_2[grepl('tlprs', res_all$Model_2)] <- 'Multi'
+res_all$Source_2[!grepl('tlprs', res_all$Model_2) & !grepl('EUR|EAS|AFR', res_all$Model_2)] <- 'Multi'
+
+res_all$Discovery_1 <- res_all$gwas_group
+res_all$Discovery_1[grepl('EUR', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'EUR'
+res_all$Discovery_1[grepl('EAS', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'EAS'
+res_all$Discovery_1[grepl('AFR', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'AFR'
+
+res_all$Discovery_2 <- res_all$gwas_group
+res_all$Discovery_2[grepl('EUR', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'EUR'
+res_all$Discovery_2[grepl('EAS', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'EAS'
+res_all$Discovery_2[grepl('AFR', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'AFR'
+
+res_all$TLPRS_target_1[grepl('EUR', res_all$Model_1) & res_all$TLPRS_1] <- 'EUR'
+res_all$TLPRS_target_1[grepl('EAS', res_all$Model_1) & res_all$TLPRS_1] <- 'EAS'
+res_all$TLPRS_target_1[grepl('AFR', res_all$Model_1) & res_all$TLPRS_1] <- 'AFR'
+res_all$TLPRS_target_1[!grepl('EUR|AFR|EAS', res_all$Model_1) & res_all$TLPRS_1] <- 'Both'
+res_all$TLPRS_target_1[res_all$TLPRS_target_1 == res_all$Target] <- 'Target Pop.'
+
+res_all$TLPRS_target_2[grepl('EUR', res_all$Model_2) & res_all$TLPRS_2] <- 'EUR'
+res_all$TLPRS_target_2[grepl('EAS', res_all$Model_2) & res_all$TLPRS_2] <- 'EAS'
+res_all$TLPRS_target_2[grepl('AFR', res_all$Model_2) & res_all$TLPRS_2] <- 'AFR'
+res_all$TLPRS_target_2[!grepl('EUR|AFR|EAS', res_all$Model_2) & res_all$TLPRS_2] <- 'Both'
+res_all$TLPRS_target_2[res_all$TLPRS_target_2 == res_all$Target] <- 'Target Pop.'
+
+# Subset to tests comparing to the Unadjusted models
+res_all <- res_all[res_all$Method_1 == res_all$Method_2, ]
+res_all <- res_all[res_all$Source_2 == 'Multi', ]
+res_all <- res_all[res_all$Test_1  == res_all$Test_2, ]
+res_all <- res_all[res_all$TLPRS_2  == F, ]
+res_all <- res_all[res_all$Target %in% c('EAS', 'AFR'),]
 
-ggplot(tmp, aes(x=Method, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
-    labs(y = "R (SE)") +
-    facet_grid(Target ~ Discovery, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+ggplot(res_all, aes(x = Method_2, y = R_diff, colour = Test_1)) +
+  geom_point(position=position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7), alpha=0.5) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(Target ~ TLPRS_target_1, scales='free', space = 'free_x')
 
 
-####
-# Create heatmap showing difference between all methods and models
-####
+######
+# Average R across phenotypes
+######
 
-# Read in results
-targ_pop=c('EUR','EAS','AFR')
-res <- list()
-for(pheno_i in prscsx_dat$labels){
-  res_i<-NULL
-  for(targ_pop_i in targ_pop){
-    if(targ_pop_i == 'EAS'){
-      disc_pop <- 'EAS'
-    }
-    if(targ_pop_i == 'AFR'){
-      disc_pop <- 'AFR'
-    }
-    if(targ_pop_i == 'EUR'){
-      disc_pop <- c('EAS','AFR')
-    }
-    for(disc_pop_i in disc_pop){
-      eval_i <-
-        fread(
-          paste0(
-            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
-            'targ_',
-            targ_pop_i,
-            '.disc_EUR_',
-            disc_pop_i,
-            '/',
-            pheno_i,
-            '/res.pred_comp.txt'
-          )
-        )
-      eval_i$target<-targ_pop_i
-      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
-      res_i<-rbind(res_i, eval_i)
-    }
-  }
-  
-  res[[pheno_i]]<-res_i
-}
-    
 library(MAd)
 
-# Average R across phenotypes
 meta_res <- NULL
 for(targ_pop_i in targ_pop){
   if(targ_pop_i == 'EAS'){
@@ -2735,12 +2849,7 @@ for(targ_pop_i in targ_pop){
   for(disc_pop_i in disc_pop){
   
     # Subset res for each scenario
-    res_i <- do.call(rbind, lapply(seq_along(res), function(i) {
-      x <- res[[i]]
-      x$pheno <- names(res)[i]
-      x <- x[x$target == targ_pop_i]
-      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
-    }))
+    res_i <- res_all[res_all$Target == targ_pop_i & res_all$gwas_group == paste0('EUR+', disc_pop_i), ]
   
     # Calculate diff SE based on p-value
     res_i$R_diff_pval[res_i$R_diff == 0] <- 1-0.001
@@ -2768,13 +2877,10 @@ for(targ_pop_i in targ_pop){
           data = res_group_i
         )
       
+      group_info <- res_group_i[1, !(names(res_group_i[1,]) %in% c('Model_1_R', 'Model_2_R', 'R_diff', 'R_diff_pval', 'R_diff_z', 'R_diff_SE')), with=F]
       meta_res <- rbind(meta_res,
                         data.table(
-                          Target = targ_pop_i,
-                          Group = group_i,
-                          gwas_group = res_group_i$gwas_group[1],
-                          Model_1 = res_group_i$Model_1[1],
-                          Model_2 = res_group_i$Model_2[1],
+                          group_info,
                           R_diff = meta_res_i$es,
                           R_diff_SE = sqrt(meta_res_i$var)
                         ))
@@ -2784,123 +2890,110 @@ for(targ_pop_i in targ_pop){
 meta_res$R_diff_z <- meta_res$R_diff / meta_res$R_diff_SE
 meta_res$R_diff_p <- 2*pnorm(-abs(meta_res$R_diff_z))
 
-# Mirror results to fill in gaps
-meta_res_symmetric <- meta_res
-meta_res_mirrored <- meta_res
-meta_res_mirrored$Model_1 <- meta_res$Model_2
-meta_res_mirrored$Model_2 <- meta_res$Model_1
-meta_res_mirrored$R_diff <- -meta_res_mirrored$R_diff
-meta_res <- rbind(meta_res_symmetric, meta_res_mirrored)
-
-meta_res$R_diff[meta_res$Model_1_Test == meta_res$Model_2_Test]<-NA
-meta_res$R_diff_catagory<-'NA'
-meta_res$R_diff_catagory[meta_res$R_diff < -0.002]<-'-0.025 - -0.002'
-meta_res$R_diff_catagory[meta_res$R_diff < -0.025]<-'-0.08 - -0.025'
-meta_res$R_diff_catagory[meta_res$R_diff < -0.08]<-'< -0.08'
-meta_res$R_diff_catagory[meta_res$R_diff > -0.002 & meta_res$R_diff < 0.002]<-'-0.002 - 0.002'
-meta_res$R_diff_catagory[meta_res$R_diff > 0.002]<-'0.002 - 0.025'
-meta_res$R_diff_catagory[meta_res$R_diff > 0.025]<-'0.025 - 0.08'
-meta_res$R_diff_catagory[meta_res$R_diff > 0.08]<-'> 0.08'
-meta_res$R_diff_catagory<-factor(meta_res$R_diff_catagory, level=rev(c('< -0.08','-0.08 - -0.025','-0.025 - -0.002','-0.002 - 0.002','0.002 - 0.025','0.025 - 0.08','> 0.08')))
-meta_res$indep_star<-' '
-meta_res$indep_star[meta_res$R_diff_p < 0.05]<-'*'
-meta_res$indep_star[meta_res$R_diff_p < 1e-3]<-'**'
-meta_res$indep_star[meta_res$R_diff_p < 1e-6]<-'***'
-
-# Annotate tests to get order correct
-meta_res$Method<-sub('\\..*','',meta_res$Model_1)
-
-meta_res$Model[grepl('top1', meta_res$Model_1)]<-'Top1'
-meta_res$Model[grepl('pseudo', meta_res$Model_1)]<-'Pseudo'
-meta_res$Model[!grepl('top1|pseudo', meta_res$Model_1)]<-'Multi'
-
-meta_res$Model2[grepl('top1', meta_res$Model_2)]<-'Top1'
-meta_res$Model2[grepl('pseudo', meta_res$Model_2)]<-'Pseudo'
-meta_res$Model2[!grepl('top1|pseudo', meta_res$Model_2)]<-'Multi'
-
-meta_res$Source[grepl('AFR|EAS|EUR', meta_res$Model_1)] <- 'Single'
-meta_res$Source[!grepl('AFR|EAS|EUR', meta_res$Model_1)] <- 'Multi'
-
-meta_res$Source2[grepl('AFR|EAS|EUR', meta_res$Model_2)] <- 'Single'
-meta_res$Source2[!grepl('AFR|EAS|EUR', meta_res$Model_2)] <- 'Multi'
+# Plot the results
+ggplot(meta_res, aes(x=Method_1, y=R_diff, fill=Test_1)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_point(
+    data = res_all,
+    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
+    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+    alpha = 0.3
+  ) +
+  geom_errorbar(
+    aes(
+      ymin = R_diff - R_diff_SE,
+      ymax = R_diff + R_diff_SE
+    ),
+    width = 0,
+    position = position_dodge(width = 0.7)
+  ) +
+  geom_point(
+    stat = "identity",
+    position = position_dodge(0.7),
+    size = 2,
+    shape = 23,
+    colour = 'black'
+  ) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  geom_vline(xintercept = 1.5, linetype = "dotted") +
+  geom_vline(xintercept = 2.5, linetype = "dotted") +
+  geom_vline(xintercept = 3.5, linetype = "dotted") +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(Target ~ TLPRS_target_1, scales='free', space = 'free_x') +
+  panel_border()
 
-meta_res$Discovery[grepl('EUR', meta_res$Model_1)] <- 'EUR'
-meta_res$Discovery[grepl('EAS', meta_res$Model_1)] <- 'EAS'
-meta_res$Discovery[grepl('AFR', meta_res$Model_1)] <- 'AFR'
-meta_res$Discovery[meta_res$Source == 'Multi'] <- meta_res$gwas_group[meta_res$Source == 'Multi']
 
-meta_res$Discovery2[grepl('EUR', meta_res$Model_2)] <- 'EUR'
-meta_res$Discovery2[grepl('EAS', meta_res$Model_2)] <- 'EAS'
-meta_res$Discovery2[grepl('AFR', meta_res$Model_2)] <- 'AFR'
-meta_res$Discovery2[meta_res$Source2 == 'Multi'] <- meta_res$gwas_group[meta_res$Source2 == 'Multi']
 
-meta_res$Method<-factor(meta_res$Method, levels=unique(meta_res$Method))
-meta_res$Model<-factor(meta_res$Model, levels=c('Top1','Pseudo','Multi'))
-meta_res$Model2<-factor(meta_res$Model2, levels=c('Top1','Pseudo','Multi'))
-meta_res$Discovery<-factor(meta_res$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
-meta_res$Discovery2<-factor(meta_res$Discovery2, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
+# Plot the results
+ggplot(meta_res[meta_res$TLPRS_target_1 == 'Both',], aes(x=Method_1, y=R_diff, fill=Test_1)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_point(
+    data = res_all[res_all$TLPRS_target_1 == 'Both',],
+    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
+    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+    alpha = 0.3
+  ) +
+  geom_errorbar(
+    aes(
+      ymin = R_diff - R_diff_SE,
+      ymax = R_diff + R_diff_SE
+    ),
+    width = 0,
+    position = position_dodge(width = 0.7)
+  ) +
+  geom_point(
+    stat = "identity",
+    position = position_dodge(0.7),
+    size = 2,
+    shape = 23,
+    colour = 'black'
+  ) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  geom_vline(xintercept = 1.5, linetype = "dotted") +
+  geom_vline(xintercept = 2.5, linetype = "dotted") +
+  geom_vline(xintercept = 3.5, linetype = "dotted") +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(Target ~ ., scales='free', space = 'free_x') +
+  panel_border()
 
-meta_res<-meta_res[order(meta_res$Discovery, meta_res$Discovery2, meta_res$Method),]
 
-heatmap_list<-NULL
-for(model_i in c('Top1','Pseudo','Multi')){
-  heatmap_list[[model_i]]<-list()
-  for(targ_pop_i in targ_pop){
-    if(targ_pop_i == 'EAS'){
-      disc_pop <- 'EAS'
-    }
-    if(targ_pop_i == 'AFR'){
-      disc_pop <- 'AFR'
-    }
-    if(targ_pop_i == 'EUR'){
-      disc_pop <- c('EAS','AFR')
-    }
-    heatmap_list[[model_i]][[targ_pop_i]]<-list()
-    for(disc_pop_i in disc_pop){
-  
-      tmp <- meta_res[meta_res$Target == targ_pop_i, ]
-      tmp <- tmp[tmp$gwas_group == paste0('EUR+', disc_pop_i), ]
-      tmp <- tmp[tmp$Model == model_i & tmp$Model2 == model_i,]
-      tmp$Model_1<-gsub('\\..*','', tmp$Model_1)
-      tmp$Model_2<-gsub('\\..*','', tmp$Model_2)
-      
-      tmp$Model_1 <- factor(
-        tmp$Model_1, 
-        levels=unique(tmp$Model_1))
-      tmp$Model_2 <- factor(
-        tmp$Model_2, 
-        levels=unique(tmp$Model_1))
-      
-      heatmap_list[[model_i]][[targ_pop_i]][[disc_pop_i]] <-
-        ggplot(data = tmp, aes(Model_2, Model_1, fill = R_diff_catagory)) +
-        geom_tile(color = "white") +
-        labs(y = 'Test', x = 'Comparison', fill = 'R difference', title = paste0('Target: ', targ_pop_i, ', Model = ', model_i)) +
-        facet_grid(Discovery ~ Discovery2, scales = 'free', space = 'free', switch="both") +
-        geom_text(
-          data = tmp,
-          aes(Model_2, Model_1, label = indep_star),
-          color = "black",
-          size = 4,
-          angle = 0,
-          vjust = 0.8
-        ) +
-        scale_fill_brewer(
-          breaks = levels(tmp$R_diff_catagory),
-          palette = "RdBu",
-          drop = F,
-          na.value = 'grey'
-        ) +
-        theme_half_open() +
-        background_grid() +
-        panel_border() +
-        theme(axis.text.x = element_text(
-          angle = 45,
-          vjust = 1,
-          hjust = 1
-        ))
-    }
-  }
-}
+# Plot the results
+ggplot(meta_res[meta_res$TLPRS_target_1 == 'Both',], aes(x=Method_1, y=R_diff, fill=Test_1)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_point(
+    data = res_all[res_all$TLPRS_target_1 == 'Both',],
+    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
+    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+    alpha = 0.3
+  ) +
+  geom_errorbar(
+    aes(
+      ymin = R_diff - R_diff_SE,
+      ymax = R_diff + R_diff_SE
+    ),
+    width = 0,
+    position = position_dodge(width = 0.7)
+  ) +
+  geom_point(
+    stat = "identity",
+    position = position_dodge(0.7),
+    size = 2,
+    shape = 23,
+    colour = 'black'
+  ) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  geom_vline(xintercept = 1.5, linetype = "dotted") +
+  geom_vline(xintercept = 2.5, linetype = "dotted") +
+  geom_vline(xintercept = 3.5, linetype = "dotted") +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(Target ~ ., scales='free', space = 'free_x') +
+  panel_border()
 
 
 ```
@@ -2914,9 +3007,6 @@ for(model_i in c('Top1','Pseudo','Multi')){
 library(data.table)
 library(ggplot2)
 library(cowplot)
-library(ggrepel)
-library(dplyr)
-library(plotly)
 
 setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 source('../functions/misc.R')
@@ -2933,6 +3023,10 @@ bm_files_i <- list.files(paste0(outdir, '/reference/benchmarks/'), full.names =
 # Subset benchmarks for pgs_methods
 bm_files_i <- bm_files_i[grepl('prep_pgs_', bm_files_i)]
 
+# Subset to benchmarks for gwas/gwas_groups in config
+scores <- list_score_files(config)
+bm_files_i <- bm_files_i[grepl(paste0('-', unique(scores$name),'.txt', collapse = '|'), bm_files_i)]
+
 # Read in benchmark files
 bm_dat_all <- do.call(rbind, lapply(bm_files_i, function(file) {
   tmp <- fread(file)
@@ -2959,6 +3053,19 @@ for(i in unique(bm_dat_all$method)){
   )
 }
 
+# Calculate average max_rss for each method
+method_avg_mem <- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg_mem <- rbind(
+    method_avg_mem,
+    data.frame(
+      Method = i,
+      Memory = mean(bm_dat_all$max_rss[bm_dat_all$method == i])
+    )
+  )
+}
+
+
 # Format the time taken nicely
 method_avg$Time_clean[method_avg$Time < 60] <-
   paste0(round(method_avg$Time[method_avg$Time < 60], 1), ' sec')
@@ -2975,5 +3082,17 @@ ggplot(method_avg, aes(x = Method, y = Time, fill = Method)) +
   background_grid() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
 
+# Format the Memory nicely
+method_avg_mem$Memory_clean <-
+  paste0(round(method_avg_mem$Memory/1000, 2), ' Gb')
+
+ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
+  geom_bar(stat = "identity", position="dodge") +
+  geom_text(aes(label = Memory_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
+  labs(x = "PGS Method", y = "Memory (Mb)") +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
+
 ```
 

From b2ab1a57ea714c2660a5951b4045932df4195a74 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 4 Dec 2024 10:38:35 +0000
Subject: [PATCH 056/153] Set --use-conda and mamba as default for pipeline in
 config

---
 pipeline/config.yaml | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/pipeline/config.yaml b/pipeline/config.yaml
index 3773f131..96a32f31 100644
--- a/pipeline/config.yaml
+++ b/pipeline/config.yaml
@@ -78,3 +78,7 @@ cores_outlier_detection: 5
 
 # Specify PGS methods that should be used by TL-PRS
 tlprs_methods: NA
+
+# Set pipeline to use conda envs and mamba to prepare envs
+use-conda: true
+conda-frontend: mamba

From c95564ae56bce42849340f1d1dec5b06336a2636 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 4 Dec 2024 11:13:53 +0000
Subject: [PATCH 057/153] Removed attempt to set use-conda and mamba flag from
 config as it is does not work

---
 pipeline/config.yaml | 3 ---
 1 file changed, 3 deletions(-)

diff --git a/pipeline/config.yaml b/pipeline/config.yaml
index 96a32f31..cd7aacf2 100644
--- a/pipeline/config.yaml
+++ b/pipeline/config.yaml
@@ -79,6 +79,3 @@ cores_outlier_detection: 5
 # Specify PGS methods that should be used by TL-PRS
 tlprs_methods: NA
 
-# Set pipeline to use conda envs and mamba to prepare envs
-use-conda: true
-conda-frontend: mamba

From 57e7e0542a5332f9fb2cb52e93a5961e410db9a5 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 4 Dec 2024 18:00:52 +0000
Subject: [PATCH 058/153] Added continuous correction of PGS for ancestry.
 Testing required

---
 .../external_score_processor.R                |  19 +-
 Scripts/pgs_methods/dbslmm.R                  |  10 +-
 Scripts/pgs_methods/lassosum.R                |   8 +
 Scripts/pgs_methods/ldpred2.R                 |   8 +
 Scripts/pgs_methods/megaprs.R                 |   8 +
 Scripts/pgs_methods/prscs.R                   |   8 +
 Scripts/pgs_methods/ptclump.R                 |   8 +
 Scripts/pgs_methods/quickprs.R                |   8 +
 Scripts/pgs_methods/sbayesr.R                 | 500 +++++++++---------
 Scripts/pgs_methods/sbayesrc.R                |   8 +
 Scripts/ref_pca/ref_pca.R                     |  11 +
 Scripts/target_scoring/target_scoring.R       |   7 +-
 .../target_scoring/target_scoring_pipeline.R  |  51 +-
 docs/pipeline_1kg_hgdp_prep.Rmd               |   6 +
 functions/pgs.R                               |  91 ++++
 pipeline/rules/pgs_methods.smk                |  41 +-
 pipeline/rules/target_scoring.smk             |  12 +-
 17 files changed, 517 insertions(+), 287 deletions(-)

diff --git a/Scripts/external_score_processor/external_score_processor.R b/Scripts/external_score_processor/external_score_processor.R
index dc34f3b1..32c1026f 100644
--- a/Scripts/external_score_processor/external_score_processor.R
+++ b/Scripts/external_score_processor/external_score_processor.R
@@ -6,6 +6,8 @@ library("optparse")
 option_list = list(
 	make_option("--ref_plink_chr", action="store", default=NULL, type='character',
 		help="Path to per chromosome reference PLINK files [required]"),
+	make_option("--ref_pcs", action="store", default=NULL, type='character',
+	  help="Reference PCs for continuous ancestry correction [optional]"),
 	make_option("--pop_data", action="store", default=NULL, type='character',
 		help="File containing the population code and location of the keep file [required]"),
 	make_option("--plink2", action="store", default='plink2', type='character',
@@ -266,17 +268,18 @@ if(nrow(targ_matched) < 0.75*n_snp_orig){
 	# Calculate scores in the full reference
 	ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'))
 
+	if(!is.null(opt$ref_pcs)){
+	  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+	  # Derive trans-ancestry PGS models and estimate PGS residual scale
+	  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+	}
+	
 	# Calculate scale within each reference population
-	pop_data <- fread(opt$pop_data)
-	pop_data<-data.table(
-	  FID=pop_data$`#IID`,
-	  IID=pop_data$`#IID`,
-	  POP=pop_data$POP
-	)
+	pop_data <- read_pop_data(opt$pop_data)
 
 	for(pop_i in unique(pop_data$POP)){
-	ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-	fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+  	ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+  	fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
 	}
 }
 
diff --git a/Scripts/pgs_methods/dbslmm.R b/Scripts/pgs_methods/dbslmm.R
index 6aec5225..4515508e 100644
--- a/Scripts/pgs_methods/dbslmm.R
+++ b/Scripts/pgs_methods/dbslmm.R
@@ -7,7 +7,9 @@ option_list = list(
 make_option("--ref_plink_chr", action="store", default=NULL, type='character',
 		help="Path to per chromosome reference PLINK files [required]"),
 make_option("--ref_keep", action="store", default=NULL, type='character',
-		help="Keep file to subset individuals in reference for clumping [optional]"),
+    help="Keep file to subset individuals in reference for clumping [optional]"),
+make_option("--ref_pcs", action="store", default=NULL, type='character',
+    help="Reference PCs for continuous ancestry correction [optional]"),
 make_option("--pop_data", action="store", default=NULL, type='character',
     help="File containing the population code and location of the keep file [required]"),
 make_option("--plink", action="store", default='plink', type='character',
@@ -248,6 +250,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads=opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/pgs_methods/lassosum.R b/Scripts/pgs_methods/lassosum.R
index cb19e145..5e15b656 100644
--- a/Scripts/pgs_methods/lassosum.R
+++ b/Scripts/pgs_methods/lassosum.R
@@ -8,6 +8,8 @@ option_list = list(
 			help="Path to genome-wide reference PLINK files [required]"),
 	make_option("--ref_keep", action="store", default=NULL, type='character',
 			help="Keep file to subset individuals in reference for clumping [optional]"),
+	make_option("--ref_pcs", action="store", default=NULL, type='character',
+	    help="Reference PCs for continuous ancestry correction [optional]"),
 	make_option("--gwas_pop", action="store", default=NULL, type='character',
 			help="Population of GWAS sample [required]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
@@ -204,6 +206,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/pgs_methods/ldpred2.R b/Scripts/pgs_methods/ldpred2.R
index 9e3ad382..15cb4655 100644
--- a/Scripts/pgs_methods/ldpred2.R
+++ b/Scripts/pgs_methods/ldpred2.R
@@ -6,6 +6,8 @@ library("optparse")
 option_list = list(
   make_option("--ref_plink_chr", action="store", default=NULL, type='character',
       help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+      help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--ldpred2_ref_dir", action="store", default=NULL, type='character',
       help="Path to directory containing LDpred2 reference data [required]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
@@ -353,6 +355,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/pgs_methods/megaprs.R b/Scripts/pgs_methods/megaprs.R
index d8c43214..6613efa2 100644
--- a/Scripts/pgs_methods/megaprs.R
+++ b/Scripts/pgs_methods/megaprs.R
@@ -8,6 +8,8 @@ option_list = list(
               help="Path to per chromosome reference PLINK files [required]"),
   make_option("--ref_keep", action="store", default=NULL, type='character',
               help="Path to keep file for reference [optional]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+              help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
               help="File containing the population code and location of the keep file [required]"),
   make_option("--plink", action="store", default='plink', type='character',
@@ -266,6 +268,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/pgs_methods/prscs.R b/Scripts/pgs_methods/prscs.R
index fa4f21a4..866926ba 100644
--- a/Scripts/pgs_methods/prscs.R
+++ b/Scripts/pgs_methods/prscs.R
@@ -6,6 +6,8 @@ library("optparse")
 option_list = list(
   make_option("--ref_plink_chr", action="store", default=NULL, type='character',
       help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+      help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
       help="File containing the population code and location of the keep file [required]"),
   make_option("--plink2", action="store", default='plink2', type='character',
@@ -203,6 +205,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/pgs_methods/ptclump.R b/Scripts/pgs_methods/ptclump.R
index f48c9bb8..46b4d4b1 100644
--- a/Scripts/pgs_methods/ptclump.R
+++ b/Scripts/pgs_methods/ptclump.R
@@ -8,6 +8,8 @@ make_option("--ref_plink_chr", action="store", default=NULL, type='character',
 		help="Path to per chromosome reference PLINK files [required]"),
 make_option("--ref_keep", action="store", default=NULL, type='character',
     help="Keep file to subset individuals in reference for clumping [optional]"),
+make_option("--ref_pcs", action="store", default=NULL, type='character',
+    help="Reference PCs for continuous ancestry correction [optional]"),
 make_option("--pop_data", action="store", default=NULL, type='character',
 		help="File containing the population code and location of the keep file [required]"),
 make_option("--plink2", action="store", default='plink2', type='character',
@@ -179,6 +181,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'))
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/pgs_methods/quickprs.R b/Scripts/pgs_methods/quickprs.R
index 45b184e8..36023b4b 100644
--- a/Scripts/pgs_methods/quickprs.R
+++ b/Scripts/pgs_methods/quickprs.R
@@ -6,6 +6,8 @@ suppressMessages(library("optparse"))
 option_list = list(
   make_option("--ref_plink_chr", action="store", default=NA, type='character',
               help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+              help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
               help="File containing the population code and location of the keep file [required]"),
   make_option("--plink", action="store", default='plink', type='character',
@@ -197,6 +199,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/pgs_methods/sbayesr.R b/Scripts/pgs_methods/sbayesr.R
index aaaa2618..981ae7ae 100644
--- a/Scripts/pgs_methods/sbayesr.R
+++ b/Scripts/pgs_methods/sbayesr.R
@@ -1,246 +1,254 @@
-#!/usr/bin/Rscript
-# This script was written by Oliver Pain whilst at King's College London University.
-start.time <- Sys.time()
-library("optparse")
-
-option_list = list(
-  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
-      help="Path to per chromosome reference PLINK files [required]"),
-  make_option("--ref_freq_chr", action="store", default=NULL, type='character',
-      help="Path to per chromosome reference PLINK .frq files [required]"),
-  make_option("--pop_data", action="store", default=NULL, type='character',
-      help="File containing the population code and location of the keep file [required]"),
-  make_option("--plink2", action="store", default='plink2', type='character',
-      help="Path PLINK v2 software binary [required]"),
-  make_option("--output", action="store", default=NULL, type='character',
-      help="Path for output files [required]"),
-  make_option("--n_cores", action="store", default=1, type='numeric',
-      help="Number of cores for parallel computing [optional]"),
-  make_option("--sumstats", action="store", default=NULL, type='character',
-      help="GWAS summary statistics in LDSC format [required]"),
-  make_option("--gctb", action="store", default=NULL, type='character',
-      help="Path to GCTB binary [required]"),
-  make_option("--impute_N", action="store", default=T, type='logical',
-      help="Logical indicating whether per variant N should imputed based on SE. [optional]"),
-  make_option("--P_max", action="store", default=NULL, type='numeric',
-      help="P-value threshold for filter variants [optional]"),
-  make_option("--robust", action="store", default=F, type='logical',
-      help="Force robust GCTB parameterisation [optional]"),
-  make_option("--test", action="store", default=NA, type='character',
-      help="Specify number of SNPs to include [optional]"),
-  make_option("--ld_matrix_chr", action="store", default=NULL, type='character',
-      help="Path to per chromosome shrunk sparse LD matrix from GCTB [required]")
-)
-
-opt = parse_args(OptionParser(option_list = option_list))
-
-# Load dependencies
-library(GenoUtils)
-library(data.table)
-source('../functions/misc.R')
-source_all('../functions')
-library(foreach)
-library(doMC)
-registerDoMC(opt$n_cores)
-
-# Check required inputs
-if(is.null(opt$ref_plink_chr)){
-  stop('--ref_plink_chr must be specified.\n')
-}
-if(is.null(opt$sumstats)){
-  stop('--sumstats must be specified.\n')
-}
-if(is.null(opt$pop_data)){
-  stop('--pop_data must be specified.\n')
-}
-if(is.null(opt$output)){
-  stop('--output must be specified.\n')
-}
-if(is.null(opt$gctb)){
-  stop('--gctb must be specified.\n')
-}
-if(is.null(opt$ld_matrix_chr)){
-  stop('--ld_matrix_chr must be specified.\n')
-}
-
-# Create output directory
-opt$output_dir <- paste0(dirname(opt$output),'/')
-system(paste0('mkdir -p ',opt$output_dir))
-
-# Create temp directory
-tmp_dir <- tempdir()
-
-# Initiate log file
-log_file <- paste0(opt$output,'.log')
-log_header(log_file = log_file, opt = opt, script = 'sbayesr.R', start.time = start.time)
-
-# If testing, change CHROMS to chr value
-if(!is.na(opt$test) && opt$test == 'NA'){
-  opt$test<-NA
-}
-if(!is.na(opt$test)){
-  CHROMS <- as.numeric(gsub('chr','',opt$test))
-}
-
-#####
-# Read in sumstats
-#####
-
-log_add(log_file = log_file, message = 'Reading in GWAS.')
-
-# Read in, check and format GWAS summary statistics
-gwas <- read_sumstats(sumstats = opt$sumstats, chr = CHROMS, log_file = log_file, req_cols = c('SNP','A1','A2','FREQ','BETA','SE','P','N'))
-
-###
-# Change to COJO format
-###
-
-gwas <- gwas[, c('SNP','A1','A2','FREQ','BETA','SE','P','N'), with=F]
-names(gwas) <- c('SNP','A1','A2','freq','b','se','p','N')
-
-# Check whether per variant sample size is available
-if(length(unique(gwas$N)) == 1){
-  per_var_N <- F
-  log_add(log_file = log_file, message = 'Per variant N is not present.')
-
-  if(opt$impute_N == T){
-    log_add(log_file = log_file, message = 'Per variant N will be imputed.')
-  }
-} else {
-  per_var_N <- T
-  log_add(log_file = log_file, message = 'Per variant N is present.')
-}
-
-# Set maximum p-value threshold
-if(!is.null(opt$P_max)){
-  gwas <- gwas[gwas$p <= opt$P_max,]
-  log_add(log_file = log_file, message = paste0('After p-value threshold of <= ',opt$P_max,', ', nrow(gwas), ' variants remain.'))
-}
-
-# Write out cojo format sumstats
-fwrite(gwas, paste0(tmp_dir,'/GWAS_sumstats_COJO.txt'), sep=' ', na = "NA", quote=F)
-
-rm(gwas)
-gc()
-
-# Record start time for test
-if(!is.na(opt$test)){
-  test_start.time <- test_start(log_file = log_file)
-}
-
-#####
-# Run GCTB SBayesR
-#####
-
-log_add(log_file = log_file, message = 'Running SBayesR analysis.')
-
-sbayesr_opt <- NULL
-if(opt$robust){
-  sbayesr_opt <- paste0(sbayesr_opt, '--robust ')
-}
-if(per_var_N == F & opt$impute_N == T){
-  sbayesr_opt <- paste0(sbayesr_opt, '--impute-n ')
-}
-
-error<-foreach(i = CHROMS, .combine = rbind, .options.multicore = list(preschedule = FALSE)) %dopar% {
-  log <- system(paste0(opt$gctb, ' --sbayes R --ldm ', opt$ld_matrix_chr, i, '.ldm.sparse --pi 0.95,0.02,0.02,0.01 --gamma 0.0,0.01,0.1,1 --gwas-summary ', tmp_dir, '/GWAS_sumstats_COJO.txt --chain-length 10000 ', sbayesr_opt, '--exclude-mhc --burn-in 2000 --out-freq 1000 --out ', tmp_dir, '/GWAS_sumstats_SBayesR.chr', i),  intern = T)
-
-  # Check whether the analysis converged
-  if(any(grepl("Analysis finished", log))){
-    if(any(grepl("MCMC cycles completed", log))){
-	    data.frame(chr=i, Log='Analysis converged')
-    } else {
-      print(log)
-      data.frame(chr=i, Log='Analysis did not converge')
-    }
-  } else {
-    print(log)
-	  data.frame(chr=i, Log='Error')
-  }
-}
-
-# Report an error if SBayesR didn't converge for all chromosomes
-if(sum(grepl('Error', error$Log) == T) > 1){
-  log_add(log_file = log_file, message = paste0('An error occurred for ', sum(grepl('Error', error$Log) == T), ' chromosomes. Retry requesting more memory or run interactively to debug.'))
-  sink(file = log_file, append = T)
-    print(error)
-  sink()
-  q()
-  n
-}
-
-# Combine per chromosome snpRes files
-snpRes<-NULL
-for(i in CHROMS){
-  snpRes <- rbind(snpRes, fread(paste0(tmp_dir, '/GWAS_sumstats_SBayesR.chr', i, '.snpRes')))
-}
-
-# Save in plink score format
-snpRes <- snpRes[,c('Name', 'A1', 'A2', 'A1Effect'), with=F]
-names(snpRes) <- c('SNP', 'A1', 'A2', 'SCORE_SBayesR')
-
-# Flip effects to match reference alleles
-ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
-score_new <- map_score(ref = ref, score = snpRes)
-
-fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
-
-if(file.exists(paste0(opt$output,'.score.gz'))){
-  system(paste0('rm ',opt$output,'.score.gz'))
-}
-system(paste0('gzip ',opt$output,'.score'))
-
-# Record end time of test
-if(!is.na(opt$test)){
-  test_finish(log_file = log_file, test_start.time = test_start.time)
-}
-
-# Combine per chromosome parRes files
-parRes_mcmc <- list()
-for(i in CHROMS){
-	parRes_mcmc[[i]] <- fread(paste0(tmp_dir, '/GWAS_sumstats_SBayesR.chr', i, '.mcmcsamples.Par'))
-}
-
-parRes <- NULL
-for(par in names(parRes_mcmc[[i]])){
-	parRes_mcmc_par <- NULL
-	for(i in CHROMS){
-		parRes_mcmc_par <- cbind(parRes_mcmc_par, parRes_mcmc[[i]][[par]])
-	}
-
-	parRes_mcmc_par_sum <- rowSums(parRes_mcmc_par)
-
-	parRes_par <- data.frame( Par = par,
-                            Mean = mean(parRes_mcmc_par_sum),
-                            SD = sd(parRes_mcmc_par_sum))
-
-	parRes <- rbind(parRes, parRes_par)
-}
-
-write.table(parRes, paste0(opt$output_dir,'/GWAS_sumstats_SBayesR.GW.parRes'), col.names=T, row.names=F, quote=F)
-log_add(log_file = log_file, message = paste0('SNP-heritability estimate is ',parRes[parRes$Par == 'hsq', names(parRes) == 'Mean']," (SD=",parRes[parRes$Par == 'hsq', names(parRes) == 'SD'],")."))
-
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
-end.time <- Sys.time()
-time.taken <- end.time - start.time
-sink(file = log_file, append = T)
-cat('Analysis finished at', as.character(end.time),'\n')
-cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
-sink()
-
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
+      help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+      help="Reference PCs for continuous ancestry correction [optional]"),
+  make_option("--ref_freq_chr", action="store", default=NULL, type='character',
+      help="Path to per chromosome reference PLINK .frq files [required]"),
+  make_option("--pop_data", action="store", default=NULL, type='character',
+      help="File containing the population code and location of the keep file [required]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+      help="Path PLINK v2 software binary [required]"),
+  make_option("--output", action="store", default=NULL, type='character',
+      help="Path for output files [required]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+      help="Number of cores for parallel computing [optional]"),
+  make_option("--sumstats", action="store", default=NULL, type='character',
+      help="GWAS summary statistics in LDSC format [required]"),
+  make_option("--gctb", action="store", default=NULL, type='character',
+      help="Path to GCTB binary [required]"),
+  make_option("--impute_N", action="store", default=T, type='logical',
+      help="Logical indicating whether per variant N should imputed based on SE. [optional]"),
+  make_option("--P_max", action="store", default=NULL, type='numeric',
+      help="P-value threshold for filter variants [optional]"),
+  make_option("--robust", action="store", default=F, type='logical',
+      help="Force robust GCTB parameterisation [optional]"),
+  make_option("--test", action="store", default=NA, type='character',
+      help="Specify number of SNPs to include [optional]"),
+  make_option("--ld_matrix_chr", action="store", default=NULL, type='character',
+      help="Path to per chromosome shrunk sparse LD matrix from GCTB [required]")
+)
+
+opt = parse_args(OptionParser(option_list = option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+library(foreach)
+library(doMC)
+registerDoMC(opt$n_cores)
+
+# Check required inputs
+if(is.null(opt$ref_plink_chr)){
+  stop('--ref_plink_chr must be specified.\n')
+}
+if(is.null(opt$sumstats)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$pop_data)){
+  stop('--pop_data must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--output must be specified.\n')
+}
+if(is.null(opt$gctb)){
+  stop('--gctb must be specified.\n')
+}
+if(is.null(opt$ld_matrix_chr)){
+  stop('--ld_matrix_chr must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
+system(paste0('mkdir -p ',opt$output_dir))
+
+# Create temp directory
+tmp_dir <- tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'sbayesr.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+#####
+# Read in sumstats
+#####
+
+log_add(log_file = log_file, message = 'Reading in GWAS.')
+
+# Read in, check and format GWAS summary statistics
+gwas <- read_sumstats(sumstats = opt$sumstats, chr = CHROMS, log_file = log_file, req_cols = c('SNP','A1','A2','FREQ','BETA','SE','P','N'))
+
+###
+# Change to COJO format
+###
+
+gwas <- gwas[, c('SNP','A1','A2','FREQ','BETA','SE','P','N'), with=F]
+names(gwas) <- c('SNP','A1','A2','freq','b','se','p','N')
+
+# Check whether per variant sample size is available
+if(length(unique(gwas$N)) == 1){
+  per_var_N <- F
+  log_add(log_file = log_file, message = 'Per variant N is not present.')
+
+  if(opt$impute_N == T){
+    log_add(log_file = log_file, message = 'Per variant N will be imputed.')
+  }
+} else {
+  per_var_N <- T
+  log_add(log_file = log_file, message = 'Per variant N is present.')
+}
+
+# Set maximum p-value threshold
+if(!is.null(opt$P_max)){
+  gwas <- gwas[gwas$p <= opt$P_max,]
+  log_add(log_file = log_file, message = paste0('After p-value threshold of <= ',opt$P_max,', ', nrow(gwas), ' variants remain.'))
+}
+
+# Write out cojo format sumstats
+fwrite(gwas, paste0(tmp_dir,'/GWAS_sumstats_COJO.txt'), sep=' ', na = "NA", quote=F)
+
+rm(gwas)
+gc()
+
+# Record start time for test
+if(!is.na(opt$test)){
+  test_start.time <- test_start(log_file = log_file)
+}
+
+#####
+# Run GCTB SBayesR
+#####
+
+log_add(log_file = log_file, message = 'Running SBayesR analysis.')
+
+sbayesr_opt <- NULL
+if(opt$robust){
+  sbayesr_opt <- paste0(sbayesr_opt, '--robust ')
+}
+if(per_var_N == F & opt$impute_N == T){
+  sbayesr_opt <- paste0(sbayesr_opt, '--impute-n ')
+}
+
+error<-foreach(i = CHROMS, .combine = rbind, .options.multicore = list(preschedule = FALSE)) %dopar% {
+  log <- system(paste0(opt$gctb, ' --sbayes R --ldm ', opt$ld_matrix_chr, i, '.ldm.sparse --pi 0.95,0.02,0.02,0.01 --gamma 0.0,0.01,0.1,1 --gwas-summary ', tmp_dir, '/GWAS_sumstats_COJO.txt --chain-length 10000 ', sbayesr_opt, '--exclude-mhc --burn-in 2000 --out-freq 1000 --out ', tmp_dir, '/GWAS_sumstats_SBayesR.chr', i),  intern = T)
+
+  # Check whether the analysis converged
+  if(any(grepl("Analysis finished", log))){
+    if(any(grepl("MCMC cycles completed", log))){
+	    data.frame(chr=i, Log='Analysis converged')
+    } else {
+      print(log)
+      data.frame(chr=i, Log='Analysis did not converge')
+    }
+  } else {
+    print(log)
+	  data.frame(chr=i, Log='Error')
+  }
+}
+
+# Report an error if SBayesR didn't converge for all chromosomes
+if(sum(grepl('Error', error$Log) == T) > 1){
+  log_add(log_file = log_file, message = paste0('An error occurred for ', sum(grepl('Error', error$Log) == T), ' chromosomes. Retry requesting more memory or run interactively to debug.'))
+  sink(file = log_file, append = T)
+    print(error)
+  sink()
+  q()
+  n
+}
+
+# Combine per chromosome snpRes files
+snpRes<-NULL
+for(i in CHROMS){
+  snpRes <- rbind(snpRes, fread(paste0(tmp_dir, '/GWAS_sumstats_SBayesR.chr', i, '.snpRes')))
+}
+
+# Save in plink score format
+snpRes <- snpRes[,c('Name', 'A1', 'A2', 'A1Effect'), with=F]
+names(snpRes) <- c('SNP', 'A1', 'A2', 'SCORE_SBayesR')
+
+# Flip effects to match reference alleles
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
+score_new <- map_score(ref = ref, score = snpRes)
+
+fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+
+if(file.exists(paste0(opt$output,'.score.gz'))){
+  system(paste0('rm ',opt$output,'.score.gz'))
+}
+system(paste0('gzip ',opt$output,'.score'))
+
+# Record end time of test
+if(!is.na(opt$test)){
+  test_finish(log_file = log_file, test_start.time = test_start.time)
+}
+
+# Combine per chromosome parRes files
+parRes_mcmc <- list()
+for(i in CHROMS){
+	parRes_mcmc[[i]] <- fread(paste0(tmp_dir, '/GWAS_sumstats_SBayesR.chr', i, '.mcmcsamples.Par'))
+}
+
+parRes <- NULL
+for(par in names(parRes_mcmc[[i]])){
+	parRes_mcmc_par <- NULL
+	for(i in CHROMS){
+		parRes_mcmc_par <- cbind(parRes_mcmc_par, parRes_mcmc[[i]][[par]])
+	}
+
+	parRes_mcmc_par_sum <- rowSums(parRes_mcmc_par)
+
+	parRes_par <- data.frame( Par = par,
+                            Mean = mean(parRes_mcmc_par_sum),
+                            SD = sd(parRes_mcmc_par_sum))
+
+	parRes <- rbind(parRes, parRes_par)
+}
+
+write.table(parRes, paste0(opt$output_dir,'/GWAS_sumstats_SBayesR.GW.parRes'), col.names=T, row.names=F, quote=F)
+log_add(log_file = log_file, message = paste0('SNP-heritability estimate is ',parRes[parRes$Par == 'hsq', names(parRes) == 'Mean']," (SD=",parRes[parRes$Par == 'hsq', names(parRes) == 'SD'],")."))
+
+####
+# Calculate mean and sd of polygenic scores
+####
+
+log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
+
+# Calculate scores in the full reference
+ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
+
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
+# Calculate scale within each reference population
+pop_data <- read_pop_data(opt$pop_data)
+
+for(pop_i in unique(pop_data$POP)){
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
+
diff --git a/Scripts/pgs_methods/sbayesrc.R b/Scripts/pgs_methods/sbayesrc.R
index 8eb747ac..5bb13ff8 100644
--- a/Scripts/pgs_methods/sbayesrc.R
+++ b/Scripts/pgs_methods/sbayesrc.R
@@ -6,6 +6,8 @@ library("optparse")
 option_list = list(
   make_option("--ref_plink_chr", action="store", default=NULL, type='character',
       help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+      help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
       help="File containing the population code and location of the keep file [required]"),
   make_option("--plink2", action="store", default='plink2', type='character',
@@ -240,6 +242,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/ref_pca/ref_pca.R b/Scripts/ref_pca/ref_pca.R
index 6a4dae91..ea6093c9 100644
--- a/Scripts/ref_pca/ref_pca.R
+++ b/Scripts/ref_pca/ref_pca.R
@@ -58,6 +58,11 @@ tmp_dir<-tempdir()
 log_file <- paste(opt$output,'.log',sep='')
 log_header(log_file = log_file, opt = opt, script = 'ref_pca.R', start.time = start.time)
 
+# Set ref_keep to NULL if NA
+if(!is.null(opt$ref_keep) && opt$ref_keep == 'NA'){
+  opt$ref_keep<-NULL
+}
+
 # If testing, change CHROMS to chr value
 if(!is.na(opt$test) && opt$test == 'NA'){
   opt$test<-NA
@@ -127,6 +132,12 @@ log_add(log_file = log_file, message = 'Computing reference PCs.')
 # Calculate PCs in the full reference
 ref_pcs<-plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.eigenvec.var.gz'))
 
+# Scale reference scores across all individuals and save a scale file
+ref_pcs_scale_TRANS <- score_mean_sd(scores = ref_pcs)
+fwrite(ref_pcs_scale_TRANS, paste0(opt$output, '.TRANS.scale'), row.names = F, quote=F, sep=' ', na='NA')
+scores_scaled<-score_scale(score=ref_pcs, ref_scale=ref_pcs_scale_TRANS)
+fwrite(scores_scaled, paste0(opt$output, '.profiles'), row.names = F, quote=F, sep=' ', na='NA')
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/target_scoring/target_scoring.R b/Scripts/target_scoring/target_scoring.R
index e145b240..11fe98fa 100644
--- a/Scripts/target_scoring/target_scoring.R
+++ b/Scripts/target_scoring/target_scoring.R
@@ -6,7 +6,7 @@ library("optparse")
 option_list = list(
 make_option("--target_plink_chr", action="store", default=NULL, type='character',
 		help="Path to per chromosome target PLINK2 files [required]"),
-make_option("--target_keep", action="store", default=NULL, type='character',
+make_option("--target_keep", action="store", default=NA, type='character',
 		help="Path to keep file for target [optional]"),
 make_option("--ref_score", action="store", default=NULL, type='character',
 		help="Path to reference scoring files [required]"),
@@ -67,6 +67,11 @@ tmp_dir<-tempdir()
 log_file <- paste0(opt$output,'.log')
 log_header(log_file = log_file, opt = opt, script = 'target_scoring.R', start.time = start.time)
 
+# Set ref_keep to NULL if NA
+if(!is.null(opt$target_keep) && opt$target_keep == 'NA'){
+  opt$target_keep<-NULL
+}
+
 # If testing, change CHROMS to chr value
 if(!is.na(opt$test) && opt$test == 'NA'){
   opt$test<-NA
diff --git a/Scripts/target_scoring/target_scoring_pipeline.R b/Scripts/target_scoring/target_scoring_pipeline.R
index da7089a9..a0838359 100644
--- a/Scripts/target_scoring/target_scoring_pipeline.R
+++ b/Scripts/target_scoring/target_scoring_pipeline.R
@@ -116,7 +116,11 @@ target_list <- read_param(config = opt$config, param = 'target_list', return_obj
 
 # Set params for plink_score
 opt$target_plink_chr <- paste0(outdir, '/', opt$name, '/geno/', opt$name, '.ref.chr')
-opt$target_keep <- paste0(outdir, '/', opt$name, '/ancestry/keep_files/model_based/', opt$population, '.keep')
+if(opt$population == 'TRANS'){
+  opt$target_keep<-NULL
+} else {
+  opt$target_keep <- paste0(outdir, '/', opt$name, '/ancestry/keep_files/model_based/', opt$population, '.keep')
+}
 refdir <- read_param(config = opt$config, param = 'refdir', return_obj = F)
 opt$ref_freq_chr <- paste0(refdir, '/freq_files/', opt$population,'/ref.', opt$population,'.chr')
 
@@ -202,19 +206,40 @@ scores<-data.table(scores_ids,
 # Scale the polygenic scores based on the reference
 ###
 
-# Read in scale file and update Param
-log_add(log_file = log_file, message = paste0('Reading in scale files.'))
-scale_files<-list()
-for(i in 1:nrow(score_files)){
-  scale_files[[paste0('score_file_', i)]]<-fread(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'-', opt$population,'.scale'))
-  scale_files[[paste0('score_file_', i)]]$Param<-gsub('SCORE_', paste0('score_file_', i, '.'), scale_files[[paste0('score_file_', i)]]$Param)
-}
+if(opt$population == 'TRANS'){
+  log_add(log_file = log_file, message = paste0('Reading in ancestry adjustment models.'))
 
-# Concatenate scale files
-all_scale<-do.call(rbind, scale_files)
-
-log_add(log_file = log_file, message = 'Scaling target polygenic scores to the reference.')
-scores<-score_scale(score=scores, ref_scale=all_scale)
+  models<-list()
+  for(i in 1:nrow(score_files)){
+    models[[paste0('score_file_', i)]]<-readRDS(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'-TRANS.model.rds'))
+    names(models[[paste0('score_file_', i)]])<-gsub('SCORE_', paste0('score_file_', i, '.'), names(models[[paste0('score_file_', i)]]))
+  }
+  
+  models <- do.call(c, unname(models))
+  
+  # Read in target projected PCs
+  target_pcs<-fread(paste0(outdir,'/',opt$name,'/pcs/projected/TRANS/',opt$name,'-TRANS.profiles'))
+  log_add(log_file = log_file, message = paste0('Reading in target reference-projected PCs.'))
+  
+  # Adjust scores
+  log_add(log_file = log_file, message = 'Adjusting target PGS for ancestry.')
+  scores <- score_adjust(score = scores, pcs = target_pcs, ref_model = models)
+} else {
+  # Read in scale file and update Param
+  log_add(log_file = log_file, message = paste0('Reading in scale files.'))
+  scale_files<-list()
+  for(i in 1:nrow(score_files)){
+    scale_files[[paste0('score_file_', i)]]<-fread(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'-', opt$population,'.scale'))
+    scale_files[[paste0('score_file_', i)]]$Param<-gsub('SCORE_', paste0('score_file_', i, '.'), scale_files[[paste0('score_file_', i)]]$Param)
+  }
+  
+  # Concatenate scale files
+  all_scale<-do.call(rbind, scale_files)
+  
+  # Scale scores
+  log_add(log_file = log_file, message = 'Scaling target polygenic scores to the reference.')
+  scores<-score_scale(score=scores, ref_scale=all_scale)
+}
 
 ###
 # Write out the target sample scores
diff --git a/docs/pipeline_1kg_hgdp_prep.Rmd b/docs/pipeline_1kg_hgdp_prep.Rmd
index ccdbf7e6..ccdb2bda 100644
--- a/docs/pipeline_1kg_hgdp_prep.Rmd
+++ b/docs/pipeline_1kg_hgdp_prep.Rmd
@@ -186,6 +186,12 @@ for(pop in unique(pop_dat$POP)){
   }
 }
 
+# Create frequency files across all reference individuals (TRANS)
+dir.create(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS'), recursive = T)
+for(i in 1:22){
+  system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.chr',i,' --chr ',i,' --freq --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS/hgdp_1kg_hm3.chr', i))
+}
+
 ####
 # Create .rds files for SNP stats
 ####
diff --git a/functions/pgs.R b/functions/pgs.R
index 71e2e9b5..a1e1686a 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -464,3 +464,94 @@ quick_prs<-function(sumstats, ref_dir, genomic_control, prs_model, n_cores = 1,
 
   return(score)
 }
+
+# Derive trans-ancestry PGS models and estimate PGS residual scale
+model_trans_pgs<-function(scores=NULL, pcs=NULL, output=NULL){
+  if(any(is.null(c(scores, pcs, output)))){
+    stop('Error: All parameters must be specified.')
+  }
+  
+  if(is.character(pcs)){
+    # Read in the reference PCs, extract PC columns, and update headers
+    pcs_dat<-fread(pcs)
+    names(pcs_dat)[1]<-'FID'
+    pcs_dat<-pcs_dat[,grepl('FID|IID|^PC', names(pcs_dat)), with=F]
+  } else {
+    pcs_dat<-pcs
+  }
+  
+  # Merge PGS and PCs
+  scores_pcs<-merge(scores, pcs_dat, by=c('FID','IID'))
+  
+  # Calculate PGS residuals
+  pcs_noid<-scores_pcs[,grepl('^PC', names(scores_pcs)), with=F]
+  
+  mod_list<-NULL
+  scores_pcs_resid<-scores_pcs
+  for(i in names(scores)[-1:-2]){
+    mod_list[[i]]<-list()
+    
+    tmp<-data.table(y=scores_pcs[[i]], pcs_noid)
+    
+    # Model differences in mean
+    pgs_pc_mean_mod<-lm(y ~ ., data=tmp)
+    
+    # Model differences in variance of residuals
+    # Use gamma distribution to constrain predicted variance to be non-negative
+    predicted_pgs <- predict(pgs_pc_mean_mod, newdata = tmp)
+    residual_pgs <- tmp$y - predicted_pgs
+    squared_residuals <- residual_pgs^2
+    squared_residuals <- pmax(squared_residuals, 1e-6)
+    
+    pgs_pc_var_mod <- glm(squared_residuals ~ ., data = tmp[, names(tmp) != 'y', with=F], family = Gamma(link = "log"))
+    predicted_pgs_var <- exp(predict(pgs_pc_var_mod, newdata = tmp))
+    
+    scores_pcs_resid[[i]]<-residual_pgs/sqrt(predicted_pgs_var)
+    
+    mod_list[[i]]$mean_model <- pgs_pc_mean_mod
+    mod_list[[i]]$var_model <- pgs_pc_var_mod
+  }
+  
+  scores_pcs_resid<-scores_pcs_resid[,grepl('FID|IID|^SCORE', names(scores_pcs_resid)), with=F]
+  
+  # Save mean and SD of PGS residuals in 'trans' population
+  # This should be approximately mean = 0 and SD = 1, but save as a sanity check
+  scores_pcs_resid_scale<-score_mean_sd(scores=scores_pcs_resid)
+  fwrite(scores_pcs_resid_scale, paste0(output,'-TRANS.scale'), sep=' ', col.names=T, quote=F)
+  
+  # Save PGS ~ PC models
+  saveRDS(mod_list, file = paste0(output,'-TRANS.model.rds'))
+}
+
+# Adjust PGS for ancestry using reference PC models
+score_adjust <- function(score, pcs, ref_model) {
+  # Convert score and pcs to data.table if not already
+  setDT(score)
+  setDT(pcs)
+  
+  # Ensure pcs is keyed on FID and IID for efficient joins
+  setkey(pcs, FID, IID)
+  
+  # Loop through each score column (skipping FID and IID)
+  for (col_name in names(score)[-1:-2]) {
+    # Retrieve models for the current score
+    mean_model <- ref_model[[col_name]]$mean_model
+    var_model <- ref_model[[col_name]]$var_model
+    
+    # Calculate residuals directly in `score`
+    score[, (col_name) := {
+      # Get corresponding rows from pcs by FID and IID
+      pc_data <- pcs[.SD, on = .(FID, IID)]
+      
+      # Predict mean and variance
+      predicted_pgs <- predict(mean_model, newdata = pc_data)
+      predicted_pgs_var <- exp(predict(var_model, newdata = pc_data))
+      
+      # Compute residuals
+      round((get(col_name) - predicted_pgs) / sqrt(predicted_pgs_var), 3)
+    }, .SDcols = c("FID", "IID", col_name)]
+  }
+  
+  # Return only relevant columns (FID, IID, and adjusted scores)
+  return(score)
+}
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 57d15f4f..2804f5ac 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -3,13 +3,15 @@ rule ref_pca_i:
   input:
     ref_input,
     rules.install_genoutils.output,
-    f"{resdir}/last_version.txt"
+    f"{resdir}/last_version.txt",
+    "../Scripts/ref_pca/ref_pca.R"
   output:
     f"{resdir}/data/ref/pc_score_files/{{population}}/ref-{{population}}-pcs.EUR.scale"
   conda:
     "../envs/analysis.yaml",
   params:
-    testing=config["testing"]
+    testing=config["testing"],
+    ref_keep=lambda wildcards: "NA" if wildcards.population == "TRANS" else f"{refdir}/keep_files/{wildcards.population}.keep"
   benchmark:
     f"{resdir}/data/benchmarks/ref_pca_i-{{population}}.txt"
   log:
@@ -17,12 +19,12 @@ rule ref_pca_i:
   shell:
     "Rscript ../Scripts/ref_pca/ref_pca.R \
       --ref_plink_chr {refdir}/ref.chr \
-      --ref_keep {refdir}/keep_files/{wildcards.population}.keep \
+      --ref_keep {params.ref_keep} \
       --pop_data {refdir}/ref.pop.txt \
       --output {resdir}/data/ref/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs \
       --test {params.testing} > {log} 2>&1"
 
-populations=["AFR","AMR","EAS","EUR","SAS"]
+populations=["AFR","AMR","CSA","EAS","EUR","MID","TRANS"]
 
 rule ref_pca:
   input: expand(f"{resdir}/data/ref/pc_score_files/{{population}}/ref-{{population}}-pcs.EUR.scale", population=populations)
@@ -75,7 +77,8 @@ rule sumstat_prep:
 
 rule prep_pgs_ptclump_i:
   input:
-    f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz"
+    f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/ptclump/{{gwas}}/ref-{{gwas}}.score.gz"
   conda:
@@ -92,6 +95,7 @@ rule prep_pgs_ptclump_i:
     "Rscript ../Scripts/pgs_methods/ptclump.R \
       --ref_plink_chr {refdir}/ref.chr \
       --ref_keep {refdir}/keep_files/{params.population}.keep \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --output {outdir}/reference/pgs_score_files/ptclump/{wildcards.gwas}/ref-{wildcards.gwas} \
       --pop_data {refdir}/ref.pop.txt \
@@ -121,7 +125,8 @@ rule prep_pgs_dbslmm_i:
     rules.download_ldsc.output,
     rules.download_hm3_snplist.output,
     rules.download_dbslmm.output,
-    rules.download_ld_blocks.output
+    rules.download_ld_blocks.output,
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/dbslmm/{{gwas}}/ref-{{gwas}}.score.gz"
   conda:
@@ -174,7 +179,8 @@ rule prep_pgs_prscs_i:
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
     rules.download_prscs_software.output,
-    lambda w: f"{resdir}/data/prscs_ref/" + prscs_ldref + "/ldblk_" + prscs_ldref + "_" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0].lower() + "/ldblk_1kg_chr1.hdf5"
+    lambda w: f"{resdir}/data/prscs_ref/" + prscs_ldref + "/ldblk_" + prscs_ldref + "_" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0].lower() + "/ldblk_1kg_chr1.hdf5",
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/prscs/{{gwas}}/ref-{{gwas}}.score.gz"
   conda:
@@ -220,7 +226,8 @@ rule prep_pgs_sbayesr_i:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
     lambda w: f"{sbayesr_ldref}/" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0] + "/map.rds",
     rules.download_gctb_ref.output,
-    rules.download_gctb_software.output
+    rules.download_gctb_software.output,
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/sbayesr/{{gwas}}/ref-{{gwas}}.score.gz"
   conda:
@@ -256,7 +263,8 @@ rule prep_pgs_lassosum_i:
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
-    rules.install_lassosum.output
+    rules.install_lassosum.output,
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/lassosum/{{gwas}}/ref-{{gwas}}.score.gz"
   benchmark:
@@ -293,7 +301,8 @@ rule prep_pgs_ldpred2_i:
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
-    lambda w: f"{ldpred2_ldref}/" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0] + "/map.rds"
+    lambda w: f"{ldpred2_ldref}/" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0] + "/map.rds",
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/ldpred2/{{gwas}}/ref-{{gwas}}.score.gz"
   benchmark:
@@ -341,7 +350,8 @@ rule prep_pgs_megaprs_i:
     rules.download_ldak_highld.output,
     rules.download_ldak.output,
     rules.download_ldak_map.output,
-    rules.download_ldak_bld.output
+    rules.download_ldak_bld.output,
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/megaprs/{{gwas}}/ref-{{gwas}}.score.gz"
   benchmark:
@@ -392,7 +402,8 @@ rule prep_pgs_quickprs_i:
     rules.download_ldak_highld.output,
     rules.download_ldak5_2.output,
     rules.download_ldak_map.output,
-    rules.download_ldak_bld.output
+    rules.download_ldak_bld.output,
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz"
   benchmark:
@@ -434,7 +445,8 @@ rule prep_pgs_sbayesrc_i:
     rules.download_gctb252_software.output,
     rules.download_sbayesrc_annot.output,
     rules.install_genoutils_sbayesrc.output,
-    rules.install_sbayesrc.output
+    rules.install_sbayesrc.output,
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/sbayesrc/{{gwas}}/ref-{{gwas}}.score.gz"
   benchmark:
@@ -501,7 +513,8 @@ rule prep_pgs_external_i:
   input:
     lambda w: score_path(w),
     ref_input,
-    rules.install_genoutils.output
+    rules.install_genoutils.output,
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     touch(f"{outdir}/reference/target_checks/prep_pgs_external_i-{{score}}.done")
   params:
diff --git a/pipeline/rules/target_scoring.smk b/pipeline/rules/target_scoring.smk
index 52d2b23e..35918fce 100644
--- a/pipeline/rules/target_scoring.smk
+++ b/pipeline/rules/target_scoring.smk
@@ -3,8 +3,10 @@ def ancestry_munge(x):
     checkpoints.ancestry_reporter.get(name=x).output[0]
     checkpoint_output = outdir + "/" + x + "/ancestry/ancestry_report.txt"
     ancestry_report_df = pd.read_table(checkpoint_output, sep=' ')
-    return ancestry_report_df['population'].tolist()
-
+    population_list = ancestry_report_df['population'].tolist()
+    population_list.append('TRANS')
+    return population_list
+    
 # Define which pgs_methods are can be applied to any GWAS population
 pgs_methods_noneur = ['ptclump','lassosum','megaprs','prscs','dbslmm']
 
@@ -25,11 +27,12 @@ rule pc_projection_i:
   conda:
     "../envs/analysis.yaml"
   params:
-    testing=config["testing"]
+    testing=config["testing"],
+    target_keep=lambda wildcards: "NA" if wildcards.population == "TRANS" else f"{outdir}/{wildcards.name}/ancestry/keep_files/model_based/{wildcards.population}.keep"
   shell:
     "Rscript ../Scripts/target_scoring/target_scoring.R \
       --target_plink_chr {outdir}/{wildcards.name}/geno/{wildcards.name}.ref.chr \
-      --target_keep {outdir}/{wildcards.name}/ancestry/keep_files/model_based/{wildcards.population}.keep \
+      --target_keep {params.target_keep} \
       --ref_freq_chr {refdir}/freq_files/{wildcards.population}/ref.{wildcards.population}.chr \
       --ref_score {resdir}/data/ref/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs.eigenvec.var.gz \
       --ref_scale {resdir}/data/ref/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs.{wildcards.population}.scale \
@@ -58,6 +61,7 @@ rule target_pgs_i:
   threads: config['cores_target_pgs']
   input:
     f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",
+    f"{outdir}/reference/target_checks/{{name}}/pc_projection-TRANS.done",
     rules.prep_pgs.input
   output:
     touch(f"{outdir}/reference/target_checks/{{name}}/target_pgs-{{population}}.done")

From 6b93059aa7e648180b217c924b3c051ae2e89d20 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 6 Dec 2024 15:50:24 +0000
Subject: [PATCH 059/153] Allowed for TRANS population in read_pgs function

---
 functions/pipeline.R | 9 +++++----
 1 file changed, 5 insertions(+), 4 deletions(-)

diff --git a/functions/pipeline.R b/functions/pipeline.R
index 1bbe3e93..36dc3c04 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -43,16 +43,17 @@ read_pgs <- function(config, name = NULL, pgs_methods = NULL, gwas = NULL, pop =
   for (name_i in target_list$name) {
     # Read in keep_list to determine populations available
     keep_list_i <- fread(paste0(outdir,'/',name_i,'/ancestry/keep_list.txt'))
-
+    pops <- c('TRANS', keep_list_i$POP)
+    
     if(!is.null(pop)){
-      if(any(!(pop %in% keep_list_i$POP))){
+      if(any(!(pop %in% pops))){
         stop(paste0('Requested pop are not present in ',name_i,' sample.'))
       }
-      keep_list_i <- keep_list_i[keep_list_i$POP %in% pop,]
+      pops <- pops[pops %in% pop]
     }
 
     pgs[[name_i]] <- list()
-    for (pop_i in keep_list_i$POP) {
+    for (pop_i in pops) {
       pgs[[name_i]][[pop_i]] <- list()
       for(score_i in 1:nrow(score_file_list)){
         gwas_i <- score_file_list$name[score_i]

From 707fa1abd43012e2843088dd2d17230c45c4f12b Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 6 Dec 2024 15:51:06 +0000
Subject: [PATCH 060/153] Updated pgs_methods.smk to allow for TRANS PGS

---
 pipeline/rules/pgs_methods.smk | 9 +++++++++
 1 file changed, 9 insertions(+)

diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 2804f5ac..7fb6ae1d 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -146,6 +146,7 @@ rule prep_pgs_dbslmm_i:
     "Rscript ../Scripts/pgs_methods/dbslmm.R \
       --ref_plink_chr {refdir}/ref.chr \
       --ref_keep {refdir}/keep_files/{params.population}.keep \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --ld_blocks {resdir}/data/ld_blocks/{params.ld_block_pop} \
       --plink {resdir}/software/plink/plink \
@@ -201,6 +202,7 @@ rule prep_pgs_prscs_i:
     export OPENBLAS_NUM_THREADS=1; \
     Rscript ../Scripts/pgs_methods/prscs.R \
     --ref_plink_chr {refdir}/ref.chr \
+    --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
     --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
     --output {outdir}/reference/pgs_score_files/prscs/{wildcards.gwas}/ref-{wildcards.gwas} \
     --pop_data {refdir}/ref.pop.txt \
@@ -241,6 +243,7 @@ rule prep_pgs_sbayesr_i:
   shell:
     "Rscript ../Scripts/pgs_methods/sbayesr.R \
       --ref_plink_chr {refdir}/ref.chr \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --gctb {resdir}/software/gctb/gctb_2.03beta_Linux/gctb \
       --ld_matrix_chr {sbayesr_ldref} \
@@ -281,6 +284,7 @@ rule prep_pgs_lassosum_i:
      --ref_plink_chr {refdir}/ref.chr \
      --ref_keep {refdir}/keep_files/{params.population}.keep \
      --gwas_pop {params.population} \
+     --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
      --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
      --output {outdir}/reference/pgs_score_files/lassosum/{wildcards.gwas}/ref-{wildcards.gwas} \
      --n_cores {threads} \
@@ -323,6 +327,7 @@ rule prep_pgs_ldpred2_i:
     Rscript ../Scripts/pgs_methods/ldpred2.R \
       --ref_plink_chr {refdir}/ref.chr \
       --ldpred2_ref_dir {ldpred2_ldref}/{params.population} \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --n_cores {threads} \
       --output {outdir}/reference/pgs_score_files/ldpred2/{wildcards.gwas}/ref-{wildcards.gwas} \
@@ -367,6 +372,7 @@ rule prep_pgs_megaprs_i:
     "Rscript ../Scripts/pgs_methods/megaprs.R \
       --ref_plink_chr {refdir}/ref.chr \
       --ref_keep {refdir}/keep_files/{params.population}.keep \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --ldak {resdir}/software/ldak/ldak5.1.linux \
       --ldak_map {resdir}/data/ldak_map/genetic_map_b37 \
@@ -419,6 +425,7 @@ rule prep_pgs_quickprs_i:
   shell:
     "Rscript ../Scripts/pgs_methods/quickprs.R \
       --ref_plink_chr {refdir}/ref.chr \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --ldak {resdir}/software/ldak5.2/ldak5.2.linux \
       --quick_prs_ref {params.quick_prs_ref} \
@@ -463,6 +470,7 @@ rule prep_pgs_sbayesrc_i:
     "export OMP_NUM_THREADS={threads}; \
     Rscript ../Scripts/pgs_methods/sbayesrc.R \
       --ref_plink_chr {refdir}/ref.chr \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --gctb {resdir}/software/gctb_2.5.2/gctb_2.5.2_Linux/gctb \
       --sbayesrc_ldref {params.sbayesrc_ldref} \
@@ -533,6 +541,7 @@ rule prep_pgs_external_i:
     "Rscript ../Scripts/external_score_processor/external_score_processor.R \
       --ref_plink_chr {refdir}/ref.chr \
       --score {params.score} \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --output {outdir}/reference/pgs_score_files/external/{wildcards.score}/ref-{wildcards.score} \
       --pop_data {refdir}/ref.pop.txt \
       --test {params.testing} > {log} 2>&1"

From 81934fffca09996acce2131bb6d4408c95b3d50d Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 6 Dec 2024 15:51:40 +0000
Subject: [PATCH 061/153] Starting adding benchmark using simulated data from
 HAPNEST

---
 docs/hapnest_benchmark.Rmd | 141 +++++++++++++++++++++++++++++++++++++
 1 file changed, 141 insertions(+)
 create mode 100644 docs/hapnest_benchmark.Rmd

diff --git a/docs/hapnest_benchmark.Rmd b/docs/hapnest_benchmark.Rmd
new file mode 100644
index 00000000..4a312823
--- /dev/null
+++ b/docs/hapnest_benchmark.Rmd
@@ -0,0 +1,141 @@
+---
+title: HapNest Benchmark
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+***
+
+Here will use simulated genetic data to benchmark GenoPred. We will simulate the genetic data using [HAPNEST](https://github.com/intervene-EU-H2020/synthetic_data).
+
+***
+
+# Set up HAPNEST
+
+```{bash}
+mkdir -p /users/k1806347/oliverpainfel/HAPNEST
+cd /users/k1806347/oliverpainfel/HAPNEST
+
+# Step 1: Download container
+singularity pull docker://sophiewharrie/intervene-synthetic-data
+
+# Step 2: Set up workspace
+mkdir -p /users/k1806347/oliverpainfel/HAPNEST/containers
+mv intervene-synthetic-data_latest.sif /users/k1806347/oliverpainfel/HAPNEST/containers/
+mkdir -p /users/k1806347/oliverpainfel/HAPNEST/data
+
+# Step 3: Initiate HAPNEST (download dependencies)
+export TMPDIR=/tmp
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif init
+
+# Step 4: Download reference data
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif fetch
+
+```
+
+***
+
+# Test run using default config
+
+Generates data for chromosome 1, for 6 populations, HapMap3 SNPs, and 1 phenotype.
+
+```{bash}
+# Step 5: Generate genotype and phenotype data
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif generate_geno 1 data/config.yaml
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif generate_pheno data/config.yaml
+
+# Step 6: Evaluate simulation (optional and slow)
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif validate data/config.yaml
+```
+
+The data simulation took about 1 minute. Evaluation takes >2 hours.
+
+***
+
+# HAPNEST released genotype and phenotype data
+
+It would probably be easier, and more reproducible to use the released version of simulated data from the HAPNEST paper. The files are very large as they are for 6.8M variants and 1M individuals. Let's start with chromosome 22 to testing things out. We can subset the files to HapMap3 variants as we download them to avoid storing so much data in first instance.
+
+***
+
+## Download genotype and phenotype
+
+```{bash}
+mkdir -p /users/k1806347/oliverpainfel/HAPNEST/released/full
+mkdir -p /users/k1806347/oliverpainfel/HAPNEST/released/subset
+module load plink2
+cd /users/k1806347/oliverpainfel/HAPNEST/released/full
+
+# Download genotype data
+# Subset the data to HapMap3 variants to save storage space
+# Might as well convert to plink2 format for efficiency
+for chr in $(seq 22 22); do
+  for file in $(echo bed bim fam); do
+    wget https://ftp.ebi.ac.uk/biostudies/fire/S-BSST/936/S-BSST936/Files/genotypes/synthetic_v1_chr-${chr}.${file}
+  done
+  
+  wget https://ftp.ebi.ac.uk/biostudies/fire/S-BSST/936/S-BSST936/Files/rsids/rsid_variant_map_list_chr22.txt
+  wget https://ftp.ebi.ac.uk/biostudies/fire/S-BSST/936/S-BSST936/Files/example/synthetic_small_v1_chr-22.bim
+  
+  awk 'NR==FNR {snp[$1]; next} $2 in snp' /users/k1806347/oliverpainfel/GenoPred/pipeline/resources/data/hm3_snplist/w_hm3.snplist rsid_variant_map_list_chr22.txt > matched_rows.txt
+
+  plink2 \
+    --bfile synthetic_v1_chr-${chr} \
+    --make-pgen \
+    --extract matched_rows.txt \
+    --out /users/k1806347/oliverpainfel/HAPNEST/released/subset/synthetic_v1_hm3_chr${chr}
+
+  rm synthetic_v1_chr-${chr}.*
+  rm matched_rows.txt
+  rm rsid_variant_map_list_chr${chr}.txt
+done
+
+# Download phenotype data
+mkdir -p /users/k1806347/oliverpainfel/HAPNEST/released/phenotype
+cd /users/k1806347/oliverpainfel/HAPNEST/released/phenotype
+wget https://ftp.ebi.ac.uk/biostudies/fire/S-BSST/936/S-BSST936/Files/synthetic_v1.sample
+for i in $(seq 1 9); do
+  wget https://ftp.ebi.ac.uk/biostudies/fire/S-BSST/936/S-BSST936/Files/phenotypes/synthetic_v1.pheno${i}
+done
+
+```
+
+Note. For some reason there only about 65% of HapMap3 variants available in the synthetic data. This will cause an error when using GenoPred as it requires a certain overlap with the default reference data. Given we are going to generate the GWAS using this data, this wouldn't actually cause any issues of SNP overlap, but there would be poor coverage of the genome which will decrease the PGS R2 values. This is not a big issue, but given it is so fast to simulate data, it is making me think we should simulate our own so we can make it exactly what we want (sample size, genetic architecture, snplist).
+
+***
+
+# Full simulation
+
+Lets modify the quickstart config.yaml to simulate data for chromosome 22, 40k individuals, EUR, EAS and AFR population, 9 phenotypes with same genetic architecture as HAPNEST paper.
+
+```{bash}
+cd /users/k1806347/oliverpainfel/HAPNEST
+
+# Generate genotype and phenotype data
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif generate_geno 8 data/config.synth_1.yaml
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif generate_pheno data/config.synth_1.yaml
+
+```
+
+
+
+***
+
+Lets split the data into training and testing subsets, as we did for UKB EUR in the CrossPop analysis. Perform a GWAS of each phenotype using 
+
+
+
+

From dd0f08e7274e7ba861840e8205baa54767b07ea4 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 6 Dec 2024 18:19:17 +0000
Subject: [PATCH 062/153] Added PGS correlation matrix and GWAS header
 interptetor to report

---
 .../pipeline_reports/indiv_report_creator.Rmd | 109 +++++++++++++++++
 .../pipeline_reports/samp_report_creator.Rmd  | 111 ++++++++++++++++++
 functions/pgs.R                               |   3 +
 functions/pipeline.R                          |  30 +++++
 4 files changed, 253 insertions(+)

diff --git a/Scripts/pipeline_reports/indiv_report_creator.Rmd b/Scripts/pipeline_reports/indiv_report_creator.Rmd
index 6f31e4b5..493869f7 100644
--- a/Scripts/pipeline_reports/indiv_report_creator.Rmd
+++ b/Scripts/pipeline_reports/indiv_report_creator.Rmd
@@ -295,6 +295,7 @@ cat0("## GWAS summary statistics \n\n")
 
 # Create a summary table for GWAS sumstats
 sumstat_qc <- NULL
+column_interp <- list()
 for(gwas in gwas_list$name) {
     log <- readLines(paste0(outdir,'/reference/gwas_sumstat/', gwas, '/', gwas, '-cleaned.log'))
     
@@ -307,6 +308,26 @@ for(gwas in gwas_list$name) {
       population = gwas_list$population[gwas_list$name == gwas],
       orig_n = orig_n,
       final_n = final_n))
+    
+    column_interp_i <-
+      log[
+        seq(
+          which(grepl("---------------", log))[3], 
+          which(grepl("---------------", log))[4])]
+
+    column_interp_i <- column_interp_i[c(-1, -length(column_interp_i))]
+    column_interp_i <- gsub('^ ', '', column_interp_i)
+    header<-column_interp_i[1]
+    data<-column_interp_i[-1]
+    data<-data[grepl('TRUE', data)]
+    original<-sub(' .*','', data)
+    interp<-gsub('  | $','',sub('.*NA     ','', data))
+    column_interp_i <- data.table(
+      original = original,
+      interp = interp)
+    names(column_interp_i)<-c('Header','Interpretation')
+
+    column_interp[[gwas]] <- column_interp_i
 }
 
 names(sumstat_qc) <- c('Name', 'Label', 'Population', 'NSNP Original', 'NSNP Final')
@@ -326,6 +347,36 @@ datatable(sumstat_qc,
 
 cat0("***\n\n")
 
+cat0("<details><summary>Show GWAS header interpretations</summary>\n\n")
+for(i in 1:length(column_interp)){
+  cat0("**",names(column_interp)[i], "** \n\n")
+  cat0("\n\n")
+  
+  # Print the datatable
+  print(htmltools::tagList(
+    datatable(
+    column_interp[[i]], 
+    rownames = FALSE,
+    options = list(
+      dom = 't',
+      ordering = FALSE,
+      columnDefs = list(
+        list(className = "dt-center", targets = '_all')
+      ),
+      scrollX = TRUE
+    ),
+    width = '50%',
+    selection = 'none'
+  )))
+  
+  cat0("***\n\n")
+
+}
+cat0("**Note.** Columns that were dropped are not shown here.\n\n")
+cat0("</details> \n\n")
+
+cat0("***\n\n")
+
 ```
 
 ```{r, results='asis', eval = all(pgs_incl, !is.null(score_list))}
@@ -374,6 +425,64 @@ cat0("***\n\n")
 
 ```{r, results='asis', eval = pgs_incl}
 
+cat0("## PGS correlation \n\n")
+cat0("This section shows the correlation between PGS in the reference sample. This is intended as a sanity check that the PGS correlations are in the expected direction, thereby confirming the GWAS alleles are being interpreted correctly.\n\n")
+
+```
+
+```{r, eval = pgs_incl, include = F}
+
+# Read in the reference pgs
+ref_pgs <- read_reference_pgs(config = params$config)
+
+# Merge all PGS into single data.frame
+for(gwas_i in names(ref_pgs)){
+  for(method_i in names(ref_pgs[[gwas_i]])){
+    names(ref_pgs[[gwas_i]][[method_i]])[3] <- paste(gwas_i,method_i, sep=':')
+  }
+}
+
+ref_pgs <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('FID','IID'), all.x = TRUE), unlist(ref_pgs, recursive=F))
+ref_pgs$FID<-NULL
+ref_pgs$IID<-NULL
+
+# Calculate correlations
+cormat<-cor(ref_pgs)
+                
+# Melt for plotting
+melted_cormat <- melt(cormat, na.rm = TRUE)
+
+plot_obj<-ggplot(melted_cormat, aes(x = Var1, y = Var2, fill = value)) +
+    geom_tile() +
+    scale_fill_gradient2(low = "blue", mid = "white", high = "red",
+                          midpoint = 0, limits = c(-1, 1),
+                          name = "Pearson\nCorrelation\n") +
+    geom_text(aes(label = round(value, 2)), color = "black", size = 4) +
+    theme_minimal() +
+    labs(x = "", y = "") +
+    theme(text = element_text(size = 16), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
+    coord_fixed()
+
+plot_file<-paste0(tempfile(),'.png')
+png(plot_file, units = 'px', res = 100, width = 400+(70*ncol(ref_pgs)), height = 100+(70*ncol(ref_pgs)))
+plot_obj
+dev.off()
+
+```
+
+
+```{r, results='asis', eval = pgs_incl}
+
+cat0("<details><summary>Show reference PGS correlation matrix</summary>\n\n")
+cat0(paste0("![](", plot_file,")"))
+cat0("**Note.** Plot shows correlation between pseudovalidated PGS across all populations, adjusted for ancestry.\n\n")
+cat0("</details> \n\n")
+cat0("***\n\n")
+
+```
+
+```{r, results='asis', eval = pgs_incl}
+
 cat0("## Target Polygenic Profile {.tabset .tabset-fade} \n\n")
 
 ```
diff --git a/Scripts/pipeline_reports/samp_report_creator.Rmd b/Scripts/pipeline_reports/samp_report_creator.Rmd
index d5ef38f3..f45eff4d 100644
--- a/Scripts/pipeline_reports/samp_report_creator.Rmd
+++ b/Scripts/pipeline_reports/samp_report_creator.Rmd
@@ -201,6 +201,7 @@ cat0("## GWAS summary statistics \n\n")
 
 # Create a summary table for GWAS sumstats
 sumstat_qc <- NULL
+column_interp <- list()
 for(gwas in gwas_list$name) {
     log <- readLines(paste0(outdir,'/reference/gwas_sumstat/', gwas, '/', gwas, '-cleaned.log'))
     
@@ -213,6 +214,26 @@ for(gwas in gwas_list$name) {
       population = gwas_list$population[gwas_list$name == gwas],
       orig_n = orig_n,
       final_n = final_n))
+    
+    column_interp_i <-
+      log[
+        seq(
+          which(grepl("---------------", log))[3], 
+          which(grepl("---------------", log))[4])]
+
+    column_interp_i <- column_interp_i[c(-1, -length(column_interp_i))]
+    column_interp_i <- gsub('^ ', '', column_interp_i)
+    header<-column_interp_i[1]
+    data<-column_interp_i[-1]
+    data<-data[grepl('TRUE', data)]
+    original<-sub(' .*','', data)
+    interp<-gsub('  | $','',sub('.*NA     ','', data))
+    column_interp_i <- data.table(
+      original = original,
+      interp = interp)
+    names(column_interp_i)<-c('Header','Interpretation')
+
+    column_interp[[gwas]] <- column_interp_i
 }
 
 names(sumstat_qc) <- c('Name', 'Label', 'Population', 'NSNP Original', 'NSNP Final')
@@ -232,6 +253,36 @@ datatable(sumstat_qc,
 
 cat0("***\n\n")
 
+cat0("<details><summary>Show GWAS header interpretations</summary>\n\n")
+for(i in 1:length(column_interp)){
+  cat0("**",names(column_interp)[i], "** \n\n")
+  cat0("\n\n")
+  
+  # Print the datatable
+  print(htmltools::tagList(
+    datatable(
+    column_interp[[i]], 
+    rownames = FALSE,
+    options = list(
+      dom = 't',
+      ordering = FALSE,
+      columnDefs = list(
+        list(className = "dt-center", targets = '_all')
+      ),
+      scrollX = TRUE
+    ),
+    width = '50%',
+    selection = 'none'
+  )))
+  
+  cat0("***\n\n")
+
+}
+cat0("**Note.** Columns that were dropped are not shown here.\n\n")
+cat0("</details> \n\n")
+
+cat0("***\n\n")
+
 ```
 
 ```{r, results='asis', eval = all(pgs_incl, !is.null(score_list))}
@@ -278,6 +329,64 @@ cat0("***\n\n")
 
 ```
 
+```{r, results='asis', eval = pgs_incl}
+
+cat0("## PGS correlation \n\n")
+cat0("This section shows the correlation between PGS in the reference sample. This is intended as a sanity check that the PGS correlations are in the expected direction, thereby confirming the GWAS alleles are being interpreted correctly.\n\n")
+
+```
+
+```{r, eval = pgs_incl, include = F}
+
+# Read in the reference pgs
+ref_pgs <- read_reference_pgs(config = params$config)
+
+# Merge all PGS into single data.frame
+for(gwas_i in names(ref_pgs)){
+  for(method_i in names(ref_pgs[[gwas_i]])){
+    names(ref_pgs[[gwas_i]][[method_i]])[3] <- paste(gwas_i,method_i, sep=':')
+  }
+}
+
+ref_pgs <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('FID','IID'), all.x = TRUE), unlist(ref_pgs, recursive=F))
+ref_pgs$FID<-NULL
+ref_pgs$IID<-NULL
+
+# Calculate correlations
+cormat<-cor(ref_pgs)
+                
+# Melt for plotting
+melted_cormat <- melt(cormat, na.rm = TRUE)
+
+plot_obj<-ggplot(melted_cormat, aes(x = Var1, y = Var2, fill = value)) +
+    geom_tile() +
+    scale_fill_gradient2(low = "blue", mid = "white", high = "red",
+                          midpoint = 0, limits = c(-1, 1),
+                          name = "Pearson\nCorrelation\n") +
+    geom_text(aes(label = round(value, 2)), color = "black", size = 4) +
+    theme_minimal() +
+    labs(x = "", y = "") +
+    theme(text = element_text(size = 16), axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1)) +
+    coord_fixed()
+
+plot_file<-paste0(tempfile(),'.png')
+png(plot_file, units = 'px', res = 100, width = 400+(70*ncol(ref_pgs)), height = 100+(70*ncol(ref_pgs)))
+plot_obj
+dev.off()
+
+```
+
+
+```{r, results='asis', eval = pgs_incl}
+
+cat0("<details><summary>Show reference PGS correlation matrix</summary>\n\n")
+cat0(paste0("![](", plot_file,")"))
+cat0("**Note.** Plot shows correlation between pseudovalidated PGS across all populations, adjusted for ancestry.\n\n")
+cat0("</details> \n\n")
+cat0("***\n\n")
+
+```
+
 ```{r, include = F, eval = all(pgs_incl, targ_incl)}
 
 # Read in prs
@@ -325,6 +434,7 @@ for(pgs_method_i in unique(pgs_pseudo$pgs_method)){
 ```{r, results='asis', eval = pgs_incl}
 
 cat0("## PGS Distribution {.tabset .tabset-fade} \n\n")
+cat0("This section shows the distribution of PGS in the target sample.\n\n")
 
 for(pgs_method_i in unique(pgs_pseudo$pgs_method)){
   # Create a new tab for each method
@@ -338,6 +448,7 @@ for(pgs_method_i in unique(pgs_pseudo$pgs_method)){
 }
 
 cat0("## {-} \n\n")
+cat0("**Note.** Plot shows distribution of pseudovalidated PGS.\n\n")
 
 ```
 
diff --git a/functions/pgs.R b/functions/pgs.R
index a1e1686a..b6e1bc8d 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -521,6 +521,9 @@ model_trans_pgs<-function(scores=NULL, pcs=NULL, output=NULL){
   
   # Save PGS ~ PC models
   saveRDS(mod_list, file = paste0(output,'-TRANS.model.rds'))
+  
+  # Save TRANS PGS in reference
+  fwrite(scores_pcs_resid, paste0(output,'-TRANS.profiles'), sep=' ', na='NA', quote=F)
 }
 
 # Adjust PGS for ancestry using reference PC models
diff --git a/functions/pipeline.R b/functions/pipeline.R
index 36dc3c04..ff6b31a4 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -337,3 +337,33 @@ read_pseudo_r <- function(config, gwas){
   return(r)
 }
 
+# Read in reference PGS
+# Read in TRANS scores (adjusted for ancestry), and restrict to pseudovalidated models
+read_reference_pgs <- function(config){
+  
+  # Identify score files
+  score_file_list <- list_score_files(config)
+  
+  # Identify outdir parameter
+  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+  
+  pgs <- list()
+  for(score_i in 1:nrow(score_file_list)){
+    gwas_i <- score_file_list$name[score_i]
+    pgs_method_i <- score_file_list$method[score_i]
+    if (is.null(pgs[[gwas_i]])) {
+      pgs[[gwas_i]] <- list()
+    }
+    pgs[[gwas_i]][[pgs_method_i]] <-
+      fread(
+        paste0(
+          outdir, '/reference/pgs_score_files/', pgs_method_i, '/',  gwas_i, '/ref-', gwas_i, '-TRANS.profiles'
+        )
+      )
+    pseudo_param <- find_pseudo(config = config, gwas = gwas_i, pgs_method = pgs_method_i)
+    pgs[[gwas_i]][[pgs_method_i]]<-pgs[[gwas_i]][[pgs_method_i]][,c('FID','IID',paste0('SCORE_',pseudo_param)), with=F]
+  }
+  
+  return(pgs)
+}
+

From 3cdcece6b0d18f7a7dc7f19b1259ef29edeaf8be Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 9 Dec 2024 12:10:13 +0000
Subject: [PATCH 063/153] Show TRANS PGS coloured by population in sample
 report

---
 .../pipeline_reports/samp_report_creator.Rmd  | 23 +++++++++++++++----
 1 file changed, 18 insertions(+), 5 deletions(-)

diff --git a/Scripts/pipeline_reports/samp_report_creator.Rmd b/Scripts/pipeline_reports/samp_report_creator.Rmd
index f45eff4d..522ab031 100644
--- a/Scripts/pipeline_reports/samp_report_creator.Rmd
+++ b/Scripts/pipeline_reports/samp_report_creator.Rmd
@@ -402,6 +402,8 @@ for(gwas in names(pgs[[1]])){
       names(tmp) <- gsub(paste0(gwas, '_'), '', names(tmp))
 
       pgs_pseudo <- rbind(pgs_pseudo, data.table(
+        FID = tmp$FID,
+        IID = tmp$IID,
         score = tmp[[pseudo_param[1]]],
         pop = pop,
         gwas = gwas,
@@ -410,20 +412,31 @@ for(gwas in names(pgs[[1]])){
   }
 }
 
+ancestry_log <- read_ancestry(config = params$config,  name = params$name)
+for(i in names(ancestry_log$keep_files)){
+  pgs_pseudo$assigned_pop[paste0(pgs_pseudo$FID, '.', pgs_pseudo$IID) %in% paste0(ancestry_log$keep_files[[i]]$V1, '.',  ancestry_log$keep_files[[i]]$V2)] <- i
+}
+pgs_pseudo$assigned_pop[is.na(pgs_pseudo$assigned_pop)]<-'Unassigned'
+
 # Plot the distribution of polygenic scores
 pgs_dist_plots <- list()
 for(pgs_method_i in unique(pgs_pseudo$pgs_method)){
   pgs_pseudo_i <- pgs_pseudo[pgs_pseudo$pgs_method %in% pgs_method_i,]
   pgs_dist_plots[[pgs_method_i]] <- 
-    ggplot(pgs_pseudo_i, aes(x=score, fill=1)) + 
+    ggplot(pgs_pseudo_i, aes(x=score, fill=assigned_pop)) + 
       geom_density(alpha=0.5) +
-      labs(x='Polygenic Z-Score', y='Density') +
+      labs(x='Polygenic Z-Score', y='Density', fill = 'Population') +
       theme_half_open() +
       background_grid() +
       panel_border() +
       facet_grid(gwas ~ pop) +
-      theme(legend.position = "none")
-  
+      theme(
+        legend.position = "bottom",        # Move legend below the plot
+        legend.title = element_text(size = 12),  # Adjust legend title size (optional)
+        legend.text = element_text(size = 10)    # Adjust legend text size (optional)
+      ) +
+      guides(fill = guide_legend(nrow = 1)) 
+
   png(paste0(tmp_folder,'/plot_', pgs_method_i,'.png'), height = 150 + (300*length(unique(pgs_pseudo_i$gwas))), width = 2000, res = 170)
     print(pgs_dist_plots[[pgs_method_i]])
   dev.off()
@@ -434,7 +447,7 @@ for(pgs_method_i in unique(pgs_pseudo$pgs_method)){
 ```{r, results='asis', eval = pgs_incl}
 
 cat0("## PGS Distribution {.tabset .tabset-fade} \n\n")
-cat0("This section shows the distribution of PGS in the target sample.\n\n")
+cat0("This section shows the distribution of PGS in the target sample. PGS are shown scaled according to each ancestry-match reference population. TRANS includes PGS across all populations after continuous adjustment for ancestry.\n\n")
 
 for(pgs_method_i in unique(pgs_pseudo$pgs_method)){
   # Create a new tab for each method

From 11913d22b52e42095bab13ac2cb7f9062926e92a Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 9 Dec 2024 13:09:04 +0000
Subject: [PATCH 064/153] Updated read_pgs function to include pseudo_only
 option, and updated report scripts to use it

---
 .../pipeline_reports/indiv_report_creator.Rmd | 35 +++++++-----------
 .../pipeline_reports/samp_report_creator.Rmd  | 37 +++++++++----------
 2 files changed, 30 insertions(+), 42 deletions(-)

diff --git a/Scripts/pipeline_reports/indiv_report_creator.Rmd b/Scripts/pipeline_reports/indiv_report_creator.Rmd
index 493869f7..015ba857 100644
--- a/Scripts/pipeline_reports/indiv_report_creator.Rmd
+++ b/Scripts/pipeline_reports/indiv_report_creator.Rmd
@@ -490,30 +490,21 @@ cat0("## Target Polygenic Profile {.tabset .tabset-fade} \n\n")
 ```{r, include = F, eval = pgs_incl}
 
 # Read in prs
-pgs <- read_pgs(config = params$config, name = params$name)[[1]]
+pgs <- read_pgs(config = params$config, name = params$name, pop = 'TRANS', pseudo_only=T)[[1]]
 
-# Retain pseudovalidated PGS for target individual
-pgs <- pgs[[top_pop]]
+# Structure PGS for target individual
 pgs_dat <- NULL
-for (gwas_i in names(pgs)) {
-  for (pgs_method_i in names(pgs[[gwas_i]])) {
-    pseudo_param <-
-      find_pseudo(config = params$config,
-                  gwas = gwas_i,
-                  pgs_method = pgs_method_i,
-                  target_pop = top_pop)
-    
-    tmp <- pgs[[gwas_i]][[pgs_method_i]]
-    names(tmp) <- gsub(paste0(gwas_i, '_'), '', names(tmp))
-    tmp <- tmp[paste0(tmp$FID, '.', tmp$IID) == params$id, pseudo_param[1], with = F]
-    tmp <- data.table(
-      score = names(tmp),
-      value = as.numeric(tmp),
-      gwas = gwas_i,
-      pgs_method = pgs_method_i
-    )
-    
-    pgs_dat <- rbind(pgs_dat, tmp)
+for(gwas in names(pgs[[1]])){
+  for(pgs_method in names(pgs[[1]][[gwas]])){
+    for(pop in names(pgs)){
+      tmp <- pgs[[pop]][[gwas]][[pgs_method]]
+      tmp <- tmp[paste0(tmp$FID, '.', tmp$IID) == params$id,]
+      names(tmp)<-c('FID','IID','value')
+      tmp$pop = pop
+      tmp$gwas = gwas
+      tmp$pgs_method = pgs_method
+      pgs_dat <- rbind(pgs_dat, tmp)
+    }
   }
 }
 
diff --git a/Scripts/pipeline_reports/samp_report_creator.Rmd b/Scripts/pipeline_reports/samp_report_creator.Rmd
index 522ab031..825912a8 100644
--- a/Scripts/pipeline_reports/samp_report_creator.Rmd
+++ b/Scripts/pipeline_reports/samp_report_creator.Rmd
@@ -390,40 +390,37 @@ cat0("***\n\n")
 ```{r, include = F, eval = all(pgs_incl, targ_incl)}
 
 # Read in prs
-pgs <- read_pgs(config = params$config, name = params$name)[[1]]
+pgs <- read_pgs(config = params$config, name = params$name, pseudo_only=T)[[1]]
 
-# Subset pseudovalidated PGS
-pgs_pseudo <- NULL
+# Structure PGS for plotting
+pgs_melt <- NULL
 for(gwas in names(pgs[[1]])){
   for(pgs_method in names(pgs[[1]][[gwas]])){
-    pseudo_param <- find_pseudo(config = params$config, gwas = gwas, pgs_method = pgs_method, target_pop = 'TRANS')
     for(pop in names(pgs)){
       tmp <- pgs[[pop]][[gwas]][[pgs_method]]
-      names(tmp) <- gsub(paste0(gwas, '_'), '', names(tmp))
-
-      pgs_pseudo <- rbind(pgs_pseudo, data.table(
-        FID = tmp$FID,
-        IID = tmp$IID,
-        score = tmp[[pseudo_param[1]]],
-        pop = pop,
-        gwas = gwas,
-        pgs_method = pgs_method))
+      names(tmp)<-c('FID','IID','score')
+      tmp$pop = pop
+      tmp$gwas = gwas
+      tmp$pgs_method = pgs_method
+      pgs_melt <- rbind(pgs_melt, tmp)
     }
   }
 }
+pgs<-pgs_melt
+rm(pgs_melt)
 
 ancestry_log <- read_ancestry(config = params$config,  name = params$name)
 for(i in names(ancestry_log$keep_files)){
-  pgs_pseudo$assigned_pop[paste0(pgs_pseudo$FID, '.', pgs_pseudo$IID) %in% paste0(ancestry_log$keep_files[[i]]$V1, '.',  ancestry_log$keep_files[[i]]$V2)] <- i
+  pgs$assigned_pop[paste0(pgs$FID, '.', pgs$IID) %in% paste0(ancestry_log$keep_files[[i]]$V1, '.',  ancestry_log$keep_files[[i]]$V2)] <- i
 }
-pgs_pseudo$assigned_pop[is.na(pgs_pseudo$assigned_pop)]<-'Unassigned'
+pgs$assigned_pop[is.na(pgs$assigned_pop)]<-'Unassigned'
 
 # Plot the distribution of polygenic scores
 pgs_dist_plots <- list()
-for(pgs_method_i in unique(pgs_pseudo$pgs_method)){
-  pgs_pseudo_i <- pgs_pseudo[pgs_pseudo$pgs_method %in% pgs_method_i,]
+for(pgs_method_i in unique(pgs$pgs_method)){
+  pgs_i <- pgs[pgs$pgs_method %in% pgs_method_i,]
   pgs_dist_plots[[pgs_method_i]] <- 
-    ggplot(pgs_pseudo_i, aes(x=score, fill=assigned_pop)) + 
+    ggplot(pgs_i, aes(x=score, fill=assigned_pop)) + 
       geom_density(alpha=0.5) +
       labs(x='Polygenic Z-Score', y='Density', fill = 'Population') +
       theme_half_open() +
@@ -437,7 +434,7 @@ for(pgs_method_i in unique(pgs_pseudo$pgs_method)){
       ) +
       guides(fill = guide_legend(nrow = 1)) 
 
-  png(paste0(tmp_folder,'/plot_', pgs_method_i,'.png'), height = 150 + (300*length(unique(pgs_pseudo_i$gwas))), width = 2000, res = 170)
+  png(paste0(tmp_folder,'/plot_', pgs_method_i,'.png'), height = 150 + (300*length(unique(pgs_i$gwas))), width = 2000, res = 170)
     print(pgs_dist_plots[[pgs_method_i]])
   dev.off()
 }
@@ -449,7 +446,7 @@ for(pgs_method_i in unique(pgs_pseudo$pgs_method)){
 cat0("## PGS Distribution {.tabset .tabset-fade} \n\n")
 cat0("This section shows the distribution of PGS in the target sample. PGS are shown scaled according to each ancestry-match reference population. TRANS includes PGS across all populations after continuous adjustment for ancestry.\n\n")
 
-for(pgs_method_i in unique(pgs_pseudo$pgs_method)){
+for(pgs_method_i in unique(pgs$pgs_method)){
   # Create a new tab for each method
   cat0("### ", pgs_method_i, "\n")
   

From 16fb7dbcfcbe0b633275ecc57bc280ce04e653f4 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 9 Dec 2024 14:51:58 +0000
Subject: [PATCH 065/153] Included parameter to select ancestry adjustment
 approach, continuous or discrete

---
 .../pipeline_reports/samp_report_creator.Rmd  |  5 ++-
 docs/pipeline_readme.Rmd                      | 20 +++++++++
 functions/pipeline.R                          | 44 +++++++++++++------
 pipeline/config.yaml                          |  2 +
 pipeline/rules/target_scoring.smk             | 37 ++++++++++++----
 5 files changed, 86 insertions(+), 22 deletions(-)

diff --git a/Scripts/pipeline_reports/samp_report_creator.Rmd b/Scripts/pipeline_reports/samp_report_creator.Rmd
index 825912a8..d86822bf 100644
--- a/Scripts/pipeline_reports/samp_report_creator.Rmd
+++ b/Scripts/pipeline_reports/samp_report_creator.Rmd
@@ -416,6 +416,9 @@ for(i in names(ancestry_log$keep_files)){
 pgs$assigned_pop[is.na(pgs$assigned_pop)]<-'Unassigned'
 
 # Plot the distribution of polygenic scores
+plot_width <- 250 + length(unique(pgs$pop)) * 250
+if(plot_width < 1000) plot_width <- 1000
+
 pgs_dist_plots <- list()
 for(pgs_method_i in unique(pgs$pgs_method)){
   pgs_i <- pgs[pgs$pgs_method %in% pgs_method_i,]
@@ -434,7 +437,7 @@ for(pgs_method_i in unique(pgs$pgs_method)){
       ) +
       guides(fill = guide_legend(nrow = 1)) 
 
-  png(paste0(tmp_folder,'/plot_', pgs_method_i,'.png'), height = 150 + (300*length(unique(pgs_i$gwas))), width = 2000, res = 170)
+  png(paste0(tmp_folder,'/plot_', pgs_method_i,'.png'), height = 150 + (300*length(unique(pgs_i$gwas))), width = plot_width, res = 170)
     print(pgs_dist_plots[[pgs_method_i]])
   dev.off()
 }
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index d068bd29..a2af92be 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -1116,6 +1116,7 @@ Reads in polygenic scores (PGS) based on the provided configuration and filters.
   - `pgs_methods` (optional): A vector of PGS methods to include. Default is NULL.
   - `gwas` (optional): A vector of GWAS to include. Default is NULL.
   - `pop` (optional): A vector of populations to include. Default is NULL.
+  - `pseudo_only` (optional): A logical parameter that determines whether to return only the PGS selected through pseudovalidation. Default is FALSE.
   
 - **Returns**
   - A list containing the filtered PGS data structured by target name, population, GWAS, and PGS method.
@@ -1399,6 +1400,25 @@ kable(gwas_groups_df, 'markdown')
 
 </div>
 
+***
+
+## Select ancestry adjustment approach
+
+The GenoPred pipeline has the capability to generate target PGS scaled according to an ancestry-matched reference, using either continuous or discrete corrections for ancestry (details provided [here]()).
+
+By default, the pipeline applies only the continuous correction for ancestry to scale the target PGS. However, users can specify the desired ancestry adjustment approach using the `pgs_scaling` parameter in the `configfile`. This parameter accepts a list of values, which can include `continuous` and `discrete`.
+
+For example, to produce target PGS using both continuous and discrete correction, specify:
+
+```{yaml}
+pgs_scaling: ['continuous', 'discrete]
+```
+
+Or, to produce target PGS only the discrete correction, specify:
+
+```{yaml}
+pgs_scaling: ['discrete']
+```
 
 ***
 
diff --git a/functions/pipeline.R b/functions/pipeline.R
index ff6b31a4..b4062e08 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -5,8 +5,7 @@ if (!require("data.table", quietly = TRUE)) {
 }
 
 # Read in PGS
-read_pgs <- function(config, name = NULL, pgs_methods = NULL, gwas = NULL, pop = NULL){
-
+read_pgs <- function(config, name = NULL, pgs_methods = NULL, gwas = NULL, pop = NULL, pseudo_only = F){
   # Read in target_list
   target_list <- read_param(config = config, param = 'target_list')
   if(!is.null(name)){
@@ -38,14 +37,25 @@ read_pgs <- function(config, name = NULL, pgs_methods = NULL, gwas = NULL, pop =
 
   # Identify outdir parameter
   outdir <- read_param(config = config, param = 'outdir', return_obj = F)
-
+  
+  # Identify pgs_scaling parameter
+  pgs_scaling <- read_param(config = config, param = 'pgs_scaling', return_obj = F)
+  
   pgs <- list()
   for (name_i in target_list$name) {
-    # Read in keep_list to determine populations available
-    keep_list_i <- fread(paste0(outdir,'/',name_i,'/ancestry/keep_list.txt'))
-    pops <- c('TRANS', keep_list_i$POP)
-    
+    pops<-NULL
+    if('continuous' %in% pgs_scaling){
+      pops <- c('TRANS', pops)
+    }
+    if('discrete' %in% pgs_scaling){
+      # Read in keep_list to determine populations available
+      keep_list_i <- fread(paste0(outdir,'/',name_i,'/ancestry/keep_list.txt'))
+      pops <- c(pops, keep_list_i$POP)
+    }
     if(!is.null(pop)){
+      if(!any('discrete' %in% pgs_scaling) & any(pop != 'TRANS')){
+        stop(paste0('Requested pop are not present in ',name_i,' sample. Only PGS adjusted using continuous ancestry correction are available due to pgs_scaling parameter in configfile.'))
+      }
       if(any(!(pop %in% pops))){
         stop(paste0('Requested pop are not present in ',name_i,' sample.'))
       }
@@ -61,12 +71,20 @@ read_pgs <- function(config, name = NULL, pgs_methods = NULL, gwas = NULL, pop =
         if (is.null(pgs[[name_i]][[pop_i]][[gwas_i]])) {
           pgs[[name_i]][[pop_i]][[gwas_i]] <- list()
         }
-        pgs[[name_i]][[pop_i]][[gwas_i]][[pgs_method_i]] <-
-            fread(
-              paste0(
-                outdir, '/', name_i, '/pgs/', pop_i, '/', pgs_method_i, '/',  gwas_i, '/', name_i, '-', gwas_i, '-', pop_i, '.profiles'
-              )
-            )
+        file_i<-paste0(outdir, '/', name_i, '/pgs/', pop_i, '/', pgs_method_i, '/',  gwas_i, '/', name_i, '-', gwas_i, '-', pop_i, '.profiles')
+        if(pseudo_only){
+          pseudo_param <- find_pseudo(config = config, gwas = gwas_i, target_pop = pop_i, pgs_method = pgs_method_i)
+
+          score_header <-
+            fread(file_i, nrows = 1)
+          score_cols <-
+            which(names(score_header) %in% c('FID', 'IID', paste0(gwas_i, '_',pseudo_param)))
+          
+          pgs[[name_i]][[pop_i]][[gwas_i]][[pgs_method_i]] <-
+            fread(cmd = paste0("cut -d' ' -f ", paste0(score_cols, collapse=','), " ", file_i))
+        } else {
+          pgs[[name_i]][[pop_i]][[gwas_i]][[pgs_method_i]] <- fread(file_i)
+        }
       }
     }
   }
diff --git a/pipeline/config.yaml b/pipeline/config.yaml
index cd7aacf2..e041cf15 100644
--- a/pipeline/config.yaml
+++ b/pipeline/config.yaml
@@ -79,3 +79,5 @@ cores_outlier_detection: 5
 # Specify PGS methods that should be used by TL-PRS
 tlprs_methods: NA
 
+# Specify PGS scaling approach
+pgs_scaling: ['continuous']
diff --git a/pipeline/rules/target_scoring.smk b/pipeline/rules/target_scoring.smk
index 35918fce..8a09c9f7 100644
--- a/pipeline/rules/target_scoring.smk
+++ b/pipeline/rules/target_scoring.smk
@@ -1,12 +1,33 @@
-# Create a function summarising which populations are present in target
-def ancestry_munge(x):
+def ancestry_munge(x, scaling='continuous'):
+    # Ensure scaling is a list
+    if not isinstance(scaling, list):
+        raise ValueError("The scaling parameter must be a list (e.g., ['discrete', 'continuous']).")
+
+    # Retrieve ancestry reporter output
     checkpoints.ancestry_reporter.get(name=x).output[0]
     checkpoint_output = outdir + "/" + x + "/ancestry/ancestry_report.txt"
+
+    # Read ancestry report
     ancestry_report_df = pd.read_table(checkpoint_output, sep=' ')
+
+    # Extract population list
     population_list = ancestry_report_df['population'].tolist()
-    population_list.append('TRANS')
-    return population_list
-    
+
+    # Handle scaling logic
+    if 'continuous' in scaling and 'discrete' not in scaling:
+        # Only continuous scaling
+        return ['TRANS']
+    elif 'continuous' in scaling and 'discrete' in scaling:
+        # Both continuous and discrete scaling
+        population_list.append('TRANS')
+        return population_list
+    elif 'discrete' in scaling and 'continuous' not in scaling:
+        # Only discrete scaling
+        return population_list
+    else:
+        # Raise an error for invalid scaling input
+        raise ValueError("Invalid value for scaling. Must include 'continuous', 'discrete', or both.")
+
 # Define which pgs_methods are can be applied to any GWAS population
 pgs_methods_noneur = ['ptclump','lassosum','megaprs','prscs','dbslmm']
 
@@ -42,7 +63,7 @@ rule pc_projection_i:
 
 rule pc_projection_all:
   input:
-    lambda w: expand(f"{outdir}/reference/target_checks/{{name}}/pc_projection-{{population}}.done", name=w.name, population=ancestry_munge("{}".format(w.name)))
+    lambda w: expand(f"{outdir}/reference/target_checks/{{name}}/pc_projection-{{population}}.done", name=w.name, population=ancestry_munge("{}".format(w.name), scaling = config["pgs_scaling"]))
   output:
     touch(f"{outdir}/reference/target_checks/{{name}}/pc_projection.done")
 
@@ -61,7 +82,7 @@ rule target_pgs_i:
   threads: config['cores_target_pgs']
   input:
     f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",
-    f"{outdir}/reference/target_checks/{{name}}/pc_projection-TRANS.done",
+    lambda w: f"{outdir}/reference/target_checks/{{name}}/pc_projection-TRANS.done" if w.population == "TRANS" else [],
     rules.prep_pgs.input
   output:
     touch(f"{outdir}/reference/target_checks/{{name}}/target_pgs-{{population}}.done")
@@ -85,7 +106,7 @@ rule target_pgs_i:
 
 rule target_pgs_all:
   input:
-    lambda w: expand(f"{outdir}/reference/target_checks/{{name}}/target_pgs-{{population}}.done", name=w.name, population = ancestry_munge(w.name))
+    lambda w: expand(f"{outdir}/reference/target_checks/{{name}}/target_pgs-{{population}}.done", name=w.name, population = ancestry_munge(w.name, scaling = config["pgs_scaling"]))
   output:
     touch(f"{outdir}/reference/target_checks/{{name}}/target_pgs.done")
 

From cb3e09633633ab158887a6854fb61f31cfb8f04d Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 3 Jan 2025 12:57:10 +0000
Subject: [PATCH 066/153] Developing HAPNEST demo

---
 docs/hapnest_benchmark.Rmd                | 487 +++++++++++++++++++++-
 pipeline/misc/hapnest/config.synth_1.yaml | 159 +++++++
 2 files changed, 643 insertions(+), 3 deletions(-)
 create mode 100644 pipeline/misc/hapnest/config.synth_1.yaml

diff --git a/docs/hapnest_benchmark.Rmd b/docs/hapnest_benchmark.Rmd
index 4a312823..a122836a 100644
--- a/docs/hapnest_benchmark.Rmd
+++ b/docs/hapnest_benchmark.Rmd
@@ -125,16 +125,497 @@ Lets modify the quickstart config.yaml to simulate data for chromosome 22, 40k i
 cd /users/k1806347/oliverpainfel/HAPNEST
 
 # Generate genotype and phenotype data
-singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif generate_geno 8 data/config.synth_1.yaml
-singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif generate_pheno data/config.synth_1.yaml
+singularity exec \
+  --bind data/:/data/ \
+  --bind /users/k1806347/oliverpainfel/GenoPred/pipeline/misc/hapnest/config.synth_1.yaml:/data/config.synth_1.yaml \
+  containers/intervene-synthetic-data_latest.sif \
+  generate_geno \
+  8 \
+  data/config.synth_1.yaml
+  
+# Note this only worked when allocating 100G RAM when using 8 threads. This is a lot more than expected based on the HAPNEST paper.
+
+singularity exec \
+  --bind data/:/data/ \
+  --bind /users/k1806347/oliverpainfel/GenoPred/pipeline/misc/hapnest/config.synth_1.yaml:/data/config.synth_1.yaml \
+  containers/intervene-synthetic-data_latest.sif \
+  generate_pheno \
+  data/config.synth_1.yaml
+  
+```
+
+***
+
+# Subsample and GWAS
+
+## Subsample
+
+```{r}
+library(data.table)
+sample <-
+  fread(
+    '/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22.sample',
+    header = F
+  )$V1
+
+pops<-unique(sample)
+fam <- fread('/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22.fam')
+fam$pop <- sample
+
+dir.create('~/oliverpainfel/Analysis/HAPNEST/synth_1', recursive = T)
+
+set.seed(1)
+for(pop_i in pops){
+  fam_pop_i <- fam[fam$pop == pop_i,]
+  train_size <- floor(0.9 * nrow(fam_pop_i))
+  train_indices <- sample(seq_len(nrow(fam_pop_i)), size = train_size)
+  
+  write.table(
+    fam_pop_i[train_indices, c('V1', 'V2'), with = F],
+    paste0(
+      '~/oliverpainfel/Analysis/HAPNEST/synth_1/training.',
+      pop_i,
+      '.txt'
+    ),
+    col.names = F,
+    row.names = F,
+    quote = F
+  )
+  
+  write.table(
+    fam_pop_i[!(seq_len(nrow(fam_pop_i)) %in% train_indices), c('V1','V2'), with=F],
+    paste0(
+      '~/oliverpainfel/Analysis/HAPNEST/synth_1/testing.',
+      pop_i,
+      '.txt'
+    ),
+    col.names = F,
+    row.names = F,
+    quote = F
+  )
+}
 
 ```
 
+***
+
+## GWAS
+
+### Perform PCA
+
+```{r}
+# conda activate .snakemake/conda/ea13b6c549c70695534894feeeecf0b3_
+setwd('~/oliverpainfel/GenoPred/pipeline/')
+
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+  make_option("--target_plink_chr", action="store", default=NULL, type='character',
+              help="Path to per chromosome target PLINK files [required]"),
+  make_option("--maf", action="store", default=0.05, type='numeric',
+              help="Minor allele frequency threshold [optional]"),
+  make_option("--geno", action="store", default=0.02, type='numeric',
+              help="Variant missingness threshold [optional]"),
+  make_option("--hwe", action="store", default=1e-6, type='numeric',
+              help="Hardy Weinberg p-value threshold. [optional]"),
+  make_option("--n_pcs", action="store", default=10, type='numeric',
+              help="Number of PCs (min=4) [optional]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+              help="Path PLINK2 software binary [required]"),
+  make_option("--keep_list", action="store", default=NULL, type='character',
+              help="File containing list of keep files and corresponding population code [optional]"),
+  make_option("--unrel", action="store", default=NA, type='character',
+              help="File containing list of unrelated individuals [optional]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+              help="Number of cores for parallel computing [optional]"),
+  make_option("--test", action="store", default=NA, type='character',
+              help="Specify test mode [optional]"),
+  make_option("--output", action="store", default=NULL, type='character',
+              help="Path for output files [required]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+opt$target_plink_chr<-'/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-'
+opt$output<-'~/oliverpainfel/Analysis/HAPNEST/synth_1/pca/'
+opt$test<-'chr22'
+
+keep_list<-data.frame(
+  file = c(
+    '~/oliverpainfel/Analysis/HAPNEST/synth_1/training.EUR.txt',
+    '~/oliverpainfel/Analysis/HAPNEST/synth_1/training.EAS.txt',
+    '~/oliverpainfel/Analysis/HAPNEST/synth_1/training.AFR.txt'),
+  POP = c('EUR','EAS','AFR')
+)
+
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+############
+# Create file listing variants in regions of long range LD
+############
+
+targ_pvar <- read_bim(opt$target_plink_chr, chr = CHROMS)
+targ_pvar <- remove_regions(dat = targ_pvar, regions = long_ld_coord)
+
+for(pop in keep_list$POP){
+  # Read in keep file for population
+  keep_file <- fread(keep_list$file[keep_list$POP == pop], header=F)
+  if(ncol(keep_file) == 1){
+    keep_file <- data.table(
+      FID = keep_file$V1,
+      IID = keep_file$V1)
+  } else {
+    keep_file <- data.table(
+      FID = keep_file$V1,
+      IID = keep_file$V2)
+  }
+
+  ###########
+  # Perform PCA on QC'd and independent variants
+  ###########
+
+  # Create QC'd SNP-list
+  target_qc_snplist <- plink_qc_snplist(bfile = opt$target_plink_chr, plink2 = opt$plink2, chr = CHROMS, keep = keep_file, maf = opt$maf, geno = opt$geno, hwe = opt$hwe, threads = opt$n_cores)
+
+  # Remove high LD regions
+  target_qc_snplist <- target_qc_snplist[target_qc_snplist %in% targ_pvar$SNP]
+
+  # Perform LD pruning
+  ld_indep <- plink_prune(bfile = opt$target_plink_chr, chr = CHROMS, keep = keep_file, plink2 = opt$plink2, extract = target_qc_snplist, threads = opt$n_cores)
+
+  # To improve efficiency, derive PCs using random subset of 1000 individuals.
+  keep_file_subset <- keep_file[sample(1000, replace = T),]
+  keep_file_subset <- keep_file_subset[!duplicated(keep_file_subset),]
+
+  # Run PCA
+  snp_weights <- plink_pca(bfile = opt$target_plink_chr, keep = keep_file_subset, chr = CHROMS, plink2 = opt$plink2, extract = ld_indep, n_pc = opt$n_pcs, threads = opt$n_cores)
+  fwrite(snp_weights, paste0(tmp_dir,'/ref.eigenvec.var'), row.names = F, quote=F, sep=' ', na='NA')
+
+  # Project into the full population
+  target_pcs <- plink_score(bfile = opt$target_plink_chr, keep = keep_file, chr = CHROMS, plink2 = opt$plink2, score = paste0(tmp_dir,'/ref.eigenvec.var'), threads = opt$n_cores)
+
+  fwrite(target_pcs, paste0(opt$output, pop,'.pcs.txt'), quote=F, sep=' ', na='NA')
+}
 
+```
 
 ***
 
-Lets split the data into training and testing subsets, as we did for UKB EUR in the CrossPop analysis. Perform a GWAS of each phenotype using 
+### Perform GWAS
+
+```{bash}
+
+module add plink2
+for pheno in $(seq 1 1); do
+    awk 'BEGIN {OFS="\t"} NR==1 {print "FID", "IID", "pheno"} NR>1 {print $1, $1, $NF}' /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno} > /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno}.plink
+    
+  for pop in $(echo EUR EAS AFR); do
+  
+    mkdir -p ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}
+    for chr in $(seq 22 22); do
+        sbatch -p neurohack_cpu --mem 20G -n 4 --wrap="plink2 \
+          --bfile /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22 \
+          --pheno /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno}.plink \
+          --1 \
+          --covar ~/oliverpainfel/Analysis/HAPNEST/synth_1/pca/${pop}.pcs.txt \
+          --covar-variance-standardize \
+          --logistic omit-ref cols=+a1freq,+ax hide-covar \
+          --maf 0.01 \
+          --geno 0.05 \
+          --out ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr${chr}"
+    done
+  done
+done
+
+# Once complete, merge results across chromosomes
+for pheno in $(seq 1 1); do
+  for pop in $(echo EUR EAS AFR); do
+    head -n 1 ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr22.pheno.glm.logistic.hybrid > ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+      for chr in $(seq 22 22); do
+        tail -n +2 ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr22.pheno.glm.logistic.hybrid >> ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+      done
+      
+      # Remove REF and ALT columns and rename AX column to A2
+      cut -f 4,5 --complement ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid | awk 'BEGIN{FS=OFS="\t"} NR==1 {$7="A2"} 1' > temp.txt && mv temp.txt ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+  
+      gzip ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+  done
+done
+
+# Issue, all the cases are african! How can we get some cases in each population
+```
+
+***
+
+# Run GenoPred
+
+## Subset HAPNEST testing data
+
+```{bash}
+mkdir -p ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing_subset
+for chr in $(seq 22 22); do
+    plink2 \
+      --bfile /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-${chr} \
+      --keep /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno}.plink \
+      --make-bed \
+      --out ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing_subset/synth_1.chr${chr}
+done
+```
+
+## Prepare config
+
+```{r}
+
+dir.create('/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config', recursive = T)
+
+# gwas_list
+sample_file<-fread('/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22.sample', header=F)$V1
+gwas_list <- NULL
+for(pheno in 1:1){
+  pheno_file <- fread(paste0('/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno', pheno,'.plink'))
+  for(pop in c('EUR','EAS','AFR')){
+    
+    pheno_file_pop <- pheno_file[sample_file == pop,]
+    
+    tmp <- data.frame(
+      name=paste0('pheno',pheno,'_',pop),
+      path=paste0('/users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno', pheno, '/pheno', pheno, '.', pop, '.GW.pheno.glm.logistic.hybrid.gz'),
+      population=pop,
+      n=NA,
+      sampling=mean(pheno_file_pop$pheno),
+      prevalence=0.5,
+      mean=NA,
+      sd=NA,
+      label=paste0('"pheno', pheno, ' (', pop, ')"')
+    )
+    
+    gwas_list <- rbind(gwas_list, tmp)
+  }
+}
+
+write.table(gwas_list, '/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/gwas_list.txt', col.names = T, row.names = F, quote = F)
+
+# target_list
+target_list <- data.frame(
+  name='hapnest',
+  path='/users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_1/testing_subset/synth_1',
+  type='plink1',
+  indiv_report=F
+)
+
+write.table(target_list, '/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/target_list.txt', col.names = T, row.names = F, quote = F)
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/output",
+  "config_file: /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/gwas_list.txt",
+  "target_list: /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/target_list.txt",
+  "pgs_methods: ['ptclump']",
+  "cores_prep_pgs: 1",
+  "cores_target_pgs: 10",
+  "testing: chr22",
+  "pgs_scaling: ['continuous', 'discrete']"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+***
+
+## Run pipeline
+
+```{bash}
+snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml output_all -n
+```
+
+***
+
+# Evaluate PGS
+
+## Create predictor lists
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('~/oliverpainfel/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Create files for EAS and AFR targets
+pop <- c('EUR','EAS','AFR')
+for(trait_i in paste0('pheno', 1:1)){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
+  scores_i <- scores[grepl(paste0('^', trait_i, '_'), scores$name),]
+  scores_i$group <- scores_i$method
+  
+  for(pop_i in pop){
+    dir.create(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/assoc_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i
+      ),
+      recursive = T
+    )
+    
+    scores_i_j <- scores_i[grepl(pop_i, scores_i$name, ignore.case = T),]
+    scores_i_j$predictor <- paste0(
+      outdir,
+      '/hapnest/pgs/',
+      'TRANS',
+      '/',
+      scores_i_j$method,
+      '/',
+      scores_i_j$name,
+      '/hapnest-',
+      scores_i_j$name,
+      '-',
+      'TRANS',
+      '.profiles'
+    )
+    
+    predictors_i <- scores_i_j[, c('predictor', 'group'), with=F]
+    
+    write.table(
+      predictors_i,
+      paste0(
+        '/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/assoc_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i,
+        '/predictor_list.ptclump.txt'
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}
+
+```
+</details>
+
+***
+
+## Run model_builder
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/GenoPred/pipeline
+conda activate model_builder
+
+for pop in $(echo EUR EAS AFR); do
+  for pheno in $(echo pheno$(seq 1 1)); do
+    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      --outcome /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.${pheno}.plink \
+      --predictors /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/assoc_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
+      --out /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/assoc_${pop}.disc_${pop}/${pheno}/res.ptclump \
+      --n_core 5 \
+      --all_model F \
+      --assoc T"
+  done
+done
+
+```
+</details>
+
+***
+
+## Plot assoc results
+
+<details><summary>Show code</summary>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in phenotypes
+pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+
+# Read in results
+pop = c('EUR','EAS','AFR')
+res_all <- NULL
+for(pheno_i in pheno_intersect){
+  res_i<-NULL
+  for(pop_i in pop){
+    assoc_i <-
+      fread(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          pop_i,
+          '.disc_',
+          pop_i,
+          '/',
+          pheno_i,
+          '/res.ptclump.assoc.txt'
+        )
+      )
+      assoc_i$Population <- pop_i
+      res_i<-rbind(res_i, assoc_i)
+  }
+  
+  res_i$Phenotype <- pheno_i
+  res_all<-rbind(res_all, res_i)
+}
+
+# Extract pT variable from Predictor
+res_all$pT <- gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.', '', res_all$Predictor))
+res_all$pT <- factor(res_all$pT, levels = unique(res_all$pT))
+
+ggplot(res_all, aes(x = Phenotype, y = BETA, fill = pT)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.8) +
+  geom_errorbar(aes(ymin=BETA-SE, ymax=BETA+SE), width=0, position=position_dodge(width = 0.8, preserve = "single")) +
+  labs(y="BETA (SE)") +
+  theme_half_open() +
+  background_grid() +
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y') +
+  scale_fill_manual(values = colorRampPalette(c("lightblue", "darkblue"))(length(unique(res_all$pT)))) +
+  facet_grid(Population ~.)
+
+```
+</details>
+
+pheno_list could be three columns: pheno scores covar. pheno describes outcome, scores is comma separated list of score names (gwas_list and score_list names, plus option of projected pcs), and covar is a covariate file to be included in association analyses.
+
+**Note.** The performance of methods that take into account functional annotations (MegaPRS, SBayesRC) could be relatively downward biased due to the phenotype simulations not simulating functional enrichments. This benchmark is really to evaluate the impact of covarying for ancestry. In future research would could also simulate functional enrichments by specifying causal variants.
 
 
 
diff --git a/pipeline/misc/hapnest/config.synth_1.yaml b/pipeline/misc/hapnest/config.synth_1.yaml
new file mode 100644
index 00000000..a548a855
--- /dev/null
+++ b/pipeline/misc/hapnest/config.synth_1.yaml
@@ -0,0 +1,159 @@
+####################################
+# GLOBAL PARAMETERS                #
+####################################
+
+global_parameters:
+  random_seed: 123
+  chromosome: 22 # "all" or a number from 1 to 22
+  superpopulation: none # "none" or a specific superpopulation (AFR, AMR, EAS, EUR, CSA, MID)
+  memory: 100000 # amount of memory available (in MB) for memory-intensive commands
+  batchsize: 10000 # batchsize for writing plink output during genotype generation
+
+####################################
+# FILEPATHS                        #
+####################################
+
+# - the chromosome number can be given as a wildcard by specifying {chromosome} in the filepath
+# - the superpopulation can be given as a wildcard by specifying {superpopulation} in the filepath
+filepaths:
+  general:
+    output_dir: data/outputs/synth_1
+    output_prefix: synth_1_chr-{chromosome}
+  genotype:
+    vcf_input_raw: data/inputs/raw/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.final.vcf.gz
+    vcf_input_processed: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.final.recode.vcf
+    vcf_metadata: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.metadata
+    popfile_raw: data/inputs/processed/1KG+HGDP/merged_pop_adjusted.tsv
+    popfile_processed: data/inputs/processed/1KG+HGDP/merged_pop.tsv
+    variant_list: data/inputs/processed/1KG+HGDP/hapmap_variant_list_chr{chromosome}.txt
+    remove_list: data/inputs/processed/1KG+HGDP/remove.txt
+    rsid_list: data/inputs/processed/1KG+HGDP/rsid_map_list_chr{chromosome}.txt
+    genetic_mapfile: data/inputs/raw/1KG+HGDP/genetic_maps/chr{chromosome}.interpolated_genetic_map
+    genetic_distfile: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.distfile
+    mutation_mapfile: data/inputs/raw/1KG+HGDP/mutation_maps/atlas.chr{chromosome}.csv
+    mutation_agefile: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.agefile
+    hap1_matrix: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.h1
+    hap2_matrix: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.h2
+  phenotype:
+    causal_list: none
+    reference: data/inputs/processed/1KG+HGDP/Africa.Annot
+    plink_override: none # can set to a value if using pre-simulated genetics input
+  software:
+    plink: plink
+    plink2: plink2
+    king: king
+    vcftools: vcftools
+    mapthin: mapthin
+    phenoalg: phenoalg
+
+
+####################################
+# GENOTYPE DATA                    #
+####################################
+
+genotype_data:
+  filetype: plink # specify either `plink` or `vcf`
+  samples:
+    use_default: false # setting this to true will ignore the custom population groups
+    custom: # add your custom population groups below if using use_default=false
+      - id: AFR
+        nsamples: 40000
+        populations: 
+          - AFR: 100
+      - id: EAS
+        nsamples: 40000
+        populations: 
+          - EAS: 100
+      - id: EUR
+        nsamples: 40000
+        populations: 
+          - EUR: 100
+    default:
+      nsamples: 1000 # used by the algorithm if use_default=true, otherwise custom population groups are used
+  # recombination rate 
+  rho:
+    AFR: 0.77
+    AMR: 0.80
+    EAS: 0.58
+    EUR: 0.68
+    CSA: 0.73
+    MID: 0.65
+  # effective population size 
+  Ne:
+    AFR: 11900
+    AMR: 10400
+    EAS: 11700
+    EUR: 11700
+    CSA: 11500
+    MID: 8100
+
+####################################
+# PHENOTYPE DATA                   #
+####################################
+
+phenotype_data:
+  nPopulation: 3
+  nTrait: 1
+  a: -0.4
+  b: -1
+  c: 0.5
+  nComponent: 1
+  PropotionGeno: 0.1,0.1,0.1
+  PropotionCovar: 0,0,0
+  Prevalence: 0.5,0.5,0.5
+  TraitCorr: 1
+  PopulationCorr: 0,0,0,0,0,0,0,0,0
+  CompWeight: 1,5,10
+  Causality:
+      UseCausalList: false # if true the algorithm will use the causal_list filepath
+      Polygenicity: 0.005 # only required if UseCausalList is false
+      Pleiotropy: 1 # only required if UseCausalList is false
+      
+####################################
+# EVALUATION                       #
+####################################
+
+# Set to true if you want the script to calculate the metric
+evaluation:
+  metrics:
+    aats: false  # nearest neighbour adversarial accuracy
+    kinship: false # relatedness, including kinship density and IBS plots
+    ld_corr: true # linkage disequilibrium (LD) correlation matrix
+    ld_decay: true # linkage disequilibrium (LD) decay plot (and distance)
+    maf: true # minor allele frequency divergences
+    pca: false # principal components analysis
+    gwas: false # GWAS, manhattan plot and qqplot
+
+
+####################################
+# OPTIMISATION                     #
+####################################
+
+# Note that this code uses a single superpopulation and ignores custom population structures
+optimisation:
+  # prior distributions - specify lower/upper bounds for uniform priors
+  priors:
+    rho:
+      uniform_lower: 0
+      uniform_upper: 3
+    Ne:
+      uniform_lower: 0
+      uniform_upper: 50000
+  # inference type - simulation-based rejection ABC or emulation-based rejection ABC
+  simulation_rejection_ABC:
+    run: true
+    n_particles: 500
+    threshold: 0.15
+    max_iter: 500
+    write_progress: true
+  emulation_rejection_ABC:
+    run: false
+    n_particles: 500
+    threshold: 0.15
+    n_design_points: 50
+    max_iter: 500
+    write_progress: true
+  # choice of summary statistic/s
+  summary_statistics:
+    ld_decay: true
+    kinship: true

From 14e55df371a19bcbdde275689bdeaee1f63a2cb0 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 3 Jan 2025 12:57:35 +0000
Subject: [PATCH 067/153] Added Rproj file

---
 GenoPred.Rproj | 13 +++++++++++++
 1 file changed, 13 insertions(+)
 create mode 100644 GenoPred.Rproj

diff --git a/GenoPred.Rproj b/GenoPred.Rproj
new file mode 100644
index 00000000..8e3c2ebc
--- /dev/null
+++ b/GenoPred.Rproj
@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

From c83fbbb721cc4d49232606f0f28a75127e03aeee Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 3 Jan 2025 12:58:28 +0000
Subject: [PATCH 068/153] Fixed plink_pca function when using plink1 files

---
 functions/plink.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/functions/plink.R b/functions/plink.R
index e41a186e..249b4165 100644
--- a/functions/plink.R
+++ b/functions/plink.R
@@ -200,14 +200,14 @@ plink_pca<-function(bfile=NULL, pfile=NULL, chr = 1:22, plink2, extract = NULL,
 
   # Subset data prior to merging
   if(!is.null(bfile)){
-    plink_subset(bfile = bfile, chr = chr, plink2 = plink2, keep = keep, extract = extract, memory = memory, out = paste0(tmp_dir,'/ref_subset_chr'), threads=threads)
+    plink_subset(bfile = bfile, chr = chr, plink2 = plink2, make_bed = T, keep = keep, extract = extract, memory = memory, out = paste0(tmp_dir,'/ref_subset_chr'), threads=threads)
   } else {
     plink_subset(pfile = pfile, chr = chr, plink2 = plink2, keep = keep, extract = extract, memory = memory, out = paste0(tmp_dir,'/ref_subset_chr'), threads=threads)
   }
 
   # Merge subset reference
   if(!is.null(bfile)){
-    plink_merge(bfile = paste0(tmp_dir,'/ref_subset_chr'), chr = chr, plink2 = plink2, keep = keep, extract = extract, flip = flip, memory = memory, out = paste0(tmp_dir,'/ref_merge'), threads=threads)
+    plink_merge(bfile = paste0(tmp_dir,'/ref_subset_chr'), make_bed = T, chr = chr, plink2 = plink2, keep = keep, extract = extract, flip = flip, memory = memory, out = paste0(tmp_dir,'/ref_merge'), threads=threads)
   } else {
     plink_merge(pfile = paste0(tmp_dir,'/ref_subset_chr'), chr = chr, plink2 = plink2, keep = keep, extract = extract, flip = flip, memory = memory, out = paste0(tmp_dir,'/ref_merge'), threads=threads)
   }

From b41369aa8743e9be3739b2fc7fc93e86b8680909 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 3 Jan 2025 15:37:04 +0000
Subject: [PATCH 069/153] Slight tweak of night mode toggle

---
 docs/styles/styles.css | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/docs/styles/styles.css b/docs/styles/styles.css
index a132b7f0..27b2217d 100644
--- a/docs/styles/styles.css
+++ b/docs/styles/styles.css
@@ -113,7 +113,7 @@
   z-index: 1000;
   display: inline-block;
   width: 60px; /* Adjusted for clear visibility of emojis */
-  height: 25px;
+  height: 30px;
 }
 
 /* Hide the default checkbox */
@@ -148,7 +148,7 @@
 .slider::before {
   content: '🌜';
   left: 0px; /* Adjust as needed */
-  height: 25px;
+  height: 26px;
   opacity: 0; /* Hide initially */
 }
 
@@ -156,7 +156,7 @@
 .slider::after {
   content: '🌞';
   right: 0px; /* Adjust as needed */
-  height: 25px;
+  height: 26px;
   opacity: 1; /* Show initially */
 }
 

From 57378fd829b69701cd2727756bca8a7be297cd10 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 3 Jan 2025 15:37:35 +0000
Subject: [PATCH 070/153] Update to demo using HAPNEST simulation

---
 docs/Images/hapnest/pgs_eval.png          |  Bin 0 -> 52778 bytes
 docs/hapnest_benchmark.Rmd                |  390 ++++---
 docs/hapnest_benchmark.html               | 1232 +++++++++++++++++++++
 pipeline/misc/hapnest/config.synth_1.yaml |    8 +-
 4 files changed, 1493 insertions(+), 137 deletions(-)
 create mode 100644 docs/Images/hapnest/pgs_eval.png
 create mode 100644 docs/hapnest_benchmark.html

diff --git a/docs/Images/hapnest/pgs_eval.png b/docs/Images/hapnest/pgs_eval.png
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diff --git a/docs/hapnest_benchmark.Rmd b/docs/hapnest_benchmark.Rmd
index a122836a..57788251 100644
--- a/docs/hapnest_benchmark.Rmd
+++ b/docs/hapnest_benchmark.Rmd
@@ -24,6 +24,7 @@ Here will use simulated genetic data to benchmark GenoPred. We will simulate the
 ***
 
 # Set up HAPNEST
+<details><summary>Show code</summary>
 
 ```{bash}
 mkdir -p /users/k1806347/oliverpainfel/HAPNEST
@@ -46,12 +47,16 @@ singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.
 
 ```
 
+</details>
+
 ***
 
 # Test run using default config
 
 Generates data for chromosome 1, for 6 populations, HapMap3 SNPs, and 1 phenotype.
 
+<details><summary>Show code</summary>
+
 ```{bash}
 # Step 5: Generate genotype and phenotype data
 singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif generate_geno 1 data/config.yaml
@@ -61,6 +66,8 @@ singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.
 singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif validate data/config.yaml
 ```
 
+</details>
+
 The data simulation took about 1 minute. Evaluation takes >2 hours.
 
 ***
@@ -73,6 +80,8 @@ It would probably be easier, and more reproducible to use the released version o
 
 ## Download genotype and phenotype
 
+<details><summary>Show code</summary>
+
 ```{bash}
 mkdir -p /users/k1806347/oliverpainfel/HAPNEST/released/full
 mkdir -p /users/k1806347/oliverpainfel/HAPNEST/released/subset
@@ -113,6 +122,8 @@ done
 
 ```
 
+</details>
+
 Note. For some reason there only about 65% of HapMap3 variants available in the synthetic data. This will cause an error when using GenoPred as it requires a certain overlap with the default reference data. Given we are going to generate the GWAS using this data, this wouldn't actually cause any issues of SNP overlap, but there would be poor coverage of the genome which will decrease the PGS R2 values. This is not a big issue, but given it is so fast to simulate data, it is making me think we should simulate our own so we can make it exactly what we want (sample size, genetic architecture, snplist).
 
 ***
@@ -121,6 +132,8 @@ Note. For some reason there only about 65% of HapMap3 variants available in the
 
 Lets modify the quickstart config.yaml to simulate data for chromosome 22, 40k individuals, EUR, EAS and AFR population, 9 phenotypes with same genetic architecture as HAPNEST paper.
 
+<details><summary>Show code</summary>
+
 ```{bash}
 cd /users/k1806347/oliverpainfel/HAPNEST
 
@@ -144,13 +157,18 @@ singularity exec \
   
 ```
 
+</details>
+
 ***
 
-# Subsample and GWAS
+# Identify unrelated individuals
 
-## Subsample
+We need to identify a group of unrelated individuals, to avoid bias in the GWAS and sample overlap between GWAS and target sample when evaluating PGS. Idenitfy unrelated individuals within each population.
+
+<details><summary>Show code</summary>
 
 ```{r}
+# Subset simulated data by population
 library(data.table)
 sample <-
   fread(
@@ -162,11 +180,113 @@ pops<-unique(sample)
 fam <- fread('/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22.fam')
 fam$pop <- sample
 
+for(i in c('EUR','EAS','AFR')){
+  fam_i <- fam[fam$pop == i,]
+  fam_i <- fam_i[, c('V1','V2'), with=F]
+  
+  write.table(
+    fam_i,
+    paste0(
+      '~/oliverpainfel/Analysis/HAPNEST/synth_1/', i,'.keep'),
+    col.names = F,
+    row.names = F,
+    quote = F
+  )
+}
+
+```
+
+```{r}
+# Identify unrelated individuals
+# conda activate .snakemake/conda/ea13b6c549c70695534894feeeecf0b3_
+setwd('~/oliverpainfel/GenoPred/pipeline/')
 dir.create('~/oliverpainfel/Analysis/HAPNEST/synth_1', recursive = T)
 
+library("optparse")
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+option_list = list(
+  make_option("--target_plink_chr", action="store", default=NULL, type='character',
+              help="Path to per chromosome target PLINK files [required]"),
+  make_option("--maf", action="store", default=0.05, type='numeric',
+              help="Minor allele frequency threshold [optional]"),
+  make_option("--geno", action="store", default=0.02, type='numeric',
+              help="Variant missingness threshold [optional]"),
+  make_option("--hwe", action="store", default=1e-6, type='numeric',
+              help="Hardy Weinberg p-value threshold. [optional]"),
+  make_option("--n_pcs", action="store", default=10, type='numeric',
+              help="Number of PCs (min=4) [optional]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+              help="Path PLINK2 software binary [required]"),
+  make_option("--keep_list", action="store", default=NULL, type='character',
+              help="File containing list of keep files and corresponding population code [optional]"),
+  make_option("--unrel", action="store", default=NA, type='character',
+              help="File containing list of unrelated individuals [optional]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+              help="Number of cores for parallel computing [optional]"),
+  make_option("--test", action="store", default=NA, type='character',
+              help="Specify test mode [optional]"),
+  make_option("--output", action="store", default=NULL, type='character',
+              help="Path for output files [required]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+opt$test<-'chr22'
+opt$target_plink_chr<-'/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-'
+opt$output<-'~/oliverpainfel/Analysis/HAPNEST/synth_1/relatedness/synth_1'
+opt$n_cores<-20
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
+system(paste0('mkdir -p ',opt$output_dir))
+
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+for(i in c('EUR','EAS','AFR')){
+
+  # Identify high quality variants
+  target_qc_snplist<-plink_qc_snplist(bfile = opt$target_plink_chr, chr = CHROMS, plink2 = opt$plink2, geno = opt$geno, maf = opt$maf, hwe = opt$hwe, keep = paste0('~/oliverpainfel/Analysis/HAPNEST/synth_1/', i,'.keep'))
+  
+  # Generate kinship matrix and list of unrelated individuals
+  plink_king(bfile = opt$target_plink_chr, chr = CHROMS, extract = target_qc_snplist, plink2 = opt$plink2, out = paste0(opt$output, '.', i), threads = opt$n_cores, keep = paste0('~/oliverpainfel/Analysis/HAPNEST/synth_1/', i,'.keep'))
+  
+}
+
+```
+
+</details>
+
+***
+
+# Subsample and GWAS
+
+## Subsample
+
+<details><summary>Show code</summary>
+
+```{r}
+library(data.table)
+
+fam <- NULL
+for(i in c('EUR','EAS','AFR')){
+  tmp <- fread(paste0('~/oliverpainfel/Analysis/HAPNEST/synth_1/relatedness/synth_1.',i,'.unrelated.keep'), header = F)
+  tmp$pop <- i
+  fam <- rbind(fam, tmp)
+}
+
 set.seed(1)
-for(pop_i in pops){
+for(pop_i in c('EUR','EAS','AFR')){
   fam_pop_i <- fam[fam$pop == pop_i,]
+  print(nrow(fam_pop_i))
   train_size <- floor(0.9 * nrow(fam_pop_i))
   train_indices <- sample(seq_len(nrow(fam_pop_i)), size = train_size)
   
@@ -197,12 +317,16 @@ for(pop_i in pops){
 
 ```
 
+</details>
+
 ***
 
 ## GWAS
 
 ### Perform PCA
 
+<details><summary>Show code</summary>
+
 ```{r}
 # conda activate .snakemake/conda/ea13b6c549c70695534894feeeecf0b3_
 setwd('~/oliverpainfel/GenoPred/pipeline/')
@@ -310,10 +434,14 @@ for(pop in keep_list$POP){
 
 ```
 
+</details>
+
 ***
 
 ### Perform GWAS
 
+<details><summary>Show code</summary>
+
 ```{bash}
 
 module add plink2
@@ -353,28 +481,38 @@ for pheno in $(seq 1 1); do
   done
 done
 
-# Issue, all the cases are african! How can we get some cases in each population
 ```
 
+</details>
+
 ***
 
 # Run GenoPred
 
 ## Subset HAPNEST testing data
 
+<details><summary>Show code</summary>
+
 ```{bash}
 mkdir -p ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing_subset
+
+cat ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing.*.txt > ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing.txt
+
 for chr in $(seq 22 22); do
     plink2 \
       --bfile /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-${chr} \
-      --keep /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno}.plink \
+      --keep ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing.txt \
       --make-bed \
       --out ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing_subset/synth_1.chr${chr}
 done
 ```
 
+</details>
+
 ## Prepare config
 
+<details><summary>Show code</summary>
+
 ```{r}
 
 dir.create('/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config', recursive = T)
@@ -432,19 +570,25 @@ write.table(config, '/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/con
 
 ```
 
+</details>
+
 ***
 
 ## Run pipeline
 
+<details><summary>Show code</summary>
+
 ```{bash}
 snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml output_all -n
 ```
 
+</details>
+
 ***
 
 # Evaluate PGS
 
-## Create predictor lists
+Evaluate PGS R2 within each population.
 
 <details><summary>Show code</summary>
 
@@ -463,160 +607,140 @@ outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 # Get a list of score files
 scores <- list_score_files(config)
 
-# Create files for EAS and AFR targets
+# Read in PGS for each population
 pop <- c('EUR','EAS','AFR')
-for(trait_i in paste0('pheno', 1:1)){
-  # Make a group containing both GWAS for each single source method
-  # Make a group for each multisource method
-  scores_i <- scores[grepl(paste0('^', trait_i, '_'), scores$name),]
-  scores_i$group <- scores_i$method
-  
-  for(pop_i in pop){
-    dir.create(
-      paste0(
-        '/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/assoc_',
-        pop_i,
-        '.disc_',
-        pop_i,
-        '/',
-        trait_i
-      ),
-      recursive = T
-    )
-    
-    scores_i_j <- scores_i[grepl(pop_i, scores_i$name, ignore.case = T),]
-    scores_i_j$predictor <- paste0(
-      outdir,
-      '/hapnest/pgs/',
-      'TRANS',
-      '/',
-      scores_i_j$method,
-      '/',
-      scores_i_j$name,
-      '/hapnest-',
-      scores_i_j$name,
-      '-',
-      'TRANS',
-      '.profiles'
-    )
-    
-    predictors_i <- scores_i_j[, c('predictor', 'group'), with=F]
+pgs <- read_pgs(config = config)
+
+# Read in phenotype data
+pheno <- fread('/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno1.plink')
+
+# Evaluate PGS (without adjustment for reference PCs)
+assoc<-NULL
+for(i in pop){
+  for(j in 1:nrow(scores)){
+    tmp <- pgs$hapnest[[i]][[scores$name[j]]][[scores$method[j]]]
+    tmp <- merge(tmp, pheno, by = c('FID','IID'))
+    tmp_pheno <- tmp$pheno
+    tmp_pgs <- tmp[, !(names(tmp) %in% c('FID','IID','pheno')), with = F]
     
-    write.table(
-      predictors_i,
-      paste0(
-        '/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/assoc_',
-        pop_i,
-        '.disc_',
-        pop_i,
-        '/',
-        trait_i,
-        '/predictor_list.ptclump.txt'
-      ),
-      col.names = T,
-      row.names = F,
-      quote = F
-    )
+    for(k in names(tmp_pgs)){
+  	  mod <- glm(tmp_pheno ~ scale(tmp_pgs[[k]]), family='binomial')
+  		obs_r2 <- cor(predict(mod), as.numeric(tmp_pheno))^2
+  	  sum_mod <- summary(mod)
+
+  	  assoc <- rbind(
+  	    assoc, 
+  	    data.table(
+    	    Predictor = k,
+    	    BETA = coef(sum_mod)[2, 1],
+    	    SE = coef(sum_mod)[2, 2],
+    	    P = coef(sum_mod)[2, 4],
+    	    Obs_R2 = obs_r2,
+      		N = length(tmp_pheno),
+      		gwas = scores$name[j],
+      		method = scores$method[j],
+      		pop = i,
+      		trans = F
+  	    )
+  	  )
+	  }
   }
 }
 
-```
-</details>
-
-***
-
-## Run model_builder
-
-<details><summary>Show code</summary>
-
-```{bash}
-cd /users/k1806347/oliverpainfel/GenoPred/pipeline
-conda activate model_builder
-
-for pop in $(echo EUR EAS AFR); do
-  for pheno in $(echo pheno$(seq 1 1)); do
-    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
-      --outcome /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.${pheno}.plink \
-      --predictors /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/assoc_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
-      --out /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/assoc_${pop}.disc_${pop}/${pheno}/res.ptclump \
-      --n_core 5 \
-      --all_model F \
-      --assoc T"
-  done
-done
-
-```
-</details>
-
-***
-
-## Plot assoc results
+# Evaluate PGS (without adjustment for reference PCs)
+assoc_trans<-NULL
+for(i in pop){
+  for(j in 1:nrow(scores)){
+    tmp <- pgs$hapnest[[i]][[scores$name[j]]][[scores$method[j]]]
+    tmp2 <- pgs$hapnest[['TRANS']][[scores$name[j]]][[scores$method[j]]]
+    tmp2 <- tmp2[tmp2$FID %in% tmp$FID,]
+    tmp <- merge(tmp2, pheno, by = c('FID','IID'))
+    tmp_pheno <- tmp$pheno
+    tmp_pgs <- tmp[, !(names(tmp) %in% c('FID','IID','pheno')), with = F]
+    
+    for(k in names(tmp_pgs)){
+  	  mod <- glm(tmp_pheno ~ scale(tmp_pgs[[k]]), family='binomial')
+  		obs_r2 <- cor(predict(mod), as.numeric(tmp_pheno))^2
+  	  sum_mod <- summary(mod)
+
+  	  assoc_trans <- rbind(
+  	    assoc_trans, 
+  	    data.table(
+    	    Predictor = k,
+    	    BETA = coef(sum_mod)[2, 1],
+    	    SE = coef(sum_mod)[2, 2],
+    	    P = coef(sum_mod)[2, 4],
+    	    Obs_R2 = obs_r2,
+      		N = length(tmp_pheno),
+      		gwas = scores$name[j],
+      		method = scores$method[j],
+      		pop = i,
+      		trans = T
+  	    )
+  	  )
+	  }
+  }
+}
 
-<details><summary>Show code</summary>
+# Check the correlation between pheno and PGS, with and without adjustments for reference PCs
+cor(assoc_trans$BETA, assoc$BETA) # 0.9996044
 
-```{r}
-setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+plot(assoc_trans$BETA, assoc$BETA)
+abline(a = 0, b = 1, col = "red", lwd = 2, lty = 2)
+# The results are almost identical. This will vary depending on whether the phenotype varies according to ancestry. This simulation confirms that the PGS R2 is not impacted when there is no correlation between ancestry and the phenotype.
 
-library(data.table)
+# Plot the results
+assoc_both <- rbind(assoc, assoc_trans)
 library(ggplot2)
 library(cowplot)
 
-source('../functions/misc.R')
-source_all('../functions')
+# Extract pT variable from Predictor
+assoc_both$pT <- gsub('e.','e-', gsub('.*0_', '', assoc_both$Predictor))
+assoc_both$pT <- factor(assoc_both$pT, levels = unique(assoc_both$pT))
 
-# Read in phenotypes
-pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
-
-# Read in results
-pop = c('EUR','EAS','AFR')
-res_all <- NULL
-for(pheno_i in pheno_intersect){
-  res_i<-NULL
-  for(pop_i in pop){
-    assoc_i <-
-      fread(
-        paste0(
-          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
-          pop_i,
-          '.disc_',
-          pop_i,
-          '/',
-          pheno_i,
-          '/res.ptclump.assoc.txt'
-        )
-      )
-      assoc_i$Population <- pop_i
-      res_i<-rbind(res_i, assoc_i)
-  }
-  
-  res_i$Phenotype <- pheno_i
-  res_all<-rbind(res_all, res_i)
-}
+# Improve labelling for plot
+assoc_both$pop <- paste0('Target = ', assoc_both$pop)
+assoc_both$pop <- factor(assoc_both$pop, levels = unique(assoc_both$pop))
 
-# Extract pT variable from Predictor
-res_all$pT <- gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.', '', res_all$Predictor))
-res_all$pT <- factor(res_all$pT, levels = unique(res_all$pT))
+assoc_both$gwas <- gsub('.*_', '', assoc_both$gwas)
+assoc_both$gwas <- paste0('Discovery = ', assoc_both$gwas)
+assoc_both$gwas <- factor(assoc_both$gwas, levels = unique(assoc_both$gwas))
 
-ggplot(res_all, aes(x = Phenotype, y = BETA, fill = pT)) +
+assoc_both$trans <- ifelse(assoc_both$trans, 'Unadjusted', 'Adjusted')
+
+setwd('/scratch_tmp/prj/oliverpainfel/GenoPred')
+dir.create('docs/Images/hapnest')
+png('docs/Images/hapnest/pgs_eval.png', res = 100, height = 700, width = 1000, units = 'px')
+
+ggplot(assoc_both, aes(x = trans, y = BETA, fill = pT)) +
   geom_hline(yintercept = 0, colour = 'darkgrey') +
   geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.8) +
   geom_errorbar(aes(ymin=BETA-SE, ymax=BETA+SE), width=0, position=position_dodge(width = 0.8, preserve = "single")) +
-  labs(y="BETA (SE)") +
+  labs(y="BETA (SE)", x=NULL) +
   theme_half_open() +
   background_grid() +
   panel_border() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
   background_grid(major = 'y', minor = 'y') +
-  scale_fill_manual(values = colorRampPalette(c("lightblue", "darkblue"))(length(unique(res_all$pT)))) +
-  facet_grid(Population ~.)
+  scale_fill_manual(values = colorRampPalette(c("lightblue", "darkblue"))(length(unique(assoc_both$pT)))) +
+  facet_grid(pop ~ gwas)
+
+dev.off()
 
 ```
-</details>
 
-pheno_list could be three columns: pheno scores covar. pheno describes outcome, scores is comma separated list of score names (gwas_list and score_list names, plus option of projected pcs), and covar is a covariate file to be included in association analyses.
+</details>
 
-**Note.** The performance of methods that take into account functional annotations (MegaPRS, SBayesRC) could be relatively downward biased due to the phenotype simulations not simulating functional enrichments. This benchmark is really to evaluate the impact of covarying for ancestry. In future research would could also simulate functional enrichments by specifying causal variants.
+</br>
 
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+![Performance of PGS when adjusted for ancestry](Images/hapnest/pgs_eval.png)
+</div>
+</div>
 
+</br>
 
+The results with and without reference-based correction for ancestry are almost identical. This will vary depending on whether the phenotype varies according to ancestry. This simulation confirms that the PGS R2 is not impacted when there is no correlation between ancestry and the phenotype.
 
+As expected the PGS derived using target-matched ancestry GWAS, perform slightly better. The difference isn't huge, due to same GWAS sample size, and cross population rG of 1.
diff --git a/docs/hapnest_benchmark.html b/docs/hapnest_benchmark.html
new file mode 100644
index 00000000..b2fc22d5
--- /dev/null
+++ b/docs/hapnest_benchmark.html
@@ -0,0 +1,1232 @@
+<!DOCTYPE html>
+
+<html>
+
+<head>
+
+<meta charset="utf-8" />
+<meta name="generator" content="pandoc" />
+<meta http-equiv="X-UA-Compatible" content="IE=EDGE" />
+
+
+
+
+<title>HapNest Benchmark</title>
+
+<script src="site_libs/header-attrs-2.26/header-attrs.js"></script>
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+      </button>
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+    </div>
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+      <ul class="nav navbar-nav">
+        
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+    Pipeline
+     
+    <span class="caret"></span>
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+  <ul class="dropdown-menu" role="menu">
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+    </li>
+    <li>
+      <a href="pipeline_technical.html">Technical documentation</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Research
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li>
+      <a href="research_index.html">Overview</a>
+    </li>
+    <li>
+      <a href="comparison_of_methods_summary.html">Polygenic Scoring Methods Comparison</a>
+    </li>
+    <li>
+      <a href="Functionally_informed_prediction.html">Quantifying Polygenic Signal Mediated by Altered Gene Expression</a>
+    </li>
+    <li>
+      <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
+    </li>
+  </ul>
+</li>
+<li>
+  <a href="more_index.html">More</a>
+</li>
+<li>
+  <a href="https://github.com/opain/GenoPred">
+    <span class="fa fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul>
+    </div><!--/.nav-collapse -->
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+</div><!--/.navbar -->
+
+<div id="header">
+
+
+
+<h1 class="title toc-ignore">HapNest Benchmark</h1>
+
+</div>
+
+
+<hr />
+<p>Here will use simulated genetic data to benchmark GenoPred. We will
+simulate the genetic data using <a
+href="https://github.com/intervene-EU-H2020/synthetic_data">HAPNEST</a>.</p>
+<hr />
+<div id="set-up-hapnest" class="section level1">
+<h1>Set up HAPNEST</h1>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>mkdir -p /users/k1806347/oliverpainfel/HAPNEST
+cd /users/k1806347/oliverpainfel/HAPNEST
+
+# Step 1: Download container
+singularity pull docker://sophiewharrie/intervene-synthetic-data
+
+# Step 2: Set up workspace
+mkdir -p /users/k1806347/oliverpainfel/HAPNEST/containers
+mv intervene-synthetic-data_latest.sif /users/k1806347/oliverpainfel/HAPNEST/containers/
+mkdir -p /users/k1806347/oliverpainfel/HAPNEST/data
+
+# Step 3: Initiate HAPNEST (download dependencies)
+export TMPDIR=/tmp
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif init
+
+# Step 4: Download reference data
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif fetch
+</code></pre>
+</details>
+<hr />
+</div>
+<div id="test-run-using-default-config" class="section level1">
+<h1>Test run using default config</h1>
+<p>Generates data for chromosome 1, for 6 populations, HapMap3 SNPs, and
+1 phenotype.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code># Step 5: Generate genotype and phenotype data
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif generate_geno 1 data/config.yaml
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif generate_pheno data/config.yaml
+
+# Step 6: Evaluate simulation (optional and slow)
+singularity exec --bind data/:/data/ containers/intervene-synthetic-data_latest.sif validate data/config.yaml</code></pre>
+</details>
+<p>The data simulation took about 1 minute. Evaluation takes &gt;2
+hours.</p>
+<hr />
+</div>
+<div id="hapnest-released-genotype-and-phenotype-data"
+class="section level1">
+<h1>HAPNEST released genotype and phenotype data</h1>
+<p>It would probably be easier, and more reproducible to use the
+released version of simulated data from the HAPNEST paper. The files are
+very large as they are for 6.8M variants and 1M individuals. Let’s start
+with chromosome 22 to testing things out. We can subset the files to
+HapMap3 variants as we download them to avoid storing so much data in
+first instance.</p>
+<hr />
+<div id="download-genotype-and-phenotype" class="section level2">
+<h2>Download genotype and phenotype</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>mkdir -p /users/k1806347/oliverpainfel/HAPNEST/released/full
+mkdir -p /users/k1806347/oliverpainfel/HAPNEST/released/subset
+module load plink2
+cd /users/k1806347/oliverpainfel/HAPNEST/released/full
+
+# Download genotype data
+# Subset the data to HapMap3 variants to save storage space
+# Might as well convert to plink2 format for efficiency
+for chr in $(seq 22 22); do
+  for file in $(echo bed bim fam); do
+    wget https://ftp.ebi.ac.uk/biostudies/fire/S-BSST/936/S-BSST936/Files/genotypes/synthetic_v1_chr-${chr}.${file}
+  done
+  
+  wget https://ftp.ebi.ac.uk/biostudies/fire/S-BSST/936/S-BSST936/Files/rsids/rsid_variant_map_list_chr22.txt
+  wget https://ftp.ebi.ac.uk/biostudies/fire/S-BSST/936/S-BSST936/Files/example/synthetic_small_v1_chr-22.bim
+  
+  awk &#39;NR==FNR {snp[$1]; next} $2 in snp&#39; /users/k1806347/oliverpainfel/GenoPred/pipeline/resources/data/hm3_snplist/w_hm3.snplist rsid_variant_map_list_chr22.txt &gt; matched_rows.txt
+
+  plink2 \
+    --bfile synthetic_v1_chr-${chr} \
+    --make-pgen \
+    --extract matched_rows.txt \
+    --out /users/k1806347/oliverpainfel/HAPNEST/released/subset/synthetic_v1_hm3_chr${chr}
+
+  rm synthetic_v1_chr-${chr}.*
+  rm matched_rows.txt
+  rm rsid_variant_map_list_chr${chr}.txt
+done
+
+# Download phenotype data
+mkdir -p /users/k1806347/oliverpainfel/HAPNEST/released/phenotype
+cd /users/k1806347/oliverpainfel/HAPNEST/released/phenotype
+wget https://ftp.ebi.ac.uk/biostudies/fire/S-BSST/936/S-BSST936/Files/synthetic_v1.sample
+for i in $(seq 1 9); do
+  wget https://ftp.ebi.ac.uk/biostudies/fire/S-BSST/936/S-BSST936/Files/phenotypes/synthetic_v1.pheno${i}
+done
+</code></pre>
+</details>
+<p>Note. For some reason there only about 65% of HapMap3 variants
+available in the synthetic data. This will cause an error when using
+GenoPred as it requires a certain overlap with the default reference
+data. Given we are going to generate the GWAS using this data, this
+wouldn’t actually cause any issues of SNP overlap, but there would be
+poor coverage of the genome which will decrease the PGS R2 values. This
+is not a big issue, but given it is so fast to simulate data, it is
+making me think we should simulate our own so we can make it exactly
+what we want (sample size, genetic architecture, snplist).</p>
+<hr />
+</div>
+</div>
+<div id="full-simulation" class="section level1">
+<h1>Full simulation</h1>
+<p>Lets modify the quickstart config.yaml to simulate data for
+chromosome 22, 40k individuals, EUR, EAS and AFR population, 9
+phenotypes with same genetic architecture as HAPNEST paper.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/HAPNEST
+
+# Generate genotype and phenotype data
+singularity exec \
+  --bind data/:/data/ \
+  --bind /users/k1806347/oliverpainfel/GenoPred/pipeline/misc/hapnest/config.synth_1.yaml:/data/config.synth_1.yaml \
+  containers/intervene-synthetic-data_latest.sif \
+  generate_geno \
+  8 \
+  data/config.synth_1.yaml
+  
+# Note this only worked when allocating 100G RAM when using 8 threads. This is a lot more than expected based on the HAPNEST paper.
+
+singularity exec \
+  --bind data/:/data/ \
+  --bind /users/k1806347/oliverpainfel/GenoPred/pipeline/misc/hapnest/config.synth_1.yaml:/data/config.synth_1.yaml \
+  containers/intervene-synthetic-data_latest.sif \
+  generate_pheno \
+  data/config.synth_1.yaml
+  </code></pre>
+</details>
+<hr />
+</div>
+<div id="identify-unrelated-individuals" class="section level1">
+<h1>Identify unrelated individuals</h1>
+<p>We need to identify a group of unrelated individuals, to avoid bias
+in the GWAS and sample overlap between GWAS and target sample when
+evaluating PGS. Idenitfy unrelated individuals within each
+population.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code># Subset simulated data by population
+library(data.table)
+sample &lt;-
+  fread(
+    &#39;/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22.sample&#39;,
+    header = F
+  )$V1
+
+pops&lt;-unique(sample)
+fam &lt;- fread(&#39;/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22.fam&#39;)
+fam$pop &lt;- sample
+
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)){
+  fam_i &lt;- fam[fam$pop == i,]
+  fam_i &lt;- fam_i[, c(&#39;V1&#39;,&#39;V2&#39;), with=F]
+  
+  write.table(
+    fam_i,
+    paste0(
+      &#39;~/oliverpainfel/Analysis/HAPNEST/synth_1/&#39;, i,&#39;.keep&#39;),
+    col.names = F,
+    row.names = F,
+    quote = F
+  )
+}</code></pre>
+<pre class="r"><code># Identify unrelated individuals
+# conda activate .snakemake/conda/ea13b6c549c70695534894feeeecf0b3_
+setwd(&#39;~/oliverpainfel/GenoPred/pipeline/&#39;)
+dir.create(&#39;~/oliverpainfel/Analysis/HAPNEST/synth_1&#39;, recursive = T)
+
+library(&quot;optparse&quot;)
+library(GenoUtils)
+library(data.table)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Create temp directory
+tmp_dir&lt;-tempdir()
+
+option_list = list(
+  make_option(&quot;--target_plink_chr&quot;, action=&quot;store&quot;, default=NULL, type=&#39;character&#39;,
+              help=&quot;Path to per chromosome target PLINK files [required]&quot;),
+  make_option(&quot;--maf&quot;, action=&quot;store&quot;, default=0.05, type=&#39;numeric&#39;,
+              help=&quot;Minor allele frequency threshold [optional]&quot;),
+  make_option(&quot;--geno&quot;, action=&quot;store&quot;, default=0.02, type=&#39;numeric&#39;,
+              help=&quot;Variant missingness threshold [optional]&quot;),
+  make_option(&quot;--hwe&quot;, action=&quot;store&quot;, default=1e-6, type=&#39;numeric&#39;,
+              help=&quot;Hardy Weinberg p-value threshold. [optional]&quot;),
+  make_option(&quot;--n_pcs&quot;, action=&quot;store&quot;, default=10, type=&#39;numeric&#39;,
+              help=&quot;Number of PCs (min=4) [optional]&quot;),
+  make_option(&quot;--plink2&quot;, action=&quot;store&quot;, default=&#39;plink2&#39;, type=&#39;character&#39;,
+              help=&quot;Path PLINK2 software binary [required]&quot;),
+  make_option(&quot;--keep_list&quot;, action=&quot;store&quot;, default=NULL, type=&#39;character&#39;,
+              help=&quot;File containing list of keep files and corresponding population code [optional]&quot;),
+  make_option(&quot;--unrel&quot;, action=&quot;store&quot;, default=NA, type=&#39;character&#39;,
+              help=&quot;File containing list of unrelated individuals [optional]&quot;),
+  make_option(&quot;--n_cores&quot;, action=&quot;store&quot;, default=1, type=&#39;numeric&#39;,
+              help=&quot;Number of cores for parallel computing [optional]&quot;),
+  make_option(&quot;--test&quot;, action=&quot;store&quot;, default=NA, type=&#39;character&#39;,
+              help=&quot;Specify test mode [optional]&quot;),
+  make_option(&quot;--output&quot;, action=&quot;store&quot;, default=NULL, type=&#39;character&#39;,
+              help=&quot;Path for output files [required]&quot;)
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+opt$test&lt;-&#39;chr22&#39;
+opt$target_plink_chr&lt;-&#39;/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-&#39;
+opt$output&lt;-&#39;~/oliverpainfel/Analysis/HAPNEST/synth_1/relatedness/synth_1&#39;
+opt$n_cores&lt;-20
+
+# Create output directory
+opt$output_dir &lt;- paste0(dirname(opt$output),&#39;/&#39;)
+system(paste0(&#39;mkdir -p &#39;,opt$output_dir))
+
+if(!is.na(opt$test)){
+  CHROMS &lt;- as.numeric(gsub(&#39;chr&#39;,&#39;&#39;,opt$test))
+}
+
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)){
+
+  # Identify high quality variants
+  target_qc_snplist&lt;-plink_qc_snplist(bfile = opt$target_plink_chr, chr = CHROMS, plink2 = opt$plink2, geno = opt$geno, maf = opt$maf, hwe = opt$hwe, keep = paste0(&#39;~/oliverpainfel/Analysis/HAPNEST/synth_1/&#39;, i,&#39;.keep&#39;))
+  
+  # Generate kinship matrix and list of unrelated individuals
+  plink_king(bfile = opt$target_plink_chr, chr = CHROMS, extract = target_qc_snplist, plink2 = opt$plink2, out = paste0(opt$output, &#39;.&#39;, i), threads = opt$n_cores, keep = paste0(&#39;~/oliverpainfel/Analysis/HAPNEST/synth_1/&#39;, i,&#39;.keep&#39;))
+  
+}</code></pre>
+</details>
+<hr />
+</div>
+<div id="subsample-and-gwas" class="section level1">
+<h1>Subsample and GWAS</h1>
+<div id="subsample" class="section level2">
+<h2>Subsample</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+
+fam &lt;- NULL
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)){
+  tmp &lt;- fread(paste0(&#39;~/oliverpainfel/Analysis/HAPNEST/synth_1/relatedness/synth_1.&#39;,i,&#39;.unrelated.keep&#39;), header = F)
+  tmp$pop &lt;- i
+  fam &lt;- rbind(fam, tmp)
+}
+
+set.seed(1)
+for(pop_i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)){
+  fam_pop_i &lt;- fam[fam$pop == pop_i,]
+  print(nrow(fam_pop_i))
+  train_size &lt;- floor(0.9 * nrow(fam_pop_i))
+  train_indices &lt;- sample(seq_len(nrow(fam_pop_i)), size = train_size)
+  
+  write.table(
+    fam_pop_i[train_indices, c(&#39;V1&#39;, &#39;V2&#39;), with = F],
+    paste0(
+      &#39;~/oliverpainfel/Analysis/HAPNEST/synth_1/training.&#39;,
+      pop_i,
+      &#39;.txt&#39;
+    ),
+    col.names = F,
+    row.names = F,
+    quote = F
+  )
+  
+  write.table(
+    fam_pop_i[!(seq_len(nrow(fam_pop_i)) %in% train_indices), c(&#39;V1&#39;,&#39;V2&#39;), with=F],
+    paste0(
+      &#39;~/oliverpainfel/Analysis/HAPNEST/synth_1/testing.&#39;,
+      pop_i,
+      &#39;.txt&#39;
+    ),
+    col.names = F,
+    row.names = F,
+    quote = F
+  )
+}</code></pre>
+</details>
+<hr />
+</div>
+<div id="gwas" class="section level2">
+<h2>GWAS</h2>
+<div id="perform-pca" class="section level3">
+<h3>Perform PCA</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code># conda activate .snakemake/conda/ea13b6c549c70695534894feeeecf0b3_
+setwd(&#39;~/oliverpainfel/GenoPred/pipeline/&#39;)
+
+start.time &lt;- Sys.time()
+library(&quot;optparse&quot;)
+
+option_list = list(
+  make_option(&quot;--target_plink_chr&quot;, action=&quot;store&quot;, default=NULL, type=&#39;character&#39;,
+              help=&quot;Path to per chromosome target PLINK files [required]&quot;),
+  make_option(&quot;--maf&quot;, action=&quot;store&quot;, default=0.05, type=&#39;numeric&#39;,
+              help=&quot;Minor allele frequency threshold [optional]&quot;),
+  make_option(&quot;--geno&quot;, action=&quot;store&quot;, default=0.02, type=&#39;numeric&#39;,
+              help=&quot;Variant missingness threshold [optional]&quot;),
+  make_option(&quot;--hwe&quot;, action=&quot;store&quot;, default=1e-6, type=&#39;numeric&#39;,
+              help=&quot;Hardy Weinberg p-value threshold. [optional]&quot;),
+  make_option(&quot;--n_pcs&quot;, action=&quot;store&quot;, default=10, type=&#39;numeric&#39;,
+              help=&quot;Number of PCs (min=4) [optional]&quot;),
+  make_option(&quot;--plink2&quot;, action=&quot;store&quot;, default=&#39;plink2&#39;, type=&#39;character&#39;,
+              help=&quot;Path PLINK2 software binary [required]&quot;),
+  make_option(&quot;--keep_list&quot;, action=&quot;store&quot;, default=NULL, type=&#39;character&#39;,
+              help=&quot;File containing list of keep files and corresponding population code [optional]&quot;),
+  make_option(&quot;--unrel&quot;, action=&quot;store&quot;, default=NA, type=&#39;character&#39;,
+              help=&quot;File containing list of unrelated individuals [optional]&quot;),
+  make_option(&quot;--n_cores&quot;, action=&quot;store&quot;, default=1, type=&#39;numeric&#39;,
+              help=&quot;Number of cores for parallel computing [optional]&quot;),
+  make_option(&quot;--test&quot;, action=&quot;store&quot;, default=NA, type=&#39;character&#39;,
+              help=&quot;Specify test mode [optional]&quot;),
+  make_option(&quot;--output&quot;, action=&quot;store&quot;, default=NULL, type=&#39;character&#39;,
+              help=&quot;Path for output files [required]&quot;)
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+opt$target_plink_chr&lt;-&#39;/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-&#39;
+opt$output&lt;-&#39;~/oliverpainfel/Analysis/HAPNEST/synth_1/pca/&#39;
+opt$test&lt;-&#39;chr22&#39;
+
+keep_list&lt;-data.frame(
+  file = c(
+    &#39;~/oliverpainfel/Analysis/HAPNEST/synth_1/training.EUR.txt&#39;,
+    &#39;~/oliverpainfel/Analysis/HAPNEST/synth_1/training.EAS.txt&#39;,
+    &#39;~/oliverpainfel/Analysis/HAPNEST/synth_1/training.AFR.txt&#39;),
+  POP = c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)
+)
+
+library(GenoUtils)
+library(data.table)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Create temp directory
+tmp_dir&lt;-tempdir()
+
+if(!is.na(opt$test)){
+  CHROMS &lt;- as.numeric(gsub(&#39;chr&#39;,&#39;&#39;,opt$test))
+}
+
+############
+# Create file listing variants in regions of long range LD
+############
+
+targ_pvar &lt;- read_bim(opt$target_plink_chr, chr = CHROMS)
+targ_pvar &lt;- remove_regions(dat = targ_pvar, regions = long_ld_coord)
+
+for(pop in keep_list$POP){
+  # Read in keep file for population
+  keep_file &lt;- fread(keep_list$file[keep_list$POP == pop], header=F)
+  if(ncol(keep_file) == 1){
+    keep_file &lt;- data.table(
+      FID = keep_file$V1,
+      IID = keep_file$V1)
+  } else {
+    keep_file &lt;- data.table(
+      FID = keep_file$V1,
+      IID = keep_file$V2)
+  }
+
+  ###########
+  # Perform PCA on QC&#39;d and independent variants
+  ###########
+
+  # Create QC&#39;d SNP-list
+  target_qc_snplist &lt;- plink_qc_snplist(bfile = opt$target_plink_chr, plink2 = opt$plink2, chr = CHROMS, keep = keep_file, maf = opt$maf, geno = opt$geno, hwe = opt$hwe, threads = opt$n_cores)
+
+  # Remove high LD regions
+  target_qc_snplist &lt;- target_qc_snplist[target_qc_snplist %in% targ_pvar$SNP]
+
+  # Perform LD pruning
+  ld_indep &lt;- plink_prune(bfile = opt$target_plink_chr, chr = CHROMS, keep = keep_file, plink2 = opt$plink2, extract = target_qc_snplist, threads = opt$n_cores)
+
+  # To improve efficiency, derive PCs using random subset of 1000 individuals.
+  keep_file_subset &lt;- keep_file[sample(1000, replace = T),]
+  keep_file_subset &lt;- keep_file_subset[!duplicated(keep_file_subset),]
+
+  # Run PCA
+  snp_weights &lt;- plink_pca(bfile = opt$target_plink_chr, keep = keep_file_subset, chr = CHROMS, plink2 = opt$plink2, extract = ld_indep, n_pc = opt$n_pcs, threads = opt$n_cores)
+  fwrite(snp_weights, paste0(tmp_dir,&#39;/ref.eigenvec.var&#39;), row.names = F, quote=F, sep=&#39; &#39;, na=&#39;NA&#39;)
+
+  # Project into the full population
+  target_pcs &lt;- plink_score(bfile = opt$target_plink_chr, keep = keep_file, chr = CHROMS, plink2 = opt$plink2, score = paste0(tmp_dir,&#39;/ref.eigenvec.var&#39;), threads = opt$n_cores)
+
+  fwrite(target_pcs, paste0(opt$output, pop,&#39;.pcs.txt&#39;), quote=F, sep=&#39; &#39;, na=&#39;NA&#39;)
+}</code></pre>
+</details>
+<hr />
+</div>
+<div id="perform-gwas" class="section level3">
+<h3>Perform GWAS</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>
+module add plink2
+for pheno in $(seq 1 1); do
+    awk &#39;BEGIN {OFS=&quot;\t&quot;} NR==1 {print &quot;FID&quot;, &quot;IID&quot;, &quot;pheno&quot;} NR&gt;1 {print $1, $1, $NF}&#39; /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno} &gt; /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno}.plink
+    
+  for pop in $(echo EUR EAS AFR); do
+  
+    mkdir -p ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}
+    for chr in $(seq 22 22); do
+        sbatch -p neurohack_cpu --mem 20G -n 4 --wrap=&quot;plink2 \
+          --bfile /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22 \
+          --pheno /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno}.plink \
+          --1 \
+          --covar ~/oliverpainfel/Analysis/HAPNEST/synth_1/pca/${pop}.pcs.txt \
+          --covar-variance-standardize \
+          --logistic omit-ref cols=+a1freq,+ax hide-covar \
+          --maf 0.01 \
+          --geno 0.05 \
+          --out ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr${chr}&quot;
+    done
+  done
+done
+
+# Once complete, merge results across chromosomes
+for pheno in $(seq 1 1); do
+  for pop in $(echo EUR EAS AFR); do
+    head -n 1 ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr22.pheno.glm.logistic.hybrid &gt; ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+      for chr in $(seq 22 22); do
+        tail -n +2 ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr22.pheno.glm.logistic.hybrid &gt;&gt; ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+      done
+      
+      # Remove REF and ALT columns and rename AX column to A2
+      cut -f 4,5 --complement ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid | awk &#39;BEGIN{FS=OFS=&quot;\t&quot;} NR==1 {$7=&quot;A2&quot;} 1&#39; &gt; temp.txt &amp;&amp; mv temp.txt ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+  
+      gzip ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+  done
+done
+</code></pre>
+</details>
+<hr />
+</div>
+</div>
+</div>
+<div id="run-genopred" class="section level1">
+<h1>Run GenoPred</h1>
+<div id="subset-hapnest-testing-data" class="section level2">
+<h2>Subset HAPNEST testing data</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>mkdir -p ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing_subset
+
+cat ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing.*.txt &gt; ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing.txt
+
+for chr in $(seq 22 22); do
+    plink2 \
+      --bfile /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-${chr} \
+      --keep ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing.txt \
+      --make-bed \
+      --out ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing_subset/synth_1.chr${chr}
+done</code></pre>
+</details>
+</div>
+<div id="prepare-config" class="section level2">
+<h2>Prepare config</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>dir.create(&#39;/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config&#39;, recursive = T)
+
+# gwas_list
+sample_file&lt;-fread(&#39;/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22.sample&#39;, header=F)$V1
+gwas_list &lt;- NULL
+for(pheno in 1:1){
+  pheno_file &lt;- fread(paste0(&#39;/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno&#39;, pheno,&#39;.plink&#39;))
+  for(pop in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)){
+    
+    pheno_file_pop &lt;- pheno_file[sample_file == pop,]
+    
+    tmp &lt;- data.frame(
+      name=paste0(&#39;pheno&#39;,pheno,&#39;_&#39;,pop),
+      path=paste0(&#39;/users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno&#39;, pheno, &#39;/pheno&#39;, pheno, &#39;.&#39;, pop, &#39;.GW.pheno.glm.logistic.hybrid.gz&#39;),
+      population=pop,
+      n=NA,
+      sampling=mean(pheno_file_pop$pheno),
+      prevalence=0.5,
+      mean=NA,
+      sd=NA,
+      label=paste0(&#39;&quot;pheno&#39;, pheno, &#39; (&#39;, pop, &#39;)&quot;&#39;)
+    )
+    
+    gwas_list &lt;- rbind(gwas_list, tmp)
+  }
+}
+
+write.table(gwas_list, &#39;/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/gwas_list.txt&#39;, col.names = T, row.names = F, quote = F)
+
+# target_list
+target_list &lt;- data.frame(
+  name=&#39;hapnest&#39;,
+  path=&#39;/users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_1/testing_subset/synth_1&#39;,
+  type=&#39;plink1&#39;,
+  indiv_report=F
+)
+
+write.table(target_list, &#39;/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/target_list.txt&#39;, col.names = T, row.names = F, quote = F)
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/output&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/gwas_list.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/target_list.txt&quot;,
+  &quot;pgs_methods: [&#39;ptclump&#39;]&quot;,
+  &quot;cores_prep_pgs: 1&quot;,
+  &quot;cores_target_pgs: 10&quot;,
+  &quot;testing: chr22&quot;,
+  &quot;pgs_scaling: [&#39;continuous&#39;, &#39;discrete&#39;]&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-pipeline" class="section level2">
+<h2>Run pipeline</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml output_all -n</code></pre>
+</details>
+<hr />
+</div>
+</div>
+<div id="evaluate-pgs" class="section level1">
+<h1>Evaluate PGS</h1>
+<p>Evaluate PGS R2 within each population.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;~/oliverpainfel/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+library(data.table)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml&#39;
+pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+# Read in PGS for each population
+pop &lt;- c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)
+pgs &lt;- read_pgs(config = config)
+
+# Read in phenotype data
+pheno &lt;- fread(&#39;/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno1.plink&#39;)
+
+# Evaluate PGS (without adjustment for reference PCs)
+assoc&lt;-NULL
+for(i in pop){
+  for(j in 1:nrow(scores)){
+    tmp &lt;- pgs$hapnest[[i]][[scores$name[j]]][[scores$method[j]]]
+    tmp &lt;- merge(tmp, pheno, by = c(&#39;FID&#39;,&#39;IID&#39;))
+    tmp_pheno &lt;- tmp$pheno
+    tmp_pgs &lt;- tmp[, !(names(tmp) %in% c(&#39;FID&#39;,&#39;IID&#39;,&#39;pheno&#39;)), with = F]
+    
+    for(k in names(tmp_pgs)){
+      mod &lt;- glm(tmp_pheno ~ scale(tmp_pgs[[k]]), family=&#39;binomial&#39;)
+        obs_r2 &lt;- cor(predict(mod), as.numeric(tmp_pheno))^2
+      sum_mod &lt;- summary(mod)
+
+      assoc &lt;- rbind(
+        assoc, 
+        data.table(
+            Predictor = k,
+            BETA = coef(sum_mod)[2, 1],
+            SE = coef(sum_mod)[2, 2],
+            P = coef(sum_mod)[2, 4],
+            Obs_R2 = obs_r2,
+            N = length(tmp_pheno),
+            gwas = scores$name[j],
+            method = scores$method[j],
+            pop = i,
+            trans = F
+        )
+      )
+      }
+  }
+}
+
+# Evaluate PGS (without adjustment for reference PCs)
+assoc_trans&lt;-NULL
+for(i in pop){
+  for(j in 1:nrow(scores)){
+    tmp &lt;- pgs$hapnest[[i]][[scores$name[j]]][[scores$method[j]]]
+    tmp2 &lt;- pgs$hapnest[[&#39;TRANS&#39;]][[scores$name[j]]][[scores$method[j]]]
+    tmp2 &lt;- tmp2[tmp2$FID %in% tmp$FID,]
+    tmp &lt;- merge(tmp2, pheno, by = c(&#39;FID&#39;,&#39;IID&#39;))
+    tmp_pheno &lt;- tmp$pheno
+    tmp_pgs &lt;- tmp[, !(names(tmp) %in% c(&#39;FID&#39;,&#39;IID&#39;,&#39;pheno&#39;)), with = F]
+    
+    for(k in names(tmp_pgs)){
+      mod &lt;- glm(tmp_pheno ~ scale(tmp_pgs[[k]]), family=&#39;binomial&#39;)
+        obs_r2 &lt;- cor(predict(mod), as.numeric(tmp_pheno))^2
+      sum_mod &lt;- summary(mod)
+
+      assoc_trans &lt;- rbind(
+        assoc_trans, 
+        data.table(
+            Predictor = k,
+            BETA = coef(sum_mod)[2, 1],
+            SE = coef(sum_mod)[2, 2],
+            P = coef(sum_mod)[2, 4],
+            Obs_R2 = obs_r2,
+            N = length(tmp_pheno),
+            gwas = scores$name[j],
+            method = scores$method[j],
+            pop = i,
+            trans = T
+        )
+      )
+      }
+  }
+}
+
+# Check the correlation between pheno and PGS, with and without adjustments for reference PCs
+cor(assoc_trans$BETA, assoc$BETA) # 0.9996044
+
+plot(assoc_trans$BETA, assoc$BETA)
+abline(a = 0, b = 1, col = &quot;red&quot;, lwd = 2, lty = 2)
+# The results are almost identical. This will vary depending on whether the phenotype varies according to ancestry. This simulation confirms that the PGS R2 is not impacted when there is no correlation between ancestry and the phenotype.
+
+# Plot the results
+assoc_both &lt;- rbind(assoc, assoc_trans)
+library(ggplot2)
+library(cowplot)
+
+# Extract pT variable from Predictor
+assoc_both$pT &lt;- gsub(&#39;e.&#39;,&#39;e-&#39;, gsub(&#39;.*0_&#39;, &#39;&#39;, assoc_both$Predictor))
+assoc_both$pT &lt;- factor(assoc_both$pT, levels = unique(assoc_both$pT))
+
+# Improve labelling for plot
+assoc_both$pop &lt;- paste0(&#39;Target = &#39;, assoc_both$pop)
+assoc_both$pop &lt;- factor(assoc_both$pop, levels = unique(assoc_both$pop))
+
+assoc_both$gwas &lt;- gsub(&#39;.*_&#39;, &#39;&#39;, assoc_both$gwas)
+assoc_both$gwas &lt;- paste0(&#39;Discovery = &#39;, assoc_both$gwas)
+assoc_both$gwas &lt;- factor(assoc_both$gwas, levels = unique(assoc_both$gwas))
+
+assoc_both$trans &lt;- ifelse(assoc_both$trans, &#39;Unadjusted&#39;, &#39;Adjusted&#39;)
+
+setwd(&#39;/scratch_tmp/prj/oliverpainfel/GenoPred&#39;)
+dir.create(&#39;docs/Images/hapnest&#39;)
+png(&#39;docs/Images/hapnest/pgs_eval.png&#39;, res = 100, height = 700, width = 1000, units = &#39;px&#39;)
+
+ggplot(assoc_both, aes(x = trans, y = BETA, fill = pT)) +
+  geom_hline(yintercept = 0, colour = &#39;darkgrey&#39;) +
+  geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.8) +
+  geom_errorbar(aes(ymin=BETA-SE, ymax=BETA+SE), width=0, position=position_dodge(width = 0.8, preserve = &quot;single&quot;)) +
+  labs(y=&quot;BETA (SE)&quot;, x=NULL) +
+  theme_half_open() +
+  background_grid() +
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) +
+  scale_fill_manual(values = colorRampPalette(c(&quot;lightblue&quot;, &quot;darkblue&quot;))(length(unique(assoc_both$pT)))) +
+  facet_grid(pop ~ gwas)
+
+dev.off()</code></pre>
+</details>
+<p></br></p>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<div class="figure">
+<img src="Images/hapnest/pgs_eval.png" alt="" />
+<p class="caption">Performance of PGS when adjusted for ancestry</p>
+</div>
+</div>
+</div>
+<p></br></p>
+<p>The results with and without reference-based correction for ancestry
+are almost identical. This will vary depending on whether the phenotype
+varies according to ancestry. This simulation confirms that the PGS R2
+is not impacted when there is no correlation between ancestry and the
+phenotype.</p>
+<p>As expected the PGS derived using target-matched ancestry GWAS,
+perform slightly better. The difference isn’t huge, due to same GWAS
+sample size, and cross population rG of 1.</p>
+</div>
+
+<!-- footer.html -->
+<hr/>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 500px;">
+<img src="Images/logo/sponsors.png">
+</div>
+</div>
+
+
+</div>
+</div>
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
+
+
+</script>
+
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
+
+<!-- code folding -->
+
+<script>
+$(document).ready(function ()  {
+
+    // temporarily add toc-ignore selector to headers for the consistency with Pandoc
+    $('.unlisted.unnumbered').addClass('toc-ignore')
+
+    // move toc-ignore selectors from section div to header
+    $('div.section.toc-ignore')
+        .removeClass('toc-ignore')
+        .children('h1,h2,h3,h4,h5').addClass('toc-ignore');
+
+    // establish options
+    var options = {
+      selectors: "h1,h2",
+      theme: "bootstrap3",
+      context: '.toc-content',
+      hashGenerator: function (text) {
+        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
+      },
+      ignoreSelector: ".toc-ignore",
+      scrollTo: 0
+    };
+    options.showAndHide = true;
+    options.smoothScroll = true;
+
+    // tocify
+    var toc = $("#TOC").tocify(options).data("toc-tocify");
+});
+</script>
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/pipeline/misc/hapnest/config.synth_1.yaml b/pipeline/misc/hapnest/config.synth_1.yaml
index a548a855..13c0214d 100644
--- a/pipeline/misc/hapnest/config.synth_1.yaml
+++ b/pipeline/misc/hapnest/config.synth_1.yaml
@@ -117,10 +117,10 @@ phenotype_data:
 evaluation:
   metrics:
     aats: false  # nearest neighbour adversarial accuracy
-    kinship: false # relatedness, including kinship density and IBS plots
-    ld_corr: true # linkage disequilibrium (LD) correlation matrix
-    ld_decay: true # linkage disequilibrium (LD) decay plot (and distance)
-    maf: true # minor allele frequency divergences
+    kinship: true # relatedness, including kinship density and IBS plots
+    ld_corr: false # linkage disequilibrium (LD) correlation matrix
+    ld_decay: false # linkage disequilibrium (LD) decay plot (and distance)
+    maf: false # minor allele frequency divergences
     pca: false # principal components analysis
     gwas: false # GWAS, manhattan plot and qqplot
 

From 01312f8f749c9c5586e4eeb4e8a4b3781c8ba778 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 3 Jan 2025 15:37:55 +0000
Subject: [PATCH 071/153] Fixed plink function when using plink1 as input

---
 functions/plink.R | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/functions/plink.R b/functions/plink.R
index 249b4165..ae8d4985 100644
--- a/functions/plink.R
+++ b/functions/plink.R
@@ -383,9 +383,9 @@ plink_king<-function(bfile=NULL, pfile=NULL, extract = NULL, chr = 1:22, plink2=
 
   # Merge per chromosome files extracting selected variants
   if(!is.null(bfile)){
-    plink_merge(bfile = bfile, chr = chr, plink2 = plink2, extract = extract_snplist, out = paste0(tmp_dir,'/merged'), threads=threads)
+    plink_merge(bfile = bfile, chr = chr, plink2 = plink2, make_bed = T, extract = extract_snplist, out = paste0(tmp_dir,'/merged'), threads=threads, keep = keep)
   } else {
-    plink_merge(pfile = pfile, chr = chr, plink2 = plink2, extract = extract_snplist, out = paste0(tmp_dir,'/merged'), threads=threads)
+    plink_merge(pfile = pfile, chr = chr, plink2 = plink2, extract = extract_snplist, out = paste0(tmp_dir,'/merged'), threads=threads, keep = keep)
   }
 
   # Run KING estimator

From 4b1c8b4bf521a5054138a371ef5a426b770f482b Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 3 Jan 2025 16:11:22 +0000
Subject: [PATCH 072/153] Minor edit to correlation matrix plot dimensions

---
 Scripts/pipeline_reports/indiv_report_creator.Rmd | 11 +++++++++--
 Scripts/pipeline_reports/samp_report_creator.Rmd  | 12 +++++++++---
 2 files changed, 18 insertions(+), 5 deletions(-)

diff --git a/Scripts/pipeline_reports/indiv_report_creator.Rmd b/Scripts/pipeline_reports/indiv_report_creator.Rmd
index 015ba857..18e36010 100644
--- a/Scripts/pipeline_reports/indiv_report_creator.Rmd
+++ b/Scripts/pipeline_reports/indiv_report_creator.Rmd
@@ -464,17 +464,24 @@ plot_obj<-ggplot(melted_cormat, aes(x = Var1, y = Var2, fill = value)) +
     coord_fixed()
 
 plot_file<-paste0(tempfile(),'.png')
-png(plot_file, units = 'px', res = 100, width = 400+(70*ncol(ref_pgs)), height = 100+(70*ncol(ref_pgs)))
+plot_height <- 150+(70*ncol(ref_pgs))
+plot_width <- 450+(70*ncol(ref_pgs))
+
+if(plot_height < 400) {plot_height <- 400}
+if(plot_width < 700) {plot_width <- 700}
+
+png(plot_file, units = 'px', res = 100, width = plot_width, height = plot_height)
 plot_obj
 dev.off()
 
+
 ```
 
 
 ```{r, results='asis', eval = pgs_incl}
 
 cat0("<details><summary>Show reference PGS correlation matrix</summary>\n\n")
-cat0(paste0("![](", plot_file,")"))
+cat0(paste0("![](", plot_file,")\n\n"))
 cat0("**Note.** Plot shows correlation between pseudovalidated PGS across all populations, adjusted for ancestry.\n\n")
 cat0("</details> \n\n")
 cat0("***\n\n")
diff --git a/Scripts/pipeline_reports/samp_report_creator.Rmd b/Scripts/pipeline_reports/samp_report_creator.Rmd
index d86822bf..3efa5999 100644
--- a/Scripts/pipeline_reports/samp_report_creator.Rmd
+++ b/Scripts/pipeline_reports/samp_report_creator.Rmd
@@ -370,7 +370,13 @@ plot_obj<-ggplot(melted_cormat, aes(x = Var1, y = Var2, fill = value)) +
     coord_fixed()
 
 plot_file<-paste0(tempfile(),'.png')
-png(plot_file, units = 'px', res = 100, width = 400+(70*ncol(ref_pgs)), height = 100+(70*ncol(ref_pgs)))
+plot_height <- 150+(70*ncol(ref_pgs))
+plot_width <- 450+(70*ncol(ref_pgs))
+
+if(plot_height < 400) {plot_height <- 400}
+if(plot_width < 700) {plot_width <- 700}
+
+png(plot_file, units = 'px', res = 100, width = plot_width, height = plot_height)
 plot_obj
 dev.off()
 
@@ -380,7 +386,7 @@ dev.off()
 ```{r, results='asis', eval = pgs_incl}
 
 cat0("<details><summary>Show reference PGS correlation matrix</summary>\n\n")
-cat0(paste0("![](", plot_file,")"))
+cat0(paste0("![](", plot_file,")\n\n"))
 cat0("**Note.** Plot shows correlation between pseudovalidated PGS across all populations, adjusted for ancestry.\n\n")
 cat0("</details> \n\n")
 cat0("***\n\n")
@@ -416,7 +422,7 @@ for(i in names(ancestry_log$keep_files)){
 pgs$assigned_pop[is.na(pgs$assigned_pop)]<-'Unassigned'
 
 # Plot the distribution of polygenic scores
-plot_width <- 250 + length(unique(pgs$pop)) * 250
+plot_width <- 250 + length(unique(pgs$pop)) * 255
 if(plot_width < 1000) plot_width <- 1000
 
 pgs_dist_plots <- list()

From 07e17bb9b44994af8ce6db10eb1d19c2fe0d7f47 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 3 Jan 2025 17:12:33 +0000
Subject: [PATCH 073/153] Updated documentation

---
 docs/pipeline_readme.Rmd   | 18 ++++++++++--
 docs/pipeline_readme.html  | 58 ++++++++++++++++++++++++++++++++++++--
 docs/styles/night-mode.css |  9 ++++++
 docs/styles/styles.css     |  7 ++++-
 4 files changed, 85 insertions(+), 7 deletions(-)

diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index a2af92be..c83b728d 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -206,7 +206,7 @@ singularity \
 
 # Pipeline configuration
 
-The pipeline is configured using a configfile, which tells the pipeline what to do, and the location of the input data listed in the target_list, gwas_list, and score_list files.
+The pipeline is configured using a `configfile`, which tells the pipeline what to do, and the location of the input data listed in the `target_list`, `gwas_list`, and `score_list` files.
 
 <div class="centered-container">
 <div class="rounded-image-container" style="width: 75%;">
@@ -1404,9 +1404,21 @@ kable(gwas_groups_df, 'markdown')
 
 ## Select ancestry adjustment approach
 
-The GenoPred pipeline has the capability to generate target PGS scaled according to an ancestry-matched reference, using either continuous or discrete corrections for ancestry (details provided [here]()).
+The GenoPred pipeline offers the ability to generate target polygenic scores (PGS) corrected for ancestry. This can be achieved through two approaches: discrete or continuous corrections for ancestry.
 
-By default, the pipeline applies only the continuous correction for ancestry to scale the target PGS. However, users can specify the desired ancestry adjustment approach using the `pgs_scaling` parameter in the `configfile`. This parameter accepts a list of values, which can include `continuous` and `discrete`.
+### Discrete Correction:
+* This approach assigns each target individual to an ancestry-matched reference population.
+* The target individual's PGS is then standardized based on the mean and standard deviation (SD) of the PGS within the corresponding reference population.
+* This is the approach used by GenoPred until v3.0.0.
+
+### Continuous Correction:
+* This method utilizes genetic principal components projected from reference populations to standardize target PGS.
+* It accounts for variation within reference populations and accommodates individuals whose ancestry does not align closely with a single reference population.
+* GenoPred uses the approach described by Linders et al. ([here](https://doi.org/10.1016/j.gim.2023.100006)), which adjust both the mean and variance of the target PGS.
+
+There is a nice explanation of these approaches on the pgsc_calc website ([here](https://pgsc-calc.readthedocs.io/en/latest/explanation/geneticancestry.html)), where they refer to the continuous approach as 'Empirical Methods', and the continuous approach as 'PCA-based methods' (specifically `Z_norm2`).
+
+By default, the GenoPred pipeline applies only the continuous correction for ancestry to scale the target PGS. However, users can specify the desired ancestry adjustment approach using the `pgs_scaling` parameter in the `configfile`. This parameter accepts a list of values, which can include `continuous` and `discrete`.
 
 For example, to produce target PGS using both continuous and discrete correction, specify:
 
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index 7d9b7fc0..d02e185c 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -627,9 +627,10 @@ <h1>Containers</h1>
 </div>
 <div id="pipeline-configuration" class="section level1">
 <h1>Pipeline configuration</h1>
-<p>The pipeline is configured using a configfile, which tells the
-pipeline what to do, and the location of the input data listed in the
-target_list, gwas_list, and score_list files.</p>
+<p>The pipeline is configured using a <code>configfile</code>, which
+tells the pipeline what to do, and the location of the input data listed
+in the <code>target_list</code>, <code>gwas_list</code>, and
+<code>score_list</code> files.</p>
 <div class="centered-container">
 <div class="rounded-image-container" style="width: 75%;">
 <p><img src="Images/pipeline_readme/input_schematic_wide.png"></p>
@@ -1831,6 +1832,9 @@ <h2><code>read_pgs</code></h2>
 is NULL.</li>
 <li><code>pop</code> (optional): A vector of populations to include.
 Default is NULL.</li>
+<li><code>pseudo_only</code> (optional): A logical parameter that
+determines whether to return only the PGS selected through
+pseudovalidation. Default is FALSE.</li>
 </ul></li>
 <li><strong>Returns</strong>
 <ul>
@@ -2186,6 +2190,54 @@ <h2>Multi-source PGS methods</h2>
 </div>
 <hr />
 </div>
+<div id="select-ancestry-adjustment-approach" class="section level2">
+<h2>Select ancestry adjustment approach</h2>
+<p>The GenoPred pipeline offers the ability to generate target polygenic
+scores (PGS) corrected for ancestry. This can be achieved through two
+approaches: discrete or continuous corrections for ancestry.</p>
+<div id="discrete-correction" class="section level3">
+<h3>Discrete Correction:</h3>
+<ul>
+<li>This approach assigns each target individual to an ancestry-matched
+reference population.</li>
+<li>The target individual’s PGS is then standardized based on the mean
+and standard deviation (SD) of the PGS within the corresponding
+reference population.</li>
+<li>This is the approach used by GenoPred until v3.0.0.</li>
+</ul>
+</div>
+<div id="continuous-correction" class="section level3">
+<h3>Continuous Correction:</h3>
+<ul>
+<li>This method utilizes genetic principal components projected from
+reference populations to standardize target PGS.</li>
+<li>It accounts for variation within reference populations and
+accommodates individuals whose ancestry does not align closely with a
+single reference population.</li>
+<li>GenoPred uses the approach described by Linders et al. (<a
+href="https://doi.org/10.1016/j.gim.2023.100006">here</a>), which adjust
+both the mean and variance of the target PGS.</li>
+</ul>
+<p>There is a nice explanation of these approaches on the pgsc_calc
+website (<a
+href="https://pgsc-calc.readthedocs.io/en/latest/explanation/geneticancestry.html">here</a>),
+where they refer to the continuous approach as ‘Empirical Methods’, and
+the continuous approach as ‘PCA-based methods’ (specifically
+<code>Z_norm2</code>).</p>
+<p>By default, the GenoPred pipeline applies only the continuous
+correction for ancestry to scale the target PGS. However, users can
+specify the desired ancestry adjustment approach using the
+<code>pgs_scaling</code> parameter in the <code>configfile</code>. This
+parameter accepts a list of values, which can include
+<code>continuous</code> and <code>discrete</code>.</p>
+<p>For example, to produce target PGS using both continuous and discrete
+correction, specify:</p>
+<pre class="yaml"><code>pgs_scaling: [&#39;continuous&#39;, &#39;discrete]</code></pre>
+<p>Or, to produce target PGS only the discrete correction, specify:</p>
+<pre class="yaml"><code>pgs_scaling: [&#39;discrete&#39;]</code></pre>
+<hr />
+</div>
+</div>
 </div>
 <div id="running-in-offline-environment" class="section level1">
 <h1>Running in offline environment</h1>
diff --git a/docs/styles/night-mode.css b/docs/styles/night-mode.css
index 8bf1cf5b..ff41b5e4 100644
--- a/docs/styles/night-mode.css
+++ b/docs/styles/night-mode.css
@@ -25,6 +25,15 @@ pre {
 p code {
     background-color: #242f39; /* Slightly different background for inline code */
     color: #82ccdd; /* Light blue for inline code text */
+    border: 1px solid #4e5a5b !important;
+}
+
+p {
+  font-weight: 300 !important;
+}
+
+li {
+  font-weight: 300 !important;
 }
 
 /* Optional: Syntax Highlighting Styles */
diff --git a/docs/styles/styles.css b/docs/styles/styles.css
index 27b2217d..9ba0b16a 100644
--- a/docs/styles/styles.css
+++ b/docs/styles/styles.css
@@ -212,7 +212,11 @@ h4 {
 }
 
 p {
-  font-weight: 300;
+  font-weight: 400;
+}
+
+li {
+  font-weight: 400;
 }
 
 /* Styling for code blocks */
@@ -229,6 +233,7 @@ pre {
 p code {
     padding: 2px 4px;
     border-radius: 5px;
+    border: 1px solid #cccccc;
 }
 
 .tocify .list-group-item {

From fd76a7a7a99aacb799ea64cb83c26771859e9686 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sat, 4 Jan 2025 13:42:38 +0000
Subject: [PATCH 074/153] Fixed bug in outlier detection when unrelated
 individuals are not specified.

---
 functions/plink.R | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/functions/plink.R b/functions/plink.R
index ae8d4985..6d0aa9ce 100644
--- a/functions/plink.R
+++ b/functions/plink.R
@@ -356,7 +356,7 @@ plink_clump<-function(bfile=NULL, pfile=NULL, plink=NULL, plink2=NULL, chr = 1:2
 }
 
 # Generate kinship matrix and identify unrelated individuals (>2nd degree)
-plink_king<-function(bfile=NULL, pfile=NULL, extract = NULL, chr = 1:22, plink2='plink2', out, keep=NA, threads = 1){
+plink_king<-function(bfile=NULL, pfile=NULL, extract = NULL, chr = 1:22, plink2='plink2', out, keep=NULL, threads = 1){
   if(is.null(bfile) & is.null(pfile)){
     stop("bfile or pfile must be specified.")
   }

From fa4ccec076ecd410054164904149a7b3a94cd671 Mon Sep 17 00:00:00 2001
From: Oliver Pain <oliver.pain@kcl.ac.uk>
Date: Mon, 6 Jan 2025 16:43:27 +0000
Subject: [PATCH 075/153] Making some notes on running on dnanexus

---
 docs/CrossPop_dnanexus.Rmd | 3220 ++++++++++++++++++++++++++++++++++++
 1 file changed, 3220 insertions(+)
 create mode 100644 docs/CrossPop_dnanexus.Rmd

diff --git a/docs/CrossPop_dnanexus.Rmd b/docs/CrossPop_dnanexus.Rmd
new file mode 100644
index 00000000..ac179b33
--- /dev/null
+++ b/docs/CrossPop_dnanexus.Rmd
@@ -0,0 +1,3220 @@
+---
+title: Cross-population evaluation of polygenic scores
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+Half way through my project implementing and evaluation cross population PGS methods, our KCL server went down, and UKB moved over to DNAnexus. Here we will set up the cross population project on DNAnexus.
+
+***
+
+# Define ancestry in UKB
+
+We will need to mount the dispensed UKB genetic data, so it can be used as an input to the GenoPred pipeline.
+
+```{bash}
+# Mount the project with the dispensed UKB data
+FUSE_MOUNT=/home/dnanexus/projects2
+mkdir -p $FUSE_MOUNT
+./dxfuse-linux -uid $(id -u) -gid $(id -g) -verbose 2 $FUSE_MOUNT project-GP0ZBqQJ5vK5kxJF3KyxgZg1
+
+# Create symlinks to the dispensed imputed genetic data
+mkdir -p /home/dnanexus/ukb/ukb_symlinks
+
+# pgen and pvar files
+for chr in $(seq 1 22);do
+  for file in $(echo bgen bgen.bgi);do
+    ln -s /home/dnanexus/projects/UKB_82087//Bulk/Imputation/Imputation\ from\ genotype\ \(TOPmed\)/ukb21007_c${chr}_b0_v1.${file} /home/dnanexus/ukb/ukb_symlinks/ukb_imp.chr${chr}.${file}
+  done
+done
+
+# Make a copy of the sample file, and set the sex column to dichotomous (otherwise plink throws an error)
+cp /home/dnanexus/projects/UKB_82087//Bulk/Imputation/Imputation\ from\ genotype\ \(TOPmed\)/ukb21007_c1_b0_v1.sample /home/dnanexus/ukb/ukb_symlinks/ukb_imp.sample
+sed -i '2s/0/D/4' /home/dnanexus/ukb/ukb_symlinks/ukb_imp.sample
+
+# Subset the files to contain variants with MAF > 0.005 and INFO 0.4
+```
+
+```{r}
+install.packages('data.table')
+install.packages('R.utils')
+library(data.table)
+for(i in 22:22){
+    snp_dat<-fread(paste0("/home/dnanexus/projects2/UKB_82087//Bulk/Imputation/Imputation from genotype (TOPmed)/helper_files/ukb21007_c",i,'_b0_v1.sites.vcf.gz'), nrows = 100000)
+    snp_dat<-snp_dat[nchar(snp_dat$REF) == 1 & nchar(snp_dat$ALT) == 1,]
+    snp_dat$ID<-paste0(snp_dat$`#CHROM`, ':', snp_dat$POS)
+    snp_dat<-snp_dat[, c('ID','INFO'), with=F]
+    snp_dat <- data.table(ID = snp_dat$ID, do.call(rbind, lapply(strsplit(snp_dat$INFO, ';'), function(x) x[1:2])))
+    names(snp_dat)<-c('ID','FREQ','R2')
+    snp_dat$FREQ<-as.numeric(gsub('AF=','', snp_dat$FREQ))
+    snp_dat$R2<-as.numeric(gsub('R2=','', snp_dat$R2))
+    snp_dat<-snp_dat[(snp_dat$FREQ > 0.005 | snp_dat$FREQ < 0.995) & snp_dat$R2 > 0.4,]
+    fwrite(snp_dat[,'ID', with=F], paste0('/home/dnanexus/ukb/maf_005.r2_4.chr',i,'.snplist'), quote=F, col.names=F)
+}
+```
+
+```{bash}
+# decompress qctool_v2.2.0-CentOS_Linux7.8.2003-x86_64.tgz
+
+
+/home/dnanexus/software/qctool_v2.0.8-CentOS\ Linux7.6.1810-x86_64/qctool \
+    -g /home/dnanexus/projects2/UKB_82087//Bulk/Imputation/Imputation\ from\ genotype\ \(TOPmed\)/ukb21007_c22_b0_v1.bgen \
+    -og /home/dnanexus/ukb/ukb.filtered.chr22.bgen \
+    -incl-positions /home/dnanexus/ukb/maf_005.r2_4.chr22.snplist
+```
+
+Create the require configuration files. 
+
+```{bash}
+mkdir -p /home/dnanexus/ukb/configs/basic
+mkdir -p /home/dnanexus/ukb/GenoPred/output
+```
+
+```{r}
+# Create target list
+target_list <- data.frame(
+  name='ukb',
+  path='/home/dnanexus/ukb/ukb_symlinks/ukb_imp',
+  type='bgen',
+  indiv_report=F
+)
+
+write.table(target_list, '/home/dnanexus/ukb/configs/basic/target_list.txt', col.names=T, row.names=F, quote=F)
+
+# Create config file
+conf <- c(
+  'outdir: /home/dnanexus/ukb/GenoPred/output',
+  'config_file: /home/dnanexus/ukb/configs/basic/config.yaml',
+  'target_list: /home/dnanexus/ukb/configs/basic/target_list.txt',
+  'testing: chr22'
+)
+
+write.table(conf, '/home/dnanexus/ukb/configs/basic/config.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+Run pipeline
+
+```{bash}
+cd /home/dnanexus/GenoPred/pipeline
+git describe --tags
+# v2.2.2-178-ge7cceca
+
+snakemake -j 4 --use-conda --configfile=/home/dnanexus/ukb/configs/basic/config.yaml ancestry_inference -n 
+```
+
+
+
+```{r}
+# Create config file
+conf <- c(
+  'outdir: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output',
+  'config_file: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/benchmark/config.yaml',
+  'gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/benchmark/gwas_list.txt',
+  'target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt',
+  "pgs_methods: ['ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs']",
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 50"
+)
+
+write.table(conf, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/benchmark/config.yaml', col.names = F, row.names = F, quote = F)
+```
+
+
+
+***
+***
+
+
+# Introduction
+
+European (EUR) GWAS are typically the largest in sample size, or even the only GWAS for certain outcomes. Here we will evaluate approaches for calculating polygenic scores (PGS) across populations. We will include single- and multi-source PGS methods, using EUR GWAS alone, or using EUR GWAS in combination with GWAS from other populations.
+
+In first instance, we will use EUR individuals in UK Biobank (UKB), to derive GWAS summary statistics, and Biobank Japan GWAS. We will evaluate PGS across populations in UKB, using outcomes available in the majority of participants to ensure sufficient sample size in non-EUR populations.
+
+***
+
+# Derive GWAS in UKB
+
+To avoid sample overlap between the EUR GWAS and the EUR target sample for evaluation, we will split EUR individuals in UKB into training and testing subsets. The GWAS will be performed in the training subset, and the PGS evaluation will occur in the testing subset.
+
+***
+
+## Run outlier detection
+
+<details><summary>Show code</summary>
+```{bash}
+cd /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/GenoPred/pipeline
+git describe --tags
+# v2.2.5-20-gb0bf674
+
+snakemake --profile slurm --use-conda --configfile=../../usr/k1806347/configs/benchmark/config.yaml outlier_detection -n 
+
+```
+</details>
+
+***
+
+## Collect phenotype data
+
+We will use the same 33 quantitative traits that were used in the PRS-CSx paper (Supp Table 10). 
+
+<details><summary>Show code</summary>
+```{bash}
+mkdir /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx
+```
+
+```{r}
+library(ukbkings)
+library(dplyr)
+library(stringr)
+library(data.table)
+
+# create data.frame showing variables used by prscsx
+prscsx_fields<-c('30620','30600','30610','30650','30160','21001','21002','30710','30680','4079','30150','30740','30750','30760','50','30030','30020','30780','30120','30050','30060','30040','30130','30140','30080','30010','30700','4080','30690','30860','30870','30000','30730')
+prscsx_trait<-c('Alanine aminotransferase','Albumin','Alkaline phosphatase','Aspartate transaminase','Basophil','Body mass index','Body weight','C-reactive protein','Calcium','Diastolic blood pressure','Eosinophil','Glucose','HbA1c','HDL-cholesterol','Height','Hematocrit','Hemoglobin','LDL-cholesterol','Lymphocyte','Mean corpuscular hemoglobin','Mean corpuscular hemoglobin concentration','Mean corpuscular volume','Monocyte','Neutrophil','Platelet','Red blood cell','Serum creatinine','Sytolic blood pressure','Total cholesterol','Total protein','Triglycerides','White blood cell','γ-glutamyl transpeptidase')
+prscsx_labels<-c('ALT','ALB','ALP','AST','BAS','BMI','BWT','CRP','Ca','DBP','EOS','GLC','HbA1c','HDL','HT','HCT','HB','LDL','LYM','MCH','MCHC','MCV','MON','NEU','PLT','RBC','CR','SBP','TC','TP','TG','WBC','GGT')
+
+prscsx_dat<-data.frame(
+  trait=prscsx_trait,
+  labels=prscsx_labels,
+  field=prscsx_fields
+)
+
+write.csv(prscsx_dat, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv', row.names = F)
+write.table(prscsx_labels, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt', col.names=F, row.names = F, quote=F)
+
+# Extract outcomes from UKB (project ukb82087)
+project_dir <- "/datasets/ukbiobank/ukb82087"
+
+system('rm /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.txt')
+f <- bio_field(project_dir)
+f %>%
+    select(field, name) %>%
+    filter(str_detect(field, paste(paste0("^", prscsx_dat$field, '-'), collapse='|'))) %>%
+    bio_field_add("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.txt")
+
+bio_phen(
+    project_dir,
+    field = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.txt",
+    out = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset"
+)
+
+system("ls -lh /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.rds")
+df <- readRDS("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.rds")
+
+# Take the first observation of each outcome
+library(tidyr)
+df_long <- df %>%
+  pivot_longer(cols = names(df)[!grepl('eid', names(df))], names_to = "variable", values_to = "outcome") %>%
+  drop_na(outcome)
+df_long$variable<-gsub('-.*','', df_long$variable)
+df_long<-df_long[!duplicated(df_long[,c('eid','variable')]),]
+
+library(data.table)
+
+for(i in 1:nrow(prscsx_dat)){
+  tmp <- df_long[df_long$variable == prscsx_dat$field[i],]
+  tmp <- data.frame(
+    eid = tmp$eid,
+    outcome = tmp$outcome
+  )
+  
+  fwrite(
+    tmp,
+    paste0(
+      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      prscsx_dat$label[i],
+      '.txt'
+    ),
+    row.names = F,
+    quote = F,
+    na = 'NA',
+    sep = '\t'
+  )
+}
+
+# Read in ancestry inference results to determine sample size per population
+# Use ancestry information from GenoPred
+keep_files<-list.files(path = '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/pcs/within_sample/', pattern = '.keep')
+
+pop_dat<-NULL
+for(i in keep_files){
+  tmp<-fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/pcs/within_sample/', i))
+  names(tmp)<-c('FID','IID')
+  tmp$POP<-gsub('.keep','', gsub('ukb.outlier_detection.','',i))
+  pop_dat<-rbind(pop_dat, tmp)
+}
+
+# Update row number IDs to project specific IDs
+psam<-fread('/scratch/prj/ukbiobank/ukb82087/imputed/ukb82087_imp_chr1_MAF1_INFO4_v1.psam')
+psam$rn<-1:nrow(psam)
+psam<-psam[,c('IID','rn'), with = F]
+
+pop_dat$FID<-NULL
+pop_dat<-merge(pop_dat, psam, by.x='IID', by.y='rn')
+pop_dat<-data.frame(
+  eid=pop_dat$IID.y,
+  POP=pop_dat$POP
+)
+
+# Merge ancestry info with phenotype data
+df_short <- dcast(df_long, eid ~ variable, value.var = "outcome")
+df_short<-merge(df_short, pop_dat, by='eid')
+
+# Remove related individuals
+greedy_related <- "/scratch/prj/ukbiobank/KCL_Data/Software/tools/GreedyRelated-master-v1.2.1/GreedyRelated"
+rel<-bio_gen_related_remove(
+      project_dir = project_dir,
+      greedy_related = greedy_related,
+      keep = df_short$eid,
+      thresh = 0.044,
+      seed = 1
+    )$eid
+
+df_short_unrel<-df_short[!(df_short$eid %in% rel),]
+
+n_table<-NULL
+for(i in 1:nrow(prscsx_dat)){
+  for(j in unique(pop_dat$POP[!is.na(pop_dat$POP)])){
+    tmp<-data.frame(
+      trait=prscsx_dat$trait[i],
+      labels=prscsx_dat$label[i],
+      field=prscsx_dat$field[i],
+      population=j,
+      n=sum(!is.na(df_short[[prscsx_dat$field[i]]][df_short$POP == j])),
+      n_unrel=sum(!is.na(df_short_unrel[[prscsx_dat$field[i]]][df_short_unrel$POP == j]))
+    )
+    n_table<-rbind(n_table, tmp)
+  }
+}
+
+write.csv(n_table, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/n_table')
+
+# Define training subset for EUR
+df_short_unrel_eur<-df_short_unrel[df_short_unrel$POP == 'EUR',]
+set.seed(1)
+train_size <- floor(0.8 * nrow(df_short_unrel_eur))
+train_indices <- sample(seq_len(nrow(df_short_unrel_eur)), size = train_size)
+
+df_short_unrel_eur_train<-df_short_unrel_eur[train_indices,]
+df_short_unrel_eur_test<-df_short_unrel_eur[-train_indices,]
+
+n_table_eur<-NULL
+for(i in 1:nrow(prscsx_dat)){
+  tmp<-data.frame(
+    trait=prscsx_dat$trait[i],
+    labels=prscsx_dat$label[i],
+    field=prscsx_dat$field[i],
+    n_train=sum(!is.na(df_short_unrel_eur_train[[prscsx_dat$field[i]]])),
+    n_test=sum(!is.na(df_short_unrel_eur_test[[prscsx_dat$field[i]]]))
+  )
+  n_table_eur<-rbind(n_table_eur, tmp)
+}
+
+write.csv(n_table_eur, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/n_table_eur')
+
+df_short_unrel$POP[df_short_unrel$eid %in% df_short_unrel_eur_train$eid]<-'EUR_train'
+df_short_unrel$POP[df_short_unrel$eid %in% df_short_unrel_eur_test$eid]<-'EUR_test'
+
+# Output phenotype data for each population
+for(i in 1:nrow(prscsx_dat)){
+  for(j in unique(df_short_unrel$POP)){
+    tmp<-df_short_unrel[df_short_unrel$POP == j,]
+    tmp <- data.frame(
+      FID = tmp$eid,
+      IID = tmp$eid,
+      outcome = tmp[[prscsx_dat$field[i]]]
+    )
+    
+    fwrite(
+      tmp,
+      paste0(
+        '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+        prscsx_dat$label[i],
+        '.unrel.', j, '.txt'
+      ),
+      row.names = F,
+      quote = F,
+      na = 'NA',
+      sep = '\t'
+    )
+    
+    # Write out with row number based IDs
+    pheno<-merge(tmp, psam, by='IID')
+    pheno<-data.frame(
+      FID=pheno$rn,
+      IID=pheno$rn,
+      outcome=pheno$outcome
+    )
+  
+    fwrite(
+      pheno,
+      paste0(
+        '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+        prscsx_dat$label[i],
+        '.unrel.', j, '.row_number.txt'
+      ),
+      row.names = F,
+      quote = F,
+      na = 'NA',
+      sep = '\t'
+    )
+  }
+}
+
+# For the EUR training GWAS, normalise and regress covariates
+# Use age, sex and PCs as covariates
+# Read in PC data released by UKB
+qc_dat<-bio_gen_sqc(project_dir)
+qc_dat<-qc_dat[,c('eid',paste0('pc',1:20))]
+df_short_unrel<-merge(df_short_unrel, qc_dat, by='eid')
+
+# Read in sex and age information
+system('rm /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.txt')
+f <- bio_field(project_dir)
+f %>%
+    select(field, name) %>%
+    filter(str_detect(field, "^21022-0.0|^31-0.0")) %>%
+    bio_field_add("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.txt")
+
+bio_phen(
+    project_dir,
+    field = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.txt",
+    out = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset"
+)
+
+system("ls -lh /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.rds")
+df <- readRDS("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.rds")
+names(df)<-gsub('-.*','',names(df))
+names(df)[names(df) == '31']<-'sex'
+names(df)[names(df) == '21022']<-'age'
+df_short_unrel<-merge(df_short_unrel, df, by='eid')
+
+# Within each population, normalise each outcome and regress out covariates
+library(RNOmni)
+covs<-c(paste0('pc',1:20), 'sex', 'age')
+df_short_unrel_eur_train<-df_short_unrel[df_short_unrel$POP == 'EUR_train',]
+for(i in 1:nrow(prscsx_dat)){
+  tmp<-df_short_unrel_eur_train[!is.na(df_short_unrel_eur_train[[prscsx_dat$field[i]]]),]
+  tmp$pheno_norm<-RNOmni::RankNorm(tmp[[prscsx_dat$field[i]]])
+  mod<-lm(as.formula(paste0('pheno_norm ~ ', paste(covs, collapse=' + '))), data=tmp)
+  tmp$pheno_norm_resid_scale<-as.numeric(scale(resid(mod)))
+  tmp<-data.frame(
+    FID=tmp$eid,
+    IID=tmp$eid,
+    outcome=tmp$pheno_norm_resid_scale
+  )
+  
+  fwrite(
+    tmp,
+    paste0(
+      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      prscsx_dat$label[i],
+      '.unrel.EUR_train.norm_resid_scale.txt'
+    ),
+    row.names = F,
+    quote = F,
+    na = 'NA',
+    sep = '\t'
+  )
+}
+
+# Convert to row number based IDs so it will work with UKB geno data from GenoPred
+for(i in 1:nrow(prscsx_dat)){
+  pheno<-fread(paste0(
+      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      prscsx_dat$label[i],
+      '.unrel.EUR_train.norm_resid_scale.txt'
+    ))
+  
+  pheno<-merge(pheno, psam, by='IID')
+  pheno<-data.frame(
+    FID=pheno$rn,
+    IID=pheno$rn,
+    outcome=pheno$outcome
+  )
+  
+  fwrite(
+    pheno,
+    paste0(
+      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      prscsx_dat$label[i],
+      '.unrel.EUR_train.norm_resid_scale.row_number.txt'
+    ),
+    row.names = F,
+    quote = F,
+    na = 'NA',
+    sep = '\t'
+  )
+}
+
+```
+</details>
+
+***
+
+## Run GWAS
+
+<details><summary>Show code</summary>
+```{bash, eval=F, echo=T}
+for pheno in $(tail -n +6 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  mkdir -p /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}
+  for chr in $(seq 1 22); do
+      sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/plink2 \
+        --pfile /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/geno/ukb.ref.chr${chr} \
+        --pheno /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.EUR_train.norm_resid_scale.row_number.txt \
+        --linear omit-ref cols=+a1freq,+ax \
+        --maf 0.01 \
+        --geno 0.05 \
+        --out /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}"
+  done
+done
+
+# Once complete, merge results across chromosomes
+for pheno in $(tail -n +6 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  head -n 1 /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.chr1.outcome.glm.linear > /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+    for chr in $(seq 1 22); do
+      tail -n +2 /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}.outcome.glm.linear >> /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+    done
+    
+    # Remove REF and ALT columns and rename AX column to A2
+    cut -f 4,5 --complement /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt | awk 'BEGIN{FS=OFS="\t"} NR==1 {$5="A2"} 1' > temp.txt && mv temp.txt /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+
+    gzip /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+done
+
+```
+</details>
+
+***
+
+# Download relevant BBJ sumstats
+
+<details><summary>Show code</summary>
+
+```{r}
+# Identify wget command for relevant phenotypes
+library(data.table)
+
+# Read in BBJ GWAS info from BBJ website
+bbj_gwas<-fread('~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas.csv')
+
+# Map BBJ trait names to those used for UKB
+bbj_gwas$bbj_labels <-
+  gsub("\\)", '', gsub(".*\\(", '', bbj_gwas$Phenotype))
+bbj_gwas$trait <- gsub(" \\(.*", '', bbj_gwas$Phenotype)
+
+bbj_gwas$Category<-NULL
+bbj_gwas$Phenotype<-NULL
+
+# Update trait labels to match what was used in prscsx paper
+bbj_gwas$trait<-gsub(' count','', bbj_gwas$trait)
+bbj_gwas$trait[bbj_gwas$trait == 'G-glutamyl transpeptidase']<-'γ-glutamyl transpeptidase'
+
+# Merge the bbj trait info with the prscsx trait info
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat <- merge(bbj_gwas, prscsx_dat, by='trait', all=T)
+
+write.csv(prscsx_dat, '~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_prscsx.csv', row.names = F)
+
+# Create column showing what label is used in the wget command
+prscsx_dat$wget_label <-
+  gsub('.v1.zip', '', gsub('.*hum0197.v3.BBJ.', '', prscsx_dat$wget))
+
+# Write a table showing label matching prscsx info and wget url
+write.table(prscsx_dat[, c('labels', 'wget', 'wget_label'), with=F], '~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt', col.names = F, row.names = F, quote = F)
+
+```
+
+```{bash}
+# wget and unzip sumstats
+for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  url=$(awk -v var="$pheno" '$1 == var {print $2}' ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
+  sbatch -p neurohack_cpu --wrap="wget -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip ${url}
+    unzip /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip -d /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx
+    rm /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip"
+done
+
+# Delete X chromosome sumstats and rename files to be consistent with prscsx sumstat info
+for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  wget_label=$(awk -v var="$pheno" '$1 == var {print $3}' ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
+  mv ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1/GWASsummary_${wget_label}_Japanese_SakaueKanai2020.auto.txt.gz ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz
+  rm -r ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1
+done
+
+# Format so BOLT P value is used by GenoPred
+for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/pigz -dc ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz | awk 'BEGIN {OFS=\"\t\"} {print \$2, \$3, \$4, \$6, \$7, \$8, \$9, \$12, \$13, \$15}' | sed '1s/P_BOLT_LMM_INF/P/' | /users/k1806347/oliverpainfel/Software/pigz -c > ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.reformat.txt.gz"
+done
+
+```
+</details>
+
+***
+
+# Download relevant UGR sumstats
+
+<details><summary>Show code</summary>
+
+```{r}
+# Identify wget command for relevant phenotypes
+library(data.table)
+
+# Read in UGR GWAS info from GWAS catalogue
+ugr_gwas<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats.csv')
+
+# Map UGR trait names to those used for UKB
+ugr_gwas$trait<-gsub(' levels','', ugr_gwas$reportedTrait)
+ugr_gwas$trait<-gsub(' count','', ugr_gwas$trait)
+
+ugr_to_prscsx <- c(
+  "Aspartate aminotransferase" = "Aspartate transaminase",
+  "Bilirubin" = NA,  # No direct match
+  "Eosinophils" = "Eosinophil",
+  "Gamma glutamyl transferase" = "γ-glutamyl transpeptidase",
+  "HDL cholesterol" = "HDL-cholesterol",
+  "Hemoglobin A1c" = "HbA1c",
+  "Hip circumference" = NA,  # No direct match
+  "LDL cholesterol" = "LDL-cholesterol",
+  "Red cell distribution width" = NA,  # No direct match
+  "Serum albumin" = "Albumin",
+  "Serum alkaline phosphatase" = "Alkaline phosphatase",
+  "Systolic blood pressure" = "Sytolic blood pressure",
+  "Triglyceride" = "Triglycerides",
+  "Waist circumference" = NA,  # No direct match
+  "Waist-hip ratio" = NA,  # No direct match
+  "Weight" = "Body weight"
+)
+
+ugr_gwas$trait <- ifelse(ugr_gwas$trait %in% names(ugr_to_prscsx),
+                                   ugr_to_prscsx[ugr_gwas$trait],
+                                   ugr_gwas$trait)
+
+# Merge the ugr trait info with the prscsx trait info
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat <- merge(ugr_gwas, prscsx_dat, by='trait')
+
+write.csv(prscsx_dat, '~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv', row.names = F)
+
+# Create column indicating wget command
+for(i in 1:nrow(prscsx_dat)){
+  if(!grepl('.txt', prscsx_dat$wget[i])){
+    print(i)
+    Sys.sleep(2)
+    log<-system(paste0('curl --max-time 10 ', gsub('http:','ftp:', prscsx_dat$summaryStatistics[i]), '/'), intern = T)
+    log<-log[grepl('annotated.txt.gz|annotated.txt', log)]
+    log<-gsub('.* ','', log)
+    prscsx_dat$wget[i]<-paste0(prscsx_dat$summaryStatistics[i], '/', log)
+  }
+}
+
+# Write a table showing label matching prscsx info and wget url
+write.table(prscsx_dat[, c('labels', 'wget'), with=F], '~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt', col.names = F, row.names = F, quote = F)
+
+```
+
+```{bash}
+# wget and unzip sumstats
+for pheno in $(cat ~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt | cut -d' ' -f 1); do
+  url=$(awk -v var="$pheno" '$1 == var {print $2}' ~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt)
+  sbatch -p cpu --wrap="wget -O ~/oliverpainfel/Data/GWAS_sumstats/UGR/${pheno}.txt.gz ${url}"
+done
+
+```
+
+```{r}
+library(future.batchtools)
+library(furrr)
+library(data.table)
+ugr_data<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv')
+
+plan(batchtools_slurm(resources = list(
+  time = "12:00:00",
+  ntasks = 2,
+  mem = "10g",
+  partition = "neurohack_cpu"
+)))
+
+furrr::future_map_dfr(1:nrow(ugr_data), function(i) {
+  print(i)
+  sumstats <- fread(paste0("~/oliverpainfel/Data/GWAS_sumstats/UGR/", ugr_data$label[i], ".txt.gz"))
+  sumstats <- sumstats[, names(sumstats) %in% c("snpid", "pval_fe", "se_fe") | grepl('^beta_|^af_|^no_', names(sumstats)), with=F]
+
+  # Extract CHR, BP, A1, A2 from snpid
+  snp_split <- tstrsplit(sumstats$snpid, ":", fixed = TRUE)
+  sumstats[, `:=`(CHR = snp_split[[1]], BP = snp_split[[2]], A1 = snp_split[[3]], A2 = snp_split[[4]])]
+
+  # Set no_ and af_ to NA if beta is NA
+  cohorts <- gsub('^no_','', names(sumstats)[grepl('^no_', names(sumstats))])
+  for (cohort in cohorts) {
+    sumstats[[paste0('no_', cohort)]][is.na(sumstats[[paste0('beta_', cohort)]])] <- NA
+    sumstats[[paste0('af_', cohort)]][is.na(sumstats[[paste0('beta_', cohort)]])] <- NA
+  }
+
+  # Calculate sample size weighted average for allele frequency
+  for (cohort in cohorts) {
+    sumstats[[paste0('af_', cohort, '_weighted')]] <- sumstats[[paste0('af_', cohort)]] * sumstats[[paste0('no_', cohort)]]
+  }
+  
+  # Calculate total N and frequency
+  sumstats[, N := rowSums(.SD, na.rm = TRUE), .SDcols = patterns("^no_")]
+  sumstats[, FREQ := rowSums(.SD, na.rm = TRUE) / N, .SDcols = patterns("weighted$")]
+
+  # Rename columns
+  setnames(sumstats, old = c('beta_fe', 'se_fe', 'pval_fe'), new = c('BETA', 'SE', 'P'))
+
+  # Select relevant columns and remove rows with missing data
+  sumstats <- sumstats[, .(CHR, BP, A1, A2, BETA, SE, P, FREQ, N)]
+  sumstats <- sumstats[complete.cases(sumstats)]
+  
+  fwrite(sumstats, paste0("~/oliverpainfel/Data/GWAS_sumstats/UGR/", ugr_data$label[i], ".reformat.txt.gz"), sep=' ', quote=F, na='NA')
+  
+})
+
+```
+
+</details>
+
+***
+
+# Estimate SNP-h2, polygenicity and rG across populations
+
+We will estimate SNP-h2 using LD-score regression, and the rG using POPCORN.
+POPCORN can estimate the SNP-h2, but it will vary according to the other GWAS included due to SNP overlap.
+Use the sumstats QC'd by GenoPred.
+To estimate polygenicity, lets use AVENGEME based on ptclump score association results. Lets generate those using GenoPred.
+
+***
+
+## Prepare configuration for GenoPred
+
+<details><summary>Show code</summary>
+
+```{r}
+######
+# gwas_list
+######
+
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+
+gwas_list_eur<-data.frame(
+  name=paste0(prscsx_dat$labels,'_UKB'),
+  path=paste0('/scratch/prj/ukbiobank/usr/ollie_pain/gwas/',prscsx_dat$labels,'/ukb.eur_train.',prscsx_dat$labels,'.GW.txt.gz'),
+  population='EUR',
+  n=NA,
+  sampling=NA,
+  prevalence=NA,
+  mean=0,
+  sd=1,
+  label=paste0('"', prscsx_dat$trait, ' (UKB)"')
+)
+
+bbj_info<-fread('~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_prscsx.csv')
+bbj_info<-bbj_info[bbj_info$labels %in% prscsx_dat$labels,]
+
+gwas_list_eas<-data.frame(
+  name=paste0(bbj_info$labels,'_BBJ'),
+  path=paste0('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.',bbj_info$labels,'.reformat.txt.gz'),
+  population='EAS',
+  n=as.numeric(gsub(',','',bbj_info$`No. samples`)),
+  sampling=NA,
+  prevalence=NA,
+  mean=0,
+  sd=1,
+  label=paste0('"', prscsx_dat$trait, ' (BBJ)"')
+)
+
+ugr_data<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv')
+ugr_data<-ugr_data[ugr_data$labels %in% prscsx_dat$labels,]
+
+gwas_list_afr<-data.frame(
+  name=paste0(ugr_data$labels,'_UGR'),
+  path=paste0('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/UGR/',ugr_data$labels,'.reformat.txt.gz'),
+  population='AFR',
+  n=NA,
+  sampling=NA,
+  prevalence=NA,
+  mean=0,
+  sd=1,
+  label=paste0('"', ugr_data$trait, ' (UGR)"')
+)
+gwas_list<-do.call(rbind, list(gwas_list_eur, gwas_list_eas, gwas_list_afr))
+
+# Create file listing phenotypes in common between AFR, EAS and EUR
+pheno <- gsub('_.*', '', gwas_list$name)
+pheno_intersect <- Reduce(intersect, 
+                           list(
+                             pheno[gwas_list$population == 'EUR'],
+                             pheno[gwas_list$population == 'EAS'],
+                             pheno[gwas_list$population == 'AFR']
+                             )
+                           )
+
+# Restrict gwas_list to intersecting phenotypes
+gwas_list<-gwas_list[pheno %in% pheno_intersect,]
+
+write.table(gwas_list, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt', col.names = T, row.names = F, quote = F)
+
+write.table(pheno_intersect, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', col.names = F, row.names = F, quote = F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output_crosspop",
+  "config_file: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml",
+  "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt",
+  "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
+  "pgs_methods: ['ptclump']",
+  "cores_prep_pgs: 1",
+  "cores_target_pgs: 20"
+)
+
+write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml', col.names = F, row.names = F, quote = F)
+
+```
+</details>
+
+***
+
+## Run pipeline
+
+```{bash}
+snakemake --profile slurm --use-conda --configfile=/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml target_pgs -n
+```
+
+***
+
+## Reformat for LDSC and POPCORN
+
+<details><summary>Show code</summary>
+```{r}
+library(data.table)
+dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats', recursive = T)
+gwas_list<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt')
+
+for(i in 1:nrow(gwas_list)){
+  if(
+    file.exists(
+      paste0(
+        "/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/",
+        gwas_list$name[i], 
+        ".sumstats.gz"))){
+    next    
+  }
+  print(i)
+  gwas_file <-
+    paste0(
+      "/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output_crosspop/reference/gwas_sumstat/",
+      gwas_list$name[i],
+      "/",
+      gwas_list$name[i],
+      "-cleaned.gz"
+    )
+  
+  gwas_header <- fread(gwas_file, nrows = 1)
+  cols_index <- which(names(gwas_header) %in% c('SNP','A1','A2','BETA','SE','P','N'))
+  
+  system(
+    paste0(
+      "zcat ",
+      gwas_file,
+      " | cut -f ", 
+      paste0(cols_index, collapse = ','),
+      " | sed -e '1s/BETA/beta/'",
+      " | /users/k1806347/oliverpainfel/Software/pigz -f",
+      " > /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/",
+      gwas_list$name[i], 
+      ".sumstats.gz"
+      )
+    )
+}
+```
+
+</details>
+
+***
+
+## Run LDSC
+
+<details><summary>Show code</summary>
+```{bash}
+conda activate ldsc
+
+for pop in $(echo EUR EAS AFR);do
+  if [ "$pop" == "EUR" ]; then
+      samp="UKB"
+  fi
+  if [ "$pop" == "EAS" ]; then
+      samp="BBJ"
+  fi
+  if [ "$pop" == "AFR" ]; then
+      samp="UGR"
+  fi
+  
+  for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats
+
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
+     --sumstats /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/${pheno}_${samp}.sumstats.gz \
+     --out /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats/${pheno}_${samp}"
+
+  done
+done
+
+for pop in $(echo EUR EAS AFR);do
+  if [ "$pop" == "EUR" ]; then
+      samp="UKB"
+  fi
+  if [ "$pop" == "EAS" ]; then
+      samp="BBJ"
+  fi
+  if [ "$pop" == "AFR" ]; then
+      samp="UGR"
+  fi
+  
+  for pheno in $(tail -n +6 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/${pheno}/${pop}
+
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \
+     --h2 /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats/${pheno}_${samp}.sumstats.gz \
+     --ref-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.${pop}.rsid \
+     --w-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.${pop}.rsid \
+     --out /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/${pheno}/${pop}/res"
+     
+  done
+done
+
+```
+
+</details>
+
+***
+
+## Calculate CSCOREs
+
+```{bash}
+
+# Subset the reference data into relevant populations
+for pop in $(echo EUR EAS AFR); do
+  mkdir -p /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp
+  for chr in $(seq 1 22); do
+    /users/k1806347/oliverpainfel/Software/plink1.9/plink \
+      --bfile /users/k1806347/oliverpainfel/Data/1KG/GenoPred/v2.0.0/ref.chr${chr} \
+      --keep /users/k1806347/oliverpainfel/Data/1KG/GenoPred/v2.0.0/keep_files/${pop}.keep \
+      --make-bed \
+      --out /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.${pop}.chr${chr}
+    done
+done
+
+conda activate popcorn
+for pop in $(echo EAS AFR); do
+  mkdir -p /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES
+  for chr in $(seq 1 22); do
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="popcorn \
+      compute \
+      -v 1 \
+      --bfile1 /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.EUR.chr${chr} \
+      --bfile2 /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.${pop}.chr${chr} \
+      /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_chr${chr}.txt"
+  done
+done
+
+for pop in $(echo EAS AFR); do
+  cat /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_chr*.txt > /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_all.txt
+done
+
+rm -r /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp
+```
+
+***
+
+## Run POPCORN
+
+<details><summary>Show code</summary>
+```{bash}
+conda activate popcorn
+for pop in $(echo EAS AFR);do
+  if [ "$pop" == "EAS" ]; then
+      samp="BBJ"
+  fi
+  if [ "$pop" == "AFR" ]; then
+      samp="UGR"
+  fi
+  
+  for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/${pheno}/EUR_${pop}
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="popcorn \
+       fit -v 3 \
+       --cfile /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_all.txt \
+       --sfile1 /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/${pheno}_UKB.sumstats.gz \
+       --sfile2 /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/${pheno}_${samp}.sumstats.gz \
+       --gen_effect \
+       /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/${pheno}/EUR_${pop}/rG_gen_effect"
+  done
+done
+
+```
+
+</details>
+
+***
+
+## Plot the LDSC and POPCORN results
+
+<details><summary>Show code</summary>
+
+```{r}
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+# Read in phenotypes
+pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+
+# Plot the heritability estimates
+h2_res <- NULL
+
+for(pop in c('AFR','EAS', 'EUR')){
+  for(pheno in pheno_intersect){
+    log <-
+      readLines(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/',
+          pheno,
+          '/',
+          pop,
+          '/res.log'
+        )
+      )
+    
+    h2 <- log[grepl('Total Observed scale h2:', log)]
+    h2_est <- as.numeric(gsub(' .*','', gsub('Total Observed scale h2: ', '', h2)))
+    h2_se <- as.numeric(gsub("\\)",'', gsub(".* \\(", '', h2)))
+    int <- log[grepl('Intercept:', log)]
+    int_est <- as.numeric(gsub(' .*','', gsub('Intercept: ', '', int)))
+    int_se <- as.numeric(gsub("\\)",'', gsub(".* \\(", '', int)))
+    lambda <- log[grepl('Lambda GC:', log)]
+    lambda <- as.numeric(gsub('.* ','', lambda))
+    
+    h2_res <- rbind(
+      h2_res,
+      data.table(
+        Population = pop,
+        Phenotype = pheno,
+        h2_est = h2_est,
+        h2_se = h2_se,
+        int_est = int_est,
+        int_se = int_se,
+        lambda = lambda
+      )
+    )
+  }
+}
+
+write.csv(h2_res, '/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results.csv', row.names = F, quote = F)
+
+ggplot(h2_res, aes(x = Phenotype, y = h2_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=h2_est-h2_se, ymax=h2_est+h2_se), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="SNP-based Heritability (SE)") +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+# Plot rG estimates
+rg_res <- NULL
+for(pop in c('AFR','EAS')){
+  for(pheno in h2_res_sig$Phenotype){
+    pop_res_i<-fread(paste0('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/', pheno, '/EUR_', pop, '/rG_gen_effect'))
+    names(pop_res_i) <- c('Test','Estimate','SE','Z','P')
+    pop_res_i <- pop_res_i[pop_res_i$Test == 'pge',]
+    pop_res_i$Population_1 <- 'EUR'
+    pop_res_i$Population_2 <- pop
+    pop_res_i$Phenotype <- pheno
+    rg_res <- rbind(rg_res, pop_res_i)
+  }
+}
+
+rg_res$Comparison <- paste0(rg_res$Population_1, ' vs. ', rg_res$Population_2)
+
+write.csv(rg_res, '/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results.csv', row.names = F, quote = F)
+
+ggplot(rg_res, aes(x = Phenotype, y = Estimate, fill = Comparison)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=Estimate-SE, ymax=Estimate+SE), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="SNP-based\nGenetic Correlation (SE)") +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+```
+</details>
+
+## AVENGEME
+
+### Create predictor lists
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Read in phenotypes
+pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+
+# Create files for EAS and AFR targets
+pop <- c('EUR','EAS','AFR')
+for(trait_i in pheno_intersect){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
+  scores_i <- scores[grepl(paste0('^', trait_i, '_'), scores$name),]
+  scores_i$group <- scores_i$method
+  
+  for(pop_i in pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    if(pop_i == 'EAS'){
+      samp_i <- 'BBJ'
+    }
+    if(pop_i == 'AFR'){
+      samp_i <- 'UGR'
+    }
+    if(pop_i == 'EUR'){
+      samp_i <- c('UKB')
+    }
+
+    dir.create(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i
+      ),
+      recursive = T
+    )
+    
+    scores_i_j <- scores_i[grepl(samp_i, scores_i$name, ignore.case = T),]
+    scores_i_j$predictor <- paste0(
+      outdir,
+      '/ukb/pgs/',
+      pop_i,
+      '/',
+      scores_i_j$method,
+      '/',
+      scores_i_j$name,
+      '/ukb-',
+      scores_i_j$name,
+      '-',
+      pop_i,
+      '.profiles'
+    )
+    
+    predictors_i <- scores_i_j[, c('predictor', 'group'), with=F]
+    
+    write.table(
+      predictors_i,
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i,
+        '/predictor_list.ptclump.txt'
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}
+
+```
+</details>
+
+***
+
+### Run model_builder
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+for pop in $(echo EUR EAS AFR); do
+  if [ "$pop" == "EUR" ]; then
+      pop2="EUR_test"
+  else
+      pop2=$pop
+  fi
+  
+  for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
+    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${pop2}.row_number.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/res.ptclump \
+      --n_core 5 \
+      --all_model F \
+      --assoc T"
+  done
+done
+
+```
+</details>
+
+***
+
+### Plot assoc results
+
+<details><summary>Show code</summary>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in phenotypes
+pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+
+# Read in results
+pop = c('EUR','EAS','AFR')
+res_all <- NULL
+for(pheno_i in pheno_intersect){
+  res_i<-NULL
+  for(pop_i in pop){
+    assoc_i <-
+      fread(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          pop_i,
+          '.disc_',
+          pop_i,
+          '/',
+          pheno_i,
+          '/res.ptclump.assoc.txt'
+        )
+      )
+      assoc_i$Population <- pop_i
+      res_i<-rbind(res_i, assoc_i)
+  }
+  
+  res_i$Phenotype <- pheno_i
+  res_all<-rbind(res_all, res_i)
+}
+
+# Extract pT variable from Predictor
+res_all$pT <- gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.', '', res_all$Predictor))
+res_all$pT <- factor(res_all$pT, levels = unique(res_all$pT))
+
+ggplot(res_all, aes(x = Phenotype, y = BETA, fill = pT)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.8) +
+  geom_errorbar(aes(ymin=BETA-SE, ymax=BETA+SE), width=0, position=position_dodge(width = 0.8, preserve = "single")) +
+  labs(y="BETA (SE)") +
+  theme_half_open() +
+  background_grid() +
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y') +
+  scale_fill_manual(values = colorRampPalette(c("lightblue", "darkblue"))(length(unique(res_all$pT)))) +
+  facet_grid(Population ~.)
+
+```
+</details>
+
+### Run AVENGEME
+
+```{r}
+
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+library(avengeme)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Get some key variables from config
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+gwas_list <- read_param(config = config, param = 'gwas_list', return_obj = T)
+
+# Read in phenotypes
+pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+
+pop = c('EUR','EAS','AFR')
+
+mod_res_all <- NULL
+for(pop_i in pop){
+  for(pheno_i in pheno_intersect){
+    gwas_i<-gwas_list$name[gwas_list$population == pop_i & grepl(paste0('^', pheno_i, '_'),  gwas_list$name)]
+      
+    res_i <-
+      fread(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          pop_i,
+          '.disc_',
+          pop_i,
+          '/',
+          pheno_i,
+          '/res.ptclump.assoc.txt'
+        )
+      )
+    
+    res_i$Z <- res_i$BETA / res_i$SE
+    
+    res_i$pT <- as.numeric(gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.', '', res_i$Predictor)))
+
+    nsnp_log <-
+      read.table(
+        paste0(
+          outdir,
+          '/reference/pgs_score_files/ptclump/',
+          gwas_i,
+          '/ref-',
+          gwas_i,
+          '.NSNP_per_pT'
+        ),
+        header = T
+      )
+    
+    nsnp<-nsnp_log$NSNP[nrow(nsnp_log)]
+    
+    disc_N <-
+      median(
+        fread(
+          paste0(
+            outdir,
+            '/reference/gwas_sumstat/',
+            gwas_i,
+            '/',
+            gwas_i,
+            '-cleaned.gz'
+          ), nrows = 10000
+        )$N
+      )
+    
+    targ_N <- res_i$N[1]
+    
+    mod_res <- estimatePolygenicModel(
+      p = res_i$Z,
+      nsnp = nsnp,
+      n = c(disc_N, targ_N),
+      pupper = c(0, res_i$pT),
+      fixvg2pi02 = T,
+      alpha = 0.05
+    )
+    
+    mod_res_all <- rbind(
+      mod_res_all,
+      data.frame(
+        Phenotype = pheno_i,
+        Population = pop_i,
+        GWAS = gwas_i,
+        nsnp = nsnp,
+        n_disc = disc_N,
+        n_targ = targ_N,
+        vg_est = mod_res$vg[1],
+        vg_lowCI = mod_res$vg[2],
+        vg_highCI = mod_res$vg[3],
+        pi0_est = mod_res$pi0[1],
+        pi0_lowCI = mod_res$pi0[2],
+        pi0_highCI = mod_res$pi0[3]
+      )
+    )
+  }
+}
+
+dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme')
+write.csv(mod_res_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv', row.names = F, quote = F)
+
+ggplot(mod_res_all, aes(x = Phenotype, y = vg_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=vg_lowCI, ymax=vg_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="SNP-based Heritability (95%CI)") +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=1 - pi0_lowCI, ymax=1 - pi0_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="Proporition non-zero\neffects (95%CI)") +
+  theme_half_open() +
+  coord_cartesian(ylim = c(0, 0.15)) + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+```
+
+***
+
+### Select GWAS for downstream analyses
+
+```{r}
+#########
+# Select 10 GWAS for downstream analysis
+#########
+# Criteria are that SNP-h2 > 0.01 in both AVENGEME and LDSC
+# Then GWAS are selected to represent a range of polygenicity and heritability, as estimated in EUR since they are most accurate
+
+library(data.table)
+
+# Read in the AVENGEME results
+avengeme <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv')
+
+# Read in the LDSC results
+ldsc <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results.csv')
+
+# Combine results
+both <- merge(avengeme, ldsc, by = c('Population','Phenotype'))
+
+# Remove GWAS that have negative SNP-h2 from LDSC in any population
+both_h2 <- both[!(both$Phenotype %in% both$Phenotype[both$vg_est < 0.01 | both$h2_est < 0.01]),]
+
+# Select GWAS representing a range of SNP-h2 from LDSC, and a range of polygenicity from AVENGEME.
+
+both_eur<-both_h2[both_h2$Population == 'EUR',]
+traits_data <- data.frame(trait = both_eur$Phenotype, heritability = both_eur$vg_est, polygenicity = both_eur$pi0_est)
+
+# Number of bins (e.g., dividing into 5 bins each for heritability and polygenicity)
+num_bins <- 5
+
+# Create bins
+traits_data$her_bin <- cut(traits_data$heritability, breaks = num_bins)
+traits_data$poly_bin <- cut(traits_data$polygenicity, breaks = num_bins)
+
+# Split data by unique bin combinations
+split_data <- split(traits_data, list(traits_data$her_bin, traits_data$poly_bin), drop = TRUE)
+
+set.seed(1)
+# Randomly select one trait from each bin combination
+selected_traits <- do.call(rbind, lapply(split_data, function(df) df[sample(nrow(df), 1), ]))
+
+# Limit to 10 traits if more than 10 unique combinations
+if (nrow(selected_traits) > 10) {
+  selected_traits <- selected_traits[sample(nrow(selected_traits), 10), ]
+}
+
+write.table(selected_traits$trait, '/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', col.names = F, row.names = F, quote = F)
+
+```
+
+***
+
+# Run GenoPred
+
+## Prepare configuration for GenoPred
+
+<details><summary>Show code</summary>
+
+```{r}
+######
+# gwas_list
+######
+library(data.table)
+# Subset original gwas_list to include selected traits
+gwas_list<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt')
+pheno<-gsub('_.*','', gwas_list$name)
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+gwas_list<-gwas_list[pheno %in% selected_traits,]
+gwas_list$label<-paste0('"', gwas_list$label, '"')
+
+write.table(gwas_list, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt', col.names = T, row.names = F, quote = F)
+
+######
+# gwas_groups
+######
+
+gwas_groups_eas<-data.frame(
+  name=paste0(selected_traits, '_UKB_BBJ'),
+  gwas=sapply(selected_traits, function(x) paste0(x,'_UKB,',x,'_BBJ')),
+  label=paste0('"', selected_traits, " (UKB+BBJ)", '"')
+)
+
+gwas_groups_afr<-data.frame(
+  name=paste0(selected_traits, '_UKB_UGR'),
+  gwas=sapply(selected_traits, function(x) paste0(x,'_UKB,',x,'_UGR')),
+  label=paste0('"', selected_traits, " (UKB+UGR)", '"')
+)
+
+gwas_groups<-rbind(gwas_groups_eas, gwas_groups_afr)
+
+write.table(gwas_groups, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt', col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output_crosspop",
+  "config_file: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml",
+  "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt",
+  "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
+  "gwas_groups: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt",
+#  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
+  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','ldpred2','megaprs','sbayesrc','prscsx','xwing','prscs']",
+#  "tlprs_methods: ['dbslmm','prscs','lassosum']",
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 50",
+  "ldpred2_inference: F",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc"
+)
+
+write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
+
+```
+</details>
+
+***
+
+## Run pipeline
+
+```{bash}
+snakemake --profile slurm --use-conda --configfile=/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml output_all -n
+```
+
+***
+
+# Evaluate PGS
+
+Lets use the model builder script which implements nested 10 fold cross validation. Similar set up to previous paper, evaluating a model containing the best PGS selected by 10-fold cross validation, a model containing the PGS selected by pseudovalidation (if available), and an elastic net model containing all PGS from a given method. We will need to update the model builder script to achieve this
+
+***
+
+## Compare all methods
+
+### Create predictor lists
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Remove tlprs
+scores<-scores[!grepl('tlprs', scores$method),]
+
+# Create files for EAS and AFR targets
+targ_pop <- c('EAS','AFR')
+for(trait_i in selected_traits){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
+  scores_i <- scores[grepl(trait_i, scores$name),]
+  scores_i$group <- scores_i$method
+  
+  for(targ_pop_i in targ_pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'BBJ'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'UGR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('BBJ','UGR')
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == 'BBJ'){
+        disc_pop_j_2 <- 'EAS'
+      }
+      if(disc_pop_j == 'UGR'){
+        disc_pop_j_2 <- 'AFR'
+      }
+
+      dir.create(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i
+        ),
+        recursive = T
+      )
+      
+      scores_i_j <- scores_i[grepl('UKB$', scores_i$name, ignore.case = F) |
+                       grepl(paste0(disc_pop_j, '$'), scores_i$name, ignore.case = T),]
+
+      scores_i_j_multi <- scores_i_j
+      # Insert path to score file
+      scores_i_j_multi$predictor <- paste0(
+        outdir,
+        '/ukb/pgs/',
+        targ_pop_i,
+        '/',
+        scores_i_j_multi$method,
+        '/',
+        scores_i_j_multi$name,
+        '/ukb-',
+        scores_i_j_multi$name,
+        '-',
+        targ_pop_i,
+        '.profiles'
+      )
+      
+      # Extract the pseudo score for each method and specify as a seperate group
+      for(i in 1:nrow(scores_i_j)) {
+        param <- find_pseudo(
+          config = config,
+          gwas = scores_i_j$name[i],
+          pgs_method = scores_i_j$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header <-
+          fread(scores_i_j_multi$predictor[i], nrows = 1)
+        score_cols <-
+          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            scores_i_j_multi$predictor[i], 
+            " > ", 
+            gsub('.profiles',
+                    '.pseudo.profiles',
+                    scores_i_j_multi$predictor[i])
+          )
+        )
+      }
+      
+      scores_i_j_pseudo <- scores_i_j_multi
+      scores_i_j_pseudo$group <- paste0(scores_i_j_multi$group, '.pseudo')
+      for(i in 1:nrow(scores_i_j_pseudo)){
+        if(grepl('UKB', scores_i_j_pseudo$name[i])){
+          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.EUR.pseudo', scores_i_j_pseudo$group[i])
+        }
+        if(grepl('BBJ', scores_i_j_pseudo$name[i])){
+          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.EAS.pseudo', scores_i_j_pseudo$group[i])
+        }
+        if(grepl('UGR', scores_i_j_pseudo$name[i])){
+          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.AFR.pseudo', scores_i_j_pseudo$group[i])
+        }
+      }
+      scores_i_j_pseudo$predictor <- gsub('.profiles',
+                                            '.pseudo.profiles',
+                                            scores_i_j_pseudo$predictor)
+      
+      # Make a group for each GWAS and single source combo
+      scores_i_j_single <- scores_i_j_multi[!(scores_i_j_multi$method %in% pgs_group_methods),]
+      scores_i_j_single$group <-
+        paste0(scores_i_j_single$method,
+               '.',
+               gsub('.*_', '', scores_i_j_single$name))
+      scores_i_j_single$group <- gsub('UKB', 'EUR', scores_i_j_single$group)
+      scores_i_j_single$group <- gsub('BBJ', 'EAS', scores_i_j_single$group)
+      scores_i_j_single$group <- gsub('UGR', 'AFR', scores_i_j_single$group)
+      
+      predictors_i<- do.call(rbind, list(
+        scores_i_j_multi, scores_i_j_pseudo, scores_i_j_single
+      ))
+      
+      predictors_i <- predictors_i[, c('predictor', 'group'), with=F]
+      
+      write.table(
+        predictors_i,
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i,
+          '/predictor_list.txt'
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}
+
+```
+</details>
+
+***
+
+### Run model_builder
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+for targ_pop in $(echo EUR EAS AFR); do
+  if [ "$targ_pop" == "EUR" ]; then
+      targ_pop2="EUR_test"
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ "$targ_pop" == "EUR" ]; then
+    disc_pop=$(echo EAS AFR)
+  fi
+  
+  if [ "$targ_pop" == "EAS" ]; then
+    disc_pop="EAS"
+  fi
+  
+  if [ "$targ_pop" == "AFR" ]; then
+    disc_pop="AFR"
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+      sbatch --mem 20G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+        --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
+        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
+        --n_core 10 \
+        --top1 T \
+        --all_model T \
+        --assoc T"
+    done
+  done
+done
+
+```
+</details>
+
+***
+
+### Plot results
+
+<details><summary>Show code</summary>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+# Calculate corelation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop = c('EAS','AFR')
+res_eval <- list()
+for(pheno_i in selected_traits){
+  res_eval_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.pred_eval.txt'
+          )
+        )
+      eval_i$Target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_eval_i<-rbind(res_eval_i, eval_i)
+    }
+  }
+  
+  res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
+  
+  res_eval_i$Model[grepl('top1', res_eval_i$Group)]<-'Top1'
+  res_eval_i$Model[grepl('pseudo', res_eval_i$Group)]<-'Pseudo'
+  res_eval_i$Model[!grepl('top1|pseudo', res_eval_i$Group)]<-'Multi'
+  
+  res_eval_i$Source<-ifelse(res_eval_i$Method %in% c(pgs_group_methods, 'all') | !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
+         
+  res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
+  res_eval_i$Discovery[grepl('EAS', res_eval_i$Group)] <- 'EAS'
+  res_eval_i$Discovery[grepl('AFR', res_eval_i$Group)] <- 'AFR'
+  res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
+  
+  res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('Top1','Pseudo','Multi'))
+  res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+  # Remove Multi model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('sbayesrc', 'quickprs') & 
+      res_eval_i$Model == 'Multi' & 
+      res_eval_i$Source == 'Single'),]
+  
+  # Remove pseudo model for methods that don't really have one (single source combo)
+  res_eval_i <- res_eval_i[!which(res_eval_i$Model == 'Pseudo' & res_eval_i$Method == 'ptclump'),]
+
+  res_eval[[pheno_i]]<-res_eval_i
+  
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_eval for each scenario
+    res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x <- res_eval[[i]]
+      x$pheno <- names(res_eval)[i]
+      x <- x[x$Target == targ_pop_i]
+      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
+    }))
+    
+    # Average res_evalults for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_eval_i$Sample<-'A'
+  
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno <-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+      
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          'res_evalults missing for ',
+          targ_pop_i,
+          ' ',
+          group_i,
+          ' ',
+          paste0(missing_pheno, collapse = ' ')
+        ))
+      }
+      
+      cors_i <- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i <-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_eval_group_i
+        )
+      
+      tmp <- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval <- rbind(meta_res_eval, tmp)
+    }
+  }
+}
+
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('Top1','Pseudo','Multi'))
+meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+meta_res_eval$R_diff<-NA
+meta_res_eval$reference_point<-F
+for(targ_pop_i in targ_pop){
+  # Calculate R compared to top1 model where GWAS matches target population
+  meta_res_eval$R_diff[meta_res_eval$Target == targ_pop_i] <- meta_res_eval$R[meta_res_eval$Target == targ_pop_i] - meta_res_eval$R[meta_res_eval$Target == targ_pop_i & meta_res_eval$Discovery == meta_res_eval$Target & meta_res_eval$Model == 'Top1']
+  meta_res_eval$reference_point[meta_res_eval$Target == targ_pop_i & meta_res_eval$Discovery == meta_res_eval$Target & meta_res_eval$Model == 'Top1'] <- T 
+}
+
+####
+# Comparison of models across methods, across target and discovery populations
+####
+
+tmp <- meta_res_eval
+
+ggplot(tmp, aes(x=Method, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
+    labs(y = "R (SE)") +
+    facet_grid(Target ~ Discovery, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Create a function to mirror pred_comp results
+mirror_comp<-function(x){
+  x_sym <- x
+  x_sym$Model_1 <- x$Model_2
+  x_sym$Model_2 <- x$Model_1
+  x_sym$R_diff <- -x_sym$R_diff
+  x_mirrored <- rbind(x, x_sym)
+  x_diag<-data.frame(
+      Model_1=unique(x_mirrored$Model_1),
+      Model_2=unique(x_mirrored$Model_1),
+      Model_1_R=x_mirrored$Model_1_R,
+      Model_2_R=x_mirrored$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp<-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
+# Read in results
+targ_pop=c('EUR','EAS','AFR')
+res_comp <- list()
+for(pheno_i in selected_traits){
+  res_comp_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      comp_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.pred_comp.txt'
+          )
+        )
+      comp_i<-mirror_comp(comp_i)
+      comp_i$Target<-targ_pop_i
+      comp_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_comp_i<-rbind(res_comp_i, comp_i)
+    }
+  }
+  
+  res_comp[[pheno_i]]<-res_comp_i
+}
+
+res_comp_all <- do.call(rbind, lapply(names(res_comp), function(name) {
+  x <- res_comp[[name]]
+  x$pheno <- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Annotate tests to get order correct
+res_comp_all$Method1<-sub('\\..*','',res_comp_all$Model_1)
+res_comp_all$Method2<-sub('\\..*','',res_comp_all$Model_2)
+
+find_model<-function(x){
+  mod <- x
+  mod[grepl('top1', x)]<-'Top1'
+  mod[grepl('pseudo', x)]<-'Pseudo'
+  mod[!grepl('top1|pseudo', x)]<-'Multi'
+  return(mod)
+}
+
+res_comp_all$Model1<-find_model(res_comp_all$Model_1)
+res_comp_all$Model2<-find_model(res_comp_all$Model_2)
+
+res_comp_all$Source1<-ifelse(res_comp_all$Method1 %in% c(pgs_group_methods, 'all') | !grepl('AFR|EAS|EUR', res_comp_all$Model_1), 'Multi', 'Single')
+res_comp_all$Source2<-ifelse(res_comp_all$Method2 %in% c(pgs_group_methods, 'all') | !grepl('AFR|EAS|EUR', res_comp_all$Model_2), 'Multi', 'Single')
+
+for(i in c('EUR','EAS','AFR')){
+  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] <- i
+  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] <- i
+}
+res_comp_all$Discovery1[res_comp_all$Source1 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source1 == 'Multi']
+res_comp_all$Discovery2[res_comp_all$Source2 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source2 == 'Multi']
+
+res_comp_all$Method1<-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
+res_comp_all$Method2<-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
+res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('Top1','Pseudo','Multi'))
+res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('Top1','Pseudo','Multi'))
+res_comp_all$Discovery1<-factor(res_comp_all$Discovery1, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
+res_comp_all$Discovery2<-factor(res_comp_all$Discovery2, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+# Remove Multi model for groups that contain one score (aka QuickPRS and SBayesRC)
+res_comp_all <- res_comp_all[
+  !(res_comp_all$Method1 %in%  c('sbayesrc', 'quickprs') & 
+    res_comp_all$Model1 == 'Multi' & 
+    res_comp_all$Source1 == 'Single'),]
+
+res_comp_all <- res_comp_all[
+  !(res_comp_all$Method2 %in%  c('sbayesrc', 'quickprs') & 
+    res_comp_all$Model2 == 'Multi' & 
+    res_comp_all$Source2 == 'Single'),]
+
+# Remove pseudo model for methods that don't really have one (single source combo)
+res_comp_all <- res_comp_all[!which(res_comp_all$Model1 == 'Pseudo' & res_comp_all$Method1 == 'ptclump'),]
+res_comp_all <- res_comp_all[!which(res_comp_all$Model2 == 'Pseudo' & res_comp_all$Method2 == 'ptclump'),]
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_comp <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_comp for each scenario
+    res_comp_i <- res_comp_all[res_comp_all$Target == targ_pop_i & res_comp_all$gwas_group == paste0('EUR+', disc_pop_i)]
+  
+    # Calculate diff SE based on p-value
+    res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] <- 1-0.001
+    res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]<-1-0.001
+    res_comp_i$R_diff_z<-qnorm(res_comp_i$R_diff_pval/2)
+    res_comp_i$R_diff_SE<-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
+        
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_comp_i$Sample<-'A'
+    res_comp_i$Group <- paste0(res_comp_i$Model_1, '_vs_', res_comp_i$Model_2)
+  
+    for(group_i in unique(res_comp_i$Group)){
+      res_comp_group_i <- res_comp_i[res_comp_i$Group == group_i,]
+      cors_i <- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
+      
+      if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
+        
+        meta_res_comp_i <-
+          agg(
+            id = Sample,
+            es = R_diff,
+            var = R_diff_SE ^ 2,
+            cor = cors_i,
+            method = "BHHR",
+            mod = NULL,
+            data = res_comp_group_i
+          )
+        
+        tmp <- res_comp_group_i[1,]
+        tmp$pheno <- NULL
+        tmp$R_diff <- meta_res_comp_i$es
+        tmp$R_diff_SE <- sqrt(meta_res_comp_i$var)
+        tmp$R_diff_z <- tmp$R_diff / tmp$R_diff_SE
+        tmp$R_diff_p <- 2*pnorm(-abs(tmp$R_diff_z))
+      } else {
+        tmp <- res_comp_group_i[1,]
+        tmp$pheno <- NULL
+        tmp$R_diff <- NA
+        tmp$R_diff_SE <- NA
+        tmp$R_diff_z <- NA
+        tmp$R_diff_p <- NA
+      }
+      meta_res_comp <- rbind(meta_res_comp, tmp)
+    }
+  }
+}
+
+# Group differences
+meta_res_comp$R_diff_catagory <- cut(
+    meta_res_comp$R_diff,
+    breaks = c(-Inf, -0.08, -0.025, -0.002, 0.002, 0.025, 0.08, Inf),
+    labels = c('< -0.08', '-0.08 - -0.025', '-0.025 - -0.002', '-0.002 - 0.002', '0.002 - 0.025', '0.025 - 0.08', '> 0.08'),
+    right = FALSE
+)
+meta_res_comp$R_diff_catagory <- factor(meta_res_comp$R_diff_catagory, levels = rev(levels(meta_res_comp$R_diff_catagory)))
+
+# Assign significance stars
+meta_res_comp$indep_star<-' '
+meta_res_comp$indep_star[meta_res_comp$R_diff_p < 0.05]<-'*'
+meta_res_comp$indep_star[meta_res_comp$R_diff_p < 1e-3]<-'**'
+meta_res_comp$indep_star[meta_res_comp$R_diff_p < 1e-6]<-'***'
+
+meta_res_comp<-meta_res_comp[order(meta_res_comp$Discovery1, meta_res_comp$Discovery2, meta_res_comp$Method1),]
+
+heatmap_list<-NULL
+for(model_i in c('Top1','Pseudo','Multi')){
+  heatmap_list[[model_i]]<-list()
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    heatmap_list[[model_i]][[targ_pop_i]]<-list()
+    for(disc_pop_i in disc_pop){
+  
+      tmp <- meta_res_comp[meta_res_comp$Target == targ_pop_i, ]
+      tmp <- tmp[tmp$gwas_group == paste0('EUR+', disc_pop_i), ]
+      tmp <- tmp[tmp$Model1 == model_i & tmp$Model2 == model_i,]
+      tmp$Model_1<-gsub('\\..*','', tmp$Model_1)
+      tmp$Model_2<-gsub('\\..*','', tmp$Model_2)
+      
+      tmp$Model_1 <- factor(
+        tmp$Model_1, 
+        levels=unique(tmp$Model_1))
+      tmp$Model_2 <- factor(
+        tmp$Model_2, 
+        levels=unique(tmp$Model_1))
+      
+      heatmap_list[[model_i]][[targ_pop_i]][[disc_pop_i]] <-
+        ggplot(data = tmp, aes(Model_2, Model_1, fill = R_diff_catagory)) +
+        geom_tile(color = "white", show.legend = TRUE) +
+        labs(y = 'Test', x = 'Comparison', fill = 'R difference', title = paste0('Target: ', targ_pop_i, ', Model = ', model_i)) +
+        facet_grid(Discovery1 ~ Discovery2, scales = 'free', space = 'free', switch="both") +
+        geom_text(
+          data = tmp,
+          aes(Model_2, Model_1, label = indep_star),
+          color = "black",
+          size = 4,
+          angle = 0,
+          vjust = 0.8
+        ) +
+        scale_fill_brewer(
+          breaks = levels(tmp$R_diff_catagory),
+          palette = "RdBu",
+          drop = F,
+          na.value = 'grey'
+        ) +
+        theme_half_open() +
+        background_grid() +
+        panel_border() +
+        theme(axis.text.x = element_text(
+          angle = 45,
+          vjust = 1,
+          hjust = 1
+        ))
+    }
+  }
+}
+
+####
+# Plot R compared to top1 model where GWAS matches target population
+####
+meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Model2 == 'Top1' & meta_res_comp$Method2 == 'ptclump' & meta_res_comp$Discovery2 == meta_res_comp$Target,]
+meta_res_comp_ptclump_top1$reference_point<-F
+meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Model1 == 'Top1' & meta_res_comp_ptclump_top1$Method1 == 'ptclump' & meta_res_comp_ptclump_top1$Discovery1 == meta_res_comp_ptclump_top1$Target]<-T
+meta_res_comp_ptclump_top1$R_diff[is.na(meta_res_comp_ptclump_top1$R_diff)]<-0
+meta_res_comp_ptclump_top1$Discovery1 <- factor(meta_res_comp_ptclump_top1$Discovery1, levels=rev(levels(meta_res_comp_ptclump_top1$Discovery1)))
+
+res_comp_all_ptclump_top1<-res_comp_all[res_comp_all$Model2 == 'Top1' & res_comp_all$Method2 == 'ptclump' & res_comp_all$Discovery2 == res_comp_all$Target,]
+res_comp_all_ptclump_top1$Discovery1 <-  factor(res_comp_all_ptclump_top1$Discovery1, levels=levels(meta_res_comp_ptclump_top1$Discovery1))
+
+# Create data to plot reference points
+meta_res_comp_reference <- meta_res_comp_ptclump_top1
+meta_res_comp_reference$R_diff[meta_res_comp_ptclump_top1$reference_point == F] <- NA
+meta_res_comp_reference$R_diff_SE [meta_res_comp_ptclump_top1$reference_point == F] <- NA
+res_comp_all_ptclump_top1$reference_point<-F
+
+ggplot(meta_res_comp_ptclump_top1, aes(x=Method1, y=R_diff , fill = Model1)) +
+    geom_point(
+        data = res_comp_all_ptclump_top1,
+        mapping = aes(x=Method1, y=R_diff, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff - R_diff_SE,
+          ymax = R_diff + R_diff_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_res_comp_reference,
+        aes(x = Method1, y = R_diff, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method1))), linetype="dotted") +
+    labs(y = "R_diff (SE)") +
+    facet_grid(Target ~ Discovery1, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+```
+</details>
+
+***
+
+## Evaluate TLPRS
+### Create predictor lists
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Subset to TLPRS scores and pseudo scores for corresponding methods
+pgs_methods <- unique(scores$method[grepl('^tlprs', scores$method)])
+pgs_methods <- c(pgs_methods, gsub('tlprs_','',pgs_methods))
+scores <- scores[scores$method %in% pgs_methods,]
+
+# Create files for EAS and AFR targets
+targ_pop <- c('EUR','EAS','AFR')
+for(trait_i in selected_traits){
+  scores_i <- scores[grepl(trait_i, scores$name),]
+  scores_i$group <- scores_i$method
+  
+  for(targ_pop_i in targ_pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'BBJ'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'UGR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('BBJ','UGR')
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == 'BBJ'){
+        disc_pop_j_2 <- 'EAS'
+      }
+      if(disc_pop_j == 'UGR'){
+        disc_pop_j_2 <- 'AFR'
+      }
+
+      dir.create(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i
+        ),
+        recursive = T
+      )
+      
+      scores_i_j <- scores_i[grepl('UKB', scores_i$name, ignore.case = F) |
+                             grepl(disc_pop_j, scores_i$name, ignore.case = T),]
+      
+      # Insert the pseudo score for the non-TLPRS methods
+      scores_i_j_pseudo <- scores_i_j[which(!grepl('^tlprs', scores_i_j$method)), ]
+      scores_i_j_pseudo$group <- paste0(scores_i_j_pseudo$group,'.pseudo')
+
+      scores_i_j_pseudo$predictor <- paste0(
+        outdir,
+        '/ukb/pgs/',
+        targ_pop_i,
+        '/',
+        scores_i_j_pseudo$method,
+        '/',
+        scores_i_j_pseudo$name,
+        '/ukb-',
+        scores_i_j_pseudo$name,
+        '-',
+        targ_pop_i,
+        '.pseudo.profiles'
+      )
+      
+      for(i in 1:nrow(scores_i_j_pseudo)) {
+        tmp <- scores_i_j_pseudo[i,]
+        param <- find_pseudo(
+          config = config,
+          gwas = tmp$name,
+          pgs_method = tmp$method,
+          target_pop = targ_pop_i
+        )
+        
+        score_header <-
+          fread(gsub('.pseudo', '', tmp$predictor), nrows = 1)
+        score_cols <-
+          which(names(score_header) %in% c('FID', 'IID', paste0(tmp$name, '_', param)))
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            gsub('.pseudo', '', tmp$predictor), 
+            " > ", tmp$predictor
+          )
+        )
+      }
+      
+      # Create disc_pop specific groups
+      scores_i_j_pseudo_disc_pop <- scores_i_j_pseudo
+      scores_i_j_pseudo_disc_pop$group[grepl('UKB', scores_i_j_pseudo_disc_pop$name)] <- paste0(scores_i_j_pseudo_disc_pop$group[grepl('UKB', scores_i_j_pseudo_disc_pop$name)], '.EUR')
+      scores_i_j_pseudo_disc_pop$group[!grepl('UKB', scores_i_j_pseudo_disc_pop$name)] <- paste0(scores_i_j_pseudo_disc_pop$group[!grepl('UKB', scores_i_j_pseudo_disc_pop$name)], '.', disc_pop_j_2)
+      
+      # Insert groups for TLPRS scores for both target populations, and target specific
+      scores_i_j_multi <- scores_i_j[which(grepl('^tlprs', scores_i_j$method)), ]
+      
+      # Insert path to score file
+      scores_i_j_multi$predictor <- paste0(
+        outdir,
+        '/ukb/pgs/',
+        targ_pop_i,
+        '/',
+        scores_i_j_multi$method,
+        '/',
+        scores_i_j_multi$name,
+        '/ukb-',
+        scores_i_j_multi$name,
+        '-',
+        targ_pop_i,
+        '.profiles'
+      )
+      
+      scores_i_j_multi_targ_pop <- scores_i_j_multi
+      scores_i_j_multi_targ_pop_both<-NULL
+      for(i in 1:nrow(scores_i_j_multi_targ_pop)){
+        score_header <-
+          fread(gsub('.pseudo', '', scores_i_j_multi_targ_pop$predictor[i]), nrows = 1)
+        score_cols_EUR <-
+          which(names(score_header) %in% c('FID', 'IID', names(score_header)[grepl('targ_EUR', names(score_header))]))
+        score_cols_targ <-
+          which(names(score_header) %in% c('FID', 'IID', names(score_header)[grepl(paste0('targ_', targ_pop_i), names(score_header))]))
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols_EUR, collapse=','),
+            " ", 
+            scores_i_j_multi_targ_pop$predictor[i], 
+            " > ", 
+            paste0(
+              outdir,
+              '/ukb/pgs/',
+              targ_pop_i,
+              '/',
+              scores_i_j_multi_targ_pop$method[i],
+              '/',
+              scores_i_j_multi_targ_pop$name[i],
+              '/ukb-',
+              scores_i_j_multi_targ_pop$name[i],
+              '-',
+              targ_pop_i,
+              '.targ_EUR.profiles'
+            )
+          )
+        )
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols_targ, collapse=','),
+            " ", 
+            scores_i_j_multi_targ_pop$predictor[i], 
+            " > ", 
+            paste0(
+              outdir,
+              '/ukb/pgs/',
+              targ_pop_i,
+              '/',
+              scores_i_j_multi_targ_pop$method[i],
+              '/',
+              scores_i_j_multi_targ_pop$name[i],
+              '/ukb-',
+              scores_i_j_multi_targ_pop$name[i],
+              '-',
+              targ_pop_i,
+              '.targ_',
+              targ_pop_i,
+              '.profiles'
+            )
+          )
+        )
+        
+        tmp<-scores_i_j_multi_targ_pop[i,]
+        tmp <- rbind(tmp, tmp)
+        tmp$predictor[1] <- paste0(
+            outdir,
+            '/ukb/pgs/',
+            targ_pop_i,
+            '/',
+            scores_i_j_multi_targ_pop$method[i],
+            '/',
+            scores_i_j_multi_targ_pop$name[i],
+            '/ukb-',
+            scores_i_j_multi_targ_pop$name[i],
+            '-',
+            targ_pop_i,
+            '.targ_EUR.profiles'
+          )
+        tmp$group[1] <- paste0(tmp$group[1], '.EUR')
+        
+        tmp$predictor[2] <- paste0(
+            outdir,
+            '/ukb/pgs/',
+            targ_pop_i,
+            '/',
+            scores_i_j_multi_targ_pop$method[i],
+            '/',
+            scores_i_j_multi_targ_pop$name[i],
+            '/ukb-',
+            scores_i_j_multi_targ_pop$name[i],
+            '-',
+            targ_pop_i,
+            '.targ_',
+            targ_pop_i,
+            '.profiles'
+          )
+        tmp$group[2] <- paste0(tmp$group[2], '.', targ_pop_i)
+
+        scores_i_j_multi_targ_pop_both <- rbind(
+          scores_i_j_multi_targ_pop_both,
+          tmp)
+      }
+      
+      predictors_i<- do.call(rbind, list(
+        scores_i_j_multi, scores_i_j_multi_targ_pop_both, scores_i_j_pseudo, scores_i_j_pseudo_disc_pop
+      ))
+      
+      predictors_i <- predictors_i[, c('predictor', 'group'), with=F]
+      
+      write.table(
+        predictors_i,
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i,
+          '/predictor_list.tlprs.txt'
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}
+
+```
+
+</details>
+
+***
+
+### Run model_builder
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+for targ_pop in $(echo EUR EAS AFR); do
+  if [ "$targ_pop" == "EUR" ]; then
+      targ_pop2="EUR_test"
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ "$targ_pop" == "EUR" ]; then
+    disc_pop=$(echo AFR EAS)
+  fi
+  
+  if [ "$targ_pop" == "EAS" ]; then
+    disc_pop="EAS"
+  fi
+  
+  if [ "$targ_pop" == "AFR" ]; then
+    disc_pop="AFR"
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+      sbatch --mem 20G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+        --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
+        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
+        --n_core 10 \
+        --top1 T \
+        --all_model F \
+        --assoc T"
+    done
+  done
+done
+
+```
+</details>
+
+***
+
+### Plot results
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-prscsx_dat[1:5,]
+
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+  
+# Define pgs_methods used
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+
+# Define gwas_list used
+gwas_list<-read_param(config = config, param = 'gwas_list')
+
+# Define gwas_groups used
+gwas_groups<-read_param(config = config, param = 'gwas_groups')
+
+# Calculate corelation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in prscsx_dat$labels){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop = c('EUR','EAS','AFR')
+res <- list()
+for(pheno_i in prscsx_dat$labels){
+  res_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.tlprs.pred_eval.txt'
+          )
+        )
+      eval_i$Target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_i<-rbind(res_i, eval_i)
+    }
+  }
+  
+  res_i$Method<-sub('\\..*','',res_i$Group)
+  res_i$Method_short<-sub('.*_','',res_i$Method)
+  res_i<-res_i[order(res_i$Method_short, res_i$Method),]
+  
+  res_i$Model[grepl('pseudo', res_i$Group)]<-'Pseudo'
+  res_i$Model[grepl('top1', res_i$Group)]<-'Top1'
+  res_i$Model[!grepl('top1|pseudo', res_i$Group)]<-'Multi'
+  res_i$Model[!grepl('tlprs', res_i$Group) & !grepl('EUR|EAS|AFR', res_i$Group) & !grepl('top1', res_i$Group)]<-'Multi'
+  
+  res_i$Source[!grepl('tlprs', res_i$Group)] <- 'Single'
+  res_i$Source[grepl('tlprs', res_i$Group)] <- 'Multi'
+  res_i$Source[!grepl('tlprs', res_i$Group) & !grepl('EUR|EAS|AFR', res_i$Group)] <- 'Multi'
+  
+  res_i$Discovery <- res_i$gwas_group
+  res_i$Discovery[grepl('EUR', res_i$Group) & res_i$Source == 'Single'] <- 'EUR'
+  res_i$Discovery[grepl('EAS', res_i$Group) & res_i$Source == 'Single'] <- 'EAS'
+  res_i$Discovery[grepl('AFR', res_i$Group) & res_i$Source == 'Single'] <- 'AFR'
+
+  res_i$Method <- gsub('tlprs_','tlprs - ', res_i$Method)
+  res_i$Method[grepl('tlprs', res_i$Method) & grepl('EUR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('EUR', res_i$Group)], " (EUR)")
+  res_i$Method[grepl('tlprs', res_i$Method) & grepl('EAS', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('EAS', res_i$Group)], " (EAS)")
+  res_i$Method[grepl('tlprs', res_i$Method) & grepl('AFR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('AFR', res_i$Group)], " (AFR)")
+  res_i$Method[grepl('tlprs', res_i$Method) & !grepl('EUR|EAS|AFR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & !grepl('EUR|EAS|AFR', res_i$Group)], " (Both)")
+
+  res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
+  res_i$Model<-factor(res_i$Model, levels=c('Top1','Pseudo','Multi'))
+  res_i$Discovery<-factor(res_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+  res[[pheno_i]]<-res_i
+  
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res for each scenario
+    res_i <- do.call(rbind, lapply(seq_along(res), function(i) {
+      x <- res[[i]]
+      x$pheno <- names(res)[i]
+      x <- x[x$Target == targ_pop_i]
+      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
+    }))
+    
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_i$Sample<-'A'
+  
+    for(group_i in unique(res_i$Group)){
+      res_group_i <- res_i[res_i$Group == group_i,]
+      cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
+      
+      meta_res_i <-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_group_i
+        )
+      
+      meta_res <- rbind(meta_res,
+                        data.table(
+                          Group = group_i,
+                          Method = res_group_i$Method[1],
+                          Model = res_group_i$Model[1],
+                          Source = res_group_i$Source[1],
+                          Discovery = res_group_i$Discovery[1],
+                          gwas_group = res_group_i$gwas_group[1],
+                          Target = targ_pop_i,
+                          R = meta_res_i$es,
+                          SE = sqrt(meta_res_i$var)
+                        ))
+    }
+  }
+}
+
+meta_res$Model<-factor(meta_res$Model, levels=c('Top1','Pseudo','Multi'))
+meta_res$Discovery<-factor(meta_res$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+####
+# Compare TLPRS to unadjusted PGS
+####
+
+meta_res_multi_pop <- meta_res[!(meta_res$Discovery %in% c('EUR','EAS','AFR')),]
+meta_res_multi_pop$original_method <- gsub(' .*', '', gsub('tlprs - ', '', meta_res_multi_pop$Method))
+meta_res_multi_pop$test[!grepl('tlprs', meta_res_multi_pop$Method)] <- 'Unadjusted'
+meta_res_multi_pop$test[grepl('tlprs', meta_res_multi_pop$Method)] <- gsub('.* ', 'TLPRS ', meta_res_multi_pop$Method[grepl('tlprs', meta_res_multi_pop$Method)])
+meta_res_multi_pop$test <- factor(meta_res_multi_pop$test, levels=unique(meta_res_multi_pop$test))
+meta_res_multi_pop$test <- gsub('AFR', 'target pop', meta_res_multi_pop$test)
+meta_res_multi_pop$test <- gsub('EAS', 'target pop', meta_res_multi_pop$test)
+
+tmp <- meta_res_multi_pop[meta_res_multi_pop$Target %in% c('EAS', 'AFR'),]
+
+ggplot(tmp, aes(x=test, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
+    labs(y = "R (SE)") +
+    facet_grid(Target ~ original_method, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+tmp <- meta_res_multi_pop[meta_res_multi_pop$Target %in% c('EAS', 'AFR'),]
+tmp <- tmp[grepl('Unadj|Both', tmp$test),]
+tmp$test <- gsub(' .*', '', tmp$test)
+tmp$test <- factor(tmp$test, levels=c('Unadjusted','TLPRS'))
+
+ggplot(tmp, aes(x=test, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
+    labs(y = "R (SE)", x = NULL) +
+    facet_grid(Target ~ original_method, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Read in results
+targ_pop=c('EUR','EAS','AFR')
+res <- list()
+for(pheno_i in prscsx_dat$labels){
+  res_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.tlprs.pred_comp.txt'
+          )
+        )
+      eval_i$Target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_i<-rbind(res_i, eval_i)
+    }
+  }
+  
+  res[[pheno_i]]<-res_i
+}
+
+#####
+# Create a plot showing relative R from TLPRS vs Unadjusted
+#####
+
+res_all <- do.call(rbind, lapply(names(res), function(name) {
+  x <- res[[name]]
+  x$pheno <- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Mirror results to fill in gaps
+res_all_symmetric <- res_all
+res_all_mirrored <- res_all
+res_all_mirrored$Model_1 <- res_all$Model_2
+res_all_mirrored$Model_2 <- res_all$Model_1
+res_all_mirrored$Model_1_R <- res_all$Model_2_R
+res_all_mirrored$Model_2_R <- res_all$Model_1_R
+res_all_mirrored$R_diff <- -res_all_mirrored$R_diff
+res_all <- rbind(res_all_symmetric, res_all_mirrored)
+
+# Subset tests where top1 TLPRS is being compared to top1 unadjusted
+res_all$Method_1 <- gsub('.*_', '', gsub('\\..*', '', res_all$Model_1))
+res_all$Method_2 <- gsub('.*_', '', gsub('\\..*', '', res_all$Model_2))
+
+res_all$TLPRS_1 <- grepl('tlprs', res_all$Model_1)
+res_all$TLPRS_2 <- grepl('tlprs', res_all$Model_2)
+
+res_all$Test_1[grepl('pseudo', res_all$Model_1)]<-'Pseudo'
+res_all$Test_1[grepl('top1', res_all$Model_1)]<-'Top1'
+res_all$Test_1[!grepl('top1|pseudo', res_all$Model_1)]<-'Multi'
+res_all$Test_1[!grepl('tlprs', res_all$Model_1) & !grepl('EUR|EAS|AFR', res_all$Model_1) & !grepl('top1', res_all$Model_1)]<-'Multi'
+
+res_all$Test_2[grepl('pseudo', res_all$Model_2)]<-'Pseudo'
+res_all$Test_2[grepl('top1', res_all$Model_2)]<-'Top1'
+res_all$Test_2[!grepl('top1|pseudo', res_all$Model_2)]<-'Multi'
+res_all$Test_2[!grepl('tlprs', res_all$Model_2) & !grepl('EUR|EAS|AFR', res_all$Model_2) & !grepl('top1', res_all$Model_2)]<-'Multi'
+
+res_all$Source_1[!grepl('tlprs', res_all$Model_1)] <- 'Single'
+res_all$Source_1[grepl('tlprs', res_all$Model_1)] <- 'Multi'
+res_all$Source_1[!grepl('tlprs', res_all$Model_1) & !grepl('EUR|EAS|AFR', res_all$Model_1)] <- 'Multi'
+
+res_all$Source_2[!grepl('tlprs', res_all$Model_2)] <- 'Single'
+res_all$Source_2[grepl('tlprs', res_all$Model_2)] <- 'Multi'
+res_all$Source_2[!grepl('tlprs', res_all$Model_2) & !grepl('EUR|EAS|AFR', res_all$Model_2)] <- 'Multi'
+
+res_all$Discovery_1 <- res_all$gwas_group
+res_all$Discovery_1[grepl('EUR', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'EUR'
+res_all$Discovery_1[grepl('EAS', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'EAS'
+res_all$Discovery_1[grepl('AFR', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'AFR'
+
+res_all$Discovery_2 <- res_all$gwas_group
+res_all$Discovery_2[grepl('EUR', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'EUR'
+res_all$Discovery_2[grepl('EAS', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'EAS'
+res_all$Discovery_2[grepl('AFR', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'AFR'
+
+res_all$TLPRS_target_1[grepl('EUR', res_all$Model_1) & res_all$TLPRS_1] <- 'EUR'
+res_all$TLPRS_target_1[grepl('EAS', res_all$Model_1) & res_all$TLPRS_1] <- 'EAS'
+res_all$TLPRS_target_1[grepl('AFR', res_all$Model_1) & res_all$TLPRS_1] <- 'AFR'
+res_all$TLPRS_target_1[!grepl('EUR|AFR|EAS', res_all$Model_1) & res_all$TLPRS_1] <- 'Both'
+res_all$TLPRS_target_1[res_all$TLPRS_target_1 == res_all$Target] <- 'Target Pop.'
+
+res_all$TLPRS_target_2[grepl('EUR', res_all$Model_2) & res_all$TLPRS_2] <- 'EUR'
+res_all$TLPRS_target_2[grepl('EAS', res_all$Model_2) & res_all$TLPRS_2] <- 'EAS'
+res_all$TLPRS_target_2[grepl('AFR', res_all$Model_2) & res_all$TLPRS_2] <- 'AFR'
+res_all$TLPRS_target_2[!grepl('EUR|AFR|EAS', res_all$Model_2) & res_all$TLPRS_2] <- 'Both'
+res_all$TLPRS_target_2[res_all$TLPRS_target_2 == res_all$Target] <- 'Target Pop.'
+
+# Subset to tests comparing to the Unadjusted models
+res_all <- res_all[res_all$Method_1 == res_all$Method_2, ]
+res_all <- res_all[res_all$Source_2 == 'Multi', ]
+res_all <- res_all[res_all$Test_1  == res_all$Test_2, ]
+res_all <- res_all[res_all$TLPRS_2  == F, ]
+res_all <- res_all[res_all$Target %in% c('EAS', 'AFR'),]
+
+ggplot(res_all, aes(x = Method_2, y = R_diff, colour = Test_1)) +
+  geom_point(position=position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7), alpha=0.5) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(Target ~ TLPRS_target_1, scales='free', space = 'free_x')
+
+
+######
+# Average R across phenotypes
+######
+
+library(MAd)
+
+meta_res <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res for each scenario
+    res_i <- res_all[res_all$Target == targ_pop_i & res_all$gwas_group == paste0('EUR+', disc_pop_i), ]
+  
+    # Calculate diff SE based on p-value
+    res_i$R_diff_pval[res_i$R_diff == 0] <- 1-0.001
+    res_i$R_diff_pval[res_i$R_diff_pval == 1]<-1-0.001
+    res_i$R_diff_z<-qnorm(res_i$R_diff_pval/2)
+    res_i$R_diff_SE<-abs(res_i$R_diff/res_i$R_diff_z)
+        
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_i$Sample<-'A'
+    res_i$Group <- paste0(res_i$Model_1, '_vs_', res_i$Model_2)
+  
+    for(group_i in unique(res_i$Group)){
+      res_group_i <- res_i[res_i$Group == group_i,]
+      cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
+      
+      meta_res_i <-
+        agg(
+          id = Sample,
+          es = R_diff,
+          var = R_diff_SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_group_i
+        )
+      
+      group_info <- res_group_i[1, !(names(res_group_i[1,]) %in% c('Model_1_R', 'Model_2_R', 'R_diff', 'R_diff_pval', 'R_diff_z', 'R_diff_SE')), with=F]
+      meta_res <- rbind(meta_res,
+                        data.table(
+                          group_info,
+                          R_diff = meta_res_i$es,
+                          R_diff_SE = sqrt(meta_res_i$var)
+                        ))
+    }
+  }
+}
+meta_res$R_diff_z <- meta_res$R_diff / meta_res$R_diff_SE
+meta_res$R_diff_p <- 2*pnorm(-abs(meta_res$R_diff_z))
+
+# Plot the results
+ggplot(meta_res, aes(x=Method_1, y=R_diff, fill=Test_1)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_point(
+    data = res_all,
+    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
+    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+    alpha = 0.3
+  ) +
+  geom_errorbar(
+    aes(
+      ymin = R_diff - R_diff_SE,
+      ymax = R_diff + R_diff_SE
+    ),
+    width = 0,
+    position = position_dodge(width = 0.7)
+  ) +
+  geom_point(
+    stat = "identity",
+    position = position_dodge(0.7),
+    size = 2,
+    shape = 23,
+    colour = 'black'
+  ) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  geom_vline(xintercept = 1.5, linetype = "dotted") +
+  geom_vline(xintercept = 2.5, linetype = "dotted") +
+  geom_vline(xintercept = 3.5, linetype = "dotted") +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(Target ~ TLPRS_target_1, scales='free', space = 'free_x') +
+  panel_border()
+
+
+
+# Plot the results
+ggplot(meta_res[meta_res$TLPRS_target_1 == 'Both',], aes(x=Method_1, y=R_diff, fill=Test_1)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_point(
+    data = res_all[res_all$TLPRS_target_1 == 'Both',],
+    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
+    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+    alpha = 0.3
+  ) +
+  geom_errorbar(
+    aes(
+      ymin = R_diff - R_diff_SE,
+      ymax = R_diff + R_diff_SE
+    ),
+    width = 0,
+    position = position_dodge(width = 0.7)
+  ) +
+  geom_point(
+    stat = "identity",
+    position = position_dodge(0.7),
+    size = 2,
+    shape = 23,
+    colour = 'black'
+  ) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  geom_vline(xintercept = 1.5, linetype = "dotted") +
+  geom_vline(xintercept = 2.5, linetype = "dotted") +
+  geom_vline(xintercept = 3.5, linetype = "dotted") +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(Target ~ ., scales='free', space = 'free_x') +
+  panel_border()
+
+
+# Plot the results
+ggplot(meta_res[meta_res$TLPRS_target_1 == 'Both',], aes(x=Method_1, y=R_diff, fill=Test_1)) +
+  geom_hline(yintercept = 0, colour = 'darkgrey') +
+  geom_point(
+    data = res_all[res_all$TLPRS_target_1 == 'Both',],
+    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
+    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+    alpha = 0.3
+  ) +
+  geom_errorbar(
+    aes(
+      ymin = R_diff - R_diff_SE,
+      ymax = R_diff + R_diff_SE
+    ),
+    width = 0,
+    position = position_dodge(width = 0.7)
+  ) +
+  geom_point(
+    stat = "identity",
+    position = position_dodge(0.7),
+    size = 2,
+    shape = 23,
+    colour = 'black'
+  ) +
+  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  geom_vline(xintercept = 1.5, linetype = "dotted") +
+  geom_vline(xintercept = 2.5, linetype = "dotted") +
+  geom_vline(xintercept = 3.5, linetype = "dotted") +
+  background_grid(major = 'y', minor = 'y') +
+  facet_grid(Target ~ ., scales='free', space = 'free_x') +
+  panel_border()
+
+
+```
+</details>
+
+***
+
+# Computational resoures
+
+```{r}
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+
+# Get some key variables from config
+config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in configuration specific benchmark files
+bm_files_i <- list.files(paste0(outdir, '/reference/benchmarks/'), full.names = T)
+
+# Subset benchmarks for pgs_methods
+bm_files_i <- bm_files_i[grepl('prep_pgs_', bm_files_i)]
+
+# Subset to benchmarks for gwas/gwas_groups in config
+scores <- list_score_files(config)
+bm_files_i <- bm_files_i[grepl(paste0('-', unique(scores$name),'.txt', collapse = '|'), bm_files_i)]
+
+# Read in benchmark files
+bm_dat_all <- do.call(rbind, lapply(bm_files_i, function(file) {
+  tmp <- fread(file)
+  tmp$file <- basename(file)
+  return(tmp)
+}))
+
+# Create rule column
+bm_dat_all$rule <- gsub('-.*','',bm_dat_all$file)
+
+# Create method column
+bm_dat_all$method <-
+  gsub('_i', '', gsub('prep_pgs_', '', bm_dat_all$rule))
+
+# Calculate average time taken for each method
+method_avg <- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg <- rbind(
+    method_avg,
+    data.frame(
+      Method = i,
+      Time = mean(bm_dat_all$s[bm_dat_all$method == i])
+    )
+  )
+}
+
+# Calculate average max_rss for each method
+method_avg_mem <- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg_mem <- rbind(
+    method_avg_mem,
+    data.frame(
+      Method = i,
+      Memory = mean(bm_dat_all$max_rss[bm_dat_all$method == i])
+    )
+  )
+}
+
+
+# Format the time taken nicely
+method_avg$Time_clean[method_avg$Time < 60] <-
+  paste0(round(method_avg$Time[method_avg$Time < 60], 1), ' sec')
+method_avg$Time_clean[method_avg$Time > 60] <-
+  paste0(round(method_avg$Time[method_avg$Time > 60] / 60, 1), ' min')
+method_avg$Time_clean[method_avg$Time > 3600] <-
+  paste0(round(method_avg$Time[method_avg$Time > 3600] / 60 / 60, 1), ' hr')
+
+ggplot(method_avg, aes(x = Method, y = Time, fill = Method)) +
+  geom_bar(stat = "identity", position="dodge") +
+  geom_text(aes(label = Time_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
+  labs(x = "PGS Method", y = "Time (s)") +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
+
+# Format the Memory nicely
+method_avg_mem$Memory_clean <-
+  paste0(round(method_avg_mem$Memory/1000, 2), ' Gb')
+
+ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
+  geom_bar(stat = "identity", position="dodge") +
+  geom_text(aes(label = Memory_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
+  labs(x = "PGS Method", y = "Memory (Mb)") +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
+
+```
+

From e77ca09b1190b67256366871645657843557fe83 Mon Sep 17 00:00:00 2001
From: Oliver Pain <oliver.pain@kcl.ac.uk>
Date: Tue, 7 Jan 2025 13:47:19 +0000
Subject: [PATCH 076/153] Minor updates to attempt on DNAnexus

---
 docs/CrossPop_dnanexus.Rmd | 62 ++++++++++++++------------------------
 1 file changed, 22 insertions(+), 40 deletions(-)

diff --git a/docs/CrossPop_dnanexus.Rmd b/docs/CrossPop_dnanexus.Rmd
index ac179b33..362af59f 100644
--- a/docs/CrossPop_dnanexus.Rmd
+++ b/docs/CrossPop_dnanexus.Rmd
@@ -38,60 +38,37 @@ We will need to mount the dispensed UKB genetic data, so it can be used as an in
 
 ```{bash}
 # Mount the project with the dispensed UKB data
-FUSE_MOUNT=/home/dnanexus/projects2
+mkdir ~/software
+cd software
+wget https://github.com/dnanexus/dxfuse/releases/download/v1.4.0/dxfuse-linux
+chmod a+x dxfuse-linux
+
+cd ~
+FUSE_MOUNT=~/projects
 mkdir -p $FUSE_MOUNT
-./dxfuse-linux -uid $(id -u) -gid $(id -g) -verbose 2 $FUSE_MOUNT project-GP0ZBqQJ5vK5kxJF3KyxgZg1
+~/software/dxfuse-linux -uid $(id -u) -gid $(id -g) -verbose 2 $FUSE_MOUNT project-GP0ZBqQJ5vK5kxJF3KyxgZg1
 
 # Create symlinks to the dispensed imputed genetic data
-mkdir -p /home/dnanexus/ukb/ukb_symlinks
+mkdir -p ~/ukb/ukb_symlinks
 
 # pgen and pvar files
 for chr in $(seq 1 22);do
   for file in $(echo bgen bgen.bgi);do
-    ln -s /home/dnanexus/projects/UKB_82087//Bulk/Imputation/Imputation\ from\ genotype\ \(TOPmed\)/ukb21007_c${chr}_b0_v1.${file} /home/dnanexus/ukb/ukb_symlinks/ukb_imp.chr${chr}.${file}
+    ln -s ~/projects/UKB_82087//Bulk/Imputation/Imputation\ from\ genotype\ \(TOPmed\)/ukb21007_c${chr}_b0_v1.${file} ~/ukb/ukb_symlinks/ukb_imp.chr${chr}.${file}
   done
 done
 
 # Make a copy of the sample file, and set the sex column to dichotomous (otherwise plink throws an error)
-cp /home/dnanexus/projects/UKB_82087//Bulk/Imputation/Imputation\ from\ genotype\ \(TOPmed\)/ukb21007_c1_b0_v1.sample /home/dnanexus/ukb/ukb_symlinks/ukb_imp.sample
-sed -i '2s/0/D/4' /home/dnanexus/ukb/ukb_symlinks/ukb_imp.sample
-
-# Subset the files to contain variants with MAF > 0.005 and INFO 0.4
-```
-
-```{r}
-install.packages('data.table')
-install.packages('R.utils')
-library(data.table)
-for(i in 22:22){
-    snp_dat<-fread(paste0("/home/dnanexus/projects2/UKB_82087//Bulk/Imputation/Imputation from genotype (TOPmed)/helper_files/ukb21007_c",i,'_b0_v1.sites.vcf.gz'), nrows = 100000)
-    snp_dat<-snp_dat[nchar(snp_dat$REF) == 1 & nchar(snp_dat$ALT) == 1,]
-    snp_dat$ID<-paste0(snp_dat$`#CHROM`, ':', snp_dat$POS)
-    snp_dat<-snp_dat[, c('ID','INFO'), with=F]
-    snp_dat <- data.table(ID = snp_dat$ID, do.call(rbind, lapply(strsplit(snp_dat$INFO, ';'), function(x) x[1:2])))
-    names(snp_dat)<-c('ID','FREQ','R2')
-    snp_dat$FREQ<-as.numeric(gsub('AF=','', snp_dat$FREQ))
-    snp_dat$R2<-as.numeric(gsub('R2=','', snp_dat$R2))
-    snp_dat<-snp_dat[(snp_dat$FREQ > 0.005 | snp_dat$FREQ < 0.995) & snp_dat$R2 > 0.4,]
-    fwrite(snp_dat[,'ID', with=F], paste0('/home/dnanexus/ukb/maf_005.r2_4.chr',i,'.snplist'), quote=F, col.names=F)
-}
-```
-
-```{bash}
-# decompress qctool_v2.2.0-CentOS_Linux7.8.2003-x86_64.tgz
-
+cp ~/projects/UKB_82087//Bulk/Imputation/Imputation\ from\ genotype\ \(TOPmed\)/ukb21007_c1_b0_v1.sample ~/ukb/ukb_symlinks/ukb_imp.sample
+sed -i '2s/0/D/4' ~/ukb/ukb_symlinks/ukb_imp.sample
 
-/home/dnanexus/software/qctool_v2.0.8-CentOS\ Linux7.6.1810-x86_64/qctool \
-    -g /home/dnanexus/projects2/UKB_82087//Bulk/Imputation/Imputation\ from\ genotype\ \(TOPmed\)/ukb21007_c22_b0_v1.bgen \
-    -og /home/dnanexus/ukb/ukb.filtered.chr22.bgen \
-    -incl-positions /home/dnanexus/ukb/maf_005.r2_4.chr22.snplist
 ```
 
 Create the require configuration files. 
 
 ```{bash}
-mkdir -p /home/dnanexus/ukb/configs/basic
-mkdir -p /home/dnanexus/ukb/GenoPred/output
+mkdir -p ~/ukb/configs/basic
+mkdir -p ~/ukb/GenoPred/output
 ```
 
 ```{r}
@@ -120,13 +97,18 @@ write.table(conf, '/home/dnanexus/ukb/configs/basic/config.yaml', col.names = F,
 Run pipeline
 
 ```{bash}
-cd /home/dnanexus/GenoPred/pipeline
+conda activate genopred
+cd ~/GenoPred/pipeline
 git describe --tags
-# v2.2.2-178-ge7cceca
+# v2.2.2-179-gfa4ccec
 
-snakemake -j 4 --use-conda --configfile=/home/dnanexus/ukb/configs/basic/config.yaml ancestry_inference -n 
+snakemake -j 1 --use-conda --configfile=/home/dnanexus/ukb/configs/basic/config.yaml ancestry_inference -n 
 ```
 
+I run into an issue where the mount breaks. I get error 'Transport endpoint is not connected.'.
+
+
+
 
 
 ```{r}

From 84389853256a07480e2517150fb7fa97ac97b81d Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 14 Jan 2025 11:07:22 +0000
Subject: [PATCH 077/153] Updating documentation

---
 docs/hapnest_benchmark.Rmd   | 33 ++++++++++++++++++++++++++++-
 docs/pipeline_readme.Rmd     |  2 +-
 docs/pipeline_readme.html    |  5 ++---
 docs/pipeline_technical.Rmd  | 18 ++++++++++++----
 docs/pipeline_technical.html | 40 +++++++++++++++++++++++++++---------
 5 files changed, 79 insertions(+), 19 deletions(-)

diff --git a/docs/hapnest_benchmark.Rmd b/docs/hapnest_benchmark.Rmd
index 57788251..f95f7564 100644
--- a/docs/hapnest_benchmark.Rmd
+++ b/docs/hapnest_benchmark.Rmd
@@ -579,7 +579,7 @@ write.table(config, '/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/con
 <details><summary>Show code</summary>
 
 ```{bash}
-snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml output_all -n
+snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml output_all outlier_detection -n
 ```
 
 </details>
@@ -727,6 +727,37 @@ ggplot(assoc_both, aes(x = trans, y = BETA, fill = pT)) +
 
 dev.off()
 
+####
+# Check correlation between phenotype and PCs
+####
+
+# Read in projected PCs
+pcs<-fread('/scratch_tmp/prj/oliverpainfel/Analysis/HAPNEST/genopred/output/hapnest/pcs/projected/TRANS/hapnest-TRANS.profiles')
+
+pcs_pheno <- merge(pcs, pheno, by=c('FID','IID'))
+
+round(cor(pcs_pheno[,-1:-2]),2)
+# There is 0 correlation between phenotype and the projected PCs.
+
+# Read in within sample PCs
+pcs_eur <- fread('/scratch_tmp/prj/oliverpainfel/Analysis/HAPNEST/genopred/output/hapnest/pcs/within_sample/hapnest.outlier_detection.EUR.PCs.txt')
+
+pcs_eur_pheno <- merge(pcs_eur, pheno, by=c('FID','IID'))
+
+round(cor(pcs_eur_pheno[,-1:-2]),2)
+# There is very little correlation between within sample PCs and phenotye (within a given population).
+
+####
+# Check correlation between PGS and PCs
+#### 
+# Check using output from reference data
+pgs_models<-readRDS("/scratch_tmp/prj/oliverpainfel/Analysis/HAPNEST/genopred/output/reference/pgs_score_files/ptclump/pheno1_EUR/ref-pheno1_EUR-TRANS.model.rds")
+
+summary(pgs_models$`SCORE_0_1e-04`$mean_model)
+summary(pgs_models$`SCORE_0_1e-04`$var_model)
+
+# There are strong correlations between PGS and the projected PCs.
+
 ```
 
 </details>
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index c83b728d..45389f4f 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -1404,7 +1404,7 @@ kable(gwas_groups_df, 'markdown')
 
 ## Select ancestry adjustment approach
 
-The GenoPred pipeline offers the ability to generate target polygenic scores (PGS) corrected for ancestry. This can be achieved through two approaches: discrete or continuous corrections for ancestry.
+The GenoPred pipeline offers two approaches for adjusting PGS for ancestry: discrete or continuous.
 
 ### Discrete Correction:
 * This approach assigns each target individual to an ancestry-matched reference population.
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index d02e185c..ff557084 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -2192,9 +2192,8 @@ <h2>Multi-source PGS methods</h2>
 </div>
 <div id="select-ancestry-adjustment-approach" class="section level2">
 <h2>Select ancestry adjustment approach</h2>
-<p>The GenoPred pipeline offers the ability to generate target polygenic
-scores (PGS) corrected for ancestry. This can be achieved through two
-approaches: discrete or continuous corrections for ancestry.</p>
+<p>The GenoPred pipeline offers two approaches for adjusting PGS for
+ancestry: discrete or continuous.</p>
 <div id="discrete-correction" class="section level3">
 <h3>Discrete Correction:</h3>
 <ul>
diff --git a/docs/pipeline_technical.Rmd b/docs/pipeline_technical.Rmd
index 146ee287..37f68557 100644
--- a/docs/pipeline_technical.Rmd
+++ b/docs/pipeline_technical.Rmd
@@ -226,19 +226,19 @@ kable(methods_table, "html", caption = "Table 2 provides a summary of each appro
 
 ***
 
-### PRS-CSx
+#### PRS-CSx
 
 PRS-CSx, an extension of PRS-CS, integrates GWAS summary statistics from multiple ancestry groups using a Bayesian framework. The method is implemented in GenoPred via the script [pgs_methods/prscsx.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscsx.R). We use summary statistics from the populations most closely matching the ancestry of the GWAS data to estimate posterior effect sizes. The LD information is derived from population-matched reference panels (e.g., 1KG). GenoPred clones PRS-CSx from GitHub (29a1148875f6ae3f2594b25579f40d4b587c5691) and uses the provided precompiled PRS-CSx binary to estimate SNP effect sizes. By default, GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the auto model, but the user can modify this behaviour using the prscs_phi parameter in the configfile. By default, GenoPred uses the PRS-CS provided 1KG-derived LD matrix data, matching the population of the GWAS sample. The user can select the UKB-derived LD matrix data to be used using the `prscs_ldref` parameter in the `configfile`. 1KG is used by default as PGS based on Yengo et al. sumstats performed significantly better in the OpenSNP target sample, when using the 1KG reference data (this may differ for other GWAS).
 
 ***
 
-### X-Wing
+#### X-Wing
 
 X-Wing is a polygenic scoring method designed to enhance genetic prediction accuracy across ancestrally diverse populations by leveraging a cross-population weighting framework. The method operates through a combination of several software tools: LOGODetect, PANTHER, and LEOPARD. GenoPred integrates this method using the script [pgs_methods/xwing.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/xwing.R). GenoPred downloads X-WING software from GitHub (e9fcc264266e0e884323311816bfe20053fd3f7a). The LD information derived from the 1KG dataset is provided by the developers of X-Wing. 
 
 ***
 
-### TL-PRS
+#### TL-PRS
 
 TL-PRS (Transfer Learning Polygenic Risk Score) is implemented using the script [pgs_methods/tlprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/tlprs.R). The method combines summary statistics from target GWAS and a reference dataset via transfer learning, iteratively updating SNP effect sizes. GenoPred uses a modified version of TL-PRS to improve efficiency when being applied to many score files (https://github.com/opain/TLPRS). No modifications to the parameters used has been altered from the original version of TL-PRS (https://github.com/ZhangchenZhao/TLPRS/commit/6c8d8905f91ce902f0cca8d5465f9952b0b3e274). Multiple learning rates (factors of 1, 10, 100, 1000) are tested in an iterative approach with 15 steps per factor to update the SNP effect sizes. GenoPred uses the 1KG+HGDP reference for LD estimation, matching the population of the GWAS sample.
 
@@ -292,7 +292,17 @@ If the user doesn't provide a list of unrelated indiviuals using the `unrel` col
 
 ## Target scoring
 
-This step calculates scores in the target sample, based on scoring files from the PGS methods, and from the reference PCA. It is carried out using the [target_scoring.R script](https://github.com/opain/GenoPred/blob/master/Scripts/target_scoring/target_scoring.R). It is performed separately for each target sample population. Scoring is performed using PLINK2 using the --read-freq function to impute missing genotypes using the ancestry-matched reference allele frequency. The scores are computed for each chromosome, then multiplied by the number of non-missing variants to get the sum score, and then added across chromosomes. Finally, the target sample scores are standardised according to the mean and SD of the score in an ancestry-matched reference.
+This step calculates scores in the target sample, based on scoring files from the PGS methods, and from the reference PCA. It is carried out using the [target_scoring.R script](https://github.com/opain/GenoPred/blob/master/Scripts/target_scoring/target_scoring.R). It is performed separately for each target sample population. Scoring is performed using PLINK2 using the --read-freq function to impute missing genotypes using the ancestry-matched reference allele frequency. The scores are computed for each chromosome, then multiplied by the number of non-missing variants to get the sum score, and then added across chromosomes. Finally, the target sample PGS are adjusted for ancestry, so they can be interpreted in units of SD from the mean in an ancestry-matched population. See [here](#ancestry-adjustment) for more information on ancestry adjustment. 
+
+***
+
+### Ancestry adjustment
+
+The GenoPred pipeline offers discrete or continuous approaches for adjusting PGS for ancestry. Previously described [here](pipeline_readme.html#select-ancestry-adjustment-approach). As of GenoPred v3.0.0, only the continuous approach is applied by default.
+
+The discrete approach involves standardising target sample PGS according the mean and SD of the PGS in the individual's assigned reference population.
+
+The continuous approach uses reference data to model the relationship between genetic principal components and the PGS, and then corrects the mean and SD of the PGS for target individuals according to their reference-projected principal component scores. This approach was used in the study by Linders et al. ([here](https://doi.org/10.1016/j.gim.2023.100006)).
 
 ***
 
diff --git a/docs/pipeline_technical.html b/docs/pipeline_technical.html
index 5da61b4a..c69bb6b4 100644
--- a/docs/pipeline_technical.html
+++ b/docs/pipeline_technical.html
@@ -1114,9 +1114,8 @@ <h3>Multi-ancestry Methods</h3>
 </table>
 </div>
 <hr />
-</div>
-<div id="prs-csx" class="section level3">
-<h3>PRS-CSx</h3>
+<div id="prs-csx" class="section level4">
+<h4>PRS-CSx</h4>
 <p>PRS-CSx, an extension of PRS-CS, integrates GWAS summary statistics
 from multiple ancestry groups using a Bayesian framework. The method is
 implemented in GenoPred via the script <a
@@ -1139,8 +1138,8 @@ <h3>PRS-CSx</h3>
 GWAS).</p>
 <hr />
 </div>
-<div id="x-wing" class="section level3">
-<h3>X-Wing</h3>
+<div id="x-wing" class="section level4">
+<h4>X-Wing</h4>
 <p>X-Wing is a polygenic scoring method designed to enhance genetic
 prediction accuracy across ancestrally diverse populations by leveraging
 a cross-population weighting framework. The method operates through a
@@ -1152,8 +1151,8 @@ <h3>X-Wing</h3>
 from the 1KG dataset is provided by the developers of X-Wing.</p>
 <hr />
 </div>
-<div id="tl-prs" class="section level3">
-<h3>TL-PRS</h3>
+<div id="tl-prs" class="section level4">
+<h4>TL-PRS</h4>
 <p>TL-PRS (Transfer Learning Polygenic Risk Score) is implemented using
 the script <a
 href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/tlprs.R">pgs_methods/tlprs.R</a>.
@@ -1172,6 +1171,7 @@ <h3>TL-PRS</h3>
 the population of the GWAS sample.</p>
 <hr />
 </div>
+</div>
 <div id="pseudovalidation" class="section level3">
 <h3>Pseudovalidation</h3>
 <p>Several polygenic scoring methods use a range of ‘tuning’ parameters
@@ -1333,10 +1333,30 @@ <h2>Target scoring</h2>
 function to impute missing genotypes using the ancestry-matched
 reference allele frequency. The scores are computed for each chromosome,
 then multiplied by the number of non-missing variants to get the sum
-score, and then added across chromosomes. Finally, the target sample
-scores are standardised according to the mean and SD of the score in an
-ancestry-matched reference.</p>
+score, and then added across chromosomes. Finally, the target sample PGS
+are adjusted for ancestry, so they can be interpreted in units of SD
+from the mean in an ancestry-matched population. See <a
+href="#ancestry-adjustment">here</a> for more information on ancestry
+adjustment.</p>
 <hr />
+<div id="ancestry-adjustment" class="section level3">
+<h3>Ancestry adjustment</h3>
+<p>The GenoPred pipeline offers discrete or continuous approaches for
+adjusting PGS for ancestry. Previously described <a
+href="pipeline_readme.html#select-ancestry-adjustment-approach">here</a>.
+As of GenoPred v3.0.0, only the continuous approach is applied by
+default.</p>
+<p>The discrete approach involves standardising target sample PGS
+according the mean and SD of the PGS in the individual’s assigned
+reference population.</p>
+<p>The continuous approach uses reference data to model the relationship
+between genetic principal components and the PGS, and then corrects the
+mean and SD of the PGS for target individuals according to their
+reference-projected principal component scores. This approach was used
+in the study by Linders et al. (<a
+href="https://doi.org/10.1016/j.gim.2023.100006">here</a>).</p>
+<hr />
+</div>
 </div>
 <div id="report-creation" class="section level2">
 <h2>Report creation</h2>

From 31627e93f92cc78fcc3d04e5c6027c50fa31586a Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 14 Jan 2025 18:04:06 +0000
Subject: [PATCH 078/153] Added some documentation on how to run on DNAnexus

---
 docs/running_on_dnanexus.Rmd        | 358 ++++++++++++
 docs/running_on_dnanexus.html       | 862 ++++++++++++++++++++++++++++
 pipeline/misc/dnanexus/dxpy_env.yml |   9 +
 3 files changed, 1229 insertions(+)
 create mode 100644 docs/running_on_dnanexus.Rmd
 create mode 100644 docs/running_on_dnanexus.html
 create mode 100644 pipeline/misc/dnanexus/dxpy_env.yml

diff --git a/docs/running_on_dnanexus.Rmd b/docs/running_on_dnanexus.Rmd
new file mode 100644
index 00000000..6319558c
--- /dev/null
+++ b/docs/running_on_dnanexus.Rmd
@@ -0,0 +1,358 @@
+---
+title: Running GenoPred on DNAnexus
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+# Introduction
+
+The UK Biobank (UKB) has recently updated its data access policy, requiring researchers to access and analyze UKB data exclusively via the UKB Research Analysis Platform (UKB-RAP), hosted by DNAnexus. This represents a significant shift in workflows, moving from institutional servers to a cloud-computing environment. DNAnexus and similar cloud-computing systems are likely to become standard for future datasets, so this guide aims to provide instructions on running GenoPred on DNAnexus, with a particular focus on analyzing UKB data. The UKB dataset presents unique challenges due to its size, making efficient analysis essential.
+
+Cloud computing involves requesting access to an instance (or virtual machine) with a specified amount of resources, such as disk space, RAM, and the number of CPU cores. Once access is granted, users must create the required software and data environment within the instance—a step that is often unfamiliar to many. After completing the desired analyses, users must export any outputs they wish to retain, as any data or software left on the instance will be deleted once it is terminated.
+
+This process of transferring software and data into and out of the instance is a significant departure from the experience of working on personal computers or institutional servers, where you can resume work seamlessly each time you log in. 
+
+***
+
+# RStudio vs. Cloud Workstation
+
+Currently, the easiest way to run GenoPred on DNAnexus is interactively, using either RStudio or a Cloud Workstation with the GenoPred software container. Each approach offers unique advantages.
+
+RStudio mounts files directly from a DNAnexus project, eliminating the need to import data. This feature saves time and disk space, particularly with large datasets like UKB genetic data.
+
+Unlike RStudio, the cloud workstation requires input data to be manually imported using dx download, which can be time-consuming and requires additional storage space. While dxfuse allows project folders to be mounted (even across projects), it is prone to instability during long analyses, such as when using UKB.
+
+Another distinction is that cloud workstations can be connected to via VScode, which some people may prefer to Rstudio.
+
+Given the advantage of stable project folder mounting in RStudio, I will demonstrate the workflow in that context. For the demo, minimal resources are required, so I will request an `mem1_ssd1_v2_x2` instance type. A similar workflow can also be applied in the Cloud Workstation, substituting the Singularity container with the GenoPred Docker container.
+
+***
+
+# Demonstration using Rstudio
+
+***
+
+## Step 0: Prepare test data
+
+To make the demonstration as similar as possible to working with UKB data, I will first upload a version of the GenoPred test data into a DNAnexus project. While it would be simpler to download the test data directly into the instance, this approach will better mimic the set up when working with UKB data.
+
+```{bash}
+# Step 1: Download the GenoPred test data from Zenodo
+wget -O test_data.tar.gz https://zenodo.org/records/10640650/files/test_data.tar.gz?download=1
+
+# Step 2: Decompress the downloaded file
+tar -xf test_data.tar.gz
+
+# Step 3: Extract only the necessary files for the demonstration
+mkdir genopred_test_data
+mv test_data/target/imputed_sample_plink2/example.chr22* genopred_test_data/
+mv test_data/reference/gwas_sumstats/BODY04.gz genopred_test_data/
+
+# Step 4: Load the Conda environment with dxpy installed
+# The Conda environment file for installing dxpy is available in the GenoPred repository
+# Path: GenoPred/pipeline/misc/dnanexus/dxpy_env.yml
+conda activate dxpy_env
+
+# Step 5: Log in to DNAnexus using an API token, if not already logged in
+# API tokens can be created on the DNAnexus website (https://platform.dnanexus.com/)
+dx login --token <token>
+
+# Step 6: Select the desired DNAnexus project
+dx select genopred_demo
+
+# Step 7: Upload the prepared data to DNAnexus
+dx upload -r genopred_test_data
+
+# Step 8: Clean up temporary files to save space
+rm -r test_data test_data.tar.gz genopred_test_data
+```
+
+***
+
+## Step 1. Install Singularity and Download the GenoPred Container
+
+Once your RStudio session has started, install Singularity via the terminal. Follow the commands below to set up Singularity, along with other essential tools:
+
+```{bash}
+# Configure keyboard layout (optional step to avoid prompts)
+echo 'keyboard-configuration keyboard-configuration/layout select us' | sudo debconf-set-selections
+echo 'keyboard-configuration keyboard-configuration/variant select English (US)' | sudo debconf-set-selections
+
+# Install required dependencies (e.g., tmux, build tools, and libraries for Singularity)
+sudo DEBIAN_FRONTEND=noninteractive apt update && \
+sudo DEBIAN_FRONTEND=noninteractive apt install -y build-essential libseccomp-dev pkg-config squashfs-tools cryptsetup golang tmux
+
+# Set the Singularity version to install
+export VERSION=3.11.0
+
+# Download and extract the Singularity source code
+wget https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-ce-${VERSION}.tar.gz
+tar -xvzf singularity-ce-${VERSION}.tar.gz
+cd singularity-ce-${VERSION}
+
+# Configure and build Singularity without SUID
+./mconfig --without-suid
+make -C builddir
+
+# Install Singularity system-wide
+sudo make -C builddir install
+
+# Return to the home directory
+cd ~
+```
+
+After installing Singularity, download the GenoPred container using the following command:
+
+```{bash}
+# Pull the GenoPred container
+singularity pull library://opain/genopred/genopred_pipeline:latest
+```
+
+<div class="note-box">
+
+**Note:** To make your workflow more efficient and reproducible, consider saving the downloaded container file in your project folder. This allows you to re-use the container in future analyses without needing to download it again.
+
+</div>
+
+***
+
+## Step 2. Import input data
+
+In a DNAnexus project, data is automatically mounted within the RStudio session and can be accessed from the /mnt/project directory. However, if the data you need is not located in the project folder, you will need to import it manually using the dx download command.
+
+For this demonstration, we are working with the small test dataset, so I will show how to import it. When working with larger datasets, such as UKB genetic data, it is often more efficient to use the mounted version of the dispensed data. This approach saves time and disk space by avoiding the need to download and store large files within the RStudio instance.
+
+```{bash}
+# View the files in your current project folder
+dx ls
+
+# More instructions on using the dx commands can be found here:
+# https://documentation.dnanexus.com/getting-started/cli-quickstart
+
+# Import the test data into the instance
+dx download -r genopred_test_data
+```
+
+***
+
+## Step 3. Set up pipeline configuration
+
+Now, we will create the configuration files required to run the GenoPred pipeline. Note that the `outdir` and `resdir` must be set to directories located outside the container to ensure proper access and storage.
+
+```{r}
+
+# Create directories for configuration and output files
+dir.create('/home/rstudio-server/genopred/config', recursive = TRUE)
+dir.create('/home/rstudio-server/genopred/output', recursive = TRUE)
+
+# Create gwas_list configuration
+gwas_list <- data.frame(
+  name = 'BODY04', 
+  path = '/home/rstudio-server/genopred_test_data/BODY04.gz',
+  population = 'EUR',
+  n = NA,
+  sampling = NA,
+  prevalence = NA,
+  mean = 0,
+  sd = 1,
+  label = '"Body Mass Index"'
+)
+
+write.table(
+  gwas_list, 
+  '/home/rstudio-server/genopred/config/gwas_list.txt', 
+  col.names = TRUE, 
+  row.names = FALSE, 
+  quote = FALSE, 
+  sep = ' '
+)
+
+# Create target_list configuration
+target_list <- data.frame(
+  name = 'example_plink1',
+  path = '/home/rstudio-server/genopred_test_data/example',
+  type = 'plink2',
+  indiv_report = FALSE
+)
+
+write.table(
+  target_list, 
+  '/home/rstudio-server/genopred/config/target_list.txt', 
+  col.names = TRUE, 
+  row.names = FALSE, 
+  quote = FALSE, 
+  sep = ' '
+)
+
+# Create main configuration file
+conf <- c(
+  'outdir: /home/rstudio-server/genopred/output',
+  'resdir: /home/rstudio-server/genopred/resources',
+  'config_file: /home/rstudio-server/genopred/config/config.yaml',
+  'gwas_list: /home/rstudio-server/genopred/config/gwas_list.txt',
+  'target_list: /home/rstudio-server/genopred/config/target_list.txt',
+  "pgs_methods: ['ptclump']",
+  'testing: chr22'
+)
+
+writeLines(
+  conf, 
+  '/home/rstudio-server/genopred/config/config.yaml'
+)
+
+```
+
+***
+
+## Step 4. Run the pipeline in the container
+
+Now we can run the GenoPred pipeline. This can be done either interactively within the container or by executing the desired commands directly. Running the pipeline interactively is often more convenient for performing a dry run before launching the full analysis.
+
+***
+
+### Start an Interactive Session in the Container
+
+To begin, start an interactive session within the Singularity container. Make sure to mount the home directory within the RStudio session to store the outputs:
+
+```{bash}
+singularity shell \
+  --bind /home/rstudio-server:/home/rstudio-server \
+  --writable-tmpfs \
+  /home/rstudio-server/genopred_pipeline_latest.sif
+```
+
+***
+
+### Run GenoPred Inside the Container
+
+Once inside the container, you can use the GenoPred pipeline as usual:
+
+```{bash}
+# Activate the GenoPred Environment:
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+# Navigate to the Pipeline Folder:
+cd /tools/GenoPred/pipeline
+
+# Perform a Dry Run: 
+# A dry run checks the pipeline's steps without executing them, helping you identify any missing dependencies or issues:
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/config.yaml output_all
+
+# Run the Pipeline: 
+# Once satisfied with the dry run, execute the pipeline:
+snakemake -j1 --use-conda --configfile=/home/rstudio-server/genopred/config/config.yaml output_all
+```
+
+After the analysis is complete, you can leave the container by typing:
+
+```{bash}
+exit
+```
+
+***
+
+## Step 5. Export ouputs to the project folder
+
+To avoid losing the outputs of your analysis when the RStudio session is terminated, you need to export the results to your DNAnexus project folder. Both the resdir (resources) and outdir (outputs) should be saved for future analyses.
+
+For simplicity and efficiency, we will compress the outputs and resources into a single tar file and then upload it to the DNAnexus project. If you plan to reuse the resources (e.g., for different pipeline configurations), you may choose to store them in separate tar files.
+
+```{bash}
+# Compress the GenoPred working directory
+cd /home/rstudio-server
+tar -cvf test_run_genopred.tar.gz genopred
+
+# Upload the GenoPred container
+dx upload genopred_pipeline_latest.sif
+
+# Upload the tar file containing pipeline resources and outputs
+dx upload test_run_genopred.tar.gz
+```
+
+Once the files are uploaded, you can safely terminate the RStudio session. Ensure the session is fully terminated by checking the Monitor tab in your DNAnexus project folder.
+
+***
+
+# Extending your analysis in the future
+
+If you want to extend your analysis without rerunning steps that have already completed, you can start a new RStudio session, import the outputs from a previous run, and resume the pipeline from within the container. Note that you will also need to import the input data used in the previous analysis.
+
+```{bash}
+# Download the Outputs from the Previous Run:
+dx download test_run_genopred.tar.gz
+tar -xvf test_run_genopred.tar.gz
+
+# Download the Singularity Container:
+dx download genopred_pipeline_latest.sif
+
+# Download the Input Data:
+dx download -r genopred_test_data
+```
+
+When using dx download for files that are not part of a tar archive, the original timestamps are lost. This may confuse GenoPred, as it will interpret the files as being updated. To fix this, reset the timestamps of the input files:
+
+```{bash}
+find /home/rstudio-server/genopred_test_data/ -type f -exec touch -t 200001010101.01 {} +
+```
+
+If the input data is accessed via the automatic mount in /mnt/project, this step is unnecessary, as the timestamps are preserved. This another advantage of mounting the input data.
+
+```{bash}
+# Start an Interactive Session in the Container:
+singularity shell \
+  --bind /home/rstudio-server:/home/rstudio-server \
+  --writable-tmpfs \
+  /home/rstudio-server/genopred_pipeline_latest.sif
+
+# Activate the GenoPred Environment:
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+# Navigate to the Pipeline Folder:
+cd /tools/GenoPred/pipeline
+
+### Check the Pipeline State: Run a dry run to verify the current state of the pipeline:
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/config.yaml output_all
+```
+
+The pipeline will indicate that there is nothing to be done if the configuration has not changed and all outputs are up to date.
+
+If an input file is updated (e.g., by changing its timestamp), the pipeline will automatically rerun only the necessary steps:
+
+```{bash}
+touch /home/rstudio-server/genopred_test_data/BODY04.gz
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/config.yaml output_all
+```
+
+This dry run will show which steps need to be re-executed due to the update.
+
+***
+
+# Troubleshooting
+
+Please post questions as an issue on the GenoPred GitHub repo [here](https://github.com/opain/GenoPred/issues).
diff --git a/docs/running_on_dnanexus.html b/docs/running_on_dnanexus.html
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+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Pipeline
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li>
+      <a href="pipeline_overview.html">Overview</a>
+    </li>
+    <li>
+      <a href="pipeline_readme.html">Instructions</a>
+    </li>
+    <li>
+      <a href="pipeline_technical.html">Technical documentation</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Research
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li>
+      <a href="research_index.html">Overview</a>
+    </li>
+    <li>
+      <a href="comparison_of_methods_summary.html">Polygenic Scoring Methods Comparison</a>
+    </li>
+    <li>
+      <a href="Functionally_informed_prediction.html">Quantifying Polygenic Signal Mediated by Altered Gene Expression</a>
+    </li>
+    <li>
+      <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
+    </li>
+  </ul>
+</li>
+<li>
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+</li>
+<li>
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+    <span class="fa fa-github fa-lg"></span>
+     
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+<div id="header">
+
+
+
+<h1 class="title toc-ignore">Running GenoPred on DNAnexus</h1>
+
+</div>
+
+
+<hr />
+<div id="introduction" class="section level1">
+<h1>Introduction</h1>
+<p>The UK Biobank (UKB) has recently updated its data access policy,
+requiring researchers to access and analyze UKB data exclusively via the
+UKB Research Analysis Platform (UKB-RAP), hosted by DNAnexus. This
+represents a significant shift in workflows, moving from institutional
+servers to a cloud-computing environment. DNAnexus and similar
+cloud-computing systems are likely to become standard for future
+datasets, so this guide aims to provide instructions on running GenoPred
+on DNAnexus, with a particular focus on analyzing UKB data. The UKB
+dataset presents unique challenges due to its size, making efficient
+analysis essential.</p>
+<p>Cloud computing involves requesting access to an instance (or virtual
+machine) with a specified amount of resources, such as disk space, RAM,
+and the number of CPU cores. Once access is granted, users must create
+the required software and data environment within the instance—a step
+that is often unfamiliar to many. After completing the desired analyses,
+users must export any outputs they wish to retain, as any data or
+software left on the instance will be deleted once it is terminated.</p>
+<p>This process of transferring software and data into and out of the
+instance is a significant departure from the experience of working on
+personal computers or institutional servers, where you can resume work
+seamlessly each time you log in.</p>
+<hr />
+</div>
+<div id="rstudio-vs.-cloud-workstation" class="section level1">
+<h1>RStudio vs. Cloud Workstation</h1>
+<p>Currently, the easiest way to run GenoPred on DNAnexus is
+interactively, using either RStudio or a Cloud Workstation with the
+GenoPred software container. Each approach offers unique advantages.</p>
+<p>RStudio mounts files directly from a DNAnexus project, eliminating
+the need to import data. This feature saves time and disk space,
+particularly with large datasets like UKB genetic data.</p>
+<p>Unlike RStudio, the cloud workstation requires input data to be
+manually imported using dx download, which can be time-consuming and
+requires additional storage space. While dxfuse allows project folders
+to be mounted (even across projects), it is prone to instability during
+long analyses, such as when using UKB.</p>
+<p>Another distinction is that cloud workstations can be connected to
+via VScode, which some people may prefer to Rstudio.</p>
+<p>Given the advantage of stable project folder mounting in RStudio, I
+will demonstrate the workflow in that context. For the demo, minimal
+resources are required, so I will request an
+<code>mem1_ssd1_v2_x2</code> instance type. A similar workflow can also
+be applied in the Cloud Workstation, substituting the Singularity
+container with the GenoPred Docker container.</p>
+<hr />
+</div>
+<div id="demonstration-using-rstudio" class="section level1">
+<h1>Demonstration using Rstudio</h1>
+<hr />
+<div id="step-0-prepare-test-data" class="section level2">
+<h2>Step 0: Prepare test data</h2>
+<p>To make the demonstration as similar as possible to working with UKB
+data, I will first upload a version of the GenoPred test data into a
+DNAnexus project. While it would be simpler to download the test data
+directly into the instance, this approach will better mimic the set up
+when working with UKB data.</p>
+<pre class="bash"><code># Step 1: Download the GenoPred test data from Zenodo
+wget -O test_data.tar.gz https://zenodo.org/records/10640650/files/test_data.tar.gz?download=1
+
+# Step 2: Decompress the downloaded file
+tar -xf test_data.tar.gz
+
+# Step 3: Extract only the necessary files for the demonstration
+mkdir genopred_test_data
+mv test_data/target/imputed_sample_plink2/example.chr22* genopred_test_data/
+mv test_data/reference/gwas_sumstats/BODY04.gz genopred_test_data/
+
+# Step 4: Load the Conda environment with dxpy installed
+# The Conda environment file for installing dxpy is available in the GenoPred repository
+# Path: GenoPred/pipeline/misc/dnanexus/dxpy_env.yml
+conda activate dxpy_env
+
+# Step 5: Log in to DNAnexus using an API token, if not already logged in
+# API tokens can be created on the DNAnexus website (https://platform.dnanexus.com/)
+dx login --token &lt;token&gt;
+
+# Step 6: Select the desired DNAnexus project
+dx select genopred_demo
+
+# Step 7: Upload the prepared data to DNAnexus
+dx upload -r genopred_test_data
+
+# Step 8: Clean up temporary files to save space
+rm -r test_data test_data.tar.gz genopred_test_data</code></pre>
+<hr />
+</div>
+<div
+id="step-1.-install-singularity-and-download-the-genopred-container"
+class="section level2">
+<h2>Step 1. Install Singularity and Download the GenoPred Container</h2>
+<p>Once your RStudio session has started, install Singularity via the
+terminal. Follow the commands below to set up Singularity, along with
+other essential tools:</p>
+<pre class="bash"><code># Configure keyboard layout (optional step to avoid prompts)
+echo &#39;keyboard-configuration keyboard-configuration/layout select us&#39; | sudo debconf-set-selections
+echo &#39;keyboard-configuration keyboard-configuration/variant select English (US)&#39; | sudo debconf-set-selections
+
+# Install required dependencies (e.g., tmux, build tools, and libraries for Singularity)
+sudo DEBIAN_FRONTEND=noninteractive apt update &amp;&amp; \
+sudo DEBIAN_FRONTEND=noninteractive apt install -y build-essential libseccomp-dev pkg-config squashfs-tools cryptsetup golang tmux
+
+# Set the Singularity version to install
+export VERSION=3.11.0
+
+# Download and extract the Singularity source code
+wget https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-ce-${VERSION}.tar.gz
+tar -xvzf singularity-ce-${VERSION}.tar.gz
+cd singularity-ce-${VERSION}
+
+# Configure and build Singularity without SUID
+./mconfig --without-suid
+make -C builddir
+
+# Install Singularity system-wide
+sudo make -C builddir install
+
+# Return to the home directory
+cd ~</code></pre>
+<p>After installing Singularity, download the GenoPred container using
+the following command:</p>
+<pre class="bash"><code># Pull the GenoPred container
+singularity pull library://opain/genopred/genopred_pipeline:latest</code></pre>
+<div class="note-box">
+<p><strong>Note:</strong> To make your workflow more efficient and
+reproducible, consider saving the downloaded container file in your
+project folder. This allows you to re-use the container in future
+analyses without needing to download it again.</p>
+</div>
+<hr />
+</div>
+<div id="step-2.-import-input-data" class="section level2">
+<h2>Step 2. Import input data</h2>
+<p>In a DNAnexus project, data is automatically mounted within the
+RStudio session and can be accessed from the /mnt/project directory.
+However, if the data you need is not located in the project folder, you
+will need to import it manually using the dx download command.</p>
+<p>For this demonstration, we are working with the small test dataset,
+so I will show how to import it. When working with larger datasets, such
+as UKB genetic data, it is often more efficient to use the mounted
+version of the dispensed data. This approach saves time and disk space
+by avoiding the need to download and store large files within the
+RStudio instance.</p>
+<pre class="bash"><code># View the files in your current project folder
+dx ls
+
+# More instructions on using the dx commands can be found here:
+# https://documentation.dnanexus.com/getting-started/cli-quickstart
+
+# Import the test data into the instance
+dx download -r genopred_test_data</code></pre>
+<hr />
+</div>
+<div id="step-3.-set-up-pipeline-configuration" class="section level2">
+<h2>Step 3. Set up pipeline configuration</h2>
+<p>Now, we will create the configuration files required to run the
+GenoPred pipeline. Note that the <code>outdir</code> and
+<code>resdir</code> must be set to directories located outside the
+container to ensure proper access and storage.</p>
+<pre class="r"><code># Create directories for configuration and output files
+dir.create(&#39;/home/rstudio-server/genopred/config&#39;, recursive = TRUE)
+dir.create(&#39;/home/rstudio-server/genopred/output&#39;, recursive = TRUE)
+
+# Create gwas_list configuration
+gwas_list &lt;- data.frame(
+  name = &#39;BODY04&#39;, 
+  path = &#39;/home/rstudio-server/genopred_test_data/BODY04.gz&#39;,
+  population = &#39;EUR&#39;,
+  n = NA,
+  sampling = NA,
+  prevalence = NA,
+  mean = 0,
+  sd = 1,
+  label = &#39;&quot;Body Mass Index&quot;&#39;
+)
+
+write.table(
+  gwas_list, 
+  &#39;/home/rstudio-server/genopred/config/gwas_list.txt&#39;, 
+  col.names = TRUE, 
+  row.names = FALSE, 
+  quote = FALSE, 
+  sep = &#39; &#39;
+)
+
+# Create target_list configuration
+target_list &lt;- data.frame(
+  name = &#39;example_plink1&#39;,
+  path = &#39;/home/rstudio-server/genopred_test_data/example&#39;,
+  type = &#39;plink2&#39;,
+  indiv_report = FALSE
+)
+
+write.table(
+  target_list, 
+  &#39;/home/rstudio-server/genopred/config/target_list.txt&#39;, 
+  col.names = TRUE, 
+  row.names = FALSE, 
+  quote = FALSE, 
+  sep = &#39; &#39;
+)
+
+# Create main configuration file
+conf &lt;- c(
+  &#39;outdir: /home/rstudio-server/genopred/output&#39;,
+  &#39;resdir: /home/rstudio-server/genopred/resources&#39;,
+  &#39;config_file: /home/rstudio-server/genopred/config/config.yaml&#39;,
+  &#39;gwas_list: /home/rstudio-server/genopred/config/gwas_list.txt&#39;,
+  &#39;target_list: /home/rstudio-server/genopred/config/target_list.txt&#39;,
+  &quot;pgs_methods: [&#39;ptclump&#39;]&quot;,
+  &#39;testing: chr22&#39;
+)
+
+writeLines(
+  conf, 
+  &#39;/home/rstudio-server/genopred/config/config.yaml&#39;
+)</code></pre>
+<hr />
+</div>
+<div id="step-4.-run-the-pipeline-in-the-container"
+class="section level2">
+<h2>Step 4. Run the pipeline in the container</h2>
+<p>Now we can run the GenoPred pipeline. This can be done either
+interactively within the container or by executing the desired commands
+directly. Running the pipeline interactively is often more convenient
+for performing a dry run before launching the full analysis.</p>
+<hr />
+<div id="start-an-interactive-session-in-the-container"
+class="section level3">
+<h3>Start an Interactive Session in the Container</h3>
+<p>To begin, start an interactive session within the Singularity
+container. Make sure to mount the home directory within the RStudio
+session to store the outputs:</p>
+<pre class="bash"><code>singularity shell \
+  --bind /home/rstudio-server:/home/rstudio-server \
+  --writable-tmpfs \
+  /home/rstudio-server/genopred_pipeline_latest.sif</code></pre>
+<hr />
+</div>
+<div id="run-genopred-inside-the-container" class="section level3">
+<h3>Run GenoPred Inside the Container</h3>
+<p>Once inside the container, you can use the GenoPred pipeline as
+usual:</p>
+<pre class="bash"><code># Activate the GenoPred Environment:
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+# Navigate to the Pipeline Folder:
+cd /tools/GenoPred/pipeline
+
+# Perform a Dry Run: 
+# A dry run checks the pipeline&#39;s steps without executing them, helping you identify any missing dependencies or issues:
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/config.yaml output_all
+
+# Run the Pipeline: 
+# Once satisfied with the dry run, execute the pipeline:
+snakemake -j1 --use-conda --configfile=/home/rstudio-server/genopred/config/config.yaml output_all</code></pre>
+<p>After the analysis is complete, you can leave the container by
+typing:</p>
+<pre class="bash"><code>exit</code></pre>
+<hr />
+</div>
+</div>
+<div id="step-5.-export-ouputs-to-the-project-folder"
+class="section level2">
+<h2>Step 5. Export ouputs to the project folder</h2>
+<p>To avoid losing the outputs of your analysis when the RStudio session
+is terminated, you need to export the results to your DNAnexus project
+folder. Both the resdir (resources) and outdir (outputs) should be saved
+for future analyses.</p>
+<p>For simplicity and efficiency, we will compress the outputs and
+resources into a single tar file and then upload it to the DNAnexus
+project. If you plan to reuse the resources (e.g., for different
+pipeline configurations), you may choose to store them in separate tar
+files.</p>
+<pre class="bash"><code># Compress the GenoPred working directory
+cd /home/rstudio-server
+tar -cvf test_run_genopred.tar.gz genopred
+
+# Upload the GenoPred container
+dx upload genopred_pipeline_latest.sif
+
+# Upload the tar file containing pipeline resources and outputs
+dx upload test_run_genopred.tar.gz</code></pre>
+<p>Once the files are uploaded, you can safely terminate the RStudio
+session. Ensure the session is fully terminated by checking the Monitor
+tab in your DNAnexus project folder.</p>
+<hr />
+</div>
+</div>
+<div id="extending-your-analysis-in-the-future" class="section level1">
+<h1>Extending your analysis in the future</h1>
+<p>If you want to extend your analysis without rerunning steps that have
+already completed, you can start a new RStudio session, import the
+outputs from a previous run, and resume the pipeline from within the
+container. Note that you will also need to import the input data used in
+the previous analysis.</p>
+<pre class="bash"><code># Download the Outputs from the Previous Run:
+dx download test_run_genopred.tar.gz
+tar -xvf test_run_genopred.tar.gz
+
+# Download the Singularity Container:
+dx download genopred_pipeline_latest.sif
+
+# Download the Input Data:
+dx download -r genopred_test_data</code></pre>
+<p>When using dx download for files that are not part of a tar archive,
+the original timestamps are lost. This may confuse GenoPred, as it will
+interpret the files as being updated. To fix this, reset the timestamps
+of the input files:</p>
+<pre class="bash"><code>find /home/rstudio-server/genopred_test_data/ -type f -exec touch -t 200001010101.01 {} +</code></pre>
+<p>If the input data is accessed via the automatic mount in
+/mnt/project, this step is unnecessary, as the timestamps are preserved.
+This another advantage of mounting the input data.</p>
+<pre class="bash"><code># Start an Interactive Session in the Container:
+singularity shell \
+  --bind /home/rstudio-server:/home/rstudio-server \
+  --writable-tmpfs \
+  /home/rstudio-server/genopred_pipeline_latest.sif
+
+# Activate the GenoPred Environment:
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+# Navigate to the Pipeline Folder:
+cd /tools/GenoPred/pipeline
+
+### Check the Pipeline State: Run a dry run to verify the current state of the pipeline:
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/config.yaml output_all</code></pre>
+<p>The pipeline will indicate that there is nothing to be done if the
+configuration has not changed and all outputs are up to date.</p>
+<p>If an input file is updated (e.g., by changing its timestamp), the
+pipeline will automatically rerun only the necessary steps:</p>
+<pre class="bash"><code>touch /home/rstudio-server/genopred_test_data/BODY04.gz
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/config.yaml output_all</code></pre>
+<p>This dry run will show which steps need to be re-executed due to the
+update.</p>
+<hr />
+</div>
+<div id="troubleshooting" class="section level1">
+<h1>Troubleshooting</h1>
+<p>Please post questions as an issue on the GenoPred GitHub repo <a
+href="https://github.com/opain/GenoPred/issues">here</a>.</p>
+</div>
+
+<!-- footer.html -->
+<hr/>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 500px;">
+<img src="Images/logo/sponsors.png">
+</div>
+</div>
+
+
+</div>
+</div>
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
+
+
+</script>
+
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
+
+<!-- code folding -->
+
+<script>
+$(document).ready(function ()  {
+
+    // temporarily add toc-ignore selector to headers for the consistency with Pandoc
+    $('.unlisted.unnumbered').addClass('toc-ignore')
+
+    // move toc-ignore selectors from section div to header
+    $('div.section.toc-ignore')
+        .removeClass('toc-ignore')
+        .children('h1,h2,h3,h4,h5').addClass('toc-ignore');
+
+    // establish options
+    var options = {
+      selectors: "h1,h2",
+      theme: "bootstrap3",
+      context: '.toc-content',
+      hashGenerator: function (text) {
+        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
+      },
+      ignoreSelector: ".toc-ignore",
+      scrollTo: 0
+    };
+    options.showAndHide = true;
+    options.smoothScroll = true;
+
+    // tocify
+    var toc = $("#TOC").tocify(options).data("toc-tocify");
+});
+</script>
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/pipeline/misc/dnanexus/dxpy_env.yml b/pipeline/misc/dnanexus/dxpy_env.yml
new file mode 100644
index 00000000..090e1981
--- /dev/null
+++ b/pipeline/misc/dnanexus/dxpy_env.yml
@@ -0,0 +1,9 @@
+name: dxpy_env
+channels:
+  - defaults
+  - conda-forge
+dependencies:
+  - python=3.9
+  - pip
+  - pip:
+      - dxpy

From 14c2141b5b3967e1dd0832af57ff5ae5a29e01ca Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 15 Jan 2025 14:59:33 +0000
Subject: [PATCH 079/153] Added DNAnexus demo for UKB

---
 docs/more_index.Rmd           |   1 +
 docs/more_index.html          |   2 +
 docs/pipeline_readme.Rmd      |   7 +
 docs/pipeline_readme.html     |   8 +
 docs/running_on_dnanexus.Rmd  | 251 ++++++++++++++++++++++++++++++-
 docs/running_on_dnanexus.html | 268 +++++++++++++++++++++++++++++++++-
 6 files changed, 529 insertions(+), 8 deletions(-)

diff --git a/docs/more_index.Rmd b/docs/more_index.Rmd
index 572a1aba..65fe974b 100644
--- a/docs/more_index.Rmd
+++ b/docs/more_index.Rmd
@@ -18,6 +18,7 @@ output:
 - Instructions - <a href="pipeline_readme.html" class="inline_button">Link</a>
 - Technical documentation - <a href="pipeline_technical.html" class="inline_button">Link</a>
 - Running in an offline environment - <a href="running_offline.html" class="inline_button">Link</a>
+- Running on DNAnexus/UKB-RAP - <a href="running_on_dnanexus.html" class="inline_button">Link</a>
 - Demonstration using 23anMe data - <a href="pipeline_23andMe.html" class="inline_button">Link</a>
 - Computational time/memory benchmark - <a href="pipeline_benchmark.html" class="inline_button">Link</a>
 - Benchmark in OpenSNP dataset - <a href="opensnp_benchmark.html" class="inline_button">Link</a>
diff --git a/docs/more_index.html b/docs/more_index.html
index 6e873868..1d2d6b8c 100644
--- a/docs/more_index.html
+++ b/docs/more_index.html
@@ -363,6 +363,8 @@ <h1>Pipeline</h1>
 <a href="pipeline_technical.html" class="inline_button">Link</a></li>
 <li>Running in an offline environment -
 <a href="running_offline.html" class="inline_button">Link</a></li>
+<li>Running on DNAnexus/UKB-RAP -
+<a href="running_on_dnanexus.html" class="inline_button">Link</a></li>
 <li>Demonstration using 23anMe data -
 <a href="pipeline_23andMe.html" class="inline_button">Link</a></li>
 <li>Computational time/memory benchmark -
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index d068bd29..72c8ca52 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -27,6 +27,7 @@ This document will provides instructions for the GenoPred pipeline. It covers th
 -   [Run using test data](#run-using-test-data)
 -   [A little about Snakemake](#a-little-about-snakemake)
 -   [Running on an HPC](#running-on-an-hpc)
+-   [Running on DNAnexus/UKB-RAP](#running-on-dnanexusukb-rap)
 -   [Requesting outputs](#requesting-outputs)
 -   [Computational resources](#computational-resources)
 -   [Additional parameters](#additional-parameters)
@@ -813,6 +814,12 @@ Before launching GenoPred, ensure your environment is clean by not loading any u
 
 ***
 
+# Running on DNAnexus/UKB-RAP
+
+See [here](running_on_dnanexus.html) for specific advice when running GenoPred on DNAnexus and the UKB-RAP.
+
+***
+
 # Requesting outputs
 
 The GenoPred pipeline has many potential outputs. Here is a detailed schematic diagram illustrating the inputs, outputs and processes of the GenoPred pipeline.
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index 7d9b7fc0..8160d43b 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -438,6 +438,8 @@ <h1 class="title toc-ignore">GenoPred Pipeline - Instructions</h1>
 <li><a href="#a-little-about-snakemake">A little about
 Snakemake</a></li>
 <li><a href="#running-on-an-hpc">Running on an HPC</a></li>
+<li><a href="#running-on-dnanexusukb-rap">Running on
+DNAnexus/UKB-RAP</a></li>
 <li><a href="#requesting-outputs">Requesting outputs</a></li>
 <li><a href="#computational-resources">Computational resources</a></li>
 <li><a href="#additional-parameters">Additional parameters</a></li>
@@ -1497,6 +1499,12 @@ <h2>Managing Module Conflicts</h2>
 <hr />
 </div>
 </div>
+<div id="running-on-dnanexusukb-rap" class="section level1">
+<h1>Running on DNAnexus/UKB-RAP</h1>
+<p>See <a href="running_on_dnanexus.html">here</a> for specific advice
+when running GenoPred on DNAnexus and the UKB-RAP.</p>
+<hr />
+</div>
 <div id="requesting-outputs" class="section level1">
 <h1>Requesting outputs</h1>
 <p>The GenoPred pipeline has many potential outputs. Here is a detailed
diff --git a/docs/running_on_dnanexus.Rmd b/docs/running_on_dnanexus.Rmd
index 6319558c..1ea1d275 100644
--- a/docs/running_on_dnanexus.Rmd
+++ b/docs/running_on_dnanexus.Rmd
@@ -236,6 +236,22 @@ Now we can run the GenoPred pipeline. This can be done either interactively with
 
 ### Start an Interactive Session in the Container
 
+To ensure that your analysis persists even when the RStudio server tab is closed, it is recommended to start the container within a tmux session. This will allow you to detach and reattach to the session as needed.
+
+Start a tmux session within the terminal by running:
+
+```{bash}
+tmux
+```
+
+This will take you into a `tmux` session. You can 'detach' from the `tmux` session by pressing `Ctrl+b d`, and reattach to the session in the future by typing:
+
+```{bash}
+tmux attach
+```
+
+Further instructions on using `tmux` can be found [here](https://www.redhat.com/en/blog/introduction-tmux-linux).
+
 To begin, start an interactive session within the Singularity container. Make sure to mount the home directory within the RStudio session to store the outputs:
 
 ```{bash}
@@ -268,6 +284,10 @@ snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/confi
 snakemake -j1 --use-conda --configfile=/home/rstudio-server/genopred/config/config.yaml output_all
 ```
 
+While this analysis is running, you can detach from the `tmux` session, close the RStudio tab, and close your browser. When you reopen the RStudio app, you may see that your session appears suspended. Do not worry—your analysis will continue running in the background.
+
+By using `tmux`, your analysis will continue to run even if the terminal session or RStudio server tab is closed.
+
 After the analysis is complete, you can leave the container by typing:
 
 ```{bash}
@@ -285,13 +305,13 @@ For simplicity and efficiency, we will compress the outputs and resources into a
 ```{bash}
 # Compress the GenoPred working directory
 cd /home/rstudio-server
-tar -cvf test_run_genopred.tar.gz genopred
+tar -cvf test_run_genopred.tar genopred
 
 # Upload the GenoPred container
 dx upload genopred_pipeline_latest.sif
 
 # Upload the tar file containing pipeline resources and outputs
-dx upload test_run_genopred.tar.gz
+dx upload test_run_genopred.tar
 ```
 
 Once the files are uploaded, you can safely terminate the RStudio session. Ensure the session is fully terminated by checking the Monitor tab in your DNAnexus project folder.
@@ -304,8 +324,8 @@ If you want to extend your analysis without rerunning steps that have already co
 
 ```{bash}
 # Download the Outputs from the Previous Run:
-dx download test_run_genopred.tar.gz
-tar -xvf test_run_genopred.tar.gz
+dx download test_run_genopred.tar
+tar -xvf test_run_genopred.tar
 
 # Download the Singularity Container:
 dx download genopred_pipeline_latest.sif
@@ -353,6 +373,229 @@ This dry run will show which steps need to be re-executed due to the update.
 
 ***
 
+# Running GenoPred with UK Biobank
+
+The UKB imputed genetic data is provided without post-imputation QC, resulting in large files. The `format_target` step of GenoPred, which reformats the target genetic data, is time-intensive but reduces file size to ~86GB, making future analyses faster and cheaper. Storing this output for reuse is highly recommended. Additionally, selecting instances with appropriate resources for each pipeline stage ensures cost efficiency, as some steps utilize multiple cores while others do not.
+
+***
+
+## Key Adjustments for UKB Data
+
+- Mounted UKB Data: Avoid duplicating the large UKB data files by using a mounted version to save time and disk space.
+- File Naming with Symbolic Links: Update file names as required by GenoPred using symbolic links to avoid duplicating data.
+- High-Resource Instance for `format_target`: Use a `mem3_ssd2_v2_x8` instance, which supports 8 processes and provides sufficient RAM and disk space for processing the large UKB files efficiently. Other steps of the pipeline will have other resources requirements.
+
+***
+
+## Workflow
+
+- Run `format_target` on a High-Resource Instance: Use the `mem3_ssd2_v2_x8` instance to efficiently complete the `format_target` step. 
+- Export and Store Processed Data: Save the reformatted data for future use, avoiding repeated processing.
+- Terminate the High-Resource Instance: After completing `format_target`, terminate the instance to minimize costs.
+- Continue Analysis on Cost-Effective Instances: For downstream steps, switch to an instance with resources tailored to those stages.
+- This approach balances efficiency and cost when working with large-scale UKB genetic data in the GenoPred pipeline.
+
+***
+
+## Step 1. Install Singularity
+
+Once your RStudio session has started, install Singularity via the terminal. Follow the commands below to set up Singularity, along with other essential tools:
+
+```{bash}
+# Configure keyboard layout (optional step to avoid prompts)
+echo 'keyboard-configuration keyboard-configuration/layout select us' | sudo debconf-set-selections
+echo 'keyboard-configuration keyboard-configuration/variant select English (US)' | sudo debconf-set-selections
+
+# Install required dependencies (e.g., tmux, build tools, and libraries for Singularity)
+sudo DEBIAN_FRONTEND=noninteractive apt update && \
+sudo DEBIAN_FRONTEND=noninteractive apt install -y build-essential libseccomp-dev pkg-config squashfs-tools cryptsetup golang tmux
+
+# Set the Singularity version to install
+export VERSION=3.11.0
+
+# Download and extract the Singularity source code
+wget https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-ce-${VERSION}.tar.gz
+tar -xvzf singularity-ce-${VERSION}.tar.gz
+cd singularity-ce-${VERSION}
+
+# Configure and build Singularity without SUID
+./mconfig --without-suid
+make -C builddir
+
+# Install Singularity system-wide
+sudo make -C builddir install
+
+# Return to the home directory
+cd ~
+```
+
+***
+
+## Step 2: Prepare input data
+
+We will use a mounted version of the UKB genetic data to save time and disk space. To meet GenoPred's file name requirements without duplicating data, we will create symbolic links. Given the size of the UKB genetic data, we will also request an instance with sufficient resources.
+
+```{bash}
+# Create symlinks to the dispensed imputed genetic data
+mkdir -p /home/rstudio-server/ukb/ukb_symlinks
+
+# Link bgen and bgen.bgi files for all chromosomes
+for chr in $(seq 1 22); do
+  for file in $(echo bgen bgen.bgi); do
+    ln -s /mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype/ukb22828_c${chr}_b0_v3.${file} /home/rstudio-server/ukb/ukb_symlinks/ukb_imp.chr${chr}.${file}
+  done
+done
+
+# Link the sample file (same for all chromosomes)
+ln -s /mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype/ukb22828_c1_b0_v3.sample /home/rstudio-server/ukb/ukb_symlinks/ukb_imp.sample
+```
+
+***
+
+## Step 3. Set up pipeline configuration
+
+Next, create the configuration files required to run the GenoPred pipeline. Ensure that `outdir` and `resdir` are set to directories outside the container for proper access and storage.
+
+```{r}
+# Create directories for configuration and output files
+dir.create('/home/rstudio-server/genopred/config/ukb/basic', recursive = T)
+dir.create('/home/rstudio-server/genopred/output', recursive = T)
+
+# Create target list
+# We are specifying the symbolic links we made for the UKB data
+target_list <- data.frame(
+  name='ukb',
+  path='/home/rstudio-server/ukb/ukb_symlinks/ukb_imp',
+  type='bgen',
+  indiv_report=F
+)
+
+write.table(
+  target_list,
+  '/home/rstudio-server/genopred/config/ukb/basic/target_list.txt',
+  col.names = T,
+  row.names = F,
+  quote = F
+)
+
+# Create config file
+conf <- c(
+  'outdir: /home/rstudio-server/genopred/output',
+  'config_file: /home/rstudio-server/genopred/config/ukb/basic/config.yaml',
+  'resdir: /home/rstudio-server/genopred/resources',
+  'target_list: /home/rstudio-server/genopred/config/ukb/basic/target_list.txt'
+)
+
+write.table(
+  conf,
+  '/home/rstudio-server/genopred/config/ukb/basic/config.yaml',
+  col.names = F,
+  row.names = F,
+  quote = F
+)
+```
+
+***
+
+## Step 4. Run the pipeline in the container
+
+Now we can run the GenoPred pipeline. This can be done either interactively within the container or by executing the desired commands directly. Running the pipeline interactively is often more convenient for performing a dry run before launching the full analysis.
+
+***
+
+### Start an Interactive Session in the Container
+
+To ensure that your analysis persists even when the RStudio server tab is closed, it is recommended to start the container within a tmux session. This will allow you to detach and reattach to the session as needed.
+
+Start a tmux session within the terminal by running:
+
+```{bash}
+tmux
+```
+
+This will take you into a `tmux` session. You can 'detach' from the `tmux` session by pressing `Ctrl+b d`, and reattach to the session in the future by typing:
+
+```{bash}
+tmux attach
+```
+
+Further instructions on using `tmux` can be found [here](https://www.redhat.com/en/blog/introduction-tmux-linux).
+
+To begin, start an interactive session within the Singularity container. Make sure to mount the home directory within the RStudio session and the directory that the symbolic links point to, to store the pipeline outputs and access the input data within the container.
+
+```{bash}
+singularity shell \
+  --bind /home/rstudio-server:/home/rstudio-server \
+  --bind /mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype:/mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype \
+  --writable-tmpfs \
+  /mnt/project/genopred_pipeline_latest.sif
+```
+
+***
+
+### Run GenoPred Inside the Container
+
+Once inside the container, you can use the GenoPred pipeline as usual. The resources provided by this instance (`mem3_ssd2_v2_x8`) will not be required for all steps in the GenoPred workflow, so to be cost efficient, I am just carrying out the `format_target` step within this instance. I will then export the data, terminate this instance, continue my analysis using a new instance with appropriate resources for downstream steps.
+
+```{bash}
+# Activate the GenoPred Environment:
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+# Navigate to the Pipeline Folder:
+cd /tools/GenoPred/pipeline
+
+# Perform a Dry Run: 
+# A dry run checks the pipeline's steps without executing them, helping you identify any missing dependencies or issues:
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/basic/config.yaml format_target
+
+# Run the Pipeline: 
+# Once satisfied with the dry run, execute the pipeline:
+snakemake -j8 --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/basic/config.yaml format_target
+```
+
+<div class="note-box">
+
+**Note:** Since the instance has 8 cores available, I use the `-j8` parameter when running `snakemake` to ensure it utilizes all 8 cores efficiently.
+
+</div>
+
+After the analysis is complete, you can leave the container by typing:
+
+```{bash}
+exit
+```
+
+In total, this analysis took ~18 hours, costing ~£6.
+
+***
+
+## Step 5. Export ouputs to the project folder
+
+To avoid losing the outputs of your analysis when the RStudio session is terminated, you need to export the results to your DNAnexus project folder. Both the resdir (resources) and outdir (outputs) should be saved for future analyses.
+
+For simplicity and efficiency, we will compress the outputs and resources into a single tar file and then upload it to the DNAnexus project. If you plan to reuse the resources (e.g., for different pipeline configurations), you may choose to store them in separate tar files.
+
+```{bash}
+# Compress and upload the GenoPred working directory
+cd /home/rstudio-server
+tar -cvf ukb_genopred.tar genopred
+dx upload ukb_genopred.tar
+```
+
+I will also tar and upload the symlinks created for the UKB data. While these could be recreated, this approach conveniently preserves the timestamps, making it easier to resume the analysis seamlessly when extending it in the future.
+
+```{bash}
+# Compress and upload the ukb directory containing symlinks
+cd /home/rstudio-server
+tar -cvf ukb_symlinks.tar ukb
+dx upload ukb_symlinks.tar
+```
+
+Once the files are uploaded, you can safely terminate the RStudio session. Ensure the session is fully terminated by checking the Monitor tab in your DNAnexus project folder.
+
+***
+
 # Troubleshooting
 
 Please post questions as an issue on the GenoPred GitHub repo [here](https://github.com/opain/GenoPred/issues).
diff --git a/docs/running_on_dnanexus.html b/docs/running_on_dnanexus.html
index 26e1e004..9bb13f98 100644
--- a/docs/running_on_dnanexus.html
+++ b/docs/running_on_dnanexus.html
@@ -659,6 +659,18 @@ <h2>Step 4. Run the pipeline in the container</h2>
 <div id="start-an-interactive-session-in-the-container"
 class="section level3">
 <h3>Start an Interactive Session in the Container</h3>
+<p>To ensure that your analysis persists even when the RStudio server
+tab is closed, it is recommended to start the container within a tmux
+session. This will allow you to detach and reattach to the session as
+needed.</p>
+<p>Start a tmux session within the terminal by running:</p>
+<pre class="bash"><code>tmux</code></pre>
+<p>This will take you into a <code>tmux</code> session. You can ‘detach’
+from the <code>tmux</code> session by pressing <code>Ctrl+b d</code>,
+and reattach to the session in the future by typing:</p>
+<pre class="bash"><code>tmux attach</code></pre>
+<p>Further instructions on using <code>tmux</code> can be found <a
+href="https://www.redhat.com/en/blog/introduction-tmux-linux">here</a>.</p>
 <p>To begin, start an interactive session within the Singularity
 container. Make sure to mount the home directory within the RStudio
 session to store the outputs:</p>
@@ -686,6 +698,13 @@ <h3>Run GenoPred Inside the Container</h3>
 # Run the Pipeline: 
 # Once satisfied with the dry run, execute the pipeline:
 snakemake -j1 --use-conda --configfile=/home/rstudio-server/genopred/config/config.yaml output_all</code></pre>
+<p>While this analysis is running, you can detach from the
+<code>tmux</code> session, close the RStudio tab, and close your
+browser. When you reopen the RStudio app, you may see that your session
+appears suspended. Do not worry—your analysis will continue running in
+the background.</p>
+<p>By using <code>tmux</code>, your analysis will continue to run even
+if the terminal session or RStudio server tab is closed.</p>
 <p>After the analysis is complete, you can leave the container by
 typing:</p>
 <pre class="bash"><code>exit</code></pre>
@@ -706,13 +725,13 @@ <h2>Step 5. Export ouputs to the project folder</h2>
 files.</p>
 <pre class="bash"><code># Compress the GenoPred working directory
 cd /home/rstudio-server
-tar -cvf test_run_genopred.tar.gz genopred
+tar -cvf test_run_genopred.tar genopred
 
 # Upload the GenoPred container
 dx upload genopred_pipeline_latest.sif
 
 # Upload the tar file containing pipeline resources and outputs
-dx upload test_run_genopred.tar.gz</code></pre>
+dx upload test_run_genopred.tar</code></pre>
 <p>Once the files are uploaded, you can safely terminate the RStudio
 session. Ensure the session is fully terminated by checking the Monitor
 tab in your DNAnexus project folder.</p>
@@ -727,8 +746,8 @@ <h1>Extending your analysis in the future</h1>
 container. Note that you will also need to import the input data used in
 the previous analysis.</p>
 <pre class="bash"><code># Download the Outputs from the Previous Run:
-dx download test_run_genopred.tar.gz
-tar -xvf test_run_genopred.tar.gz
+dx download test_run_genopred.tar
+tar -xvf test_run_genopred.tar
 
 # Download the Singularity Container:
 dx download genopred_pipeline_latest.sif
@@ -768,6 +787,247 @@ <h1>Extending your analysis in the future</h1>
 update.</p>
 <hr />
 </div>
+<div id="running-genopred-with-uk-biobank" class="section level1">
+<h1>Running GenoPred with UK Biobank</h1>
+<p>The UKB imputed genetic data is provided without post-imputation QC,
+resulting in large files. The <code>format_target</code> step of
+GenoPred, which reformats the target genetic data, is time-intensive but
+reduces file size to ~86GB, making future analyses faster and cheaper.
+Storing this output for reuse is highly recommended. Additionally,
+selecting instances with appropriate resources for each pipeline stage
+ensures cost efficiency, as some steps utilize multiple cores while
+others do not.</p>
+<hr />
+<div id="key-adjustments-for-ukb-data" class="section level2">
+<h2>Key Adjustments for UKB Data</h2>
+<ul>
+<li>Mounted UKB Data: Avoid duplicating the large UKB data files by
+using a mounted version to save time and disk space.</li>
+<li>File Naming with Symbolic Links: Update file names as required by
+GenoPred using symbolic links to avoid duplicating data.</li>
+<li>High-Resource Instance for <code>format_target</code>: Use a
+<code>mem3_ssd2_v2_x8</code> instance, which supports 8 processes and
+provides sufficient RAM and disk space for processing the large UKB
+files efficiently. Other steps of the pipeline will have other resources
+requirements.</li>
+</ul>
+<hr />
+</div>
+<div id="workflow" class="section level2">
+<h2>Workflow</h2>
+<ul>
+<li>Run <code>format_target</code> on a High-Resource Instance: Use the
+<code>mem3_ssd2_v2_x8</code> instance to efficiently complete the
+<code>format_target</code> step.</li>
+<li>Export and Store Processed Data: Save the reformatted data for
+future use, avoiding repeated processing.</li>
+<li>Terminate the High-Resource Instance: After completing
+<code>format_target</code>, terminate the instance to minimize
+costs.</li>
+<li>Continue Analysis on Cost-Effective Instances: For downstream steps,
+switch to an instance with resources tailored to those stages.</li>
+<li>This approach balances efficiency and cost when working with
+large-scale UKB genetic data in the GenoPred pipeline.</li>
+</ul>
+<hr />
+</div>
+<div id="step-1.-install-singularity" class="section level2">
+<h2>Step 1. Install Singularity</h2>
+<p>Once your RStudio session has started, install Singularity via the
+terminal. Follow the commands below to set up Singularity, along with
+other essential tools:</p>
+<pre class="bash"><code># Configure keyboard layout (optional step to avoid prompts)
+echo &#39;keyboard-configuration keyboard-configuration/layout select us&#39; | sudo debconf-set-selections
+echo &#39;keyboard-configuration keyboard-configuration/variant select English (US)&#39; | sudo debconf-set-selections
+
+# Install required dependencies (e.g., tmux, build tools, and libraries for Singularity)
+sudo DEBIAN_FRONTEND=noninteractive apt update &amp;&amp; \
+sudo DEBIAN_FRONTEND=noninteractive apt install -y build-essential libseccomp-dev pkg-config squashfs-tools cryptsetup golang tmux
+
+# Set the Singularity version to install
+export VERSION=3.11.0
+
+# Download and extract the Singularity source code
+wget https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-ce-${VERSION}.tar.gz
+tar -xvzf singularity-ce-${VERSION}.tar.gz
+cd singularity-ce-${VERSION}
+
+# Configure and build Singularity without SUID
+./mconfig --without-suid
+make -C builddir
+
+# Install Singularity system-wide
+sudo make -C builddir install
+
+# Return to the home directory
+cd ~</code></pre>
+<hr />
+</div>
+<div id="step-2-prepare-input-data" class="section level2">
+<h2>Step 2: Prepare input data</h2>
+<p>We will use a mounted version of the UKB genetic data to save time
+and disk space. To meet GenoPred’s file name requirements without
+duplicating data, we will create symbolic links. Given the size of the
+UKB genetic data, we will also request an instance with sufficient
+resources.</p>
+<pre class="bash"><code># Create symlinks to the dispensed imputed genetic data
+mkdir -p /home/rstudio-server/ukb/ukb_symlinks
+
+# Link bgen and bgen.bgi files for all chromosomes
+for chr in $(seq 1 22); do
+  for file in $(echo bgen bgen.bgi); do
+    ln -s /mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype/ukb22828_c${chr}_b0_v3.${file} /home/rstudio-server/ukb/ukb_symlinks/ukb_imp.chr${chr}.${file}
+  done
+done
+
+# Link the sample file (same for all chromosomes)
+ln -s /mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype/ukb22828_c1_b0_v3.sample /home/rstudio-server/ukb/ukb_symlinks/ukb_imp.sample</code></pre>
+<hr />
+</div>
+<div id="step-3.-set-up-pipeline-configuration-1"
+class="section level2">
+<h2>Step 3. Set up pipeline configuration</h2>
+<p>Next, create the configuration files required to run the GenoPred
+pipeline. Ensure that <code>outdir</code> and <code>resdir</code> are
+set to directories outside the container for proper access and
+storage.</p>
+<pre class="r"><code># Create directories for configuration and output files
+dir.create(&#39;/home/rstudio-server/genopred/config/ukb/basic&#39;, recursive = T)
+dir.create(&#39;/home/rstudio-server/genopred/output&#39;, recursive = T)
+
+# Create target list
+# We are specifying the symbolic links we made for the UKB data
+target_list &lt;- data.frame(
+  name=&#39;ukb&#39;,
+  path=&#39;/home/rstudio-server/ukb/ukb_symlinks/ukb_imp&#39;,
+  type=&#39;bgen&#39;,
+  indiv_report=F
+)
+
+write.table(
+  target_list,
+  &#39;/home/rstudio-server/genopred/config/ukb/basic/target_list.txt&#39;,
+  col.names = T,
+  row.names = F,
+  quote = F
+)
+
+# Create config file
+conf &lt;- c(
+  &#39;outdir: /home/rstudio-server/genopred/output&#39;,
+  &#39;config_file: /home/rstudio-server/genopred/config/ukb/basic/config.yaml&#39;,
+  &#39;resdir: /home/rstudio-server/genopred/resources&#39;,
+  &#39;target_list: /home/rstudio-server/genopred/config/ukb/basic/target_list.txt&#39;
+)
+
+write.table(
+  conf,
+  &#39;/home/rstudio-server/genopred/config/ukb/basic/config.yaml&#39;,
+  col.names = F,
+  row.names = F,
+  quote = F
+)</code></pre>
+<hr />
+</div>
+<div id="step-4.-run-the-pipeline-in-the-container-1"
+class="section level2">
+<h2>Step 4. Run the pipeline in the container</h2>
+<p>Now we can run the GenoPred pipeline. This can be done either
+interactively within the container or by executing the desired commands
+directly. Running the pipeline interactively is often more convenient
+for performing a dry run before launching the full analysis.</p>
+<hr />
+<div id="start-an-interactive-session-in-the-container-1"
+class="section level3">
+<h3>Start an Interactive Session in the Container</h3>
+<p>To ensure that your analysis persists even when the RStudio server
+tab is closed, it is recommended to start the container within a tmux
+session. This will allow you to detach and reattach to the session as
+needed.</p>
+<p>Start a tmux session within the terminal by running:</p>
+<pre class="bash"><code>tmux</code></pre>
+<p>This will take you into a <code>tmux</code> session. You can ‘detach’
+from the <code>tmux</code> session by pressing <code>Ctrl+b d</code>,
+and reattach to the session in the future by typing:</p>
+<pre class="bash"><code>tmux attach</code></pre>
+<p>Further instructions on using <code>tmux</code> can be found <a
+href="https://www.redhat.com/en/blog/introduction-tmux-linux">here</a>.</p>
+<p>To begin, start an interactive session within the Singularity
+container. Make sure to mount the home directory within the RStudio
+session and the directory that the symbolic links point to, to store the
+pipeline outputs and access the input data within the container.</p>
+<pre class="bash"><code>singularity shell \
+  --bind /home/rstudio-server:/home/rstudio-server \
+  --bind /mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype:/mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype \
+  --writable-tmpfs \
+  /mnt/project/genopred_pipeline_latest.sif</code></pre>
+<hr />
+</div>
+<div id="run-genopred-inside-the-container-1" class="section level3">
+<h3>Run GenoPred Inside the Container</h3>
+<p>Once inside the container, you can use the GenoPred pipeline as
+usual. The resources provided by this instance
+(<code>mem3_ssd2_v2_x8</code>) will not be required for all steps in the
+GenoPred workflow, so to be cost efficient, I am just carrying out the
+<code>format_target</code> step within this instance. I will then export
+the data, terminate this instance, continue my analysis using a new
+instance with appropriate resources for downstream steps.</p>
+<pre class="bash"><code># Activate the GenoPred Environment:
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+# Navigate to the Pipeline Folder:
+cd /tools/GenoPred/pipeline
+
+# Perform a Dry Run: 
+# A dry run checks the pipeline&#39;s steps without executing them, helping you identify any missing dependencies or issues:
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/basic/config.yaml format_target
+
+# Run the Pipeline: 
+# Once satisfied with the dry run, execute the pipeline:
+snakemake -j8 --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/basic/config.yaml format_target</code></pre>
+<div class="note-box">
+<p><strong>Note:</strong> Since the instance has 8 cores available, I
+use the <code>-j8</code> parameter when running <code>snakemake</code>
+to ensure it utilizes all 8 cores efficiently.</p>
+</div>
+<p>After the analysis is complete, you can leave the container by
+typing:</p>
+<pre class="bash"><code>exit</code></pre>
+<p>In total, this analysis took ~18 hours, costing ~£6.</p>
+<hr />
+</div>
+</div>
+<div id="step-5.-export-ouputs-to-the-project-folder-1"
+class="section level2">
+<h2>Step 5. Export ouputs to the project folder</h2>
+<p>To avoid losing the outputs of your analysis when the RStudio session
+is terminated, you need to export the results to your DNAnexus project
+folder. Both the resdir (resources) and outdir (outputs) should be saved
+for future analyses.</p>
+<p>For simplicity and efficiency, we will compress the outputs and
+resources into a single tar file and then upload it to the DNAnexus
+project. If you plan to reuse the resources (e.g., for different
+pipeline configurations), you may choose to store them in separate tar
+files.</p>
+<pre class="bash"><code># Compress and upload the GenoPred working directory
+cd /home/rstudio-server
+tar -cvf ukb_genopred.tar genopred
+dx upload ukb_genopred.tar</code></pre>
+<p>I will also tar and upload the symlinks created for the UKB data.
+While these could be recreated, this approach conveniently preserves the
+timestamps, making it easier to resume the analysis seamlessly when
+extending it in the future.</p>
+<pre class="bash"><code># Compress and upload the ukb directory containing symlinks
+cd /home/rstudio-server
+tar -cvf ukb_symlinks.tar ukb
+dx upload ukb_symlinks.tar</code></pre>
+<p>Once the files are uploaded, you can safely terminate the RStudio
+session. Ensure the session is fully terminated by checking the Monitor
+tab in your DNAnexus project folder.</p>
+<hr />
+</div>
+</div>
 <div id="troubleshooting" class="section level1">
 <h1>Troubleshooting</h1>
 <p>Please post questions as an issue on the GenoPred GitHub repo <a

From adab8602a89f676959face68297fa77e6d145671 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 20 Jan 2025 10:44:03 +0000
Subject: [PATCH 080/153] Updated documentation for running on DNAnexus

---
 .../ukb_relative_remover.R                    |  87 +++++
 docs/running_on_dnanexus.Rmd                  | 359 +++++++++++++++--
 docs/running_on_dnanexus.html                 | 369 ++++++++++++++++--
 3 files changed, 759 insertions(+), 56 deletions(-)
 create mode 100644 Scripts/ukb_relative_remover/ukb_relative_remover.R

diff --git a/Scripts/ukb_relative_remover/ukb_relative_remover.R b/Scripts/ukb_relative_remover/ukb_relative_remover.R
new file mode 100644
index 00000000..d3ee9d04
--- /dev/null
+++ b/Scripts/ukb_relative_remover/ukb_relative_remover.R
@@ -0,0 +1,87 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+suppressMessages(library("optparse"))
+
+option_list = list(
+  make_option("--keep", action="store", default=NA, type='character',
+              help="File listing individuals to keep - only first column used [optional]"),
+  make_option("--rel_file", action="store", default=NA, type='character',
+              help="UKB relatedness file [required]"),
+  make_option("--rel_thresh", action="store", default=0.044, type='numeric',
+              help="Kingship threshold [optional]"),
+  make_option("--seed", action="store", default=1234, type='numeric',
+              help="Seed number [optional]"),
+  make_option("--GreedyRelated", action="store", default=NA, type='character',
+              help="Path to GreedyRelated binary [required]"),
+  make_option("--output", action="store", default='./unrelated', type='character',
+              help="Output file name [optional]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+sink(file = paste(opt$output,'.log',sep=''), append = F)
+cat("#################################################################
+# ukb_relative_remover.R
+# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
+#################################################################
+Analysis started at",as.character(start.time),'
+Options are:\n')
+print(opt)
+cat('Analysis started at',as.character(start.time),'\n')
+sink()
+
+####
+# Format UKB relatedness files for greedyRelated (modified from Joni's script)
+####
+library(data.table)
+
+# Load rel_file
+Related<-fread(opt$rel_file)
+
+# Add "Pair"
+Related$Pair<-1:dim(Related)[1]
+
+# Rename "Kinship" to "Factor"
+names(Related)[names(Related) == "Kinship"] <- "Factor"
+
+# Add both IDs
+Related_Long.0<-Related[,c("ID1","Pair","Factor")]
+Related_Long.1<-Related[,c("ID2","Pair","Factor")]
+
+names(Related_Long.0)[1]<-"ID"
+names(Related_Long.1)[1]<-"ID"
+
+Related_Long<-rbind(Related_Long.0, Related_Long.1)
+
+# Sort by pairs
+Related_Final<-Related_Long[order(Related_Long$Pair), ]
+
+fwrite(Related_Final, file=paste0(opt$output,'.rel_file_temp'), sep=' ', quote=F)
+
+######
+# Now run greedyRelated
+######
+
+if(is.na(opt$keep)){
+  system(paste0(opt$GreedyRelated,' -r ',opt$output,'.rel_file_temp -t ', opt$rel_thresh,' -s ',opt$seed,' > ',opt$out,'.rel_temp'))
+} else {
+  system(paste0(opt$GreedyRelated,' -r ',opt$output,'.rel_file_temp -keep ',opt$keep,' -t ', opt$rel_thresh,' -s ',opt$seed,' > ',opt$out,'.rel_temp'))
+}
+
+# Format output
+system(paste0("cut -f 1,2 ", opt$out,'.rel_temp',' > ',opt$out,'.related'))
+
+######
+# Delete temporary file
+######
+
+system(paste0('rm ',opt$output,'.rel_file_temp'))
+system(paste0('rm ',opt$output,'.rel_temp'))
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = paste(opt$output,'.log',sep=''), append = T)
+cat('Analysis finished at',as.character(end.time),'\n')
+cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
+sink()
diff --git a/docs/running_on_dnanexus.Rmd b/docs/running_on_dnanexus.Rmd
index 1ea1d275..89d24ff5 100644
--- a/docs/running_on_dnanexus.Rmd
+++ b/docs/running_on_dnanexus.Rmd
@@ -125,6 +125,12 @@ sudo make -C builddir install
 cd ~
 ```
 
+<div class="note-box">
+
+**Note:** This will need to be done for every new instance of Rstudio that you want to run GenoPred. For convenience, you can store the code in a shell script.
+
+</div>
+
 After installing Singularity, download the GenoPred container using the following command:
 
 ```{bash}
@@ -399,39 +405,18 @@ The UKB imputed genetic data is provided without post-imputation QC, resulting i
 
 ## Step 1. Install Singularity
 
-Once your RStudio session has started, install Singularity via the terminal. Follow the commands below to set up Singularity, along with other essential tools:
+Once your RStudio session has started, install Singularity via the terminal. We will use the shell script we created [here](#step-1-install-singularity-and-download-the-genopred-container):
 
 ```{bash}
-# Configure keyboard layout (optional step to avoid prompts)
-echo 'keyboard-configuration keyboard-configuration/layout select us' | sudo debconf-set-selections
-echo 'keyboard-configuration keyboard-configuration/variant select English (US)' | sudo debconf-set-selections
-
-# Install required dependencies (e.g., tmux, build tools, and libraries for Singularity)
-sudo DEBIAN_FRONTEND=noninteractive apt update && \
-sudo DEBIAN_FRONTEND=noninteractive apt install -y build-essential libseccomp-dev pkg-config squashfs-tools cryptsetup golang tmux
-
-# Set the Singularity version to install
-export VERSION=3.11.0
-
-# Download and extract the Singularity source code
-wget https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-ce-${VERSION}.tar.gz
-tar -xvzf singularity-ce-${VERSION}.tar.gz
-cd singularity-ce-${VERSION}
-
-# Configure and build Singularity without SUID
-./mconfig --without-suid
-make -C builddir
-
-# Install Singularity system-wide
-sudo make -C builddir install
-
-# Return to the home directory
-cd ~
+# Download, update permissions and run script to install singularity
+dx download install_singularity.sh
+chmod a+x install_singularity.sh
+./install_singularity
 ```
 
 ***
 
-## Step 2: Prepare input data
+## Step 2. Prepare input data
 
 We will use a mounted version of the UKB genetic data to save time and disk space. To meet GenoPred's file name requirements without duplicating data, we will create symbolic links. Given the size of the UKB genetic data, we will also request an instance with sufficient resources.
 
@@ -596,6 +581,326 @@ Once the files are uploaded, you can safely terminate the RStudio session. Ensur
 
 ***
 
+## Step 6. Ancestry Inference and Within Target QC
+
+The ancestry inference step is required prior to polygenic scoring, so we will do this now. In the same session, we will also perform the within-sample QC and project reference principal components, which generate other useful outputs.
+
+***
+
+### Start an Rstudio session
+
+Neither of the these steps require much RAM. The within-sample QC can leverage multiple cores, but ancestry inference doesn't. We need enough disk space to import the output from the previous run, but not much more. In this demonstration I am using a `mem2_ssd1_v2_x8` instance, which seemed to work well.
+
+***
+
+### Install Singularity
+
+Once your RStudio session has started, install Singularity via the terminal. We will use the shell script we created [here](#step-1-install-singularity-and-download-the-genopred-container):
+
+```{bash}
+# Download, update permissions and run script to install singularity
+dx download install_singularity.sh
+chmod a+x install_singularity.sh
+./install_singularity
+```
+
+***
+
+### Import the data from previous session
+
+We are going to extend our previous analysis UKB using GenoPred. We need to recreate the environment we had before.
+
+```{bash}
+# Download and decompress the symlinks previous run of GenoPred
+# Decompressing the mounted data save time and disk space
+tar -xvf /mnt/project/ukb_symlinks.tar -C ~/
+tar -xvf /mnt/project/ukb_genopred.tar -C ~/
+```
+
+***
+
+### Estimate relatedness
+
+Although GenoPred can estimate relatedness from scratch, UKB is very large and it will be time consuming. Instead, we will use the kinship matrix released by UKBto identify a list of unrelated individuals, using the software [GreedyRelated](https://gitlab.com/choishingwan/GreedyRelated), a script I have written previously [ukb_relative_remover.R](https://github.com/opain/GenoPred/tree/master/Scripts/ukb_relative_remover/ukb_relative_remover.R). 
+
+```{bash}
+# Install optparse in R
+Rscript -e "install.packages('optparse', repos='https://cloud.r-project.org/')"
+
+# Download and give permission to GreedyRelated binary for linux
+wget https://gitlab.com/-/project/14754196/uploads/6fdc44072a8a866cb77c2cf91f68d662/GreedyRelated_linux
+chmod a+x GreedyRelated_linux
+
+# Identify list of related individuals (using threshold of 0.044)
+Rscript ukb_relative_remover.R \
+  --rel_file /mnt/project/Bulk/Genotype\ Results/Genotype\ calls/ukb_rel.dat \
+  --rel_thresh 0.044 \
+  --seed 1 \
+  --GreedyRelated ./GreedyRelated_linux \
+  --output ukb/ukb82087
+```
+
+```{r}
+# Identify a list of unrelated individuals by removing this list of related indivduals from the full list of individuals in the UKB .fam file
+library(data.table)
+related <- fread('ukb/ukb82087.related')$V1
+fam<-fread('/mnt/project/Bulk/Genotype Results/Genotype calls/ukb22418_c1_b0_v2.fam')$V1
+
+unrelated<-fam[!(fam %in% related)]
+write.table(unrelated, 'ukb/ukb82087.unrelated', col.names=F, row.names = F, quote = F)
+```
+
+***
+
+### Update configuration for relatedness
+
+We must update the target_list configuration file for GenoPred to indicate the location of the file indicating unrelated individuals.
+
+```{r}
+target_list <- data.frame(
+  name='ukb',
+  path='/home/rstudio-server/ukb/ukb_symlinks/ukb_imp',
+  type='bgen',
+  indiv_report=F,
+  unrel='/home/rstudio-server/ukb/ukb82087.unrelated'
+)
+
+write.table(
+  target_list,
+  '/home/rstudio-server/genopred/config/ukb/basic/target_list.txt',
+  col.names = T,
+  row.names = F,
+  quote = F
+)
+```
+
+***
+
+### Run pipeline
+
+```{bash}
+# Start a tmux session to ensure the analysis persists even the connection is lost
+tmux
+
+# Start an interactive session inside the GenoPred container
+singularity shell \
+  --bind /home/rstudio-server:/home/rstudio-server \
+  --bind /mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype:/mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype \
+  --writable-tmpfs \
+  /mnt/project/genopred_pipeline_latest.sif
+
+# Activate the GenoPred Environment:
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+# Navigate to the Pipeline Folder:
+cd /tools/GenoPred/pipeline
+
+# Perform a Dry Run: 
+# A dry run checks the pipeline's steps without executing them, helping you identify any missing dependencies or issues
+# We can see that GenoPred will pick up where it left off, and won't rerun steps it ran before.
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/basic/config.yaml ancestry_inference outlier_detection pc_projection
+
+# Run the analysis. Here I am using 8 cores since I am using an instance with 8 cores available (mem2_ssd1_v2_x8).
+snakemake -j8 --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/basic/config.yaml ancestry_inference outlier_detection pc_projection
+
+```
+
+***
+
+### Package and export outputs
+
+Once the analysis is complete, we will compress and `dx upload` the output. The `genopred` output folder will contain the contents of the previous run as well (the reformatted UKB data), so we can delete the old version of the `ukb_genopred.tar` file in our project folder after the upload of the new version is complete. The same goes for the `ukb`/`ukb_symlinks.tar` folder - We need to reupload this since we are now storing the list of unrelated individuals in there.
+
+```{bash}
+# Compress and upload the GenoPred working directory
+cd /home/rstudio-server
+tar -cvf ukb_genopred.tar genopred
+dx upload ukb_genopred.tar
+
+# Compress and upload the ukb directory containing symlinks and list of unrelated individuals
+tar -cvf ukb_symlinks.tar ukb
+dx upload ukb_symlinks.tar
+```
+
+***
+
+## Step 7. Generating score files
+
+Score files can be generated using GenoPred either on DNAnexus or on other platforms, as this step does not require access to UK Biobank (UKB) data.
+
+Notably, score files generated in one instance of GenoPred (or with other software) can be reused as input for another instance of GenoPred. For example, you can:
+
+1. Generate score files using GenoPred on an institutional server (e.g., for free or with existing resources).
+
+2. Copy these score files to DNAnexus and use them to perform target sample scoring in the UKB dataset on DNAnexus.
+
+There are already several demonstrations of running GenoPred on DNAnexus in this document. The same setup can be used to generate score files. So, I will focus on demonstrating the more common scenario of importing PGS score files from a previous run of GenoPred, to be used for target sample scoring in UKB on DNAnexus.
+
+There are two approaches for using scores files from a different run of GenoPred. The score file can be reformated to the PGS catalogue format, and included in the `score_list`, but this requires one set of weights per file, which is inefficient, and looses some functionality downstream such as the function to return the pseudovalidated score (`find_pseudo()`). An alternative solution, is to copy the input GWAS sumstats, QC'd sumstats, and the PGS score files, which provides full downstream functionality.
+
+The most convenient solution will depend on your needs. If you just want to use a relatively fast PGS method, then you might as well run on DNAnexus as it won't cost much. If you want to use computationally intensive methods, then you may want to save money by running on your institutional server, and then importing the output to DNAnexus. If you only want to use a single score from the computationally intensive method, then exporting that score alone and specifying it using the `score_list` will be most convenient. However, if you want full functionality of GenoPred, whilst running PGS methods on a different server, then copying the entire GWAS and score file directories from a previous run onto DNAnexus is needed. I will demonstrate only the final scenario as it is the most convoluted.
+
+***
+
+## Step 8. Target sampling scoring
+
+Here I will use a score file generated using GenoPred previously. The score file was generated using the an coronary artery disease GWAS and the `ptclump` method. I have uploaded it to my DNAnexus project from my institutional server using the dx upload function (similar to I did in [this section]((#step-0-prepare-test-data))).
+
+```{bash}
+# Package and upload the required sumstats and score files to the DNAnexus project folder.
+mkdir -p genopred_scores/gwas_sumstat
+mkdir -p genopred_scores/pgs_score_files/sbayesr
+
+cp ~/oliverpainfel/GenoPred/pipeline/example_input/gwas_list.txt genopred_scores/
+cp -r ~/oliverpainfel/GenoPred/pipeline/test_data/output/test1/reference/gwas_sumstat/COAD01 genopred_scores/gwas_sumstat/
+cp -r ~/oliverpainfel/GenoPred/pipeline/test_data/output/test1/reference/pgs_score_files/ptclump/COAD01 genopred_scores/pgs_score_files/ptclump/
+
+tar -cvf genopred_scores.tar genopred_scores
+dx upload genopred_scores.tar
+```
+
+Now I will spin up a new instance in Rstudio to perform target sample scoring in UKB.
+Using `mem2_ssd1_v2_x4` instance.
+
+- Install Singularity (same as before - I put it in a shell script to make it easier to run)
+
+```{bash}
+dx download install_singularity.sh
+chmod a+x install_singularity.sh
+./install_singularity.sh
+```
+
+- Import the data from previous session (Decompress from the mounted version to save disk space and time)
+
+```{bash}
+# Import GenoPred inputs relating to UKB
+tar -xvf /mnt/project/ukb_symlinks.tar -C ~/
+tar -xvf /mnt/project/ukb_genopred.tar -C ~/
+
+# Import GenoPred outputs from PGS methods, and move into the apropriate genopred folder 
+tar -xvf /mnt/project/genopred_scores.tar -C ~/
+mv genopred_scores/gwas_sumstat ~/genopred/output/reference/
+mv genopred_scores/pgs_score_files ~/genopred/output/reference/
+```
+
+- Update configuration to specify the GWAS, score files and PGS methods to use.
+
+The gwas_list and pgs_methods should match the configuration used to generate the score files. However, we will need to create empty files to represent the original raw GWAS summary statistics, which we did not copy over to DNAnexus.
+
+```{r}
+# Make an empty file to represent the unQC'd sumstats
+library(data.table)
+gwas_list<-fread('~/genopred_scores/gwas_list.txt')
+gwas_list<-gwas_list[gwas_list$name == 'COAD01',]
+dir.create('/home/rstudio-server/raw_sumstats')
+for(i in 1:nrow(gwas_list)){
+  path <- paste0('/home/rstudio-server/raw_sumstats/', gwas_list$name[i],'.txt')
+  file.create(path)
+  gwas_list$path[i] <- path
+}
+
+gwas_list$label<-paste0("\"", gwas_list$label, "\"")
+
+dir.create('/home/rstudio-server/genopred/config/ukb/demo')
+
+write.table(
+  gwas_list,
+  '/home/rstudio-server/genopred/config/ukb/demo/gwas_list.txt',
+  col.names = T,
+  row.names = F,
+  quote = F
+)
+
+# Create config file
+conf <- c(
+  'outdir: /home/rstudio-server/genopred/output',
+  'config_file: /home/rstudio-server/genopred/config/ukb/demo/config.yaml',
+  'resdir: /home/rstudio-server/genopred/resources',
+  'gwas_list: /home/rstudio-server/genopred/config/ukb/demo/gwas_list.txt',
+  "pgs_methods: ['ptclump']",
+  'target_list: /home/rstudio-server/genopred/config/ukb/basic/target_list.txt',
+  'cores_target_pgs: 1'
+)
+
+write.table(
+  conf,
+  '/home/rstudio-server/genopred/config/ukb/demo/config.yaml',
+  col.names = F,
+  row.names = F,
+  quote = F
+)
+
+```
+
+- Run pipeline
+
+```{bash}
+# Start a tmux session
+tmux
+
+# Start interactive session in container
+singularity shell \
+  --bind /home/rstudio-server:/home/rstudio-server \
+  --bind /mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype:/mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype \
+  --writable-tmpfs \
+  /mnt/project/genopred_pipeline_latest.sif
+
+# Activate the GenoPred Environment:
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+# Navigate to the Pipeline Folder:
+cd /tools/GenoPred/pipeline
+
+# It will think the score files need to be recreated due to the sumstat paths changing. So touch the outputs of prep_pgs
+# This just updates the file timestamps for step prior to prep_pgs so the pipeline doesn't think it needs to recreate them due to the raw sumstats being newer than the score files.
+snakemake -t -j1 --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/demo/config.yaml prep_pgs
+
+# Perform a Dry Run: 
+# A dry run checks the pipeline's steps without executing them, helping you identify any missing dependencies or issues.
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/demo/config.yaml output_all
+
+# We can see it will only run target scoring and downstream steps, as it should.
+# Now we can run using four cores, matching the resources available in our instance (mem2_ssd1_v2_x4)
+snakemake -j4 --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/demo/config.yaml output_all
+```
+
+- Package and export PGS
+
+You could tar and export the entire `genopred` folder again, or you could export just the files you need, such as the score files and the report.
+
+```{bash}
+# Upload report
+dx upload genopred/output/ukb/reports/ukb-report.html
+```
+
+It could also be convenient to store the PGS in an .RDS file, and then export that file.
+
+```{bash}
+export LC_ALL=C
+export LANG=C
+```
+
+```{r}
+setwd('/tools/GenoPred/pipeline')
+library(data.table)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in PGS
+pgs <- read_pgs(config = '/home/rstudio-server/genopred/config/ukb/demo/config.yaml')
+
+saveRDS(pgs, file = "/home/rstudio-server/ukb_pgs_COAD01.rds")
+```
+
+```{bash}
+dx upload ukb_pgs_COAD01.rds
+```
+
+***
+
 # Troubleshooting
 
 Please post questions as an issue on the GenoPred GitHub repo [here](https://github.com/opain/GenoPred/issues).
diff --git a/docs/running_on_dnanexus.html b/docs/running_on_dnanexus.html
index 9bb13f98..ec97293a 100644
--- a/docs/running_on_dnanexus.html
+++ b/docs/running_on_dnanexus.html
@@ -548,6 +548,11 @@ <h2>Step 1. Install Singularity and Download the GenoPred Container</h2>
 
 # Return to the home directory
 cd ~</code></pre>
+<div class="note-box">
+<p><strong>Note:</strong> This will need to be done for every new
+instance of Rstudio that you want to run GenoPred. For convenience, you
+can store the code in a shell script.</p>
+</div>
 <p>After installing Singularity, download the GenoPred container using
 the following command:</p>
 <pre class="bash"><code># Pull the GenoPred container
@@ -834,37 +839,16 @@ <h2>Workflow</h2>
 <div id="step-1.-install-singularity" class="section level2">
 <h2>Step 1. Install Singularity</h2>
 <p>Once your RStudio session has started, install Singularity via the
-terminal. Follow the commands below to set up Singularity, along with
-other essential tools:</p>
-<pre class="bash"><code># Configure keyboard layout (optional step to avoid prompts)
-echo &#39;keyboard-configuration keyboard-configuration/layout select us&#39; | sudo debconf-set-selections
-echo &#39;keyboard-configuration keyboard-configuration/variant select English (US)&#39; | sudo debconf-set-selections
-
-# Install required dependencies (e.g., tmux, build tools, and libraries for Singularity)
-sudo DEBIAN_FRONTEND=noninteractive apt update &amp;&amp; \
-sudo DEBIAN_FRONTEND=noninteractive apt install -y build-essential libseccomp-dev pkg-config squashfs-tools cryptsetup golang tmux
-
-# Set the Singularity version to install
-export VERSION=3.11.0
-
-# Download and extract the Singularity source code
-wget https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-ce-${VERSION}.tar.gz
-tar -xvzf singularity-ce-${VERSION}.tar.gz
-cd singularity-ce-${VERSION}
-
-# Configure and build Singularity without SUID
-./mconfig --without-suid
-make -C builddir
-
-# Install Singularity system-wide
-sudo make -C builddir install
-
-# Return to the home directory
-cd ~</code></pre>
+terminal. We will use the shell script we created <a
+href="#step-1-install-singularity-and-download-the-genopred-container">here</a>:</p>
+<pre class="bash"><code># Download, update permissions and run script to install singularity
+dx download install_singularity.sh
+chmod a+x install_singularity.sh
+./install_singularity</code></pre>
 <hr />
 </div>
-<div id="step-2-prepare-input-data" class="section level2">
-<h2>Step 2: Prepare input data</h2>
+<div id="step-2.-prepare-input-data" class="section level2">
+<h2>Step 2. Prepare input data</h2>
 <p>We will use a mounted version of the UKB genetic data to save time
 and disk space. To meet GenoPred’s file name requirements without
 duplicating data, we will create symbolic links. Given the size of the
@@ -1027,6 +1011,333 @@ <h2>Step 5. Export ouputs to the project folder</h2>
 tab in your DNAnexus project folder.</p>
 <hr />
 </div>
+<div id="step-6.-ancestry-inference-and-within-target-qc"
+class="section level2">
+<h2>Step 6. Ancestry Inference and Within Target QC</h2>
+<p>The ancestry inference step is required prior to polygenic scoring,
+so we will do this now. In the same session, we will also perform the
+within-sample QC and project reference principal components, which
+generate other useful outputs.</p>
+<hr />
+<div id="start-an-rstudio-session" class="section level3">
+<h3>Start an Rstudio session</h3>
+<p>Neither of the these steps require much RAM. The within-sample QC can
+leverage multiple cores, but ancestry inference doesn’t. We need enough
+disk space to import the output from the previous run, but not much
+more. In this demonstration I am using a <code>mem2_ssd1_v2_x8</code>
+instance, which seemed to work well.</p>
+<hr />
+</div>
+<div id="install-singularity" class="section level3">
+<h3>Install Singularity</h3>
+<p>Once your RStudio session has started, install Singularity via the
+terminal. We will use the shell script we created <a
+href="#step-1-install-singularity-and-download-the-genopred-container">here</a>:</p>
+<pre class="bash"><code># Download, update permissions and run script to install singularity
+dx download install_singularity.sh
+chmod a+x install_singularity.sh
+./install_singularity</code></pre>
+<hr />
+</div>
+<div id="import-the-data-from-previous-session" class="section level3">
+<h3>Import the data from previous session</h3>
+<p>We are going to extend our previous analysis UKB using GenoPred. We
+need to recreate the environment we had before.</p>
+<pre class="bash"><code># Download and decompress the symlinks previous run of GenoPred
+# Decompressing the mounted data save time and disk space
+tar -xvf /mnt/project/ukb_symlinks.tar -C ~/
+tar -xvf /mnt/project/ukb_genopred.tar -C ~/</code></pre>
+<hr />
+</div>
+<div id="estimate-relatedness" class="section level3">
+<h3>Estimate relatedness</h3>
+<p>Although GenoPred can estimate relatedness from scratch, UKB is very
+large and it will be time consuming. Instead, we will use the kinship
+matrix released by UKBto identify a list of unrelated individuals, using
+the software <a
+href="https://gitlab.com/choishingwan/GreedyRelated">GreedyRelated</a>,
+a script I have written previously <a
+href="https://github.com/opain/GenoPred/tree/master/Scripts/ukb_relative_remover/ukb_relative_remover.R">ukb_relative_remover.R</a>.</p>
+<pre class="bash"><code># Install optparse in R
+Rscript -e &quot;install.packages(&#39;optparse&#39;, repos=&#39;https://cloud.r-project.org/&#39;)&quot;
+
+# Download and give permission to GreedyRelated binary for linux
+wget https://gitlab.com/-/project/14754196/uploads/6fdc44072a8a866cb77c2cf91f68d662/GreedyRelated_linux
+chmod a+x GreedyRelated_linux
+
+# Identify list of related individuals (using threshold of 0.044)
+Rscript ukb_relative_remover.R \
+  --rel_file /mnt/project/Bulk/Genotype\ Results/Genotype\ calls/ukb_rel.dat \
+  --rel_thresh 0.044 \
+  --seed 1 \
+  --GreedyRelated ./GreedyRelated_linux \
+  --output ukb/ukb82087</code></pre>
+<pre class="r"><code># Identify a list of unrelated individuals by removing this list of related indivduals from the full list of individuals in the UKB .fam file
+library(data.table)
+related &lt;- fread(&#39;ukb/ukb82087.related&#39;)$V1
+fam&lt;-fread(&#39;/mnt/project/Bulk/Genotype Results/Genotype calls/ukb22418_c1_b0_v2.fam&#39;)$V1
+
+unrelated&lt;-fam[!(fam %in% related)]
+write.table(unrelated, &#39;ukb/ukb82087.unrelated&#39;, col.names=F, row.names = F, quote = F)</code></pre>
+<hr />
+</div>
+<div id="update-configuration-for-relatedness" class="section level3">
+<h3>Update configuration for relatedness</h3>
+<p>We must update the target_list configuration file for GenoPred to
+indicate the location of the file indicating unrelated individuals.</p>
+<pre class="r"><code>target_list &lt;- data.frame(
+  name=&#39;ukb&#39;,
+  path=&#39;/home/rstudio-server/ukb/ukb_symlinks/ukb_imp&#39;,
+  type=&#39;bgen&#39;,
+  indiv_report=F,
+  unrel=&#39;/home/rstudio-server/ukb/ukb82087.unrelated&#39;
+)
+
+write.table(
+  target_list,
+  &#39;/home/rstudio-server/genopred/config/ukb/basic/target_list.txt&#39;,
+  col.names = T,
+  row.names = F,
+  quote = F
+)</code></pre>
+<hr />
+</div>
+<div id="run-pipeline" class="section level3">
+<h3>Run pipeline</h3>
+<pre class="bash"><code># Start a tmux session to ensure the analysis persists even the connection is lost
+tmux
+
+# Start an interactive session inside the GenoPred container
+singularity shell \
+  --bind /home/rstudio-server:/home/rstudio-server \
+  --bind /mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype:/mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype \
+  --writable-tmpfs \
+  /mnt/project/genopred_pipeline_latest.sif
+
+# Activate the GenoPred Environment:
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+# Navigate to the Pipeline Folder:
+cd /tools/GenoPred/pipeline
+
+# Perform a Dry Run: 
+# A dry run checks the pipeline&#39;s steps without executing them, helping you identify any missing dependencies or issues
+# We can see that GenoPred will pick up where it left off, and won&#39;t rerun steps it ran before.
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/basic/config.yaml ancestry_inference outlier_detection pc_projection
+
+# Run the analysis. Here I am using 8 cores since I am using an instance with 8 cores available (mem2_ssd1_v2_x8).
+snakemake -j8 --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/basic/config.yaml ancestry_inference outlier_detection pc_projection
+</code></pre>
+<hr />
+</div>
+<div id="package-and-export-outputs" class="section level3">
+<h3>Package and export outputs</h3>
+<p>Once the analysis is complete, we will compress and
+<code>dx upload</code> the output. The <code>genopred</code> output
+folder will contain the contents of the previous run as well (the
+reformatted UKB data), so we can delete the old version of the
+<code>ukb_genopred.tar</code> file in our project folder after the
+upload of the new version is complete. The same goes for the
+<code>ukb</code>/<code>ukb_symlinks.tar</code> folder - We need to
+reupload this since we are now storing the list of unrelated individuals
+in there.</p>
+<pre class="bash"><code># Compress and upload the GenoPred working directory
+cd /home/rstudio-server
+tar -cvf ukb_genopred.tar genopred
+dx upload ukb_genopred.tar
+
+# Compress and upload the ukb directory containing symlinks and list of unrelated individuals
+tar -cvf ukb_symlinks.tar ukb
+dx upload ukb_symlinks.tar</code></pre>
+<hr />
+</div>
+</div>
+<div id="step-7.-generating-score-files" class="section level2">
+<h2>Step 7. Generating score files</h2>
+<p>Score files can be generated using GenoPred either on DNAnexus or on
+other platforms, as this step does not require access to UK Biobank
+(UKB) data.</p>
+<p>Notably, score files generated in one instance of GenoPred (or with
+other software) can be reused as input for another instance of GenoPred.
+For example, you can:</p>
+<ol style="list-style-type: decimal">
+<li><p>Generate score files using GenoPred on an institutional server
+(e.g., for free or with existing resources).</p></li>
+<li><p>Copy these score files to DNAnexus and use them to perform target
+sample scoring in the UKB dataset on DNAnexus.</p></li>
+</ol>
+<p>There are already several demonstrations of running GenoPred on
+DNAnexus in this document. The same setup can be used to generate score
+files. So, I will focus on demonstrating the more common scenario of
+importing PGS score files from a previous run of GenoPred, to be used
+for target sample scoring in UKB on DNAnexus.</p>
+<p>There are two approaches for using scores files from a different run
+of GenoPred. The score file can be reformated to the PGS catalogue
+format, and included in the <code>score_list</code>, but this requires
+one set of weights per file, which is inefficient, and looses some
+functionality downstream such as the function to return the
+pseudovalidated score (<code>find_pseudo()</code>). An alternative
+solution, is to copy the input GWAS sumstats, QC’d sumstats, and the PGS
+score files, which provides full downstream functionality.</p>
+<p>The most convenient solution will depend on your needs. If you just
+want to use a relatively fast PGS method, then you might as well run on
+DNAnexus as it won’t cost much. If you want to use computationally
+intensive methods, then you may want to save money by running on your
+institutional server, and then importing the output to DNAnexus. If you
+only want to use a single score from the computationally intensive
+method, then exporting that score alone and specifying it using the
+<code>score_list</code> will be most convenient. However, if you want
+full functionality of GenoPred, whilst running PGS methods on a
+different server, then copying the entire GWAS and score file
+directories from a previous run onto DNAnexus is needed. I will
+demonstrate only the final scenario as it is the most convoluted.</p>
+<hr />
+</div>
+<div id="step-8.-target-sampling-scoring" class="section level2">
+<h2>Step 8. Target sampling scoring</h2>
+<p>Here I will use a score file generated using GenoPred previously. The
+score file was generated using the an coronary artery disease GWAS and
+the <code>ptclump</code> method. I have uploaded it to my DNAnexus
+project from my institutional server using the dx upload function
+(similar to I did in <a href="(#step-0-prepare-test-data)">this
+section</a>).</p>
+<pre class="bash"><code># Package and upload the required sumstats and score files to the DNAnexus project folder.
+mkdir -p genopred_scores/gwas_sumstat
+mkdir -p genopred_scores/pgs_score_files/sbayesr
+
+cp ~/oliverpainfel/GenoPred/pipeline/example_input/gwas_list.txt genopred_scores/
+cp -r ~/oliverpainfel/GenoPred/pipeline/test_data/output/test1/reference/gwas_sumstat/COAD01 genopred_scores/gwas_sumstat/
+cp -r ~/oliverpainfel/GenoPred/pipeline/test_data/output/test1/reference/pgs_score_files/ptclump/COAD01 genopred_scores/pgs_score_files/ptclump/
+
+tar -cvf genopred_scores.tar genopred_scores
+dx upload genopred_scores.tar</code></pre>
+<p>Now I will spin up a new instance in Rstudio to perform target sample
+scoring in UKB. Using <code>mem2_ssd1_v2_x4</code> instance.</p>
+<ul>
+<li>Install Singularity (same as before - I put it in a shell script to
+make it easier to run)</li>
+</ul>
+<pre class="bash"><code>dx download install_singularity.sh
+chmod a+x install_singularity.sh
+./install_singularity.sh</code></pre>
+<ul>
+<li>Import the data from previous session (Decompress from the mounted
+version to save disk space and time)</li>
+</ul>
+<pre class="bash"><code># Import GenoPred inputs relating to UKB
+tar -xvf /mnt/project/ukb_symlinks.tar -C ~/
+tar -xvf /mnt/project/ukb_genopred.tar -C ~/
+
+# Import GenoPred outputs from PGS methods, and move into the apropriate genopred folder 
+tar -xvf /mnt/project/genopred_scores.tar -C ~/
+mv genopred_scores/gwas_sumstat ~/genopred/output/reference/
+mv genopred_scores/pgs_score_files ~/genopred/output/reference/</code></pre>
+<ul>
+<li>Update configuration to specify the GWAS, score files and PGS
+methods to use.</li>
+</ul>
+<p>The gwas_list and pgs_methods should match the configuration used to
+generate the score files. However, we will need to create empty files to
+represent the original raw GWAS summary statistics, which we did not
+copy over to DNAnexus.</p>
+<pre class="r"><code># Make an empty file to represent the unQC&#39;d sumstats
+library(data.table)
+gwas_list&lt;-fread(&#39;~/genopred_scores/gwas_list.txt&#39;)
+gwas_list&lt;-gwas_list[gwas_list$name == &#39;COAD01&#39;,]
+dir.create(&#39;/home/rstudio-server/raw_sumstats&#39;)
+for(i in 1:nrow(gwas_list)){
+  path &lt;- paste0(&#39;/home/rstudio-server/raw_sumstats/&#39;, gwas_list$name[i],&#39;.txt&#39;)
+  file.create(path)
+  gwas_list$path[i] &lt;- path
+}
+
+gwas_list$label&lt;-paste0(&quot;\&quot;&quot;, gwas_list$label, &quot;\&quot;&quot;)
+
+dir.create(&#39;/home/rstudio-server/genopred/config/ukb/demo&#39;)
+
+write.table(
+  gwas_list,
+  &#39;/home/rstudio-server/genopred/config/ukb/demo/gwas_list.txt&#39;,
+  col.names = T,
+  row.names = F,
+  quote = F
+)
+
+# Create config file
+conf &lt;- c(
+  &#39;outdir: /home/rstudio-server/genopred/output&#39;,
+  &#39;config_file: /home/rstudio-server/genopred/config/ukb/demo/config.yaml&#39;,
+  &#39;resdir: /home/rstudio-server/genopred/resources&#39;,
+  &#39;gwas_list: /home/rstudio-server/genopred/config/ukb/demo/gwas_list.txt&#39;,
+  &quot;pgs_methods: [&#39;ptclump&#39;]&quot;,
+  &#39;target_list: /home/rstudio-server/genopred/config/ukb/basic/target_list.txt&#39;,
+  &#39;cores_target_pgs: 1&#39;
+)
+
+write.table(
+  conf,
+  &#39;/home/rstudio-server/genopred/config/ukb/demo/config.yaml&#39;,
+  col.names = F,
+  row.names = F,
+  quote = F
+)</code></pre>
+<ul>
+<li>Run pipeline</li>
+</ul>
+<pre class="bash"><code># Start a tmux session
+tmux
+
+# Start interactive session in container
+singularity shell \
+  --bind /home/rstudio-server:/home/rstudio-server \
+  --bind /mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype:/mnt/project/Bulk/Imputation/UKB\ imputation\ from\ genotype \
+  --writable-tmpfs \
+  /mnt/project/genopred_pipeline_latest.sif
+
+# Activate the GenoPred Environment:
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+# Navigate to the Pipeline Folder:
+cd /tools/GenoPred/pipeline
+
+# It will think the score files need to be recreated due to the sumstat paths changing. So touch the outputs of prep_pgs
+# This just updates the file timestamps for step prior to prep_pgs so the pipeline doesn&#39;t think it needs to recreate them due to the raw sumstats being newer than the score files.
+snakemake -t -j1 --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/demo/config.yaml prep_pgs
+
+# Perform a Dry Run: 
+# A dry run checks the pipeline&#39;s steps without executing them, helping you identify any missing dependencies or issues.
+snakemake -n --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/demo/config.yaml output_all
+
+# We can see it will only run target scoring and downstream steps, as it should.
+# Now we can run using four cores, matching the resources available in our instance (mem2_ssd1_v2_x4)
+snakemake -j4 --use-conda --configfile=/home/rstudio-server/genopred/config/ukb/demo/config.yaml output_all</code></pre>
+<ul>
+<li>Package and export PGS</li>
+</ul>
+<p>You could tar and export the entire <code>genopred</code> folder
+again, or you could export just the files you need, such as the score
+files and the report.</p>
+<pre class="bash"><code># Upload report
+dx upload genopred/output/ukb/reports/ukb-report.html</code></pre>
+<p>It could also be convenient to store the PGS in an .RDS file, and
+then export that file.</p>
+<pre class="bash"><code>export LC_ALL=C
+export LANG=C</code></pre>
+<pre class="r"><code>setwd(&#39;/tools/GenoPred/pipeline&#39;)
+library(data.table)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in PGS
+pgs &lt;- read_pgs(config = &#39;/home/rstudio-server/genopred/config/ukb/demo/config.yaml&#39;)
+
+saveRDS(pgs, file = &quot;/home/rstudio-server/ukb_pgs_COAD01.rds&quot;)</code></pre>
+<pre class="bash"><code>dx upload ukb_pgs_COAD01.rds</code></pre>
+<hr />
+</div>
 </div>
 <div id="troubleshooting" class="section level1">
 <h1>Troubleshooting</h1>

From a0831982a76e285245a6afa8d8103b5dac327819 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 21 Jan 2025 13:51:02 +0000
Subject: [PATCH 081/153] updates for cross pop methods

---
 Scripts/pgs_methods/bridgeprs.R               | 10 ++-
 Scripts/pgs_methods/prscsx.R                  | 36 ++++++----
 Scripts/pgs_methods/quickprs_multi.R          | 27 +++-----
 Scripts/pgs_methods/tlprs.R                   | 10 ++-
 Scripts/pgs_methods/xwing.R                   | 60 ++++++++++++-----
 docs/prep_quickprs_ref.Rmd                    |  4 +-
 functions/pgs.R                               | 67 +++++++++++++++++++
 .../misc/cross_pop_test/config_cross_pop.yaml |  2 +-
 pipeline/misc/opensnp/config_cross_pop.yaml   |  5 +-
 pipeline/rules/dependencies.smk               |  9 ++-
 pipeline/rules/pgs_methods.smk                | 22 ++++--
 11 files changed, 188 insertions(+), 64 deletions(-)

diff --git a/Scripts/pgs_methods/bridgeprs.R b/Scripts/pgs_methods/bridgeprs.R
index b561e34a..edfff838 100644
--- a/Scripts/pgs_methods/bridgeprs.R
+++ b/Scripts/pgs_methods/bridgeprs.R
@@ -4,8 +4,10 @@ start.time <- Sys.time()
 library("optparse")
 
 option_list = list(
-  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
+  make_option("--ref_plink_chr", action="store", default=NA, type='character',
       help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+      help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
       help="File containing the population code and location of the keep file [required]"),
   make_option("--plink2", action="store", default='plink2', type='character',
@@ -259,6 +261,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/pgs_methods/prscsx.R b/Scripts/pgs_methods/prscsx.R
index b39eaeea..d67d5ca4 100644
--- a/Scripts/pgs_methods/prscsx.R
+++ b/Scripts/pgs_methods/prscsx.R
@@ -4,32 +4,34 @@ start.time <- Sys.time()
 library("optparse")
 
 option_list = list(
-  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
-      help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_plink_chr", action="store", default=NA, type='character',
+              help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+              help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
-      help="File containing the population code and location of the keep file [required]"),
+              help="File containing the population code and location of the keep file [required]"),
   make_option("--plink2", action="store", default='plink2', type='character',
-      help="Path PLINK v2 software binary [optional]"),
+              help="Path PLINK v2 software binary [optional]"),
   make_option("--output", action="store", default=NULL, type='character',
-      help="Path for output files [required]"),
+              help="Path for output files [required]"),
   make_option("--memory", action="store", default=5000, type='numeric',
-      help="Memory limit [optional]"),
+              help="Memory limit [optional]"),
   make_option("--sumstats", action="store", default=NULL, type='character',
-      help="Comma-seperated list of GWAS summary statistics [required]"),
+              help="Comma-seperated list of GWAS summary statistics [required]"),
   make_option("--populations", action="store", default=NULL, type='character',
-      help="Comma-seperated list of population codes matching GWAS [required]"),
+              help="Comma-seperated list of population codes matching GWAS [required]"),
   make_option("--prscsx_path", action="store", default=NULL, type='character',
-      help="Path to PRS-CSx executable [required]"),
+              help="Path to PRS-CSx executable [required]"),
   make_option("--n_cores", action="store", default=1, type='numeric',
-      help="Number of cores for parallel computing [optional]"),
+              help="Number of cores for parallel computing [optional]"),
   make_option("--prscsx_ref_path", action="store", default=NULL, type='character',
-      help="Comma-seperated list of PRS-CSx reference data [required]"),
+              help="Comma-seperated list of PRS-CSx reference data [required]"),
   make_option("--test", action="store", default=NA, type='character',
-      help="Specify number of SNPs to include [optional]"),
+              help="Specify number of SNPs to include [optional]"),
   make_option("--phi_param", action="store", default='auto', type='character',
-      help="Path to PRScs reference [optional]"),
+              help="Path to PRScs reference [optional]"),
   make_option("--seed", action="store", default=1, type='numeric',
-      help="Seed number for PRScs [optional]")
+              help="Seed number for PRScs [optional]")
 )
 
 opt = parse_args(OptionParser(option_list = option_list))
@@ -233,6 +235,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/pgs_methods/quickprs_multi.R b/Scripts/pgs_methods/quickprs_multi.R
index 09082fc5..0f35572a 100644
--- a/Scripts/pgs_methods/quickprs_multi.R
+++ b/Scripts/pgs_methods/quickprs_multi.R
@@ -6,6 +6,8 @@ suppressMessages(library("optparse"))
 option_list = list(
   make_option("--ref_plink_chr", action="store", default=NA, type='character',
               help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+              help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
               help="File containing the population code and location of the keep file [required]"),
   make_option("--plink", action="store", default='plink', type='character',
@@ -242,22 +244,7 @@ for(targ_pop in populations){
 }
 
 # Average weights across repeats
-# Not using X-Wing due to bug when weights are 0
-avg_weights <- list()
-for(targ_pop in populations){
-
-  weights_prefix <- paste0(tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep')
-
-  cal_avg_rel_weights <- function(path){
-    weights_file <- fread(path)
-    weights_non_0 <-  ifelse(weights_file$Weights < 0, 0, weights_file$Weights)
-    rel_weights <- weights_non_0/(sum(weights_non_0))
-    return(rel_weights)
-  }
-
-  rel_weights_list <- lapply(c(1:4), function(i) cal_avg_rel_weights(paste0(weights_prefix, i, ".txt")))
-  avg_weights[[targ_pop]] <- as.numeric(colMeans(as.data.frame(do.call(rbind, rel_weights_list)), na.rm=T))
-}
+mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'))
 
 ####
 # Combine score files
@@ -276,7 +263,7 @@ for(targ_pop in populations){
   mix_weights<-avg_weights[[targ_pop]]
   score_all_tmp<-score_all
   for(i in populations){
-    score_all_tmp[[paste0('SCORE_targ_', i)]] <- score_all[[paste0('SCORE_targ_', i)]] * mix_weights[which(populations == targ_pop)]
+    score_all_tmp[[paste0('SCORE_targ_', i)]] <- score_all[[paste0('SCORE_targ_', i)]] * mix_weights[which(populations == i)]
   }
   # Take average of weighted scores
   score_all[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <- rowSums(score_all_tmp[, grepl('SCORE_', names(score_all_tmp)), with = F])
@@ -319,6 +306,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/pgs_methods/tlprs.R b/Scripts/pgs_methods/tlprs.R
index 731b0557..e1531ac4 100644
--- a/Scripts/pgs_methods/tlprs.R
+++ b/Scripts/pgs_methods/tlprs.R
@@ -6,8 +6,10 @@ library("optparse")
 option_list = list(
   make_option("--config", action="store", default=NULL, type='character',
       help="Pipeline configuration file. Required when pseudo_only is TRUE [optional]"),
-  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
+  make_option("--ref_plink_chr", action="store", default=NA, type='character',
       help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+      help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
       help="File containing the population code and location of the keep file [required]"),
   make_option("--ref_keep_dir", action="store", default=NULL, type='character',
@@ -275,6 +277,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/Scripts/pgs_methods/xwing.R b/Scripts/pgs_methods/xwing.R
index 53815881..95c41bbc 100644
--- a/Scripts/pgs_methods/xwing.R
+++ b/Scripts/pgs_methods/xwing.R
@@ -4,8 +4,12 @@ start.time <- Sys.time()
 library("optparse")
 
 option_list = list(
-  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
+  make_option("--ref_plink_chr", action="store", default=NA, type='character',
       help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_freq_chr", action="store", default=NULL, type='character',
+      help="Path to per chromosome reference PLINK2 .afreq files [required]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+      help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--xwing_repo", action="store", default=NULL, type='character',
       help="Path to X-WING repo [required]"),
   make_option("--logodetect_ref", action="store", default=NULL, type='character',
@@ -51,6 +55,9 @@ registerDoMC(opt$n_cores)
 if(is.null(opt$ref_plink_chr)){
   stop('--ref_plink_chr must be specified.\n')
 }
+if(is.null(opt$ref_freq_chr)){
+  stop('--ref_freq_chr must be specified.\n')
+}
 if(is.null(opt$sumstats)){
   stop('--sumstats must be specified.\n')
 }
@@ -116,7 +123,8 @@ for(i in 1:length(sumstats)){
 
   # Read in, check and format GWAS summary statistics
   gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('CHR', 'SNP', 'BP', 'A1', 'A2', 'BETA', 'P', 'N'))
-
+  # TESTING subset!!!!
+  gwas <- gwas[1:1000,]
   # Store average sample size
   gwas_N <- c(gwas_N, round(mean(gwas$N), 0))
   gwas$N<-NULL
@@ -190,6 +198,9 @@ log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list
       '--pst_pop ', pst_pop, ' ',
       '--out_name output ',
       '--seed 1 ',
+      # TESTING !!!
+      '--n_iter 200 ',
+      '--n_burnin 100 ',
       '--out_dir ', tmp_dir, '/PANTHER/post_targ_', targ_pop
     )
 
@@ -259,6 +270,9 @@ log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list
       '--pst_pop ', pst_pop, ' ',
       '--out_name output_', index, ' ',
       '--seed 1 ',
+      # TESTING !!!
+      '--n_iter 200 ',
+      '--n_burnin 100 ',
       '--out_dir ', tmp_dir, '/LEOPARD/post_targ_', targ_pop
     )
 
@@ -290,9 +304,7 @@ for(targ_pop in populations){
   dir.create(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop), recursive = T)
 
   for(i in 1:4){
-
     targ_gwas_valid_n<-fread(paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', i, '_train_valid_N.txt'))$N_valid
-
     system(paste0(
       'Rscript ', opt$xwing_repo, '/LEOPARD_Weights.R --beta_file ', paste0(paste0(tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i, '_', populations, '_Post.txt'), collapse=','), ' --valid_file ', tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', i, '_valid.txt --n_valid ', targ_gwas_valid_n ,' --ref_prefix ', opt$leopard_ref,'/', targ_pop, '/', targ_pop, '_part3 --out ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep', i, '.txt'
     ))
@@ -300,33 +312,43 @@ for(targ_pop in populations){
 }
 
 # Average weights across repeats
-for(targ_pop in populations){
-  system(paste0(
-    'Rscript ', opt$xwing_repo, '/LEOPARD_Avg.R --weights_prefix ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep --rep 4 --out ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_avg.txt'
-  ))
-}
+mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'))
 
 ####
 # Combine score files
 ####
 
+# Read in reference population data
+pop_data <- read_pop_data(opt$pop_data)
+
+# Read in reference SNP data
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
+
 # We should combine the raw PANTHER score files for each population,
 # and then combine using mixing weights for each population
 score_all<-NULL
 for(targ_pop in populations){
-  mix_weights<-fread(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_avg.txt'))
+  # Read in the .freq file for target population
+  freq_data <- read_frq(freq_dir = opt$ref_freq_chr, population = targ_pop, chr = CHROMS)
+
   score_pop <- NULL
   for(pst_pop in populations){
+    
+    # Read in SNP-weights
     score_i<-NULL
     for(chr in CHROMS){
       score_i_chr<-fread(paste0(tmp_dir, '/PANTHER/post_targ_', targ_pop, '/output_', pst_pop, '_pst_eff_phiauto_chr', chr,'.txt'), header=F)
       score_i<-rbind(score_i, score_i_chr)
     }
-
-    score_i$V6 <- score_i$V6 * mix_weights$Weights[grepl(paste0(pst_pop,'_Post.txt'), mix_weights$Path)]
-
-    names(score_i)<-c('CHR','SNP', 'BP', 'A1', 'A2', paste0('SCORE_targ_', targ_pop, '_pst_', pst_pop))
-    score_i<-score_i[, c('SNP', 'A1', 'A2', paste0('SCORE_targ_', targ_pop, '_pst_', pst_pop)), with=F]
+    names(score_i) <- c('CHR','SNP','BP','A1','A2','BETA')
+    score_i <- score_i[, c('SNP','A1','A2','BETA'), with = F]
+    
+    # Centre SNP-weights for target population
+    score_i <- centre_weights(score = score_i, freq = freq_data)
+    
+    # Adjust SNP-weights according to mixing weights
+    score_i$BETA <- score_i$BETA * mix_weights[[targ_pop]][which(populations == pst_pop)]
+    names(score_i)<-c('SNP', 'A1', 'A2', paste0('SCORE_targ_', targ_pop, '_pst_', pst_pop))
 
     if(is.null(score_pop)){
       score_pop<-score_i
@@ -334,10 +356,10 @@ for(targ_pop in populations){
       score_pop<-merge(score_pop, score_i, by=c('SNP','A1','A2'), all=T)
     }
   }
+  
   # Take average of weighted scores
   score_pop[is.na(score_pop)]<-0
   score_pop[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <- rowSums(score_pop[, grepl('SCORE_', names(score_pop)), with = F])
-
   if(is.null(score_all)){
     score_all<-score_pop
   } else {
@@ -374,6 +396,12 @@ log_add(log_file = log_file, message = 'Calculating polygenic scores in referenc
 # Calculate scores in the full reference
 ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
 
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
 # Calculate scale within each reference population
 pop_data <- read_pop_data(opt$pop_data)
 
diff --git a/docs/prep_quickprs_ref.Rmd b/docs/prep_quickprs_ref.Rmd
index d3a54acb..6bb4773b 100644
--- a/docs/prep_quickprs_ref.Rmd
+++ b/docs/prep_quickprs_ref.Rmd
@@ -127,8 +127,6 @@ Only the correlations part needs to be derived using subset data
 Follow X-wing strategy first - Split into three parts
 
 ```{r}
-
-
 # Load dependencies
 library(GenoUtils)
 library(data.table)
@@ -146,7 +144,7 @@ opt$ldak_map<-'resources/data/ldak_map/genetic_map_b37'
 opt$ldak_tag<-'resources/data/ldak_bld'
 opt$ldak_highld<-'resources/data/ldak_highld/highld.txt'
 opt$ldak<-'/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/software/ldak5.2/ldak5.2.linux'
-opt$n_cores<-10
+opt$n_cores<-20
 
 for(pop in c('EUR','EAS','AFR')){
   opt$output <- paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/', pop, '.hm3/', pop, '.hm3')
diff --git a/functions/pgs.R b/functions/pgs.R
index b6e1bc8d..cb443058 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -183,6 +183,22 @@ read_pvar<-function(dat, chr = 1:22){
   return(pvar)
 }
 
+# Read in the .freq file for target population
+read_frq<-function(freq_dir, population, chr){
+  freq_data<-NULL
+  for(i in chr){
+    tmp<-fread(paste0(freq_dir,'/',population,'/ref.',population,'.chr',i,'.afreq'))
+    tmp<-data.table(
+      SNP = tmp$ID,
+      A1 = tmp$ALT,
+      A2 = tmp$REF,
+      FREQ = tmp$ALT_FREQS
+    )
+    freq_data<-rbind(freq_data, tmp)
+  }
+  return(freq_data)
+}
+
 # Remove variants within genomic regions (REF: PMC2443852)
 remove_regions<-function(dat, regions){
   exclude<-NULL
@@ -558,3 +574,54 @@ score_adjust <- function(score, pcs, ref_model) {
   # Return only relevant columns (FID, IID, and adjusted scores)
   return(score)
 }
+
+# Helper function to calculate relative weights for a single file from LEOPARD
+cal_avg_rel_weights <- function(path){
+  weights_file <- fread(path)
+  weights_non_0 <-  ifelse(weights_file$Weights < 0, 0, weights_file$Weights)
+  rel_weights <- weights_non_0/(sum(weights_non_0))
+  return(rel_weights)
+}
+
+# Function to calculate average weights across replications from LEOPARD
+calculate_avg_weights <- function(populations, leopard_dir) {
+  avg_weights <- list()
+  
+  # Iterate over populations
+  for (targ_pop in populations) {
+    # Define the file prefix for the population
+    weights_prefix <- paste0(leopard_dir, '/weights_', targ_pop, '/output_LEOPARD_weights_rep')
+    
+    # Calculate relative weights for each replication
+    rel_weights_list <- lapply(1:4, function(i) {
+      cal_avg_rel_weights(paste0(weights_prefix, i, ".txt"))
+    })
+    
+    # Calculate the average weights across replications
+    avg_weights[[targ_pop]] <- as.numeric(colMeans(do.call(rbind, rel_weights_list), na.rm = TRUE))
+  }
+  
+  return(avg_weights)
+}
+
+# Centre SNP-weights
+centre_weights <- function(score, freq){
+  # Sort and flip according to reference data
+  score <- map_score(ref = ref, score = score)
+  
+  # Sort and flip freq according to reference just in case they are different
+  freq <- map_score(ref = ref, score = freq)
+  
+  # Calculate mean genotype dosage and denominator
+  freq$MeanGenotype <- 2 * freq$FREQ
+  denominator <- sum(freq$MeanGenotype^2)
+  
+  for(i in names(score)[!(names(score) %in% c('SNP','A1','A2'))]){
+    # Calculate mean of PGS
+    mean_pgs <- sum(score[[i]] * freq$MeanGenotype)
+    
+    # Adjust the SNP-weights so PGS is centered
+    score[[i]] <- score[[i]] - (mean_pgs / denominator) * freq$MeanGenotype
+  }
+  return(score)
+}
\ No newline at end of file
diff --git a/pipeline/misc/cross_pop_test/config_cross_pop.yaml b/pipeline/misc/cross_pop_test/config_cross_pop.yaml
index 89bd3be0..b19ea02e 100644
--- a/pipeline/misc/cross_pop_test/config_cross_pop.yaml
+++ b/pipeline/misc/cross_pop_test/config_cross_pop.yaml
@@ -4,7 +4,7 @@ config_file: misc/cross_pop_test/config_cross_pop.yaml
 gwas_list: misc/cross_pop_test/gwas_list.txt
 target_list: misc/dev/test_data/config/target_list.txt
 gwas_groups: misc/cross_pop_test/gwas_groups.txt
-pgs_methods: ['bridgeprs']
+pgs_methods: ['ptclump', 'xwing']
 testing: chr22
 cores_target_pgs: 1
 mem_target_pgs: 2000
diff --git a/pipeline/misc/opensnp/config_cross_pop.yaml b/pipeline/misc/opensnp/config_cross_pop.yaml
index 6c24f335..d4858ae5 100644
--- a/pipeline/misc/opensnp/config_cross_pop.yaml
+++ b/pipeline/misc/opensnp/config_cross_pop.yaml
@@ -2,7 +2,6 @@ outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1
 config_file: misc/opensnp/config_cross_pop.yaml
 gwas_list: misc/opensnp/gwas_list.txt
 target_list: misc/opensnp/target_list.txt
-pgs_methods: ['dbslmm','tlprs','bridgeprs']
-cores_prep_pgs: 20
+pgs_methods: ['ptclump', 'megaprs','xwing']
+testing: chr22
 gwas_groups: misc/cross_pop_test/gwas_groups.txt
-tlprs_methods: ['dbslmm']
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 86a2e7b5..61dc3be6 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -933,7 +933,7 @@ rule download_ldak_highld:
     """
 
 # Download LDAK V5.2 for QuickPRS
-# This is a temporary solution. Only this version works for QuickPRS
+# Only this version works for QuickPRS
 rule download_ldak5_2:
   output:
     f"{resdir}/software/ldak5.2/ldak5.2.linux"
@@ -944,10 +944,13 @@ rule download_ldak5_2:
   shell:
     """
     {{
-      cp /users/k1806347/oliverpainfel/Software/ldak5.2.linux {resdir}/software/ldak5.2/ldak5.2.linux
+      rm -r -f {resdir}/software/ldak5.2; \
+      mkdir -p {resdir}/software/ldak5.2; \
+      wget --no-check-certificate -O {resdir}/software/ldak5.2/ldak5.2.linux "https://drive.google.com/uc?export=download&id=19knXZnbPNDz3J5dBKeVyZZoe6iZnLPEk"; \
+      chmod a+x {resdir}/software/ldak5.2/ldak5.2.linux
     }} > {log} 2>&1
     """
-
+    
 # Download LDAK V6
 rule download_ldak6:
   output:
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 7fb6ae1d..b4435043 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -585,7 +585,8 @@ rule prep_pgs_prscsx_i:
     rules.download_prscsx_software.output,
     lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
     lambda w: expand(f"{resdir}/data/prscs_ref/{prscs_ldref}/ldblk_{prscs_ldref}_{{population}}/ldblk_{prscs_ldref}_chr1.hdf5", population=[pop.lower() for pop in get_populations(w.gwas_group)]),
-    f"{resdir}/data/prscs_ref/{prscs_ldref}/snpinfo_mult_{prscs_ldref}_hm3"
+    f"{resdir}/data/prscs_ref/{prscs_ldref}/snpinfo_mult_{prscs_ldref}_hm3",
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/prscsx/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   conda:
@@ -607,6 +608,7 @@ rule prep_pgs_prscsx_i:
     export OPENBLAS_NUM_THREADS=1; \
     Rscript ../Scripts/pgs_methods/prscsx.R \
       --ref_plink_chr {refdir}/ref.chr \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {params.sumstats} \
       --populations {params.populations} \
       --prscsx_ref_path {resdir}/data/prscs_ref/{prscs_ldref} \
@@ -632,13 +634,15 @@ rule prep_pgs_xwing_i:
   threads: config['cores_prep_pgs']
   input:
     rules.download_xwing_software.output,
+    rules.install_genoutils_xwing.output,
     rules.download_leopard_panther_snp_data.output,
     lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
     lambda w: expand(f"{resdir}/data/prscs_ref/{prscs_ldref}/ldblk_{prscs_ldref}_{{population}}/ldblk_{prscs_ldref}_chr1.hdf5", population=[pop.lower() for pop in get_populations(w.gwas_group)]),
     f"{resdir}/data/prscs_ref/{prscs_ldref}/snpinfo_mult_{prscs_ldref}_hm3",
     lambda w: expand(f"{resdir}/data/PANTHER_LEOPARD_1kg_ref/ldblk_1kg_{{population}}/ldblk_1kg_chr13.hdf5", population=[pop.lower() for pop in get_populations(w.gwas_group)]),
     lambda w: expand(f"{resdir}/data/LEOPARD_1kg_ref/{{population}}/{{population}}_part1.bed", population=get_populations(w.gwas_group)),
-    lambda w: expand(f"{resdir}/data/LOGODetect_1kg_ref/{{population}}/1000G_{{population}}_QC.bim", population=get_populations(w.gwas_group))
+    lambda w: expand(f"{resdir}/data/LOGODetect_1kg_ref/{{population}}/1000G_{{population}}_QC.bim", population=get_populations(w.gwas_group)),
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/xwing/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   conda:
@@ -659,6 +663,8 @@ rule prep_pgs_xwing_i:
     export OPENBLAS_NUM_THREADS=1; \
     Rscript ../Scripts/pgs_methods/xwing.R \
       --ref_plink_chr {refdir}/ref.chr \
+      --ref_freq_chr {refdir}/freq_files \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {params.sumstats} \
       --populations {params.populations} \
       --logodetect_ref {resdir}/data/LOGODetect_1kg_ref \
@@ -687,7 +693,8 @@ rule prep_pgs_tlprs_i:
   input:
     rules.install_tlprs.output,
     lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
-    lambda w: expand(f"{outdir}/reference/pgs_score_files/{{method}}/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group), method=w.method)
+    lambda w: expand(f"{outdir}/reference/pgs_score_files/{{method}}/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group), method=w.method),
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/tlprs_{{method}}/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   conda:
@@ -707,6 +714,7 @@ rule prep_pgs_tlprs_i:
     Rscript ../Scripts/pgs_methods/tlprs.R \
       --config {params.config_file} \
       --ref_plink_chr {refdir}/ref.chr \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {params.sumstats} \
       --scores {params.scores} \
       --populations {params.populations} \
@@ -735,7 +743,8 @@ rule prep_pgs_quickprs_multi_i:
     rules.download_ldak_highld.output,
     rules.download_ldak5_2.output,
     rules.download_ldak_map.output,
-    rules.download_ldak_bld.output
+    rules.download_ldak_bld.output,
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/quickprs_multi/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   benchmark:
@@ -751,6 +760,7 @@ rule prep_pgs_quickprs_multi_i:
   shell:
     "Rscript ../Scripts/pgs_methods/quickprs_multi.R \
       --ref_plink_chr {refdir}/ref.chr \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {params.sumstats} \
       --populations {params.populations} \
       --ldak {resdir}/software/ldak5.2/ldak5.2.linux \
@@ -775,7 +785,8 @@ rule prep_pgs_bridgeprs_i:
   threads: config['cores_prep_pgs']
   input:
     rules.download_bridgeprs_software.output,
-    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas_group}}/{{gwas_group}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))
+    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas_group}}/{{gwas_group}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/bridgeprs/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   conda:
@@ -792,6 +803,7 @@ rule prep_pgs_bridgeprs_i:
     """
     Rscript ../Scripts/pgs_methods/bridgeprs.R \
       --ref_plink_chr {refdir}/ref.chr \
+      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {params.sumstats} \
       --populations {params.populations} \
       --pop_data {refdir}/ref.pop.txt \

From 961b899b1f5bbbffdde1b21e1788646d64635b14 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 21 Jan 2025 18:16:12 +0000
Subject: [PATCH 082/153] Updates to cross pop set up

---
 Scripts/pgs_methods/quickprs_multi.R        | 10 +--
 Scripts/pgs_methods/xwing.R                 | 11 +---
 docs/prep_quickprs_ref.Rmd                  | 15 ++---
 functions/pgs.R                             | 12 +++-
 pipeline/config.yaml                        |  3 +
 pipeline/misc/opensnp/config_cross_pop.yaml |  4 +-
 pipeline/rules/dependencies.smk             | 68 ++++++++++-----------
 pipeline/rules/pgs_methods.smk              |  7 +--
 8 files changed, 67 insertions(+), 63 deletions(-)

diff --git a/Scripts/pgs_methods/quickprs_multi.R b/Scripts/pgs_methods/quickprs_multi.R
index 0f35572a..95914148 100644
--- a/Scripts/pgs_methods/quickprs_multi.R
+++ b/Scripts/pgs_methods/quickprs_multi.R
@@ -115,7 +115,7 @@ for(i in 1:length(sumstats)){
   gwas_N <- c(gwas_N, round(mean(gwas$n), 0))
 
   # Check overlap between GWAS and LDAK reference
-  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', populations[i], '.hm3')
+  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', populations[i])
   ldak_hm3_file <- list.files(quick_prs_ref_pop_i)
   ldak_hm3_file <- ldak_hm3_file[grepl('.cors.bim', ldak_hm3_file)][1]
   ldak_hm3 <- fread(paste0(quick_prs_ref_pop_i, '/', ldak_hm3_file))
@@ -146,7 +146,7 @@ score_full <- list()
 for(i in 1:length(sumstats)){
   score_full[[populations[i]]] <- quick_prs(
     sumstats = paste0(tmp_dir,'/GWAS_sumstats_temp', i, '.txt'),
-    ref_dir = paste0(opt$quick_prs_ref, '/', populations[i], '.hm3'),
+    ref_dir = paste0(opt$quick_prs_ref, '/', populations[i]),
     genomic_control = opt$genomic_control,
     n_cores = opt$n_cores,
     prs_model = opt$prs_model)
@@ -161,7 +161,7 @@ log_add(log_file = log_file, message = 'Subsampling sumstats.')
 dir.create(paste0(tmp_dir,'/LEOPARD/sampled_sumstats'), recursive = T)
 
 for(i in 1:length(sumstats)){
-  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', populations[i], '.hm3')
+  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', populations[i])
   ref_files <- list.files(quick_prs_ref_pop_i)
   ref_files <- gsub('.bed', '', ref_files[grepl('subset_1.bed', ref_files)])
   system(
@@ -200,7 +200,7 @@ for(i in 1:length(sumstats)){
 
     score_subset[[populations[i]]][[paste0('subset_', j)]] <- quick_prs(
       sumstats = paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.reformat.txt'),
-      ref_dir = paste0(opt$quick_prs_ref, '/', populations[i], '.hm3'),
+      ref_dir = paste0(opt$quick_prs_ref, '/', populations[i]),
       genomic_control = opt$genomic_control,
       n_cores = opt$n_cores,
       ref_subset = '2',
@@ -220,7 +220,7 @@ log_add(log_file = log_file, message = 'Estimating the linear combination weight
 
 for(targ_pop in populations){
   dir.create(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop), recursive = T)
-  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', targ_pop, '.hm3')
+  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', targ_pop)
   ref_files <- list.files(quick_prs_ref_pop_i)
   ref_files <- gsub('.bed', '', ref_files[grepl('subset_3.bed', ref_files)])
 
diff --git a/Scripts/pgs_methods/xwing.R b/Scripts/pgs_methods/xwing.R
index 95c41bbc..d52432ef 100644
--- a/Scripts/pgs_methods/xwing.R
+++ b/Scripts/pgs_methods/xwing.R
@@ -123,8 +123,7 @@ for(i in 1:length(sumstats)){
 
   # Read in, check and format GWAS summary statistics
   gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('CHR', 'SNP', 'BP', 'A1', 'A2', 'BETA', 'P', 'N'))
-  # TESTING subset!!!!
-  gwas <- gwas[1:1000,]
+
   # Store average sample size
   gwas_N <- c(gwas_N, round(mean(gwas$N), 0))
   gwas$N<-NULL
@@ -198,9 +197,6 @@ log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list
       '--pst_pop ', pst_pop, ' ',
       '--out_name output ',
       '--seed 1 ',
-      # TESTING !!!
-      '--n_iter 200 ',
-      '--n_burnin 100 ',
       '--out_dir ', tmp_dir, '/PANTHER/post_targ_', targ_pop
     )
 
@@ -270,9 +266,6 @@ log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list
       '--pst_pop ', pst_pop, ' ',
       '--out_name output_', index, ' ',
       '--seed 1 ',
-      # TESTING !!!
-      '--n_iter 200 ',
-      '--n_burnin 100 ',
       '--out_dir ', tmp_dir, '/LEOPARD/post_targ_', targ_pop
     )
 
@@ -312,7 +305,7 @@ for(targ_pop in populations){
 }
 
 # Average weights across repeats
-mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'))
+mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'), log_file = log_file)
 
 ####
 # Combine score files
diff --git a/docs/prep_quickprs_ref.Rmd b/docs/prep_quickprs_ref.Rmd
index 6bb4773b..f785d9ab 100644
--- a/docs/prep_quickprs_ref.Rmd
+++ b/docs/prep_quickprs_ref.Rmd
@@ -36,7 +36,7 @@ QuickPRS only seems to work for v5.2, so let make the reference data using this
 ## Full reference
 
 ```{r}
-
+# conda activate .snakemake/conda/329e0288cb99508f5e6c50a0996b234c_
 # Load dependencies
 library(GenoUtils)
 library(data.table)
@@ -57,7 +57,7 @@ opt$ldak<-'/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resour
 opt$n_cores<-20
 
 for(pop in c('EUR','EAS','AFR')){
-  opt$output <- paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/', pop, '.hm3/', pop, '.hm3')
+  opt$output <- paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3/', pop, '/', pop)
   opt$output_dir <- paste0(dirname(opt$output),'/')
   system(paste0('mkdir -p ',opt$output_dir))
 
@@ -127,6 +127,7 @@ Only the correlations part needs to be derived using subset data
 Follow X-wing strategy first - Split into three parts
 
 ```{r}
+# conda activate .snakemake/conda/329e0288cb99508f5e6c50a0996b234c_
 # Load dependencies
 library(GenoUtils)
 library(data.table)
@@ -147,7 +148,7 @@ opt$ldak<-'/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resour
 opt$n_cores<-20
 
 for(pop in c('EUR','EAS','AFR')){
-  opt$output <- paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/', pop, '.hm3/', pop, '.hm3')
+  opt$output <- paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset/', pop, '/', pop)
   opt$output_dir <- paste0(dirname(opt$output),'/')
   system(paste0('mkdir -p ',opt$output_dir))
 
@@ -198,10 +199,9 @@ for(pop in c('EUR','EAS','AFR')){
   for(subset_i in 1:3){
     # Save plink format files
     system(paste0(opt$plink, ' --bfile ', tmp_dir, '/ref_merge --keep ', tmp_dir, '/keep', subset_i, ' --make-bed --out ', opt$output, '.subset_', subset_i))
-    
-    # Save frq files
-    system(paste0(opt$plink, ' --bfile ', opt$output, '.subset_', subset_i, ' --freq --out ', opt$output, '.subset_', subset_i))
-    
+  }
+  
+  for(subset_i in 2:2){
     # Calculate predictor-predictor correlations
     system(paste0(opt$ldak,' --calc-cors ', tmp_dir, '/tmp.subset_', subset_i, ' --keep ', tmp_dir, '/keep', subset_i, ' --bfile ', tmp_dir, '/ref_merge --window-cm 3 --max-threads ', opt$n_cores))
       
@@ -211,6 +211,7 @@ for(pop in c('EUR','EAS','AFR')){
     }
   }
 
+
   system(paste0('rm -r ', tmp_dir, '/*'))
 }
 
diff --git a/functions/pgs.R b/functions/pgs.R
index cb443058..22e33f55 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -584,7 +584,7 @@ cal_avg_rel_weights <- function(path){
 }
 
 # Function to calculate average weights across replications from LEOPARD
-calculate_avg_weights <- function(populations, leopard_dir) {
+calculate_avg_weights <- function(populations, leopard_dir, log_file = NULL) {
   avg_weights <- list()
   
   # Iterate over populations
@@ -601,6 +601,16 @@ calculate_avg_weights <- function(populations, leopard_dir) {
     avg_weights[[targ_pop]] <- as.numeric(colMeans(do.call(rbind, rel_weights_list), na.rm = TRUE))
   }
   
+  log_add(log_file = log_file, message = '------------------------')
+  for(i in names(avg_weights)){
+    for(j in populations){
+      log_add(log_file = log_file, message = paste0("LEOPARD weights - ", i, " target: "))
+      log_add(log_file = log_file, message = paste0(j, ' = ', avg_weights[[i]][which(populations == j)]))
+      mix_weights
+    }
+    log_add(log_file = log_file, message = '------------------------')
+  }
+  
   return(avg_weights)
 }
 
diff --git a/pipeline/config.yaml b/pipeline/config.yaml
index e041cf15..5e051fd9 100644
--- a/pipeline/config.yaml
+++ b/pipeline/config.yaml
@@ -46,6 +46,9 @@ sbayesrc_ldref: NA
 # Specify reference for QuickPRS
 quickprs_ldref: NA
 
+# Specify reference for QuickPRS-Multi
+quickprs_multi_ldref: NA
+
 # Specify reference for LDpred2
 ldpred2_ldref: NA
 
diff --git a/pipeline/misc/opensnp/config_cross_pop.yaml b/pipeline/misc/opensnp/config_cross_pop.yaml
index d4858ae5..d36889a5 100644
--- a/pipeline/misc/opensnp/config_cross_pop.yaml
+++ b/pipeline/misc/opensnp/config_cross_pop.yaml
@@ -2,6 +2,8 @@ outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1
 config_file: misc/opensnp/config_cross_pop.yaml
 gwas_list: misc/opensnp/gwas_list.txt
 target_list: misc/opensnp/target_list.txt
-pgs_methods: ['ptclump', 'megaprs','xwing']
+pgs_methods: ['ptclump', 'megaprs','xwing','quickprs']
 testing: chr22
 gwas_groups: misc/cross_pop_test/gwas_groups.txt
+cores_prep_pgs: 10 # Set to 20 when running xwing
+quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3
\ No newline at end of file
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 61dc3be6..bc9541d0 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -312,20 +312,28 @@ if 'sbayesr' in config['pgs_methods']:
 
 # Set quickprs reference path
 if config['quickprs_ldref'] == 'NA':
-  quickprs_ldref=f"{resdir}/data/quickprs_ref"
+  quickprs_ldref=f"{resdir}/data/quickprs"
+  
+  # Check if gwas_list contains invalid populations
+  valid_pops = {'EUR', 'EAS', 'AFR', 'SAS'}
+  invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
+
+  if invalid_pops:
+    raise ValueError(
+      f"Default quickprs reference data is only available for EUR, EAS, AFR and SAS populations. For other populations, please provide your own quickprs reference data using the quickprs_ldref parameter."
+    )
 else:
   quickprs_ldref=config['quickprs_ldref']
 
-# Check the quickprs ldref data is present for the required populations in the gwas_list
-if 'quickprs' in config['pgs_methods']:
-  for pop in gwas_list_df['population'].unique():
-    path = f"{quickprs_ldref}/{pop}.hm3"
-    # Check if required files exists
-    cors_file = os.path.join(path, f"{pop}.hm3.cors.bin")
-    if not os.path.exists(cors_file):
-      print(f"File not found: {cors_file}")
-      raise FileNotFoundError(f"Required file not found: {cors_file}. quickprs reference data must include .cors.bin for all populations.")
-
+  # Check the quickprs ldref data is present for the required populations in the gwas_list
+  if 'quickprs' in config['pgs_methods']:
+    for pop in gwas_list_df['population'].unique():
+      path = f"{quickprs_ldref}/{pop}"
+      # Check if required files exists
+      cors_file = os.path.join(path, f"{pop}.cors.bin")
+      if not os.path.exists(cors_file):
+        print(f"File not found: {cors_file}")
+        raise FileNotFoundError(f"Required file not found: {cors_file}. quickprs reference data must include .cors.bin for all populations when quickprs_ldref is specified.")
 
 # Set sbayesrc reference path
 if config['sbayesrc_ldref'] == 'NA':
@@ -950,48 +958,35 @@ rule download_ldak5_2:
       chmod a+x {resdir}/software/ldak5.2/ldak5.2.linux
     }} > {log} 2>&1
     """
-    
-# Download LDAK V6
-rule download_ldak6:
-  output:
-    f"{resdir}/software/ldak6/ldak6.linux"
-  benchmark:
-    f"{resdir}/data/benchmarks/download_ldak6.txt"
-  log:
-    f"{resdir}/data/logs/download_ldak6.log"
-  shell:
-    """
-    {{
-      rm -r -f {resdir}/software/ldak6; \
-      mkdir -p {resdir}/software/ldak6; \
-      wget --no-check-certificate -O {resdir}/software/ldak6/ldak6.linux https://github.com/dougspeed/LDAK/raw/main/ldak6.linux; \
-      chmod a+x {resdir}/software/ldak6/ldak6.linux
-    }} > {log} 2>&1
-    """
 
-
-# Download LDAK QuickPRS refernce data
+# NOTE. This doesn't currently work as the reference data on LDAK website isn't in the right format for LDAK 5.1, 5.2, or 6
+# In due course we will update this to download from our own repo. For the timebeing. The user must specify the reference data themselves
 rule download_quickprs_ref:
   output:
-    f"{resdir}/data/quickprs/{{population}}.hapmap/{{population}}.hapmap.cors.root"
+    f"{resdir}/data/quickprs/{{population}}/{{population}}.cors.bin"
   benchmark:
     f"{resdir}/data/benchmarks/download_quickprs_ref-{{population}}.txt"
   log:
     f"{resdir}/data/logs/download_quickprs_ref-{{population}}.log"
+  params:
+    pop_code=lambda wildcards: {'EUR': 'gbr', 'SAS': 'sas', 'EAS': 'eas', 'AFR': 'afr'}[wildcards.population]
   shell:
     """
     {{
       mkdir -p {resdir}/data/quickprs; \
-      rm -r -f {resdir}/data/quickprs/{wildcards.population}.hapmap; \
-      wget --no-check-certificate -O {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz https://genetics.ghpc.au.dk/doug/{wildcards.population}.hapmap.tar.gz; \
+      rm -r -f {resdir}/data/quickprs/{wildcards.population}; \
+      wget --no-check-certificate -O {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz https://genetics.ghpc.au.dk/doug/{params.pop_code}.hapmap.tar.gz; \
       tar -zxvf {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz -C {resdir}/data/quickprs/; \
+      mv {resdir}/data/quickprs/{params.pop_code}.hapmap {resdir}/data/quickprs/{wildcards.population}; \
+      find {resdir}/data/quickprs/{wildcards.population} -type f -name '*{params.pop_code}*' -exec bash -c 'mv \"$0\" \"${{0//{params.pop_code}/{wildcards.population}}}\"' {{}} \; \
+      find {resdir}/data/quickprs/{wildcards.population} -type f -name '*hapmap*' -exec bash -c 'mv \"$0\" \"${{0//.hapmap./.}}\"' {{}} \; \
       rm {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz
     }} > {log} 2>&1
     """
 
 rule download_quickprs_ref_all:
   input:
-    lambda w: expand(f"{resdir}/data/quickprs/{{population}}.hapmap.cors.root", population=['gbr','sas','eas','afr'])
+    lambda w: expand(f"{resdir}/data/quickprs/{{population}}/{{population}}.cors.bin", population=['EUR', 'SAS', 'EAS', 'AFR'])
 
 # Download preprocessed reference data (1KG+HGDP HapMap3)
 rule download_default_ref:
@@ -1347,7 +1342,8 @@ rule get_all_resources:
     rules.download_ldak_map.output,
     rules.download_ldak_bld.output,
     rules.download_ldak_highld.output,
-    rules.download_default_ref.output
+    rules.download_default_ref.output,
+    rules.download_quickprs_ref_all.output
   output:
     touch(f"{resdir}/software/get_all_resources.done")
 
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index b4435043..d4e8cae9 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -395,8 +395,8 @@ def get_quickprs_reference_path(w, gwas_list_df, resdir):
   population = gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0]
 
   # Return the full path string
-  return f"{quickprs_ldref}/{population}.hm3/{population}.hm3.cors.bin"
-
+  return f"{quickprs_ldref}/{population}/{population}.cors.bin"
+  
 rule prep_pgs_quickprs_i:
   resources:
     mem_mb=20000,
@@ -420,7 +420,6 @@ rule prep_pgs_quickprs_i:
     "../envs/analysis.yaml"
   params:
     population= lambda w: gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0],
-    quick_prs_ref= lambda w: f"{quickprs_ldref}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}.hm3",
     testing=config["testing"]
   shell:
     "Rscript ../Scripts/pgs_methods/quickprs.R \
@@ -428,7 +427,7 @@ rule prep_pgs_quickprs_i:
       --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --ldak {resdir}/software/ldak5.2/ldak5.2.linux \
-      --quick_prs_ref {params.quick_prs_ref} \
+      --quick_prs_ref {quickprs_ldref}/{params.population} \
       --n_cores {threads} \
       --output {outdir}/reference/pgs_score_files/quickprs/{wildcards.gwas}/ref-{wildcards.gwas} \
       --pop_data {refdir}/ref.pop.txt \

From f7fd4d2bd91597619ce83e2ec8bcbc6c23dba756 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 21 Jan 2025 18:46:58 +0000
Subject: [PATCH 083/153] Updates

---
 pipeline/misc/opensnp/config_cross_pop.yaml |  3 +-
 pipeline/rules/dependencies.smk             | 46 ++++++++++++++-------
 pipeline/rules/pgs_methods.smk              |  2 +-
 3 files changed, 34 insertions(+), 17 deletions(-)

diff --git a/pipeline/misc/opensnp/config_cross_pop.yaml b/pipeline/misc/opensnp/config_cross_pop.yaml
index d36889a5..6930648d 100644
--- a/pipeline/misc/opensnp/config_cross_pop.yaml
+++ b/pipeline/misc/opensnp/config_cross_pop.yaml
@@ -6,4 +6,5 @@ pgs_methods: ['ptclump', 'megaprs','xwing','quickprs']
 testing: chr22
 gwas_groups: misc/cross_pop_test/gwas_groups.txt
 cores_prep_pgs: 10 # Set to 20 when running xwing
-quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3
\ No newline at end of file
+quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3
+quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset
\ No newline at end of file
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index bc9541d0..950c5b18 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -311,22 +311,22 @@ if 'sbayesr' in config['pgs_methods']:
         raise FileNotFoundError(f"Required file not found: {ld_file}. SBayesR reference data must include files for all chromosomes.")
 
 # Set quickprs reference path
-if config['quickprs_ldref'] == 'NA':
-  quickprs_ldref=f"{resdir}/data/quickprs"
+if any(method in config['pgs_methods'] for method in ['quickprs', 'quickprs_multi']):
+  if config['quickprs_ldref'] == 'NA':
+    quickprs_ldref=f"{resdir}/data/quickprs"
+    
+    # Check if gwas_list contains invalid populations
+    valid_pops = {'EUR', 'EAS', 'AFR', 'SAS'}
+    invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
   
-  # Check if gwas_list contains invalid populations
-  valid_pops = {'EUR', 'EAS', 'AFR', 'SAS'}
-  invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
-
-  if invalid_pops:
-    raise ValueError(
-      f"Default quickprs reference data is only available for EUR, EAS, AFR and SAS populations. For other populations, please provide your own quickprs reference data using the quickprs_ldref parameter."
-    )
-else:
-  quickprs_ldref=config['quickprs_ldref']
-
-  # Check the quickprs ldref data is present for the required populations in the gwas_list
-  if 'quickprs' in config['pgs_methods']:
+    if invalid_pops:
+      raise ValueError(
+        f"Default quickprs reference data is only available for EUR, EAS, AFR and SAS populations. For other populations, please provide your own quickprs reference data using the quickprs_ldref parameter."
+      )
+  else:
+    quickprs_ldref=config['quickprs_ldref']
+  
+    # Check the quickprs ldref data is present for the required populations in the gwas_list
     for pop in gwas_list_df['population'].unique():
       path = f"{quickprs_ldref}/{pop}"
       # Check if required files exists
@@ -335,6 +335,22 @@ else:
         print(f"File not found: {cors_file}")
         raise FileNotFoundError(f"Required file not found: {cors_file}. quickprs reference data must include .cors.bin for all populations when quickprs_ldref is specified.")
 
+# Set quickprs_multi reference path
+quickprs_multi_ldref=config['quickprs_multi_ldref']
+if 'quickprs_multi' in config['pgs_methods']:
+  missing_files = []
+  for pop in gwas_list_df['population'].unique():
+    path = f"{quickprs_multi_ldref}/{pop}"
+    # Check if required files exists
+    if not os.path.exists(f"{path}/{pop}.subset_1.bed"):
+      missing_files.append(f"{path}/{pop}.subset_1.bed")
+    if not os.path.exists(f"{path}/{pop}.subset_2.bed"):
+      missing_files.append(f"{path}/{pop}.subset_2.bed")
+    if not os.path.exists(f"{path}/{pop}.subset_3.bed"):
+      missing_files.append(f"{path}/{pop}.subset_3.bed")
+    if missing_files:
+      raise FileNotFoundError(f"The following quickprs_multi reference data are missing: {', '.join(missing_files)}")
+
 # Set sbayesrc reference path
 if config['sbayesrc_ldref'] == 'NA':
   sbayesrc_ldref=f"{resdir}/data/sbayesrc_ref"
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index d4e8cae9..aea71b0f 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -737,7 +737,7 @@ rule prep_pgs_quickprs_multi_i:
     time_min=5000
   threads: config['cores_prep_pgs']
   input:
-    lambda w: expand(f"{quickprs_ldref}/{{population}}.hm3/{{population}}.hm3.cors.bin", population=[pop for pop in get_populations(w.gwas_group)]),
+    #lambda w: expand(f"{quickprs_ldref}/{{population}}/{{population}}.cors.bin", population=[pop for pop in get_populations(w.gwas_group)]),
     lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
     rules.download_ldak_highld.output,
     rules.download_ldak5_2.output,

From d18e0910635347ad611576d0b8fd4eb7e61f0d17 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 22 Jan 2025 13:29:37 +0000
Subject: [PATCH 084/153] Updates

---
 Scripts/pgs_methods/quickprs.R              | 22 ++---
 Scripts/pgs_methods/quickprs_multi.R        | 91 ++++++++++++---------
 Scripts/pgs_methods/xwing.R                 |  5 +-
 docs/prep_quickprs_ref.Rmd                  |  4 +
 functions/pgs.R                             | 54 +++++++++---
 pipeline/misc/opensnp/config_cross_pop.yaml |  2 +-
 pipeline/rules/pgs_methods.smk              | 13 +--
 7 files changed, 119 insertions(+), 72 deletions(-)

diff --git a/Scripts/pgs_methods/quickprs.R b/Scripts/pgs_methods/quickprs.R
index 36023b4b..0e534b8c 100644
--- a/Scripts/pgs_methods/quickprs.R
+++ b/Scripts/pgs_methods/quickprs.R
@@ -22,8 +22,8 @@ option_list = list(
               help="GWAS summary statistics [optional]"),
   make_option("--ldak", action="store", default=NA, type='character',
               help="Path to ldak v5.2 executable [required]"),
-  make_option("--quick_prs_ref", action="store", default=NA, type='character',
-              help="Path to folder containing ldak quick prs reference [required]"),
+  make_option("--quickprs_ldref", action="store", default=NA, type='character',
+              help="Path to folder containing ldak quickprs reference [required]"),
   make_option("--n_cores", action="store", default=1, type='numeric',
               help="Number of cores for parallel computing [optional]"),
   make_option("--prs_model", action="store", default='bayesr', type='character',
@@ -58,8 +58,8 @@ if(is.null(opt$output)){
 if(is.null(opt$ldak)){
   stop('--ldak must be specified.\n')
 }
-if(is.null(opt$quick_prs_ref)){
-  stop('--quick_prs_ref must be specified.\n')
+if(is.null(opt$quickprs_ldref)){
+  stop('--quickprs_ldref must be specified.\n')
 }
 
 # Create output directory
@@ -98,9 +98,9 @@ gwas<-gwas[,c('Predictor','A1','A2','N','Z')]
 names(gwas)<-c('Predictor','A1','A2','n','Z')
 
 # Check overlap between GWAS and LDAK reference
-ldak_hm3_file <- list.files(opt$quick_prs_ref)
+ldak_hm3_file <- list.files(opt$quickprs_ldref)
 ldak_hm3_file <- ldak_hm3_file[grepl('.cors.bim', ldak_hm3_file)][1]
-ldak_hm3 <- fread(paste0(opt$quick_prs_ref, '/', ldak_hm3_file))
+ldak_hm3 <- fread(paste0(opt$quickprs_ldref, '/', ldak_hm3_file))
 ldak_hm3 <- ldak_hm3[ldak_hm3$V1 %in% CHROMS,]
 ref_overlap <- sum(gwas$Predictor %in% ldak_hm3$V2) / nrow(ldak_hm3)
 
@@ -122,15 +122,15 @@ if(!is.na(opt$test)){
 ############
 
 # Calculate Per-Predictor Heritabilities.
-ref_files<-list.files(opt$quick_prs_ref)
+ref_files<-list.files(opt$quickprs_ldref)
 
 tagging_file<-ref_files[grepl('quickprs.tagging',ref_files)]
 matrix_file<-ref_files[grepl('quickprs.matrix',ref_files)]
 
 if(opt$genomic_control == F){
-  system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --tagfile ', opt$quick_prs_ref, '/', tagging_file, ' --summary ', tmp_dir, '/GWAS_sumstats_temp.txt --matrix ', opt$quick_prs_ref, '/', matrix_file, ' --max-threads ', opt$n_cores, ' --check-sums NO'))
+  system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --tagfile ', opt$quickprs_ldref, '/', tagging_file, ' --summary ', tmp_dir, '/GWAS_sumstats_temp.txt --matrix ', opt$quickprs_ldref, '/', matrix_file, ' --max-threads ', opt$n_cores, ' --check-sums NO'))
 } else{
-  system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --genomic-control YES --tagfile ', opt$quick_prs_ref, '/', tagging_file, ' --summary ', tmp_dir, '/GWAS_sumstats_temp.txt --matrix ', opt$quick_prs_ref, '/', matrix_file, ' --max-threads ', opt$n_cores, ' --check-sums NO'))
+  system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --genomic-control YES --tagfile ', opt$quickprs_ldref, '/', tagging_file, ' --summary ', tmp_dir, '/GWAS_sumstats_temp.txt --matrix ', opt$quickprs_ldref, '/', matrix_file, ' --max-threads ', opt$n_cores, ' --check-sums NO'))
 }
 
 ldak_res_her<-fread(paste0(tmp_dir,'/bld.ldak.hers'))
@@ -145,7 +145,7 @@ cor_file_prefix<-gsub('.cors.bin','',ref_files[grepl('.cors.bin',ref_files) & !g
 
 log_add(log_file = log_file, message = paste0('Running MegaPRS: ',opt$prs_model,' model.'))
 
-system(paste0(opt$ldak,' --mega-prs ',tmp_dir,'/mega_full --model ',opt$prs_model,' --cors ',opt$quick_prs_ref,'/',cor_file_prefix,' --ind-hers ',tmp_dir,'/bld.ldak.ind.hers --summary ',tmp_dir,'/GWAS_sumstats_temp.txt --high-LD ',opt$quick_prs_ref,'/highld.snps --cv-proportion 0.1 --window-cm 1 --max-threads ',opt$n_cores,' --extract ',tmp_dir,'/GWAS_sumstats_temp.txt'))
+system(paste0(opt$ldak,' --mega-prs ',tmp_dir,'/mega_full --model ',opt$prs_model,' --cors ',opt$quickprs_ldref,'/',cor_file_prefix,' --ind-hers ',tmp_dir,'/bld.ldak.ind.hers --summary ',tmp_dir,'/GWAS_sumstats_temp.txt --high-LD ',opt$quickprs_ldref,'/highld.snps --cv-proportion 0.1 --window-cm 1 --max-threads ',opt$n_cores,' --extract ',tmp_dir,'/GWAS_sumstats_temp.txt'))
 
 # Save the parameters file
 system(paste0('cp ',tmp_dir,'/mega_full.parameters ',opt$output,'.model_param.txt'))
@@ -157,7 +157,7 @@ system(paste0('cp ',tmp_dir,'/mega_full.cors ',opt$output,'.pseudoval.txt'))
 ldak_res_cors <- fread(paste0(tmp_dir, '/mega_full.cors'), nThread = opt$n_cores)
 best_score <- ldak_res_cors[which.max(ldak_res_cors$Correlation),]
 
-log_add(log_file = log_file, message = paste0('Model ', gsub('Score_','',best_score$V1[1]),' is identified as the best with correlation of ', best_score$V2))
+log_add(log_file = log_file, message = paste0('Model ', gsub('Score_','',best_score$Model[1]),' is identified as the best with correlation of ', best_score$Correlation[1]))
 
 ######
 # Format final score file
diff --git a/Scripts/pgs_methods/quickprs_multi.R b/Scripts/pgs_methods/quickprs_multi.R
index 95914148..0017808e 100644
--- a/Scripts/pgs_methods/quickprs_multi.R
+++ b/Scripts/pgs_methods/quickprs_multi.R
@@ -6,6 +6,8 @@ suppressMessages(library("optparse"))
 option_list = list(
   make_option("--ref_plink_chr", action="store", default=NA, type='character',
               help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_freq_chr", action="store", default=NULL, type='character',
+              help="Path to per chromosome reference PLINK2 .afreq files [required]"),
   make_option("--ref_pcs", action="store", default=NULL, type='character',
               help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
@@ -24,8 +26,10 @@ option_list = list(
               help="Comma-seperated list of population codes matching GWAS [required]"),
   make_option("--ldak", action="store", default=NA, type='character',
               help="Path to ldak v5.2 executable [required]"),
-  make_option("--quick_prs_ref", action="store", default=NA, type='character',
-              help="Path to folder containing ldak quick prs reference [required]"),
+  make_option("--quickprs_ldref", action="store", default=NA, type='character',
+              help="Path to folder containing ldak quickprs reference [required]"),
+  make_option("--quickprs_multi_ldref", action="store", default=NA, type='character',
+              help="Path to folder containing ldak quickprs_multi reference [required]"),
   make_option("--n_cores", action="store", default=1, type='numeric',
               help="Number of cores for parallel computing [optional]"),
   make_option("--prs_model", action="store", default='bayesr', type='character',
@@ -50,6 +54,9 @@ source_all('../functions')
 if(is.null(opt$ref_plink_chr)){
   stop('--ref_plink_chr must be specified.\n')
 }
+if(is.null(opt$ref_freq_chr)){
+  stop('--ref_freq_chr must be specified.\n')
+}
 if(is.null(opt$sumstats)){
   stop('--sumstats must be specified.\n')
 }
@@ -62,8 +69,11 @@ if(is.null(opt$output)){
 if(is.null(opt$ldak)){
   stop('--ldak must be specified.\n')
 }
-if(is.null(opt$quick_prs_ref)){
-  stop('--quick_prs_ref must be specified.\n')
+if(is.null(opt$quickprs_ldref)){
+  stop('--quickprs_ldref must be specified.\n')
+}
+if(is.null(opt$quickprs_multi_ldref)){
+  stop('--quickprs_multi_ldref must be specified.\n')
 }
 if(is.null(opt$populations)){
   stop('--populations must be specified.\n')
@@ -115,10 +125,10 @@ for(i in 1:length(sumstats)){
   gwas_N <- c(gwas_N, round(mean(gwas$n), 0))
 
   # Check overlap between GWAS and LDAK reference
-  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', populations[i])
-  ldak_hm3_file <- list.files(quick_prs_ref_pop_i)
+  quickprs_ldref_pop_i <- paste0(opt$quickprs_ldref, '/', populations[i])
+  ldak_hm3_file <- list.files(quickprs_ldref_pop_i)
   ldak_hm3_file <- ldak_hm3_file[grepl('.cors.bim', ldak_hm3_file)][1]
-  ldak_hm3 <- fread(paste0(quick_prs_ref_pop_i, '/', ldak_hm3_file))
+  ldak_hm3 <- fread(paste0(quickprs_ldref_pop_i, '/', ldak_hm3_file))
   ldak_hm3 <- ldak_hm3[ldak_hm3$V1 %in% CHROMS,]
   ref_overlap <- sum(gwas$Predictor %in% ldak_hm3$V2) / nrow(ldak_hm3)
 
@@ -146,7 +156,7 @@ score_full <- list()
 for(i in 1:length(sumstats)){
   score_full[[populations[i]]] <- quick_prs(
     sumstats = paste0(tmp_dir,'/GWAS_sumstats_temp', i, '.txt'),
-    ref_dir = paste0(opt$quick_prs_ref, '/', populations[i]),
+    ref_dir = paste0(opt$quickprs_ldref, '/', populations[i]),
     genomic_control = opt$genomic_control,
     n_cores = opt$n_cores,
     prs_model = opt$prs_model)
@@ -161,8 +171,8 @@ log_add(log_file = log_file, message = 'Subsampling sumstats.')
 dir.create(paste0(tmp_dir,'/LEOPARD/sampled_sumstats'), recursive = T)
 
 for(i in 1:length(sumstats)){
-  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', populations[i])
-  ref_files <- list.files(quick_prs_ref_pop_i)
+  quickprs_multi_ldref_pop_i <- paste0(opt$quickprs_multi_ldref, '/', populations[i])
+  ref_files <- list.files(quickprs_multi_ldref_pop_i)
   ref_files <- gsub('.bed', '', ref_files[grepl('subset_1.bed', ref_files)])
   system(
     paste0(
@@ -172,7 +182,7 @@ for(i in 1:length(sumstats)){
       '--sumstats ', tmp_dir, '/GWAS_sumstats_temp', i, '.txt ',
       '--n_gwas ', gwas_N[i], ' ',
       '--train_prop 0.75 ',
-      '--ref_prefix ', quick_prs_ref_pop_i, '/', ref_files, ' ',
+      '--ref_prefix ', quickprs_multi_ldref_pop_i, '/', ref_files, ' ',
       '--seed 1 ',
       '--rep 4 ',
       '--out_prefix ', tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i
@@ -198,9 +208,10 @@ for(i in 1:length(sumstats)){
     sumstats_tmp <- sumstats_tmp[, c('Predictor','A1','A2','n','P','Direction'), with = F]
     fwrite(sumstats_tmp, paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.reformat.txt'), sep=' ')
 
-    score_subset[[populations[i]]][[paste0('subset_', j)]] <- quick_prs(
+    score_subset[[populations[i]]][[paste0('subset_', j)]] <- quickprs(
       sumstats = paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.reformat.txt'),
-      ref_dir = paste0(opt$quick_prs_ref, '/', populations[i]),
+      quickprs_ldref = paste0(opt$quickprs_ldref, '/', populations[i]),
+      quickprs_multi_ldref = paste0(opt$quickprs_multi_ldref, '/', populations[i]),
       genomic_control = opt$genomic_control,
       n_cores = opt$n_cores,
       ref_subset = '2',
@@ -220,8 +231,8 @@ log_add(log_file = log_file, message = 'Estimating the linear combination weight
 
 for(targ_pop in populations){
   dir.create(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop), recursive = T)
-  quick_prs_ref_pop_i <- paste0(opt$quick_prs_ref, '/', targ_pop)
-  ref_files <- list.files(quick_prs_ref_pop_i)
+  quickprs_multi_ldref_pop_i <- paste0(opt$quickprs_multi_ldref, '/', targ_pop)
+  ref_files <- list.files(quickprs_multi_ldref_pop_i)
   ref_files <- gsub('.bed', '', ref_files[grepl('subset_3.bed', ref_files)])
 
   for(j in 1:4){
@@ -237,14 +248,14 @@ for(targ_pop in populations){
       '--beta_file ', tmp_dir, '/LEOPARD/post_targ_', populations[1], '/output_', j, '_', populations[1], '_Post.txt',',',tmp_dir, '/LEOPARD/post_targ_', populations[2], '/output_', j, '_', populations[2], '_Post.txt ',
       '--valid_file ', tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_valid.reformat.txt ',
       '--n_valid ', targ_gwas_valid_n ,' ',
-      '--ref_prefix ', quick_prs_ref_pop_i, '/', ref_files, ' ',
+      '--ref_prefix ', quickprs_multi_ldref_pop_i, '/', ref_files, ' ',
       '--out ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep', j, '.txt'
     ))
   }
 }
 
 # Average weights across repeats
-mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'))
+mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'), log_file = log_file)
 
 ####
 # Combine score files
@@ -252,39 +263,43 @@ mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = pa
 
 log_add(log_file = log_file, message = 'Creating score file.')
 
-# We should combine the raw QuickPRS score files for each population,
-# and then combine using mixing weights for each population
+# Combine the scores from each population
 score_all <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE), score_full)
 names(score_all)[grepl('BETA', names(score_all))]<-paste0('SCORE_targ_', populations)
 score_all[is.na(score_all)]<-0
 
-for(targ_pop in populations){
-  # Read in weights for target population
-  mix_weights<-avg_weights[[targ_pop]]
-  score_all_tmp<-score_all
-  for(i in populations){
-    score_all_tmp[[paste0('SCORE_targ_', i)]] <- score_all[[paste0('SCORE_targ_', i)]] * mix_weights[which(populations == i)]
-  }
-  # Take average of weighted scores
-  score_all[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <- rowSums(score_all_tmp[, grepl('SCORE_', names(score_all_tmp)), with = F])
-}
-
-names(score_all)[names(score_all) == 'SNP']<-'Predictor'
+# Read in reference SNP data
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
 
 # Change IDs to RSIDs
-ref_pvar <- read_pvar(dat = opt$ref_plink_chr, chr = CHROMS)
-ref_pvar$Predictor<-paste0(ref_pvar$CHR,':',ref_pvar$BP)
-score_all<-merge(score_all, ref_pvar[,c('Predictor','SNP'), with=F], by='Predictor')
+ref$Predictor<-paste0(ref$CHR,':',ref$BP)
+names(score_all)[names(score_all) == 'SNP'] <- 'Predictor'
+score_all<-merge(score_all, ref[,c('Predictor','SNP'), with=F], by='Predictor')
 score_all<-score_all[, c('SNP', 'A1', 'A2', names(score_all)[grepl('SCORE_', names(score_all))]), with=F]
 
 # Flip effects to match reference alleles
-ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
-score_new <- map_score(ref = ref, score = score_all)
+score_all <- map_score(ref = ref, score = score_all)
+
+# Calculate linear combination of scores using mixing weights for each target population
+score_weighted <- score_all
+for(targ_pop in populations){
+  # Read in the .freq file for target population
+  freq_data <- read_frq(freq_dir = opt$ref_freq_chr, population = targ_pop, chr = CHROMS)
+  
+  # Centre SNP-weights for target population
+  score_i <- centre_weights(score = score_all, freq = freq_data, ref = ref)
+  
+  # Linearly combine scores using mixing weights for target population
+  score_weighted[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <-
+    calculate_weighted_scores(score = score_i,
+                              mix_weights = mix_weights,
+                              targ_pop = targ_pop)
+}
 
 # Reduce number of significant figures to save space
-score_new[, (4:ncol(score_new)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_new)]
+score_weighted[, (4:ncol(score_all)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_all)]
 
-fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+fwrite(score_weighted, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
 
 if(file.exists(paste0(opt$output,'.score.gz'))){
   system(paste0('rm ',opt$output,'.score.gz'))
diff --git a/Scripts/pgs_methods/xwing.R b/Scripts/pgs_methods/xwing.R
index d52432ef..2601e63e 100644
--- a/Scripts/pgs_methods/xwing.R
+++ b/Scripts/pgs_methods/xwing.R
@@ -311,9 +311,6 @@ mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = pa
 # Combine score files
 ####
 
-# Read in reference population data
-pop_data <- read_pop_data(opt$pop_data)
-
 # Read in reference SNP data
 ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
 
@@ -337,7 +334,7 @@ for(targ_pop in populations){
     score_i <- score_i[, c('SNP','A1','A2','BETA'), with = F]
     
     # Centre SNP-weights for target population
-    score_i <- centre_weights(score = score_i, freq = freq_data)
+    score_i <- centre_weights(score = score_i, freq = freq_data, ref = ref)
     
     # Adjust SNP-weights according to mixing weights
     score_i$BETA <- score_i$BETA * mix_weights[[targ_pop]][which(populations == pst_pop)]
diff --git a/docs/prep_quickprs_ref.Rmd b/docs/prep_quickprs_ref.Rmd
index f785d9ab..6871e09e 100644
--- a/docs/prep_quickprs_ref.Rmd
+++ b/docs/prep_quickprs_ref.Rmd
@@ -211,6 +211,10 @@ for(pop in c('EUR','EAS','AFR')){
     }
   }
 
+  # Calculate freq files for subset 3, as required by LEOPARD
+  for(subset_i in 3:3){
+    system(paste0(opt$plink, ' --bfile ', opt$output, '.subset_', subset_i, ' --freq --out ', opt$output, '.subset_', subset_i))
+  }
 
   system(paste0('rm -r ', tmp_dir, '/*'))
 }
diff --git a/functions/pgs.R b/functions/pgs.R
index 22e33f55..bcea5887 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -72,6 +72,11 @@ list_score_files <- function(config){
 
 # Flip effects in score file to match A1 reference
 map_score<-function(ref, score){
+  if(!all(c('SNP','A1','A2') %in% names(ref)) | !all(c('SNP','A1','A2') %in% names(score))){
+    stop('ref and score must contain SNP, A1 and A2 columns.')
+  }
+  
+  ref <- ref[, c('SNP','A1','A2'), with = F]
   tmp <- merge(ref, score, by = 'SNP', all.x=T, sort = F)
   flip <- which(tmp$A1.x != tmp$A1.y)
   tmp <- as.matrix(tmp[, -1:-5, drop = FALSE])
@@ -431,24 +436,29 @@ read_score <- function(score, chr = 1:22, log_file = NULL){
 }
 
 
-quick_prs<-function(sumstats, ref_dir, genomic_control, prs_model, n_cores = 1, ref_subset = NULL){
+quickprs<-function(sumstats, quickprs_ldref, quickprs_multi_ldref = NULL, genomic_control, prs_model, n_cores = 1, ref_subset = NULL){
   tmp_dir<-tempfile()
   dir.create(tmp_dir)
-
+  
+  # Check if quickprs_multi_ldref and ref_subset are both NULL or both non-NULL
+  if (xor(is.null(quickprs_multi_ldref), is.null(ref_subset))) {
+    stop("Both 'quickprs_multi_ldref' and 'ref_subset' must either be NULL or non-NULL.")
+  }
+  
   ######
   # Estimate Per-Predictor Heritabilities
   ######
 
   # Calculate Per-Predictor Heritabilities.
-  ref_files<-list.files(ref_dir)
+  quickprs_ldref_files<-list.files(quickprs_ldref)
 
-  tagging_file<-ref_files[grepl('quickprs.tagging',ref_files)]
-  matrix_file<-ref_files[grepl('quickprs.matrix',ref_files)]
+  tagging_file<-quickprs_ldref_files[grepl('quickprs.tagging',quickprs_ldref_files)]
+  matrix_file<-quickprs_ldref_files[grepl('quickprs.matrix',quickprs_ldref_files)]
 
   if(opt$genomic_control == F){
-    system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --tagfile ', ref_dir, '/', tagging_file, ' --summary ', sumstats, ' --matrix ', ref_dir, '/', matrix_file, ' --max-threads ', n_cores, ' --check-sums NO'))
+    system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --tagfile ', quickprs_ldref, '/', tagging_file, ' --summary ', sumstats, ' --matrix ', quickprs_ldref, '/', matrix_file, ' --max-threads ', n_cores, ' --check-sums NO'))
   } else{
-    system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --genomic-control YES --tagfile ', ref_dir, '/', tagging_file, ' --summary ', sumstats, ' --matrix ', ref_dir, '/', matrix_file, ' --max-threads ', n_cores, ' --check-sums NO'))
+    system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --genomic-control YES --tagfile ', quickprs_ldref, '/', tagging_file, ' --summary ', sumstats, ' --matrix ', quickprs_ldref, '/', matrix_file, ' --max-threads ', n_cores, ' --check-sums NO'))
   }
 
   ldak_res_her<-fread(paste0(tmp_dir,'/bld.ldak.hers'))
@@ -458,12 +468,15 @@ quick_prs<-function(sumstats, ref_dir, genomic_control, prs_model, n_cores = 1,
   ######
 
   if(!is.null(ref_subset)){
-    cor_file_prefix<-gsub('.cors.bin','',ref_files[grepl(paste0('subset_', ref_subset, '.cors.bin'),ref_files)])
+    quickprs_multi_ldref_files<-list.files(quickprs_multi_ldref)
+    ref_dir <- quickprs_multi_ldref
+    cor_file_prefix<-gsub('.cors.bin','',quickprs_multi_ldref_files[grepl(paste0('subset_', ref_subset, '.cors.bin'),quickprs_multi_ldref_files)])
   } else {
-    cor_file_prefix<-gsub('.cors.bin','',ref_files[grepl('.cors.bin',ref_files) & !grepl('subset', ref_files)])
+    cor_file_prefix<-gsub('.cors.bin','',quickprs_ldref_files[grepl('.cors.bin',quickprs_ldref_files) & !grepl('subset', quickprs_ldref_files)])
+    ref_dir <- quickprs_ldref
   }
 
-  system(paste0(opt$ldak,' --mega-prs ',tmp_dir,'/mega_full --model ', prs_model,' --cors ', ref_dir, '/', cor_file_prefix, ' --ind-hers ', tmp_dir, '/bld.ldak.ind.hers --summary ', sumstats, ' --high-LD ', ref_dir, '/highld.snps --cv-proportion 0.1 --window-cm 1 --max-threads ', n_cores,' --extract ', sumstats))
+  system(paste0(opt$ldak,' --mega-prs ',tmp_dir,'/mega_full --model ', prs_model,' --cors ', ref_dir, '/', cor_file_prefix, ' --ind-hers ', tmp_dir, '/bld.ldak.ind.hers --summary ', sumstats, ' --high-LD ', quickprs_ldref, '/highld.snps --cv-proportion 0.1 --window-cm 1 --max-threads ', n_cores,' --extract ', sumstats))
 
   # Identify the best fitting model
   ldak_res_cors <- fread(paste0(tmp_dir, '/mega_full.cors'), nThread = n_cores)
@@ -603,10 +616,9 @@ calculate_avg_weights <- function(populations, leopard_dir, log_file = NULL) {
   
   log_add(log_file = log_file, message = '------------------------')
   for(i in names(avg_weights)){
+    log_add(log_file = log_file, message = paste0("LEOPARD weights - ", i, " target: "))
     for(j in populations){
-      log_add(log_file = log_file, message = paste0("LEOPARD weights - ", i, " target: "))
       log_add(log_file = log_file, message = paste0(j, ' = ', avg_weights[[i]][which(populations == j)]))
-      mix_weights
     }
     log_add(log_file = log_file, message = '------------------------')
   }
@@ -615,7 +627,7 @@ calculate_avg_weights <- function(populations, leopard_dir, log_file = NULL) {
 }
 
 # Centre SNP-weights
-centre_weights <- function(score, freq){
+centre_weights <- function(score, freq, ref){
   # Sort and flip according to reference data
   score <- map_score(ref = ref, score = score)
   
@@ -634,4 +646,20 @@ centre_weights <- function(score, freq){
     score[[i]] <- score[[i]] - (mean_pgs / denominator) * freq$MeanGenotype
   }
   return(score)
+}
+
+# Linearly combine scores using mixing weights for target population
+calculate_weighted_scores <- function(score, targ_pop, mix_weights) {
+  if(!all((names(score) %in% c('SNP','A1','A2', paste0('SCORE_targ_', names(mix_weights)))))){
+    stop(paste0('score should only contain columns SNP, A1, A2, ', paste(paste0('SCORE_targ_', names(mix_weights)), collapse=', ')))
+  }
+  score_weighted<-score
+  for(disc_pop in names(mix_weights)){
+    score_tmp <- score[[paste0('SCORE_targ_', disc_pop)]]
+    weight_tmp <- mix_weights[[targ_pop]][which(names(mix_weights) == disc_pop)]
+    score_weighted[[paste0('SCORE_targ_', disc_pop)]] <- score_tmp * weight_tmp
+  }
+  score_combined <- rowSums(score_weighted[, grepl('SCORE_', names(score_weighted)), with = FALSE])
+  
+  return(score_combined)
 }
\ No newline at end of file
diff --git a/pipeline/misc/opensnp/config_cross_pop.yaml b/pipeline/misc/opensnp/config_cross_pop.yaml
index 6930648d..dddf6ba1 100644
--- a/pipeline/misc/opensnp/config_cross_pop.yaml
+++ b/pipeline/misc/opensnp/config_cross_pop.yaml
@@ -2,7 +2,7 @@ outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1
 config_file: misc/opensnp/config_cross_pop.yaml
 gwas_list: misc/opensnp/gwas_list.txt
 target_list: misc/opensnp/target_list.txt
-pgs_methods: ['ptclump', 'megaprs','xwing','quickprs']
+pgs_methods: ['ptclump', 'megaprs','xwing','quickprs','quickprs_multi']
 testing: chr22
 gwas_groups: misc/cross_pop_test/gwas_groups.txt
 cores_prep_pgs: 10 # Set to 20 when running xwing
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index aea71b0f..30bf7df0 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -390,7 +390,7 @@ rule prep_pgs_megaprs:
 # LDAK QuickPRS
 ##
 
-def get_quickprs_reference_path(w, gwas_list_df, resdir):
+def get_quickprs_ldref_path(w, gwas_list_df, resdir):
   # Get the population from the GWAS list
   population = gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0]
 
@@ -404,7 +404,7 @@ rule prep_pgs_quickprs_i:
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
-    lambda w: get_quickprs_reference_path(w, gwas_list_df, resdir),
+    lambda w: get_quickprs_ldref_path(w, gwas_list_df, resdir),
     rules.download_ldak_highld.output,
     rules.download_ldak5_2.output,
     rules.download_ldak_map.output,
@@ -427,7 +427,7 @@ rule prep_pgs_quickprs_i:
       --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --ldak {resdir}/software/ldak5.2/ldak5.2.linux \
-      --quick_prs_ref {quickprs_ldref}/{params.population} \
+      --quickprs_ldref {quickprs_ldref}/{params.population} \
       --n_cores {threads} \
       --output {outdir}/reference/pgs_score_files/quickprs/{wildcards.gwas}/ref-{wildcards.gwas} \
       --pop_data {refdir}/ref.pop.txt \
@@ -737,7 +737,8 @@ rule prep_pgs_quickprs_multi_i:
     time_min=5000
   threads: config['cores_prep_pgs']
   input:
-    #lambda w: expand(f"{quickprs_ldref}/{{population}}/{{population}}.cors.bin", population=[pop for pop in get_populations(w.gwas_group)]),
+    lambda w: expand(f"{quickprs_ldref}/{{population}}/{{population}}.cors.bin", population=[pop for pop in get_populations(w.gwas_group)]),
+    lambda w: expand(f"{quickprs_multi_ldref}/{{population}}/{{population}}.subset_1.bed", population=[pop for pop in get_populations(w.gwas_group)]),
     lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
     rules.download_ldak_highld.output,
     rules.download_ldak5_2.output,
@@ -759,11 +760,13 @@ rule prep_pgs_quickprs_multi_i:
   shell:
     "Rscript ../Scripts/pgs_methods/quickprs_multi.R \
       --ref_plink_chr {refdir}/ref.chr \
+      --ref_freq_chr {refdir}/freq_files \
       --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {params.sumstats} \
       --populations {params.populations} \
       --ldak {resdir}/software/ldak5.2/ldak5.2.linux \
-      --quick_prs_ref {quickprs_ldref} \
+      --quickprs_ldref {quickprs_ldref} \
+      --quickprs_multi_ldref {quickprs_multi_ldref} \
       --xwing_repo {resdir}/software/xwing \
       --n_cores {threads} \
       --output {outdir}/reference/pgs_score_files/quickprs_multi/{wildcards.gwas_group}/ref-{wildcards.gwas_group} \

From 2f0585321e3d1619127cf2d817b6f3da232b6e0c Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 22 Jan 2025 15:55:00 +0000
Subject: [PATCH 085/153] Updates

---
 Scripts/pgs_methods/quickprs_multi.R          |  4 +-
 functions/pipeline.R                          |  2 +-
 .../misc/cross_pop_test/cross_pop_test.Rmd    | 95 ++++++-------------
 pipeline/misc/cross_pop_test/gwas_groups.txt  |  1 +
 .../misc/opensnp/config_cross_pop_gw.yaml     |  9 ++
 pipeline/misc/opensnp/gwas_list.txt           |  2 +
 6 files changed, 44 insertions(+), 69 deletions(-)
 create mode 100644 pipeline/misc/opensnp/config_cross_pop_gw.yaml

diff --git a/Scripts/pgs_methods/quickprs_multi.R b/Scripts/pgs_methods/quickprs_multi.R
index 0017808e..870c141c 100644
--- a/Scripts/pgs_methods/quickprs_multi.R
+++ b/Scripts/pgs_methods/quickprs_multi.R
@@ -154,9 +154,9 @@ log_add(log_file = log_file, message = 'Running QuickPRS using full sumstats.')
 
 score_full <- list()
 for(i in 1:length(sumstats)){
-  score_full[[populations[i]]] <- quick_prs(
+  score_full[[populations[i]]] <- quickprs(
     sumstats = paste0(tmp_dir,'/GWAS_sumstats_temp', i, '.txt'),
-    ref_dir = paste0(opt$quickprs_ldref, '/', populations[i]),
+    quickprs_ldref = paste0(opt$quickprs_ldref, '/', populations[i]),
     genomic_control = opt$genomic_control,
     n_cores = opt$n_cores,
     prs_model = opt$prs_model)
diff --git a/functions/pipeline.R b/functions/pipeline.R
index b4062e08..c6e96e80 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -378,7 +378,7 @@ read_reference_pgs <- function(config){
           outdir, '/reference/pgs_score_files/', pgs_method_i, '/',  gwas_i, '/ref-', gwas_i, '-TRANS.profiles'
         )
       )
-    pseudo_param <- find_pseudo(config = config, gwas = gwas_i, pgs_method = pgs_method_i)
+    pseudo_param <- find_pseudo(config = config, gwas = gwas_i, pgs_method = pgs_method_i, target_pop = 'TRANS')
     pgs[[gwas_i]][[pgs_method_i]]<-pgs[[gwas_i]][[pgs_method_i]][,c('FID','IID',paste0('SCORE_',pseudo_param)), with=F]
   }
   
diff --git a/pipeline/misc/cross_pop_test/cross_pop_test.Rmd b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
index 40bef4f6..d887a603 100644
--- a/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
+++ b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
@@ -47,8 +47,11 @@ write.table(config, 'misc/opensnp/config_cross_pop.yaml', col.names = F, row.nam
 
 ```{bash}
 snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_cross_pop.yaml output_all -n
+snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_cross_pop_gw.yaml output_all -n
 ```
 
+***
+
 ## Evaluate PGS
 
 <details><summary>Show code</summary>
@@ -68,7 +71,10 @@ source_all('../functions')
 pheno <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno/height.txt')
 
 # Read in PGS
-pgs <- read_pgs(config = 'misc/opensnp/config_cross_pop.yaml', name = 'opensnp')$opensnp
+pgs <- read_pgs(config = 'misc/opensnp/config_cross_pop_gw.yaml', name = 'opensnp')$opensnp
+
+# Read in PGS
+ancestry <- read_ancestry(config = 'misc/opensnp/config_cross_pop_gw.yaml', name = 'opensnp')
 
 # Estimate correlation between pheno and pgs
 cor <- NULL
@@ -78,6 +84,9 @@ for(pop_i in names(pgs)){
       pgs_i <- pgs[[pop_i]][[gwas_i]][[pgs_method_i]]
       pheno_pgs<-merge(pheno, pgs_i, by = c('FID','IID'))
       
+      # Restrict to EUR
+      pheno_pgs <- pheno_pgs[pheno_pgs$FID %in% ancestry$keep_files$EUR$V1,]
+      
       for(model_i in names(pgs_i)[-1:-2]){
         y <- scale(pheno_pgs$height)
         x <- scale(pheno_pgs[[model_i]])
@@ -104,47 +113,43 @@ for(pop_i in names(pgs)){
   }
 }
 
-# The European sample is the only one large enough for interpretable results
-# Subset EUR results
-cor_eur <- cor[cor$pop == 'EUR', ]
-
 # Restrict to best and and pseudoval only
-cor_eur_subset <- NULL
-for(pop_i in unique(cor_eur$pop)){
-  for(gwas_i in unique(cor_eur$gwas[cor_eur$pop == pop_i])){
-    for(pgs_method_i in unique(cor_eur$pgs_method[cor_eur$pop == pop_i & cor_eur$gwas == gwas_i])){
+cor_subset <- NULL
+for(pop_i in unique(cor$pop)){
+  for(gwas_i in unique(cor$gwas[cor$pop == pop_i])){
+    for(pgs_method_i in unique(cor$pgs_method[cor$pop == pop_i & cor$gwas == gwas_i])){
       
       # Subset relevant results
-      cor_eur_i <- cor_eur[
-        cor_eur$pop == pop_i &
-        cor_eur$gwas == gwas_i &
-        cor_eur$pgs_method == pgs_method_i,]
+      cor_i <- cor[
+        cor$pop == pop_i &
+        cor$gwas == gwas_i &
+        cor$pgs_method == pgs_method_i,]
   
       # Top R
       if(pgs_method_i %in% c('ptclump','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm') | any(grepl('tlprs', pgs_method_i))){
-        top_i <- cor_eur_i[which(cor_eur_i$r == max(cor_eur_i$r, na.rm = T))[1],]
+        top_i <- cor_i[which(cor_i$r == max(cor_i$r, na.rm = T))[1],]
         top_i$model <- 'Top'
-        cor_eur_subset <- rbind(cor_eur_subset, top_i)
+        cor_subset <- rbind(cor_subset, top_i)
       }
   
       # PseudoVal
-      if(pgs_method_i %in% c('ptclump','sbayesr','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm')){
-        cor_eur_i$name <- gsub(paste0(gwas_i, '_'), '', cor_eur_i$name)
-        pseudo_param <- find_pseudo(config = 'misc/opensnp/config_cross_pop.yaml', gwas = gwas_i, pgs_method = pgs_method_i)
-        pseudo_i <- cor_eur_i[cor_eur_i$name == pseudo_param,]
+      if(pgs_method_i %in% c('ptclump','sbayesr','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm','quickprs','quickprs_multi')){
+        cor_i$name <- gsub(paste0(gwas_i, '_'), '', cor_i$name)
+        pseudo_param <- find_pseudo(config = 'misc/opensnp/config_cross_pop_gw.yaml', gwas = gwas_i, pgs_method = pgs_method_i, target_pop = 'EUR')
+        pseudo_i <- cor_i[cor_i$name == pseudo_param,]
         pseudo_i$model <- 'Pseudo'
-        cor_eur_subset <- rbind(cor_eur_subset, pseudo_i)
+        cor_subset <- rbind(cor_subset, pseudo_i)
       }
     }
   }
 }
 
 # Plot the results
-cor_eur_subset$model <- factor(cor_eur_subset$model, levels = c('Top','Pseudo','External'))
+cor_subset$model <- factor(cor_subset$model, levels = c('Top','Pseudo','External'))
 dir.create('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP')
 
-plot_obj_eur <- 
-  ggplot(cor_eur_subset, aes(x = pgs_method, y = r, fill = model)) +
+plot_obj <- 
+  ggplot(cor_subset, aes(x = pgs_method, y = r, fill = model)) +
   geom_bar(stat = "identity", position = position_dodge2(preserve = "single"), width = 0.7) +
   geom_errorbar(
     aes(ymin = r - se, ymax = r + se),
@@ -155,7 +160,7 @@ plot_obj_eur <-
     y = "Correlation (SE)",
     x = 'PGS Method',
     fill = 'Model',
-    title = paste0("OpenSNP - EUR\n(N = ", cor_eur_subset$n[1], ")")
+    title = paste0("OpenSNP - EUR\n(N = ", cor_subset$n[1], ")")
   ) +
   theme_half_open() +
   background_grid() +
@@ -163,46 +168,4 @@ plot_obj_eur <-
         plot.title = element_text(hjust = 0.5, size=12)) +
   facet_grid(. ~ gwas, scales = 'free_x', space = 'free_x')
 
-
-cor_eur_tlprs<-cor_eur[cor_eur$pgs_method == 'tlprs_ptclump',]
-cor_eur_tlprs[grepl('EUR_0_0.01', cor_eur_tlprs$name),]
-
-
-cor_eas <- cor[cor$pop == 'EAS', ]
-# Restrict to best and and pseudoval only
-cor_eas_subset <- NULL
-for(pop_i in unique(cor_eas$pop)){
-  for(gwas_i in unique(cor_eas$gwas[cor_eas$pop == pop_i])){
-    for(pgs_method_i in unique(cor_eas$pgs_method[cor_eas$pop == pop_i & cor_eas$gwas == gwas_i])){
-      
-      # Subset relevant results
-      cor_eas_i <- cor_eas[
-        cor_eas$pop == pop_i &
-        cor_eas$gwas == gwas_i &
-        cor_eas$pgs_method == pgs_method_i,]
-  
-      # Top R
-      if(pgs_method_i %in% c('ptclump','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm') | any(grepl('tlprs', pgs_method_i))){
-        top_i <- cor_eas_i[which(cor_eas_i$r^2 == max(cor_eas_i$r^2, na.rm = T))[1],]
-        top_i$model <- 'Top'
-        cor_eas_subset <- rbind(cor_eas_subset, top_i)
-      }
-  
-      # PseudoVal
-      if(pgs_method_i %in% c('ptclump','sbayesr','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm')){
-        cor_eas_i$name <- gsub(paste0(gwas_i, '_'), '', cor_eas_i$name)
-        pseudo_param <- find_pseudo(config = 'misc/opensnp/config_cross_pop.yaml', gwas = gwas_i, pgs_method = pgs_method_i)
-        pseudo_i <- cor_eas_i[cor_eas_i$name == pseudo_param,]
-        pseudo_i$model <- 'Pseudo'
-        cor_eas_subset <- rbind(cor_eas_subset, pseudo_i)
-      }
-    }
-  }
-}
-
-# Plot the results
-cor_eas_subset$model <- factor(cor_eur_subset$model, levels = c('Top','Pseudo','External'))
-dir.create('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP')
-cor_eas_tlprs<-cor_eas[cor_eas$pgs_method == 'tlprs_ptclump',]
-
 ```
diff --git a/pipeline/misc/cross_pop_test/gwas_groups.txt b/pipeline/misc/cross_pop_test/gwas_groups.txt
index 213fc7ff..524768a0 100644
--- a/pipeline/misc/cross_pop_test/gwas_groups.txt
+++ b/pipeline/misc/cross_pop_test/gwas_groups.txt
@@ -1,2 +1,3 @@
 name gwas label
 height yengo_eur,yengo_eas "Height (EUR+EAS)"
+HT_UKB_UGR HT_UKB,HT_UGR "HT (UKB+UGR)"
\ No newline at end of file
diff --git a/pipeline/misc/opensnp/config_cross_pop_gw.yaml b/pipeline/misc/opensnp/config_cross_pop_gw.yaml
new file mode 100644
index 00000000..ea4adf36
--- /dev/null
+++ b/pipeline/misc/opensnp/config_cross_pop_gw.yaml
@@ -0,0 +1,9 @@
+outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_gw
+config_file: misc/opensnp/config_cross_pop_gw.yaml
+gwas_list: misc/opensnp/gwas_list.txt
+target_list: misc/opensnp/target_list.txt
+pgs_methods: ['ptclump', 'megaprs', 'quickprs', 'quickprs_multi']
+gwas_groups: misc/cross_pop_test/gwas_groups.txt
+cores_prep_pgs: 10 # Set to 20 when running xwing
+quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3
+quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset
diff --git a/pipeline/misc/opensnp/gwas_list.txt b/pipeline/misc/opensnp/gwas_list.txt
index ced82a73..ce104316 100644
--- a/pipeline/misc/opensnp/gwas_list.txt
+++ b/pipeline/misc/opensnp/gwas_list.txt
@@ -1,3 +1,5 @@
 name path population n sampling prevalence mean sd label
 yengo_eur /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eur.txt EUR NA NA NA NA NA "Yengo 2022 Height EUR"
 yengo_eas /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eas.txt EAS NA NA NA NA NA "Yengo 2022 Height EAS"
+HT_UKB /scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/gwas/HT/ukb.eur_train.HT.GW.txt.gz EUR NA NA NA 0 1 "Height (UKB)"
+HT_UGR /scratch/prj/oliverpainfel/recovered/Data/GWAS_sumstats/UGR/HT.reformat.txt.gz AFR NA NA NA 0 1 "Height (UGR)"
\ No newline at end of file

From 98cec61656e3c43610e662ccc523b5db4df16eba Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 22 Jan 2025 18:14:22 +0000
Subject: [PATCH 086/153] Rerunning UKB crosspop analysis

---
 docs/CrossPop.Rmd | 176 ++++++++++++++++++++++++++++++++++++++--------
 1 file changed, 146 insertions(+), 30 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 9824e4a9..fcb3fddc 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -42,16 +42,132 @@ To avoid sample overlap between the EUR GWAS and the EUR target sample for evalu
 
 ***
 
-## Run outlier detection
+## Perform ancestry inference in UKB
+
+### Create target_list
+
+<div class="shallow-break"></div>
 
 <details><summary>Show code</summary>
+<br/>
+
+<h3>Create symlinks</h3>
+
+We will create symlinks to the imputed genotype data for UKB. We will use the pgen format data for computationl efficiency and those restricted to MAF >= 1% and INFO >= 0.4. We are using genetic data that is not application specific, so the data doesn't need to be reprocessed for each application. Therefore we will use row number IDs for the .psam file so they can be connected to application specific data downstream.
+
 ```{bash}
-cd /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/GenoPred/pipeline
-git describe --tags
-# v2.2.5-20-gb0bf674
+mkdir -p /users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks
+
+# pgen and pvar files
+for chr in $(seq 1 22);do
+  for file in $(echo pgen pvar);do
+    ln -s /datasets/ukbiobank/June2017/Imputed/ukb_imp_chr${chr}_v3_MAF1_INFO4.${file} /users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/ukb_imp_maf1_info4.chr${chr}.${file}
+  done
+done
+```
+
+```{r}
+# Make .psam 
+n = 487409
+psam <- data.frame(FID = 1:487409,
+                   IID = 1:487409)
+names(psam)[1]<-'#FID'
+write.table(psam, '/users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/rownumber.psam', col.names=T, row.names = F, quote = F)
+```
+
+```{bash}
+for chr in $(seq 1 22);do
+  ln -s /users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/rownumber.psam /users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/ukb_imp_maf1_info4.chr${chr}.psam
+done
+```
+
+***
+
+<h3>Create list of unrelated individuals</h3>
+
+```{r}
+library(ukbkings)
+library(data.table)
+
+psam<-fread('/scratch/prj/ukbiobank/recovered/ukb82087/imputed/ukb82087_imp_chr1_MAF1_INFO4_v1.psam')
+psam$rn<-1:nrow(psam)
+
+project_dir <- "/datasets/ukbiobank/ukb82087"
+greedy_related <- "/scratch/prj/ukbiobank/recovered/KCL_Data/Software/tools/GreedyRelated-master-v1.2.1/GreedyRelated"
+
+# Create a list of unrelated individuals irrespective of a phenotype
+psam_unrel_all <- psam[!(
+  psam$IID %in% bio_gen_related_remove(
+    project_dir = project_dir,
+    greedy_related = greedy_related,
+    thresh = 0.044,
+    seed = 1
+  )$eid
+), ]
+
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/phenotypes')
+
+write.table(psam_unrel_all$IID, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.txt', row.names=F, col.names=F, quote=F)
+write.table(psam_unrel_all$rn, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt', row.names=F, col.names=F, quote=F)
+```
+
+***
 
-snakemake --profile slurm --use-conda --configfile=../../usr/k1806347/configs/benchmark/config.yaml outlier_detection -n 
+<h3>Create target_list</h3>
 
+```{bash}
+mkdir -p /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic
+```
+
+```{r}
+target_list <- data.frame(
+  name='ukb',
+  path='/users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/ukb_imp_maf1_info4',
+  type='plink2',
+  indiv_report=F,
+  unrel='/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt'
+)
+
+write.table(target_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt', col.names=T, row.names=F, quote=F)
+```
+
+</details>
+
+***
+
+## configfile
+
+<details><summary>Show code</summary>
+
+```{r}
+# Create config file
+conf <- c(
+  'outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output',
+  'config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/config.yaml',
+  'target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt'
+)
+
+write.table(conf, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/config.yaml', col.names = F, row.names = F, quote = F)
+```
+
+</details>
+
+***
+
+## Run pipeline
+
+<details><summary>Show code</summary>
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+git describe --tags
+#v2.2.2-213-g2f05853
+
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/config.yaml \
+  outlier_detection \
+  -n 
 ```
 </details>
 
@@ -63,7 +179,7 @@ We will use the same 33 quantitative traits that were used in the PRS-CSx paper
 
 <details><summary>Show code</summary>
 ```{bash}
-mkdir /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx
+mkdir /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx
 ```
 
 ```{r}
@@ -83,27 +199,27 @@ prscsx_dat<-data.frame(
   field=prscsx_fields
 )
 
-write.csv(prscsx_dat, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv', row.names = F)
-write.table(prscsx_labels, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt', col.names=F, row.names = F, quote=F)
+write.csv(prscsx_dat, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv', row.names = F)
+write.table(prscsx_labels, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt', col.names=F, row.names = F, quote=F)
 
 # Extract outcomes from UKB (project ukb82087)
 project_dir <- "/datasets/ukbiobank/ukb82087"
 
-system('rm /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.txt')
+system('rm /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.txt')
 f <- bio_field(project_dir)
 f %>%
     select(field, name) %>%
     filter(str_detect(field, paste(paste0("^", prscsx_dat$field, '-'), collapse='|'))) %>%
-    bio_field_add("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.txt")
+    bio_field_add("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.txt")
 
 bio_phen(
     project_dir,
-    field = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.txt",
-    out = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset"
+    field = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.txt",
+    out = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset"
 )
 
-system("ls -lh /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.rds")
-df <- readRDS("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_field_subset.rds")
+system("ls -lh /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.rds")
+df <- readRDS("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.rds")
 
 # Take the first observation of each outcome
 library(tidyr)
@@ -125,7 +241,7 @@ for(i in 1:nrow(prscsx_dat)){
   fwrite(
     tmp,
     paste0(
-      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
       prscsx_dat$label[i],
       '.txt'
     ),
@@ -138,11 +254,11 @@ for(i in 1:nrow(prscsx_dat)){
 
 # Read in ancestry inference results to determine sample size per population
 # Use ancestry information from GenoPred
-keep_files<-list.files(path = '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/pcs/within_sample/', pattern = '.keep')
+keep_files<-list.files(path = '/users/k1806347/oliverpainfel/Data/ukb/GenoPredPipe/output/ukb/pcs/within_sample/', pattern = '.keep')
 
 pop_dat<-NULL
 for(i in keep_files){
-  tmp<-fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/pcs/within_sample/', i))
+  tmp<-fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/GenoPredPipe/output/ukb/pcs/within_sample/', i))
   names(tmp)<-c('FID','IID')
   tmp$POP<-gsub('.keep','', gsub('ukb.outlier_detection.','',i))
   pop_dat<-rbind(pop_dat, tmp)
@@ -191,7 +307,7 @@ for(i in 1:nrow(prscsx_dat)){
   }
 }
 
-write.csv(n_table, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/n_table')
+write.csv(n_table, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/n_table')
 
 # Define training subset for EUR
 df_short_unrel_eur<-df_short_unrel[df_short_unrel$POP == 'EUR',]
@@ -214,7 +330,7 @@ for(i in 1:nrow(prscsx_dat)){
   n_table_eur<-rbind(n_table_eur, tmp)
 }
 
-write.csv(n_table_eur, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/n_table_eur')
+write.csv(n_table_eur, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/n_table_eur')
 
 df_short_unrel$POP[df_short_unrel$eid %in% df_short_unrel_eur_train$eid]<-'EUR_train'
 df_short_unrel$POP[df_short_unrel$eid %in% df_short_unrel_eur_test$eid]<-'EUR_test'
@@ -232,7 +348,7 @@ for(i in 1:nrow(prscsx_dat)){
     fwrite(
       tmp,
       paste0(
-        '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+        '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
         prscsx_dat$label[i],
         '.unrel.', j, '.txt'
       ),
@@ -253,7 +369,7 @@ for(i in 1:nrow(prscsx_dat)){
     fwrite(
       pheno,
       paste0(
-        '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+        '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
         prscsx_dat$label[i],
         '.unrel.', j, '.row_number.txt'
       ),
@@ -273,21 +389,21 @@ qc_dat<-qc_dat[,c('eid',paste0('pc',1:20))]
 df_short_unrel<-merge(df_short_unrel, qc_dat, by='eid')
 
 # Read in sex and age information
-system('rm /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.txt')
+system('rm /users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt')
 f <- bio_field(project_dir)
 f %>%
     select(field, name) %>%
     filter(str_detect(field, "^21022-0.0|^31-0.0")) %>%
-    bio_field_add("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.txt")
+    bio_field_add("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt")
 
 bio_phen(
     project_dir,
-    field = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.txt",
-    out = "/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset"
+    field = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt",
+    out = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset"
 )
 
-system("ls -lh /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.rds")
-df <- readRDS("/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/age_sex_field_subset.rds")
+system("ls -lh /users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.rds")
+df <- readRDS("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.rds")
 names(df)<-gsub('-.*','',names(df))
 names(df)[names(df) == '31']<-'sex'
 names(df)[names(df) == '21022']<-'age'
@@ -311,7 +427,7 @@ for(i in 1:nrow(prscsx_dat)){
   fwrite(
     tmp,
     paste0(
-      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
       prscsx_dat$label[i],
       '.unrel.EUR_train.norm_resid_scale.txt'
     ),
@@ -325,7 +441,7 @@ for(i in 1:nrow(prscsx_dat)){
 # Convert to row number based IDs so it will work with UKB geno data from GenoPred
 for(i in 1:nrow(prscsx_dat)){
   pheno<-fread(paste0(
-      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
       prscsx_dat$label[i],
       '.unrel.EUR_train.norm_resid_scale.txt'
     ))
@@ -340,7 +456,7 @@ for(i in 1:nrow(prscsx_dat)){
   fwrite(
     pheno,
     paste0(
-      '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/',
+      '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
       prscsx_dat$label[i],
       '.unrel.EUR_train.norm_resid_scale.row_number.txt'
     ),

From 36cd06d83016b8bfd4ef676858ffc9b1cfa978bd Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 22 Jan 2025 23:40:29 +0000
Subject: [PATCH 087/153] continuing crosspop analysis in ukb

---
 docs/CrossPop.Rmd | 132 +++++++++++++++++++++++-----------------------
 1 file changed, 66 insertions(+), 66 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index fcb3fddc..994591ed 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -135,7 +135,7 @@ write.table(target_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/config
 
 ***
 
-## configfile
+### Create configfile
 
 <details><summary>Show code</summary>
 
@@ -154,7 +154,7 @@ write.table(conf, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic
 
 ***
 
-## Run pipeline
+### Run pipeline
 
 <details><summary>Show code</summary>
 ```{bash}
@@ -166,8 +166,7 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/config.yaml \
-  outlier_detection \
-  -n 
+  outlier_detection
 ```
 </details>
 
@@ -199,6 +198,7 @@ prscsx_dat<-data.frame(
   field=prscsx_fields
 )
 
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx')
 write.csv(prscsx_dat, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv', row.names = F)
 write.table(prscsx_labels, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt', col.names=F, row.names = F, quote=F)
 
@@ -254,18 +254,18 @@ for(i in 1:nrow(prscsx_dat)){
 
 # Read in ancestry inference results to determine sample size per population
 # Use ancestry information from GenoPred
-keep_files<-list.files(path = '/users/k1806347/oliverpainfel/Data/ukb/GenoPredPipe/output/ukb/pcs/within_sample/', pattern = '.keep')
+keep_files<-list.files(path = '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/pcs/within_sample/', pattern = '.keep')
 
 pop_dat<-NULL
 for(i in keep_files){
-  tmp<-fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/GenoPredPipe/output/ukb/pcs/within_sample/', i))
+  tmp<-fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/pcs/within_sample/', i))
   names(tmp)<-c('FID','IID')
   tmp$POP<-gsub('.keep','', gsub('ukb.outlier_detection.','',i))
   pop_dat<-rbind(pop_dat, tmp)
 }
 
 # Update row number IDs to project specific IDs
-psam<-fread('/scratch/prj/ukbiobank/ukb82087/imputed/ukb82087_imp_chr1_MAF1_INFO4_v1.psam')
+psam<-fread('/scratch/prj/ukbiobank/recovered/ukb82087/imputed/ukb82087_imp_chr1_MAF1_INFO4_v1.psam')
 psam$rn<-1:nrow(psam)
 psam<-psam[,c('IID','rn'), with = F]
 
@@ -281,7 +281,7 @@ df_short <- dcast(df_long, eid ~ variable, value.var = "outcome")
 df_short<-merge(df_short, pop_dat, by='eid')
 
 # Remove related individuals
-greedy_related <- "/scratch/prj/ukbiobank/KCL_Data/Software/tools/GreedyRelated-master-v1.2.1/GreedyRelated"
+greedy_related <- "/scratch/prj/ukbiobank/recovered/KCL_Data/Software/tools/GreedyRelated-master-v1.2.1/GreedyRelated"
 rel<-bio_gen_related_remove(
       project_dir = project_dir,
       greedy_related = greedy_related,
@@ -476,30 +476,30 @@ for(i in 1:nrow(prscsx_dat)){
 
 <details><summary>Show code</summary>
 ```{bash, eval=F, echo=T}
-for pheno in $(tail -n +6 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
-  mkdir -p /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}
+for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+  mkdir -p /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}
   for chr in $(seq 1 22); do
       sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/plink2 \
-        --pfile /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output/ukb/geno/ukb.ref.chr${chr} \
-        --pheno /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.EUR_train.norm_resid_scale.row_number.txt \
+        --pfile /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
+        --pheno /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.EUR_train.norm_resid_scale.row_number.txt \
         --linear omit-ref cols=+a1freq,+ax \
         --maf 0.01 \
         --geno 0.05 \
-        --out /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}"
+        --out /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}"
   done
 done
 
 # Once complete, merge results across chromosomes
-for pheno in $(tail -n +6 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
-  head -n 1 /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.chr1.outcome.glm.linear > /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+for pheno in $(tail -n +6 /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+  head -n 1 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.chr1.outcome.glm.linear > /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
     for chr in $(seq 1 22); do
-      tail -n +2 /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}.outcome.glm.linear >> /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+      tail -n +2 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}.outcome.glm.linear >> /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
     done
     
     # Remove REF and ALT columns and rename AX column to A2
-    cut -f 4,5 --complement /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt | awk 'BEGIN{FS=OFS="\t"} NR==1 {$5="A2"} 1' > temp.txt && mv temp.txt /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+    cut -f 4,5 --complement /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt | awk 'BEGIN{FS=OFS="\t"} NR==1 {$5="A2"} 1' > temp.txt && mv temp.txt /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
 
-    gzip /scratch/prj/ukbiobank/usr/ollie_pain/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+    gzip /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
 done
 
 ```
@@ -531,7 +531,7 @@ bbj_gwas$trait<-gsub(' count','', bbj_gwas$trait)
 bbj_gwas$trait[bbj_gwas$trait == 'G-glutamyl transpeptidase']<-'γ-glutamyl transpeptidase'
 
 # Merge the bbj trait info with the prscsx trait info
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv')
 prscsx_dat <- merge(bbj_gwas, prscsx_dat, by='trait', all=T)
 
 write.csv(prscsx_dat, '~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_prscsx.csv', row.names = F)
@@ -547,7 +547,7 @@ write.table(prscsx_dat[, c('labels', 'wget', 'wget_label'), with=F], '~/oliverpa
 
 ```{bash}
 # wget and unzip sumstats
-for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
   url=$(awk -v var="$pheno" '$1 == var {print $2}' ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
   sbatch -p neurohack_cpu --wrap="wget -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip ${url}
     unzip /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip -d /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx
@@ -555,14 +555,14 @@ for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscs
 done
 
 # Delete X chromosome sumstats and rename files to be consistent with prscsx sumstat info
-for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
   wget_label=$(awk -v var="$pheno" '$1 == var {print $3}' ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
   mv ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1/GWASsummary_${wget_label}_Japanese_SakaueKanai2020.auto.txt.gz ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz
   rm -r ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1
 done
 
 # Format so BOLT P value is used by GenoPred
-for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
 sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/pigz -dc ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz | awk 'BEGIN {OFS=\"\t\"} {print \$2, \$3, \$4, \$6, \$7, \$8, \$9, \$12, \$13, \$15}' | sed '1s/P_BOLT_LMM_INF/P/' | /users/k1806347/oliverpainfel/Software/pigz -c > ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.reformat.txt.gz"
 done
 
@@ -610,7 +610,7 @@ ugr_gwas$trait <- ifelse(ugr_gwas$trait %in% names(ugr_to_prscsx),
                                    ugr_gwas$trait)
 
 # Merge the ugr trait info with the prscsx trait info
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv')
 prscsx_dat <- merge(ugr_gwas, prscsx_dat, by='trait')
 
 write.csv(prscsx_dat, '~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv', row.names = F)
@@ -714,11 +714,11 @@ To estimate polygenicity, lets use AVENGEME based on ptclump score association r
 # gwas_list
 ######
 
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv')
 
 gwas_list_eur<-data.frame(
   name=paste0(prscsx_dat$labels,'_UKB'),
-  path=paste0('/scratch/prj/ukbiobank/usr/ollie_pain/gwas/',prscsx_dat$labels,'/ukb.eur_train.',prscsx_dat$labels,'.GW.txt.gz'),
+  path=paste0('/users/k1806347/oliverpainfel/Data/ukb/gwas/',prscsx_dat$labels,'/ukb.eur_train.',prscsx_dat$labels,'.GW.txt.gz'),
   population='EUR',
   n=NA,
   sampling=NA,
@@ -772,25 +772,25 @@ pheno_intersect <- Reduce(intersect,
 # Restrict gwas_list to intersecting phenotypes
 gwas_list<-gwas_list[pheno %in% pheno_intersect,]
 
-write.table(gwas_list, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt', col.names = T, row.names = F, quote = F)
+write.table(gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt', col.names = T, row.names = F, quote = F)
 
-write.table(pheno_intersect, '/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', col.names = F, row.names = F, quote = F)
+write.table(pheno_intersect, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt', col.names = F, row.names = F, quote = F)
 
 ######
 # config
 ######
 
 config<-c(
-  "outdir: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output_crosspop",
-  "config_file: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml",
-  "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt",
-  "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
   "pgs_methods: ['ptclump']",
   "cores_prep_pgs: 1",
   "cores_target_pgs: 20"
 )
 
-write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml', col.names = F, row.names = F, quote = F)
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml', col.names = F, row.names = F, quote = F)
 
 ```
 </details>
@@ -800,7 +800,7 @@ write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k180
 ## Run pipeline
 
 ```{bash}
-snakemake --profile slurm --use-conda --configfile=/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml target_pgs -n
+snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml target_pgs -n
 ```
 
 ***
@@ -811,7 +811,7 @@ snakemake --profile slurm --use-conda --configfile=/scratch/prj/ukbiobank/usr/ol
 ```{r}
 library(data.table)
 dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats', recursive = T)
-gwas_list<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt')
+gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt')
 
 for(i in 1:nrow(gwas_list)){
   if(
@@ -825,7 +825,7 @@ for(i in 1:nrow(gwas_list)){
   print(i)
   gwas_file <-
     paste0(
-      "/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output_crosspop/reference/gwas_sumstat/",
+      "/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/",
       gwas_list$name[i],
       "/",
       gwas_list$name[i],
@@ -872,7 +872,7 @@ for pop in $(echo EUR EAS AFR);do
       samp="UGR"
   fi
   
-  for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
     mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats
 
     sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
@@ -893,7 +893,7 @@ for pop in $(echo EUR EAS AFR);do
       samp="UGR"
   fi
   
-  for pheno in $(tail -n +6 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  for pheno in $(tail -n +6 /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
     mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/${pheno}/${pop}
 
     sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \
@@ -962,7 +962,7 @@ for pop in $(echo EAS AFR);do
       samp="UGR"
   fi
   
-  for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+  for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
     mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/${pheno}/EUR_${pop}
     sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="popcorn \
        fit -v 3 \
@@ -991,7 +991,7 @@ library(ggplot2)
 library(cowplot)
 
 # Read in phenotypes
-pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
 
 # Plot the heritability estimates
 h2_res <- NULL
@@ -1086,7 +1086,7 @@ source_all('../functions')
 library(data.table)
 
 # Get some key variables from config
-config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml'
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml'
 pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
@@ -1094,7 +1094,7 @@ outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 scores <- list_score_files(config)
 
 # Read in phenotypes
-pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
 
 # Create files for EAS and AFR targets
 pop <- c('EUR','EAS','AFR')
@@ -1184,9 +1184,9 @@ for pop in $(echo EUR EAS AFR); do
       pop2=$pop
   fi
   
-  for pheno in $(cat /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
+  for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
     sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
-      --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${pop2}.row_number.txt \
+      --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${pop2}.row_number.txt \
       --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
       --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/res.ptclump \
       --n_core 5 \
@@ -1215,7 +1215,7 @@ source('../functions/misc.R')
 source_all('../functions')
 
 # Read in phenotypes
-pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
 
 # Read in results
 pop = c('EUR','EAS','AFR')
@@ -1278,12 +1278,12 @@ source('../functions/misc.R')
 source_all('../functions')
 
 # Get some key variables from config
-config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config_all.yaml'
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml'
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 gwas_list <- read_param(config = config, param = 'gwas_list', return_obj = T)
 
 # Read in phenotypes
-pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
 
 pop = c('EUR','EAS','AFR')
 
@@ -1458,13 +1458,13 @@ write.table(selected_traits$trait, '/users/k1806347/oliverpainfel/Analyses/cross
 ######
 library(data.table)
 # Subset original gwas_list to include selected traits
-gwas_list<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt')
+gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt')
 pheno<-gsub('_.*','', gwas_list$name)
 selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
 gwas_list<-gwas_list[pheno %in% selected_traits,]
 gwas_list$label<-paste0('"', gwas_list$label, '"')
 
-write.table(gwas_list, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt', col.names = T, row.names = F, quote = F)
+write.table(gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt', col.names = T, row.names = F, quote = F)
 
 ######
 # gwas_groups
@@ -1484,18 +1484,18 @@ gwas_groups_afr<-data.frame(
 
 gwas_groups<-rbind(gwas_groups_eas, gwas_groups_afr)
 
-write.table(gwas_groups, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt', col.names = T, row.names = F, quote = F)
+write.table(gwas_groups, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt', col.names = T, row.names = F, quote = F)
 
 ######
 # config
 ######
 
 config<-c(
-  "outdir: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/output_crosspop",
-  "config_file: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml",
-  "gwas_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list.txt",
-  "target_list: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/basic/target_list.txt",
-  "gwas_groups: /scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_groups.txt",
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
+  "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
 #  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
   "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','ldpred2','megaprs','sbayesrc','prscsx','xwing','prscs']",
 #  "tlprs_methods: ['dbslmm','prscs','lassosum']",
@@ -1507,7 +1507,7 @@ config<-c(
   "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc"
 )
 
-write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
 
 ```
 </details>
@@ -1517,7 +1517,7 @@ write.table(config, '/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k180
 ## Run pipeline
 
 ```{bash}
-snakemake --profile slurm --use-conda --configfile=/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml output_all -n
+snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml output_all -n
 ```
 
 ***
@@ -1542,7 +1542,7 @@ source_all('../functions')
 library(data.table)
 
 # Get some key variables from config
-config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
 pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
@@ -1730,7 +1730,7 @@ for targ_pop in $(echo EUR EAS AFR); do
   for disc_pop_i in ${disc_pop}; do
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
       sbatch --mem 20G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
-        --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+        --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
         --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
         --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
         --n_core 10 \
@@ -1773,7 +1773,7 @@ for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
   }
   pheno_pop_i <- list()
   for(pheno_i in selected_traits){
-    pheno_pop_i[[pheno_i]] <- fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
     names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
   }
   
@@ -2289,7 +2289,7 @@ source_all('../functions')
 library(data.table)
 
 # Get some key variables from config
-config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
@@ -2572,9 +2572,9 @@ for targ_pop in $(echo EUR EAS AFR); do
   fi
   
   for disc_pop_i in ${disc_pop}; do
-    for pheno in $(head -n 5 /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_labels.txt); do
+    for pheno in $(head -n 5 /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
       sbatch --mem 20G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
-        --outcome /scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+        --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
         --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
         --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
         --n_core 10 \
@@ -2606,10 +2606,10 @@ source('../functions/misc.R')
 source_all('../functions')
 
 # Read in list of outcomes 
-prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv')
+prscsx_dat<-fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv')
 prscsx_dat<-prscsx_dat[1:5,]
 
-config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
   
 # Define pgs_methods used
 pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
@@ -2630,7 +2630,7 @@ for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
   }
   pheno_pop_i <- list()
   for(pheno_i in prscsx_dat$labels){
-    pheno_pop_i[[pheno_i]] <- fread(paste0('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
     names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
   }
   
@@ -3129,7 +3129,7 @@ source('../functions/misc.R')
 source_all('../functions')
 
 # Get some key variables from config
-config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml'
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
 pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 

From fb75bb9015881941c4b841eb749264201a24227f Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 23 Jan 2025 08:58:55 +0000
Subject: [PATCH 088/153] Changed it so update to configfile name doesnt
 trigger rerun. Should be fine due to params within the config file being
 checked

---
 docs/CrossPop.Rmd              | 25 ++++++++++++++++++++-----
 pipeline/rules/pgs_methods.smk |  1 -
 pipeline/rules/target_qc.smk   |  2 --
 3 files changed, 20 insertions(+), 8 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 994591ed..6c85121b 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -166,7 +166,7 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/config.yaml \
-  outlier_detection
+  outlier_detection -n
 ```
 </details>
 
@@ -490,7 +490,7 @@ for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prsc
 done
 
 # Once complete, merge results across chromosomes
-for pheno in $(tail -n +6 /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
   head -n 1 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.chr1.outcome.glm.linear > /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
     for chr in $(seq 1 22); do
       tail -n +2 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}.outcome.glm.linear >> /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
@@ -502,6 +502,9 @@ for pheno in $(tail -n +6 /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prsc
     gzip /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
 done
 
+# Delete per chromosome files
+rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*/*chr*
+
 ```
 </details>
 
@@ -557,13 +560,17 @@ done
 # Delete X chromosome sumstats and rename files to be consistent with prscsx sumstat info
 for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
   wget_label=$(awk -v var="$pheno" '$1 == var {print $3}' ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
-  mv ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1/GWASsummary_${wget_label}_Japanese_SakaueKanai2020.auto.txt.gz ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz
+if [ "$pheno" == "HT" ]; then
+    mv ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1/GWASsummary_Height_Japanese_SakaueKanai2020.auto.txt.gz ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.HT.txt.gz
+  else
+    mv ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1/GWASsummary_${wget_label}_Japanese_SakaueKanai2020.auto.txt.gz ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz
+  fi
   rm -r ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1
 done
 
 # Format so BOLT P value is used by GenoPred
 for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
-sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/pigz -dc ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz | awk 'BEGIN {OFS=\"\t\"} {print \$2, \$3, \$4, \$6, \$7, \$8, \$9, \$12, \$13, \$15}' | sed '1s/P_BOLT_LMM_INF/P/' | /users/k1806347/oliverpainfel/Software/pigz -c > ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.reformat.txt.gz"
+sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/pigz/pigz -dc ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz | awk 'BEGIN {OFS=\"\t\"} {print \$2, \$3, \$4, \$6, \$7, \$8, \$9, \$12, \$13, \$15}' | sed '1s/P_BOLT_LMM_INF/P/' | /users/k1806347/oliverpainfel/Software/pigz/pigz -c > ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.reformat.txt.gz"
 done
 
 ```
@@ -616,6 +623,7 @@ prscsx_dat <- merge(ugr_gwas, prscsx_dat, by='trait')
 write.csv(prscsx_dat, '~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv', row.names = F)
 
 # Create column indicating wget command
+prscsx_dat$wget<-NA
 for(i in 1:nrow(prscsx_dat)){
   if(!grepl('.txt', prscsx_dat$wget[i])){
     print(i)
@@ -626,6 +634,7 @@ for(i in 1:nrow(prscsx_dat)){
     prscsx_dat$wget[i]<-paste0(prscsx_dat$summaryStatistics[i], '/', log)
   }
 }
+# Note this has to be run a few times due to some requests being blocked.
 
 # Write a table showing label matching prscsx info and wget url
 write.table(prscsx_dat[, c('labels', 'wget'), with=F], '~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt', col.names = F, row.names = F, quote = F)
@@ -714,6 +723,8 @@ To estimate polygenicity, lets use AVENGEME based on ptclump score association r
 # gwas_list
 ######
 
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop')
+
 prscsx_dat<-fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv')
 
 gwas_list_eur<-data.frame(
@@ -800,7 +811,11 @@ write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/cro
 ## Run pipeline
 
 ```{bash}
-snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml target_pgs -n
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml \
+  target_pgs -n
 ```
 
 ***
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 30bf7df0..5d9c6c70 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -51,7 +51,6 @@ if 'gwas_list' in config:
     params:
       outdir=config["outdir"],
       refdir=config["refdir"],
-      config_file = config["config_file"],
       testing = config['testing'],
       population= lambda w: gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0],
       n= lambda w: gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'n'].iloc[0],
diff --git a/pipeline/rules/target_qc.smk b/pipeline/rules/target_qc.smk
index dd893fbe..390199d5 100644
--- a/pipeline/rules/target_qc.smk
+++ b/pipeline/rules/target_qc.smk
@@ -31,7 +31,6 @@ if 'target_list' in config:
       "../envs/analysis.yaml"
     params:
       outdir=config["outdir"],
-      config_file = config["config_file"],
       name= lambda w: target_list_df.loc[target_list_df['name'] == "{}".format(w.name), 'name'].iloc[0],
       path= lambda w: target_list_df.loc[target_list_df['name'] == "{}".format(w.name), 'path'].iloc[0]
     shell:
@@ -110,7 +109,6 @@ if 'target_list' in config:
     params:
       outdir=config["outdir"],
       refdir=config["refdir"],
-      config_file = config["config_file"],
       testing=config["testing"],
       prefix= lambda w: target_prefix(name = w.name),
       type= lambda w: target_type(name = w.name)

From d4e5f8d8d7d3ff142ea02f5eec1ac78d9b4b3141 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sat, 25 Jan 2025 00:06:24 +0000
Subject: [PATCH 089/153] Updates

---
 docs/CrossPop.Rmd | 45 ++++++++++++++++++++++++---------------------
 functions/pgs.R   |  2 +-
 2 files changed, 25 insertions(+), 22 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 6c85121b..05abd9aa 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -857,7 +857,7 @@ for(i in 1:nrow(gwas_list)){
       " | cut -f ", 
       paste0(cols_index, collapse = ','),
       " | sed -e '1s/BETA/beta/'",
-      " | /users/k1806347/oliverpainfel/Software/pigz -f",
+      " | /users/k1806347/oliverpainfel/Software/pigz/pigz -f",
       " > /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/",
       gwas_list$name[i], 
       ".sumstats.gz"
@@ -887,7 +887,7 @@ for pop in $(echo EUR EAS AFR);do
       samp="UGR"
   fi
   
-  for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+  for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
     mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats
 
     sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
@@ -908,7 +908,7 @@ for pop in $(echo EUR EAS AFR);do
       samp="UGR"
   fi
   
-  for pheno in $(tail -n +6 /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+  for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
     mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/${pheno}/${pop}
 
     sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \
@@ -934,15 +934,15 @@ done
 for pop in $(echo EUR EAS AFR); do
   mkdir -p /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp
   for chr in $(seq 1 22); do
-    /users/k1806347/oliverpainfel/Software/plink1.9/plink \
-      --bfile /users/k1806347/oliverpainfel/Data/1KG/GenoPred/v2.0.0/ref.chr${chr} \
-      --keep /users/k1806347/oliverpainfel/Data/1KG/GenoPred/v2.0.0/keep_files/${pop}.keep \
+    /users/k1806347/oliverpainfel/Software/plink2 \
+      --pfile /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr${chr} \
+      --keep /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files/${pop}.keep \
       --make-bed \
       --out /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.${pop}.chr${chr}
     done
 done
 
-conda activate popcorn
+conda activate /scratch/prj/oliverpainfel/recovered/.conda/envs/popcorn
 for pop in $(echo EAS AFR); do
   mkdir -p /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES
   for chr in $(seq 1 22); do
@@ -960,6 +960,7 @@ for pop in $(echo EAS AFR); do
 done
 
 rm -r /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp
+rm /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_*_CSCORES/*chr*.txt
 ```
 
 ***
@@ -1061,7 +1062,7 @@ ggplot(h2_res, aes(x = Phenotype, y = h2_est, fill = Population)) +
 # Plot rG estimates
 rg_res <- NULL
 for(pop in c('AFR','EAS')){
-  for(pheno in h2_res_sig$Phenotype){
+  for(pheno in h2_res$Phenotype){
     pop_res_i<-fread(paste0('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/', pheno, '/EUR_', pop, '/rG_gen_effect'))
     names(pop_res_i) <- c('Test','Estimate','SE','Z','P')
     pop_res_i <- pop_res_i[pop_res_i$Test == 'pge',]
@@ -1077,8 +1078,8 @@ rg_res$Comparison <- paste0(rg_res$Population_1, ' vs. ', rg_res$Population_2)
 write.csv(rg_res, '/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results.csv', row.names = F, quote = F)
 
 ggplot(rg_res, aes(x = Phenotype, y = Estimate, fill = Comparison)) +
-  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
-  geom_errorbar(aes(ymin=Estimate-SE, ymax=Estimate+SE), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  geom_bar(stat="identity", position=position_dodge(), width = 0.7) +
+  geom_errorbar(aes(ymin=Estimate-SE, ymax=Estimate+SE), width=.2, position=position_dodge(width = 0.7)) +
   labs(y="SNP-based\nGenetic Correlation (SE)") +
   theme_half_open() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
@@ -1146,17 +1147,13 @@ for(trait_i in pheno_intersect){
     scores_i_j <- scores_i[grepl(samp_i, scores_i$name, ignore.case = T),]
     scores_i_j$predictor <- paste0(
       outdir,
-      '/ukb/pgs/',
-      pop_i,
-      '/',
+      '/ukb/pgs/TRANS/',
       scores_i_j$method,
       '/',
       scores_i_j$name,
       '/ukb-',
       scores_i_j$name,
-      '-',
-      pop_i,
-      '.profiles'
+      '-TRANS.profiles'
     )
     
     predictors_i <- scores_i_j[, c('predictor', 'group'), with=F]
@@ -1512,14 +1509,16 @@ config<-c(
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
 #  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
-  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','ldpred2','megaprs','sbayesrc','prscsx','xwing','prscs']",
+#  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','ldpred2','megaprs','sbayesrc','prscsx','xwing','prscs']",
+  "pgs_methods: ['ptclump','quickprs','quickprs_multi']",
 #  "tlprs_methods: ['dbslmm','prscs','lassosum']",
   "cores_prep_pgs: 10",
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
-  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2",
-  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs",
-  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc"
+#  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3",
+  "quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset"
+#  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc"
 )
 
 write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
@@ -1532,7 +1531,11 @@ write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/cro
 ## Run pipeline
 
 ```{bash}
-snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml output_all -n
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml \
+  output_all -n
 ```
 
 ***
diff --git a/functions/pgs.R b/functions/pgs.R
index bcea5887..73bf32bf 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -530,7 +530,7 @@ model_trans_pgs<-function(scores=NULL, pcs=NULL, output=NULL){
     predicted_pgs <- predict(pgs_pc_mean_mod, newdata = tmp)
     residual_pgs <- tmp$y - predicted_pgs
     squared_residuals <- residual_pgs^2
-    squared_residuals <- pmax(squared_residuals, 1e-6)
+    squared_residuals <- pmax(squared_residuals, 1e-4)
     
     pgs_pc_var_mod <- glm(squared_residuals ~ ., data = tmp[, names(tmp) != 'y', with=F], family = Gamma(link = "log"))
     predicted_pgs_var <- exp(predict(pgs_pc_var_mod, newdata = tmp))

From afb7dcd87b6b22df6c55d798343dabdb1b3518e5 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sat, 25 Jan 2025 10:17:48 +0000
Subject: [PATCH 090/153] Update

---
 docs/CrossPop.Rmd | 24 ++++++++++--------------
 1 file changed, 10 insertions(+), 14 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 05abd9aa..b013828d 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1510,7 +1510,7 @@ config<-c(
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
 #  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
 #  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','ldpred2','megaprs','sbayesrc','prscsx','xwing','prscs']",
-  "pgs_methods: ['ptclump','quickprs','quickprs_multi']",
+  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','megaprs','prscs']",
 #  "tlprs_methods: ['dbslmm','prscs','lassosum']",
   "cores_prep_pgs: 10",
   "cores_target_pgs: 50",
@@ -1620,17 +1620,13 @@ for(trait_i in selected_traits){
       # Insert path to score file
       scores_i_j_multi$predictor <- paste0(
         outdir,
-        '/ukb/pgs/',
-        targ_pop_i,
-        '/',
+        '/ukb/pgs/TRANS/',
         scores_i_j_multi$method,
         '/',
         scores_i_j_multi$name,
         '/ukb-',
         scores_i_j_multi$name,
-        '-',
-        targ_pop_i,
-        '.profiles'
+        '-TRANS.profiles'
       )
       
       # Extract the pseudo score for each method and specify as a seperate group
@@ -1655,8 +1651,8 @@ for(trait_i in selected_traits){
             scores_i_j_multi$predictor[i], 
             " > ", 
             gsub('.profiles',
-                    '.pseudo.profiles',
-                    scores_i_j_multi$predictor[i])
+                 paste0('.', targ_pop_i, '_pseudo.profiles'),
+                 scores_i_j_multi$predictor[i])
           )
         )
       }
@@ -1674,9 +1670,9 @@ for(trait_i in selected_traits){
           scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.AFR.pseudo', scores_i_j_pseudo$group[i])
         }
       }
-      scores_i_j_pseudo$predictor <- gsub('.profiles',
-                                            '.pseudo.profiles',
-                                            scores_i_j_pseudo$predictor)
+      scores_i_j_pseudo$predictor <- gsub('.profiles', 
+                                          paste0('.', targ_pop_i, '_pseudo.profiles'),
+                                          scores_i_j_pseudo$predictor)
       
       # Make a group for each GWAS and single source combo
       scores_i_j_single <- scores_i_j_multi[!(scores_i_j_multi$method %in% pgs_group_methods),]
@@ -1726,7 +1722,7 @@ for(trait_i in selected_traits){
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-for targ_pop in $(echo EUR EAS AFR); do
+for targ_pop in $(echo EAS AFR); do
   if [ "$targ_pop" == "EUR" ]; then
       targ_pop2="EUR_test"
   else
@@ -1781,7 +1777,7 @@ source_all('../functions')
 # Read in list of outcomes 
 selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
 
-# Calculate corelation between all phenotypes in each target population
+# Calculate correlation between all phenotypes in each target population
 cors <- list()
 for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
   if(pop_i == 'EUR'){

From c8ba3911652214c3b57e3067e4468f9b1a4b7884 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sun, 26 Jan 2025 19:17:56 +0000
Subject: [PATCH 091/153] Updates

---
 docs/CrossPop.Rmd               |  10 +-
 docs/prep_ldpred2_ldmat.Rmd     | 192 ++++++++++++++++----------------
 docs/prep_sbayesrc_ref.Rmd      |  16 +--
 pipeline/rules/dependencies.smk |   2 +-
 pipeline/rules/pgs_methods.smk  |   4 +-
 5 files changed, 111 insertions(+), 113 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index b013828d..a4d6830d 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1510,15 +1510,15 @@ config<-c(
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
 #  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
 #  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','ldpred2','megaprs','sbayesrc','prscsx','xwing','prscs']",
-  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','megaprs','prscs']",
+  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
 #  "tlprs_methods: ['dbslmm','prscs','lassosum']",
   "cores_prep_pgs: 10",
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
-#  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
   "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3",
-  "quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset"
-#  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc"
+  "quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
 )
 
 write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
@@ -1535,7 +1535,7 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml \
-  output_all -n
+  prep_pgs -n
 ```
 
 ***
diff --git a/docs/prep_ldpred2_ldmat.Rmd b/docs/prep_ldpred2_ldmat.Rmd
index d085c53e..356973a6 100644
--- a/docs/prep_ldpred2_ldmat.Rmd
+++ b/docs/prep_ldpred2_ldmat.Rmd
@@ -25,32 +25,38 @@ pre code, pre, code {
 
 ***
 
-# 1KG
+# 1KG+HGDP
 
-Here we will calculate LD matrices for LDpred2 using HapMap3 SNPs in the 1KG super populations. This is to allow fair comparison across methods for a cross-population polygenic scoring study. 
+Here we will calculate LD matrices for LDpred2 using HapMap3 SNPs in the 1KG+HGDP super populations. This is to allow fair comparison across methods for a cross-population polygenic scoring study. 
 
 The code will follow that used to generate the LD matrices for EUR, in this paper ([link](https://doi.org/10.1016/j.xhgg.2022.100136)).
 
+***
+
+## Full reference
+
 ```{bash}
-# Subset 1KG into super populations and merge across chromosomes
+# Subset 1KG+HGDP into super populations and merge across chromosomes
 for pop in $(echo EUR EAS AFR); do
-  mkdir -p /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}
+  mkdir -p /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3/${pop}
 
   # Create mergelist
-  rm -f /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/mergelist.txt
+  rm -f /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3/${pop}/mergelist.txt
   for chr in $(seq 1 22); do
-    echo ~/oliverpainfel/Data/1KG/GenoPred/v2.0.0/ref.chr${chr} >> /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/mergelist.txt
+    echo /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr${chr} >> /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3/${pop}/mergelist.txt
   done
   
-  ~/oliverpainfel/Software/plink1.9/plink \
-    --merge-list /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/mergelist.txt \
+  /users/k1806347/oliverpainfel/Software/plink2_linux_avx2_20241011/plink2 \
+    --pmerge-list /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3/${pop}/mergelist.txt \
     --make-bed \
-    --keep /users/k1806347/oliverpainfel/Data/1KG/GenoPred/v2.0.0/keep_files/${pop}.keep \
-    --out /users/k1806347/oliverpainfel/Data/1KG/ldpred2/${pop}/ref.${pop}.GW
+    --keep /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files/${pop}.keep \
+    --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3/${pop}/ref.${pop}.GW
 done
 ```
 
 ```{r}
+# export OPENBLAS_NUM_THREADS=1
+# conda activate .snakemake/conda/329e0288cb99508f5e6c50a0996b234c_
 library(bigsnpr)
 library(bigreadr)
 
@@ -59,7 +65,7 @@ for(pop in c('EUR', 'EAS', 'AFR')){
   # Read in reference data
   snp_readBed(
     paste0(
-      '/users/k1806347/oliverpainfel/Data/1KG/ldpred2/',
+      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
       pop,
       '/ref.',
       pop,
@@ -71,7 +77,7 @@ for(pop in c('EUR', 'EAS', 'AFR')){
   ref <-
     snp_attach(
       paste0(
-        '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+        '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
         pop,
         '/ref.',
         pop,
@@ -86,7 +92,7 @@ for(pop in c('EUR', 'EAS', 'AFR')){
   # Save imputed reference
   ref$genotypes<-G_imp
   saveRDS(ref, paste0(
-        '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+        '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
         pop,
         '/ref.',
         pop,
@@ -104,16 +110,16 @@ library(future.batchtools)
 library(furrr)
 
 for(pop in c('EUR', 'EAS', 'AFR')){
-  obj.1000G <- snp_attach(paste0(
-          '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+  obj.hgdp_1kg <- snp_attach(paste0(
+          '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
           pop,
           '/ref.',
           pop,
           '.GW.rds'
         ))
-  G <- obj.1000G$genotypes
-  CHR <- obj.1000G$map$chromosome
-  POS <- obj.1000G$map$physical.pos
+  G <- obj.hgdp_1kg$genotypes
+  CHR <- obj.hgdp_1kg$map$chromosome
+  POS <- obj.hgdp_1kg$map$physical.pos
   POS2 <- snp_asGeneticPos(CHR, POS, dir ='/users/k1806347/oliverpainfel/Data/Genetic_Map/CEU', ncores = nb_cores()) # Note this is not appropriate but I think Florian did the same as used distance from UKB
   MAF <- snp_MAF(G, ncores = nb_cores())
   keep <- (MAF > 0.02)
@@ -137,7 +143,7 @@ for(pop in c('EUR', 'EAS', 'AFR')){
         ncores = nb_cores()
       )
     }, file = paste0(
-      '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
       pop,
       '/LD_chr',
       chr,
@@ -169,7 +175,7 @@ for(pop in c('EUR', 'EAS', 'AFR')){
         ifelse(best_grp[corr0T@i + 1L] == best_grp[corr0T@j + 1L], corr0T@x, 0)
       as(Matrix::drop0(corr0T), "symmetricMatrix")
     }, file = paste0(
-      '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
       pop,
       '/LD_with_blocks_chr',
       chr,
@@ -183,7 +189,7 @@ for(pop in c('EUR', 'EAS', 'AFR')){
   saveRDS(
     all_final_grp,
     paste0(
-      '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
       pop,
       '/all_final_grp.rds'
     )
@@ -192,7 +198,7 @@ for(pop in c('EUR', 'EAS', 'AFR')){
   ###
   # Create map.rds
   ###
-  map <- obj.1000G$map[keep,]
+  map <- obj.hgdp_1kg$map[keep,]
 
   # Compute allele frequency
   map$af<-big_colstats(G, ind.col = which(keep), ncores = nb_cores())$sum / (2 * nrow(G))
@@ -202,7 +208,7 @@ for(pop in c('EUR', 'EAS', 'AFR')){
     cat(chr, ".. ", sep = "")
     corr_chr <- readRDS(
       paste0(
-        '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+        '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
         pop,
         '/LD_chr',
         chr,
@@ -218,98 +224,91 @@ for(pop in c('EUR', 'EAS', 'AFR')){
   saveRDS(
     map,
     paste0(
-      '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
       pop,
       '/map.rds'
     )
   )
 }
 
-
 ```
 
 ***
 
-# 1KG+HGDP
-
-Here we will calculate LD matrices for LDpred2 using HapMap3 SNPs in the 1KG+HGDP super populations. This is to allow fair comparison across methods for a cross-population polygenic scoring study. 
+## Split reference
 
-The code will follow that used to generate the LD matrices for EUR, in this paper ([link](https://doi.org/10.1016/j.xhgg.2022.100136)).
 
-```{bash}
-# Subset 1KG+HGDP into super populations and merge across chromosomes
-for pop in $(echo EUR EAS AFR); do
-  mkdir -p /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/${pop}
-  mkdir -p /users/k1806347/oliverpainfel/Data/hgdp_1kg/prscs/ldblk_hgdp_1kg_$(echo ${pop} | tr '[:upper:]' '[:lower:]')
+To use GWAS subsampling techniques
+Only the correlations part needs to be derived using subset data
+Follow X-wing strategy first - Split into three parts
 
-  # Create mergelist
-  rm -f /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/${pop}/mergelist.txt
-  for chr in $(seq 1 22); do
-    echo /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} >> /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/${pop}/mergelist.txt
-  done
-  
-  /users/k1806347/oliverpainfel/Software/plink2_linux_avx2_20241011/plink2 \
-    --pmerge-list /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/${pop}/mergelist.txt \
-    --make-bed \
-    --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/keep_files/${pop}.keep \
-    --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/${pop}/ref.${pop}.GW
-done
-```
+- Subset the plink1 files into three parts
+- Calculate LD matrices in subset 2
+- Calculate freq files for subset 3
 
 ```{r}
-library(bigsnpr)
-library(bigreadr)
-
-# Create object for genetic data in ldpred2 format, and impute missing values
-for(pop in c('EUR', 'EAS', 'AFR')){
-  # Read in reference data
-  snp_readBed(
-    paste0(
-      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
-      pop,
-      '/ref.',
-      pop,
-      '.GW.bed'
-    )
-  )
+# conda activate .snakemake/conda/329e0288cb99508f5e6c50a0996b234c_
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
   
-  # Attach the ref object in R session
-  ref <-
-    snp_attach(
-      paste0(
-        '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
-        pop,
-        '/ref.',
-        pop,
-        '.GW.rds'
-      )
-    )
-  G <- ref$genotypes
-
-  #### Impute missing values (bigsnpr can't handle missing data in most functions)
-  G_imp <- snp_fastImputeSimple(G, method = "mean2", ncores = nb_cores())
-
-  # Save imputed reference
-  ref$genotypes<-G_imp
-  saveRDS(ref, paste0(
-        '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
-        pop,
-        '/ref.',
-        pop,
-        '.GW.rds'
-      ))
-}
-
-################
-# Now calculate LD matrices following code from https://doi.org/10.1016/j.xhgg.2022.100136
-
 library(dplyr)
 library(bigreadr)
 library(bigsnpr)
 library(future.batchtools)
 library(furrr)
 
-for(pop in c('EUR', 'EAS', 'AFR')){
+# Create temp directory
+tmp_dir<-tempdir()
+
+opt<-list()
+opt$ref_plink_chr<-'resources/data/ref/ref.chr'
+opt$plink<-'plink'
+opt$plink2<-'plink2'
+
+for(pop in c('EUR','EAS','AFR')){
+  opt$output <- paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/subset/', pop, '/', pop)
+  opt$output_dir <- paste0(dirname(opt$output),'/')
+  system(paste0('mkdir -p ',opt$output_dir))
+
+  opt$ref_keep<-paste0('resources/data/ref/keep_files/', pop, '.keep')
+  
+  keep <- fread(opt$ref_keep)
+  
+  # Split reference into three groups
+  lines <- readLines(opt$ref_keep)
+  set.seed(1)
+  shuffled_lines <- sample(lines)
+
+  # Calculate the split points
+  n <- length(shuffled_lines)
+  split1 <- floor(n / 3)
+  split2 <- 2 * split1
+  
+  # Split the lines into three groups
+  keep1 <- shuffled_lines[1:split1]
+  keep2 <- shuffled_lines[(split1 + 1):split2]
+  keep3 <- shuffled_lines[(split2 + 1):n]
+  
+  # Write each group to a separate file
+  write.table(data.frame(0, keep1), paste0(tmp_dir, "/keep1"), col.names = F, row.names = F, quote = F)
+  write.table(data.frame(0, keep2), paste0(tmp_dir, "/keep2"), col.names = F, row.names = F, quote = F)
+  write.table(data.frame(0, keep3), paste0(tmp_dir, "/keep3"), col.names = F, row.names = F, quote = F)
+
+  for(subset_i in 1:3){
+    # Save plink format files
+    system(paste0(opt$plink, ' --bfile ', tmp_dir, '/ref_merge --keep ', tmp_dir, '/keep', subset_i, ' --make-bed --out ', opt$output, '.subset_', subset_i))
+  }
+
+  # Calculate freq files for subset 3, as required by LEOPARD
+  for(subset_i in 3:3){
+    system(paste0(opt$plink, ' --bfile ', opt$output, '.subset_', subset_i, ' --freq --out ', opt$output, '.subset_', subset_i))
+  }
+
+  # Read in 1KG+HGDP rds file
+  # Subset to subset 2 individuals
   obj.hgdp_1kg <- snp_attach(paste0(
           '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
           pop,
@@ -343,7 +342,7 @@ for(pop in c('EUR', 'EAS', 'AFR')){
         ncores = nb_cores()
       )
     }, file = paste0(
-      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/',
+      '/users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/subset',
       pop,
       '/LD_chr',
       chr,
@@ -429,11 +428,10 @@ for(pop in c('EUR', 'EAS', 'AFR')){
       '/map.rds'
     )
   )
+  
+  system(paste0('rm -r ', tmp_dir, '/*'))
 }
 
 ```
 
 
-
-
-
diff --git a/docs/prep_sbayesrc_ref.Rmd b/docs/prep_sbayesrc_ref.Rmd
index 91d70db6..696255d9 100644
--- a/docs/prep_sbayesrc_ref.Rmd
+++ b/docs/prep_sbayesrc_ref.Rmd
@@ -30,26 +30,26 @@ pre code, pre, code {
 Here we will prepare reference data for SBayesRC using HapMap3 SNPs in the 1KG+HGDP super populations. This is to allow fair comparison across methods for a cross-population polygenic scoring study. 
 
 ```{bash}
-conda activate sbayesrc
+conda activate sbayesrc # /users/k1806347/oliverpainfel/Software/sbayesrc.yaml
 module add parallel/20220522-gcc-13.2.0
 
 for pop in EUR EAS AFR; do
 
 # Set up directories and variables
-mkdir -p "/users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}"
-outDir="/users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/${pop}.hm3"
-rm -f /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/mergelist.txt
+mkdir -p "/users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3/${pop}"
+outDir="/users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3/${pop}/${pop}"
+rm -f /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3/${pop}/mergelist.txt
 
 # Create the merge list for PLINK
 for chr in $(seq 1 22); do
-  echo /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} >> /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/mergelist.txt
+  echo /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr${chr} >> /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/mergelist.txt
 done
 
 # Run PLINK to merge chromosomes and filter for the population
 /users/k1806347/oliverpainfel/Software/plink2_linux_avx2_20241011/plink2 \
   --pmerge-list /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/mergelist.txt \
   --make-bed \
-  --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/keep_files/${pop}.keep \
+  --keep /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files/${pop}.keep \
   --maf 0.01 \
   --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ref.${pop}.GW
 
@@ -112,9 +112,9 @@ for pop in EUR EAS AFR; do
   rm $outDir/ld.sh
   rm -r $outDir/snplist
   rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ref.${pop}.GW*
-  rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/${pop}.hm3.log
+  rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/hm3/${pop}.log
   rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ma_file.txt
   rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/mergelist.txt
 done
-
+  
 ```
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 950c5b18..b5bc4d94 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -362,7 +362,7 @@ if 'sbayesrc' in config['pgs_methods']:
   for pop in gwas_list_df['population'].unique():
     path = f"{sbayesrc_ldref}/{pop}"
     # Check if required files exists
-    cors_file = os.path.join(path, f"{pop}.hm3/ldm.info")
+    cors_file = os.path.join(path, f"ldm.info")
     if not os.path.exists(cors_file):
       print(f"File not found: {cors_file}")
       raise FileNotFoundError(f"Required file not found: {cors_file}. sbayesrc reference data must include ldm.info for all populations.")
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 5d9c6c70..584e0565 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -446,7 +446,7 @@ rule prep_pgs_sbayesrc_i:
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
-    lambda w: f"{sbayesrc_ldref}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}.hm3/ldm.info",
+    lambda w: f"{sbayesrc_ldref}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}/ldm.info",
     rules.download_gctb252_software.output,
     rules.download_sbayesrc_annot.output,
     rules.install_genoutils_sbayesrc.output,
@@ -462,7 +462,7 @@ rule prep_pgs_sbayesrc_i:
     "../envs/sbayesrc.yaml"
   params:
     population= lambda w: gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0],
-    sbayesrc_ldref= lambda w: f"{sbayesrc_ldref}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}.hm3",
+    sbayesrc_ldref= lambda w: f"{sbayesrc_ldref}/{gwas_list_df.loc[gwas_list_df['name'] == w.gwas, 'population'].iloc[0]}",
     testing=config["testing"]
   shell:
     "export OMP_NUM_THREADS={threads}; \

From 459186656c6ed5a6c812618fed6d5655381b1fa1 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 28 Jan 2025 10:28:36 +0000
Subject: [PATCH 092/153] Added in model_builder_top1.R

---
 Scripts/model_builder/model_builder_top1.R | 468 +++++++++++++++++++++
 1 file changed, 468 insertions(+)
 create mode 100644 Scripts/model_builder/model_builder_top1.R

diff --git a/Scripts/model_builder/model_builder_top1.R b/Scripts/model_builder/model_builder_top1.R
new file mode 100644
index 00000000..43b45f4d
--- /dev/null
+++ b/Scripts/model_builder/model_builder_top1.R
@@ -0,0 +1,468 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+suppressMessages(library("optparse"))
+
+option_list = list(
+make_option("--outcome", action="store", default=NULL, type='character',
+    help="File containing outcome data [required]"),
+make_option("--predictors", action="store", default=NULL, type='character',
+    help="File listing files containing predictors, with a groups column for model comparison [required]"),
+make_option("--n_fold", action="store", default=10, type='numeric',
+    help="Number of folds in for cross-validation [optional]"),
+make_option("--n_core", action="store", default=1, type='numeric',
+    help="Number of cores for parallel computing [optional]"),
+make_option("--keep", action="store", default=NULL, type='character',
+    help="File containing list of individuals to include in analysis [optional]"),
+make_option("--outcome_pop_prev", action="store", default=NULL, type='numeric',
+    help="Prevalence of outcome in the general population [optional]"),
+make_option("--out", action="store", default=NULL, type='character',
+    help="Prefix for output files [required]"),
+make_option("--pred_miss", action="store", default=0.1, type='numeric',
+    help="Proportion of missing values allowed in predictor [optional]"),
+make_option("--export_models", action="store", default=T, type='logical',
+    help="Export model coefficients [optional]"),
+make_option("--seed", action="store", default=1, type='numeric',
+    help="Set seed number [optional]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+suppressMessages(library(GenoUtils))
+suppressMessages(library(data.table))
+source('../functions/misc.R')
+source_all('../functions')
+suppressMessages(library(doMC))
+suppressMessages(library(caret))
+suppressMessages(library(pROC))
+suppressMessages(library(verification))
+suppressMessages(library(psych))
+registerDoMC(opt$n_core)
+
+# Check required inputs
+if(is.null(opt$outcome)){
+  stop('--outcome must be specified.\n')
+}
+if(is.null(opt$predictors)){
+  stop('--predictors must be specified.\n')
+}
+if(is.null(opt$out)){
+  stop('--out must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$out), '/')
+system(paste0('mkdir -p ', opt$output_dir))
+
+# Create temp directory
+tmp_dir <- tempdir()
+
+# Create directory for final models to be saved
+if(opt$export_models){
+  system(paste0('mkdir -p ', opt$output_dir, '/final_models'))
+}
+
+# Initiate log file
+log_file <- paste0(opt$out,'.log')
+log_header(log_file = log_file, opt = opt, script = 'model_builder.R', start.time = start.time)
+
+###########
+# Read in predictors
+###########
+
+predictors_file <- fread(opt$predictors)
+
+if(nrow(predictors_file) > 1){
+  predictors <- foreach(i = 1:nrow(predictors_file)) %dopar% {
+    read_predictor(x = predictors_file$predictor[i], pred_miss = opt$pred_miss, file_index = i)
+  }
+
+  group_list <- do.call(rbind, lapply(1:nrow(predictors_file), function(predfile) {
+    data.table(multi = predictors_file$multi[predfile], top1 = predictors_file$top1[predfile], predictor = names(predictors[[predfile]])[-1])
+  }))
+
+  predictors <- Reduce(function(x, y) merge(x, y, by = "IID"), predictors)
+
+  log_add(log_file = log_file, message = paste0('After merging the ', nrow(predictors_file), ' predictors files, ', ncol(predictors)-1, ' predictors remain.'))
+  log_add(log_file = log_file, message = paste0('After merging the ', nrow(predictors_file), ' predictors files, ', nrow(predictors), ' individuals remain.'))
+} else {
+  predictors <- read_predictor(x = predictors_file$predictor[1], pred_miss = opt$pred_miss)
+  group_list <- data.table(multi = predictors_file$multi[1], top1 = predictors_file$top1[1], predictor = names(predictors)[-1])
+}
+
+# Remove predictors with zero variance
+nz_var <- sapply(predictors[, -1, with = FALSE], function(col) var(col) != 0)
+if(sum(!nz_var) > 1){
+  log_add(log_file = log_file, message = paste0(sum(!nz_var), ' predictors have zero variance and will be excluded from downstream analyes.'))
+}
+if(all(!(nz_var))){
+  stop('All predictors have zero variance.')
+}
+predictors <- predictors[, c(TRUE, nz_var), with = FALSE]
+group_list <- group_list[group_list$predictor %in% names(predictors),]
+
+###########
+# Create list of groups for downstream comparison
+###########
+
+group_list$group <- paste0(group_list$multi,'-',group_list$top1)
+
+# Remove identical predictors within each group
+group_list_non_identical <- NULL
+for(i in unique(group_list$group)){
+  if(sum(group_list$group == i) > 1){
+    ident <- group_list$predictor[group_list$group == i][
+      duplicated(
+        as.list(
+          predictors[, group_list$predictor[group_list$group == i], with = F]))]
+
+    group_list_non_identical <- rbind(
+      group_list_non_identical,
+      group_list[group_list$group == i & !(group_list$predictor %in% ident),]
+    )
+
+    if(length(ident) > 0){
+      log_add(log_file = log_file, message = paste0(length(ident), ' duplicate predictors removed from group ', i))
+    }
+  } else {
+    group_list_non_identical <- rbind(
+      group_list_non_identical,
+      group_list[group_list$group == i,]
+    )
+  }
+}
+group_list <- group_list_non_identical
+
+# Calculate the number of predictors in each group
+for(i in unique(group_list$multi)){
+  group_list$n_multi[group_list$multi == i] <- sum(group_list$multi == i)
+  for(j in unique(group_list$top1)){
+    group_list$n_top1[group_list$multi == i & group_list$top1 == j] <- sum(group_list$multi == i & group_list$top1 == j)
+  }
+}
+
+write.table(group_list[!duplicated(group_list$group), c('group','n_multi','n_top1'), with = F], paste0(opt$out,'.group_list.txt'), col.names=T, row.names=F, quote=F)
+log_add(log_file = log_file, message = paste0('List of groups saved as ',opt$out,'.group_list.txt.'))
+
+###########
+# Read in the outcome data
+###########
+
+outcome<-read_outcome(x = opt$outcome, keep = opt$keep)
+
+# Determine whether outcome is binary or continuous and format accordingly
+if (length(unique(outcome$outcome_var)) > 2) {
+  family <- 'gaussian'
+}
+if (length(unique(outcome$outcome_var)) == 2) {
+  family <- 'binomial'
+  outcome$outcome_var <- factor(outcome$outcome_var, labels = c('CONTROL', 'CASE'))
+}
+
+log_add(log_file = log_file, message = paste0('Phenotype is ', ifelse(family == 'binomial', 'binary', 'quantitative'),'.'))
+
+###########
+# Merge the outcome and predictors
+###########
+
+outcome_predictors <- merge(outcome, predictors, by='IID')
+
+rm(outcome, predictors)
+
+log_add(log_file = log_file, message = paste0(nrow(outcome_predictors),' individuals have both phenotypic and predictor data.'))
+
+# Report the size of the combined outcome and predictor data
+log_add(log_file = log_file, message = paste0('Data to be carried foward is ',format(object.size(outcome_predictors), units='auto'),'.'))
+
+############
+# Prediction modelling
+############
+
+# We will derive top1 models, where the best predictor within each multi-top1 combo is evaluated using cross-validation
+# We will then derive multi models, combining top1 predictors within each multi group, agian using cross-validation.
+
+# Split the sample into opt$n_outer_fold folds
+set.seed(opt$seed)
+d <- sample(1:nrow(outcome_predictors))
+train_ind <- createFolds(d, k = opt$n_fold, returnTrain=TRUE)
+
+# Create objects to store outputs
+indep_pred_list <- list()
+
+####
+# Generate predictions using single best predictor from each group, identifying the best predictor using training data, and then evaluating in the test data
+####
+# We will include the groups with a single predictor here as well for convenience, although no feature selection is required.
+
+# Initialise progress log
+log_message <- 'Generate predictions using top1 models for each group... '
+progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
+
+top1_indep_pred <- foreach(i = 1:length(unique(group_list$group)), .combine = 'c') %dopar% {
+  group_name<-paste0(unique(group_list$group)[i], '.top1')
+  indep_pred <- NULL
+  for(outer_val in 1:opt$n_fold){
+    # Subset training and testing data
+    cv_dat <- subset_train_test(dat = outcome_predictors, train_ind = train_ind, fold = outer_val)
+    
+    # Subset variables in group
+    pred_name <- group_list$predictor[group_list$group == unique(group_list$group)[i]]
+    cv_dat$train$x <- cv_dat$train$x[, pred_name, with = F]
+    
+    # Evaluate each predictor in training data
+    # NOTE. Should we be using the RMSE to select the best predictor within a group.
+    top1_res<-NULL
+    for(pred_i in names(cv_dat$train$x)){
+      res_pred_i <- cor(cv_dat$train$y, cv_dat$train$x[[pred_i]], use='p')
+      top1_res <- rbind(
+        top1_res,
+        data.table(
+          pred = pred_i,
+          cor = res_pred_i)
+      )
+    }
+    top_pred <- top1_res$pred[which.max(abs(top1_res$cor))]
+    
+    # Build model using best predictor
+    train_tmp <- data.table(y = cv_dat$train$y, x = cv_dat$train$x[[top_pred]])
+    train_mod <- glm(y ~ x, family=family, data=train_tmp)
+    
+    # Evaluate best performing predictor in test data
+    test_tmp <- data.table(x = cv_dat$test$x[[top_pred]])
+    indep_pred_i <- predict(object = train_mod, newdata = test_tmp, type = "response")
+    indep_pred_i <- data.table(obs = cv_dat$test$y, pred = indep_pred_i)
+    
+    # Save test set predictions from each outer loop
+    indep_pred <- rbind(indep_pred, indep_pred_i)
+  }
+  
+  # Derive and export final model using all data
+  if(opt$export_models){
+    top1_res<-NULL
+    for(pred_i in names(cv_dat$train$x)){
+      res_pred_i <- cor(outcome_predictors$outcome_var, outcome_predictors[[pred_i]], use='p')
+      top1_res <- rbind(
+        top1_res,
+        data.table(
+          pred = pred_i,
+          cor = res_pred_i)
+      )
+    }
+    top_pred <- top1_res$pred[which.max(abs(top1_res$cor))]
+    
+    train_mod <- glm(as.formula(paste0('outcome_var ~ ', top_pred)), family=family, data=outcome_predictors)
+    
+    export_final_model(model = train_mod,
+                       group = group_name,
+                       outdir = paste0(opt$output_dir,
+                                       '/final_models'))
+  }
+  
+  # Update progress log
+  progress <- update_progress_file(progress_file)
+  update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(unique(group_list$group))*100),'%'))
+  
+  # Output results
+  setNames(list(indep_pred), group_name)
+}
+
+indep_pred_list <- c(indep_pred_list, top1_indep_pred)
+update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
+
+####
+# Generate predictions using model containing top1 predictores from each multi group
+####
+
+# Initialise progress log
+log_message <- 'Generate predictions using multi models for each group... '
+progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
+
+multi <- foreach(i = 1:length(unique(group_list$multi)), .combine = 'c') %dopar% {
+  group_name<-paste0(unique(group_list$multi)[i], '.multi')
+  indep_pred <- NULL
+  for(outer_val in 1:opt$n_fold){
+    # Subset training and testing data
+    cv_dat <- subset_train_test(dat = outcome_predictors, train_ind = train_ind, fold = outer_val)
+    
+    top_pred_all <- NULL
+    for(top1_group in unique(group_list$top1[group_list$multi == unique(group_list$multi)[i]])){
+      # Subset variables in group
+      pred_name <- group_list$predictor[
+        group_list$top1 == top1_group & 
+        group_list$multi == unique(group_list$multi)[i]]
+      
+      cv_dat_subset <- cv_dat
+      cv_dat_subset$train$x <- cv_dat$train$x[, pred_name, with = F]
+      
+      # Evaluate each predictor in training data
+      # NOTE. Should we be using the RMSE to select the best predictor within a group.
+      top1_res<-NULL
+      for(pred_i in names(cv_dat_subset$train$x)){
+        res_pred_i <- cor(cv_dat_subset$train$y, cv_dat_subset$train$x[[pred_i]], use='p')
+        top1_res <- rbind(
+          top1_res,
+          data.table(
+            pred = pred_i,
+            cor = res_pred_i)
+        )
+      }
+      top_pred <- top1_res$pred[which.max(abs(top1_res$cor))]
+      
+      top_pred_all <- c(top_pred_all, top_pred)
+    }
+  
+    # Build model using top1 predictors
+    train_tmp <- data.table(y = cv_dat$train$y, cv_dat$train$x[,top_pred_all, with=F])
+    train_mod <- glm(y ~ ., family=family, data=train_tmp)
+    
+    # Evaluate best performing predictor in test data
+    test_tmp <- data.table(cv_dat$test$x[,top_pred_all, with=F])
+    indep_pred_i <- predict(object = train_mod, newdata = test_tmp, type = "response")
+    indep_pred_i <- data.table(obs = cv_dat$test$y, pred = indep_pred_i)
+    
+    # Save test set predictions from each outer loop
+    indep_pred <- rbind(indep_pred, indep_pred_i)
+  }
+  
+  # Derive and export final model using all data
+  if(opt$export_models){
+    top_pred_all <- NULL
+    for(top1_group in unique(group_list$top1[group_list$multi == unique(group_list$multi)[i]])){
+      # Subset variables in group
+      pred_name <- group_list$predictor[
+        group_list$top1 == top1_group & 
+          group_list$multi == unique(group_list$multi)[i]]
+      
+      # Evaluate each predictor in training data
+      # NOTE. Should we be using the RMSE to select the best predictor within a group.
+      top1_res<-NULL
+      for(pred_i in pred_name){
+        res_pred_i <- cor(outcome_predictors$outcome_var, outcome_predictors[[pred_i]], use='p')
+        top1_res <- rbind(
+          top1_res,
+          data.table(
+            pred = pred_i,
+            cor = res_pred_i)
+        )
+      }
+      top_pred <- top1_res$pred[which.max(abs(top1_res$cor))]
+      
+      top_pred_all <- c(top_pred_all, top_pred)
+    }
+    
+    # Build model using top1 predictors
+    train_tmp <- data.table(y = outcome_predictors$outcome_var, outcome_predictors[,top_pred_all, with=F])
+    train_mod <- glm(y ~ ., family=family, data=train_tmp)
+    
+    export_final_model(model = train_mod,
+                       group = group_name,
+                       outdir = paste0(opt$output_dir,
+                                       '/final_models'))
+  }
+  
+  # Update progress log
+  progress <- update_progress_file(progress_file)
+  update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(unique(group_list$multi))*100),'%'))
+  
+  # Output results
+  setNames(list(indep_pred), group_name)
+}
+
+indep_pred_list <- c(indep_pred_list, multi)
+update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
+
+###################
+# Evaluate all models
+###################
+
+# Initialise progress log
+log_message <- 'Evaluating all models... '
+progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
+
+pred_eval_all <- foreach(i = 1:length(indep_pred_list), .combine=rbind) %dopar% {
+  # Update progress log
+  progress <- update_progress_file(progress_file)
+  update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(indep_pred_list)*100),'%'))
+
+  data.table(
+      Group = names(indep_pred_list)[i],
+      eval_pred(
+        obs = indep_pred_list[[i]]$obs,
+        pred = indep_pred_list[[i]]$pred,
+        family = family
+      )
+    )
+}
+
+update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
+
+# Write out the results
+write.table(pred_eval_all, paste0(opt$out,'.pred_eval.txt'), col.names=T, row.names=F, quote=F)
+log_add(log_file = log_file, message = paste0('Model evaluation results saved as ',opt$out,'.pred_eval.txt.'))
+
+###################
+# Compare predictive utiliy of the different models
+###################
+
+if(length(pred_eval_all$Group) > 1){
+
+  # Initialise progress log
+  log_message <- 'Comparing model performance... '
+  progress_file <- initialise_progress(log_message = log_message, log_file = log_file)
+
+  # Identify number of pairwise comparisons
+  models <- pred_eval_all$Group
+  model_comps <- data.table(t(combn(models, 2)))
+
+  comp_res_all <- foreach(i = 1:length(models), .combine=rbind) %dopar% {
+    group1 <- models[i]
+    comp_res_i <- NULL
+    for(group2 in model_comps$V2[model_comps$V1 == group1]){
+
+    	group1_r <- cor(scale(as.numeric(indep_pred_list[[group1]]$obs)), scale(indep_pred_list[[group1]]$pred))[1]
+      group2_r <- cor(scale(as.numeric(indep_pred_list[[group2]]$obs)), scale(indep_pred_list[[group2]]$pred))[1]
+
+      r_diff <- group1_r - group2_r
+
+      group1_group2_r <- cor(scale(indep_pred_list[[group1]]$pred), scale(indep_pred_list[[group2]]$pred))
+
+      r_diff_p <-
+        paired.r(
+          xy = group1_r,
+          xz = group2_r,
+          yz = group1_group2_r,
+          n = length(scale(indep_pred_list[[group1]]$pred)),
+          twotailed = T
+        )$p[1]
+
+      comp_res <- data.table(
+        Model_1 = group1,
+        Model_2 = group2,
+        Model_1_R = group1_r,
+        Model_2_R = group2_r,
+        R_diff = r_diff,
+        R_diff_pval = r_diff_p
+      )
+
+      comp_res_i <- rbind(comp_res_i, comp_res)
+    }
+
+    # Update progress log
+    progress <- update_progress_file(progress_file)
+    update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(models)*100),'%'))
+
+    comp_res_i
+  }
+
+  update_log_file(log_file = log_file, message = paste0(log_message, 'Done!'))
+
+  # Write out the results
+  write.table(comp_res_all, paste0(opt$out,'.pred_comp.txt'), col.names=T, row.names=F, quote=F)
+  log_add(log_file = log_file, message = paste0('Model comparison results saved as ',opt$out,'.pred_comp.txt.'))
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+log_add(log_file = log_file, message = paste0('Analysis finished at ',as.character(end.time)))
+log_add(log_file = log_file, message = paste0('Analysis duration was ',as.character(round(time.taken,2)),attr(time.taken, 'units')))

From 87e911f41a970e626d91852264075c9bcd8b05e6 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 28 Jan 2025 10:28:56 +0000
Subject: [PATCH 093/153] Update error catching in sbayesrc

---
 Scripts/pgs_methods/sbayesrc.R | 26 +++++++++++++++++++-------
 1 file changed, 19 insertions(+), 7 deletions(-)

diff --git a/Scripts/pgs_methods/sbayesrc.R b/Scripts/pgs_methods/sbayesrc.R
index 5bb13ff8..ddb1c5b5 100644
--- a/Scripts/pgs_methods/sbayesrc.R
+++ b/Scripts/pgs_methods/sbayesrc.R
@@ -162,9 +162,9 @@ tryCatch(
   },
   error = function(e) {
     # Check if the error message matches the specific issue
-    if (grepl("All correlations are negative, this may indicate errors in summary data.", e$message)) {
-      message("Specific error encountered. Retrying with btune = FALSE...")
-      log_add(log_file = log_file, message = 'Specific error encountered. Retrying with btune = FALSE...')
+    if (grepl("All correlations are negative, this may indicate errors in summary data.", conditionMessage(e))) {
+      message("Specific error encountered: All correlations are negative. Retrying with btune = FALSE...")
+      log_add(log_file = log_file, message = 'Specific error encountered: All correlations are negative. Retrying with btune = FALSE...')
       # Retry with the btune parameter set to FALSE
       SBayesRC::sbayesrc(
         mafile = paste0(tmp_dir, '/tidy.imp.ma'),
@@ -174,7 +174,7 @@ tryCatch(
         bTune = FALSE,
         log2file = FALSE
       )
-    } else if (grepl("An unexpected error occurred: Warning, the best parameter is the minimumn threshold, we suggest to expand the tuning grid by specify lower tuning value", e$message)) {
+    } else if (grepl("Warning, the best parameter is the minimumn threshold", conditionMessage(e))) {
         message("Specific error encountered. Retrying with tuneStep=c(0.995, 0.9, 0.8, 0.7, 0.6)...")
         log_add(log_file = log_file, message = 'Specific error encountered. Retrying with btune = FALSE...')
         # Retry with the tuneStep=c(0.995, 0.9, 0.8, 0.7, 0.6)
@@ -186,14 +186,26 @@ tryCatch(
           tuneStep=c(0.995, 0.9, 0.8, 0.7, 0.6),
           log2file = FALSE
         )
-      } else {
+    } else if (grepl("Invalid tune outputs, all correlations are invalid number", conditionMessage(e))) {
+      message("Specific error encountered: Invalid tune outputs. Setting bTune=FALSE...")
+      log_add(log_file = log_file, message = 'Specific error encountered: Invalid tune outputs. Setting bTune=FALSE...')
+      # Retry with bTune=FALSE
+      SBayesRC::sbayesrc(
+        mafile = paste0(tmp_dir, '/tidy.imp.ma'),
+        LDdir = opt$sbayesrc_ldref,
+        outPrefix = paste0(tmp_dir, '/sbrc'),
+        annot = opt$sbayesrc_annot,
+        bTune = FALSE,
+        log2file = FALSE
+      )
+    } else {
         # For any other error, rethrow the error and stop the script
-      stop("An unexpected error occurred: ", e$message)
+      stop("An unexpected error occurred: ", conditionMessage(e))
     }
   },
   warning = function(w) {
     message("A warning occurred while running SBayesRC::sbayesrc:")
-    message(w)
+    message(conditionMessage(e))
   }
 )
 

From 28ac80fff360f8713acbe5a81a253d78f604a4b6 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 29 Jan 2025 16:39:22 +0000
Subject: [PATCH 094/153] Updates to crosspop analysis

---
 docs/CrossPop.Rmd              | 336 +++++++++++++++++++++++++--------
 functions/constants.R          |   2 +-
 pipeline/rules/pgs_methods.smk |   4 +-
 3 files changed, 259 insertions(+), 83 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index a4d6830d..967e70fa 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1508,11 +1508,9 @@ config<-c(
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt",
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
-#  "pgs_methods: ['ptclump','dbslmm','prscsx','xwing','tlprs','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc']",
-#  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','ldpred2','megaprs','sbayesrc','prscsx','xwing','prscs']",
-  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
+  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx','xwing']",
 #  "tlprs_methods: ['dbslmm','prscs','lassosum']",
-  "cores_prep_pgs: 10",
+  "cores_prep_pgs: 10", # xwing run with 20 cores
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
   "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
@@ -1548,6 +1546,18 @@ Lets use the model builder script which implements nested 10 fold cross validati
 
 ## Compare all methods
 
+We want to see:
+- Performance of pseudo and top1 models for single-source methods
+- Performance of pseudo and top1 models for multi-source methods
+- Performance of multi-source methods:
+  - Using crossval for tuning step 1 and 2
+  - Using pseudoval for tuning step 1 and 2
+  - Using pseudoval for tuning step 1 and crossval for tuning step 2
+
+To achieve this. Will need to define groups of predictors for step 1 modelling, and groups that should then be linearly combined.
+
+***
+
 ### Create predictor lists
 
 <details><summary>Show code</summary>
@@ -1576,13 +1586,10 @@ scores<-scores[!grepl('tlprs', scores$method),]
 # Create files for EAS and AFR targets
 targ_pop <- c('EAS','AFR')
 for(trait_i in selected_traits){
-  # Make a group containing both GWAS for each single source method
-  # Make a group for each multisource method
   scores_i <- scores[grepl(trait_i, scores$name),]
-  scores_i$group <- scores_i$method
+  scores_i$multi <- scores_i$method
   
   for(targ_pop_i in targ_pop){
-    # Subset GWAS based on EUR and/or targ_pop_i
     if(targ_pop_i == 'EAS'){
       disc_pop <- 'BBJ'
     }
@@ -1613,82 +1620,121 @@ for(trait_i in selected_traits){
         recursive = T
       )
       
-      scores_i_j <- scores_i[grepl('UKB$', scores_i$name, ignore.case = F) |
-                       grepl(paste0(disc_pop_j, '$'), scores_i$name, ignore.case = T),]
+      scores_i_j <- scores_i[
+        (grepl('UKB$', scores_i$name, ignore.case = F) | 
+         grepl(paste0(disc_pop_j, '$'), scores_i$name, ignore.case = T)),]
 
-      scores_i_j_multi <- scores_i_j
       # Insert path to score file
-      scores_i_j_multi$predictor <- paste0(
+      scores_i_j$predictor <- paste0(
         outdir,
         '/ukb/pgs/TRANS/',
-        scores_i_j_multi$method,
+        scores_i_j$method,
         '/',
-        scores_i_j_multi$name,
+        scores_i_j$name,
         '/ukb-',
-        scores_i_j_multi$name,
+        scores_i_j$name,
         '-TRANS.profiles'
       )
       
+      ####
+      # Make groups single source methods
+      ####
+      
+      scores_i_j_single_top1 <- scores_i_j[!(scores_i_j$method %in% pgs_group_methods),]
+
+      # Create top1 column indicating which predictors top1 models should be derived
+      scores_i_j_single_top1$top1[grepl('UKB', scores_i_j_single_top1$name, ignore.case = F)] <- 'EUR'
+      scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] <- disc_pop_j_2
+      
+      ####
+      # Make groups containing pseudo scores for single source methods
+      ####
+
       # Extract the pseudo score for each method and specify as a seperate group
-      for(i in 1:nrow(scores_i_j)) {
+      for(i in 1:nrow(scores_i_j_single_top1)) {
         param <- find_pseudo(
           config = config,
-          gwas = scores_i_j$name[i],
-          pgs_method = scores_i_j$method[i],
+          gwas = scores_i_j_single_top1$name[i],
+          pgs_method = scores_i_j_single_top1$method[i],
           target_pop = targ_pop_i
         )
         
         score_header <-
-          fread(scores_i_j_multi$predictor[i], nrows = 1)
+          fread(scores_i_j_single_top1$predictor[i], nrows = 1)
         score_cols <-
-          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
+          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_single_top1$name[i], '_', param)))
         
         system(
           paste0(
             "cut -d' ' -f ", 
             paste0(score_cols, collapse=','),
             " ", 
-            scores_i_j_multi$predictor[i], 
+            scores_i_j_single_top1$predictor[i], 
             " > ", 
             gsub('.profiles',
                  paste0('.', targ_pop_i, '_pseudo.profiles'),
-                 scores_i_j_multi$predictor[i])
+                 scores_i_j_single_top1$predictor[i])
           )
         )
       }
       
-      scores_i_j_pseudo <- scores_i_j_multi
-      scores_i_j_pseudo$group <- paste0(scores_i_j_multi$group, '.pseudo')
-      for(i in 1:nrow(scores_i_j_pseudo)){
-        if(grepl('UKB', scores_i_j_pseudo$name[i])){
-          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.EUR.pseudo', scores_i_j_pseudo$group[i])
-        }
-        if(grepl('BBJ', scores_i_j_pseudo$name[i])){
-          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.EAS.pseudo', scores_i_j_pseudo$group[i])
-        }
-        if(grepl('UGR', scores_i_j_pseudo$name[i])){
-          scores_i_j_pseudo$group[i] <- gsub('.pseudo', '.AFR.pseudo', scores_i_j_pseudo$group[i])
-        }
+      scores_i_j_single_pseudo <- scores_i_j_single_top1
+      scores_i_j_single_pseudo$multi <- paste0(scores_i_j_single_pseudo$multi, '.pseudo')
+
+      scores_i_j_single_pseudo$predictor <- gsub('.profiles', 
+                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
+                                    scores_i_j_single_pseudo$predictor)
+
+      ####
+      # Make groups for multi-single-source pseudo scores
+      ####
+      
+      scores_i_j_multi_single_pseudo <- scores_i_j[grepl('_multi$', scores_i_j$method),]
+
+      # Extract the pseudo score for each method and specify as a seperate group
+      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
+        param <- find_pseudo(
+          config = config,
+          gwas = scores_i_j_multi_single_pseudo$name[i],
+          pgs_method = scores_i_j_multi_single_pseudo$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header <-
+          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
+        score_cols <-
+          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi_single_pseudo$name[i], '_', param)))
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            scores_i_j_multi_single_pseudo$predictor[i], 
+            " > ", 
+            gsub('.profiles',
+                 paste0('.', targ_pop_i, '_pseudo.profiles'),
+                 scores_i_j_multi_single_pseudo$predictor[i])
+          )
+        )
       }
-      scores_i_j_pseudo$predictor <- gsub('.profiles', 
-                                          paste0('.', targ_pop_i, '_pseudo.profiles'),
-                                          scores_i_j_pseudo$predictor)
       
-      # Make a group for each GWAS and single source combo
-      scores_i_j_single <- scores_i_j_multi[!(scores_i_j_multi$method %in% pgs_group_methods),]
-      scores_i_j_single$group <-
-        paste0(scores_i_j_single$method,
-               '.',
-               gsub('.*_', '', scores_i_j_single$name))
-      scores_i_j_single$group <- gsub('UKB', 'EUR', scores_i_j_single$group)
-      scores_i_j_single$group <- gsub('BBJ', 'EAS', scores_i_j_single$group)
-      scores_i_j_single$group <- gsub('UGR', 'AFR', scores_i_j_single$group)
+      scores_i_j_multi_single_pseudo$multi <- paste0(scores_i_j_multi_single_pseudo$multi, '.pseudo')
+
+      scores_i_j_multi_single_pseudo$predictor <- gsub('.profiles', 
+                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
+                                    scores_i_j_multi_single_pseudo$predictor)
       
+      scores_i_j_multi_single_pseudo$top1<-paste0('EUR_', disc_pop_j_2)
+
+      ####
+      # Combine the different predictor groups
+      ####
       predictors_i<- do.call(rbind, list(
-        scores_i_j_multi, scores_i_j_pseudo, scores_i_j_single
+        scores_i_j_single_top1, scores_i_j_single_pseudo, scores_i_j_multi_single_pseudo 
       ))
       
-      predictors_i <- predictors_i[, c('predictor', 'group'), with=F]
+      predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
       
       write.table(
         predictors_i,
@@ -1743,14 +1789,11 @@ for targ_pop in $(echo EAS AFR); do
   
   for disc_pop_i in ${disc_pop}; do
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
-      sbatch --mem 20G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      sbatch --mem 10G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
         --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
         --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
         --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
-        --n_core 10 \
-        --top1 T \
-        --all_model T \
-        --assoc T"
+        --n_core 5"
     done
   done
 done
@@ -1833,31 +1876,47 @@ for(pheno_i in selected_traits){
   }
   
   res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
+  res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
   
-  res_eval_i$Model[grepl('top1', res_eval_i$Group)]<-'Top1'
-  res_eval_i$Model[grepl('pseudo', res_eval_i$Group)]<-'Pseudo'
-  res_eval_i$Model[!grepl('top1|pseudo', res_eval_i$Group)]<-'Multi'
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTrain'
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTrain'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTrain'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTrain'
+  res_eval_i$Model[grepl('quickprs_multi', res_eval_i$Group)]<-'SumStatTrain'
+  
+  
+  res_eval_i$Source<-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | 
+    !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
   
-  res_eval_i$Source<-ifelse(res_eval_i$Method %in% c(pgs_group_methods, 'all') | !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
-         
   res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
   res_eval_i$Discovery[grepl('EAS', res_eval_i$Group)] <- 'EAS'
   res_eval_i$Discovery[grepl('AFR', res_eval_i$Group)] <- 'AFR'
   res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
   
   res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
-  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('Top1','Pseudo','Multi'))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
   res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-  # Remove Multi model for groups that contain one score (aka QuickPRS and SBayesRC)
+  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
   res_eval_i <- res_eval_i[
-    !(res_eval_i$Method %in%  c('sbayesrc', 'quickprs') & 
-      res_eval_i$Model == 'Multi' & 
-      res_eval_i$Source == 'Single'),]
+    !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
+      res_eval_i$Model %in% c('IndivTrain','Multi-IndivTrain')),]
   
-  # Remove pseudo model for methods that don't really have one (single source combo)
-  res_eval_i <- res_eval_i[!which(res_eval_i$Model == 'Pseudo' & res_eval_i$Method == 'ptclump'),]
+  # Remove pseudo model for methods that don't really have one 
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('ptclump') & 
+      res_eval_i$Model %in% c('SumStatTrain','Multi-SumStatTrain')),]
 
+  # Remove any duplicate models
+  res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
+    "Target", "Method", "Model", "Source", "Discovery"
+  )]),]
+  
   res_eval[[pheno_i]]<-res_eval_i
   
 }
@@ -1938,36 +1997,153 @@ for(targ_pop_i in targ_pop){
   }
 }
 
-meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('Top1','Pseudo','Multi'))
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
 meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-meta_res_eval$R_diff<-NA
-meta_res_eval$reference_point<-F
-for(targ_pop_i in targ_pop){
-  # Calculate R compared to top1 model where GWAS matches target population
-  meta_res_eval$R_diff[meta_res_eval$Target == targ_pop_i] <- meta_res_eval$R[meta_res_eval$Target == targ_pop_i] - meta_res_eval$R[meta_res_eval$Target == targ_pop_i & meta_res_eval$Discovery == meta_res_eval$Target & meta_res_eval$Model == 'Top1']
-  meta_res_eval$reference_point[meta_res_eval$Target == targ_pop_i & meta_res_eval$Discovery == meta_res_eval$Target & meta_res_eval$Model == 'Top1'] <- T 
-}
-
 ####
 # Comparison of models across methods, across target and discovery populations
 ####
 
 tmp <- meta_res_eval
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
+tmp$label[grepl('Multi', tmp$Model)] <- paste0(tmp$label[grepl('Multi', tmp$Model)], '-Multi')
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+
+tmp$Target <- paste0(tmp$Target, ' Target')
+
+model_palette <- c(
+  "IndivTrain" = "#F8766D",
+  "SumStatTrain" = "#7CAE00",
+  "Multi-IndivTrain" = "#00BFC4",
+  "Multi-SumStatTrain" = "#C77CFF"
+)
+
+tmp$Model <- factor(tmp$Model, levels = names(model_palette))
 
-ggplot(tmp, aes(x=Method, y=R , fill = Model)) +
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
     #geom_hline(yintercept = 0) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
                   position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
-    labs(y = "R (SE)") +
-    facet_grid(Target ~ Discovery, scales='free', space = 'free_x') +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
     theme_half_open() +
     background_grid() + 
     panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
+    scale_fill_manual(values = model_palette)
+
+ggplot(tmp[tmp$Discovery_clean == 'Target-matched GWAS',], aes(x=label, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(. ~ Target, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
+    scale_fill_manual(values = model_palette)
+
+ggplot(tmp[tmp$Discovery_clean == 'EUR GWAS',], aes(x=label, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(. ~ Target, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
+    scale_fill_manual(values = model_palette)
+
+tmp_multi <- tmp[tmp$Discovery_clean == 'Both',]
+tmp_multi <- tmp
+tmp_multi$Model[tmp_multi$Model != 'SumStatTrain'] <- 'IndivTrain'
+tmp_multi$Model[tmp_multi$Model == 'SumStatTrain'] <- 'SumStatTrain'
+tmp_multi <- tmp_multi[!duplicated(tmp_multi[, c('label','Target','Discovery_clean','Model'), with=F]),]
+
+ggplot(tmp_multi, aes(x=label, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
+    scale_fill_manual(values = model_palette)
+
+tmp_multi_pseudo <- tmp[tmp$Discovery_clean == 'Both',]
+tmp_multi_pseudo <- tmp_multi_pseudo[!duplicated(tmp_multi_pseudo[, c('label','Target'), with=F]),]
+tmp_multi_pseudo$Model <- 'IndivTrain'
+
+ggplot(tmp_multi_pseudo, aes(x=label, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(. ~ Target, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
+    scale_fill_manual(values = model_palette)
+
+tmp_crossval <- tmp[tmp$Model %in% c('IndivTrain','Multi-IndivTrain'),]
+tmp_crossval$Model <- 'IndivTrain'
+
+ggplot(tmp_crossval, aes(x=label, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "none") +
+    scale_fill_manual(values = model_palette)
+
+tmp_pseudoval <- tmp[tmp$Model %in% c('SumStatTrain'),]
+
+ggplot(tmp_pseudoval, aes(x=label, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "none") +
+    scale_fill_manual(values = model_palette)
 
 ####
 # Create heatmap showing difference between all methods and models
diff --git a/functions/constants.R b/functions/constants.R
index 0842bef6..a8de2a58 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -37,7 +37,7 @@ ref_pop <- data.frame(
 
 # Make a data.frame giving labels to the 1KG reference populations
 pgs_method_labels <- data.frame(
-  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','tlprs','quickprs'),
+  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','tlprs','quickprs_multi'),
   label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','TL-PRS','QuickPRS-Multi')
 )
 pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),]
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 584e0565..326f6247 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -577,7 +577,7 @@ checkpoint score_reporter:
 rule prep_pgs_prscsx_i:
   resources:
     mem_mb=2000*config['cores_prep_pgs'],
-    time_min=5000
+    time_min=2800
   threads: config['cores_prep_pgs']
   input:
     rules.download_prscsx_software.output,
@@ -628,7 +628,7 @@ rule prep_pgs_prscsx:
 rule prep_pgs_xwing_i:
   resources:
     mem_mb=2000*config['cores_prep_pgs'],
-    time_min=10000
+    time_min=2800
   threads: config['cores_prep_pgs']
   input:
     rules.download_xwing_software.output,

From ef36c724b3bf11c49ef93450974537a171247da7 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 29 Jan 2025 18:13:19 +0000
Subject: [PATCH 095/153] Update

---
 docs/CrossPop.Rmd | 128 +++++++++++++++++++++++++++++++++++++++++++++-
 1 file changed, 127 insertions(+), 1 deletion(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 967e70fa..23beba6a 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1582,6 +1582,7 @@ scores <- list_score_files(config)
 
 # Remove tlprs
 scores<-scores[!grepl('tlprs', scores$method),]
+scores<-scores[!grepl('xwing', scores$method),]
 
 # Create files for EAS and AFR targets
 targ_pop <- c('EAS','AFR')
@@ -1727,11 +1728,136 @@ for(trait_i in selected_traits){
       
       scores_i_j_multi_single_pseudo$top1<-paste0('EUR_', disc_pop_j_2)
 
+      ####
+      # Make groups for the Multi-Source methods
+      ####
+      
+      scores_i_j_multi <- scores_i_j[(scores_i_j$method %in% pgs_group_methods) &
+                                           !grepl('_multi$', scores_i_j$method),]
+
+      # Split top1 scores by target population
+      scores_i_j_multi_top1<-NULL
+      for(i in 1:nrow(scores_i_j_multi)){
+        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
+        
+        for(pop in c('EUR', disc_pop_j_2)){
+          
+          if(scores_i_j_multi$method[i] == 'prscsx'){
+            score_cols <-
+              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi'), names(score_header)))
+          }
+          
+          system(
+            paste0(
+              "cut -d' ' -f ", 
+              paste0(score_cols, collapse=','),
+              " ", 
+              scores_i_j_multi$predictor[i], 
+              " > ", 
+              gsub('.profiles',
+                   paste0('.', pop, '_grid.profiles'),
+                   scores_i_j_multi$predictor[i])
+            )
+          )
+          
+          tmp <- scores_i_j_multi[i,]
+          tmp$multi <- paste0(tmp$multi, '.grid')
+          tmp$top1 <- pop
+          tmp$predictor <-
+              gsub('.profiles',
+                   paste0('.', pop, '_grid.profiles'),
+                   scores_i_j_multi$predictor[i])
+          
+          scores_i_j_multi_top1 <- rbind(scores_i_j_multi_top1, tmp)
+        }
+      }
+
+      # Split pop-specific pseudo scores by target population
+      scores_i_j_multi_pop_pseudo<-NULL
+      for(i in 1:nrow(scores_i_j_multi)){
+        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
+        
+        for(pop in c('EUR', disc_pop_j_2)){
+          
+          if(scores_i_j_multi$method[i] == 'prscsx'){
+            score_cols <-
+              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi_auto'), names(score_header)))
+          }
+          
+          system(
+            paste0(
+              "cut -d' ' -f ", 
+              paste0(score_cols, collapse=','),
+              " ", 
+              scores_i_j_multi$predictor[i], 
+              " > ", 
+              gsub('.profiles',
+                   paste0('.', pop, '_pseudo.profiles'),
+                   scores_i_j_multi$predictor[i])
+            )
+          )
+          
+          tmp <- scores_i_j_multi[i,]
+          tmp$multi <- paste0(tmp$multi, '.pop_pseudo')
+          tmp$top1 <- pop
+          tmp$predictor <-
+              gsub('.profiles',
+                   paste0('.', pop, '_pseudo.profiles'),
+                   scores_i_j_multi$predictor[i])
+          
+          scores_i_j_multi_pop_pseudo <- rbind(scores_i_j_multi_pop_pseudo, tmp)
+        }
+      }
+      
+      # Create pseudo score for multi-source methods
+      scores_i_j_multi_pseudo<-NULL
+      for(i in 1:nrow(scores_i_j_multi)) {
+        param <- find_pseudo(
+          config = config,
+          gwas = scores_i_j_multi$name[i],
+          pgs_method = scores_i_j_multi$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header <-
+          fread(scores_i_j_multi$predictor[i], nrows = 1)
+        score_cols <-
+          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
+
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            scores_i_j_multi$predictor[i], 
+            " > ", 
+            gsub('.profiles',
+                 paste0('.pseudo.profiles'),
+                 scores_i_j_multi$predictor[i])
+          )
+        )
+        
+        tmp <- scores_i_j_multi[i,]
+        tmp$multi <- paste0(tmp$multi, '.pseudo')
+        tmp$top1 <- paste0('EUR_', disc_pop_j_2)
+        tmp$predictor <-
+            gsub('.profiles',
+                 paste0('.pseudo.profiles'),
+                 scores_i_j_multi$predictor[i])
+        
+        scores_i_j_multi_pseudo <- rbind(scores_i_j_multi_pseudo, tmp)
+      }
+
       ####
       # Combine the different predictor groups
       ####
       predictors_i<- do.call(rbind, list(
-        scores_i_j_single_top1, scores_i_j_single_pseudo, scores_i_j_multi_single_pseudo 
+        scores_i_j_single_top1, 
+        scores_i_j_single_pseudo, 
+        scores_i_j_multi_single_pseudo,
+        scores_i_j_multi_top1,
+        scores_i_j_multi_pop_pseudo,
+        scores_i_j_multi_pseudo
       ))
       
       predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]

From 4a6f8e560b9e01d84e8665994707548dff53c4b7 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 30 Jan 2025 17:09:27 +0000
Subject: [PATCH 096/153] Updates

---
 docs/CrossPop.Rmd            | 155 +++------
 docs/model_builder_demo.Rmd  | 144 ++++++++
 docs/model_builder_demo.html | 653 +++++++++++++++++++++++++++++++++++
 3 files changed, 848 insertions(+), 104 deletions(-)
 create mode 100644 docs/model_builder_demo.Rmd
 create mode 100644 docs/model_builder_demo.html

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 23beba6a..38be5f4a 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1915,7 +1915,7 @@ for targ_pop in $(echo EAS AFR); do
   
   for disc_pop_i in ${disc_pop}; do
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
-      sbatch --mem 10G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+      sbatch --mem 10G -n 5 -p interruptible_cpu --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
         --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
         --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
         --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
@@ -2013,6 +2013,7 @@ for(pheno_i in selected_traits){
   res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
                    grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTrain'
   res_eval_i$Model[grepl('quickprs_multi', res_eval_i$Group)]<-'SumStatTrain'
+  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTrain'
   
   
   res_eval_i$Source<-ifelse(
@@ -2038,6 +2039,11 @@ for(pheno_i in selected_traits){
     !(res_eval_i$Method %in%  c('ptclump') & 
       res_eval_i$Model %in% c('SumStatTrain','Multi-SumStatTrain')),]
 
+  # Remove top1 models for PRS-CSx
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('prscsx') & 
+      grepl('top1', res_eval_i$Group)),]
+  
   # Remove any duplicate models
   res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
     "Target", "Method", "Model", "Source", "Discovery"
@@ -2130,9 +2136,11 @@ meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','E
 # Comparison of models across methods, across target and discovery populations
 ####
 
+# Format results for nice plot
 tmp <- meta_res_eval
 tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
-tmp$label[grepl('Multi', tmp$Model)] <- paste0(tmp$label[grepl('Multi', tmp$Model)], '-Multi')
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)], '-Multi')
+tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
 tmp$Discovery_clean <- as.character(tmp$Discovery)
 tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
 tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
@@ -2153,6 +2161,7 @@ model_palette <- c(
 
 tmp$Model <- factor(tmp$Model, levels = names(model_palette))
 
+# Plot everything
 ggplot(tmp, aes(x=label, y=R , fill = Model)) +
     #geom_hline(yintercept = 0) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
@@ -2168,36 +2177,7 @@ ggplot(tmp, aes(x=label, y=R , fill = Model)) +
     theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
     scale_fill_manual(values = model_palette)
 
-ggplot(tmp[tmp$Discovery_clean == 'Target-matched GWAS',], aes(x=label, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(. ~ Target, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
-    scale_fill_manual(values = model_palette)
-
-ggplot(tmp[tmp$Discovery_clean == 'EUR GWAS',], aes(x=label, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(. ~ Target, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
-    scale_fill_manual(values = model_palette)
-
+# Simplify results showing results only with or without training data
 tmp_multi <- tmp[tmp$Discovery_clean == 'Both',]
 tmp_multi <- tmp
 tmp_multi$Model[tmp_multi$Model != 'SumStatTrain'] <- 'IndivTrain'
@@ -2219,58 +2199,6 @@ ggplot(tmp_multi, aes(x=label, y=R , fill = Model)) +
     theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
     scale_fill_manual(values = model_palette)
 
-tmp_multi_pseudo <- tmp[tmp$Discovery_clean == 'Both',]
-tmp_multi_pseudo <- tmp_multi_pseudo[!duplicated(tmp_multi_pseudo[, c('label','Target'), with=F]),]
-tmp_multi_pseudo$Model <- 'IndivTrain'
-
-ggplot(tmp_multi_pseudo, aes(x=label, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(. ~ Target, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
-    scale_fill_manual(values = model_palette)
-
-tmp_crossval <- tmp[tmp$Model %in% c('IndivTrain','Multi-IndivTrain'),]
-tmp_crossval$Model <- 'IndivTrain'
-
-ggplot(tmp_crossval, aes(x=label, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "none") +
-    scale_fill_manual(values = model_palette)
-
-tmp_pseudoval <- tmp[tmp$Model %in% c('SumStatTrain'),]
-
-ggplot(tmp_pseudoval, aes(x=label, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), legend.position = "none") +
-    scale_fill_manual(values = model_palette)
-
 ####
 # Create heatmap showing difference between all methods and models
 ####
@@ -2295,7 +2223,7 @@ mirror_comp<-function(x){
 }
   
 # Read in results
-targ_pop=c('EUR','EAS','AFR')
+targ_pop=c('EAS','AFR')
 res_comp <- list()
 for(pheno_i in selected_traits){
   res_comp_i<-NULL
@@ -2341,13 +2269,19 @@ res_comp_all <- do.call(rbind, lapply(names(res_comp), function(name) {
 
 # Annotate tests to get order correct
 res_comp_all$Method1<-sub('\\..*','',res_comp_all$Model_1)
+res_comp_all$Method1<-gsub('-.*','', res_comp_all$Method1)
 res_comp_all$Method2<-sub('\\..*','',res_comp_all$Model_2)
+res_comp_all$Method2<-gsub('-.*','', res_comp_all$Method2)
 
 find_model<-function(x){
   mod <- x
-  mod[grepl('top1', x)]<-'Top1'
-  mod[grepl('pseudo', x)]<-'Pseudo'
-  mod[!grepl('top1|pseudo', x)]<-'Multi'
+  mod[grepl('top1$', x) & !grepl('pseudo', x)] <- 'IndivTrain'
+  mod[grepl('top1$', x) & grepl('pseudo', x)] <- 'SumStatTrain'
+  mod[grepl('multi$', x) & !grepl('pseudo', x)] <- 'Multi-IndivTrain'
+  mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTrain'
+  mod[grepl('quickprs_multi', x)] <- 'SumStatTrain'
+  mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTrain'
+  
   return(mod)
 }
 
@@ -2366,25 +2300,37 @@ res_comp_all$Discovery2[res_comp_all$Source2 == 'Multi'] <- res_comp_all$gwas_gr
 
 res_comp_all$Method1<-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
 res_comp_all$Method2<-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
-res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('Top1','Pseudo','Multi'))
-res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('Top1','Pseudo','Multi'))
+res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
 res_comp_all$Discovery1<-factor(res_comp_all$Discovery1, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
 res_comp_all$Discovery2<-factor(res_comp_all$Discovery2, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-# Remove Multi model for groups that contain one score (aka QuickPRS and SBayesRC)
+# Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
 res_comp_all <- res_comp_all[
-  !(res_comp_all$Method1 %in%  c('sbayesrc', 'quickprs') & 
-    res_comp_all$Model1 == 'Multi' & 
-    res_comp_all$Source1 == 'Single'),]
+!(res_comp_all$Method1 %in%  c('quickprs','sbayesrc') & 
+  res_comp_all$Model1 %in% c('IndivTrain','Multi-IndivTrain')),]
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method2 %in%  c('quickprs','sbayesrc') & 
+  res_comp_all$Model2 %in% c('IndivTrain','Multi-IndivTrain')),]
 
+# Remove pseudo model for methods that don't really have one 
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method1 %in%  c('ptclump') & 
+  res_comp_all$Model1 %in% c('SumStatTrain','Multi-SumStatTrain')),]
 res_comp_all <- res_comp_all[
-  !(res_comp_all$Method2 %in%  c('sbayesrc', 'quickprs') & 
-    res_comp_all$Model2 == 'Multi' & 
-    res_comp_all$Source2 == 'Single'),]
+!(res_comp_all$Method2 %in%  c('ptclump') & 
+  res_comp_all$Model2 %in% c('SumStatTrain','Multi-SumStatTrain')),]
 
-# Remove pseudo model for methods that don't really have one (single source combo)
-res_comp_all <- res_comp_all[!which(res_comp_all$Model1 == 'Pseudo' & res_comp_all$Method1 == 'ptclump'),]
-res_comp_all <- res_comp_all[!which(res_comp_all$Model2 == 'Pseudo' & res_comp_all$Method2 == 'ptclump'),]
+# Remove top1 models for PRS-CSx
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method1 %in%  c('prscsx') & 
+  grepl('top1', res_comp_all$Model_1)),]
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method2 %in%  c('prscsx') & 
+  grepl('top1', res_comp_all$Model_2)),]
+
+# Remove any comparisons
+res_comp_all <- res_comp_all[!duplicated(res_comp_all[, c("Target", "Method1", "Model1", "Source1", "Discovery1", "Method2", "Model2", "Source2", "Discovery2")]),]
 
 library(MAd)
 
@@ -2470,7 +2416,7 @@ meta_res_comp$indep_star[meta_res_comp$R_diff_p < 1e-6]<-'***'
 meta_res_comp<-meta_res_comp[order(meta_res_comp$Discovery1, meta_res_comp$Discovery2, meta_res_comp$Method1),]
 
 heatmap_list<-NULL
-for(model_i in c('Top1','Pseudo','Multi')){
+for(model_i in c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain')){
   heatmap_list[[model_i]]<-list()
   for(targ_pop_i in targ_pop){
     if(targ_pop_i == 'EAS'){
@@ -2532,13 +2478,14 @@ for(model_i in c('Top1','Pseudo','Multi')){
 ####
 # Plot R compared to top1 model where GWAS matches target population
 ####
-meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Model2 == 'Top1' & meta_res_comp$Method2 == 'ptclump' & meta_res_comp$Discovery2 == meta_res_comp$Target,]
+
+meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Model2 == 'IndivTrain' & meta_res_comp$Method2 == 'ptclump' & meta_res_comp$Discovery2 == meta_res_comp$Target,]
 meta_res_comp_ptclump_top1$reference_point<-F
-meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Model1 == 'Top1' & meta_res_comp_ptclump_top1$Method1 == 'ptclump' & meta_res_comp_ptclump_top1$Discovery1 == meta_res_comp_ptclump_top1$Target]<-T
+meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Model1 == 'IndivTrain' & meta_res_comp_ptclump_top1$Method1 == 'ptclump' & meta_res_comp_ptclump_top1$Discovery1 == meta_res_comp_ptclump_top1$Target]<-T
 meta_res_comp_ptclump_top1$R_diff[is.na(meta_res_comp_ptclump_top1$R_diff)]<-0
 meta_res_comp_ptclump_top1$Discovery1 <- factor(meta_res_comp_ptclump_top1$Discovery1, levels=rev(levels(meta_res_comp_ptclump_top1$Discovery1)))
 
-res_comp_all_ptclump_top1<-res_comp_all[res_comp_all$Model2 == 'Top1' & res_comp_all$Method2 == 'ptclump' & res_comp_all$Discovery2 == res_comp_all$Target,]
+res_comp_all_ptclump_top1<-res_comp_all[res_comp_all$Model2 == 'IndivTrain' & res_comp_all$Method2 == 'ptclump' & res_comp_all$Discovery2 == res_comp_all$Target,]
 res_comp_all_ptclump_top1$Discovery1 <-  factor(res_comp_all_ptclump_top1$Discovery1, levels=levels(meta_res_comp_ptclump_top1$Discovery1))
 
 # Create data to plot reference points
diff --git a/docs/model_builder_demo.Rmd b/docs/model_builder_demo.Rmd
new file mode 100644
index 00000000..9379a586
--- /dev/null
+++ b/docs/model_builder_demo.Rmd
@@ -0,0 +1,144 @@
+---
+title: Model Builder Top1 Script Demo
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+This document provides a demonstration of the `model_builder_top1.R` script. This script derives and evaluates predictive models using a structured approach:
+
+1. **Top1 Model**: Identifies the best predictor within a specified `top1` group.
+2. **Multi Model**: Constructs a generalized linear model (GLM) incorporating all best predictors from different `top1` groups within the same `multi` group.
+3. **Evaluation**: Uses cross-validation to train and evaluate both `top1` and `multi` models.
+4. **Comparison**: Applies Williams' test to compare the predictive utility of the models (using `psych::paired.r` function).
+
+***
+
+## Input Files
+
+### Outcome File (`--outcome`)
+
+- A file containing the outcome data to be predicted.
+- Column names are required.
+- Acceptable delimiters: space, tab, or comma.
+- Required format:
+
+  1. **FID**: Family ID
+  2. **IID**: Individual ID
+  3. **Outcome Variable** (any column name is acceptable)
+
+### Predictor List File (`--predictors`)
+
+- A file listing the predictor data files and their respective grouping information.
+- Must contain three columns with headers:
+
+  - **predictor**: Path to the predictor data file.
+  - **multi**: Grouping variable specifying which `top1` models should be combined into a `multi` model.
+  - **top1**: Group variable indicating which predictor files should be considered together when creating a `top1` model.
+
+Each **predictor file** must follow this format:
+
+- Column names are required.
+- Acceptable delimiters: space, tab, or comma.
+- Required format:
+
+  1. **FID**: Family ID
+  2. **IID**: Individual ID
+  3. Additional columns: Predictor variables (any column names are acceptable)
+
+***
+
+## Generating Demo Data
+
+Below is an example script to simulate a phenotype and polygenic scores (PGS) across different p-value thresholds, derived from two separate GWAS datasets.
+
+```{r}
+# Create a directory for the demo
+dir.create('~/test_model_builder')
+set.seed(1)
+n <- 1000
+ids <- 1:n
+data <- data.frame(FID = ids, IID = ids, outcome = rnorm(n))
+
+# Simulate PGS for two GWAS datasets
+for(i in 1:2){
+  pgs_tmp <- data$outcome + rnorm(n)
+  for(j in c(0.05, 0.01, 0.5)){
+    data[[paste0('pgs',i,'_',j)]] <- pgs_tmp + rnorm(n, 0, j*5)
+  }
+}
+
+# Save outcome data
+write.table(data[, c('FID','IID','outcome')], '~/test_model_builder/outcome.txt', col.names=TRUE, row.names=FALSE, quote=FALSE)
+
+# Save PGS data from each GWAS
+write.table(data[, grepl('FID|IID|pgs1', names(data))], '~/test_model_builder/pgs1.txt', col.names=TRUE, row.names=FALSE, quote=FALSE)
+write.table(data[, grepl('FID|IID|pgs2', names(data))], '~/test_model_builder/pgs2.txt', col.names=TRUE, row.names=FALSE, quote=FALSE)
+```
+
+***
+
+## Creating the Predictor List File
+
+The following script generates a predictor list file, linking predictor files to their respective `multi` and `top1` groups.
+
+```{r}
+pred <- data.frame(
+  predictor = c('~/test_model_builder/pgs1.txt','~/test_model_builder/pgs2.txt'),
+  multi = 'combo',
+  top1 = c('pgs1','pgs2')
+)
+
+write.table(pred, '~/test_model_builder/predictor_list.txt', col.names=TRUE, row.names=FALSE, quote=FALSE)
+```
+
+***
+
+## Running the Model Builder Script
+
+To execute the `model_builder_top1.R` script, activate the appropriate Conda environment and run the script with the required input files. Note, a .yaml file to create the model_builder environment can be found [here](https://github.com/opain/GenoPred/blob/gwas_grouping/pipeline/envs/model_builder.yaml).
+
+```{bash}
+conda activate model_builder
+
+Rscript ../Scripts/model_builder/model_builder_top1.R \
+  --outcome ~/test_model_builder/outcome.txt \
+  --predictors ~/test_model_builder/predictor_list.txt \
+  --out ~/test_model_builder/res
+```
+
+***
+
+## Output Files
+
+Upon execution, the script will generate the following outputs:
+
+- `<out>.log` - Log file recording the execution process.
+- `<out>.pred_eval.txt` - Evaluation results indicating the predictive utility of each model.
+- `<out>.pred_comp.txt` - Comparative results of predictive utility across models.
+- `<out>.group_list.txt` - Summary of the number of predictors used in each `multi` and `top1` model.
+
+
diff --git a/docs/model_builder_demo.html b/docs/model_builder_demo.html
new file mode 100644
index 00000000..35b7fe8c
--- /dev/null
+++ b/docs/model_builder_demo.html
@@ -0,0 +1,653 @@
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+      <a href="research_index.html">Overview</a>
+    </li>
+    <li>
+      <a href="comparison_of_methods_summary.html">Polygenic Scoring Methods Comparison</a>
+    </li>
+    <li>
+      <a href="Functionally_informed_prediction.html">Quantifying Polygenic Signal Mediated by Altered Gene Expression</a>
+    </li>
+    <li>
+      <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
+    </li>
+  </ul>
+</li>
+<li>
+  <a href="more_index.html">More</a>
+</li>
+<li>
+  <a href="https://github.com/opain/GenoPred">
+    <span class="fa fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul>
+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+<div id="header">
+
+
+
+<h1 class="title toc-ignore">Model Builder Top1 Script Demo</h1>
+
+</div>
+
+
+<hr />
+<p>This document provides a demonstration of the
+<code>model_builder_top1.R</code> script. This script derives and
+evaluates predictive models using a structured approach:</p>
+<ol style="list-style-type: decimal">
+<li><strong>Top1 Model</strong>: Identifies the best predictor within a
+specified <code>top1</code> group.</li>
+<li><strong>Multi Model</strong>: Constructs a generalized linear model
+(GLM) incorporating all best predictors from different <code>top1</code>
+groups within the same <code>multi</code> group.</li>
+<li><strong>Evaluation</strong>: Uses cross-validation to train and
+evaluate both <code>top1</code> and <code>multi</code> models.</li>
+<li><strong>Comparison</strong>: Applies Williams’ test to compare the
+predictive utility of the models (using <code>psych::paired.r</code>
+function).</li>
+</ol>
+<hr />
+<div id="input-files" class="section level2">
+<h2>Input Files</h2>
+<div id="outcome-file---outcome" class="section level3">
+<h3>Outcome File (<code>--outcome</code>)</h3>
+<ul>
+<li><p>A file containing the outcome data to be predicted.</p></li>
+<li><p>Column names are required.</p></li>
+<li><p>Acceptable delimiters: space, tab, or comma.</p></li>
+<li><p>Required format:</p>
+<ol style="list-style-type: decimal">
+<li><strong>FID</strong>: Family ID</li>
+<li><strong>IID</strong>: Individual ID</li>
+<li><strong>Outcome Variable</strong> (any column name is
+acceptable)</li>
+</ol></li>
+</ul>
+</div>
+<div id="predictor-list-file---predictors" class="section level3">
+<h3>Predictor List File (<code>--predictors</code>)</h3>
+<ul>
+<li><p>A file listing the predictor data files and their respective
+grouping information.</p></li>
+<li><p>Must contain three columns with headers:</p>
+<ul>
+<li><strong>predictor</strong>: Path to the predictor data file.</li>
+<li><strong>multi</strong>: Grouping variable specifying which
+<code>top1</code> models should be combined into a <code>multi</code>
+model.</li>
+<li><strong>top1</strong>: Group variable indicating which predictor
+files should be considered together when creating a <code>top1</code>
+model.</li>
+</ul></li>
+</ul>
+<p>Each <strong>predictor file</strong> must follow this format:</p>
+<ul>
+<li><p>Column names are required.</p></li>
+<li><p>Acceptable delimiters: space, tab, or comma.</p></li>
+<li><p>Required format:</p>
+<ol style="list-style-type: decimal">
+<li><strong>FID</strong>: Family ID</li>
+<li><strong>IID</strong>: Individual ID</li>
+<li>Additional columns: Predictor variables (any column names are
+acceptable)</li>
+</ol></li>
+</ul>
+<hr />
+</div>
+</div>
+<div id="generating-demo-data" class="section level2">
+<h2>Generating Demo Data</h2>
+<p>Below is an example script to simulate a phenotype and polygenic
+scores (PGS) across different p-value thresholds, derived from two
+separate GWAS datasets.</p>
+<pre class="r"><code># Create a directory for the demo
+dir.create(&#39;~/test_model_builder&#39;)
+set.seed(1)
+n &lt;- 1000
+ids &lt;- 1:n
+data &lt;- data.frame(FID = ids, IID = ids, outcome = rnorm(n))
+
+# Simulate PGS for two GWAS datasets
+for(i in 1:2){
+  pgs_tmp &lt;- data$outcome + rnorm(n)
+  for(j in c(0.05, 0.01, 0.5)){
+    data[[paste0(&#39;pgs&#39;,i,&#39;_&#39;,j)]] &lt;- pgs_tmp + rnorm(n, 0, j*5)
+  }
+}
+
+# Save outcome data
+write.table(data[, c(&#39;FID&#39;,&#39;IID&#39;,&#39;outcome&#39;)], &#39;~/test_model_builder/outcome.txt&#39;, col.names=TRUE, row.names=FALSE, quote=FALSE)
+
+# Save PGS data from each GWAS
+write.table(data[, grepl(&#39;FID|IID|pgs1&#39;, names(data))], &#39;~/test_model_builder/pgs1.txt&#39;, col.names=TRUE, row.names=FALSE, quote=FALSE)
+write.table(data[, grepl(&#39;FID|IID|pgs2&#39;, names(data))], &#39;~/test_model_builder/pgs2.txt&#39;, col.names=TRUE, row.names=FALSE, quote=FALSE)</code></pre>
+<hr />
+</div>
+<div id="creating-the-predictor-list-file" class="section level2">
+<h2>Creating the Predictor List File</h2>
+<p>The following script generates a predictor list file, linking
+predictor files to their respective <code>multi</code> and
+<code>top1</code> groups.</p>
+<pre class="r"><code>pred &lt;- data.frame(
+  predictor = c(&#39;~/test_model_builder/pgs1.txt&#39;,&#39;~/test_model_builder/pgs2.txt&#39;),
+  multi = &#39;combo&#39;,
+  top1 = c(&#39;pgs1&#39;,&#39;pgs2&#39;)
+)
+
+write.table(pred, &#39;~/test_model_builder/predictor_list.txt&#39;, col.names=TRUE, row.names=FALSE, quote=FALSE)</code></pre>
+<hr />
+</div>
+<div id="running-the-model-builder-script" class="section level2">
+<h2>Running the Model Builder Script</h2>
+<p>To execute the <code>model_builder_top1.R</code> script, activate the
+appropriate Conda environment and run the script with the required input
+files. Note, a .yaml file to create the model_builder environment can be
+found <a
+href="https://github.com/opain/GenoPred/blob/gwas_grouping/pipeline/envs/model_builder.yaml">here</a>.</p>
+<pre class="bash"><code>conda activate model_builder
+
+Rscript ../Scripts/model_builder/model_builder_top1.R \
+  --outcome ~/test_model_builder/outcome.txt \
+  --predictors ~/test_model_builder/predictor_list.txt \
+  --out ~/test_model_builder/res</code></pre>
+<hr />
+</div>
+<div id="output-files" class="section level2">
+<h2>Output Files</h2>
+<p>Upon execution, the script will generate the following outputs:</p>
+<ul>
+<li><code>&lt;out&gt;.log</code> - Log file recording the execution
+process.</li>
+<li><code>&lt;out&gt;.pred_eval.txt</code> - Evaluation results
+indicating the predictive utility of each model.</li>
+<li><code>&lt;out&gt;.pred_comp.txt</code> - Comparative results of
+predictive utility across models.</li>
+<li><code>&lt;out&gt;.group_list.txt</code> - Summary of the number of
+predictors used in each <code>multi</code> and <code>top1</code>
+model.</li>
+</ul>
+</div>
+
+<!-- footer.html -->
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+
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+}
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From 84977bbb91a4f24da1af4dc4b7eec3482ac42a1f Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 31 Jan 2025 15:41:19 +0000
Subject: [PATCH 097/153] Updates

---
 docs/pgs_shift.Rmd | 84 ++++++++++++++++++++++++++++++++++++++++++++++
 functions/pgs.R    |  5 +--
 2 files changed, 87 insertions(+), 2 deletions(-)
 create mode 100644 docs/pgs_shift.Rmd

diff --git a/docs/pgs_shift.Rmd b/docs/pgs_shift.Rmd
new file mode 100644
index 00000000..4fd7bca1
--- /dev/null
+++ b/docs/pgs_shift.Rmd
@@ -0,0 +1,84 @@
+---
+title: Cross-population evaluation of polygenic scores
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+This document will explore a shift in the PGS distribution reported in UKB when using the latest anorexia nervosa GWAS. 
+
+I will run this analysis myself using UKB as a target sample.
+
+```{r}
+######
+# gwas_list
+######
+
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift')
+
+gwas_list<-data.frame(
+  name='ANOR07',
+  path='/scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/Miryam/project1/sum_stats/ANOR07.gz',
+  population='EUR',
+  n=NA,
+  sampling=NA,
+  prevalence=NA,
+  mean=0,
+  sd=1,
+  label=paste0('"Anorexia Nervosa"')
+)
+
+write.table(gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/gwas_list_all.txt', col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/config.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/gwas_list_all.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
+  "pgs_methods: ['quickprs']",
+  "cores_prep_pgs: 1",
+  "cores_target_pgs: 1",
+  "pgs_scaling: ['continuous', 'discrete']"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/config.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+
+```{bash}
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/config.yaml \
+  /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/target_checks/ukb/target_pgs-TRANS.done \
+  /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/target_checks/ukb/target_pgs-EUR.done \
+  -n
+```
diff --git a/functions/pgs.R b/functions/pgs.R
index 73bf32bf..ec128387 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -65,8 +65,9 @@ list_score_files <- function(config){
     combos <- rbind(combos, expand.grid(name = gwas_groups$name, method = pgs_methods_list))
   }
 
-  combos <- data.table(apply(combos, 2, as.character))
-
+  combos <- data.table(combos)
+  combos <- combos[, lapply(.SD, as.character)]
+  
   return(combos)
 }
 

From ac2de83d581e7063868ff4d51d1416b47806360c Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 4 Feb 2025 09:23:14 +0000
Subject: [PATCH 098/153] Updates to reference for trans PGS, and some
 documentation updates.

---
 .../pipeline_reports/indiv_report_creator.Rmd |  17 +-
 docs/CrossPop.Rmd                             |   2 +-
 docs/pgs_shift.Rmd                            | 280 ++++++++++++++++++
 docs/pipeline_1kg_hgdp_prep.Rmd               |  23 ++
 docs/pipeline_readme.Rmd                      |   3 +-
 docs/pipeline_readme.html                     |   7 +-
 docs/pipeline_technical.Rmd                   |   3 +-
 docs/pipeline_technical.html                  |   7 +-
 pipeline/rules/dependencies.smk               |  50 +++-
 pipeline/rules/pgs_methods.smk                |  16 +-
 10 files changed, 363 insertions(+), 45 deletions(-)

diff --git a/Scripts/pipeline_reports/indiv_report_creator.Rmd b/Scripts/pipeline_reports/indiv_report_creator.Rmd
index 18e36010..5155a349 100644
--- a/Scripts/pipeline_reports/indiv_report_creator.Rmd
+++ b/Scripts/pipeline_reports/indiv_report_creator.Rmd
@@ -189,19 +189,12 @@ model_pred <- ancestry$model_pred[
 
 top_pop <- names(model_pred)[model_pred == max(model_pred)] 
 
-# If user specified reference was used, recreate population label data.frame
-if(!is.na(refdir)){
-  ref_pop<-fread(paste0(refdir,'/ref.keep.list'), header=F)
-  names(ref_pop)<-c('pop','label')
-  ref_pop$label<-ref_pop$pop
-}
-
 if(nrow(keep_all) == 0){
   cat0("- The target individual did not match well to any reference population.\n") 
-  cat0("- The indivdual best matches the ", ref_pop$label[ref_pop$pop == top_pop], " reference population, but did not reach the ", ancestry_prob_thresh*100, "% probability threshold.\n\n")
+  cat0("- The indivdual best matches the ", top_pop, " reference population, but did not reach the ", ancestry_prob_thresh*100, "% probability threshold.\n\n")
   pgs_incl <- F
 } else {
-  cat0("- The target individual matches the ", ref_pop$label[ref_pop$pop == keep_all$pop], " reference population with a probability >", ancestry_prob_thresh*100, "%.\n\n")
+  cat0("- The target individual matches the ", top_pop, " reference population with a probability >", ancestry_prob_thresh*100, "%.\n\n")
 }
 
 ```
@@ -212,14 +205,12 @@ if(nrow(keep_all) == 0){
 
 dat <- melt(round(model_pred,2))
 dat[dat == 0] <-NA
-dat <- merge(dat, ref_pop, by.x = 'variable', by.y = 'pop')
-dat$label <- gsub(' ', '\n', dat$label)
 dat$annot <- paste0(dat$value*100, '%')
 y_max <- ifelse(max(dat$value, na.rm = T) > ancestry_prob_thresh, 105, 100)
 
 png(paste0(tmp_folder,'/plot_ancestry.png'), height = 450, width = 800, res = 120)
 
-  ggplot(dat, aes(x=label, y=value*100, fill=label)) +
+  ggplot(dat, aes(x=variable, y=value*100, fill=variable)) +
     geom_chicklet(radius = grid::unit(1, 'mm')) +
     geom_hline(yintercept = ancestry_prob_thresh*100, linetype = 'dashed') +
     labs(x='Population', y="Probability (%)") +
@@ -877,7 +868,7 @@ for(pgs_method_i in unique(all_dat$pgs_method)){
     pgs_dat_i <- pgs_dat[pgs_dat$gwas == gwas_i & pgs_dat$pgs_method == pgs_method_i,]
     abs_res_i <- abs_res[abs_res$pgs_method == pgs_method_i & abs_res$gwas == gwas_i,]
 
-    cat0('- Your PGS Z-score for ', all_dat$label[all_dat$gwas == gwas_i], ' is ', pgs_dat_i$value,' which is higher than ', paste0(round(100*pnorm(pgs_dat_i$value),1),'%'),' of other ', ref_pop$label[ref_pop$pop == top_pop],' individuals.\n\n')
+    cat0('- Your PGS Z-score for ', all_dat$label[all_dat$gwas == gwas_i], ' is ', pgs_dat_i$value,' which is higher than ', paste0(round(100*pnorm(pgs_dat_i$value),1),'%'),' of other ', top_pop,' individuals.\n\n')
     
     if(pgs_method_i != 'external'){
       if(is.na(gwas_list$prevalence[gwas_list$name == gwas_i])){
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 38be5f4a..e1c8404f 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1533,7 +1533,7 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml \
-  prep_pgs -n
+  prep_pgs  -n
 ```
 
 ***
diff --git a/docs/pgs_shift.Rmd b/docs/pgs_shift.Rmd
index 4fd7bca1..c8bb4f20 100644
--- a/docs/pgs_shift.Rmd
+++ b/docs/pgs_shift.Rmd
@@ -82,3 +82,283 @@ snakemake \
   /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/target_checks/ukb/target_pgs-EUR.done \
   -n
 ```
+
+```{r}
+#conda activate model_builder
+library(data.table)
+
+pgs_eur<-fread('~/oliverpainfel/Data/ukb/GenoPred/output/ukb/pgs/EUR/quickprs/ANOR07/ukb-ANOR07-EUR.profiles')
+pgs_trans<-fread('~/oliverpainfel/Data/ukb/GenoPred/output/ukb/pgs/TRANS/quickprs/ANOR07/ukb-ANOR07-TRANS.profiles')
+
+pgs_trans<- pgs_trans[pgs_trans$FID %in% pgs_eur$FID,]
+
+mean(pgs_eur$ANOR07_quickprs) # -0.4638782
+mean(pgs_trans$ANOR07_quickprs) # -0.4323459
+
+ref_pgs_trans<- fread('~/oliverpainfel/Data/ukb/GenoPred/output/reference/pgs_score_files/quickprs/ANOR07/ref-ANOR07-TRANS.profiles')
+mean(ref_pgs_trans$SCORE_quickprs) # 7.60805e-06
+
+ref_eur<-fread('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files/EUR.keep', header=F)
+ref_pgs_trans<-ref_pgs_trans[ref_pgs_trans$FID %in% ref_eur$V1,]
+mean(ref_pgs_trans$SCORE_quickprs) # -0.4323459
+hist(ref_pgs_trans$SCORE_quickprs)
+
+names(ref_pgs_trans)[names(ref_pgs_trans) == 'SCORE_quickprs']<-'ANOR07_quickprs'
+ref_pgs_trans$group<-'ref'
+pgs_trans$group<-'ukb'
+both_pgs_trans<-rbind(ref_pgs_trans, pgs_trans)
+
+library(ggplot2)
+ggplot(both_pgs_trans, aes(x = ANOR07_quickprs, fill = group)) +
+  geom_density(alpha = 0.5) +  # alpha controls transparency
+  theme_minimal()
+
+```
+
+This replicates the issue of there being a shift in the PGS distribution.
+
+Test whether the within UKB PCs correlate with the AN PGS. This could indicate that there are ancestry effects that the reference is not capturing well
+
+```{r}
+pcs<-fread('~/oliverpainfel/Data/ukb/GenoPred/output/ukb/pcs/within_sample/ukb.outlier_detection.EUR.PCs.txt')
+
+pgs_trans_pcs<-merge(pgs_trans, pcs, by=c('FID','IID'))
+
+round(cor(pgs_trans_pcs[, grepl('ANOR07_quickprs|PC', names(pgs_trans_pcs)), with=F]),2)
+
+pcs<-fread('~/oliverpainfel/Data/ukb/GenoPred/output/ukb/pcs/projected/TRANS/ukb-TRANS.profiles')
+
+pgs_trans_pcs<-merge(pgs_trans, pcs, by=c('FID','IID'))
+
+round(cor(pgs_trans_pcs[, grepl('ANOR07_quickprs|PC', names(pgs_trans_pcs)), with=F]),2)
+```
+
+There aren't any strong correlations with UKB PCs and the PGS, but this is because we are looking at PCs within the EUR.
+
+Check whether the projected PCs in UKB overlap nicely with UKB projected PCs
+
+```{r}
+ukb_pcs<-fread('~/oliverpainfel/Data/ukb/GenoPred/output/ukb/pcs/projected/TRANS/ukb-TRANS.profiles')
+ukb_pcs$group<-'ukb'
+ref_pcs<-fread('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles')
+ref_pcs$group<-'ref'
+
+both_pcs<-rbind(ukb_pcs, ref_pcs)
+
+ggplot(both_pcs, aes(x=PC1, y=PC2, colour = group)) +
+  geom_point()
+  
+```
+
+There is a sligth contraction towards the mean in UKB, which is expected due to missing SNPs. This could be leading to an under correction of the PGS for ancestry?
+
+
+Lets see whether we see the same thing for OpenSNP.
+
+```{r}
+
+######
+# config
+######
+
+config<-c(
+  'outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test6',
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/config_opensnp.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/gwas_list_all.txt",
+  'target_list: misc/opensnp/target_list.txt',
+  "pgs_methods: ['quickprs']",
+  "cores_prep_pgs: 1",
+  "cores_target_pgs: 1",
+  "pgs_scaling: ['continuous', 'discrete']"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/config_opensnp.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+```{bash}
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/config_opensnp.yaml \
+  target_pgs \
+  -n
+```
+
+```{r}
+#conda activate model_builder
+library(data.table)
+
+pgs_eur<-fread('~/oliverpainfel/Data/OpenSNP/GenoPred/test6/opensnp/pgs/EUR/quickprs/ANOR07/opensnp-ANOR07-EUR.profiles')
+
+pgs_trans<-fread('~/oliverpainfel/Data/OpenSNP/GenoPred/test6/opensnp/pgs/TRANS/quickprs/ANOR07/opensnp-ANOR07-TRANS.profiles')
+
+pgs_trans<- pgs_trans[pgs_trans$FID %in% pgs_eur$FID,]
+
+mean(pgs_eur$ANOR07_quickprs) # -0.2224865
+mean(pgs_trans$ANOR07_quickprs) # 0.07088288
+
+ref_pgs_trans<- fread('~/oliverpainfel/Data/ukb/GenoPred/output/reference/pgs_score_files/quickprs/ANOR07/ref-ANOR07-TRANS.profiles')
+mean(ref_pgs_trans$SCORE_quickprs) # 7.60805e-06
+
+ref_eur<-fread('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files/EUR.keep', header=F)
+ref_pgs_trans<-ref_pgs_trans[ref_pgs_trans$FID %in% ref_eur$V1,]
+mean(ref_pgs_trans$SCORE_quickprs) # 0.016452
+hist(ref_pgs_trans$SCORE_quickprs)
+
+names(ref_pgs_trans)[names(ref_pgs_trans) == 'SCORE_quickprs']<-'ANOR07_quickprs'
+ref_pgs_trans$group<-'ref'
+pgs_trans$group<-'opensnp'
+both_pgs_trans<-rbind(ref_pgs_trans, pgs_trans)
+
+library(ggplot2)
+ggplot(both_pgs_trans, aes(x = ANOR07_quickprs, fill = group)) +
+  geom_density(alpha = 0.5) +  # alpha controls transparency
+  theme_minimal()
+
+```
+
+This shows there is also a skew in the opensnp data, but regressing out the reference PC effects centres it nicely. This is different to what we see in UKB. There must be some structure in UKB that is not well represented in the reference.
+
+Looking at the PC plots from the ancestry inference step from UKB and opensnp, UKB does seem to have more diversity than the reference, and there appears to be a slight shift for PC1. 
+
+```{r}
+opensnp_pcs<-fread('~/oliverpainfel/Data/OpenSNP/GenoPred/test6/opensnp/pcs/projected/TRANS/opensnp-TRANS.profiles')
+opensnp_pcs$group<-'opensnp'
+ref_pcs<-fread('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles')
+ref_pcs$group<-'ref'
+
+both_pcs<-rbind(opensnp_pcs, ref_pcs)
+both_pcs$group<-factor(both_pcs$group, levels=c('opensnp','ref'))
+ggplot(both_pcs, aes(x=PC1, y=PC2, colour = group)) +
+  geom_point() +
+  geom_point(data = both_pcs[both_pcs$group == 'opensnp',], aes(x=PC1, y=PC2, colour = group))
+  
+```
+
+
+I think it is important to check whether this shift occurs even when there is perfect overlap between variants in the reference and target sample (no missingness). This would require us to make a copy of the reference only containing UKB SNPs.
+
+Identify list of SNPs with complete data in UKB.
+
+```{r}
+dir.create('~/test')
+
+for(i in 1:22){
+  system(paste0('~/oliverpainfel/Software/plink2 --pfile ~/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr', i, ' --missing --out ~/test/ukb.chr', i))
+}
+
+library(data.table)
+vmiss<-NULL
+for(i in 1:22){
+  vmiss<-rbind(vmiss, fread(paste0('/users/k1806347/test/ukb.chr',i,'.vmiss')))
+}
+
+snp_keep<-vmiss$ID[vmiss$F_MISS < 0.005] # This retains 589k variants
+
+write.table(snp_keep, '/users/k1806347/test/snp_keep.txt', col.names = F, row.names = F, quote = F)
+
+dir.create('~/oliverpainfel/Data/hgdp_1kg/ukb_overlap/')
+for(i in 1:22){
+  system(paste0('~/oliverpainfel/Software/plink2 --pfile ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr', i, ' --extract /users/k1806347/test/snp_keep.txt --make-pgen --out ~/oliverpainfel/Data/hgdp_1kg/ukb_overlap/ref.chr', i))
+}
+
+for(i in 1:22){
+  rds<-readRDS(paste0('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr', i, '.rds'))
+  rds<-rds[rds$SNP %in% snp_keep,]
+  saveRDS(rds, paste0('~/oliverpainfel/Data/hgdp_1kg/ukb_overlap/ref.chr', i, '.rds'))
+}
+
+system('cp -r ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files ~/oliverpainfel/Data/hgdp_1kg/ukb_overlap/')
+system('cp -r ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/freq_files ~/oliverpainfel/Data/hgdp_1kg/ukb_overlap/')
+system('cp ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.pop.txt ~/oliverpainfel/Data/hgdp_1kg/ukb_overlap/')
+system('cp ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.keep.list ~/oliverpainfel/Data/hgdp_1kg/ukb_overlap/')
+```
+
+
+```{r}
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_test",
+  "refdir: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ukb_overlap",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/config_test.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/gwas_list_all.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
+  "pgs_methods: ['quickprs']",
+  "cores_prep_pgs: 1",
+  "cores_target_pgs: 1",
+  "pgs_scaling: ['continuous', 'discrete']"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/config_test.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+```{bash}
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/config_test.yaml \
+  /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_test/reference/target_checks/ukb/target_pgs-TRANS.done -n
+```
+
+```{r}
+#conda activate model_builder
+library(data.table)
+
+pgs_trans<-fread('~/oliverpainfel/Data/ukb/GenoPred/output_test/ukb/pgs/TRANS/quickprs/ANOR07/ukb-ANOR07-TRANS.profiles')
+
+ancestry<-read_ancestry(config = '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/config_test.yaml', name = 'ukb')
+pgs_trans<- pgs_trans[pgs_trans$FID %in% ancestry$keep_files$EUR$V1,]
+
+mean(pgs_trans$ANOR07_quickprs) # -0.4086925
+
+```
+
+OK. This indicatest this shift is not caused by missing SNPs, as with nearly no missingness, there is still a substantial PGS shift. The ancestry PCs align really nicely with the reference also.
+
+Maybe we should try increasing the number of PCs that are regressed out? I have run with 20 projected PCs.
+
+```{r}
+#conda activate model_builder
+library(data.table)
+
+pgs_trans<-fread('~/oliverpainfel/Data/ukb/GenoPred/output_test/ukb/pgs/TRANS/quickprs/ANOR07/ukb-ANOR07-TRANS.profiles')
+
+ancestry<-read_ancestry(config = '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/pgs_shift/config_test.yaml', name = 'ukb')
+pgs_trans<- pgs_trans[pgs_trans$FID %in% ancestry$keep_files$EUR$V1,]
+
+mean(pgs_trans$ANOR07_quickprs) # -0.3901739
+
+```
+
+Increasing the number of projected PCs doesn't make a difference. This indicates that whatever structure is shifting the PGS in UKB doesn't exist in the reference. It would be interesting to see whether using the UKB sample as the reference would resolve the situation in UKB, whilst also correctly scaling the PGS in opensnp. However, given UKB is not a representative sample due to sampling bias, I don't think this is a good solution.
+
+Test whether the within-ukb PCs provided by UKB are correlated with the trans PGS.
+
+```{r}
+qc_dat<-fread('/datasets/ukbiobank/ukb82087/genotyped/ukb82087_sqc_v2.txt')
+# 28-67 are PCs 1-40
+qc_dat<-qc_dat[, c(1,2,28:67), with=F]
+names(qc_dat)<-c('FID','IID', paste0('PC', 1:40))
+
+# Convert to row number based IDs
+psam<-fread('/scratch/prj/ukbiobank/recovered/ukb82087/imputed/ukb82087_imp_chr1_MAF1_INFO4_v1.psam')
+psam$rn<-1:nrow(psam)
+psam<-psam[,c('IID','rn'), with = F]
+
+qc_dat<-merge(qc_dat, psam, by='IID')
+qc_dat$FID<-qc_dat$rn
+qc_dat$IID<-qc_dat$rn
+qc_dat$rn<-NULL
+
+pgs_trans<-fread('~/oliverpainfel/Data/ukb/GenoPred/output_test/ukb/pgs/TRANS/quickprs/ANOR07/ukb-ANOR07-TRANS.profiles')
+pgs_trans_pcs<-merge(pgs_trans, qc_dat, by=c('FID','IID'))
+
+round(cor(pgs_trans_pcs[, -1:-2]),2)
+```
+
diff --git a/docs/pipeline_1kg_hgdp_prep.Rmd b/docs/pipeline_1kg_hgdp_prep.Rmd
index ccdb2bda..a101413a 100644
--- a/docs/pipeline_1kg_hgdp_prep.Rmd
+++ b/docs/pipeline_1kg_hgdp_prep.Rmd
@@ -278,6 +278,29 @@ tar -czvf genopred_1kg_hgdp.tar.gz ref
 
 ```
 
+```{bash}
+
+# Copy over all the relavent files
+mkdir -p /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref
+
+for chr in $(seq 1 22); do
+  for file in $(echo pgen pvar psam rds); do
+    cp /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr${chr}.${file} /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr}.${file}
+  done
+done
+
+cp /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.pop.txt /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.pop.txt
+
+cp -r /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/
+
+cp -r /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/freq_files /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/
+
+# Compress folder and upload to zenodo
+cd /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred
+tar -czvf genopred_1kg_hgdp.tar.gz ref
+
+```
+
 ***
 
 # Create subset of Pan UKB LD scores
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 07a58573..9b6a0325 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -42,7 +42,7 @@ For a technical details of the GenoPred pipeline, click [here](pipeline_technica
 
 Please cite our preprint when using the pipeline:
 
-- "Pain, O. et al. "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." MedRxiv 2024. https://doi.org/10.1101/2024.06.12.24308843
+- "Pain, O. et al. "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." Bioinformatics 2024. https://doi.org/10.1093/bioinformatics/btae551
 
 If relevant, please also cite our paper comparing polygenic scoring methods and describing the reference-standardised approach:
 
@@ -1295,7 +1295,6 @@ Users can provide their own reference data using the `refdir` parameter in the `
   ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data with RSIDs in SNP column)
   ├── ref.chr[1-22].rds (SNP data - refer to default ref data for format)
   ├── ref.pop.txt (Population data for reference individuals - with header)
-  ├── ref.keep.list (lists keep files for each population - columns pop and path - no header)
   ├── keep_files
   │   └──[pop].keep (keep files for each population - no header)
   └── freq_files
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index 5fbfb156..63cd8231 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -454,9 +454,9 @@ <h1 class="title toc-ignore">GenoPred Pipeline - Instructions</h1>
 <p>Please cite our preprint when using the pipeline:</p>
 <ul>
 <li>“Pain, O. et al. ”The GenoPred Pipeline: A Comprehensive and
-Scalable Pipeline for Polygenic Scoring.” MedRxiv 2024. <a
-href="https://doi.org/10.1101/2024.06.12.24308843"
-class="uri">https://doi.org/10.1101/2024.06.12.24308843</a></li>
+Scalable Pipeline for Polygenic Scoring.” Bioinformatics 2024. <a
+href="https://doi.org/10.1093/bioinformatics/btae551"
+class="uri">https://doi.org/10.1093/bioinformatics/btae551</a></li>
 </ul>
 <p>If relevant, please also cite our paper comparing polygenic scoring
 methods and describing the reference-standardised approach:</p>
@@ -2057,7 +2057,6 @@ <h2>Using your own reference</h2>
   ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data with RSIDs in SNP column)
   ├── ref.chr[1-22].rds (SNP data - refer to default ref data for format)
   ├── ref.pop.txt (Population data for reference individuals - with header)
-  ├── ref.keep.list (lists keep files for each population - columns pop and path - no header)
   ├── keep_files
   │   └──[pop].keep (keep files for each population - no header)
   └── freq_files
diff --git a/docs/pipeline_technical.Rmd b/docs/pipeline_technical.Rmd
index 37f68557..66d361b3 100644
--- a/docs/pipeline_technical.Rmd
+++ b/docs/pipeline_technical.Rmd
@@ -26,7 +26,7 @@ This document provides technical details of the GenoPred pipeline. The GenoPred
 
 Please cite our preprint when using the pipeline:
 
-- "Pain, O. et al. "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." MedRxiv 2024. https://doi.org/10.1101/2024.06.12.24308843
+- "Pain, O. et al. "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." Bioinformatics 2024. https://doi.org/10.1093/bioinformatics/btae551
 
 If relevant, please also cite our paper comparing polygenic scoring methods and describing the reference-standardised approach:
 
@@ -72,7 +72,6 @@ Users can provide there own reference data using the `refdir` parameter in the `
   ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data with RSIDs in SNP column)
   ├── ref.chr[1-22].rds (SNP data - refer to default ref data for format)
   ├── ref.pop.txt (Population data for reference individuals - with header)
-  ├── ref.keep.list (lists keep files for each population - columns pop and path - no header)
   ├── keep_files
   │   └──[pop].keep (keep files for each population - no header)
   └── freq_files
diff --git a/docs/pipeline_technical.html b/docs/pipeline_technical.html
index c69bb6b4..c50d522c 100644
--- a/docs/pipeline_technical.html
+++ b/docs/pipeline_technical.html
@@ -440,9 +440,9 @@ <h1>Introduction</h1>
 <p>Please cite our preprint when using the pipeline:</p>
 <ul>
 <li>“Pain, O. et al. ”The GenoPred Pipeline: A Comprehensive and
-Scalable Pipeline for Polygenic Scoring.” MedRxiv 2024. <a
-href="https://doi.org/10.1101/2024.06.12.24308843"
-class="uri">https://doi.org/10.1101/2024.06.12.24308843</a></li>
+Scalable Pipeline for Polygenic Scoring.” Bioinformatics 2024. <a
+href="https://doi.org/10.1093/bioinformatics/btae551"
+class="uri">https://doi.org/10.1093/bioinformatics/btae551</a></li>
 </ul>
 <p>If relevant, please also cite our paper comparing polygenic scoring
 methods and describing the reference-standardised approach:</p>
@@ -509,7 +509,6 @@ <h2>Reference genetic data</h2>
   ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data with RSIDs in SNP column)
   ├── ref.chr[1-22].rds (SNP data - refer to default ref data for format)
   ├── ref.pop.txt (Population data for reference individuals - with header)
-  ├── ref.keep.list (lists keep files for each population - columns pop and path - no header)
   ├── keep_files
   │   └──[pop].keep (keep files for each population - no header)
   └── freq_files
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index b5bc4d94..79ef13bd 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -367,19 +367,47 @@ if 'sbayesrc' in config['pgs_methods']:
       print(f"File not found: {cors_file}")
       raise FileNotFoundError(f"Required file not found: {cors_file}. sbayesrc reference data must include ldm.info for all populations.")
 
-# Set refdir parameter
-# If refdir is NA, set refdir to '${resdir}/data/ref'
+####
+# Check reference data
+####
 if config['refdir'] == 'NA':
-  refdir=f"{resdir}/data/ref"
-  ref_input=f"{refdir}/ref.pop.txt"
+    refdir = f"{resdir}/data/ref"
 else:
-  refdir=config['refdir']
-  ref_input = [os.path.join(refdir, f"ref.chr{i}.{ext}") for i in get_chr_range(testing = config['testing']) for ext in ['pgen', 'pvar', 'psam', 'rds']] + \
-                 [os.path.join(refdir, file_name) for file_name in ['ref.pop.txt', 'ref.keep.list']]
-
-  for full_path in ref_input:
-      if not os.path.exists(full_path):
-          raise FileNotFoundError(f"File not found: {full_path}. Check reference data format.")
+    refdir = config['refdir']
+
+ref_input = [os.path.join(refdir, f"ref.chr{i}.{ext}") for i in get_chr_range(testing=config['testing']) for ext in ['pgen', 'pvar', 'psam', 'rds']]
+ref_input.append(os.path.join(refdir, 'ref.pop.txt'))
+
+# Read populations from ref.pop.txt
+populations = set()
+ref_pop_file = os.path.join(refdir, 'ref.pop.txt')
+if os.path.exists(ref_pop_file):
+    with open(ref_pop_file, 'r') as f:
+        next(f)  # Skip header
+        for line in f:
+            parts = line.strip().split()
+            if len(parts) == 2:
+                populations.add(parts[1])
+
+# Check keep files for populations in ref.pop.txt
+keep_dir = os.path.join(refdir, "keep_files")
+for pop in populations:
+    keep_file = os.path.join(keep_dir, f"{pop}.keep")
+    ref_input.append(keep_file)
+
+# Check frequency files for populations in ref.pop.txt and TRANS
+freq_dir = os.path.join(refdir, "freq_files")
+for pop in list(populations) + ['TRANS']:
+    for i in range(1, 23):  # Chromosomes 1-22
+        freq_file = os.path.join(freq_dir, pop, f"ref.{pop}.chr{i}.afreq")
+        ref_input.append(freq_file)
+
+# Verify that all required files exist
+for full_path in ref_input:
+    if not os.path.exists(full_path):
+        raise FileNotFoundError(f"File not found: {full_path}. Check reference data format.")
+
+#####
 
 # Check valid pgs_methods are specified
 def check_pgs_methods(x):
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 326f6247..87a17dfc 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -174,7 +174,7 @@ rule prep_pgs_dbslmm:
 rule prep_pgs_prscs_i:
   resources:
     mem_mb=2000*config['cores_prep_pgs'],
-    time_min=5000
+    time_min=2800
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
@@ -300,7 +300,7 @@ rule prep_pgs_lassosum:
 rule prep_pgs_ldpred2_i:
   resources:
     mem_mb=30000,
-    time_min=5000
+    time_min=2800
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
@@ -347,7 +347,7 @@ rule prep_pgs_ldpred2:
 rule prep_pgs_megaprs_i:
   resources:
     mem_mb=20000,
-    time_min=5000
+    time_min=2800
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
@@ -399,7 +399,7 @@ def get_quickprs_ldref_path(w, gwas_list_df, resdir):
 rule prep_pgs_quickprs_i:
   resources:
     mem_mb=20000,
-    time_min=5000
+    time_min=2800
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
@@ -442,7 +442,7 @@ rule prep_pgs_quickprs:
 rule prep_pgs_sbayesrc_i:
   resources:
     mem_mb=20000,
-    time_min=5000
+    time_min=2800
   threads: config['cores_prep_pgs']
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
@@ -686,7 +686,7 @@ rule prep_pgs_xwing:
 rule prep_pgs_tlprs_i:
   resources:
     mem_mb=10000,
-    time_min=5000
+    time_min=2800
   threads: config['cores_prep_pgs']
   input:
     rules.install_tlprs.output,
@@ -733,7 +733,7 @@ rule prep_pgs_tlprs:
 rule prep_pgs_quickprs_multi_i:
   resources:
     mem_mb=20000,
-    time_min=5000
+    time_min=2800
   threads: config['cores_prep_pgs']
   input:
     lambda w: expand(f"{quickprs_ldref}/{{population}}/{{population}}.cors.bin", population=[pop for pop in get_populations(w.gwas_group)]),
@@ -782,7 +782,7 @@ rule prep_pgs_quickprs_multi:
 rule prep_pgs_bridgeprs_i:
   resources:
     mem_mb=2000*config['cores_prep_pgs'],
-    time_min=5000
+    time_min=2800
   threads: config['cores_prep_pgs']
   input:
     rules.download_bridgeprs_software.output,

From 44b6feed670c6ca304cbcb3afdd076f682f79697 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 5 Feb 2025 18:47:47 +0000
Subject: [PATCH 099/153] Updated reference data for TRANS PGS

---
 pipeline/envs/pipeline.yaml     |  3 ++
 pipeline/rules/dependencies.smk | 65 +++++++++++++++++----------------
 2 files changed, 36 insertions(+), 32 deletions(-)

diff --git a/pipeline/envs/pipeline.yaml b/pipeline/envs/pipeline.yaml
index 8aa9c9c2..e2f80b0e 100644
--- a/pipeline/envs/pipeline.yaml
+++ b/pipeline/envs/pipeline.yaml
@@ -9,3 +9,6 @@ dependencies:
   - snakemake-minimal=7.32.3
   - pandas=2.0.3
   - ghostscript=10.02.1
+  - pip
+  - pip:
+      - gdown
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 79ef13bd..51e6038d 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -372,40 +372,41 @@ if 'sbayesrc' in config['pgs_methods']:
 ####
 if config['refdir'] == 'NA':
     refdir = f"{resdir}/data/ref"
+    ref_input=f"{refdir}/ref.pop.txt"
 else:
     refdir = config['refdir']
 
-ref_input = [os.path.join(refdir, f"ref.chr{i}.{ext}") for i in get_chr_range(testing=config['testing']) for ext in ['pgen', 'pvar', 'psam', 'rds']]
-ref_input.append(os.path.join(refdir, 'ref.pop.txt'))
-
-# Read populations from ref.pop.txt
-populations = set()
-ref_pop_file = os.path.join(refdir, 'ref.pop.txt')
-if os.path.exists(ref_pop_file):
-    with open(ref_pop_file, 'r') as f:
-        next(f)  # Skip header
-        for line in f:
-            parts = line.strip().split()
-            if len(parts) == 2:
-                populations.add(parts[1])
-
-# Check keep files for populations in ref.pop.txt
-keep_dir = os.path.join(refdir, "keep_files")
-for pop in populations:
-    keep_file = os.path.join(keep_dir, f"{pop}.keep")
-    ref_input.append(keep_file)
-
-# Check frequency files for populations in ref.pop.txt and TRANS
-freq_dir = os.path.join(refdir, "freq_files")
-for pop in list(populations) + ['TRANS']:
-    for i in range(1, 23):  # Chromosomes 1-22
-        freq_file = os.path.join(freq_dir, pop, f"ref.{pop}.chr{i}.afreq")
-        ref_input.append(freq_file)
-
-# Verify that all required files exist
-for full_path in ref_input:
-    if not os.path.exists(full_path):
-        raise FileNotFoundError(f"File not found: {full_path}. Check reference data format.")
+    ref_input = [os.path.join(refdir, f"ref.chr{i}.{ext}") for i in get_chr_range(testing=config['testing']) for ext in ['pgen', 'pvar', 'psam', 'rds']]
+    ref_input.append(os.path.join(refdir, 'ref.pop.txt'))
+    
+    # Read populations from ref.pop.txt
+    populations = set()
+    ref_pop_file = os.path.join(refdir, 'ref.pop.txt')
+    if os.path.exists(ref_pop_file):
+        with open(ref_pop_file, 'r') as f:
+            next(f)  # Skip header
+            for line in f:
+                parts = line.strip().split()
+                if len(parts) == 2:
+                    populations.add(parts[1])
+    
+    # Check keep files for populations in ref.pop.txt
+    keep_dir = os.path.join(refdir, "keep_files")
+    for pop in populations:
+        keep_file = os.path.join(keep_dir, f"{pop}.keep")
+        ref_input.append(keep_file)
+    
+    # Check frequency files for populations in ref.pop.txt and TRANS
+    freq_dir = os.path.join(refdir, "freq_files")
+    for pop in list(populations) + ['TRANS']:
+        for i in range(1, 23):  # Chromosomes 1-22
+            freq_file = os.path.join(freq_dir, pop, f"ref.{pop}.chr{i}.afreq")
+            ref_input.append(freq_file)
+    
+    # Verify that all required files exist
+    for full_path in ref_input:
+        if not os.path.exists(full_path):
+            raise FileNotFoundError(f"File not found: {full_path}. Check reference data format.")
 
 #####
 
@@ -1045,7 +1046,7 @@ rule download_default_ref:
     {{
       rm -r {resdir}/data/ref; \
       mkdir -p {resdir}/data/ref; \
-      wget --no-check-certificate -O {resdir}/data/ref/genopred_1kg_hgdp.tar.gz https://zenodo.org/records/10666983/files/genopred_1kg_hgdp.tar.gz?download=1; \
+      gdown --id 1vYH6V-7F68Ji1vy9TaH0ysjmdYJFef-f -O resources/data/ref/genopred_1kg_hgdp.tar.gz; \
       tar -xzvf {resdir}/data/ref/genopred_1kg_hgdp.tar.gz -C {resdir}/data/ref/; \
       mv {resdir}/data/ref/ref/* {resdir}/data/ref/; \
       rm -r {resdir}/data/ref/ref; \

From cf4fce38263c28638051743b3ff2e7d336cc23fb Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 5 Feb 2025 19:40:12 +0000
Subject: [PATCH 100/153] Update to test data for TRANS PGS

---
 docs/pipeline_1kg_hgdp_prep.Rmd               |   23 -
 docs/pipeline_1kg_hgdp_prep.html              |    8 +-
 .../freq_files/TRANS/ref.TRANS.chr22.afreq    | 1001 +++++++++++++++++
 pipeline/misc/dev/test_setup.Rmd              |    1 +
 pipeline/rules/target_scoring.smk             |    2 +-
 5 files changed, 1010 insertions(+), 25 deletions(-)
 create mode 100644 pipeline/misc/dev/test_data/ref/freq_files/TRANS/ref.TRANS.chr22.afreq

diff --git a/docs/pipeline_1kg_hgdp_prep.Rmd b/docs/pipeline_1kg_hgdp_prep.Rmd
index a101413a..ccdb2bda 100644
--- a/docs/pipeline_1kg_hgdp_prep.Rmd
+++ b/docs/pipeline_1kg_hgdp_prep.Rmd
@@ -278,29 +278,6 @@ tar -czvf genopred_1kg_hgdp.tar.gz ref
 
 ```
 
-```{bash}
-
-# Copy over all the relavent files
-mkdir -p /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref
-
-for chr in $(seq 1 22); do
-  for file in $(echo pgen pvar psam rds); do
-    cp /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr${chr}.${file} /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr}.${file}
-  done
-done
-
-cp /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.pop.txt /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.pop.txt
-
-cp -r /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/
-
-cp -r /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/freq_files /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/
-
-# Compress folder and upload to zenodo
-cd /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred
-tar -czvf genopred_1kg_hgdp.tar.gz ref
-
-```
-
 ***
 
 # Create subset of Pan UKB LD scores
diff --git a/docs/pipeline_1kg_hgdp_prep.html b/docs/pipeline_1kg_hgdp_prep.html
index c3088db5..66cedffa 100644
--- a/docs/pipeline_1kg_hgdp_prep.html
+++ b/docs/pipeline_1kg_hgdp_prep.html
@@ -13,7 +13,7 @@
 
 <title>GenoPred Pipeline - Reference preparation</title>
 
-<script src="site_libs/header-attrs-2.25/header-attrs.js"></script>
+<script src="site_libs/header-attrs-2.26/header-attrs.js"></script>
 <script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
 <meta name="viewport" content="width=device-width, initial-scale=1" />
 <link href="site_libs/bootstrap-3.3.5/css/cosmo.min.css" rel="stylesheet" />
@@ -594,6 +594,12 @@ <h1>Format and output required reference files</h1>
   }
 }
 
+# Create frequency files across all reference individuals (TRANS)
+dir.create(paste0(&#39;/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS&#39;), recursive = T)
+for(i in 1:22){
+  system(paste0(&#39;plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.chr&#39;,i,&#39; --chr &#39;,i,&#39; --freq --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS/hgdp_1kg_hm3.chr&#39;, i))
+}
+
 ####
 # Create .rds files for SNP stats
 ####
diff --git a/pipeline/misc/dev/test_data/ref/freq_files/TRANS/ref.TRANS.chr22.afreq b/pipeline/misc/dev/test_data/ref/freq_files/TRANS/ref.TRANS.chr22.afreq
new file mode 100644
index 00000000..032635e8
--- /dev/null
+++ b/pipeline/misc/dev/test_data/ref/freq_files/TRANS/ref.TRANS.chr22.afreq
@@ -0,0 +1,1001 @@
+#CHROM ID REF ALT ALT_FREQS OBS_CT
+22 rs7287144 G A 0.784787 6626
+22 rs5748662 A G 0.826186 6576
+22 rs5994034 C T 0.137228 6624
+22 rs4010554 A C 0.720344 6626
+22 rs4010558 A G 0.709404 6614
+22 rs3954571 T C 0.535024 6624
+22 rs11089179 A G 0.258321 6550
+22 rs9604821 G A 0.647508 6542
+22 rs2379965 C T 0.64221 6624
+22 rs2379981 G A 0.930064 6606
+22 rs4535153 C T 0.925976 6606
+22 rs5747620 T C 0.43431 6622
+22 rs17430900 G A 0.00437802 6624
+22 rs9605903 T C 0.188349 6626
+22 rs5747940 T C 0.441727 6624
+22 rs5746647 G T 0.911259 6626
+22 rs16980739 C T 0.151826 6626
+22 rs9605927 T C 0.0372886 6624
+22 rs5747968 G T 0.753096 6622
+22 rs2236639 A G 0.863417 6626
+22 rs5747988 A G 0.862511 6626
+22 rs5746664 A C 0.855116 6626
+22 rs5747999 C A 0.590127 6624
+22 rs2070501 A G 0.525355 6626
+22 rs11089263 C A 0.504264 6566
+22 rs2096537 A C 0.763738 6624
+22 rs16984366 T C 0.21214 6392
+22 rs2154615 C T 0.0952884 6622
+22 rs8137637 T G 0.204799 6626
+22 rs4410381 G A 0.20827 6602
+22 rs9604967 C T 0.0784787 6626
+22 rs5993671 G T 0.764724 6622
+22 rs5993792 T C 0.187292 6626
+22 rs5992472 G A 0.728343 6626
+22 rs4819849 A G 0.287957 6626
+22 rs9605028 A G 0.0738002 6626
+22 rs1892844 A G 0.105795 6626
+22 rs2529883 C T 0.0953818 6626
+22 rs17432784 C T 0.0698239 6588
+22 rs2845379 T C 0.639191 6624
+22 rs2845380 A G 0.805463 6626
+22 rs2247281 G A 0.631754 6626
+22 rs2845346 C T 0.906278 6626
+22 rs2845347 C T 0.9099 6626
+22 rs16981635 C A 0.0581044 6626
+22 rs1807512 T C 0.231048 6622
+22 rs5748593 T C 0.841835 6626
+22 rs17433377 G A 0.106312 6622
+22 rs4390844 C T 0.903562 6626
+22 rs2381107 C T 0.905852 6596
+22 rs4819535 T C 0.143963 6460
+22 rs5748648 G A 0.137338 6626
+22 rs738045 G A 0.334793 6622
+22 rs7284996 T C 0.335548 6622
+22 rs5748651 G A 0.0483092 6624
+22 rs2385714 T C 0.205527 6622
+22 rs2080203 T G 0.196768 6622
+22 rs5748657 C T 0.458786 6624
+22 rs2072467 T C 0.04754 6626
+22 rs2072466 C T 0.855267 6626
+22 rs7291429 T G 0.560236 6624
+22 rs9605155 G T 0.377566 6624
+22 rs5748664 A G 0.882584 6626
+22 rs874835 A G 0.378019 6624
+22 rs874836 A G 0.774004 6624
+22 rs5994031 C T 0.576261 6622
+22 rs2215842 C T 0.576087 6624
+22 rs2192431 T G 0.203351 6624
+22 rs175138 G A 0.576087 6624
+22 rs175139 C T 0.576087 6624
+22 rs983305 G A 0.370426 6614
+22 rs9606478 G A 0.585749 6624
+22 rs175140 T G 0.169082 6624
+22 rs1860944 G A 0.358092 6624
+22 rs7287430 G A 0.380435 6624
+22 rs175145 A G 0.768941 6626
+22 rs175146 A G 0.169031 6626
+22 rs175147 G T 0.538822 6620
+22 rs16981741 A G 0.188198 6626
+22 rs175148 T C 0.540459 6624
+22 rs175149 A G 0.540459 6624
+22 rs9606481 T C 0.388319 6626
+22 rs17363716 T C 0.184878 6626
+22 rs165652 T G 0.749623 6626
+22 rs165757 G A 0.756341 6624
+22 rs17435801 T C 0.184878 6626
+22 rs175152 A C 0.779656 6626
+22 rs165645 C T 0.499698 6620
+22 rs165611 T C 0.509964 6624
+22 rs5746906 A G 0.0990039 6626
+22 rs165670 C T 0.504378 6624
+22 rs165778 T C 0.841382 6626
+22 rs16981765 T C 0.0576691 6624
+22 rs165810 C A 0.543012 6626
+22 rs2075120 C T 0.184545 6600
+22 rs175154 A G 0.810444 6626
+22 rs165808 C T 0.841684 6626
+22 rs165890 T C 0.808633 6626
+22 rs2041608 T G 0.0721401 6626
+22 rs12169910 C T 0.0707376 6616
+22 rs165886 G A 0.803049 6626
+22 rs165698 T C 0.840779 6626
+22 rs165608 G A 0.621564 6622
+22 rs165790 T C 0.840477 6626
+22 rs165667 C A 0.841081 6626
+22 rs7291500 T G 0.0828554 6626
+22 rs4006343 A G 0.7737 6540
+22 rs738043 G A 0.153491 6274
+22 rs5748728 G A 0.738304 6626
+22 rs4586727 G A 0.389308 6622
+22 rs17444804 C T 0.0461817 6626
+22 rs1860945 T C 0.242379 6626
+22 rs737936 G A 0.130697 6626
+22 rs16981833 C T 0.0591609 6626
+22 rs7288841 C T 0.115338 6624
+22 rs9606534 T C 0.180048 6626
+22 rs7292561 T C 0.325483 6624
+22 rs7293026 T C 0.76555 6624
+22 rs13058496 A G 0.130244 6626
+22 rs8136206 C A 0.81965 6626
+22 rs887755 A G 0.387413 6626
+22 rs759081 T C 0.803264 6618
+22 rs5992587 C T 0.402446 6622
+22 rs11703901 C T 0.130094 6626
+22 rs2003568 T C 0.192784 6624
+22 rs12485066 G A 0.060821 6626
+22 rs5748744 A G 0.445501 6624
+22 rs5992589 G A 0.246528 6624
+22 rs9306242 A G 0.308424 6624
+22 rs9605179 A G 0.318237 6624
+22 rs5992590 C T 0.683726 6624
+22 rs5748747 A C 0.816425 6624
+22 rs5994097 T C 0.167371 6626
+22 rs9618937 G A 0.282071 6626
+22 rs17806382 G A 0.0377302 6626
+22 rs5748748 T C 0.0938726 6626
+22 rs5748752 C T 0.816823 6622
+22 rs5994102 T C 0.156901 6622
+22 rs5748755 C T 0.819143 6624
+22 rs2385785 G A 0.212258 6624
+22 rs5748756 A C 0.285326 6624
+22 rs5994104 T C 0.434802 6626
+22 rs933461 G A 0.811594 6624
+22 rs1981707 T C 0.311896 6624
+22 rs4819923 T G 0.406099 6624
+22 rs5748760 A G 0.815424 6626
+22 rs5748762 T C 0.154589 6624
+22 rs2385786 G A 0.484903 6624
+22 rs1990483 C T 0.360012 6622
+22 rs5994110 A G 0.145726 6622
+22 rs17733785 T C 0.0766677 6626
+22 rs8137298 A C 0.228041 6626
+22 rs11914017 T C 0.106733 6624
+22 rs11089387 T C 0.336809 6618
+22 rs5992598 C T 0.20003 6624
+22 rs7287116 T C 0.199426 6624
+22 rs1024732 G A 0.378285 6622
+22 rs5748765 A C 0.54529 6624
+22 rs5748766 T G 0.205252 6626
+22 rs4819925 C T 0.81482 6626
+22 rs2041607 G A 0.356992 6622
+22 rs5992600 T C 0.138134 6624
+22 rs759076 G A 0.808273 6624
+22 rs757630 T C 0.845155 6626
+22 rs4819932 C A 0.804197 6624
+22 rs9618953 T C 0.0963164 6624
+22 rs4819934 G A 0.0611413 6624
+22 rs4819935 C A 0.816974 6622
+22 rs4819936 G A 0.0605192 6626
+22 rs5992604 G A 0.425272 6624
+22 rs9618954 C A 0.163598 6626
+22 rs17806634 T C 0.0253547 6626
+22 rs2399152 T C 0.187443 6626
+22 rs4819938 T C 0.374698 6624
+22 rs5748798 G A 0.366546 6624
+22 rs5746961 C T 0.0858738 6626
+22 rs7291404 G A 0.135225 6626
+22 rs11913227 T C 0.0753549 6622
+22 rs5994128 A G 0.751132 6626
+22 rs5994129 G A 0.465418 6622
+22 rs917838 T C 0.358135 6626
+22 rs2192155 A G 0.820404 6626
+22 rs738031 G T 0.121226 6624
+22 rs2399153 A G 0.875189 6626
+22 rs5746962 A G 0.461957 6624
+22 rs4141523 G T 0.878962 6626
+22 rs4819940 G A 0.53276 6624
+22 rs17806741 G A 0.0508602 6626
+22 rs5994130 G A 0.251887 6626
+22 rs17204993 T C 0.135115 6624
+22 rs2399168 C A 0.308726 6624
+22 rs2908526 G A 0.757018 6626
+22 rs5748829 A G 0.0813462 6626
+22 rs928831 T C 0.0733474 6626
+22 rs2845394 A G 0.428162 6626
+22 rs2845393 T G 0.398702 6624
+22 rs881623 T C 0.75732 6626
+22 rs947822 T C 0.549215 6624
+22 rs2845388 A G 0.878019 6624
+22 rs2845384 G A 0.757928 6622
+22 rs7285901 T G 0.182518 6624
+22 rs2845408 A G 0.757471 6626
+22 rs16981964 T C 0.0813462 6626
+22 rs10483090 T C 0.128433 6626
+22 rs12159416 T C 0.185481 6626
+22 rs16981972 C T 0.030486 6626
+22 rs9606592 T G 0.340024 6626
+22 rs5748845 C T 0.110776 6626
+22 rs5994155 C A 0.757246 6624
+22 rs5748850 T G 0.654845 6626
+22 rs2041629 A G 0.670592 6624
+22 rs4819956 A C 0.348778 6626
+22 rs4819553 G A 0.755961 6626
+22 rs4819958 A G 0.756112 6626
+22 rs4819554 G A 0.755961 6626
+22 rs2241042 A C 0.411259 6626
+22 rs2241043 C T 0.470269 6626
+22 rs5748863 G A 0.456962 6622
+22 rs6518660 A G 0.185174 6610
+22 rs13053889 C T 0.142965 6624
+22 rs9606615 T C 0.342741 6626
+22 rs2241044 A C 0.420163 6626
+22 rs2302519 C T 0.284722 6624
+22 rs2241046 C T 0.836251 6626
+22 rs2241049 A G 0.367643 6626
+22 rs6518661 G A 0.279052 6626
+22 rs879577 C T 0.256341 6624
+22 rs879576 G A 0.116244 6624
+22 rs2229151 G A 0.118775 6626
+22 rs879575 C T 0.160429 6626
+22 rs1468488 T C 0.265168 6626
+22 rs2895332 G A 0.72833 6622
+22 rs887796 G A 0.843043 6626
+22 rs738034 G A 0.49019 6626
+22 rs3827278 C A 0.188198 6626
+22 rs4819962 T C 0.622585 6624
+22 rs1047929 T C 0.189556 6626
+22 rs11160 G A 0.17965 6624
+22 rs971768 G A 0.172653 6626
+22 rs7289082 A G 0.22457 6626
+22 rs12170331 G A 0.17965 6624
+22 rs974396 A G 0.20329 6626
+22 rs738033 G T 0.179348 6624
+22 rs2277830 A C 0.0235436 6626
+22 rs3827295 G A 0.168126 6626
+22 rs5994165 G A 0.465439 6626
+22 rs5992629 G A 0.879378 6624
+22 rs5748871 A G 0.451102 6626
+22 rs5746996 C A 0.379644 6622
+22 rs4819964 G A 0.865379 6626
+22 rs5748875 T C 0.415761 6624
+22 rs4819965 G T 0.85904 6626
+22 rs9605222 G T 0.0635041 6598
+22 rs5748883 T C 0.359644 6626
+22 rs5747004 C A 0.110323 6626
+22 rs5994170 A G 0.382815 6622
+22 rs4819558 C T 0.259433 6626
+22 rs4819559 A C 0.256944 6624
+22 rs5994175 G T 0.318292 6626
+22 rs740422 C T 0.158467 6626
+22 rs741139 A G 0.158467 6626
+22 rs5747007 A C 0.0336553 6626
+22 rs7287119 C T 0.169837 6624
+22 rs5747008 G A 0.88198 6626
+22 rs2302521 G A 0.128735 6626
+22 rs3788268 T C 0.882699 6624
+22 rs1034859 C A 0.551011 6626
+22 rs3788269 G A 0.0775732 6626
+22 rs5994180 A G 0.129641 6626
+22 rs5748917 C T 0.511319 6626
+22 rs4423695 C T 0.892243 6626
+22 rs5994186 G A 0.448068 6624
+22 rs5994187 G A 0.448083 6626
+22 rs5748919 C T 0.440085 6626
+22 rs5748923 T G 0.397343 6624
+22 rs5748926 T C 0.72291 6626
+22 rs2401071 A G 0.280948 6624
+22 rs1125471 C A 0.349487 6624
+22 rs1076102 A G 0.396739 6624
+22 rs1135909 T C 0.227273 6622
+22 rs3171599 A G 0.278147 6626
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+22 rs463271 T C 0.647387 6622
+22 rs462904 T G 0.66073 6626
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+22 rs460036 C T 0.203592 6626
+22 rs455550 A G 0.262832 6624
+22 rs7288409 A G 0.282779 6620
+22 rs7284964 A G 0.0792333 6626
+22 rs7289593 A G 0.0567462 6626
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+22 rs5992985 C T 0.454696 6622
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+22 rs11703744 G A 0.0647841 6622
+22 rs17207304 T C 0.130888 6624
+22 rs5992165 G A 0.449743 6626
+22 rs17742344 G A 0.112202 6622
+22 rs9618193 T C 0.140507 6626
+22 rs2401503 T C 0.174464 6626
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+22 rs17809705 C T 0.131301 6626
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+22 rs17742403 G A 0.0718382 6626
+22 rs5992991 C A 0.0635376 6626
+22 rs5992166 C T 0.0871075 6624
+22 rs4819666 A G 0.439312 6624
+22 rs8139802 G T 0.128283 6626
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+22 rs464541 C T 0.589852 6622
+22 rs17207360 C T 0.145391 6596
+22 rs361818 G A 0.832478 6626
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+22 rs362128 C T 0.853003 6626
+22 rs5992997 T G 0.107035 6624
+22 rs361594 T C 0.416012 6620
+22 rs362250 C T 0.260338 6626
+22 rs467998 A C 0.112285 6626
+22 rs467504 T C 0.60423 6620
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+22 rs361893 T C 0.276487 6626
+22 rs461517 A G 0.112436 6626
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+22 rs361800 G A 0.0956837 6626
+22 rs362043 G T 0.308877 6624
+22 rs7290794 C T 0.0546498 6624
+22 rs361834 G A 0.611228 6626
+22 rs464901 T C 0.419813 6622
+22 rs361540 A G 0.603984 6626
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+22 rs362133 T C 0.268116 6624
+22 rs362195 G A 0.140507 6626
+22 rs2234329 G T 0.197162 6624
+22 rs2234331 C T 0.00347117 6626
+22 rs361776 G A 0.211504 6624
+22 rs362249 A G 0.546045 6624
+22 rs10427839 C T 0.0603213 6598
+22 rs7286465 A G 0.0691216 6626
+22 rs361557 A G 0.517231 6616
+22 rs8140197 G A 0.204559 6624
+22 rs2234338 C T 0.0490492 6626
+22 rs12159975 C T 0.0490492 6626
+22 rs2540620 A C 0.433746 6626
+22 rs5746516 T G 0.0273166 6626
+22 rs4819668 G A 0.147299 6626
+22 rs5747490 A C 0.148959 6626
+22 rs5747492 A G 0.327993 6616
+22 rs361534 C T 0.592145 6620
+22 rs5747494 C A 0.180665 6620
+22 rs9618216 C T 0.13587 6624
+22 rs5993013 G A 0.219807 6624
+22 rs5992185 A C 0.294446 6626
+22 rs2252257 G A 0.383756 6624
+22 rs2543953 A G 0.399698 6620
+22 rs3180408 C T 0.290094 6622
+22 rs4819484 G A 0.793432 6608
+22 rs2543958 T G 0.00342253 6428
+22 rs2005883 C T 0.0258216 5538
+22 rs5747927 G A 0.0823313 5988
+22 rs9605907 A G 0.677414 6606
+22 rs5993445 T C 0.25898 6626
+22 rs2540627 A G 0.620924 6624
+22 rs2077200 A G 0.574426 6624
+22 rs7288481 G A 0.268267 6624
+22 rs2019061 G A 0.426653 6626
+22 rs9680846 C T 0.277241 6626
+22 rs11914070 C T 0.163043 6624
+22 rs2078743 G A 0.0665761 6624
+22 rs2540648 A G 0.0246001 6626
+22 rs5747950 A G 0.553275 6626
+22 rs11089247 C T 0.458346 6626
+22 rs2041780 G A 0.664956 6626
+22 rs2023634 A G 0.0446725 6626
+22 rs2518805 A G 0.0947781 6626
+22 rs7292750 G A 0.0531241 6626
+22 rs2075198 A G 0.140658 6626
+22 rs1210635 A C 0.525807 6626
+22 rs2075197 C T 0.0203743 6626
+22 rs1210637 T C 0.570695 6620
+22 rs3804048 G A 0.0808935 6626
+22 rs7291719 C T 0.077875 6626
+22 rs1210638 T C 0.671597 6626
+22 rs2913 G A 0.527317 6626
+22 rs7287699 G A 0.0889191 6624
+22 rs2023635 A G 0.343948 6626
+22 rs17810512 A C 0.221383 6622
+22 rs5993462 C T 0.0904014 6626
+22 rs5746651 G A 0.143914 6622
+22 rs5993463 C T 0.416692 6614
+22 rs4431035 A G 0.201691 6624
+22 rs7288491 C T 0.0387866 6626
+22 rs10483099 C T 0.240036 6624
+22 rs17810548 C T 0.145726 6622
+22 rs3747041 C T 0.0341081 6626
+22 rs2871006 G T 0.39511 6626
+22 rs3848856 G T 0.157052 6622
+22 rs2073776 C T 0.451842 6624
+22 rs6623 C T 0.250832 6614
+22 rs10160 A G 0.531259 6622
+22 rs2072123 A G 0.428464 6626
+22 rs5992353 C A 0.428722 6622
+22 rs2283641 C T 0.47042 6626
+22 rs2073775 T C 0.530797 6624
+22 rs6518516 A G 0.429369 6626
+22 rs6518517 A G 0.427558 6626
+22 rs715590 A G 0.469043 6622
+22 rs715599 G A 0.117185 6622
+22 rs1001896 G A 0.245246 6626
+22 rs5992354 T C 0.428313 6626
+22 rs2057754 T C 0.18363 6622
+22 rs10854539 G A 0.14482 6622
+22 rs8138035 A G 0.428442 6624
+22 rs5992356 G A 0.245545 6622
+22 rs2238735 G A 0.0416541 6626
+22 rs2238740 G A 0.180157 6622
+22 rs2238743 T G 0.530042 6624
+22 rs5993488 G A 0.427105 6626
+22 rs5993489 G A 0.14428 6626
+22 rs2238748 T C 0.469807 6624
+22 rs5993492 T C 0.530939 6626
+22 rs6518522 A G 0.166465 6626
+22 rs2238751 G A 0.182614 6626
+22 rs2238754 C T 0.089345 6626
+22 rs1557847 A G 0.428464 6626
+22 rs5993516 G A 0.144475 6624
+22 rs2800960 C T 0.919559 6626
+22 rs2800969 C T 0.245921 6620
+22 rs2800980 C T 0.471006 6622
+22 rs1934895 A G 0.140809 6626
+22 rs2525036 T C 0.425747 6626
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+22 rs10483100 T C 0.166667 6624
+22 rs2800954 G A 0.425702 6622
+22 rs8135308 T G 0.146695 6626
+22 rs2189490 C T 0.527015 6626
+22 rs2066240 G A 0.0917597 6626
+22 rs1034727 C T 0.333031 6624
+22 rs2800981 G A 0.527174 6624
+22 rs2525079 T C 0.333484 6624
+22 rs5993533 A G 0.333031 6624
+22 rs712966 A G 0.333535 6626
+22 rs807741 T G 0.929822 6626
+22 rs807750 A G 0.467693 6624
+22 rs807751 A G 0.536523 6626
+22 rs807753 T C 0.468297 6624
+22 rs807756 T G 0.971325 6626
+22 rs807757 G A 0.213682 6622
+22 rs807758 T C 0.506649 6618
+22 rs807759 A G 0.497736 6626
+22 rs17811029 G A 0.182216 6624
+22 rs8139221 T C 0.366355 6622
+22 rs12233351 T C 0.0516149 6626
+22 rs1052763 C T 0.265097 6624
+22 rs1052773 G A 0.207729 6624
+22 rs2285857 T C 0.26872 6624
+22 rs2285858 C A 0.260187 6626
+22 rs929282 C T 0.041805 6626
+22 rs712965 G A 0.102777 6626
+22 rs2240111 C T 0.166566 6622
+22 rs2240112 T C 0.362662 6626
+22 rs16983371 T C 0.309387 6626
+22 rs5747997 C T 0.435859 6626
+22 rs17743887 C T 0.0141865 6626
+22 rs3765611 T C 0.0416541 6626
+22 rs17743906 T C 0.0959855 6626
+22 rs737923 A G 0.486262 6624
+22 rs715544 G A 0.166315 6626
+22 rs4819776 C T 0.274826 6626
+22 rs4819778 C T 0.517205 6626
+22 rs5992372 A G 0.757923 6626
+22 rs2275901 G A 0.355418 6626
+22 rs12484548 T C 0.234078 6626
+22 rs12484562 T G 0.243284 6626
+22 rs11089255 T C 0.243431 6622
+22 rs12628176 A G 0.242754 6624
+22 rs5993546 G A 0.224336 6624
+22 rs2096376 C T 0.860398 6626
+22 rs5748014 C A 0.48247 4906
+22 rs5748015 G A 0.143433 4964
+22 rs5993549 C T 0.243133 6626
+22 rs4819782 A G 0.283278 6626
+22 rs13053237 T C 0.0540459 6624
+22 rs807670 A C 0.639602 6626
+22 rs5748021 T G 0.17054 6626
+22 rs712964 T C 0.664202 6626
+22 rs712960 A C 0.447011 6624
+22 rs807673 G A 0.310236 6624
+22 rs712959 C T 0.602022 6626
+22 rs2298271 C T 0.151826 6626
+22 rs2298270 G T 0.106503 6582
+22 rs5746671 A G 0.415183 6626
+22 rs807667 G A 0.840779 6626
+22 rs2040771 C T 0.37383 6626
+22 rs712958 T C 0.751736 6626
diff --git a/pipeline/misc/dev/test_setup.Rmd b/pipeline/misc/dev/test_setup.Rmd
index ca63a012..591d0bda 100644
--- a/pipeline/misc/dev/test_setup.Rmd
+++ b/pipeline/misc/dev/test_setup.Rmd
@@ -44,6 +44,7 @@ for(i in pops){
   fwrite(freq_i, paste0('misc/dev/test_data/ref/freq_files/',i,'/ref.',i,'.chr22.afreq'), quote=F, sep=' ', na='NA')
 }
 ```
+
 ```{bash}
 cd ~/oliverpainfel/Software/MyGit/GenoPred/pipeline
 
diff --git a/pipeline/rules/target_scoring.smk b/pipeline/rules/target_scoring.smk
index 8a09c9f7..2365035a 100644
--- a/pipeline/rules/target_scoring.smk
+++ b/pipeline/rules/target_scoring.smk
@@ -78,7 +78,7 @@ rule pc_projection:
 rule target_pgs_i:
   resources:
     mem_mb=config['mem_target_pgs'],
-    time_min=1600
+    time_min=4000
   threads: config['cores_target_pgs']
   input:
     f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",

From 89963386588a5ef970da8f8990017bce6125abcc Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 5 Feb 2025 20:14:50 +0000
Subject: [PATCH 101/153] Updated test_data

---
 docs/pipeline_readme.Rmd  | 5 +++--
 docs/pipeline_readme.html | 5 +++--
 2 files changed, 6 insertions(+), 4 deletions(-)

diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 9b6a0325..ad732d06 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -521,8 +521,9 @@ Once you have installed GenoPred, we can run the pipeline using the test data.
 First, we need to download and decompress the test data. Do this within the `GenoPred/pipeline` folder.
 
 ```{bash}
-# Download from Zenodo
-wget -O test_data.tar.gz https://zenodo.org/records/10640650/files/test_data.tar.gz?download=1
+# Download from Google Drive
+conda activate genopred
+gdown --id 1C4AwDnY_hJ4ilGneMlAjwEKghzss5PeG -O test_data.tar.gz
 
 # Decompress
 tar -xf test_data.tar.gz
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index 63cd8231..efa2dde9 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -1026,8 +1026,9 @@ <h1>Run using test data</h1>
 <h2>Step 1: Download the test data</h2>
 <p>First, we need to download and decompress the test data. Do this
 within the <code>GenoPred/pipeline</code> folder.</p>
-<pre class="bash"><code># Download from Zenodo
-wget -O test_data.tar.gz https://zenodo.org/records/10640650/files/test_data.tar.gz?download=1
+<pre class="bash"><code># Download from Google Drive
+conda activate genopred
+gdown --id 1C4AwDnY_hJ4ilGneMlAjwEKghzss5PeG -O test_data.tar.gz
 
 # Decompress
 tar -xf test_data.tar.gz

From ea1d24db481b6a2d41e4ac5ea39668ae6e0bcdc0 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 7 Feb 2025 12:58:20 +0000
Subject: [PATCH 102/153] Updates

---
 docs/CrossPop.Rmd                  | 156 +++++++++++++++++++++++++----
 pipeline/example_input/config.yaml |   2 +-
 2 files changed, 137 insertions(+), 21 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index e1c8404f..ec88bec8 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1580,10 +1580,6 @@ selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_su
 # Get a list of score files
 scores <- list_score_files(config)
 
-# Remove tlprs
-scores<-scores[!grepl('tlprs', scores$method),]
-scores<-scores[!grepl('xwing', scores$method),]
-
 # Create files for EAS and AFR targets
 targ_pop <- c('EAS','AFR')
 for(trait_i in selected_traits){
@@ -1736,8 +1732,9 @@ for(trait_i in selected_traits){
                                            !grepl('_multi$', scores_i_j$method),]
 
       # Split top1 scores by target population
+      # This doesn't apply to xwing because it only has pop-specific pseudo scores
       scores_i_j_multi_top1<-NULL
-      for(i in 1:nrow(scores_i_j_multi)){
+      for(i in 1:which(scores_i_j_multi$method %in% c('prscsx'))){
         score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
         
         for(pop in c('EUR', disc_pop_j_2)){
@@ -1746,6 +1743,10 @@ for(trait_i in selected_traits){
             score_cols <-
               which(grepl(paste0('^FID$|^IID$|_', pop, '_phi'), names(score_header)))
           }
+          if(scores_i_j_multi$method[i] == 'xwing'){
+            score_cols <-
+              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst'), names(score_header)))
+          }
           
           system(
             paste0(
@@ -1778,11 +1779,14 @@ for(trait_i in selected_traits){
         score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
         
         for(pop in c('EUR', disc_pop_j_2)){
-          
           if(scores_i_j_multi$method[i] == 'prscsx'){
             score_cols <-
               which(grepl(paste0('^FID$|^IID$|_', pop, '_phi_auto'), names(score_header)))
           }
+          if(scores_i_j_multi$method[i] == 'xwing'){
+            score_cols <-
+              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst_', pop), names(score_header)))
+          }
           
           system(
             paste0(
@@ -1915,7 +1919,7 @@ for targ_pop in $(echo EAS AFR); do
   
   for disc_pop_i in ${disc_pop}; do
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
-      sbatch --mem 10G -n 5 -p interruptible_cpu --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+      sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
         --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
         --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
         --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
@@ -2012,9 +2016,10 @@ for(pheno_i in selected_traits){
                    !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTrain'
   res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
                    grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTrain'
+  
   res_eval_i$Model[grepl('quickprs_multi', res_eval_i$Group)]<-'SumStatTrain'
   res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTrain'
-  
+  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTrain'
   
   res_eval_i$Source<-ifelse(
     res_eval_i$Method %in% pgs_group_methods | 
@@ -2039,9 +2044,9 @@ for(pheno_i in selected_traits){
     !(res_eval_i$Method %in%  c('ptclump') & 
       res_eval_i$Model %in% c('SumStatTrain','Multi-SumStatTrain')),]
 
-  # Remove top1 models for PRS-CSx
+  # Remove top1 models for QuickPRS-Multi, PRS-CSx, X-wing
   res_eval_i <- res_eval_i[
-    !(res_eval_i$Method %in%  c('prscsx') & 
+    !(res_eval_i$Method %in%  c('prscsx', 'xwing', 'quickprs_multi') & 
       grepl('top1', res_eval_i$Group)),]
   
   # Remove any duplicate models
@@ -2281,6 +2286,7 @@ find_model<-function(x){
   mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTrain'
   mod[grepl('quickprs_multi', x)] <- 'SumStatTrain'
   mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTrain'
+  mod[x == 'xwing.pseudo.multi'] <- 'SumStatTrain'
   
   return(mod)
 }
@@ -2323,14 +2329,14 @@ res_comp_all <- res_comp_all[
 
 # Remove top1 models for PRS-CSx
 res_comp_all <- res_comp_all[
-!(res_comp_all$Method1 %in%  c('prscsx') & 
+!(res_comp_all$Method1 %in%  c('prscsx','xwing','quickprs_multi') & 
   grepl('top1', res_comp_all$Model_1)),]
 res_comp_all <- res_comp_all[
-!(res_comp_all$Method2 %in%  c('prscsx') & 
+!(res_comp_all$Method2 %in%  c('prscsx','xwing','quickprs_multi') & 
   grepl('top1', res_comp_all$Model_2)),]
 
 # Remove any comparisons
-res_comp_all <- res_comp_all[!duplicated(res_comp_all[, c("Target", "Method1", "Model1", "Source1", "Discovery1", "Method2", "Model2", "Source2", "Discovery2")]),]
+res_comp_all <- res_comp_all[!duplicated(res_comp_all[, c("Target", "Method1", "Model1", "Source1", "Discovery1", "Method2", "Model2", "Source2", "Discovery2",'pheno')]),]
 
 library(MAd)
 
@@ -2381,6 +2387,14 @@ for(targ_pop_i in targ_pop){
         
         tmp <- res_comp_group_i[1,]
         tmp$pheno <- NULL
+        tmp$Model_1_R <-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &
+                            meta_res_eval$Target == targ_pop_i &
+                            meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i)]
+        tmp$Model_2_R <-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &
+                            meta_res_eval$Target == targ_pop_i &
+                            meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i)]
         tmp$R_diff <- meta_res_comp_i$es
         tmp$R_diff_SE <- sqrt(meta_res_comp_i$var)
         tmp$R_diff_z <- tmp$R_diff / tmp$R_diff_SE
@@ -2494,10 +2508,63 @@ meta_res_comp_reference$R_diff[meta_res_comp_ptclump_top1$reference_point == F]
 meta_res_comp_reference$R_diff_SE [meta_res_comp_ptclump_top1$reference_point == F] <- NA
 res_comp_all_ptclump_top1$reference_point<-F
 
-ggplot(meta_res_comp_ptclump_top1, aes(x=Method1, y=R_diff , fill = Model1)) +
+# Format results for nice plot
+model_palette <- c(
+  "IndivTrain" = "#F8766D",
+  "SumStatTrain" = "#7CAE00",
+  "Multi-IndivTrain" = "#00BFC4",
+  "Multi-SumStatTrain" = "#C77CFF"
+)
+
+meta_tmp <- meta_res_comp_ptclump_top1
+meta_tmp <- merge(meta_tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method')
+meta_tmp$label[grepl('Multi', meta_tmp$Model1) & !(meta_tmp$Method1 %in% pgs_group_methods)] <- paste0(meta_tmp$label[grepl('Multi', meta_tmp$Model1) & !(meta_tmp$Method1 %in% pgs_group_methods)], '-Multi')
+meta_tmp$label <- factor(meta_tmp$label, levels = unique(meta_tmp$label[order(!(grepl('Multi', meta_tmp$label)), meta_tmp$label)]))
+meta_tmp$Discovery_clean <- as.character(meta_tmp$Discovery1)
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 == 'EUR'] <- 'EUR GWAS'
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Single'] <- 'Target-matched GWAS'
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Multi'] <- 'Both'
+meta_tmp$Discovery_clean <- factor(meta_tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+meta_tmp$Target <- paste0(meta_tmp$Target, ' Target')
+meta_tmp$Model1 <- factor(meta_tmp$Model1, levels = names(model_palette))
+
+meta_tmp_ref <- meta_res_comp_reference
+meta_tmp_ref <- merge(meta_tmp_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method')
+meta_tmp_ref$label[grepl('Multi', meta_tmp_ref$Model1) & !(meta_tmp_ref$Method1 %in% pgs_group_methods)] <- paste0(meta_tmp_ref$label[grepl('Multi', meta_tmp_ref$Model1) & !(meta_tmp_ref$Method1 %in% pgs_group_methods)], '-Multi')
+meta_tmp_ref$label <- factor(meta_tmp_ref$label, levels = unique(meta_tmp_ref$label[order(!(grepl('Multi', meta_tmp_ref$label)), meta_tmp_ref$label)]))
+meta_tmp_ref$Discovery_clean <- as.character(meta_tmp_ref$Discovery1)
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 == 'EUR'] <- 'EUR GWAS'
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != 'EUR' & meta_tmp_ref$Source1 == 'Single'] <- 'Target-matched GWAS'
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != 'EUR' & meta_tmp_ref$Source1 == 'Multi'] <- 'Both'
+meta_tmp_ref$Discovery_clean <- factor(meta_tmp_ref$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+meta_tmp_ref$Target <- paste0(meta_tmp_ref$Target, ' Target')
+meta_tmp_ref$Model1 <- factor(meta_tmp_ref$Model1, levels = names(model_palette))
+
+tmp <- res_comp_all_ptclump_top1
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method')
+tmp$label[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods)], '-Multi')
+tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
+tmp$Discovery_clean <- as.character(tmp$Discovery1)
+tmp$Discovery_clean[tmp$Discovery1 == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model1 <- factor(tmp$Model1, levels = names(model_palette))
+
+ggplot(meta_tmp, aes(x=label, y=R_diff , fill = Model1)) +
     geom_point(
-        data = res_comp_all_ptclump_top1,
-        mapping = aes(x=Method1, y=R_diff, colour=Model1),
+        data = tmp,
+        mapping = aes(x=label, y=R_diff, colour=Model1),
         position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
         alpha = 0.3
       ) +
@@ -2517,8 +2584,8 @@ ggplot(meta_res_comp_ptclump_top1, aes(x=Method1, y=R_diff , fill = Model1)) +
       shape = 23
     ) +
     geom_point(
-        data = meta_res_comp_reference,
-        aes(x = Method1, y = R_diff, fill = Model1),
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff, fill = Model1),
         stat = "identity",
         position = position_dodge(0.7), # Ensure same dodge as other points
         size = 3,    # Increase size for emphasis
@@ -2526,9 +2593,58 @@ ggplot(meta_res_comp_ptclump_top1, aes(x=Method1, y=R_diff , fill = Model1)) +
         stroke = 1.5,
         show.legend=F
     ) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method1))), linetype="dotted") +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype="dotted") +
     labs(y = "R_diff (SE)") +
-    facet_grid(Target ~ Discovery1, scales='free', space = 'free_x') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+
+# Plot as % change
+meta_tmp$R_diff_perc <- meta_tmp$R_diff / meta_tmp$Model_2_R
+meta_tmp_ref$R_diff_perc <- meta_tmp_ref$R_diff / meta_tmp_ref$Model_2_R
+tmp$R_diff_perc <- tmp$R_diff / tmp$Model_2_R
+
+meta_tmp$R_diff_perc_SE <- meta_tmp$R_diff_SE / meta_tmp$Model_2_R
+
+ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
+#    geom_point(
+#        data = tmp,
+#        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+#        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+#        alpha = 0.3
+#      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype="dotted") +
+    labs(y = "R diff. (SE)") +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
     theme_half_open() +
     background_grid() + 
     panel_border() + 
diff --git a/pipeline/example_input/config.yaml b/pipeline/example_input/config.yaml
index be9b39e0..4e79d58d 100644
--- a/pipeline/example_input/config.yaml
+++ b/pipeline/example_input/config.yaml
@@ -23,7 +23,7 @@ testing: chr22
 cores_prep_pgs: 5
 
 # Specify number of cores for target scoring
-cores_target_pgs: 1
+cores_target_pgs: 5
 
 # Specify memory in megabytes for target scoring
 mem_target_pgs: 2000

From 212c7008d15ef6f63a53d4e446a40b80517f7cd6 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 7 Feb 2025 13:33:24 +0000
Subject: [PATCH 103/153] Parallelised the TRANS PGS adjustment

---
 functions/pgs.R | 47 ++++++++++++++++++++++++++++++++---------------
 1 file changed, 32 insertions(+), 15 deletions(-)

diff --git a/functions/pgs.R b/functions/pgs.R
index ec128387..8f4added 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -556,35 +556,52 @@ model_trans_pgs<-function(scores=NULL, pcs=NULL, output=NULL){
   fwrite(scores_pcs_resid, paste0(output,'-TRANS.profiles'), sep=' ', na='NA', quote=F)
 }
 
-# Adjust PGS for ancestry using reference PC models
+# Adjust PGS for ancestry using reference PC models with parallel processing
 score_adjust <- function(score, pcs, ref_model) {
-  # Convert score and pcs to data.table if not already
-  setDT(score)
-  setDT(pcs)
+  # Store original column order
+  original_order <- names(score)
   
   # Ensure pcs is keyed on FID and IID for efficient joins
   setkey(pcs, FID, IID)
+
+  # Get list of score columns (excluding FID and IID)
+  score_cols <- setdiff(names(score), c("FID", "IID"))
   
-  # Loop through each score column (skipping FID and IID)
-  for (col_name in names(score)[-1:-2]) {
-    # Retrieve models for the current score
-    mean_model <- ref_model[[col_name]]$mean_model
-    var_model <- ref_model[[col_name]]$var_model
+  # Process columns in chunks to reduce memory load
+  chunk_size <- 200
+  for (i in seq(1, length(score_cols), by = chunk_size)) {
+    # Get a subset of columns to process in this iteration
+    chunk <- score_cols[i:min(i + chunk_size - 1, length(score_cols))]
     
-    # Calculate residuals directly in `score`
-    score[, (col_name) := {
-      # Get corresponding rows from pcs by FID and IID
-      pc_data <- pcs[.SD, on = .(FID, IID)]
+    # Parallel computation using foreach
+    chunk_results <- foreach(col_name = chunk, .packages = c("data.table")) %dopar% {
+      # Retrieve models for the current score
+      mean_model <- ref_model[[col_name]]$mean_model
+      var_model <- ref_model[[col_name]]$var_model
+      
+      # Get corresponding rows from pcs
+      pc_data <- pcs[score, on = .(FID, IID)]
       
       # Predict mean and variance
       predicted_pgs <- predict(mean_model, newdata = pc_data)
       predicted_pgs_var <- exp(predict(var_model, newdata = pc_data))
       
       # Compute residuals
-      round((get(col_name) - predicted_pgs) / sqrt(predicted_pgs_var), 3)
-    }, .SDcols = c("FID", "IID", col_name)]
+      adjusted_score <- round((score[[col_name]] - predicted_pgs) / sqrt(predicted_pgs_var), 3)
+      
+      # Return results as a list (column name + values)
+      list(col_name = col_name, values = adjusted_score)
+    }
+    
+    # **Write results back to `score` immediately, to avoid holding large objects in memory**
+    for (res in chunk_results) {
+      set(score, j = res$col_name, value = res$values)
+    }
   }
   
+  # Reorder columns to match original order
+  setcolorder(score, original_order)
+  
   # Return only relevant columns (FID, IID, and adjusted scores)
   return(score)
 }

From 4c943242a8767c9043f95eb60cd8447d038345e3 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 7 Feb 2025 15:08:33 +0000
Subject: [PATCH 104/153] Avoided redundency in quickprs_multi

---
 Scripts/pgs_methods/quickprs_multi.R | 28 +++++++++-------------------
 pipeline/rules/pgs_methods.smk       |  3 +++
 2 files changed, 12 insertions(+), 19 deletions(-)

diff --git a/Scripts/pgs_methods/quickprs_multi.R b/Scripts/pgs_methods/quickprs_multi.R
index 870c141c..284a818e 100644
--- a/Scripts/pgs_methods/quickprs_multi.R
+++ b/Scripts/pgs_methods/quickprs_multi.R
@@ -69,9 +69,6 @@ if(is.null(opt$output)){
 if(is.null(opt$ldak)){
   stop('--ldak must be specified.\n')
 }
-if(is.null(opt$quickprs_ldref)){
-  stop('--quickprs_ldref must be specified.\n')
-}
 if(is.null(opt$quickprs_multi_ldref)){
   stop('--quickprs_multi_ldref must be specified.\n')
 }
@@ -105,6 +102,9 @@ log_add(log_file = log_file, message = paste0(length(sumstats), ' sets of GWAS h
 # Split opt$populations
 populations<-unlist(strsplit(opt$populations, ','))
 
+# Split opt$scores
+score_files<-unlist(strsplit(opt$scores, ','))
+
 #####
 # Format the sumstats
 #####
@@ -154,12 +154,8 @@ log_add(log_file = log_file, message = 'Running QuickPRS using full sumstats.')
 
 score_full <- list()
 for(i in 1:length(sumstats)){
-  score_full[[populations[i]]] <- quickprs(
-    sumstats = paste0(tmp_dir,'/GWAS_sumstats_temp', i, '.txt'),
-    quickprs_ldref = paste0(opt$quickprs_ldref, '/', populations[i]),
-    genomic_control = opt$genomic_control,
-    n_cores = opt$n_cores,
-    prs_model = opt$prs_model)
+  score_full[[populations[i]]] <- fread(paste0(score_files[i]))
+  names(score_full[[populations[i]]])[4] <- paste0('SCORE_targ_', populations[i])
 }
 
 #####
@@ -254,6 +250,7 @@ for(targ_pop in populations){
   }
 }
 
+
 # Average weights across repeats
 mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'), log_file = log_file)
 
@@ -265,18 +262,8 @@ log_add(log_file = log_file, message = 'Creating score file.')
 
 # Combine the scores from each population
 score_all <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE), score_full)
-names(score_all)[grepl('BETA', names(score_all))]<-paste0('SCORE_targ_', populations)
 score_all[is.na(score_all)]<-0
 
-# Read in reference SNP data
-ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
-
-# Change IDs to RSIDs
-ref$Predictor<-paste0(ref$CHR,':',ref$BP)
-names(score_all)[names(score_all) == 'SNP'] <- 'Predictor'
-score_all<-merge(score_all, ref[,c('Predictor','SNP'), with=F], by='Predictor')
-score_all<-score_all[, c('SNP', 'A1', 'A2', names(score_all)[grepl('SCORE_', names(score_all))]), with=F]
-
 # Flip effects to match reference alleles
 score_all <- map_score(ref = ref, score = score_all)
 
@@ -296,6 +283,9 @@ for(targ_pop in populations){
                               targ_pop = targ_pop)
 }
 
+# Only retain weighted score columns
+score_weighted <- score_weighted[, c('SNP','A1','A2', names(score_weighted)[grepl('_weighted$', names(score_weighted))]), with=F]
+
 # Reduce number of significant figures to save space
 score_weighted[, (4:ncol(score_all)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_all)]
 
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 87a17dfc..96d649dc 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -736,6 +736,7 @@ rule prep_pgs_quickprs_multi_i:
     time_min=2800
   threads: config['cores_prep_pgs']
   input:
+    lambda w: expand(f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group)),
     lambda w: expand(f"{quickprs_ldref}/{{population}}/{{population}}.cors.bin", population=[pop for pop in get_populations(w.gwas_group)]),
     lambda w: expand(f"{quickprs_multi_ldref}/{{population}}/{{population}}.subset_1.bed", population=[pop for pop in get_populations(w.gwas_group)]),
     lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
@@ -754,6 +755,7 @@ rule prep_pgs_quickprs_multi_i:
     "../envs/xwing.yaml"
   params:
     sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
+    scores= lambda w: ",".join(expand(f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group))),
     populations= lambda w: ",".join(get_populations(w.gwas_group)),
     testing=config["testing"]
   shell:
@@ -762,6 +764,7 @@ rule prep_pgs_quickprs_multi_i:
       --ref_freq_chr {refdir}/freq_files \
       --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {params.sumstats} \
+      --scores {params.scores} \
       --populations {params.populations} \
       --ldak {resdir}/software/ldak5.2/ldak5.2.linux \
       --quickprs_ldref {quickprs_ldref} \

From 47a896ae632689a8e82089ed9e238ec8f10ee2bc Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 7 Feb 2025 20:07:34 +0000
Subject: [PATCH 105/153] Bug fix in quickprs_multi

---
 Scripts/pgs_methods/quickprs_multi.R | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/Scripts/pgs_methods/quickprs_multi.R b/Scripts/pgs_methods/quickprs_multi.R
index 284a818e..ac1ad8b3 100644
--- a/Scripts/pgs_methods/quickprs_multi.R
+++ b/Scripts/pgs_methods/quickprs_multi.R
@@ -22,6 +22,8 @@ option_list = list(
               help="Memory limit [optional]"),
   make_option("--sumstats", action="store", default=NULL, type='character',
               help="Comma-seperated list of GWAS summary statistics [required]"),
+  make_option("--scores", action="store", default=NULL, type='character',
+              help="Comma-seperated list of score files [required]"),
   make_option("--populations", action="store", default=NULL, type='character',
               help="Comma-seperated list of population codes matching GWAS [required]"),
   make_option("--ldak", action="store", default=NA, type='character',
@@ -264,8 +266,8 @@ log_add(log_file = log_file, message = 'Creating score file.')
 score_all <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE), score_full)
 score_all[is.na(score_all)]<-0
 
-# Flip effects to match reference alleles
-score_all <- map_score(ref = ref, score = score_all)
+# Read in reference SNP data
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
 
 # Calculate linear combination of scores using mixing weights for each target population
 score_weighted <- score_all

From 59117e2000c46ca450d7ce715408e1f22fb1ef45 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sat, 8 Feb 2025 13:48:01 +0000
Subject: [PATCH 106/153] Updates

---
 docs/CrossPop_dnanexus.Rmd   | 184 +++++++++++++++++++++++++++++++++++
 docs/running_on_dnanexus.Rmd |   2 +-
 2 files changed, 185 insertions(+), 1 deletion(-)

diff --git a/docs/CrossPop_dnanexus.Rmd b/docs/CrossPop_dnanexus.Rmd
index 362af59f..228ffe90 100644
--- a/docs/CrossPop_dnanexus.Rmd
+++ b/docs/CrossPop_dnanexus.Rmd
@@ -107,6 +107,190 @@ snakemake -j 1 --use-conda --configfile=/home/dnanexus/ukb/configs/basic/config.
 
 I run into an issue where the mount breaks. I get error 'Transport endpoint is not connected.'.
 
+***
+
+# Make a reproducible example of mounting issue
+
+This will help the DNAnexus team fix it.
+
+```{bash}
+dx run cloud_workstation --instance-type mem2_hdd2_v2_x2 -imax_session_length=48h --allow-ssh
+```
+
+```{bash}
+# Start a tmux session
+tmux
+
+mkdir ~/software
+cd ~/software
+
+# Download PLINK2
+wget https://s3.amazonaws.com/plink2-assets/alpha6/plink2_linux_avx2_20250129.zip
+unzip plink2_linux_avx2_20250129.zip
+
+# Download and install dxfuse
+wget https://github.com/dnanexus/dxfuse/releases/download/v1.4.1/dxfuse-linux
+chmod a+x dxfuse-linux
+
+# Mount the UKB genetic data
+mkdir -p ~/projects
+~/software/dxfuse-linux -uid $(id -u) -gid $(id -g) -verbose 2 ~/projects project-Gx7VQQ8JbZj8yPqKFF1bFZxy
+
+# Convert UKB imputed genetic data to plink2 format
+mkdir -p ~/ukb/
+
+~/software/plink2 \
+  --bgen ~/projects/Oliver_Pain_Fellowship/Bulk/Imputation/UKB\ imputation\ from\ genotype/ukb22828_c22_b0_v3.bgen ref-last \
+  --sample ~/projects/Oliver_Pain_Fellowship/Bulk/Imputation/UKB\ imputation\ from\ genotype/ukb22828_c22_b0_v3.sample \
+  --make-pgen 'pvar-cols=' \
+  --threads 1 \
+  --out ~/ukb/ukb22828_c22_b0_v3
+
+# There is no error when running like this.
+# See whether there is an error when running within GenoPred.
+```
+
+```{bash}
+# Start a tmux session
+tmux
+
+mkdir ~/software
+cd ~/software
+
+# Download and install dxfuse
+wget https://github.com/dnanexus/dxfuse/releases/download/v1.4.1/dxfuse-linux
+chmod a+x dxfuse-linux
+
+# Mount the UKB genetic data
+mkdir -p ~/projects
+~/software/dxfuse-linux -uid $(id -u) -gid $(id -g) -verbose 2 ~/projects project-Gx7VQQ8JbZj8yPqKFF1bFZxy
+
+# Create symlinks to imputed UKB data
+mkdir -p ~/ukb/ukb_symlinks
+
+for chr in $(seq 1 22);do
+  for file in $(echo bgen bgen.bgi);do
+    ln -s ~/projects/Oliver_Pain_Fellowship/Bulk/Imputation/UKB\ imputation\ from\ genotype/ukb22828_c${chr}_b0_v3.${file} ~/ukb/ukb_symlinks/ukb_imp.chr${chr}.${file}
+  done
+done
+
+ln -s ~/projects/Oliver_Pain_Fellowship/Bulk/Imputation/UKB\ imputation\ from\ genotype/ukb22828_c22_b0_v3.sample ~/ukb/ukb_symlinks/ukb_imp.sample
+
+# Create the require configuration files. 
+mkdir -p ~/ukb/configs/basic
+mkdir -p ~/ukb/GenoPred/output
+```
+
+```{r}
+# Create target list
+target_list <- data.frame(
+  name='ukb',
+  path='/home/dnanexus/ukb/ukb_symlinks/ukb_imp',
+  type='bgen',
+  indiv_report=F
+)
+
+write.table(target_list, '/home/dnanexus/ukb/configs/basic/target_list.txt', col.names=T, row.names=F, quote=F)
+
+# Create config file
+conf <- c(
+  'outdir: /home/dnanexus/ukb/GenoPred/output',
+  'config_file: /home/dnanexus/ukb/configs/basic/config.yaml',
+  'resdir: /home/dnanexus/ukb/GenoPred/resources',
+  'target_list: /home/dnanexus/ukb/configs/basic/target_list.txt',
+  'testing: chr22'
+)
+
+write.table(conf, '/home/dnanexus/ukb/configs/basic/config.yaml', col.names = F, row.names = F, quote = F)
+```
+
+```{bash}
+cd
+
+# Download GenoPred docker
+docker \
+  pull \
+  opaino/genopred_pipeline:latest
+  
+# Start interactive session inside container
+docker run \
+  --user $(id -u):$(id -g) \
+  --rm \
+  -it \
+  -v /home/dnanexus:/home/dnanexus \
+  -v /tools/GenoPred/pipeline:/tmp \
+  opaino/genopred_pipeline:latest
+
+# Activate GenoPred environment
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+cd /tools/GenoPred/pipeline
+git config --global --add safe.directory /tools/GenoPred
+
+snakemake -j 1 --use-conda --configfile=/home/dnanexus/ukb/configs/basic/config.yaml ancestry_inference -n
+
+# I am struggling to get GenoPred to work inside the docker image due to write permissions in the .snakemake folders. Doing this might help     -v /tmp:/mnt \
+
+```
+
+```{bash}
+# Configure keyboard layout (optional step to avoid prompts)
+echo 'keyboard-configuration keyboard-configuration/layout select us' | sudo debconf-set-selections
+echo 'keyboard-configuration keyboard-configuration/variant select English (US)' | sudo debconf-set-selections
+
+# Install required dependencies (e.g., tmux, build tools, and libraries for Singularity)
+sudo DEBIAN_FRONTEND=noninteractive apt update && \
+sudo DEBIAN_FRONTEND=noninteractive apt install -y build-essential libglib2.0-dev libseccomp-dev pkg-config squashfs-tools cryptsetup golang tmux
+
+# Set the Singularity version to install
+export VERSION=3.11.0
+
+# Download and extract the Singularity source code
+wget https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-ce-${VERSION}.tar.gz
+tar -xvzf singularity-ce-${VERSION}.tar.gz
+cd singularity-ce-${VERSION}
+
+# Configure and build Singularity without SUID
+./mconfig --without-suid
+make -C builddir
+
+# Install Singularity system-wide
+sudo make -C builddir install
+
+# Return to the home directory
+cd ~
+```
+
+```{bash}
+# Connect to DNAnexus project   
+unset DX_WORKSPACE_ID
+dx cd $DX_PROJECT_CONTEXT_ID:
+
+# Download, update permissions and run script to install singularity
+dx download install_singularity.sh
+chmod a+x install_singularity.sh
+./install_singularitysh
+```
+
+```{bash}
+# Start interactive session inside singularity
+singularity shell \
+  --bind /home/dnanexus:/home/dnanexus \
+  --bind /tmp:/mnt \
+  --writable-tmpfs \
+  /home/dnanexus/projects/Oliver_Pain_Fellowship/genopred_pipeline_latest.sif
+  
+# Activate GenoPred environment
+source /opt/mambaforge/etc/profile.d/conda.sh
+conda activate genopred
+
+cd /tools/GenoPred/pipeline
+
+snakemake -j 1 --use-conda --configfile=/home/dnanexus/ukb/configs/basic/config.yaml ancestry_inference -n
+
+```
+***
 
 
 
diff --git a/docs/running_on_dnanexus.Rmd b/docs/running_on_dnanexus.Rmd
index 89d24ff5..d4c3f215 100644
--- a/docs/running_on_dnanexus.Rmd
+++ b/docs/running_on_dnanexus.Rmd
@@ -411,7 +411,7 @@ Once your RStudio session has started, install Singularity via the terminal. We
 # Download, update permissions and run script to install singularity
 dx download install_singularity.sh
 chmod a+x install_singularity.sh
-./install_singularity
+./install_singularitysh
 ```
 
 ***

From f5ea3b8b9695ca247842a323c42cb0c42b4e7082 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 12 Feb 2025 11:54:44 +0000
Subject: [PATCH 107/153] Updating quickprs to used adjusted LEOPARD weights

---
 Scripts/pgs_methods/quickprs_multi.R |  60 ++++-
 docs/CrossPop.Rmd                    | 368 ++++++++++++++++++++++++++-
 docs/CrossPop_dnanexus.Rmd           |  64 +----
 functions/pgs.R                      |   8 +
 pipeline/rules/pgs_methods.smk       |   3 +-
 5 files changed, 423 insertions(+), 80 deletions(-)

diff --git a/Scripts/pgs_methods/quickprs_multi.R b/Scripts/pgs_methods/quickprs_multi.R
index ac1ad8b3..ec0ac7fb 100644
--- a/Scripts/pgs_methods/quickprs_multi.R
+++ b/Scripts/pgs_methods/quickprs_multi.R
@@ -149,10 +149,10 @@ if(!is.na(opt$test)){
 }
 
 #####
-# Run QuickPRS using full sumstats
+# Read QuickPRS scores when using full sumstats
 #####
 
-log_add(log_file = log_file, message = 'Running QuickPRS using full sumstats.')
+log_add(log_file = log_file, message = 'Reading in QuickPRS scores when using full sumstats.')
 
 score_full <- list()
 for(i in 1:length(sumstats)){
@@ -252,10 +252,41 @@ for(targ_pop in populations){
   }
 }
 
-
 # Average weights across repeats
 mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'), log_file = log_file)
 
+####
+# Adjust weights to correspond to PGS with SD of 1
+####
+
+# Read in the scale files for the original score files
+scale_all <- NULL
+for(i in 1:length(score_files)){
+  scale_file <- gsub('.score.gz', '', score_files[i])
+  scale_file <- paste0(scale_file, '-', populations[i], '.scale')
+  scale_file <- fread(scale_file)
+  
+  scale_all <- rbind(
+    scale_all, 
+    data.table(
+      SD = scale_file$SD,
+      Discovery = populations[i]
+    )
+  )
+}
+
+# Adjust weights to correspond to PGS with SD of 1
+log_add(log_file = log_file, message = '------------------------')
+for(i in populations){
+  mix_weights[[i]] <- adjust_weights(weights = mix_weights[[i]], pgs_sd = scale_all$SD)
+  
+  log_add(log_file = log_file, message = paste0("Adjusted LEOPARD weights - ", i, " target: "))
+  for(j in populations){
+    log_add(log_file = log_file, message = paste0(j, ' = ', mix_weights[[i]][which(populations == j)]))
+  }
+  log_add(log_file = log_file, message = '------------------------')
+}
+
 ####
 # Combine score files
 ####
@@ -266,8 +297,12 @@ log_add(log_file = log_file, message = 'Creating score file.')
 score_all <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE), score_full)
 score_all[is.na(score_all)]<-0
 
-# Read in reference SNP data
+# Read in reference SNP and population data
 ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
+pop_data <- read_pop_data(opt$pop_data)
+
+# Subset PGS to SNPs in ref (useful when testing)
+score_all <- score_all[score_all$SNP %in% ref$SNP,]
 
 # Calculate linear combination of scores using mixing weights for each target population
 score_weighted <- score_all
@@ -278,6 +313,23 @@ for(targ_pop in populations){
   # Centre SNP-weights for target population
   score_i <- centre_weights(score = score_all, freq = freq_data, ref = ref)
   
+  ###
+  # Scale weights to give PGS SD of 1 in target population
+  ###
+  
+  # Calculate scores in reference, and scale weights accordingly
+  fwrite(score_i, paste0(tmp_dir,'/tmp.',targ_pop,'.score'), col.names=T, sep=' ', quote=F)
+  
+  # Calc score in target sample
+  ref_pgs <- plink_score(pfile = opt$ref_plink_chr, keep = pop_data[pop_data$POP == targ_pop, c('FID'), with=F], chr = CHROMS, plink2 = opt$plink2, score = paste0(tmp_dir,'/tmp.',targ_pop,'.score'), threads = opt$n_cores)
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs)
+  
+  # Rescale SNP-weights according to PGS SD in target
+  for(i in populations){
+    scaling_factor <- 1 / ref_pgs_scale_i$SD[ref_pgs_scale_i$Param == paste0('SCORE_targ_', i)]
+    score_i[[paste0('SCORE_targ_', i)]] <- score_i[[paste0('SCORE_targ_', i)]] * scaling_factor
+  }
+  
   # Linearly combine scores using mixing weights for target population
   score_weighted[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <-
     calculate_weighted_scores(score = score_i,
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index ec88bec8..69d35558 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1898,6 +1898,8 @@ for(trait_i in selected_traits){
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
+rm /users/k1806347/oliverpainfel/Analyses/crosspop/targ_*.disc_EUR*/*/res*
+
 for targ_pop in $(echo EAS AFR); do
   if [ "$targ_pop" == "EUR" ]; then
       targ_pop2="EUR_test"
@@ -2213,6 +2215,8 @@ mirror_comp<-function(x){
   x_sym <- x
   x_sym$Model_1 <- x$Model_2
   x_sym$Model_2 <- x$Model_1
+  x_sym$Model_1_R <- x$Model_2_R
+  x_sym$Model_2_R <- x$Model_1_R
   x_sym$R_diff <- -x_sym$R_diff
   x_mirrored <- rbind(x, x_sym)
   x_diag<-data.frame(
@@ -2493,13 +2497,13 @@ for(model_i in c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTr
 # Plot R compared to top1 model where GWAS matches target population
 ####
 
-meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Model2 == 'IndivTrain' & meta_res_comp$Method2 == 'ptclump' & meta_res_comp$Discovery2 == meta_res_comp$Target,]
+meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Model2 == 'IndivTrain' & meta_res_comp$Method2 == 'ptclump' & meta_res_comp$Discovery2 == 'EUR',]
 meta_res_comp_ptclump_top1$reference_point<-F
-meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Model1 == 'IndivTrain' & meta_res_comp_ptclump_top1$Method1 == 'ptclump' & meta_res_comp_ptclump_top1$Discovery1 == meta_res_comp_ptclump_top1$Target]<-T
+meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Model1 == 'IndivTrain' & meta_res_comp_ptclump_top1$Method1 == 'ptclump' & meta_res_comp_ptclump_top1$Discovery1 == 'EUR']<-T
 meta_res_comp_ptclump_top1$R_diff[is.na(meta_res_comp_ptclump_top1$R_diff)]<-0
 meta_res_comp_ptclump_top1$Discovery1 <- factor(meta_res_comp_ptclump_top1$Discovery1, levels=rev(levels(meta_res_comp_ptclump_top1$Discovery1)))
 
-res_comp_all_ptclump_top1<-res_comp_all[res_comp_all$Model2 == 'IndivTrain' & res_comp_all$Method2 == 'ptclump' & res_comp_all$Discovery2 == res_comp_all$Target,]
+res_comp_all_ptclump_top1<-res_comp_all[res_comp_all$Model2 == 'IndivTrain' & res_comp_all$Method2 == 'ptclump' & res_comp_all$Discovery2 == 'EUR',]
 res_comp_all_ptclump_top1$Discovery1 <-  factor(res_comp_all_ptclump_top1$Discovery1, levels=levels(meta_res_comp_ptclump_top1$Discovery1))
 
 # Create data to plot reference points
@@ -2610,12 +2614,12 @@ tmp$R_diff_perc <- tmp$R_diff / tmp$Model_2_R
 meta_tmp$R_diff_perc_SE <- meta_tmp$R_diff_SE / meta_tmp$Model_2_R
 
 ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
-#    geom_point(
-#        data = tmp,
-#        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
-#        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
-#        alpha = 0.3
-#      ) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
     geom_hline(yintercept = 0) +
       geom_errorbar(
         aes(
@@ -2650,11 +2654,311 @@ ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
     panel_border() + 
     theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
 
+ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype="dotted") +
+    labs(y = "R diff. (SE)") +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+# Simplify results showing results only with or without training data
+meta_tmp_simple <- meta_tmp
+meta_tmp_simple$Model1<-as.character(meta_tmp_simple$Model1)
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 == 'Multi-SumStatTrain'] <- 'IndivTrain of SumStatTrain'
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 == 'Multi-IndivTrain'] <- 'IndivTrain'
+meta_tmp_ref_simple$Model1<-factor(meta_tmp_simple$Model1, levels = c('IndivTrain', 'SumStatTrain', 'IndivTrain of SumStatTrain'))
+meta_tmp_simple$Model2<-as.character(meta_tmp_simple$Model2)
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 == 'Multi-SumStatTrain'] <- 'IndivTrain of SumStatTrain'
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 == 'Multi-IndivTrain'] <- 'IndivTrain'
+meta_tmp_simple$Model2<-factor(meta_tmp_simple$Model2, levels = c('IndivTrain', 'SumStatTrain', 'IndivTrain of SumStatTrain'))
+meta_tmp_simple <- meta_tmp_simple[!duplicated(meta_tmp_simple[, c('label','Target','Discovery_clean','Model1','Model2'), with=F]),]
+
+meta_tmp_ref_simple <- meta_tmp_ref
+meta_tmp_ref_simple$Model1<-as.character(meta_tmp_ref_simple$Model1)
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == 'Multi-SumStatTrain'] <- 'IndivTrain of SumStatTrain'
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == 'Multi-IndivTrain'] <- 'IndivTrain'
+meta_tmp_ref_simple$Model1<-factor(meta_tmp_ref_simple$Model1, levels = c('IndivTrain', 'SumStatTrain', 'IndivTrain of SumStatTrain'))
+meta_tmp_ref_simple$Model2<-as.character(meta_tmp_ref_simple$Model2)
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == 'Multi-SumStatTrain'] <- 'IndivTrain of SumStatTrain'
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == 'Multi-IndivTrain'] <- 'IndivTrain'
+meta_tmp_ref_simple$Model2<-factor(meta_tmp_ref_simple$Model2, levels = c('IndivTrain', 'SumStatTrain', 'IndivTrain of SumStatTrain'))
+
+meta_tmp_ref_simple <- meta_tmp_ref_simple[!duplicated(meta_tmp_ref_simple[, c('label','Target','Discovery_clean','Model1','Model2'), with=F]),]
+
+# Export plot for poster presentation
+png('poster_plot1.png', width = 54, height = 36, res= 300, units = 'cm')
+ggplot(meta_tmp_simple, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        size = 1,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 7,
+      stroke = 1,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple,
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 7,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1,
+        show.legend=F
+    ) +
+   scale_fill_manual(values = c(
+        "IndivTrain" = "#F8766D",   # Red
+        "SumStatTrain" = "#00BFC4", # Green
+        "IndivTrain of SumStatTrain" = "#CCFFFF" # Purple
+    )) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype="dotted", size = 1) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(size = 24, angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        axis.text.y = element_text(size = 24),  # Increase y-axis labels
+        axis.title = element_text(size = 24),  # Increase axis titles
+        strip.text = element_text(size = 24),  # Increase facet labels
+        legend.text = element_text(size = 24, hjust = 0.5),  # Increase legend text
+        legend.position = "top",
+        legend.key.spacing.x = unit(5, "cm"),
+        legend.justification = "center"
+    )
+dev.off()
+
 ```
 </details>
 
 ***
 
+### Check LEOPARD weights
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Read in gwas_groups
+gwas_groups<-read_param(config = config, param = 'gwas_groups', return_obj = T)
+
+# Read in gwas_list
+gwas_list<-read_param(config = config, param = 'gwas_list', return_obj = T)
+
+###
+# Read in weights estimated by LEOPARD
+###
+# Read in raw weights from QuickPRS, and standardise them according to the scale of the population specific PGS
+
+methods<-c('quickprs_multi')
+leopard_weights<-NULL
+for(method_i in methods){
+  scores_method<-scores$name[scores$method == method_i]
+  for(i in selected_traits){
+    scores_i <- scores_method[grepl(paste0('^', i,'_'), scores_method)]
+    for(j in scores_i){
+        log <- readLines(paste0(outdir, '/reference/pgs_score_files/', method_i,'/', j, '/ref-', j, '.log'))
+        populations <- unlist(strsplit(gsub('populations| ','', log[grepl('^ populations', log)]), ','))
+        
+        weight_row <- which(grepl(paste0('LEOPARD weights - ', populations[populations != 'EUR'],' target'), log))
+        weight_row <- weight_row + 1:2
+        log<-log[weight_row]
+        log <- as.numeric(gsub('.* ', '', log))
+        
+        contributing_gwas <- unlist(strsplit(gwas_groups$gwas[gwas_groups$name == j], ','))
+        
+        scale_all <- NULL
+        for(k in contributing_gwas){
+          scale<-fread(paste0(outdir, '/reference/pgs_score_files/quickprs/', k, '/ref-', k, '-', populations[populations != 'EUR'], '.scale'))
+          scale_all <- rbind(
+            scale_all, 
+            data.table(
+              SD = scale$SD,
+              Discovery = gwas_list$population[gwas_list$name == k]
+            )
+          )
+        }
+        
+        weights <-
+          data.table(
+            Target =  populations[populations != 'EUR'],
+            Discovery = populations,
+            Weight = round(log, 3),
+            Trait = i,
+            Method = method_i
+          )
+        
+        weights <- merge(weights, scale_all, by = 'Discovery')
+        
+        # Adjust weights to standardize for PGS SD = 1
+        adjust_weights <- function(weights, pgs_sd) {
+          adjusted_weights <- weights * pgs_sd
+          # Normalize weights so they sum to 1
+          normalized_weights <- adjusted_weights * (1 / sum(adjusted_weights))
+          return(normalized_weights)
+        }
+        
+        weights$Adjusted_Weight <- adjust_weights(
+          weights$Weight,
+          weights$SD)
+
+        leopard_weights <- rbind(leopard_weights, weights)
+    }
+  }
+
+}
+
+
+obs_weights<-NULL
+for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_methods)]){
+  
+  scores_method<-scores$name[scores$method == method_i]
+  ###
+  # Read in weights when training in UKB
+  ###
+  
+  for(i in selected_traits){
+    for(j in c('EAS','AFR')){
+      model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/targ_', j, '.disc_EUR_', j, '/', i, '/final_models/', method_i, '.pseudo.multi.final_model.txt'))
+      model<-model[-1,]
+      names(model) <- c('x', 'BETA')
+      model$Discovery[grepl('UKB', model$x)]<-'EUR'
+      model$Discovery[grepl('BBJ', model$x)]<-'EAS'
+      model$Discovery[grepl('UGR', model$x)]<-'AFR'
+      model$Target <- j
+      model$Weight <- model$BETA/sum(model$BETA)
+      model$Trait <- i
+      model$Method <- method_i
+      model<-model[,c('Target','Discovery','Weight','Method','Trait'), with=F]
+      obs_weights<-rbind(obs_weights, model)
+    }
+  }
+}
+
+ggplot(obs_weights[obs_weights$Target == obs_weights$Discovery,], aes(x = Trait, y = Weight, fill = Method)) +
+  geom_bar(position="dodge", stat="identity") +
+  facet_grid(. ~ Target)
+
+
+###
+# Combine and compare
+###
+
+leopard_weights$SD <- NULL
+leopard_weights_adjusted <- leopard_weights
+leopard_weights_adjusted$Weight <- leopard_weights_adjusted$Adjusted_Weight
+leopard_weights$Adjusted_Weight <- NULL
+leopard_weights_adjusted$Adjusted_Weight <- NULL
+
+leopard_weights$Method <- 'LEOPARD'
+leopard_weights_adjusted$Method <- 'LEOPARD adjusted'
+
+both <- do.call(rbind, list(obs_weights, leopard_weights, leopard_weights_adjusted))
+both <- both[both$Discovery != 'EUR',]
+both$Target <- paste0('Target = ', both$Target)
+
+ggplot(both, aes(x = Trait, y = Weight, fill = Method)) +
+  geom_bar(position="dodge", stat="identity") +
+  facet_grid(. ~ Target)
+
+ggplot(both[both$Method %in% c('LEOPARD','LEOPARD adjusted','sbayesrc'),], aes(x = Trait, y = Weight, fill = Method)) +
+  geom_bar(position="dodge", stat="identity") +
+  facet_grid(. ~ Target)
+
+both$Discovery<-NULL
+both_wide <- reshape(both, 
+                     idvar = c("Trait", "Target"), 
+                     timevar = "Method", 
+                     direction = "wide")
+
+cor(both_wide[, -1:-2])
+
+obvs_vs_leopard <- merge(obs_weights, leopard_weights_adjusted, by=c('Target','Discovery','Trait'))
+
+ggplot(obvs_vs_leopard, aes(x=Weight.x, y=Weight.y, colour = Method.x)) +
+  geom_point() +
+  geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "black")
+
+mean_abs_diff <- aggregate(abs(obvs_vs_leopard$Weight.x - obvs_vs_leopard$Weight.y),
+                           by = list(Method = obvs_vs_leopard$Method.x, Population = obvs_vs_leopard$Target),
+                           FUN = mean)
+
+# Rename columns
+colnames(mean_abs_diff) <- c("Method", "Mean_Absolute_Difference")
+
+# It looks like the observed weights are highly correlated across methods, but the LEOPARD weights are not. The LEOPARD weights seem more similar to observed for QuickPRS than LDpred2.
+
+# The correlation and absolute difference between leopard and observed weights is actually quite similar between methods. LEOPARD is slightly better for QuickPRS, but I am not sure it explains the difference in pseudo PGS. I think it is likley due to the difference in PGS variance when applying the LEOPARD weights.
+
+# The LEOPARD weights seem less accurate when the PGS are scaled, but these weights could be used for any PGS method. Lets see how well this works for SBayesRC.I am running quickprs_multi again, without scaling using LEOPARD, but still scaling the quickprs weights. This will indicate whether it is the scaling of the weights or scaling within LEOPARD that is making it worse. 
+
+# Scaling within LEOPARD makes the weights worse for the EAS target, but it is scaling the PGS before applying weights that makes the PGS worse for AFR. This kind of makes sense as the scale of the EAS and EUR PGS are typically similar. 
+
+```
+
+***
+
 ## Evaluate TLPRS
 ### Create predictor lists
 
@@ -3536,31 +3840,37 @@ bm_dat_all$rule <- gsub('-.*','',bm_dat_all$file)
 bm_dat_all$method <-
   gsub('_i', '', gsub('prep_pgs_', '', bm_dat_all$rule))
 
+bm_dat_all <- merge(bm_dat_all, pgs_method_labels, by = 'method')
+
 # Calculate average time taken for each method
 method_avg <- NULL
-for(i in unique(bm_dat_all$method)){
+for(i in unique(bm_dat_all$label)){
   method_avg <- rbind(
     method_avg,
     data.frame(
+      method = pgs_method_labels$method[pgs_method_labels$label == i],
       Method = i,
-      Time = mean(bm_dat_all$s[bm_dat_all$method == i])
+      Time = mean(bm_dat_all$s[bm_dat_all$label == i])
     )
   )
 }
 
+# Times X-Wing time by two since it used 20 cores, but other methods used 10
+method_avg$Time[method_avg$method == 'xwing'] <- method_avg$Time[method_avg$method == 'xwing'] * 2
+
 # Calculate average max_rss for each method
 method_avg_mem <- NULL
-for(i in unique(bm_dat_all$method)){
+for(i in unique(bm_dat_all$label)){
   method_avg_mem <- rbind(
     method_avg_mem,
     data.frame(
+      method = pgs_method_labels$method[pgs_method_labels$label == i],
       Method = i,
-      Memory = mean(bm_dat_all$max_rss[bm_dat_all$method == i])
+      Memory = mean(bm_dat_all$max_rss[bm_dat_all$label == i])
     )
   )
 }
 
-
 # Format the time taken nicely
 method_avg$Time_clean[method_avg$Time < 60] <-
   paste0(round(method_avg$Time[method_avg$Time < 60], 1), ' sec')
@@ -3569,6 +3879,36 @@ method_avg$Time_clean[method_avg$Time > 60] <-
 method_avg$Time_clean[method_avg$Time > 3600] <-
   paste0(round(method_avg$Time[method_avg$Time > 3600] / 60 / 60, 1), ' hr')
 
+# Convert time in seconds to hours
+method_avg$Time_hour <- method_avg$Time / 60/60
+
+# Seperate methods by single or multi source
+method_avg$Type[!(method_avg$method %in% pgs_group_methods)]<-'Single-source'
+method_avg$Type[method_avg$method %in% pgs_group_methods]<-'Multi-source'
+
+method_avg$Type<-factor(method_avg$Type, levels = c('Single-source','Multi-source'))
+method_avg$Method <- factor(method_avg$Method, levels = c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "QuickPRS-Multi", "PRS-CSx", "X-Wing"))
+
+
+
+time <- ggplot(method_avg, aes(x = Method, y = Time_hour, fill = Method)) +
+  geom_bar(stat = "identity", position="dodge") +
+  geom_text(aes(label = Time_clean), vjust = 0.5, angle = 90, hjust = -0.2, position = position_dodge(width = 0.9), size = 7) +
+  labs(x = NULL, y = "Time (hours)") +
+  ylim(0, max(method_avg$Time_hour) + (max(method_avg$Time_hour)/5)) +
+  facet_grid(~ Type, scales='free', space = 'free_x') +
+  theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() + 
+  theme(axis.text.x = element_text(size = 24, angle = 45, hjust = 1), legend.position="none",
+        axis.text.y = element_text(size = 24),  # Increase y-axis labels
+        axis.title = element_text(size = 24),  # Increase axis titles
+        strip.text = element_text(size = 24))
+
+png('poster_plot2.png', width = 22, height = 31.5, res= 300, units = 'cm')
+  time
+dev.off()
+
 ggplot(method_avg, aes(x = Method, y = Time, fill = Method)) +
   geom_bar(stat = "identity", position="dodge") +
   geom_text(aes(label = Time_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
diff --git a/docs/CrossPop_dnanexus.Rmd b/docs/CrossPop_dnanexus.Rmd
index 228ffe90..538e936f 100644
--- a/docs/CrossPop_dnanexus.Rmd
+++ b/docs/CrossPop_dnanexus.Rmd
@@ -204,76 +204,17 @@ conf <- c(
 write.table(conf, '/home/dnanexus/ukb/configs/basic/config.yaml', col.names = F, row.names = F, quote = F)
 ```
 
-```{bash}
-cd
-
-# Download GenoPred docker
-docker \
-  pull \
-  opaino/genopred_pipeline:latest
-  
-# Start interactive session inside container
-docker run \
-  --user $(id -u):$(id -g) \
-  --rm \
-  -it \
-  -v /home/dnanexus:/home/dnanexus \
-  -v /tools/GenoPred/pipeline:/tmp \
-  opaino/genopred_pipeline:latest
-
-# Activate GenoPred environment
-source /opt/mambaforge/etc/profile.d/conda.sh
-conda activate genopred
-
-cd /tools/GenoPred/pipeline
-git config --global --add safe.directory /tools/GenoPred
-
-snakemake -j 1 --use-conda --configfile=/home/dnanexus/ukb/configs/basic/config.yaml ancestry_inference -n
-
-# I am struggling to get GenoPred to work inside the docker image due to write permissions in the .snakemake folders. Doing this might help     -v /tmp:/mnt \
-
-```
-
-```{bash}
-# Configure keyboard layout (optional step to avoid prompts)
-echo 'keyboard-configuration keyboard-configuration/layout select us' | sudo debconf-set-selections
-echo 'keyboard-configuration keyboard-configuration/variant select English (US)' | sudo debconf-set-selections
-
-# Install required dependencies (e.g., tmux, build tools, and libraries for Singularity)
-sudo DEBIAN_FRONTEND=noninteractive apt update && \
-sudo DEBIAN_FRONTEND=noninteractive apt install -y build-essential libglib2.0-dev libseccomp-dev pkg-config squashfs-tools cryptsetup golang tmux
-
-# Set the Singularity version to install
-export VERSION=3.11.0
-
-# Download and extract the Singularity source code
-wget https://github.com/sylabs/singularity/releases/download/v${VERSION}/singularity-ce-${VERSION}.tar.gz
-tar -xvzf singularity-ce-${VERSION}.tar.gz
-cd singularity-ce-${VERSION}
-
-# Configure and build Singularity without SUID
-./mconfig --without-suid
-make -C builddir
-
-# Install Singularity system-wide
-sudo make -C builddir install
-
-# Return to the home directory
-cd ~
-```
-
 ```{bash}
 # Connect to DNAnexus project   
 unset DX_WORKSPACE_ID
 dx cd $DX_PROJECT_CONTEXT_ID:
 
 # Download, update permissions and run script to install singularity
+# Note. I was not able to run snakemake within docker on the workstation for some reason due to write restrictions within the docker environment.
 dx download install_singularity.sh
 chmod a+x install_singularity.sh
 ./install_singularitysh
-```
 
-```{bash}
 # Start interactive session inside singularity
 singularity shell \
   --bind /home/dnanexus:/home/dnanexus \
@@ -289,7 +230,10 @@ cd /tools/GenoPred/pipeline
 
 snakemake -j 1 --use-conda --configfile=/home/dnanexus/ukb/configs/basic/config.yaml ancestry_inference -n
 
+# This worked with no error. This is odd as it failed before. Maybe it is because I am using the container? Or maybe it is because I updated the dxfuse version.
 ```
+
+
 ***
 
 
diff --git a/functions/pgs.R b/functions/pgs.R
index 8f4added..a49120ec 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -680,4 +680,12 @@ calculate_weighted_scores <- function(score, targ_pop, mix_weights) {
   score_combined <- rowSums(score_weighted[, grepl('SCORE_', names(score_weighted)), with = FALSE])
   
   return(score_combined)
+}
+
+# Adjust weights to correspond to PGS with SD of 1
+adjust_weights <- function(weights, pgs_sd) {
+  adjusted_weights <- weights * pgs_sd
+  # Normalize weights so they sum to 1
+  normalized_weights <- adjusted_weights * (1 / sum(adjusted_weights))
+  return(normalized_weights)
 }
\ No newline at end of file
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 96d649dc..2580b8c8 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -732,8 +732,7 @@ rule prep_pgs_tlprs:
 
 rule prep_pgs_quickprs_multi_i:
   resources:
-    mem_mb=20000,
-    time_min=2800
+    mem_mb=10000
   threads: config['cores_prep_pgs']
   input:
     lambda w: expand(f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group)),

From e136b2ccef378fedb4d3522ac961bd68c431c725 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 12 Feb 2025 15:33:21 +0000
Subject: [PATCH 108/153] Updating integration of LEOPARD

---
 Scripts/pgs_methods/apply_leopard_weights.R | 207 ++++++++++++++++
 Scripts/pgs_methods/leopard_quickprs.R      | 260 ++++++++++++++++++++
 docs/CrossPop.Rmd                           | 204 ++++++++-------
 pipeline/config.yaml                        |   5 +-
 pipeline/rules/pgs_methods.smk              | 145 +++++++----
 5 files changed, 664 insertions(+), 157 deletions(-)
 create mode 100644 Scripts/pgs_methods/apply_leopard_weights.R
 create mode 100644 Scripts/pgs_methods/leopard_quickprs.R

diff --git a/Scripts/pgs_methods/apply_leopard_weights.R b/Scripts/pgs_methods/apply_leopard_weights.R
new file mode 100644
index 00000000..2d94595f
--- /dev/null
+++ b/Scripts/pgs_methods/apply_leopard_weights.R
@@ -0,0 +1,207 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+suppressMessages(library("optparse"))
+
+option_list = list(
+  make_option("--config", action="store", default=NA, type='character',
+              help="Path to config file [required]"),
+  make_option("--method", action="store", default=NA, type='character',
+              help="PGS method [required]"),
+  make_option("--gwas_group", action="store", default=NA, type='character',
+              help="GWAS group [required]"),
+  make_option("--test", action="store", default=NA, type='character',
+              help="Specify number of SNPs to include [optional]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Check required inputs
+if(is.null(opt$config)){
+  stop('--config must be specified.\n')
+}
+if(is.null(opt$method)){
+  stop('--method must be specified.\n')
+}
+if(is.null(opt$gwas_group)){
+  stop('--gwas_group must be specified.\n')
+}
+
+# Identify outdir from config file
+outdir <- read_param(config = opt$config, 'outdir', return_obj = F)
+system(paste0('mkdir -p ', outdir, '/reference/pgs_score_files/', opt$method,'_multi/', opt$gwas_group))
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+# Set output prefix
+opt$output<-paste0(outdir, '/reference/pgs_score_files/', opt$method,'_multi/', opt$gwas_group,'/ref-', opt$gwas_group)
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'apply_leopard_weights.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+# Read in gwas_groups
+gwas_groups <- read_param(config = opt$config, param = 'gwas_groups')
+gwas_groups <- gwas_groups[gwas_groups$name == opt$gwas_group,]
+
+# Read in gwas_list
+gwas_list <- read_param(config = opt$config, param = 'gwas_list')
+gwas_list <- gwas_list[gwas_list$name %in% unlist(strsplit(gwas_groups$gwas, ',')),]
+
+# Split opt$scores
+score_files<-paste0(outdir, '/reference/pgs_score_files/', opt$method, '/', gwas_list$name, '/ref-', gwas_list$name, '.score.gz')
+
+#####
+# Read in score files and subset pseudo score
+#####
+
+log_add(log_file = log_file, message = 'Reading in QuickPRS scores when using full sumstats.')
+
+score_full <- list()
+for(i in 1:nrow(gwas_list)){
+  param <- find_pseudo(
+    config = opt$config,
+    gwas = gwas_list$name[i],
+    pgs_method = opt$method,
+    target_pop = gwas_list$population[i]
+  )
+  
+  score_header <-
+    fread(score_files[i], nrows = 1)
+  score_cols <-
+    which(names(score_header) %in% c('SNP','A1','A2', paste0('SCORE_', param)))
+  
+  score_full[[populations[i]]] <- fread(cmd = 
+    paste0(
+      "cut -d' ' -f ", 
+      paste0(score_cols, collapse=','),
+      " ", 
+      score_files[i])
+    )
+  
+  names(score_full[[populations[i]]])[4] <- paste0('SCORE_targ_', populations[i])
+}
+
+####
+# Read in the mixing weights
+####
+
+mix_weights <- readRDS(paste0(outdir, '/reference/pgs_score_files/leopard/', opt$gwas_group, '/ref-', opt$gwas_group, '.weights.rds'))
+
+####
+# Combine score files
+####
+
+log_add(log_file = log_file, message = 'Creating score file.')
+
+# Combine the scores from each population
+score_all <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE), score_full)
+score_all[is.na(score_all)]<-0
+
+# Read in reference SNP and population data
+refdir <- read_param(config = opt$config, param = 'refdir', return_obj = F)
+opt$ref_plink_chr <- paste0(refdir, '/ref.chr')
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
+opt$pop_data <- paste0(refdir, '/ref.pop.txt ')
+pop_data <- read_pop_data(opt$pop_data)
+opt$ref_freq_chr <- paste0(refdir, '/freq_files ')
+
+# Subset PGS to SNPs in ref (useful when testing)
+score_all <- score_all[score_all$SNP %in% ref$SNP,]
+
+# Calculate linear combination of scores using mixing weights for each target population
+score_weighted <- score_all
+for(targ_pop in populations){
+  # Read in the .freq file for target population
+  freq_data <- read_frq(freq_dir = opt$ref_freq_chr, population = targ_pop, chr = CHROMS)
+  
+  # Centre SNP-weights for target population
+  score_i <- centre_weights(score = score_all, freq = freq_data, ref = ref)
+  
+  ###
+  # Scale weights to give PGS SD of 1 in target population
+  ###
+  
+  # Calculate scores in reference, and scale weights accordingly
+  fwrite(score_i, paste0(tmp_dir,'/tmp.',targ_pop,'.score'), col.names=T, sep=' ', quote=F)
+  
+  # Calc score in target sample
+  ref_pgs <- plink_score(pfile = opt$ref_plink_chr, keep = pop_data[pop_data$POP == targ_pop, c('FID'), with=F], chr = CHROMS, score = paste0(tmp_dir,'/tmp.',targ_pop,'.score'))
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs)
+  
+  # Rescale SNP-weights according to PGS SD in target
+  for(i in populations){
+    scaling_factor <- 1 / ref_pgs_scale_i$SD[ref_pgs_scale_i$Param == paste0('SCORE_targ_', i)]
+    score_i[[paste0('SCORE_targ_', i)]] <- score_i[[paste0('SCORE_targ_', i)]] * scaling_factor
+  }
+  
+  # Linearly combine scores using mixing weights for target population
+  score_weighted[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <-
+    calculate_weighted_scores(score = score_i,
+                              mix_weights = mix_weights,
+                              targ_pop = targ_pop)
+}
+
+# Only retain weighted score columns
+score_weighted <- score_weighted[, c('SNP','A1','A2', names(score_weighted)[grepl('_weighted$', names(score_weighted))]), with=F]
+
+# Reduce number of significant figures to save space
+score_weighted[, (4:ncol(score_all)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_all)]
+
+fwrite(score_weighted, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+
+if(file.exists(paste0(opt$output,'.score.gz'))){
+  system(paste0('rm ',opt$output,'.score.gz'))
+}
+
+system(paste0('gzip ',opt$output,'.score'))
+
+# Record end time of test
+if(!is.na(opt$test)){
+  test_finish(log_file = log_file, test_start.time = test_start.time)
+}
+
+####
+# Calculate mean and sd of polygenic scores
+####
+
+log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
+
+# Calculate scores in the full reference
+ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
+
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
+# Calculate scale within each reference population
+pop_data <- read_pop_data(opt$pop_data)
+
+for(pop_i in unique(pop_data$POP)){
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
diff --git a/Scripts/pgs_methods/leopard_quickprs.R b/Scripts/pgs_methods/leopard_quickprs.R
new file mode 100644
index 00000000..8925fd1d
--- /dev/null
+++ b/Scripts/pgs_methods/leopard_quickprs.R
@@ -0,0 +1,260 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+suppressMessages(library("optparse"))
+
+option_list = list(
+  make_option("--output", action="store", default='NA', type='character',
+              help="Path for output files [required]"),
+  make_option("--sumstats", action="store", default=NULL, type='character',
+              help="Comma-seperated list of GWAS summary statistics [required]"),
+  make_option("--scores", action="store", default=NULL, type='character',
+              help="Comma-seperated list of score files [required]"),
+  make_option("--populations", action="store", default=NULL, type='character',
+              help="Comma-seperated list of population codes matching GWAS [required]"),
+  make_option("--ldak", action="store", default=NA, type='character',
+              help="Path to ldak v5.2 executable [required]"),
+  make_option("--quickprs_ldref", action="store", default=NA, type='character',
+              help="Path to folder containing ldak quickprs reference [required]"),
+  make_option("--quickprs_multi_ldref", action="store", default=NA, type='character',
+              help="Path to folder containing ldak quickprs_multi reference [required]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+              help="Number of cores for parallel computing [optional]"),
+  make_option("--prs_model", action="store", default='bayesr', type='character',
+              help="Model used for deriving SNP-weights [optional]"),
+  make_option("--genomic_control", action="store", default=F, type='logical',
+              help="Logical indicating whether genomic control was applied to GWAS [optional]"),
+  make_option("--xwing_repo", action="store", default=NULL, type='character',
+              help="Path to X-WING repo [required]"),
+  make_option("--test", action="store", default=NA, type='character',
+              help="Specify number of SNPs to include [optional]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Check required inputs
+if(is.null(opt$sumstats)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$scores)){
+  stop('--scores must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--output must be specified.\n')
+}
+if(is.null(opt$ldak)){
+  stop('--ldak must be specified.\n')
+}
+if(is.null(opt$quickprs_multi_ldref)){
+  stop('--quickprs_multi_ldref must be specified.\n')
+}
+if(is.null(opt$populations)){
+  stop('--populations must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
+system(paste0('mkdir -p ',opt$output_dir))
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'leopard_quickprs', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+# Split opt$sumstats
+sumstats<-unlist(strsplit(opt$sumstats, ','))
+log_add(log_file = log_file, message = paste0(length(sumstats), ' sets of GWAS have been provided.'))
+
+# Split opt$populations
+populations<-unlist(strsplit(opt$populations, ','))
+
+# Split opt$scores
+score_files<-unlist(strsplit(opt$scores, ','))
+
+#####
+# Format the sumstats
+#####
+
+gwas_N<-NULL
+for(i in 1:length(sumstats)){
+  log_add(log_file = log_file, message = paste0('Reading in GWAS ', i))
+
+  # Read in, check and format GWAS summary statistics
+  gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('CHR','BP','SNP','A1','A2','BETA','SE','N','P'))
+
+  # Format for LDAK
+  snplist <- gwas$SNP
+  gwas$Z <- gwas$BETA / gwas$SE
+  gwas$Predictor<-paste0(gwas$CHR, ':', gwas$BP)
+  gwas<-gwas[,c('Predictor','A1','A2','N','Z','CHR','BP','Predictor','BETA','P')]
+  names(gwas)<-c('Predictor','A1','A2','n','Z','CHR','BP','SNP','BETA','P')
+  gwas_N <- c(gwas_N, round(mean(gwas$n), 0))
+
+  # Check overlap between GWAS and LDAK reference
+  quickprs_ldref_pop_i <- paste0(opt$quickprs_ldref, '/', populations[i])
+  ldak_hm3_file <- list.files(quickprs_ldref_pop_i)
+  ldak_hm3_file <- ldak_hm3_file[grepl('.cors.bim', ldak_hm3_file)][1]
+  ldak_hm3 <- fread(paste0(quickprs_ldref_pop_i, '/', ldak_hm3_file))
+  ldak_hm3 <- ldak_hm3[ldak_hm3$V1 %in% CHROMS,]
+  ref_overlap <- sum(gwas$Predictor %in% ldak_hm3$V2) / nrow(ldak_hm3)
+
+  log_add(log_file = log_file, message = paste0('GWAS ', i,'-reference overlap is ', round(ref_overlap * 100, 2), '%.'))
+
+  # Subset GWAS to LDAK reference data
+  gwas <- gwas[gwas$Predictor %in% ldak_hm3$V2, ]
+
+  # Output formatted sumstats
+  fwrite(gwas, paste0(tmp_dir,'/GWAS_sumstats_temp', i, '.txt'), sep=' ')
+}
+
+#####
+# Subsample GWAS sumstats
+#####
+
+log_add(log_file = log_file, message = 'Subsampling sumstats.')
+
+dir.create(paste0(tmp_dir,'/LEOPARD/sampled_sumstats'), recursive = T)
+
+for(i in 1:length(sumstats)){
+  quickprs_multi_ldref_pop_i <- paste0(opt$quickprs_multi_ldref, '/', populations[i])
+  ref_files <- list.files(quickprs_multi_ldref_pop_i)
+  ref_files <- gsub('.bed', '', ref_files[grepl('subset_1.bed', ref_files)])
+  system(
+    paste0(
+      'Rscript ',
+      opt$xwing_repo,
+      '/LEOPARD_Sim.R ',
+      '--sumstats ', tmp_dir, '/GWAS_sumstats_temp', i, '.txt ',
+      '--n_gwas ', gwas_N[i], ' ',
+      '--train_prop 0.75 ',
+      '--ref_prefix ', quickprs_multi_ldref_pop_i, '/', ref_files, ' ',
+      '--seed 1 ',
+      '--rep 4 ',
+      '--out_prefix ', tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i
+    )
+  )
+}
+
+#####
+# Run QuickPRS using subsampled sumstats
+#####
+
+log_add(log_file = log_file, message = 'Running QuickPRS on subsampled sumstats.')
+
+score_subset <- list()
+for(i in 1:length(sumstats)){
+  dir.create(paste0(tmp_dir, '/LEOPARD/post_targ_', populations[i]))
+  score_subset[[populations[i]]] <- list()
+  for(j in 1:4){
+    sumstats_tmp <- fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.txt'))
+    n_tmp <- fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train_valid_N.txt'))
+    sumstats_tmp$n <- n_tmp$N_train
+    sumstats_tmp$Direction <-sign(sumstats_tmp$BETA)
+    sumstats_tmp <- sumstats_tmp[, c('Predictor','A1','A2','n','P','Direction'), with = F]
+    fwrite(sumstats_tmp, paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.reformat.txt'), sep=' ')
+
+    score_subset[[populations[i]]][[paste0('subset_', j)]] <- quickprs(
+      sumstats = paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.reformat.txt'),
+      quickprs_ldref = paste0(opt$quickprs_ldref, '/', populations[i]),
+      quickprs_multi_ldref = paste0(opt$quickprs_multi_ldref, '/', populations[i]),
+      genomic_control = opt$genomic_control,
+      n_cores = opt$n_cores,
+      ref_subset = '2',
+      prs_model = opt$prs_model)
+
+    fwrite(score_subset[[populations[i]]][[paste0('subset_', j)]],
+           paste0(tmp_dir, '/LEOPARD/post_targ_', populations[i], '/output_', j, '_', populations[i], '_Post.txt'),
+           sep=' ')
+  }
+}
+
+#####
+# Estimating the linear combination weights
+#####
+
+log_add(log_file = log_file, message = 'Estimating the linear combination weights.')
+
+for(targ_pop in populations){
+  dir.create(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop), recursive = T)
+  quickprs_multi_ldref_pop_i <- paste0(opt$quickprs_multi_ldref, '/', targ_pop)
+  ref_files <- list.files(quickprs_multi_ldref_pop_i)
+  ref_files <- gsub('.bed', '', ref_files[grepl('subset_3.bed', ref_files)])
+
+  for(j in 1:4){
+    targ_gwas_valid_n<-fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_train_valid_N.txt'))$N_valid
+
+    sumstats_tmp <- fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_valid.txt'))
+    sumstats_tmp$SNP<-sumstats_tmp$Predictor
+    sumstats_tmp <- sumstats_tmp[, c('SNP','CHR','BP','A1','A2','BETA','P'), with = F]
+    fwrite(sumstats_tmp, paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_valid.reformat.txt'), sep=' ')
+
+    system(paste0(
+      'Rscript ', opt$xwing_repo, '/LEOPARD_Weights.R ',
+      '--beta_file ', tmp_dir, '/LEOPARD/post_targ_', populations[1], '/output_', j, '_', populations[1], '_Post.txt',',',tmp_dir, '/LEOPARD/post_targ_', populations[2], '/output_', j, '_', populations[2], '_Post.txt ',
+      '--valid_file ', tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_valid.reformat.txt ',
+      '--n_valid ', targ_gwas_valid_n ,' ',
+      '--ref_prefix ', quickprs_multi_ldref_pop_i, '/', ref_files, ' ',
+      '--out ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep', j, '.txt'
+    ))
+  }
+}
+
+# Average weights across repeats
+mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'), log_file = log_file)
+
+####
+# Adjust weights to correspond to PGS with SD of 1
+####
+
+# Read in the scale files for the original score files
+scale_all <- NULL
+for(i in 1:length(score_files)){
+  scale_file <- gsub('.score.gz', '', score_files[i])
+  scale_file <- paste0(scale_file, '-', populations[i], '.scale')
+  scale_file <- fread(scale_file)
+  
+  scale_all <- rbind(
+    scale_all, 
+    data.table(
+      SD = scale_file$SD,
+      Discovery = populations[i]
+    )
+  )
+}
+
+# Adjust weights to correspond to PGS with SD of 1
+log_add(log_file = log_file, message = '------------------------')
+for(i in populations){
+  mix_weights[[i]] <- adjust_weights(weights = mix_weights[[i]], pgs_sd = scale_all$SD)
+  
+  log_add(log_file = log_file, message = paste0("Adjusted LEOPARD weights - ", i, " target: "))
+  for(j in populations){
+    log_add(log_file = log_file, message = paste0(j, ' = ', mix_weights[[i]][which(populations == j)]))
+  }
+  log_add(log_file = log_file, message = '------------------------')
+}
+
+saveRDS(mix_weights, paste0(opt$output, '.weights.rds'))
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 69d35558..2042d50a 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1921,11 +1921,13 @@ for targ_pop in $(echo EAS AFR); do
   
   for disc_pop_i in ${disc_pop}; do
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
-      sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
-        --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
-        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
-        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
-        --n_core 5"
+      if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt" ]; then
+        sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+          --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
+          --n_core 5"
+      fi
     done
   done
 done
@@ -2780,6 +2782,8 @@ dev.off()
 
 ### Check LEOPARD weights
 
+Here we will compare the LEOPARD estimated weights for population specific PGS, to the weights estimated using observed data in the UKB target sample.
+
 ```{r}
 setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 
@@ -2800,84 +2804,48 @@ outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 # Get a list of score files
 scores <- list_score_files(config)
 
-# Read in gwas_groups
-gwas_groups<-read_param(config = config, param = 'gwas_groups', return_obj = T)
-
-# Read in gwas_list
-gwas_list<-read_param(config = config, param = 'gwas_list', return_obj = T)
-
 ###
 # Read in weights estimated by LEOPARD
 ###
-# Read in raw weights from QuickPRS, and standardise them according to the scale of the population specific PGS
+# Currently, we are only using LEOPARD to estimate the weights for QuickPRS PGS.
+# Read in the weights adjusted to account for standardised PGS (SD = 1).
+# Read in the weights for the non-EUR target sample (i.e. either AFR or EAS).
 
-methods<-c('quickprs_multi')
 leopard_weights<-NULL
-for(method_i in methods){
-  scores_method<-scores$name[scores$method == method_i]
-  for(i in selected_traits){
-    scores_i <- scores_method[grepl(paste0('^', i,'_'), scores_method)]
-    for(j in scores_i){
-        log <- readLines(paste0(outdir, '/reference/pgs_score_files/', method_i,'/', j, '/ref-', j, '.log'))
-        populations <- unlist(strsplit(gsub('populations| ','', log[grepl('^ populations', log)]), ','))
-        
-        weight_row <- which(grepl(paste0('LEOPARD weights - ', populations[populations != 'EUR'],' target'), log))
-        weight_row <- weight_row + 1:2
-        log<-log[weight_row]
-        log <- as.numeric(gsub('.* ', '', log))
-        
-        contributing_gwas <- unlist(strsplit(gwas_groups$gwas[gwas_groups$name == j], ','))
-        
-        scale_all <- NULL
-        for(k in contributing_gwas){
-          scale<-fread(paste0(outdir, '/reference/pgs_score_files/quickprs/', k, '/ref-', k, '-', populations[populations != 'EUR'], '.scale'))
-          scale_all <- rbind(
-            scale_all, 
-            data.table(
-              SD = scale$SD,
-              Discovery = gwas_list$population[gwas_list$name == k]
-            )
-          )
-        }
-        
-        weights <-
-          data.table(
-            Target =  populations[populations != 'EUR'],
-            Discovery = populations,
-            Weight = round(log, 3),
-            Trait = i,
-            Method = method_i
-          )
-        
-        weights <- merge(weights, scale_all, by = 'Discovery')
-        
-        # Adjust weights to standardize for PGS SD = 1
-        adjust_weights <- function(weights, pgs_sd) {
-          adjusted_weights <- weights * pgs_sd
-          # Normalize weights so they sum to 1
-          normalized_weights <- adjusted_weights * (1 / sum(adjusted_weights))
-          return(normalized_weights)
-        }
-        
-        weights$Adjusted_Weight <- adjust_weights(
-          weights$Weight,
-          weights$SD)
-
-        leopard_weights <- rbind(leopard_weights, weights)
-    }
+scores_quickprs <- scores$name[scores$method == 'quickprs_multi']
+for(i in selected_traits){
+  scores_i <- scores_quickprs[grepl(paste0('^', i,'_'), scores_quickprs)]
+  for(j in scores_i){
+      log <- readLines(paste0(outdir, '/reference/pgs_score_files/quickprs_multi/', j, '/ref-', j, '.log'))
+      populations <- unlist(strsplit(gsub('populations| ','', log[grepl('^ populations', log)]), ','))
+      
+      weight_row <- which(grepl(paste0('Adjusted LEOPARD weights - ', populations[populations != 'EUR'],' target'), log))
+      weight_row <- weight_row + 1:2
+      log <- log[weight_row]
+      log <- as.numeric(gsub('.* ', '', log))
+      
+      weights <-
+        data.table(
+          Target =  populations[populations != 'EUR'],
+          Discovery = populations,
+          Weight = round(log, 3),
+          Trait = i,
+          Method = 'quickprs_multi'
+        )
+      
+      leopard_weights <- rbind(leopard_weights, weights)
   }
-
 }
 
+#####
+# Read in the PGS weights estimated using UKB data
+#####
+# Read in the final model coefficients for models containing pseudo PGS from single-source methods
 
 obs_weights<-NULL
 for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_methods)]){
-  
   scores_method<-scores$name[scores$method == method_i]
-  ###
-  # Read in weights when training in UKB
-  ###
-  
+
   for(i in selected_traits){
     for(j in c('EAS','AFR')){
       model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/targ_', j, '.disc_EUR_', j, '/', i, '/final_models/', method_i, '.pseudo.multi.final_model.txt'))
@@ -2896,64 +2864,94 @@ for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_met
   }
 }
 
-ggplot(obs_weights[obs_weights$Target == obs_weights$Discovery,], aes(x = Trait, y = Weight, fill = Method)) +
-  geom_bar(position="dodge", stat="identity") +
-  facet_grid(. ~ Target)
-
-
 ###
 # Combine and compare
 ###
 
-leopard_weights$SD <- NULL
-leopard_weights_adjusted <- leopard_weights
-leopard_weights_adjusted$Weight <- leopard_weights_adjusted$Adjusted_Weight
-leopard_weights$Adjusted_Weight <- NULL
-leopard_weights_adjusted$Adjusted_Weight <- NULL
-
 leopard_weights$Method <- 'LEOPARD'
-leopard_weights_adjusted$Method <- 'LEOPARD adjusted'
-
-both <- do.call(rbind, list(obs_weights, leopard_weights, leopard_weights_adjusted))
+both <- do.call(rbind, list(obs_weights, leopard_weights))
 both <- both[both$Discovery != 'EUR',]
 both$Target <- paste0('Target = ', both$Target)
 
+# Plot the estimated and observed weights
 ggplot(both, aes(x = Trait, y = Weight, fill = Method)) +
   geom_bar(position="dodge", stat="identity") +
-  facet_grid(. ~ Target)
-
-ggplot(both[both$Method %in% c('LEOPARD','LEOPARD adjusted','sbayesrc'),], aes(x = Trait, y = Weight, fill = Method)) +
-  geom_bar(position="dodge", stat="identity") +
-  facet_grid(. ~ Target)
+  facet_grid(. ~ Target) +
+  theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border()
 
+# Calculate correlation between weights from each method
 both$Discovery<-NULL
 both_wide <- reshape(both, 
                      idvar = c("Trait", "Target"), 
                      timevar = "Method", 
                      direction = "wide")
+names(both_wide) <- gsub('Weight.', '', names(both_wide))
+
+cor_matrix_EAS <- cor(both_wide[both_wide$Target == 'Target = EAS', -1:-2])
+cor_matrix_AFR <- cor(both_wide[both_wide$Target == 'Target = AFR', -1:-2])
+
+# Convert correlation matrix to long format for ggplot
+cor_df_EAS <- melt(cor_matrix_EAS)
+cor_df_AFR <- melt(cor_matrix_AFR)
+cor_df_EAS$Target <- 'Target = EAS'
+cor_df_AFR$Target <- 'Target = AFR'
+cor_df <- rbind(cor_df_AFR, cor_df_EAS)
+
+# Create ggplot correlation heatmap with text inside
+ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
+  geom_tile(color = "white") +  # Tile plot with white borders
+  geom_text(aes(label = round(value, 2)), color = "black") +  # Add correlation values
+  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +  # Color scale
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1),
+    axis.title = element_blank()
+  ) +
+  facet_grid(. ~ Target) +
+  labs(fill = "Correlation")
 
-cor(both_wide[, -1:-2])
-
-obvs_vs_leopard <- merge(obs_weights, leopard_weights_adjusted, by=c('Target','Discovery','Trait'))
+# Plot comparison between observed and LEOPARD weights
+obvs_vs_leopard <- merge(obs_weights, leopard_weights, by=c('Target','Discovery','Trait'))
 
 ggplot(obvs_vs_leopard, aes(x=Weight.x, y=Weight.y, colour = Method.x)) +
+  geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "black") +
   geom_point() +
-  geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "black")
+  theme_half_open() +
+  panel_border() + 
+  facet_grid(. ~ Target) +
+  labs(y = 'LEOPARD weight', x = 'Observed weight') +
+  xlim(c(0,1)) +
+  ylim(c(0,1))
 
 mean_abs_diff <- aggregate(abs(obvs_vs_leopard$Weight.x - obvs_vs_leopard$Weight.y),
                            by = list(Method = obvs_vs_leopard$Method.x, Population = obvs_vs_leopard$Target),
                            FUN = mean)
 
 # Rename columns
-colnames(mean_abs_diff) <- c("Method", "Mean_Absolute_Difference")
-
-# It looks like the observed weights are highly correlated across methods, but the LEOPARD weights are not. The LEOPARD weights seem more similar to observed for QuickPRS than LDpred2.
-
-# The correlation and absolute difference between leopard and observed weights is actually quite similar between methods. LEOPARD is slightly better for QuickPRS, but I am not sure it explains the difference in pseudo PGS. I think it is likley due to the difference in PGS variance when applying the LEOPARD weights.
-
-# The LEOPARD weights seem less accurate when the PGS are scaled, but these weights could be used for any PGS method. Lets see how well this works for SBayesRC.I am running quickprs_multi again, without scaling using LEOPARD, but still scaling the quickprs weights. This will indicate whether it is the scaling of the weights or scaling within LEOPARD that is making it worse. 
-
-# Scaling within LEOPARD makes the weights worse for the EAS target, but it is scaling the PGS before applying weights that makes the PGS worse for AFR. This kind of makes sense as the scale of the EAS and EUR PGS are typically similar. 
+colnames(mean_abs_diff) <- c("Method", "Target", "Mean_Absolute_Difference")
+
+#    Method                   Target  Mean_Absolute_Difference
+#    dbslmm                      AFR                0.19474126
+#  lassosum                      AFR                0.30388943
+#   ldpred2                      AFR                0.16107677
+#   megaprs                      AFR                0.22283358
+#     prscs                      AFR                0.17876755
+#   ptclump                      AFR                0.17443263
+#  quickprs                      AFR                0.18643385
+#  sbayesrc                      AFR                0.19114802
+#    dbslmm                      EAS                0.09235866
+#  lassosum                      EAS                0.09133249
+#   ldpred2                      EAS                0.09829542
+#   megaprs                      EAS                0.12050745
+#     prscs                      EAS                0.08538271
+#   ptclump                      EAS                0.10710789
+#  quickprs                      EAS                0.11950164
+#  sbayesrc                      EAS                0.10211742
+
+# The adjusted LEOPARD weights look good. It is less accurate for the AFR target, probably due to limited GWAS sample size and LD reference mismatch. The correlation between the observed pgs weights is quite consistent across methods as well. Given how fast QuickPRS is, and that preliminary analysis shows other methods might be more sensitive to small reference data, I think we should use the QuickPRS-based LEOPARD weights for other PGS methods as well.
 
 ```
 
diff --git a/pipeline/config.yaml b/pipeline/config.yaml
index 5e051fd9..eb44f5a0 100644
--- a/pipeline/config.yaml
+++ b/pipeline/config.yaml
@@ -22,9 +22,12 @@ score_list: NA
 # Specify location of gwas_groups file
 gwas_groups: NA
 
-# Specify pgs_methods ('ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs','prscsx','quickprs','sbayesrc')
+# Specify pgs_methods ('ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs','prscsx','quickprs','sbayesrc','leopard')
 pgs_methods: NA
 
+# Specify whether LEOPARD should be used to combine population-specific PGS
+leopard: true
+
 # Specify p-value thresholds for ptclump
 ptclump_pts: ['5e-8', '1e-6', '1e-4', '1e-2', '0.1', '0.2', '0.3', '0.4', '0.5', '1']
 
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 2580b8c8..0eed3a40 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -568,6 +568,96 @@ checkpoint score_reporter:
 # Multi-ancestry methods
 ###########
 
+####
+# LEOPARD 
+####
+
+# Estimate weights for population-specific PGS from single-source methods
+rule leopard_quickprs_i:
+  resources:
+    mem_mb=10000,
+    time_min=1000
+  threads: config['cores_prep_pgs']
+  input:
+    lambda w: expand(f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group)),
+    lambda w: expand(f"{quickprs_ldref}/{{population}}/{{population}}.cors.bin", population=[pop for pop in get_populations(w.gwas_group)]),
+    lambda w: expand(f"{quickprs_multi_ldref}/{{population}}/{{population}}.subset_1.bed", population=[pop for pop in get_populations(w.gwas_group)]),
+    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
+    rules.download_ldak_highld.output,
+    rules.download_ldak5_2.output,
+    rules.download_ldak_map.output,
+    rules.download_ldak_bld.output
+  output:
+    f"{outdir}/reference/pgs_score_files/leopard/{{gwas_group}}/ref-{{gwas_group}}.weights.rds"
+  benchmark:
+    f"{outdir}/reference/benchmarks/leopard_quickprs_i-{{gwas_group}}.txt"
+  log:
+    f"{outdir}/reference/logs/leopard_quickprs_i-{{gwas_group}}.log"
+  conda:
+    "../envs/xwing.yaml"
+  params:
+    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
+    scores= lambda w: ",".join(expand(f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group))),
+    populations= lambda w: ",".join(get_populations(w.gwas_group)),
+    testing=config["testing"]
+  shell:
+    "Rscript ../Scripts/pgs_methods/leopard_quickprs.R \
+      --sumstats {params.sumstats} \
+      --scores {params.scores} \
+      --populations {params.populations} \
+      --ldak {resdir}/software/ldak5.2/ldak5.2.linux \
+      --quickprs_ldref {quickprs_ldref} \
+      --quickprs_multi_ldref {quickprs_multi_ldref} \
+      --xwing_repo {resdir}/software/xwing \
+      --n_cores {threads} \
+      --output {outdir}/reference/pgs_score_files/leopard/{wildcards.gwas_group}/ref-{wildcards.gwas_group} \
+      --test {params.testing} > {log} 2>&1"
+
+rule leopard_quickprs:
+  input: expand(f"{outdir}/reference/pgs_score_files/leopard/{{gwas_group}}/ref-{{gwas_group}}.weights.rds", gwas_group=gwas_groups_df['name'])
+
+####
+# Combine single-source PGS using LEOPARD weights
+####
+
+# Define the single_source methods
+single_source_methods = {"ptclump", "dbslmm", "prscs", "sbayesr", "sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs"}
+
+# Find which single source methods have been requested
+requested_single_source_methods = list(single_source_methods.intersection(pgs_methods_all))
+
+rule prep_pgs_multi_i:
+  input:
+    f"{outdir}/reference/pgs_score_files/leopard/{{gwas_group}}/ref-{{gwas_group}}.weights.rds",
+    lambda w: expand(f"{outdir}/reference/pgs_score_files/{{method}}/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group), method = w.method),
+    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+  output:
+    f"{outdir}/reference/pgs_score_files/{{method}}_multi/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
+  benchmark:
+    f"{outdir}/reference/benchmarks/prep_pgs_multi_i-{{gwas_group}}-{{method}}.txt"
+  log:
+    f"{outdir}/reference/logs/prep_pgs_multi_i-{{gwas_group}}-{{method}}.log"
+  conda:
+    "../envs/xwing.yaml"
+  params:
+    scores= lambda w: ",".join(expand(f"{outdir}/reference/pgs_score_files/{{method}}/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group), method = w.method)),
+    populations= lambda w: ",".join(get_populations(w.gwas_group)),
+    testing=config["testing"],
+    config_file = config["config_file"]
+  shell:
+    "Rscript ../Scripts/pgs_methods/quickprs_multi.R \
+      --config {params.config_file} \
+      --gwas_group {wildcards.gwas_group} \
+      --method {wildcards.method} \
+      --test {params.testing} > {log} 2>&1"
+
+rule prep_pgs_multi:
+  input: expand(f"{outdir}/reference/pgs_score_files/{{method}}_multi/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'], method = requested_single_source_methods)
+
+#########
+# Jointly optimised methods
+#########
+
 ####
 # PRS-CSx
 ####
@@ -726,57 +816,6 @@ rule prep_pgs_tlprs_i:
 rule prep_pgs_tlprs:
   input: expand(f"{outdir}/reference/pgs_score_files/tlprs_{{method}}/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'], method=config["tlprs_methods"])
 
-##
-# LDAK QuickPRS Multi
-##
-
-rule prep_pgs_quickprs_multi_i:
-  resources:
-    mem_mb=10000
-  threads: config['cores_prep_pgs']
-  input:
-    lambda w: expand(f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group)),
-    lambda w: expand(f"{quickprs_ldref}/{{population}}/{{population}}.cors.bin", population=[pop for pop in get_populations(w.gwas_group)]),
-    lambda w: expand(f"{quickprs_multi_ldref}/{{population}}/{{population}}.subset_1.bed", population=[pop for pop in get_populations(w.gwas_group)]),
-    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
-    rules.download_ldak_highld.output,
-    rules.download_ldak5_2.output,
-    rules.download_ldak_map.output,
-    rules.download_ldak_bld.output,
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
-  output:
-    f"{outdir}/reference/pgs_score_files/quickprs_multi/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
-  benchmark:
-    f"{outdir}/reference/benchmarks/prep_pgs_quickprs_multi_i-{{gwas_group}}.txt"
-  log:
-    f"{outdir}/reference/logs/prep_pgs_quickprs_multi_i-{{gwas_group}}.log"
-  conda:
-    "../envs/xwing.yaml"
-  params:
-    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
-    scores= lambda w: ",".join(expand(f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group))),
-    populations= lambda w: ",".join(get_populations(w.gwas_group)),
-    testing=config["testing"]
-  shell:
-    "Rscript ../Scripts/pgs_methods/quickprs_multi.R \
-      --ref_plink_chr {refdir}/ref.chr \
-      --ref_freq_chr {refdir}/freq_files \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
-      --sumstats {params.sumstats} \
-      --scores {params.scores} \
-      --populations {params.populations} \
-      --ldak {resdir}/software/ldak5.2/ldak5.2.linux \
-      --quickprs_ldref {quickprs_ldref} \
-      --quickprs_multi_ldref {quickprs_multi_ldref} \
-      --xwing_repo {resdir}/software/xwing \
-      --n_cores {threads} \
-      --output {outdir}/reference/pgs_score_files/quickprs_multi/{wildcards.gwas_group}/ref-{wildcards.gwas_group} \
-      --pop_data {refdir}/ref.pop.txt \
-      --test {params.testing} > {log} 2>&1"
-
-rule prep_pgs_quickprs_multi:
-  input: expand(f"{outdir}/reference/pgs_score_files/quickprs_multi/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'])
-
 ####
 # BridgePRS
 ####
@@ -847,14 +886,14 @@ if 'sbayesrc' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_sbayesrc.input)
 if 'external' in pgs_methods_all:
   pgs_methods_input.append(rules.score_reporter.output)
+if config["leopard"]:
+  pgs_methods_input.append(rules.prep_pgs_multi.output)
 if 'prscsx' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_prscsx.input)
 if 'xwing' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_xwing.input)
 if 'tlprs' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_tlprs.input)
-if 'quickprs_multi' in pgs_methods_all:
-  pgs_methods_input.append(rules.prep_pgs_quickprs_multi.input)
 if 'bridgeprs' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_bridgeprs.input)
 

From 6b989750db6960d163a5b313685f6d4ba04c4320 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 17 Feb 2025 11:23:03 +0000
Subject: [PATCH 109/153] Updates

---
 Scripts/pgs_methods/apply_leopard_weights.R |  26 +-
 docs/CrossPop.Rmd                           | 327 +++++++++++++++-----
 functions/constants.R                       |   8 +-
 functions/pgs.R                             |  16 +-
 functions/pipeline.R                        |   2 +-
 pipeline/config.yaml                        |   8 +-
 pipeline/rules/dependencies.smk             |  37 ++-
 pipeline/rules/pgs_methods.smk              |  12 +-
 pipeline/rules/target_scoring.smk           |   2 +-
 9 files changed, 321 insertions(+), 117 deletions(-)

diff --git a/Scripts/pgs_methods/apply_leopard_weights.R b/Scripts/pgs_methods/apply_leopard_weights.R
index 2d94595f..c9dc68e2 100644
--- a/Scripts/pgs_methods/apply_leopard_weights.R
+++ b/Scripts/pgs_methods/apply_leopard_weights.R
@@ -86,15 +86,13 @@ for(i in 1:nrow(gwas_list)){
   score_cols <-
     which(names(score_header) %in% c('SNP','A1','A2', paste0('SCORE_', param)))
   
-  score_full[[populations[i]]] <- fread(cmd = 
+  score_full[[gwas_list$population[i]]] <- fread(cmd = 
     paste0(
-      "cut -d' ' -f ", 
-      paste0(score_cols, collapse=','),
-      " ", 
-      score_files[i])
+      "zcat ", score_files[i], " | cut -d' ' -f ", 
+      paste0(score_cols, collapse=','))
     )
   
-  names(score_full[[populations[i]]])[4] <- paste0('SCORE_targ_', populations[i])
+  names(score_full[[gwas_list$population[i]]])[4] <- paste0('SCORE_targ_', gwas_list$population[i])
 }
 
 ####
@@ -117,16 +115,16 @@ score_all[is.na(score_all)]<-0
 refdir <- read_param(config = opt$config, param = 'refdir', return_obj = F)
 opt$ref_plink_chr <- paste0(refdir, '/ref.chr')
 ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
-opt$pop_data <- paste0(refdir, '/ref.pop.txt ')
+opt$pop_data <- paste0(refdir, '/ref.pop.txt')
 pop_data <- read_pop_data(opt$pop_data)
-opt$ref_freq_chr <- paste0(refdir, '/freq_files ')
+opt$ref_freq_chr <- paste0(refdir, '/freq_files')
 
 # Subset PGS to SNPs in ref (useful when testing)
 score_all <- score_all[score_all$SNP %in% ref$SNP,]
 
 # Calculate linear combination of scores using mixing weights for each target population
 score_weighted <- score_all
-for(targ_pop in populations){
+for(targ_pop in gwas_list$population){
   # Read in the .freq file for target population
   freq_data <- read_frq(freq_dir = opt$ref_freq_chr, population = targ_pop, chr = CHROMS)
   
@@ -141,11 +139,11 @@ for(targ_pop in populations){
   fwrite(score_i, paste0(tmp_dir,'/tmp.',targ_pop,'.score'), col.names=T, sep=' ', quote=F)
   
   # Calc score in target sample
-  ref_pgs <- plink_score(pfile = opt$ref_plink_chr, keep = pop_data[pop_data$POP == targ_pop, c('FID'), with=F], chr = CHROMS, score = paste0(tmp_dir,'/tmp.',targ_pop,'.score'))
+  ref_pgs <- plink_score(pfile = opt$ref_plink_chr, plink2 = 'plink2', keep = pop_data[pop_data$POP == targ_pop, c('FID'), with=F], chr = CHROMS, score = paste0(tmp_dir,'/tmp.',targ_pop,'.score'))
   ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs)
   
   # Rescale SNP-weights according to PGS SD in target
-  for(i in populations){
+  for(i in gwas_list$population){
     scaling_factor <- 1 / ref_pgs_scale_i$SD[ref_pgs_scale_i$Param == paste0('SCORE_targ_', i)]
     score_i[[paste0('SCORE_targ_', i)]] <- score_i[[paste0('SCORE_targ_', i)]] * scaling_factor
   }
@@ -183,7 +181,11 @@ if(!is.na(opt$test)){
 log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
 
 # Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
+ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = 'plink2', score = paste0(opt$output,'.score.gz'))
+
+resdir <- read_param(config = opt$config, param = 'resdir', return_obj = F)
+
+opt$ref_pcs <- paste0(resdir, '/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles')
 
 if(!is.null(opt$ref_pcs)){
   log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 2042d50a..6f0d7022 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1368,6 +1368,7 @@ for(pop_i in pop){
         Population = pop_i,
         GWAS = gwas_i,
         nsnp = nsnp,
+        max_r2 = max(res_i$Obs_R2),
         n_disc = disc_N,
         n_targ = targ_N,
         vg_est = mod_res$vg[1],
@@ -1401,6 +1402,25 @@ ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) +
   theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
   background_grid(major = 'y', minor = 'y')
 
+ggplot(mod_res_all, aes(x = Phenotype, y = max_r2, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  labs(y="Max R2") +
+  theme_half_open() +
+  coord_cartesian(ylim = c(0, 0.15)) + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+# 
+hist(mod_res_all$max_r2)
+hist(mod_res_all$max_r2[mod_res_all$Population == 'EUR'])
+hist(mod_res_all$max_r2[mod_res_all$Population == 'EAS'])
+hist(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])
+
+summary(mod_res_all$max_r2)
+summary(mod_res_all$max_r2[mod_res_all$Population == 'EUR'])
+summary(mod_res_all$max_r2[mod_res_all$Population == 'EAS'])
+summary(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])
+
 ```
 
 ***
@@ -1429,7 +1449,6 @@ both <- merge(avengeme, ldsc, by = c('Population','Phenotype'))
 both_h2 <- both[!(both$Phenotype %in% both$Phenotype[both$vg_est < 0.01 | both$h2_est < 0.01]),]
 
 # Select GWAS representing a range of SNP-h2 from LDSC, and a range of polygenicity from AVENGEME.
-
 both_eur<-both_h2[both_h2$Population == 'EUR',]
 traits_data <- data.frame(trait = both_eur$Phenotype, heritability = both_eur$vg_est, polygenicity = both_eur$pi0_est)
 
@@ -1454,6 +1473,154 @@ if (nrow(selected_traits) > 10) {
 
 write.table(selected_traits$trait, '/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', col.names = F, row.names = F, quote = F)
 
+# Plot max R2 for selected traits
+mod_res_all <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv')
+mod_res_all_selected <- mod_res_all[mod_res_all$Phenotype %in% selected_traits$trait,]
+
+ggplot(mod_res_all_selected, aes(x = Phenotype, y = max_r2, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  labs(y="Max R2") +
+  theme_half_open() +
+  coord_cartesian(ylim = c(0, 0.15)) + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+# 
+hist(mod_res_all$max_r2)
+hist(mod_res_all$max_r2[mod_res_all$Population == 'EUR'])
+hist(mod_res_all$max_r2[mod_res_all$Population == 'EAS'])
+hist(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])
+
+summary(mod_res_all$max_r2)
+summary(mod_res_all$max_r2[mod_res_all$Population == 'EUR'])
+summary(mod_res_all$max_r2[mod_res_all$Population == 'EAS'])
+summary(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])
+
+round(sqrt(min(mod_res_all$max_r2[mod_res_all$Population == 'EUR'])), 2)
+round(sqrt(max(mod_res_all$max_r2[mod_res_all$Population == 'EUR'])), 2)
+round(sqrt(min(mod_res_all$max_r2[mod_res_all$Population == 'EAS'])), 2)
+round(sqrt(max(mod_res_all$max_r2[mod_res_all$Population == 'EAS'])), 2)
+round(sqrt(min(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])), 2)
+round(sqrt(max(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])), 2)
+
+```
+
+***
+
+# Make a descriptives table of GWAS
+
+```{r}
+library(data.table)
+
+#####
+# Trait names, labels, and URLs
+#####
+
+###
+# UKB
+###
+ukb <- fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv')
+names(ukb) <- c('trait', 'labels','field')
+trait_labels <- ukb[, c('trait','labels'), with=F]
+ukb<-ukb[, c('trait','field'), with=F]
+ukb$sample <- 'UKB'
+ukb$population <- 'EUR'
+ukb$url<-NA
+
+###
+# BBJ
+###
+bbj <- fread('~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_prscsx.csv')
+bbj <- bbj[, c('trait', 'wget'), with = F]
+names(bbj) <- c('trait', 'url')
+bbj$sample <- 'BBJ'
+bbj$population <- 'EAS'
+bbj$field <- NA
+
+###
+# UGR
+###
+ugr <- fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv')
+ugr <- ugr[, c('trait', 'summaryStatistics'), with = F]
+names(ugr) <- c('trait','url')
+ugr$sample <- 'UGR'
+ugr$population <- 'AFR'
+ugr$field <- NA
+
+info_all <- do.call(rbind, list(ukb, bbj, ugr))
+info_all<-merge(info_all, trait_labels, by='trait')
+
+#####
+# Sample size, SNP-h2 and polygenicity
+#####
+
+# Read in the AVENGEME and LDSC results
+avengeme <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv')
+ldsc <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results.csv')
+both <- merge(avengeme, ldsc, by = c('Population','Phenotype'))
+
+# Format for descriptives table
+both$h2_avengeme<- paste0(
+  round(both$vg_est,2), 
+  " (95%CI = ", 
+  round(both$vg_lowCI, 2), 
+  " - " , 
+  round(both$vg_highCI, 2), ")")
+
+both$pi0_avengeme <- paste0(
+  round(both$pi0_est,2), 
+  " (95%CI = ", 
+  round(both$pi0_lowCI, 2), 
+  " - " , 
+  round(both$pi0_highCI, 2), ")")
+
+both$h2_ldsc <- paste0(
+  round(both$h2_est,2), 
+  " (SE = ", 
+  round(both$h2_se, 2), 
+  ")")
+
+both$int_ldsc <- paste0(
+  round(both$int_est,2), 
+  " (SE = ", 
+  round(both$int_se, 2), 
+  ")")
+
+both<-both[, c('Population','Phenotype','n_disc','n_targ','h2_avengeme','pi0_avengeme','h2_ldsc','int_ldsc','lambda'), with = F]
+names(both)[1:2]<-c('population','labels')
+
+info_all <- merge(info_all, both, by = c('labels','population'))
+info_all$n_disc<-round(info_all$n_disc, 0)
+info_all$n_targ<-round(info_all$n_targ, 0)
+
+info_all<-info_all[, c('labels','trait','population','sample','n_disc','n_targ','h2_avengeme','pi0_avengeme','h2_ldsc','int_ldsc','lambda','field','url'), with=F]
+names(info_all) <- c('Trait Label', 'Trait Description', 'Ancestry', 'GWAS Sample', 'GWAS N', 'Target N',"SNP-h2 (AVENGEME)","pi0 (AVENGEME)","SNP-h2 (LDSC)","Intercept (LDSC)",'Lambda', 'UKB Field', 'URL')
+
+# Add in column indicating whether the trait was used in downstream PGS comparison
+selected_traits <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+info_all$`Selected` <- info_all$`Trait Label` %in% selected_traits
+
+write.csv(info_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv', row.names=F)
+
+# Estimate the mean and SD of sample size within each population for selected traits
+info_all_selected<-info_all[info_all$Selected == T,]
+n_dat <- NULL
+for(i in unique(info_all_selected$`GWAS Sample`)){
+  n_dat <-rbind(
+    n_dat,
+    data.table(
+      sample = i,
+      gwas_n_median = round(median(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      gwas_n_mean = round(mean(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      gwas_n_sd = round(sd(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_median = round(median(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_mean = round(mean(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_sd = round(sd(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i]))
+    )
+  )
+}
+
 ```
 
 ***
@@ -1510,6 +1677,7 @@ config<-c(
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
   "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx','xwing']",
 #  "tlprs_methods: ['dbslmm','prscs','lassosum']",
+  "leopard_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
   "cores_prep_pgs: 10", # xwing run with 20 cores
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
@@ -1581,7 +1749,7 @@ selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_su
 scores <- list_score_files(config)
 
 # Create files for EAS and AFR targets
-targ_pop <- c('EAS','AFR')
+targ_pop <- c('EUR','EAS','AFR')
 for(trait_i in selected_traits){
   scores_i <- scores[grepl(trait_i, scores$name),]
   scores_i$multi <- scores_i$method
@@ -1637,7 +1805,9 @@ for(trait_i in selected_traits){
       # Make groups single source methods
       ####
       
-      scores_i_j_single_top1 <- scores_i_j[!(scores_i_j$method %in% pgs_group_methods),]
+      scores_i_j_single_top1 <-
+        scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &
+                     !grepl('_multi$', scores_i_j$method), ]
 
       # Create top1 column indicating which predictors top1 models should be derived
       scores_i_j_single_top1$top1[grepl('UKB', scores_i_j_single_top1$name, ignore.case = F)] <- 'EUR'
@@ -1688,7 +1858,7 @@ for(trait_i in selected_traits){
       
       scores_i_j_multi_single_pseudo <- scores_i_j[grepl('_multi$', scores_i_j$method),]
 
-      # Extract the pseudo score for each method and specify as a seperate group
+      # Extract the pseudo score for each method and specify as a separate group
       for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
         param <- find_pseudo(
           config = config,
@@ -1728,8 +1898,7 @@ for(trait_i in selected_traits){
       # Make groups for the Multi-Source methods
       ####
       
-      scores_i_j_multi <- scores_i_j[(scores_i_j$method %in% pgs_group_methods) &
-                                           !grepl('_multi$', scores_i_j$method),]
+      scores_i_j_multi <- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
 
       # Split top1 scores by target population
       # This doesn't apply to xwing because it only has pop-specific pseudo scores
@@ -1836,7 +2005,7 @@ for(trait_i in selected_traits){
             scores_i_j_multi$predictor[i], 
             " > ", 
             gsub('.profiles',
-                 paste0('.pseudo.profiles'),
+                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
                  scores_i_j_multi$predictor[i])
           )
         )
@@ -1846,7 +2015,7 @@ for(trait_i in selected_traits){
         tmp$top1 <- paste0('EUR_', disc_pop_j_2)
         tmp$predictor <-
             gsub('.profiles',
-                 paste0('.pseudo.profiles'),
+                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
                  scores_i_j_multi$predictor[i])
         
         scores_i_j_multi_pseudo <- rbind(scores_i_j_multi_pseudo, tmp)
@@ -1898,9 +2067,7 @@ for(trait_i in selected_traits){
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-rm /users/k1806347/oliverpainfel/Analyses/crosspop/targ_*.disc_EUR*/*/res*
-
-for targ_pop in $(echo EAS AFR); do
+for targ_pop in $(echo EUR); do
   if [ "$targ_pop" == "EUR" ]; then
       targ_pop2="EUR_test"
   else
@@ -1920,6 +2087,7 @@ for targ_pop in $(echo EAS AFR); do
   fi
   
   for disc_pop_i in ${disc_pop}; do
+    #rm /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop}/*/res*
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
       if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt" ]; then
         sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
@@ -1975,7 +2143,7 @@ for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
 }
 
 # Read in results
-targ_pop = c('EAS','AFR')
+targ_pop = c('EUR','EAS','AFR')
 res_eval <- list()
 for(pheno_i in selected_traits){
   res_eval_i<-NULL
@@ -2021,12 +2189,12 @@ for(pheno_i in selected_traits){
   res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
                    grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTrain'
   
-  res_eval_i$Model[grepl('quickprs_multi', res_eval_i$Group)]<-'SumStatTrain'
+  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTrain'
   res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTrain'
   res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTrain'
   
   res_eval_i$Source<-ifelse(
-    res_eval_i$Method %in% pgs_group_methods | 
+    res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
     !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
   
   res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
@@ -2045,17 +2213,17 @@ for(pheno_i in selected_traits){
   
   # Remove pseudo model for methods that don't really have one 
   res_eval_i <- res_eval_i[
-    !(res_eval_i$Method %in%  c('ptclump') & 
+    !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
       res_eval_i$Model %in% c('SumStatTrain','Multi-SumStatTrain')),]
 
-  # Remove top1 models for QuickPRS-Multi, PRS-CSx, X-wing
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
   res_eval_i <- res_eval_i[
-    !(res_eval_i$Method %in%  c('prscsx', 'xwing', 'quickprs_multi') & 
+    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method)) & 
       grepl('top1', res_eval_i$Group)),]
   
   # Remove any duplicate models
   res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
-    "Target", "Method", "Model", "Source", "Discovery"
+    "Target", "Method", "Model", "Source", "Discovery","gwas_group"
   )]),]
   
   res_eval[[pheno_i]]<-res_eval_i
@@ -2145,8 +2313,16 @@ meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','E
 # Comparison of models across methods, across target and discovery populations
 ####
 
+model_palette <- c(
+  "IndivTrain" = "#F8766D",
+  "SumStatTrain" = "#7CAE00",
+  "Multi-IndivTrain" = "#00BFC4",
+  "Multi-SumStatTrain" = "#C77CFF"
+)
+
 # Format results for nice plot
 tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
 tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
 tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)], '-Multi')
 tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
@@ -2161,13 +2337,6 @@ tmp$Discovery_clean <- factor(tmp$Discovery_clean,
 
 tmp$Target <- paste0(tmp$Target, ' Target')
 
-model_palette <- c(
-  "IndivTrain" = "#F8766D",
-  "SumStatTrain" = "#7CAE00",
-  "Multi-IndivTrain" = "#00BFC4",
-  "Multi-SumStatTrain" = "#C77CFF"
-)
-
 tmp$Model <- factor(tmp$Model, levels = names(model_palette))
 
 # Plot everything
@@ -2208,6 +2377,60 @@ ggplot(tmp_multi, aes(x=label, y=R , fill = Model)) +
     theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
     scale_fill_manual(values = model_palette)
 
+
+# Format results for nice plot
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target == 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)], '-Multi')
+tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery == 'EAS'] <- 'EAS GWAS'
+tmp$Discovery_clean[tmp$Discovery == 'AFR'] <- 'AFR GWAS'
+tmp$Discovery_clean[tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c(
+                                         'EUR GWAS',
+                                         'EAS GWAS',
+                                         'AFR GWAS',
+                                         'Both'))
+
+tmp$Target <- paste0(tmp$Target, ' Target')
+
+tmp$Model <- factor(tmp$Model, levels = names(model_palette))
+
+# Plot everything
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(gwas_group ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
+    scale_fill_manual(values = model_palette)
+
+ggplot(tmp[tmp$Discovery_clean %in% c('EUR GWAS','Both'),], aes(x=label, y=R , fill = Model)) +
+    #geom_hline(yintercept = 0) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(gwas_group ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
+    scale_fill_manual(values = model_palette)
+
 ####
 # Create heatmap showing difference between all methods and models
 ####
@@ -2290,7 +2513,7 @@ find_model<-function(x){
   mod[grepl('top1$', x) & grepl('pseudo', x)] <- 'SumStatTrain'
   mod[grepl('multi$', x) & !grepl('pseudo', x)] <- 'Multi-IndivTrain'
   mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTrain'
-  mod[grepl('quickprs_multi', x)] <- 'SumStatTrain'
+  mod[grepl('_multi', x)] <- 'SumStatTrain'
   mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTrain'
   mod[x == 'xwing.pseudo.multi'] <- 'SumStatTrain'
   
@@ -2327,18 +2550,18 @@ res_comp_all <- res_comp_all[
 
 # Remove pseudo model for methods that don't really have one 
 res_comp_all <- res_comp_all[
-!(res_comp_all$Method1 %in%  c('ptclump') & 
+!(res_comp_all$Method1 %in%  c('ptclump','ptclump_multi') & 
   res_comp_all$Model1 %in% c('SumStatTrain','Multi-SumStatTrain')),]
 res_comp_all <- res_comp_all[
-!(res_comp_all$Method2 %in%  c('ptclump') & 
+!(res_comp_all$Method2 %in%  c('ptclump','ptclump_multi') & 
   res_comp_all$Model2 %in% c('SumStatTrain','Multi-SumStatTrain')),]
 
 # Remove top1 models for PRS-CSx
 res_comp_all <- res_comp_all[
-!(res_comp_all$Method1 %in%  c('prscsx','xwing','quickprs_multi') & 
+!(grepl('prscsx|xwing|_multi', res_comp_all$Method1) & 
   grepl('top1', res_comp_all$Model_1)),]
 res_comp_all <- res_comp_all[
-!(res_comp_all$Method2 %in%  c('prscsx','xwing','quickprs_multi') & 
+!(grepl('prscsx|xwing|_multi', res_comp_all$Method2) & 
   grepl('top1', res_comp_all$Model_2)),]
 
 # Remove any comparisons
@@ -2615,47 +2838,7 @@ tmp$R_diff_perc <- tmp$R_diff / tmp$Model_2_R
 
 meta_tmp$R_diff_perc_SE <- meta_tmp$R_diff_SE / meta_tmp$Model_2_R
 
-ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
-    geom_point(
-        data = tmp,
-        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
-        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
-        alpha = 0.3
-      ) +
-    geom_hline(yintercept = 0) +
-      geom_errorbar(
-        aes(
-          ymin = R_diff_perc - R_diff_perc_SE,
-          ymax = R_diff_perc + R_diff_perc_SE
-        ),
-        width = 0,
-        position = position_dodge(width = 0.7)
-      ) +    
-    geom_point(
-      stat = "identity",
-      position = position_dodge(0.7),
-      size = 3,
-      shape = 23
-    ) +
-    geom_point(
-        data = meta_tmp_ref,
-        aes(x = label, y = R_diff_perc, fill = Model1),
-        stat = "identity",
-        position = position_dodge(0.7), # Ensure same dodge as other points
-        size = 3,    # Increase size for emphasis
-        shape = 22,
-        stroke = 1.5,
-        show.legend=F
-    ) +
-    scale_y_continuous(labels = percent_format()) +
-    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype="dotted") +
-    labs(y = "R diff. (SE)") +
-    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-
+library(scales)
 ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
     geom_point(
         data = tmp,
@@ -2702,7 +2885,7 @@ meta_tmp_simple <- meta_tmp
 meta_tmp_simple$Model1<-as.character(meta_tmp_simple$Model1)
 meta_tmp_simple$Model1[meta_tmp_simple$Model1 == 'Multi-SumStatTrain'] <- 'IndivTrain of SumStatTrain'
 meta_tmp_simple$Model1[meta_tmp_simple$Model1 == 'Multi-IndivTrain'] <- 'IndivTrain'
-meta_tmp_ref_simple$Model1<-factor(meta_tmp_simple$Model1, levels = c('IndivTrain', 'SumStatTrain', 'IndivTrain of SumStatTrain'))
+meta_tmp_simple$Model1<-factor(meta_tmp_simple$Model1, levels = c('IndivTrain', 'SumStatTrain', 'IndivTrain of SumStatTrain'))
 meta_tmp_simple$Model2<-as.character(meta_tmp_simple$Model2)
 meta_tmp_simple$Model2[meta_tmp_simple$Model2 == 'Multi-SumStatTrain'] <- 'IndivTrain of SumStatTrain'
 meta_tmp_simple$Model2[meta_tmp_simple$Model2 == 'Multi-IndivTrain'] <- 'IndivTrain'
diff --git a/functions/constants.R b/functions/constants.R
index a8de2a58..4c2f4edd 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -37,14 +37,14 @@ ref_pop <- data.frame(
 
 # Make a data.frame giving labels to the 1KG reference populations
 pgs_method_labels <- data.frame(
-  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','tlprs','quickprs_multi'),
-  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','TL-PRS','QuickPRS-Multi')
+  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','tlprs','ptclump_multi','dbslmm_multi','ldpred2_multi','sbayesr_multi','sbayesrc_multi','lassosum_multi','prscs_multi','megaprs_multi','quickprs_multi'),
+  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','TL-PRS','pT+clump-Multi','DBSLMM-Multi','LDpred2-Multi','SBayesR-Multi','SBayesRC-Multi','lassosum-Multi','PRS-CS-Multi','MegaPRS-Multi','QuickPRS-Multi')
 )
 pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),]
 
-# Make vector indicating pgs_methods that can be applied to non-european GWAS
+# Make vector indicating single source pgs_methods that can be applied to non-european GWAS
 pgs_methods_noneur <- c('ptclump','lassosum','megaprs','prscs','dbslmm','ldpred2','quickprs','sbayesrc')
 
 # Make vector indicating pgs_methods that are to be applied to gwas_groups
-pgs_group_methods <- c('prscsx','xwing','tlprs','quickprs_multi')
+pgs_group_methods <- c('prscsx','xwing')
 
diff --git a/functions/pgs.R b/functions/pgs.R
index a49120ec..5ed03511 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -54,14 +54,20 @@ list_score_files <- function(config){
     # Retain methods that are applied to groups of gwas
     pgs_methods_list <- pgs_methods_list[(pgs_methods_list %in% pgs_group_methods)]
 
-    if('tlprs' %in% pgs_methods_list){
-      # For TL-PRS, list combos for tlprs_methods
-      tlprs_methods_list <- read_param(config = config, param = 'tlprs_methods', return_obj = F)
-      combos <- rbind(combos, expand.grid(name = gwas_groups$name, method = paste0('tlprs_', tlprs_methods_list)))
+    # For TL-PRS, list combos for tlprs_methods
+    tlprs_methods<-read_param(config = config, param = 'tlprs_methods', return_obj = F)
+    if(length(tlprs_methods) > 1 || !is.na(tlprs_methods)){
+      combos <- rbind(combos, expand.grid(name = gwas_groups$name, method = paste0('tlprs_', tlprs_methods)))
+    }
+    
+    # For LEOPARD, list combos for leopard_methods
+    leopard_methods<-read_param(config = config, param = 'leopard_methods', return_obj = F)
+    if(length(leopard_methods) > 1 || !is.na(leopard_methods)){
+      combos <- rbind(combos, expand.grid(name = gwas_groups$name, method = paste0(leopard_methods,'_multi')))
     }
 
     # Provide combos for other methods applied to groups of gwas
-    pgs_methods_list <- pgs_methods_list[pgs_methods_list != 'tlprs']
+    pgs_methods_list <- pgs_methods_list[!(pgs_methods_list %in% c('tlprs','leopard'))]
     combos <- rbind(combos, expand.grid(name = gwas_groups$name, method = pgs_methods_list))
   }
 
diff --git a/functions/pipeline.R b/functions/pipeline.R
index c6e96e80..f65bedd6 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -310,7 +310,7 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
     }
     pseudo_val <- paste0('targ_', target_pop, '_weighted')
   }
-  if(pgs_method == 'quickprs_multi'){
+  if(grepl('_multi$', pgs_method)){
     if(!is.null(target_pop) && target_pop == 'TRANS'){
       cat('No pseudovalidation for TRANS target population available for xwing.\n')
       cat(paste0('Returning result for ', gwas_list$population[1],' target population.\n'))
diff --git a/pipeline/config.yaml b/pipeline/config.yaml
index eb44f5a0..4e115c6e 100644
--- a/pipeline/config.yaml
+++ b/pipeline/config.yaml
@@ -22,12 +22,9 @@ score_list: NA
 # Specify location of gwas_groups file
 gwas_groups: NA
 
-# Specify pgs_methods ('ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs','prscsx','quickprs','sbayesrc','leopard')
+# Specify pgs_methods ('ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs','quickprs','sbayesrc','prscsx','xwing')
 pgs_methods: NA
 
-# Specify whether LEOPARD should be used to combine population-specific PGS
-leopard: true
-
 # Specify p-value thresholds for ptclump
 ptclump_pts: ['5e-8', '1e-6', '1e-4', '1e-2', '0.1', '0.2', '0.3', '0.4', '0.5', '1']
 
@@ -85,5 +82,8 @@ cores_outlier_detection: 5
 # Specify PGS methods that should be used by TL-PRS
 tlprs_methods: NA
 
+# Specify PGS methods that should be reweighted according to LEOPARD ('ptclump','dbslmm','prscs','lassosum','ldpred2','megaprs','quickprs','sbayesrc')
+leopard_methods: NA
+
 # Specify PGS scaling approach
 pgs_scaling: ['continuous']
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 51e6038d..044b71de 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -311,7 +311,7 @@ if 'sbayesr' in config['pgs_methods']:
         raise FileNotFoundError(f"Required file not found: {ld_file}. SBayesR reference data must include files for all chromosomes.")
 
 # Set quickprs reference path
-if any(method in config['pgs_methods'] for method in ['quickprs', 'quickprs_multi']):
+if (config["leopard_methods"] and config["leopard_methods"] != "NA") or "quickprs" in config["pgs_methods"]:
   if config['quickprs_ldref'] == 'NA':
     quickprs_ldref=f"{resdir}/data/quickprs"
     
@@ -337,7 +337,7 @@ if any(method in config['pgs_methods'] for method in ['quickprs', 'quickprs_mult
 
 # Set quickprs_multi reference path
 quickprs_multi_ldref=config['quickprs_multi_ldref']
-if 'quickprs_multi' in config['pgs_methods']:
+if config["leopard_methods"] and config["leopard_methods"] != "NA":
   missing_files = []
   for pop in gwas_list_df['population'].unique():
     path = f"{quickprs_multi_ldref}/{pop}"
@@ -417,7 +417,7 @@ def check_pgs_methods(x):
         return
 
     valid_pgs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesr","sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs", "xwing", "prscsx", "tlprs","quickprs_multi"
+        "ptclump", "dbslmm", "prscs", "sbayesr","sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs", "xwing", "prscsx", "tlprs"
     }
 
     invalid_methods = [method for method in x if method not in valid_pgs_methods]
@@ -431,19 +431,13 @@ check_pgs_methods(config['pgs_methods'])
 # Check valid tlprs_methods are specified
 def check_tlprs_methods(config):
     valid_tlprs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesr", "sbayesrc", "lassosum", "ldpred2", "megaprs","quickprs"
+        "ptclump", "dbslmm", "prscs", "sbayesrc", "lassosum", "ldpred2", "megaprs","quickprs"
     }
 
-    # Check if 'tlprs' is in the pgs_methods list
-    if 'tlprs' in config.get('pgs_methods', []):
-        # Check if tlprs_methods is defined and not None/NA
-        tlprs_methods = config.get('tlprs_methods')
-
-        if tlprs_methods is None or tlprs_methods == 'NA':
-            raise ValueError("tlprs_methods must be specified when 'tlprs' is included in pgs_methods.")
-
+    # Check if 'tlprs_methods' is empty
+    if config["tlprs_methods"] and config["tlprs_methods"] != "NA":
         # Check for invalid methods
-        invalid_methods = [method for method in tlprs_methods if method not in valid_tlprs_methods]
+        invalid_methods = [method for method in config["tlprs_methods"] if method not in valid_tlprs_methods]
 
         if invalid_methods:
             raise ValueError(f"Invalid tlprs_methods specified: {', '.join(invalid_methods)}. "
@@ -451,6 +445,23 @@ def check_tlprs_methods(config):
 
 check_tlprs_methods(config)
 
+# Check valid leopard_methods are specified
+def check_leopard_methods(config):
+    valid_leopard_methods = {
+        "ptclump", "dbslmm", "prscs", "sbayesrc", "lassosum", "ldpred2", "megaprs","quickprs"
+    }
+
+    # Check if 'leopard_methods' is empty
+    if config["leopard_methods"] and config["leopard_methods"] != "NA":
+        # Check for invalid methods
+        invalid_methods = [method for method in config["leopard_methods"] if method not in valid_leopard_methods]
+
+        if invalid_methods:
+            raise ValueError(f"Invalid leopard_methods specified: {', '.join(invalid_methods)}. "
+                             f"Valid methods are: {', '.join(valid_leopard_methods)}.")
+
+check_leopard_methods(config)
+
 ########
 # Check for repo version updates
 ########
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 0eed3a40..618b9bb8 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -620,8 +620,8 @@ rule leopard_quickprs:
 # Combine single-source PGS using LEOPARD weights
 ####
 
-# Define the single_source methods
-single_source_methods = {"ptclump", "dbslmm", "prscs", "sbayesr", "sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs"}
+# Define the single_source methods that can be applied to non-EUR data
+single_source_methods = {"ptclump", "dbslmm", "prscs", "sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs"}
 
 # Find which single source methods have been requested
 requested_single_source_methods = list(single_source_methods.intersection(pgs_methods_all))
@@ -645,7 +645,7 @@ rule prep_pgs_multi_i:
     testing=config["testing"],
     config_file = config["config_file"]
   shell:
-    "Rscript ../Scripts/pgs_methods/quickprs_multi.R \
+    "Rscript ../Scripts/pgs_methods/apply_leopard_weights.R \
       --config {params.config_file} \
       --gwas_group {wildcards.gwas_group} \
       --method {wildcards.method} \
@@ -886,8 +886,8 @@ if 'sbayesrc' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_sbayesrc.input)
 if 'external' in pgs_methods_all:
   pgs_methods_input.append(rules.score_reporter.output)
-if config["leopard"]:
-  pgs_methods_input.append(rules.prep_pgs_multi.output)
+if config["leopard_methods"] and config["leopard_methods"] != "NA":
+  pgs_methods_input.append(rules.prep_pgs_multi.input)
 if 'prscsx' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_prscsx.input)
 if 'xwing' in pgs_methods_all:
@@ -900,3 +900,5 @@ if 'bridgeprs' in pgs_methods_all:
 rule prep_pgs:
   input:
     pgs_methods_input
+    
+  
\ No newline at end of file
diff --git a/pipeline/rules/target_scoring.smk b/pipeline/rules/target_scoring.smk
index 2365035a..6d361a04 100644
--- a/pipeline/rules/target_scoring.smk
+++ b/pipeline/rules/target_scoring.smk
@@ -78,7 +78,7 @@ rule pc_projection:
 rule target_pgs_i:
   resources:
     mem_mb=config['mem_target_pgs'],
-    time_min=4000
+    time_min=1000
   threads: config['cores_target_pgs']
   input:
     f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",

From f9d7e05f462d3ab420211713db2b1e51dbafa946 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 18 Feb 2025 17:38:01 +0000
Subject: [PATCH 110/153] Updates

---
 docs/CrossPop.Rmd | 341 ++++++++++++++++++++++++----------------------
 1 file changed, 180 insertions(+), 161 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 6f0d7022..57d1a4b5 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -2067,7 +2067,7 @@ for(trait_i in selected_traits){
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-for targ_pop in $(echo EUR); do
+for targ_pop in $(echo EUR EAS AFR); do
   if [ "$targ_pop" == "EUR" ]; then
       targ_pop2="EUR_test"
   else
@@ -2121,6 +2121,7 @@ source_all('../functions')
 
 # Read in list of outcomes 
 selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
 
 # Calculate correlation between all phenotypes in each target population
 cors <- list()
@@ -2230,6 +2231,76 @@ for(pheno_i in selected_traits){
   
 }
 
+res_eval_simp <- NULL
+for(pheno_i in selected_traits){
+  tmp <- res_eval[[pheno_i]]
+  tmp$Trait <- pheno_i
+  
+  # Insert nice PGS method names
+  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
+  tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)], '-Multi')
+  tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
+  
+  # Simplify result to either SumStatTrain or IndivTrain
+  tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
+  tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+  tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery','Model'), with=F]),]
+  
+  res_eval_simp <- rbind(res_eval_simp, tmp)
+}
+
+# Plot results for each phenotype separately
+dir.create('~/oliverpainfel/Analyses/crosspop/plots')
+
+for(pheno_i in selected_traits){
+  tmp <- res_eval_simp[res_eval_simp$Trait == pheno_i,]
+
+  tmp$Discovery_clean <- as.character(tmp$Discovery)
+  tmp$Discovery_clean <- paste0(tmp$Discovery_clean, ' GWAS')
+  tmp$Target <- paste0(tmp$Target, ' Target')
+
+  png(paste0('~/oliverpainfel/Analyses/crosspop/plots/', pheno_i,'.png'), res=300, width = 3200, height = 2000, units = 'px')
+  plot_tmp<-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x=NULL, fill = NULL, title = info_all$`Trait Description`[info_all$`Trait Label` == pheno_i]) +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+  print(plot_tmp)
+  dev.off()
+}
+
+# Compare performance of each method in each target population across values of GWAS sample size
+for(targ_pop_i in targ_pop){
+    tmp <- res_eval_simp[res_eval_simp$Target == targ_pop_i,]
+    for(disc_pop_i in unique(tmp$Discovery)){
+      tmp2 <- tmp[tmp$Discovery == disc_pop_i,]
+      
+      ggplot(tmp2, aes(x=label, y=R , fill = Model)) +
+        geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                      width = 0,
+                      position = position_dodge(width = 1)) +
+        geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+        geom_vline(xintercept = seq(1.5, length(unique(tmp2$Trait))), linetype="dotted") +
+        labs(y = "R (SE)", x=NULL, fill = NULL, title = info_all$`Trait Description`[info_all$`Trait Label` == pheno_i]) +
+        facet_grid(. ~ Trait, scales='free', space = 'free_x') +
+        theme_half_open() +
+        background_grid(major = 'y', minor = 'y') + 
+        panel_border() + 
+        theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+      
+    }
+}
+
 ####
 # Average results across phenotypes
 ####
@@ -2309,18 +2380,7 @@ for(targ_pop_i in targ_pop){
 meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
 meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-####
-# Comparison of models across methods, across target and discovery populations
-####
-
-model_palette <- c(
-  "IndivTrain" = "#F8766D",
-  "SumStatTrain" = "#7CAE00",
-  "Multi-IndivTrain" = "#00BFC4",
-  "Multi-SumStatTrain" = "#C77CFF"
-)
-
-# Format results for nice plot
+# Plot average performance across phenotypes for AFR and EAS targets
 tmp <- meta_res_eval
 tmp <- tmp[tmp$Target != 'EUR',]
 tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
@@ -2334,14 +2394,13 @@ tmp$Discovery_clean <- factor(tmp$Discovery_clean,
                               levels = c('Target-matched GWAS',
                                          'EUR GWAS',
                                          'Both'))
-
 tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
+tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
-tmp$Model <- factor(tmp$Model, levels = names(model_palette))
-
-# Plot everything
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
 ggplot(tmp, aes(x=label, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
                   position = position_dodge(width = 1)) +
@@ -2350,86 +2409,13 @@ ggplot(tmp, aes(x=label, y=R , fill = Model)) +
     labs(y = "R (SE)", x='Method') +
     facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
     theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
-    scale_fill_manual(values = model_palette)
-
-# Simplify results showing results only with or without training data
-tmp_multi <- tmp[tmp$Discovery_clean == 'Both',]
-tmp_multi <- tmp
-tmp_multi$Model[tmp_multi$Model != 'SumStatTrain'] <- 'IndivTrain'
-tmp_multi$Model[tmp_multi$Model == 'SumStatTrain'] <- 'SumStatTrain'
-tmp_multi <- tmp_multi[!duplicated(tmp_multi[, c('label','Target','Discovery_clean','Model'), with=F]),]
-
-ggplot(tmp_multi, aes(x=label, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
-    scale_fill_manual(values = model_palette)
-
-
-# Format results for nice plot
-tmp <- meta_res_eval
-tmp <- tmp[tmp$Target == 'EUR',]
-tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
-tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)], '-Multi')
-tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
-tmp$Discovery_clean <- as.character(tmp$Discovery)
-tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
-tmp$Discovery_clean[tmp$Discovery == 'EAS'] <- 'EAS GWAS'
-tmp$Discovery_clean[tmp$Discovery == 'AFR'] <- 'AFR GWAS'
-tmp$Discovery_clean[tmp$Source == 'Multi'] <- 'Both'
-tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
-                              levels = c(
-                                         'EUR GWAS',
-                                         'EAS GWAS',
-                                         'AFR GWAS',
-                                         'Both'))
-
-tmp$Target <- paste0(tmp$Target, ' Target')
-
-tmp$Model <- factor(tmp$Model, levels = names(model_palette))
-
-# Plot everything
-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(gwas_group ~ Discovery_clean, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
-    scale_fill_manual(values = model_palette)
-
-ggplot(tmp[tmp$Discovery_clean %in% c('EUR GWAS','Both'),], aes(x=label, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(gwas_group ~ Discovery_clean, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
+    background_grid(major = 'y', minor = 'y') + 
     panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +
-    scale_fill_manual(values = model_palette)
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
 
 ####
 # Create heatmap showing difference between all methods and models
@@ -2457,7 +2443,7 @@ mirror_comp<-function(x){
 }
   
 # Read in results
-targ_pop=c('EAS','AFR')
+targ_pop=c('EUR','EAS','AFR')
 res_comp <- list()
 for(pheno_i in selected_traits){
   res_comp_i<-NULL
@@ -2654,67 +2640,82 @@ meta_res_comp$R_diff_catagory <- factor(meta_res_comp$R_diff_catagory, levels =
 meta_res_comp$indep_star<-' '
 meta_res_comp$indep_star[meta_res_comp$R_diff_p < 0.05]<-'*'
 meta_res_comp$indep_star[meta_res_comp$R_diff_p < 1e-3]<-'**'
-meta_res_comp$indep_star[meta_res_comp$R_diff_p < 1e-6]<-'***'
+# meta_res_comp$indep_star[meta_res_comp$R_diff_p < 1e-6]<-'***'
 
 meta_res_comp<-meta_res_comp[order(meta_res_comp$Discovery1, meta_res_comp$Discovery2, meta_res_comp$Method1),]
 
-heatmap_list<-NULL
-for(model_i in c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain')){
-  heatmap_list[[model_i]]<-list()
-  for(targ_pop_i in targ_pop){
-    if(targ_pop_i == 'EAS'){
-      disc_pop <- 'EAS'
-    }
-    if(targ_pop_i == 'AFR'){
-      disc_pop <- 'AFR'
-    }
-    if(targ_pop_i == 'EUR'){
-      disc_pop <- c('EAS','AFR')
-    }
-    heatmap_list[[model_i]][[targ_pop_i]]<-list()
-    for(disc_pop_i in disc_pop){
-  
-      tmp <- meta_res_comp[meta_res_comp$Target == targ_pop_i, ]
-      tmp <- tmp[tmp$gwas_group == paste0('EUR+', disc_pop_i), ]
-      tmp <- tmp[tmp$Model1 == model_i & tmp$Model2 == model_i,]
-      tmp$Model_1<-gsub('\\..*','', tmp$Model_1)
-      tmp$Model_2<-gsub('\\..*','', tmp$Model_2)
-      
-      tmp$Model_1 <- factor(
-        tmp$Model_1, 
-        levels=unique(tmp$Model_1))
-      tmp$Model_2 <- factor(
-        tmp$Model_2, 
-        levels=unique(tmp$Model_1))
-      
-      heatmap_list[[model_i]][[targ_pop_i]][[disc_pop_i]] <-
-        ggplot(data = tmp, aes(Model_2, Model_1, fill = R_diff_catagory)) +
-        geom_tile(color = "white", show.legend = TRUE) +
-        labs(y = 'Test', x = 'Comparison', fill = 'R difference', title = paste0('Target: ', targ_pop_i, ', Model = ', model_i)) +
-        facet_grid(Discovery1 ~ Discovery2, scales = 'free', space = 'free', switch="both") +
-        geom_text(
-          data = tmp,
-          aes(Model_2, Model_1, label = indep_star),
-          color = "black",
-          size = 4,
-          angle = 0,
-          vjust = 0.8
-        ) +
-        scale_fill_brewer(
-          breaks = levels(tmp$R_diff_catagory),
-          palette = "RdBu",
-          drop = F,
-          na.value = 'grey'
-        ) +
-        theme_half_open() +
-        background_grid() +
-        panel_border() +
-        theme(axis.text.x = element_text(
-          angle = 45,
-          vjust = 1,
-          hjust = 1
-        ))
-    }
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+
+    tmp <- meta_res_comp[meta_res_comp$Target == targ_pop_i, ]
+
+    tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method')
+    names(tmp)[names(tmp) == 'label'] <- 'label1'
+    tmp <- merge(tmp, pgs_method_labels, by.x = 'Method2', by.y = 'method')
+    names(tmp)[names(tmp) == 'label'] <- 'label2'
+    
+    tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods)] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods)], '-Multi')
+    tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods)] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods)], '-Multi')
+    
+    tmp$Model1[tmp$Model1 != 'SumStatTrain'] <- 'IndivTrain'
+    tmp$Model1[tmp$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
+    tmp$Model2[tmp$Model2 != 'SumStatTrain'] <- 'IndivTrain'
+    tmp$Model2[tmp$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+    
+    tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+    tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+    tmp$label1 <- paste0(tmp$label1," (", ifelse(tmp$Model1 == 'SumStatTrain', 'ST', 'IT'), ")")
+    tmp$label2 <- paste0(tmp$label2," (", ifelse(tmp$Model2 == 'SumStatTrain', 'ST', 'IT'), ")")
+    
+    tmp <- tmp[tmp$gwas_group == paste0('EUR+', disc_pop_i), ]
+
+    tmp$label1 <- factor(
+      tmp$label1, 
+      levels=unique(tmp$label1))
+    tmp$label2 <- factor(
+      tmp$label2, 
+      levels=unique(tmp$label2))
+    
+    plot_tmp <- ggplot(data = tmp, aes(label2, label1, fill = R_diff_catagory)) +
+      geom_tile(color = "white", show.legend = TRUE) +
+      labs(y = 'Test', x = 'Comparison', fill = 'R difference', title = paste0('Target: ', targ_pop_i)) +
+      facet_grid(Discovery1 ~ Discovery2, scales = 'free', space = 'free', switch="both") +
+      geom_text(
+        data = tmp,
+        aes(label2, label1, label = indep_star),
+        color = "black",
+        size = 4,
+        angle = 0,
+        vjust = 0.8
+      ) +
+      scale_fill_brewer(
+        breaks = levels(tmp$R_diff_catagory),
+        palette = "RdBu",
+        drop = F,
+        na.value = 'grey'
+      ) +
+      theme_half_open() +
+      background_grid() +
+      panel_border() +
+      theme(axis.text.x = element_text(
+        angle = 45,
+        vjust = 1,
+        hjust = 1
+      ))
+    
+    png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_diff.Discovery_EUR_', disc_pop_i,'.Target_', targ_pop_i, '.png'), res=300, width = 4200, height = 3000, units = 'px')
+      print(plot_tmp)
+    dev.off()
   }
 }
 
@@ -4001,7 +4002,7 @@ outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 bm_files_i <- list.files(paste0(outdir, '/reference/benchmarks/'), full.names = T)
 
 # Subset benchmarks for pgs_methods
-bm_files_i <- bm_files_i[grepl('prep_pgs_', bm_files_i)]
+bm_files_i <- bm_files_i[grepl('prep_pgs_|leopard_quickprs_', bm_files_i)]
 
 # Subset to benchmarks for gwas/gwas_groups in config
 scores <- list_score_files(config)
@@ -4021,7 +4022,9 @@ bm_dat_all$rule <- gsub('-.*','',bm_dat_all$file)
 bm_dat_all$method <-
   gsub('_i', '', gsub('prep_pgs_', '', bm_dat_all$rule))
 
-bm_dat_all <- merge(bm_dat_all, pgs_method_labels, by = 'method')
+bm_dat_all <- merge(bm_dat_all, pgs_method_labels, by = 'method', all.x=T)
+
+bm_dat_all$label[bm_dat_all$method == 'leopard_quickprs']<-"LEOPARD (QuickPRS)"
 
 # Calculate average time taken for each method
 method_avg <- NULL
@@ -4029,7 +4032,7 @@ for(i in unique(bm_dat_all$label)){
   method_avg <- rbind(
     method_avg,
     data.frame(
-      method = pgs_method_labels$method[pgs_method_labels$label == i],
+      method = bm_dat_all$method[bm_dat_all$label == i][1],
       Method = i,
       Time = mean(bm_dat_all$s[bm_dat_all$label == i])
     )
@@ -4052,6 +4055,9 @@ for(i in unique(bm_dat_all$label)){
   )
 }
 
+# Divide X-Wing memory by two, since it used 20 cores, but other methods used 10
+method_avg$Memory[method_avg$method == 'xwing'] <- method_avg$Memory[method_avg$method == 'xwing'] /2
+
 # Format the time taken nicely
 method_avg$Time_clean[method_avg$Time < 60] <-
   paste0(round(method_avg$Time[method_avg$Time < 60], 1), ' sec')
@@ -4066,9 +4072,22 @@ method_avg$Time_hour <- method_avg$Time / 60/60
 # Seperate methods by single or multi source
 method_avg$Type[!(method_avg$method %in% pgs_group_methods)]<-'Single-source'
 method_avg$Type[method_avg$method %in% pgs_group_methods]<-'Multi-source'
+method_avg$Type[method_avg$method == 'leopard_quickprs']<-'Tuning'
+
+method_avg$Type<-factor(method_avg$Type, levels = c('Single-source','Multi-source','Tuning'))
+method_avg$Method <- factor(method_avg$Method, levels = c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "QuickPRS-Multi", "PRS-CSx", "X-Wing","LEOPARD (QuickPRS)"))
+
+ggplot(method_avg, aes(x = Method, y = Time_hour, fill = Method)) +
+  geom_bar(stat = "identity", position="dodge") +
+  geom_text(aes(label = Time_clean), vjust = 0.5, angle = 90, hjust = -0.2, position = position_dodge(width = 0.9)) +
+  labs(x = NULL, y = "Time (hours)") +
+  ylim(0, max(method_avg$Time_hour) + (max(method_avg$Time_hour)/5)) +
+  facet_grid(~ Type, scales='free', space = 'free_x') +
+  theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
 
-method_avg$Type<-factor(method_avg$Type, levels = c('Single-source','Multi-source'))
-method_avg$Method <- factor(method_avg$Method, levels = c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "QuickPRS-Multi", "PRS-CSx", "X-Wing"))
 
 
 

From 0c3f7614753da3e0dc89a33e8f2c75de85977075 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 20 Feb 2025 11:26:31 +0000
Subject: [PATCH 111/153] Updates

---
 docs/CrossPop.Rmd     | 438 ++++++++++++++++++++++++++++++++++++------
 functions/constants.R |   2 +-
 2 files changed, 376 insertions(+), 64 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 57d1a4b5..f536ed0e 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1817,7 +1817,7 @@ for(trait_i in selected_traits){
       # Make groups containing pseudo scores for single source methods
       ####
 
-      # Extract the pseudo score for each method and specify as a seperate group
+      # Extract the pseudo score for each method and specify as a separate group
       for(i in 1:nrow(scores_i_j_single_top1)) {
         param <- find_pseudo(
           config = config,
@@ -2020,7 +2020,7 @@ for(trait_i in selected_traits){
         
         scores_i_j_multi_pseudo <- rbind(scores_i_j_multi_pseudo, tmp)
       }
-
+      
       ####
       # Combine the different predictor groups
       ####
@@ -2035,6 +2035,14 @@ for(trait_i in selected_traits){
       
       predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
       
+      ####
+      # Make a group that will combined all population specific PGS
+      ####
+      
+      predictors_i_all <- predictors_i[predictors_i$top1 %in% c('EUR','AFR','EAS'),]
+      predictors_i_all$multi <- 'all'
+      predictors_i<-rbind(predictors_i, predictors_i_all)
+      
       write.table(
         predictors_i,
         paste0(
@@ -2067,6 +2075,8 @@ for(trait_i in selected_traits){
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/targ_*.disc_EUR_*/*/res*
+
 for targ_pop in $(echo EUR EAS AFR); do
   if [ "$targ_pop" == "EUR" ]; then
       targ_pop2="EUR_test"
@@ -2087,7 +2097,6 @@ for targ_pop in $(echo EUR EAS AFR); do
   fi
   
   for disc_pop_i in ${disc_pop}; do
-    #rm /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop}/*/res*
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
       if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt" ]; then
         sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
@@ -2231,15 +2240,19 @@ for(pheno_i in selected_traits){
   
 }
 
+# Create vector defining or of methods in plots
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi", "PRS-CSx", "X-Wing", "All") 
+
 res_eval_simp <- NULL
 for(pheno_i in selected_traits){
   tmp <- res_eval[[pheno_i]]
   tmp$Trait <- pheno_i
   
   # Insert nice PGS method names
-  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
-  tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)], '-Multi')
-  tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
+  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+  tmp$label[is.na(tmp$label)] <- 'All'
+  tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+  tmp$label <- factor(tmp$label, levels = model_order)
   
   # Simplify result to either SumStatTrain or IndivTrain
   tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
@@ -2249,17 +2262,105 @@ for(pheno_i in selected_traits){
   res_eval_simp <- rbind(res_eval_simp, tmp)
 }
 
+# Count the number of traits each method is best
+tmp <- res_eval_simp[res_eval_simp$label != 'All',]
+best_groups <-
+  do.call(rbind, by(tmp, list(
+    tmp$Target,
+    tmp$gwas_group,
+    tmp$Trait
+  ), function(subset) {
+    subset[which.max(subset$R),]  # Select row with max R
+  }))
+
+best_counts <- as.data.frame(table(paste0(best_groups$label,':', best_groups$Model), best_groups$gwas_group, best_groups$Target))
+
+# Rename columns
+colnames(best_counts) <- c("label", "gwas_group", "Target", "count")
+best_counts$Model<-gsub('.*:','',best_counts$label)
+best_counts$label<-gsub(':.*','',best_counts$label)
+best_counts$label <- factor(best_counts$label, levels = model_order)
+
+# Remove zero counts to declutter the plot
+best_counts <- best_counts[best_counts$count > 0, ]
+
+# Create the plot
+ggplot(best_counts[best_counts$Target != 'EUR',], aes(x = label, y = count, fill = Model)) +
+  geom_bar(stat = "identity", position = "dodge") +
+  facet_wrap(~ Target, scales = 'free_x') +
+  theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() + 
+  labs(
+    title = "Number of times each method is the best",
+    x = "Method",
+    y = "Count",
+    fill = "GWAS Group"
+  ) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+#############################
+# Identify best methods that improved prediction over next best method by 2% for any trait
+# Filter out 'All' from the data
+tmp <- res_eval_simp[res_eval_simp$label != 'All',]
+
+# Identify the best method for each trait, but only if it improves by >2%
+best_groups <- do.call(rbind, by(tmp, list(tmp$Target, tmp$gwas_group, tmp$Trait), function(subset) {
+  if (nrow(subset) > 1) {
+    # Sort by R in descending order
+    subset <- subset[order(-subset$R), ]
+    # Check if the best method is more than 2% better than the second best
+    if ((subset$R[1] - subset$R[2]) / subset$R[2] > 0.02) {
+      return(subset[1, ])  # Return the best method if criteria met
+    } 
+  } else {
+    return(subset[1, ])  # Handle cases with only one method
+  }
+  return(NULL)  # Return NULL if criteria not met
+}))
+
+# Create a count table with label and model combined
+best_counts <- as.data.frame(table(paste0(best_groups$label,':', best_groups$Model), 
+                                   best_groups$gwas_group, best_groups$Target))
+
+# Rename columns
+colnames(best_counts) <- c("label", "gwas_group", "Target", "count")
+best_counts$Model <- gsub('.*:', '', best_counts$label)
+best_counts$label <- gsub(':.*', '', best_counts$label)
+best_counts$label <- factor(best_counts$label, levels = model_order)
+
+# Remove zero counts to declutter the plot
+best_counts <- best_counts[best_counts$count > 0, ]
+
+# Create the plot
+library(ggplot2)
+ggplot(best_counts[best_counts$Target != 'EUR',], aes(x = label, y = count, fill = Model)) +
+  geom_bar(stat = "identity", position = "dodge") +
+  facet_wrap(~ Target, scales = 'free_x') +
+  theme_minimal() +
+  labs(
+    title = "Number of times each method is the best (with >2% improvement)",
+    x = "Method",
+    y = "Count",
+    fill = "Model"
+  ) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+
+#############################
+
+
 # Plot results for each phenotype separately
 dir.create('~/oliverpainfel/Analyses/crosspop/plots')
 
 for(pheno_i in selected_traits){
   tmp <- res_eval_simp[res_eval_simp$Trait == pheno_i,]
-
+  #tmp <- tmp[tmp$Target != 'EUR',]
   tmp$Discovery_clean <- as.character(tmp$Discovery)
   tmp$Discovery_clean <- paste0(tmp$Discovery_clean, ' GWAS')
   tmp$Target <- paste0(tmp$Target, ' Target')
 
-  png(paste0('~/oliverpainfel/Analyses/crosspop/plots/', pheno_i,'.png'), res=300, width = 3200, height = 2000, units = 'px')
+  png(paste0('~/oliverpainfel/Analyses/crosspop/plots/', pheno_i,'.png'), res=300, width = 3400, height = 2000, units = 'px')
   plot_tmp<-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
@@ -2280,6 +2381,7 @@ for(pheno_i in selected_traits){
 }
 
 # Compare performance of each method in each target population across values of GWAS sample size
+# Because we cannot hold all other things constant, this plot is confusing. I think relative performance might be easier to read.
 for(targ_pop_i in targ_pop){
     tmp <- res_eval_simp[res_eval_simp$Target == targ_pop_i,]
     for(disc_pop_i in unique(tmp$Discovery)){
@@ -2383,9 +2485,10 @@ meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','E
 # Plot average performance across phenotypes for AFR and EAS targets
 tmp <- meta_res_eval
 tmp <- tmp[tmp$Target != 'EUR',]
-tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
-tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)], '-Multi')
-tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
 tmp$Discovery_clean <- as.character(tmp$Discovery)
 tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
 tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
@@ -2417,6 +2520,69 @@ ggplot(tmp, aes(x=label, y=R , fill = Model)) +
           legend.justification = "center")
 dev.off()
 
+# Plot average performance across phenotypes for EUR using AFR or EAS GWAS
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target == 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean <- paste0(tmp$Discovery_clean, ' GWAS')
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
+tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.png'), res=300, width = 4000, height = 1500, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+# Plot performance of -multi models trained using LEOPARD vs using indiv-level data
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)], '-multi')
+tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
+tmp<-tmp[grepl('multi', tmp$label),]
+tmp <- tmp[tmp$Model != 'Multi-IndivTrain',]
+tmp$Model<-as.character(tmp$Model)
+tmp$Model[tmp$Model != 'SumStatTrain']<-'IndivTrain'
+tmp$Model[tmp$Model == 'SumStatTrain']<-'LEOPARD'
+tmp$Target <- paste0(tmp$Target, ' Target')
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_leopard.png'), res=300, width = 1500, height = 2000, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
 ####
 # Create heatmap showing difference between all methods and models
 ####
@@ -2509,9 +2675,9 @@ find_model<-function(x){
 res_comp_all$Model1<-find_model(res_comp_all$Model_1)
 res_comp_all$Model2<-find_model(res_comp_all$Model_2)
 
-res_comp_all$Source1<-ifelse(res_comp_all$Method1 %in% c(pgs_group_methods, 'all') | !grepl('AFR|EAS|EUR', res_comp_all$Model_1), 'Multi', 'Single')
-res_comp_all$Source2<-ifelse(res_comp_all$Method2 %in% c(pgs_group_methods, 'all') | !grepl('AFR|EAS|EUR', res_comp_all$Model_2), 'Multi', 'Single')
-
+res_comp_all$Source1<-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method1) | !grepl('AFR|EAS|EUR', res_comp_all$Model_1), 'Multi', 'Single')
+res_comp_all$Source2<-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method2) | !grepl('AFR|EAS|EUR', res_comp_all$Model_2), 'Multi', 'Single')
+  
 for(i in c('EUR','EAS','AFR')){
   res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] <- i
   res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] <- i
@@ -2553,6 +2719,26 @@ res_comp_all <- res_comp_all[
 # Remove any comparisons
 res_comp_all <- res_comp_all[!duplicated(res_comp_all[, c("Target", "Method1", "Model1", "Source1", "Discovery1", "Method2", "Model2", "Source2", "Discovery2",'pheno')]),]
 
+res_comp_all$r_diff_rel <- res_comp_all$R_diff / res_comp_all$Model_2_R
+
+# Calculate relative improvement for ldpred2-multi vs ldpred2 as example
+tmp_ldpred2 <- res_comp_all[res_comp_all$Model_1 == 'ldpred2.multi' & 
+                    grepl('ldpred2-', res_comp_all$Model_2) &
+                    res_comp_all$Target == 'AFR',]
+tmp_ldpred2 <- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+tmp_ldpred2 <- tmp_ldpred2[!duplicated(tmp_ldpred2$pheno),]
+round(min(tmp_ldpred2$r_diff_rel)*100, 1)
+round(max(tmp_ldpred2$r_diff_rel)*100, 1)
+
+tmp_ldpred2 <- res_comp_all[res_comp_all$Model_1 == 'ldpred2.multi' & 
+                    grepl('ldpred2-', res_comp_all$Model_2) &
+                    res_comp_all$Target == 'EAS',]
+tmp_ldpred2 <- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+tmp_ldpred2 <- tmp_ldpred2[!duplicated(tmp_ldpred2$pheno),]
+round(min(tmp_ldpred2$r_diff_rel)*100, 1)
+round(max(tmp_ldpred2$r_diff_rel)*100, 1)
+
+
 library(MAd)
 
 # Average R across phenotypes
@@ -2627,6 +2813,32 @@ for(targ_pop_i in targ_pop){
   }
 }
 
+meta_res_comp$R_diff_perc <- meta_res_comp$R_diff / meta_res_comp$Model_2_R
+  
+# Extract average improvement for ldpred2-multi vs ldpred2 as example
+tmp_ldpred2 <- meta_res_comp[meta_res_comp$Model_1 == 'ldpred2.multi' & 
+                    grepl('ldpred2-', meta_res_comp$Model_2) &
+                    meta_res_comp$Target == 'AFR',]
+tmp_ldpred2 <- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+round(min(tmp_ldpred2$R_diff_perc)*100, 1)
+
+tmp_ldpred2 <- meta_res_comp[meta_res_comp$Model_1 == 'ldpred2.multi' & 
+                    grepl('ldpred2-', meta_res_comp$Model_2) &
+                    meta_res_comp$Target == 'EAS',]
+tmp_ldpred2 <- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+round(min(tmp_ldpred2$R_diff_perc)*100, 1)
+
+# Compare QuickPRS-Multi vs QuickPRS to evaluate LEOPARD performance
+tmp_quickprs <- meta_res_comp[meta_res_comp$Model_1 == 'quickprs_multi.pseudo.multi' & 
+                                meta_res_comp$Model_2 == 'quickprs.pseudo.multi' &
+                    meta_res_comp$Target == 'AFR',]
+round(min(tmp_quickprs$R_diff_perc)*100, 1)
+
+tmp_quickprs <- meta_res_comp[meta_res_comp$Model_1 == 'quickprs_multi.pseudo.multi' & 
+                                meta_res_comp$Model_2 == 'quickprs.pseudo.multi' &
+                    meta_res_comp$Target == 'EAS',]
+round(min(tmp_quickprs$R_diff_perc)*100, 1)
+
 # Group differences
 meta_res_comp$R_diff_catagory <- cut(
     meta_res_comp$R_diff,
@@ -2658,13 +2870,15 @@ for(targ_pop_i in targ_pop){
 
     tmp <- meta_res_comp[meta_res_comp$Target == targ_pop_i, ]
 
-    tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method')
+    tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+    tmp$label[is.na(tmp$label)] <- 'All'
     names(tmp)[names(tmp) == 'label'] <- 'label1'
-    tmp <- merge(tmp, pgs_method_labels, by.x = 'Method2', by.y = 'method')
+    tmp <- merge(tmp, pgs_method_labels, by.x = 'Method2', by.y = 'method', all.x = T)
+    tmp$label[is.na(tmp$label)] <- 'All'
     names(tmp)[names(tmp) == 'label'] <- 'label2'
     
-    tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods)] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods)], '-Multi')
-    tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods)] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods)], '-Multi')
+    tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'], '-multi')
+    tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'], '-multi')
     
     tmp$Model1[tmp$Model1 != 'SumStatTrain'] <- 'IndivTrain'
     tmp$Model1[tmp$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
@@ -2674,17 +2888,18 @@ for(targ_pop_i in targ_pop){
     tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
     tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
 
+    tmp$label1 <- factor(tmp$label1, levels = model_order)
+    tmp$label2 <- factor(tmp$label2, levels = model_order)
+
+    tmp<-tmp[order(tmp$label1, tmp$label2),]
+    
     tmp$label1 <- paste0(tmp$label1," (", ifelse(tmp$Model1 == 'SumStatTrain', 'ST', 'IT'), ")")
     tmp$label2 <- paste0(tmp$label2," (", ifelse(tmp$Model2 == 'SumStatTrain', 'ST', 'IT'), ")")
+
+    tmp$label1 <- factor(tmp$label1, levels = unique(tmp$label1))
+    tmp$label2 <- factor(tmp$label2, levels = unique(tmp$label2))
     
     tmp <- tmp[tmp$gwas_group == paste0('EUR+', disc_pop_i), ]
-
-    tmp$label1 <- factor(
-      tmp$label1, 
-      levels=unique(tmp$label1))
-    tmp$label2 <- factor(
-      tmp$label2, 
-      levels=unique(tmp$label2))
     
     plot_tmp <- ggplot(data = tmp, aes(label2, label1, fill = R_diff_catagory)) +
       geom_tile(color = "white", show.legend = TRUE) +
@@ -2713,15 +2928,16 @@ for(targ_pop_i in targ_pop){
         hjust = 1
       ))
     
-    png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_diff.Discovery_EUR_', disc_pop_i,'.Target_', targ_pop_i, '.png'), res=300, width = 4200, height = 3000, units = 'px')
+    png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_diff.Discovery_EUR_', disc_pop_i,'.Target_', targ_pop_i, '.png'), res=300, width = 4400, height = 3200, units = 'px')
       print(plot_tmp)
     dev.off()
   }
 }
 
 ####
-# Plot R compared to top1 model where GWAS matches target population
+# Plot relative improvement of methods
 ####
+# Use ptclump IndivTrain using EUR GWAS as the reference, as provides an interpretable scale
 
 meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Model2 == 'IndivTrain' & meta_res_comp$Method2 == 'ptclump' & meta_res_comp$Discovery2 == 'EUR',]
 meta_res_comp_ptclump_top1$reference_point<-F
@@ -2738,18 +2954,11 @@ meta_res_comp_reference$R_diff[meta_res_comp_ptclump_top1$reference_point == F]
 meta_res_comp_reference$R_diff_SE [meta_res_comp_ptclump_top1$reference_point == F] <- NA
 res_comp_all_ptclump_top1$reference_point<-F
 
-# Format results for nice plot
-model_palette <- c(
-  "IndivTrain" = "#F8766D",
-  "SumStatTrain" = "#7CAE00",
-  "Multi-IndivTrain" = "#00BFC4",
-  "Multi-SumStatTrain" = "#C77CFF"
-)
-
 meta_tmp <- meta_res_comp_ptclump_top1
-meta_tmp <- merge(meta_tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method')
-meta_tmp$label[grepl('Multi', meta_tmp$Model1) & !(meta_tmp$Method1 %in% pgs_group_methods)] <- paste0(meta_tmp$label[grepl('Multi', meta_tmp$Model1) & !(meta_tmp$Method1 %in% pgs_group_methods)], '-Multi')
-meta_tmp$label <- factor(meta_tmp$label, levels = unique(meta_tmp$label[order(!(grepl('Multi', meta_tmp$label)), meta_tmp$label)]))
+meta_tmp <- merge(meta_tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+meta_tmp$label[is.na(meta_tmp$label)] <- 'All'
+meta_tmp$label[grepl('Multi', meta_tmp$Model1) & !(meta_tmp$Method1 %in% pgs_group_methods) & meta_tmp$label != 'All'] <- paste0(meta_tmp$label[grepl('Multi', meta_tmp$Model1) & !(meta_tmp$Method1 %in% pgs_group_methods) & meta_tmp$label != 'All'], '-multi')
+meta_tmp$label <- factor(meta_tmp$label, levels = model_order)
 meta_tmp$Discovery_clean <- as.character(meta_tmp$Discovery1)
 meta_tmp$Discovery_clean[meta_tmp$Discovery1 == 'EUR'] <- 'EUR GWAS'
 meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Single'] <- 'Target-matched GWAS'
@@ -2762,9 +2971,10 @@ meta_tmp$Target <- paste0(meta_tmp$Target, ' Target')
 meta_tmp$Model1 <- factor(meta_tmp$Model1, levels = names(model_palette))
 
 meta_tmp_ref <- meta_res_comp_reference
-meta_tmp_ref <- merge(meta_tmp_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method')
-meta_tmp_ref$label[grepl('Multi', meta_tmp_ref$Model1) & !(meta_tmp_ref$Method1 %in% pgs_group_methods)] <- paste0(meta_tmp_ref$label[grepl('Multi', meta_tmp_ref$Model1) & !(meta_tmp_ref$Method1 %in% pgs_group_methods)], '-Multi')
-meta_tmp_ref$label <- factor(meta_tmp_ref$label, levels = unique(meta_tmp_ref$label[order(!(grepl('Multi', meta_tmp_ref$label)), meta_tmp_ref$label)]))
+meta_tmp_ref <- merge(meta_tmp_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+meta_tmp_ref$label[is.na(meta_tmp_ref$label)] <- 'All'
+meta_tmp_ref$label[grepl('Multi', meta_tmp_ref$Model1) & !(meta_tmp_ref$Method1 %in% pgs_group_methods) & meta_tmp_ref$label != 'All'] <- paste0(meta_tmp_ref$label[grepl('Multi', meta_tmp_ref$Model1) & !(meta_tmp_ref$Method1 %in% pgs_group_methods) & meta_tmp_ref$label != 'All'], '-multi')
+meta_tmp_ref$label <- factor(meta_tmp_ref$label, levels = model_order)
 meta_tmp_ref$Discovery_clean <- as.character(meta_tmp_ref$Discovery1)
 meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 == 'EUR'] <- 'EUR GWAS'
 meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != 'EUR' & meta_tmp_ref$Source1 == 'Single'] <- 'Target-matched GWAS'
@@ -2777,9 +2987,10 @@ meta_tmp_ref$Target <- paste0(meta_tmp_ref$Target, ' Target')
 meta_tmp_ref$Model1 <- factor(meta_tmp_ref$Model1, levels = names(model_palette))
 
 tmp <- res_comp_all_ptclump_top1
-tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method')
-tmp$label[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods)], '-Multi')
-tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
 tmp$Discovery_clean <- as.character(tmp$Discovery1)
 tmp$Discovery_clean[tmp$Discovery1 == 'EUR'] <- 'EUR GWAS'
 tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Single'] <- 'Target-matched GWAS'
@@ -2883,27 +3094,128 @@ ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
 
 # Simplify results showing results only with or without training data
 meta_tmp_simple <- meta_tmp
-meta_tmp_simple$Model1<-as.character(meta_tmp_simple$Model1)
-meta_tmp_simple$Model1[meta_tmp_simple$Model1 == 'Multi-SumStatTrain'] <- 'IndivTrain of SumStatTrain'
-meta_tmp_simple$Model1[meta_tmp_simple$Model1 == 'Multi-IndivTrain'] <- 'IndivTrain'
-meta_tmp_simple$Model1<-factor(meta_tmp_simple$Model1, levels = c('IndivTrain', 'SumStatTrain', 'IndivTrain of SumStatTrain'))
-meta_tmp_simple$Model2<-as.character(meta_tmp_simple$Model2)
-meta_tmp_simple$Model2[meta_tmp_simple$Model2 == 'Multi-SumStatTrain'] <- 'IndivTrain of SumStatTrain'
-meta_tmp_simple$Model2[meta_tmp_simple$Model2 == 'Multi-IndivTrain'] <- 'IndivTrain'
-meta_tmp_simple$Model2<-factor(meta_tmp_simple$Model2, levels = c('IndivTrain', 'SumStatTrain', 'IndivTrain of SumStatTrain'))
-meta_tmp_simple <- meta_tmp_simple[!duplicated(meta_tmp_simple[, c('label','Target','Discovery_clean','Model1','Model2'), with=F]),]
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 != 'SumStatTrain'] <- 'IndivTrain'
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 != 'SumStatTrain'] <- 'IndivTrain'
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+meta_tmp_simple<-meta_tmp_simple[meta_tmp_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_simple<-meta_tmp_simple[meta_tmp_simple$Model_2 %in% res_eval_simp$Group,]
 
 meta_tmp_ref_simple <- meta_tmp_ref
-meta_tmp_ref_simple$Model1<-as.character(meta_tmp_ref_simple$Model1)
-meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == 'Multi-SumStatTrain'] <- 'IndivTrain of SumStatTrain'
-meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == 'Multi-IndivTrain'] <- 'IndivTrain'
-meta_tmp_ref_simple$Model1<-factor(meta_tmp_ref_simple$Model1, levels = c('IndivTrain', 'SumStatTrain', 'IndivTrain of SumStatTrain'))
-meta_tmp_ref_simple$Model2<-as.character(meta_tmp_ref_simple$Model2)
-meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == 'Multi-SumStatTrain'] <- 'IndivTrain of SumStatTrain'
-meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == 'Multi-IndivTrain'] <- 'IndivTrain'
-meta_tmp_ref_simple$Model2<-factor(meta_tmp_ref_simple$Model2, levels = c('IndivTrain', 'SumStatTrain', 'IndivTrain of SumStatTrain'))
-
-meta_tmp_ref_simple <- meta_tmp_ref_simple[!duplicated(meta_tmp_ref_simple[, c('label','Target','Discovery_clean','Model1','Model2'), with=F]),]
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 != 'SumStatTrain'] <- 'IndivTrain'
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 != 'SumStatTrain'] <- 'IndivTrain'
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+meta_tmp_ref_simple<-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_ref_simple<-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_2 %in% res_eval_simp$Group,]
+
+tmp_simple <- tmp
+tmp_simple$Model1[tmp_simple$Model1 != 'SumStatTrain'] <- 'IndivTrain'
+tmp_simple$Model1[tmp_simple$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
+tmp_simple$Model2[tmp_simple$Model2 != 'SumStatTrain'] <- 'IndivTrain'
+tmp_simple$Model2[tmp_simple$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+tmp_simple<-tmp_simple[tmp_simple$Model_1 %in% res_eval_simp$Group,]
+tmp_simple<-tmp_simple[tmp_simple$Model_2 %in% res_eval_simp$Group,]
+
+# Export plot for manuscript
+png('~/oliverpainfel/Analyses/crosspop/plots/average_r.perc_improv.png', width = 3200, height = 2000, res= 300, units = 'px')
+ggplot(meta_tmp_simple[meta_tmp_simple$Target != 'EUR Target',], aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp_simple[tmp_simple$Target != 'EUR Target',],
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target != 'EUR Target',],
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 4,
+        shape = 22,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype="dotted") +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = "top",
+        legend.key.spacing.x = unit(2, "cm"),
+        legend.justification = "center"
+    )
+dev.off()
+
+# Plot for EUR
+meta_tmp_simple$Discovery_clean <- paste0(meta_tmp_simple$Discovery1,' GWAS')
+meta_tmp_ref_simple$Discovery_clean <- paste0(meta_tmp_ref_simple$Discovery1,' GWAS')
+tmp_simple$Discovery_clean <- paste0(tmp_simple$Discovery1,' GWAS')
+
+png('~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.perc_improv.png', width = 4000, height = 1500, res= 300, units = 'px')
+ggplot(meta_tmp_simple[meta_tmp_simple$Target == 'EUR Target',], aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp_simple[tmp_simple$Target == 'EUR Target',],
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target == 'EUR Target',],
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 4,
+        shape = 22,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype="dotted") +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = "top",
+        legend.key.spacing.x = unit(2, "cm"),
+        legend.justification = "center"
+    )
+dev.off()
 
 # Export plot for poster presentation
 png('poster_plot1.png', width = 54, height = 36, res= 300, units = 'cm')
diff --git a/functions/constants.R b/functions/constants.R
index 4c2f4edd..60a4d98c 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -38,7 +38,7 @@ ref_pop <- data.frame(
 # Make a data.frame giving labels to the 1KG reference populations
 pgs_method_labels <- data.frame(
   method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','tlprs','ptclump_multi','dbslmm_multi','ldpred2_multi','sbayesr_multi','sbayesrc_multi','lassosum_multi','prscs_multi','megaprs_multi','quickprs_multi'),
-  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','TL-PRS','pT+clump-Multi','DBSLMM-Multi','LDpred2-Multi','SBayesR-Multi','SBayesRC-Multi','lassosum-Multi','PRS-CS-Multi','MegaPRS-Multi','QuickPRS-Multi')
+  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','TL-PRS','pT+clump-multi','DBSLMM-multi','LDpred2-multi','SBayesR-multi','SBayesRC-multi','lassosum-multi','PRS-CS-multi','MegaPRS-multi','QuickPRS-multi')
 )
 pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),]
 

From 4192f4d5acd02cb9a45381d2b1887f33c06daf29 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 25 Feb 2025 08:25:43 +0000
Subject: [PATCH 112/153] Updates

---
 docs/CrossPop.Rmd               | 667 +++++++++++++++++++++++---------
 functions/constants.R           |   4 +-
 pipeline/rules/dependencies.smk |   2 +-
 pipeline/rules/pgs_methods.smk  |   4 +-
 4 files changed, 494 insertions(+), 183 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index f536ed0e..f3d442be 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1675,8 +1675,8 @@ config<-c(
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt",
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
-  "pgs_methods: ['ptclump','quickprs','quickprs_multi','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx','xwing']",
-#  "tlprs_methods: ['dbslmm','prscs','lassosum']",
+  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx','xwing']",
+  "tlprs_methods: ['dbslmm']",
   "leopard_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
   "cores_prep_pgs: 10", # xwing run with 20 cores
   "cores_target_pgs: 50",
@@ -1701,7 +1701,7 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml \
-  prep_pgs  -n
+  target_pgs  -n
 ```
 
 ***
@@ -2738,6 +2738,62 @@ tmp_ldpred2 <- tmp_ldpred2[!duplicated(tmp_ldpred2$pheno),]
 round(min(tmp_ldpred2$r_diff_rel)*100, 1)
 round(max(tmp_ldpred2$r_diff_rel)*100, 1)
 
+# Calculate relative improvement for sbayesrc-multi vs sbayesrc in EUR target as example
+tmp_sbayesrc <- res_comp_all[res_comp_all$Model_1 == 'sbayesrc.pseudo.multi' & 
+                    grepl('sbayesrc.pseudo-', res_comp_all$Model_2) &
+                    res_comp_all$Target == 'EUR' &
+                    res_comp_all$Discovery1 == 'EUR+EAS' &
+                    res_comp_all$Discovery2 == 'EUR',]
+tmp_sbayesrc <- tmp_sbayesrc[order(-tmp_sbayesrc$Model_2_R),]
+round(min(tmp_sbayesrc$r_diff_rel)*100, 1)
+round(max(tmp_sbayesrc$r_diff_rel)*100, 1)
+
+tmp_sbayesrc <- res_comp_all[res_comp_all$Model_1 == 'sbayesrc.pseudo.multi' & 
+                    grepl('sbayesrc.pseudo-', res_comp_all$Model_2) &
+                    res_comp_all$Target == 'EUR' &
+                    res_comp_all$Discovery1 == 'EUR+AFR' &
+                    res_comp_all$Discovery2 == 'EUR',]
+tmp_sbayesrc <- tmp_sbayesrc[order(-tmp_sbayesrc$Model_2_R),]
+round(min(tmp_sbayesrc$r_diff_rel)*100, 1)
+round(max(tmp_sbayesrc$r_diff_rel)*100, 1)
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTrain or SumStatTrain result
+tmp <- res_comp_all
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label1'
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method2', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label2'
+
+tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'], '-multi')
+tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'], '-multi')
+
+tmp$Model1[tmp$Model1 != 'SumStatTrain'] <- 'IndivTrain'
+tmp$Model1[tmp$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
+tmp$Model2[tmp$Model2 != 'SumStatTrain'] <- 'IndivTrain'
+tmp$Model2[tmp$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+
+tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` <- paste0(tmp$label1, ' - ', tmp$Model1, ' - ', tmp$Discovery1)
+tmp$`Model 2` <- paste0(tmp$label2, ' - ', tmp$Model2, ' - ', tmp$Discovery2)
+
+tmp <- tmp[, c('Target', 'pheno', 'Model 1', 'Model 2', 'Model_1_R', 'Model_2_R', 'R_diff', 'R_diff_pval'), with=F]
+names(tmp) <- c('Target', 'Trait','Model 1', 'Model 2', "R (Model 1)", "R (Model 2)", "R difference (Model 1 R - Model 2 R)", "R difference p-value")
+
+tmp<-tmp[order(tmp$Target, tmp$Trait, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` <- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` <- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` <- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, '~/oliverpainfel/Analyses/crosspop/r_diff.csv', row.names=F)
+
+###########
 
 library(MAd)
 
@@ -2828,6 +2884,35 @@ tmp_ldpred2 <- meta_res_comp[meta_res_comp$Model_1 == 'ldpred2.multi' &
 tmp_ldpred2 <- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
 round(min(tmp_ldpred2$R_diff_perc)*100, 1)
 
+# Extract average improvement for sbayesrc-multi vs sbayesrc in EUR as example
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_1 == 'sbayesrc.pseudo.multi' & 
+                    grepl('sbayesrc.pseudo', meta_res_comp$Model_2) &
+                    meta_res_comp$Target == 'EUR' &
+                    meta_res_comp$Discovery1 == 'EUR+AFR' &
+                    meta_res_comp$Discovery2 == 'EUR',]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_1 == 'sbayesrc.pseudo.multi' & 
+                    grepl('sbayesrc.pseudo', meta_res_comp$Model_2) &
+                    meta_res_comp$Target == 'EUR' &
+                    meta_res_comp$Discovery1 == 'EUR+EAS' &
+                    meta_res_comp$Discovery2 == 'EUR',]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+
+# Extract average improvement for sbayesrc in EUR compared to all model
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_2 == 'sbayesrc.pseudo-EUR.top1' &
+                    meta_res_comp$Model_1 == 'all-EUR.top1' &
+                    meta_res_comp$Target == 'AFR',]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+tmp_sbayesrc$R_diff_p
+
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_2 == 'sbayesrc.pseudo-EUR.top1' &
+                    meta_res_comp$Model_1 == 'all-EUR.top1' &
+                    meta_res_comp$Target == 'EAS',]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+tmp_sbayesrc$R_diff_p
+
+
 # Compare QuickPRS-Multi vs QuickPRS to evaluate LEOPARD performance
 tmp_quickprs <- meta_res_comp[meta_res_comp$Model_1 == 'quickprs_multi.pseudo.multi' & 
                                 meta_res_comp$Model_2 == 'quickprs.pseudo.multi' &
@@ -2839,6 +2924,82 @@ tmp_quickprs <- meta_res_comp[meta_res_comp$Model_1 == 'quickprs_multi.pseudo.mu
                     meta_res_comp$Target == 'EAS',]
 round(min(tmp_quickprs$R_diff_perc)*100, 1)
 
+# Compare all.multi method to next best method
+tmp_all <- meta_res_comp[meta_res_comp$Model_1 == 'all.multi' &
+                    meta_res_comp$Target == 'AFR' &
+                    meta_res_comp$Source2 == 'Multi',]
+tmp_all <- tmp_all[order(tmp_all$R_diff),]
+tmp_all <- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+tmp_all <- meta_res_comp[meta_res_comp$Model_1 == 'all.multi' &
+                    meta_res_comp$Target == 'EAS' &
+                    meta_res_comp$Source2 == 'Multi',]
+tmp_all <- tmp_all[order(tmp_all$R_diff),]
+tmp_all <- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+# Compare all.multi method to next best method
+tmp_all <- meta_res_comp[meta_res_comp$Model_1 == 'all-AFR.top1' &
+                    meta_res_comp$Target == 'AFR' &
+                    meta_res_comp$Discovery1 == 'AFR' &
+                    meta_res_comp$Discovery2 == 'AFR',]
+tmp_all <- tmp_all[order(tmp_all$R_diff),]
+tmp_all <- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+tmp_all <- meta_res_comp[meta_res_comp$Model_1 == 'all-EAS.top1' &
+                    meta_res_comp$Target == 'EAS' &
+                    meta_res_comp$Discovery1 == 'EAS' &
+                    meta_res_comp$Discovery2 == 'EAS',]
+tmp_all <- tmp_all[order(tmp_all$R_diff),]
+tmp_all <- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTrain or SumStatTrain result
+tmp <- meta_res_comp
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label1'
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method2', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label2'
+
+tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'], '-multi')
+tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'], '-multi')
+
+tmp$Model1[tmp$Model1 != 'SumStatTrain'] <- 'IndivTrain'
+tmp$Model1[tmp$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
+tmp$Model2[tmp$Model2 != 'SumStatTrain'] <- 'IndivTrain'
+tmp$Model2[tmp$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+
+tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` <- paste0(tmp$label1, ' - ', tmp$Model1, ' - ', tmp$Discovery1)
+tmp$`Model 2` <- paste0(tmp$label2, ' - ', tmp$Model2, ' - ', tmp$Discovery2)
+
+tmp$`Percentage change (R difference / Model 2 R)` <- paste0(round(tmp$R_diff_perc * 100, 1), '%')
+
+tmp <- tmp[, c('Target', 'Model 1', 'Model 2', 'Model_1_R', 'Model_2_R', 'R_diff',"Percentage change (R difference / Model 2 R)", 'R_diff_p'), with=F]
+names(tmp) <- c('Target','Model 1', 'Model 2', "R (Model 1)", "R (Model 2)", "R difference (Model 1 R - Model 2 R)", "Percentage change (R difference / Model 2 R)", "R difference p-value")
+
+tmp<-tmp[order(tmp$Target, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` <- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` <- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` <- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, '~/oliverpainfel/Analyses/crosspop/r_diff_average.csv', row.names=F)
+
+############
+
 # Group differences
 meta_res_comp$R_diff_catagory <- cut(
     meta_res_comp$R_diff,
@@ -2939,13 +3100,13 @@ for(targ_pop_i in targ_pop){
 ####
 # Use ptclump IndivTrain using EUR GWAS as the reference, as provides an interpretable scale
 
-meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Model2 == 'IndivTrain' & meta_res_comp$Method2 == 'ptclump' & meta_res_comp$Discovery2 == 'EUR',]
+meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Method2 == 'all' & meta_res_comp$Source2 == 'Multi',]
 meta_res_comp_ptclump_top1$reference_point<-F
-meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Model1 == 'IndivTrain' & meta_res_comp_ptclump_top1$Method1 == 'ptclump' & meta_res_comp_ptclump_top1$Discovery1 == 'EUR']<-T
+meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Method1 == 'all' & meta_res_comp_ptclump_top1$Source1 == 'Multi']<-T
 meta_res_comp_ptclump_top1$R_diff[is.na(meta_res_comp_ptclump_top1$R_diff)]<-0
 meta_res_comp_ptclump_top1$Discovery1 <- factor(meta_res_comp_ptclump_top1$Discovery1, levels=rev(levels(meta_res_comp_ptclump_top1$Discovery1)))
 
-res_comp_all_ptclump_top1<-res_comp_all[res_comp_all$Model2 == 'IndivTrain' & res_comp_all$Method2 == 'ptclump' & res_comp_all$Discovery2 == 'EUR',]
+res_comp_all_ptclump_top1<-res_comp_all[res_comp_all$Method2 == 'all' & res_comp_all$Source2 == 'Multi',]
 res_comp_all_ptclump_top1$Discovery1 <-  factor(res_comp_all_ptclump_top1$Discovery1, levels=levels(meta_res_comp_ptclump_top1$Discovery1))
 
 # Create data to plot reference points
@@ -3170,6 +3331,10 @@ meta_tmp_simple$Discovery_clean <- paste0(meta_tmp_simple$Discovery1,' GWAS')
 meta_tmp_ref_simple$Discovery_clean <- paste0(meta_tmp_ref_simple$Discovery1,' GWAS')
 tmp_simple$Discovery_clean <- paste0(tmp_simple$Discovery1,' GWAS')
 
+meta_tmp_simple<-meta_tmp_simple[!duplicated(meta_tmp_simple[, c('label', 'Discovery_clean', 'Model1'), with=F]),]
+meta_tmp_ref_simple<-meta_tmp_ref_simple[!duplicated(meta_tmp_ref_simple[, c('label', 'Discovery_clean', 'Model1'), with=F]),]
+tmp_simple<-tmp_simple[!duplicated(tmp_simple[, c('label', 'Discovery_clean', 'Model1','pheno'), with=F]),]
+
 png('~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.perc_improv.png', width = 4000, height = 1500, res= 300, units = 'px')
 ggplot(meta_tmp_simple[meta_tmp_simple$Target == 'EUR Target',], aes(x=label, y=R_diff_perc , fill = Model1)) +
     geom_point(
@@ -3217,60 +3382,6 @@ ggplot(meta_tmp_simple[meta_tmp_simple$Target == 'EUR Target',], aes(x=label, y=
     )
 dev.off()
 
-# Export plot for poster presentation
-png('poster_plot1.png', width = 54, height = 36, res= 300, units = 'cm')
-ggplot(meta_tmp_simple, aes(x=label, y=R_diff_perc , fill = Model1)) +
-    geom_hline(yintercept = 0) +
-      geom_errorbar(
-        aes(
-          ymin = R_diff_perc - R_diff_perc_SE,
-          ymax = R_diff_perc + R_diff_perc_SE
-        ),
-        width = 0,
-        size = 1,
-        position = position_dodge(width = 0.7)
-      ) +    
-    geom_point(
-      stat = "identity",
-      position = position_dodge(0.7),
-      size = 7,
-      stroke = 1,
-      shape = 23
-    ) +
-    geom_point(
-        data = meta_tmp_ref_simple,
-        aes(x = label, y = R_diff_perc, fill = Model1),
-        stat = "identity",
-        position = position_dodge(0.7), # Ensure same dodge as other points
-        size = 7,    # Increase size for emphasis
-        shape = 22,
-        stroke = 1,
-        show.legend=F
-    ) +
-   scale_fill_manual(values = c(
-        "IndivTrain" = "#F8766D",   # Red
-        "SumStatTrain" = "#00BFC4", # Green
-        "IndivTrain of SumStatTrain" = "#CCFFFF" # Purple
-    )) +
-    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype="dotted", size = 1) +
-    scale_y_continuous(labels = percent_format()) +
-    labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) +
-    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid(major = 'y', minor = 'y') + 
-    panel_border() + 
-    theme(
-        axis.text.x = element_text(size = 24, angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
-        axis.text.y = element_text(size = 24),  # Increase y-axis labels
-        axis.title = element_text(size = 24),  # Increase axis titles
-        strip.text = element_text(size = 24),  # Increase facet labels
-        legend.text = element_text(size = 24, hjust = 0.5),  # Increase legend text
-        legend.position = "top",
-        legend.key.spacing.x = unit(5, "cm"),
-        legend.justification = "center"
-    )
-dev.off()
-
 ```
 </details>
 
@@ -3301,32 +3412,24 @@ outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 scores <- list_score_files(config)
 
 ###
-# Read in weights estimated by LEOPARD
+# Read in weights estimated by LEOPARD (QuickPRS)
 ###
-# Currently, we are only using LEOPARD to estimate the weights for QuickPRS PGS.
-# Read in the weights adjusted to account for standardised PGS (SD = 1).
-# Read in the weights for the non-EUR target sample (i.e. either AFR or EAS).
 
 leopard_weights<-NULL
 scores_quickprs <- scores$name[scores$method == 'quickprs_multi']
 for(i in selected_traits){
   scores_i <- scores_quickprs[grepl(paste0('^', i,'_'), scores_quickprs)]
   for(j in scores_i){
-      log <- readLines(paste0(outdir, '/reference/pgs_score_files/quickprs_multi/', j, '/ref-', j, '.log'))
-      populations <- unlist(strsplit(gsub('populations| ','', log[grepl('^ populations', log)]), ','))
-      
-      weight_row <- which(grepl(paste0('Adjusted LEOPARD weights - ', populations[populations != 'EUR'],' target'), log))
-      weight_row <- weight_row + 1:2
-      log <- log[weight_row]
-      log <- as.numeric(gsub('.* ', '', log))
+      weights_file <- readRDS(paste0(outdir, '/reference/pgs_score_files/leopard/', j, '/ref-', j, '.weights.rds'))
+      weights_file <- data.frame(weights_file)
       
       weights <-
         data.table(
-          Target =  populations[populations != 'EUR'],
-          Discovery = populations,
-          Weight = round(log, 3),
+          Target = do.call(c, lapply(names(weights_file), function(x) rep(x, 2))),
+          Discovery = names(weights_file),
+          Weight = do.call(c, lapply(weights_file, function(x) x)),
           Trait = i,
-          Method = 'quickprs_multi'
+          Method = 'LEOPARD'
         )
       
       leopard_weights <- rbind(leopard_weights, weights)
@@ -3336,26 +3439,42 @@ for(i in selected_traits){
 #####
 # Read in the PGS weights estimated using UKB data
 #####
-# Read in the final model coefficients for models containing pseudo PGS from single-source methods
+# Read in the final model coefficients for multi-source methods
 
 obs_weights<-NULL
 for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_methods)]){
   scores_method<-scores$name[scores$method == method_i]
+  method_i <- gsub('_multi','', method_i)
 
   for(i in selected_traits){
-    for(j in c('EAS','AFR')){
-      model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/targ_', j, '.disc_EUR_', j, '/', i, '/final_models/', method_i, '.pseudo.multi.final_model.txt'))
-      model<-model[-1,]
-      names(model) <- c('x', 'BETA')
-      model$Discovery[grepl('UKB', model$x)]<-'EUR'
-      model$Discovery[grepl('BBJ', model$x)]<-'EAS'
-      model$Discovery[grepl('UGR', model$x)]<-'AFR'
-      model$Target <- j
-      model$Weight <- model$BETA/sum(model$BETA)
-      model$Trait <- i
-      model$Method <- method_i
-      model<-model[,c('Target','Discovery','Weight','Method','Trait'), with=F]
-      obs_weights<-rbind(obs_weights, model)
+    for(j in c('EAS','AFR','EUR')){
+      if(j == 'EUR'){
+        pops <- c('EAS','AFR')
+      } else {
+        pops <- j
+      }
+      
+      for(k in pops){
+        model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/targ_', j, '.disc_EUR_', k, '/', i, '/final_models/', method_i, '.pseudo.multi.final_model.txt'))
+        model<-model[-1,]
+        
+        # Set weight to zero if negative, as this is what LEOPARD does
+        if(any(model$V2 < 0)){
+          model$V2[model$V2 < 0] <- 0
+          model$V2[model$V2 > 0] <- 1
+        }
+        
+        names(model) <- c('x', 'BETA')
+        model$Discovery[grepl('UKB', model$x)]<-'EUR'
+        model$Discovery[grepl('BBJ', model$x)]<-'EAS'
+        model$Discovery[grepl('UGR', model$x)]<-'AFR'
+        model$Target <- j
+        model$Weight <- model$BETA/sum(model$BETA)
+        model$Trait <- i
+        model$Method <- method_i
+        model<-model[,c('Target','Discovery','Weight','Method','Trait'), with=F]
+        obs_weights<-rbind(obs_weights, model)
+      }
     }
   }
 }
@@ -3364,29 +3483,70 @@ for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_met
 # Combine and compare
 ###
 
-leopard_weights$Method <- 'LEOPARD'
 both <- do.call(rbind, list(obs_weights, leopard_weights))
-both <- both[both$Discovery != 'EUR',]
-both$Target <- paste0('Target = ', both$Target)
+both<-merge(both, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x=T, sort = F)
+both$label[is.na(both$label)] <- both$Method[is.na(both$label)]
+both$label <- factor(both$label, levels=unique(both$label))
+
+# Plot non-EUR target first
+tmp <- both[both$Target != 'EUR',]
+tmp <- tmp[tmp$Discovery != 'EUR',]
+
+# Set LEOPARD to black fill
+default_colors <- hue_pal()(9)
+names(default_colors) <- levels(tmp$label)
+default_colors["LEOPARD"] <- "black"
 
 # Plot the estimated and observed weights
-ggplot(both, aes(x = Trait, y = Weight, fill = Method)) +
-  geom_bar(position="dodge", stat="identity") +
-  facet_grid(. ~ Target) +
+png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights.png', units = 'px', res = 300, width = 2500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Target ~ .) +
   theme_half_open() +
+  labs(title = 'Weight of target ancestry-matched PGS', fill = NULL) +
   background_grid(major = 'y', minor = 'y') + 
-  panel_border()
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
+
+# Plot EUR target
+tmp <- both[both$Target == 'EUR',]
+tmp <- tmp[tmp$Discovery != 'EUR',]
+
+# Set LEOPARD to black fill
+default_colors <- hue_pal()(9)
+names(default_colors) <- levels(tmp$label)
+default_colors["LEOPARD"] <- "black"
+
+# Plot the estimated and observed weights
+png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_eur.png', units = 'px', res = 300, width = 2500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Discovery ~ .) +
+  theme_half_open() +
+  labs(title = 'Weight of non-EUR PGS for EUR Target', fill = NULL) +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
 
 # Calculate correlation between weights from each method
-both$Discovery<-NULL
-both_wide <- reshape(both, 
+# Start with non-EUR target
+tmp <- both[both$Target != 'EUR',]
+tmp$Discovery<-NULL
+tmp_wide <- reshape(tmp, 
                      idvar = c("Trait", "Target"), 
-                     timevar = "Method", 
+                     timevar = "label", 
                      direction = "wide")
-names(both_wide) <- gsub('Weight.', '', names(both_wide))
 
-cor_matrix_EAS <- cor(both_wide[both_wide$Target == 'Target = EAS', -1:-2])
-cor_matrix_AFR <- cor(both_wide[both_wide$Target == 'Target = AFR', -1:-2])
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+
+cor_matrix_EAS <- cor(tmp_wide[tmp_wide$Target == 'EAS', -1:-2])
+cor_matrix_AFR <- cor(tmp_wide[tmp_wide$Target == 'AFR', -1:-2])
 
 # Convert correlation matrix to long format for ggplot
 cor_df_EAS <- melt(cor_matrix_EAS)
@@ -3396,6 +3556,7 @@ cor_df_AFR$Target <- 'Target = AFR'
 cor_df <- rbind(cor_df_AFR, cor_df_EAS)
 
 # Create ggplot correlation heatmap with text inside
+png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_corr.png', units = 'px', width = 3300, height = 1700, res = 300)
 ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
   geom_tile(color = "white") +  # Tile plot with white borders
   geom_text(aes(label = round(value, 2)), color = "black") +  # Add correlation values
@@ -3408,46 +3569,43 @@ ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
   ) +
   facet_grid(. ~ Target) +
   labs(fill = "Correlation")
+dev.off()
+
+# Start with non-EUR target
+tmp <- both[both$Target == 'EUR',]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
 
-# Plot comparison between observed and LEOPARD weights
-obvs_vs_leopard <- merge(obs_weights, leopard_weights, by=c('Target','Discovery','Trait'))
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+
+cor_matrix_EAS <- cor(tmp_wide[tmp_wide$Discovery == 'EAS', -1:-2])
+cor_matrix_AFR <- cor(tmp_wide[tmp_wide$Discovery == 'AFR', -1:-2])
+
+# Convert correlation matrix to long format for ggplot
+cor_df_EAS <- melt(cor_matrix_EAS)
+cor_df_AFR <- melt(cor_matrix_AFR)
+cor_df_EAS$Target <- 'Discovery = EAS + EUR'
+cor_df_AFR$Target <- 'Discovery = AFR + EUR'
+cor_df <- rbind(cor_df_AFR, cor_df_EAS)
 
-ggplot(obvs_vs_leopard, aes(x=Weight.x, y=Weight.y, colour = Method.x)) +
-  geom_abline(slope = 1, intercept = 0, linetype = "dashed", color = "black") +
-  geom_point() +
+# Create ggplot correlation heatmap with text inside
+png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_corr_eur.png', units = 'px', width = 3300, height = 1700, res = 300)
+ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
+  geom_tile(color = "white") +  # Tile plot with white borders
+  geom_text(aes(label = round(value, 2)), color = "black") +  # Add correlation values
+  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +  # Color scale
   theme_half_open() +
   panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1),
+    axis.title = element_blank()
+  ) +
   facet_grid(. ~ Target) +
-  labs(y = 'LEOPARD weight', x = 'Observed weight') +
-  xlim(c(0,1)) +
-  ylim(c(0,1))
-
-mean_abs_diff <- aggregate(abs(obvs_vs_leopard$Weight.x - obvs_vs_leopard$Weight.y),
-                           by = list(Method = obvs_vs_leopard$Method.x, Population = obvs_vs_leopard$Target),
-                           FUN = mean)
-
-# Rename columns
-colnames(mean_abs_diff) <- c("Method", "Target", "Mean_Absolute_Difference")
-
-#    Method                   Target  Mean_Absolute_Difference
-#    dbslmm                      AFR                0.19474126
-#  lassosum                      AFR                0.30388943
-#   ldpred2                      AFR                0.16107677
-#   megaprs                      AFR                0.22283358
-#     prscs                      AFR                0.17876755
-#   ptclump                      AFR                0.17443263
-#  quickprs                      AFR                0.18643385
-#  sbayesrc                      AFR                0.19114802
-#    dbslmm                      EAS                0.09235866
-#  lassosum                      EAS                0.09133249
-#   ldpred2                      EAS                0.09829542
-#   megaprs                      EAS                0.12050745
-#     prscs                      EAS                0.08538271
-#   ptclump                      EAS                0.10710789
-#  quickprs                      EAS                0.11950164
-#  sbayesrc                      EAS                0.10211742
-
-# The adjusted LEOPARD weights look good. It is less accurate for the AFR target, probably due to limited GWAS sample size and LD reference mismatch. The correlation between the observed pgs weights is quite consistent across methods as well. Given how fast QuickPRS is, and that preliminary analysis shows other methods might be more sensitive to small reference data, I think we should use the QuickPRS-based LEOPARD weights for other PGS methods as well.
+  labs(fill = "Correlation")
+dev.off()
 
 ```
 
@@ -4338,6 +4496,10 @@ bm_dat_all <- merge(bm_dat_all, pgs_method_labels, by = 'method', all.x=T)
 
 bm_dat_all$label[bm_dat_all$method == 'leopard_quickprs']<-"LEOPARD (QuickPRS)"
 
+#############
+# Time
+#############
+
 # Calculate average time taken for each method
 method_avg <- NULL
 for(i in unique(bm_dat_all$label)){
@@ -4354,21 +4516,26 @@ for(i in unique(bm_dat_all$label)){
 # Times X-Wing time by two since it used 20 cores, but other methods used 10
 method_avg$Time[method_avg$method == 'xwing'] <- method_avg$Time[method_avg$method == 'xwing'] * 2
 
-# Calculate average max_rss for each method
-method_avg_mem <- NULL
-for(i in unique(bm_dat_all$label)){
-  method_avg_mem <- rbind(
-    method_avg_mem,
-    data.frame(
-      method = pgs_method_labels$method[pgs_method_labels$label == i],
-      Method = i,
-      Memory = mean(bm_dat_all$max_rss[bm_dat_all$label == i])
-    )
-  )
-}
+# Divide the multi-source methods (PRS-CSx and X-Wing by 2 so it is time per GWAS)
+method_avg$Time[method_avg$method %in% c('prscsx','xwing','leopard_quickprs')] <- method_avg$Time[ method_avg$method %in% c('prscsx','xwing','leopard_quickprs')] / 2
 
-# Divide X-Wing memory by two, since it used 20 cores, but other methods used 10
-method_avg$Memory[method_avg$method == 'xwing'] <- method_avg$Memory[method_avg$method == 'xwing'] /2
+# Approximate times for either tuning or grid only
+method_avg$Model <- 'Full'
+
+tmp <- method_avg[method_avg$method == 'prscs' & method_avg$Model == 'Full',] 
+tmp$Model <- 'auto'
+tmp$Time <- tmp$Time * (1/5)
+method_avg<-rbind(method_avg, tmp)
+
+tmp <- method_avg[method_avg$method == 'prscsx' & method_avg$Model == 'Full',] 
+tmp$Model <- 'auto'
+tmp$Time <- tmp$Time * (1/5)
+method_avg<-rbind(method_avg, tmp)
+
+tmp <- method_avg[method_avg$method == 'xwing' & method_avg$Model == 'Full',] 
+tmp$Model <- 'grid'
+tmp$Time <- tmp$Time * (2/10)
+method_avg<-rbind(method_avg, tmp)
 
 # Format the time taken nicely
 method_avg$Time_clean[method_avg$Time < 60] <-
@@ -4389,7 +4556,7 @@ method_avg$Type[method_avg$method == 'leopard_quickprs']<-'Tuning'
 method_avg$Type<-factor(method_avg$Type, levels = c('Single-source','Multi-source','Tuning'))
 method_avg$Method <- factor(method_avg$Method, levels = c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "QuickPRS-Multi", "PRS-CSx", "X-Wing","LEOPARD (QuickPRS)"))
 
-ggplot(method_avg, aes(x = Method, y = Time_hour, fill = Method)) +
+ggplot(method_avg, aes(x = Method, y = Time_hour, fill = Model)) +
   geom_bar(stat = "identity", position="dodge") +
   geom_text(aes(label = Time_clean), vjust = 0.5, angle = 90, hjust = -0.2, position = position_dodge(width = 0.9)) +
   labs(x = NULL, y = "Time (hours)") +
@@ -4398,36 +4565,32 @@ ggplot(method_avg, aes(x = Method, y = Time_hour, fill = Method)) +
   theme_half_open() +
   background_grid(major = 'y', minor = 'y') + 
   panel_border() + 
-  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
-
-
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
 
+method_avg <- method_avg[method_avg$Model == 'Full',]
+method_avg <- method_avg[, c('Method','Time_hour')]
+method_avg$Time_hour <- round(method_avg$Time_hour, 2)
+names(method_avg)<-c('Method',"Time (hrs)")
 
-time <- ggplot(method_avg, aes(x = Method, y = Time_hour, fill = Method)) +
-  geom_bar(stat = "identity", position="dodge") +
-  geom_text(aes(label = Time_clean), vjust = 0.5, angle = 90, hjust = -0.2, position = position_dodge(width = 0.9), size = 7) +
-  labs(x = NULL, y = "Time (hours)") +
-  ylim(0, max(method_avg$Time_hour) + (max(method_avg$Time_hour)/5)) +
-  facet_grid(~ Type, scales='free', space = 'free_x') +
-  theme_half_open() +
-  background_grid(major = 'y', minor = 'y') + 
-  panel_border() + 
-  theme(axis.text.x = element_text(size = 24, angle = 45, hjust = 1), legend.position="none",
-        axis.text.y = element_text(size = 24),  # Increase y-axis labels
-        axis.title = element_text(size = 24),  # Increase axis titles
-        strip.text = element_text(size = 24))
+#############
+# Memory
+#############
 
-png('poster_plot2.png', width = 22, height = 31.5, res= 300, units = 'cm')
-  time
-dev.off()
+# Calculate average max_rss for each method
+method_avg_mem <- NULL
+for(i in unique(bm_dat_all$label)){
+  method_avg_mem <- rbind(
+    method_avg_mem,
+    data.frame(
+      method = bm_dat_all$method[bm_dat_all$label == i][1],
+      Method = i,
+      Memory = mean(bm_dat_all$max_rss[bm_dat_all$label == i])
+    )
+  )
+}
 
-ggplot(method_avg, aes(x = Method, y = Time, fill = Method)) +
-  geom_bar(stat = "identity", position="dodge") +
-  geom_text(aes(label = Time_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
-  labs(x = "PGS Method", y = "Time (s)") +
-  theme_half_open() +
-  background_grid() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
+# Divide X-Wing memory by two, since it used 20 cores, but other methods used 10
+method_avg_mem$Memory[method_avg_mem$method == 'xwing'] /2
 
 # Format the Memory nicely
 method_avg_mem$Memory_clean <-
@@ -4441,5 +4604,153 @@ ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
   background_grid() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
 
+method_avg_mem$Memory_gb <- method_avg_mem$Memory/1000
+method_avg_mem <- method_avg_mem[, c('Method','Memory_gb')]
+method_avg_mem$Memory_gb <- round(method_avg_mem$Memory_gb, 2)
+names(method_avg_mem)<-c('Method',"Memory (Gb)")
+
+method_avg<-merge(method_avg, method_avg_mem, by = 'Method')
+
+write.csv(method_avg, '~/oliverpainfel/Analyses/crosspop/time_memory.csv', row.names=F)
 ```
 
+***
+
+# Sensitivity analysis using 1KG reference
+
+PRS-CS, PRS-CSx and X-Wing all use the 1KG reference sample, whereas the other methods are using the 1KG+HGDP reference sample. We should check whether this difference is impacting our conclusions.
+
+***
+
+## Subset AFR and EAS individuals in UKB data
+
+To make this quicker, focus on evaluating the PGS methods in the AFR and EAS target individuals in UKB. This will avoid reprocessing the full UKB data.
+
+```{r}
+library(data.table)
+
+keep <- NULL
+for (i in c('AFR', 'EAS')) {
+  keep <- rbind(keep, fread(
+    paste0(
+      '~/oliverpainfel/Data/ukb/GenoPred/output/ukb/ancestry/keep_files/model_based/',
+      i,
+      '.keep'
+    )
+  ))
+}
+
+write.table(
+  keep,
+  '~/oliverpainfel/Data/ukb/afr_eas.keep',
+  row.names = F,
+  col.names = F,
+  quote = F
+)
+
+```
+
+```{bash}
+mkdir ~/oliverpainfel/Data/ukb/afr_eas_subset
+
+for chr in $(seq 1 22); do
+  ~/oliverpainfel/Software/plink2 \
+    --pfile ~/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
+    --keep ~/oliverpainfel/Data/ukb/afr_eas.keep \
+    --make-pgen \
+    --out ~/oliverpainfel/Data/ukb/afr_eas_subset/ukb.chr${chr}
+done
+
+```
+
+***
+
+## Create 1KG only GenoPred reference data
+
+Subset the 1KG+HGDP reference data to include only 1KG individuals.
+
+```{bash}
+mkdir -p ~/oliverpainfel/Data/1kg/genopred/
+cp -r ~/oliverpainfel/Data/hgdp_1kg/genopred/ref ~/oliverpainfel/Data/1kg/genopred/
+rm ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr*.p*
+```
+
+```{r}
+library(data.table)
+
+ref<- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr1.psam')
+ref<-ref[ref$Project == 'gnomAD_1kG',]
+
+write.table(ref[,1, drop = F], '~/oliverpainfel/Data/1kg/1kg.keep', col.names=F, row.names=F, quote=F)
+```
+
+```{bash}
+for chr in $(seq 1 22); do
+  ~/oliverpainfel/Software/plink2 \
+    --pfile ~/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} \
+    --keep ~/oliverpainfel/Data/1kg/1kg.keep \
+    --make-pgen \
+    --out ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr${chr}
+done
+```
+
+***
+
+## Run pipeline
+
+To save time, run using PGS methods that do not need pre-processed LD matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary from the 1KG+HGDP results, then expand to other methods (LDpred2, SBayesRC, QuickPRS).
+
+```{r}
+library(data.table)
+
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only')
+
+######
+# target_list
+######
+target_list <- data.frame(
+  name='ukb',
+  path='/users/k1806347/oliverpainfel/Data/ukb/afr_eas_subset/ukb',
+  type='plink2',
+  indiv_report=F,
+  unrel='/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt'
+)
+
+write.table(target_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt', col.names=T, row.names=F, quote=F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_1kgref",
+  "refdir: /users/k1806347/oliverpainfel/Data/1kg/genopred/ref",
+  "resdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/resdir_1kgref",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt",
+  "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
+  "pgs_methods: ['ptclump','dbslmm','lassosum','megaprs']",
+#  "leopard_methods: ['ptclump','dbslmm','lassosum','megaprs']",
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 10"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+```{bash}
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \
+  output_all -n
+```
+
+
+
+
+
+
+
diff --git a/functions/constants.R b/functions/constants.R
index 60a4d98c..d6c6d888 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -37,8 +37,8 @@ ref_pop <- data.frame(
 
 # Make a data.frame giving labels to the 1KG reference populations
 pgs_method_labels <- data.frame(
-  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','tlprs','ptclump_multi','dbslmm_multi','ldpred2_multi','sbayesr_multi','sbayesrc_multi','lassosum_multi','prscs_multi','megaprs_multi','quickprs_multi'),
-  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','TL-PRS','pT+clump-multi','DBSLMM-multi','LDpred2-multi','SBayesR-multi','SBayesRC-multi','lassosum-multi','PRS-CS-multi','MegaPRS-multi','QuickPRS-multi')
+  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','ptclump_multi','dbslmm_multi','ldpred2_multi','sbayesr_multi','sbayesrc_multi','lassosum_multi','prscs_multi','megaprs_multi','quickprs_multi'),
+  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','pT+clump-multi','DBSLMM-multi','LDpred2-multi','SBayesR-multi','SBayesRC-multi','lassosum-multi','PRS-CS-multi','MegaPRS-multi','QuickPRS-multi')
 )
 pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),]
 
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 044b71de..c4c1b582 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -417,7 +417,7 @@ def check_pgs_methods(x):
         return
 
     valid_pgs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesr","sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs", "xwing", "prscsx", "tlprs"
+        "ptclump", "dbslmm", "prscs", "sbayesr","sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs", "xwing", "prscsx"
     }
 
     invalid_methods = [method for method in x if method not in valid_pgs_methods]
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 618b9bb8..6e84e1a7 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -888,12 +888,12 @@ if 'external' in pgs_methods_all:
   pgs_methods_input.append(rules.score_reporter.output)
 if config["leopard_methods"] and config["leopard_methods"] != "NA":
   pgs_methods_input.append(rules.prep_pgs_multi.input)
+if config["tlprs_methods"] and config["tlprs_methods"] != "NA":
+  pgs_methods_input.append(rules.prep_pgs_tlprs.input)
 if 'prscsx' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_prscsx.input)
 if 'xwing' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_xwing.input)
-if 'tlprs' in pgs_methods_all:
-  pgs_methods_input.append(rules.prep_pgs_tlprs.input)
 if 'bridgeprs' in pgs_methods_all:
   pgs_methods_input.append(rules.prep_pgs_bridgeprs.input)
 

From d3f63be373fb4bcc10c786b99171a320c74fe339 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 25 Feb 2025 17:19:42 +0000
Subject: [PATCH 113/153] Updates

---
 docs/CrossPop.Rmd     | 2405 ++++++++++++++++++++++++-----------------
 functions/constants.R |    4 +-
 2 files changed, 1394 insertions(+), 1015 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index f3d442be..a5d9aca0 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -2482,6 +2482,8 @@ for(targ_pop_i in targ_pop){
 meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
 meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
+write.csv(meta_res_eval, '~/oliverpainfel/Analyses/crosspop/r_eval.csv', row.names = F)
+
 # Plot average performance across phenotypes for AFR and EAS targets
 tmp <- meta_res_eval
 tmp <- tmp[tmp$Target != 'EUR',]
@@ -3611,1146 +3613,1523 @@ dev.off()
 
 ***
 
-## Evaluate TLPRS
-### Create predictor lists
-
-<details><summary>Show code</summary>
+# Computational resoures
 
 ```{r}
+library(data.table)
+library(ggplot2)
+library(cowplot)
 
 setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 source('../functions/misc.R')
 source_all('../functions')
-library(data.table)
 
 # Get some key variables from config
 config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
-# Read in list of outcomes 
-selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
-
-# Get a list of score files
-scores <- list_score_files(config)
+# Read in configuration specific benchmark files
+bm_files_i <- list.files(paste0(outdir, '/reference/benchmarks/'), full.names = T)
 
-# Subset to TLPRS scores and pseudo scores for corresponding methods
-pgs_methods <- unique(scores$method[grepl('^tlprs', scores$method)])
-pgs_methods <- c(pgs_methods, gsub('tlprs_','',pgs_methods))
-scores <- scores[scores$method %in% pgs_methods,]
+# Subset benchmarks for pgs_methods
+bm_files_i <- bm_files_i[grepl('prep_pgs_|leopard_quickprs_', bm_files_i)]
 
-# Create files for EAS and AFR targets
-targ_pop <- c('EUR','EAS','AFR')
-for(trait_i in selected_traits){
-  scores_i <- scores[grepl(trait_i, scores$name),]
-  scores_i$group <- scores_i$method
-  
-  for(targ_pop_i in targ_pop){
-    # Subset GWAS based on EUR and/or targ_pop_i
-    if(targ_pop_i == 'EAS'){
-      disc_pop <- 'BBJ'
-    }
-    if(targ_pop_i == 'AFR'){
-      disc_pop <- 'UGR'
-    }
-    if(targ_pop_i == 'EUR'){
-      disc_pop <- c('BBJ','UGR')
-    }
-    
-    for(disc_pop_j in disc_pop){
-      if(disc_pop_j == 'BBJ'){
-        disc_pop_j_2 <- 'EAS'
-      }
-      if(disc_pop_j == 'UGR'){
-        disc_pop_j_2 <- 'AFR'
-      }
+# Subset to benchmarks for gwas/gwas_groups in config
+scores <- list_score_files(config)
+bm_files_i <- bm_files_i[grepl(paste0('-', unique(scores$name),'.txt', collapse = '|'), bm_files_i)]
 
-      dir.create(
-        paste0(
-          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
-          targ_pop_i,
-          '.disc_EUR_',
-          disc_pop_j_2,
-          '/',
-          trait_i
-        ),
-        recursive = T
-      )
-      
-      scores_i_j <- scores_i[grepl('UKB', scores_i$name, ignore.case = F) |
-                             grepl(disc_pop_j, scores_i$name, ignore.case = T),]
-      
-      # Insert the pseudo score for the non-TLPRS methods
-      scores_i_j_pseudo <- scores_i_j[which(!grepl('^tlprs', scores_i_j$method)), ]
-      scores_i_j_pseudo$group <- paste0(scores_i_j_pseudo$group,'.pseudo')
+# Read in benchmark files
+bm_dat_all <- do.call(rbind, lapply(bm_files_i, function(file) {
+  tmp <- fread(file)
+  tmp$file <- basename(file)
+  return(tmp)
+}))
 
-      scores_i_j_pseudo$predictor <- paste0(
-        outdir,
-        '/ukb/pgs/',
-        targ_pop_i,
-        '/',
-        scores_i_j_pseudo$method,
-        '/',
-        scores_i_j_pseudo$name,
-        '/ukb-',
-        scores_i_j_pseudo$name,
-        '-',
-        targ_pop_i,
-        '.pseudo.profiles'
-      )
-      
-      for(i in 1:nrow(scores_i_j_pseudo)) {
-        tmp <- scores_i_j_pseudo[i,]
-        param <- find_pseudo(
-          config = config,
-          gwas = tmp$name,
-          pgs_method = tmp$method,
-          target_pop = targ_pop_i
-        )
-        
-        score_header <-
-          fread(gsub('.pseudo', '', tmp$predictor), nrows = 1)
-        score_cols <-
-          which(names(score_header) %in% c('FID', 'IID', paste0(tmp$name, '_', param)))
-        
-        system(
-          paste0(
-            "cut -d' ' -f ", 
-            paste0(score_cols, collapse=','),
-            " ", 
-            gsub('.pseudo', '', tmp$predictor), 
-            " > ", tmp$predictor
-          )
-        )
-      }
-      
-      # Create disc_pop specific groups
-      scores_i_j_pseudo_disc_pop <- scores_i_j_pseudo
-      scores_i_j_pseudo_disc_pop$group[grepl('UKB', scores_i_j_pseudo_disc_pop$name)] <- paste0(scores_i_j_pseudo_disc_pop$group[grepl('UKB', scores_i_j_pseudo_disc_pop$name)], '.EUR')
-      scores_i_j_pseudo_disc_pop$group[!grepl('UKB', scores_i_j_pseudo_disc_pop$name)] <- paste0(scores_i_j_pseudo_disc_pop$group[!grepl('UKB', scores_i_j_pseudo_disc_pop$name)], '.', disc_pop_j_2)
-      
-      # Insert groups for TLPRS scores for both target populations, and target specific
-      scores_i_j_multi <- scores_i_j[which(grepl('^tlprs', scores_i_j$method)), ]
-      
-      # Insert path to score file
-      scores_i_j_multi$predictor <- paste0(
-        outdir,
-        '/ukb/pgs/',
-        targ_pop_i,
-        '/',
-        scores_i_j_multi$method,
-        '/',
-        scores_i_j_multi$name,
-        '/ukb-',
-        scores_i_j_multi$name,
-        '-',
-        targ_pop_i,
-        '.profiles'
-      )
-      
-      scores_i_j_multi_targ_pop <- scores_i_j_multi
-      scores_i_j_multi_targ_pop_both<-NULL
-      for(i in 1:nrow(scores_i_j_multi_targ_pop)){
-        score_header <-
-          fread(gsub('.pseudo', '', scores_i_j_multi_targ_pop$predictor[i]), nrows = 1)
-        score_cols_EUR <-
-          which(names(score_header) %in% c('FID', 'IID', names(score_header)[grepl('targ_EUR', names(score_header))]))
-        score_cols_targ <-
-          which(names(score_header) %in% c('FID', 'IID', names(score_header)[grepl(paste0('targ_', targ_pop_i), names(score_header))]))
-        
-        system(
-          paste0(
-            "cut -d' ' -f ", 
-            paste0(score_cols_EUR, collapse=','),
-            " ", 
-            scores_i_j_multi_targ_pop$predictor[i], 
-            " > ", 
-            paste0(
-              outdir,
-              '/ukb/pgs/',
-              targ_pop_i,
-              '/',
-              scores_i_j_multi_targ_pop$method[i],
-              '/',
-              scores_i_j_multi_targ_pop$name[i],
-              '/ukb-',
-              scores_i_j_multi_targ_pop$name[i],
-              '-',
-              targ_pop_i,
-              '.targ_EUR.profiles'
-            )
-          )
-        )
-        
-        system(
-          paste0(
-            "cut -d' ' -f ", 
-            paste0(score_cols_targ, collapse=','),
-            " ", 
-            scores_i_j_multi_targ_pop$predictor[i], 
-            " > ", 
-            paste0(
-              outdir,
-              '/ukb/pgs/',
-              targ_pop_i,
-              '/',
-              scores_i_j_multi_targ_pop$method[i],
-              '/',
-              scores_i_j_multi_targ_pop$name[i],
-              '/ukb-',
-              scores_i_j_multi_targ_pop$name[i],
-              '-',
-              targ_pop_i,
-              '.targ_',
-              targ_pop_i,
-              '.profiles'
-            )
-          )
-        )
-        
-        tmp<-scores_i_j_multi_targ_pop[i,]
-        tmp <- rbind(tmp, tmp)
-        tmp$predictor[1] <- paste0(
-            outdir,
-            '/ukb/pgs/',
-            targ_pop_i,
-            '/',
-            scores_i_j_multi_targ_pop$method[i],
-            '/',
-            scores_i_j_multi_targ_pop$name[i],
-            '/ukb-',
-            scores_i_j_multi_targ_pop$name[i],
-            '-',
-            targ_pop_i,
-            '.targ_EUR.profiles'
-          )
-        tmp$group[1] <- paste0(tmp$group[1], '.EUR')
-        
-        tmp$predictor[2] <- paste0(
-            outdir,
-            '/ukb/pgs/',
-            targ_pop_i,
-            '/',
-            scores_i_j_multi_targ_pop$method[i],
-            '/',
-            scores_i_j_multi_targ_pop$name[i],
-            '/ukb-',
-            scores_i_j_multi_targ_pop$name[i],
-            '-',
-            targ_pop_i,
-            '.targ_',
-            targ_pop_i,
-            '.profiles'
-          )
-        tmp$group[2] <- paste0(tmp$group[2], '.', targ_pop_i)
+# Create rule column
+bm_dat_all$rule <- gsub('-.*','',bm_dat_all$file)
 
-        scores_i_j_multi_targ_pop_both <- rbind(
-          scores_i_j_multi_targ_pop_both,
-          tmp)
-      }
-      
-      predictors_i<- do.call(rbind, list(
-        scores_i_j_multi, scores_i_j_multi_targ_pop_both, scores_i_j_pseudo, scores_i_j_pseudo_disc_pop
-      ))
-      
-      predictors_i <- predictors_i[, c('predictor', 'group'), with=F]
-      
-      write.table(
-        predictors_i,
-        paste0(
-          '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
-          targ_pop_i,
-          '.disc_EUR_',
-          disc_pop_j_2,
-          '/',
-          trait_i,
-          '/predictor_list.tlprs.txt'
-        ),
-        col.names = T,
-        row.names = F,
-        quote = F
-      )
-    }
-  }
-}
+# Create method column
+bm_dat_all$method <-
+  gsub('_i', '', gsub('prep_pgs_', '', bm_dat_all$rule))
 
-```
+bm_dat_all <- merge(bm_dat_all, pgs_method_labels, by = 'method', all.x=T)
 
-</details>
+bm_dat_all$label[bm_dat_all$method == 'leopard_quickprs']<-"LEOPARD (QuickPRS)"
 
-***
+#############
+# Time
+#############
 
-### Run model_builder
+# Calculate average time taken for each method
+method_avg <- NULL
+for(i in unique(bm_dat_all$label)){
+  method_avg <- rbind(
+    method_avg,
+    data.frame(
+      method = bm_dat_all$method[bm_dat_all$label == i][1],
+      Method = i,
+      Time = mean(bm_dat_all$s[bm_dat_all$label == i])
+    )
+  )
+}
 
-<details><summary>Show code</summary>
+# Times X-Wing time by two since it used 20 cores, but other methods used 10
+method_avg$Time[method_avg$method == 'xwing'] <- method_avg$Time[method_avg$method == 'xwing'] * 2
 
-```{bash}
-cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
-conda activate model_builder
+# Divide the multi-source methods (PRS-CSx and X-Wing by 2 so it is time per GWAS)
+method_avg$Time[method_avg$method %in% c('prscsx','xwing','leopard_quickprs')] <- method_avg$Time[ method_avg$method %in% c('prscsx','xwing','leopard_quickprs')] / 2
 
-for targ_pop in $(echo EUR EAS AFR); do
-  if [ "$targ_pop" == "EUR" ]; then
-      targ_pop2="EUR_test"
-  else
-      targ_pop2=$targ_pop
-  fi
-  
-  if [ "$targ_pop" == "EUR" ]; then
-    disc_pop=$(echo AFR EAS)
-  fi
-  
-  if [ "$targ_pop" == "EAS" ]; then
-    disc_pop="EAS"
-  fi
-  
-  if [ "$targ_pop" == "AFR" ]; then
-    disc_pop="AFR"
-  fi
-  
-  for disc_pop_i in ${disc_pop}; do
-    for pheno in $(head -n 5 /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
-      sbatch --mem 20G -n 10 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
-        --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
-        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
-        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
-        --n_core 10 \
-        --top1 T \
-        --all_model F \
-        --assoc T"
-    done
-  done
-done
+# Approximate times for either tuning or grid only
+method_avg$Model <- 'Full'
 
-```
-</details>
+tmp <- method_avg[method_avg$method == 'prscs' & method_avg$Model == 'Full',] 
+tmp$Model <- 'auto'
+tmp$Time <- tmp$Time * (1/5)
+method_avg<-rbind(method_avg, tmp)
 
-***
+tmp <- method_avg[method_avg$method == 'prscsx' & method_avg$Model == 'Full',] 
+tmp$Model <- 'auto'
+tmp$Time <- tmp$Time * (1/5)
+method_avg<-rbind(method_avg, tmp)
 
-### Plot results
+tmp <- method_avg[method_avg$method == 'xwing' & method_avg$Model == 'Full',] 
+tmp$Model <- 'grid'
+tmp$Time <- tmp$Time * (2/10)
+method_avg<-rbind(method_avg, tmp)
 
-<details><summary>Show code</summary>
+# Format the time taken nicely
+method_avg$Time_clean[method_avg$Time < 60] <-
+  paste0(round(method_avg$Time[method_avg$Time < 60], 1), ' sec')
+method_avg$Time_clean[method_avg$Time > 60] <-
+  paste0(round(method_avg$Time[method_avg$Time > 60] / 60, 1), ' min')
+method_avg$Time_clean[method_avg$Time > 3600] <-
+  paste0(round(method_avg$Time[method_avg$Time > 3600] / 60 / 60, 1), ' hr')
 
-```{r}
+# Convert time in seconds to hours
+method_avg$Time_hour <- method_avg$Time / 60/60
 
-setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+# Seperate methods by single or multi source
+method_avg$Type[!(method_avg$method %in% pgs_group_methods)]<-'Single-source'
+method_avg$Type[method_avg$method %in% pgs_group_methods]<-'Multi-source'
+method_avg$Type[method_avg$method == 'leopard_quickprs']<-'Tuning'
 
-library(data.table)
-library(ggplot2)
-library(cowplot)
+method_avg$Type<-factor(method_avg$Type, levels = c('Single-source','Multi-source','Tuning'))
+method_avg$Method <- factor(method_avg$Method, levels = c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "QuickPRS-Multi", "PRS-CSx", "X-Wing","LEOPARD (QuickPRS)"))
 
-source('../functions/misc.R')
-source_all('../functions')
+ggplot(method_avg, aes(x = Method, y = Time_hour, fill = Model)) +
+  geom_bar(stat = "identity", position="dodge") +
+  geom_text(aes(label = Time_clean), vjust = 0.5, angle = 90, hjust = -0.2, position = position_dodge(width = 0.9)) +
+  labs(x = NULL, y = "Time (hours)") +
+  ylim(0, max(method_avg$Time_hour) + (max(method_avg$Time_hour)/5)) +
+  facet_grid(~ Type, scales='free', space = 'free_x') +
+  theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
 
-# Read in list of outcomes 
-prscsx_dat<-fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv')
-prscsx_dat<-prscsx_dat[1:5,]
+method_avg <- method_avg[method_avg$Model == 'Full',]
+method_avg <- method_avg[, c('Method','Time_hour')]
+method_avg$Time_hour <- round(method_avg$Time_hour, 2)
+names(method_avg)<-c('Method',"Time (hrs)")
 
-config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
-  
-# Define pgs_methods used
-pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+#############
+# Memory
+#############
 
-# Define gwas_list used
-gwas_list<-read_param(config = config, param = 'gwas_list')
+# Calculate average max_rss for each method
+method_avg_mem <- NULL
+for(i in unique(bm_dat_all$label)){
+  method_avg_mem <- rbind(
+    method_avg_mem,
+    data.frame(
+      method = bm_dat_all$method[bm_dat_all$label == i][1],
+      Method = i,
+      Memory = mean(bm_dat_all$max_rss[bm_dat_all$label == i])
+    )
+  )
+}
 
-# Define gwas_groups used
-gwas_groups<-read_param(config = config, param = 'gwas_groups')
+# Divide X-Wing memory by two, since it used 20 cores, but other methods used 10
+method_avg_mem$Memory[method_avg_mem$method == 'xwing'] /2
 
-# Calculate corelation between all phenotypes in each target population
-cors <- list()
-for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
-  if(pop_i == 'EUR'){
-    pop_i_2 <- 'EUR_test'
-  } else {
-    pop_i_2 <- pop_i
-  }
-  pheno_pop_i <- list()
-  for(pheno_i in prscsx_dat$labels){
-    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
-    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
-  }
-  
-  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+# Format the Memory nicely
+method_avg_mem$Memory_clean <-
+  paste0(round(method_avg_mem$Memory/1000, 2), ' Gb')
 
-  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
-  cors[[pop_i]] <- cors_i
-}
+ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
+  geom_bar(stat = "identity", position="dodge") +
+  geom_text(aes(label = Memory_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
+  labs(x = "PGS Method", y = "Memory (Mb)") +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
 
-# Read in results
-targ_pop = c('EUR','EAS','AFR')
-res <- list()
-for(pheno_i in prscsx_dat$labels){
-  res_i<-NULL
-  for(targ_pop_i in targ_pop){
-    if(targ_pop_i == 'EAS'){
-      disc_pop <- 'EAS'
-    }
-    if(targ_pop_i == 'AFR'){
-      disc_pop <- 'AFR'
-    }
-    if(targ_pop_i == 'EUR'){
-      disc_pop <- c('EAS','AFR')
-    }
-    for(disc_pop_i in disc_pop){
-      eval_i <-
-        fread(
-          paste0(
-            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
-            'targ_',
-            targ_pop_i,
-            '.disc_EUR_',
-            disc_pop_i,
-            '/',
-            pheno_i,
-            '/res.tlprs.pred_eval.txt'
-          )
-        )
-      eval_i$Target<-targ_pop_i
-      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
-      res_i<-rbind(res_i, eval_i)
-    }
-  }
-  
-  res_i$Method<-sub('\\..*','',res_i$Group)
-  res_i$Method_short<-sub('.*_','',res_i$Method)
-  res_i<-res_i[order(res_i$Method_short, res_i$Method),]
-  
-  res_i$Model[grepl('pseudo', res_i$Group)]<-'Pseudo'
-  res_i$Model[grepl('top1', res_i$Group)]<-'Top1'
-  res_i$Model[!grepl('top1|pseudo', res_i$Group)]<-'Multi'
-  res_i$Model[!grepl('tlprs', res_i$Group) & !grepl('EUR|EAS|AFR', res_i$Group) & !grepl('top1', res_i$Group)]<-'Multi'
-  
-  res_i$Source[!grepl('tlprs', res_i$Group)] <- 'Single'
-  res_i$Source[grepl('tlprs', res_i$Group)] <- 'Multi'
-  res_i$Source[!grepl('tlprs', res_i$Group) & !grepl('EUR|EAS|AFR', res_i$Group)] <- 'Multi'
-  
-  res_i$Discovery <- res_i$gwas_group
-  res_i$Discovery[grepl('EUR', res_i$Group) & res_i$Source == 'Single'] <- 'EUR'
-  res_i$Discovery[grepl('EAS', res_i$Group) & res_i$Source == 'Single'] <- 'EAS'
-  res_i$Discovery[grepl('AFR', res_i$Group) & res_i$Source == 'Single'] <- 'AFR'
-
-  res_i$Method <- gsub('tlprs_','tlprs - ', res_i$Method)
-  res_i$Method[grepl('tlprs', res_i$Method) & grepl('EUR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('EUR', res_i$Group)], " (EUR)")
-  res_i$Method[grepl('tlprs', res_i$Method) & grepl('EAS', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('EAS', res_i$Group)], " (EAS)")
-  res_i$Method[grepl('tlprs', res_i$Method) & grepl('AFR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & grepl('AFR', res_i$Group)], " (AFR)")
-  res_i$Method[grepl('tlprs', res_i$Method) & !grepl('EUR|EAS|AFR', res_i$Group)] <- paste0(res_i$Method[grepl('tlprs', res_i$Method) & !grepl('EUR|EAS|AFR', res_i$Group)], " (Both)")
-
-  res_i$Method<-factor(res_i$Method, levels=unique(res_i$Method))
-  res_i$Model<-factor(res_i$Model, levels=c('Top1','Pseudo','Multi'))
-  res_i$Discovery<-factor(res_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
-
-  res[[pheno_i]]<-res_i
-  
-}
+method_avg_mem$Memory_gb <- method_avg_mem$Memory/1000
+method_avg_mem <- method_avg_mem[, c('Method','Memory_gb')]
+method_avg_mem$Memory_gb <- round(method_avg_mem$Memory_gb, 2)
+names(method_avg_mem)<-c('Method',"Memory (Gb)")
 
-####
-# Average results across phenotypes
-####
+method_avg<-merge(method_avg, method_avg_mem, by = 'Method')
 
-library(MAd)
+write.csv(method_avg, '~/oliverpainfel/Analyses/crosspop/time_memory.csv', row.names=F)
+```
 
-# Average R across phenotypes
-meta_res <- NULL
-for(targ_pop_i in targ_pop){
-  if(targ_pop_i == 'EAS'){
-    disc_pop <- 'EAS'
-  }
-  if(targ_pop_i == 'AFR'){
-    disc_pop <- 'AFR'
-  }
-  if(targ_pop_i == 'EUR'){
-    disc_pop <- c('EAS','AFR')
-  }
-  for(disc_pop_i in disc_pop){
-  
-    # Subset res for each scenario
-    res_i <- do.call(rbind, lapply(seq_along(res), function(i) {
-      x <- res[[i]]
-      x$pheno <- names(res)[i]
-      x <- x[x$Target == targ_pop_i]
-      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
-    }))
-    
-    # Average results for each test across phenotypes
-    # Use MAd to account for correlation between them
-    res_i$Sample<-'A'
-  
-    for(group_i in unique(res_i$Group)){
-      res_group_i <- res_i[res_i$Group == group_i,]
-      cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
-      
-      meta_res_i <-
-        agg(
-          id = Sample,
-          es = R,
-          var = SE ^ 2,
-          cor = cors_i,
-          method = "BHHR",
-          mod = NULL,
-          data = res_group_i
-        )
-      
-      meta_res <- rbind(meta_res,
-                        data.table(
-                          Group = group_i,
-                          Method = res_group_i$Method[1],
-                          Model = res_group_i$Model[1],
-                          Source = res_group_i$Source[1],
-                          Discovery = res_group_i$Discovery[1],
-                          gwas_group = res_group_i$gwas_group[1],
-                          Target = targ_pop_i,
-                          R = meta_res_i$es,
-                          SE = sqrt(meta_res_i$var)
-                        ))
-    }
-  }
-}
+***
 
-meta_res$Model<-factor(meta_res$Model, levels=c('Top1','Pseudo','Multi'))
-meta_res$Discovery<-factor(meta_res$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+# Sensitivity analysis using 1KG reference
 
-####
-# Compare TLPRS to unadjusted PGS
-####
+PRS-CS, PRS-CSx and X-Wing all use the 1KG reference sample, whereas the other methods are using the 1KG+HGDP reference sample. We should check whether this difference is impacting our conclusions.
 
-meta_res_multi_pop <- meta_res[!(meta_res$Discovery %in% c('EUR','EAS','AFR')),]
-meta_res_multi_pop$original_method <- gsub(' .*', '', gsub('tlprs - ', '', meta_res_multi_pop$Method))
-meta_res_multi_pop$test[!grepl('tlprs', meta_res_multi_pop$Method)] <- 'Unadjusted'
-meta_res_multi_pop$test[grepl('tlprs', meta_res_multi_pop$Method)] <- gsub('.* ', 'TLPRS ', meta_res_multi_pop$Method[grepl('tlprs', meta_res_multi_pop$Method)])
-meta_res_multi_pop$test <- factor(meta_res_multi_pop$test, levels=unique(meta_res_multi_pop$test))
-meta_res_multi_pop$test <- gsub('AFR', 'target pop', meta_res_multi_pop$test)
-meta_res_multi_pop$test <- gsub('EAS', 'target pop', meta_res_multi_pop$test)
+***
 
-tmp <- meta_res_multi_pop[meta_res_multi_pop$Target %in% c('EAS', 'AFR'),]
+## Subset AFR and EAS individuals in UKB data
 
-ggplot(tmp, aes(x=test, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
-    labs(y = "R (SE)") +
-    facet_grid(Target ~ original_method, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+To make this quicker, focus on evaluating the PGS methods in the AFR and EAS target individuals in UKB. This will avoid reprocessing the full UKB data.
 
-tmp <- meta_res_multi_pop[meta_res_multi_pop$Target %in% c('EAS', 'AFR'),]
-tmp <- tmp[grepl('Unadj|Both', tmp$test),]
-tmp$test <- gsub(' .*', '', tmp$test)
-tmp$test <- factor(tmp$test, levels=c('Unadjusted','TLPRS'))
+```{r}
+library(data.table)
 
-ggplot(tmp, aes(x=test, y=R , fill = Model)) +
-    #geom_hline(yintercept = 0) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=2, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$Method))), linetype="dotted") +
-    labs(y = "R (SE)", x = NULL) +
-    facet_grid(Target ~ original_method, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid() + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+keep <- NULL
+for (i in c('AFR', 'EAS')) {
+  keep <- rbind(keep, fread(
+    paste0(
+      '~/oliverpainfel/Data/ukb/GenoPred/output/ukb/ancestry/keep_files/model_based/',
+      i,
+      '.keep'
+    )
+  ))
+}
 
-####
-# Create heatmap showing difference between all methods and models
-####
+write.table(
+  keep,
+  '~/oliverpainfel/Data/ukb/afr_eas.keep',
+  row.names = F,
+  col.names = F,
+  quote = F
+)
 
-# Read in results
-targ_pop=c('EUR','EAS','AFR')
-res <- list()
-for(pheno_i in prscsx_dat$labels){
-  res_i<-NULL
-  for(targ_pop_i in targ_pop){
-    if(targ_pop_i == 'EAS'){
-      disc_pop <- 'EAS'
-    }
-    if(targ_pop_i == 'AFR'){
-      disc_pop <- 'AFR'
-    }
-    if(targ_pop_i == 'EUR'){
-      disc_pop <- c('EAS','AFR')
-    }
-    for(disc_pop_i in disc_pop){
-      eval_i <-
-        fread(
-          paste0(
-            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
-            'targ_',
-            targ_pop_i,
-            '.disc_EUR_',
-            disc_pop_i,
-            '/',
-            pheno_i,
-            '/res.tlprs.pred_comp.txt'
-          )
-        )
-      eval_i$Target<-targ_pop_i
-      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
-      res_i<-rbind(res_i, eval_i)
-    }
-  }
-  
-  res[[pheno_i]]<-res_i
-}
+```
 
-#####
-# Create a plot showing relative R from TLPRS vs Unadjusted
-#####
+```{bash}
+mkdir ~/oliverpainfel/Data/ukb/afr_eas_subset
 
-res_all <- do.call(rbind, lapply(names(res), function(name) {
-  x <- res[[name]]
-  x$pheno <- name  # Add a new column with the name of the element
-  x  # Return the updated dataframe
-}))
+for chr in $(seq 1 22); do
+  ~/oliverpainfel/Software/plink2 \
+    --pfile ~/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
+    --keep ~/oliverpainfel/Data/ukb/afr_eas.keep \
+    --make-pgen \
+    --out ~/oliverpainfel/Data/ukb/afr_eas_subset/ukb.chr${chr}
+done
 
-# Mirror results to fill in gaps
-res_all_symmetric <- res_all
-res_all_mirrored <- res_all
-res_all_mirrored$Model_1 <- res_all$Model_2
-res_all_mirrored$Model_2 <- res_all$Model_1
-res_all_mirrored$Model_1_R <- res_all$Model_2_R
-res_all_mirrored$Model_2_R <- res_all$Model_1_R
-res_all_mirrored$R_diff <- -res_all_mirrored$R_diff
-res_all <- rbind(res_all_symmetric, res_all_mirrored)
-
-# Subset tests where top1 TLPRS is being compared to top1 unadjusted
-res_all$Method_1 <- gsub('.*_', '', gsub('\\..*', '', res_all$Model_1))
-res_all$Method_2 <- gsub('.*_', '', gsub('\\..*', '', res_all$Model_2))
-
-res_all$TLPRS_1 <- grepl('tlprs', res_all$Model_1)
-res_all$TLPRS_2 <- grepl('tlprs', res_all$Model_2)
-
-res_all$Test_1[grepl('pseudo', res_all$Model_1)]<-'Pseudo'
-res_all$Test_1[grepl('top1', res_all$Model_1)]<-'Top1'
-res_all$Test_1[!grepl('top1|pseudo', res_all$Model_1)]<-'Multi'
-res_all$Test_1[!grepl('tlprs', res_all$Model_1) & !grepl('EUR|EAS|AFR', res_all$Model_1) & !grepl('top1', res_all$Model_1)]<-'Multi'
-
-res_all$Test_2[grepl('pseudo', res_all$Model_2)]<-'Pseudo'
-res_all$Test_2[grepl('top1', res_all$Model_2)]<-'Top1'
-res_all$Test_2[!grepl('top1|pseudo', res_all$Model_2)]<-'Multi'
-res_all$Test_2[!grepl('tlprs', res_all$Model_2) & !grepl('EUR|EAS|AFR', res_all$Model_2) & !grepl('top1', res_all$Model_2)]<-'Multi'
-
-res_all$Source_1[!grepl('tlprs', res_all$Model_1)] <- 'Single'
-res_all$Source_1[grepl('tlprs', res_all$Model_1)] <- 'Multi'
-res_all$Source_1[!grepl('tlprs', res_all$Model_1) & !grepl('EUR|EAS|AFR', res_all$Model_1)] <- 'Multi'
-
-res_all$Source_2[!grepl('tlprs', res_all$Model_2)] <- 'Single'
-res_all$Source_2[grepl('tlprs', res_all$Model_2)] <- 'Multi'
-res_all$Source_2[!grepl('tlprs', res_all$Model_2) & !grepl('EUR|EAS|AFR', res_all$Model_2)] <- 'Multi'
-
-res_all$Discovery_1 <- res_all$gwas_group
-res_all$Discovery_1[grepl('EUR', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'EUR'
-res_all$Discovery_1[grepl('EAS', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'EAS'
-res_all$Discovery_1[grepl('AFR', res_all$Model_1) & res_all$Source_1 == 'Single'] <- 'AFR'
-
-res_all$Discovery_2 <- res_all$gwas_group
-res_all$Discovery_2[grepl('EUR', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'EUR'
-res_all$Discovery_2[grepl('EAS', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'EAS'
-res_all$Discovery_2[grepl('AFR', res_all$Model_2) & res_all$Source_2 == 'Single'] <- 'AFR'
-
-res_all$TLPRS_target_1[grepl('EUR', res_all$Model_1) & res_all$TLPRS_1] <- 'EUR'
-res_all$TLPRS_target_1[grepl('EAS', res_all$Model_1) & res_all$TLPRS_1] <- 'EAS'
-res_all$TLPRS_target_1[grepl('AFR', res_all$Model_1) & res_all$TLPRS_1] <- 'AFR'
-res_all$TLPRS_target_1[!grepl('EUR|AFR|EAS', res_all$Model_1) & res_all$TLPRS_1] <- 'Both'
-res_all$TLPRS_target_1[res_all$TLPRS_target_1 == res_all$Target] <- 'Target Pop.'
-
-res_all$TLPRS_target_2[grepl('EUR', res_all$Model_2) & res_all$TLPRS_2] <- 'EUR'
-res_all$TLPRS_target_2[grepl('EAS', res_all$Model_2) & res_all$TLPRS_2] <- 'EAS'
-res_all$TLPRS_target_2[grepl('AFR', res_all$Model_2) & res_all$TLPRS_2] <- 'AFR'
-res_all$TLPRS_target_2[!grepl('EUR|AFR|EAS', res_all$Model_2) & res_all$TLPRS_2] <- 'Both'
-res_all$TLPRS_target_2[res_all$TLPRS_target_2 == res_all$Target] <- 'Target Pop.'
-
-# Subset to tests comparing to the Unadjusted models
-res_all <- res_all[res_all$Method_1 == res_all$Method_2, ]
-res_all <- res_all[res_all$Source_2 == 'Multi', ]
-res_all <- res_all[res_all$Test_1  == res_all$Test_2, ]
-res_all <- res_all[res_all$TLPRS_2  == F, ]
-res_all <- res_all[res_all$Target %in% c('EAS', 'AFR'),]
-
-ggplot(res_all, aes(x = Method_2, y = R_diff, colour = Test_1)) +
-  geom_point(position=position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7), alpha=0.5) +
-  labs(y = "Difference in\nCorrelations (SE)", x = '') +
-  theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  background_grid(major = 'y', minor = 'y') +
-  facet_grid(Target ~ TLPRS_target_1, scales='free', space = 'free_x')
+```
 
+***
 
-######
-# Average R across phenotypes
-######
+## Create 1KG only GenoPred reference data
 
-library(MAd)
+Subset the 1KG+HGDP reference data to include only 1KG individuals.
 
-meta_res <- NULL
-for(targ_pop_i in targ_pop){
-  if(targ_pop_i == 'EAS'){
-    disc_pop <- 'EAS'
-  }
-  if(targ_pop_i == 'AFR'){
-    disc_pop <- 'AFR'
-  }
-  if(targ_pop_i == 'EUR'){
-    disc_pop <- c('EAS','AFR')
-  }
-  for(disc_pop_i in disc_pop){
-  
-    # Subset res for each scenario
-    res_i <- res_all[res_all$Target == targ_pop_i & res_all$gwas_group == paste0('EUR+', disc_pop_i), ]
-  
-    # Calculate diff SE based on p-value
-    res_i$R_diff_pval[res_i$R_diff == 0] <- 1-0.001
-    res_i$R_diff_pval[res_i$R_diff_pval == 1]<-1-0.001
-    res_i$R_diff_z<-qnorm(res_i$R_diff_pval/2)
-    res_i$R_diff_SE<-abs(res_i$R_diff/res_i$R_diff_z)
-        
-    # Average results for each test across phenotypes
-    # Use MAd to account for correlation between them
-    res_i$Sample<-'A'
-    res_i$Group <- paste0(res_i$Model_1, '_vs_', res_i$Model_2)
-  
-    for(group_i in unique(res_i$Group)){
-      res_group_i <- res_i[res_i$Group == group_i,]
-      cors_i <- cors[[targ_pop_i]][unique(res_group_i$pheno), unique(res_group_i$pheno)]
-      
-      meta_res_i <-
-        agg(
-          id = Sample,
-          es = R_diff,
-          var = R_diff_SE ^ 2,
-          cor = cors_i,
-          method = "BHHR",
-          mod = NULL,
-          data = res_group_i
-        )
-      
-      group_info <- res_group_i[1, !(names(res_group_i[1,]) %in% c('Model_1_R', 'Model_2_R', 'R_diff', 'R_diff_pval', 'R_diff_z', 'R_diff_SE')), with=F]
-      meta_res <- rbind(meta_res,
-                        data.table(
-                          group_info,
-                          R_diff = meta_res_i$es,
-                          R_diff_SE = sqrt(meta_res_i$var)
-                        ))
-    }
-  }
-}
-meta_res$R_diff_z <- meta_res$R_diff / meta_res$R_diff_SE
-meta_res$R_diff_p <- 2*pnorm(-abs(meta_res$R_diff_z))
-
-# Plot the results
-ggplot(meta_res, aes(x=Method_1, y=R_diff, fill=Test_1)) +
-  geom_hline(yintercept = 0, colour = 'darkgrey') +
-  geom_point(
-    data = res_all,
-    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
-    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
-    alpha = 0.3
-  ) +
-  geom_errorbar(
-    aes(
-      ymin = R_diff - R_diff_SE,
-      ymax = R_diff + R_diff_SE
-    ),
-    width = 0,
-    position = position_dodge(width = 0.7)
-  ) +
-  geom_point(
-    stat = "identity",
-    position = position_dodge(0.7),
-    size = 2,
-    shape = 23,
-    colour = 'black'
-  ) +
-  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
-  theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  geom_vline(xintercept = 1.5, linetype = "dotted") +
-  geom_vline(xintercept = 2.5, linetype = "dotted") +
-  geom_vline(xintercept = 3.5, linetype = "dotted") +
-  background_grid(major = 'y', minor = 'y') +
-  facet_grid(Target ~ TLPRS_target_1, scales='free', space = 'free_x') +
-  panel_border()
-
-
-
-# Plot the results
-ggplot(meta_res[meta_res$TLPRS_target_1 == 'Both',], aes(x=Method_1, y=R_diff, fill=Test_1)) +
-  geom_hline(yintercept = 0, colour = 'darkgrey') +
-  geom_point(
-    data = res_all[res_all$TLPRS_target_1 == 'Both',],
-    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
-    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
-    alpha = 0.3
-  ) +
-  geom_errorbar(
-    aes(
-      ymin = R_diff - R_diff_SE,
-      ymax = R_diff + R_diff_SE
-    ),
-    width = 0,
-    position = position_dodge(width = 0.7)
-  ) +
-  geom_point(
-    stat = "identity",
-    position = position_dodge(0.7),
-    size = 2,
-    shape = 23,
-    colour = 'black'
-  ) +
-  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
-  theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  geom_vline(xintercept = 1.5, linetype = "dotted") +
-  geom_vline(xintercept = 2.5, linetype = "dotted") +
-  geom_vline(xintercept = 3.5, linetype = "dotted") +
-  background_grid(major = 'y', minor = 'y') +
-  facet_grid(Target ~ ., scales='free', space = 'free_x') +
-  panel_border()
+```{bash}
+mkdir -p ~/oliverpainfel/Data/1kg/genopred/
+cp -r ~/oliverpainfel/Data/hgdp_1kg/genopred/ref ~/oliverpainfel/Data/1kg/genopred/
+rm ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr*.p*
+```
 
+```{r}
+library(data.table)
 
-# Plot the results
-ggplot(meta_res[meta_res$TLPRS_target_1 == 'Both',], aes(x=Method_1, y=R_diff, fill=Test_1)) +
-  geom_hline(yintercept = 0, colour = 'darkgrey') +
-  geom_point(
-    data = res_all[res_all$TLPRS_target_1 == 'Both',],
-    mapping = aes(x=Method_1, y=R_diff, colour=Test_1),
-    position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
-    alpha = 0.3
-  ) +
-  geom_errorbar(
-    aes(
-      ymin = R_diff - R_diff_SE,
-      ymax = R_diff + R_diff_SE
-    ),
-    width = 0,
-    position = position_dodge(width = 0.7)
-  ) +
-  geom_point(
-    stat = "identity",
-    position = position_dodge(0.7),
-    size = 2,
-    shape = 23,
-    colour = 'black'
-  ) +
-  labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') +
-  theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  geom_vline(xintercept = 1.5, linetype = "dotted") +
-  geom_vline(xintercept = 2.5, linetype = "dotted") +
-  geom_vline(xintercept = 3.5, linetype = "dotted") +
-  background_grid(major = 'y', minor = 'y') +
-  facet_grid(Target ~ ., scales='free', space = 'free_x') +
-  panel_border()
+ref<- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr1.psam')
+ref<-ref[ref$Project == 'gnomAD_1kG',]
 
+write.table(ref[,1, drop = F], '~/oliverpainfel/Data/1kg/1kg.keep', col.names=F, row.names=F, quote=F)
+```
 
+```{bash}
+for chr in $(seq 1 22); do
+  ~/oliverpainfel/Software/plink2 \
+    --pfile ~/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} \
+    --keep ~/oliverpainfel/Data/1kg/1kg.keep \
+    --make-pgen \
+    --out ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr${chr}
+done
 ```
-</details>
 
 ***
 
-# Computational resoures
+## Run pipeline
+
+To save time, run using PGS methods that do not need pre-processed LD matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary from the 1KG+HGDP results, then expand to other methods (LDpred2, SBayesRC, QuickPRS).
 
 ```{r}
 library(data.table)
-library(ggplot2)
-library(cowplot)
 
-setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
-source('../functions/misc.R')
-source_all('../functions')
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only')
 
-# Get some key variables from config
-config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
-pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
-outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+######
+# target_list
+######
+target_list <- data.frame(
+  name='ukb',
+  path='/users/k1806347/oliverpainfel/Data/ukb/afr_eas_subset/ukb',
+  type='plink2',
+  indiv_report=F,
+  unrel='/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt'
+)
 
-# Read in configuration specific benchmark files
-bm_files_i <- list.files(paste0(outdir, '/reference/benchmarks/'), full.names = T)
+write.table(target_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt', col.names=T, row.names=F, quote=F)
 
-# Subset benchmarks for pgs_methods
-bm_files_i <- bm_files_i[grepl('prep_pgs_|leopard_quickprs_', bm_files_i)]
+######
+# config
+######
 
-# Subset to benchmarks for gwas/gwas_groups in config
-scores <- list_score_files(config)
-bm_files_i <- bm_files_i[grepl(paste0('-', unique(scores$name),'.txt', collapse = '|'), bm_files_i)]
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_1kgref",
+  "refdir: /users/k1806347/oliverpainfel/Data/1kg/genopred/ref",
+  "resdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/resdir_1kgref",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt",
+  "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
+  "pgs_methods: ['ptclump','dbslmm','lassosum','megaprs']",
+#  "leopard_methods: ['ptclump','dbslmm','lassosum','megaprs']",
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 10"
+)
 
-# Read in benchmark files
-bm_dat_all <- do.call(rbind, lapply(bm_files_i, function(file) {
-  tmp <- fread(file)
-  tmp$file <- basename(file)
-  return(tmp)
-}))
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml', col.names = F, row.names = F, quote = F)
 
-# Create rule column
-bm_dat_all$rule <- gsub('-.*','',bm_dat_all$file)
+```
 
-# Create method column
-bm_dat_all$method <-
-  gsub('_i', '', gsub('prep_pgs_', '', bm_dat_all$rule))
+```{bash}
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \
+  output_all -n
+```
 
-bm_dat_all <- merge(bm_dat_all, pgs_method_labels, by = 'method', all.x=T)
+***
 
-bm_dat_all$label[bm_dat_all$method == 'leopard_quickprs']<-"LEOPARD (QuickPRS)"
+## Evaluate PGS
 
-#############
-# Time
-#############
+<details><summary>Show code</summary>
 
-# Calculate average time taken for each method
-method_avg <- NULL
-for(i in unique(bm_dat_all$label)){
-  method_avg <- rbind(
-    method_avg,
-    data.frame(
-      method = bm_dat_all$method[bm_dat_all$label == i][1],
-      Method = i,
-      Time = mean(bm_dat_all$s[bm_dat_all$label == i])
-    )
-  )
-}
+```{r}
 
-# Times X-Wing time by two since it used 20 cores, but other methods used 10
-method_avg$Time[method_avg$method == 'xwing'] <- method_avg$Time[method_avg$method == 'xwing'] * 2
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
 
-# Divide the multi-source methods (PRS-CSx and X-Wing by 2 so it is time per GWAS)
-method_avg$Time[method_avg$method %in% c('prscsx','xwing','leopard_quickprs')] <- method_avg$Time[ method_avg$method %in% c('prscsx','xwing','leopard_quickprs')] / 2
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
-# Approximate times for either tuning or grid only
-method_avg$Model <- 'Full'
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
 
-tmp <- method_avg[method_avg$method == 'prscs' & method_avg$Model == 'Full',] 
-tmp$Model <- 'auto'
-tmp$Time <- tmp$Time * (1/5)
-method_avg<-rbind(method_avg, tmp)
+# Get a list of score files
+scores <- list_score_files(config)
 
-tmp <- method_avg[method_avg$method == 'prscsx' & method_avg$Model == 'Full',] 
-tmp$Model <- 'auto'
-tmp$Time <- tmp$Time * (1/5)
-method_avg<-rbind(method_avg, tmp)
+# Create files for EAS and AFR targets
+targ_pop <- c('EAS','AFR')
+for(trait_i in selected_traits){
+  scores_i <- scores[grepl(trait_i, scores$name),]
+  scores_i$multi <- scores_i$method
+  
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'BBJ'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'UGR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('BBJ','UGR')
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == 'BBJ'){
+        disc_pop_j_2 <- 'EAS'
+      }
+      if(disc_pop_j == 'UGR'){
+        disc_pop_j_2 <- 'AFR'
+      }
 
-tmp <- method_avg[method_avg$method == 'xwing' & method_avg$Model == 'Full',] 
-tmp$Model <- 'grid'
-tmp$Time <- tmp$Time * (2/10)
-method_avg<-rbind(method_avg, tmp)
+      dir.create(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i
+        ),
+        recursive = T
+      )
+      
+      scores_i_j <- scores_i[
+        (grepl('UKB$', scores_i$name, ignore.case = F) | 
+         grepl(paste0(disc_pop_j, '$'), scores_i$name, ignore.case = T)),]
 
-# Format the time taken nicely
-method_avg$Time_clean[method_avg$Time < 60] <-
-  paste0(round(method_avg$Time[method_avg$Time < 60], 1), ' sec')
-method_avg$Time_clean[method_avg$Time > 60] <-
-  paste0(round(method_avg$Time[method_avg$Time > 60] / 60, 1), ' min')
-method_avg$Time_clean[method_avg$Time > 3600] <-
-  paste0(round(method_avg$Time[method_avg$Time > 3600] / 60 / 60, 1), ' hr')
+      # Insert path to score file
+      scores_i_j$predictor <- paste0(
+        outdir,
+        '/ukb/pgs/TRANS/',
+        scores_i_j$method,
+        '/',
+        scores_i_j$name,
+        '/ukb-',
+        scores_i_j$name,
+        '-TRANS.profiles'
+      )
+      
+      ####
+      # Make groups single source methods
+      ####
+      
+      scores_i_j_single_top1 <-
+        scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &
+                     !grepl('_multi$', scores_i_j$method), ]
 
-# Convert time in seconds to hours
-method_avg$Time_hour <- method_avg$Time / 60/60
+      # Create top1 column indicating which predictors top1 models should be derived
+      scores_i_j_single_top1$top1[grepl('UKB', scores_i_j_single_top1$name, ignore.case = F)] <- 'EUR'
+      scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] <- disc_pop_j_2
+      
+      ####
+      # Make groups containing pseudo scores for single source methods
+      ####
 
-# Seperate methods by single or multi source
-method_avg$Type[!(method_avg$method %in% pgs_group_methods)]<-'Single-source'
-method_avg$Type[method_avg$method %in% pgs_group_methods]<-'Multi-source'
-method_avg$Type[method_avg$method == 'leopard_quickprs']<-'Tuning'
-
-method_avg$Type<-factor(method_avg$Type, levels = c('Single-source','Multi-source','Tuning'))
-method_avg$Method <- factor(method_avg$Method, levels = c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "QuickPRS-Multi", "PRS-CSx", "X-Wing","LEOPARD (QuickPRS)"))
-
-ggplot(method_avg, aes(x = Method, y = Time_hour, fill = Model)) +
-  geom_bar(stat = "identity", position="dodge") +
-  geom_text(aes(label = Time_clean), vjust = 0.5, angle = 90, hjust = -0.2, position = position_dodge(width = 0.9)) +
-  labs(x = NULL, y = "Time (hours)") +
-  ylim(0, max(method_avg$Time_hour) + (max(method_avg$Time_hour)/5)) +
-  facet_grid(~ Type, scales='free', space = 'free_x') +
-  theme_half_open() +
-  background_grid(major = 'y', minor = 'y') + 
-  panel_border() + 
-  theme(axis.text.x = element_text(angle = 45, hjust = 1))
-
-method_avg <- method_avg[method_avg$Model == 'Full',]
-method_avg <- method_avg[, c('Method','Time_hour')]
-method_avg$Time_hour <- round(method_avg$Time_hour, 2)
-names(method_avg)<-c('Method',"Time (hrs)")
+      # Extract the pseudo score for each method and specify as a separate group
+      for(i in 1:nrow(scores_i_j_single_top1)) {
+        param <- find_pseudo(
+          config = config,
+          gwas = scores_i_j_single_top1$name[i],
+          pgs_method = scores_i_j_single_top1$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header <-
+          fread(scores_i_j_single_top1$predictor[i], nrows = 1)
+        score_cols <-
+          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_single_top1$name[i], '_', param)))
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            scores_i_j_single_top1$predictor[i], 
+            " > ", 
+            gsub('.profiles',
+                 paste0('.', targ_pop_i, '_pseudo.profiles'),
+                 scores_i_j_single_top1$predictor[i])
+          )
+        )
+      }
+      
+      scores_i_j_single_pseudo <- scores_i_j_single_top1
+      scores_i_j_single_pseudo$multi <- paste0(scores_i_j_single_pseudo$multi, '.pseudo')
 
-#############
-# Memory
-#############
+      scores_i_j_single_pseudo$predictor <- gsub('.profiles', 
+                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
+                                    scores_i_j_single_pseudo$predictor)
 
-# Calculate average max_rss for each method
-method_avg_mem <- NULL
-for(i in unique(bm_dat_all$label)){
-  method_avg_mem <- rbind(
-    method_avg_mem,
-    data.frame(
-      method = bm_dat_all$method[bm_dat_all$label == i][1],
-      Method = i,
-      Memory = mean(bm_dat_all$max_rss[bm_dat_all$label == i])
-    )
-  )
+#      ####
+#      # Make groups for multi-single-source pseudo scores
+#      ####
+#      
+#      scores_i_j_multi_single_pseudo <- scores_i_j[grepl('_multi$', scores_i_j$method),]
+#
+#      # Extract the pseudo score for each method and specify as a separate group
+#      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
+#        param <- find_pseudo(
+#          config = config,
+#          gwas = scores_i_j_multi_single_pseudo$name[i],
+#          pgs_method = scores_i_j_multi_single_pseudo$method[i],
+#          target_pop = targ_pop_i
+#        )
+#        
+#        score_header <-
+#          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
+#        score_cols <-
+#          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi_single_pseudo$name[i], '_', param)))
+#        
+#        system(
+#          paste0(
+#            "cut -d' ' -f ", 
+#            paste0(score_cols, collapse=','),
+#            " ", 
+#            scores_i_j_multi_single_pseudo$predictor[i], 
+#            " > ", 
+#            gsub('.profiles',
+#                 paste0('.', targ_pop_i, '_pseudo.profiles'),
+#                 scores_i_j_multi_single_pseudo$predictor[i])
+#          )
+#        )
+#      }
+#      
+#      scores_i_j_multi_single_pseudo$multi <- paste0(scores_i_j_multi_single_pseudo$multi, '.pseudo')
+#
+#      scores_i_j_multi_single_pseudo$predictor <- gsub('.profiles', 
+#                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
+#                                    scores_i_j_multi_single_pseudo$predictor)
+#      
+#      scores_i_j_multi_single_pseudo$top1<-paste0('EUR_', disc_pop_j_2)
+#
+#      ####
+#      # Make groups for the Multi-Source methods
+#      ####
+#      
+#      scores_i_j_multi <- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
+#
+#      # Split top1 scores by target population
+#      # This doesn't apply to xwing because it only has pop-specific pseudo scores
+#      scores_i_j_multi_top1<-NULL
+#      for(i in 1:which(scores_i_j_multi$method %in% c('prscsx'))){
+#        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
+#        
+#        for(pop in c('EUR', disc_pop_j_2)){
+#          
+#          if(scores_i_j_multi$method[i] == 'prscsx'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi'), names(score_header)))
+#          }
+#          if(scores_i_j_multi$method[i] == 'xwing'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst'), names(score_header)))
+#          }
+#          
+#          system(
+#            paste0(
+#              "cut -d' ' -f ", 
+#              paste0(score_cols, collapse=','),
+#              " ", 
+#              scores_i_j_multi$predictor[i], 
+#              " > ", 
+#              gsub('.profiles',
+#                   paste0('.', pop, '_grid.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#            )
+#          )
+#          
+#          tmp <- scores_i_j_multi[i,]
+#          tmp$multi <- paste0(tmp$multi, '.grid')
+#          tmp$top1 <- pop
+#          tmp$predictor <-
+#              gsub('.profiles',
+#                   paste0('.', pop, '_grid.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#          
+#          scores_i_j_multi_top1 <- rbind(scores_i_j_multi_top1, tmp)
+#        }
+#      }
+#
+#      # Split pop-specific pseudo scores by target population
+#      scores_i_j_multi_pop_pseudo<-NULL
+#      for(i in 1:nrow(scores_i_j_multi)){
+#        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
+#        
+#        for(pop in c('EUR', disc_pop_j_2)){
+#          if(scores_i_j_multi$method[i] == 'prscsx'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi_auto'), names(score_header)))
+#          }
+#          if(scores_i_j_multi$method[i] == 'xwing'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst_', pop), names(score_header)))
+#          }
+#          
+#          system(
+#            paste0(
+#              "cut -d' ' -f ", 
+#              paste0(score_cols, collapse=','),
+#              " ", 
+#              scores_i_j_multi$predictor[i], 
+#              " > ", 
+#              gsub('.profiles',
+#                   paste0('.', pop, '_pseudo.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#            )
+#          )
+#          
+#          tmp <- scores_i_j_multi[i,]
+#          tmp$multi <- paste0(tmp$multi, '.pop_pseudo')
+#          tmp$top1 <- pop
+#          tmp$predictor <-
+#              gsub('.profiles',
+#                   paste0('.', pop, '_pseudo.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#          
+#          scores_i_j_multi_pop_pseudo <- rbind(scores_i_j_multi_pop_pseudo, tmp)
+#        }
+#      }
+#      
+#      # Create pseudo score for multi-source methods
+#      scores_i_j_multi_pseudo<-NULL
+#      for(i in 1:nrow(scores_i_j_multi)) {
+#        param <- find_pseudo(
+#          config = config,
+#          gwas = scores_i_j_multi$name[i],
+#          pgs_method = scores_i_j_multi$method[i],
+#          target_pop = targ_pop_i
+#        )
+#        
+#        score_header <-
+#          fread(scores_i_j_multi$predictor[i], nrows = 1)
+#        score_cols <-
+#          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
+#
+#        system(
+#          paste0(
+#            "cut -d' ' -f ", 
+#            paste0(score_cols, collapse=','),
+#            " ", 
+#            scores_i_j_multi$predictor[i], 
+#            " > ", 
+#            gsub('.profiles',
+#                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
+#                 scores_i_j_multi$predictor[i])
+#          )
+#        )
+#        
+#        tmp <- scores_i_j_multi[i,]
+#        tmp$multi <- paste0(tmp$multi, '.pseudo')
+#        tmp$top1 <- paste0('EUR_', disc_pop_j_2)
+#        tmp$predictor <-
+#            gsub('.profiles',
+#                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
+#                 scores_i_j_multi$predictor[i])
+#        
+#        scores_i_j_multi_pseudo <- rbind(scores_i_j_multi_pseudo, tmp)
+#      }
+      
+      ####
+      # Combine the different predictor groups
+      ####
+      predictors_i<- do.call(rbind, list(
+        scores_i_j_single_top1, 
+        scores_i_j_single_pseudo#, 
+#        scores_i_j_multi_single_pseudo,
+#        scores_i_j_multi_top1,
+#        scores_i_j_multi_pop_pseudo,
+#        scores_i_j_multi_pseudo
+      ))
+      
+      predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
+      
+      write.table(
+        predictors_i,
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i,
+          '/predictor_list.txt'
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
 }
 
-# Divide X-Wing memory by two, since it used 20 cores, but other methods used 10
-method_avg_mem$Memory[method_avg_mem$method == 'xwing'] /2
-
-# Format the Memory nicely
-method_avg_mem$Memory_clean <-
-  paste0(round(method_avg_mem$Memory/1000, 2), ' Gb')
-
-ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
-  geom_bar(stat = "identity", position="dodge") +
-  geom_text(aes(label = Memory_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
-  labs(x = "PGS Method", y = "Memory (Mb)") +
-  theme_half_open() +
-  background_grid() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
-
-method_avg_mem$Memory_gb <- method_avg_mem$Memory/1000
-method_avg_mem <- method_avg_mem[, c('Method','Memory_gb')]
-method_avg_mem$Memory_gb <- round(method_avg_mem$Memory_gb, 2)
-names(method_avg_mem)<-c('Method',"Memory (Gb)")
-
-method_avg<-merge(method_avg, method_avg_mem, by = 'Method')
-
-write.csv(method_avg, '~/oliverpainfel/Analyses/crosspop/time_memory.csv', row.names=F)
 ```
+</details>
 
 ***
 
-# Sensitivity analysis using 1KG reference
-
-PRS-CS, PRS-CSx and X-Wing all use the 1KG reference sample, whereas the other methods are using the 1KG+HGDP reference sample. We should check whether this difference is impacting our conclusions.
-
-***
-
-## Subset AFR and EAS individuals in UKB data
-
-To make this quicker, focus on evaluating the PGS methods in the AFR and EAS target individuals in UKB. This will avoid reprocessing the full UKB data.
-
-```{r}
-library(data.table)
-
-keep <- NULL
-for (i in c('AFR', 'EAS')) {
-  keep <- rbind(keep, fread(
-    paste0(
-      '~/oliverpainfel/Data/ukb/GenoPred/output/ukb/ancestry/keep_files/model_based/',
-      i,
-      '.keep'
-    )
-  ))
-}
-
-write.table(
-  keep,
-  '~/oliverpainfel/Data/ukb/afr_eas.keep',
-  row.names = F,
-  col.names = F,
-  quote = F
-)
+### Run model_builder
 
-```
+<details><summary>Show code</summary>
 
 ```{bash}
-mkdir ~/oliverpainfel/Data/ukb/afr_eas_subset
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
 
-for chr in $(seq 1 22); do
-  ~/oliverpainfel/Software/plink2 \
-    --pfile ~/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
-    --keep ~/oliverpainfel/Data/ukb/afr_eas.keep \
-    --make-pgen \
-    --out ~/oliverpainfel/Data/ukb/afr_eas_subset/ukb.chr${chr}
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_*.disc_EUR_*/*/res*
+
+for targ_pop in $(echo EAS AFR); do
+  if [ "$targ_pop" == "EUR" ]; then
+      targ_pop2="EUR_test"
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ "$targ_pop" == "EUR" ]; then
+    disc_pop=$(echo EAS AFR)
+  fi
+  
+  if [ "$targ_pop" == "EAS" ]; then
+    disc_pop="EAS"
+  fi
+  
+  if [ "$targ_pop" == "AFR" ]; then
+    disc_pop="AFR"
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+      if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt" ]; then
+        sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+          --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
+          --n_core 5"
+      fi
+    done
+  done
 done
 
 ```
+</details>
 
 ***
 
-## Create 1KG only GenoPred reference data
-
-Subset the 1KG+HGDP reference data to include only 1KG individuals.
+### Plot results
 
-```{bash}
-mkdir -p ~/oliverpainfel/Data/1kg/genopred/
-cp -r ~/oliverpainfel/Data/hgdp_1kg/genopred/ref ~/oliverpainfel/Data/1kg/genopred/
-rm ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr*.p*
-```
+<details><summary>Show code</summary>
 
 ```{r}
-library(data.table)
-
-ref<- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr1.psam')
-ref<-ref[ref$Project == 'gnomAD_1kG',]
-
-write.table(ref[,1, drop = F], '~/oliverpainfel/Data/1kg/1kg.keep', col.names=F, row.names=F, quote=F)
-```
-
-```{bash}
-for chr in $(seq 1 22); do
-  ~/oliverpainfel/Software/plink2 \
-    --pfile ~/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} \
-    --keep ~/oliverpainfel/Data/1kg/1kg.keep \
-    --make-pgen \
-    --out ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr${chr}
-done
-```
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 
-***
+library(data.table)
+library(ggplot2)
+library(cowplot)
 
-## Run pipeline
+source('../functions/misc.R')
+source_all('../functions')
 
-To save time, run using PGS methods that do not need pre-processed LD matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary from the 1KG+HGDP results, then expand to other methods (LDpred2, SBayesRC, QuickPRS).
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
 
-```{r}
-library(data.table)
+# Calculate correlation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
 
-dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only')
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
 
-######
-# target_list
-######
-target_list <- data.frame(
-  name='ukb',
-  path='/users/k1806347/oliverpainfel/Data/ukb/afr_eas_subset/ukb',
-  type='plink2',
-  indiv_report=F,
-  unrel='/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt'
+# Read in results
+targ_pop = c('EAS','AFR')
+res_eval <- list()
+for(pheno_i in selected_traits){
+  res_eval_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.pred_eval.txt'
+          )
+        )
+      eval_i$Target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_eval_i<-rbind(res_eval_i, eval_i)
+    }
+  }
+  
+  res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
+  res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
+  
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTrain'
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTrain'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTrain'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTrain'
+  
+  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTrain'
+  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTrain'
+  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTrain'
+  
+  res_eval_i$Source<-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
+    !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
+  
+  res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
+  res_eval_i$Discovery[grepl('EAS', res_eval_i$Group)] <- 'EAS'
+  res_eval_i$Discovery[grepl('AFR', res_eval_i$Group)] <- 'AFR'
+  res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
+  
+  res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+  res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
+      res_eval_i$Model %in% c('IndivTrain','Multi-IndivTrain')),]
+  
+  # Remove pseudo model for methods that don't really have one 
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
+      res_eval_i$Model %in% c('SumStatTrain','Multi-SumStatTrain')),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
+  res_eval_i <- res_eval_i[
+    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method)) & 
+      grepl('top1', res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
+    "Target", "Method", "Model", "Source", "Discovery","gwas_group"
+  )]),]
+  
+  res_eval[[pheno_i]]<-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi", "PRS-CSx", "X-Wing", "All") 
+
+res_eval_simp <- NULL
+for(pheno_i in selected_traits){
+  tmp <- res_eval[[pheno_i]]
+  tmp$Trait <- pheno_i
+  
+  # Insert nice PGS method names
+  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+  tmp$label[is.na(tmp$label)] <- 'All'
+  tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+  tmp$label <- factor(tmp$label, levels = model_order)
+  
+  # Simplify result to either SumStatTrain or IndivTrain
+  tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
+  tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+  tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery','Model'), with=F]),]
+  
+  res_eval_simp <- rbind(res_eval_simp, tmp)
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_eval for each scenario
+    res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x <- res_eval[[i]]
+      x$pheno <- names(res_eval)[i]
+      x <- x[x$Target == targ_pop_i]
+      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
+    }))
+    
+    # Average res_evalults for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_eval_i$Sample<-'A'
+  
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno <-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+      
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          'res_evalults missing for ',
+          targ_pop_i,
+          ' ',
+          group_i,
+          ' ',
+          paste0(missing_pheno, collapse = ' ')
+        ))
+      }
+      
+      cors_i <- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i <-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_eval_group_i
+        )
+      
+      tmp <- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval <- rbind(meta_res_eval, tmp)
+    }
+  }
+}
+
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
+tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+# The results look very similar to when using 1KG+HGDP. 
+
+###################
+# Plot a comparison between the runs using different references
+
+# Read in results using 1KG+HGDP reference
+main_results<-fread('~/oliverpainfel/Analyses/crosspop/r_eval.csv')
+sens_results<-meta_res_eval
+
+tmp <- main_results
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
+tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+main_results <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
+
+tmp <- sens_results
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
+tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+sens_results <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
+
+main_results<-main_results[main_results$Method %in% sens_results$Method,]
+main_results<-main_results[main_results$Target %in% sens_results$Target,]
+
+sens_results$Reference <- '1KG'
+main_results$Reference <- '1KG+HGDP'
+
+both_results <- rbind(main_results, sens_results)
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/comparison_to_main_result.png', units = 'px', res = 300, width=4000, height=2500)
+ggplot(both_results, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean + Reference, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+  
+```
+</details>
+
+***
+
+# TL-PRS
+
+Run using AFR and EAS subset in UKB to make it quicker to run. This is the main interest when running TL-PRS anyway. 
+
+***
+
+## Run pipeline
+
+To save time, run using PGS methods that do not need pre-processed LD matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary from the 1KG+HGDP results, then expand to other methods (LDpred2, SBayesRC, QuickPRS).
+
+```{r}
+library(data.table)
+
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs')
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_tlprs",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt",
+  "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
+  "pgs_methods: ['quickprs','dbslmm','ldpred2','sbayesrc']",
+  "tlprs_methods: ['quickprs','dbslmm','ldpred2','sbayesrc']",
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 50",
+  "prscs_phi: ['auto']",
+  "ldpred2_model: ['auto']",
+  "ldpred2_inference: F",
+  "dbslmm_h2f: ['1']",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
 )
 
-write.table(target_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt', col.names=T, row.names=F, quote=F)
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+```{bash}
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml \
+  output_all -n
+```
+
+***
+
+## Create predictor lists
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Create files for EAS and AFR targets
+targ_pop <- c('EAS','AFR')
+for(trait_i in selected_traits){
+  scores_i <- scores[grepl(trait_i, scores$name),]
+
+  for(targ_pop_i in targ_pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'BBJ'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'UGR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('BBJ','UGR')
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == 'BBJ'){
+        disc_pop_j_2 <- 'EAS'
+      }
+      if(disc_pop_j == 'UGR'){
+        disc_pop_j_2 <- 'AFR'
+      }
+
+      dir.create(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i
+        ),
+        recursive = T
+      )
+      
+      scores_i_j <- scores_i[
+        (grepl('UKB$', scores_i$name, ignore.case = F) | 
+         grepl(paste0(disc_pop_j, '$'), scores_i$name, ignore.case = T)),]
+
+      scores_i_j$predictor <- paste0(
+        outdir,
+        '/ukb/pgs/TRANS/',
+        scores_i_j$method,
+        '/',
+        scores_i_j$name,
+        '/ukb-',
+        scores_i_j$name,
+        '-TRANS.profiles'
+      )
+      
+      #####
+      # List single-source PGS
+      #####
+      # These are actually pseudoval scores (as per the config)
+      scores_i_j_single <- scores_i_j[!grepl('tlprs', scores_i_j$method),]
+      
+      scores_i_j_single$top1[grepl('UKB', scores_i_j_single$name, ignore.case = F)] <- 'EUR'
+      scores_i_j_single$top1[grepl(disc_pop_j, scores_i_j_single$name, ignore.case = F)] <- disc_pop_j_2
+      scores_i_j_single$multi <- paste0(scores_i_j_single$method,'.pseudo')
+      
+      #####
+      # List tlprs scores (split by target population)
+      #####
+      scores_i_j_tlprs <- scores_i_j[grepl('tlprs', scores_i_j$method),]
+      scores_i_j_tlprs$multi <- scores_i_j_tlprs$method
+      
+      scores_i_j_tlprs_pop<-NULL
+      for(i in 1:nrow(scores_i_j_tlprs)){
+        score_header<-fread(scores_i_j_tlprs$predictor[i], nrow = 1)
+        
+        for(pop in c('EUR', disc_pop_j_2)){
+          score_cols <- which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_'), names(score_header)))
+
+          system(
+            paste0(
+              "cut -d' ' -f ", 
+              paste0(score_cols, collapse=','),
+              " ", 
+              scores_i_j_tlprs$predictor[i], 
+              " > ", 
+              gsub('.profiles',
+                   paste0('.targ_', pop, '.profiles'),
+                   scores_i_j_tlprs$predictor[i])
+            )
+          )
+          
+          tmp <- scores_i_j_tlprs[i,]
+          tmp$multi <- paste0(tmp$multi, '.pop')
+          tmp$top1 <- pop
+          tmp$predictor <-
+              gsub('.profiles',
+                   paste0('.targ_', pop, '.profiles'),
+                   scores_i_j_tlprs$predictor[i])
+          
+          scores_i_j_tlprs_pop <- rbind(scores_i_j_tlprs_pop, tmp)
+        }
+      }
+
+      predictors_i<- do.call(rbind, list(
+        scores_i_j_single, scores_i_j_tlprs_pop
+      ))
+      
+      predictors_i <- predictors_i[, c('predictor', 'top1','multi'), with=F]
+      
+      write.table(
+        predictors_i,
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i,
+          '/predictor_list.tlprs.txt'
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}
+
+```
+
+</details>
+
+***
+
+## Run model_builder
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_*.disc_EUR_*/*/res*
+
+for targ_pop in $(echo EAS AFR); do
+  if [ "$targ_pop" == "EUR" ]; then
+      targ_pop2="EUR_test"
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ "$targ_pop" == "EUR" ]; then
+    disc_pop=$(echo AFR EAS)
+  fi
+  
+  if [ "$targ_pop" == "EAS" ]; then
+    disc_pop="EAS"
+  fi
+  
+  if [ "$targ_pop" == "AFR" ]; then
+    disc_pop="AFR"
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+      if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs.pred_comp.txt" ]; then
+        sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+          --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
+          --n_core 5"
+      fi
+    done
+  done
+done
+```
+</details>
+
+***
 
-######
-# config
-######
+## Plot results
 
-config<-c(
-  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_1kgref",
-  "refdir: /users/k1806347/oliverpainfel/Data/1kg/genopred/ref",
-  "resdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/resdir_1kgref",
-  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml",
-  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt",
-  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt",
-  "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
-  "pgs_methods: ['ptclump','dbslmm','lassosum','megaprs']",
-#  "leopard_methods: ['ptclump','dbslmm','lassosum','megaprs']",
-  "cores_prep_pgs: 10",
-  "cores_target_pgs: 10"
-)
+<details><summary>Show code</summary>
 
-write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml', col.names = F, row.names = F, quote = F)
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 
-```
+library(data.table)
+library(ggplot2)
+library(cowplot)
 
-```{bash}
-snakemake \
-  --profile slurm \
-  --use-conda \
-  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \
-  output_all -n
-```
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+
+# Calculate correlation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop = c('EAS','AFR')
+res_eval <- list()
+for(pheno_i in selected_traits){
+  res_eval_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.tlprs.pred_eval.txt'
+          )
+        )
+      eval_i$Target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_eval_i<-rbind(res_eval_i, eval_i)
+    }
+  }
+  
+  res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
+  res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
+  
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTrain'
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTrain'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTrain'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTrain'
+  
+  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTrain'
+  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTrain'
+  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTrain'
+  
+  res_eval_i$Source<-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
+    !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
+  
+  res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
+  res_eval_i$Discovery[grepl('EAS', res_eval_i$Group)] <- 'EAS'
+  res_eval_i$Discovery[grepl('AFR', res_eval_i$Group)] <- 'AFR'
+  res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
+  
+  res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+  res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
+      res_eval_i$Model %in% c('IndivTrain','Multi-IndivTrain')),]
+  
+  # Remove pseudo model for methods that don't really have one 
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
+      res_eval_i$Model %in% c('SumStatTrain','Multi-SumStatTrain')),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing, TL-*
+  res_eval_i <- res_eval_i[
+    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method) | grepl('^tlprs_', res_eval_i$Method)) & 
+      grepl('top1', res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
+    "Target", "Method", "Model", "Source", "Discovery","gwas_group"
+  )]),]
+  
+  res_eval[[pheno_i]]<-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi","TL-DBSLMM", "PRS-CSx", "X-Wing", "All") 
+
+####
+# Average results across phenotypes
+####
 
+library(MAd)
 
+# Average R across phenotypes
+meta_res_eval <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_eval for each scenario
+    res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x <- res_eval[[i]]
+      x$pheno <- names(res_eval)[i]
+      x <- x[x$Target == targ_pop_i]
+      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
+    }))
+    
+    # Average res_evalults for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_eval_i$Sample<-'A'
+  
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno <-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+      
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          'res_evalults missing for ',
+          targ_pop_i,
+          ' ',
+          group_i,
+          ' ',
+          paste0(missing_pheno, collapse = ' ')
+        ))
+      }
+      
+      cors_i <- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i <-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_eval_group_i
+        )
+      
+      tmp <- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval <- rbind(meta_res_eval, tmp)
+    }
+  }
+}
 
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All' & !grepl('^tlprs_', tmp$Method)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All' & !grepl('^tlprs_', tmp$Method)], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
+tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
+png(paste0('~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
 
+# TL-PRS does seem to provide a slight advantage  
+```
 
diff --git a/functions/constants.R b/functions/constants.R
index d6c6d888..0841115f 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -37,8 +37,8 @@ ref_pop <- data.frame(
 
 # Make a data.frame giving labels to the 1KG reference populations
 pgs_method_labels <- data.frame(
-  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','ptclump_multi','dbslmm_multi','ldpred2_multi','sbayesr_multi','sbayesrc_multi','lassosum_multi','prscs_multi','megaprs_multi','quickprs_multi'),
-  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','pT+clump-multi','DBSLMM-multi','LDpred2-multi','SBayesR-multi','SBayesRC-multi','lassosum-multi','PRS-CS-multi','MegaPRS-multi','QuickPRS-multi')
+  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','ptclump_multi','dbslmm_multi','ldpred2_multi','sbayesr_multi','sbayesrc_multi','lassosum_multi','prscs_multi','megaprs_multi','quickprs_multi','tlprs_dbslmm'),
+  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','pT+clump-multi','DBSLMM-multi','LDpred2-multi','SBayesR-multi','SBayesRC-multi','lassosum-multi','PRS-CS-multi','MegaPRS-multi','QuickPRS-multi','TL-DBSLMM')
 )
 pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),]
 

From c3cfcade1a9196fd629a5fea8d641af746b1932e Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 26 Feb 2025 17:53:05 +0000
Subject: [PATCH 114/153] Updates

---
 docs/CrossPop.Rmd               |  553 ++-
 docs/CrossPop.html              | 5977 +++++++++++++++++++++++++++++++
 docs/pipeline_readme.Rmd        |   14 +-
 docs/pipeline_readme.html       |   67 +-
 functions/constants.R           |    4 +-
 pipeline/rules/dependencies.smk |    2 +-
 6 files changed, 6582 insertions(+), 35 deletions(-)
 create mode 100644 docs/CrossPop.html

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index a5d9aca0..0ca80c99 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -32,8 +32,6 @@ pre code, pre, code {
 
 European (EUR) GWAS are typically the largest in sample size, or even the only GWAS for certain outcomes. Here we will evaluate approaches for calculating polygenic scores (PGS) across populations. We will include single- and multi-source PGS methods, using EUR GWAS alone, or using EUR GWAS in combination with GWAS from other populations.
 
-In first instance, we will use EUR individuals in UK Biobank (UKB), to derive GWAS summary statistics, and Biobank Japan GWAS. We will evaluate PGS across populations in UKB, using outcomes available in the majority of participants to ensure sufficient sample size in non-EUR populations.
-
 ***
 
 # Derive GWAS in UKB
@@ -810,6 +808,8 @@ write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/cro
 
 ## Run pipeline
 
+<details><summary>Show code</summary>
+
 ```{bash}
 snakemake \
   --profile slurm \
@@ -818,6 +818,8 @@ snakemake \
   target_pgs -n
 ```
 
+</details>
+
 ***
 
 ## Reformat for LDSC and POPCORN
@@ -928,6 +930,8 @@ done
 
 ## Calculate CSCOREs
 
+<details><summary>Show code</summary>
+
 ```{bash}
 
 # Subset the reference data into relevant populations
@@ -963,6 +967,8 @@ rm -r /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp
 rm /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_*_CSCORES/*chr*.txt
 ```
 
+</details>
+
 ***
 
 ## Run POPCORN
@@ -1277,6 +1283,8 @@ ggplot(res_all, aes(x = Phenotype, y = BETA, fill = pT)) +
 
 ### Run AVENGEME
 
+<details><summary>Show code</summary>
+
 ```{r}
 
 setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -1423,10 +1431,14 @@ summary(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])
 
 ```
 
+</details>
+
 ***
 
 ### Select GWAS for downstream analyses
 
+<details><summary>Show code</summary>
+
 ```{r}
 #########
 # Select 10 GWAS for downstream analysis
@@ -1505,10 +1517,14 @@ round(sqrt(max(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])), 2)
 
 ```
 
+</details>
+
 ***
 
 # Make a descriptives table of GWAS
 
+<details><summary>Show code</summary>
+
 ```{r}
 library(data.table)
 
@@ -1623,6 +1639,8 @@ for(i in unique(info_all_selected$`GWAS Sample`)){
 
 ```
 
+</details>
+
 ***
 
 # Run GenoPred
@@ -1696,6 +1714,8 @@ write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/cro
 
 ## Run pipeline
 
+<details><summary>Show code</summary>
+
 ```{bash}
 snakemake \
   --profile slurm \
@@ -1704,6 +1724,8 @@ snakemake \
   target_pgs  -n
 ```
 
+</details>
+
 ***
 
 # Evaluate PGS
@@ -3393,6 +3415,8 @@ dev.off()
 
 Here we will compare the LEOPARD estimated weights for population specific PGS, to the weights estimated using observed data in the UKB target sample.
 
+<details><summary>Show code</summary>
+
 ```{r}
 setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 
@@ -3611,10 +3635,105 @@ dev.off()
 
 ```
 
+</details>
+
+***
+
+# Investigate PRS-CS vs. PRS-CSx performance
+
+PRS-CS-multi is performing better than PRS-CSx often, which contrasts with the original PRS-CSx paper. Try to understand why this is.
+
+```{r}
+# Read in the PGS for EAS target from PRS-CS and PRS-CSx
+prscs_eur <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/ukb/pgs/TRANS/prscs/TC_UKB/ukb-TC_UKB-TRANS.profiles')
+prscs_eas <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/ukb/pgs/TRANS/prscs/TC_BBJ/ukb-TC_BBJ-TRANS.profiles')
+prscsx <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/ukb/pgs/TRANS/prscsx/TC_UKB_BBJ/ukb-TC_UKB_BBJ-TRANS.profiles')
+
+# Read in the phenotype data
+pheno <- fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/TC.unrel.EAS.row_number.txt')
+
+# Merge all the data
+data <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('FID','IID')), list(pheno, prscs_eur, prscs_eas, prscsx))
+
+assoc_res <- data.frame(
+  PGS = names(data)[-1:-3],
+  R = cor(data[,-1:-2], use='p')[-1,1]
+)
+rownames(assoc_res)<-NULL
+
+assoc_res<-assoc_res[!grepl('META', assoc_res$PGS),]
+assoc_res$phi <- gsub('.*_phi_','',assoc_res$PGS)
+assoc_res$phi <- factor(assoc_res$phi, levels = c('1e+00','1e-02','1e-04','1e-06','auto'))
+assoc_res$method <- ifelse(grepl('UKB_BBJ', assoc_res$PGS), 'PRS-CSx', 'PRS-CS')
+assoc_res$pop <- NA
+assoc_res$pop[assoc_res$method == 'PRS-CS' & grepl('UKB', assoc_res$PGS)] <- 'EUR'
+assoc_res$pop[assoc_res$method == 'PRS-CS' & grepl('BBJ', assoc_res$PGS)] <- 'EAS'
+assoc_res$pop[assoc_res$method == 'PRS-CSx' & grepl('EUR', assoc_res$PGS)] <- 'EUR'
+assoc_res$pop[assoc_res$method == 'PRS-CSx' & grepl('EAS', assoc_res$PGS)] <- 'EAS'
+
+library(ggplot2)
+library(cowplot)
+
+ggplot(assoc_res, aes(x=pop, y=R , fill = phi)) +
+    geom_bar(stat="identity", position=position_dodge(0.7), width = 0.7) +
+    labs(y = "R", x='Method') +
+    facet_grid(. ~ method, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+# This shows nicely the poorer performance of the EAS PGS from PRS-CSx and than PRS-CS.
+# Why is this? Have a look at the SNP-weights.
+
+prscs_eur <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/reference/pgs_score_files/prscs/TC_UKB/ref-TC_UKB.score.gz')
+prscs_eas <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/reference/pgs_score_files/prscs/TC_BBJ/ref-TC_BBJ.score.gz')
+prscsx <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/reference/pgs_score_files/prscsx/TC_UKB_BBJ/ref-TC_UKB_BBJ.score.gz')
+
+prscsx<-prscsx[, !(grepl('META', names(prscsx))), with=F]
+
+names(prscs_eur) <- gsub('SCORE', 'prscs_eur', names(prscs_eur))
+names(prscs_eas) <- gsub('SCORE', 'prscs_eas', names(prscs_eas))
+names(prscsx) <- gsub('SCORE', 'prscsx', names(prscsx))
+
+data <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1', 'A2'), sort = F), list(prscs_eur, prscs_eas, prscsx))
+data[data == 0]<-NA
+
+na_count <- sapply(data[,-1:-3], function(x) sum(is.na(x)))
+na_table <- data.frame(Column = names(na_count), NA_Count = na_count)
+
+# check correlation between PRS-CS and PRS-CSx variant that are shared and not shared across GWAS
+shared_var <- data[!is.na(data$`prscs_eur_phi_1e-04`) & !is.na(data$`prscs_eas_phi_1e-04`), ]
+nonshared_var <- data[is.na(data$`prscs_eur_phi_1e-04`) & !is.na(data$`prscs_eas_phi_1e-04`), ]
+
+nrow(shared_var)
+nrow(nonshared_var)
+
+cor(shared_var$`prscs_eas_phi_1e-04`, shared_var$`prscsx_EAS_phi_1e-04`)
+cor(nonshared_var$`prscs_eas_phi_1e-04`, nonshared_var$`prscsx_EAS_phi_1e-04`)
+
+nonshared_var[, c('SNP','A1','A2','prscs_eas_phi_1e-04','prscsx_EAS_phi_1e-04'), with=F]
+
+
+shared_var <- data[!is.na(data$`prscs_eur_phi_1e-04`) & !is.na(data$`prscs_eas_phi_1e-04`), ]
+nonshared_var <- data[!is.na(data$`prscs_eur_phi_1e-04`) & is.na(data$`prscs_eas_phi_1e-04`), ]
+
+nrow(shared_var)
+nrow(nonshared_var)
+
+cor(shared_var$`prscs_eur_phi_1e-04`, shared_var$`prscsx_EUR_phi_1e-04`)
+cor(nonshared_var$`prscs_eur_phi_1e-04`, nonshared_var$`prscsx_EUR_phi_1e-04`)
+
+nonshared_var[, c('SNP','A1','A2','prscs_eas_phi_1e-04','prscsx_EAS_phi_1e-04'), with=F]
+
+```
+
 ***
 
 # Computational resoures
 
+<details><summary>Show code</summary>
+
 ```{r}
 library(data.table)
 library(ggplot2)
@@ -3775,6 +3894,8 @@ method_avg<-merge(method_avg, method_avg_mem, by = 'Method')
 write.csv(method_avg, '~/oliverpainfel/Analyses/crosspop/time_memory.csv', row.names=F)
 ```
 
+</details>
+
 ***
 
 # Sensitivity analysis using 1KG reference
@@ -3787,6 +3908,8 @@ PRS-CS, PRS-CSx and X-Wing all use the 1KG reference sample, whereas the other m
 
 To make this quicker, focus on evaluating the PGS methods in the AFR and EAS target individuals in UKB. This will avoid reprocessing the full UKB data.
 
+<details><summary>Show code</summary>
+
 ```{r}
 library(data.table)
 
@@ -3824,12 +3947,16 @@ done
 
 ```
 
+</details>
+
 ***
 
 ## Create 1KG only GenoPred reference data
 
 Subset the 1KG+HGDP reference data to include only 1KG individuals.
 
+<details><summary>Show code</summary>
+
 ```{bash}
 mkdir -p ~/oliverpainfel/Data/1kg/genopred/
 cp -r ~/oliverpainfel/Data/hgdp_1kg/genopred/ref ~/oliverpainfel/Data/1kg/genopred/
@@ -3855,12 +3982,16 @@ for chr in $(seq 1 22); do
 done
 ```
 
+</details>
+
 ***
 
 ## Run pipeline
 
 To save time, run using PGS methods that do not need pre-processed LD matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary from the 1KG+HGDP results, then expand to other methods (LDpred2, SBayesRC, QuickPRS).
 
+<details><summary>Show code</summary>
+
 ```{r}
 library(data.table)
 
@@ -3909,6 +4040,8 @@ snakemake \
   output_all -n
 ```
 
+</details>
+
 ***
 
 ## Evaluate PGS
@@ -4644,6 +4777,8 @@ Run using AFR and EAS subset in UKB to make it quicker to run. This is the main
 
 To save time, run using PGS methods that do not need pre-processed LD matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary from the 1KG+HGDP results, then expand to other methods (LDpred2, SBayesRC, QuickPRS).
 
+<details><summary>Show code</summary>
+
 ```{r}
 library(data.table)
 
@@ -4684,6 +4819,8 @@ snakemake \
   output_all -n
 ```
 
+</details>
+
 ***
 
 ## Create predictor lists
@@ -4998,7 +5135,7 @@ for(pheno_i in selected_traits){
 
   # Remove top1 models for *-Multi, PRS-CSx, X-wing, TL-*
   res_eval_i <- res_eval_i[
-    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method) | grepl('^tlprs_', res_eval_i$Method)) & 
+    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method)) & 
       grepl('top1', res_eval_i$Group)),]
   
   # Remove any duplicate models
@@ -5011,7 +5148,7 @@ for(pheno_i in selected_traits){
 }
 
 # Create vector defining or of methods in plots
-model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi","TL-DBSLMM", "PRS-CSx", "X-Wing", "All") 
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi","TL-DBSLMM","TL-LDpred2","TL-QuickPRS","TL-SBayesRC", "PRS-CSx", "X-Wing", "All") 
 
 ####
 # Average results across phenotypes
@@ -5112,15 +5249,26 @@ tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
 tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
 tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
-png(paste0('~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+dir.create('~/oliverpainfel/Analyses/crosspop/tlprs/plots')
+
+# Plot unidirectional TL-PRS (as it was intended), comparing the unadjusted EUR PGS to the EUR PGS that has been adjusted according to the target-matched GWAS
+tmp_tlprs_uni <- tmp[grepl('tlprs', tmp$Method) & !grepl('pop-EUR.top1', tmp$Group) & tmp$Source == 'Single', ]
+tmp_tlprs_uni$Type <- 'TL-PRS'
+tmp_unadj <- tmp[!grepl('tlprs', tmp$Method) & tmp$Discovery == 'EUR', ]
+tmp_unadj$Type <- 'Original'
+tmp_both <- rbind(tmp_unadj, tmp_tlprs_uni)
+tmp_both$label<-gsub('TL-','',tmp_both$label)
+tmp_both$Type<-factor(tmp_both$Type, levels = c('Original','TL-PRS'))
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.png'), res=300, width = 2000, height = 1600, units = 'px')
+ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
                   position = position_dodge(width = 1)) +
     geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
     geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
     labs(y = "R (SE)", x='Method') +
-    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    facet_grid(Target ~ ., scales='free', space = 'free_x') +
     theme_half_open() +
     background_grid(major = 'y', minor = 'y') + 
     panel_border() + 
@@ -5130,6 +5278,395 @@ ggplot(tmp, aes(x=label, y=R , fill = Model)) +
           legend.justification = "center")
 dev.off()
 
-# TL-PRS does seem to provide a slight advantage  
+tmp_tlprs_uni <- tmp[grepl('tlprs', tmp$Method) & grepl('pop-EUR.top1', tmp$Group) & tmp$Source == 'Single', ]
+tmp_tlprs_uni$Type <- 'TL-PRS'
+tmp_unadj <- tmp[!grepl('tlprs', tmp$Method) & tmp$Discovery == 'EUR', ]
+tmp_unadj$Type <- 'Original'
+tmp_both <- rbind(tmp_unadj, tmp_tlprs_uni)
+tmp_both$label<-gsub('TL-','',tmp_both$label)
+tmp_both$Type<-factor(tmp_both$Type, levels = c('Original','TL-PRS'))
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.targ_EUR.png'), res=300, width = 2000, height = 1600, units = 'px')
+ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ ., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+# Have one column per method, but fill according to Original EUR PGS, Original matched-PGS, TL-PRS EUR Target, TL-PRS non-EUR Target, TL-PRS Multi, and Original-Multi
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$Type <- NA
+tmp$Type[grepl('tlprs', tmp$Method) & grepl('pop-EUR.top1', tmp$Group)]<-"TL-PRS (EUR PGS tuned to target)"
+tmp$Type[grepl('tlprs', tmp$Method) & !grepl('pop-EUR.top1', tmp$Group) & tmp$Source == 'Single']<-"TL-PRS (Target-matched PGS tuned to EUR)"
+tmp$Type[!grepl('tlprs', tmp$Method) & tmp$Discovery == 'EUR']<-"Original (EUR PGS)"
+tmp$Type[!grepl('tlprs', tmp$Method) & tmp$Discovery != 'EUR' & tmp$Source == 'Single']<-"Original (Target-matched PGS)"
+tmp$Type[grepl('tlprs', tmp$Method) & grepl('multi', tmp$Group)]<-"TL-PRS-multi"
+tmp$Type[!grepl('tlprs', tmp$Method) & grepl('multi', tmp$Group)]<-"Original-multi"
+tmp <- tmp[!is.na(tmp$Type),]
+tmp$Type<-factor(tmp$Type, levels=c("Original (EUR PGS)", "Original (Target-matched PGS)", "TL-PRS (EUR PGS tuned to target)", "TL-PRS (Target-matched PGS tuned to EUR)", "Original-multi", "TL-PRS-multi"))
+tmp$label<-gsub('TL-','',tmp$label)
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png'), res=300, width = 4000, height = 2200, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Type)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ ., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+#########################
+# Check significance of differences between TL-PRS and unadjusted approaches
+########################
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Create a function to mirror pred_comp results
+mirror_comp<-function(x){
+  x_sym <- x
+  x_sym$Model_1 <- x$Model_2
+  x_sym$Model_2 <- x$Model_1
+  x_sym$Model_1_R <- x$Model_2_R
+  x_sym$Model_2_R <- x$Model_1_R
+  x_sym$R_diff <- -x_sym$R_diff
+  x_mirrored <- rbind(x, x_sym)
+  x_diag<-data.frame(
+      Model_1=unique(x_mirrored$Model_1),
+      Model_2=unique(x_mirrored$Model_1),
+      Model_1_R=x_mirrored$Model_1_R,
+      Model_2_R=x_mirrored$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp<-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
+# Read in results
+targ_pop=c('EAS','AFR')
+res_comp <- list()
+for(pheno_i in selected_traits){
+  res_comp_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      comp_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.tlprs.pred_comp.txt'
+          )
+        )
+      comp_i<-mirror_comp(comp_i)
+      comp_i$Target<-targ_pop_i
+      comp_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_comp_i<-rbind(res_comp_i, comp_i)
+    }
+  }
+  
+  res_comp[[pheno_i]]<-res_comp_i
+}
+
+res_comp_all <- do.call(rbind, lapply(names(res_comp), function(name) {
+  x <- res_comp[[name]]
+  x$pheno <- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Annotate tests to get order correct
+res_comp_all$Method1<-sub('\\..*','',res_comp_all$Model_1)
+res_comp_all$Method1<-gsub('-.*','', res_comp_all$Method1)
+res_comp_all$Method2<-sub('\\..*','',res_comp_all$Model_2)
+res_comp_all$Method2<-gsub('-.*','', res_comp_all$Method2)
+
+find_model<-function(x){
+  mod <- x
+  mod[grepl('top1$', x) & !grepl('pseudo', x)] <- 'IndivTrain'
+  mod[grepl('top1$', x) & grepl('pseudo', x)] <- 'SumStatTrain'
+  mod[grepl('multi$', x) & !grepl('pseudo', x)] <- 'Multi-IndivTrain'
+  mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTrain'
+  mod[grepl('_multi', x)] <- 'SumStatTrain'
+  mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTrain'
+  mod[x == 'xwing.pseudo.multi'] <- 'SumStatTrain'
+  
+  return(mod)
+}
+
+res_comp_all$Model1<-find_model(res_comp_all$Model_1)
+res_comp_all$Model2<-find_model(res_comp_all$Model_2)
+
+res_comp_all$Source1<-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method1) | !grepl('AFR|EAS|EUR', res_comp_all$Model_1), 'Multi', 'Single')
+res_comp_all$Source2<-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method2) | !grepl('AFR|EAS|EUR', res_comp_all$Model_2), 'Multi', 'Single')
+  
+for(i in c('EUR','EAS','AFR')){
+  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] <- i
+  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] <- i
+}
+res_comp_all$Discovery1[res_comp_all$Source1 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source1 == 'Multi']
+res_comp_all$Discovery2[res_comp_all$Source2 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source2 == 'Multi']
+
+res_comp_all$Method1<-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
+res_comp_all$Method2<-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
+res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+res_comp_all$Discovery1<-factor(res_comp_all$Discovery1, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
+res_comp_all$Discovery2<-factor(res_comp_all$Discovery2, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+# Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method1 %in%  c('quickprs','sbayesrc') & 
+  res_comp_all$Model1 %in% c('IndivTrain','Multi-IndivTrain')),]
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method2 %in%  c('quickprs','sbayesrc') & 
+  res_comp_all$Model2 %in% c('IndivTrain','Multi-IndivTrain')),]
+
+# Remove pseudo model for methods that don't really have one 
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method1 %in%  c('ptclump','ptclump_multi') & 
+  res_comp_all$Model1 %in% c('SumStatTrain','Multi-SumStatTrain')),]
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method2 %in%  c('ptclump','ptclump_multi') & 
+  res_comp_all$Model2 %in% c('SumStatTrain','Multi-SumStatTrain')),]
+
+# Remove top1 models for PRS-CSx
+res_comp_all <- res_comp_all[
+!(grepl('prscsx|xwing|_multi', res_comp_all$Method1) & 
+  grepl('top1', res_comp_all$Model_1)),]
+res_comp_all <- res_comp_all[
+!(grepl('prscsx|xwing|_multi', res_comp_all$Method2) & 
+  grepl('top1', res_comp_all$Model_2)),]
+
+# Remove any comparisons
+res_comp_all <- res_comp_all[!duplicated(res_comp_all[, c("Target", "Method1", "Model1", "Source1", "Discovery1", "Method2", "Model2", "Source2", "Discovery2",'pheno')]),]
+
+###########
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_comp <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_comp for each scenario
+    res_comp_i <- res_comp_all[res_comp_all$Target == targ_pop_i & res_comp_all$gwas_group == paste0('EUR+', disc_pop_i)]
+  
+    # Calculate diff SE based on p-value
+    res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] <- 1-0.001
+    res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]<-1-0.001
+    res_comp_i$R_diff_z<-qnorm(res_comp_i$R_diff_pval/2)
+    res_comp_i$R_diff_SE<-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
+        
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_comp_i$Sample<-'A'
+    res_comp_i$Group <- paste0(res_comp_i$Model_1, '_vs_', res_comp_i$Model_2)
+  
+    for(group_i in unique(res_comp_i$Group)){
+      res_comp_group_i <- res_comp_i[res_comp_i$Group == group_i,]
+      cors_i <- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
+      
+      if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
+        
+        meta_res_comp_i <-
+          agg(
+            id = Sample,
+            es = R_diff,
+            var = R_diff_SE ^ 2,
+            cor = cors_i,
+            method = "BHHR",
+            mod = NULL,
+            data = res_comp_group_i
+          )
+        
+        tmp <- res_comp_group_i[1,]
+        tmp$pheno <- NULL
+        tmp$Model_1_R <-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &
+                            meta_res_eval$Target == targ_pop_i &
+                            meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i)]
+        tmp$Model_2_R <-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &
+                            meta_res_eval$Target == targ_pop_i &
+                            meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i)]
+        tmp$R_diff <- meta_res_comp_i$es
+        tmp$R_diff_SE <- sqrt(meta_res_comp_i$var)
+        tmp$R_diff_z <- tmp$R_diff / tmp$R_diff_SE
+        tmp$R_diff_p <- 2*pnorm(-abs(tmp$R_diff_z))
+      } else {
+        tmp <- res_comp_group_i[1,]
+        tmp$pheno <- NULL
+        tmp$R_diff <- NA
+        tmp$R_diff_SE <- NA
+        tmp$R_diff_z <- NA
+        tmp$R_diff_p <- NA
+      }
+      meta_res_comp <- rbind(meta_res_comp, tmp)
+    }
+  }
+}
+
+meta_res_comp$R_diff_perc <- meta_res_comp$R_diff / meta_res_comp$Model_2_R
+  
+# Compare IndivTrain SBayesRC-multi to TL-SBayesRC-multi
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_2 == 'sbayesrc.pseudo.multi' & 
+                                meta_res_comp$Model_1 == 'tlprs_sbayesrc.pop.multi' &
+                    meta_res_comp$Target == 'AFR',]
+round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
+tmp_sbayesrc$R_diff_p
+
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_2 == 'sbayesrc.pseudo.multi' & 
+                                meta_res_comp$Model_1 == 'tlprs_sbayesrc.pop.multi' &
+                    meta_res_comp$Target == 'EAS',]
+round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
+tmp_sbayesrc$R_diff_p
+
+```
+
+</details>
+
+***
+
+## Computational resoures
+
+<details><summary>Show code</summary>
+
+```{r}
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in configuration specific benchmark files
+bm_files_i <- list.files(paste0(outdir, '/reference/benchmarks/'), full.names = T)
+
+# Subset benchmarks for pgs_methods
+bm_files_i <- bm_files_i[grepl('prep_pgs_tlprs', bm_files_i)]
+
+# Read in benchmark files
+bm_dat_all <- do.call(rbind, lapply(bm_files_i, function(file) {
+  tmp <- fread(file)
+  tmp$file <- basename(file)
+  return(tmp)
+}))
+
+# Create rule column
+bm_dat_all$rule <- gsub('-.*','',bm_dat_all$file)
+
+# Create method column
+bm_dat_all$method <-
+  gsub('_i', '', gsub('prep_pgs_', '', bm_dat_all$rule))
+
+#############
+# Time
+#############
+
+# Calculate average time taken for each method
+method_avg <- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg <- rbind(
+    method_avg,
+    data.frame(
+      Method = i,
+      Time = mean(bm_dat_all$s[bm_dat_all$method == i])
+    )
+  )
+}
+
+# Convert time in seconds to hours
+method_avg$Time_hour <- method_avg$Time / 60/60
+method_avg$Time_hour <- round(method_avg$Time_hour, 2)
+
+#This is for bidirectional TL-PRS
+
+#############
+# Memory
+#############
+
+# Calculate average max_rss for each method
+method_avg_mem <- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg_mem <- rbind(
+    method_avg_mem,
+    data.frame(
+      Method = i,
+      Memory = mean(bm_dat_all$max_rss[bm_dat_all$method == i])
+    )
+  )
+}
+
+# Format the Memory nicely
+method_avg_mem$Memory_clean <-
+  paste0(round(method_avg_mem$Memory/1000, 2), ' Gb')
+
+ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
+  geom_bar(stat = "identity", position="dodge") +
+  geom_text(aes(label = Memory_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
+  labs(x = "PGS Method", y = "Memory (Mb)") +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
+
+method_avg_mem$Memory_gb <- method_avg_mem$Memory/1000
+method_avg_mem <- method_avg_mem[, c('Method','Memory_gb')]
+method_avg_mem$Memory_gb <- round(method_avg_mem$Memory_gb, 2)
+names(method_avg_mem)<-c('Method',"Memory (Gb)")
+
+method_avg<-merge(method_avg, method_avg_mem, by = 'Method')
+
+write.csv(method_avg, '~/oliverpainfel/Analyses/crosspop/time_memory.csv', row.names=F)
 ```
 
+</details>
diff --git a/docs/CrossPop.html b/docs/CrossPop.html
new file mode 100644
index 00000000..1550d0e4
--- /dev/null
+++ b/docs/CrossPop.html
@@ -0,0 +1,5977 @@
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+    </div>
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+      <ul class="nav navbar-nav">
+        
+      </ul>
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+        <li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Pipeline
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li>
+      <a href="pipeline_overview.html">Overview</a>
+    </li>
+    <li>
+      <a href="pipeline_readme.html">Instructions</a>
+    </li>
+    <li>
+      <a href="pipeline_technical.html">Technical documentation</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Research
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li>
+      <a href="research_index.html">Overview</a>
+    </li>
+    <li>
+      <a href="comparison_of_methods_summary.html">Polygenic Scoring Methods Comparison</a>
+    </li>
+    <li>
+      <a href="Functionally_informed_prediction.html">Quantifying Polygenic Signal Mediated by Altered Gene Expression</a>
+    </li>
+    <li>
+      <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
+    </li>
+  </ul>
+</li>
+<li>
+  <a href="more_index.html">More</a>
+</li>
+<li>
+  <a href="https://github.com/opain/GenoPred">
+    <span class="fa fa-github fa-lg"></span>
+     
+  </a>
+</li>
+      </ul>
+    </div><!--/.nav-collapse -->
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+</div><!--/.navbar -->
+
+<div id="header">
+
+
+
+<h1 class="title toc-ignore">Cross-population evaluation of polygenic
+scores</h1>
+
+</div>
+
+
+<hr />
+<div id="introduction" class="section level1">
+<h1>Introduction</h1>
+<p>European (EUR) GWAS are typically the largest in sample size, or even
+the only GWAS for certain outcomes. Here we will evaluate approaches for
+calculating polygenic scores (PGS) across populations. We will include
+single- and multi-source PGS methods, using EUR GWAS alone, or using EUR
+GWAS in combination with GWAS from other populations.</p>
+<hr />
+</div>
+<div id="derive-gwas-in-ukb" class="section level1">
+<h1>Derive GWAS in UKB</h1>
+<p>To avoid sample overlap between the EUR GWAS and the EUR target
+sample for evaluation, we will split EUR individuals in UKB into
+training and testing subsets. The GWAS will be performed in the training
+subset, and the PGS evaluation will occur in the testing subset.</p>
+<hr />
+<div id="perform-ancestry-inference-in-ukb" class="section level2">
+<h2>Perform ancestry inference in UKB</h2>
+<div id="create-target_list" class="section level3">
+<h3>Create target_list</h3>
+<div class="shallow-break">
+
+</div>
+<details>
+<summary>
+Show code
+</summary>
+<p><br/></p>
+<h3>
+Create symlinks
+</h3>
+<p>We will create symlinks to the imputed genotype data for UKB. We will
+use the pgen format data for computationl efficiency and those
+restricted to MAF &gt;= 1% and INFO &gt;= 0.4. We are using genetic data
+that is not application specific, so the data doesn’t need to be
+reprocessed for each application. Therefore we will use row number IDs
+for the .psam file so they can be connected to application specific data
+downstream.</p>
+<pre class="bash"><code>mkdir -p /users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks
+
+# pgen and pvar files
+for chr in $(seq 1 22);do
+  for file in $(echo pgen pvar);do
+    ln -s /datasets/ukbiobank/June2017/Imputed/ukb_imp_chr${chr}_v3_MAF1_INFO4.${file} /users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/ukb_imp_maf1_info4.chr${chr}.${file}
+  done
+done</code></pre>
+<pre class="r"><code># Make .psam 
+n = 487409
+psam &lt;- data.frame(FID = 1:487409,
+                   IID = 1:487409)
+names(psam)[1]&lt;-&#39;#FID&#39;
+write.table(psam, &#39;/users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/rownumber.psam&#39;, col.names=T, row.names = F, quote = F)</code></pre>
+<pre class="bash"><code>for chr in $(seq 1 22);do
+  ln -s /users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/rownumber.psam /users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/ukb_imp_maf1_info4.chr${chr}.psam
+done</code></pre>
+<hr />
+<h3>
+Create list of unrelated individuals
+</h3>
+<pre class="r"><code>library(ukbkings)
+library(data.table)
+
+psam&lt;-fread(&#39;/scratch/prj/ukbiobank/recovered/ukb82087/imputed/ukb82087_imp_chr1_MAF1_INFO4_v1.psam&#39;)
+psam$rn&lt;-1:nrow(psam)
+
+project_dir &lt;- &quot;/datasets/ukbiobank/ukb82087&quot;
+greedy_related &lt;- &quot;/scratch/prj/ukbiobank/recovered/KCL_Data/Software/tools/GreedyRelated-master-v1.2.1/GreedyRelated&quot;
+
+# Create a list of unrelated individuals irrespective of a phenotype
+psam_unrel_all &lt;- psam[!(
+  psam$IID %in% bio_gen_related_remove(
+    project_dir = project_dir,
+    greedy_related = greedy_related,
+    thresh = 0.044,
+    seed = 1
+  )$eid
+), ]
+
+dir.create(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes&#39;)
+
+write.table(psam_unrel_all$IID, &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.txt&#39;, row.names=F, col.names=F, quote=F)
+write.table(psam_unrel_all$rn, &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt&#39;, row.names=F, col.names=F, quote=F)</code></pre>
+<hr />
+<h3>
+Create target_list
+</h3>
+<pre class="bash"><code>mkdir -p /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic</code></pre>
+<pre class="r"><code>target_list &lt;- data.frame(
+  name=&#39;ukb&#39;,
+  path=&#39;/users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/ukb_imp_maf1_info4&#39;,
+  type=&#39;plink2&#39;,
+  indiv_report=F,
+  unrel=&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt&#39;
+)
+
+write.table(target_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&#39;, col.names=T, row.names=F, quote=F)</code></pre>
+</details>
+<hr />
+</div>
+<div id="create-configfile" class="section level3">
+<h3>Create configfile</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code># Create config file
+conf &lt;- c(
+  &#39;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&#39;,
+  &#39;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/config.yaml&#39;,
+  &#39;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&#39;
+)
+
+write.table(conf, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/config.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-pipeline" class="section level3">
+<h3>Run pipeline</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+git describe --tags
+#v2.2.2-213-g2f05853
+
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/config.yaml \
+  outlier_detection -n</code></pre>
+</details>
+<hr />
+</div>
+</div>
+<div id="collect-phenotype-data" class="section level2">
+<h2>Collect phenotype data</h2>
+<p>We will use the same 33 quantitative traits that were used in the
+PRS-CSx paper (Supp Table 10).</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>mkdir /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx</code></pre>
+<pre class="r"><code>library(ukbkings)
+library(dplyr)
+library(stringr)
+library(data.table)
+
+# create data.frame showing variables used by prscsx
+prscsx_fields&lt;-c(&#39;30620&#39;,&#39;30600&#39;,&#39;30610&#39;,&#39;30650&#39;,&#39;30160&#39;,&#39;21001&#39;,&#39;21002&#39;,&#39;30710&#39;,&#39;30680&#39;,&#39;4079&#39;,&#39;30150&#39;,&#39;30740&#39;,&#39;30750&#39;,&#39;30760&#39;,&#39;50&#39;,&#39;30030&#39;,&#39;30020&#39;,&#39;30780&#39;,&#39;30120&#39;,&#39;30050&#39;,&#39;30060&#39;,&#39;30040&#39;,&#39;30130&#39;,&#39;30140&#39;,&#39;30080&#39;,&#39;30010&#39;,&#39;30700&#39;,&#39;4080&#39;,&#39;30690&#39;,&#39;30860&#39;,&#39;30870&#39;,&#39;30000&#39;,&#39;30730&#39;)
+prscsx_trait&lt;-c(&#39;Alanine aminotransferase&#39;,&#39;Albumin&#39;,&#39;Alkaline phosphatase&#39;,&#39;Aspartate transaminase&#39;,&#39;Basophil&#39;,&#39;Body mass index&#39;,&#39;Body weight&#39;,&#39;C-reactive protein&#39;,&#39;Calcium&#39;,&#39;Diastolic blood pressure&#39;,&#39;Eosinophil&#39;,&#39;Glucose&#39;,&#39;HbA1c&#39;,&#39;HDL-cholesterol&#39;,&#39;Height&#39;,&#39;Hematocrit&#39;,&#39;Hemoglobin&#39;,&#39;LDL-cholesterol&#39;,&#39;Lymphocyte&#39;,&#39;Mean corpuscular hemoglobin&#39;,&#39;Mean corpuscular hemoglobin concentration&#39;,&#39;Mean corpuscular volume&#39;,&#39;Monocyte&#39;,&#39;Neutrophil&#39;,&#39;Platelet&#39;,&#39;Red blood cell&#39;,&#39;Serum creatinine&#39;,&#39;Sytolic blood pressure&#39;,&#39;Total cholesterol&#39;,&#39;Total protein&#39;,&#39;Triglycerides&#39;,&#39;White blood cell&#39;,&#39;γ-glutamyl transpeptidase&#39;)
+prscsx_labels&lt;-c(&#39;ALT&#39;,&#39;ALB&#39;,&#39;ALP&#39;,&#39;AST&#39;,&#39;BAS&#39;,&#39;BMI&#39;,&#39;BWT&#39;,&#39;CRP&#39;,&#39;Ca&#39;,&#39;DBP&#39;,&#39;EOS&#39;,&#39;GLC&#39;,&#39;HbA1c&#39;,&#39;HDL&#39;,&#39;HT&#39;,&#39;HCT&#39;,&#39;HB&#39;,&#39;LDL&#39;,&#39;LYM&#39;,&#39;MCH&#39;,&#39;MCHC&#39;,&#39;MCV&#39;,&#39;MON&#39;,&#39;NEU&#39;,&#39;PLT&#39;,&#39;RBC&#39;,&#39;CR&#39;,&#39;SBP&#39;,&#39;TC&#39;,&#39;TP&#39;,&#39;TG&#39;,&#39;WBC&#39;,&#39;GGT&#39;)
+
+prscsx_dat&lt;-data.frame(
+  trait=prscsx_trait,
+  labels=prscsx_labels,
+  field=prscsx_fields
+)
+
+dir.create(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx&#39;)
+write.csv(prscsx_dat, &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv&#39;, row.names = F)
+write.table(prscsx_labels, &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt&#39;, col.names=F, row.names = F, quote=F)
+
+# Extract outcomes from UKB (project ukb82087)
+project_dir &lt;- &quot;/datasets/ukbiobank/ukb82087&quot;
+
+system(&#39;rm /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.txt&#39;)
+f &lt;- bio_field(project_dir)
+f %&gt;%
+    select(field, name) %&gt;%
+    filter(str_detect(field, paste(paste0(&quot;^&quot;, prscsx_dat$field, &#39;-&#39;), collapse=&#39;|&#39;))) %&gt;%
+    bio_field_add(&quot;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.txt&quot;)
+
+bio_phen(
+    project_dir,
+    field = &quot;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.txt&quot;,
+    out = &quot;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset&quot;
+)
+
+system(&quot;ls -lh /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.rds&quot;)
+df &lt;- readRDS(&quot;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.rds&quot;)
+
+# Take the first observation of each outcome
+library(tidyr)
+df_long &lt;- df %&gt;%
+  pivot_longer(cols = names(df)[!grepl(&#39;eid&#39;, names(df))], names_to = &quot;variable&quot;, values_to = &quot;outcome&quot;) %&gt;%
+  drop_na(outcome)
+df_long$variable&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, df_long$variable)
+df_long&lt;-df_long[!duplicated(df_long[,c(&#39;eid&#39;,&#39;variable&#39;)]),]
+
+library(data.table)
+
+for(i in 1:nrow(prscsx_dat)){
+  tmp &lt;- df_long[df_long$variable == prscsx_dat$field[i],]
+  tmp &lt;- data.frame(
+    eid = tmp$eid,
+    outcome = tmp$outcome
+  )
+  
+  fwrite(
+    tmp,
+    paste0(
+      &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;,
+      prscsx_dat$label[i],
+      &#39;.txt&#39;
+    ),
+    row.names = F,
+    quote = F,
+    na = &#39;NA&#39;,
+    sep = &#39;\t&#39;
+  )
+}
+
+# Read in ancestry inference results to determine sample size per population
+# Use ancestry information from GenoPred
+keep_files&lt;-list.files(path = &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/pcs/within_sample/&#39;, pattern = &#39;.keep&#39;)
+
+pop_dat&lt;-NULL
+for(i in keep_files){
+  tmp&lt;-fread(paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/pcs/within_sample/&#39;, i))
+  names(tmp)&lt;-c(&#39;FID&#39;,&#39;IID&#39;)
+  tmp$POP&lt;-gsub(&#39;.keep&#39;,&#39;&#39;, gsub(&#39;ukb.outlier_detection.&#39;,&#39;&#39;,i))
+  pop_dat&lt;-rbind(pop_dat, tmp)
+}
+
+# Update row number IDs to project specific IDs
+psam&lt;-fread(&#39;/scratch/prj/ukbiobank/recovered/ukb82087/imputed/ukb82087_imp_chr1_MAF1_INFO4_v1.psam&#39;)
+psam$rn&lt;-1:nrow(psam)
+psam&lt;-psam[,c(&#39;IID&#39;,&#39;rn&#39;), with = F]
+
+pop_dat$FID&lt;-NULL
+pop_dat&lt;-merge(pop_dat, psam, by.x=&#39;IID&#39;, by.y=&#39;rn&#39;)
+pop_dat&lt;-data.frame(
+  eid=pop_dat$IID.y,
+  POP=pop_dat$POP
+)
+
+# Merge ancestry info with phenotype data
+df_short &lt;- dcast(df_long, eid ~ variable, value.var = &quot;outcome&quot;)
+df_short&lt;-merge(df_short, pop_dat, by=&#39;eid&#39;)
+
+# Remove related individuals
+greedy_related &lt;- &quot;/scratch/prj/ukbiobank/recovered/KCL_Data/Software/tools/GreedyRelated-master-v1.2.1/GreedyRelated&quot;
+rel&lt;-bio_gen_related_remove(
+      project_dir = project_dir,
+      greedy_related = greedy_related,
+      keep = df_short$eid,
+      thresh = 0.044,
+      seed = 1
+    )$eid
+
+df_short_unrel&lt;-df_short[!(df_short$eid %in% rel),]
+
+n_table&lt;-NULL
+for(i in 1:nrow(prscsx_dat)){
+  for(j in unique(pop_dat$POP[!is.na(pop_dat$POP)])){
+    tmp&lt;-data.frame(
+      trait=prscsx_dat$trait[i],
+      labels=prscsx_dat$label[i],
+      field=prscsx_dat$field[i],
+      population=j,
+      n=sum(!is.na(df_short[[prscsx_dat$field[i]]][df_short$POP == j])),
+      n_unrel=sum(!is.na(df_short_unrel[[prscsx_dat$field[i]]][df_short_unrel$POP == j]))
+    )
+    n_table&lt;-rbind(n_table, tmp)
+  }
+}
+
+write.csv(n_table, &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/n_table&#39;)
+
+# Define training subset for EUR
+df_short_unrel_eur&lt;-df_short_unrel[df_short_unrel$POP == &#39;EUR&#39;,]
+set.seed(1)
+train_size &lt;- floor(0.8 * nrow(df_short_unrel_eur))
+train_indices &lt;- sample(seq_len(nrow(df_short_unrel_eur)), size = train_size)
+
+df_short_unrel_eur_train&lt;-df_short_unrel_eur[train_indices,]
+df_short_unrel_eur_test&lt;-df_short_unrel_eur[-train_indices,]
+
+n_table_eur&lt;-NULL
+for(i in 1:nrow(prscsx_dat)){
+  tmp&lt;-data.frame(
+    trait=prscsx_dat$trait[i],
+    labels=prscsx_dat$label[i],
+    field=prscsx_dat$field[i],
+    n_train=sum(!is.na(df_short_unrel_eur_train[[prscsx_dat$field[i]]])),
+    n_test=sum(!is.na(df_short_unrel_eur_test[[prscsx_dat$field[i]]]))
+  )
+  n_table_eur&lt;-rbind(n_table_eur, tmp)
+}
+
+write.csv(n_table_eur, &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/n_table_eur&#39;)
+
+df_short_unrel$POP[df_short_unrel$eid %in% df_short_unrel_eur_train$eid]&lt;-&#39;EUR_train&#39;
+df_short_unrel$POP[df_short_unrel$eid %in% df_short_unrel_eur_test$eid]&lt;-&#39;EUR_test&#39;
+
+# Output phenotype data for each population
+for(i in 1:nrow(prscsx_dat)){
+  for(j in unique(df_short_unrel$POP)){
+    tmp&lt;-df_short_unrel[df_short_unrel$POP == j,]
+    tmp &lt;- data.frame(
+      FID = tmp$eid,
+      IID = tmp$eid,
+      outcome = tmp[[prscsx_dat$field[i]]]
+    )
+    
+    fwrite(
+      tmp,
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;,
+        prscsx_dat$label[i],
+        &#39;.unrel.&#39;, j, &#39;.txt&#39;
+      ),
+      row.names = F,
+      quote = F,
+      na = &#39;NA&#39;,
+      sep = &#39;\t&#39;
+    )
+    
+    # Write out with row number based IDs
+    pheno&lt;-merge(tmp, psam, by=&#39;IID&#39;)
+    pheno&lt;-data.frame(
+      FID=pheno$rn,
+      IID=pheno$rn,
+      outcome=pheno$outcome
+    )
+  
+    fwrite(
+      pheno,
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;,
+        prscsx_dat$label[i],
+        &#39;.unrel.&#39;, j, &#39;.row_number.txt&#39;
+      ),
+      row.names = F,
+      quote = F,
+      na = &#39;NA&#39;,
+      sep = &#39;\t&#39;
+    )
+  }
+}
+
+# For the EUR training GWAS, normalise and regress covariates
+# Use age, sex and PCs as covariates
+# Read in PC data released by UKB
+qc_dat&lt;-bio_gen_sqc(project_dir)
+qc_dat&lt;-qc_dat[,c(&#39;eid&#39;,paste0(&#39;pc&#39;,1:20))]
+df_short_unrel&lt;-merge(df_short_unrel, qc_dat, by=&#39;eid&#39;)
+
+# Read in sex and age information
+system(&#39;rm /users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt&#39;)
+f &lt;- bio_field(project_dir)
+f %&gt;%
+    select(field, name) %&gt;%
+    filter(str_detect(field, &quot;^21022-0.0|^31-0.0&quot;)) %&gt;%
+    bio_field_add(&quot;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt&quot;)
+
+bio_phen(
+    project_dir,
+    field = &quot;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt&quot;,
+    out = &quot;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset&quot;
+)
+
+system(&quot;ls -lh /users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.rds&quot;)
+df &lt;- readRDS(&quot;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.rds&quot;)
+names(df)&lt;-gsub(&#39;-.*&#39;,&#39;&#39;,names(df))
+names(df)[names(df) == &#39;31&#39;]&lt;-&#39;sex&#39;
+names(df)[names(df) == &#39;21022&#39;]&lt;-&#39;age&#39;
+df_short_unrel&lt;-merge(df_short_unrel, df, by=&#39;eid&#39;)
+
+# Within each population, normalise each outcome and regress out covariates
+library(RNOmni)
+covs&lt;-c(paste0(&#39;pc&#39;,1:20), &#39;sex&#39;, &#39;age&#39;)
+df_short_unrel_eur_train&lt;-df_short_unrel[df_short_unrel$POP == &#39;EUR_train&#39;,]
+for(i in 1:nrow(prscsx_dat)){
+  tmp&lt;-df_short_unrel_eur_train[!is.na(df_short_unrel_eur_train[[prscsx_dat$field[i]]]),]
+  tmp$pheno_norm&lt;-RNOmni::RankNorm(tmp[[prscsx_dat$field[i]]])
+  mod&lt;-lm(as.formula(paste0(&#39;pheno_norm ~ &#39;, paste(covs, collapse=&#39; + &#39;))), data=tmp)
+  tmp$pheno_norm_resid_scale&lt;-as.numeric(scale(resid(mod)))
+  tmp&lt;-data.frame(
+    FID=tmp$eid,
+    IID=tmp$eid,
+    outcome=tmp$pheno_norm_resid_scale
+  )
+  
+  fwrite(
+    tmp,
+    paste0(
+      &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;,
+      prscsx_dat$label[i],
+      &#39;.unrel.EUR_train.norm_resid_scale.txt&#39;
+    ),
+    row.names = F,
+    quote = F,
+    na = &#39;NA&#39;,
+    sep = &#39;\t&#39;
+  )
+}
+
+# Convert to row number based IDs so it will work with UKB geno data from GenoPred
+for(i in 1:nrow(prscsx_dat)){
+  pheno&lt;-fread(paste0(
+      &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;,
+      prscsx_dat$label[i],
+      &#39;.unrel.EUR_train.norm_resid_scale.txt&#39;
+    ))
+  
+  pheno&lt;-merge(pheno, psam, by=&#39;IID&#39;)
+  pheno&lt;-data.frame(
+    FID=pheno$rn,
+    IID=pheno$rn,
+    outcome=pheno$outcome
+  )
+  
+  fwrite(
+    pheno,
+    paste0(
+      &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;,
+      prscsx_dat$label[i],
+      &#39;.unrel.EUR_train.norm_resid_scale.row_number.txt&#39;
+    ),
+    row.names = F,
+    quote = F,
+    na = &#39;NA&#39;,
+    sep = &#39;\t&#39;
+  )
+}</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-gwas" class="section level2">
+<h2>Run GWAS</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+  mkdir -p /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}
+  for chr in $(seq 1 22); do
+      sbatch -p neurohack_cpu --wrap=&quot;/users/k1806347/oliverpainfel/Software/plink2 \
+        --pfile /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
+        --pheno /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.EUR_train.norm_resid_scale.row_number.txt \
+        --linear omit-ref cols=+a1freq,+ax \
+        --maf 0.01 \
+        --geno 0.05 \
+        --out /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}&quot;
+  done
+done
+
+# Once complete, merge results across chromosomes
+for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+  head -n 1 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.chr1.outcome.glm.linear &gt; /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+    for chr in $(seq 1 22); do
+      tail -n +2 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.chr${chr}.outcome.glm.linear &gt;&gt; /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+    done
+    
+    # Remove REF and ALT columns and rename AX column to A2
+    cut -f 4,5 --complement /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt | awk &#39;BEGIN{FS=OFS=&quot;\t&quot;} NR==1 {$5=&quot;A2&quot;} 1&#39; &gt; temp.txt &amp;&amp; mv temp.txt /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+
+    gzip /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+done
+
+# Delete per chromosome files
+rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*/*chr*
+</code></pre>
+</details>
+<hr />
+</div>
+</div>
+<div id="download-relevant-bbj-sumstats" class="section level1">
+<h1>Download relevant BBJ sumstats</h1>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code># Identify wget command for relevant phenotypes
+library(data.table)
+
+# Read in BBJ GWAS info from BBJ website
+bbj_gwas&lt;-fread(&#39;~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas.csv&#39;)
+
+# Map BBJ trait names to those used for UKB
+bbj_gwas$bbj_labels &lt;-
+  gsub(&quot;\\)&quot;, &#39;&#39;, gsub(&quot;.*\\(&quot;, &#39;&#39;, bbj_gwas$Phenotype))
+bbj_gwas$trait &lt;- gsub(&quot; \\(.*&quot;, &#39;&#39;, bbj_gwas$Phenotype)
+
+bbj_gwas$Category&lt;-NULL
+bbj_gwas$Phenotype&lt;-NULL
+
+# Update trait labels to match what was used in prscsx paper
+bbj_gwas$trait&lt;-gsub(&#39; count&#39;,&#39;&#39;, bbj_gwas$trait)
+bbj_gwas$trait[bbj_gwas$trait == &#39;G-glutamyl transpeptidase&#39;]&lt;-&#39;γ-glutamyl transpeptidase&#39;
+
+# Merge the bbj trait info with the prscsx trait info
+prscsx_dat&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv&#39;)
+prscsx_dat &lt;- merge(bbj_gwas, prscsx_dat, by=&#39;trait&#39;, all=T)
+
+write.csv(prscsx_dat, &#39;~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_prscsx.csv&#39;, row.names = F)
+
+# Create column showing what label is used in the wget command
+prscsx_dat$wget_label &lt;-
+  gsub(&#39;.v1.zip&#39;, &#39;&#39;, gsub(&#39;.*hum0197.v3.BBJ.&#39;, &#39;&#39;, prscsx_dat$wget))
+
+# Write a table showing label matching prscsx info and wget url
+write.table(prscsx_dat[, c(&#39;labels&#39;, &#39;wget&#39;, &#39;wget_label&#39;), with=F], &#39;~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<pre class="bash"><code># wget and unzip sumstats
+for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+  url=$(awk -v var=&quot;$pheno&quot; &#39;$1 == var {print $2}&#39; ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
+  sbatch -p neurohack_cpu --wrap=&quot;wget -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip ${url}
+    unzip /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip -d /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx
+    rm /users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/${pheno}.zip&quot;
+done
+
+# Delete X chromosome sumstats and rename files to be consistent with prscsx sumstat info
+for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+  wget_label=$(awk -v var=&quot;$pheno&quot; &#39;$1 == var {print $3}&#39; ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
+if [ &quot;$pheno&quot; == &quot;HT&quot; ]; then
+    mv ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1/GWASsummary_Height_Japanese_SakaueKanai2020.auto.txt.gz ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.HT.txt.gz
+  else
+    mv ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1/GWASsummary_${wget_label}_Japanese_SakaueKanai2020.auto.txt.gz ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz
+  fi
+  rm -r ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/hum0197.v3.BBJ.${wget_label}.v1
+done
+
+# Format so BOLT P value is used by GenoPred
+for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+sbatch -p neurohack_cpu --wrap=&quot;/users/k1806347/oliverpainfel/Software/pigz/pigz -dc ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.txt.gz | awk &#39;BEGIN {OFS=\&quot;\t\&quot;} {print \$2, \$3, \$4, \$6, \$7, \$8, \$9, \$12, \$13, \$15}&#39; | sed &#39;1s/P_BOLT_LMM_INF/P/&#39; | /users/k1806347/oliverpainfel/Software/pigz/pigz -c &gt; ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${pheno}.reformat.txt.gz&quot;
+done
+</code></pre>
+</details>
+<hr />
+</div>
+<div id="download-relevant-ugr-sumstats" class="section level1">
+<h1>Download relevant UGR sumstats</h1>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code># Identify wget command for relevant phenotypes
+library(data.table)
+
+# Read in UGR GWAS info from GWAS catalogue
+ugr_gwas&lt;-fread(&#39;~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats.csv&#39;)
+
+# Map UGR trait names to those used for UKB
+ugr_gwas$trait&lt;-gsub(&#39; levels&#39;,&#39;&#39;, ugr_gwas$reportedTrait)
+ugr_gwas$trait&lt;-gsub(&#39; count&#39;,&#39;&#39;, ugr_gwas$trait)
+
+ugr_to_prscsx &lt;- c(
+  &quot;Aspartate aminotransferase&quot; = &quot;Aspartate transaminase&quot;,
+  &quot;Bilirubin&quot; = NA,  # No direct match
+  &quot;Eosinophils&quot; = &quot;Eosinophil&quot;,
+  &quot;Gamma glutamyl transferase&quot; = &quot;γ-glutamyl transpeptidase&quot;,
+  &quot;HDL cholesterol&quot; = &quot;HDL-cholesterol&quot;,
+  &quot;Hemoglobin A1c&quot; = &quot;HbA1c&quot;,
+  &quot;Hip circumference&quot; = NA,  # No direct match
+  &quot;LDL cholesterol&quot; = &quot;LDL-cholesterol&quot;,
+  &quot;Red cell distribution width&quot; = NA,  # No direct match
+  &quot;Serum albumin&quot; = &quot;Albumin&quot;,
+  &quot;Serum alkaline phosphatase&quot; = &quot;Alkaline phosphatase&quot;,
+  &quot;Systolic blood pressure&quot; = &quot;Sytolic blood pressure&quot;,
+  &quot;Triglyceride&quot; = &quot;Triglycerides&quot;,
+  &quot;Waist circumference&quot; = NA,  # No direct match
+  &quot;Waist-hip ratio&quot; = NA,  # No direct match
+  &quot;Weight&quot; = &quot;Body weight&quot;
+)
+
+ugr_gwas$trait &lt;- ifelse(ugr_gwas$trait %in% names(ugr_to_prscsx),
+                                   ugr_to_prscsx[ugr_gwas$trait],
+                                   ugr_gwas$trait)
+
+# Merge the ugr trait info with the prscsx trait info
+prscsx_dat&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv&#39;)
+prscsx_dat &lt;- merge(ugr_gwas, prscsx_dat, by=&#39;trait&#39;)
+
+write.csv(prscsx_dat, &#39;~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv&#39;, row.names = F)
+
+# Create column indicating wget command
+prscsx_dat$wget&lt;-NA
+for(i in 1:nrow(prscsx_dat)){
+  if(!grepl(&#39;.txt&#39;, prscsx_dat$wget[i])){
+    print(i)
+    Sys.sleep(2)
+    log&lt;-system(paste0(&#39;curl --max-time 10 &#39;, gsub(&#39;http:&#39;,&#39;ftp:&#39;, prscsx_dat$summaryStatistics[i]), &#39;/&#39;), intern = T)
+    log&lt;-log[grepl(&#39;annotated.txt.gz|annotated.txt&#39;, log)]
+    log&lt;-gsub(&#39;.* &#39;,&#39;&#39;, log)
+    prscsx_dat$wget[i]&lt;-paste0(prscsx_dat$summaryStatistics[i], &#39;/&#39;, log)
+  }
+}
+# Note this has to be run a few times due to some requests being blocked.
+
+# Write a table showing label matching prscsx info and wget url
+write.table(prscsx_dat[, c(&#39;labels&#39;, &#39;wget&#39;), with=F], &#39;~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<pre class="bash"><code># wget and unzip sumstats
+for pheno in $(cat ~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt | cut -d&#39; &#39; -f 1); do
+  url=$(awk -v var=&quot;$pheno&quot; &#39;$1 == var {print $2}&#39; ~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_wget.txt)
+  sbatch -p cpu --wrap=&quot;wget -O ~/oliverpainfel/Data/GWAS_sumstats/UGR/${pheno}.txt.gz ${url}&quot;
+done
+</code></pre>
+<pre class="r"><code>library(future.batchtools)
+library(furrr)
+library(data.table)
+ugr_data&lt;-fread(&#39;~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv&#39;)
+
+plan(batchtools_slurm(resources = list(
+  time = &quot;12:00:00&quot;,
+  ntasks = 2,
+  mem = &quot;10g&quot;,
+  partition = &quot;neurohack_cpu&quot;
+)))
+
+furrr::future_map_dfr(1:nrow(ugr_data), function(i) {
+  print(i)
+  sumstats &lt;- fread(paste0(&quot;~/oliverpainfel/Data/GWAS_sumstats/UGR/&quot;, ugr_data$label[i], &quot;.txt.gz&quot;))
+  sumstats &lt;- sumstats[, names(sumstats) %in% c(&quot;snpid&quot;, &quot;pval_fe&quot;, &quot;se_fe&quot;) | grepl(&#39;^beta_|^af_|^no_&#39;, names(sumstats)), with=F]
+
+  # Extract CHR, BP, A1, A2 from snpid
+  snp_split &lt;- tstrsplit(sumstats$snpid, &quot;:&quot;, fixed = TRUE)
+  sumstats[, `:=`(CHR = snp_split[[1]], BP = snp_split[[2]], A1 = snp_split[[3]], A2 = snp_split[[4]])]
+
+  # Set no_ and af_ to NA if beta is NA
+  cohorts &lt;- gsub(&#39;^no_&#39;,&#39;&#39;, names(sumstats)[grepl(&#39;^no_&#39;, names(sumstats))])
+  for (cohort in cohorts) {
+    sumstats[[paste0(&#39;no_&#39;, cohort)]][is.na(sumstats[[paste0(&#39;beta_&#39;, cohort)]])] &lt;- NA
+    sumstats[[paste0(&#39;af_&#39;, cohort)]][is.na(sumstats[[paste0(&#39;beta_&#39;, cohort)]])] &lt;- NA
+  }
+
+  # Calculate sample size weighted average for allele frequency
+  for (cohort in cohorts) {
+    sumstats[[paste0(&#39;af_&#39;, cohort, &#39;_weighted&#39;)]] &lt;- sumstats[[paste0(&#39;af_&#39;, cohort)]] * sumstats[[paste0(&#39;no_&#39;, cohort)]]
+  }
+  
+  # Calculate total N and frequency
+  sumstats[, N := rowSums(.SD, na.rm = TRUE), .SDcols = patterns(&quot;^no_&quot;)]
+  sumstats[, FREQ := rowSums(.SD, na.rm = TRUE) / N, .SDcols = patterns(&quot;weighted$&quot;)]
+
+  # Rename columns
+  setnames(sumstats, old = c(&#39;beta_fe&#39;, &#39;se_fe&#39;, &#39;pval_fe&#39;), new = c(&#39;BETA&#39;, &#39;SE&#39;, &#39;P&#39;))
+
+  # Select relevant columns and remove rows with missing data
+  sumstats &lt;- sumstats[, .(CHR, BP, A1, A2, BETA, SE, P, FREQ, N)]
+  sumstats &lt;- sumstats[complete.cases(sumstats)]
+  
+  fwrite(sumstats, paste0(&quot;~/oliverpainfel/Data/GWAS_sumstats/UGR/&quot;, ugr_data$label[i], &quot;.reformat.txt.gz&quot;), sep=&#39; &#39;, quote=F, na=&#39;NA&#39;)
+  
+})</code></pre>
+</details>
+<hr />
+</div>
+<div id="estimate-snp-h2-polygenicity-and-rg-across-populations"
+class="section level1">
+<h1>Estimate SNP-h2, polygenicity and rG across populations</h1>
+<p>We will estimate SNP-h2 using LD-score regression, and the rG using
+POPCORN. POPCORN can estimate the SNP-h2, but it will vary according to
+the other GWAS included due to SNP overlap. Use the sumstats QC’d by
+GenoPred. To estimate polygenicity, lets use AVENGEME based on ptclump
+score association results. Lets generate those using GenoPred.</p>
+<hr />
+<div id="prepare-configuration-for-genopred" class="section level2">
+<h2>Prepare configuration for GenoPred</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>######
+# gwas_list
+######
+
+dir.create(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop&#39;)
+
+prscsx_dat&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv&#39;)
+
+gwas_list_eur&lt;-data.frame(
+  name=paste0(prscsx_dat$labels,&#39;_UKB&#39;),
+  path=paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/gwas/&#39;,prscsx_dat$labels,&#39;/ukb.eur_train.&#39;,prscsx_dat$labels,&#39;.GW.txt.gz&#39;),
+  population=&#39;EUR&#39;,
+  n=NA,
+  sampling=NA,
+  prevalence=NA,
+  mean=0,
+  sd=1,
+  label=paste0(&#39;&quot;&#39;, prscsx_dat$trait, &#39; (UKB)&quot;&#39;)
+)
+
+bbj_info&lt;-fread(&#39;~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_prscsx.csv&#39;)
+bbj_info&lt;-bbj_info[bbj_info$labels %in% prscsx_dat$labels,]
+
+gwas_list_eas&lt;-data.frame(
+  name=paste0(bbj_info$labels,&#39;_BBJ&#39;),
+  path=paste0(&#39;/users/k1806347/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.&#39;,bbj_info$labels,&#39;.reformat.txt.gz&#39;),
+  population=&#39;EAS&#39;,
+  n=as.numeric(gsub(&#39;,&#39;,&#39;&#39;,bbj_info$`No. samples`)),
+  sampling=NA,
+  prevalence=NA,
+  mean=0,
+  sd=1,
+  label=paste0(&#39;&quot;&#39;, prscsx_dat$trait, &#39; (BBJ)&quot;&#39;)
+)
+
+ugr_data&lt;-fread(&#39;~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv&#39;)
+ugr_data&lt;-ugr_data[ugr_data$labels %in% prscsx_dat$labels,]
+
+gwas_list_afr&lt;-data.frame(
+  name=paste0(ugr_data$labels,&#39;_UGR&#39;),
+  path=paste0(&#39;/users/k1806347/oliverpainfel/Data/GWAS_sumstats/UGR/&#39;,ugr_data$labels,&#39;.reformat.txt.gz&#39;),
+  population=&#39;AFR&#39;,
+  n=NA,
+  sampling=NA,
+  prevalence=NA,
+  mean=0,
+  sd=1,
+  label=paste0(&#39;&quot;&#39;, ugr_data$trait, &#39; (UGR)&quot;&#39;)
+)
+gwas_list&lt;-do.call(rbind, list(gwas_list_eur, gwas_list_eas, gwas_list_afr))
+
+# Create file listing phenotypes in common between AFR, EAS and EUR
+pheno &lt;- gsub(&#39;_.*&#39;, &#39;&#39;, gwas_list$name)
+pheno_intersect &lt;- Reduce(intersect, 
+                           list(
+                             pheno[gwas_list$population == &#39;EUR&#39;],
+                             pheno[gwas_list$population == &#39;EAS&#39;],
+                             pheno[gwas_list$population == &#39;AFR&#39;]
+                             )
+                           )
+
+# Restrict gwas_list to intersecting phenotypes
+gwas_list&lt;-gwas_list[pheno %in% pheno_intersect,]
+
+write.table(gwas_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt&#39;, col.names = T, row.names = F, quote = F)
+
+write.table(pheno_intersect, &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt&#39;, col.names = F, row.names = F, quote = F)
+
+######
+# config
+######
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&quot;,
+  &quot;pgs_methods: [&#39;ptclump&#39;]&quot;,
+  &quot;cores_prep_pgs: 1&quot;,
+  &quot;cores_target_pgs: 20&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-pipeline-1" class="section level2">
+<h2>Run pipeline</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml \
+  target_pgs -n</code></pre>
+</details>
+<hr />
+</div>
+<div id="reformat-for-ldsc-and-popcorn" class="section level2">
+<h2>Reformat for LDSC and POPCORN</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+dir.create(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats&#39;, recursive = T)
+gwas_list&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt&#39;)
+
+for(i in 1:nrow(gwas_list)){
+  if(
+    file.exists(
+      paste0(
+        &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/&quot;,
+        gwas_list$name[i], 
+        &quot;.sumstats.gz&quot;))){
+    next    
+  }
+  print(i)
+  gwas_file &lt;-
+    paste0(
+      &quot;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/&quot;,
+      gwas_list$name[i],
+      &quot;/&quot;,
+      gwas_list$name[i],
+      &quot;-cleaned.gz&quot;
+    )
+  
+  gwas_header &lt;- fread(gwas_file, nrows = 1)
+  cols_index &lt;- which(names(gwas_header) %in% c(&#39;SNP&#39;,&#39;A1&#39;,&#39;A2&#39;,&#39;BETA&#39;,&#39;SE&#39;,&#39;P&#39;,&#39;N&#39;))
+  
+  system(
+    paste0(
+      &quot;zcat &quot;,
+      gwas_file,
+      &quot; | cut -f &quot;, 
+      paste0(cols_index, collapse = &#39;,&#39;),
+      &quot; | sed -e &#39;1s/BETA/beta/&#39;&quot;,
+      &quot; | /users/k1806347/oliverpainfel/Software/pigz/pigz -f&quot;,
+      &quot; &gt; /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/&quot;,
+      gwas_list$name[i], 
+      &quot;.sumstats.gz&quot;
+      )
+    )
+}</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-ldsc" class="section level2">
+<h2>Run LDSC</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>conda activate ldsc
+
+for pop in $(echo EUR EAS AFR);do
+  if [ &quot;$pop&quot; == &quot;EUR&quot; ]; then
+      samp=&quot;UKB&quot;
+  fi
+  if [ &quot;$pop&quot; == &quot;EAS&quot; ]; then
+      samp=&quot;BBJ&quot;
+  fi
+  if [ &quot;$pop&quot; == &quot;AFR&quot; ]; then
+      samp=&quot;UGR&quot;
+  fi
+  
+  for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats
+
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap=&quot;/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
+     --sumstats /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/${pheno}_${samp}.sumstats.gz \
+     --out /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats/${pheno}_${samp}&quot;
+
+  done
+done
+
+for pop in $(echo EUR EAS AFR);do
+  if [ &quot;$pop&quot; == &quot;EUR&quot; ]; then
+      samp=&quot;UKB&quot;
+  fi
+  if [ &quot;$pop&quot; == &quot;EAS&quot; ]; then
+      samp=&quot;BBJ&quot;
+  fi
+  if [ &quot;$pop&quot; == &quot;AFR&quot; ]; then
+      samp=&quot;UGR&quot;
+  fi
+  
+  for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/${pheno}/${pop}
+
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap=&quot;/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \
+     --h2 /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats/${pheno}_${samp}.sumstats.gz \
+     --ref-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.${pop}.rsid \
+     --w-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.${pop}.rsid \
+     --out /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/${pheno}/${pop}/res&quot;
+     
+  done
+done
+</code></pre>
+</details>
+<hr />
+</div>
+<div id="calculate-cscores" class="section level2">
+<h2>Calculate CSCOREs</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>
+# Subset the reference data into relevant populations
+for pop in $(echo EUR EAS AFR); do
+  mkdir -p /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp
+  for chr in $(seq 1 22); do
+    /users/k1806347/oliverpainfel/Software/plink2 \
+      --pfile /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr${chr} \
+      --keep /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files/${pop}.keep \
+      --make-bed \
+      --out /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.${pop}.chr${chr}
+    done
+done
+
+conda activate /scratch/prj/oliverpainfel/recovered/.conda/envs/popcorn
+for pop in $(echo EAS AFR); do
+  mkdir -p /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES
+  for chr in $(seq 1 22); do
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap=&quot;popcorn \
+      compute \
+      -v 1 \
+      --bfile1 /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.EUR.chr${chr} \
+      --bfile2 /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.${pop}.chr${chr} \
+      /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_chr${chr}.txt&quot;
+  done
+done
+
+for pop in $(echo EAS AFR); do
+  cat /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_chr*.txt &gt; /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_all.txt
+done
+
+rm -r /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp
+rm /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_*_CSCORES/*chr*.txt</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-popcorn" class="section level2">
+<h2>Run POPCORN</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>conda activate popcorn
+for pop in $(echo EAS AFR);do
+  if [ &quot;$pop&quot; == &quot;EAS&quot; ]; then
+      samp=&quot;BBJ&quot;
+  fi
+  if [ &quot;$pop&quot; == &quot;AFR&quot; ]; then
+      samp=&quot;UGR&quot;
+  fi
+  
+  for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/${pheno}/EUR_${pop}
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap=&quot;popcorn \
+       fit -v 3 \
+       --cfile /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${pop}_CSCORES/scores_all.txt \
+       --sfile1 /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/${pheno}_UKB.sumstats.gz \
+       --sfile2 /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/${pheno}_${samp}.sumstats.gz \
+       --gen_effect \
+       /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/${pheno}/EUR_${pop}/rG_gen_effect&quot;
+  done
+done
+</code></pre>
+</details>
+<hr />
+</div>
+<div id="plot-the-ldsc-and-popcorn-results" class="section level2">
+<h2>Plot the LDSC and POPCORN results</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+library(ggplot2)
+library(cowplot)
+
+# Read in phenotypes
+pheno_intersect &lt;- read.table(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt&#39;, header=F)$V1
+
+# Plot the heritability estimates
+h2_res &lt;- NULL
+
+for(pop in c(&#39;AFR&#39;,&#39;EAS&#39;, &#39;EUR&#39;)){
+  for(pheno in pheno_intersect){
+    log &lt;-
+      readLines(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/&#39;,
+          pheno,
+          &#39;/&#39;,
+          pop,
+          &#39;/res.log&#39;
+        )
+      )
+    
+    h2 &lt;- log[grepl(&#39;Total Observed scale h2:&#39;, log)]
+    h2_est &lt;- as.numeric(gsub(&#39; .*&#39;,&#39;&#39;, gsub(&#39;Total Observed scale h2: &#39;, &#39;&#39;, h2)))
+    h2_se &lt;- as.numeric(gsub(&quot;\\)&quot;,&#39;&#39;, gsub(&quot;.* \\(&quot;, &#39;&#39;, h2)))
+    int &lt;- log[grepl(&#39;Intercept:&#39;, log)]
+    int_est &lt;- as.numeric(gsub(&#39; .*&#39;,&#39;&#39;, gsub(&#39;Intercept: &#39;, &#39;&#39;, int)))
+    int_se &lt;- as.numeric(gsub(&quot;\\)&quot;,&#39;&#39;, gsub(&quot;.* \\(&quot;, &#39;&#39;, int)))
+    lambda &lt;- log[grepl(&#39;Lambda GC:&#39;, log)]
+    lambda &lt;- as.numeric(gsub(&#39;.* &#39;,&#39;&#39;, lambda))
+    
+    h2_res &lt;- rbind(
+      h2_res,
+      data.table(
+        Population = pop,
+        Phenotype = pheno,
+        h2_est = h2_est,
+        h2_se = h2_se,
+        int_est = int_est,
+        int_se = int_se,
+        lambda = lambda
+      )
+    )
+  }
+}
+
+write.csv(h2_res, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results.csv&#39;, row.names = F, quote = F)
+
+ggplot(h2_res, aes(x = Phenotype, y = h2_est, fill = Population)) +
+  geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.7) +
+  geom_errorbar(aes(ymin=h2_est-h2_se, ymax=h2_est+h2_se), width=.2, position=position_dodge(width = 0.7, preserve = &quot;single&quot;)) +
+  labs(y=&quot;SNP-based Heritability (SE)&quot;) +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
+
+# Plot rG estimates
+rg_res &lt;- NULL
+for(pop in c(&#39;AFR&#39;,&#39;EAS&#39;)){
+  for(pheno in h2_res$Phenotype){
+    pop_res_i&lt;-fread(paste0(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/&#39;, pheno, &#39;/EUR_&#39;, pop, &#39;/rG_gen_effect&#39;))
+    names(pop_res_i) &lt;- c(&#39;Test&#39;,&#39;Estimate&#39;,&#39;SE&#39;,&#39;Z&#39;,&#39;P&#39;)
+    pop_res_i &lt;- pop_res_i[pop_res_i$Test == &#39;pge&#39;,]
+    pop_res_i$Population_1 &lt;- &#39;EUR&#39;
+    pop_res_i$Population_2 &lt;- pop
+    pop_res_i$Phenotype &lt;- pheno
+    rg_res &lt;- rbind(rg_res, pop_res_i)
+  }
+}
+
+rg_res$Comparison &lt;- paste0(rg_res$Population_1, &#39; vs. &#39;, rg_res$Population_2)
+
+write.csv(rg_res, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results.csv&#39;, row.names = F, quote = F)
+
+ggplot(rg_res, aes(x = Phenotype, y = Estimate, fill = Comparison)) +
+  geom_bar(stat=&quot;identity&quot;, position=position_dodge(), width = 0.7) +
+  geom_errorbar(aes(ymin=Estimate-SE, ymax=Estimate+SE), width=.2, position=position_dodge(width = 0.7)) +
+  labs(y=&quot;SNP-based\nGenetic Correlation (SE)&quot;) +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)</code></pre>
+</details>
+</div>
+<div id="avengeme" class="section level2">
+<h2>AVENGEME</h2>
+<div id="create-predictor-lists" class="section level3">
+<h3>Create predictor lists</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+library(data.table)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml&#39;
+pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+# Read in phenotypes
+pheno_intersect &lt;- read.table(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt&#39;, header=F)$V1
+
+# Create files for EAS and AFR targets
+pop &lt;- c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)
+for(trait_i in pheno_intersect){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
+  scores_i &lt;- scores[grepl(paste0(&#39;^&#39;, trait_i, &#39;_&#39;), scores$name),]
+  scores_i$group &lt;- scores_i$method
+  
+  for(pop_i in pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    if(pop_i == &#39;EAS&#39;){
+      samp_i &lt;- &#39;BBJ&#39;
+    }
+    if(pop_i == &#39;AFR&#39;){
+      samp_i &lt;- &#39;UGR&#39;
+    }
+    if(pop_i == &#39;EUR&#39;){
+      samp_i &lt;- c(&#39;UKB&#39;)
+    }
+
+    dir.create(
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/targ_&#39;,
+        pop_i,
+        &#39;.disc_&#39;,
+        pop_i,
+        &#39;/&#39;,
+        trait_i
+      ),
+      recursive = T
+    )
+    
+    scores_i_j &lt;- scores_i[grepl(samp_i, scores_i$name, ignore.case = T),]
+    scores_i_j$predictor &lt;- paste0(
+      outdir,
+      &#39;/ukb/pgs/TRANS/&#39;,
+      scores_i_j$method,
+      &#39;/&#39;,
+      scores_i_j$name,
+      &#39;/ukb-&#39;,
+      scores_i_j$name,
+      &#39;-TRANS.profiles&#39;
+    )
+    
+    predictors_i &lt;- scores_i_j[, c(&#39;predictor&#39;, &#39;group&#39;), with=F]
+    
+    write.table(
+      predictors_i,
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/targ_&#39;,
+        pop_i,
+        &#39;.disc_&#39;,
+        pop_i,
+        &#39;/&#39;,
+        trait_i,
+        &#39;/predictor_list.ptclump.txt&#39;
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-model_builder" class="section level3">
+<h3>Run model_builder</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+for pop in $(echo EUR EAS AFR); do
+  if [ &quot;$pop&quot; == &quot;EUR&quot; ]; then
+      pop2=&quot;EUR_test&quot;
+  else
+      pop2=$pop
+  fi
+  
+  for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
+    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap=&quot;Rscript ../Scripts/model_builder/model_builder.R \
+      --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${pop2}.row_number.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${pop}.disc_${pop}/${pheno}/res.ptclump \
+      --n_core 5 \
+      --all_model F \
+      --assoc T&quot;
+  done
+done
+</code></pre>
+</details>
+<hr />
+</div>
+<div id="plot-assoc-results" class="section level3">
+<h3>Plot assoc results</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in phenotypes
+pheno_intersect &lt;- read.table(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt&#39;, header=F)$V1
+
+# Read in results
+pop = c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)
+res_all &lt;- NULL
+for(pheno_i in pheno_intersect){
+  res_i&lt;-NULL
+  for(pop_i in pop){
+    assoc_i &lt;-
+      fread(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/targ_&#39;,
+          pop_i,
+          &#39;.disc_&#39;,
+          pop_i,
+          &#39;/&#39;,
+          pheno_i,
+          &#39;/res.ptclump.assoc.txt&#39;
+        )
+      )
+      assoc_i$Population &lt;- pop_i
+      res_i&lt;-rbind(res_i, assoc_i)
+  }
+  
+  res_i$Phenotype &lt;- pheno_i
+  res_all&lt;-rbind(res_all, res_i)
+}
+
+# Extract pT variable from Predictor
+res_all$pT &lt;- gsub(&#39;e.&#39;,&#39;e-&#39;, gsub(&#39;.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.&#39;, &#39;&#39;, res_all$Predictor))
+res_all$pT &lt;- factor(res_all$pT, levels = unique(res_all$pT))
+
+ggplot(res_all, aes(x = Phenotype, y = BETA, fill = pT)) +
+  geom_hline(yintercept = 0, colour = &#39;darkgrey&#39;) +
+  geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.8) +
+  geom_errorbar(aes(ymin=BETA-SE, ymax=BETA+SE), width=0, position=position_dodge(width = 0.8, preserve = &quot;single&quot;)) +
+  labs(y=&quot;BETA (SE)&quot;) +
+  theme_half_open() +
+  background_grid() +
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) +
+  scale_fill_manual(values = colorRampPalette(c(&quot;lightblue&quot;, &quot;darkblue&quot;))(length(unique(res_all$pT)))) +
+  facet_grid(Population ~.)</code></pre>
+</details>
+</div>
+<div id="run-avengeme" class="section level3">
+<h3>Run AVENGEME</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+library(avengeme)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml&#39;
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+gwas_list &lt;- read_param(config = config, param = &#39;gwas_list&#39;, return_obj = T)
+
+# Read in phenotypes
+pheno_intersect &lt;- read.table(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt&#39;, header=F)$V1
+
+pop = c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)
+
+mod_res_all &lt;- NULL
+for(pop_i in pop){
+  for(pheno_i in pheno_intersect){
+    gwas_i&lt;-gwas_list$name[gwas_list$population == pop_i &amp; grepl(paste0(&#39;^&#39;, pheno_i, &#39;_&#39;),  gwas_list$name)]
+      
+    res_i &lt;-
+      fread(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/targ_&#39;,
+          pop_i,
+          &#39;.disc_&#39;,
+          pop_i,
+          &#39;/&#39;,
+          pheno_i,
+          &#39;/res.ptclump.assoc.txt&#39;
+        )
+      )
+    
+    res_i$Z &lt;- res_i$BETA / res_i$SE
+    
+    res_i$pT &lt;- as.numeric(gsub(&#39;e.&#39;,&#39;e-&#39;, gsub(&#39;.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.&#39;, &#39;&#39;, res_i$Predictor)))
+
+    nsnp_log &lt;-
+      read.table(
+        paste0(
+          outdir,
+          &#39;/reference/pgs_score_files/ptclump/&#39;,
+          gwas_i,
+          &#39;/ref-&#39;,
+          gwas_i,
+          &#39;.NSNP_per_pT&#39;
+        ),
+        header = T
+      )
+    
+    nsnp&lt;-nsnp_log$NSNP[nrow(nsnp_log)]
+    
+    disc_N &lt;-
+      median(
+        fread(
+          paste0(
+            outdir,
+            &#39;/reference/gwas_sumstat/&#39;,
+            gwas_i,
+            &#39;/&#39;,
+            gwas_i,
+            &#39;-cleaned.gz&#39;
+          ), nrows = 10000
+        )$N
+      )
+    
+    targ_N &lt;- res_i$N[1]
+    
+    mod_res &lt;- estimatePolygenicModel(
+      p = res_i$Z,
+      nsnp = nsnp,
+      n = c(disc_N, targ_N),
+      pupper = c(0, res_i$pT),
+      fixvg2pi02 = T,
+      alpha = 0.05
+    )
+    
+    mod_res_all &lt;- rbind(
+      mod_res_all,
+      data.frame(
+        Phenotype = pheno_i,
+        Population = pop_i,
+        GWAS = gwas_i,
+        nsnp = nsnp,
+        max_r2 = max(res_i$Obs_R2),
+        n_disc = disc_N,
+        n_targ = targ_N,
+        vg_est = mod_res$vg[1],
+        vg_lowCI = mod_res$vg[2],
+        vg_highCI = mod_res$vg[3],
+        pi0_est = mod_res$pi0[1],
+        pi0_lowCI = mod_res$pi0[2],
+        pi0_highCI = mod_res$pi0[3]
+      )
+    )
+  }
+}
+
+dir.create(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme&#39;)
+write.csv(mod_res_all, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv&#39;, row.names = F, quote = F)
+
+ggplot(mod_res_all, aes(x = Phenotype, y = vg_est, fill = Population)) +
+  geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.7) +
+  geom_errorbar(aes(ymin=vg_lowCI, ymax=vg_highCI), width=.2, position=position_dodge(width = 0.7, preserve = &quot;single&quot;)) +
+  labs(y=&quot;SNP-based Heritability (95%CI)&quot;) +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
+
+ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) +
+  geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.7) +
+  geom_errorbar(aes(ymin=1 - pi0_lowCI, ymax=1 - pi0_highCI), width=.2, position=position_dodge(width = 0.7, preserve = &quot;single&quot;)) +
+  labs(y=&quot;Proporition non-zero\neffects (95%CI)&quot;) +
+  theme_half_open() +
+  coord_cartesian(ylim = c(0, 0.15)) + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
+
+ggplot(mod_res_all, aes(x = Phenotype, y = max_r2, fill = Population)) +
+  geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.7) +
+  labs(y=&quot;Max R2&quot;) +
+  theme_half_open() +
+  coord_cartesian(ylim = c(0, 0.15)) + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
+
+# 
+hist(mod_res_all$max_r2)
+hist(mod_res_all$max_r2[mod_res_all$Population == &#39;EUR&#39;])
+hist(mod_res_all$max_r2[mod_res_all$Population == &#39;EAS&#39;])
+hist(mod_res_all$max_r2[mod_res_all$Population == &#39;AFR&#39;])
+
+summary(mod_res_all$max_r2)
+summary(mod_res_all$max_r2[mod_res_all$Population == &#39;EUR&#39;])
+summary(mod_res_all$max_r2[mod_res_all$Population == &#39;EAS&#39;])
+summary(mod_res_all$max_r2[mod_res_all$Population == &#39;AFR&#39;])</code></pre>
+</details>
+<hr />
+</div>
+<div id="select-gwas-for-downstream-analyses" class="section level3">
+<h3>Select GWAS for downstream analyses</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>#########
+# Select 10 GWAS for downstream analysis
+#########
+# Criteria are that SNP-h2 &gt; 0.01 in both AVENGEME and LDSC
+# Then GWAS are selected to represent a range of polygenicity and heritability, as estimated in EUR since they are most accurate
+
+library(data.table)
+
+# Read in the AVENGEME results
+avengeme &lt;- fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv&#39;)
+
+# Read in the LDSC results
+ldsc &lt;- fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results.csv&#39;)
+
+# Combine results
+both &lt;- merge(avengeme, ldsc, by = c(&#39;Population&#39;,&#39;Phenotype&#39;))
+
+# Remove GWAS that have negative SNP-h2 from LDSC in any population
+both_h2 &lt;- both[!(both$Phenotype %in% both$Phenotype[both$vg_est &lt; 0.01 | both$h2_est &lt; 0.01]),]
+
+# Select GWAS representing a range of SNP-h2 from LDSC, and a range of polygenicity from AVENGEME.
+both_eur&lt;-both_h2[both_h2$Population == &#39;EUR&#39;,]
+traits_data &lt;- data.frame(trait = both_eur$Phenotype, heritability = both_eur$vg_est, polygenicity = both_eur$pi0_est)
+
+# Number of bins (e.g., dividing into 5 bins each for heritability and polygenicity)
+num_bins &lt;- 5
+
+# Create bins
+traits_data$her_bin &lt;- cut(traits_data$heritability, breaks = num_bins)
+traits_data$poly_bin &lt;- cut(traits_data$polygenicity, breaks = num_bins)
+
+# Split data by unique bin combinations
+split_data &lt;- split(traits_data, list(traits_data$her_bin, traits_data$poly_bin), drop = TRUE)
+
+set.seed(1)
+# Randomly select one trait from each bin combination
+selected_traits &lt;- do.call(rbind, lapply(split_data, function(df) df[sample(nrow(df), 1), ]))
+
+# Limit to 10 traits if more than 10 unique combinations
+if (nrow(selected_traits) &gt; 10) {
+  selected_traits &lt;- selected_traits[sample(nrow(selected_traits), 10), ]
+}
+
+write.table(selected_traits$trait, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, col.names = F, row.names = F, quote = F)
+
+# Plot max R2 for selected traits
+mod_res_all &lt;- fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv&#39;)
+mod_res_all_selected &lt;- mod_res_all[mod_res_all$Phenotype %in% selected_traits$trait,]
+
+ggplot(mod_res_all_selected, aes(x = Phenotype, y = max_r2, fill = Population)) +
+  geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.7) +
+  labs(y=&quot;Max R2&quot;) +
+  theme_half_open() +
+  coord_cartesian(ylim = c(0, 0.15)) + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
+
+# 
+hist(mod_res_all$max_r2)
+hist(mod_res_all$max_r2[mod_res_all$Population == &#39;EUR&#39;])
+hist(mod_res_all$max_r2[mod_res_all$Population == &#39;EAS&#39;])
+hist(mod_res_all$max_r2[mod_res_all$Population == &#39;AFR&#39;])
+
+summary(mod_res_all$max_r2)
+summary(mod_res_all$max_r2[mod_res_all$Population == &#39;EUR&#39;])
+summary(mod_res_all$max_r2[mod_res_all$Population == &#39;EAS&#39;])
+summary(mod_res_all$max_r2[mod_res_all$Population == &#39;AFR&#39;])
+
+round(sqrt(min(mod_res_all$max_r2[mod_res_all$Population == &#39;EUR&#39;])), 2)
+round(sqrt(max(mod_res_all$max_r2[mod_res_all$Population == &#39;EUR&#39;])), 2)
+round(sqrt(min(mod_res_all$max_r2[mod_res_all$Population == &#39;EAS&#39;])), 2)
+round(sqrt(max(mod_res_all$max_r2[mod_res_all$Population == &#39;EAS&#39;])), 2)
+round(sqrt(min(mod_res_all$max_r2[mod_res_all$Population == &#39;AFR&#39;])), 2)
+round(sqrt(max(mod_res_all$max_r2[mod_res_all$Population == &#39;AFR&#39;])), 2)</code></pre>
+</details>
+<hr />
+</div>
+</div>
+</div>
+<div id="make-a-descriptives-table-of-gwas" class="section level1">
+<h1>Make a descriptives table of GWAS</h1>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+
+#####
+# Trait names, labels, and URLs
+#####
+
+###
+# UKB
+###
+ukb &lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv&#39;)
+names(ukb) &lt;- c(&#39;trait&#39;, &#39;labels&#39;,&#39;field&#39;)
+trait_labels &lt;- ukb[, c(&#39;trait&#39;,&#39;labels&#39;), with=F]
+ukb&lt;-ukb[, c(&#39;trait&#39;,&#39;field&#39;), with=F]
+ukb$sample &lt;- &#39;UKB&#39;
+ukb$population &lt;- &#39;EUR&#39;
+ukb$url&lt;-NA
+
+###
+# BBJ
+###
+bbj &lt;- fread(&#39;~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_prscsx.csv&#39;)
+bbj &lt;- bbj[, c(&#39;trait&#39;, &#39;wget&#39;), with = F]
+names(bbj) &lt;- c(&#39;trait&#39;, &#39;url&#39;)
+bbj$sample &lt;- &#39;BBJ&#39;
+bbj$population &lt;- &#39;EAS&#39;
+bbj$field &lt;- NA
+
+###
+# UGR
+###
+ugr &lt;- fread(&#39;~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv&#39;)
+ugr &lt;- ugr[, c(&#39;trait&#39;, &#39;summaryStatistics&#39;), with = F]
+names(ugr) &lt;- c(&#39;trait&#39;,&#39;url&#39;)
+ugr$sample &lt;- &#39;UGR&#39;
+ugr$population &lt;- &#39;AFR&#39;
+ugr$field &lt;- NA
+
+info_all &lt;- do.call(rbind, list(ukb, bbj, ugr))
+info_all&lt;-merge(info_all, trait_labels, by=&#39;trait&#39;)
+
+#####
+# Sample size, SNP-h2 and polygenicity
+#####
+
+# Read in the AVENGEME and LDSC results
+avengeme &lt;- fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv&#39;)
+ldsc &lt;- fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results.csv&#39;)
+both &lt;- merge(avengeme, ldsc, by = c(&#39;Population&#39;,&#39;Phenotype&#39;))
+
+# Format for descriptives table
+both$h2_avengeme&lt;- paste0(
+  round(both$vg_est,2), 
+  &quot; (95%CI = &quot;, 
+  round(both$vg_lowCI, 2), 
+  &quot; - &quot; , 
+  round(both$vg_highCI, 2), &quot;)&quot;)
+
+both$pi0_avengeme &lt;- paste0(
+  round(both$pi0_est,2), 
+  &quot; (95%CI = &quot;, 
+  round(both$pi0_lowCI, 2), 
+  &quot; - &quot; , 
+  round(both$pi0_highCI, 2), &quot;)&quot;)
+
+both$h2_ldsc &lt;- paste0(
+  round(both$h2_est,2), 
+  &quot; (SE = &quot;, 
+  round(both$h2_se, 2), 
+  &quot;)&quot;)
+
+both$int_ldsc &lt;- paste0(
+  round(both$int_est,2), 
+  &quot; (SE = &quot;, 
+  round(both$int_se, 2), 
+  &quot;)&quot;)
+
+both&lt;-both[, c(&#39;Population&#39;,&#39;Phenotype&#39;,&#39;n_disc&#39;,&#39;n_targ&#39;,&#39;h2_avengeme&#39;,&#39;pi0_avengeme&#39;,&#39;h2_ldsc&#39;,&#39;int_ldsc&#39;,&#39;lambda&#39;), with = F]
+names(both)[1:2]&lt;-c(&#39;population&#39;,&#39;labels&#39;)
+
+info_all &lt;- merge(info_all, both, by = c(&#39;labels&#39;,&#39;population&#39;))
+info_all$n_disc&lt;-round(info_all$n_disc, 0)
+info_all$n_targ&lt;-round(info_all$n_targ, 0)
+
+info_all&lt;-info_all[, c(&#39;labels&#39;,&#39;trait&#39;,&#39;population&#39;,&#39;sample&#39;,&#39;n_disc&#39;,&#39;n_targ&#39;,&#39;h2_avengeme&#39;,&#39;pi0_avengeme&#39;,&#39;h2_ldsc&#39;,&#39;int_ldsc&#39;,&#39;lambda&#39;,&#39;field&#39;,&#39;url&#39;), with=F]
+names(info_all) &lt;- c(&#39;Trait Label&#39;, &#39;Trait Description&#39;, &#39;Ancestry&#39;, &#39;GWAS Sample&#39;, &#39;GWAS N&#39;, &#39;Target N&#39;,&quot;SNP-h2 (AVENGEME)&quot;,&quot;pi0 (AVENGEME)&quot;,&quot;SNP-h2 (LDSC)&quot;,&quot;Intercept (LDSC)&quot;,&#39;Lambda&#39;, &#39;UKB Field&#39;, &#39;URL&#39;)
+
+# Add in column indicating whether the trait was used in downstream PGS comparison
+selected_traits &lt;- fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+
+info_all$`Selected` &lt;- info_all$`Trait Label` %in% selected_traits
+
+write.csv(info_all, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv&#39;, row.names=F)
+
+# Estimate the mean and SD of sample size within each population for selected traits
+info_all_selected&lt;-info_all[info_all$Selected == T,]
+n_dat &lt;- NULL
+for(i in unique(info_all_selected$`GWAS Sample`)){
+  n_dat &lt;-rbind(
+    n_dat,
+    data.table(
+      sample = i,
+      gwas_n_median = round(median(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      gwas_n_mean = round(mean(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      gwas_n_sd = round(sd(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_median = round(median(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_mean = round(mean(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_sd = round(sd(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i]))
+    )
+  )
+}</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-genopred" class="section level1">
+<h1>Run GenoPred</h1>
+<div id="prepare-configuration-for-genopred-1" class="section level2">
+<h2>Prepare configuration for GenoPred</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>######
+# gwas_list
+######
+library(data.table)
+# Subset original gwas_list to include selected traits
+gwas_list&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt&#39;)
+pheno&lt;-gsub(&#39;_.*&#39;,&#39;&#39;, gwas_list$name)
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+gwas_list&lt;-gwas_list[pheno %in% selected_traits,]
+gwas_list$label&lt;-paste0(&#39;&quot;&#39;, gwas_list$label, &#39;&quot;&#39;)
+
+write.table(gwas_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&#39;, col.names = T, row.names = F, quote = F)
+
+######
+# gwas_groups
+######
+
+gwas_groups_eas&lt;-data.frame(
+  name=paste0(selected_traits, &#39;_UKB_BBJ&#39;),
+  gwas=sapply(selected_traits, function(x) paste0(x,&#39;_UKB,&#39;,x,&#39;_BBJ&#39;)),
+  label=paste0(&#39;&quot;&#39;, selected_traits, &quot; (UKB+BBJ)&quot;, &#39;&quot;&#39;)
+)
+
+gwas_groups_afr&lt;-data.frame(
+  name=paste0(selected_traits, &#39;_UKB_UGR&#39;),
+  gwas=sapply(selected_traits, function(x) paste0(x,&#39;_UKB,&#39;,x,&#39;_UGR&#39;)),
+  label=paste0(&#39;&quot;&#39;, selected_traits, &quot; (UKB+UGR)&quot;, &#39;&quot;&#39;)
+)
+
+gwas_groups&lt;-rbind(gwas_groups_eas, gwas_groups_afr)
+
+write.table(gwas_groups, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt&#39;, col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&quot;,
+  &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt&quot;,
+  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;,&#39;prscsx&#39;,&#39;xwing&#39;]&quot;,
+  &quot;tlprs_methods: [&#39;dbslmm&#39;]&quot;,
+  &quot;leopard_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
+  &quot;cores_prep_pgs: 10&quot;, # xwing run with 20 cores
+  &quot;cores_target_pgs: 50&quot;,
+  &quot;ldpred2_inference: F&quot;,
+  &quot;ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3&quot;,
+  &quot;quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3&quot;,
+  &quot;quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset&quot;,
+  &quot;sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-pipeline-2" class="section level2">
+<h2>Run pipeline</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml \
+  target_pgs  -n</code></pre>
+</details>
+<hr />
+</div>
+</div>
+<div id="evaluate-pgs" class="section level1">
+<h1>Evaluate PGS</h1>
+<p>Lets use the model builder script which implements nested 10 fold
+cross validation. Similar set up to previous paper, evaluating a model
+containing the best PGS selected by 10-fold cross validation, a model
+containing the PGS selected by pseudovalidation (if available), and an
+elastic net model containing all PGS from a given method. We will need
+to update the model builder script to achieve this</p>
+<hr />
+<div id="compare-all-methods" class="section level2">
+<h2>Compare all methods</h2>
+<p>We want to see: - Performance of pseudo and top1 models for
+single-source methods - Performance of pseudo and top1 models for
+multi-source methods - Performance of multi-source methods: - Using
+crossval for tuning step 1 and 2 - Using pseudoval for tuning step 1 and
+2 - Using pseudoval for tuning step 1 and crossval for tuning step 2</p>
+<p>To achieve this. Will need to define groups of predictors for step 1
+modelling, and groups that should then be linearly combined.</p>
+<hr />
+<div id="create-predictor-lists-1" class="section level3">
+<h3>Create predictor lists</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+library(data.table)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml&#39;
+pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+# Create files for EAS and AFR targets
+targ_pop &lt;- c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)
+for(trait_i in selected_traits){
+  scores_i &lt;- scores[grepl(trait_i, scores$name),]
+  scores_i$multi &lt;- scores_i$method
+  
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;BBJ&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;UGR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;BBJ&#39;,&#39;UGR&#39;)
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == &#39;BBJ&#39;){
+        disc_pop_j_2 &lt;- &#39;EAS&#39;
+      }
+      if(disc_pop_j == &#39;UGR&#39;){
+        disc_pop_j_2 &lt;- &#39;AFR&#39;
+      }
+
+      dir.create(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_j_2,
+          &#39;/&#39;,
+          trait_i
+        ),
+        recursive = T
+      )
+      
+      scores_i_j &lt;- scores_i[
+        (grepl(&#39;UKB$&#39;, scores_i$name, ignore.case = F) | 
+         grepl(paste0(disc_pop_j, &#39;$&#39;), scores_i$name, ignore.case = T)),]
+
+      # Insert path to score file
+      scores_i_j$predictor &lt;- paste0(
+        outdir,
+        &#39;/ukb/pgs/TRANS/&#39;,
+        scores_i_j$method,
+        &#39;/&#39;,
+        scores_i_j$name,
+        &#39;/ukb-&#39;,
+        scores_i_j$name,
+        &#39;-TRANS.profiles&#39;
+      )
+      
+      ####
+      # Make groups single source methods
+      ####
+      
+      scores_i_j_single_top1 &lt;-
+        scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &amp;
+                     !grepl(&#39;_multi$&#39;, scores_i_j$method), ]
+
+      # Create top1 column indicating which predictors top1 models should be derived
+      scores_i_j_single_top1$top1[grepl(&#39;UKB&#39;, scores_i_j_single_top1$name, ignore.case = F)] &lt;- &#39;EUR&#39;
+      scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] &lt;- disc_pop_j_2
+      
+      ####
+      # Make groups containing pseudo scores for single source methods
+      ####
+
+      # Extract the pseudo score for each method and specify as a separate group
+      for(i in 1:nrow(scores_i_j_single_top1)) {
+        param &lt;- find_pseudo(
+          config = config,
+          gwas = scores_i_j_single_top1$name[i],
+          pgs_method = scores_i_j_single_top1$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header &lt;-
+          fread(scores_i_j_single_top1$predictor[i], nrows = 1)
+        score_cols &lt;-
+          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_single_top1$name[i], &#39;_&#39;, param)))
+        
+        system(
+          paste0(
+            &quot;cut -d&#39; &#39; -f &quot;, 
+            paste0(score_cols, collapse=&#39;,&#39;),
+            &quot; &quot;, 
+            scores_i_j_single_top1$predictor[i], 
+            &quot; &gt; &quot;, 
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                 scores_i_j_single_top1$predictor[i])
+          )
+        )
+      }
+      
+      scores_i_j_single_pseudo &lt;- scores_i_j_single_top1
+      scores_i_j_single_pseudo$multi &lt;- paste0(scores_i_j_single_pseudo$multi, &#39;.pseudo&#39;)
+
+      scores_i_j_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                    paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                                    scores_i_j_single_pseudo$predictor)
+
+      ####
+      # Make groups for multi-single-source pseudo scores
+      ####
+      
+      scores_i_j_multi_single_pseudo &lt;- scores_i_j[grepl(&#39;_multi$&#39;, scores_i_j$method),]
+
+      # Extract the pseudo score for each method and specify as a separate group
+      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
+        param &lt;- find_pseudo(
+          config = config,
+          gwas = scores_i_j_multi_single_pseudo$name[i],
+          pgs_method = scores_i_j_multi_single_pseudo$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header &lt;-
+          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
+        score_cols &lt;-
+          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_multi_single_pseudo$name[i], &#39;_&#39;, param)))
+        
+        system(
+          paste0(
+            &quot;cut -d&#39; &#39; -f &quot;, 
+            paste0(score_cols, collapse=&#39;,&#39;),
+            &quot; &quot;, 
+            scores_i_j_multi_single_pseudo$predictor[i], 
+            &quot; &gt; &quot;, 
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                 scores_i_j_multi_single_pseudo$predictor[i])
+          )
+        )
+      }
+      
+      scores_i_j_multi_single_pseudo$multi &lt;- paste0(scores_i_j_multi_single_pseudo$multi, &#39;.pseudo&#39;)
+
+      scores_i_j_multi_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                    paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                                    scores_i_j_multi_single_pseudo$predictor)
+      
+      scores_i_j_multi_single_pseudo$top1&lt;-paste0(&#39;EUR_&#39;, disc_pop_j_2)
+
+      ####
+      # Make groups for the Multi-Source methods
+      ####
+      
+      scores_i_j_multi &lt;- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
+
+      # Split top1 scores by target population
+      # This doesn&#39;t apply to xwing because it only has pop-specific pseudo scores
+      scores_i_j_multi_top1&lt;-NULL
+      for(i in 1:which(scores_i_j_multi$method %in% c(&#39;prscsx&#39;))){
+        score_header&lt;-fread(scores_i_j_multi$predictor[i], nrow = 1)
+        
+        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
+          
+          if(scores_i_j_multi$method[i] == &#39;prscsx&#39;){
+            score_cols &lt;-
+              which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi&#39;), names(score_header)))
+          }
+          if(scores_i_j_multi$method[i] == &#39;xwing&#39;){
+            score_cols &lt;-
+              which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst&#39;), names(score_header)))
+          }
+          
+          system(
+            paste0(
+              &quot;cut -d&#39; &#39; -f &quot;, 
+              paste0(score_cols, collapse=&#39;,&#39;),
+              &quot; &quot;, 
+              scores_i_j_multi$predictor[i], 
+              &quot; &gt; &quot;, 
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
+                   scores_i_j_multi$predictor[i])
+            )
+          )
+          
+          tmp &lt;- scores_i_j_multi[i,]
+          tmp$multi &lt;- paste0(tmp$multi, &#39;.grid&#39;)
+          tmp$top1 &lt;- pop
+          tmp$predictor &lt;-
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
+                   scores_i_j_multi$predictor[i])
+          
+          scores_i_j_multi_top1 &lt;- rbind(scores_i_j_multi_top1, tmp)
+        }
+      }
+
+      # Split pop-specific pseudo scores by target population
+      scores_i_j_multi_pop_pseudo&lt;-NULL
+      for(i in 1:nrow(scores_i_j_multi)){
+        score_header&lt;-fread(scores_i_j_multi$predictor[i], nrow = 1)
+        
+        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
+          if(scores_i_j_multi$method[i] == &#39;prscsx&#39;){
+            score_cols &lt;-
+              which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi_auto&#39;), names(score_header)))
+          }
+          if(scores_i_j_multi$method[i] == &#39;xwing&#39;){
+            score_cols &lt;-
+              which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst_&#39;, pop), names(score_header)))
+          }
+          
+          system(
+            paste0(
+              &quot;cut -d&#39; &#39; -f &quot;, 
+              paste0(score_cols, collapse=&#39;,&#39;),
+              &quot; &quot;, 
+              scores_i_j_multi$predictor[i], 
+              &quot; &gt; &quot;, 
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
+                   scores_i_j_multi$predictor[i])
+            )
+          )
+          
+          tmp &lt;- scores_i_j_multi[i,]
+          tmp$multi &lt;- paste0(tmp$multi, &#39;.pop_pseudo&#39;)
+          tmp$top1 &lt;- pop
+          tmp$predictor &lt;-
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
+                   scores_i_j_multi$predictor[i])
+          
+          scores_i_j_multi_pop_pseudo &lt;- rbind(scores_i_j_multi_pop_pseudo, tmp)
+        }
+      }
+      
+      # Create pseudo score for multi-source methods
+      scores_i_j_multi_pseudo&lt;-NULL
+      for(i in 1:nrow(scores_i_j_multi)) {
+        param &lt;- find_pseudo(
+          config = config,
+          gwas = scores_i_j_multi$name[i],
+          pgs_method = scores_i_j_multi$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header &lt;-
+          fread(scores_i_j_multi$predictor[i], nrows = 1)
+        score_cols &lt;-
+          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_multi$name[i], &#39;_&#39;, param)))
+
+        system(
+          paste0(
+            &quot;cut -d&#39; &#39; -f &quot;, 
+            paste0(score_cols, collapse=&#39;,&#39;),
+            &quot; &quot;, 
+            scores_i_j_multi$predictor[i], 
+            &quot; &gt; &quot;, 
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
+                 scores_i_j_multi$predictor[i])
+          )
+        )
+        
+        tmp &lt;- scores_i_j_multi[i,]
+        tmp$multi &lt;- paste0(tmp$multi, &#39;.pseudo&#39;)
+        tmp$top1 &lt;- paste0(&#39;EUR_&#39;, disc_pop_j_2)
+        tmp$predictor &lt;-
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
+                 scores_i_j_multi$predictor[i])
+        
+        scores_i_j_multi_pseudo &lt;- rbind(scores_i_j_multi_pseudo, tmp)
+      }
+      
+      ####
+      # Combine the different predictor groups
+      ####
+      predictors_i&lt;- do.call(rbind, list(
+        scores_i_j_single_top1, 
+        scores_i_j_single_pseudo, 
+        scores_i_j_multi_single_pseudo,
+        scores_i_j_multi_top1,
+        scores_i_j_multi_pop_pseudo,
+        scores_i_j_multi_pseudo
+      ))
+      
+      predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;multi&#39;,&#39;top1&#39;), with=F]
+      
+      ####
+      # Make a group that will combined all population specific PGS
+      ####
+      
+      predictors_i_all &lt;- predictors_i[predictors_i$top1 %in% c(&#39;EUR&#39;,&#39;AFR&#39;,&#39;EAS&#39;),]
+      predictors_i_all$multi &lt;- &#39;all&#39;
+      predictors_i&lt;-rbind(predictors_i, predictors_i_all)
+      
+      write.table(
+        predictors_i,
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_j_2,
+          &#39;/&#39;,
+          trait_i,
+          &#39;/predictor_list.txt&#39;
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-model_builder-1" class="section level3">
+<h3>Run model_builder</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/targ_*.disc_EUR_*/*/res*
+
+for targ_pop in $(echo EUR EAS AFR); do
+  if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
+      targ_pop2=&quot;EUR_test&quot;
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
+    disc_pop=$(echo EAS AFR)
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;EAS&quot; ]; then
+    disc_pop=&quot;EAS&quot;
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;AFR&quot; ]; then
+    disc_pop=&quot;AFR&quot;
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+      if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt&quot; ]; then
+        sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
+          --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
+          --n_core 5&quot;
+      fi
+    done
+  done
+done
+</code></pre>
+</details>
+<hr />
+</div>
+<div id="plot-results" class="section level3">
+<h3>Plot results</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+info_all &lt;- fread(&#39;~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv&#39;)
+
+# Calculate correlation between all phenotypes in each target population
+cors &lt;- list()
+for(pop_i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;CSA&#39;,&#39;AMR&#39;)){
+  if(pop_i == &#39;EUR&#39;){
+    pop_i_2 &lt;- &#39;EUR_test&#39;
+  } else {
+    pop_i_2 &lt;- pop_i
+  }
+  pheno_pop_i &lt;- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] &lt;- fread(paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;, pheno_i, &#39;.unrel.&#39;, pop_i_2, &#39;.row_number.txt&#39;))
+    names(pheno_pop_i[[pheno_i]])[3] &lt;- pheno_i
+  }
+  
+  pheno_pop_i_merged &lt;- merged_df &lt;- Reduce(function(x, y) merge(x, y, all = TRUE, by = c(&#39;FID&#39;,&#39;IID&#39;)), pheno_pop_i)
+
+  cors_i &lt;- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use=&#39;p&#39;))
+  cors[[pop_i]] &lt;- cors_i
+}
+
+# Read in results
+targ_pop = c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)
+res_eval &lt;- list()
+for(pheno_i in selected_traits){
+  res_eval_i&lt;-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;EAS&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;AFR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i &lt;-
+        fread(
+          paste0(
+            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/&#39;,
+            &#39;targ_&#39;,
+            targ_pop_i,
+            &#39;.disc_EUR_&#39;,
+            disc_pop_i,
+            &#39;/&#39;,
+            pheno_i,
+            &#39;/res.pred_eval.txt&#39;
+          )
+        )
+      eval_i$Target&lt;-targ_pop_i
+      eval_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
+      res_eval_i&lt;-rbind(res_eval_i, eval_i)
+    }
+  }
+  
+  res_eval_i$Method&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_eval_i$Group)
+  res_eval_i$Method&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_eval_i$Method)
+  
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTrain&#39;
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTrain&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTrain&#39;
+  
+  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;prscsx.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;xwing.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
+  
+  res_eval_i$Source&lt;-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_eval_i$Method) | 
+    !grepl(&#39;EUR|EAS|AFR&#39;, res_eval_i$Group), &#39;Multi&#39;, &#39;Single&#39;)
+  
+  res_eval_i$Discovery[grepl(&#39;EUR&#39;, res_eval_i$Group)] &lt;- &#39;EUR&#39;
+  res_eval_i$Discovery[grepl(&#39;EAS&#39;, res_eval_i$Group)] &lt;- &#39;EAS&#39;
+  res_eval_i$Discovery[grepl(&#39;AFR&#39;, res_eval_i$Group)] &lt;- &#39;AFR&#39;
+  res_eval_i$Discovery[res_eval_i$Source == &#39;Multi&#39;] &lt;- res_eval_i$gwas_group[res_eval_i$Source == &#39;Multi&#39;]
+  
+  res_eval_i$Method&lt;-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+  res_eval_i$Discovery&lt;-factor(res_eval_i$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+  
+  # Remove pseudo model for methods that don&#39;t really have one 
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
+  res_eval_i &lt;- res_eval_i[
+    !((res_eval_i$Method %in%  c(&#39;prscsx&#39;, &#39;xwing&#39;) | grepl(&#39;_multi$&#39;, res_eval_i$Method)) &amp; 
+      grepl(&#39;top1&#39;, res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i &lt;- res_eval_i[!duplicated(res_eval_i[, c(
+    &quot;Target&quot;, &quot;Method&quot;, &quot;Model&quot;, &quot;Source&quot;, &quot;Discovery&quot;,&quot;gwas_group&quot;
+  )]),]
+  
+  res_eval[[pheno_i]]&lt;-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;, &quot;DBSLMM-multi&quot;, &quot;lassosum-multi&quot;, &quot;LDpred2-multi&quot;, &quot;MegaPRS-multi&quot;, &quot;PRS-CS-multi&quot;, &quot;pT+clump-multi&quot;, &quot;QuickPRS-multi&quot;, &quot;SBayesRC-multi&quot;, &quot;PRS-CSx&quot;, &quot;X-Wing&quot;, &quot;All&quot;) 
+
+res_eval_simp &lt;- NULL
+for(pheno_i in selected_traits){
+  tmp &lt;- res_eval[[pheno_i]]
+  tmp$Trait &lt;- pheno_i
+  
+  # Insert nice PGS method names
+  tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+  tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+  tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+  tmp$label &lt;- factor(tmp$label, levels = model_order)
+  
+  # Simplify result to either SumStatTrain or IndivTrain
+  tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+  tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+  tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery&#39;,&#39;Model&#39;), with=F]),]
+  
+  res_eval_simp &lt;- rbind(res_eval_simp, tmp)
+}
+
+# Count the number of traits each method is best
+tmp &lt;- res_eval_simp[res_eval_simp$label != &#39;All&#39;,]
+best_groups &lt;-
+  do.call(rbind, by(tmp, list(
+    tmp$Target,
+    tmp$gwas_group,
+    tmp$Trait
+  ), function(subset) {
+    subset[which.max(subset$R),]  # Select row with max R
+  }))
+
+best_counts &lt;- as.data.frame(table(paste0(best_groups$label,&#39;:&#39;, best_groups$Model), best_groups$gwas_group, best_groups$Target))
+
+# Rename columns
+colnames(best_counts) &lt;- c(&quot;label&quot;, &quot;gwas_group&quot;, &quot;Target&quot;, &quot;count&quot;)
+best_counts$Model&lt;-gsub(&#39;.*:&#39;,&#39;&#39;,best_counts$label)
+best_counts$label&lt;-gsub(&#39;:.*&#39;,&#39;&#39;,best_counts$label)
+best_counts$label &lt;- factor(best_counts$label, levels = model_order)
+
+# Remove zero counts to declutter the plot
+best_counts &lt;- best_counts[best_counts$count &gt; 0, ]
+
+# Create the plot
+ggplot(best_counts[best_counts$Target != &#39;EUR&#39;,], aes(x = label, y = count, fill = Model)) +
+  geom_bar(stat = &quot;identity&quot;, position = &quot;dodge&quot;) +
+  facet_wrap(~ Target, scales = &#39;free_x&#39;) +
+  theme_half_open() +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+  panel_border() + 
+  labs(
+    title = &quot;Number of times each method is the best&quot;,
+    x = &quot;Method&quot;,
+    y = &quot;Count&quot;,
+    fill = &quot;GWAS Group&quot;
+  ) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+#############################
+# Identify best methods that improved prediction over next best method by 2% for any trait
+# Filter out &#39;All&#39; from the data
+tmp &lt;- res_eval_simp[res_eval_simp$label != &#39;All&#39;,]
+
+# Identify the best method for each trait, but only if it improves by &gt;2%
+best_groups &lt;- do.call(rbind, by(tmp, list(tmp$Target, tmp$gwas_group, tmp$Trait), function(subset) {
+  if (nrow(subset) &gt; 1) {
+    # Sort by R in descending order
+    subset &lt;- subset[order(-subset$R), ]
+    # Check if the best method is more than 2% better than the second best
+    if ((subset$R[1] - subset$R[2]) / subset$R[2] &gt; 0.02) {
+      return(subset[1, ])  # Return the best method if criteria met
+    } 
+  } else {
+    return(subset[1, ])  # Handle cases with only one method
+  }
+  return(NULL)  # Return NULL if criteria not met
+}))
+
+# Create a count table with label and model combined
+best_counts &lt;- as.data.frame(table(paste0(best_groups$label,&#39;:&#39;, best_groups$Model), 
+                                   best_groups$gwas_group, best_groups$Target))
+
+# Rename columns
+colnames(best_counts) &lt;- c(&quot;label&quot;, &quot;gwas_group&quot;, &quot;Target&quot;, &quot;count&quot;)
+best_counts$Model &lt;- gsub(&#39;.*:&#39;, &#39;&#39;, best_counts$label)
+best_counts$label &lt;- gsub(&#39;:.*&#39;, &#39;&#39;, best_counts$label)
+best_counts$label &lt;- factor(best_counts$label, levels = model_order)
+
+# Remove zero counts to declutter the plot
+best_counts &lt;- best_counts[best_counts$count &gt; 0, ]
+
+# Create the plot
+library(ggplot2)
+ggplot(best_counts[best_counts$Target != &#39;EUR&#39;,], aes(x = label, y = count, fill = Model)) +
+  geom_bar(stat = &quot;identity&quot;, position = &quot;dodge&quot;) +
+  facet_wrap(~ Target, scales = &#39;free_x&#39;) +
+  theme_minimal() +
+  labs(
+    title = &quot;Number of times each method is the best (with &gt;2% improvement)&quot;,
+    x = &quot;Method&quot;,
+    y = &quot;Count&quot;,
+    fill = &quot;Model&quot;
+  ) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+
+#############################
+
+
+# Plot results for each phenotype separately
+dir.create(&#39;~/oliverpainfel/Analyses/crosspop/plots&#39;)
+
+for(pheno_i in selected_traits){
+  tmp &lt;- res_eval_simp[res_eval_simp$Trait == pheno_i,]
+  #tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+  tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+  tmp$Discovery_clean &lt;- paste0(tmp$Discovery_clean, &#39; GWAS&#39;)
+  tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+
+  png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots/&#39;, pheno_i,&#39;.png&#39;), res=300, width = 3400, height = 2000, units = &#39;px&#39;)
+  plot_tmp&lt;-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=NULL, fill = NULL, title = info_all$`Trait Description`[info_all$`Trait Label` == pheno_i]) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+  print(plot_tmp)
+  dev.off()
+}
+
+# Compare performance of each method in each target population across values of GWAS sample size
+# Because we cannot hold all other things constant, this plot is confusing. I think relative performance might be easier to read.
+for(targ_pop_i in targ_pop){
+    tmp &lt;- res_eval_simp[res_eval_simp$Target == targ_pop_i,]
+    for(disc_pop_i in unique(tmp$Discovery)){
+      tmp2 &lt;- tmp[tmp$Discovery == disc_pop_i,]
+      
+      ggplot(tmp2, aes(x=label, y=R , fill = Model)) +
+        geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                      width = 0,
+                      position = position_dodge(width = 1)) +
+        geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+        geom_vline(xintercept = seq(1.5, length(unique(tmp2$Trait))), linetype=&quot;dotted&quot;) +
+        labs(y = &quot;R (SE)&quot;, x=NULL, fill = NULL, title = info_all$`Trait Description`[info_all$`Trait Label` == pheno_i]) +
+        facet_grid(. ~ Trait, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+        theme_half_open() +
+        background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+        panel_border() + 
+        theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+      
+    }
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval &lt;- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == &#39;EAS&#39;){
+    disc_pop &lt;- &#39;EAS&#39;
+  }
+  if(targ_pop_i == &#39;AFR&#39;){
+    disc_pop &lt;- &#39;AFR&#39;
+  }
+  if(targ_pop_i == &#39;EUR&#39;){
+    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_eval for each scenario
+    res_eval_i &lt;- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x &lt;- res_eval[[i]]
+      x$pheno &lt;- names(res_eval)[i]
+      x &lt;- x[x$Target == targ_pop_i]
+      x &lt;- x[x$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+    }))
+    
+    # Average res_evalults for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_eval_i$Sample&lt;-&#39;A&#39;
+  
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i &lt;- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno &lt;-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+      
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          &#39;res_evalults missing for &#39;,
+          targ_pop_i,
+          &#39; &#39;,
+          group_i,
+          &#39; &#39;,
+          paste0(missing_pheno, collapse = &#39; &#39;)
+        ))
+      }
+      
+      cors_i &lt;- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i &lt;-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = &quot;BHHR&quot;,
+          mod = NULL,
+          data = res_eval_group_i
+        )
+      
+      tmp &lt;- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval &lt;- rbind(meta_res_eval, tmp)
+    }
+  }
+}
+
+meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+meta_res_eval$Discovery&lt;-factor(meta_res_eval$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+write.csv(meta_res_eval, &#39;~/oliverpainfel/Analyses/crosspop/r_eval.csv&#39;, row.names = F)
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r.png&#39;), res=300, width = 3200, height = 2000, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+# Plot average performance across phenotypes for EUR using AFR or EAS GWAS
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target == &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean &lt;- paste0(tmp$Discovery_clean, &#39; GWAS&#39;)
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.png&#39;), res=300, width = 4000, height = 1500, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+# Plot performance of -multi models trained using LEOPARD vs using indiv-level data
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;)
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods)] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods)], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = unique(tmp$label[order(!(grepl(&#39;Multi&#39;, tmp$label)), tmp$label)]))
+tmp&lt;-tmp[grepl(&#39;multi&#39;, tmp$label),]
+tmp &lt;- tmp[tmp$Model != &#39;Multi-IndivTrain&#39;,]
+tmp$Model&lt;-as.character(tmp$Model)
+tmp$Model[tmp$Model != &#39;SumStatTrain&#39;]&lt;-&#39;IndivTrain&#39;
+tmp$Model[tmp$Model == &#39;SumStatTrain&#39;]&lt;-&#39;LEOPARD&#39;
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r_leopard.png&#39;), res=300, width = 1500, height = 2000, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Create a function to mirror pred_comp results
+mirror_comp&lt;-function(x){
+  x_sym &lt;- x
+  x_sym$Model_1 &lt;- x$Model_2
+  x_sym$Model_2 &lt;- x$Model_1
+  x_sym$Model_1_R &lt;- x$Model_2_R
+  x_sym$Model_2_R &lt;- x$Model_1_R
+  x_sym$R_diff &lt;- -x_sym$R_diff
+  x_mirrored &lt;- rbind(x, x_sym)
+  x_diag&lt;-data.frame(
+      Model_1=unique(x_mirrored$Model_1),
+      Model_2=unique(x_mirrored$Model_1),
+      Model_1_R=x_mirrored$Model_1_R,
+      Model_2_R=x_mirrored$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp&lt;-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
+# Read in results
+targ_pop=c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)
+res_comp &lt;- list()
+for(pheno_i in selected_traits){
+  res_comp_i&lt;-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;EAS&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;AFR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+    }
+    for(disc_pop_i in disc_pop){
+      comp_i &lt;-
+        fread(
+          paste0(
+            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/&#39;,
+            &#39;targ_&#39;,
+            targ_pop_i,
+            &#39;.disc_EUR_&#39;,
+            disc_pop_i,
+            &#39;/&#39;,
+            pheno_i,
+            &#39;/res.pred_comp.txt&#39;
+          )
+        )
+      comp_i&lt;-mirror_comp(comp_i)
+      comp_i$Target&lt;-targ_pop_i
+      comp_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
+      res_comp_i&lt;-rbind(res_comp_i, comp_i)
+    }
+  }
+  
+  res_comp[[pheno_i]]&lt;-res_comp_i
+}
+
+res_comp_all &lt;- do.call(rbind, lapply(names(res_comp), function(name) {
+  x &lt;- res_comp[[name]]
+  x$pheno &lt;- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Annotate tests to get order correct
+res_comp_all$Method1&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_1)
+res_comp_all$Method1&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method1)
+res_comp_all$Method2&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_2)
+res_comp_all$Method2&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method2)
+
+find_model&lt;-function(x){
+  mod &lt;- x
+  mod[grepl(&#39;top1$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;IndivTrain&#39;
+  mod[grepl(&#39;top1$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;SumStatTrain&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-IndivTrain&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-SumStatTrain&#39;
+  mod[grepl(&#39;_multi&#39;, x)] &lt;- &#39;SumStatTrain&#39;
+  mod[x == &#39;prscsx.pseudo.multi&#39;] &lt;- &#39;SumStatTrain&#39;
+  mod[x == &#39;xwing.pseudo.multi&#39;] &lt;- &#39;SumStatTrain&#39;
+  
+  return(mod)
+}
+
+res_comp_all$Model1&lt;-find_model(res_comp_all$Model_1)
+res_comp_all$Model2&lt;-find_model(res_comp_all$Model_2)
+
+res_comp_all$Source1&lt;-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method1) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_1), &#39;Multi&#39;, &#39;Single&#39;)
+res_comp_all$Source2&lt;-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method2) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_2), &#39;Multi&#39;, &#39;Single&#39;)
+  
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)){
+  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] &lt;- i
+  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] &lt;- i
+}
+res_comp_all$Discovery1[res_comp_all$Source1 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source1 == &#39;Multi&#39;]
+res_comp_all$Discovery2[res_comp_all$Source2 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source2 == &#39;Multi&#39;]
+
+res_comp_all$Method1&lt;-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
+res_comp_all$Method2&lt;-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
+res_comp_all$Model1&lt;-factor(res_comp_all$Model1, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+res_comp_all$Model2&lt;-factor(res_comp_all$Model2, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+res_comp_all$Discovery1&lt;-factor(res_comp_all$Discovery1, levels=rev(c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;)))
+res_comp_all$Discovery2&lt;-factor(res_comp_all$Discovery2, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+# Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method1 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+  res_comp_all$Model1 %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method2 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+  res_comp_all$Model2 %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+
+# Remove pseudo model for methods that don&#39;t really have one 
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method1 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+  res_comp_all$Model1 %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method2 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+  res_comp_all$Model2 %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+
+# Remove top1 models for PRS-CSx
+res_comp_all &lt;- res_comp_all[
+!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method1) &amp; 
+  grepl(&#39;top1&#39;, res_comp_all$Model_1)),]
+res_comp_all &lt;- res_comp_all[
+!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method2) &amp; 
+  grepl(&#39;top1&#39;, res_comp_all$Model_2)),]
+
+# Remove any comparisons
+res_comp_all &lt;- res_comp_all[!duplicated(res_comp_all[, c(&quot;Target&quot;, &quot;Method1&quot;, &quot;Model1&quot;, &quot;Source1&quot;, &quot;Discovery1&quot;, &quot;Method2&quot;, &quot;Model2&quot;, &quot;Source2&quot;, &quot;Discovery2&quot;,&#39;pheno&#39;)]),]
+
+res_comp_all$r_diff_rel &lt;- res_comp_all$R_diff / res_comp_all$Model_2_R
+
+# Calculate relative improvement for ldpred2-multi vs ldpred2 as example
+tmp_ldpred2 &lt;- res_comp_all[res_comp_all$Model_1 == &#39;ldpred2.multi&#39; &amp; 
+                    grepl(&#39;ldpred2-&#39;, res_comp_all$Model_2) &amp;
+                    res_comp_all$Target == &#39;AFR&#39;,]
+tmp_ldpred2 &lt;- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+tmp_ldpred2 &lt;- tmp_ldpred2[!duplicated(tmp_ldpred2$pheno),]
+round(min(tmp_ldpred2$r_diff_rel)*100, 1)
+round(max(tmp_ldpred2$r_diff_rel)*100, 1)
+
+tmp_ldpred2 &lt;- res_comp_all[res_comp_all$Model_1 == &#39;ldpred2.multi&#39; &amp; 
+                    grepl(&#39;ldpred2-&#39;, res_comp_all$Model_2) &amp;
+                    res_comp_all$Target == &#39;EAS&#39;,]
+tmp_ldpred2 &lt;- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+tmp_ldpred2 &lt;- tmp_ldpred2[!duplicated(tmp_ldpred2$pheno),]
+round(min(tmp_ldpred2$r_diff_rel)*100, 1)
+round(max(tmp_ldpred2$r_diff_rel)*100, 1)
+
+# Calculate relative improvement for sbayesrc-multi vs sbayesrc in EUR target as example
+tmp_sbayesrc &lt;- res_comp_all[res_comp_all$Model_1 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                    grepl(&#39;sbayesrc.pseudo-&#39;, res_comp_all$Model_2) &amp;
+                    res_comp_all$Target == &#39;EUR&#39; &amp;
+                    res_comp_all$Discovery1 == &#39;EUR+EAS&#39; &amp;
+                    res_comp_all$Discovery2 == &#39;EUR&#39;,]
+tmp_sbayesrc &lt;- tmp_sbayesrc[order(-tmp_sbayesrc$Model_2_R),]
+round(min(tmp_sbayesrc$r_diff_rel)*100, 1)
+round(max(tmp_sbayesrc$r_diff_rel)*100, 1)
+
+tmp_sbayesrc &lt;- res_comp_all[res_comp_all$Model_1 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                    grepl(&#39;sbayesrc.pseudo-&#39;, res_comp_all$Model_2) &amp;
+                    res_comp_all$Target == &#39;EUR&#39; &amp;
+                    res_comp_all$Discovery1 == &#39;EUR+AFR&#39; &amp;
+                    res_comp_all$Discovery2 == &#39;EUR&#39;,]
+tmp_sbayesrc &lt;- tmp_sbayesrc[order(-tmp_sbayesrc$Model_2_R),]
+round(min(tmp_sbayesrc$r_diff_rel)*100, 1)
+round(max(tmp_sbayesrc$r_diff_rel)*100, 1)
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTrain or SumStatTrain result
+tmp &lt;- res_comp_all
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label1&#39;
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method2&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label2&#39;
+
+tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;] &lt;- paste0(tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;] &lt;- paste0(tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;], &#39;-multi&#39;)
+
+tmp$Model1[tmp$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp$Model1[tmp$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp$Model2[tmp$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp$Model2[tmp$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+
+tmp&lt;-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp&lt;-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` &lt;- paste0(tmp$label1, &#39; - &#39;, tmp$Model1, &#39; - &#39;, tmp$Discovery1)
+tmp$`Model 2` &lt;- paste0(tmp$label2, &#39; - &#39;, tmp$Model2, &#39; - &#39;, tmp$Discovery2)
+
+tmp &lt;- tmp[, c(&#39;Target&#39;, &#39;pheno&#39;, &#39;Model 1&#39;, &#39;Model 2&#39;, &#39;Model_1_R&#39;, &#39;Model_2_R&#39;, &#39;R_diff&#39;, &#39;R_diff_pval&#39;), with=F]
+names(tmp) &lt;- c(&#39;Target&#39;, &#39;Trait&#39;,&#39;Model 1&#39;, &#39;Model 2&#39;, &quot;R (Model 1)&quot;, &quot;R (Model 2)&quot;, &quot;R difference (Model 1 R - Model 2 R)&quot;, &quot;R difference p-value&quot;)
+
+tmp&lt;-tmp[order(tmp$Target, tmp$Trait, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` &lt;- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` &lt;- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` &lt;- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, &#39;~/oliverpainfel/Analyses/crosspop/r_diff.csv&#39;, row.names=F)
+
+###########
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_comp &lt;- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == &#39;EAS&#39;){
+    disc_pop &lt;- &#39;EAS&#39;
+  }
+  if(targ_pop_i == &#39;AFR&#39;){
+    disc_pop &lt;- &#39;AFR&#39;
+  }
+  if(targ_pop_i == &#39;EUR&#39;){
+    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_comp for each scenario
+    res_comp_i &lt;- res_comp_all[res_comp_all$Target == targ_pop_i &amp; res_comp_all$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+  
+    # Calculate diff SE based on p-value
+    res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] &lt;- 1-0.001
+    res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]&lt;-1-0.001
+    res_comp_i$R_diff_z&lt;-qnorm(res_comp_i$R_diff_pval/2)
+    res_comp_i$R_diff_SE&lt;-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
+        
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_comp_i$Sample&lt;-&#39;A&#39;
+    res_comp_i$Group &lt;- paste0(res_comp_i$Model_1, &#39;_vs_&#39;, res_comp_i$Model_2)
+  
+    for(group_i in unique(res_comp_i$Group)){
+      res_comp_group_i &lt;- res_comp_i[res_comp_i$Group == group_i,]
+      cors_i &lt;- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
+      
+      if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
+        
+        meta_res_comp_i &lt;-
+          agg(
+            id = Sample,
+            es = R_diff,
+            var = R_diff_SE ^ 2,
+            cor = cors_i,
+            method = &quot;BHHR&quot;,
+            mod = NULL,
+            data = res_comp_group_i
+          )
+        
+        tmp &lt;- res_comp_group_i[1,]
+        tmp$pheno &lt;- NULL
+        tmp$Model_1_R &lt;-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &amp;
+                            meta_res_eval$Target == targ_pop_i &amp;
+                            meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+        tmp$Model_2_R &lt;-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &amp;
+                            meta_res_eval$Target == targ_pop_i &amp;
+                            meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+        tmp$R_diff &lt;- meta_res_comp_i$es
+        tmp$R_diff_SE &lt;- sqrt(meta_res_comp_i$var)
+        tmp$R_diff_z &lt;- tmp$R_diff / tmp$R_diff_SE
+        tmp$R_diff_p &lt;- 2*pnorm(-abs(tmp$R_diff_z))
+      } else {
+        tmp &lt;- res_comp_group_i[1,]
+        tmp$pheno &lt;- NULL
+        tmp$R_diff &lt;- NA
+        tmp$R_diff_SE &lt;- NA
+        tmp$R_diff_z &lt;- NA
+        tmp$R_diff_p &lt;- NA
+      }
+      meta_res_comp &lt;- rbind(meta_res_comp, tmp)
+    }
+  }
+}
+
+meta_res_comp$R_diff_perc &lt;- meta_res_comp$R_diff / meta_res_comp$Model_2_R
+  
+# Extract average improvement for ldpred2-multi vs ldpred2 as example
+tmp_ldpred2 &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;ldpred2.multi&#39; &amp; 
+                    grepl(&#39;ldpred2-&#39;, meta_res_comp$Model_2) &amp;
+                    meta_res_comp$Target == &#39;AFR&#39;,]
+tmp_ldpred2 &lt;- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+round(min(tmp_ldpred2$R_diff_perc)*100, 1)
+
+tmp_ldpred2 &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;ldpred2.multi&#39; &amp; 
+                    grepl(&#39;ldpred2-&#39;, meta_res_comp$Model_2) &amp;
+                    meta_res_comp$Target == &#39;EAS&#39;,]
+tmp_ldpred2 &lt;- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+round(min(tmp_ldpred2$R_diff_perc)*100, 1)
+
+# Extract average improvement for sbayesrc-multi vs sbayesrc in EUR as example
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                    grepl(&#39;sbayesrc.pseudo&#39;, meta_res_comp$Model_2) &amp;
+                    meta_res_comp$Target == &#39;EUR&#39; &amp;
+                    meta_res_comp$Discovery1 == &#39;EUR+AFR&#39; &amp;
+                    meta_res_comp$Discovery2 == &#39;EUR&#39;,]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                    grepl(&#39;sbayesrc.pseudo&#39;, meta_res_comp$Model_2) &amp;
+                    meta_res_comp$Target == &#39;EUR&#39; &amp;
+                    meta_res_comp$Discovery1 == &#39;EUR+EAS&#39; &amp;
+                    meta_res_comp$Discovery2 == &#39;EUR&#39;,]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+
+# Extract average improvement for sbayesrc in EUR compared to all model
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_2 == &#39;sbayesrc.pseudo-EUR.top1&#39; &amp;
+                    meta_res_comp$Model_1 == &#39;all-EUR.top1&#39; &amp;
+                    meta_res_comp$Target == &#39;AFR&#39;,]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+tmp_sbayesrc$R_diff_p
+
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_2 == &#39;sbayesrc.pseudo-EUR.top1&#39; &amp;
+                    meta_res_comp$Model_1 == &#39;all-EUR.top1&#39; &amp;
+                    meta_res_comp$Target == &#39;EAS&#39;,]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+tmp_sbayesrc$R_diff_p
+
+
+# Compare QuickPRS-Multi vs QuickPRS to evaluate LEOPARD performance
+tmp_quickprs &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;quickprs_multi.pseudo.multi&#39; &amp; 
+                                meta_res_comp$Model_2 == &#39;quickprs.pseudo.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;AFR&#39;,]
+round(min(tmp_quickprs$R_diff_perc)*100, 1)
+
+tmp_quickprs &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;quickprs_multi.pseudo.multi&#39; &amp; 
+                                meta_res_comp$Model_2 == &#39;quickprs.pseudo.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;EAS&#39;,]
+round(min(tmp_quickprs$R_diff_perc)*100, 1)
+
+# Compare all.multi method to next best method
+tmp_all &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;all.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;AFR&#39; &amp;
+                    meta_res_comp$Source2 == &#39;Multi&#39;,]
+tmp_all &lt;- tmp_all[order(tmp_all$R_diff),]
+tmp_all &lt;- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+tmp_all &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;all.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;EAS&#39; &amp;
+                    meta_res_comp$Source2 == &#39;Multi&#39;,]
+tmp_all &lt;- tmp_all[order(tmp_all$R_diff),]
+tmp_all &lt;- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+# Compare all.multi method to next best method
+tmp_all &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;all-AFR.top1&#39; &amp;
+                    meta_res_comp$Target == &#39;AFR&#39; &amp;
+                    meta_res_comp$Discovery1 == &#39;AFR&#39; &amp;
+                    meta_res_comp$Discovery2 == &#39;AFR&#39;,]
+tmp_all &lt;- tmp_all[order(tmp_all$R_diff),]
+tmp_all &lt;- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+tmp_all &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;all-EAS.top1&#39; &amp;
+                    meta_res_comp$Target == &#39;EAS&#39; &amp;
+                    meta_res_comp$Discovery1 == &#39;EAS&#39; &amp;
+                    meta_res_comp$Discovery2 == &#39;EAS&#39;,]
+tmp_all &lt;- tmp_all[order(tmp_all$R_diff),]
+tmp_all &lt;- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTrain or SumStatTrain result
+tmp &lt;- meta_res_comp
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label1&#39;
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method2&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label2&#39;
+
+tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;] &lt;- paste0(tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;] &lt;- paste0(tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;], &#39;-multi&#39;)
+
+tmp$Model1[tmp$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp$Model1[tmp$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp$Model2[tmp$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp$Model2[tmp$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+
+tmp&lt;-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp&lt;-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` &lt;- paste0(tmp$label1, &#39; - &#39;, tmp$Model1, &#39; - &#39;, tmp$Discovery1)
+tmp$`Model 2` &lt;- paste0(tmp$label2, &#39; - &#39;, tmp$Model2, &#39; - &#39;, tmp$Discovery2)
+
+tmp$`Percentage change (R difference / Model 2 R)` &lt;- paste0(round(tmp$R_diff_perc * 100, 1), &#39;%&#39;)
+
+tmp &lt;- tmp[, c(&#39;Target&#39;, &#39;Model 1&#39;, &#39;Model 2&#39;, &#39;Model_1_R&#39;, &#39;Model_2_R&#39;, &#39;R_diff&#39;,&quot;Percentage change (R difference / Model 2 R)&quot;, &#39;R_diff_p&#39;), with=F]
+names(tmp) &lt;- c(&#39;Target&#39;,&#39;Model 1&#39;, &#39;Model 2&#39;, &quot;R (Model 1)&quot;, &quot;R (Model 2)&quot;, &quot;R difference (Model 1 R - Model 2 R)&quot;, &quot;Percentage change (R difference / Model 2 R)&quot;, &quot;R difference p-value&quot;)
+
+tmp&lt;-tmp[order(tmp$Target, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` &lt;- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` &lt;- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` &lt;- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, &#39;~/oliverpainfel/Analyses/crosspop/r_diff_average.csv&#39;, row.names=F)
+
+############
+
+# Group differences
+meta_res_comp$R_diff_catagory &lt;- cut(
+    meta_res_comp$R_diff,
+    breaks = c(-Inf, -0.08, -0.025, -0.002, 0.002, 0.025, 0.08, Inf),
+    labels = c(&#39;&lt; -0.08&#39;, &#39;-0.08 - -0.025&#39;, &#39;-0.025 - -0.002&#39;, &#39;-0.002 - 0.002&#39;, &#39;0.002 - 0.025&#39;, &#39;0.025 - 0.08&#39;, &#39;&gt; 0.08&#39;),
+    right = FALSE
+)
+meta_res_comp$R_diff_catagory &lt;- factor(meta_res_comp$R_diff_catagory, levels = rev(levels(meta_res_comp$R_diff_catagory)))
+
+# Assign significance stars
+meta_res_comp$indep_star&lt;-&#39; &#39;
+meta_res_comp$indep_star[meta_res_comp$R_diff_p &lt; 0.05]&lt;-&#39;*&#39;
+meta_res_comp$indep_star[meta_res_comp$R_diff_p &lt; 1e-3]&lt;-&#39;**&#39;
+# meta_res_comp$indep_star[meta_res_comp$R_diff_p &lt; 1e-6]&lt;-&#39;***&#39;
+
+meta_res_comp&lt;-meta_res_comp[order(meta_res_comp$Discovery1, meta_res_comp$Discovery2, meta_res_comp$Method1),]
+
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == &#39;EAS&#39;){
+    disc_pop &lt;- &#39;EAS&#39;
+  }
+  if(targ_pop_i == &#39;AFR&#39;){
+    disc_pop &lt;- &#39;AFR&#39;
+  }
+  if(targ_pop_i == &#39;EUR&#39;){
+    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+  }
+  for(disc_pop_i in disc_pop){
+
+    tmp &lt;- meta_res_comp[meta_res_comp$Target == targ_pop_i, ]
+
+    tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+    tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+    names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label1&#39;
+    tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method2&#39;, by.y = &#39;method&#39;, all.x = T)
+    tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+    names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label2&#39;
+    
+    tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;] &lt;- paste0(tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;], &#39;-multi&#39;)
+    tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;] &lt;- paste0(tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;], &#39;-multi&#39;)
+    
+    tmp$Model1[tmp$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+    tmp$Model1[tmp$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+    tmp$Model2[tmp$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+    tmp$Model2[tmp$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+    
+    tmp&lt;-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+    tmp&lt;-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+    tmp$label1 &lt;- factor(tmp$label1, levels = model_order)
+    tmp$label2 &lt;- factor(tmp$label2, levels = model_order)
+
+    tmp&lt;-tmp[order(tmp$label1, tmp$label2),]
+    
+    tmp$label1 &lt;- paste0(tmp$label1,&quot; (&quot;, ifelse(tmp$Model1 == &#39;SumStatTrain&#39;, &#39;ST&#39;, &#39;IT&#39;), &quot;)&quot;)
+    tmp$label2 &lt;- paste0(tmp$label2,&quot; (&quot;, ifelse(tmp$Model2 == &#39;SumStatTrain&#39;, &#39;ST&#39;, &#39;IT&#39;), &quot;)&quot;)
+
+    tmp$label1 &lt;- factor(tmp$label1, levels = unique(tmp$label1))
+    tmp$label2 &lt;- factor(tmp$label2, levels = unique(tmp$label2))
+    
+    tmp &lt;- tmp[tmp$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i), ]
+    
+    plot_tmp &lt;- ggplot(data = tmp, aes(label2, label1, fill = R_diff_catagory)) +
+      geom_tile(color = &quot;white&quot;, show.legend = TRUE) +
+      labs(y = &#39;Test&#39;, x = &#39;Comparison&#39;, fill = &#39;R difference&#39;, title = paste0(&#39;Target: &#39;, targ_pop_i)) +
+      facet_grid(Discovery1 ~ Discovery2, scales = &#39;free&#39;, space = &#39;free&#39;, switch=&quot;both&quot;) +
+      geom_text(
+        data = tmp,
+        aes(label2, label1, label = indep_star),
+        color = &quot;black&quot;,
+        size = 4,
+        angle = 0,
+        vjust = 0.8
+      ) +
+      scale_fill_brewer(
+        breaks = levels(tmp$R_diff_catagory),
+        palette = &quot;RdBu&quot;,
+        drop = F,
+        na.value = &#39;grey&#39;
+      ) +
+      theme_half_open() +
+      background_grid() +
+      panel_border() +
+      theme(axis.text.x = element_text(
+        angle = 45,
+        vjust = 1,
+        hjust = 1
+      ))
+    
+    png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r_diff.Discovery_EUR_&#39;, disc_pop_i,&#39;.Target_&#39;, targ_pop_i, &#39;.png&#39;), res=300, width = 4400, height = 3200, units = &#39;px&#39;)
+      print(plot_tmp)
+    dev.off()
+  }
+}
+
+####
+# Plot relative improvement of methods
+####
+# Use ptclump IndivTrain using EUR GWAS as the reference, as provides an interpretable scale
+
+meta_res_comp_ptclump_top1&lt;-meta_res_comp[meta_res_comp$Method2 == &#39;all&#39; &amp; meta_res_comp$Source2 == &#39;Multi&#39;,]
+meta_res_comp_ptclump_top1$reference_point&lt;-F
+meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Method1 == &#39;all&#39; &amp; meta_res_comp_ptclump_top1$Source1 == &#39;Multi&#39;]&lt;-T
+meta_res_comp_ptclump_top1$R_diff[is.na(meta_res_comp_ptclump_top1$R_diff)]&lt;-0
+meta_res_comp_ptclump_top1$Discovery1 &lt;- factor(meta_res_comp_ptclump_top1$Discovery1, levels=rev(levels(meta_res_comp_ptclump_top1$Discovery1)))
+
+res_comp_all_ptclump_top1&lt;-res_comp_all[res_comp_all$Method2 == &#39;all&#39; &amp; res_comp_all$Source2 == &#39;Multi&#39;,]
+res_comp_all_ptclump_top1$Discovery1 &lt;-  factor(res_comp_all_ptclump_top1$Discovery1, levels=levels(meta_res_comp_ptclump_top1$Discovery1))
+
+# Create data to plot reference points
+meta_res_comp_reference &lt;- meta_res_comp_ptclump_top1
+meta_res_comp_reference$R_diff[meta_res_comp_ptclump_top1$reference_point == F] &lt;- NA
+meta_res_comp_reference$R_diff_SE [meta_res_comp_ptclump_top1$reference_point == F] &lt;- NA
+res_comp_all_ptclump_top1$reference_point&lt;-F
+
+meta_tmp &lt;- meta_res_comp_ptclump_top1
+meta_tmp &lt;- merge(meta_tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+meta_tmp$label[is.na(meta_tmp$label)] &lt;- &#39;All&#39;
+meta_tmp$label[grepl(&#39;Multi&#39;, meta_tmp$Model1) &amp; !(meta_tmp$Method1 %in% pgs_group_methods) &amp; meta_tmp$label != &#39;All&#39;] &lt;- paste0(meta_tmp$label[grepl(&#39;Multi&#39;, meta_tmp$Model1) &amp; !(meta_tmp$Method1 %in% pgs_group_methods) &amp; meta_tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+meta_tmp$label &lt;- factor(meta_tmp$label, levels = model_order)
+meta_tmp$Discovery_clean &lt;- as.character(meta_tmp$Discovery1)
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != &#39;EUR&#39; &amp; meta_tmp$Source1 == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != &#39;EUR&#39; &amp; meta_tmp$Source1 == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+meta_tmp$Discovery_clean &lt;- factor(meta_tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+meta_tmp$Target &lt;- paste0(meta_tmp$Target, &#39; Target&#39;)
+meta_tmp$Model1 &lt;- factor(meta_tmp$Model1, levels = names(model_palette))
+
+meta_tmp_ref &lt;- meta_res_comp_reference
+meta_tmp_ref &lt;- merge(meta_tmp_ref, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+meta_tmp_ref$label[is.na(meta_tmp_ref$label)] &lt;- &#39;All&#39;
+meta_tmp_ref$label[grepl(&#39;Multi&#39;, meta_tmp_ref$Model1) &amp; !(meta_tmp_ref$Method1 %in% pgs_group_methods) &amp; meta_tmp_ref$label != &#39;All&#39;] &lt;- paste0(meta_tmp_ref$label[grepl(&#39;Multi&#39;, meta_tmp_ref$Model1) &amp; !(meta_tmp_ref$Method1 %in% pgs_group_methods) &amp; meta_tmp_ref$label != &#39;All&#39;], &#39;-multi&#39;)
+meta_tmp_ref$label &lt;- factor(meta_tmp_ref$label, levels = model_order)
+meta_tmp_ref$Discovery_clean &lt;- as.character(meta_tmp_ref$Discovery1)
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != &#39;EUR&#39; &amp; meta_tmp_ref$Source1 == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != &#39;EUR&#39; &amp; meta_tmp_ref$Source1 == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+meta_tmp_ref$Discovery_clean &lt;- factor(meta_tmp_ref$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+meta_tmp_ref$Target &lt;- paste0(meta_tmp_ref$Target, &#39; Target&#39;)
+meta_tmp_ref$Model1 &lt;- factor(meta_tmp_ref$Model1, levels = names(model_palette))
+
+tmp &lt;- res_comp_all_ptclump_top1
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery1)
+tmp$Discovery_clean[tmp$Discovery1 == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery1 != &#39;EUR&#39; &amp; tmp$Source1 == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery1 != &#39;EUR&#39; &amp; tmp$Source1 == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model1 &lt;- factor(tmp$Model1, levels = names(model_palette))
+
+ggplot(meta_tmp, aes(x=label, y=R_diff , fill = Model1)) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff - R_diff_SE,
+          ymax = R_diff + R_diff_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff, fill = Model1),
+        stat = &quot;identity&quot;,
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R_diff (SE)&quot;) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+
+# Plot as % change
+meta_tmp$R_diff_perc &lt;- meta_tmp$R_diff / meta_tmp$Model_2_R
+meta_tmp_ref$R_diff_perc &lt;- meta_tmp_ref$R_diff / meta_tmp_ref$Model_2_R
+tmp$R_diff_perc &lt;- tmp$R_diff / tmp$Model_2_R
+
+meta_tmp$R_diff_perc_SE &lt;- meta_tmp$R_diff_SE / meta_tmp$Model_2_R
+
+library(scales)
+ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = &quot;identity&quot;,
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R diff. (SE)&quot;) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+# Simplify results showing results only with or without training data
+meta_tmp_simple &lt;- meta_tmp
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+meta_tmp_simple&lt;-meta_tmp_simple[meta_tmp_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_simple&lt;-meta_tmp_simple[meta_tmp_simple$Model_2 %in% res_eval_simp$Group,]
+
+meta_tmp_ref_simple &lt;- meta_tmp_ref
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+meta_tmp_ref_simple&lt;-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_ref_simple&lt;-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_2 %in% res_eval_simp$Group,]
+
+tmp_simple &lt;- tmp
+tmp_simple$Model1[tmp_simple$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp_simple$Model1[tmp_simple$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp_simple$Model2[tmp_simple$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp_simple$Model2[tmp_simple$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp_simple&lt;-tmp_simple[tmp_simple$Model_1 %in% res_eval_simp$Group,]
+tmp_simple&lt;-tmp_simple[tmp_simple$Model_2 %in% res_eval_simp$Group,]
+
+# Export plot for manuscript
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r.perc_improv.png&#39;, width = 3200, height = 2000, res= 300, units = &#39;px&#39;)
+ggplot(meta_tmp_simple[meta_tmp_simple$Target != &#39;EUR Target&#39;,], aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp_simple[tmp_simple$Target != &#39;EUR Target&#39;,],
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target != &#39;EUR Target&#39;,],
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = &quot;identity&quot;,
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 4,
+        shape = 22,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype=&quot;dotted&quot;) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = &quot;Relative Improvement (SE)&quot;, fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(2, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;
+    )
+dev.off()
+
+# Plot for EUR
+meta_tmp_simple$Discovery_clean &lt;- paste0(meta_tmp_simple$Discovery1,&#39; GWAS&#39;)
+meta_tmp_ref_simple$Discovery_clean &lt;- paste0(meta_tmp_ref_simple$Discovery1,&#39; GWAS&#39;)
+tmp_simple$Discovery_clean &lt;- paste0(tmp_simple$Discovery1,&#39; GWAS&#39;)
+
+meta_tmp_simple&lt;-meta_tmp_simple[!duplicated(meta_tmp_simple[, c(&#39;label&#39;, &#39;Discovery_clean&#39;, &#39;Model1&#39;), with=F]),]
+meta_tmp_ref_simple&lt;-meta_tmp_ref_simple[!duplicated(meta_tmp_ref_simple[, c(&#39;label&#39;, &#39;Discovery_clean&#39;, &#39;Model1&#39;), with=F]),]
+tmp_simple&lt;-tmp_simple[!duplicated(tmp_simple[, c(&#39;label&#39;, &#39;Discovery_clean&#39;, &#39;Model1&#39;,&#39;pheno&#39;), with=F]),]
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.perc_improv.png&#39;, width = 4000, height = 1500, res= 300, units = &#39;px&#39;)
+ggplot(meta_tmp_simple[meta_tmp_simple$Target == &#39;EUR Target&#39;,], aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp_simple[tmp_simple$Target == &#39;EUR Target&#39;,],
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target == &#39;EUR Target&#39;,],
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = &quot;identity&quot;,
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 4,
+        shape = 22,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype=&quot;dotted&quot;) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = &quot;Relative Improvement (SE)&quot;, fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(2, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;
+    )
+dev.off()</code></pre>
+</details>
+<hr />
+</div>
+<div id="check-leopard-weights" class="section level3">
+<h3>Check LEOPARD weights</h3>
+<p>Here we will compare the LEOPARD estimated weights for population
+specific PGS, to the weights estimated using observed data in the UKB
+target sample.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml&#39;
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+###
+# Read in weights estimated by LEOPARD (QuickPRS)
+###
+
+leopard_weights&lt;-NULL
+scores_quickprs &lt;- scores$name[scores$method == &#39;quickprs_multi&#39;]
+for(i in selected_traits){
+  scores_i &lt;- scores_quickprs[grepl(paste0(&#39;^&#39;, i,&#39;_&#39;), scores_quickprs)]
+  for(j in scores_i){
+      weights_file &lt;- readRDS(paste0(outdir, &#39;/reference/pgs_score_files/leopard/&#39;, j, &#39;/ref-&#39;, j, &#39;.weights.rds&#39;))
+      weights_file &lt;- data.frame(weights_file)
+      
+      weights &lt;-
+        data.table(
+          Target = do.call(c, lapply(names(weights_file), function(x) rep(x, 2))),
+          Discovery = names(weights_file),
+          Weight = do.call(c, lapply(weights_file, function(x) x)),
+          Trait = i,
+          Method = &#39;LEOPARD&#39;
+        )
+      
+      leopard_weights &lt;- rbind(leopard_weights, weights)
+  }
+}
+
+#####
+# Read in the PGS weights estimated using UKB data
+#####
+# Read in the final model coefficients for multi-source methods
+
+obs_weights&lt;-NULL
+for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_methods)]){
+  scores_method&lt;-scores$name[scores$method == method_i]
+  method_i &lt;- gsub(&#39;_multi&#39;,&#39;&#39;, method_i)
+
+  for(i in selected_traits){
+    for(j in c(&#39;EAS&#39;,&#39;AFR&#39;,&#39;EUR&#39;)){
+      if(j == &#39;EUR&#39;){
+        pops &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+      } else {
+        pops &lt;- j
+      }
+      
+      for(k in pops){
+        model &lt;- fread(paste0(&#39;~/oliverpainfel/Analyses/crosspop/targ_&#39;, j, &#39;.disc_EUR_&#39;, k, &#39;/&#39;, i, &#39;/final_models/&#39;, method_i, &#39;.pseudo.multi.final_model.txt&#39;))
+        model&lt;-model[-1,]
+        
+        # Set weight to zero if negative, as this is what LEOPARD does
+        if(any(model$V2 &lt; 0)){
+          model$V2[model$V2 &lt; 0] &lt;- 0
+          model$V2[model$V2 &gt; 0] &lt;- 1
+        }
+        
+        names(model) &lt;- c(&#39;x&#39;, &#39;BETA&#39;)
+        model$Discovery[grepl(&#39;UKB&#39;, model$x)]&lt;-&#39;EUR&#39;
+        model$Discovery[grepl(&#39;BBJ&#39;, model$x)]&lt;-&#39;EAS&#39;
+        model$Discovery[grepl(&#39;UGR&#39;, model$x)]&lt;-&#39;AFR&#39;
+        model$Target &lt;- j
+        model$Weight &lt;- model$BETA/sum(model$BETA)
+        model$Trait &lt;- i
+        model$Method &lt;- method_i
+        model&lt;-model[,c(&#39;Target&#39;,&#39;Discovery&#39;,&#39;Weight&#39;,&#39;Method&#39;,&#39;Trait&#39;), with=F]
+        obs_weights&lt;-rbind(obs_weights, model)
+      }
+    }
+  }
+}
+
+###
+# Combine and compare
+###
+
+both &lt;- do.call(rbind, list(obs_weights, leopard_weights))
+both&lt;-merge(both, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x=T, sort = F)
+both$label[is.na(both$label)] &lt;- both$Method[is.na(both$label)]
+both$label &lt;- factor(both$label, levels=unique(both$label))
+
+# Plot non-EUR target first
+tmp &lt;- both[both$Target != &#39;EUR&#39;,]
+tmp &lt;- tmp[tmp$Discovery != &#39;EUR&#39;,]
+
+# Set LEOPARD to black fill
+default_colors &lt;- hue_pal()(9)
+names(default_colors) &lt;- levels(tmp$label)
+default_colors[&quot;LEOPARD&quot;] &lt;- &quot;black&quot;
+
+# Plot the estimated and observed weights
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/leopard_weights.png&#39;, units = &#39;px&#39;, res = 300, width = 2500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat=&quot;identity&quot;, colour = &#39;black&#39;, size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Target ~ .) +
+  theme_half_open() +
+  labs(title = &#39;Weight of target ancestry-matched PGS&#39;, fill = NULL) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
+
+# Plot EUR target
+tmp &lt;- both[both$Target == &#39;EUR&#39;,]
+tmp &lt;- tmp[tmp$Discovery != &#39;EUR&#39;,]
+
+# Set LEOPARD to black fill
+default_colors &lt;- hue_pal()(9)
+names(default_colors) &lt;- levels(tmp$label)
+default_colors[&quot;LEOPARD&quot;] &lt;- &quot;black&quot;
+
+# Plot the estimated and observed weights
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_eur.png&#39;, units = &#39;px&#39;, res = 300, width = 2500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat=&quot;identity&quot;, colour = &#39;black&#39;, size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Discovery ~ .) +
+  theme_half_open() +
+  labs(title = &#39;Weight of non-EUR PGS for EUR Target&#39;, fill = NULL) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
+
+# Calculate correlation between weights from each method
+# Start with non-EUR target
+tmp &lt;- both[both$Target != &#39;EUR&#39;,]
+tmp$Discovery&lt;-NULL
+tmp_wide &lt;- reshape(tmp, 
+                     idvar = c(&quot;Trait&quot;, &quot;Target&quot;), 
+                     timevar = &quot;label&quot;, 
+                     direction = &quot;wide&quot;)
+
+names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+
+cor_matrix_EAS &lt;- cor(tmp_wide[tmp_wide$Target == &#39;EAS&#39;, -1:-2])
+cor_matrix_AFR &lt;- cor(tmp_wide[tmp_wide$Target == &#39;AFR&#39;, -1:-2])
+
+# Convert correlation matrix to long format for ggplot
+cor_df_EAS &lt;- melt(cor_matrix_EAS)
+cor_df_AFR &lt;- melt(cor_matrix_AFR)
+cor_df_EAS$Target &lt;- &#39;Target = EAS&#39;
+cor_df_AFR$Target &lt;- &#39;Target = AFR&#39;
+cor_df &lt;- rbind(cor_df_AFR, cor_df_EAS)
+
+# Create ggplot correlation heatmap with text inside
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_corr.png&#39;, units = &#39;px&#39;, width = 3300, height = 1700, res = 300)
+ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
+  geom_tile(color = &quot;white&quot;) +  # Tile plot with white borders
+  geom_text(aes(label = round(value, 2)), color = &quot;black&quot;) +  # Add correlation values
+  scale_fill_gradient2(low = &quot;blue&quot;, mid = &quot;white&quot;, high = &quot;red&quot;, midpoint = 0) +  # Color scale
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1),
+    axis.title = element_blank()
+  ) +
+  facet_grid(. ~ Target) +
+  labs(fill = &quot;Correlation&quot;)
+dev.off()
+
+# Start with non-EUR target
+tmp &lt;- both[both$Target == &#39;EUR&#39;,]
+tmp$Target&lt;-NULL
+tmp_wide &lt;- reshape(tmp, 
+                     idvar = c(&quot;Trait&quot;, &quot;Discovery&quot;), 
+                     timevar = &quot;label&quot;, 
+                     direction = &quot;wide&quot;)
+
+names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+
+cor_matrix_EAS &lt;- cor(tmp_wide[tmp_wide$Discovery == &#39;EAS&#39;, -1:-2])
+cor_matrix_AFR &lt;- cor(tmp_wide[tmp_wide$Discovery == &#39;AFR&#39;, -1:-2])
+
+# Convert correlation matrix to long format for ggplot
+cor_df_EAS &lt;- melt(cor_matrix_EAS)
+cor_df_AFR &lt;- melt(cor_matrix_AFR)
+cor_df_EAS$Target &lt;- &#39;Discovery = EAS + EUR&#39;
+cor_df_AFR$Target &lt;- &#39;Discovery = AFR + EUR&#39;
+cor_df &lt;- rbind(cor_df_AFR, cor_df_EAS)
+
+# Create ggplot correlation heatmap with text inside
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_corr_eur.png&#39;, units = &#39;px&#39;, width = 3300, height = 1700, res = 300)
+ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
+  geom_tile(color = &quot;white&quot;) +  # Tile plot with white borders
+  geom_text(aes(label = round(value, 2)), color = &quot;black&quot;) +  # Add correlation values
+  scale_fill_gradient2(low = &quot;blue&quot;, mid = &quot;white&quot;, high = &quot;red&quot;, midpoint = 0) +  # Color scale
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1),
+    axis.title = element_blank()
+  ) +
+  facet_grid(. ~ Target) +
+  labs(fill = &quot;Correlation&quot;)
+dev.off()</code></pre>
+</details>
+<hr />
+</div>
+</div>
+</div>
+<div id="computational-resoures" class="section level1">
+<h1>Computational resoures</h1>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+library(ggplot2)
+library(cowplot)
+
+setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml&#39;
+pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Read in configuration specific benchmark files
+bm_files_i &lt;- list.files(paste0(outdir, &#39;/reference/benchmarks/&#39;), full.names = T)
+
+# Subset benchmarks for pgs_methods
+bm_files_i &lt;- bm_files_i[grepl(&#39;prep_pgs_|leopard_quickprs_&#39;, bm_files_i)]
+
+# Subset to benchmarks for gwas/gwas_groups in config
+scores &lt;- list_score_files(config)
+bm_files_i &lt;- bm_files_i[grepl(paste0(&#39;-&#39;, unique(scores$name),&#39;.txt&#39;, collapse = &#39;|&#39;), bm_files_i)]
+
+# Read in benchmark files
+bm_dat_all &lt;- do.call(rbind, lapply(bm_files_i, function(file) {
+  tmp &lt;- fread(file)
+  tmp$file &lt;- basename(file)
+  return(tmp)
+}))
+
+# Create rule column
+bm_dat_all$rule &lt;- gsub(&#39;-.*&#39;,&#39;&#39;,bm_dat_all$file)
+
+# Create method column
+bm_dat_all$method &lt;-
+  gsub(&#39;_i&#39;, &#39;&#39;, gsub(&#39;prep_pgs_&#39;, &#39;&#39;, bm_dat_all$rule))
+
+bm_dat_all &lt;- merge(bm_dat_all, pgs_method_labels, by = &#39;method&#39;, all.x=T)
+
+bm_dat_all$label[bm_dat_all$method == &#39;leopard_quickprs&#39;]&lt;-&quot;LEOPARD (QuickPRS)&quot;
+
+#############
+# Time
+#############
+
+# Calculate average time taken for each method
+method_avg &lt;- NULL
+for(i in unique(bm_dat_all$label)){
+  method_avg &lt;- rbind(
+    method_avg,
+    data.frame(
+      method = bm_dat_all$method[bm_dat_all$label == i][1],
+      Method = i,
+      Time = mean(bm_dat_all$s[bm_dat_all$label == i])
+    )
+  )
+}
+
+# Times X-Wing time by two since it used 20 cores, but other methods used 10
+method_avg$Time[method_avg$method == &#39;xwing&#39;] &lt;- method_avg$Time[method_avg$method == &#39;xwing&#39;] * 2
+
+# Divide the multi-source methods (PRS-CSx and X-Wing by 2 so it is time per GWAS)
+method_avg$Time[method_avg$method %in% c(&#39;prscsx&#39;,&#39;xwing&#39;,&#39;leopard_quickprs&#39;)] &lt;- method_avg$Time[ method_avg$method %in% c(&#39;prscsx&#39;,&#39;xwing&#39;,&#39;leopard_quickprs&#39;)] / 2
+
+# Approximate times for either tuning or grid only
+method_avg$Model &lt;- &#39;Full&#39;
+
+tmp &lt;- method_avg[method_avg$method == &#39;prscs&#39; &amp; method_avg$Model == &#39;Full&#39;,] 
+tmp$Model &lt;- &#39;auto&#39;
+tmp$Time &lt;- tmp$Time * (1/5)
+method_avg&lt;-rbind(method_avg, tmp)
+
+tmp &lt;- method_avg[method_avg$method == &#39;prscsx&#39; &amp; method_avg$Model == &#39;Full&#39;,] 
+tmp$Model &lt;- &#39;auto&#39;
+tmp$Time &lt;- tmp$Time * (1/5)
+method_avg&lt;-rbind(method_avg, tmp)
+
+tmp &lt;- method_avg[method_avg$method == &#39;xwing&#39; &amp; method_avg$Model == &#39;Full&#39;,] 
+tmp$Model &lt;- &#39;grid&#39;
+tmp$Time &lt;- tmp$Time * (2/10)
+method_avg&lt;-rbind(method_avg, tmp)
+
+# Format the time taken nicely
+method_avg$Time_clean[method_avg$Time &lt; 60] &lt;-
+  paste0(round(method_avg$Time[method_avg$Time &lt; 60], 1), &#39; sec&#39;)
+method_avg$Time_clean[method_avg$Time &gt; 60] &lt;-
+  paste0(round(method_avg$Time[method_avg$Time &gt; 60] / 60, 1), &#39; min&#39;)
+method_avg$Time_clean[method_avg$Time &gt; 3600] &lt;-
+  paste0(round(method_avg$Time[method_avg$Time &gt; 3600] / 60 / 60, 1), &#39; hr&#39;)
+
+# Convert time in seconds to hours
+method_avg$Time_hour &lt;- method_avg$Time / 60/60
+
+# Seperate methods by single or multi source
+method_avg$Type[!(method_avg$method %in% pgs_group_methods)]&lt;-&#39;Single-source&#39;
+method_avg$Type[method_avg$method %in% pgs_group_methods]&lt;-&#39;Multi-source&#39;
+method_avg$Type[method_avg$method == &#39;leopard_quickprs&#39;]&lt;-&#39;Tuning&#39;
+
+method_avg$Type&lt;-factor(method_avg$Type, levels = c(&#39;Single-source&#39;,&#39;Multi-source&#39;,&#39;Tuning&#39;))
+method_avg$Method &lt;- factor(method_avg$Method, levels = c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;, &quot;QuickPRS-Multi&quot;, &quot;PRS-CSx&quot;, &quot;X-Wing&quot;,&quot;LEOPARD (QuickPRS)&quot;))
+
+ggplot(method_avg, aes(x = Method, y = Time_hour, fill = Model)) +
+  geom_bar(stat = &quot;identity&quot;, position=&quot;dodge&quot;) +
+  geom_text(aes(label = Time_clean), vjust = 0.5, angle = 90, hjust = -0.2, position = position_dodge(width = 0.9)) +
+  labs(x = NULL, y = &quot;Time (hours)&quot;) +
+  ylim(0, max(method_avg$Time_hour) + (max(method_avg$Time_hour)/5)) +
+  facet_grid(~ Type, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+  theme_half_open() +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+  panel_border() + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+
+method_avg &lt;- method_avg[method_avg$Model == &#39;Full&#39;,]
+method_avg &lt;- method_avg[, c(&#39;Method&#39;,&#39;Time_hour&#39;)]
+method_avg$Time_hour &lt;- round(method_avg$Time_hour, 2)
+names(method_avg)&lt;-c(&#39;Method&#39;,&quot;Time (hrs)&quot;)
+
+#############
+# Memory
+#############
+
+# Calculate average max_rss for each method
+method_avg_mem &lt;- NULL
+for(i in unique(bm_dat_all$label)){
+  method_avg_mem &lt;- rbind(
+    method_avg_mem,
+    data.frame(
+      method = bm_dat_all$method[bm_dat_all$label == i][1],
+      Method = i,
+      Memory = mean(bm_dat_all$max_rss[bm_dat_all$label == i])
+    )
+  )
+}
+
+# Divide X-Wing memory by two, since it used 20 cores, but other methods used 10
+method_avg_mem$Memory[method_avg_mem$method == &#39;xwing&#39;] /2
+
+# Format the Memory nicely
+method_avg_mem$Memory_clean &lt;-
+  paste0(round(method_avg_mem$Memory/1000, 2), &#39; Gb&#39;)
+
+ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
+  geom_bar(stat = &quot;identity&quot;, position=&quot;dodge&quot;) +
+  geom_text(aes(label = Memory_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
+  labs(x = &quot;PGS Method&quot;, y = &quot;Memory (Mb)&quot;) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position=&quot;none&quot;)
+
+method_avg_mem$Memory_gb &lt;- method_avg_mem$Memory/1000
+method_avg_mem &lt;- method_avg_mem[, c(&#39;Method&#39;,&#39;Memory_gb&#39;)]
+method_avg_mem$Memory_gb &lt;- round(method_avg_mem$Memory_gb, 2)
+names(method_avg_mem)&lt;-c(&#39;Method&#39;,&quot;Memory (Gb)&quot;)
+
+method_avg&lt;-merge(method_avg, method_avg_mem, by = &#39;Method&#39;)
+
+write.csv(method_avg, &#39;~/oliverpainfel/Analyses/crosspop/time_memory.csv&#39;, row.names=F)</code></pre>
+</details>
+<hr />
+</div>
+<div id="sensitivity-analysis-using-1kg-reference"
+class="section level1">
+<h1>Sensitivity analysis using 1KG reference</h1>
+<p>PRS-CS, PRS-CSx and X-Wing all use the 1KG reference sample, whereas
+the other methods are using the 1KG+HGDP reference sample. We should
+check whether this difference is impacting our conclusions.</p>
+<hr />
+<div id="subset-afr-and-eas-individuals-in-ukb-data"
+class="section level2">
+<h2>Subset AFR and EAS individuals in UKB data</h2>
+<p>To make this quicker, focus on evaluating the PGS methods in the AFR
+and EAS target individuals in UKB. This will avoid reprocessing the full
+UKB data.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+
+keep &lt;- NULL
+for (i in c(&#39;AFR&#39;, &#39;EAS&#39;)) {
+  keep &lt;- rbind(keep, fread(
+    paste0(
+      &#39;~/oliverpainfel/Data/ukb/GenoPred/output/ukb/ancestry/keep_files/model_based/&#39;,
+      i,
+      &#39;.keep&#39;
+    )
+  ))
+}
+
+write.table(
+  keep,
+  &#39;~/oliverpainfel/Data/ukb/afr_eas.keep&#39;,
+  row.names = F,
+  col.names = F,
+  quote = F
+)</code></pre>
+<pre class="bash"><code>mkdir ~/oliverpainfel/Data/ukb/afr_eas_subset
+
+for chr in $(seq 1 22); do
+  ~/oliverpainfel/Software/plink2 \
+    --pfile ~/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
+    --keep ~/oliverpainfel/Data/ukb/afr_eas.keep \
+    --make-pgen \
+    --out ~/oliverpainfel/Data/ukb/afr_eas_subset/ukb.chr${chr}
+done
+</code></pre>
+</details>
+<hr />
+</div>
+<div id="create-1kg-only-genopred-reference-data"
+class="section level2">
+<h2>Create 1KG only GenoPred reference data</h2>
+<p>Subset the 1KG+HGDP reference data to include only 1KG
+individuals.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>mkdir -p ~/oliverpainfel/Data/1kg/genopred/
+cp -r ~/oliverpainfel/Data/hgdp_1kg/genopred/ref ~/oliverpainfel/Data/1kg/genopred/
+rm ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr*.p*</code></pre>
+<pre class="r"><code>library(data.table)
+
+ref&lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr1.psam&#39;)
+ref&lt;-ref[ref$Project == &#39;gnomAD_1kG&#39;,]
+
+write.table(ref[,1, drop = F], &#39;~/oliverpainfel/Data/1kg/1kg.keep&#39;, col.names=F, row.names=F, quote=F)</code></pre>
+<pre class="bash"><code>for chr in $(seq 1 22); do
+  ~/oliverpainfel/Software/plink2 \
+    --pfile ~/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} \
+    --keep ~/oliverpainfel/Data/1kg/1kg.keep \
+    --make-pgen \
+    --out ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr${chr}
+done</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-pipeline-3" class="section level2">
+<h2>Run pipeline</h2>
+<p>To save time, run using PGS methods that do not need pre-processed LD
+matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary
+from the 1KG+HGDP results, then expand to other methods (LDpred2,
+SBayesRC, QuickPRS).</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+
+dir.create(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only&#39;)
+
+######
+# target_list
+######
+target_list &lt;- data.frame(
+  name=&#39;ukb&#39;,
+  path=&#39;/users/k1806347/oliverpainfel/Data/ukb/afr_eas_subset/ukb&#39;,
+  type=&#39;plink2&#39;,
+  indiv_report=F,
+  unrel=&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt&#39;
+)
+
+write.table(target_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt&#39;, col.names=T, row.names=F, quote=F)
+
+######
+# config
+######
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_1kgref&quot;,
+  &quot;refdir: /users/k1806347/oliverpainfel/Data/1kg/genopred/ref&quot;,
+  &quot;resdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/resdir_1kgref&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt&quot;,
+  &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt&quot;,
+  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;]&quot;,
+#  &quot;leopard_methods: [&#39;ptclump&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;]&quot;,
+  &quot;cores_prep_pgs: 10&quot;,
+  &quot;cores_target_pgs: 10&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<pre class="bash"><code>snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \
+  output_all -n</code></pre>
+</details>
+<hr />
+</div>
+<div id="evaluate-pgs-1" class="section level2">
+<h2>Evaluate PGS</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+library(data.table)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml&#39;
+pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+# Create files for EAS and AFR targets
+targ_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+for(trait_i in selected_traits){
+  scores_i &lt;- scores[grepl(trait_i, scores$name),]
+  scores_i$multi &lt;- scores_i$method
+  
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;BBJ&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;UGR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;BBJ&#39;,&#39;UGR&#39;)
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == &#39;BBJ&#39;){
+        disc_pop_j_2 &lt;- &#39;EAS&#39;
+      }
+      if(disc_pop_j == &#39;UGR&#39;){
+        disc_pop_j_2 &lt;- &#39;AFR&#39;
+      }
+
+      dir.create(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_j_2,
+          &#39;/&#39;,
+          trait_i
+        ),
+        recursive = T
+      )
+      
+      scores_i_j &lt;- scores_i[
+        (grepl(&#39;UKB$&#39;, scores_i$name, ignore.case = F) | 
+         grepl(paste0(disc_pop_j, &#39;$&#39;), scores_i$name, ignore.case = T)),]
+
+      # Insert path to score file
+      scores_i_j$predictor &lt;- paste0(
+        outdir,
+        &#39;/ukb/pgs/TRANS/&#39;,
+        scores_i_j$method,
+        &#39;/&#39;,
+        scores_i_j$name,
+        &#39;/ukb-&#39;,
+        scores_i_j$name,
+        &#39;-TRANS.profiles&#39;
+      )
+      
+      ####
+      # Make groups single source methods
+      ####
+      
+      scores_i_j_single_top1 &lt;-
+        scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &amp;
+                     !grepl(&#39;_multi$&#39;, scores_i_j$method), ]
+
+      # Create top1 column indicating which predictors top1 models should be derived
+      scores_i_j_single_top1$top1[grepl(&#39;UKB&#39;, scores_i_j_single_top1$name, ignore.case = F)] &lt;- &#39;EUR&#39;
+      scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] &lt;- disc_pop_j_2
+      
+      ####
+      # Make groups containing pseudo scores for single source methods
+      ####
+
+      # Extract the pseudo score for each method and specify as a separate group
+      for(i in 1:nrow(scores_i_j_single_top1)) {
+        param &lt;- find_pseudo(
+          config = config,
+          gwas = scores_i_j_single_top1$name[i],
+          pgs_method = scores_i_j_single_top1$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header &lt;-
+          fread(scores_i_j_single_top1$predictor[i], nrows = 1)
+        score_cols &lt;-
+          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_single_top1$name[i], &#39;_&#39;, param)))
+        
+        system(
+          paste0(
+            &quot;cut -d&#39; &#39; -f &quot;, 
+            paste0(score_cols, collapse=&#39;,&#39;),
+            &quot; &quot;, 
+            scores_i_j_single_top1$predictor[i], 
+            &quot; &gt; &quot;, 
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                 scores_i_j_single_top1$predictor[i])
+          )
+        )
+      }
+      
+      scores_i_j_single_pseudo &lt;- scores_i_j_single_top1
+      scores_i_j_single_pseudo$multi &lt;- paste0(scores_i_j_single_pseudo$multi, &#39;.pseudo&#39;)
+
+      scores_i_j_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                    paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                                    scores_i_j_single_pseudo$predictor)
+
+#      ####
+#      # Make groups for multi-single-source pseudo scores
+#      ####
+#      
+#      scores_i_j_multi_single_pseudo &lt;- scores_i_j[grepl(&#39;_multi$&#39;, scores_i_j$method),]
+#
+#      # Extract the pseudo score for each method and specify as a separate group
+#      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
+#        param &lt;- find_pseudo(
+#          config = config,
+#          gwas = scores_i_j_multi_single_pseudo$name[i],
+#          pgs_method = scores_i_j_multi_single_pseudo$method[i],
+#          target_pop = targ_pop_i
+#        )
+#        
+#        score_header &lt;-
+#          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
+#        score_cols &lt;-
+#          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_multi_single_pseudo$name[i], &#39;_&#39;, param)))
+#        
+#        system(
+#          paste0(
+#            &quot;cut -d&#39; &#39; -f &quot;, 
+#            paste0(score_cols, collapse=&#39;,&#39;),
+#            &quot; &quot;, 
+#            scores_i_j_multi_single_pseudo$predictor[i], 
+#            &quot; &gt; &quot;, 
+#            gsub(&#39;.profiles&#39;,
+#                 paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+#                 scores_i_j_multi_single_pseudo$predictor[i])
+#          )
+#        )
+#      }
+#      
+#      scores_i_j_multi_single_pseudo$multi &lt;- paste0(scores_i_j_multi_single_pseudo$multi, &#39;.pseudo&#39;)
+#
+#      scores_i_j_multi_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+#                                    paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+#                                    scores_i_j_multi_single_pseudo$predictor)
+#      
+#      scores_i_j_multi_single_pseudo$top1&lt;-paste0(&#39;EUR_&#39;, disc_pop_j_2)
+#
+#      ####
+#      # Make groups for the Multi-Source methods
+#      ####
+#      
+#      scores_i_j_multi &lt;- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
+#
+#      # Split top1 scores by target population
+#      # This doesn&#39;t apply to xwing because it only has pop-specific pseudo scores
+#      scores_i_j_multi_top1&lt;-NULL
+#      for(i in 1:which(scores_i_j_multi$method %in% c(&#39;prscsx&#39;))){
+#        score_header&lt;-fread(scores_i_j_multi$predictor[i], nrow = 1)
+#        
+#        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
+#          
+#          if(scores_i_j_multi$method[i] == &#39;prscsx&#39;){
+#            score_cols &lt;-
+#              which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi&#39;), names(score_header)))
+#          }
+#          if(scores_i_j_multi$method[i] == &#39;xwing&#39;){
+#            score_cols &lt;-
+#              which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst&#39;), names(score_header)))
+#          }
+#          
+#          system(
+#            paste0(
+#              &quot;cut -d&#39; &#39; -f &quot;, 
+#              paste0(score_cols, collapse=&#39;,&#39;),
+#              &quot; &quot;, 
+#              scores_i_j_multi$predictor[i], 
+#              &quot; &gt; &quot;, 
+#              gsub(&#39;.profiles&#39;,
+#                   paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
+#                   scores_i_j_multi$predictor[i])
+#            )
+#          )
+#          
+#          tmp &lt;- scores_i_j_multi[i,]
+#          tmp$multi &lt;- paste0(tmp$multi, &#39;.grid&#39;)
+#          tmp$top1 &lt;- pop
+#          tmp$predictor &lt;-
+#              gsub(&#39;.profiles&#39;,
+#                   paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
+#                   scores_i_j_multi$predictor[i])
+#          
+#          scores_i_j_multi_top1 &lt;- rbind(scores_i_j_multi_top1, tmp)
+#        }
+#      }
+#
+#      # Split pop-specific pseudo scores by target population
+#      scores_i_j_multi_pop_pseudo&lt;-NULL
+#      for(i in 1:nrow(scores_i_j_multi)){
+#        score_header&lt;-fread(scores_i_j_multi$predictor[i], nrow = 1)
+#        
+#        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
+#          if(scores_i_j_multi$method[i] == &#39;prscsx&#39;){
+#            score_cols &lt;-
+#              which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi_auto&#39;), names(score_header)))
+#          }
+#          if(scores_i_j_multi$method[i] == &#39;xwing&#39;){
+#            score_cols &lt;-
+#              which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst_&#39;, pop), names(score_header)))
+#          }
+#          
+#          system(
+#            paste0(
+#              &quot;cut -d&#39; &#39; -f &quot;, 
+#              paste0(score_cols, collapse=&#39;,&#39;),
+#              &quot; &quot;, 
+#              scores_i_j_multi$predictor[i], 
+#              &quot; &gt; &quot;, 
+#              gsub(&#39;.profiles&#39;,
+#                   paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
+#                   scores_i_j_multi$predictor[i])
+#            )
+#          )
+#          
+#          tmp &lt;- scores_i_j_multi[i,]
+#          tmp$multi &lt;- paste0(tmp$multi, &#39;.pop_pseudo&#39;)
+#          tmp$top1 &lt;- pop
+#          tmp$predictor &lt;-
+#              gsub(&#39;.profiles&#39;,
+#                   paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
+#                   scores_i_j_multi$predictor[i])
+#          
+#          scores_i_j_multi_pop_pseudo &lt;- rbind(scores_i_j_multi_pop_pseudo, tmp)
+#        }
+#      }
+#      
+#      # Create pseudo score for multi-source methods
+#      scores_i_j_multi_pseudo&lt;-NULL
+#      for(i in 1:nrow(scores_i_j_multi)) {
+#        param &lt;- find_pseudo(
+#          config = config,
+#          gwas = scores_i_j_multi$name[i],
+#          pgs_method = scores_i_j_multi$method[i],
+#          target_pop = targ_pop_i
+#        )
+#        
+#        score_header &lt;-
+#          fread(scores_i_j_multi$predictor[i], nrows = 1)
+#        score_cols &lt;-
+#          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_multi$name[i], &#39;_&#39;, param)))
+#
+#        system(
+#          paste0(
+#            &quot;cut -d&#39; &#39; -f &quot;, 
+#            paste0(score_cols, collapse=&#39;,&#39;),
+#            &quot; &quot;, 
+#            scores_i_j_multi$predictor[i], 
+#            &quot; &gt; &quot;, 
+#            gsub(&#39;.profiles&#39;,
+#                 paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
+#                 scores_i_j_multi$predictor[i])
+#          )
+#        )
+#        
+#        tmp &lt;- scores_i_j_multi[i,]
+#        tmp$multi &lt;- paste0(tmp$multi, &#39;.pseudo&#39;)
+#        tmp$top1 &lt;- paste0(&#39;EUR_&#39;, disc_pop_j_2)
+#        tmp$predictor &lt;-
+#            gsub(&#39;.profiles&#39;,
+#                 paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
+#                 scores_i_j_multi$predictor[i])
+#        
+#        scores_i_j_multi_pseudo &lt;- rbind(scores_i_j_multi_pseudo, tmp)
+#      }
+      
+      ####
+      # Combine the different predictor groups
+      ####
+      predictors_i&lt;- do.call(rbind, list(
+        scores_i_j_single_top1, 
+        scores_i_j_single_pseudo#, 
+#        scores_i_j_multi_single_pseudo,
+#        scores_i_j_multi_top1,
+#        scores_i_j_multi_pop_pseudo,
+#        scores_i_j_multi_pseudo
+      ))
+      
+      predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;multi&#39;,&#39;top1&#39;), with=F]
+      
+      write.table(
+        predictors_i,
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_j_2,
+          &#39;/&#39;,
+          trait_i,
+          &#39;/predictor_list.txt&#39;
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}</code></pre>
+</details>
+<hr />
+<div id="run-model_builder-2" class="section level3">
+<h3>Run model_builder</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_*.disc_EUR_*/*/res*
+
+for targ_pop in $(echo EAS AFR); do
+  if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
+      targ_pop2=&quot;EUR_test&quot;
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
+    disc_pop=$(echo EAS AFR)
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;EAS&quot; ]; then
+    disc_pop=&quot;EAS&quot;
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;AFR&quot; ]; then
+    disc_pop=&quot;AFR&quot;
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+      if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt&quot; ]; then
+        sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
+          --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
+          --n_core 5&quot;
+      fi
+    done
+  done
+done
+</code></pre>
+</details>
+<hr />
+</div>
+<div id="plot-results-1" class="section level3">
+<h3>Plot results</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+info_all &lt;- fread(&#39;~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv&#39;)
+
+# Calculate correlation between all phenotypes in each target population
+cors &lt;- list()
+for(pop_i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;CSA&#39;,&#39;AMR&#39;)){
+  if(pop_i == &#39;EUR&#39;){
+    pop_i_2 &lt;- &#39;EUR_test&#39;
+  } else {
+    pop_i_2 &lt;- pop_i
+  }
+  pheno_pop_i &lt;- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] &lt;- fread(paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;, pheno_i, &#39;.unrel.&#39;, pop_i_2, &#39;.row_number.txt&#39;))
+    names(pheno_pop_i[[pheno_i]])[3] &lt;- pheno_i
+  }
+  
+  pheno_pop_i_merged &lt;- merged_df &lt;- Reduce(function(x, y) merge(x, y, all = TRUE, by = c(&#39;FID&#39;,&#39;IID&#39;)), pheno_pop_i)
+
+  cors_i &lt;- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use=&#39;p&#39;))
+  cors[[pop_i]] &lt;- cors_i
+}
+
+# Read in results
+targ_pop = c(&#39;EAS&#39;,&#39;AFR&#39;)
+res_eval &lt;- list()
+for(pheno_i in selected_traits){
+  res_eval_i&lt;-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;EAS&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;AFR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i &lt;-
+        fread(
+          paste0(
+            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/&#39;,
+            &#39;targ_&#39;,
+            targ_pop_i,
+            &#39;.disc_EUR_&#39;,
+            disc_pop_i,
+            &#39;/&#39;,
+            pheno_i,
+            &#39;/res.pred_eval.txt&#39;
+          )
+        )
+      eval_i$Target&lt;-targ_pop_i
+      eval_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
+      res_eval_i&lt;-rbind(res_eval_i, eval_i)
+    }
+  }
+  
+  res_eval_i$Method&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_eval_i$Group)
+  res_eval_i$Method&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_eval_i$Method)
+  
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTrain&#39;
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTrain&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTrain&#39;
+  
+  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;prscsx.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;xwing.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
+  
+  res_eval_i$Source&lt;-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_eval_i$Method) | 
+    !grepl(&#39;EUR|EAS|AFR&#39;, res_eval_i$Group), &#39;Multi&#39;, &#39;Single&#39;)
+  
+  res_eval_i$Discovery[grepl(&#39;EUR&#39;, res_eval_i$Group)] &lt;- &#39;EUR&#39;
+  res_eval_i$Discovery[grepl(&#39;EAS&#39;, res_eval_i$Group)] &lt;- &#39;EAS&#39;
+  res_eval_i$Discovery[grepl(&#39;AFR&#39;, res_eval_i$Group)] &lt;- &#39;AFR&#39;
+  res_eval_i$Discovery[res_eval_i$Source == &#39;Multi&#39;] &lt;- res_eval_i$gwas_group[res_eval_i$Source == &#39;Multi&#39;]
+  
+  res_eval_i$Method&lt;-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+  res_eval_i$Discovery&lt;-factor(res_eval_i$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+  
+  # Remove pseudo model for methods that don&#39;t really have one 
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
+  res_eval_i &lt;- res_eval_i[
+    !((res_eval_i$Method %in%  c(&#39;prscsx&#39;, &#39;xwing&#39;) | grepl(&#39;_multi$&#39;, res_eval_i$Method)) &amp; 
+      grepl(&#39;top1&#39;, res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i &lt;- res_eval_i[!duplicated(res_eval_i[, c(
+    &quot;Target&quot;, &quot;Method&quot;, &quot;Model&quot;, &quot;Source&quot;, &quot;Discovery&quot;,&quot;gwas_group&quot;
+  )]),]
+  
+  res_eval[[pheno_i]]&lt;-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;, &quot;DBSLMM-multi&quot;, &quot;lassosum-multi&quot;, &quot;LDpred2-multi&quot;, &quot;MegaPRS-multi&quot;, &quot;PRS-CS-multi&quot;, &quot;pT+clump-multi&quot;, &quot;QuickPRS-multi&quot;, &quot;SBayesRC-multi&quot;, &quot;PRS-CSx&quot;, &quot;X-Wing&quot;, &quot;All&quot;) 
+
+res_eval_simp &lt;- NULL
+for(pheno_i in selected_traits){
+  tmp &lt;- res_eval[[pheno_i]]
+  tmp$Trait &lt;- pheno_i
+  
+  # Insert nice PGS method names
+  tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+  tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+  tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+  tmp$label &lt;- factor(tmp$label, levels = model_order)
+  
+  # Simplify result to either SumStatTrain or IndivTrain
+  tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+  tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+  tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery&#39;,&#39;Model&#39;), with=F]),]
+  
+  res_eval_simp &lt;- rbind(res_eval_simp, tmp)
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval &lt;- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == &#39;EAS&#39;){
+    disc_pop &lt;- &#39;EAS&#39;
+  }
+  if(targ_pop_i == &#39;AFR&#39;){
+    disc_pop &lt;- &#39;AFR&#39;
+  }
+  if(targ_pop_i == &#39;EUR&#39;){
+    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_eval for each scenario
+    res_eval_i &lt;- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x &lt;- res_eval[[i]]
+      x$pheno &lt;- names(res_eval)[i]
+      x &lt;- x[x$Target == targ_pop_i]
+      x &lt;- x[x$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+    }))
+    
+    # Average res_evalults for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_eval_i$Sample&lt;-&#39;A&#39;
+  
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i &lt;- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno &lt;-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+      
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          &#39;res_evalults missing for &#39;,
+          targ_pop_i,
+          &#39; &#39;,
+          group_i,
+          &#39; &#39;,
+          paste0(missing_pheno, collapse = &#39; &#39;)
+        ))
+      }
+      
+      cors_i &lt;- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i &lt;-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = &quot;BHHR&quot;,
+          mod = NULL,
+          data = res_eval_group_i
+        )
+      
+      tmp &lt;- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval &lt;- rbind(meta_res_eval, tmp)
+    }
+  }
+}
+
+meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+meta_res_eval$Discovery&lt;-factor(meta_res_eval$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/average_r.png&#39;), res=300, width = 3200, height = 2000, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+# The results look very similar to when using 1KG+HGDP. 
+
+###################
+# Plot a comparison between the runs using different references
+
+# Read in results using 1KG+HGDP reference
+main_results&lt;-fread(&#39;~/oliverpainfel/Analyses/crosspop/r_eval.csv&#39;)
+sens_results&lt;-meta_res_eval
+
+tmp &lt;- main_results
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+main_results &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+
+tmp &lt;- sens_results
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+sens_results &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+
+main_results&lt;-main_results[main_results$Method %in% sens_results$Method,]
+main_results&lt;-main_results[main_results$Target %in% sens_results$Target,]
+
+sens_results$Reference &lt;- &#39;1KG&#39;
+main_results$Reference &lt;- &#39;1KG+HGDP&#39;
+
+both_results &lt;- rbind(main_results, sens_results)
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/comparison_to_main_result.png&#39;, units = &#39;px&#39;, res = 300, width=4000, height=2500)
+ggplot(both_results, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ Discovery_clean + Reference, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()</code></pre>
+</details>
+<hr />
+</div>
+</div>
+</div>
+<div id="tl-prs" class="section level1">
+<h1>TL-PRS</h1>
+<p>Run using AFR and EAS subset in UKB to make it quicker to run. This
+is the main interest when running TL-PRS anyway.</p>
+<hr />
+<div id="run-pipeline-4" class="section level2">
+<h2>Run pipeline</h2>
+<p>To save time, run using PGS methods that do not need pre-processed LD
+matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary
+from the 1KG+HGDP results, then expand to other methods (LDpred2,
+SBayesRC, QuickPRS).</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+
+dir.create(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs&#39;)
+
+######
+# config
+######
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_tlprs&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt&quot;,
+  &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt&quot;,
+  &quot;pgs_methods: [&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
+  &quot;tlprs_methods: [&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
+  &quot;cores_prep_pgs: 10&quot;,
+  &quot;cores_target_pgs: 50&quot;,
+  &quot;prscs_phi: [&#39;auto&#39;]&quot;,
+  &quot;ldpred2_model: [&#39;auto&#39;]&quot;,
+  &quot;ldpred2_inference: F&quot;,
+  &quot;dbslmm_h2f: [&#39;1&#39;]&quot;,
+  &quot;ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3&quot;,
+  &quot;quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3&quot;,
+  &quot;sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<pre class="bash"><code>snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml \
+  output_all -n</code></pre>
+</details>
+<hr />
+</div>
+<div id="create-predictor-lists-2" class="section level2">
+<h2>Create predictor lists</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+library(data.table)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&#39;
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+# Create files for EAS and AFR targets
+targ_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+for(trait_i in selected_traits){
+  scores_i &lt;- scores[grepl(trait_i, scores$name),]
+
+  for(targ_pop_i in targ_pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;BBJ&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;UGR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;BBJ&#39;,&#39;UGR&#39;)
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == &#39;BBJ&#39;){
+        disc_pop_j_2 &lt;- &#39;EAS&#39;
+      }
+      if(disc_pop_j == &#39;UGR&#39;){
+        disc_pop_j_2 &lt;- &#39;AFR&#39;
+      }
+
+      dir.create(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_j_2,
+          &#39;/&#39;,
+          trait_i
+        ),
+        recursive = T
+      )
+      
+      scores_i_j &lt;- scores_i[
+        (grepl(&#39;UKB$&#39;, scores_i$name, ignore.case = F) | 
+         grepl(paste0(disc_pop_j, &#39;$&#39;), scores_i$name, ignore.case = T)),]
+
+      scores_i_j$predictor &lt;- paste0(
+        outdir,
+        &#39;/ukb/pgs/TRANS/&#39;,
+        scores_i_j$method,
+        &#39;/&#39;,
+        scores_i_j$name,
+        &#39;/ukb-&#39;,
+        scores_i_j$name,
+        &#39;-TRANS.profiles&#39;
+      )
+      
+      #####
+      # List single-source PGS
+      #####
+      # These are actually pseudoval scores (as per the config)
+      scores_i_j_single &lt;- scores_i_j[!grepl(&#39;tlprs&#39;, scores_i_j$method),]
+      
+      scores_i_j_single$top1[grepl(&#39;UKB&#39;, scores_i_j_single$name, ignore.case = F)] &lt;- &#39;EUR&#39;
+      scores_i_j_single$top1[grepl(disc_pop_j, scores_i_j_single$name, ignore.case = F)] &lt;- disc_pop_j_2
+      scores_i_j_single$multi &lt;- paste0(scores_i_j_single$method,&#39;.pseudo&#39;)
+      
+      #####
+      # List tlprs scores (split by target population)
+      #####
+      scores_i_j_tlprs &lt;- scores_i_j[grepl(&#39;tlprs&#39;, scores_i_j$method),]
+      scores_i_j_tlprs$multi &lt;- scores_i_j_tlprs$method
+      
+      scores_i_j_tlprs_pop&lt;-NULL
+      for(i in 1:nrow(scores_i_j_tlprs)){
+        score_header&lt;-fread(scores_i_j_tlprs$predictor[i], nrow = 1)
+        
+        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
+          score_cols &lt;- which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_&#39;), names(score_header)))
+
+          system(
+            paste0(
+              &quot;cut -d&#39; &#39; -f &quot;, 
+              paste0(score_cols, collapse=&#39;,&#39;),
+              &quot; &quot;, 
+              scores_i_j_tlprs$predictor[i], 
+              &quot; &gt; &quot;, 
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.targ_&#39;, pop, &#39;.profiles&#39;),
+                   scores_i_j_tlprs$predictor[i])
+            )
+          )
+          
+          tmp &lt;- scores_i_j_tlprs[i,]
+          tmp$multi &lt;- paste0(tmp$multi, &#39;.pop&#39;)
+          tmp$top1 &lt;- pop
+          tmp$predictor &lt;-
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.targ_&#39;, pop, &#39;.profiles&#39;),
+                   scores_i_j_tlprs$predictor[i])
+          
+          scores_i_j_tlprs_pop &lt;- rbind(scores_i_j_tlprs_pop, tmp)
+        }
+      }
+
+      predictors_i&lt;- do.call(rbind, list(
+        scores_i_j_single, scores_i_j_tlprs_pop
+      ))
+      
+      predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;top1&#39;,&#39;multi&#39;), with=F]
+      
+      write.table(
+        predictors_i,
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_j_2,
+          &#39;/&#39;,
+          trait_i,
+          &#39;/predictor_list.tlprs.txt&#39;
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}</code></pre>
+</details>
+<hr />
+</div>
+<div id="run-model_builder-3" class="section level2">
+<h2>Run model_builder</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_*.disc_EUR_*/*/res*
+
+for targ_pop in $(echo EAS AFR); do
+  if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
+      targ_pop2=&quot;EUR_test&quot;
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
+    disc_pop=$(echo AFR EAS)
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;EAS&quot; ]; then
+    disc_pop=&quot;EAS&quot;
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;AFR&quot; ]; then
+    disc_pop=&quot;AFR&quot;
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+      if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs.pred_comp.txt&quot; ]; then
+        sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
+          --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
+          --n_core 5&quot;
+      fi
+    done
+  done
+done</code></pre>
+</details>
+<hr />
+</div>
+<div id="plot-results-2" class="section level2">
+<h2>Plot results</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+info_all &lt;- fread(&#39;~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv&#39;)
+
+# Calculate correlation between all phenotypes in each target population
+cors &lt;- list()
+for(pop_i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;CSA&#39;,&#39;AMR&#39;)){
+  if(pop_i == &#39;EUR&#39;){
+    pop_i_2 &lt;- &#39;EUR_test&#39;
+  } else {
+    pop_i_2 &lt;- pop_i
+  }
+  pheno_pop_i &lt;- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] &lt;- fread(paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;, pheno_i, &#39;.unrel.&#39;, pop_i_2, &#39;.row_number.txt&#39;))
+    names(pheno_pop_i[[pheno_i]])[3] &lt;- pheno_i
+  }
+  
+  pheno_pop_i_merged &lt;- merged_df &lt;- Reduce(function(x, y) merge(x, y, all = TRUE, by = c(&#39;FID&#39;,&#39;IID&#39;)), pheno_pop_i)
+
+  cors_i &lt;- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use=&#39;p&#39;))
+  cors[[pop_i]] &lt;- cors_i
+}
+
+# Read in results
+targ_pop = c(&#39;EAS&#39;,&#39;AFR&#39;)
+res_eval &lt;- list()
+for(pheno_i in selected_traits){
+  res_eval_i&lt;-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;EAS&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;AFR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i &lt;-
+        fread(
+          paste0(
+            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/&#39;,
+            &#39;targ_&#39;,
+            targ_pop_i,
+            &#39;.disc_EUR_&#39;,
+            disc_pop_i,
+            &#39;/&#39;,
+            pheno_i,
+            &#39;/res.tlprs.pred_eval.txt&#39;
+          )
+        )
+      eval_i$Target&lt;-targ_pop_i
+      eval_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
+      res_eval_i&lt;-rbind(res_eval_i, eval_i)
+    }
+  }
+  
+  res_eval_i$Method&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_eval_i$Group)
+  res_eval_i$Method&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_eval_i$Method)
+  
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTrain&#39;
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTrain&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTrain&#39;
+  
+  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;prscsx.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;xwing.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
+  
+  res_eval_i$Source&lt;-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_eval_i$Method) | 
+    !grepl(&#39;EUR|EAS|AFR&#39;, res_eval_i$Group), &#39;Multi&#39;, &#39;Single&#39;)
+  
+  res_eval_i$Discovery[grepl(&#39;EUR&#39;, res_eval_i$Group)] &lt;- &#39;EUR&#39;
+  res_eval_i$Discovery[grepl(&#39;EAS&#39;, res_eval_i$Group)] &lt;- &#39;EAS&#39;
+  res_eval_i$Discovery[grepl(&#39;AFR&#39;, res_eval_i$Group)] &lt;- &#39;AFR&#39;
+  res_eval_i$Discovery[res_eval_i$Source == &#39;Multi&#39;] &lt;- res_eval_i$gwas_group[res_eval_i$Source == &#39;Multi&#39;]
+  
+  res_eval_i$Method&lt;-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+  res_eval_i$Discovery&lt;-factor(res_eval_i$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+  
+  # Remove pseudo model for methods that don&#39;t really have one 
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing, TL-*
+  res_eval_i &lt;- res_eval_i[
+    !((res_eval_i$Method %in%  c(&#39;prscsx&#39;, &#39;xwing&#39;) | grepl(&#39;_multi$&#39;, res_eval_i$Method)) &amp; 
+      grepl(&#39;top1&#39;, res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i &lt;- res_eval_i[!duplicated(res_eval_i[, c(
+    &quot;Target&quot;, &quot;Method&quot;, &quot;Model&quot;, &quot;Source&quot;, &quot;Discovery&quot;,&quot;gwas_group&quot;
+  )]),]
+  
+  res_eval[[pheno_i]]&lt;-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;, &quot;DBSLMM-multi&quot;, &quot;lassosum-multi&quot;, &quot;LDpred2-multi&quot;, &quot;MegaPRS-multi&quot;, &quot;PRS-CS-multi&quot;, &quot;pT+clump-multi&quot;, &quot;QuickPRS-multi&quot;, &quot;SBayesRC-multi&quot;,&quot;TL-DBSLMM&quot;,&quot;TL-LDpred2&quot;,&quot;TL-QuickPRS&quot;,&quot;TL-SBayesRC&quot;, &quot;PRS-CSx&quot;, &quot;X-Wing&quot;, &quot;All&quot;) 
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval &lt;- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == &#39;EAS&#39;){
+    disc_pop &lt;- &#39;EAS&#39;
+  }
+  if(targ_pop_i == &#39;AFR&#39;){
+    disc_pop &lt;- &#39;AFR&#39;
+  }
+  if(targ_pop_i == &#39;EUR&#39;){
+    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_eval for each scenario
+    res_eval_i &lt;- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x &lt;- res_eval[[i]]
+      x$pheno &lt;- names(res_eval)[i]
+      x &lt;- x[x$Target == targ_pop_i]
+      x &lt;- x[x$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+    }))
+    
+    # Average res_evalults for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_eval_i$Sample&lt;-&#39;A&#39;
+  
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i &lt;- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno &lt;-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+      
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          &#39;res_evalults missing for &#39;,
+          targ_pop_i,
+          &#39; &#39;,
+          group_i,
+          &#39; &#39;,
+          paste0(missing_pheno, collapse = &#39; &#39;)
+        ))
+      }
+      
+      cors_i &lt;- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i &lt;-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = &quot;BHHR&quot;,
+          mod = NULL,
+          data = res_eval_group_i
+        )
+      
+      tmp &lt;- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval &lt;- rbind(meta_res_eval, tmp)
+    }
+  }
+}
+
+meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+meta_res_eval$Discovery&lt;-factor(meta_res_eval$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39; &amp; !grepl(&#39;^tlprs_&#39;, tmp$Method)] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39; &amp; !grepl(&#39;^tlprs_&#39;, tmp$Method)], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
+tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+
+dir.create(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots&#39;)
+
+# Plot unidirectional TL-PRS (as it was intended), comparing the unadjusted EUR PGS to the EUR PGS that has been adjusted according to the target-matched GWAS
+tmp_tlprs_uni &lt;- tmp[grepl(&#39;tlprs&#39;, tmp$Method) &amp; !grepl(&#39;pop-EUR.top1&#39;, tmp$Group) &amp; tmp$Source == &#39;Single&#39;, ]
+tmp_tlprs_uni$Type &lt;- &#39;TL-PRS&#39;
+tmp_unadj &lt;- tmp[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery == &#39;EUR&#39;, ]
+tmp_unadj$Type &lt;- &#39;Original&#39;
+tmp_both &lt;- rbind(tmp_unadj, tmp_tlprs_uni)
+tmp_both$label&lt;-gsub(&#39;TL-&#39;,&#39;&#39;,tmp_both$label)
+tmp_both$Type&lt;-factor(tmp_both$Type, levels = c(&#39;Original&#39;,&#39;TL-PRS&#39;))
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.png&#39;), res=300, width = 2000, height = 1600, units = &#39;px&#39;)
+ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+tmp_tlprs_uni &lt;- tmp[grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;pop-EUR.top1&#39;, tmp$Group) &amp; tmp$Source == &#39;Single&#39;, ]
+tmp_tlprs_uni$Type &lt;- &#39;TL-PRS&#39;
+tmp_unadj &lt;- tmp[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery == &#39;EUR&#39;, ]
+tmp_unadj$Type &lt;- &#39;Original&#39;
+tmp_both &lt;- rbind(tmp_unadj, tmp_tlprs_uni)
+tmp_both$label&lt;-gsub(&#39;TL-&#39;,&#39;&#39;,tmp_both$label)
+tmp_both$Type&lt;-factor(tmp_both$Type, levels = c(&#39;Original&#39;,&#39;TL-PRS&#39;))
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.targ_EUR.png&#39;), res=300, width = 2000, height = 1600, units = &#39;px&#39;)
+ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+# Have one column per method, but fill according to Original EUR PGS, Original matched-PGS, TL-PRS EUR Target, TL-PRS non-EUR Target, TL-PRS Multi, and Original-Multi
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$Type &lt;- NA
+tmp$Type[grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;pop-EUR.top1&#39;, tmp$Group)]&lt;-&quot;TL-PRS (EUR PGS tuned to target)&quot;
+tmp$Type[grepl(&#39;tlprs&#39;, tmp$Method) &amp; !grepl(&#39;pop-EUR.top1&#39;, tmp$Group) &amp; tmp$Source == &#39;Single&#39;]&lt;-&quot;TL-PRS (Target-matched PGS tuned to EUR)&quot;
+tmp$Type[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery == &#39;EUR&#39;]&lt;-&quot;Original (EUR PGS)&quot;
+tmp$Type[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;]&lt;-&quot;Original (Target-matched PGS)&quot;
+tmp$Type[grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;multi&#39;, tmp$Group)]&lt;-&quot;TL-PRS-multi&quot;
+tmp$Type[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;multi&#39;, tmp$Group)]&lt;-&quot;Original-multi&quot;
+tmp &lt;- tmp[!is.na(tmp$Type),]
+tmp$Type&lt;-factor(tmp$Type, levels=c(&quot;Original (EUR PGS)&quot;, &quot;Original (Target-matched PGS)&quot;, &quot;TL-PRS (EUR PGS tuned to target)&quot;, &quot;TL-PRS (Target-matched PGS tuned to EUR)&quot;, &quot;Original-multi&quot;, &quot;TL-PRS-multi&quot;))
+tmp$label&lt;-gsub(&#39;TL-&#39;,&#39;&#39;,tmp$label)
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png&#39;), res=300, width = 4000, height = 2200, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Type)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+#########################
+# Check significance of differences between TL-PRS and unadjusted approaches
+########################
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Create a function to mirror pred_comp results
+mirror_comp&lt;-function(x){
+  x_sym &lt;- x
+  x_sym$Model_1 &lt;- x$Model_2
+  x_sym$Model_2 &lt;- x$Model_1
+  x_sym$Model_1_R &lt;- x$Model_2_R
+  x_sym$Model_2_R &lt;- x$Model_1_R
+  x_sym$R_diff &lt;- -x_sym$R_diff
+  x_mirrored &lt;- rbind(x, x_sym)
+  x_diag&lt;-data.frame(
+      Model_1=unique(x_mirrored$Model_1),
+      Model_2=unique(x_mirrored$Model_1),
+      Model_1_R=x_mirrored$Model_1_R,
+      Model_2_R=x_mirrored$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp&lt;-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
+# Read in results
+targ_pop=c(&#39;EAS&#39;,&#39;AFR&#39;)
+res_comp &lt;- list()
+for(pheno_i in selected_traits){
+  res_comp_i&lt;-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;EAS&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;AFR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+    }
+    for(disc_pop_i in disc_pop){
+      comp_i &lt;-
+        fread(
+          paste0(
+            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/&#39;,
+            &#39;targ_&#39;,
+            targ_pop_i,
+            &#39;.disc_EUR_&#39;,
+            disc_pop_i,
+            &#39;/&#39;,
+            pheno_i,
+            &#39;/res.tlprs.pred_comp.txt&#39;
+          )
+        )
+      comp_i&lt;-mirror_comp(comp_i)
+      comp_i$Target&lt;-targ_pop_i
+      comp_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
+      res_comp_i&lt;-rbind(res_comp_i, comp_i)
+    }
+  }
+  
+  res_comp[[pheno_i]]&lt;-res_comp_i
+}
+
+res_comp_all &lt;- do.call(rbind, lapply(names(res_comp), function(name) {
+  x &lt;- res_comp[[name]]
+  x$pheno &lt;- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Annotate tests to get order correct
+res_comp_all$Method1&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_1)
+res_comp_all$Method1&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method1)
+res_comp_all$Method2&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_2)
+res_comp_all$Method2&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method2)
+
+find_model&lt;-function(x){
+  mod &lt;- x
+  mod[grepl(&#39;top1$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;IndivTrain&#39;
+  mod[grepl(&#39;top1$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;SumStatTrain&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-IndivTrain&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-SumStatTrain&#39;
+  mod[grepl(&#39;_multi&#39;, x)] &lt;- &#39;SumStatTrain&#39;
+  mod[x == &#39;prscsx.pseudo.multi&#39;] &lt;- &#39;SumStatTrain&#39;
+  mod[x == &#39;xwing.pseudo.multi&#39;] &lt;- &#39;SumStatTrain&#39;
+  
+  return(mod)
+}
+
+res_comp_all$Model1&lt;-find_model(res_comp_all$Model_1)
+res_comp_all$Model2&lt;-find_model(res_comp_all$Model_2)
+
+res_comp_all$Source1&lt;-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method1) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_1), &#39;Multi&#39;, &#39;Single&#39;)
+res_comp_all$Source2&lt;-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method2) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_2), &#39;Multi&#39;, &#39;Single&#39;)
+  
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)){
+  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] &lt;- i
+  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] &lt;- i
+}
+res_comp_all$Discovery1[res_comp_all$Source1 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source1 == &#39;Multi&#39;]
+res_comp_all$Discovery2[res_comp_all$Source2 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source2 == &#39;Multi&#39;]
+
+res_comp_all$Method1&lt;-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
+res_comp_all$Method2&lt;-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
+res_comp_all$Model1&lt;-factor(res_comp_all$Model1, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+res_comp_all$Model2&lt;-factor(res_comp_all$Model2, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+res_comp_all$Discovery1&lt;-factor(res_comp_all$Discovery1, levels=rev(c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;)))
+res_comp_all$Discovery2&lt;-factor(res_comp_all$Discovery2, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+# Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method1 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+  res_comp_all$Model1 %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method2 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+  res_comp_all$Model2 %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+
+# Remove pseudo model for methods that don&#39;t really have one 
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method1 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+  res_comp_all$Model1 %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method2 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+  res_comp_all$Model2 %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+
+# Remove top1 models for PRS-CSx
+res_comp_all &lt;- res_comp_all[
+!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method1) &amp; 
+  grepl(&#39;top1&#39;, res_comp_all$Model_1)),]
+res_comp_all &lt;- res_comp_all[
+!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method2) &amp; 
+  grepl(&#39;top1&#39;, res_comp_all$Model_2)),]
+
+# Remove any comparisons
+res_comp_all &lt;- res_comp_all[!duplicated(res_comp_all[, c(&quot;Target&quot;, &quot;Method1&quot;, &quot;Model1&quot;, &quot;Source1&quot;, &quot;Discovery1&quot;, &quot;Method2&quot;, &quot;Model2&quot;, &quot;Source2&quot;, &quot;Discovery2&quot;,&#39;pheno&#39;)]),]
+
+###########
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_comp &lt;- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == &#39;EAS&#39;){
+    disc_pop &lt;- &#39;EAS&#39;
+  }
+  if(targ_pop_i == &#39;AFR&#39;){
+    disc_pop &lt;- &#39;AFR&#39;
+  }
+  if(targ_pop_i == &#39;EUR&#39;){
+    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_comp for each scenario
+    res_comp_i &lt;- res_comp_all[res_comp_all$Target == targ_pop_i &amp; res_comp_all$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+  
+    # Calculate diff SE based on p-value
+    res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] &lt;- 1-0.001
+    res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]&lt;-1-0.001
+    res_comp_i$R_diff_z&lt;-qnorm(res_comp_i$R_diff_pval/2)
+    res_comp_i$R_diff_SE&lt;-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
+        
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_comp_i$Sample&lt;-&#39;A&#39;
+    res_comp_i$Group &lt;- paste0(res_comp_i$Model_1, &#39;_vs_&#39;, res_comp_i$Model_2)
+  
+    for(group_i in unique(res_comp_i$Group)){
+      res_comp_group_i &lt;- res_comp_i[res_comp_i$Group == group_i,]
+      cors_i &lt;- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
+      
+      if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
+        
+        meta_res_comp_i &lt;-
+          agg(
+            id = Sample,
+            es = R_diff,
+            var = R_diff_SE ^ 2,
+            cor = cors_i,
+            method = &quot;BHHR&quot;,
+            mod = NULL,
+            data = res_comp_group_i
+          )
+        
+        tmp &lt;- res_comp_group_i[1,]
+        tmp$pheno &lt;- NULL
+        tmp$Model_1_R &lt;-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &amp;
+                            meta_res_eval$Target == targ_pop_i &amp;
+                            meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+        tmp$Model_2_R &lt;-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &amp;
+                            meta_res_eval$Target == targ_pop_i &amp;
+                            meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+        tmp$R_diff &lt;- meta_res_comp_i$es
+        tmp$R_diff_SE &lt;- sqrt(meta_res_comp_i$var)
+        tmp$R_diff_z &lt;- tmp$R_diff / tmp$R_diff_SE
+        tmp$R_diff_p &lt;- 2*pnorm(-abs(tmp$R_diff_z))
+      } else {
+        tmp &lt;- res_comp_group_i[1,]
+        tmp$pheno &lt;- NULL
+        tmp$R_diff &lt;- NA
+        tmp$R_diff_SE &lt;- NA
+        tmp$R_diff_z &lt;- NA
+        tmp$R_diff_p &lt;- NA
+      }
+      meta_res_comp &lt;- rbind(meta_res_comp, tmp)
+    }
+  }
+}
+
+meta_res_comp$R_diff_perc &lt;- meta_res_comp$R_diff / meta_res_comp$Model_2_R
+  
+# Compare IndivTrain SBayesRC-multi to TL-SBayesRC-multi
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_2 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                                meta_res_comp$Model_1 == &#39;tlprs_sbayesrc.pop.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;AFR&#39;,]
+round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
+tmp_sbayesrc$R_diff_p
+
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_2 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                                meta_res_comp$Model_1 == &#39;tlprs_sbayesrc.pop.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;EAS&#39;,]
+round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
+tmp_sbayesrc$R_diff_p</code></pre>
+</details>
+<hr />
+</div>
+<div id="computational-resoures-1" class="section level2">
+<h2>Computational resoures</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+library(ggplot2)
+library(cowplot)
+
+setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&#39;
+pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Read in configuration specific benchmark files
+bm_files_i &lt;- list.files(paste0(outdir, &#39;/reference/benchmarks/&#39;), full.names = T)
+
+# Subset benchmarks for pgs_methods
+bm_files_i &lt;- bm_files_i[grepl(&#39;prep_pgs_tlprs&#39;, bm_files_i)]
+
+# Read in benchmark files
+bm_dat_all &lt;- do.call(rbind, lapply(bm_files_i, function(file) {
+  tmp &lt;- fread(file)
+  tmp$file &lt;- basename(file)
+  return(tmp)
+}))
+
+# Create rule column
+bm_dat_all$rule &lt;- gsub(&#39;-.*&#39;,&#39;&#39;,bm_dat_all$file)
+
+# Create method column
+bm_dat_all$method &lt;-
+  gsub(&#39;_i&#39;, &#39;&#39;, gsub(&#39;prep_pgs_&#39;, &#39;&#39;, bm_dat_all$rule))
+
+#############
+# Time
+#############
+
+# Calculate average time taken for each method
+method_avg &lt;- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg &lt;- rbind(
+    method_avg,
+    data.frame(
+      Method = i,
+      Time = mean(bm_dat_all$s[bm_dat_all$method == i])
+    )
+  )
+}
+
+# Convert time in seconds to hours
+method_avg$Time_hour &lt;- method_avg$Time / 60/60
+method_avg$Time_hour &lt;- round(method_avg$Time_hour, 2)
+
+#This is for bidirectional TL-PRS
+
+#############
+# Memory
+#############
+
+# Calculate average max_rss for each method
+method_avg_mem &lt;- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg_mem &lt;- rbind(
+    method_avg_mem,
+    data.frame(
+      Method = i,
+      Memory = mean(bm_dat_all$max_rss[bm_dat_all$method == i])
+    )
+  )
+}
+
+# Format the Memory nicely
+method_avg_mem$Memory_clean &lt;-
+  paste0(round(method_avg_mem$Memory/1000, 2), &#39; Gb&#39;)
+
+ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
+  geom_bar(stat = &quot;identity&quot;, position=&quot;dodge&quot;) +
+  geom_text(aes(label = Memory_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
+  labs(x = &quot;PGS Method&quot;, y = &quot;Memory (Mb)&quot;) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position=&quot;none&quot;)
+
+method_avg_mem$Memory_gb &lt;- method_avg_mem$Memory/1000
+method_avg_mem &lt;- method_avg_mem[, c(&#39;Method&#39;,&#39;Memory_gb&#39;)]
+method_avg_mem$Memory_gb &lt;- round(method_avg_mem$Memory_gb, 2)
+names(method_avg_mem)&lt;-c(&#39;Method&#39;,&quot;Memory (Gb)&quot;)
+
+method_avg&lt;-merge(method_avg, method_avg_mem, by = &#39;Method&#39;)
+
+write.csv(method_avg, &#39;~/oliverpainfel/Analyses/crosspop/time_memory.csv&#39;, row.names=F)</code></pre>
+</details>
+</div>
+</div>
+
+<!-- footer.html -->
+<hr/>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 500px;">
+<img src="Images/logo/sponsors.png">
+</div>
+</div>
+
+
+</div>
+</div>
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
+
+
+</script>
+
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
+
+<!-- code folding -->
+
+<script>
+$(document).ready(function ()  {
+
+    // temporarily add toc-ignore selector to headers for the consistency with Pandoc
+    $('.unlisted.unnumbered').addClass('toc-ignore')
+
+    // move toc-ignore selectors from section div to header
+    $('div.section.toc-ignore')
+        .removeClass('toc-ignore')
+        .children('h1,h2,h3,h4,h5').addClass('toc-ignore');
+
+    // establish options
+    var options = {
+      selectors: "h1,h2",
+      theme: "bootstrap3",
+      context: '.toc-content',
+      hashGenerator: function (text) {
+        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
+      },
+      ignoreSelector: ".toc-ignore",
+      scrollTo: 0
+    };
+    options.showAndHide = true;
+    options.smoothScroll = true;
+
+    // tocify
+    var toc = $("#TOC").tocify(options).data("toc-tocify");
+});
+</script>
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index ad732d06..2abeb5d6 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -273,7 +273,7 @@ config <- list(
     pgs_methods = list(
       description = 'List of polygenic scoring methods to run',
       example = "`['ptclump','dbslmm']`",
-      note = "Options are: `ptclump`, `dbslmm`, `prscs`, `sbayesr`, `lassosum`, `ldpred2`, `megaprs`, `prscsx`, `xwing`, `tlprs`. **Note.** `sbayesr` and `ldpred2` are only implemented for GWAS of EUR ancestry. **Note.** when using multi-source methods (`prscsx`, `xwing`, `tlprs`), the `gwas_groups` parameter must also be provided. See [here](#multi-source-pgs-methods) for more information."
+      note = "Options are: `ptclump`, `dbslmm`, `prscs`, `sbayesr`, `sbayesrc`, `lassosum`, `ldpred2`, `megaprs`, `quickprs`, `prscsx`, `xwing`. **Note.** by default, `quickprs`, `sbayesr`, `sbayesrc` and `ldpred2` are only implemented for GWAS of EUR ancestry. To be applicable to other populations, the user must indicate where the relevent LD reference data is stored (See [here](#specifying-alternative-reference-data-for-pgs-methods. **Note.** when using multi-source methods (`prscsx`, `xwing`), the `gwas_groups` parameter must also be provided. See [here](#multi-source-pgs-methods) for more information."
     ),
     testing = list(
       description = 'Controls testing mode',
@@ -1401,11 +1401,9 @@ kable(gwas_groups_df, 'markdown')
 
 </details>
 
-<div class="note-box">
-
-**Note:** when specifying `tlprs` in the `pgs_methods` list in the `configfile`, you must also specify an additional `tlprs_methods` parameter in the `configfile`, which indicates the PGS methods that TL-PRS will be applied to. For example: `tlprs_methods: ['ptclump','dbslmm']`.
+Another multi-source PGS approach is TL-PRS, which tunes an existing PGS model based on GWAS summary statistics from another population. GenoPred can automate this process. The user must specify which PGS methods they want TL-PRS to be applied to, using the `tlprs_methods` parameter in the `configfile`. For example: `tlprs_methods: ['lassosum','dbslmm']`. Currently, TL-PRS is only applied to the PGS from a given method selected using pseudovalidation. TL-PRS can currently only be applied to the following PGS methods `ptclump`, `dbslmm`, `prscs`, `sbayesrc`, `lassosum`, `ldpred2`, `megaprs`, and `quickprs`.
 
-</div>
+An alternative multi-source PGS approach, is to linearly combine population-specific PGS derived using single-source methods. GenoPred provides use a method called LEOPARD to estimate the optimal linear combination population-specific PGS using summary statistics alone. To calculate PGS based on this estimated linear combination using GenoPred, the user must specify which methods they wish the PGS to be combined using the `leopard_methods` parameter in the `configfile`. For example: `leopard_methods: ['lassosum','dbslmm']`. Currently, this LEOPARD weighting is only applied to the PGS from a given method selected using pseudovalidation. LEOPARD can currently only be applied to the following PGS methods `ptclump`, `dbslmm`, `prscs`, `sbayesrc`, `lassosum`, `ldpred2`, `megaprs`, and `quickprs`.
 
 ***
 
@@ -1439,6 +1437,12 @@ Or, to produce target PGS only the discrete correction, specify:
 pgs_scaling: ['discrete']
 ```
 
+***
+
+## Specifying alternative reference data for PGS methods
+
+
+
 ***
 
 # Running in offline environment
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index efa2dde9..0b2085d0 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -669,10 +669,10 @@ <h2><code>configfile</code></h2>
 </summary>
 <table>
 <colgroup>
-<col width="3%" />
-<col width="12%" />
-<col width="7%" />
-<col width="77%" />
+<col width="2%" />
+<col width="10%" />
+<col width="5%" />
+<col width="81%" />
 </colgroup>
 <thead>
 <tr class="header">
@@ -734,13 +734,18 @@ <h2><code>configfile</code></h2>
 <td align="left">List of polygenic scoring methods to run</td>
 <td align="left"><code>['ptclump','dbslmm']</code></td>
 <td align="left">Options are: <code>ptclump</code>, <code>dbslmm</code>,
-<code>prscs</code>, <code>sbayesr</code>, <code>lassosum</code>,
-<code>ldpred2</code>, <code>megaprs</code>, <code>prscsx</code>,
-<code>xwing</code>, <code>tlprs</code>. <strong>Note.</strong>
-<code>sbayesr</code> and <code>ldpred2</code> are only implemented for
-GWAS of EUR ancestry. <strong>Note.</strong> when using multi-source
-methods (<code>prscsx</code>, <code>xwing</code>, <code>tlprs</code>),
-the <code>gwas_groups</code> parameter must also be provided. See <a
+<code>prscs</code>, <code>sbayesr</code>, <code>sbayesrc</code>,
+<code>lassosum</code>, <code>ldpred2</code>, <code>megaprs</code>,
+<code>quickprs</code>, <code>prscsx</code>, <code>xwing</code>.
+<strong>Note.</strong> by default, <code>quickprs</code>,
+<code>sbayesr</code>, <code>sbayesrc</code> and <code>ldpred2</code> are
+only implemented for GWAS of EUR ancestry. To be applicable to other
+populations, the user must indicate where the relevent LD reference data
+is stored (See
+[here](#specifying-alternative-reference-data-for-pgs-methods.
+<strong>Note.</strong> when using multi-source methods
+(<code>prscsx</code>, <code>xwing</code>), the <code>gwas_groups</code>
+parameter must also be provided. See <a
 href="#multi-source-pgs-methods">here</a> for more information.</td>
 </tr>
 <tr class="even">
@@ -2188,14 +2193,33 @@ <h2>Multi-source PGS methods</h2>
 </tbody>
 </table>
 </details>
-<div class="note-box">
-<p><strong>Note:</strong> when specifying <code>tlprs</code> in the
-<code>pgs_methods</code> list in the <code>configfile</code>, you must
-also specify an additional <code>tlprs_methods</code> parameter in the
-<code>configfile</code>, which indicates the PGS methods that TL-PRS
-will be applied to. For example:
-<code>tlprs_methods: ['ptclump','dbslmm']</code>.</p>
-</div>
+<p>Another multi-source PGS approach is TL-PRS, which tunes an existing
+PGS model based on GWAS summary statistics from another population.
+GenoPred can automate this process. The user must specify which PGS
+methods they want TL-PRS to be applied to, using the
+<code>tlprs_methods</code> parameter in the <code>configfile</code>. For
+example: <code>tlprs_methods: ['lassosum','dbslmm']</code>. Currently,
+TL-PRS is only applied to the PGS from a given method selected using
+pseudovalidation. TL-PRS can currently only be applied to the following
+PGS methods <code>ptclump</code>, <code>dbslmm</code>,
+<code>prscs</code>, <code>sbayesrc</code>, <code>lassosum</code>,
+<code>ldpred2</code>, <code>megaprs</code>, and
+<code>quickprs</code>.</p>
+<p>An alternative multi-source PGS approach, is to linearly combine
+population-specific PGS derived using single-source methods. GenoPred
+provides use a method called LEOPARD to estimate the optimal linear
+combination population-specific PGS using summary statistics alone. To
+calculate PGS based on this estimated linear combination using GenoPred,
+the user must specify which methods they wish the PGS to be combined
+using the <code>leopard_methods</code> parameter in the
+<code>configfile</code>. For example:
+<code>leopard_methods: ['lassosum','dbslmm']</code>. Currently, this
+LEOPARD weighting is only applied to the PGS from a given method
+selected using pseudovalidation. LEOPARD can currently only be applied
+to the following PGS methods <code>ptclump</code>, <code>dbslmm</code>,
+<code>prscs</code>, <code>sbayesrc</code>, <code>lassosum</code>,
+<code>ldpred2</code>, <code>megaprs</code>, and
+<code>quickprs</code>.</p>
 <hr />
 </div>
 <div id="select-ancestry-adjustment-approach" class="section level2">
@@ -2245,6 +2269,11 @@ <h3>Continuous Correction:</h3>
 <hr />
 </div>
 </div>
+<div id="specifying-alternative-reference-data-for-pgs-methods"
+class="section level2">
+<h2>Specifying alternative reference data for PGS methods</h2>
+<hr />
+</div>
 </div>
 <div id="running-in-offline-environment" class="section level1">
 <h1>Running in offline environment</h1>
diff --git a/functions/constants.R b/functions/constants.R
index 0841115f..d04161bb 100644
--- a/functions/constants.R
+++ b/functions/constants.R
@@ -37,8 +37,8 @@ ref_pop <- data.frame(
 
 # Make a data.frame giving labels to the 1KG reference populations
 pgs_method_labels <- data.frame(
-  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','ptclump_multi','dbslmm_multi','ldpred2_multi','sbayesr_multi','sbayesrc_multi','lassosum_multi','prscs_multi','megaprs_multi','quickprs_multi','tlprs_dbslmm'),
-  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','pT+clump-multi','DBSLMM-multi','LDpred2-multi','SBayesR-multi','SBayesRC-multi','lassosum-multi','PRS-CS-multi','MegaPRS-multi','QuickPRS-multi','TL-DBSLMM')
+  method = c('ptclump','dbslmm','ldpred2','sbayesr','sbayesrc','lassosum','prscs','megaprs','quickprs','external','prscsx','xwing','ptclump_multi','dbslmm_multi','ldpred2_multi','sbayesr_multi','sbayesrc_multi','lassosum_multi','prscs_multi','megaprs_multi','quickprs_multi','tlprs_dbslmm','tlprs_sbayesrc','tlprs_quickprs','tlprs_ldpred2'),
+  label = c('pT+clump','DBSLMM','LDpred2','SBayesR','SBayesRC','lassosum','PRS-CS','MegaPRS','QuickPRS','External','PRS-CSx','X-Wing','pT+clump-multi','DBSLMM-multi','LDpred2-multi','SBayesR-multi','SBayesRC-multi','lassosum-multi','PRS-CS-multi','MegaPRS-multi','QuickPRS-multi','TL-DBSLMM','TL-SBayesRC','TL-QuickPRS','TL-LDpred2')
 )
 pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),]
 
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index c4c1b582..430891aa 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -431,7 +431,7 @@ check_pgs_methods(config['pgs_methods'])
 # Check valid tlprs_methods are specified
 def check_tlprs_methods(config):
     valid_tlprs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesrc", "lassosum", "ldpred2", "megaprs","quickprs"
+        "ptclump", "dbslmm", "prscs", "sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs"
     }
 
     # Check if 'tlprs_methods' is empty

From 9ab33cbbc8f496d5e5c69dfd022d7cd70005ae1d Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 28 Feb 2025 16:04:07 +0000
Subject: [PATCH 115/153] Updates

---
 Scripts/pgs_methods/prscsx.R              |  18 +-
 docs/CrossPop.Rmd                         |   7 +-
 docs/GenoClust.Rmd                        | 899 ++++++++++++++++++++++
 functions/pgs.R                           |  25 +-
 pipeline/misc/hapnest/config.synth_2.yaml | 146 ++++
 5 files changed, 1082 insertions(+), 13 deletions(-)
 create mode 100644 docs/GenoClust.Rmd
 create mode 100644 pipeline/misc/hapnest/config.synth_2.yaml

diff --git a/Scripts/pgs_methods/prscsx.R b/Scripts/pgs_methods/prscsx.R
index d67d5ca4..4b336b3b 100644
--- a/Scripts/pgs_methods/prscsx.R
+++ b/Scripts/pgs_methods/prscsx.R
@@ -181,6 +181,9 @@ log <- foreach(i = 1:nrow(jobs), .combine = c, .options.multicore = list(presche
 # Combine score files
 ####
 
+# Read in ref to harmonise score files
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
+
 score_all<-NULL
 for(pop_i in c(unlist(strsplit(opt$populations, ',')), 'META')){
   score_pop<-NULL
@@ -199,21 +202,18 @@ for(pop_i in c(unlist(strsplit(opt$populations, ',')), 'META')){
     }
     score_pop<-cbind(score_pop, score_phi)
   }
+
+  # Sort and flip effects to match reference alleles
+  score_pop <- map_score(ref = ref, score = score_pop)
+  
   if(pop_i == c(unlist(strsplit(opt$populations, ',')), 'META')[1]){
     score_all<-score_pop
   } else {
-    score_all<-merge(score_all, score_pop[, !(names(score_pop) %in% c('A1','A2')), with=F], by='SNP', all=T)
+    score_all<-cbind(score_all, score_pop[, !(names(score_pop) %in% c('SNP','A1','A2')), with=F])
   }
 }
 
-# Replace NA values with 0
-score_all[is.na(score_all)] <- 0
-
-# Flip effects to match reference alleles
-ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
-score_new <- map_score(ref = ref, score = score_all)
-
-fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+fwrite(score_all, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
 
 if(file.exists(paste0(opt$output,'.score.gz'))){
   system(paste0('rm ',opt$output,'.score.gz'))
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 0ca80c99..b1fdda5d 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -3712,8 +3712,8 @@ nrow(nonshared_var)
 cor(shared_var$`prscs_eas_phi_1e-04`, shared_var$`prscsx_EAS_phi_1e-04`)
 cor(nonshared_var$`prscs_eas_phi_1e-04`, nonshared_var$`prscsx_EAS_phi_1e-04`)
 
-nonshared_var[, c('SNP','A1','A2','prscs_eas_phi_1e-04','prscsx_EAS_phi_1e-04'), with=F]
-
+prscs_eas[prscs_eas$SNP %in% nonshared_var$SNP[1:5],]
+prscsx[prscsx$SNP %in% nonshared_var$SNP[1:5],]
 
 shared_var <- data[!is.na(data$`prscs_eur_phi_1e-04`) & !is.na(data$`prscs_eas_phi_1e-04`), ]
 nonshared_var <- data[!is.na(data$`prscs_eur_phi_1e-04`) & is.na(data$`prscs_eas_phi_1e-04`), ]
@@ -3726,6 +3726,9 @@ cor(nonshared_var$`prscs_eur_phi_1e-04`, nonshared_var$`prscsx_EUR_phi_1e-04`)
 
 nonshared_var[, c('SNP','A1','A2','prscs_eas_phi_1e-04','prscsx_EAS_phi_1e-04'), with=F]
 
+# The correlation between non-shared SNP weights from EAS PRS-CS and PRS-CSx PGS is strongly negative.
+# Indicates something weird is going on. I should check this isn't something to do with how I am outputting the SCORE file.
+
 ```
 
 ***
diff --git a/docs/GenoClust.Rmd b/docs/GenoClust.Rmd
new file mode 100644
index 00000000..ed2a04c1
--- /dev/null
+++ b/docs/GenoClust.Rmd
@@ -0,0 +1,899 @@
+---
+title: Cross-population evaluation of polygenic scores
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+In this study, we will investigate the utility if polygenic scores for stratifying individual into aetiological subtypes, and the predictive utility of modelling interactions between polygenic score and subtype membership. Our initial investigation will look at pathways specific PGS.
+
+We have previously conducted a very simple proof of concept study [here](https://opain.github.io/GenoClust/proof_of_principle.html). Here will conduct a more realistic simulation to determine feasibility and refine the methodology, and then apply the methodology to complex disease of interest.
+
+Things to think about:
+- Derive clusters using cases only, and then project in to full sample, or derive clusters within full sample?
+- Clustering method: K-means, Gaussian mixture model, hierarchical clustering
+
+***
+
+# Proof-of-concept
+
+## Simulated data
+
+We could simulate genotype and phenotype data using HAPNEST, two create two phenotypes with causal variants within different pathways.
+
+Simulate two binary phenotypes, with a genetic correlation of 0. Specify causal variants present within a gene set for each disease:
+- Disease 1: GO:0006281 DNA repair
+- Disease 2: GO:0048167 — Regulation of synaptic plasticity
+
+***
+
+### Define causal variants
+
+Download GO data to identify genes within these sets. 
+
+```{r}
+# Read in .gmt file
+gmt <- readLines('/scratch/prj/oliverpainfel/recovered/Data/MSigDB/c5.go.bp.v7.5.1.entrez.gmt')
+gene_sets <- strsplit(gmt, "\t")
+gene_list <- setNames(lapply(gene_sets, `[`, -c(1,2)), sapply(gene_sets, `[`, 1))
+
+# Subset sets of interest
+gene_list <- gene_list[ c('GOBP_DNA_REPAIR', 'GOBP_REGULATION_OF_SYNAPTIC_PLASTICITY')]
+
+# Identify variants within genes within each set
+gene_loc <- fread('/scratch/prj/oliverpainfel/recovered/Data/Gene_Locations/MAGMA_NCBI37.3/NCBI37.3.gene.loc')
+names(gene_loc)<-c('ID','CHR','START','STOP','STRAND','NAME')
+gene_loc <- gene_loc[gene_loc$CHR == 22,] #out simulation will only use chr22 data
+
+# Read in hm3 SNP-list
+rsids<-fread('~/oliverpainfel/Data/HAPNEST/data/inputs/processed/1KG+HGDP/rsid_map_list_chr22.txt')
+snplist<-fread('~/oliverpainfel/Data/HAPNEST/data/inputs/processed/1KG+HGDP/hapmap_variant_list_chr22.txt', header=F)$V1
+snplist_tab<-data.frame(do.call(rbind, lapply(strsplit(snplist, split = ':'), function(x) c(x))))
+names(snplist_tab) <- c('CHR', 'BP', 'A1', 'A2')
+snplist_tab$CHR <- gsub('chr', '', snplist_tab$CHR)
+rsids<-rsids[match(snplist, rsids$id_hg38),]
+snplist_tab$SNP <- rsids$rsid
+
+# Create SNP-list for each gene set
+dir.create('~/oliverpainfel/Data/HAPNEST/synth_2', recursive = T)
+
+set.seed(1)
+snp_sets <- list()
+for(i in names(gene_list)){
+  snp_sets[[i]] <- NULL
+  genes <- gene_list[[i]]
+  genes <- genes[genes %in% gene_loc$ID]
+  for(k in genes){
+    CHR <- gene_loc$CHR[gene_loc$ID == k] 
+    START <- gene_loc$START[gene_loc$ID == k] 
+    STOP <- gene_loc$STOP[gene_loc$ID == k]
+    
+    snp_set <- snplist_tab$SNP[which(snplist_tab$CHR == CHR & snplist_tab$BP >= START & snplist_tab$BP <= STOP)]
+    if(!(length(snp_set) > 0)){
+      next
+    }
+    
+    # Select 1 variant from each gene to be causal
+    snp_sets[[i]] <- c(snp_sets[[i]], sample(snp_set, 1))
+  }
+  snp_sets[[i]] <- unique(snp_sets[[i]])
+}
+
+# 
+causal_var<-data.frame(SNP = unlist(snp_sets))
+for(i in 1:length(snp_sets)){
+  causal_var[[paste0('Trait_', i)]]<-as.numeric(causal_var$SNP %in% snp_sets[[i]])
+}
+
+# Save as csv
+write.table(causal_var, '~/oliverpainfel/Data/HAPNEST/synth_2/causal.snplist', row.names = F, col.names = F, quote = F, sep=',')
+
+```
+
+***
+
+### Run HAPNEST
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd ~/oliverpainfel/Data/HAPNEST
+
+# Generate genotype and phenotype data
+singularity exec \
+  --bind data/:/data/ \
+  --bind /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/misc/hapnest/config.synth_2.yaml:/data/config.synth_2.yaml \
+  containers/intervene-synthetic-data_latest.sif \
+  generate_geno \
+  8 \
+  data/config.synth_2.yaml
+  
+# Keep getting an error due to memory allocation. In the end I ran it with 1 core
+
+singularity exec \
+  --bind data/:/data/ \
+  --bind /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/misc/hapnest/config.synth_2.yaml:/data/config.synth_2.yaml \
+  containers/intervene-synthetic-data_latest.sif \
+  generate_pheno \
+  data/config.synth_2.yaml
+  
+```
+
+</details>
+
+***
+
+### Run GSEA
+
+We need to run a GWAS of each phenotyepe, and then MAGMA to see whether our simulation has lead to an enrichment of selected pathways
+
+***
+
+#### Perform PCA
+
+<details><summary>Show code</summary>
+
+```{r}
+# conda activate .snakemake/conda/ea13b6c549c70695534894feeeecf0b3_
+setwd('~/oliverpainfel/GenoPred/pipeline/')
+
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+  make_option("--target_plink_chr", action="store", default=NULL, type='character',
+              help="Path to per chromosome target PLINK files [required]"),
+  make_option("--maf", action="store", default=0.05, type='numeric',
+              help="Minor allele frequency threshold [optional]"),
+  make_option("--geno", action="store", default=0.02, type='numeric',
+              help="Variant missingness threshold [optional]"),
+  make_option("--hwe", action="store", default=1e-6, type='numeric',
+              help="Hardy Weinberg p-value threshold. [optional]"),
+  make_option("--n_pcs", action="store", default=10, type='numeric',
+              help="Number of PCs (min=4) [optional]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+              help="Path PLINK2 software binary [required]"),
+  make_option("--keep_list", action="store", default=NULL, type='character',
+              help="File containing list of keep files and corresponding population code [optional]"),
+  make_option("--unrel", action="store", default=NA, type='character',
+              help="File containing list of unrelated individuals [optional]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+              help="Number of cores for parallel computing [optional]"),
+  make_option("--test", action="store", default=NA, type='character',
+              help="Specify test mode [optional]"),
+  make_option("--output", action="store", default=NULL, type='character',
+              help="Path for output files [required]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+opt$target_plink_chr<-'/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-'
+opt$output<-'~/oliverpainfel/Analysis/HAPNEST/synth_1/pca/'
+opt$test<-'chr22'
+
+keep_list<-data.frame(
+  file = c(
+    '~/oliverpainfel/Analysis/HAPNEST/synth_1/training.EUR.txt',
+    '~/oliverpainfel/Analysis/HAPNEST/synth_1/training.EAS.txt',
+    '~/oliverpainfel/Analysis/HAPNEST/synth_1/training.AFR.txt'),
+  POP = c('EUR','EAS','AFR')
+)
+
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+############
+# Create file listing variants in regions of long range LD
+############
+
+targ_pvar <- read_bim(opt$target_plink_chr, chr = CHROMS)
+targ_pvar <- remove_regions(dat = targ_pvar, regions = long_ld_coord)
+
+for(pop in keep_list$POP){
+  # Read in keep file for population
+  keep_file <- fread(keep_list$file[keep_list$POP == pop], header=F)
+  if(ncol(keep_file) == 1){
+    keep_file <- data.table(
+      FID = keep_file$V1,
+      IID = keep_file$V1)
+  } else {
+    keep_file <- data.table(
+      FID = keep_file$V1,
+      IID = keep_file$V2)
+  }
+
+  ###########
+  # Perform PCA on QC'd and independent variants
+  ###########
+
+  # Create QC'd SNP-list
+  target_qc_snplist <- plink_qc_snplist(bfile = opt$target_plink_chr, plink2 = opt$plink2, chr = CHROMS, keep = keep_file, maf = opt$maf, geno = opt$geno, hwe = opt$hwe, threads = opt$n_cores)
+
+  # Remove high LD regions
+  target_qc_snplist <- target_qc_snplist[target_qc_snplist %in% targ_pvar$SNP]
+
+  # Perform LD pruning
+  ld_indep <- plink_prune(bfile = opt$target_plink_chr, chr = CHROMS, keep = keep_file, plink2 = opt$plink2, extract = target_qc_snplist, threads = opt$n_cores)
+
+  # To improve efficiency, derive PCs using random subset of 1000 individuals.
+  keep_file_subset <- keep_file[sample(1000, replace = T),]
+  keep_file_subset <- keep_file_subset[!duplicated(keep_file_subset),]
+
+  # Run PCA
+  snp_weights <- plink_pca(bfile = opt$target_plink_chr, keep = keep_file_subset, chr = CHROMS, plink2 = opt$plink2, extract = ld_indep, n_pc = opt$n_pcs, threads = opt$n_cores)
+  fwrite(snp_weights, paste0(tmp_dir,'/ref.eigenvec.var'), row.names = F, quote=F, sep=' ', na='NA')
+
+  # Project into the full population
+  target_pcs <- plink_score(bfile = opt$target_plink_chr, keep = keep_file, chr = CHROMS, plink2 = opt$plink2, score = paste0(tmp_dir,'/ref.eigenvec.var'), threads = opt$n_cores)
+
+  fwrite(target_pcs, paste0(opt$output, pop,'.pcs.txt'), quote=F, sep=' ', na='NA')
+}
+
+```
+
+</details>
+
+***
+
+#### Perform GWAS
+
+<details><summary>Show code</summary>
+
+```{bash}
+
+module add plink2
+for pheno in $(seq 1 1); do
+    awk 'BEGIN {OFS="\t"} NR==1 {print "FID", "IID", "pheno"} NR>1 {print $1, $1, $NF}' /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno} > /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno}.plink
+    
+  for pop in $(echo EUR EAS AFR); do
+  
+    mkdir -p ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}
+    for chr in $(seq 22 22); do
+        sbatch -p neurohack_cpu --mem 20G -n 4 --wrap="plink2 \
+          --bfile /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22 \
+          --pheno /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno}.plink \
+          --1 \
+          --covar ~/oliverpainfel/Analysis/HAPNEST/synth_1/pca/${pop}.pcs.txt \
+          --covar-variance-standardize \
+          --logistic omit-ref cols=+a1freq,+ax hide-covar \
+          --maf 0.01 \
+          --geno 0.05 \
+          --out ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr${chr}"
+    done
+  done
+done
+
+# Once complete, merge results across chromosomes
+for pheno in $(seq 1 1); do
+  for pop in $(echo EUR EAS AFR); do
+    head -n 1 ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr22.pheno.glm.logistic.hybrid > ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+      for chr in $(seq 22 22); do
+        tail -n +2 ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr22.pheno.glm.logistic.hybrid >> ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+      done
+      
+      # Remove REF and ALT columns and rename AX column to A2
+      cut -f 4,5 --complement ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid | awk 'BEGIN{FS=OFS="\t"} NR==1 {$7="A2"} 1' > temp.txt && mv temp.txt ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+  
+      gzip ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
+  done
+done
+
+```
+
+</details>
+
+***
+
+#### Run MAGMA
+
+<details><summary>Show code</summary>
+
+```{bash}
+
+
+
+```
+
+</details>
+
+***
+
+## Artificial misdiagnosis
+
+Look at the UKB independent sumstats that I have used for PGS comparison. Check PGS R2 and gene set enrichment.
+
+T2D and IBD both have strong PGS R2, and presumably different gene set enrichment.
+
+***
+
+### Run GSEA
+
+```{bash}
+
+mkdir -p ~/oliverpainfel/Analyses/GenoClust/artificial/gwas
+cp /scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/GenoPredPipe/output/reference/gwas_sumstat/GCST004773/GCST004773-cleaned.gz ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/T2D.gz
+cp /scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/GenoPredPipe/output/reference/gwas_sumstat/GCST004131/GCST004131-cleaned.gz ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/IBD.gz
+cp /scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/GenoPredPipe/output/reference/gwas_sumstat/GCST90013445/GCST90013445-cleaned.gz ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/T1D.gz
+cp /scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/GenoPredPipe/output/reference/gwas_sumstat/GCST90013534/GCST90013534-cleaned.gz ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/RA.gz
+cp /scratch/prj/oliverpainfel/recovered/Data/GWAS_sumstats/prs_comparison/cleaned/COAD01.cleaned.gz ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/CAD.gz
+
+/scratch/prj/oliverpainfel/recovered/Data/GWAS_sumstats/prs_comparison/cleaned/
+
+gunzip ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/T2D.gz
+gunzip ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/IBD.gz
+gunzip ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/T1D.gz
+gunzip ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/RA.gz
+gunzip ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/CAD.gz
+
+mkdir -p ~/oliverpainfel/Analyses/GenoClust/artificial/magma/
+
+# Gene association
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --bfile /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma_ref/g1000_eur \
+  --pval ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/T2D use=SNP,P ncol=N \
+  --gene-annot /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma/NCBI37.3.genes.annot \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/T2D_gene_level
+
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --bfile /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma_ref/g1000_eur \
+  --pval ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/IBD use=SNP,P ncol=N \
+  --gene-annot /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma/NCBI37.3.genes.annot \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/IBD_gene_level
+
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --bfile /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma_ref/g1000_eur \
+  --pval ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/T1D use=SNP,P ncol=N \
+  --gene-annot /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma/NCBI37.3.genes.annot \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/T1D_gene_level
+
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --bfile /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma_ref/g1000_eur \
+  --pval ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/RA use=SNP,P ncol=N \
+  --gene-annot /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma/NCBI37.3.genes.annot \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/RA_gene_level
+
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --bfile /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma_ref/g1000_eur \
+  --pval ~/oliverpainfel/Analyses/GenoClust/artificial/gwas/CAD use=SNP,P ncol=N \
+  --gene-annot /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma/NCBI37.3.genes.annot \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/CAD_gene_level
+
+# GSEA
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --gene-results ~/oliverpainfel/Analyses/GenoClust/artificial/magma/T2D_gene_level.genes.raw \
+  --set-annot /scratch/prj/oliverpainfel/recovered/Data/MSigDB/c2.all.v7.5.1.entrez.gmt \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/T2D_gsea
+# Surprisingly T2D has very few significantly enriched gene sets
+
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --gene-results ~/oliverpainfel/Analyses/GenoClust/artificial/magma/IBD_gene_level.genes.raw \
+  --set-annot /scratch/prj/oliverpainfel/recovered/Data/MSigDB/c2.all.v7.5.1.entrez.gmt \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/IBD_gsea
+  
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --gene-results ~/oliverpainfel/Analyses/GenoClust/artificial/magma/T1D_gene_level.genes.raw \
+  --set-annot /scratch/prj/oliverpainfel/recovered/Data/MSigDB/c2.all.v7.5.1.entrez.gmt \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/T1D_gsea
+  
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --gene-results ~/oliverpainfel/Analyses/GenoClust/artificial/magma/RA_gene_level.genes.raw \
+  --set-annot /scratch/prj/oliverpainfel/recovered/Data/MSigDB/c2.all.v7.5.1.entrez.gmt \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/RA_gsea
+
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --gene-results ~/oliverpainfel/Analyses/GenoClust/artificial/magma/CAD_gene_level.genes.raw \
+  --set-annot /scratch/prj/oliverpainfel/recovered/Data/MSigDB/c2.all.v7.5.1.entrez.gmt \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/CAD_gsea
+  
+```
+
+```{r}
+# Compare GSEA results across traits
+T2D<-fread('/users/k1806347/oliverpainfel/Analyses/GenoClust/artificial/magma/T2D_gsea.gsa.out', skip = 3)
+IBD<-fread('/users/k1806347/oliverpainfel/Analyses/GenoClust/artificial/magma/IBD_gsea.gsa.out', skip = 3)
+T1D<-fread('/users/k1806347/oliverpainfel/Analyses/GenoClust/artificial/magma/T1D_gsea.gsa.out', skip = 3)
+RA<-fread('/users/k1806347/oliverpainfel/Analyses/GenoClust/artificial/magma/RA_gsea.gsa.out', skip = 3)
+CAD<-fread('/users/k1806347/oliverpainfel/Analyses/GenoClust/artificial/magma/CAD_gsea.gsa.out', skip = 3)
+
+T2D <- T2D[, c('FULL_NAME','BETA','P'), with=F]
+T2D$TRAIT <- 'T2D'
+IBD <- IBD[, c('FULL_NAME','BETA','P'), with=F]
+IBD$TRAIT <- 'IBD'
+T1D <- T1D[, c('FULL_NAME','BETA','P'), with=F]
+T1D$TRAIT <- 'T1D'
+RA <- RA[, c('FULL_NAME','BETA','P'), with=F]
+RA$TRAIT <- 'RA'
+CAD <- CAD[, c('FULL_NAME','BETA','P'), with=F]
+CAD$TRAIT <- 'CAD'
+
+both <- rbind(T2D, IBD, T1D, RA, CAD)
+
+both_wide <- reshape(both, 
+                     idvar = "FULL_NAME", 
+                     timevar = "TRAIT", 
+                     direction = "wide")
+
+cor(both_wide[, grepl('BETA', names(both_wide)), with = F], use='p')
+
+both_wide$FDR.T2D <- p.adjust(both_wide$P.T2D, method = 'fdr')
+both_wide$FDR.IBD <- p.adjust(both_wide$P.IBD, method = 'fdr')
+both_wide$FDR.T1D <- p.adjust(both_wide$P.T1D, method = 'fdr')
+both_wide$FDR.RA <- p.adjust(both_wide$P.RA, method = 'fdr')
+both_wide$FDR.CAD <- p.adjust(both_wide$P.CAD, method = 'fdr')
+
+library(VennDiagram)
+
+venn.plot <- venn.diagram(
+  x = list(T2D = both_wide$FULL_NAME[both_wide$FDR.T2D < 0.05], IBD = both_wide$FULL_NAME[both_wide$FDR.IBD < 0.05]),
+  filename = NULL,
+  output = TRUE
+)
+
+dev.off()
+grid.draw(venn.plot)
+
+venn.plot <- venn.diagram(
+  x = list(T1D = both_wide$FULL_NAME[which(both_wide$FDR.T1D < 0.05)], IBD = both_wide$FULL_NAME[which(both_wide$FDR.IBD < 0.05)]),
+  filename = NULL,
+  output = TRUE
+)
+
+dev.off()
+grid.draw(venn.plot)
+
+venn.plot <- venn.diagram(
+  x = list(RA = both_wide$FULL_NAME[which(both_wide$FDR.RA < 0.05)], IBD = both_wide$FULL_NAME[which(both_wide$FDR.IBD < 0.05)]),
+  filename = NULL,
+  output = TRUE
+)
+
+dev.off()
+grid.draw(venn.plot)
+
+venn.plot <- venn.diagram(
+  x = list(RA = both_wide$FULL_NAME[which(both_wide$FDR.RA < 0.05)], T1D = both_wide$FULL_NAME[which(both_wide$FDR.T1D < 0.05)]),
+  filename = NULL,
+  output = TRUE
+)
+
+dev.off()
+grid.draw(venn.plot)
+
+venn.plot <- venn.diagram(
+  x = list(RA = both_wide$FULL_NAME[which(both_wide$FDR.RA < 0.05)], CAD = both_wide$FULL_NAME[which(both_wide$FDR.CAD < 0.05)]),
+  filename = NULL,
+  output = TRUE
+)
+
+dev.off()
+grid.draw(venn.plot)
+
+venn.plot <- venn.diagram(
+  x = list(IBD = both_wide$FULL_NAME[which(both_wide$FDR.IBD < 0.05)], CAD = both_wide$FULL_NAME[which(both_wide$FDR.CAD < 0.05)]),
+  filename = NULL,
+  output = TRUE
+)
+
+dev.off()
+grid.draw(venn.plot)
+
+```
+***
+
+### Meta-analyse GWAS
+
+Lets use RA and CAD as they have a low correlation, but decent number of significant sets.
+
+```{bash, eval=F, echo=T}
+
+# Run METAL
+/scratch/prj/oliverpainfel/recovered/Software/generic-metal/metal
+
+SCHEME SAMPLESIZE
+MARKER SNP
+WEIGHT N
+ALLELE A1 A2
+EFFECT BETA
+STDERR SE
+PVALUE P
+PROCESS /scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/CAD
+PROCESS /scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/RA
+OUTFILE /scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/mix_CAD_RA .tbl
+
+ANALYZE
+
+QUIT
+
+```
+
+```{r, eval=F, echo=T}
+# Format the METAL output
+library(data.table)
+
+# Read in the sumstats
+meta<-fread('/scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/mix_CAD_RA1.tbl')
+
+# Remove SNPs that were not present in both studies
+meta <- meta[!grepl('\\?', meta$Direction),]
+
+# Format for GenoPred
+names(meta)<-c('SNP','A1','A2','N','BETA','P','Direction')
+meta$A1<-toupper(meta$A1)
+meta$A2<-toupper(meta$A2)
+meta$SE<-1
+meta<-meta[,c('SNP','A1','A2','BETA','SE','N','P'),with=F]
+
+fwrite(meta, '/scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/mix_CAD_RA.tbl.reformat', quote=F, sep=' ', na='NA')
+
+```
+
+```{bash}
+# Rerun MAGMA to see how the results compare to the original GWAS
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --bfile /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma_ref/g1000_eur \
+  --pval /scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/mix_CAD_RA.tbl.reformat use=SNP,P ncol=N \
+  --gene-annot /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma/NCBI37.3.genes.annot \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/mix_CAD_RA_gene_level
+
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --gene-results ~/oliverpainfel/Analyses/GenoClust/artificial/magma/mix_CAD_RA_gene_level.genes.raw \
+  --set-annot /scratch/prj/oliverpainfel/recovered/Data/MSigDB/c2.all.v7.5.1.entrez.gmt \
+  --out ~/oliverpainfel/Analyses/GenoClust/artificial/magma/mix_CAD_RA_gsea
+  
+```
+
+```{r}
+# Compare GSEA results across traits
+RA<-fread('/users/k1806347/oliverpainfel/Analyses/GenoClust/artificial/magma/RA_gsea.gsa.out', skip = 3)
+CAD<-fread('/users/k1806347/oliverpainfel/Analyses/GenoClust/artificial/magma/CAD_gsea.gsa.out', skip = 3)
+MIX<-fread('/users/k1806347/oliverpainfel/Analyses/GenoClust/artificial/magma/mix_CAD_RA_gsea.gsa.out', skip = 3)
+
+RA <- RA[, c('FULL_NAME','BETA','P'), with=F]
+RA$TRAIT <- 'RA'
+CAD <- CAD[, c('FULL_NAME','BETA','P'), with=F]
+CAD$TRAIT <- 'CAD'
+MIX <- MIX[, c('FULL_NAME','BETA','P'), with=F]
+MIX$TRAIT <- 'MIX'
+
+both <- rbind(RA, CAD, MIX)
+
+both_wide <- reshape(both, 
+                     idvar = "FULL_NAME", 
+                     timevar = "TRAIT", 
+                     direction = "wide")
+
+cor(both_wide[, grepl('BETA', names(both_wide)), with = F], use='p')
+
+both_wide$FDR.RA <- p.adjust(both_wide$P.RA, method = 'fdr')
+both_wide$FDR.CAD <- p.adjust(both_wide$P.CAD, method = 'fdr')
+both_wide$FDR.MIX <- p.adjust(both_wide$P.MIX, method = 'fdr')
+
+library(VennDiagram)
+
+venn.plot <- venn.diagram(
+  x = list(RA = both_wide$FULL_NAME[both_wide$FDR.RA < 0.05], 
+           CAD = both_wide$FULL_NAME[both_wide$FDR.CAD < 0.05],
+           MIX = both_wide$FULL_NAME[both_wide$FDR.MIX < 0.05]),
+  filename = NULL,
+  output = TRUE
+)
+
+dev.off()
+grid.draw(venn.plot)
+
+# It is still picking up some pathways for both disorders, but also many new pathways.
+both_wide[both_wide$FDR.MIX < 0.05 & both_wide$FDR.RA > 0.05 & both_wide$FDR.CAD > 0.05,]
+```
+
+***
+
+### Create phenotype data in UKB
+
+```{r}
+#####
+# CAD - Use definition from https://doi.org/10.1001/jama.2019.22241
+#####
+
+library(ukbkings)
+library(dplyr)
+library(stringr)
+
+# Extract ICD code and sex from UKB
+project_dir <- "/datasets/ukbiobank/ukb82087"
+f <- bio_field(project_dir)
+f %>%
+    select(field, name) %>%
+    filter(str_detect(field, "^31-0.0|41270|41271|41272|20002|20004|6150")) %>%
+    bio_field_add("~/oliverpainfel/Data/ukb/phenotypes/cad_field_subset.txt")
+
+bio_phen(
+    project_dir,
+    field = "~/oliverpainfel/Data/ukb/phenotypes/cad_field_subset.txt",
+    out = "~/oliverpainfel/Data/ukb/phenotypes/cad_field_subset"
+)
+
+df <- readRDS("~/oliverpainfel/Data/ukb/phenotypes/cad_field_subset.rds")
+
+# Load required libraries
+library(dplyr)
+
+# Assume 'ukb_data' is your loaded data frame
+# ukb_data <- read.csv("your_data_file.csv") # Example for loading data
+
+# CAD code definitions
+cad_icd10 <- c("I21", "I22", "I23", "I241", "I252")
+cad_icd9 <- c("410", "411", "412", "42789")
+cad_opcs4 <- paste0("K", c("401", "402", "403", "404",
+                           "411", "412", "413", "414",
+                           "451", "452", "453", "454", "455"))
+cad_self_reported <- c("1075")
+cad_operation <- c("1070", "1095")
+cad_vascular <- c("1")
+
+# Columns to search in
+icd10_cols <- grep("^41270", names(df), value = TRUE) # ICD-10 columns
+icd9_cols <- grep("^41271", names(df), value = TRUE) # ICD-9 columns
+opcs4_cols <- grep("^41272", names(df), value = TRUE) # OPCS-4 columns
+self_report_cols <- grep("^20002", names(df), value = TRUE) # Self-reported illness
+operation_cols <- grep("^20004", names(df), value = TRUE) # Operation codes
+vascular_cols <- grep("^6150", names(df), value = TRUE) # Vascular/heart problems
+
+# Create a binary CAD phenotype
+df <- df %>%
+  mutate(
+    CAD = if_else(
+      rowSums(across(all_of(icd10_cols), ~ . %in% cad_icd10), na.rm = TRUE) > 0 |
+      rowSums(across(all_of(icd9_cols), ~ . %in% cad_icd9), na.rm = TRUE) > 0 |
+      rowSums(across(all_of(opcs4_cols), ~ . %in% cad_opcs4), na.rm = TRUE) > 0 |
+      rowSums(across(all_of(self_report_cols), ~ . %in% cad_self_reported), na.rm = TRUE) > 0 |
+      rowSums(across(all_of(operation_cols), ~ . %in% cad_operation), na.rm = TRUE) > 0 |
+      rowSums(across(all_of(vascular_cols), ~ . %in% cad_vascular), na.rm = TRUE) > 0,
+      1, 0
+    )
+  )
+
+# Summary of CAD cases
+table(df$CAD)
+
+cad <- data.frame(eid = df$eid, CAD = df$CAD)
+
+#####
+# RA
+#####
+
+keep_files<-list.files(path = '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/pcs/within_sample/', pattern = '.keep')
+
+pop_dat<-NULL
+for(i in keep_files){
+  tmp<-fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/pcs/within_sample/', i))
+  names(tmp)<-c('FID','IID')
+  tmp$POP<-gsub('.keep','', gsub('ukb.outlier_detection.','',i))
+  pop_dat<-rbind(pop_dat, tmp)
+}
+
+# Update row number IDs to project specific IDs
+psam<-fread('/scratch/prj/ukbiobank/recovered/ukb82087/imputed/ukb82087_imp_chr1_MAF1_INFO4_v1.psam')
+psam$rn<-1:nrow(psam)
+psam<-psam[,c('IID','rn'), with = F]
+
+pop_dat$FID<-NULL
+pop_dat<-merge(pop_dat, psam, by.x='IID', by.y='rn')
+pop_dat<-data.frame(
+  eid=pop_dat$IID.y,
+  POP=pop_dat$POP
+)
+
+# This has been defined already during our UKB benchmark
+ra <- fread('/scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/phenotypes/ra.unrel.txt')
+names(ra)<-c('eid','RA')
+
+both <- merge(ra, cad, by='eid')
+both$MIX <- ifelse(both$RA == 1 | both$CAD == 1, 1, 0)
+nrow(both[both$RA == 1 & both$CAD == 1,])
+
+# Restrict analysis to EUR individuals
+both<-both[both$eid %in% pop_dat$eid[pop_dat$POP == 'EUR']]
+
+# There are only 954  RA cases but >22k CAD cases. This could make it hard for clustering to pick out this cluster - Lets see.
+# These are unrelated individuals in UKB already.
+# To speed things up, lets use a subset of 25k controls
+set.seed(1)
+controls<-both$eid[both$MIX == 0]
+keep<-sample(controls, 25000)
+both<-both[!(both$eid %in% controls[!(controls %in% keep)])]
+
+write.table(both, '~/oliverpainfel/Data/ukb/phenotypes/ra_cad_mix.subset.txt', row.names = F, col.names = T, quote = F)
+
+# Save with row.number IDs
+both<-merge(both, psam, by.x='eid', by.y='IID')
+
+for(i in c('RA','CAD','MIX')){
+  pheno<-data.frame(
+    FID=both$rn,
+    IID=both$rn,
+    outcome=both[[i]]
+  )
+  write.table(pheno, paste0('~/oliverpainfel/Data/ukb/phenotypes/ra_cad_mix.subset.', i, '_only.txt'), row.names = F, col.names = T, quote = F)
+}
+
+keep<-data.frame(FID = both$rn, IID = both$rn)
+
+write.table(keep, '~/oliverpainfel/Data/ukb/phenotypes/ra_cad_mix.subset.keep', row.names = F, col.names = F, quote = F)
+
+```
+
+***
+
+### Run GenoPred
+
+```{bash}
+mkdir ~/oliverpainfel/Data/ukb/genoclust_subset
+
+for chr in $(seq 1 22); do
+  ~/oliverpainfel/Software/plink2 \
+    --pfile ~/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
+    --keep ~/oliverpainfel/Data/ukb/phenotypes/ra_cad_mix.subset.keep \
+    --make-pgen \
+    --out ~/oliverpainfel/Data/ukb/genoclust_subset/ukb.chr${chr}
+done
+
+```
+
+```{r}
+library(data.table)
+
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset')
+
+######
+# target_list
+######
+
+target_list <- data.frame(
+  name='ukb',
+  path='/users/k1806347/oliverpainfel/Data/ukb/genoclust_subset/ukb',
+  type='plink2',
+  indiv_report=F,
+  unrel='/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt'
+)
+
+write.table(target_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/target_list.txt', col.names=T, row.names=F, quote=F)
+
+######
+# gwas_list
+######
+
+gwas_list<-data.frame(
+  name=c('CAD','RA','MIX'),
+  path=c(
+    '/scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/CAD',
+    '/scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/RA',
+    '/scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/mix_CAD_RA.tbl.reformat'
+  ),
+  population='EUR',
+  n=NA,
+  sampling=NA,
+  prevalence=NA,
+  mean=NA,
+  sd=NA,
+  label=paste0('"', c('CAD','RA','MIX'),'"')
+)
+
+write.table(gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/gwas_list.txt', col.names=T, row.names=F, quote=F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_genoclust",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/gwas_list.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/target_list.txt",
+  "pgs_methods: ['sbayesrc']",
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 10",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate genopred
+
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml \
+  output_all -n
+```
+
+***
+
+### Check correlation
+
+Check correlation between the PGS for CAD, RA and MIX with CAD, RA and MIX outcomes.
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline')
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Define pgs_methods used
+pgs_methods <- read_param(config = '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml', param = 'pgs_methods', return_obj = F)
+
+# Read in PGS
+pgs <- read_pgs(config = '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml', name = 'ukb')$ukb
+pgs <- Reduce(function(x, y) merge(x, y, by = c("FID", "IID"), all = TRUE), lapply(pgs$TRANS, function(x) x[[1]]))
+
+# Read in outcome data
+outcome_names<-c('CAD','RA','MIX')
+outcomes <- list()
+for(i in outcome_names){
+  outcomes[[i]]<-fread(paste0('~/oliverpainfel/Data/ukb/phenotypes/ra_cad_mix.subset.', i, '_only.txt'))
+  names(outcomes[[i]])[names(outcomes[[i]]) == 'outcome']<-paste0(i,'_outcome')
+}
+outcomes <- Reduce(function(x, y) merge(x, y, by = c("FID", "IID"), all = TRUE), outcomes)
+
+# Test correlation
+both <- merge(pgs, outcomes, by = c('FID', 'IID'))
+
+round(cor(both[,-1:-2]), 2)
+
+#              CAD_SBayesRC RA_SBayesRC MIX_SBayesRC CAD_outcome RA_outcome MIX_outcome
+# CAD_SBayesRC         1.00       -0.01         0.24        0.20      -0.02        0.20
+# RA_SBayesRC         -0.01        1.00         0.87        0.00       0.12        0.03
+# MIX_SBayesRC         0.24        0.87         1.00        0.06       0.11        0.09
+# CAD_outcome          0.20        0.00         0.06        1.00      -0.10        0.97
+# RA_outcome          -0.02        0.12         0.11       -0.10       1.00        0.15
+# MIX_outcome          0.20        0.03         0.09        0.97       0.15        1.00
+
+# This looks good so far:
+# - The MIX PGS is more correlated with the RA PGS than CAD PGS, due to larger genetic effects on RA
+# - The MIX outcome is more correlated with the CAD outcome, due to large number of CAD cases
+# - The MIX PGS predicts CAD and RA outcome worse that CAD and RA PGS respectively
+
+# The reason for PGS stratification is becoming clearer - If we were to extract genetic effects relevant to CAD, then the CAD PGS would predict CAD cases better. Likewise for RA.
+
+# It would be interesting to see whether we can cluster individuals based on these scores alone. This would be similar to the T2D paper, which stratifies PGS by relationship with other traits. Our pathway specific strategy will take a different approach.
+```
+
+
diff --git a/functions/pgs.R b/functions/pgs.R
index 5ed03511..0d06f304 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -79,8 +79,29 @@ list_score_files <- function(config){
 
 # Flip effects in score file to match A1 reference
 map_score<-function(ref, score){
-  if(!all(c('SNP','A1','A2') %in% names(ref)) | !all(c('SNP','A1','A2') %in% names(score))){
-    stop('ref and score must contain SNP, A1 and A2 columns.')
+  # Check if required columns exist
+  required_cols <- c('SNP', 'A1', 'A2')
+  if (!all(required_cols %in% names(ref)) | 
+      !all(required_cols %in% names(score))) {
+    stop('ref and score must contain SNP, A1, and A2 columns.')
+  }
+  
+  # Valid alleles
+  valid_alleles <- c('A', 'T', 'C', 'G')
+  
+  # Check for NA or invalid alleles in A1 and A2 columns
+  for (col in c('A1', 'A2')) {
+    if (any(is.na(ref[[col]])) | any(!ref[[col]] %in% valid_alleles)) {
+      stop(paste('Invalid allele values detected in ref column:', col))
+    }
+    if (any(is.na(score[[col]])) | any(!score[[col]] %in% valid_alleles)) {
+      stop(paste('Invalid allele values detected in score column:', col))
+    }
+  }
+  
+  # Check for NA values in SNP column
+  if (any(is.na(ref$SNP)) | any(is.na(score$SNP))) {
+    stop('NA values detected in SNP column of ref or score.')
   }
   
   ref <- ref[, c('SNP','A1','A2'), with = F]
diff --git a/pipeline/misc/hapnest/config.synth_2.yaml b/pipeline/misc/hapnest/config.synth_2.yaml
new file mode 100644
index 00000000..f1f42e09
--- /dev/null
+++ b/pipeline/misc/hapnest/config.synth_2.yaml
@@ -0,0 +1,146 @@
+####################################
+# GLOBAL PARAMETERS                #
+####################################
+
+global_parameters:
+  random_seed: 123
+  chromosome: 22 # "all" or a number from 1 to 22
+  superpopulation: EUR # "none" or a specific superpopulation (AFR, AMR, EAS, EUR, CSA, MID)
+  memory: 5000 # amount of memory available (in MB) for memory-intensive commands
+  batchsize: 10000 # batchsize for writing plink output during genotype generation
+
+####################################
+# FILEPATHS                        #
+####################################
+
+# - the chromosome number can be given as a wildcard by specifying {chromosome} in the filepath
+# - the superpopulation can be given as a wildcard by specifying {superpopulation} in the filepath
+filepaths:
+  general:
+    output_dir: data/outputs/synth_2
+    output_prefix: synth_2_chr-{chromosome}
+  genotype:
+    vcf_input_raw: data/inputs/raw/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.final.vcf.gz
+    vcf_input_processed: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.final.recode.vcf
+    vcf_metadata: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.metadata
+    popfile_raw: data/inputs/processed/1KG+HGDP/merged_pop_adjusted.tsv
+    popfile_processed: data/inputs/processed/1KG+HGDP/merged_pop.tsv
+    variant_list: data/inputs/processed/1KG+HGDP/hapmap_variant_list_chr{chromosome}.txt
+    remove_list: data/inputs/processed/1KG+HGDP/remove.txt
+    rsid_list: data/inputs/processed/1KG+HGDP/rsid_map_list_chr{chromosome}.txt
+    genetic_mapfile: data/inputs/raw/1KG+HGDP/genetic_maps/chr{chromosome}.interpolated_genetic_map
+    genetic_distfile: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.distfile
+    mutation_mapfile: data/inputs/raw/1KG+HGDP/mutation_maps/atlas.chr{chromosome}.csv
+    mutation_agefile: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.agefile
+    hap1_matrix: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.h1
+    hap2_matrix: data/inputs/processed/1KG+HGDP/1KG+HGDP.chr{chromosome}.hapmap.h2
+  phenotype:
+    causal_list: data/causal.snplist
+    reference: data/inputs/processed/1KG+HGDP/Africa.Annot
+    plink_override: none # can set to a value if using pre-simulated genetics input
+  software:
+    plink: plink
+    plink2: plink2
+    king: king
+    vcftools: vcftools
+    mapthin: mapthin
+    phenoalg: phenoalg
+
+
+####################################
+# GENOTYPE DATA                    #
+####################################
+
+genotype_data:
+  filetype: plink # specify either `plink` or `vcf`
+  samples:
+    use_default: true # setting this to true will ignore the custom population groups
+    default:
+      nsamples: 100000 # used by the algorithm if use_default=true, otherwise custom population groups are used
+  # recombination rate 
+  rho:
+    AFR: 0.77
+    AMR: 0.80
+    EAS: 0.58
+    EUR: 0.68
+    CSA: 0.73
+    MID: 0.65
+  # effective population size 
+  Ne:
+    AFR: 11900
+    AMR: 10400
+    EAS: 11700
+    EUR: 11700
+    CSA: 11500
+    MID: 8100
+
+####################################
+# PHENOTYPE DATA                   #
+####################################
+
+phenotype_data:
+  nPopulation: 1
+  nTrait: 2
+  a: -0.4
+  b: -1
+  c: 0.5
+  nComponent: 1
+  PropotionGeno: 0.8,0.8
+  PropotionCovar: 0,0
+  Prevalence: 0.5,0.5
+  TraitCorr: 1,0,0,1
+  PopulationCorr: 0
+  CompWeight: 1,5,10
+  Causality:
+      UseCausalList: true # if true the algorithm will use the causal_list filepath
+      Polygenicity: 0.005 # only required if UseCausalList is false
+      Pleiotropy: 1 # only required if UseCausalList is false
+      
+####################################
+# EVALUATION                       #
+####################################
+
+# Set to true if you want the script to calculate the metric
+evaluation:
+  metrics:
+    aats: false  # nearest neighbour adversarial accuracy
+    kinship: true # relatedness, including kinship density and IBS plots
+    ld_corr: false # linkage disequilibrium (LD) correlation matrix
+    ld_decay: false # linkage disequilibrium (LD) decay plot (and distance)
+    maf: false # minor allele frequency divergences
+    pca: false # principal components analysis
+    gwas: false # GWAS, manhattan plot and qqplot
+
+
+####################################
+# OPTIMISATION                     #
+####################################
+
+# Note that this code uses a single superpopulation and ignores custom population structures
+optimisation:
+  # prior distributions - specify lower/upper bounds for uniform priors
+  priors:
+    rho:
+      uniform_lower: 0
+      uniform_upper: 3
+    Ne:
+      uniform_lower: 0
+      uniform_upper: 50000
+  # inference type - simulation-based rejection ABC or emulation-based rejection ABC
+  simulation_rejection_ABC:
+    run: true
+    n_particles: 500
+    threshold: 0.15
+    max_iter: 500
+    write_progress: true
+  emulation_rejection_ABC:
+    run: false
+    n_particles: 500
+    threshold: 0.15
+    n_design_points: 50
+    max_iter: 500
+    write_progress: true
+  # choice of summary statistic/s
+  summary_statistics:
+    ld_decay: true
+    kinship: true

From bd20e07b96859874bcd7edf7ed432dac8ec70a9c Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 4 Mar 2025 08:31:19 +0000
Subject: [PATCH 116/153] Updates

---
 Scripts/pgs_methods/leopard_quickprs.R |    2 +-
 docs/CrossPop.Rmd                      |  179 ++--
 docs/GenoClust.Rmd                     | 1044 +++++++++++++++++++++++-
 3 files changed, 1126 insertions(+), 99 deletions(-)

diff --git a/Scripts/pgs_methods/leopard_quickprs.R b/Scripts/pgs_methods/leopard_quickprs.R
index 8925fd1d..7de31a18 100644
--- a/Scripts/pgs_methods/leopard_quickprs.R
+++ b/Scripts/pgs_methods/leopard_quickprs.R
@@ -206,7 +206,7 @@ for(targ_pop in populations){
 
     system(paste0(
       'Rscript ', opt$xwing_repo, '/LEOPARD_Weights.R ',
-      '--beta_file ', tmp_dir, '/LEOPARD/post_targ_', populations[1], '/output_', j, '_', populations[1], '_Post.txt',',',tmp_dir, '/LEOPARD/post_targ_', populations[2], '/output_', j, '_', populations[2], '_Post.txt ',
+      '--beta_file ', paste(paste0(tmp_dir, '/LEOPARD/post_targ_', populations, '/output_', j, '_', populations, '_Post.txt'), collapse = ','), ' ',
       '--valid_file ', tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_valid.reformat.txt ',
       '--n_valid ', targ_gwas_valid_n ,' ',
       '--ref_prefix ', quickprs_multi_ldref_pop_i, '/', ref_files, ' ',
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index b1fdda5d..92077c4f 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1694,7 +1694,6 @@ config<-c(
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
   "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx','xwing']",
-  "tlprs_methods: ['dbslmm']",
   "leopard_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
   "cores_prep_pgs: 10", # xwing run with 20 cores
   "cores_target_pgs: 50",
@@ -3305,6 +3304,12 @@ tmp_simple<-tmp_simple[tmp_simple$Model_2 %in% res_eval_simp$Group,]
 # Export plot for manuscript
 png('~/oliverpainfel/Analyses/crosspop/plots/average_r.perc_improv.png', width = 3200, height = 2000, res= 300, units = 'px')
 ggplot(meta_tmp_simple[meta_tmp_simple$Target != 'EUR Target',], aes(x=label, y=R_diff_perc , fill = Model1)) +
+#    geom_boxplot(
+#      data = tmp_simple[tmp_simple$Target != 'EUR Target',],
+#        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+#        position = position_dodge(0.7),
+#        alpha = 0.3
+#      ) +
     geom_point(
         data = tmp_simple[tmp_simple$Target != 'EUR Target',],
         mapping = aes(x=label, y=R_diff_perc, colour=Model1),
@@ -3639,100 +3644,6 @@ dev.off()
 
 ***
 
-# Investigate PRS-CS vs. PRS-CSx performance
-
-PRS-CS-multi is performing better than PRS-CSx often, which contrasts with the original PRS-CSx paper. Try to understand why this is.
-
-```{r}
-# Read in the PGS for EAS target from PRS-CS and PRS-CSx
-prscs_eur <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/ukb/pgs/TRANS/prscs/TC_UKB/ukb-TC_UKB-TRANS.profiles')
-prscs_eas <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/ukb/pgs/TRANS/prscs/TC_BBJ/ukb-TC_BBJ-TRANS.profiles')
-prscsx <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/ukb/pgs/TRANS/prscsx/TC_UKB_BBJ/ukb-TC_UKB_BBJ-TRANS.profiles')
-
-# Read in the phenotype data
-pheno <- fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/TC.unrel.EAS.row_number.txt')
-
-# Merge all the data
-data <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('FID','IID')), list(pheno, prscs_eur, prscs_eas, prscsx))
-
-assoc_res <- data.frame(
-  PGS = names(data)[-1:-3],
-  R = cor(data[,-1:-2], use='p')[-1,1]
-)
-rownames(assoc_res)<-NULL
-
-assoc_res<-assoc_res[!grepl('META', assoc_res$PGS),]
-assoc_res$phi <- gsub('.*_phi_','',assoc_res$PGS)
-assoc_res$phi <- factor(assoc_res$phi, levels = c('1e+00','1e-02','1e-04','1e-06','auto'))
-assoc_res$method <- ifelse(grepl('UKB_BBJ', assoc_res$PGS), 'PRS-CSx', 'PRS-CS')
-assoc_res$pop <- NA
-assoc_res$pop[assoc_res$method == 'PRS-CS' & grepl('UKB', assoc_res$PGS)] <- 'EUR'
-assoc_res$pop[assoc_res$method == 'PRS-CS' & grepl('BBJ', assoc_res$PGS)] <- 'EAS'
-assoc_res$pop[assoc_res$method == 'PRS-CSx' & grepl('EUR', assoc_res$PGS)] <- 'EUR'
-assoc_res$pop[assoc_res$method == 'PRS-CSx' & grepl('EAS', assoc_res$PGS)] <- 'EAS'
-
-library(ggplot2)
-library(cowplot)
-
-ggplot(assoc_res, aes(x=pop, y=R , fill = phi)) +
-    geom_bar(stat="identity", position=position_dodge(0.7), width = 0.7) +
-    labs(y = "R", x='Method') +
-    facet_grid(. ~ method, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid(major = 'y', minor = 'y') + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-
-# This shows nicely the poorer performance of the EAS PGS from PRS-CSx and than PRS-CS.
-# Why is this? Have a look at the SNP-weights.
-
-prscs_eur <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/reference/pgs_score_files/prscs/TC_UKB/ref-TC_UKB.score.gz')
-prscs_eas <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/reference/pgs_score_files/prscs/TC_BBJ/ref-TC_BBJ.score.gz')
-prscsx <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/reference/pgs_score_files/prscsx/TC_UKB_BBJ/ref-TC_UKB_BBJ.score.gz')
-
-prscsx<-prscsx[, !(grepl('META', names(prscsx))), with=F]
-
-names(prscs_eur) <- gsub('SCORE', 'prscs_eur', names(prscs_eur))
-names(prscs_eas) <- gsub('SCORE', 'prscs_eas', names(prscs_eas))
-names(prscsx) <- gsub('SCORE', 'prscsx', names(prscsx))
-
-data <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1', 'A2'), sort = F), list(prscs_eur, prscs_eas, prscsx))
-data[data == 0]<-NA
-
-na_count <- sapply(data[,-1:-3], function(x) sum(is.na(x)))
-na_table <- data.frame(Column = names(na_count), NA_Count = na_count)
-
-# check correlation between PRS-CS and PRS-CSx variant that are shared and not shared across GWAS
-shared_var <- data[!is.na(data$`prscs_eur_phi_1e-04`) & !is.na(data$`prscs_eas_phi_1e-04`), ]
-nonshared_var <- data[is.na(data$`prscs_eur_phi_1e-04`) & !is.na(data$`prscs_eas_phi_1e-04`), ]
-
-nrow(shared_var)
-nrow(nonshared_var)
-
-cor(shared_var$`prscs_eas_phi_1e-04`, shared_var$`prscsx_EAS_phi_1e-04`)
-cor(nonshared_var$`prscs_eas_phi_1e-04`, nonshared_var$`prscsx_EAS_phi_1e-04`)
-
-prscs_eas[prscs_eas$SNP %in% nonshared_var$SNP[1:5],]
-prscsx[prscsx$SNP %in% nonshared_var$SNP[1:5],]
-
-shared_var <- data[!is.na(data$`prscs_eur_phi_1e-04`) & !is.na(data$`prscs_eas_phi_1e-04`), ]
-nonshared_var <- data[!is.na(data$`prscs_eur_phi_1e-04`) & is.na(data$`prscs_eas_phi_1e-04`), ]
-
-nrow(shared_var)
-nrow(nonshared_var)
-
-cor(shared_var$`prscs_eur_phi_1e-04`, shared_var$`prscsx_EUR_phi_1e-04`)
-cor(nonshared_var$`prscs_eur_phi_1e-04`, nonshared_var$`prscsx_EUR_phi_1e-04`)
-
-nonshared_var[, c('SNP','A1','A2','prscs_eas_phi_1e-04','prscsx_EAS_phi_1e-04'), with=F]
-
-# The correlation between non-shared SNP weights from EAS PRS-CS and PRS-CSx PGS is strongly negative.
-# Indicates something weird is going on. I should check this isn't something to do with how I am outputting the SCORE file.
-
-```
-
-***
-
 # Computational resoures
 
 <details><summary>Show code</summary>
@@ -5673,3 +5584,81 @@ write.csv(method_avg, '~/oliverpainfel/Analyses/crosspop/time_memory.csv', row.n
 ```
 
 </details>
+
+***
+
+# With 2+ GWAS
+
+Extend analysis to include gwas_groups including AFR EAS and EUR GWAS. Only some multi-source methods should be applicable here, including LEOPARD, PRS-CSx, and X-Wing. Given X-Wing with LEOPARD is slow, limit X-Wing analysis to the IndivTrain model alone.
+
+***
+
+## Prepare configuration for GenoPred
+
+<details><summary>Show code</summary>
+
+```{r}
+
+library(data.table)
+
+# Subset original gwas_list to include selected traits
+gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt')
+pheno<-gsub('_.*','', gwas_list$name)
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+gwas_list<-gwas_list[pheno %in% selected_traits,]
+gwas_list$label<-paste0('"', gwas_list$label, '"')
+
+######
+# gwas_groups
+######
+
+gwas_groups_three_pop<-data.frame(
+  name=paste0(selected_traits, '_UKB_BBJ_UGR'),
+  gwas=sapply(selected_traits, function(x) paste0(x,'_UKB,',x,'_BBJ,',x,'_UGR')),
+  label=paste0('"', selected_traits, " (UKB+BBJ+UGR)", '"')
+)
+
+write.table(gwas_groups_three_pop, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_three_pop.txt', col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
+  "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_three_pop.txt",
+  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx','xwing']",
+  "leopard_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
+  "cores_prep_pgs: 10", # xwing run with 20 cores
+  "cores_target_pgs: 50",
+  "ldpred2_inference: F",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3",
+  "quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml', col.names = F, row.names = F, quote = F)
+
+```
+</details>
+
+***
+
+## Run pipeline
+
+<details><summary>Show code</summary>
+
+```{bash}
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml \
+  target_pgs  -n
+```
+
+</details>
+
diff --git a/docs/GenoClust.Rmd b/docs/GenoClust.Rmd
index ed2a04c1..caf19e96 100644
--- a/docs/GenoClust.Rmd
+++ b/docs/GenoClust.Rmd
@@ -140,6 +140,58 @@ singularity exec \
   generate_pheno \
   data/config.synth_2.yaml
   
+# Can't figure out how run with causal variants specified. Lets do this using R
+  
+```
+
+```{r}
+library(bigsnpr)
+
+# Attach the bigSNP object from a previously saved `.rds` file
+geno_file <- "your_genotype_data.rds"  # Update this with your actual file
+bigSNP <- snp_attach(geno_file)
+
+# Access the genotype matrix
+G <- bigSNP$genotypes
+
+# Get SNP names
+snp_names <- bigSNP$map$marker.ID
+
+set.seed(123)  # For reproducibility
+
+# Define number of causal SNPs
+num_causal <- 100
+
+# Randomly select causal SNPs from available markers
+causal_snps <- sample(snp_names, num_causal)
+
+# Assign effect sizes (from normal distribution)
+effect_sizes <- rnorm(num_causal, mean = 0, sd = 1)
+
+# Print the first few selected causal SNPs
+head(causal_snps)
+
+# Extract genotype indices of causal SNPs
+causal_indices <- match(causal_snps, snp_names)
+
+# Compute genetic component efficiently
+G_causal <- big_prodVec(G, effect_sizes, ind.col = causal_indices)
+
+h2 <- 0.5  # Desired heritability
+
+# Compute variance of genetic component
+var_G <- var(G_causal)
+
+# Compute required environmental variance
+var_E <- (var_G / h2) - var_G
+
+# Generate environmental noise
+E <- rnorm(length(G_causal), mean = 0, sd = sqrt(var_E))
+
+# Compute final phenotype
+Y <- G_causal + E
+
+h2_observed <- var(G_causal) / (var(G_causal) + var(E))
 ```
 
 </details>
@@ -795,11 +847,16 @@ write.table(target_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/config
 ######
 
 gwas_list<-data.frame(
-  name=c('CAD','RA','MIX'),
+  name=c('CAD','RA','MIX','T2D','T1D', 'IBD','BMI', 'HbA1c'),
   path=c(
     '/scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/CAD',
     '/scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/RA',
-    '/scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/mix_CAD_RA.tbl.reformat'
+    '/scratch_tmp/prj/oliverpainfel/Analyses/GenoClust/artificial/gwas/mix_CAD_RA.tbl.reformat',
+    '/scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/GenoPredPipe/output/reference/gwas_sumstat/GCST004773/GCST004773-cleaned.gz',
+    '/scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/GenoPredPipe/output/reference/gwas_sumstat/GCST90013445/GCST90013445-cleaned.gz',
+    '/scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/GenoPredPipe/output/reference/gwas_sumstat/GCST004131/GCST004131-cleaned.gz',
+    '/scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/GenoPredPipe/output/reference/gwas_sumstat/GCST002783/GCST002783-cleaned.gz',
+    '/scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/GenoPredPipe/output/reference/gwas_sumstat/GCST007954/GCST007954-cleaned.gz'
   ),
   population='EUR',
   n=NA,
@@ -807,7 +864,7 @@ gwas_list<-data.frame(
   prevalence=NA,
   mean=NA,
   sd=NA,
-  label=paste0('"', c('CAD','RA','MIX'),'"')
+  label=paste0('"', c('CAD','RA','MIX','T2D','T1D', 'IBD','BMI', 'HbA1c'),'"')
 )
 
 write.table(gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/gwas_list.txt', col.names=T, row.names=F, quote=F)
@@ -894,6 +951,987 @@ round(cor(both[,-1:-2]), 2)
 # The reason for PGS stratification is becoming clearer - If we were to extract genetic effects relevant to CAD, then the CAD PGS would predict CAD cases better. Likewise for RA.
 
 # It would be interesting to see whether we can cluster individuals based on these scores alone. This would be similar to the T2D paper, which stratifies PGS by relationship with other traits. Our pathway specific strategy will take a different approach.
+
+library(pROC)
+
+auc(
+  both$MIX_outcome,
+  predict(glm(MIX_outcome ~ RA_SBayesRC + CAD_SBayesRC + MIX_SBayesRC, data = both), newdata = both))
+
+auc(
+  both$MIX_outcome,
+  predict(glm(MIX_outcome ~ MIX_SBayesRC, data = both), newdata = both))
+
+auc(
+  both$MIX_outcome,
+  predict(glm(MIX_outcome ~ RA_SBayesRC + CAD_SBayesRC, data = both), newdata = both))
+
+```
+
+***
+
+### Cluster based on genome-wide PGS
+
+```{r}
+
+library(NbClust)
+
+both$group<-NULL
+both$group[both$CAD_outcome == 1]<-'CAD'
+both$group[both$RA_outcome == 1]<-'RA'
+both$group[both$CAD_outcome == 1 & both$RA_outcome == 1]<-'BOTH'
+both$group[both$CAD_outcome != 1 & both$RA_outcome != 1]<-'CONTROL'
+
+# Extract PGS data for MIX cases only
+both_cases <- both[which(both$MIX_outcome == 1),]
+pgs_cases <- both_cases[, c('CAD_SBayesRC', 'RA_SBayesRC'), with=F]
+pgs_cases <- scale(pgs_cases) 
+# Not sure whether I should be scaling since they are reference standardised
+
+# Determine optimal number of clusters
+n_clust_sol <-
+  NbClust(
+    data = pgs_cases[1:2000,],
+    distance = "euclidean",
+    min.nc = 2,
+    max.nc = 10,
+    method = 'ward.D2',
+    index = 'all'
+  )
+
+n_clust_opt<-length(unique(n_clust_sol$Best.partition))
+
+#####
+# K-means
+#####
+
+# Now try k-means clustering with 3 clusters
+k_res<-kmeans(pgs_cases, n_clust_opt)
+
+# Plot the mean of each group
+k_res_centers<-data.frame(Group=as.character(1:n_clust_opt),
+                          k_res$centers)
+
+library(reshape2)
+k_res_centers_melt<-melt(k_res_centers, id='Group')
+
+ggplot(k_res_centers_melt, aes(x=variable, y=value, group=Group, color=Group)) +
+  geom_point(size=5) +
+  geom_line() +
+  labs(x='Polygenic Score', y='Cluster Mean', title='Mean Polygenic Score Across Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust=1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#####
+# Hierarchical clustering
+#####
+
+# Compute distance matrix
+dist_matrix <- dist(pgs_cases, method = "euclidean")
+
+# Perform hierarchical clustering
+hclust_res <- hclust(dist_matrix, method = "ward.D2")
+
+# Cut tree into n_clust_opt clusters
+hclust_clusters <- cutree(hclust_res, k = n_clust_opt)
+
+# Convert clusters to a factor for plotting
+hclust_clusters <- as.factor(hclust_clusters)
+
+# Create a data frame with cluster assignments
+pgs_cases_clustered <- data.frame(pgs_cases, Cluster = hclust_clusters)
+
+# Compute the mean of each cluster for each PGS
+hclust_centers <- aggregate(. ~ Cluster, data = pgs_cases_clustered, FUN = mean)
+
+# Reshape the data for plotting
+hclust_centers_melt <- melt(hclust_centers, id = "Cluster")
+
+# Convert Cluster to character for consistent plotting
+hclust_centers_melt$Cluster <- as.character(hclust_centers_melt$Cluster)
+
+ggplot(hclust_centers_melt, aes(x = variable, y = value, group = Cluster, color = Cluster)) +
+  geom_point(size = 5) +
+  geom_line() +
+  labs(x = 'Polygenic Score', y = 'Cluster Mean', title = 'Mean Polygenic Score Across Hierarchical Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#####
+# Gaussian mixture model
+#####
+library(mclust)
+
+gmm_model <- Mclust(pgs_cases, G = n_clust_opt)
+summary(gmm_model)
+
+gmm_clusters <- gmm_model$classification  # Hard cluster labels
+gmm_probabilities <- gmm_model$z  # Soft probabilities for each cluster
+
+# Create a data frame with cluster assignments
+pgs_cases_clustered <- data.frame(pgs_cases, Cluster = gmm_clusters)
+
+# Compute the mean of each cluster for each PGS
+gmm_centers <- aggregate(. ~ Cluster, data = pgs_cases_clustered, FUN = mean)
+
+# Reshape the data for plotting
+gmm_centers_melt <- melt(gmm_centers, id = "Cluster")
+
+# Convert Cluster to character for consistent plotting
+gmm_centers_melt$Cluster <- as.character(gmm_centers_melt$Cluster)
+
+ggplot(gmm_centers_melt, aes(x = variable, y = value, group = Cluster, color = Cluster)) +
+  geom_point(size = 5) +
+  geom_line() +
+  labs(x = 'Polygenic Score', y = 'Cluster Mean', title = 'Mean Polygenic Score Across GMM Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#########
+# Compare the clusters to the case groups
+k_means_conf_matrix <- table(k_res$cluster, both_cases$group)
+hclust_D2_conf_matrix <- table(hclust_clusters, both_cases$group)
+gmm_conf_matrix <- table(gmm_clusters, both_cases$group)
+
+library(mclust)
+adjustedRandIndex(k_res$cluster, both_cases$group)
+adjustedRandIndex(hclust_clusters, both_cases$group)
+adjustedRandIndex(gmm_clusters, both_cases$group)
+
+# The accuracy of the hclust solution is higher
+pca_res <- prcomp(pgs_cases)
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(k_res$cluster))) + 
+  geom_point()
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(hclust_clusters))) + 
+  geom_point()
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(gmm_clusters))) + 
+  geom_point()
+
+ggplot(both_cases, aes(RA_SBayesRC, CAD_SBayesRC, color = as.factor(k_res$cluster))) + 
+  geom_point()
+ggplot(both_cases, aes(RA_SBayesRC, CAD_SBayesRC, color = as.factor(hclust_clusters))) + 
+  geom_point()
+ggplot(both_cases, aes(RA_SBayesRC, CAD_SBayesRC, color = as.factor(gmm_clusters))) + 
+  geom_point()
+
+ggplot(both_cases, aes(RA_SBayesRC, CAD_SBayesRC, color = as.factor(group))) + 
+  geom_point()
+
+ggplot(both_cases[both_cases$group != 'BOTH',], aes(x = RA_SBayesRC, y = CAD_SBayesRC, color = as.factor(group))) +
+  geom_density_2d() +  # Density contours
+  theme_minimal()
+
+library(Rtsne)
+
+tsne_res <- Rtsne(pgs_cases[!duplicated(pgs_cases),])
+
+ggplot(data.frame(tsne_res$Y), aes(
+  x = X1,
+  y = X2,
+  color = as.factor(k_res$cluster[!duplicated(pgs_cases)])
+)) + 
+  geom_point() +
+  labs(color = 'Cluster')
+
+ggplot(data.frame(tsne_res$Y), aes(
+  x = X1,
+  y = X2,
+  color = as.factor(hclust_clusters[!duplicated(pgs_cases)])
+)) + 
+  geom_point() +
+  labs(color = 'Cluster')
+
+ggplot(data.frame(tsne_res$Y), aes(
+  x = X1,
+  y = X2,
+  color = as.factor(gmm_clusters[!duplicated(pgs_cases)])
+)) + 
+  geom_point() +
+  labs(color = 'Cluster')
+
+
+########
+# Predict MIX outcome 
+
+# Project cluster solutions into full dataset
+both_cluster<-both
+
+library(clue)
+# kmeans
+kmeans_pred<-cl_predict(k_res, newdata = both, type = "memberships")
+both_cluster$kmean<-apply(kmeans_pred, 1, function(x) which(x == 1))
+
+# hclust
+centroids <- aggregate(pgs_cases, list(hclust_clusters), mean)[, -1]  # Remove the cluster ID column
+assign_to_nearest_cluster <- function(new_data, centroids) {
+  apply(as.matrix(new_data), 1, function(row) {
+    which.min(colSums((t(centroids) - row)^2))  # Compute Euclidean distance to centroids
+  })
+}
+both_cluster$hclust <-
+  assign_to_nearest_cluster(
+    new_data = both[, c('CAD_SBayesRC', 'RA_SBayesRC'), with = F], centroids = centroids)
+
+# gmm
+both_cluster$gmm <-
+  predict(gmm_model, 
+          newdata = both[, c('CAD_SBayesRC', 'RA_SBayesRC'), with = F])$classification
+both_cluster$gmm_prob <-
+  predict(gmm_model, 
+          newdata = both[, c('CAD_SBayesRC', 'RA_SBayesRC'), with = F])$z[,1]
+
+# MIX_SBayesRC only
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  MIX_SBayesRC")),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# MIX_SBayesRC + disorder specific PGS
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  MIX_SBayesRC + CAD_SBayesRC + RA_SBayesRC")),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# kmeans interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  ", 
+                      paste0(names(both_cluster)[grepl('SBayesRC', names(both_cluster))], '*as.factor(kmean)', collapse=' + '))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# hclust interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  ", 
+                      paste0(names(both_cluster)[grepl('SBayesRC', names(both_cluster))], '*as.factor(hclust)', collapse=' + '))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# gmm interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  ", 
+                      paste0(names(both_cluster)[grepl('SBayesRC', names(both_cluster))], '*as.factor(gmm)', collapse=' + '))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# Even when defining clusters in the cases, the improvement in prediction is minimal < 1% relative improvement. However, there is a significant interaction between the RA PGS and the cluster that represents RA cases, so the concept is holding to some extent.
+
+# Would pathway specific scores help? The best PGS predicting RA or CAD subgroups would be RA or CAD PGS, so pathway specific PGS are unlikely to improve clustering/prediction. This isn't working well because the PGS are weak predictors...
+
+# Including the disease specific PGS improves prediction a lot over the MIX PGS alone. I think this is hoovering up a lot of the interaction effects. 
+
+# Rerun using full sample to define clusters
+
+#####################
+
+library(NbClust)
+
+both$group<-NULL
+both$group[both$CAD_outcome == 1]<-'CAD'
+both$group[both$RA_outcome == 1]<-'RA'
+both$group[both$CAD_outcome == 1 & both$RA_outcome == 1]<-'BOTH'
+both$group[both$CAD_outcome != 1 & both$RA_outcome != 1]<-'CONTROL'
+
+# Extract PGS data for MIX cases only
+pgs_all <- both[, c('CAD_SBayesRC', 'RA_SBayesRC'), with=F]
+pgs_all <- scale(pgs_all) 
+# Not sure whether I should be scaling since they are reference standardised
+
+# Determine optimal number of clusters
+n_clust_sol <-
+  NbClust(
+    data = pgs_all[1:2000,],
+    distance = "euclidean",
+    min.nc = 2,
+    max.nc = 10,
+    method = 'ward.D2',
+    index = 'all'
+  )
+
+n_clust_opt<-length(unique(n_clust_sol$Best.partition))
+
+#####
+# K-means
+#####
+
+# Now try k-means clustering with 3 clusters
+k_res<-kmeans(pgs_all, n_clust_opt)
+
+# Plot the mean of each group
+k_res_centers<-data.frame(Group=as.character(1:n_clust_opt),
+                          k_res$centers)
+
+library(reshape2)
+k_res_centers_melt<-melt(k_res_centers, id='Group')
+
+ggplot(k_res_centers_melt, aes(x=variable, y=value, group=Group, color=Group)) +
+  geom_point(size=5) +
+  geom_line() +
+  labs(x='Polygenic Score', y='Cluster Mean', title='Mean Polygenic Score Across Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust=1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#####
+# Hierarchical clustering
+#####
+
+# Compute distance matrix
+dist_matrix <- dist(pgs_all, method = "euclidean")
+
+# Perform hierarchical clustering
+hclust_res <- hclust(dist_matrix, method = "ward.D2")
+
+# Cut tree into n_clust_opt clusters
+hclust_clusters <- cutree(hclust_res, k = n_clust_opt)
+
+# Convert clusters to a factor for plotting
+hclust_clusters <- as.factor(hclust_clusters)
+
+# Create a data frame with cluster assignments
+pgs_all_clustered <- data.frame(pgs_all, Cluster = hclust_clusters)
+
+# Compute the mean of each cluster for each PGS
+hclust_centers <- aggregate(. ~ Cluster, data = pgs_all_clustered, FUN = mean)
+
+# Reshape the data for plotting
+hclust_centers_melt <- melt(hclust_centers, id = "Cluster")
+
+# Convert Cluster to character for consistent plotting
+hclust_centers_melt$Cluster <- as.character(hclust_centers_melt$Cluster)
+
+ggplot(hclust_centers_melt, aes(x = variable, y = value, group = Cluster, color = Cluster)) +
+  geom_point(size = 5) +
+  geom_line() +
+  labs(x = 'Polygenic Score', y = 'Cluster Mean', title = 'Mean Polygenic Score Across Hierarchical Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#####
+# Gaussian mixture model
+#####
+library(mclust)
+
+gmm_model <- Mclust(pgs_all, G = n_clust_opt)
+summary(gmm_model)
+
+gmm_clusters <- gmm_model$classification  # Hard cluster labels
+gmm_probabilities <- gmm_model$z  # Soft probabilities for each cluster
+
+# Create a data frame with cluster assignments
+pgs_all_clustered <- data.frame(pgs_all, Cluster = gmm_clusters)
+
+# Compute the mean of each cluster for each PGS
+gmm_centers <- aggregate(. ~ Cluster, data = pgs_all_clustered, FUN = mean)
+
+# Reshape the data for plotting
+gmm_centers_melt <- melt(gmm_centers, id = "Cluster")
+
+# Convert Cluster to character for consistent plotting
+gmm_centers_melt$Cluster <- as.character(gmm_centers_melt$Cluster)
+
+ggplot(gmm_centers_melt, aes(x = variable, y = value, group = Cluster, color = Cluster)) +
+  geom_point(size = 5) +
+  geom_line() +
+  labs(x = 'Polygenic Score', y = 'Cluster Mean', title = 'Mean Polygenic Score Across GMM Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#########
+# Compare the clusters to the case groups
+k_means_conf_matrix <- table(k_res$cluster, both$group)
+hclust_D2_conf_matrix <- table(hclust_clusters, both$group)
+gmm_conf_matrix <- table(assigned_clusters, both$group)
+
+library(mclust)
+adjustedRandIndex(k_res$cluster, both$group)
+adjustedRandIndex(hclust_clusters, both$group)
+adjustedRandIndex(gmm_clusters, both$group)
+
+# The accuracy of the hclust solution is higher
+pca_res <- prcomp(pgs_all)
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(k_res$cluster))) + geom_point()
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(hclust_clusters))) + geom_point()
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(gmm_clusters))) + geom_point()
+
+########
+# Predict MIX outcome 
+
+# Project cluster solutions into full dataset
+both_cluster<-both
+
+both_cluster$kmean<-k_res$cluster
+both_cluster$hclust<-hclust_clusters
+both_cluster$gmm<-gmm_clusters
+
+# MIX_SBayesRC only
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  MIX_SBayesRC")),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# MIX_SBayesRC + disorder specific PGS
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  MIX_SBayesRC + CAD_SBayesRC + RA_SBayesRC")),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# No interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  ", 
+                      paste(names(both_cluster)[grepl('SBayesRC', names(both_cluster))], collapse = ' + '))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# kmeans interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  ", 
+                      paste0(names(both_cluster)[grepl('SBayesRC', names(both_cluster))], '*as.factor(kmean)', collapse=' + '))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# hclust interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  ", 
+                      paste0(names(both_cluster)[grepl('SBayesRC', names(both_cluster))], '*as.factor(hclust)', collapse=' + '))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# gmm interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  ", 
+                      paste0(names(both_cluster)[grepl('SBayesRC', names(both_cluster))], '*as.factor(gmm)', collapse=' + '))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# Full sample show slightly better prediction, but there is poorer seperate of case subtypes.
+
+```
+
+***
+
+Try adding in PGS for secondary traits.
+
+```{r}
+library(NbClust)
+
+both$group<-NULL
+both$group[both$CAD_outcome == 1]<-'CAD'
+both$group[both$RA_outcome == 1]<-'RA'
+both$group[both$CAD_outcome == 1 & both$RA_outcome == 1]<-'BOTH'
+both$group[both$CAD_outcome != 1 & both$RA_outcome != 1]<-'CONTROL'
+
+# Extract PGS data for MIX cases only
+both_cases <- both[which(both$MIX_outcome == 1),]
+pgs_cases <- both_cases[, grepl('SBayesRC', names(both_cases)), with=F]
+pgs_cases <- scale(pgs_cases) 
+# Not sure whether I should be scaling since they are reference standardised
+
+# Determine optimal number of clusters
+n_clust_sol <-
+  NbClust(
+    data = pgs_cases[1:2000,],
+    distance = "euclidean",
+    min.nc = 2,
+    max.nc = 10,
+    method = 'ward.D2',
+    index = 'all'
+  )
+
+n_clust_opt<-length(unique(n_clust_sol$Best.partition))
+
+#####
+# K-means
+#####
+
+# Now try k-means clustering with 3 clusters
+k_res<-kmeans(pgs_cases, n_clust_opt)
+
+# Plot the mean of each group
+k_res_centers<-data.frame(Group=as.character(1:n_clust_opt),
+                          k_res$centers)
+
+library(reshape2)
+k_res_centers_melt<-melt(k_res_centers, id='Group')
+
+ggplot(k_res_centers_melt, aes(x=variable, y=value, group=Group, color=Group)) +
+  geom_point(size=5) +
+  geom_line() +
+  labs(x='Polygenic Score', y='Cluster Mean', title='Mean Polygenic Score Across Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust=1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#####
+# Hierarchical clustering
+#####
+
+# Compute distance matrix
+dist_matrix <- dist(pgs_cases, method = "euclidean")
+
+# Perform hierarchical clustering
+hclust_res <- hclust(dist_matrix, method = "ward.D2")
+
+# Cut tree into n_clust_opt clusters
+hclust_clusters <- cutree(hclust_res, k = n_clust_opt)
+
+# Convert clusters to a factor for plotting
+hclust_clusters <- as.factor(hclust_clusters)
+
+# Create a data frame with cluster assignments
+pgs_cases_clustered <- data.frame(pgs_cases, Cluster = hclust_clusters)
+
+# Compute the mean of each cluster for each PGS
+hclust_centers <- aggregate(. ~ Cluster, data = pgs_cases_clustered, FUN = mean)
+
+# Reshape the data for plotting
+hclust_centers_melt <- melt(hclust_centers, id = "Cluster")
+
+# Convert Cluster to character for consistent plotting
+hclust_centers_melt$Cluster <- as.character(hclust_centers_melt$Cluster)
+
+ggplot(hclust_centers_melt, aes(x = variable, y = value, group = Cluster, color = Cluster)) +
+  geom_point(size = 5) +
+  geom_line() +
+  labs(x = 'Polygenic Score', y = 'Cluster Mean', title = 'Mean Polygenic Score Across Hierarchical Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#####
+# Gaussian mixture model
+#####
+library(mclust)
+
+gmm_model <- Mclust(pgs_cases, G = n_clust_opt)
+summary(gmm_model)
+
+gmm_clusters <- gmm_model$classification  # Hard cluster labels
+gmm_probabilities <- gmm_model$z  # Soft probabilities for each cluster
+
+# Create a data frame with cluster assignments
+pgs_cases_clustered <- data.frame(pgs_cases, Cluster = gmm_clusters)
+
+# Compute the mean of each cluster for each PGS
+gmm_centers <- aggregate(. ~ Cluster, data = pgs_cases_clustered, FUN = mean)
+
+# Reshape the data for plotting
+gmm_centers_melt <- melt(gmm_centers, id = "Cluster")
+
+# Convert Cluster to character for consistent plotting
+gmm_centers_melt$Cluster <- as.character(gmm_centers_melt$Cluster)
+
+ggplot(gmm_centers_melt, aes(x = variable, y = value, group = Cluster, color = Cluster)) +
+  geom_point(size = 5) +
+  geom_line() +
+  labs(x = 'Polygenic Score', y = 'Cluster Mean', title = 'Mean Polygenic Score Across GMM Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#########
+# Compare the clusters to the case groups
+k_means_conf_matrix <- table(k_res$cluster, both_cases$group)
+hclust_D2_conf_matrix <- table(hclust_clusters, both_cases$group)
+gmm_conf_matrix <- table(gmm_clusters, both_cases$group)
+
+library(mclust)
+adjustedRandIndex(k_res$cluster, both_cases$group)
+adjustedRandIndex(hclust_clusters, both_cases$group)
+adjustedRandIndex(gmm_clusters, both_cases$group)
+
+# The accuracy of the hclust solution is higher
+pca_res <- prcomp(pgs_cases)
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(k_res$cluster))) + 
+  geom_point()
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(hclust_clusters))) + 
+  geom_point()
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(gmm_clusters))) + 
+  geom_point()
+
+ggplot(both_cases, aes(RA_SBayesRC, CAD_SBayesRC, color = as.factor(k_res$cluster))) + 
+  geom_point()
+ggplot(both_cases, aes(RA_SBayesRC, CAD_SBayesRC, color = as.factor(hclust_clusters))) + 
+  geom_point()
+ggplot(both_cases, aes(RA_SBayesRC, CAD_SBayesRC, color = as.factor(gmm_clusters))) + 
+  geom_point()
+
+ggplot(both_cases, aes(RA_SBayesRC, CAD_SBayesRC, color = as.factor(group))) + 
+  geom_point()
+
+ggplot(both_cases[both_cases$group != 'BOTH',], aes(x = RA_SBayesRC, y = CAD_SBayesRC, color = as.factor(group))) +
+  geom_density_2d() +  # Density contours
+  theme_minimal()
+
+library(Rtsne)
+
+tsne_res <- Rtsne(pgs_cases[!duplicated(pgs_cases),])
+
+ggplot(data.frame(tsne_res$Y), aes(
+  x = X1,
+  y = X2,
+  color = as.factor(k_res$cluster[!duplicated(pgs_cases)])
+)) + 
+  geom_point() +
+  labs(color = 'Cluster')
+
+ggplot(data.frame(tsne_res$Y), aes(
+  x = X1,
+  y = X2,
+  color = as.factor(hclust_clusters[!duplicated(pgs_cases)])
+)) + 
+  geom_point() +
+  labs(color = 'Cluster')
+
+ggplot(data.frame(tsne_res$Y), aes(
+  x = X1,
+  y = X2,
+  color = as.factor(gmm_clusters[!duplicated(pgs_cases)])
+)) + 
+  geom_point() +
+  labs(color = 'Cluster')
+
+
+########
+# Predict MIX outcome 
+
+# Project cluster solutions into full dataset
+both_cluster<-both
+
+library(clue)
+# kmeans
+kmeans_pred<-cl_predict(k_res, newdata = both, type = "memberships")
+both_cluster$kmean<-apply(kmeans_pred, 1, function(x) which(x == 1))
+
+# hclust
+centroids <- aggregate(pgs_cases, list(hclust_clusters), mean)[, -1]  # Remove the cluster ID column
+assign_to_nearest_cluster <- function(new_data, centroids) {
+  apply(as.matrix(new_data), 1, function(row) {
+    which.min(colSums((t(centroids) - row)^2))  # Compute Euclidean distance to centroids
+  })
+}
+both_cluster$hclust <-
+  assign_to_nearest_cluster(
+    new_data = both[, grepl('SBayesRC', names(both_cases)), with = F], centroids = centroids)
+
+# gmm
+both_cluster$gmm <-
+  predict(gmm_model, 
+          newdata = both[, grepl('SBayesRC', names(both_cases)), with = F])$classification
+both_cluster$gmm_prob <-
+  predict(gmm_model, 
+          newdata = both[, grepl('SBayesRC', names(both_cases)), with = F])$z[,1]
+
+
+# MIX_SBayesRC only
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  MIX_SBayesRC")),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# MIX_SBayesRC + disorder specific PGS
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  MIX_SBayesRC + CAD_SBayesRC + RA_SBayesRC")),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# No interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  ", 
+                      paste(names(both_cluster)[grepl('SBayesRC', names(both_cluster))], collapse = ' + '))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# kmeans interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  ", 
+                      paste0(names(both_cluster)[grepl('SBayesRC', names(both_cluster))], '*as.factor(kmean)', collapse=' + '))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# hclust interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  ", 
+                      paste0(names(both_cluster)[grepl('SBayesRC', names(both_cluster))], '*as.factor(hclust)', collapse=' + '))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# gmm interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  ", 
+                      paste0(names(both_cluster)[grepl('SBayesRC', names(both_cluster))], '*as.factor(gmm)', collapse=' + '))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# 
+
+```
+
+***
+
+See whether non-negative matrix factorisation could recapitulate disease-specific PGS.
+
+Steps:
+  - Perform LD clumping on MIX GWAS
+  - Create matrix of GWAS Z-scores for top MIX hits, and disease specific GWAS
+  - Perform non-negative matrix factorisation
+  - Generate new PGS based on factor loadings for each variant.
+  - Evaluate performance in UKB
+
+```{r}
+# conda activate .snakemake/conda/329e0288cb99508f5e6c50a0996b234c_
+library("optparse")
+
+option_list = list(
+make_option("--ref_plink_chr", action="store", default=NULL, type='character',
+		help="Path to per chromosome reference PLINK files [required]"),
+make_option("--ref_keep", action="store", default=NULL, type='character',
+    help="Keep file to subset individuals in reference for clumping [optional]"),
+make_option("--plink2", action="store", default='plink2', type='character',
+    help="Path PLINKv2 software binary [required]"),
+make_option("--sumstats", action="store", default=NULL, type='character',
+		help="GWAS summary statistics in LDSC format [required]"),
+make_option("--pTs", action="store", default='5e-8,1e-6,1e-4,1e-2,0.1,0.2,0.3,0.4,0.5,1', type='character',
+		help="List of p-value thresholds for scoring [optional]"),
+make_option("--nested", action="store", default=T, type='logical',
+    help="Specify as F to use non-overlapping p-value intervals [optional]"),
+make_option("--top_hla", action="store", default=T, type='logical',
+		help="Retain only top assocaited variant in HLA/MHC region [optional]")
+)
+
+opt = parse_args(OptionParser(option_list = option_list))
+
+opt$ref_plink_chr<-'resources/data/ref/ref.chr'
+opt$ref_keep<-'resources/data/ref/keep_files/EUR.keep'
+opt$sumstats<-'~/oliverpainfel/Data/ukb/GenoPred/output_genoclust/reference/gwas_sumstat/MIX/MIX-cleaned.gz'
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+#####
+# Format pT option
+#####
+
+opt$pTs <- as.numeric(unlist(strsplit(opt$pTs, ',')))
+
+#####
+# Read in sumstats
+#####
+
+# Read in, check and format GWAS summary statistics
+gwas <- read_sumstats(sumstats = opt$sumstats, chr = CHROMS, extract = opt$extract, req_cols = c('CHR','BP','SNP','A1','A2','BETA','P'))
+
+#####
+# Prepare for ptclump
+#####
+
+if(opt$top_hla){
+  # Assumes BP column is GRCh37
+  hla <- gwas[(gwas$CHR == 6 & gwas$BP > 28e6 & gwas$BP < 34e6),]
+  top_hla <- hla$SNP[hla$P == min(hla$P)][1]
+  gwas <- gwas[!(gwas$CHR == 6 & gwas$BP > 28e6 & gwas$BP < 34e6 & gwas$SNP != top_hla),]
+}
+
+#####
+# Clump SNPs in GWAS based on LD in the reference
+#####
+
+clumped <- plink_clump(pfile = opt$ref_plink_chr, plink2 = opt$plink2, chr = CHROMS, sumstats = gwas, keep = opt$ref_keep)
+
+gwas_clumped<-gwas[gwas$SNP %in% clumped,]
+
+write.table(gwas_clumped, '~/oliverpainfel/Analyses/GenoClust/artificial/gwas/mix_CAD_RA.clumped', row.names=F, quote=F)
+
+```
+
+```{r}
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+mix_clumped <- fread('~/oliverpainfel/Analyses/GenoClust/artificial/gwas/mix_CAD_RA.clumped')
+mix_clumped <- mix_clumped[mix_clumped$P < 5e-8,]
+
+mix_gwas<-fread('~/oliverpainfel/Data/ukb/GenoPred/output_genoclust/reference/gwas_sumstat/MIX/MIX-cleaned.gz')
+cad_gwas<-fread('~/oliverpainfel/Data/ukb/GenoPred/output_genoclust/reference/gwas_sumstat/CAD/CAD-cleaned.gz')
+ra_gwas<-fread('~/oliverpainfel/Data/ukb/GenoPred/output_genoclust/reference/gwas_sumstat/RA/RA-cleaned.gz')
+
+
+# Would normally have to find proxies or impute.
+mix_gwas <- mix_gwas[mix_gwas$SNP %in% mix_clumped$SNP,]
+cad_gwas <- cad_gwas[cad_gwas$SNP %in% mix_clumped$SNP,]
+ra_gwas <- ra_gwas[ra_gwas$SNP %in% mix_clumped$SNP,]
+
+# Flip mix_gwas to be risk increasing
+mix_gwas$A1_new[mix_gwas$BETA < 0]<-mix_gwas$A2[mix_gwas$BETA < 0]
+mix_gwas$A2_new[mix_gwas$BETA < 0]<-mix_gwas$A1[mix_gwas$BETA < 0]
+mix_gwas$A1[!is.na(mix_gwas$A1_new)]<-mix_gwas$A1_new[!is.na(mix_gwas$A1_new)]
+mix_gwas$A2[!is.na(mix_gwas$A2_new)]<-mix_gwas$A2_new[!is.na(mix_gwas$A2_new)]
+mix_gwas$BETA[mix_gwas$BETA < 0]<- -mix_gwas$BETA[mix_gwas$BETA < 0]
+
+# Compute Z score
+cad_gwas$Z_CAD<-cad_gwas$BETA/cad_gwas$SE
+ra_gwas$Z_RA<-ra_gwas$BETA/ra_gwas$SE
+
+# Scale Z scores by sample size
+cad_gwas$Z_MIX <- cad_gwas$Z_MIX * sqrt(cad_gwas$N)
+ra_gwas$Z_MIX <- ra_gwas$Z_MIX * sqrt(ra_gwas$N)
+
+# Flip effects to match reference alleles
+ref <- mix_gwas[, c('SNP','A1','A2'), with=F]
+cad_gwas<-cad_gwas[,c('SNP','A1','A2','Z_CAD'), with=F]
+ra_gwas<-ra_gwas[,c('SNP','A1','A2','Z_RA'), with=F]
+cad_gwas <- map_score(ref = ref, score = cad_gwas)
+ra_gwas <- map_score(ref = ref, score = ra_gwas)
+
+z_dat<-Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c("SNP", "A1", "A2"), all = TRUE), 
+       list(
+         cad_gwas,
+         ra_gwas))
+
+library(NMF)
+
+# Separate positive and negative Z-scores
+z_dat <- z_dat[,-1:-3]
+
+# Create positive matrix (keep only positive values, set negatives to 0)
+Z_pos <- z_dat
+Z_pos[Z_pos < 0] <- 0
+
+# Create negative matrix (keep only negative values, set positives to 0, then multiply by -1)
+Z_neg <- z_dat
+Z_neg[Z_neg > 0] <- 0
+Z_neg<-abs(Z_neg)
+
+# Combine them side by side (columns are doubled)
+X_matrix <- cbind(Z_pos, Z_neg)
+
+# Convert to matrix format
+X_matrix <- as.matrix(X_matrix)
+
+# Determine optimal K (Number of Clusters)
+nmf_rank <- 2:5
+nmf_estimates <- nmf(X_matrix, nmf_rank, method = "brunet", nrun = 10)
+
+# Extract best rank (K)
+best_K <- nmf_rank[which.min(nmf_estimates$measures$dispersion)]
+print(paste("Optimal K:", best_K))
+
+# Run final NMF with optimal K
+nmf_result <- nmf(X_matrix, rank = best_K, method = "brunet")
+
+# Extract factorized matrices W and H
+W <- basis(nmf_result)  # SNP-cluster associations
+H <- coef(nmf_result)   # Trait-cluster associations
+
+H_normalized <- apply(H, 2, function(x) x / sum(x))
+
+# Visualize contribution of MIX GWAS to CAD vs. RA
+barplot(H_normalized, beside = TRUE)
+
+# Apply cut off of 0.75 to weights
+W[W < 0.75] <- 0
+
+# Create score file based on cluster weights
+score <- data.frame(ref, W)
+
+dir.create('~/oliverpainfel/Analyses/GenoClust/artificial/nmf/')
+for(i in 1:ncol(W)){
+  score_i <- data.frame(ref, W[,i])
+  names(score_i)<-c('rsID','effect_allele','other_allele', 'effect_weight')
+  write.table(
+    score_i,
+    paste0(
+      '~/oliverpainfel/Analyses/GenoClust/artificial/nmf/cluster_',
+      i,
+      '.score'
+    ),
+    row.names = F,
+    quote = F
+  )
+}
+
+```
+
+```{r}
+library(data.table)
+
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset')
+
+######
+# score_list
+######
+
+score_list<-data.frame(
+  name=c('nmf_1','nmf_2'),
+  path=c(
+    '/users/k1806347/oliverpainfel/Analyses/GenoClust/artificial/nmf/cluster_1.score',
+    '/users/k1806347/oliverpainfel/Analyses/GenoClust/artificial/nmf/cluster_2.score'
+  ),
+  label=paste0('"', c('nmf_1','nmf_2'),'"')
+)
+
+write.table(score_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/score_list.txt', col.names=T, row.names=F, quote=F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_genoclust",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/gwas_list.txt",
+  "score_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/score_list.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/target_list.txt",
+  "pgs_methods: ['sbayesrc']",
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 10",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate genopred
+
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml \
+  output_all -n
 ```
 
+I haven't fully understood this NMF process yet. 
 

From fee3a867ac3163be3b322a1ce63b73aedb957c42 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 4 Mar 2025 13:39:21 +0000
Subject: [PATCH 117/153] Update

---
 Scripts/pgs_methods/xwing.R     | 199 +++++++++++++++++---------------
 docs/CrossPop.Rmd               |   4 +-
 functions/pgs.R                 |  42 +++++--
 pipeline/rules/dependencies.smk |   3 +
 pipeline/rules/pgs_methods.smk  |   4 +-
 5 files changed, 144 insertions(+), 108 deletions(-)

diff --git a/Scripts/pgs_methods/xwing.R b/Scripts/pgs_methods/xwing.R
index 2601e63e..24e17d1e 100644
--- a/Scripts/pgs_methods/xwing.R
+++ b/Scripts/pgs_methods/xwing.R
@@ -28,6 +28,8 @@ option_list = list(
       help="Path for output files [required]"),
   make_option("--memory", action="store", default=5000, type='numeric',
       help="Memory limit [optional]"),
+  make_option("--leopard", action="store", default=T, type='logical',
+      help="Logical indicating whether LEOPARD analysis should be run [optional]"),
   make_option("--sumstats", action="store", default=NULL, type='character',
       help="Comma-seperated list of GWAS summary statistics [required]"),
   make_option("--populations", action="store", default=NULL, type='character',
@@ -210,103 +212,105 @@ log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list
   }
 }
 
-##
-# Run LEOPARD to subsample GWAS
-##
-dir.create(paste0(tmp_dir,'/LEOPARD/sampled_sumstats'), recursive = T)
-
-for(i in 1:length(sumstats)){
-  system(paste0(
-    'Rscript ', opt$xwing_repo, '/LEOPARD_Sim.R ',
-    '--sumstats ', tmp_dir, '/GWAS_sumstats_', i,'_temp.txt ',
-    '--n_gwas ', gwas_N[i], ' ',
-    '--train_prop 0.75 ',
-    '--ref_prefix ', opt$leopard_ref,'/', populations[i], '/', populations[i], '_part1 ',
-    '--seed ', opt$seed, ' ',
-    '--rep 4 ',
-    '--out_prefix ', tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', i
-  ))
-}
-
-##
-# Run PANTHER on subsampled GWAS
-##
-
-combinations <- expand.grid(targ_pop = populations, pst_pop = populations, chr = CHROMS, index = 1:4)
-
-file.remove(paste0(tmp_dir, '/checker.txt'))
-log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
-  if(!file.exists(paste0(tmp_dir, '/checker.txt'))) {
-    targ_pop <- combinations$targ_pop[i]
-    pst_pop <- combinations$pst_pop[i]
-    chr <- combinations$chr[i]
-    index <- combinations$index[i]
-
-    dir.create(paste0(tmp_dir,'/LEOPARD/post_targ_', targ_pop), recursive = T)
-    dir.create(paste0(tmp_dir,'/LEOPARD/post_collect_targ_', targ_pop), recursive = T)
-
-    sumstats_i <- paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt')
-    sumstats_i[populations == targ_pop] <- paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', index, '_train.txt')
-
-    targ_gwas_train_n<-fread(paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', index, '_train_valid_N.txt'))$N_train
-
-    gwas_N_i<-gwas_N
-    gwas_N_i[populations == targ_pop] <- targ_gwas_train_n
-
-    command<-paste0(
-      'python ', opt$xwing_repo, '/PANTHER.py ',
-      '--ref_dir ', opt$panther_leopard_ref, ' ',
-      '--bim_prefix ', tmp_dir,'/ref.chr',chr,' ',
-      '--sumstats ', paste0(sumstats_i, collapse=','), ' ',
-      '--n_gwas ', paste(gwas_N_i, collapse=','), ' ',
-      '--anno_file ', paste0(paste0(tmp_dir, '/LOGODetect/targ_', targ_pop, '_annot_', populations, '.txt'), collapse=','), ' ',
-      '--chrom ', chr, ' ',
-      '--pop ', opt$populations ,' ',
-      '--target_pop ', targ_pop,' ',
-      '--pst_pop ', pst_pop, ' ',
-      '--out_name output_', index, ' ',
-      '--seed 1 ',
-      '--out_dir ', tmp_dir, '/LEOPARD/post_targ_', targ_pop
-    )
-
-    # Run command
-    log_i <- system(command)
-
-    # Check for an error
-    if(log_i != 0){
-      write("", paste0(tmp_dir, '/checker.txt'))
+if(opt$leopard){
+  ##
+  # Run LEOPARD to subsample GWAS
+  ##
+  dir.create(paste0(tmp_dir,'/LEOPARD/sampled_sumstats'), recursive = T)
+  
+  for(i in 1:length(sumstats)){
+    system(paste0(
+      'Rscript ', opt$xwing_repo, '/LEOPARD_Sim.R ',
+      '--sumstats ', tmp_dir, '/GWAS_sumstats_', i,'_temp.txt ',
+      '--n_gwas ', gwas_N[i], ' ',
+      '--train_prop 0.75 ',
+      '--ref_prefix ', opt$leopard_ref,'/', populations[i], '/', populations[i], '_part1 ',
+      '--seed ', opt$seed, ' ',
+      '--rep 4 ',
+      '--out_prefix ', tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', i
+    ))
+  }
+  
+  ##
+  # Run PANTHER on subsampled GWAS
+  ##
+  
+  combinations <- expand.grid(targ_pop = populations, pst_pop = populations, chr = CHROMS, index = 1:4)
+  
+  file.remove(paste0(tmp_dir, '/checker.txt'))
+  log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+    if(!file.exists(paste0(tmp_dir, '/checker.txt'))) {
+      targ_pop <- combinations$targ_pop[i]
+      pst_pop <- combinations$pst_pop[i]
+      chr <- combinations$chr[i]
+      index <- combinations$index[i]
+  
+      dir.create(paste0(tmp_dir,'/LEOPARD/post_targ_', targ_pop), recursive = T)
+      dir.create(paste0(tmp_dir,'/LEOPARD/post_collect_targ_', targ_pop), recursive = T)
+  
+      sumstats_i <- paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt')
+      sumstats_i[populations == targ_pop] <- paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', index, '_train.txt')
+  
+      targ_gwas_train_n<-fread(paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', index, '_train_valid_N.txt'))$N_train
+  
+      gwas_N_i<-gwas_N
+      gwas_N_i[populations == targ_pop] <- targ_gwas_train_n
+  
+      command<-paste0(
+        'python ', opt$xwing_repo, '/PANTHER.py ',
+        '--ref_dir ', opt$panther_leopard_ref, ' ',
+        '--bim_prefix ', tmp_dir,'/ref.chr',chr,' ',
+        '--sumstats ', paste0(sumstats_i, collapse=','), ' ',
+        '--n_gwas ', paste(gwas_N_i, collapse=','), ' ',
+        '--anno_file ', paste0(paste0(tmp_dir, '/LOGODetect/targ_', targ_pop, '_annot_', populations, '.txt'), collapse=','), ' ',
+        '--chrom ', chr, ' ',
+        '--pop ', opt$populations ,' ',
+        '--target_pop ', targ_pop,' ',
+        '--pst_pop ', pst_pop, ' ',
+        '--out_name output_', index, ' ',
+        '--seed 1 ',
+        '--out_dir ', tmp_dir, '/LEOPARD/post_targ_', targ_pop
+      )
+  
+      # Run command
+      log_i <- system(command)
+  
+      # Check for an error
+      if(log_i != 0){
+        write("", paste0(tmp_dir, '/checker.txt'))
+      }
     }
   }
-}
-
-for(targ_pop in populations){
-  for(pst_pop in populations){
-    for(i in 1:4){
-      system(paste0('cat ', tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i,'_', pst_pop, '_pst_eff_phiauto_chr*.txt > ', tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i,'_', pst_pop, '_Post.txt'))
-      system(paste0("sed  -i '1iCHR\tSNP\tBP\tA1\tA2\tBETA' ", tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i, '_', pst_pop, '_Post.txt'))
+  
+  for(targ_pop in populations){
+    for(pst_pop in populations){
+      for(i in 1:4){
+        system(paste0('cat ', tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i,'_', pst_pop, '_pst_eff_phiauto_chr*.txt > ', tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i,'_', pst_pop, '_Post.txt'))
+        system(paste0("sed  -i '1iCHR\tSNP\tBP\tA1\tA2\tBETA' ", tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i, '_', pst_pop, '_Post.txt'))
+      }
     }
   }
-}
-
-##
-# Run LEOPARD to to find optimal weights for GWAS from each population
-##
-
-# Estimating the linear combination weights
-for(targ_pop in populations){
-  dir.create(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop), recursive = T)
-
-  for(i in 1:4){
-    targ_gwas_valid_n<-fread(paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', i, '_train_valid_N.txt'))$N_valid
-    system(paste0(
-      'Rscript ', opt$xwing_repo, '/LEOPARD_Weights.R --beta_file ', paste0(paste0(tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i, '_', populations, '_Post.txt'), collapse=','), ' --valid_file ', tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', i, '_valid.txt --n_valid ', targ_gwas_valid_n ,' --ref_prefix ', opt$leopard_ref,'/', targ_pop, '/', targ_pop, '_part3 --out ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep', i, '.txt'
-    ))
+  
+  ##
+  # Run LEOPARD to to find optimal weights for GWAS from each population
+  ##
+  
+  # Estimating the linear combination weights
+  for(targ_pop in populations){
+    dir.create(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop), recursive = T)
+  
+    for(i in 1:4){
+      targ_gwas_valid_n<-fread(paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', i, '_train_valid_N.txt'))$N_valid
+      system(paste0(
+        'Rscript ', opt$xwing_repo, '/LEOPARD_Weights.R --beta_file ', paste0(paste0(tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i, '_', populations, '_Post.txt'), collapse=','), ' --valid_file ', tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', i, '_valid.txt --n_valid ', targ_gwas_valid_n ,' --ref_prefix ', opt$leopard_ref,'/', targ_pop, '/', targ_pop, '_part3 --out ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep', i, '.txt'
+      ))
+    }
   }
+  
+  # Average weights across repeats
+  mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'), log_file = log_file)
 }
 
-# Average weights across repeats
-mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'), log_file = log_file)
-
 ####
 # Combine score files
 ####
@@ -315,7 +319,7 @@ mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = pa
 ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
 
 # We should combine the raw PANTHER score files for each population,
-# and then combine using mixing weights for each population
+# and then combine using mixing weights for each population if LEOPARD was run
 score_all<-NULL
 for(targ_pop in populations){
   # Read in the .freq file for target population
@@ -336,8 +340,10 @@ for(targ_pop in populations){
     # Centre SNP-weights for target population
     score_i <- centre_weights(score = score_i, freq = freq_data, ref = ref)
     
-    # Adjust SNP-weights according to mixing weights
-    score_i$BETA <- score_i$BETA * mix_weights[[targ_pop]][which(populations == pst_pop)]
+    if(opt$leopard){
+      # Adjust SNP-weights according to mixing weights
+      score_i$BETA <- score_i$BETA * mix_weights[[targ_pop]][which(populations == pst_pop)]
+    }
     names(score_i)<-c('SNP', 'A1', 'A2', paste0('SCORE_targ_', targ_pop, '_pst_', pst_pop))
 
     if(is.null(score_pop)){
@@ -347,9 +353,12 @@ for(targ_pop in populations){
     }
   }
   
-  # Take average of weighted scores
   score_pop[is.na(score_pop)]<-0
-  score_pop[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <- rowSums(score_pop[, grepl('SCORE_', names(score_pop)), with = F])
+  
+  if(opt$leopard){
+    # Take average of weighted scores
+    score_pop[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <- rowSums(score_pop[, grepl('SCORE_', names(score_pop)), with = F])
+  }
   if(is.null(score_all)){
     score_all<-score_pop
   } else {
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 92077c4f..dc380a88 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -5626,11 +5626,11 @@ write.table(gwas_groups_three_pop, '/users/k1806347/oliverpainfel/Data/ukb/GenoP
 
 config<-c(
   "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
-  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml",
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt",
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_three_pop.txt",
-  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx','xwing']",
+  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx']", # xwing removed for time sake
   "leopard_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
   "cores_prep_pgs: 10", # xwing run with 20 cores
   "cores_target_pgs: 50",
diff --git a/functions/pgs.R b/functions/pgs.R
index 0d06f304..78f033d3 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -47,30 +47,54 @@ list_score_files <- function(config){
   # Read in gwas_groups
   gwas_groups <- read_param(config = config, param = 'gwas_groups')
 
+  # Methods implemented when GWAS groups contains only 2 GWAS
   if(!is.null(gwas_groups)){
+    # Identify gwas_groups containing >2 GWAS
+    gwas_groups_2 <- gwas_groups[sapply(gwas_groups$gwas, function(x) sum(strsplit(x, ",")[[1]] != "") == 2), ]
+    
     # Identify PGS methods to be included
     pgs_methods_list <- read_param(config = config, param = 'pgs_methods', return_obj = F)
 
-    # Retain methods that are applied to groups of gwas
-    pgs_methods_list <- pgs_methods_list[(pgs_methods_list %in% pgs_group_methods)]
-
+    # Retain methods that are applied to groups with only 2 gwas
+    pgs_methods_list <- pgs_methods_list[(pgs_methods_list %in% c('prscsx', 'xwing'))]
+    
+    # Provide combos for methods applied to groups of gwas
+    combos <- rbind(combos, expand.grid(name = gwas_groups_2$name, method = pgs_methods_list))
+    
     # For TL-PRS, list combos for tlprs_methods
     tlprs_methods<-read_param(config = config, param = 'tlprs_methods', return_obj = F)
     if(length(tlprs_methods) > 1 || !is.na(tlprs_methods)){
-      combos <- rbind(combos, expand.grid(name = gwas_groups$name, method = paste0('tlprs_', tlprs_methods)))
+      combos <- rbind(combos, expand.grid(name = gwas_groups_2$name, method = paste0('tlprs_', tlprs_methods)))
     }
     
     # For LEOPARD, list combos for leopard_methods
     leopard_methods<-read_param(config = config, param = 'leopard_methods', return_obj = F)
     if(length(leopard_methods) > 1 || !is.na(leopard_methods)){
-      combos <- rbind(combos, expand.grid(name = gwas_groups$name, method = paste0(leopard_methods,'_multi')))
+      combos <- rbind(combos, expand.grid(name = gwas_groups_2$name, method = paste0(leopard_methods,'_multi')))
     }
-
-    # Provide combos for other methods applied to groups of gwas
-    pgs_methods_list <- pgs_methods_list[!(pgs_methods_list %in% c('tlprs','leopard'))]
-    combos <- rbind(combos, expand.grid(name = gwas_groups$name, method = pgs_methods_list))
   }
 
+  # Methods implemented when GWAS groups contain >2 GWAS
+  if(!is.null(gwas_groups)){
+    # Identify gwas_groups with more than 2 gwas
+    gwas_groups_more <- gwas_groups[sapply(gwas_groups$gwas, function(x) sum(strsplit(x, ",")[[1]] != "") > 2), ]
+    
+    # Identify PGS methods to be included
+    pgs_methods_list <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+    
+    # Retain methods that are applied to groups with only 2 gwas
+    pgs_methods_list <- pgs_methods_list[(pgs_methods_list %in% c('prscsx'))]
+    
+    # Provide combos for methods applied to groups of gwas
+    combos <- rbind(combos, expand.grid(name = gwas_groups_more$name, method = pgs_methods_list))
+    
+    # For LEOPARD, list combos for leopard_methods
+    leopard_methods<-read_param(config = config, param = 'leopard_methods', return_obj = F)
+    if(length(leopard_methods) > 1 || !is.na(leopard_methods)){
+      combos <- rbind(combos, expand.grid(name = gwas_groups_more$name, method = paste0(leopard_methods,'_multi')))
+    }
+  }
+  
   combos <- data.table(combos)
   combos <- combos[, lapply(.SD, as.character)]
   
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 430891aa..1acc5401 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -231,6 +231,9 @@ missing_gwas = set(gwas_groups_gwas) - set(gwas_list_names)
 if missing_gwas:
     raise ValueError(f"The following GWAS are in gwas_groups but missing in gwas_list: {', '.join(missing_gwas)}")
 
+# Subset gwas_groups to those with 2 GWAS specified
+gwas_groups_df_two = gwas_groups_df[gwas_groups_df['gwas'].str.count(',') == 1]
+
 ###
 # Check there are no duplicate values in name columns of gwas_list, score_list, gwas_groups
 ###
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 6e84e1a7..5b2ba882 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -767,7 +767,7 @@ rule prep_pgs_xwing_i:
     """
 
 rule prep_pgs_xwing:
-  input: expand(f"{outdir}/reference/pgs_score_files/xwing/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'])
+  input: expand(f"{outdir}/reference/pgs_score_files/xwing/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df_two['name'])
 
 ####
 # TL-PRS
@@ -814,7 +814,7 @@ rule prep_pgs_tlprs_i:
     """
 
 rule prep_pgs_tlprs:
-  input: expand(f"{outdir}/reference/pgs_score_files/tlprs_{{method}}/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'], method=config["tlprs_methods"])
+  input: expand(f"{outdir}/reference/pgs_score_files/tlprs_{{method}}/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df_two['name'], method=config["tlprs_methods"])
 
 ####
 # BridgePRS

From 1b89f5322c54071b3238f7f34aa1488be8d5be60 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 4 Mar 2025 20:47:14 +0000
Subject: [PATCH 118/153] Update

---
 Scripts/magma/magma_set_conditional.R         | 140 +++++++++
 Scripts/magma/set_extractor.R                 |  95 ++++++
 Scripts/magma/set_reporter.R                  |  45 +++
 .../target_scoring_partioned_pipeline.R       | 293 ++++++++++++++++++
 docs/CrossPop.Rmd                             |   2 +-
 docs/GenoClust.Rmd                            |   9 +-
 pipeline/Snakefile                            |   1 +
 pipeline/rules/magma.smk                      | 236 ++++++++++++++
 8 files changed, 819 insertions(+), 2 deletions(-)
 create mode 100644 Scripts/magma/magma_set_conditional.R
 create mode 100644 Scripts/magma/set_extractor.R
 create mode 100644 Scripts/magma/set_reporter.R
 create mode 100644 Scripts/target_scoring/target_scoring_partioned_pipeline.R
 create mode 100644 pipeline/rules/magma.smk

diff --git a/Scripts/magma/magma_set_conditional.R b/Scripts/magma/magma_set_conditional.R
new file mode 100644
index 00000000..ab3caff8
--- /dev/null
+++ b/Scripts/magma/magma_set_conditional.R
@@ -0,0 +1,140 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+suppressMessages(library("optparse"))
+
+option_list = list(
+  make_option("--gwas", action="store", default=NA, type='character',
+              help="GWAS ID [required]"),
+  make_option("--config", action="store", default=NA, type='character',
+              help="config file [required]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Check required inputs
+if(is.null(opt$config)){
+  stop('--config must be specified.\n')
+}
+if(is.null(opt$gwas)){
+  stop('--gwas must be specified.\n')
+}
+
+# Identify outdir from config file
+outdir <- read_param(config = opt$config, 'outdir', return_obj = F)
+
+# Identify resdir from config file
+resdir <- read_param(config = opt$config, 'resdir', return_obj = F)
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+# Initiate log file
+log_file <- paste0(outdir,"/reference/gwas_sumstat/",opt$gwas,'/magma/magma_set_conditional.log')
+log_header(log_file = log_file, opt = opt, script = 'magma_set_conditional.R', start.time = start.time)
+
+# Read in the MAGMA gene set enrichment results
+sets_enrich<-fread(cmd=paste0("grep -v '^#' ",outdir,"/reference/gwas_sumstat/",opt$gwas,'/magma/magma_set_level.gsa.out'))
+
+# Insert FULL_NAME column if not present
+if(all(names(sets_enrich) != 'FULL_NAME')){
+  sets_enrich$FULL_NAME<-sets_enrich$VARIABLE
+}
+
+# Remove gene sets with <5 genes present
+sets_enrich<-sets_enrich[sets_enrich$NGENES >= 5,]
+
+# Select FDR significant sets
+# Note. this could be a parameter to be tuned
+sets_enrich$P.FDR<-p.adjust(sets_enrich$P, method = 'fdr')
+sets_enrich<-sets_enrich[sets_enrich$P.FDR <= 0.05,]
+
+log_add(log_file = log_file, message = paste0(nrow(sets_enrich)," sets are FDR significant."))
+
+# If more than 1 sig set, perform conditional analysis
+if(nrow(sets_enrich) > 1){
+  
+  log_add(log_file = log_file, message = "Performing conditional analysis...")
+  
+  # Read in .gmt file
+  gmt_file <- read_param(config = opt$config, 'gene_sets', return_obj = F)
+  set_annot<-readLines(gmt_file)
+  set_ids<-sapply(strsplit(set_annot, '\t'),"[[",1)
+  
+  # Subset .gmt to contain enriched sets
+  set_annot<-set_annot[set_ids %in% sets_enrich$FULL_NAME]
+  writeLines(set_annot, paste0(tmp_dir, "/sig_sets.gmt"))
+  
+  # Sort results by p-value
+  sets_enrich<-sets_enrich[order(sets_enrich$P),]
+  
+  # Now condition each set on the most significant sets until all are independently significant
+  i<-1
+  
+  sets_indep<-sets_enrich
+  while(1){
+    if(nrow(sets_indep) <= i){
+      break
+    }
+    
+    set_i<-sets_indep$FULL_NAME[1:i]
+    
+    log<-system(paste0(
+      resdir, "/software/magma/magma",
+      " --gene-results ",outdir,"/reference/gwas_sumstat/",opt$gwas,"/magma/magma_gene_level.genes.raw",
+      " --set-annot ",tmp_dir, "/sig_sets.gmt",
+      " --model direction-sets=greater condition-hide=",paste(set_i,collapse=','),
+      " --out ",tmp_dir, "/res"
+    ), intern = T)
+    
+    if(any(grepl('ERROR - running gene-level regression: could not invert design matrix of conditioned-on variables; variables are collinear with each other', log))){
+      print(log)
+      print('ERROR: There was too much multicolinearity between sets.')
+      q()
+    }
+    
+    # Read in the results
+    cond_res<-fread(cmd=paste0("grep -v '^#' ",tmp_dir, '/res.gsa.out'))
+    
+    # Insert FULL_NAME column if not present
+    if(all(names(cond_res) != 'FULL_NAME')){
+      cond_res$FULL_NAME<-cond_res$VARIABLE
+    }
+    
+    # Remove sets from sets_indep that are no longer significant (P>0.01)
+    cond_res<-cond_res[cond_res$P < 0.01,]
+    sets_indep<-sets_indep[sets_indep$FULL_NAME %in% c(set_i,cond_res$FULL_NAME),]
+    
+    i<-i+1
+  }
+  
+  # Save file listing significant and independent sets
+  write.table(sets_indep$FULL_NAME, paste0(outdir,"/reference/gwas_sumstat/",opt$gwas,'/magma/sig_indep_sets.txt'), row.names=F, col.names=F, quote=F)
+  
+  log_add(log_file = log_file, message = paste0(nrow(sets_indep), " independent sets remain."))
+}
+
+# If 1 sig set, no conditional analysis required
+if(nrow(sets_enrich) == 1){
+  log_add(log_file = log_file, message = "No conditional analysis required.")
+  
+  # Save file listing significant and independent sets
+  write.table(sets_enrich$FULL_NAME, paste0(outdir,"/reference/gwas_sumstat/",opt$gwas,'/magma/sig_indep_sets.txt'), row.names=F, col.names=F, quote=F)
+}
+
+if(nrow(sets_enrich) == 0){
+  log_add(log_file = log_file, message = 'No conditional analysis required')
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
\ No newline at end of file
diff --git a/Scripts/magma/set_extractor.R b/Scripts/magma/set_extractor.R
new file mode 100644
index 00000000..770ea204
--- /dev/null
+++ b/Scripts/magma/set_extractor.R
@@ -0,0 +1,95 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+suppressMessages(library("optparse"))
+
+option_list = list(
+  make_option("--gwas", action="store", default=NA, type='character',
+              help="GWAS ID [required]"),
+  make_option("--config", action="store", default=NA, type='character',
+              help="config file [required]")
+)
+
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Check required inputs
+if(is.null(opt$config)){
+  stop('--config must be specified.\n')
+}
+if(is.null(opt$gwas)){
+  stop('--gwas must be specified.\n')
+}
+
+# Identify outdir from config file
+outdir <- read_param(config = opt$config, 'outdir', return_obj = F)
+
+# Identify resdir from config file
+resdir <- read_param(config = opt$config, 'resdir', return_obj = F)
+
+# Identify refdir from config file
+refdir <- read_param(config = opt$config, 'refdir', return_obj = F)
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+# Initiate log file
+log_file <- paste0(outdir,"/reference/gwas_sumstat/",opt$gwas,'/magma/set_extractor.log')
+log_header(log_file = log_file, opt = opt, script = 'set_extractor.R', start.time = start.time)
+
+# Read in significant and independent gene sets
+if(!file.exists(paste0(outdir,'/reference/gwas_sumstat/',opt$gwas,'/magma/sig_indep_sets.txt'))){
+  log_add(log_file = log_file, message = 'No sets were FDR significant.')
+} else {
+  
+  set_enrich<-read.table(paste0(outdir,'/reference/gwas_sumstat/',opt$gwas,'/magma/sig_indep_sets.txt'), header=F)$V1
+  
+  # Read in .gmt file
+  gmt_file <- read_param(config = opt$config, 'gene_sets', return_obj = F)
+  set_annot<-readLines(gmt_file)
+  set_ids<-sapply(strsplit(set_annot, '\t'),"[[",1)
+  
+  # Subset .gmt to contain enriched sets
+  set_annot<-set_annot[set_ids %in% set_enrich]
+  
+  # Save list of significant sets
+  dir.create(paste0(outdir,'/reference/gwas_sumstat/',opt$gwas,'/magma/snplists'))
+  
+  # Read in MAGMA gene locations file
+  annot<-readLines(paste0(resdir, '/data/magma/NCBI37.3.genes.annot'))[-1:-2]
+  annot<-strsplit(annot, '\t')
+  annot_ids<-sapply(annot,"[[",1)
+  
+  ref <- read_pvar(paste0(refdir, '/ref.chr'))
+  ref_snps <- ref$SNP
+  
+  for(set_i in 1:length(set_annot)){
+    set_annot_i<-unlist(strsplit(set_annot[[set_i]], '\t'))
+    
+    set_id<-set_annot_i[1]
+    genes<-set_annot_i[-1:-2]
+    
+    annot_subset<-annot[annot_ids %in% genes]
+    
+    snps<-unique(do.call(c, annot_subset))
+    snps<-snps[grepl('^rs', snps)]
+    snps<-snps[!is.na(snps)]
+    
+    snps<-snps[snps %in% ref_snps]
+    
+    write.table(snps, paste0(outdir,'/reference/gwas_sumstat/',opt$gwas,'/magma/snplists/',set_id,'.snplist'), col.names=F, row.names=F, quote=F)
+  }
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
\ No newline at end of file
diff --git a/Scripts/magma/set_reporter.R b/Scripts/magma/set_reporter.R
new file mode 100644
index 00000000..2fa35689
--- /dev/null
+++ b/Scripts/magma/set_reporter.R
@@ -0,0 +1,45 @@
+#!/usr/bin/Rscript
+# Save start time
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+suppressMessages(library("optparse"))
+
+option_list = list(
+  make_option("--config", action="store", default=NA, type='character',
+              help="config file [required]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Check required inputs
+if(is.null(opt$config)){
+  stop('--config must be specified.\n')
+}
+
+# Identify outdir from config file
+outdir <- read_param(config = opt$config, 'outdir', return_obj = F)
+
+# Read in gwas_list from config file
+gwas_list <- read_param(config = opt$config, 'gwas_list', return_obj = T)
+
+set_res<-NULL
+for(gwas_i in gwas_list$name){
+  if(!file.exists(paste0(outdir,'/reference/gwas_sumstat/',gwas_i,'/magma/sig_indep_sets.txt'))){
+    set_res<-rbind(set_res, data.frame(name=gwas_i,
+                                       n_sig=0))
+  } else {
+    
+    set_enrich<-read.table(paste0(outdir,'/reference/gwas_sumstat/',gwas_i,'/magma/sig_indep_sets.txt'), header=F)$V1
+    
+    set_res<-rbind(set_res, data.frame(name=gwas_i,
+                                       n_sig=length(set_enrich)))
+  }
+}
+
+write.table(set_res, paste0(outdir,'/reference/gwas_sumstat/set_reporter.txt'), row.names=F, col.names=T, quote=F)
diff --git a/Scripts/target_scoring/target_scoring_partioned_pipeline.R b/Scripts/target_scoring/target_scoring_partioned_pipeline.R
new file mode 100644
index 00000000..499d0ae6
--- /dev/null
+++ b/Scripts/target_scoring/target_scoring_partioned_pipeline.R
@@ -0,0 +1,293 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+make_option("--config", action="store", default=NULL, type='character',
+    help="Pipeline configuration file [required]"),
+make_option("--name", action="store", default=NULL, type='character',
+    help="Name of target sample [required]"),
+make_option("--population", action="store", default=NULL, type='character',
+    help="Population in target sample to extract [required]"),
+make_option("--plink2", action="store", default='plink2', type='character',
+		help="Path PLINK v2 software binary [optional]"),
+make_option("--n_cores", action="store", default=1, type='numeric',
+    help="Number of cores to use [optional]"),
+make_option("--test", action="store", default=NA, type='character',
+    help="Specify number of SNPs to include [optional]"),
+make_option("--memory", action="store", default=5000, type='numeric',
+		help="Memory limit [optional]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+library(foreach)
+library(doMC)
+registerDoMC(opt$n_cores)
+
+# Check required inputs
+if(is.null(opt$config)){
+  stop('--config must be specified.\n')
+}
+if(is.null(opt$name)){
+  stop('--name must be specified.\n')
+}
+if(is.null(opt$population)){
+  stop('--population must be specified.\n')
+}
+
+# Read in outdir
+outdir <- read_param(config = opt$config, param = 'outdir', return_obj = F)
+
+# Create output directory
+opt$output_dir <- paste0(outdir, '/', opt$name, '/pgs/', opt$population)
+system(paste0('mkdir -p ',opt$output_dir))
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output, '_', format(Sys.time(), "%Y-%m-%d_%H-%M-%S"), '.log')
+log_header(log_file = log_file, opt = opt, script = 'target_scoring_partitioned_pipeline.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+# Identify score files to be combined
+score_files<-list_partitioned_score_files(opt$config)
+
+# Check whether score files or target genetic data are newer than target pgs
+if(!is.null(score_files)){
+  ancestry_reporter_file<-paste0(outdir, '/reference/target_checks/', opt$name, '/ancestry_reporter.done')
+  ancestry_reporter_file_time <- file.info(ancestry_reporter_file)$mtime
+  set_reporter_file <- paste0(outdir, '/reference/benchmarks/set_reporter.txt')
+  set_reporter<-fread(set_reporter_file)
+  set_reporter_file_time <- file.info(set_reporter_file)$mtime
+  
+  # Remove score files for gwas that have no significant sets
+  score_files<-score_files[score_files$name %in% set_reporter$name[set_reporter$n_sig > 0,]]
+  
+  score_files_to_do <- data.table()
+  for(i in 1:nrow(score_files)){
+    pgs_i <- paste0(outdir, '/', opt$name,'/pgs/', opt$population,'/', score_files$method[i],'/', score_files$name[i],'/', opt$name,'-', score_files$name[i],'-',opt$population,'.partitioned.profiles')
+    score_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '.score.gz')
+    if(!file.exists(pgs_i)){
+      score_files_to_do <- rbind(score_files_to_do, score_files[i,])
+    } else {
+      score_i_time <- file.info(score_i)$mtime
+      pgs_i_time <- file.info(pgs_i)$mtime
+      if (score_i_time > pgs_i_time | ancestry_reporter_file_time > pgs_i_time | set_reporter_file_time > pgs_i_time) {
+        score_files_to_do <- rbind(score_files_to_do, score_files[i,])
+        system(paste0('rm ', pgs_i))
+      }
+    }
+  }
+  log_add(log_file = log_file, message = paste0('After checking timestamps, ', nrow(score_files_to_do), '/', nrow(score_files), ' score files will be used for target scoring.'))
+  score_files <- score_files_to_do
+}
+
+if(is.null(score_files) || nrow(score_files) == 0){
+  log_add(log_file = log_file, message = paste0('No score files to be used for target scoring.'))
+  end.time <- Sys.time()
+  time.taken <- end.time - start.time
+  sink(file = paste(opt$output,'.log',sep=''), append = T)
+  cat('Analysis finished at',as.character(end.time),'\n')
+  cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
+  sink()
+  quit(save = "no", status = 0)
+}
+
+# Read in target_list
+target_list <- read_param(config = opt$config, param = 'target_list', return_obj = T)
+
+# Set params for plink_score
+opt$target_plink_chr <- paste0(outdir, '/', opt$name, '/geno/', opt$name, '.ref.chr')
+if(opt$population == 'TRANS'){
+  opt$target_keep<-NULL
+} else {
+  opt$target_keep <- paste0(outdir, '/', opt$name, '/ancestry/keep_files/model_based/', opt$population, '.keep')
+}
+refdir <- read_param(config = opt$config, param = 'refdir', return_obj = F)
+opt$ref_freq_chr <- paste0(refdir, '/freq_files/', opt$population,'/ref.', opt$population,'.chr')
+
+# Read in reference SNP data
+ref <- read_pvar(paste0(refdir, '/ref.chr'), chr = CHROMS)[, c('CHR','SNP','A1','A2'), with=F]
+
+# Identify SNPs within sets
+set_snps<-NULL
+for(i in unique(score_files$name)){
+  set_enrich<-read.table(paste0(outdir,'/reference/gwas_sumstat/',i,'/magma/sig_indep_sets.txt'), header=F)$V1
+  for(j in set_enrich){
+    set_snps <- c(set_snps, fread(paste0(outdir,'/reference/gwas_sumstat/',i,'/magma/snplists/',j,'.snplist')), header=F)$V1
+  }
+}
+set_snps <- unique(set_snps)
+ref$extract <- ifelse(ref$SNP %in% set_snps, T, F)
+
+# We will process score files and perform target scoring for one chromosome for efficiency
+for(chr_i in CHROMS){
+  log_add(log_file = log_file, message = '########################')
+  log_add(log_file = log_file, message = paste0('Processing chromosome ', chr_i,':'))
+
+  #####
+  # Combine score files
+  #####
+  # Only retain pseudo score
+  
+  # Create row number index to subset score files by chromosome
+  row_index <- format(which(ref$CHR == chr_i & ref$extract == T) + 1, scientific = FALSE)
+  write.table(row_index, paste0(tmp_dir,'/row_index.txt'), row.names=F, quote=F, col.names = F)
+  ref_subset <- ref[row_index,]
+  
+  # Create file containing SNP, A1, and A2 information for each chromosome
+  fwrite(ref[ref$CHR == chr_i, c('SNP','A1','A2'), with=F], paste0(tmp_dir,'/map.txt'), row.names=F, quote=F, sep=' ')
+
+  # Extract process score files for each name (gwas/score) in parallel
+  foreach(i = 1:nrow(score_files), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+    param <- find_pseudo(
+      config = opt$config,
+      gwas = score_files$name[i],
+      pgs_method = score_files$method[i],
+      target_pop = opt$population
+    )
+    
+    score_header <- fread(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],".score.gz"), nrows = 1)
+    score_cols <- which(names(score_header) == paste0('SCORE_', param))
+    
+    system(paste0(
+      'zcat ', outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],".score.gz | ",
+      "cut -d' ' ", score_cols, " - | ",  # Keep pseudo score
+      'awk \'NR==FNR {rows[$1]; next} FNR==1 || FNR in rows\' ', paste0(tmp_dir,'/row_index.txt'), ' - | ',  # Corrected to retain the header and process indexed rows
+      "sed '1 s/SCORE_/", paste0('score_file_', i,'.'), "/g' > ",  # Replace SCORE in the header
+      tmp_dir, '/tmp_score.', paste0(score_files$method[i], '.', score_files$name[i]), '.txt'
+    ))
+    
+    # Create stratified score files
+    score_i <- fread(paste0(tmp_dir, '/tmp_score.', paste0(score_files$method[i], '.', score_files$name[i]), '.txt'))
+    set_enrich_i <-read.table(paste0(outdir,'/reference/gwas_sumstat/', score_files$name[i],'/magma/sig_indep_sets.txt'), header=F)$V1
+    for(j in 1:length(set_enrich_i)){
+      snplist_i <- fread(paste0(outdir,'/reference/gwas_sumstat/',i,'/magma/snplists/',set_enrich_i[j],'.snplist'), header=F)$V1
+      score_i[[paste0(names(score_i)[1], '.set_', j)]] <- score_i[[1]]
+      score_i[[paste0(names(score_i)[1], '.set_', j)]][!(ref_subset$SNP %in% snplist_i)] <- 0
+    }
+    fwrite(score_i, paste0(tmp_dir, '/tmp_score.', paste0(score_files$method[i], '.', score_files$name[i]), '.txt'), sep=' ', quote=F)
+  }
+
+  # Paste files together in batches
+  # Set number of batches according to the number of score files to combine
+  num_batches <- max(c(1, min(c(opt$n_cores, floor(nrow(score_files) / 2)))))
+  tmp_score_files <- paste0(tmp_dir,'/tmp_score.',score_files$method,'.',score_files$name,'.txt')
+  set.seed(1)
+  batches <- split(sample(tmp_score_files), rep(1:num_batches, length.out = length(tmp_score_files)))
+  log_add(log_file = log_file, message = paste0('Aggregating score files in ', num_batches,' batches.'))
+  foreach(i = 1:length(batches), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+    system(paste0("paste -d ' ' ", paste(batches[[i]], collapse = " "),' > ',tmp_dir,'/tmp_batch_',i))
+    system(paste0('rm ', paste(batches[[i]], collapse = " ")))
+  }
+
+  # Paste batches together
+  log_add(log_file = log_file, message = paste0('Aggregating batched score files.'))
+  tmp_batch_files <- paste0(tmp_dir,'/tmp_batch_',1:length(batches))
+  system(paste0("paste -d ' ' ", tmp_dir,'/map.txt ', paste(tmp_batch_files, collapse = " "), ' > ', tmp_dir, '/all_score.txt'))
+  system(paste0('rm ', paste(tmp_batch_files, collapse = " ")))
+
+  # Perform polygenic risk scoring
+  scores_i <-
+    plink_score(
+      pfile = opt$target_plink_chr,
+      chr = chr_i,
+      plink2 = opt$plink2,
+      score = paste0(tmp_dir,'/all_score.txt'),
+      keep = opt$target_keep,
+      frq = opt$ref_freq_chr,
+      threads = opt$n_cores
+    )
+
+  # Sum scores across chromosomes
+  if(chr_i == CHROMS[1]){
+    scores_ids <- scores_i[, 1:2, with = F]
+    current_scores <- as.matrix(scores_i[, -1:-2, with = FALSE])
+    scores <- current_scores
+  } else {
+    current_scores <- as.matrix(scores_i[, -1:-2, with = FALSE])
+    scores <- scores + current_scores
+  }
+
+  system(paste0('rm ', tmp_dir, '/all_score.txt'))
+  system(paste0('rm ', tmp_dir, '/row_index.txt'))
+  system(paste0('rm ', tmp_dir, '/map.txt'))
+}
+
+# Combine score with IDs
+scores<-data.table(scores_ids,
+                   scores)
+
+###
+# Scale the polygenic scores based on the reference
+###
+
+if(opt$population == 'TRANS'){
+  log_add(log_file = log_file, message = paste0('Reading in ancestry adjustment models.'))
+
+  models<-list()
+  for(i in 1:nrow(score_files)){
+    models[[paste0('score_file_', i)]]<-readRDS(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'-TRANS.model.rds'))
+    names(models[[paste0('score_file_', i)]])<-gsub('SCORE_', paste0('score_file_', i, '.'), names(models[[paste0('score_file_', i)]]))
+  }
+  
+  models <- do.call(c, unname(models))
+  
+  # Read in target projected PCs
+  target_pcs<-fread(paste0(outdir,'/',opt$name,'/pcs/projected/TRANS/',opt$name,'-TRANS.profiles'))
+  log_add(log_file = log_file, message = paste0('Reading in target reference-projected PCs.'))
+  
+  # Adjust scores
+  log_add(log_file = log_file, message = 'Adjusting target PGS for ancestry.')
+  scores <- score_adjust(score = scores, pcs = target_pcs, ref_model = models)
+} else {
+  # Read in scale file and update Param
+  log_add(log_file = log_file, message = paste0('Reading in scale files.'))
+  scale_files<-list()
+  for(i in 1:nrow(score_files)){
+    scale_files[[paste0('score_file_', i)]]<-fread(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'-', opt$population,'.scale'))
+    scale_files[[paste0('score_file_', i)]]$Param<-gsub('SCORE_', paste0('score_file_', i, '.'), scale_files[[paste0('score_file_', i)]]$Param)
+  }
+  
+  # Concatenate scale files
+  all_scale<-do.call(rbind, scale_files)
+  
+  # Scale scores
+  log_add(log_file = log_file, message = 'Scaling target polygenic scores to the reference.')
+  scores<-score_scale(score=scores, ref_scale=all_scale)
+}
+
+###
+# Write out the target sample scores
+###
+
+for(i in 1:nrow(score_files)){
+  scores_i <- scores[, c('FID','IID', names(scores)[grepl(paste0('^score_file_', i, '\\.'), names(scores))]), with=F]
+  names(scores_i) <- gsub(paste0('^score_file_', i, '\\.'), paste0(score_files$name[i], '_'), names(scores_i))
+  dir.create(paste0(outdir, '/', opt$name,'/pgs/', opt$population,'/', score_files$method[i],'/', score_files$name[i]), recursive = T)
+  fwrite(scores_i, paste0(outdir, '/', opt$name,'/pgs/', opt$population,'/', score_files$method[i],'/', score_files$name[i],'/', opt$name,'-', score_files$name[i],'-',opt$population,'.profiles'), sep=' ', na='NA', quote=F)
+}
+
+log_add(log_file = log_file, message = paste0('Saved polygenic scores.'))
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at',as.character(end.time),'\n')
+cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
+sink()
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index dc380a88..fc8b0011 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -2740,7 +2740,7 @@ res_comp_all <- res_comp_all[
   grepl('top1', res_comp_all$Model_2)),]
 
 # Remove any comparisons
-res_comp_all <- res_comp_all[!duplicated(res_comp_all[, c("Target", "Method1", "Model1", "Source1", "Discovery1", "Method2", "Model2", "Source2", "Discovery2",'pheno')]),]
+res_comp_all <- res_comp_all[!duplicated(res_comp_all[, c("Target", "gwas_group", "Method1", "Model1", "Source1", "Discovery1", "Method2", "Model2", "Source2", "Discovery2",'pheno')]),]
 
 res_comp_all$r_diff_rel <- res_comp_all$R_diff / res_comp_all$Model_2_R
 
diff --git a/docs/GenoClust.Rmd b/docs/GenoClust.Rmd
index caf19e96..8c7395ed 100644
--- a/docs/GenoClust.Rmd
+++ b/docs/GenoClust.Rmd
@@ -881,7 +881,8 @@ config<-c(
   "pgs_methods: ['sbayesrc']",
   "cores_prep_pgs: 10",
   "cores_target_pgs: 10",
-  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3",
+  "gene_sets: /scratch/prj/oliverpainfel/recovered/Data/MSigDB/c2.all.v7.5.1.entrez.gmt"
 )
 
 write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml', col.names = F, row.names = F, quote = F)
@@ -897,6 +898,12 @@ snakemake \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml \
   output_all -n
+  
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml \
+  magma_annot -n
 ```
 
 ***
diff --git a/pipeline/Snakefile b/pipeline/Snakefile
index 575a214f..15d1efdb 100644
--- a/pipeline/Snakefile
+++ b/pipeline/Snakefile
@@ -18,5 +18,6 @@ wildcard_constraints:
 include: "rules/dependencies.smk"
 include: "rules/target_qc.smk"
 include: "rules/pgs_methods.smk"
+include: "rules/magma.smk"
 include: "rules/target_scoring.smk"
 include: "rules/report.smk"
diff --git a/pipeline/rules/magma.smk b/pipeline/rules/magma.smk
new file mode 100644
index 00000000..d9efb790
--- /dev/null
+++ b/pipeline/rules/magma.smk
@@ -0,0 +1,236 @@
+# This code has been adapted from GenoDisc
+
+####
+# Download MAGMA
+####
+
+rule download_magma:
+  output:
+    f"{resdir}/software/magma/magma"
+  conda:
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/download_magma.txt"
+  log:
+    f"{outdir}/reference/logs/download_magma.log"
+  shell:
+    "wget --no-check-certificate -O {resdir}/software/magma.zip https://vu.data.surfsara.nl/index.php/s/zkKbNeNOZAhFXZB/download; \
+     unzip resources/software/magma.zip -d {resdir}/software/magma; \
+     rm {resdir}/software/magma.zip > {log} 2>&1"
+
+####
+# Download MAGMA gene locations
+####
+
+rule download_magma_gene_loc:
+  output:
+    f"{resdir}/data/magma/NCBI37.3.gene.loc"
+  conda:
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/download_magma_gene_loc.txt"
+  log:
+    f"{outdir}/reference/logs/download_magma_gene_loc.log"
+  shell:
+    "wget --no-check-certificate -O {resdir}/data/magma.zip https://vu.data.surfsara.nl/index.php/s/Pj2orwuF2JYyKxq/download; \
+     unzip {resdir}/data/magma.zip -d {resdir}/data/magma; \
+     rm {resdir}/data/magma.zip > {log} 2>&1"
+
+####
+# Download MAGMA reference
+####
+
+rule download_magma_ref:
+  output:
+    f"{resdir}/data/magma_ref/g1000_eur.bed"
+  conda:
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/download_magma_ref.txt"
+  log:
+    f"{outdir}/reference/logs/download_magma_ref.log"
+  shell:
+    "wget --no-check-certificate -O {resdir}/data/magma.zip https://vu.data.surfsara.nl/index.php/s/VZNByNwpD8qqINe/download; \
+     unzip {resdir}/data/magma.zip -d {resdir}/data/magma_ref; \
+     rm {resdir}/data/magma.zip > {log} 2>&1"
+
+####
+# Create MAGMA annotation file
+####
+
+rule magma_annot:
+  input:
+    rules.download_magma.output,
+    rules.download_magma_gene_loc.output,
+    rules.download_magma_ref.output
+  output:
+    f"{resdir}/data/magma/NCBI37.3.genes.annot"
+  conda: 
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/magma_annot.txt"
+  log:
+    f"{outdir}/reference/logs/magma_annot.log"
+  shell:
+    "{resdir}/software/magma/magma \
+      --annotate window=35,10 \
+      --snp-loc {resdir}/data/magma_ref/g1000_eur.bim \
+      --gene-loc {resdir}/data/magma/NCBI37.3.gene.loc \
+      --out {resdir}/data/magma/NCBI37.3 > {log} 2>&1"
+
+####
+# MAGMA
+####
+
+# Gene level association analysis
+rule magma_gene_level:
+  input:
+    f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
+    rules.magma_annot.output
+  output:
+    f"{outdir}/reference/gwas_sumstat/{{gwas}}/magma/magma_gene_level.genes.raw"
+  conda: 
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/magma_gene_level-{{gwas}}.txt"
+  log:
+    f"{outdir}/reference/logs/magma_gene_level-{{gwas}}.log"
+  shell:
+    "gzip -f -d -c {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz > {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned; \
+     mkdir -p {outdir}/reference/gwas_sumstat/{wildcards.gwas}/magma/; \
+     resources/software/magma/magma \
+      --bfile {resdir}/data/magma_ref/g1000_eur \
+      --pval {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned use=SNP,P ncol=N \
+      --gene-annot {resdir}/data/magma/NCBI37.3.genes.annot \
+      --out {outdir}/reference/gwas_sumstat/{wildcards.gwas}/magma/magma_gene_level; \
+     rm {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned > {log} 2>&1"
+
+######
+# Gene set enrichment analysis
+######
+
+rule magma_gene_set_level:
+  input:
+    f"{outdir}/reference/gwas_sumstat/{{gwas}}/magma/magma_gene_level.genes.raw"
+  output:
+    f"{outdir}/reference/gwas_sumstat/{{gwas}}/magma/magma_set_level.gsa.out"
+  conda: 
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/magma_gene_set_level-{{gwas}}.txt"
+  log:
+    f"{outdir}/reference/logs/magma_gene_set_level-{{gwas}}.log"
+  params:
+    gene_sets=config['gene_sets']
+  shell:
+    "resources/software/magma/magma \
+      --gene-results {outdir}/reference/gwas_sumstat/{wildcards.gwas}/magma/magma_gene_level.genes.raw \
+      --set-annot {params.gene_sets} \
+      --model direction-sets=greater \
+      --out {outdir}/reference/gwas_sumstat/{wildcards.gwas}/magma/magma_set_level > {log} 2>&1"
+
+# Run conditional analysis
+rule magma_set_conditional:
+  input:
+    f"{outdir}/reference/gwas_sumstat/{{gwas}}/magma/magma_set_level.gsa.out"
+  output:
+    touch(f"{outdir}/reference/gwas_sumstat/{{gwas}}/magma/magma_set_conditional.done")
+  conda:
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/magma_set_conditional-{{gwas}}.txt"
+  log:
+    f"{outdir}/reference/logs/magma_set_conditional-{{gwas}}.log"
+  params:
+    config_file= config['config_file']
+  shell:
+    "Rscript ../Scripts/magma/magma_set_conditional.R \
+      --config {params.config_file} \
+      --gwas {wildcards.gwas} > {log} 2>&1"
+
+# Create SNP-lists for enriched gene sets
+rule create_set_snplists:
+  input:
+    f"{outdir}/reference/gwas_sumstat/{{gwas}}/magma/magma_set_conditional.done"
+  output:
+    touch(f"{outdir}/reference/gwas_sumstat/{{gwas}}/magma/snplists/create_set_snplists.done")
+  conda:
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/create_set_snplists-{{gwas}}.txt"
+  log:
+    f"{outdir}/reference/logs/create_set_snplists-{{gwas}}.log"
+  params:
+    config_file=config['config_file']
+  shell:
+    "Rscript ../Scripts/magma/set_extractor.R \
+      --config {params.config_file} \
+      --gwas {wildcards.gwas} > {log} 2>&1"
+  
+rule run_create_set_snplists:
+  input: 
+      lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/magma/snplists/create_set_snplists.done", gwas=gwas_list_df_eur['name'])
+  output: 
+      touch(f"{outdir}/reference/gwas_sumstat/create_set_snplists_all_gwas.done")
+
+# Create a file listing gwas with significant gene sets/set-specific SNP lists
+checkpoint set_reporter:
+  input:
+    f"{outdir}/reference/gwas_sumstat/create_set_snplists_all_gwas.done"
+  output:
+    f"{outdir}/reference/gwas_sumstat/set_reporter.txt"
+  conda:
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/set_reporter.txt"
+  log:
+    f"{outdir}/reference/logs/set_reporter.log"
+  params:
+    config_file=config['config_file']
+  shell:
+    "Rscript ../Scripts/magma/set_reporter/set_reporter.R \
+      --config {params.config_file}"
+      
+########
+# Calculate stratified PGS
+########
+
+rule target_pgs_partitioned_i:
+  resources:
+    mem_mb=config['mem_target_pgs'],
+    time_min=1000
+  threads: config['cores_target_pgs']
+  input:
+    f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",
+    f"{outdir}/reference/gwas_sumstat/set_reporter.txt",
+    lambda w: f"{outdir}/reference/target_checks/{{name}}/pc_projection-TRANS.done" if w.population == "TRANS" else [],
+    rules.prep_pgs.input
+  output:
+    touch(f"{outdir}/reference/target_checks/{{name}}/target_pgs_partitioned-{{population}}.done")
+  benchmark:
+    f"{outdir}/reference/benchmarks/target_pgs_partitioned_i-{{name}}-{{population}}.txt"
+  log:
+    f"{outdir}/reference/logs/target_pgs_partitioned_i-{{name}}-{{population}}.log"
+  conda:
+    "../envs/analysis.yaml"
+  params:
+    testing=config["testing"],
+    config_file = config["config_file"]
+  shell:
+    "Rscript ../Scripts/target_scoring/target_scoring_partitioned_pipeline.R \
+      --config {params.config_file} \
+      --name {wildcards.name} \
+      --population {wildcards.population} \
+      --plink2 plink2 \
+      --test {params.testing} \
+      --n_cores {threads} > {log} 2>&1"
+
+rule target_pgs_partitioned_all:
+  input:
+    lambda w: expand(f"{outdir}/reference/target_checks/{{name}}/target_pgs_partitioned-{{population}}.done", name=w.name, population = ancestry_munge(w.name, scaling = config["pgs_scaling"]))
+  output:
+    touch(f"{outdir}/reference/target_checks/{{name}}/target_pgs_partitioned.done")
+
+rule target_pgs_partitioned:
+  input:
+    expand(f"{outdir}/reference/target_checks/{{name}}/target_pgs_partitioned.done", name=target_list_df['name'])

From 5420883b921b9793a0c50899170a004ac5c992c7 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 7 Mar 2025 10:43:32 +0000
Subject: [PATCH 119/153] Updates

---
 Scripts/pgs_methods/pgs_stratifier.R          | 179 ++++++
 ... => target_scoring_partitioned_pipeline.R} |  64 +-
 .../target_scoring/target_scoring_pipeline.R  |   4 +-
 docs/CrossPop.Rmd                             | 351 +++++++++++
 docs/GenoClust.Rmd                            | 578 ++++++++++++++----
 functions/pipeline.R                          |  99 +++
 functions/plink.R                             |   5 +-
 pipeline/Snakefile                            |   2 +-
 pipeline/rules/magma.smk                      |  29 +-
 9 files changed, 1159 insertions(+), 152 deletions(-)
 create mode 100644 Scripts/pgs_methods/pgs_stratifier.R
 rename Scripts/target_scoring/{target_scoring_partioned_pipeline.R => target_scoring_partitioned_pipeline.R} (81%)

diff --git a/Scripts/pgs_methods/pgs_stratifier.R b/Scripts/pgs_methods/pgs_stratifier.R
new file mode 100644
index 00000000..5cd11d70
--- /dev/null
+++ b/Scripts/pgs_methods/pgs_stratifier.R
@@ -0,0 +1,179 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+make_option("--config", action="store", default=NULL, type='character',
+    help="Pipeline configuration file [required]"),
+make_option("--plink2", action="store", default='plink2', type='character',
+		help="Path PLINK v2 software binary [optional]"),
+make_option("--n_cores", action="store", default=1, type='numeric',
+    help="Number of cores to use [optional]"),
+make_option("--test", action="store", default=NA, type='character',
+    help="Specify number of SNPs to include [optional]"),
+make_option("--memory", action="store", default=5000, type='numeric',
+		help="Memory limit [optional]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+library(foreach)
+library(doMC)
+registerDoMC(opt$n_cores)
+
+# Check required inputs
+if(is.null(opt$config)){
+  stop('--config must be specified.\n')
+}
+
+# Read in outdir
+outdir <- read_param(config = opt$config, param = 'outdir', return_obj = F)
+
+# Read in refdir
+refdir <- read_param(config = opt$config, param = 'refdir', return_obj = F)
+
+# Read in resdir
+resdir <- read_param(config = opt$config, param = 'resdir', return_obj = F)
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+# Initiate log file
+log_file <- paste0(outdir, '/reference/pgs_score_files/stratifier_', format(Sys.time(), "%Y-%m-%d_%H-%M-%S"), '.log')
+log_header(log_file = log_file, opt = opt, script = 'target_scoring_partitioned_pipeline.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+# Identify score files to be combined
+score_files<-list_score_files(opt$config)
+
+# Restrict to single source PGS
+score_files <- score_files[!(score_files$method %in% pgs_group_methods) & !grepl('tlprs|leopard', score_files$method),]
+
+# Check which score files need to be partitioned score files or target genetic data are newer than target pgs
+if(!is.null(score_files)){
+  set_reporter_file <- paste0(outdir, '/reference/gwas_sumstat/set_reporter.txt')
+  set_reporter<-fread(set_reporter_file)
+  set_reporter_file_time <- file.info(set_reporter_file)$mtime
+
+  # Remove score files for gwas that have no significant sets
+  score_files<-score_files[score_files$name %in% set_reporter$name[set_reporter$n_sig > 0],]
+  
+  score_files_to_do <- data.table()
+  for(i in 1:nrow(score_files)){
+    score_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '.score.gz')
+    score_partitioned_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '.partitioned.score.gz')
+    if(!file.exists(score_partitioned_i)){
+      score_files_to_do <- rbind(score_files_to_do, score_files[i,])
+    } else {
+      score_i_time <- file.info(score_i)$mtime
+      score_partitioned_i_time <- file.info(score_partitioned_i)$mtime
+      if (score_i_time > pgs_i_time | set_reporter_file_time > score_partitioned_i_time) {
+        score_files_to_do <- rbind(score_files_to_do, score_files[i,])
+        system(paste0('rm ', pgs_i))
+      }
+    }
+  }
+  log_add(log_file = log_file, message = paste0('After checking timestamps, ', nrow(score_files_to_do), '/', nrow(score_files), ' score files will be partitioned.'))
+  score_files <- score_files_to_do
+}
+
+if(is.null(score_files) || nrow(score_files) == 0){
+  log_add(log_file = log_file, message = paste0('No score files to be partitioned.'))
+  end.time <- Sys.time()
+  time.taken <- end.time - start.time
+  sink(file = paste(opt$output,'.log',sep=''), append = T)
+  cat('Analysis finished at',as.character(end.time),'\n')
+  cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
+  sink()
+  quit(save = "no", status = 0)
+}
+
+# Stratify score files (pseudo only)
+log <- foreach(i = 1:nrow(score_files), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+  # Read in snplists
+  set_enrich<-read.table(paste0(outdir,'/reference/gwas_sumstat/',score_files$name[i],'/magma/sig_indep_sets.txt'), header=F)$V1
+
+  param <- find_pseudo(
+    config = opt$config,
+    gwas = score_files$name[i],
+    pgs_method = score_files$method[i]
+  )
+    
+  score_header <- fread(
+    paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-', score_files$name[i],".score.gz"), 
+    nrows = 1)
+  score_cols <- which(names(score_header) %in% c('SNP', 'A1', 'A2', paste0('SCORE_', param)))
+  
+  # Create stratified score files
+  score_i <- fread(cmd = paste0(
+    'zcat ', outdir, '/reference/pgs_score_files/', score_files$method[i], '/', score_files$name[i], '/ref-', score_files$name[i], ".score.gz | ",
+    "cut -d' ' -f ", paste(score_cols, collapse =','), " - ")) # Keep pseudo score
+  
+  for(k in 1:length(set_enrich)){
+    snplist_k <- fread(paste0(outdir,'/reference/gwas_sumstat/',score_files$name[i],'/magma/snplists/',set_enrich[k],'.snplist'), header=F)$V1
+    score_i[[paste0(names(score_i)[4], '.set_', k)]] <- score_i[[4]]
+    score_i[[paste0(names(score_i)[4], '.set_', k)]][!(score_i$SNP %in% snplist_k)] <- 0
+  }
+  
+  # Remove unstratified PGS
+  score_i<-score_i[,-4]
+  
+  # Save score file
+  file_name <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-', score_files$name[i],".stratified")
+  fwrite(score_i, 
+         paste0(file_name, '.score'), 
+         col.names=T, sep=' ', quote=F)
+  
+  if(file.exists(paste0(file_name, ".score.gz"))){
+    system(paste0('rm ', file_name, ".score.gz"))
+  }
+  system(paste0('gzip ', file_name, '.score'))
+  
+  # Calculate scores in the full reference
+  # Subset to variants with non-zero effect
+  extract_snplist <- score_i$SNP[rowSums(abs(score_i[,-1:-3])) != 0]
+  ref_pgs <-
+    plink_score(
+      pfile = paste0(refdir, '/ref.chr'),
+      chr = CHROMS,
+      plink2 = opt$plink2,
+      extract = extract_snplist,
+      score = paste0(file_name, '.score.gz'),
+      threads = opt$n_cores
+    )
+  
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(
+    scores = ref_pgs,
+    pcs = paste0(resdir, '/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles'),
+    output = file_name
+  )
+  
+  # Calculate scale within each reference population
+  pop_data <- read_pop_data(paste0(refdir, '/ref.pop.txt'))
+  
+  for(pop_i in unique(pop_data$POP)){
+    ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+    fwrite(ref_pgs_scale_i, paste0(file_name, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+  }
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at',as.character(end.time),'\n')
+cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
+sink()
diff --git a/Scripts/target_scoring/target_scoring_partioned_pipeline.R b/Scripts/target_scoring/target_scoring_partitioned_pipeline.R
similarity index 81%
rename from Scripts/target_scoring/target_scoring_partioned_pipeline.R
rename to Scripts/target_scoring/target_scoring_partitioned_pipeline.R
index 499d0ae6..00902f99 100644
--- a/Scripts/target_scoring/target_scoring_partioned_pipeline.R
+++ b/Scripts/target_scoring/target_scoring_partitioned_pipeline.R
@@ -46,14 +46,14 @@ if(is.null(opt$population)){
 outdir <- read_param(config = opt$config, param = 'outdir', return_obj = F)
 
 # Create output directory
-opt$output_dir <- paste0(outdir, '/', opt$name, '/pgs/', opt$population)
-system(paste0('mkdir -p ',opt$output_dir))
+opt$output <- paste0(outdir, '/', opt$name, '/pgs/', opt$population)
+system(paste0('mkdir -p ',opt$output))
 
 # Create temp directory
 tmp_dir<-tempdir()
 
 # Initiate log file
-log_file <- paste0(opt$output, '_', format(Sys.time(), "%Y-%m-%d_%H-%M-%S"), '.log')
+log_file <- paste0(opt$output, '_partitioned_', format(Sys.time(), "%Y-%m-%d_%H-%M-%S"), '.log')
 log_header(log_file = log_file, opt = opt, script = 'target_scoring_partitioned_pipeline.R', start.time = start.time)
 
 # If testing, change CHROMS to chr value
@@ -65,29 +65,32 @@ if(!is.na(opt$test)){
 }
 
 # Identify score files to be combined
-score_files<-list_partitioned_score_files(opt$config)
+score_files<-list_score_files(opt$config)
+
+# Restrict to single source PGS
+score_files <- score_files[!(score_files$method %in% pgs_group_methods) & !grepl('tlprs|leopard', score_files$method),]
 
 # Check whether score files or target genetic data are newer than target pgs
 if(!is.null(score_files)){
   ancestry_reporter_file<-paste0(outdir, '/reference/target_checks/', opt$name, '/ancestry_reporter.done')
   ancestry_reporter_file_time <- file.info(ancestry_reporter_file)$mtime
-  set_reporter_file <- paste0(outdir, '/reference/benchmarks/set_reporter.txt')
-  set_reporter<-fread(set_reporter_file)
-  set_reporter_file_time <- file.info(set_reporter_file)$mtime
   
+  set_reporter_file <- paste0(outdir, '/reference/gwas_sumstat/set_reporter.txt')
+  set_reporter<-fread(set_reporter_file)
+
   # Remove score files for gwas that have no significant sets
-  score_files<-score_files[score_files$name %in% set_reporter$name[set_reporter$n_sig > 0,]]
+  score_files<-score_files[score_files$name %in% set_reporter$name[set_reporter$n_sig > 0],]
   
   score_files_to_do <- data.table()
   for(i in 1:nrow(score_files)){
     pgs_i <- paste0(outdir, '/', opt$name,'/pgs/', opt$population,'/', score_files$method[i],'/', score_files$name[i],'/', opt$name,'-', score_files$name[i],'-',opt$population,'.partitioned.profiles')
-    score_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '.score.gz')
+    score_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '.stratified.score.gz')
     if(!file.exists(pgs_i)){
       score_files_to_do <- rbind(score_files_to_do, score_files[i,])
     } else {
       score_i_time <- file.info(score_i)$mtime
       pgs_i_time <- file.info(pgs_i)$mtime
-      if (score_i_time > pgs_i_time | ancestry_reporter_file_time > pgs_i_time | set_reporter_file_time > pgs_i_time) {
+      if (score_i_time > pgs_i_time | ancestry_reporter_file_time > pgs_i_time) {
         score_files_to_do <- rbind(score_files_to_do, score_files[i,])
         system(paste0('rm ', pgs_i))
       }
@@ -101,16 +104,13 @@ if(is.null(score_files) || nrow(score_files) == 0){
   log_add(log_file = log_file, message = paste0('No score files to be used for target scoring.'))
   end.time <- Sys.time()
   time.taken <- end.time - start.time
-  sink(file = paste(opt$output,'.log',sep=''), append = T)
+  sink(file = log_file, append = T)
   cat('Analysis finished at',as.character(end.time),'\n')
   cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
   sink()
   quit(save = "no", status = 0)
 }
 
-# Read in target_list
-target_list <- read_param(config = opt$config, param = 'target_list', return_obj = T)
-
 # Set params for plink_score
 opt$target_plink_chr <- paste0(outdir, '/', opt$name, '/geno/', opt$name, '.ref.chr')
 if(opt$population == 'TRANS'){
@@ -129,7 +129,7 @@ set_snps<-NULL
 for(i in unique(score_files$name)){
   set_enrich<-read.table(paste0(outdir,'/reference/gwas_sumstat/',i,'/magma/sig_indep_sets.txt'), header=F)$V1
   for(j in set_enrich){
-    set_snps <- c(set_snps, fread(paste0(outdir,'/reference/gwas_sumstat/',i,'/magma/snplists/',j,'.snplist')), header=F)$V1
+    set_snps <- c(set_snps, fread(paste0(outdir,'/reference/gwas_sumstat/',i,'/magma/snplists/',j,'.snplist'), header=F)$V1)
   }
 }
 set_snps <- unique(set_snps)
@@ -148,40 +148,20 @@ for(chr_i in CHROMS){
   # Create row number index to subset score files by chromosome
   row_index <- format(which(ref$CHR == chr_i & ref$extract == T) + 1, scientific = FALSE)
   write.table(row_index, paste0(tmp_dir,'/row_index.txt'), row.names=F, quote=F, col.names = F)
-  ref_subset <- ref[row_index,]
+  ref_subset <- ref[as.numeric(row_index),]
   
   # Create file containing SNP, A1, and A2 information for each chromosome
-  fwrite(ref[ref$CHR == chr_i, c('SNP','A1','A2'), with=F], paste0(tmp_dir,'/map.txt'), row.names=F, quote=F, sep=' ')
+  fwrite(ref_subset[, c('SNP','A1','A2'), with=F], paste0(tmp_dir,'/map.txt'), row.names=F, quote=F, sep=' ')
 
   # Extract process score files for each name (gwas/score) in parallel
   foreach(i = 1:nrow(score_files), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
-    param <- find_pseudo(
-      config = opt$config,
-      gwas = score_files$name[i],
-      pgs_method = score_files$method[i],
-      target_pop = opt$population
-    )
-    
-    score_header <- fread(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],".score.gz"), nrows = 1)
-    score_cols <- which(names(score_header) == paste0('SCORE_', param))
-    
     system(paste0(
-      'zcat ', outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],".score.gz | ",
-      "cut -d' ' ", score_cols, " - | ",  # Keep pseudo score
+      'zcat ', outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],".stratified.score.gz | ",
+      "cut -d' ' --complement -f1-3 | ",  # Keep relevant columns, remove first 3
       'awk \'NR==FNR {rows[$1]; next} FNR==1 || FNR in rows\' ', paste0(tmp_dir,'/row_index.txt'), ' - | ',  # Corrected to retain the header and process indexed rows
       "sed '1 s/SCORE_/", paste0('score_file_', i,'.'), "/g' > ",  # Replace SCORE in the header
       tmp_dir, '/tmp_score.', paste0(score_files$method[i], '.', score_files$name[i]), '.txt'
     ))
-    
-    # Create stratified score files
-    score_i <- fread(paste0(tmp_dir, '/tmp_score.', paste0(score_files$method[i], '.', score_files$name[i]), '.txt'))
-    set_enrich_i <-read.table(paste0(outdir,'/reference/gwas_sumstat/', score_files$name[i],'/magma/sig_indep_sets.txt'), header=F)$V1
-    for(j in 1:length(set_enrich_i)){
-      snplist_i <- fread(paste0(outdir,'/reference/gwas_sumstat/',i,'/magma/snplists/',set_enrich_i[j],'.snplist'), header=F)$V1
-      score_i[[paste0(names(score_i)[1], '.set_', j)]] <- score_i[[1]]
-      score_i[[paste0(names(score_i)[1], '.set_', j)]][!(ref_subset$SNP %in% snplist_i)] <- 0
-    }
-    fwrite(score_i, paste0(tmp_dir, '/tmp_score.', paste0(score_files$method[i], '.', score_files$name[i]), '.txt'), sep=' ', quote=F)
   }
 
   # Paste files together in batches
@@ -242,7 +222,7 @@ if(opt$population == 'TRANS'){
 
   models<-list()
   for(i in 1:nrow(score_files)){
-    models[[paste0('score_file_', i)]]<-readRDS(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'-TRANS.model.rds'))
+    models[[paste0('score_file_', i)]]<-readRDS(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'.stratified-TRANS.model.rds'))
     names(models[[paste0('score_file_', i)]])<-gsub('SCORE_', paste0('score_file_', i, '.'), names(models[[paste0('score_file_', i)]]))
   }
   
@@ -260,7 +240,7 @@ if(opt$population == 'TRANS'){
   log_add(log_file = log_file, message = paste0('Reading in scale files.'))
   scale_files<-list()
   for(i in 1:nrow(score_files)){
-    scale_files[[paste0('score_file_', i)]]<-fread(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'-', opt$population,'.scale'))
+    scale_files[[paste0('score_file_', i)]]<-fread(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'.stratified-', opt$population,'.scale'))
     scale_files[[paste0('score_file_', i)]]$Param<-gsub('SCORE_', paste0('score_file_', i, '.'), scale_files[[paste0('score_file_', i)]]$Param)
   }
   
@@ -280,7 +260,7 @@ for(i in 1:nrow(score_files)){
   scores_i <- scores[, c('FID','IID', names(scores)[grepl(paste0('^score_file_', i, '\\.'), names(scores))]), with=F]
   names(scores_i) <- gsub(paste0('^score_file_', i, '\\.'), paste0(score_files$name[i], '_'), names(scores_i))
   dir.create(paste0(outdir, '/', opt$name,'/pgs/', opt$population,'/', score_files$method[i],'/', score_files$name[i]), recursive = T)
-  fwrite(scores_i, paste0(outdir, '/', opt$name,'/pgs/', opt$population,'/', score_files$method[i],'/', score_files$name[i],'/', opt$name,'-', score_files$name[i],'-',opt$population,'.profiles'), sep=' ', na='NA', quote=F)
+  fwrite(scores_i, paste0(outdir, '/', opt$name,'/pgs/', opt$population,'/', score_files$method[i],'/', score_files$name[i],'/', opt$name,'-', score_files$name[i],'-',opt$population,'.partitioned.profiles'), sep=' ', na='NA', quote=F)
 }
 
 log_add(log_file = log_file, message = paste0('Saved polygenic scores.'))
diff --git a/Scripts/target_scoring/target_scoring_pipeline.R b/Scripts/target_scoring/target_scoring_pipeline.R
index a0838359..6c7f54c8 100644
--- a/Scripts/target_scoring/target_scoring_pipeline.R
+++ b/Scripts/target_scoring/target_scoring_pipeline.R
@@ -48,8 +48,8 @@ if(is.null(opt$population)){
 outdir <- read_param(config = opt$config, param = 'outdir', return_obj = F)
 
 # Create output directory
-opt$output_dir <- paste0(outdir, '/', opt$name, '/pgs/', opt$population)
-system(paste0('mkdir -p ',opt$output_dir))
+opt$output <- paste0(outdir, '/', opt$name, '/pgs/', opt$population)
+system(paste0('mkdir -p ',opt$output))
 
 # Create temp directory
 tmp_dir<-tempdir()
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index fc8b0011..fd2272c8 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -5662,3 +5662,354 @@ snakemake \
 
 </details>
 
+### Create predictor lists
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Create files for EAS and AFR targets
+targ_pop <- c('EUR','EAS','AFR')
+for(trait_i in selected_traits){
+  scores_i <- scores[grepl(trait_i, scores$name),]
+  scores_i$multi <- scores_i$method
+  
+  for(targ_pop_i in targ_pop){
+    disc_pop_j <- c('UGR','BBJ','UKB')
+    disc_pop_j_2 <- c('AFR','EAS','EUR')
+
+    dir.create(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+        targ_pop_i,
+        '.disc_', paste(disc_pop_j_2, collapse = '_'),
+        '/',
+        trait_i
+      ),
+      recursive = T
+    )
+    
+    # Insert path to score file
+    scores_i$predictor <- paste0(
+      outdir,
+      '/ukb/pgs/TRANS/',
+      scores_i$method,
+      '/',
+      scores_i$name,
+      '/ukb-',
+      scores_i$name,
+      '-TRANS.profiles'
+    )
+      
+    ####
+    # Make groups single source methods
+    ####
+    
+    scores_i_single_top1 <-
+      scores_i[!(scores_i$method %in% pgs_group_methods) &
+                   !grepl('_multi$', scores_i$method), ]
+
+    # Create top1 column indicating which predictors top1 models should be derived
+    scores_i_single_top1$top1[grepl('UKB', scores_i_single_top1$name, ignore.case = F)] <- 'EUR'
+    scores_i_single_top1$top1[grepl('BBJ', scores_i_single_top1$name, ignore.case = F)] <- 'EAS'
+    scores_i_single_top1$top1[grepl('UGR', scores_i_single_top1$name, ignore.case = F)] <- 'AFR'
+        
+    ####
+    # Make groups containing pseudo scores for single source methods
+    ####
+
+    # Extract the pseudo score for each method and specify as a separate group
+    # This can be skipped as it was done before
+    for(i in 1:nrow(scores_i_single_top1)) {
+      param <- find_pseudo(
+        config = config,
+        gwas = scores_i_single_top1$name[i],
+        pgs_method = scores_i_single_top1$method[i],
+        target_pop = targ_pop_i
+      )
+      
+      score_header <-
+        fread(scores_i_single_top1$predictor[i], nrows = 1)
+      score_cols <-
+        which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_single_top1$name[i], '_', param)))
+      
+      system(
+        paste0(
+          "cut -d' ' -f ", 
+          paste0(score_cols, collapse=','),
+          " ", 
+          scores_i_single_top1$predictor[i], 
+          " > ", 
+          gsub('.profiles',
+               paste0('.', targ_pop_i, '_pseudo.profiles'),
+               scores_i_single_top1$predictor[i])
+        )
+      )
+    }
+    
+    scores_i_single_pseudo <- scores_i_single_top1
+    scores_i_single_pseudo$multi <- paste0(scores_i_single_pseudo$multi, '.pseudo')
+
+    scores_i_single_pseudo$predictor <- gsub('.profiles', 
+                                  paste0('.', targ_pop_i, '_pseudo.profiles'),
+                                  scores_i_single_pseudo$predictor)
+
+    ####
+    # Make groups for multi-single-source pseudo scores
+    ####
+    
+    scores_i_multi_single_pseudo <- scores_i[grepl('_multi$', scores_i$method),]
+
+    # Extract the pseudo score for each method and specify as a separate group
+    for(i in 1:nrow(scores_i_multi_single_pseudo)) {
+      param <- find_pseudo(
+        config = config,
+        gwas = scores_i_multi_single_pseudo$name[i],
+        pgs_method = scores_i_multi_single_pseudo$method[i],
+        target_pop = targ_pop_i
+      )
+      
+      score_header <-
+        fread(scores_i_multi_single_pseudo$predictor[i], nrows = 1)
+      score_cols <-
+        which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_multi_single_pseudo$name[i], '_', param)))
+      
+      system(
+        paste0(
+          "cut -d' ' -f ", 
+          paste0(score_cols, collapse=','),
+          " ", 
+          scores_i_multi_single_pseudo$predictor[i], 
+          " > ", 
+          gsub('.profiles',
+               paste0('.', targ_pop_i, '_pseudo.profiles'),
+               scores_i_multi_single_pseudo$predictor[i])
+        )
+      )
+    }
+    
+    scores_i_multi_single_pseudo$multi <- paste0(scores_i_multi_single_pseudo$multi, '.pseudo')
+
+    scores_i_multi_single_pseudo$predictor <- gsub('.profiles', 
+                                  paste0('.', targ_pop_i, '_pseudo.profiles'),
+                                  scores_i_multi_single_pseudo$predictor)
+    
+    scores_i_multi_single_pseudo$top1<-paste(disc_pop_j_2, collapse = '_')
+
+    ####
+    # Make groups for the Multi-Source methods
+    ####
+    
+    scores_i_multi <- scores_i[(scores_i$method %in% pgs_group_methods),]
+
+    # Split top1 scores by target population
+    # This doesn't apply to xwing because it only has pop-specific pseudo scores
+    scores_i_multi_top1<-NULL
+    for(i in 1:which(scores_i_multi$method %in% c('prscsx'))){
+      score_header<-fread(scores_i_multi$predictor[i], nrow = 1)
+      
+      for(pop in c('EUR', 'AFR', 'EAS')){
+        
+        if(scores_i_multi$method[i] == 'prscsx'){
+          score_cols <-
+            which(grepl(paste0('^FID$|^IID$|_', pop, '_phi'), names(score_header)))
+        }
+        if(scores_i_multi$method[i] == 'xwing'){
+          score_cols <-
+            which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst'), names(score_header)))
+        }
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            scores_i_multi$predictor[i], 
+            " > ", 
+            gsub('.profiles',
+                 paste0('.', pop, '_grid.profiles'),
+                 scores_i_multi$predictor[i])
+          )
+        )
+        
+        tmp <- scores_i_multi[i,]
+        tmp$multi <- paste0(tmp$multi, '.grid')
+        tmp$top1 <- pop
+        tmp$predictor <-
+            gsub('.profiles',
+                 paste0('.', pop, '_grid.profiles'),
+                 scores_i_multi$predictor[i])
+        
+        scores_i_multi_top1 <- rbind(scores_i_multi_top1, tmp)
+      }
+    }
+
+    # Split pop-specific pseudo scores by target population
+    scores_i_multi_pop_pseudo<-NULL
+    for(i in 1:nrow(scores_i_multi)){
+      score_header<-fread(scores_i_multi$predictor[i], nrow = 1)
+      
+      for(pop in c('EUR', 'AFR', 'EAS')){
+        if(scores_i_multi$method[i] == 'prscsx'){
+          score_cols <-
+            which(grepl(paste0('^FID$|^IID$|_', pop, '_phi_auto'), names(score_header)))
+        }
+        if(scores_i_multi$method[i] == 'xwing'){
+          score_cols <-
+            which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst_', pop), names(score_header)))
+        }
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            scores_i_multi$predictor[i], 
+            " > ", 
+            gsub('.profiles',
+                 paste0('.', pop, '_pseudo.profiles'),
+                 scores_i_multi$predictor[i])
+          )
+        )
+        
+        tmp <- scores_i_multi[i,]
+        tmp$multi <- paste0(tmp$multi, '.pop_pseudo')
+        tmp$top1 <- pop
+        tmp$predictor <-
+            gsub('.profiles',
+                 paste0('.', pop, '_pseudo.profiles'),
+                 scores_i_multi$predictor[i])
+        
+        scores_i_multi_pop_pseudo <- rbind(scores_i_multi_pop_pseudo, tmp)
+      }
+    }
+    
+    # Create pseudo score for multi-source methods
+    scores_i_multi_pseudo<-NULL
+    for(i in 1:nrow(scores_i_multi)) {
+      param <- find_pseudo(
+        config = config,
+        gwas = scores_i_multi$name[i],
+        pgs_method = scores_i_multi$method[i],
+        target_pop = targ_pop_i
+      )
+      
+      score_header <-
+        fread(scores_i_multi$predictor[i], nrows = 1)
+      score_cols <-
+        which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_multi$name[i], '_', param)))
+
+      system(
+        paste0(
+          "cut -d' ' -f ", 
+          paste0(score_cols, collapse=','),
+          " ", 
+          scores_i_multi$predictor[i], 
+          " > ", 
+          gsub('.profiles',
+               paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
+               scores_i_multi$predictor[i])
+        )
+      )
+      
+      tmp <- scores_i_multi[i,]
+      tmp$multi <- paste0(tmp$multi, '.pseudo')
+      tmp$top1 <- paste(disc_pop_j_2, collapse = '_')
+      tmp$predictor <-
+          gsub('.profiles',
+               paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
+               scores_i_multi$predictor[i])
+      
+      scores_i_multi_pseudo <- rbind(scores_i_multi_pseudo, tmp)
+    }
+    
+    ####
+    # Combine the different predictor groups
+    ####
+    predictors_i<- do.call(rbind, list(
+      scores_i_single_top1, 
+      scores_i_single_pseudo, 
+      scores_i_multi_single_pseudo,
+      scores_i_multi_top1,
+      scores_i_multi_pop_pseudo,
+      scores_i_multi_pseudo
+    ))
+    
+    predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
+    
+    ####
+    # Make a group that will combined all population specific PGS
+    ####
+    
+    predictors_i_all <- predictors_i[predictors_i$top1 %in% c('EUR','AFR','EAS'),]
+    predictors_i_all$multi <- 'all'
+    predictors_i<-rbind(predictors_i, predictors_i_all)
+    
+    write.table(
+      predictors_i,
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+        targ_pop_i,
+        '.disc_', paste(disc_pop_j_2, collapse = '_'),
+        '/',
+        trait_i,
+        '/predictor_list.txt'
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}
+
+```
+</details>
+
+***
+
+### Run model_builder
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/targ_*.disc_AFR_EAS_EUR/*/res*
+
+for targ_pop in $(echo EUR EAS AFR); do
+  if [ "$targ_pop" == "EUR" ]; then
+      targ_pop2="EUR_test"
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+    if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_AFR_EAS_EUR/${pheno}/res.pred_comp.txt" ]; then
+      sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+        --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_AFR_EAS_EUR/${pheno}/predictor_list.txt \
+        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_AFR_EAS_EUR/${pheno}/res \
+        --n_core 5"
+    fi
+  done
+done
+
+```
+</details>
diff --git a/docs/GenoClust.Rmd b/docs/GenoClust.Rmd
index 8c7395ed..01e9b50c 100644
--- a/docs/GenoClust.Rmd
+++ b/docs/GenoClust.Rmd
@@ -145,11 +145,12 @@ singularity exec \
 ```
 
 ```{r}
+#conda activate .snakemake/conda/329e0288cb99508f5e6c50a0996b234c_
 library(bigsnpr)
 
 # Attach the bigSNP object from a previously saved `.rds` file
-geno_file <- "your_genotype_data.rds"  # Update this with your actual file
-bigSNP <- snp_attach(geno_file)
+rds <- snp_readBed('~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/synth_2_chr-22.bed', backingfile = tempfile())
+bigSNP <- snp_attach(rds)
 
 # Access the genotype matrix
 G <- bigSNP$genotypes
@@ -157,41 +158,61 @@ G <- bigSNP$genotypes
 # Get SNP names
 snp_names <- bigSNP$map$marker.ID
 
-set.seed(123)  # For reproducibility
-
-# Define number of causal SNPs
-num_causal <- 100
+# Read in causal variants
+library(data.table)
+effect_sizes<-fread('~/oliverpainfel/Data/HAPNEST/synth_2/causal.snplist')
+names(effect_sizes)<-c('rsid','trait_1','trait_2')
 
-# Randomly select causal SNPs from available markers
-causal_snps <- sample(snp_names, num_causal)
+rsids<-fread('~/oliverpainfel/Data/HAPNEST/data/inputs/processed/1KG+HGDP/rsid_map_list_chr22.txt')
+effect_sizes<-merge(effect_sizes, rsids, by='rsid')
 
-# Assign effect sizes (from normal distribution)
-effect_sizes <- rnorm(num_causal, mean = 0, sd = 1)
+h2 <- 0.05  # Desired heritability
+set.seed(1)
+traits<-list()
+for(i in 1:2){
+  effect_sizes_i<-effect_sizes[effect_sizes[[paste0('trait_',i)]] == 1,]
+  causal_indices <- match(effect_sizes_i$id_hg38, snp_names)
+  G_causal <- big_prodVec(G, effect_sizes_i[[paste0('trait_',i)]], ind.col = causal_indices)
+  var_G <- var(G_causal)
+  var_E <- (var_G / h2) - var_G
+  E <- rnorm(length(G_causal), mean = 0, sd = sqrt(var_E))
+  Y <- G_causal + E
+  traits[[paste0('trait_', i)]]<-Y
+  print(var(G_causal) / (var(G_causal) + var(E)))
+}
 
-# Print the first few selected causal SNPs
-head(causal_snps)
+cor(do.call(cbind, traits))
 
-# Extract genotype indices of causal SNPs
-causal_indices <- match(causal_snps, snp_names)
+library(bigsnpr)
 
-# Compute genetic component efficiently
-G_causal <- big_prodVec(G, effect_sizes, ind.col = causal_indices)
+# Prepare the phenotype
+Y1 <- traits[["trait_1"]]
+Y2 <- traits[["trait_2"]]
 
-h2 <- 0.5  # Desired heritability
+# Perform GWAS for trait 1
+gwas_1 <- big_univLinReg(G, Y1)
 
-# Compute variance of genetic component
-var_G <- var(G_causal)
+# Perform GWAS for trait 2
+gwas_2 <- big_univLinReg(G, Y2)
 
-# Compute required environmental variance
-var_E <- (var_G / h2) - var_G
+# Combine results into a data frame
+gwas_results <- data.frame(
+  SNP = snp_names,
+  beta_trait1 = gwas_1$estim,
+  beta_trait2 = gwas_2$estim,
+  se_trait1 = gwas_1$std.err,
+  se_trait2 = gwas_2$std.err
+)
 
-# Generate environmental noise
-E <- rnorm(length(G_causal), mean = 0, sd = sqrt(var_E))
+gwas_results$p_trait1 <- 2*pnorm(q=abs(gwas_results$beta_trait1/gwas_results$se_trait1), lower.tail=FALSE)
+gwas_results$p_trait2 <- 2*pnorm(q=abs(gwas_results$beta_trait2/gwas_results$se_trait2), lower.tail=FALSE)
 
-# Compute final phenotype
-Y <- G_causal + E
+# Looks good. Lets proceed with these simulated phenotypes
+write.table(data.frame(FID = bigSNP$fam$family.ID, IID = bigSNP$fam$sample.ID, outcome = traits[["trait_1"]]), 
+            '~/oliverpainfel/Data/HAPNEST/synth_2/trait_1.txt', row.names = F, quote=F)
+write.table(data.frame(FID = bigSNP$fam$family.ID, IID = bigSNP$fam$sample.ID, outcome = traits[["trait_2"]]), 
+            '~/oliverpainfel/Data/HAPNEST/synth_2/trait_2.txt', row.names = F, quote=F)
 
-h2_observed <- var(G_causal) / (var(G_causal) + var(E))
 ```
 
 </details>
@@ -242,18 +263,10 @@ option_list = list(
 
 opt = parse_args(OptionParser(option_list=option_list))
 
-opt$target_plink_chr<-'/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-'
-opt$output<-'~/oliverpainfel/Analysis/HAPNEST/synth_1/pca/'
+opt$target_plink_chr<-'~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/synth_2_chr-'
+opt$output<-'~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/pca/'
 opt$test<-'chr22'
 
-keep_list<-data.frame(
-  file = c(
-    '~/oliverpainfel/Analysis/HAPNEST/synth_1/training.EUR.txt',
-    '~/oliverpainfel/Analysis/HAPNEST/synth_1/training.EAS.txt',
-    '~/oliverpainfel/Analysis/HAPNEST/synth_1/training.AFR.txt'),
-  POP = c('EUR','EAS','AFR')
-)
-
 library(GenoUtils)
 library(data.table)
 source('../functions/misc.R')
@@ -273,45 +286,32 @@ if(!is.na(opt$test)){
 targ_pvar <- read_bim(opt$target_plink_chr, chr = CHROMS)
 targ_pvar <- remove_regions(dat = targ_pvar, regions = long_ld_coord)
 
-for(pop in keep_list$POP){
-  # Read in keep file for population
-  keep_file <- fread(keep_list$file[keep_list$POP == pop], header=F)
-  if(ncol(keep_file) == 1){
-    keep_file <- data.table(
-      FID = keep_file$V1,
-      IID = keep_file$V1)
-  } else {
-    keep_file <- data.table(
-      FID = keep_file$V1,
-      IID = keep_file$V2)
-  }
+###########
+# Perform PCA on QC'd and independent variants
+###########
 
-  ###########
-  # Perform PCA on QC'd and independent variants
-  ###########
+# Create QC'd SNP-list
+target_qc_snplist <- plink_qc_snplist(bfile = opt$target_plink_chr, plink2 = opt$plink2, chr = CHROMS, maf = opt$maf, geno = opt$geno, hwe = opt$hwe, threads = opt$n_cores)
 
-  # Create QC'd SNP-list
-  target_qc_snplist <- plink_qc_snplist(bfile = opt$target_plink_chr, plink2 = opt$plink2, chr = CHROMS, keep = keep_file, maf = opt$maf, geno = opt$geno, hwe = opt$hwe, threads = opt$n_cores)
+# Remove high LD regions
+target_qc_snplist <- target_qc_snplist[target_qc_snplist %in% targ_pvar$SNP]
 
-  # Remove high LD regions
-  target_qc_snplist <- target_qc_snplist[target_qc_snplist %in% targ_pvar$SNP]
+# Perform LD pruning
+ld_indep <- plink_prune(bfile = opt$target_plink_chr, chr = CHROMS, plink2 = opt$plink2, extract = target_qc_snplist, threads = opt$n_cores)
 
-  # Perform LD pruning
-  ld_indep <- plink_prune(bfile = opt$target_plink_chr, chr = CHROMS, keep = keep_file, plink2 = opt$plink2, extract = target_qc_snplist, threads = opt$n_cores)
+# To improve efficiency, derive PCs using random subset of 1000 individuals.
+fam<-fread('~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/synth_2_chr-22.fam')
+fam_subset <- fam[sample(1000, replace = F),]
 
-  # To improve efficiency, derive PCs using random subset of 1000 individuals.
-  keep_file_subset <- keep_file[sample(1000, replace = T),]
-  keep_file_subset <- keep_file_subset[!duplicated(keep_file_subset),]
+# Run PCA
+snp_weights <- plink_pca(bfile = opt$target_plink_chr, keep = fam_subset, chr = CHROMS, plink2 = opt$plink2, extract = ld_indep, n_pc = opt$n_pcs, threads = opt$n_cores)
+fwrite(snp_weights, paste0(tmp_dir,'/ref.eigenvec.var'), row.names = F, quote=F, sep=' ', na='NA')
 
-  # Run PCA
-  snp_weights <- plink_pca(bfile = opt$target_plink_chr, keep = keep_file_subset, chr = CHROMS, plink2 = opt$plink2, extract = ld_indep, n_pc = opt$n_pcs, threads = opt$n_cores)
-  fwrite(snp_weights, paste0(tmp_dir,'/ref.eigenvec.var'), row.names = F, quote=F, sep=' ', na='NA')
+# Project into the full population
+target_pcs <- plink_score(bfile = opt$target_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(tmp_dir,'/ref.eigenvec.var'), threads = opt$n_cores)
 
-  # Project into the full population
-  target_pcs <- plink_score(bfile = opt$target_plink_chr, keep = keep_file, chr = CHROMS, plink2 = opt$plink2, score = paste0(tmp_dir,'/ref.eigenvec.var'), threads = opt$n_cores)
-
-  fwrite(target_pcs, paste0(opt$output, pop,'.pcs.txt'), quote=F, sep=' ', na='NA')
-}
+dir.create('~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/pca')
+fwrite(target_pcs, paste0(opt$output,'pcs.txt'), quote=F, sep=' ', na='NA')
 
 ```
 
@@ -326,44 +326,43 @@ for(pop in keep_list$POP){
 ```{bash}
 
 module add plink2
-for pheno in $(seq 1 1); do
-    awk 'BEGIN {OFS="\t"} NR==1 {print "FID", "IID", "pheno"} NR>1 {print $1, $1, $NF}' /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno} > /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno}.plink
-    
-  for pop in $(echo EUR EAS AFR); do
-  
-    mkdir -p ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}
+for pheno in $(seq 1 2); do
+    mkdir -p ~/oliverpainfel/Analysis/HAPNEST/synth_2/gwas/pheno${pheno}
     for chr in $(seq 22 22); do
         sbatch -p neurohack_cpu --mem 20G -n 4 --wrap="plink2 \
-          --bfile /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22 \
-          --pheno /users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr.pheno${pheno}.plink \
+          --bfile ~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/synth_2_chr-${chr} \
+          --pheno ~/oliverpainfel/Data/HAPNEST/synth_2/trait_${pheno}.txt \
           --1 \
-          --covar ~/oliverpainfel/Analysis/HAPNEST/synth_1/pca/${pop}.pcs.txt \
+          --covar ~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/pca/pcs.txt \
           --covar-variance-standardize \
-          --logistic omit-ref cols=+a1freq,+ax hide-covar \
+          --linear omit-ref cols=+a1freq,+ax hide-covar \
           --maf 0.01 \
           --geno 0.05 \
-          --out ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr${chr}"
+          --out ~/oliverpainfel/Analysis/HAPNEST/synth_2/gwas/pheno${pheno}/pheno${pheno}.chr${chr}"
     done
-  done
 done
 
-# Once complete, merge results across chromosomes
-for pheno in $(seq 1 1); do
-  for pop in $(echo EUR EAS AFR); do
-    head -n 1 ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr22.pheno.glm.logistic.hybrid > ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
-      for chr in $(seq 22 22); do
-        tail -n +2 ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.chr22.pheno.glm.logistic.hybrid >> ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
-      done
-      
-      # Remove REF and ALT columns and rename AX column to A2
-      cut -f 4,5 --complement ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid | awk 'BEGIN{FS=OFS="\t"} NR==1 {$7="A2"} 1' > temp.txt && mv temp.txt ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
-  
-      gzip ~/oliverpainfel/Analysis/HAPNEST/synth_1/gwas/pheno${pheno}/pheno${pheno}.${pop}.GW.pheno.glm.logistic.hybrid
-  done
-done
+```
 
+```{r}
+library(data.table)
+  rsids<-fread('~/oliverpainfel/Data/HAPNEST/data/inputs/processed/1KG+HGDP/rsid_map_list_chr22.txt')
+
+for(i in 2:2){
+  ss <- fread(paste0('/users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_2/gwas/pheno', i, '/pheno', i,'.chr22.outcome.glm.linear'))
+  ss$REF<-NULL
+  ss$ALT<-NULL
+  names(ss)[names(ss) == 'AX'] <- 'A2'
+  
+  ss<-merge(ss, rsids, by.x = 'ID', by.y = 'id_hg38')
+  ss$ID <- ss$rsid
+  ss$rsid <- NULL
+  
+  fwrite(ss, paste0('/users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_2/gwas/pheno', i, '/pheno', i,'.chr22.outcome.glm.linear'), row.names = F, quote=F, sep=' ', na='NA')
+}
 ```
 
+
 </details>
 
 ***
@@ -374,8 +373,56 @@ done
 
 ```{bash}
 
+# Gene association
+mkdir /users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_2/magma
+
+for pheno in $(seq 2 2); do
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --bfile /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma_ref/g1000_eur \
+  --pval /users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_2/gwas/pheno${pheno}/pheno${pheno}.chr22.outcome.glm.linear use=ID,P ncol=OBS_CT \
+  --gene-model snp-wise=top \
+  --gene-annot /scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/data/magma/NCBI37.3.genes.annot \
+  --out /users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_2/magma/pheno${pheno}_gene_level
+done
+
+# GSEA
+for pheno in $(seq 2 2); do
+/scratch/prj/oliverpainfel/recovered/Software/MyGit/GenoDisc/pipeline/resources/software/magma/magma \
+  --gene-results /users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_2/magma/pheno${pheno}_gene_level.genes.raw \
+  --set-annot /scratch/prj/oliverpainfel/recovered/Data/MSigDB/c5.go.bp.v7.5.1.entrez.gmt \
+  --out /users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_2/magma/pheno${pheno}_gsea
+done
+
+```
+
+```{r}
+# Investigate gene level associations
+# Why are the gene sets not showing as enriched
+res<-fread('/users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_2/magma/pheno2_gene_level.genes.out')
+
+# Read in .gmt file
+gmt <- readLines('/scratch/prj/oliverpainfel/recovered/Data/MSigDB/c5.go.bp.v7.5.1.entrez.gmt')
+gene_sets <- strsplit(gmt, "\t")
+gene_list <- setNames(lapply(gene_sets, `[`, -c(1,2)), sapply(gene_sets, `[`, 1))
+
+# Subset sets of interest
+gene_list <- gene_list[ c('GOBP_DNA_REPAIR', 'GOBP_REGULATION_OF_SYNAPTIC_PLASTICITY')]
 
+res[res$GENE %in% gene_list[['GOBP_DNA_REPAIR']],]
+res[res$GENE %in% gene_list[['GOBP_REGULATION_OF_SYNAPTIC_PLASTICITY']],]
 
+# Read in causal variants
+causal_var<-fread('~/oliverpainfel/Data/HAPNEST/synth_2/causal.snplist')
+rsids<-fread('~/oliverpainfel/Data/HAPNEST/data/inputs/processed/1KG+HGDP/rsid_map_list_chr22.txt')
+causal_var<-merge(causal_var, rsids, by.x='V1', by.y='rsid')
+causal_var<-causal_var[order(causal_var$id_hg38),]
+
+# Many of the genes are not coming up as significant, despite containing SNPs with crazy high P values. Try running MAGMA with top,1 gene model. This could pick up the signal better. This helps, but gene p values are capped at 1e-50, and many genes are still non-significant. Manually check top SNP p values within these genes.
+# Read in SNPs for gene 9463
+gwas <- fread(paste0('/users/k1806347/oliverpainfel/Analysis/HAPNEST/synth_2/gwas/pheno2/pheno2.chr22.outcome.glm.linear'))
+mean(abs(gwas[gwas$POS > 38418262 & gwas$POS < 38481708,]$T_STAT))
+# The gene is highly enriched. Why is MAGMA not finding this.
+# Make sure the SNPs we are setting as causal are in the MAGMA analysis/annot file
 ```
 
 </details>
@@ -897,18 +944,14 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml \
-  output_all -n
-  
-snakemake \
-  --profile slurm \
-  --use-conda \
-  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml \
-  magma_annot -n
+  output_all target_pgs_partitioned -n
 ```
 
 ***
 
-### Check correlation
+### Genome-wide PGS
+
+#### Check correlation
 
 Check correlation between the PGS for CAD, RA and MIX with CAD, RA and MIX outcomes.
 
@@ -977,7 +1020,7 @@ auc(
 
 ***
 
-### Cluster based on genome-wide PGS
+#### Cluster
 
 ```{r}
 
@@ -1697,6 +1740,335 @@ cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
 
 ***
 
+### Partitioned PGS
+
+#### Correlation
+
+```{r}
+# Read in the partitioned PGS
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline')
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in PGS
+pgs_gw <- read_pgs(config = '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml', name = 'ukb', pseudo_only = T, gwas=c('RA','CAD','MIX'))$ukb
+
+pgs_gw <- Reduce(function(x, y) merge(x, y, by = c("FID", "IID"), all = TRUE), lapply(pgs_gw$TRANS, function(x) x[[1]]))
+
+# Read in PGS
+pgs_partitioned <- read_pgs_2(config = '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/genoclust_subset/config.yaml', name = 'ukb', partitioned = T, gwas=c('RA','CAD','MIX'))$ukb
+
+pgs_partitioned <- Reduce(function(x, y) merge(x, y, by = c("FID", "IID"), all = TRUE), lapply(pgs_partitioned$TRANS, function(x) x[[1]]))
+
+pgs <- merge(pgs_gw, pgs_partitioned, by = c('FID', 'IID'))
+cor_mat<-cor(pgs[,-1:-2])
+round(cor_mat[, !grepl('set', colnames(cor_mat))], 2)
+
+# Read in outcome data
+outcome_names<-c('CAD','RA','MIX')
+outcomes <- list()
+for(i in outcome_names){
+  outcomes[[i]]<-fread(paste0('~/oliverpainfel/Data/ukb/phenotypes/ra_cad_mix.subset.', i, '_only.txt'))
+  names(outcomes[[i]])[names(outcomes[[i]]) == 'outcome']<-paste0(i,'_outcome')
+}
+outcomes <- Reduce(function(x, y) merge(x, y, by = c("FID", "IID"), all = TRUE), outcomes)
+
+both<-merge(outcomes, pgs, by = c('FID','IID'))
+
+# Predict each outcome using GW and stratified PGS
+library(glmnet)
+
+res<-NULL
+for(i in c('MIX','RA','CAD')){
+  out1 <-
+    cv.glmnet(x = as.matrix(both[, grepl(paste0(i, '_SBayesRC'), names(both)), with = F]), y = both$MIX_outcome)
+  
+  out2 <-
+    cv.glmnet(x = as.matrix(cbind(0, both[, paste0(i, '_SBayesRC'), with = F])), y = both$MIX_outcome)
+
+  # Get predicted values using the optimal lambda
+  pred1 <- predict(out1, newx = as.matrix(both[, grepl(paste0(i, '_SBayesRC'), names(both)), with = F]), s = "lambda.min")
+  pred2 <- predict(out2, newx = as.matrix(cbind(0, both[, paste0(i, '_SBayesRC'), with = F])), s = "lambda.min")
+  
+  # Compute correlation (Observed vs. Predicted)
+  cor1 <- cor(pred1, both$MIX_outcome)
+  cor2 <- cor(pred2, both$MIX_outcome)
+  
+  res<-rbind(res,
+             data.frame(
+               outcome = i,
+               model = c('gw-pgs + p-pgs', 'gw-pgs'),
+               cor = c(cor1, cor2), 
+               n_pred = c(1, sum(grepl(paste0(i, '_SBayesRC'), names(both))))
+             ))
+}
+
+ggplot(res, aes(x=outcome, y = cor, fill = model)) +
+  geom_bar(stat="identity", position = position_dodge())
+
+res_wide<-reshape(res, direction = 'wide', timevar = 'model', idvar = 'outcome')
+res_wide$diff<-res_wide$`cor.gw-pgs + p-pgs` - res_wide$`cor.gw-pgs`
+res_wide$perc_diff<-res_wide$diff / res_wide$`cor.gw-pgs`
+
+#   outcome cor.gw-pgs + p-pgs n_pred.gw-pgs + p-pgs cor.gw-pgs n_pred.gw-pgs         diff   perc_diff
+# 1     MIX         0.09020498                     1 0.08504137            33 0.0051636170 0.060718886
+# 3      RA         0.03816091                     1 0.03159778            44 0.0065631312 0.207708618
+# 5     CAD         0.19752735                     1 0.19699081             6 0.0005365409 0.002723685
+
+# The absolute and relative improvement is highest for RA, then MIX, with very little improvement for CAD.
+# The number of pathway specific PGS is also highest for RA and then MIX.
+# The improvement could be due to over fitting.
+# Confusing that the CAD PGS is so strong, but the number sig gene sets is higher for RA. I suppose this is due to the CAD GWAS being larger, but also more complex biologically.
+
+```
+
+***
+
+#### Clustering
+
+```{r}
+
+library(NbClust)
+
+both$group<-NULL
+both$group[both$CAD_outcome == 1]<-'CAD'
+both$group[both$RA_outcome == 1]<-'RA'
+both$group[both$CAD_outcome == 1 & both$RA_outcome == 1]<-'BOTH'
+both$group[both$CAD_outcome != 1 & both$RA_outcome != 1]<-'CONTROL'
+
+# Extract PGS data for MIX cases only
+both_cases <- both[which(both$MIX_outcome == 1),]
+pgs_cases <- both_cases[, grepl('MIX_SBayesRC', names(both_cases)), with=F]
+pgs_cases <- scale(pgs_cases) 
+# Not sure whether I should be scaling since they are reference standardised
+
+# Determine optimal number of clusters
+n_clust_sol <-
+  NbClust(
+    data = pgs_cases[1:2000,],
+    distance = "euclidean",
+    min.nc = 2,
+    max.nc = 10,
+    method = 'ward.D2',
+    index = 'all'
+  )
+
+n_clust_opt<-length(unique(n_clust_sol$Best.partition))
+
+#####
+# K-means
+#####
+
+# Now try k-means clustering with 3 clusters
+k_res<-kmeans(pgs_cases, n_clust_opt)
+
+# Plot the mean of each group
+k_res_centers<-data.frame(Group=as.character(1:n_clust_opt),
+                          k_res$centers)
+
+library(reshape2)
+k_res_centers_melt<-melt(k_res_centers, id='Group')
+
+ggplot(k_res_centers_melt, aes(x=variable, y=value, group=Group, color=Group)) +
+  geom_point(size=5) +
+  geom_line() +
+  labs(x='Polygenic Score', y='Cluster Mean', title='Mean Polygenic Score Across Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust=1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#####
+# Hierarchical clustering
+#####
+
+# Compute distance matrix
+dist_matrix <- dist(pgs_cases, method = "euclidean")
+
+# Perform hierarchical clustering
+hclust_res <- hclust(dist_matrix, method = "ward.D2")
+
+# Cut tree into n_clust_opt clusters
+hclust_clusters <- cutree(hclust_res, k = n_clust_opt)
+
+# Convert clusters to a factor for plotting
+hclust_clusters <- as.factor(hclust_clusters)
+
+# Create a data frame with cluster assignments
+pgs_cases_clustered <- data.frame(pgs_cases, Cluster = hclust_clusters)
+
+# Compute the mean of each cluster for each PGS
+hclust_centers <- aggregate(. ~ Cluster, data = pgs_cases_clustered, FUN = mean)
+
+# Reshape the data for plotting
+hclust_centers_melt <- melt(hclust_centers, id = "Cluster")
+
+# Convert Cluster to character for consistent plotting
+hclust_centers_melt$Cluster <- as.character(hclust_centers_melt$Cluster)
+
+ggplot(hclust_centers_melt, aes(x = variable, y = value, group = Cluster, color = Cluster)) +
+  geom_point(size = 5) +
+  geom_line() +
+  labs(x = 'Polygenic Score', y = 'Cluster Mean', title = 'Mean Polygenic Score Across Hierarchical Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#####
+# Gaussian mixture model
+#####
+library(mclust)
+
+gmm_model <- Mclust(pgs_cases, G = n_clust_opt)
+summary(gmm_model)
+
+gmm_clusters <- gmm_model$classification  # Hard cluster labels
+gmm_probabilities <- gmm_model$z  # Soft probabilities for each cluster
+
+# Create a data frame with cluster assignments
+pgs_cases_clustered <- data.frame(pgs_cases, Cluster = gmm_clusters)
+
+# Compute the mean of each cluster for each PGS
+gmm_centers <- aggregate(. ~ Cluster, data = pgs_cases_clustered, FUN = mean)
+
+# Reshape the data for plotting
+gmm_centers_melt <- melt(gmm_centers, id = "Cluster")
+
+# Convert Cluster to character for consistent plotting
+gmm_centers_melt$Cluster <- as.character(gmm_centers_melt$Cluster)
+
+ggplot(gmm_centers_melt, aes(x = variable, y = value, group = Cluster, color = Cluster)) +
+  geom_point(size = 5) +
+  geom_line() +
+  labs(x = 'Polygenic Score', y = 'Cluster Mean', title = 'Mean Polygenic Score Across GMM Clusters') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  background_grid(major = 'y', minor = 'y')
+
+#########
+# Compare the clusters to the case groups
+k_means_conf_matrix <- table(k_res$cluster, both_cases$group)
+hclust_D2_conf_matrix <- table(hclust_clusters, both_cases$group)
+gmm_conf_matrix <- table(gmm_clusters, both_cases$group)
+
+library(mclust)
+adjustedRandIndex(k_res$cluster, both_cases$group)
+adjustedRandIndex(hclust_clusters, both_cases$group)
+adjustedRandIndex(gmm_clusters, both_cases$group)
+
+# The accuracy of the hclust solution is higher
+pca_res <- prcomp(pgs_cases)
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(k_res$cluster))) + 
+  geom_point()
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(hclust_clusters))) + 
+  geom_point()
+ggplot(data.frame(pca_res$x), aes(PC1, PC2, color = as.factor(gmm_clusters))) + 
+  geom_point()
+
+library(Rtsne)
+
+tsne_res <- Rtsne(pgs_cases[!duplicated(pgs_cases),])
+
+ggplot(data.frame(tsne_res$Y), aes(
+  x = X1,
+  y = X2,
+  color = as.factor(k_res$cluster[!duplicated(pgs_cases)])
+)) + 
+  geom_point() +
+  labs(color = 'Cluster')
+
+ggplot(data.frame(tsne_res$Y), aes(
+  x = X1,
+  y = X2,
+  color = as.factor(hclust_clusters[!duplicated(pgs_cases)])
+)) + 
+  geom_point() +
+  labs(color = 'Cluster')
+
+ggplot(data.frame(tsne_res$Y), aes(
+  x = X1,
+  y = X2,
+  color = as.factor(gmm_clusters[!duplicated(pgs_cases)])
+)) + 
+  geom_point() +
+  labs(color = 'Cluster')
+
+
+########
+# Predict MIX outcome 
+
+# Project cluster solutions into full dataset
+both_cluster<-both
+
+library(clue)
+# kmeans
+kmeans_pred<-cl_predict(k_res, newdata = both, type = "memberships")
+both_cluster$kmean<-apply(kmeans_pred, 1, function(x) which(x == 1))
+
+# hclust
+centroids <- aggregate(pgs_cases, list(hclust_clusters), mean)[, -1]  # Remove the cluster ID column
+assign_to_nearest_cluster <- function(new_data, centroids) {
+  apply(as.matrix(new_data), 1, function(row) {
+    which.min(colSums((t(centroids) - row)^2))  # Compute Euclidean distance to centroids
+  })
+}
+both_cluster$hclust <-
+  assign_to_nearest_cluster(
+    new_data = both[, grepl('MIX_SBayesRC', names(both_cases)), with = F], centroids = centroids)
+
+# gmm
+both_cluster$gmm <-
+  predict(gmm_model, 
+          newdata = both[, grepl('MIX_SBayesRC', names(both_cases)), with = F])$classification
+both_cluster$gmm_prob <-
+  predict(gmm_model, 
+          newdata = both[, grepl('MIX_SBayesRC', names(both_cases)), with = F])$z[,1]
+
+# MIX_SBayesRC only
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  MIX_SBayesRC")),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# MIX_SBayesRC + pathway specific PGS
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~  MIX_SBayesRC + ", paste(names(both_cluster)[grepl('MIX_SBayesRC', names(both_cluster))], collapse='+'))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# kmeans interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~ ", paste(names(both_cluster)[grepl('MIX_SBayesRC', names(both_cluster))], '*as.factor(kmean)', collapse='+'))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# hclust interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~ ", paste(names(both_cluster)[grepl('MIX_SBayesRC', names(both_cluster))], '*as.factor(hclust)', collapse='+'))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# gmm interactions
+sum_mod<-summary(
+  mod<-glm(
+    as.formula(paste0("MIX_outcome ~ ", paste(names(both_cluster)[grepl('MIX_SBayesRC', names(both_cluster))], '*as.factor(gmm)', collapse='+'))),
+    data = both_cluster))
+cor(both_cluster$MIX_outcome, predict(mod, data=both_cluster))
+
+# Prediction does improve, but likely due to over fitting. Given extreme heterogeneity in this example, I am not that.
+```
+
+***
+
+### Non-negative factorisation
+
 See whether non-negative matrix factorisation could recapitulate disease-specific PGS.
 
 Steps:
diff --git a/functions/pipeline.R b/functions/pipeline.R
index f65bedd6..ce9d85be 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -92,6 +92,105 @@ read_pgs <- function(config, name = NULL, pgs_methods = NULL, gwas = NULL, pop =
   return(pgs)
 }
 
+# Read in PGS
+read_pgs_2 <- function(config, name = NULL, pgs_methods = NULL, gwas = NULL, pop = NULL, pseudo_only = F, partitioned = F){
+  # Identify outdir parameter
+  outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+  
+  # Read in target_list
+  target_list <- read_param(config = config, param = 'target_list')
+  if(!is.null(name)){
+    if(any(!(name %in% target_list$name))){
+      stop('Requested target samples are not present in target_list')
+    }
+    name_i <- name
+    target_list <- target_list[target_list$name %in% name_i,]
+  }
+  
+  # Identify score files
+  score_file_list <- list_score_files(config)
+  
+  # If partitioned, restrict to single source methods, and gwas with sig sets
+  if(partitioned){
+    score_file_list <- score_file_list[!(score_file_list$method %in% pgs_group_methods) & !grepl('tlprs|leopard', score_file_list$method),]
+    
+    set_reporter_file <- paste0(outdir, '/reference/gwas_sumstat/set_reporter.txt')
+    set_reporter<-fread(set_reporter_file)
+    score_file_list<-score_file_list[score_file_list$name %in% set_reporter$name[set_reporter$n_sig > 0],]
+    
+    part<-'.partitioned'
+  }
+  
+  # Subset requested gwas
+  if(!is.null(gwas)){
+    if(any(!(gwas %in% score_file_list$name))){
+      stop('Requested GWAS are not present in gwas_list/score_list')
+    }
+    score_file_list<-score_file_list[score_file_list$name %in% gwas,]
+  }
+  
+  # Subset requested pgs_methods
+  if(!is.null(pgs_methods)){
+    if(any(!(pgs_methods %in% score_file_list$method))){
+      stop('Requested PGS methods are not present in gwas_list/score_list')
+    }
+    score_file_list<-score_file_list[score_file_list$method %in% pgs_methods,]
+  }
+  
+  # Identify pgs_scaling parameter
+  pgs_scaling <- read_param(config = config, param = 'pgs_scaling', return_obj = F)
+  
+  pgs <- list()
+  for (name_i in target_list$name) {
+    pops<-NULL
+    if('continuous' %in% pgs_scaling){
+      pops <- c('TRANS', pops)
+    }
+    if('discrete' %in% pgs_scaling){
+      # Read in keep_list to determine populations available
+      keep_list_i <- fread(paste0(outdir,'/',name_i,'/ancestry/keep_list.txt'))
+      pops <- c(pops, keep_list_i$POP)
+    }
+    if(!is.null(pop)){
+      if(!any('discrete' %in% pgs_scaling) & any(pop != 'TRANS')){
+        stop(paste0('Requested pop are not present in ',name_i,' sample. Only PGS adjusted using continuous ancestry correction are available due to pgs_scaling parameter in configfile.'))
+      }
+      if(any(!(pop %in% pops))){
+        stop(paste0('Requested pop are not present in ',name_i,' sample.'))
+      }
+      pops <- pops[pops %in% pop]
+    }
+    
+    pgs[[name_i]] <- list()
+    for (pop_i in pops) {
+      pgs[[name_i]][[pop_i]] <- list()
+      for(score_i in 1:nrow(score_file_list)){
+        gwas_i <- score_file_list$name[score_i]
+        pgs_method_i <- score_file_list$method[score_i]
+        if (is.null(pgs[[name_i]][[pop_i]][[gwas_i]])) {
+          pgs[[name_i]][[pop_i]][[gwas_i]] <- list()
+        }
+        file_i<-paste0(outdir, '/', name_i, '/pgs/', pop_i, '/', pgs_method_i, '/',  gwas_i, '/', name_i, '-', gwas_i, '-', pop_i, part, '.profiles')
+        if(pseudo_only){
+          pseudo_param <- find_pseudo(config = config, gwas = gwas_i, target_pop = pop_i, pgs_method = pgs_method_i)
+          
+          score_header <-
+            fread(file_i, nrows = 1)
+          score_cols <-
+            which(names(score_header) %in% c('FID', 'IID', paste0(gwas_i, '_',pseudo_param)))
+          
+          pgs[[name_i]][[pop_i]][[gwas_i]][[pgs_method_i]] <-
+            fread(cmd = paste0("cut -d' ' -f ", paste0(score_cols, collapse=','), " ", file_i))
+        } else {
+          pgs[[name_i]][[pop_i]][[gwas_i]][[pgs_method_i]] <- fread(file_i)
+        }
+      }
+    }
+  }
+  
+  return(pgs)
+}
+
 # Create function to read in parameters in the config file
 read_param <- function(config, param, return_obj = T){
   library(yaml)
diff --git a/functions/plink.R b/functions/plink.R
index 6d0aa9ce..3e32c325 100644
--- a/functions/plink.R
+++ b/functions/plink.R
@@ -451,11 +451,12 @@ plink_score<-function(bfile=NULL, pfile=NULL, score, keep=NULL, extract=NULL, ch
     plink_opt<-paste0(plink_opt, '--score-col-nums 4 ')
   }
   # Calculate score in the target sample
+  scores <- NULL
   for(chr_i in chr){
     cmd<-paste0(plink_opt,'--chr ',chr_i,' --out ',tmp_folder,'/profiles.chr',chr_i,' --threads ',threads)
     cmd <- gsub('CHROMOSOME_NUMBER', chr_i, cmd)
     exit_status <- system(cmd, intern=FALSE)
-    if (exit_status != 0) {
+    if (exit_status != 0 & exit_status != 13) {
       stop()
     }
 
@@ -466,7 +467,7 @@ plink_score<-function(bfile=NULL, pfile=NULL, score, keep=NULL, extract=NULL, ch
       # Delete file to save disk space
       system(paste0('rm ', tmp_folder, '/profiles.chr', chr_i, '.sscore'))
 
-      if(chr_i == chr[1]){
+      if(is.null(scores)){
         names(sscore)<-gsub('\\#', '', names(sscore))
         scores_ids <- sscore[, names(sscore) %in% c('FID', 'IID'), with = F]
         if (ncol(scores_ids) == 1) {
diff --git a/pipeline/Snakefile b/pipeline/Snakefile
index 15d1efdb..6730d8ee 100644
--- a/pipeline/Snakefile
+++ b/pipeline/Snakefile
@@ -18,6 +18,6 @@ wildcard_constraints:
 include: "rules/dependencies.smk"
 include: "rules/target_qc.smk"
 include: "rules/pgs_methods.smk"
-include: "rules/magma.smk"
+#include: "rules/magma.smk"
 include: "rules/target_scoring.smk"
 include: "rules/report.smk"
diff --git a/pipeline/rules/magma.smk b/pipeline/rules/magma.smk
index d9efb790..cc26b9c2 100644
--- a/pipeline/rules/magma.smk
+++ b/pipeline/rules/magma.smk
@@ -188,13 +188,38 @@ checkpoint set_reporter:
   params:
     config_file=config['config_file']
   shell:
-    "Rscript ../Scripts/magma/set_reporter/set_reporter.R \
+    "Rscript ../Scripts/magma/set_reporter.R \
       --config {params.config_file}"
       
 ########
 # Calculate stratified PGS
 ########
 
+# Prepare score files for stratified PGS
+rule pgs_stratifier:
+  input:
+    f"{outdir}/reference/gwas_sumstat/set_reporter.txt",
+    rules.prep_pgs.input
+  threads: config['cores_prep_pgs']
+  output:
+    touch(f"{outdir}/reference/pgs_score_files/pgs_stratifier.done"),
+  conda:
+    "../envs/analysis.yaml"
+  benchmark:
+    f"{outdir}/reference/benchmarks/pgs_stratifier.txt"
+  log:
+    f"{outdir}/reference/logs/pgs_stratifier.log"
+  params:
+    testing=config["testing"],
+    config_file = config["config_file"]
+  shell:
+    "Rscript ../Scripts/pgs_methods/pgs_stratifier.R \
+      --config {params.config_file} \
+      --plink2 plink2 \
+      --test {params.testing} \
+      --n_cores {threads} > {log} 2>&1"
+
+# Target sample scoring
 rule target_pgs_partitioned_i:
   resources:
     mem_mb=config['mem_target_pgs'],
@@ -204,7 +229,7 @@ rule target_pgs_partitioned_i:
     f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",
     f"{outdir}/reference/gwas_sumstat/set_reporter.txt",
     lambda w: f"{outdir}/reference/target_checks/{{name}}/pc_projection-TRANS.done" if w.population == "TRANS" else [],
-    rules.prep_pgs.input
+    f"{outdir}/reference/pgs_score_files/pgs_stratifier.done"
   output:
     touch(f"{outdir}/reference/target_checks/{{name}}/target_pgs_partitioned-{{population}}.done")
   benchmark:

From 8f070203a5462b2a9b7657197192ed1edea7fb6f Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 11 Mar 2025 09:26:59 +0000
Subject: [PATCH 120/153] Updates

---
 Scripts/ref_scoring/ref_scoring.R             | 216 ++++++++++++++++++
 .../target_scoring/target_scoring_pipeline.R  |   6 +-
 docs/CrossPop.Rmd                             |   2 +-
 docs/pipeline_readme.Rmd                      |  16 +-
 docs/pipeline_readme.html                     |  20 +-
 functions/pgs.R                               |  34 ++-
 pipeline/rules/pgs_methods.smk                |  99 +++++---
 pipeline/rules/target_scoring.smk             |  10 +-
 8 files changed, 335 insertions(+), 68 deletions(-)
 create mode 100644 Scripts/ref_scoring/ref_scoring.R

diff --git a/Scripts/ref_scoring/ref_scoring.R b/Scripts/ref_scoring/ref_scoring.R
new file mode 100644
index 00000000..727cca34
--- /dev/null
+++ b/Scripts/ref_scoring/ref_scoring.R
@@ -0,0 +1,216 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+make_option("--config", action="store", default=NULL, type='character',
+    help="Pipeline configuration file [required]"),
+make_option("--continuous", action="store", default=T, type='logical',
+    help="Logical indicating whether or not continuous correction for ancestry is required [optional]"),
+make_option("--plink2", action="store", default='plink2', type='character',
+    help="Path PLINK v2 software binary [optional]"),
+make_option("--n_cores", action="store", default=1, type='numeric',
+    help="Number of cores to use [optional]"),
+make_option("--test", action="store", default=NA, type='character',
+    help="Specify number of SNPs to include [optional]"),
+make_option("--memory", action="store", default=5000, type='numeric',
+		help="Memory limit [optional]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+library(foreach)
+library(doMC)
+registerDoMC(opt$n_cores)
+
+# Check required inputs
+if(is.null(opt$config)){
+  stop('--config must be specified.\n')
+}
+
+# Read in outdir
+outdir <- read_param(config = opt$config, param = 'outdir', return_obj = F)
+
+# Create output directory
+opt$output <- paste0(outdir, '/reference/pgs_score_files/ref_scoring')
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output, '_', format(Sys.time(), "%Y-%m-%d_%H-%M-%S"), '.log')
+log_header(log_file = log_file, opt = opt, script = 'ref_scoring_pipeline.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+# Identify score files to be combined
+score_files<-list_score_files(opt$config)
+
+# Check whether score files or target genetic data are newer than target pgs
+if(!is.null(score_files)){
+  ref_pcs_file_time <- NULL
+  if(opt$continuous){
+    ref_pcs_file<-paste0(outdir, '/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale')
+    ref_pcs_file_time <- file.info(ref_pcs_file)$mtime
+  }
+  
+  score_files_to_do <- data.table()
+  for(i in 1:nrow(score_files)){
+    pgs_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '.EUR.profiles')
+    score_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '.score.gz')
+    if(!file.exists(pgs_i)){
+      score_files_to_do <- rbind(score_files_to_do, score_files[i,])
+    } else {
+      score_i_time <- file.info(score_i)$mtime
+      pgs_i_time <- file.info(pgs_i)$mtime
+      if (score_i_time > pgs_i_time | (!is.null(ref_pcs_file_time) && ref_pcs_file_time > pgs_i_time)) {
+        score_files_to_do <- rbind(score_files_to_do, score_files[i,])
+        system(paste0('rm ', pgs_i))
+      }
+    }
+  }
+  log_add(log_file = log_file, message = paste0('After checking timestamps, ', nrow(score_files_to_do), '/', nrow(score_files), ' score files will be used for reference scoring.'))
+  score_files <- score_files_to_do
+}
+
+if(is.null(score_files) || nrow(score_files) == 0){
+  log_add(log_file = log_file, message = paste0('No score files to be used for reference scoring.'))
+  end.time <- Sys.time()
+  time.taken <- end.time - start.time
+  sink(file = paste(opt$output,'.log',sep=''), append = T)
+  cat('Analysis finished at',as.character(end.time),'\n')
+  cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
+  sink()
+  quit(save = "no", status = 0)
+}
+
+# Set params for plink_score
+refdir <- read_param(config = opt$config, param = 'refdir', return_obj = F)
+opt$ref_plink_chr <- paste0(refdir, '/ref.chr')
+
+# Read in reference SNP data
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('CHR','SNP','A1','A2'), with=F]
+
+# We will process score files and perform target scoring for one chromosome for efficiency
+for(chr_i in CHROMS){
+  log_add(log_file = log_file, message = '########################')
+  log_add(log_file = log_file, message = paste0('Processing chromosome ', chr_i,':'))
+
+  #####
+  # Combine score files
+  #####
+  # Create row number index to subset score files by chromosome
+  row_index <- format(which(ref$CHR == chr_i) + 1, scientific = FALSE)
+  write.table(row_index, paste0(tmp_dir,'/row_index.txt'), row.names=F, quote=F, col.names = F)
+
+  # Create file containing SNP, A1, and A2 information for each chromosome
+  fwrite(ref[ref$CHR == chr_i, c('SNP','A1','A2'), with=F], paste0(tmp_dir,'/map.txt'), row.names=F, quote=F, sep=' ')
+
+  # Extract process score files for each name (gwas/score) in parallel
+  foreach(i = 1:nrow(score_files), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+    system(paste0(
+      'zcat ', outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],".score.gz | ",
+      'awk \'NR==FNR {rows[$1]; next} FNR==1 || FNR in rows\' ', paste0(tmp_dir,'/row_index.txt'), ' - | ',  # Corrected to retain the header and process indexed rows
+      "cut -d' ' --complement -f1-3 | ",  # Keep relevant columns, remove first 3
+      "sed '1 s/SCORE_/", paste0('score_file_', i,'.'), "/g' > ",  # Replace SCORE in the header
+      tmp_dir, '/tmp_score.', paste0(score_files$method[i], '.', score_files$name[i]), '.txt'
+    ))
+  }
+
+  # Paste files together in batches
+  # Set number of batches according to the number of score files to combine
+  num_batches <- max(c(1, min(c(opt$n_cores, floor(nrow(score_files) / 2)))))
+  tmp_score_files <- paste0(tmp_dir,'/tmp_score.',score_files$method,'.',score_files$name,'.txt')
+  set.seed(1)
+  batches <- split(sample(tmp_score_files), rep(1:num_batches, length.out = length(tmp_score_files)))
+  log_add(log_file = log_file, message = paste0('Aggregating score files in ', num_batches,' batches.'))
+  foreach(i = 1:length(batches), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
+    system(paste0("paste -d ' ' ", paste(batches[[i]], collapse = " "),' > ',tmp_dir,'/tmp_batch_',i))
+    system(paste0('rm ', paste(batches[[i]], collapse = " ")))
+  }
+
+  # Paste batches together
+  log_add(log_file = log_file, message = paste0('Aggregating batched score files.'))
+  tmp_batch_files <- paste0(tmp_dir,'/tmp_batch_',1:length(batches))
+  system(paste0("paste -d ' ' ", tmp_dir,'/map.txt ', paste(tmp_batch_files, collapse = " "), ' > ', tmp_dir, '/all_score.txt'))
+  system(paste0('rm ', paste(tmp_batch_files, collapse = " ")))
+
+  # Perform polygenic risk scoring
+  scores_i <-
+    plink_score(
+      pfile = opt$ref_plink_chr,
+      chr = chr_i,
+      plink2 = opt$plink2,
+      score = paste0(tmp_dir,'/all_score.txt'),
+      threads = opt$n_cores
+    )
+  
+  # Sum scores across chromosomes
+  if(chr_i == CHROMS[1]){
+    scores_ids <- scores_i[, 1:2, with = F]
+    current_scores <- as.matrix(scores_i[, -1:-2, with = FALSE])
+    scores <- current_scores
+  } else {
+    current_scores <- as.matrix(scores_i[, -1:-2, with = FALSE])
+    scores <- scores + current_scores
+  }
+
+  system(paste0('rm ', tmp_dir, '/all_score.txt'))
+  system(paste0('rm ', tmp_dir, '/row_index.txt'))
+  system(paste0('rm ', tmp_dir, '/map.txt'))
+  rm(scores_i)
+  rm(current_scores)
+  gc()
+}
+
+# Combine score with IDs
+scores<-data.table(scores_ids,
+                   scores)
+
+###
+# Scale the polygenic scores based on the reference
+###
+
+log_add(log_file = log_file, message = paste0('Adjusting PGS for ancestry.'))
+
+pop_data <- read_pop_data(paste0(refdir, '/ref.pop.txt'))
+
+for(i in 1:nrow(score_files)){
+  scores_i <- scores[, c('FID','IID', names(scores)[grepl(paste0('^score_file_', i, '\\.'), names(scores))]), with=F]
+  names(scores_i) <- gsub(paste0('^score_file_', i, '\\.'), 'SCORE_', names(scores_i))
+
+  output_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i], '/', score_files$name[i], '/ref-', score_files$name[i])
+
+  if(opt$continuous){
+    # Derive trans-ancestry PGS models and estimate PGS residual scale
+    model_trans_pgs(scores=scores_i, pcs=paste0(outdir, '/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles'), output=output_i)
+  }
+  
+  # Calculate scale within each reference population
+  for(pop_i in unique(pop_data$POP)){
+    ref_pgs_scale_i <- score_mean_sd(scores = scores_i, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+    fwrite(ref_pgs_scale_i, paste0(output_i, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+    scores_i_pop<-scores_i[paste0(scores_i$FID, '_', scores_i$IID) %in% paste0(pop_data$FID[pop_data$POP == pop_i], '_', pop_data$IID[pop_data$POP == pop_i]),]
+    scores_i_pop<-score_scale(score=scores_i_pop, ref_scale=ref_pgs_scale_i)
+    fwrite(scores_i_pop, paste0(output_i, '-', pop_i, '.profiles'), sep=' ', na='NA', quote=F)
+  }
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at',as.character(end.time),'\n')
+cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
+sink()
diff --git a/Scripts/target_scoring/target_scoring_pipeline.R b/Scripts/target_scoring/target_scoring_pipeline.R
index 6c7f54c8..fd22343d 100644
--- a/Scripts/target_scoring/target_scoring_pipeline.R
+++ b/Scripts/target_scoring/target_scoring_pipeline.R
@@ -76,7 +76,7 @@ if(!is.null(score_files)){
   score_files_to_do <- data.table()
   for(i in 1:nrow(score_files)){
     pgs_i <- paste0(outdir, '/', opt$name,'/pgs/', opt$population,'/', score_files$method[i],'/', score_files$name[i],'/', opt$name,'-', score_files$name[i],'-',opt$population,'.profiles')
-    score_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '.score.gz')
+    score_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '-', opt$population, '.scale')
     if(!file.exists(pgs_i)){
       score_files_to_do <- rbind(score_files_to_do, score_files[i,])
     } else {
@@ -196,6 +196,9 @@ for(chr_i in CHROMS){
   system(paste0('rm ', tmp_dir, '/all_score.txt'))
   system(paste0('rm ', tmp_dir, '/row_index.txt'))
   system(paste0('rm ', tmp_dir, '/map.txt'))
+  rm(scores_i)
+  rm(current_scores)
+  gc()
 }
 
 # Combine score with IDs
@@ -211,6 +214,7 @@ if(opt$population == 'TRANS'){
 
   models<-list()
   for(i in 1:nrow(score_files)){
+    print(i)
     models[[paste0('score_file_', i)]]<-readRDS(paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i],'-TRANS.model.rds'))
     names(models[[paste0('score_file_', i)]])<-gsub('SCORE_', paste0('score_file_', i, '.'), names(models[[paste0('score_file_', i)]]))
   }
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index fd2272c8..5a56d5d6 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -5657,7 +5657,7 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml \
-  target_pgs  -n
+  prep_pgs  -n
 ```
 
 </details>
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 2abeb5d6..56d53fc4 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -1048,21 +1048,19 @@ Outputs specific to the configuration used are stored in the `outdir` specified
       ├── pgs_score_files (score files for polygenic scoring)
       │   └── [pgs method] (pgs for each pgs method)
       │       └── [gwas/score name] (pgs for each GWAS or score file)
+      ├── pc_score_files (score files for reference principal components)
       ├── benchmarks (pipeline benchmark data)
       └── target_checks (pipeline logs)
 ```
 
-Some additional outputs that are independent of the configuration used, are store in the resources folder of the `GenoPred/pipeline` folder:
+Some additional outputs that are independent of the configuration used, are store in the resource directory, which is by default the `GenoPred/pipeline/resources` folder. The location of the resources directory can be altered using the `resdir` parameter in the `configfile`:
 
 ```{}
-GenoPred/pipeline
-  └── resources
-      ├── data (downloaded data)
-      │   ├── target_checks (pipeline logs)
-      │   ├── benchmarks (pipeline benchmark data)      
-      │   └── ref (default reference data)
-      │       └── pc_score_files (score files for reference pcs)
-      └── software (downloaded software)
+[resdir]
+  ├── data (downloaded data)
+  │   ├── target_checks (pipeline logs)
+  │   └── benchmarks (pipeline benchmark data)      
+  └── software (downloaded software)
 ```
 
 ***
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index 0b2085d0..d4f0942c 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -1766,19 +1766,19 @@ <h2>Output structure</h2>
       ├── pgs_score_files (score files for polygenic scoring)
       │   └── [pgs method] (pgs for each pgs method)
       │       └── [gwas/score name] (pgs for each GWAS or score file)
+      ├── pc_score_files (score files for reference principal components)
       ├── benchmarks (pipeline benchmark data)
       └── target_checks (pipeline logs)</code></pre>
 <p>Some additional outputs that are independent of the configuration
-used, are store in the resources folder of the
-<code>GenoPred/pipeline</code> folder:</p>
-<pre><code>GenoPred/pipeline
-  └── resources
-      ├── data (downloaded data)
-      │   ├── target_checks (pipeline logs)
-      │   ├── benchmarks (pipeline benchmark data)      
-      │   └── ref (default reference data)
-      │       └── pc_score_files (score files for reference pcs)
-      └── software (downloaded software)</code></pre>
+used, are store in the resource directory, which is by default the
+<code>GenoPred/pipeline/resources</code> folder. The location of the
+resources directory can be altered using the <code>resdir</code>
+parameter in the <code>configfile</code>:</p>
+<pre><code>[resdir]
+  ├── data (downloaded data)
+  │   ├── target_checks (pipeline logs)
+  │   └── benchmarks (pipeline benchmark data)      
+  └── software (downloaded software)</code></pre>
 <hr />
 </div>
 </div>
diff --git a/functions/pgs.R b/functions/pgs.R
index 78f033d3..4dda6669 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -142,7 +142,7 @@ map_score<-function(ref, score){
 # Calculate mean and sd of scores in file with plink .sscore format
 score_mean_sd<-function(scores, keep=NULL){
     if(!is.null(keep)){
-        scores<-scores[paste0(scores$FID, '_', scores$FID) %in% paste0(keep$FID, '_', keep$FID),]
+        scores<-scores[paste0(scores$FID, '_', scores$IID) %in% paste0(keep$FID, '_', keep$IID),]
     }
 
     scale<-data.table(  Param=names(scores)[-1:-2],
@@ -589,8 +589,8 @@ model_trans_pgs<-function(scores=NULL, pcs=NULL, output=NULL){
     
     scores_pcs_resid[[i]]<-residual_pgs/sqrt(predicted_pgs_var)
     
-    mod_list[[i]]$mean_model <- pgs_pc_mean_mod
-    mod_list[[i]]$var_model <- pgs_pc_var_mod
+    mod_list[[i]]$mean_model <- compact_lm(pgs_pc_mean_mod)
+    mod_list[[i]]$var_model <- compact_lm(pgs_pc_var_mod)
   }
   
   scores_pcs_resid<-scores_pcs_resid[,grepl('FID|IID|^SCORE', names(scores_pcs_resid)), with=F]
@@ -607,6 +607,24 @@ model_trans_pgs<-function(scores=NULL, pcs=NULL, output=NULL){
   fwrite(scores_pcs_resid, paste0(output,'-TRANS.profiles'), sep=' ', na='NA', quote=F)
 }
 
+# Remove unused parts of model object for prediction
+compact_lm <- function(cm) {
+  # just in case we forgot to set
+  # y=FALSE and model=FALSE
+  cm$y = c()
+  cm$model = c()
+  
+  cm$residuals = c()
+  cm$fitted.values = c()
+  cm$effects = c()
+  cm$qr$qr = c()
+  cm$linear.predictors = c()
+  cm$weights = c()
+  cm$prior.weights = c()
+  cm$data = c()
+  cm
+}
+
 # Adjust PGS for ancestry using reference PC models with parallel processing
 score_adjust <- function(score, pcs, ref_model) {
   # Store original column order
@@ -614,7 +632,7 @@ score_adjust <- function(score, pcs, ref_model) {
   
   # Ensure pcs is keyed on FID and IID for efficient joins
   setkey(pcs, FID, IID)
-
+  
   # Get list of score columns (excluding FID and IID)
   score_cols <- setdiff(names(score), c("FID", "IID"))
   
@@ -624,8 +642,10 @@ score_adjust <- function(score, pcs, ref_model) {
     # Get a subset of columns to process in this iteration
     chunk <- score_cols[i:min(i + chunk_size - 1, length(score_cols))]
     
-    # Parallel computation using foreach
-    chunk_results <- foreach(col_name = chunk, .packages = c("data.table")) %dopar% {
+    # Parallel computation using mclapply()
+    chunk_results <- mclapply(chunk, function(col_name) {
+      print(col_name)
+      
       # Retrieve models for the current score
       mean_model <- ref_model[[col_name]]$mean_model
       var_model <- ref_model[[col_name]]$var_model
@@ -642,7 +662,7 @@ score_adjust <- function(score, pcs, ref_model) {
       
       # Return results as a list (column name + values)
       list(col_name = col_name, values = adjusted_score)
-    }
+    }, mc.cores = getDoParWorkers())
     
     # **Write results back to `score` immediately, to avoid holding large objects in memory**
     for (res in chunk_results) {
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 5b2ba882..06f7ff15 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -6,28 +6,28 @@ rule ref_pca_i:
     f"{resdir}/last_version.txt",
     "../Scripts/ref_pca/ref_pca.R"
   output:
-    f"{resdir}/data/ref/pc_score_files/{{population}}/ref-{{population}}-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/{{population}}/ref-{{population}}-pcs.EUR.scale"
   conda:
     "../envs/analysis.yaml",
   params:
     testing=config["testing"],
     ref_keep=lambda wildcards: "NA" if wildcards.population == "TRANS" else f"{refdir}/keep_files/{wildcards.population}.keep"
   benchmark:
-    f"{resdir}/data/benchmarks/ref_pca_i-{{population}}.txt"
+    f"{outdir}/reference/benchmarks/ref_pca_i-{{population}}.txt"
   log:
-    f"{resdir}/data/logs/ref_pca_i-{{population}}.log"
+    f"{outdir}/reference/logs/ref_pca_i-{{population}}.log"
   shell:
     "Rscript ../Scripts/ref_pca/ref_pca.R \
       --ref_plink_chr {refdir}/ref.chr \
       --ref_keep {params.ref_keep} \
       --pop_data {refdir}/ref.pop.txt \
-      --output {resdir}/data/ref/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs \
+      --output {outdir}/reference/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs \
       --test {params.testing} > {log} 2>&1"
 
 populations=["AFR","AMR","CSA","EAS","EUR","MID","TRANS"]
 
 rule ref_pca:
-  input: expand(f"{resdir}/data/ref/pc_score_files/{{population}}/ref-{{population}}-pcs.EUR.scale", population=populations)
+  input: expand(f"{outdir}/reference/pc_score_files/{{population}}/ref-{{population}}-pcs.EUR.scale", population=populations)
 
 ##
 # QC and format GWAS summary statistics
@@ -77,7 +77,7 @@ rule sumstat_prep:
 rule prep_pgs_ptclump_i:
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/ptclump/{{gwas}}/ref-{{gwas}}.score.gz"
   conda:
@@ -94,7 +94,7 @@ rule prep_pgs_ptclump_i:
     "Rscript ../Scripts/pgs_methods/ptclump.R \
       --ref_plink_chr {refdir}/ref.chr \
       --ref_keep {refdir}/keep_files/{params.population}.keep \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --output {outdir}/reference/pgs_score_files/ptclump/{wildcards.gwas}/ref-{wildcards.gwas} \
       --pop_data {refdir}/ref.pop.txt \
@@ -125,7 +125,7 @@ rule prep_pgs_dbslmm_i:
     rules.download_hm3_snplist.output,
     rules.download_dbslmm.output,
     rules.download_ld_blocks.output,
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/dbslmm/{{gwas}}/ref-{{gwas}}.score.gz"
   conda:
@@ -145,7 +145,7 @@ rule prep_pgs_dbslmm_i:
     "Rscript ../Scripts/pgs_methods/dbslmm.R \
       --ref_plink_chr {refdir}/ref.chr \
       --ref_keep {refdir}/keep_files/{params.population}.keep \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --ld_blocks {resdir}/data/ld_blocks/{params.ld_block_pop} \
       --plink {resdir}/software/plink/plink \
@@ -180,7 +180,7 @@ rule prep_pgs_prscs_i:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
     rules.download_prscs_software.output,
     lambda w: f"{resdir}/data/prscs_ref/" + prscs_ldref + "/ldblk_" + prscs_ldref + "_" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0].lower() + "/ldblk_1kg_chr1.hdf5",
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/prscs/{{gwas}}/ref-{{gwas}}.score.gz"
   conda:
@@ -201,7 +201,7 @@ rule prep_pgs_prscs_i:
     export OPENBLAS_NUM_THREADS=1; \
     Rscript ../Scripts/pgs_methods/prscs.R \
     --ref_plink_chr {refdir}/ref.chr \
-    --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+    --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
     --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
     --output {outdir}/reference/pgs_score_files/prscs/{wildcards.gwas}/ref-{wildcards.gwas} \
     --pop_data {refdir}/ref.pop.txt \
@@ -228,7 +228,7 @@ rule prep_pgs_sbayesr_i:
     lambda w: f"{sbayesr_ldref}/" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0] + "/map.rds",
     rules.download_gctb_ref.output,
     rules.download_gctb_software.output,
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/sbayesr/{{gwas}}/ref-{{gwas}}.score.gz"
   conda:
@@ -242,7 +242,7 @@ rule prep_pgs_sbayesr_i:
   shell:
     "Rscript ../Scripts/pgs_methods/sbayesr.R \
       --ref_plink_chr {refdir}/ref.chr \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --gctb {resdir}/software/gctb/gctb_2.03beta_Linux/gctb \
       --ld_matrix_chr {sbayesr_ldref} \
@@ -266,7 +266,7 @@ rule prep_pgs_lassosum_i:
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
     rules.install_lassosum.output,
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/lassosum/{{gwas}}/ref-{{gwas}}.score.gz"
   benchmark:
@@ -283,7 +283,7 @@ rule prep_pgs_lassosum_i:
      --ref_plink_chr {refdir}/ref.chr \
      --ref_keep {refdir}/keep_files/{params.population}.keep \
      --gwas_pop {params.population} \
-     --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+     --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
      --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
      --output {outdir}/reference/pgs_score_files/lassosum/{wildcards.gwas}/ref-{wildcards.gwas} \
      --n_cores {threads} \
@@ -305,7 +305,7 @@ rule prep_pgs_ldpred2_i:
   input:
     f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
     lambda w: f"{ldpred2_ldref}/" + gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0] + "/map.rds",
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/ldpred2/{{gwas}}/ref-{{gwas}}.score.gz"
   benchmark:
@@ -326,7 +326,7 @@ rule prep_pgs_ldpred2_i:
     Rscript ../Scripts/pgs_methods/ldpred2.R \
       --ref_plink_chr {refdir}/ref.chr \
       --ldpred2_ref_dir {ldpred2_ldref}/{params.population} \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --n_cores {threads} \
       --output {outdir}/reference/pgs_score_files/ldpred2/{wildcards.gwas}/ref-{wildcards.gwas} \
@@ -355,7 +355,7 @@ rule prep_pgs_megaprs_i:
     rules.download_ldak.output,
     rules.download_ldak_map.output,
     rules.download_ldak_bld.output,
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/megaprs/{{gwas}}/ref-{{gwas}}.score.gz"
   benchmark:
@@ -371,7 +371,7 @@ rule prep_pgs_megaprs_i:
     "Rscript ../Scripts/pgs_methods/megaprs.R \
       --ref_plink_chr {refdir}/ref.chr \
       --ref_keep {refdir}/keep_files/{params.population}.keep \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --ldak {resdir}/software/ldak/ldak5.1.linux \
       --ldak_map {resdir}/data/ldak_map/genetic_map_b37 \
@@ -408,7 +408,7 @@ rule prep_pgs_quickprs_i:
     rules.download_ldak5_2.output,
     rules.download_ldak_map.output,
     rules.download_ldak_bld.output,
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz"
   benchmark:
@@ -423,7 +423,7 @@ rule prep_pgs_quickprs_i:
   shell:
     "Rscript ../Scripts/pgs_methods/quickprs.R \
       --ref_plink_chr {refdir}/ref.chr \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --ldak {resdir}/software/ldak5.2/ldak5.2.linux \
       --quickprs_ldref {quickprs_ldref}/{params.population} \
@@ -451,7 +451,7 @@ rule prep_pgs_sbayesrc_i:
     rules.download_sbayesrc_annot.output,
     rules.install_genoutils_sbayesrc.output,
     rules.install_sbayesrc.output,
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/sbayesrc/{{gwas}}/ref-{{gwas}}.score.gz"
   benchmark:
@@ -468,7 +468,7 @@ rule prep_pgs_sbayesrc_i:
     "export OMP_NUM_THREADS={threads}; \
     Rscript ../Scripts/pgs_methods/sbayesrc.R \
       --ref_plink_chr {refdir}/ref.chr \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --gctb {resdir}/software/gctb_2.5.2/gctb_2.5.2_Linux/gctb \
       --sbayesrc_ldref {params.sbayesrc_ldref} \
@@ -520,7 +520,7 @@ rule prep_pgs_external_i:
     lambda w: score_path(w),
     ref_input,
     rules.install_genoutils.output,
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     touch(f"{outdir}/reference/target_checks/prep_pgs_external_i-{{score}}.done")
   params:
@@ -539,7 +539,7 @@ rule prep_pgs_external_i:
     "Rscript ../Scripts/external_score_processor/external_score_processor.R \
       --ref_plink_chr {refdir}/ref.chr \
       --score {params.score} \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --output {outdir}/reference/pgs_score_files/external/{wildcards.score}/ref-{wildcards.score} \
       --pop_data {refdir}/ref.pop.txt \
       --test {params.testing} > {log} 2>&1"
@@ -630,7 +630,7 @@ rule prep_pgs_multi_i:
   input:
     f"{outdir}/reference/pgs_score_files/leopard/{{gwas_group}}/ref-{{gwas_group}}.weights.rds",
     lambda w: expand(f"{outdir}/reference/pgs_score_files/{{method}}/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group), method = w.method),
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/{{method}}_multi/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   benchmark:
@@ -674,7 +674,7 @@ rule prep_pgs_prscsx_i:
     lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
     lambda w: expand(f"{resdir}/data/prscs_ref/{prscs_ldref}/ldblk_{prscs_ldref}_{{population}}/ldblk_{prscs_ldref}_chr1.hdf5", population=[pop.lower() for pop in get_populations(w.gwas_group)]),
     f"{resdir}/data/prscs_ref/{prscs_ldref}/snpinfo_mult_{prscs_ldref}_hm3",
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/prscsx/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   conda:
@@ -696,7 +696,7 @@ rule prep_pgs_prscsx_i:
     export OPENBLAS_NUM_THREADS=1; \
     Rscript ../Scripts/pgs_methods/prscsx.R \
       --ref_plink_chr {refdir}/ref.chr \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {params.sumstats} \
       --populations {params.populations} \
       --prscsx_ref_path {resdir}/data/prscs_ref/{prscs_ldref} \
@@ -730,7 +730,7 @@ rule prep_pgs_xwing_i:
     lambda w: expand(f"{resdir}/data/PANTHER_LEOPARD_1kg_ref/ldblk_1kg_{{population}}/ldblk_1kg_chr13.hdf5", population=[pop.lower() for pop in get_populations(w.gwas_group)]),
     lambda w: expand(f"{resdir}/data/LEOPARD_1kg_ref/{{population}}/{{population}}_part1.bed", population=get_populations(w.gwas_group)),
     lambda w: expand(f"{resdir}/data/LOGODetect_1kg_ref/{{population}}/1000G_{{population}}_QC.bim", population=get_populations(w.gwas_group)),
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/xwing/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   conda:
@@ -752,7 +752,7 @@ rule prep_pgs_xwing_i:
     Rscript ../Scripts/pgs_methods/xwing.R \
       --ref_plink_chr {refdir}/ref.chr \
       --ref_freq_chr {refdir}/freq_files \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {params.sumstats} \
       --populations {params.populations} \
       --logodetect_ref {resdir}/data/LOGODetect_1kg_ref \
@@ -782,7 +782,7 @@ rule prep_pgs_tlprs_i:
     rules.install_tlprs.output,
     lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
     lambda w: expand(f"{outdir}/reference/pgs_score_files/{{method}}/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group), method=w.method),
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/tlprs_{{method}}/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   conda:
@@ -802,7 +802,7 @@ rule prep_pgs_tlprs_i:
     Rscript ../Scripts/pgs_methods/tlprs.R \
       --config {params.config_file} \
       --ref_plink_chr {refdir}/ref.chr \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {params.sumstats} \
       --scores {params.scores} \
       --populations {params.populations} \
@@ -828,7 +828,7 @@ rule prep_pgs_bridgeprs_i:
   input:
     rules.download_bridgeprs_software.output,
     lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas_group}}/{{gwas_group}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
-    f"{resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/bridgeprs/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   conda:
@@ -845,7 +845,7 @@ rule prep_pgs_bridgeprs_i:
     """
     Rscript ../Scripts/pgs_methods/bridgeprs.R \
       --ref_plink_chr {refdir}/ref.chr \
-      --ref_pcs {resdir}/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {params.sumstats} \
       --populations {params.populations} \
       --pop_data {refdir}/ref.pop.txt \
@@ -901,4 +901,33 @@ rule prep_pgs:
   input:
     pgs_methods_input
     
-  
\ No newline at end of file
+##########################
+
+# Calculate PGS in reference data
+rule ref_pgs:
+  resources:
+    mem_mb=config['mem_target_pgs'],
+    time_min=2800
+  threads: config['cores_target_pgs']
+  input:
+    lambda w: f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale" if 'continuous' in config["pgs_scaling"] else [],
+    rules.prep_pgs.input
+  output:
+    touch(f"{outdir}/reference/pgs_score_files/ref_pgs.done")
+  benchmark:
+    f"{outdir}/reference/benchmarks/ref_pgs.txt"
+  log:
+    f"{outdir}/reference/logs/ref_pgs.log"
+  conda:
+    "../envs/analysis.yaml"
+  params:
+    continuous="T" if 'continuous' in config["pgs_scaling"] else "F",
+    testing=config["testing"],
+    config_file = config["config_file"]
+  shell:
+    "Rscript ../Scripts/ref_scoring/ref_scoring.R \
+      --config {params.config_file} \
+      --continuous {params.continuous} \
+      --plink2 plink2 \
+      --test {params.testing} \
+      --n_cores {threads} > {log} 2>&1"
diff --git a/pipeline/rules/target_scoring.smk b/pipeline/rules/target_scoring.smk
index 6d361a04..a27281ff 100644
--- a/pipeline/rules/target_scoring.smk
+++ b/pipeline/rules/target_scoring.smk
@@ -38,7 +38,7 @@ pgs_methods_noneur = ['ptclump','lassosum','megaprs','prscs','dbslmm']
 rule pc_projection_i:
   input:
     f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",
-    f"{resdir}/data/ref/pc_score_files/{{population}}/ref-{{population}}-pcs.EUR.scale"
+    f"{outdir}/reference/pc_score_files/{{population}}/ref-{{population}}-pcs.EUR.scale"
   output:
     touch(f"{outdir}/reference/target_checks/{{name}}/pc_projection-{{population}}.done")
   benchmark:
@@ -55,8 +55,8 @@ rule pc_projection_i:
       --target_plink_chr {outdir}/{wildcards.name}/geno/{wildcards.name}.ref.chr \
       --target_keep {params.target_keep} \
       --ref_freq_chr {refdir}/freq_files/{wildcards.population}/ref.{wildcards.population}.chr \
-      --ref_score {resdir}/data/ref/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs.eigenvec.var.gz \
-      --ref_scale {resdir}/data/ref/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs.{wildcards.population}.scale \
+      --ref_score {outdir}/reference/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs.eigenvec.var.gz \
+      --ref_scale {outdir}/reference/pc_score_files/{wildcards.population}/ref-{wildcards.population}-pcs.{wildcards.population}.scale \
       --plink2 plink2 \
       --test {params.testing} \
       --output {outdir}/{wildcards.name}/pcs/projected/{wildcards.population}/{wildcards.name}-{wildcards.population} > {log} 2>&1"
@@ -78,12 +78,12 @@ rule pc_projection:
 rule target_pgs_i:
   resources:
     mem_mb=config['mem_target_pgs'],
-    time_min=1000
+    time_min=2800
   threads: config['cores_target_pgs']
   input:
     f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done",
     lambda w: f"{outdir}/reference/target_checks/{{name}}/pc_projection-TRANS.done" if w.population == "TRANS" else [],
-    rules.prep_pgs.input
+    rules.ref_pgs.output
   output:
     touch(f"{outdir}/reference/target_checks/{{name}}/target_pgs-{{population}}.done")
   benchmark:

From 4f3d7bfbd995bfe775dd89ea449681b4ac999fb0 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 18 Mar 2025 09:55:47 +0000
Subject: [PATCH 121/153] Updates

---
 Scripts/ref_scoring/ref_scoring.R |   4 +-
 docs/CrossPop.Rmd                 | 267 ++++++++++++++----------------
 docs/GenoClust.Rmd                | 207 +++++++++++++++++++++++
 functions/pgs.R                   |  64 +++----
 pipeline/misc/dev/test_setup.Rmd  |   1 +
 pipeline/misc/opensnp/config.yaml |   7 +-
 6 files changed, 371 insertions(+), 179 deletions(-)

diff --git a/Scripts/ref_scoring/ref_scoring.R b/Scripts/ref_scoring/ref_scoring.R
index 727cca34..2044ecb8 100644
--- a/Scripts/ref_scoring/ref_scoring.R
+++ b/Scripts/ref_scoring/ref_scoring.R
@@ -68,7 +68,7 @@ if(!is.null(score_files)){
   
   score_files_to_do <- data.table()
   for(i in 1:nrow(score_files)){
-    pgs_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '.EUR.profiles')
+    pgs_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '-EUR.profiles')
     score_i <- paste0(outdir, '/reference/pgs_score_files/', score_files$method[i],'/', score_files$name[i],'/ref-',score_files$name[i], '.score.gz')
     if(!file.exists(pgs_i)){
       score_files_to_do <- rbind(score_files_to_do, score_files[i,])
@@ -89,7 +89,7 @@ if(is.null(score_files) || nrow(score_files) == 0){
   log_add(log_file = log_file, message = paste0('No score files to be used for reference scoring.'))
   end.time <- Sys.time()
   time.taken <- end.time - start.time
-  sink(file = paste(opt$output,'.log',sep=''), append = T)
+  sink(file = log_file, append = T)
   cat('Analysis finished at',as.character(end.time),'\n')
   cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n')
   sink()
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 5a56d5d6..d0c6097c 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -2120,7 +2120,7 @@ for targ_pop in $(echo EUR EAS AFR); do
   for disc_pop_i in ${disc_pop}; do
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
       if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt" ]; then
-        sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+        sbatch --mem 10G -n 5 --exclude=erc-hpc-comp058 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
           --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
           --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
           --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
@@ -2212,17 +2212,17 @@ for(pheno_i in selected_traits){
   res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
   
   res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
-                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTrain'
+                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTune'
   res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
-                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTrain'
+                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTune'
   res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
-                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTrain'
+                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTune'
   res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
-                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTrain'
+                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTune'
   
-  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTrain'
-  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTrain'
-  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTrain'
+  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTune'
   
   res_eval_i$Source<-ifelse(
     res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
@@ -2234,18 +2234,18 @@ for(pheno_i in selected_traits){
   res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
   
   res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
-  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
   res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
   res_eval_i <- res_eval_i[
     !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
-      res_eval_i$Model %in% c('IndivTrain','Multi-IndivTrain')),]
+      res_eval_i$Model %in% c('IndivTune','Multi-IndivTune')),]
   
   # Remove pseudo model for methods that don't really have one 
   res_eval_i <- res_eval_i[
     !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
-      res_eval_i$Model %in% c('SumStatTrain','Multi-SumStatTrain')),]
+      res_eval_i$Model %in% c('SumStatTune','Multi-SumStatTune')),]
 
   # Remove top1 models for *-Multi, PRS-CSx, X-wing
   res_eval_i <- res_eval_i[
@@ -2275,9 +2275,9 @@ for(pheno_i in selected_traits){
   tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
   tmp$label <- factor(tmp$label, levels = model_order)
   
-  # Simplify result to either SumStatTrain or IndivTrain
-  tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
-  tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+  # Simplify result to either SumStatTune or IndivTune
+  tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+  tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
   tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery','Model'), with=F]),]
   
   res_eval_simp <- rbind(res_eval_simp, tmp)
@@ -2401,29 +2401,6 @@ for(pheno_i in selected_traits){
   dev.off()
 }
 
-# Compare performance of each method in each target population across values of GWAS sample size
-# Because we cannot hold all other things constant, this plot is confusing. I think relative performance might be easier to read.
-for(targ_pop_i in targ_pop){
-    tmp <- res_eval_simp[res_eval_simp$Target == targ_pop_i,]
-    for(disc_pop_i in unique(tmp$Discovery)){
-      tmp2 <- tmp[tmp$Discovery == disc_pop_i,]
-      
-      ggplot(tmp2, aes(x=label, y=R , fill = Model)) +
-        geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                      width = 0,
-                      position = position_dodge(width = 1)) +
-        geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-        geom_vline(xintercept = seq(1.5, length(unique(tmp2$Trait))), linetype="dotted") +
-        labs(y = "R (SE)", x=NULL, fill = NULL, title = info_all$`Trait Description`[info_all$`Trait Label` == pheno_i]) +
-        facet_grid(. ~ Trait, scales='free', space = 'free_x') +
-        theme_half_open() +
-        background_grid(major = 'y', minor = 'y') + 
-        panel_border() + 
-        theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-      
-    }
-}
-
 ####
 # Average results across phenotypes
 ####
@@ -2500,7 +2477,7 @@ for(targ_pop_i in targ_pop){
   }
 }
 
-meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
 meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
 write.csv(meta_res_eval, '~/oliverpainfel/Analyses/crosspop/r_eval.csv', row.names = F)
@@ -2521,8 +2498,8 @@ tmp$Discovery_clean <- factor(tmp$Discovery_clean,
                                          'EUR GWAS',
                                          'Both'))
 tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
-tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
 tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
 png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
@@ -2553,8 +2530,8 @@ tmp$label <- factor(tmp$label, levels = model_order)
 tmp$Discovery_clean <- as.character(tmp$Discovery)
 tmp$Discovery_clean <- paste0(tmp$Discovery_clean, ' GWAS')
 tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
-tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
 tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
 png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.png'), res=300, width = 4000, height = 1500, units = 'px')
@@ -2582,10 +2559,10 @@ tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
 tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)], '-multi')
 tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
 tmp<-tmp[grepl('multi', tmp$label),]
-tmp <- tmp[tmp$Model != 'Multi-IndivTrain',]
+tmp <- tmp[tmp$Model != 'Multi-IndivTune',]
 tmp$Model<-as.character(tmp$Model)
-tmp$Model[tmp$Model != 'SumStatTrain']<-'IndivTrain'
-tmp$Model[tmp$Model == 'SumStatTrain']<-'LEOPARD'
+tmp$Model[tmp$Model != 'SumStatTune']<-'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune']<-'LEOPARD'
 tmp$Target <- paste0(tmp$Target, ' Target')
 
 png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_leopard.png'), res=300, width = 1500, height = 2000, units = 'px')
@@ -2684,13 +2661,13 @@ res_comp_all$Method2<-gsub('-.*','', res_comp_all$Method2)
 
 find_model<-function(x){
   mod <- x
-  mod[grepl('top1$', x) & !grepl('pseudo', x)] <- 'IndivTrain'
-  mod[grepl('top1$', x) & grepl('pseudo', x)] <- 'SumStatTrain'
-  mod[grepl('multi$', x) & !grepl('pseudo', x)] <- 'Multi-IndivTrain'
-  mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTrain'
-  mod[grepl('_multi', x)] <- 'SumStatTrain'
-  mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTrain'
-  mod[x == 'xwing.pseudo.multi'] <- 'SumStatTrain'
+  mod[grepl('top1$', x) & !grepl('pseudo', x)] <- 'IndivTune'
+  mod[grepl('top1$', x) & grepl('pseudo', x)] <- 'SumStatTune'
+  mod[grepl('multi$', x) & !grepl('pseudo', x)] <- 'Multi-IndivTune'
+  mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTune'
+  mod[grepl('_multi', x)] <- 'SumStatTune'
+  mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTune'
+  mod[x == 'xwing.pseudo.multi'] <- 'SumStatTune'
   
   return(mod)
 }
@@ -2710,26 +2687,26 @@ res_comp_all$Discovery2[res_comp_all$Source2 == 'Multi'] <- res_comp_all$gwas_gr
 
 res_comp_all$Method1<-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
 res_comp_all$Method2<-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
-res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
-res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
 res_comp_all$Discovery1<-factor(res_comp_all$Discovery1, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
 res_comp_all$Discovery2<-factor(res_comp_all$Discovery2, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-# Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+# Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
 res_comp_all <- res_comp_all[
 !(res_comp_all$Method1 %in%  c('quickprs','sbayesrc') & 
-  res_comp_all$Model1 %in% c('IndivTrain','Multi-IndivTrain')),]
+  res_comp_all$Model1 %in% c('IndivTune','Multi-IndivTune')),]
 res_comp_all <- res_comp_all[
 !(res_comp_all$Method2 %in%  c('quickprs','sbayesrc') & 
-  res_comp_all$Model2 %in% c('IndivTrain','Multi-IndivTrain')),]
+  res_comp_all$Model2 %in% c('IndivTune','Multi-IndivTune')),]
 
 # Remove pseudo model for methods that don't really have one 
 res_comp_all <- res_comp_all[
 !(res_comp_all$Method1 %in%  c('ptclump','ptclump_multi') & 
-  res_comp_all$Model1 %in% c('SumStatTrain','Multi-SumStatTrain')),]
+  res_comp_all$Model1 %in% c('SumStatTune','Multi-SumStatTune')),]
 res_comp_all <- res_comp_all[
 !(res_comp_all$Method2 %in%  c('ptclump','ptclump_multi') & 
-  res_comp_all$Model2 %in% c('SumStatTrain','Multi-SumStatTrain')),]
+  res_comp_all$Model2 %in% c('SumStatTune','Multi-SumStatTune')),]
 
 # Remove top1 models for PRS-CSx
 res_comp_all <- res_comp_all[
@@ -2783,7 +2760,7 @@ round(max(tmp_sbayesrc$r_diff_rel)*100, 1)
 #####
 # Export a csv containing difference results for all traits
 #####
-# Simplify to contain only IndivTrain or SumStatTrain result
+# Simplify to contain only IndivTune or SumStatTune result
 tmp <- res_comp_all
 tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
 tmp$label[is.na(tmp$label)] <- 'All'
@@ -2795,10 +2772,10 @@ names(tmp)[names(tmp) == 'label'] <- 'label2'
 tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'], '-multi')
 tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'], '-multi')
 
-tmp$Model1[tmp$Model1 != 'SumStatTrain'] <- 'IndivTrain'
-tmp$Model1[tmp$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
-tmp$Model2[tmp$Model2 != 'SumStatTrain'] <- 'IndivTrain'
-tmp$Model2[tmp$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+tmp$Model1[tmp$Model1 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model1[tmp$Model1 == 'SumStatTune'] <- 'SumStatTune'
+tmp$Model2[tmp$Model2 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model2[tmp$Model2 == 'SumStatTune'] <- 'SumStatTune'
 
 tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
 tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
@@ -2986,7 +2963,7 @@ tmp_all$R_diff_p
 #####
 # Export a csv containing difference results for all traits
 #####
-# Simplify to contain only IndivTrain or SumStatTrain result
+# Simplify to contain only IndivTune or SumStatTune result
 tmp <- meta_res_comp
 tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
 tmp$label[is.na(tmp$label)] <- 'All'
@@ -2998,10 +2975,10 @@ names(tmp)[names(tmp) == 'label'] <- 'label2'
 tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'], '-multi')
 tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'], '-multi')
 
-tmp$Model1[tmp$Model1 != 'SumStatTrain'] <- 'IndivTrain'
-tmp$Model1[tmp$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
-tmp$Model2[tmp$Model2 != 'SumStatTrain'] <- 'IndivTrain'
-tmp$Model2[tmp$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+tmp$Model1[tmp$Model1 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model1[tmp$Model1 == 'SumStatTune'] <- 'SumStatTune'
+tmp$Model2[tmp$Model2 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model2[tmp$Model2 == 'SumStatTune'] <- 'SumStatTune'
 
 tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
 tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
@@ -3064,10 +3041,10 @@ for(targ_pop_i in targ_pop){
     tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'], '-multi')
     tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'], '-multi')
     
-    tmp$Model1[tmp$Model1 != 'SumStatTrain'] <- 'IndivTrain'
-    tmp$Model1[tmp$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
-    tmp$Model2[tmp$Model2 != 'SumStatTrain'] <- 'IndivTrain'
-    tmp$Model2[tmp$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+    tmp$Model1[tmp$Model1 != 'SumStatTune'] <- 'IndivTune'
+    tmp$Model1[tmp$Model1 == 'SumStatTune'] <- 'SumStatTune'
+    tmp$Model2[tmp$Model2 != 'SumStatTune'] <- 'IndivTune'
+    tmp$Model2[tmp$Model2 == 'SumStatTune'] <- 'SumStatTune'
     
     tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
     tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
@@ -3077,8 +3054,8 @@ for(targ_pop_i in targ_pop){
 
     tmp<-tmp[order(tmp$label1, tmp$label2),]
     
-    tmp$label1 <- paste0(tmp$label1," (", ifelse(tmp$Model1 == 'SumStatTrain', 'ST', 'IT'), ")")
-    tmp$label2 <- paste0(tmp$label2," (", ifelse(tmp$Model2 == 'SumStatTrain', 'ST', 'IT'), ")")
+    tmp$label1 <- paste0(tmp$label1," (", ifelse(tmp$Model1 == 'SumStatTune', 'ST', 'IT'), ")")
+    tmp$label2 <- paste0(tmp$label2," (", ifelse(tmp$Model2 == 'SumStatTune', 'ST', 'IT'), ")")
 
     tmp$label1 <- factor(tmp$label1, levels = unique(tmp$label1))
     tmp$label2 <- factor(tmp$label2, levels = unique(tmp$label2))
@@ -3121,7 +3098,7 @@ for(targ_pop_i in targ_pop){
 ####
 # Plot relative improvement of methods
 ####
-# Use ptclump IndivTrain using EUR GWAS as the reference, as provides an interpretable scale
+# Use ptclump IndivTune using EUR GWAS as the reference, as provides an interpretable scale
 
 meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Method2 == 'all' & meta_res_comp$Source2 == 'Multi',]
 meta_res_comp_ptclump_top1$reference_point<-F
@@ -3152,7 +3129,7 @@ meta_tmp$Discovery_clean <- factor(meta_tmp$Discovery_clean,
                                          'EUR GWAS',
                                          'Both'))
 meta_tmp$Target <- paste0(meta_tmp$Target, ' Target')
-meta_tmp$Model1 <- factor(meta_tmp$Model1, levels = names(model_palette))
+meta_tmp$Model1 <- factor(meta_tmp$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
 
 meta_tmp_ref <- meta_res_comp_reference
 meta_tmp_ref <- merge(meta_tmp_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
@@ -3168,7 +3145,7 @@ meta_tmp_ref$Discovery_clean <- factor(meta_tmp_ref$Discovery_clean,
                                          'EUR GWAS',
                                          'Both'))
 meta_tmp_ref$Target <- paste0(meta_tmp_ref$Target, ' Target')
-meta_tmp_ref$Model1 <- factor(meta_tmp_ref$Model1, levels = names(model_palette))
+meta_tmp_ref$Model1 <- factor(meta_tmp_ref$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
 
 tmp <- res_comp_all_ptclump_top1
 tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
@@ -3184,7 +3161,7 @@ tmp$Discovery_clean <- factor(tmp$Discovery_clean,
                                          'EUR GWAS',
                                          'Both'))
 tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model1 <- factor(tmp$Model1, levels = names(model_palette))
+tmp$Model1 <- factor(tmp$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
 
 ggplot(meta_tmp, aes(x=label, y=R_diff , fill = Model1)) +
     geom_point(
@@ -3278,26 +3255,26 @@ ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
 
 # Simplify results showing results only with or without training data
 meta_tmp_simple <- meta_tmp
-meta_tmp_simple$Model1[meta_tmp_simple$Model1 != 'SumStatTrain'] <- 'IndivTrain'
-meta_tmp_simple$Model1[meta_tmp_simple$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
-meta_tmp_simple$Model2[meta_tmp_simple$Model2 != 'SumStatTrain'] <- 'IndivTrain'
-meta_tmp_simple$Model2[meta_tmp_simple$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 == 'SumStatTune'] <- 'SumStatTune'
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 == 'SumStatTune'] <- 'SumStatTune'
 meta_tmp_simple<-meta_tmp_simple[meta_tmp_simple$Model_1 %in% res_eval_simp$Group,]
 meta_tmp_simple<-meta_tmp_simple[meta_tmp_simple$Model_2 %in% res_eval_simp$Group,]
 
 meta_tmp_ref_simple <- meta_tmp_ref
-meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 != 'SumStatTrain'] <- 'IndivTrain'
-meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
-meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 != 'SumStatTrain'] <- 'IndivTrain'
-meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == 'SumStatTune'] <- 'SumStatTune'
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == 'SumStatTune'] <- 'SumStatTune'
 meta_tmp_ref_simple<-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_1 %in% res_eval_simp$Group,]
 meta_tmp_ref_simple<-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_2 %in% res_eval_simp$Group,]
 
 tmp_simple <- tmp
-tmp_simple$Model1[tmp_simple$Model1 != 'SumStatTrain'] <- 'IndivTrain'
-tmp_simple$Model1[tmp_simple$Model1 == 'SumStatTrain'] <- 'SumStatTrain'
-tmp_simple$Model2[tmp_simple$Model2 != 'SumStatTrain'] <- 'IndivTrain'
-tmp_simple$Model2[tmp_simple$Model2 == 'SumStatTrain'] <- 'SumStatTrain'
+tmp_simple$Model1[tmp_simple$Model1 != 'SumStatTune'] <- 'IndivTune'
+tmp_simple$Model1[tmp_simple$Model1 == 'SumStatTune'] <- 'SumStatTune'
+tmp_simple$Model2[tmp_simple$Model2 != 'SumStatTune'] <- 'IndivTune'
+tmp_simple$Model2[tmp_simple$Model2 == 'SumStatTune'] <- 'SumStatTune'
 tmp_simple<-tmp_simple[tmp_simple$Model_1 %in% res_eval_simp$Group,]
 tmp_simple<-tmp_simple[tmp_simple$Model_2 %in% res_eval_simp$Group,]
 
@@ -3575,6 +3552,7 @@ tmp_wide <- reshape(tmp,
                      direction = "wide")
 
 names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
 
 cor_matrix_EAS <- cor(tmp_wide[tmp_wide$Target == 'EAS', -1:-2])
 cor_matrix_AFR <- cor(tmp_wide[tmp_wide$Target == 'AFR', -1:-2])
@@ -3611,6 +3589,7 @@ tmp_wide <- reshape(tmp,
                      direction = "wide")
 
 names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
 
 cor_matrix_EAS <- cor(tmp_wide[tmp_wide$Discovery == 'EAS', -1:-2])
 cor_matrix_AFR <- cor(tmp_wide[tmp_wide$Discovery == 'AFR', -1:-2])
@@ -4415,17 +4394,17 @@ for(pheno_i in selected_traits){
   res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
   
   res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
-                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTrain'
+                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTune'
   res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
-                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTrain'
+                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTune'
   res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
-                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTrain'
+                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTune'
   res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
-                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTrain'
+                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTune'
   
-  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTrain'
-  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTrain'
-  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTrain'
+  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTune'
   
   res_eval_i$Source<-ifelse(
     res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
@@ -4437,18 +4416,18 @@ for(pheno_i in selected_traits){
   res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
   
   res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
-  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
   res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
   res_eval_i <- res_eval_i[
     !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
-      res_eval_i$Model %in% c('IndivTrain','Multi-IndivTrain')),]
+      res_eval_i$Model %in% c('IndivTune','Multi-IndivTune')),]
   
   # Remove pseudo model for methods that don't really have one 
   res_eval_i <- res_eval_i[
     !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
-      res_eval_i$Model %in% c('SumStatTrain','Multi-SumStatTrain')),]
+      res_eval_i$Model %in% c('SumStatTune','Multi-SumStatTune')),]
 
   # Remove top1 models for *-Multi, PRS-CSx, X-wing
   res_eval_i <- res_eval_i[
@@ -4478,9 +4457,9 @@ for(pheno_i in selected_traits){
   tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
   tmp$label <- factor(tmp$label, levels = model_order)
   
-  # Simplify result to either SumStatTrain or IndivTrain
-  tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
-  tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+  # Simplify result to either SumStatTune or IndivTune
+  tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+  tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
   tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery','Model'), with=F]),]
   
   res_eval_simp <- rbind(res_eval_simp, tmp)
@@ -4562,7 +4541,7 @@ for(targ_pop_i in targ_pop){
   }
 }
 
-meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
 meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
 # Plot average performance across phenotypes for AFR and EAS targets
@@ -4581,8 +4560,8 @@ tmp$Discovery_clean <- factor(tmp$Discovery_clean,
                                          'EUR GWAS',
                                          'Both'))
 tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
-tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
 tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
 png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
@@ -4627,8 +4606,8 @@ tmp$Discovery_clean <- factor(tmp$Discovery_clean,
                                          'EUR GWAS',
                                          'Both'))
 tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
-tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
 main_results <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
 tmp <- sens_results
@@ -4646,8 +4625,8 @@ tmp$Discovery_clean <- factor(tmp$Discovery_clean,
                                          'EUR GWAS',
                                          'Both'))
 tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
-tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
 sens_results <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
 main_results<-main_results[main_results$Method %in% sens_results$Method,]
@@ -5012,17 +4991,17 @@ for(pheno_i in selected_traits){
   res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
   
   res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
-                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTrain'
+                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTune'
   res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
-                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTrain'
+                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTune'
   res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
-                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTrain'
+                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTune'
   res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
-                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTrain'
+                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTune'
   
-  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTrain'
-  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTrain'
-  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTrain'
+  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTune'
   
   res_eval_i$Source<-ifelse(
     res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
@@ -5034,18 +5013,18 @@ for(pheno_i in selected_traits){
   res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
   
   res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
-  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
   res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
   res_eval_i <- res_eval_i[
     !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
-      res_eval_i$Model %in% c('IndivTrain','Multi-IndivTrain')),]
+      res_eval_i$Model %in% c('IndivTune','Multi-IndivTune')),]
   
   # Remove pseudo model for methods that don't really have one 
   res_eval_i <- res_eval_i[
     !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
-      res_eval_i$Model %in% c('SumStatTrain','Multi-SumStatTrain')),]
+      res_eval_i$Model %in% c('SumStatTune','Multi-SumStatTune')),]
 
   # Remove top1 models for *-Multi, PRS-CSx, X-wing, TL-*
   res_eval_i <- res_eval_i[
@@ -5140,7 +5119,7 @@ for(targ_pop_i in targ_pop){
   }
 }
 
-meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
 meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
 # Plot average performance across phenotypes for AFR and EAS targets
@@ -5159,8 +5138,8 @@ tmp$Discovery_clean <- factor(tmp$Discovery_clean,
                                          'EUR GWAS',
                                          'Both'))
 tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model[tmp$Model != 'SumStatTrain'] <- 'IndivTrain'
-tmp$Model[tmp$Model == 'SumStatTrain'] <- 'SumStatTrain'
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
 tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
 dir.create('~/oliverpainfel/Analyses/crosspop/tlprs/plots')
@@ -5330,13 +5309,13 @@ res_comp_all$Method2<-gsub('-.*','', res_comp_all$Method2)
 
 find_model<-function(x){
   mod <- x
-  mod[grepl('top1$', x) & !grepl('pseudo', x)] <- 'IndivTrain'
-  mod[grepl('top1$', x) & grepl('pseudo', x)] <- 'SumStatTrain'
-  mod[grepl('multi$', x) & !grepl('pseudo', x)] <- 'Multi-IndivTrain'
-  mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTrain'
-  mod[grepl('_multi', x)] <- 'SumStatTrain'
-  mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTrain'
-  mod[x == 'xwing.pseudo.multi'] <- 'SumStatTrain'
+  mod[grepl('top1$', x) & !grepl('pseudo', x)] <- 'IndivTune'
+  mod[grepl('top1$', x) & grepl('pseudo', x)] <- 'SumStatTune'
+  mod[grepl('multi$', x) & !grepl('pseudo', x)] <- 'Multi-IndivTune'
+  mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTune'
+  mod[grepl('_multi', x)] <- 'SumStatTune'
+  mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTune'
+  mod[x == 'xwing.pseudo.multi'] <- 'SumStatTune'
   
   return(mod)
 }
@@ -5356,26 +5335,26 @@ res_comp_all$Discovery2[res_comp_all$Source2 == 'Multi'] <- res_comp_all$gwas_gr
 
 res_comp_all$Method1<-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
 res_comp_all$Method2<-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
-res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
-res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('IndivTrain','SumStatTrain','Multi-IndivTrain','Multi-SumStatTrain'))
+res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
 res_comp_all$Discovery1<-factor(res_comp_all$Discovery1, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
 res_comp_all$Discovery2<-factor(res_comp_all$Discovery2, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-# Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+# Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
 res_comp_all <- res_comp_all[
 !(res_comp_all$Method1 %in%  c('quickprs','sbayesrc') & 
-  res_comp_all$Model1 %in% c('IndivTrain','Multi-IndivTrain')),]
+  res_comp_all$Model1 %in% c('IndivTune','Multi-IndivTune')),]
 res_comp_all <- res_comp_all[
 !(res_comp_all$Method2 %in%  c('quickprs','sbayesrc') & 
-  res_comp_all$Model2 %in% c('IndivTrain','Multi-IndivTrain')),]
+  res_comp_all$Model2 %in% c('IndivTune','Multi-IndivTune')),]
 
 # Remove pseudo model for methods that don't really have one 
 res_comp_all <- res_comp_all[
 !(res_comp_all$Method1 %in%  c('ptclump','ptclump_multi') & 
-  res_comp_all$Model1 %in% c('SumStatTrain','Multi-SumStatTrain')),]
+  res_comp_all$Model1 %in% c('SumStatTune','Multi-SumStatTune')),]
 res_comp_all <- res_comp_all[
 !(res_comp_all$Method2 %in%  c('ptclump','ptclump_multi') & 
-  res_comp_all$Model2 %in% c('SumStatTrain','Multi-SumStatTrain')),]
+  res_comp_all$Model2 %in% c('SumStatTune','Multi-SumStatTune')),]
 
 # Remove top1 models for PRS-CSx
 res_comp_all <- res_comp_all[
@@ -5466,7 +5445,7 @@ for(targ_pop_i in targ_pop){
 
 meta_res_comp$R_diff_perc <- meta_res_comp$R_diff / meta_res_comp$Model_2_R
   
-# Compare IndivTrain SBayesRC-multi to TL-SBayesRC-multi
+# Compare IndivTune SBayesRC-multi to TL-SBayesRC-multi
 tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_2 == 'sbayesrc.pseudo.multi' & 
                                 meta_res_comp$Model_1 == 'tlprs_sbayesrc.pop.multi' &
                     meta_res_comp$Target == 'AFR',]
@@ -5589,7 +5568,7 @@ write.csv(method_avg, '~/oliverpainfel/Analyses/crosspop/time_memory.csv', row.n
 
 # With 2+ GWAS
 
-Extend analysis to include gwas_groups including AFR EAS and EUR GWAS. Only some multi-source methods should be applicable here, including LEOPARD, PRS-CSx, and X-Wing. Given X-Wing with LEOPARD is slow, limit X-Wing analysis to the IndivTrain model alone.
+Extend analysis to include gwas_groups including AFR EAS and EUR GWAS. Only some multi-source methods should be applicable here, including LEOPARD, PRS-CSx, and X-Wing. Given X-Wing with LEOPARD is slow, limit X-Wing analysis to the IndivTune model alone.
 
 ***
 
@@ -5657,7 +5636,7 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml \
-  prep_pgs  -n
+  target_pgs  -n
 ```
 
 </details>
diff --git a/docs/GenoClust.Rmd b/docs/GenoClust.Rmd
index 01e9b50c..d3d72eda 100644
--- a/docs/GenoClust.Rmd
+++ b/docs/GenoClust.Rmd
@@ -48,6 +48,182 @@ Simulate two binary phenotypes, with a genetic correlation of 0. Specify causal
 - Disease 1: GO:0006281 DNA repair
 - Disease 2: GO:0048167 — Regulation of synaptic plasticity
 
+***
+
+### Simulate genetic data
+
+Simulate chr22 genotype data for 100k EUR individuals, using HAPNEST.
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd ~/oliverpainfel/Data/HAPNEST
+
+# Generate genotype and phenotype data
+singularity exec \
+  --bind data/:/data/ \
+  --bind /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/misc/hapnest/config.synth_2.yaml:/data/config.synth_2.yaml \
+  containers/intervene-synthetic-data_latest.sif \
+  generate_geno \
+  8 \
+  data/config.synth_2.yaml
+  
+# Keep getting an error due to memory allocation. In the end I ran it with 1 core
+  
+```
+
+</details>
+
+***
+
+### Simulate phenotype data
+
+We want to simulate pairs of phenotypes that have a range of heritability, polygenicity, and genetic correlation. Specifically, we want to ensure these traits are enriched for gene-sets, with differing degrees of overlap between the pairs of traits. 
+
+```{r}
+#conda activate .snakemake/conda/329e0288cb99508f5e6c50a0996b234c_
+library(bigsnpr)
+
+# Attach the bigSNP object from a previously saved `.rds` file
+rds <- snp_readBed('~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/synth_2_chr-22.bed', backingfile = tempfile())
+bigSNP <- snp_attach(rds)
+G <- bigSNP$genotypes
+map <- bigSNP$map
+
+# Read gene ontology sets (.gmt)
+gmt <- readLines('/scratch/prj/oliverpainfel/recovered/Data/MSigDB/c5.go.bp.v7.5.1.entrez.gmt')
+gene_sets <- strsplit(gmt, "\t")
+gene_list <- setNames(lapply(gene_sets, `[`, -c(1,2)), sapply(gene_sets, `[`, 1))
+
+# Read in gene annotations
+gene_loc <- fread('/scratch/prj/oliverpainfel/recovered/Data/Gene_Locations/MAGMA_NCBI37.3/NCBI37.3.gene.loc')
+names(gene_loc)<-c('ID','CHR','START','STOP','STRAND','NAME')
+gene_loc <- gene_loc[gene_loc$CHR == 22,] #out simulation will only use chr22 data
+
+# Annotate SNPs with genes
+map$gene <- sapply(1:nrow(map), function(i) {
+  chr <- map$chromosome[i]
+  pos <- map$physical.pos[i]
+  gene_match <- gene_annot %>% 
+    filter(chr == chromosome & pos >= start & pos <= end) %>%
+    pull(gene_symbol)
+  if(length(gene_match) > 0) paste(gene_match, collapse = ",") else NA
+})
+
+# SNPs in enriched gene sets
+snp_gene_sets <- lapply(gene_sets, function(genes) {
+  snps_in_set <- map %>% filter(grepl(paste(genes, collapse="|"), gene))
+  snps_in_set$snp_id
+})
+
+# Set simulation parameters
+set.seed(1)
+
+h2_trait1 <- 0.3
+h2_trait2 <- 0.4
+rg <- 0.5  # genetic correlation
+polygenicity_fraction <- 0.01  # fraction of causal SNPs
+overlap_fraction <- 0.5  # fraction of overlapping causal SNPs
+
+# Select causal variants for each trait
+n_snps <- ncol(G)
+n_causal <- ceiling(polygenicity_fraction * n_snps)
+causal_snps_trait1 <- sample(unlist(snp_gene_sets), n_causal)
+
+# Overlap between traits
+n_overlap <- ceiling(overlap_fraction * n_causal)
+causal_snps_overlap <- sample(causal_snps_trait1, n_overlap)
+remaining_snps <- setdiff(unlist(snp_gene_sets), causal_snps_overlap)
+causal_snps_trait2 <- c(causal_snps_overlap, sample(remaining_snps, n_causal - n_overlap))
+
+# Generate effect sizes for each trait
+library(MASS)
+Sigma <- matrix(c(1, rg, rg, 1), ncol=2)
+betas_raw <- mvrnorm(n_causal, mu=c(0,0), Sigma=Sigma)
+
+# Adjust betas to meet desired heritability
+scale_betas <- function(G, snps, h2, betas_raw_col) {
+  G_sub <- G[, snps]
+  var_g <- var(big_prodVec(G_sub, betas_raw_col))
+  scale_factor <- sqrt(h2 / var_g)
+  betas_raw_col * scale_factor
+}
+
+beta_trait1 <- scale_betas(G, causal_snps_trait1, h2_trait1, betas_raw[,1])
+beta_trait2 <- scale_betas(G, causal_snps_trait2, h2_trait2, betas_raw[,2])
+
+# Simulate phenotyp data
+n_indiv <- nrow(G)
+
+simulate_pheno <- function(G, causal_snps, betas, h2) {
+  genetic_component <- big_prodVec(G[, causal_snps], betas)
+  env_sd <- sqrt(var(genetic_component)*(1-h2)/h2)
+  genetic_component + rnorm(n_indiv, mean=0, sd=env_sd)
+}
+
+pheno_trait1 <- simulate_pheno(G, causal_snps_trait1, beta_trait1, h2_trait1)
+pheno_trait2 <- simulate_pheno(G, causal_snps_trait2, beta_trait2, h2_trait2)
+
+phenotypes <- data.frame(trait1 = pheno_trait1, trait2 = pheno_trait2)
+
+# Read in causal variants
+library(data.table)
+effect_sizes<-fread('~/oliverpainfel/Data/HAPNEST/synth_2/causal.snplist')
+names(effect_sizes)<-c('rsid','trait_1','trait_2')
+
+rsids<-fread('~/oliverpainfel/Data/HAPNEST/data/inputs/processed/1KG+HGDP/rsid_map_list_chr22.txt')
+effect_sizes<-merge(effect_sizes, rsids, by='rsid')
+
+h2 <- 0.05  # Desired heritability
+set.seed(1)
+traits<-list()
+for(i in 1:2){
+  effect_sizes_i<-effect_sizes[effect_sizes[[paste0('trait_',i)]] == 1,]
+  causal_indices <- match(effect_sizes_i$id_hg38, snp_names)
+  G_causal <- big_prodVec(G, effect_sizes_i[[paste0('trait_',i)]], ind.col = causal_indices)
+  var_G <- var(G_causal)
+  var_E <- (var_G / h2) - var_G
+  E <- rnorm(length(G_causal), mean = 0, sd = sqrt(var_E))
+  Y <- G_causal + E
+  traits[[paste0('trait_', i)]]<-Y
+  print(var(G_causal) / (var(G_causal) + var(E)))
+}
+
+cor(do.call(cbind, traits))
+
+library(bigsnpr)
+
+# Prepare the phenotype
+Y1 <- traits[["trait_1"]]
+Y2 <- traits[["trait_2"]]
+
+# Perform GWAS for trait 1
+gwas_1 <- big_univLinReg(G, Y1)
+
+# Perform GWAS for trait 2
+gwas_2 <- big_univLinReg(G, Y2)
+
+# Combine results into a data frame
+gwas_results <- data.frame(
+  SNP = snp_names,
+  beta_trait1 = gwas_1$estim,
+  beta_trait2 = gwas_2$estim,
+  se_trait1 = gwas_1$std.err,
+  se_trait2 = gwas_2$std.err
+)
+
+gwas_results$p_trait1 <- 2*pnorm(q=abs(gwas_results$beta_trait1/gwas_results$se_trait1), lower.tail=FALSE)
+gwas_results$p_trait2 <- 2*pnorm(q=abs(gwas_results$beta_trait2/gwas_results$se_trait2), lower.tail=FALSE)
+
+# Looks good. Lets proceed with these simulated phenotypes
+write.table(data.frame(FID = bigSNP$fam$family.ID, IID = bigSNP$fam$sample.ID, outcome = traits[["trait_1"]]), 
+            '~/oliverpainfel/Data/HAPNEST/synth_2/trait_1.txt', row.names = F, quote=F)
+write.table(data.frame(FID = bigSNP$fam$family.ID, IID = bigSNP$fam$sample.ID, outcome = traits[["trait_2"]]), 
+            '~/oliverpainfel/Data/HAPNEST/synth_2/trait_2.txt', row.names = F, quote=F)
+
+```
+
+
 ***
 
 ### Define causal variants
@@ -1824,6 +2000,37 @@ res_wide$perc_diff<-res_wide$diff / res_wide$`cor.gw-pgs`
 # The improvement could be due to over fitting.
 # Confusing that the CAD PGS is so strong, but the number sig gene sets is higher for RA. I suppose this is due to the CAD GWAS being larger, but also more complex biologically.
 
+# As example of disease stratification. Predict CAD vs RA, using MIX GW PGS and paritinoed PGS.
+both$CAD_vs_RA_outcome <- NA
+both$CAD_vs_RA_outcome[both$CAD_outcome == 1 | both$RA_outcome == 1] <- 0
+both$CAD_vs_RA_outcome[both$CAD_outcome == 1] <- 1
+both$CAD_vs_RA_outcome[both$CAD_outcome == 1 & both$RA_outcome == 1] <- NA
+both_tmp<-both[complete.cases(both),]
+
+i<-'MIX'
+out1 <-
+  cv.glmnet(x = as.matrix(both_tmp[, grepl(paste0(i, '_SBayesRC'), names(both_tmp)), with = F]), y = both_tmp$CAD_vs_RA_outcome)
+
+out2 <-
+  cv.glmnet(x = as.matrix(cbind(0, both_tmp[, paste0(i, '_SBayesRC'), with = F])), y = both_tmp$CAD_vs_RA_outcome)
+
+# Get predicted values using the optimal lambda
+pred1 <- predict(out1, newx = as.matrix(both_tmp[, grepl(paste0(i, '_SBayesRC'), names(both_tmp)), with = F]), s = "lambda.min")
+pred2 <- predict(out2, newx = as.matrix(cbind(0, both_tmp[, paste0(i, '_SBayesRC'), with = F])), s = "lambda.min")
+
+# Compute correlation (Observed vs. Predicted)
+cor1 <- cor(pred1, both_tmp$CAD_vs_RA_outcome)
+cor2 <- cor(pred2, both_tmp$CAD_vs_RA_outcome)
+
+tmp <- data.frame(
+  outcome = 'CAD vs RA',
+  model = c('gw-pgs + p-pgs', 'gw-pgs'),
+  cor = c(cor1, cor2)
+)
+
+ggplot(tmp, aes(x=outcome, y = cor, fill = model)) +
+  geom_bar(stat="identity", position = position_dodge())
+
 ```
 
 ***
diff --git a/functions/pgs.R b/functions/pgs.R
index 4dda6669..6059a6a5 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -626,54 +626,60 @@ compact_lm <- function(cm) {
 }
 
 # Adjust PGS for ancestry using reference PC models with parallel processing
-score_adjust <- function(score, pcs, ref_model) {
-  # Store original column order
+score_adjust <- function(score, pcs, ref_model, chunk_size = 10) {
   original_order <- names(score)
   
-  # Ensure pcs is keyed on FID and IID for efficient joins
+  # Ensure 'pcs' keyed for fast lookup
   setkey(pcs, FID, IID)
   
-  # Get list of score columns (excluding FID and IID)
+  # List of score columns
   score_cols <- setdiff(names(score), c("FID", "IID"))
   
-  # Process columns in chunks to reduce memory load
-  chunk_size <- 200
-  for (i in seq(1, length(score_cols), by = chunk_size)) {
-    # Get a subset of columns to process in this iteration
-    chunk <- score_cols[i:min(i + chunk_size - 1, length(score_cols))]
+  # Split into chunks for memory efficiency
+  score_chunks <- split(score_cols, ceiling(seq_along(score_cols) / chunk_size))
+  
+  # Match PC rows by FID/IID directly (minimal memory usage)
+  matched_idx <- pcs[score[, .(FID, IID)], which = TRUE, nomatch = 0]
+  
+  # Process each chunk sequentially to manage RAM
+  for (chunk in score_chunks) {
     
-    # Parallel computation using mclapply()
+    # Run in parallel across columns within chunk
     chunk_results <- mclapply(chunk, function(col_name) {
-      print(col_name)
+      cat("Processing:", col_name, "\n")
       
-      # Retrieve models for the current score
+      # Fetch models for current score column
       mean_model <- ref_model[[col_name]]$mean_model
-      var_model <- ref_model[[col_name]]$var_model
-      
-      # Get corresponding rows from pcs
-      pc_data <- pcs[score, on = .(FID, IID)]
+      var_model  <- ref_model[[col_name]]$var_model
       
-      # Predict mean and variance
-      predicted_pgs <- predict(mean_model, newdata = pc_data)
-      predicted_pgs_var <- exp(predict(var_model, newdata = pc_data))
+      # Pre-allocate adjusted score vector
+      adjusted_score <- rep(NA_real_, nrow(score))
       
-      # Compute residuals
-      adjusted_score <- round((score[[col_name]] - predicted_pgs) / sqrt(predicted_pgs_var), 3)
+      # Predict mean and variance using matched PCs
+      if (length(matched_idx) > 0) {
+        adjusted_score[matched_idx] <- round(
+          (score[[col_name]][matched_idx] - predict(mean_model, newdata = pcs[matched_idx])) / 
+            sqrt(exp(predict(var_model, newdata = pcs[matched_idx]))),
+          3
+        )
+      }
       
-      # Return results as a list (column name + values)
-      list(col_name = col_name, values = adjusted_score)
-    }, mc.cores = getDoParWorkers())
+      adjusted_score
+    }, mc.cores = min(getDoParWorkers(), 10))
     
-    # **Write results back to `score` immediately, to avoid holding large objects in memory**
-    for (res in chunk_results) {
-      set(score, j = res$col_name, value = res$values)
+    # Write results directly back to 'score' object by reference
+    for (idx in seq_along(chunk)) {
+      set(score, j = chunk[idx], value = chunk_results[[idx]])
     }
+    
+    # Explicitly clean memory after each chunk
+    rm(chunk_results)
+    gc()
+    
   }
   
-  # Reorder columns to match original order
   setcolorder(score, original_order)
   
-  # Return only relevant columns (FID, IID, and adjusted scores)
   return(score)
 }
 
diff --git a/pipeline/misc/dev/test_setup.Rmd b/pipeline/misc/dev/test_setup.Rmd
index 591d0bda..63736f58 100644
--- a/pipeline/misc/dev/test_setup.Rmd
+++ b/pipeline/misc/dev/test_setup.Rmd
@@ -80,6 +80,7 @@ ss<-ss[ss$SNP %in% test_snps,]
 dir.create('misc/dev/test_data/gwas')
 fwrite(ss, 'misc/dev/test_data/gwas/BODY04.gz', quote=F, sep=' ', na='NA')
 ```
+
 ***
 
 # External score file
diff --git a/pipeline/misc/opensnp/config.yaml b/pipeline/misc/opensnp/config.yaml
index 7fb0e15e..27c8f361 100644
--- a/pipeline/misc/opensnp/config.yaml
+++ b/pipeline/misc/opensnp/config.yaml
@@ -1,8 +1,7 @@
-outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test7
+outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test6
 config_file: misc/opensnp/config.yaml
 gwas_list: misc/opensnp/gwas_list.txt
 score_list: misc/opensnp/score_list.txt
 target_list: misc/opensnp/target_list.txt
-pgs_methods: ['xwing','tlprs','ptclump']
-testing: chr22
-cores_prep_pgs: 10
+pgs_methods: ['ptclump','dbslmm']
+testing: NA

From 3e57a80377b6a4aaffd03dcf8e6cf24f044109d0 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 20 Mar 2025 11:47:02 +0000
Subject: [PATCH 122/153] Updates

---
 Scripts/model_builder/model_builder_top1.R |  36 +-
 docs/CrossPop.Rmd                          | 428 ++++++++++++++++++++-
 functions/model_builder.R                  |   9 +-
 pipeline/rules/pgs_methods.smk             |   8 +-
 4 files changed, 437 insertions(+), 44 deletions(-)

diff --git a/Scripts/model_builder/model_builder_top1.R b/Scripts/model_builder/model_builder_top1.R
index 43b45f4d..2042d8e2 100644
--- a/Scripts/model_builder/model_builder_top1.R
+++ b/Scripts/model_builder/model_builder_top1.R
@@ -67,6 +67,23 @@ if(opt$export_models){
 log_file <- paste0(opt$out,'.log')
 log_header(log_file = log_file, opt = opt, script = 'model_builder.R', start.time = start.time)
 
+###########
+# Read in the outcome data
+###########
+
+outcome<-read_outcome(x = opt$outcome, keep = opt$keep)
+
+# Determine whether outcome is binary or continuous and format accordingly
+if (length(unique(outcome$outcome_var)) > 2) {
+  family <- 'gaussian'
+}
+if (length(unique(outcome$outcome_var)) == 2) {
+  family <- 'binomial'
+  outcome$outcome_var <- factor(outcome$outcome_var, labels = c('CONTROL', 'CASE'))
+}
+
+log_add(log_file = log_file, message = paste0('Phenotype is ', ifelse(family == 'binomial', 'binary', 'quantitative'),'.'))
+
 ###########
 # Read in predictors
 ###########
@@ -75,7 +92,7 @@ predictors_file <- fread(opt$predictors)
 
 if(nrow(predictors_file) > 1){
   predictors <- foreach(i = 1:nrow(predictors_file)) %dopar% {
-    read_predictor(x = predictors_file$predictor[i], pred_miss = opt$pred_miss, file_index = i)
+    read_predictor(x = predictors_file$predictor[i], pred_miss = opt$pred_miss, file_index = i, keep = outcome$IID)
   }
 
   group_list <- do.call(rbind, lapply(1:nrow(predictors_file), function(predfile) {
@@ -145,23 +162,6 @@ for(i in unique(group_list$multi)){
 write.table(group_list[!duplicated(group_list$group), c('group','n_multi','n_top1'), with = F], paste0(opt$out,'.group_list.txt'), col.names=T, row.names=F, quote=F)
 log_add(log_file = log_file, message = paste0('List of groups saved as ',opt$out,'.group_list.txt.'))
 
-###########
-# Read in the outcome data
-###########
-
-outcome<-read_outcome(x = opt$outcome, keep = opt$keep)
-
-# Determine whether outcome is binary or continuous and format accordingly
-if (length(unique(outcome$outcome_var)) > 2) {
-  family <- 'gaussian'
-}
-if (length(unique(outcome$outcome_var)) == 2) {
-  family <- 'binomial'
-  outcome$outcome_var <- factor(outcome$outcome_var, labels = c('CONTROL', 'CASE'))
-}
-
-log_add(log_file = log_file, message = paste0('Phenotype is ', ifelse(family == 'binomial', 'binary', 'quantitative'),'.'))
-
 ###########
 # Merge the outcome and predictors
 ###########
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index d0c6097c..6284c830 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -5653,21 +5653,28 @@ source_all('../functions')
 library(data.table)
 
 # Get some key variables from config
-config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml'
-pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
-outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+config_2_pop<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
+config_3_pop<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml'
+pgs_methods <- read_param(config = config_3_pop, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config_3_pop, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
 selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
 
 # Get a list of score files
-scores <- list_score_files(config)
+scores_2_pop <- list_score_files(config_2_pop)
+scores_3_pop <- list_score_files(config_3_pop)
+scores <- rbind(scores_2_pop, scores_3_pop)
+scores <- scores[!duplicated(scores),]
+
+# Remove xwing 
+scores <- scores[scores$method != 'xwing', ]
 
 # Create files for EAS and AFR targets
-targ_pop <- c('EUR','EAS','AFR')
+targ_pop <- c('EAS','AFR')
 for(trait_i in selected_traits){
-  scores_i <- scores[grepl(trait_i, scores$name),]
-  scores_i$multi <- scores_i$method
+  scores_j <- scores[grepl(trait_i, scores$name),]
+  scores_j$multi <- scores_j$method
   
   for(targ_pop_i in targ_pop){
     disc_pop_j <- c('UGR','BBJ','UKB')
@@ -5685,6 +5692,8 @@ for(trait_i in selected_traits){
     )
     
     # Insert path to score file
+    scores_i <- scores_j[!grepl(paste0('UKB_', disc_pop_j[!(disc_pop_j_2 %in% c('EUR', targ_pop_i))], '$'), scores_j$name),]
+    
     scores_i$predictor <- paste0(
       outdir,
       '/ukb/pgs/TRANS/',
@@ -5695,7 +5704,7 @@ for(trait_i in selected_traits){
       scores_i$name,
       '-TRANS.profiles'
     )
-      
+    
     ####
     # Make groups single source methods
     ####
@@ -5717,7 +5726,7 @@ for(trait_i in selected_traits){
     # This can be skipped as it was done before
     for(i in 1:nrow(scores_i_single_top1)) {
       param <- find_pseudo(
-        config = config,
+        config = ifelse(scores_i_single_top1$name[i] %in% scores_2_pop$name, config_2_pop, config_3_pop),
         gwas = scores_i_single_top1$name[i],
         pgs_method = scores_i_single_top1$method[i],
         target_pop = targ_pop_i
@@ -5758,7 +5767,7 @@ for(trait_i in selected_traits){
     # Extract the pseudo score for each method and specify as a separate group
     for(i in 1:nrow(scores_i_multi_single_pseudo)) {
       param <- find_pseudo(
-        config = config,
+        config = ifelse(scores_i_multi_single_pseudo$name[i] %in% scores_2_pop$name, config_2_pop, config_3_pop),
         gwas = scores_i_multi_single_pseudo$name[i],
         pgs_method = scores_i_multi_single_pseudo$method[i],
         target_pop = targ_pop_i
@@ -5800,10 +5809,14 @@ for(trait_i in selected_traits){
     # Split top1 scores by target population
     # This doesn't apply to xwing because it only has pop-specific pseudo scores
     scores_i_multi_top1<-NULL
-    for(i in 1:which(scores_i_multi$method %in% c('prscsx'))){
+    for(i in which(scores_i_multi$method %in% c('prscsx'))){
       score_header<-fread(scores_i_multi$predictor[i], nrow = 1)
       
-      for(pop in c('EUR', 'AFR', 'EAS')){
+      pops <- gsub(paste0(trait_i, '_'),'', scores_i_multi$name[i])
+      pops <- unlist(strsplit(pops, '_'))
+      pops <- disc_pop_j_2[disc_pop_j %in% pops]
+
+      for(pop in pops){
         
         if(scores_i_multi$method[i] == 'prscsx'){
           score_cols <-
@@ -5844,7 +5857,11 @@ for(trait_i in selected_traits){
     for(i in 1:nrow(scores_i_multi)){
       score_header<-fread(scores_i_multi$predictor[i], nrow = 1)
       
-      for(pop in c('EUR', 'AFR', 'EAS')){
+      pops <- gsub(paste0(trait_i, '_'),'', scores_i_multi$name[i])
+      pops <- unlist(strsplit(pops, '_'))
+      pops <- disc_pop_j_2[disc_pop_j %in% pops]
+
+      for(pop in pops){
         if(scores_i_multi$method[i] == 'prscsx'){
           score_cols <-
             which(grepl(paste0('^FID$|^IID$|_', pop, '_phi_auto'), names(score_header)))
@@ -5883,7 +5900,7 @@ for(trait_i in selected_traits){
     scores_i_multi_pseudo<-NULL
     for(i in 1:nrow(scores_i_multi)) {
       param <- find_pseudo(
-        config = config,
+        config = ifelse(scores_i_multi$name[i] %in% scores_2_pop$name, config_2_pop, config_3_pop),
         gwas = scores_i_multi$name[i],
         pgs_method = scores_i_multi$method[i],
         target_pop = targ_pop_i
@@ -5930,8 +5947,6 @@ for(trait_i in selected_traits){
       scores_i_multi_pseudo
     ))
     
-    predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
-    
     ####
     # Make a group that will combined all population specific PGS
     ####
@@ -5940,8 +5955,29 @@ for(trait_i in selected_traits){
     predictors_i_all$multi <- 'all'
     predictors_i<-rbind(predictors_i, predictors_i_all)
     
+    ####
+    # Split into pairwise groups (2 pop groups)
+    ####
+    
+    afr_eur <- predictors_i[!grepl('BBJ', predictors_i$name),]
+    afr_eur$multi <- paste0(afr_eur$multi, '.EUR_AFR')
+    afr_eur$top1[afr_eur$top1 == 'AFR_EAS_EUR'] <- 'AFR_EUR'
+    
+    eas_eur <- predictors_i[!grepl('UGR', predictors_i$name),]
+    eas_eur$multi <- paste0(eas_eur$multi, '.EUR_EAS')
+    eas_eur$top1[eas_eur$top1 == 'AFR_EAS_EUR'] <- 'EAS_EUR'
+
+    one_or_three <- predictors_i[!grepl('UKB_BBJ$', predictors_i$name) &
+                                   !grepl('UKB_UGR$', predictors_i$name),]
+    
+    predictors_clean <- do.call(rbind, list(
+      afr_eur, eas_eur, one_or_three
+    ))
+    predictors_clean <- predictors_clean[, c('predictor', 'multi','top1'), with=F]
+
+        
     write.table(
-      predictors_i,
+      predictors_clean,
       paste0(
         '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
         targ_pop_i,
@@ -5972,7 +6008,7 @@ conda activate model_builder
 
 #rm /users/k1806347/oliverpainfel/Analyses/crosspop/targ_*.disc_AFR_EAS_EUR/*/res*
 
-for targ_pop in $(echo EUR EAS AFR); do
+for targ_pop in $(echo EAS AFR); do
   if [ "$targ_pop" == "EUR" ]; then
       targ_pop2="EUR_test"
   else
@@ -5981,14 +6017,366 @@ for targ_pop in $(echo EUR EAS AFR); do
   
   for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
     if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_AFR_EAS_EUR/${pheno}/res.pred_comp.txt" ]; then
-      sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+      sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
         --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
         --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_AFR_EAS_EUR/${pheno}/predictor_list.txt \
         --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_AFR_EAS_EUR/${pheno}/res \
-        --n_core 5"
+        --n_core 1"
     fi
   done
 done
 
 ```
 </details>
+
+***
+
+### Plot results
+
+<details><summary>Show code</summary>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+
+# Calculate correlation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop = c('EAS','AFR')
+res_eval <- list()
+for(pheno_i in selected_traits){
+  res_eval_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    eval_i <-
+      fread(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/',
+          'targ_',
+          targ_pop_i,
+          '.disc_AFR_EAS_EUR/',
+          pheno_i,
+          '/res.pred_eval.txt'
+        )
+      )
+    eval_i$Target<-targ_pop_i
+    eval_i$gwas_group <- 'EUR+AFR+EAS'
+    res_eval_i<-rbind(res_eval_i, eval_i)
+  }
+  
+  res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
+  res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
+  
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTune'
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTune'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTune'
+  
+  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[grepl('prscsx.pseudo', res_eval_i$Group)]<-'SumStatTune'
+
+  res_eval_i$Source<-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
+    !grepl('-EUR|-EAS|-AFR', res_eval_i$Group), 'Multi', 'Single')
+  
+  res_eval_i$Group <- gsub('\\.multi', '-multi', res_eval_i$Group)
+  res_eval_i$Group_short <- gsub('.*\\.', '', gsub('-.*', '', res_eval_i$Group)) 
+  res_eval_i$n_gwas <- 3
+  res_eval_i$n_gwas[grepl('EUR_', res_eval_i$Group_short)] <- 2
+  res_eval_i$n_gwas[res_eval_i$Source == 'Single'] <- 1
+
+  res_eval_i$Discovery[grepl('-EUR', res_eval_i$Group)] <- 'EUR'
+  res_eval_i$Discovery[grepl('-EAS', res_eval_i$Group)] <- 'EAS'
+  res_eval_i$Discovery[grepl('-AFR', res_eval_i$Group)] <- 'AFR'
+  res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
+  res_eval_i$Discovery[res_eval_i$n_gwas == 2] <- gsub('_', '+', res_eval_i$Group_short[res_eval_i$n_gwas == 2])
+  
+  res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+  res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS','EUR+AFR+EAS'))
+
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
+      res_eval_i$Model %in% c('IndivTune','Multi-IndivTune')),]
+  
+  # Remove pseudo model for methods that don't really have one 
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
+      res_eval_i$Model %in% c('SumStatTune','Multi-SumStatTune')),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
+  res_eval_i <- res_eval_i[
+    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method)) & 
+      grepl('top1', res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
+    "Target", "Method", "Model", "Source", "Discovery","gwas_group"
+  )]),]
+  
+  res_eval[[pheno_i]]<-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi", "PRS-CSx", "X-Wing", "All") 
+
+res_eval_simp <- NULL
+for(pheno_i in selected_traits){
+  tmp <- res_eval[[pheno_i]]
+  tmp$Trait <- pheno_i
+  
+  # Insert nice PGS method names
+  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+  tmp$label[is.na(tmp$label)] <- 'All'
+  tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+  tmp$label <- factor(tmp$label, levels = model_order)
+  
+  # Simplify result to either SumStatTune or IndivTune
+  tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+  tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+  tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery','Model'), with=F]),]
+  
+  res_eval_simp <- rbind(res_eval_simp, tmp)
+}
+
+# Plot results for each phenotype separately
+dir.create('~/oliverpainfel/Analyses/crosspop/plots_three_pop')
+
+for(pheno_i in selected_traits){
+  tmp <- res_eval_simp[res_eval_simp$Trait == pheno_i,]
+
+  # Remove single GWAS results
+  tmp <- tmp[tmp$n_gwas != 1,]
+  
+  # Restrict to target matched + EUR and All GWAS
+  tmp <- tmp[!(tmp$Target == 'AFR' & tmp$Discovery == 'EUR+EAS'),]
+  tmp <- tmp[!(tmp$Target == 'EAS' & tmp$Discovery == 'EUR+AFR'),]
+  tmp$Discovery_clean <- as.character(tmp$Discovery)
+  tmp$Discovery_clean[(tmp$Target == 'EAS' & tmp$Discovery == 'EUR+EAS')]<-'Target-matched + EUR GWAS'
+  tmp$Discovery_clean[(tmp$Target == 'AFR' & tmp$Discovery == 'EUR+AFR')]<-'Target-matched + EUR GWAS'
+  tmp$Discovery_clean[tmp$Discovery == 'EUR+AFR+EAS']<-'AFR + EAS + EUR GWAS'
+  tmp$Discovery_clean <- factor(tmp$Discovery_clean, levels = c(
+    'Target-matched + EUR GWAS', 'AFR + EAS + EUR GWAS'
+  ))
+  
+  tmp$Target <- paste0(tmp$Target, ' Target')
+
+  png(paste0('~/oliverpainfel/Analyses/crosspop/plots_three_pop/', pheno_i,'.png'), res=300, width = 3400, height = 2000, units = 'px')
+  plot_tmp<-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x=NULL, fill = NULL, title = info_all$`Trait Description`[info_all$`Trait Label` == pheno_i]) +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+  print(plot_tmp)
+  dev.off()
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval <- NULL
+for(targ_pop_i in targ_pop){
+  # Subset res_eval for each scenario
+  res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+    x <- res_eval[[i]]
+    x$pheno <- names(res_eval)[i]
+    x <- x[x$Target == targ_pop_i]
+  }))
+  
+  # Average res_evalults for each test across phenotypes
+  # Use MAd to account for correlation between them
+  res_eval_i$Sample<-'A'
+
+  for(group_i in unique(res_eval_i$Group)){
+    res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
+    missing_pheno <-
+      colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+    
+    if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+      print(paste0(
+        'res_evalults missing for ',
+        targ_pop_i,
+        ' ',
+        group_i,
+        ' ',
+        paste0(missing_pheno, collapse = ' ')
+      ))
+    }
+    
+    cors_i <- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+    
+    meta_res_eval_i <-
+      agg(
+        id = Sample,
+        es = R,
+        var = SE ^ 2,
+        cor = cors_i,
+        method = "BHHR",
+        mod = NULL,
+        data = res_eval_group_i
+      )
+    
+    tmp <- data.table(Group = group_i,
+                      Method = res_eval_group_i$Method[1],
+                      Model = res_eval_group_i$Model[1],
+                      Source = res_eval_group_i$Source[1],
+                      Discovery = res_eval_group_i$Discovery[1],
+                      gwas_group = res_eval_group_i$gwas_group[1],
+                      n_gwas = res_eval_group_i$n_gwas[1],
+                      Target = targ_pop_i,
+                      R = meta_res_eval_i$es,
+                      SE = sqrt(meta_res_eval_i$var))
+    
+    meta_res_eval <- rbind(meta_res_eval, tmp)
+  }
+}
+
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS','EUR+AFR+EAS'))
+
+write.csv(meta_res_eval, '~/oliverpainfel/Analyses/crosspop/r_eval_three_pop.csv', row.names = F)
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery','Model'), with=F]),]
+
+# Remove single GWAS results
+tmp <- tmp[tmp$n_gwas != 1,]
+
+# Restrict to target matched + EUR and All GWAS
+tmp <- tmp[!(tmp$Target == 'AFR' & tmp$Discovery == 'EUR+EAS'),]
+tmp <- tmp[!(tmp$Target == 'EAS' & tmp$Discovery == 'EUR+AFR'),]
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[(tmp$Target == 'EAS' & tmp$Discovery == 'EUR+EAS')]<-'Target-matched + EUR GWAS'
+tmp$Discovery_clean[(tmp$Target == 'AFR' & tmp$Discovery == 'EUR+AFR')]<-'Target-matched + EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery == 'EUR+AFR+EAS']<-'AFR + EAS + EUR GWAS'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, levels = c(
+  'Target-matched + EUR GWAS', 'AFR + EAS + EUR GWAS'
+))
+
+tmp$Target <- paste0(tmp$Target, ' Target')
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots_three_pop/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+```
+</details>
+
+***
+
+# Using GWAS in GenoPred pipeline paper
+
+This is to provide more confidence in the performance of SBayesRC and QuickPRS relative to other methods, when using GWAS that are not from single samples, typically large meta-analyses. This is a common scenario and some methods might be more robust to mispecification than others.
+
+***
+
+## Prepare configuration
+
+***
+
+## Run pipeline
+
+***
+
+## Run model_builder
+
+***
+
+## Plot results
+
+***
+
+***
+
+Additional things we could check:
+
+- Performance when using PAGE GWAS - It is a mixture of non-EUR populations, mainly AFR and AMR. The same size is larger than UGR. It was used in SBayesRC and PRS-CSx paper
+- Downsample BBJ GWAS
+- Larger reference sample size
+- Larger SNP set
+- Performance of single source methods using GWAS that were in original GenoPred pipeline (meta-analyses)
+
+All of these additional tests could be restricted to UKB GWAS sumstats. They have been done to some extent already in the SBayesRC paper. 
+I think the most interesting points are:
+- Looking at performance using downsampled UKB sumstats
+- Looking at performance using GWAS from GenoPred pipe study
+
+
+
+
+
diff --git a/functions/model_builder.R b/functions/model_builder.R
index 1478a233..d978ce3c 100644
--- a/functions/model_builder.R
+++ b/functions/model_builder.R
@@ -17,13 +17,18 @@ h2l_R2 <- function(k, r2, p) {
 }
 
 # Functions for reading in predictor file
-read_predictor<-function(x, pred_miss, file_index = NULL){
+read_predictor<-function(x, pred_miss, file_index = NULL, keep = NULL){
   # Read in predictor file
   tmp <- fread(x)
-
+  
   # Create a column that combines FID and IID
   tmp <- combine_fid_iid(tmp)
 
+  if(!is.null(keep)){
+    # Restrict to keep
+    tmp <- tmp[tmp$IID %in% keep,]
+  }
+  
   # Remove variables with > opt$pred_miss missing values
   tmp <- filter_columns_by_missing(tmp, threshold = opt$pred_miss)
 
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 06f7ff15..31dc2c41 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -827,7 +827,7 @@ rule prep_pgs_bridgeprs_i:
   threads: config['cores_prep_pgs']
   input:
     rules.download_bridgeprs_software.output,
-    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas_group}}/{{gwas_group}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
+    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group)),
     f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
   output:
     f"{outdir}/reference/pgs_score_files/bridgeprs/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
@@ -838,7 +838,7 @@ rule prep_pgs_bridgeprs_i:
   log:
     f"{outdir}/reference/logs/prep_pgs_bridgeprs_i-{{gwas_group}}.log"
   params:
-    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas_group}}/{{gwas_group}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
+    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
     populations= lambda w: ",".join(get_populations(w.gwas_group)),
     testing=config["testing"]
   shell:
@@ -856,8 +856,8 @@ rule prep_pgs_bridgeprs_i:
     """
 
 rule prep_pgs_bridgeprs:
-  input: expand(f"{outdir}/reference/pgs_score_files/bridgeprs/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'])
-
+  input: expand(f"{outdir}/reference/pgs_score_files/bridgeprs/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df_two['name'])
+  
 ###############################################
 
 ##

From ff144bfdf9e54b9deeb42f3d5a256bad8bcce3ed Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 20 Mar 2025 16:17:27 +0000
Subject: [PATCH 123/153] Updates

---
 docs/CrossPop.Rmd | 198 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 198 insertions(+)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 6284c830..7fbe4bb3 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -6337,6 +6337,8 @@ dev.off()
 ```
 </details>
 
+Relative performance of methods is unchanged. Slight absolute improvement for the AFR target, but not for the EAS target.
+
 ***
 
 # Using GWAS in GenoPred pipeline paper
@@ -6347,10 +6349,63 @@ This is to provide more confidence in the performance of SBayesRC and QuickPRS r
 
 ## Prepare configuration
 
+We can use the gwas_list from the GenoPred pipeline paper. Just make the new configuration file.
+
+```{r}
+######
+# gwas_list
+######
+
+library(data.table)
+gwas_list <- fread('/scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/benchmark/gwas_list.txt')
+
+gwas_list$path <- gsub('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/gwas_sumstats/',
+                       '/users/k1806347/oliverpainfel/Data/GWAS_sumstats/genopred_pipeline_paper/',
+                       gwas_list$path)
+
+gwas_list$label=paste0('"', gwas_list$label, '"')
+
+write.table(gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_meta.txt', col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_meta.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_meta.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
+  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 50",
+  "ldpred2_inference: F",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3",
+  "quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_meta.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
 ***
 
 ## Run pipeline
 
+<details><summary>Show code</summary>
+
+```{bash}
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_meta.yaml \
+  target_pgs -n
+```
+
+</details>
+
 ***
 
 ## Run model_builder
@@ -6361,6 +6416,149 @@ This is to provide more confidence in the performance of SBayesRC and QuickPRS r
 
 ***
 
+# Run methods using downsampled UKB sumstats
+
+It seems the performance of methods varies across EAS and AFR datasets. This could be due to the difference in sample size. To explore this, lets run the methods on EUR GWAS generated using UKB, using a range of sample sizes.
+
+***
+
+## Downsample GWAS
+
+```{r}
+library(data.table)
+
+# Read in phenotype file
+subsample_n<-c(5, 15, 45, 135)
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+set.seed(1)
+
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/subsample')
+for(i in selected_traits){
+  pheno_i_dat <- fread(
+    paste0(
+      '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
+      i,
+      '.unrel.EUR_train.norm_resid_scale.row_number.txt'
+    )
+  )
+  
+  for(n in subsample_n){
+    tmp <- pheno_i_dat[sample(1:nrow(pheno_i_dat), size = n*1000),]
+    fwrite(tmp, paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/subsample/', i, '.',n,'.txt'),
+           sep=' ',
+           na='NA',
+           quote=F)
+  }
+} 
+```
+
+```{bash}
+for pheno in $(echo TC HT HB); do
+  for n in $(echo 5 15 45 135); do
+    mkdir -p /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled
+    for chr in $(seq 1 22); do
+      if [ ! -f "/users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}.outcome.glm.linear" ]; then
+        sbatch -p interruptible_cpu --wrap="/users/k1806347/oliverpainfel/Software/plink2 \
+          --pfile /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
+          --pheno /users/k1806347/oliverpainfel/Data/ukb/phenotypes/subsample/${pheno}.${n}.txt \
+          --linear omit-ref cols=+a1freq,+ax \
+          --maf 0.01 \
+          --geno 0.05 \
+          --out /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}"
+      fi
+    done
+  done
+done
+
+# Once complete, merge results across chromosomes
+for pheno in $(echo TC HT HB); do
+  for n in $(echo 5 15 45 135); do
+    head -n 1 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr1.outcome.glm.linear > /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
+      for chr in $(seq 1 22); do
+        tail -n +2 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}.outcome.glm.linear >> /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
+      done
+      
+      # Remove REF and ALT columns and rename AX column to A2
+      cut -f 4,5 --complement /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt | awk 'BEGIN{FS=OFS="\t"} NR==1 {$5="A2"} 1' > temp.txt && mv temp.txt /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
+  
+      gzip /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
+  done
+done
+
+# Delete per chromosome files
+rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*_subsampled/*chr*
+
+```
+
+***
+
+## Prepare configuration
+
+```{r}
+######
+# gwas_list
+######
+
+gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt')
+pheno<-gsub('_.*','', gwas_list$name)
+gwas_list<-gwas_list[pheno %in% c('TC', 'HT', 'HB'),]
+gwas_list<-gwas_list[gwas_list$population == 'EUR',]
+gwas_list$pheno<-gsub('_UKB','',gwas_list$name)
+
+gwas_list_subsampled <- NULL
+for(n in c(5, 15, 45, 135)){
+  gwas_list_tmp<-gwas_list
+  
+  gwas_list_tmp$name <-
+    paste0(
+      gwas_list_tmp$name, '_', n, 'K'
+    )
+  gwas_list_tmp$path <-
+    paste0(
+      '/users/k1806347/oliverpainfel/Data/ukb/gwas/',
+      gwas_list_tmp$pheno,
+      '_subsampled/ukb.eur_train.',
+      gwas_list_tmp$pheno,
+      '.',
+      n,
+      '.GW.txt'
+    )
+  gwas_list_tmp$label <-
+    paste0(
+      gsub("\\)", paste0(' - ', n,"K)"), gwas_list_tmp$label)
+    )
+  
+  gwas_list_subsampled <- rbind(gwas_list_subsampled, gwas_list_tmp)
+}
+
+gwas_list_subsampled$pheno<-NULL
+
+gwas_list_subsampled$label<-paste0('"', gwas_list_subsampled$label, '"')
+
+write.table(gwas_list_subsampled, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt', col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
+  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 50",
+  "ldpred2_inference: F",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3",
+  "quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml', col.names = F, row.names = F, quote = F)
+```
+
 ***
 
 Additional things we could check:

From 49859f1d98815a2c7b92b7a767d444a9f49420ca Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 24 Mar 2025 15:30:00 +0000
Subject: [PATCH 124/153] Updates

---
 Scripts/model_builder/model_builder_top1.R  |   8 +-
 Scripts/pgs_methods/apply_leopard_weights.R |  27 -
 Scripts/pgs_methods/bridgeprs.R             |  23 -
 Scripts/pgs_methods/dbslmm.R                |  23 -
 Scripts/pgs_methods/lassosum.R              |  23 -
 Scripts/pgs_methods/ldpred2.R               |  23 -
 Scripts/pgs_methods/megaprs.R               |  23 -
 Scripts/pgs_methods/prscs.R                 |  23 -
 Scripts/pgs_methods/prscsx.R                |  23 -
 Scripts/pgs_methods/ptclump.R               |  23 -
 Scripts/pgs_methods/quickprs.R              |  23 -
 Scripts/pgs_methods/quickprs_multi.R        | 387 ---------------
 Scripts/pgs_methods/sbayesr.R               |  23 -
 Scripts/pgs_methods/sbayesrc.R              |  23 -
 Scripts/pgs_methods/tlprs.R                 |  23 -
 Scripts/pgs_methods/xwing.R                 |  23 -
 docs/CrossPop.Rmd                           | 524 +++++++++++++++++++-
 docs/prep_quickprs_ref.Rmd                  |  19 +
 functions/model_builder.R                   |   4 +-
 pipeline/rules/dependencies.smk             | 125 ++++-
 20 files changed, 646 insertions(+), 747 deletions(-)
 delete mode 100644 Scripts/pgs_methods/quickprs_multi.R

diff --git a/Scripts/model_builder/model_builder_top1.R b/Scripts/model_builder/model_builder_top1.R
index 2042d8e2..65e14c1f 100644
--- a/Scripts/model_builder/model_builder_top1.R
+++ b/Scripts/model_builder/model_builder_top1.R
@@ -214,7 +214,7 @@ top1_indep_pred <- foreach(i = 1:length(unique(group_list$group)), .combine = 'c
     # NOTE. Should we be using the RMSE to select the best predictor within a group.
     top1_res<-NULL
     for(pred_i in names(cv_dat$train$x)){
-      res_pred_i <- cor(cv_dat$train$y, cv_dat$train$x[[pred_i]], use='p')
+      res_pred_i <- cor(as.numeric(cv_dat$train$y), cv_dat$train$x[[pred_i]], use='p')
       top1_res <- rbind(
         top1_res,
         data.table(
@@ -241,7 +241,7 @@ top1_indep_pred <- foreach(i = 1:length(unique(group_list$group)), .combine = 'c
   if(opt$export_models){
     top1_res<-NULL
     for(pred_i in names(cv_dat$train$x)){
-      res_pred_i <- cor(outcome_predictors$outcome_var, outcome_predictors[[pred_i]], use='p')
+      res_pred_i <- cor(as.numeric(outcome_predictors$outcome_var), outcome_predictors[[pred_i]], use='p')
       top1_res <- rbind(
         top1_res,
         data.table(
@@ -299,7 +299,7 @@ multi <- foreach(i = 1:length(unique(group_list$multi)), .combine = 'c') %dopar%
       # NOTE. Should we be using the RMSE to select the best predictor within a group.
       top1_res<-NULL
       for(pred_i in names(cv_dat_subset$train$x)){
-        res_pred_i <- cor(cv_dat_subset$train$y, cv_dat_subset$train$x[[pred_i]], use='p')
+        res_pred_i <- cor(as.numeric(cv_dat_subset$train$y), cv_dat_subset$train$x[[pred_i]], use='p')
         top1_res <- rbind(
           top1_res,
           data.table(
@@ -338,7 +338,7 @@ multi <- foreach(i = 1:length(unique(group_list$multi)), .combine = 'c') %dopar%
       # NOTE. Should we be using the RMSE to select the best predictor within a group.
       top1_res<-NULL
       for(pred_i in pred_name){
-        res_pred_i <- cor(outcome_predictors$outcome_var, outcome_predictors[[pred_i]], use='p')
+        res_pred_i <- cor(as.numeric(outcome_predictors$outcome_var), outcome_predictors[[pred_i]], use='p')
         top1_res <- rbind(
           top1_res,
           data.table(
diff --git a/Scripts/pgs_methods/apply_leopard_weights.R b/Scripts/pgs_methods/apply_leopard_weights.R
index c9dc68e2..e79f6e72 100644
--- a/Scripts/pgs_methods/apply_leopard_weights.R
+++ b/Scripts/pgs_methods/apply_leopard_weights.R
@@ -174,33 +174,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = 'plink2', score = paste0(opt$output,'.score.gz'))
-
-resdir <- read_param(config = opt$config, param = 'resdir', return_obj = F)
-
-opt$ref_pcs <- paste0(resdir, '/data/ref/pc_score_files/TRANS/ref-TRANS-pcs.profiles')
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/bridgeprs.R b/Scripts/pgs_methods/bridgeprs.R
index edfff838..d1371466 100644
--- a/Scripts/pgs_methods/bridgeprs.R
+++ b/Scripts/pgs_methods/bridgeprs.R
@@ -252,29 +252,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/dbslmm.R b/Scripts/pgs_methods/dbslmm.R
index 4515508e..2df25cc0 100644
--- a/Scripts/pgs_methods/dbslmm.R
+++ b/Scripts/pgs_methods/dbslmm.R
@@ -241,29 +241,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads=opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/lassosum.R b/Scripts/pgs_methods/lassosum.R
index 5e15b656..99062bc7 100644
--- a/Scripts/pgs_methods/lassosum.R
+++ b/Scripts/pgs_methods/lassosum.R
@@ -197,29 +197,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/ldpred2.R b/Scripts/pgs_methods/ldpred2.R
index 15cb4655..e57ef353 100644
--- a/Scripts/pgs_methods/ldpred2.R
+++ b/Scripts/pgs_methods/ldpred2.R
@@ -346,29 +346,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 ####
 # Perform inference using LDpred2-auto
 ####
diff --git a/Scripts/pgs_methods/megaprs.R b/Scripts/pgs_methods/megaprs.R
index 6613efa2..a2749fee 100644
--- a/Scripts/pgs_methods/megaprs.R
+++ b/Scripts/pgs_methods/megaprs.R
@@ -259,29 +259,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/prscs.R b/Scripts/pgs_methods/prscs.R
index 866926ba..c77bccdf 100644
--- a/Scripts/pgs_methods/prscs.R
+++ b/Scripts/pgs_methods/prscs.R
@@ -196,29 +196,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/prscsx.R b/Scripts/pgs_methods/prscsx.R
index 4b336b3b..cdb63cf6 100644
--- a/Scripts/pgs_methods/prscsx.R
+++ b/Scripts/pgs_methods/prscsx.R
@@ -226,29 +226,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/ptclump.R b/Scripts/pgs_methods/ptclump.R
index 46b4d4b1..5ac821c9 100644
--- a/Scripts/pgs_methods/ptclump.R
+++ b/Scripts/pgs_methods/ptclump.R
@@ -172,29 +172,6 @@ for(i in 1:nrow(range_list)){
 
 fwrite(range_list, paste0(opt$output, '.NSNP_per_pT'), sep='\t')
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'))
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/quickprs.R b/Scripts/pgs_methods/quickprs.R
index 0e534b8c..d200b093 100644
--- a/Scripts/pgs_methods/quickprs.R
+++ b/Scripts/pgs_methods/quickprs.R
@@ -190,29 +190,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/quickprs_multi.R b/Scripts/pgs_methods/quickprs_multi.R
deleted file mode 100644
index ec0ac7fb..00000000
--- a/Scripts/pgs_methods/quickprs_multi.R
+++ /dev/null
@@ -1,387 +0,0 @@
-#!/usr/bin/Rscript
-# This script was written by Oliver Pain whilst at King's College London University.
-start.time <- Sys.time()
-suppressMessages(library("optparse"))
-
-option_list = list(
-  make_option("--ref_plink_chr", action="store", default=NA, type='character',
-              help="Path to per chromosome reference PLINK files [required]"),
-  make_option("--ref_freq_chr", action="store", default=NULL, type='character',
-              help="Path to per chromosome reference PLINK2 .afreq files [required]"),
-  make_option("--ref_pcs", action="store", default=NULL, type='character',
-              help="Reference PCs for continuous ancestry correction [optional]"),
-  make_option("--pop_data", action="store", default=NULL, type='character',
-              help="File containing the population code and location of the keep file [required]"),
-  make_option("--plink", action="store", default='plink', type='character',
-              help="Path PLINK v1.9 software binary [optional]"),
-  make_option("--plink2", action="store", default='plink2', type='character',
-              help="Path PLINK v2 software binary [optional]"),
-  make_option("--output", action="store", default='NA', type='character',
-              help="Path for output files [required]"),
-  make_option("--memory", action="store", default=5000, type='numeric',
-              help="Memory limit [optional]"),
-  make_option("--sumstats", action="store", default=NULL, type='character',
-              help="Comma-seperated list of GWAS summary statistics [required]"),
-  make_option("--scores", action="store", default=NULL, type='character',
-              help="Comma-seperated list of score files [required]"),
-  make_option("--populations", action="store", default=NULL, type='character',
-              help="Comma-seperated list of population codes matching GWAS [required]"),
-  make_option("--ldak", action="store", default=NA, type='character',
-              help="Path to ldak v5.2 executable [required]"),
-  make_option("--quickprs_ldref", action="store", default=NA, type='character',
-              help="Path to folder containing ldak quickprs reference [required]"),
-  make_option("--quickprs_multi_ldref", action="store", default=NA, type='character',
-              help="Path to folder containing ldak quickprs_multi reference [required]"),
-  make_option("--n_cores", action="store", default=1, type='numeric',
-              help="Number of cores for parallel computing [optional]"),
-  make_option("--prs_model", action="store", default='bayesr', type='character',
-              help="Model used for deriving SNP-weights [optional]"),
-  make_option("--genomic_control", action="store", default=F, type='logical',
-              help="Logical indicating whether genomic control was applied to GWAS [optional]"),
-  make_option("--xwing_repo", action="store", default=NULL, type='character',
-              help="Path to X-WING repo [required]"),
-  make_option("--test", action="store", default=NA, type='character',
-              help="Specify number of SNPs to include [optional]")
-)
-
-opt = parse_args(OptionParser(option_list=option_list))
-
-# Load dependencies
-library(GenoUtils)
-library(data.table)
-source('../functions/misc.R')
-source_all('../functions')
-
-# Check required inputs
-if(is.null(opt$ref_plink_chr)){
-  stop('--ref_plink_chr must be specified.\n')
-}
-if(is.null(opt$ref_freq_chr)){
-  stop('--ref_freq_chr must be specified.\n')
-}
-if(is.null(opt$sumstats)){
-  stop('--sumstats must be specified.\n')
-}
-if(is.null(opt$pop_data)){
-  stop('--pop_data must be specified.\n')
-}
-if(is.null(opt$output)){
-  stop('--output must be specified.\n')
-}
-if(is.null(opt$ldak)){
-  stop('--ldak must be specified.\n')
-}
-if(is.null(opt$quickprs_multi_ldref)){
-  stop('--quickprs_multi_ldref must be specified.\n')
-}
-if(is.null(opt$populations)){
-  stop('--populations must be specified.\n')
-}
-
-# Create output directory
-opt$output_dir <- paste0(dirname(opt$output),'/')
-system(paste0('mkdir -p ',opt$output_dir))
-
-# Create temp directory
-tmp_dir<-tempdir()
-
-# Initiate log file
-log_file <- paste0(opt$output,'.log')
-log_header(log_file = log_file, opt = opt, script = 'quickprs_multi.R', start.time = start.time)
-
-# If testing, change CHROMS to chr value
-if(!is.na(opt$test) && opt$test == 'NA'){
-  opt$test<-NA
-}
-if(!is.na(opt$test)){
-  CHROMS <- as.numeric(gsub('chr','',opt$test))
-}
-
-# Split opt$sumstats
-sumstats<-unlist(strsplit(opt$sumstats, ','))
-log_add(log_file = log_file, message = paste0(length(sumstats), ' sets of GWAS have been provided.'))
-
-# Split opt$populations
-populations<-unlist(strsplit(opt$populations, ','))
-
-# Split opt$scores
-score_files<-unlist(strsplit(opt$scores, ','))
-
-#####
-# Format the sumstats
-#####
-
-gwas_N<-NULL
-for(i in 1:length(sumstats)){
-  log_add(log_file = log_file, message = paste0('Reading in GWAS ', i))
-
-  # Read in, check and format GWAS summary statistics
-  gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('CHR','BP','SNP','A1','A2','BETA','SE','N','P'))
-
-  # Format for LDAK
-  snplist <- gwas$SNP
-  gwas$Z <- gwas$BETA / gwas$SE
-  gwas$Predictor<-paste0(gwas$CHR, ':', gwas$BP)
-  gwas<-gwas[,c('Predictor','A1','A2','N','Z','CHR','BP','Predictor','BETA','P')]
-  names(gwas)<-c('Predictor','A1','A2','n','Z','CHR','BP','SNP','BETA','P')
-  gwas_N <- c(gwas_N, round(mean(gwas$n), 0))
-
-  # Check overlap between GWAS and LDAK reference
-  quickprs_ldref_pop_i <- paste0(opt$quickprs_ldref, '/', populations[i])
-  ldak_hm3_file <- list.files(quickprs_ldref_pop_i)
-  ldak_hm3_file <- ldak_hm3_file[grepl('.cors.bim', ldak_hm3_file)][1]
-  ldak_hm3 <- fread(paste0(quickprs_ldref_pop_i, '/', ldak_hm3_file))
-  ldak_hm3 <- ldak_hm3[ldak_hm3$V1 %in% CHROMS,]
-  ref_overlap <- sum(gwas$Predictor %in% ldak_hm3$V2) / nrow(ldak_hm3)
-
-  log_add(log_file = log_file, message = paste0('GWAS ', i,'-reference overlap is ', round(ref_overlap * 100, 2), '%.'))
-
-  # Subset GWAS to LDAK reference data
-  gwas <- gwas[gwas$Predictor %in% ldak_hm3$V2, ]
-
-  # Output formatted sumstats
-  fwrite(gwas, paste0(tmp_dir,'/GWAS_sumstats_temp', i, '.txt'), sep=' ')
-}
-
-# Record start time for test
-if(!is.na(opt$test)){
-  test_start.time <- test_start(log_file = log_file)
-}
-
-#####
-# Read QuickPRS scores when using full sumstats
-#####
-
-log_add(log_file = log_file, message = 'Reading in QuickPRS scores when using full sumstats.')
-
-score_full <- list()
-for(i in 1:length(sumstats)){
-  score_full[[populations[i]]] <- fread(paste0(score_files[i]))
-  names(score_full[[populations[i]]])[4] <- paste0('SCORE_targ_', populations[i])
-}
-
-#####
-# Subsample GWAS sumstats
-#####
-
-log_add(log_file = log_file, message = 'Subsampling sumstats.')
-
-dir.create(paste0(tmp_dir,'/LEOPARD/sampled_sumstats'), recursive = T)
-
-for(i in 1:length(sumstats)){
-  quickprs_multi_ldref_pop_i <- paste0(opt$quickprs_multi_ldref, '/', populations[i])
-  ref_files <- list.files(quickprs_multi_ldref_pop_i)
-  ref_files <- gsub('.bed', '', ref_files[grepl('subset_1.bed', ref_files)])
-  system(
-    paste0(
-      'Rscript ',
-      opt$xwing_repo,
-      '/LEOPARD_Sim.R ',
-      '--sumstats ', tmp_dir, '/GWAS_sumstats_temp', i, '.txt ',
-      '--n_gwas ', gwas_N[i], ' ',
-      '--train_prop 0.75 ',
-      '--ref_prefix ', quickprs_multi_ldref_pop_i, '/', ref_files, ' ',
-      '--seed 1 ',
-      '--rep 4 ',
-      '--out_prefix ', tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i
-    )
-  )
-}
-
-#####
-# Run QuickPRS using subsampled sumstats
-#####
-
-log_add(log_file = log_file, message = 'Running QuickPRS on subsampled sumstats.')
-
-score_subset <- list()
-for(i in 1:length(sumstats)){
-  dir.create(paste0(tmp_dir, '/LEOPARD/post_targ_', populations[i]))
-  score_subset[[populations[i]]] <- list()
-  for(j in 1:4){
-    sumstats_tmp <- fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.txt'))
-    n_tmp <- fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train_valid_N.txt'))
-    sumstats_tmp$n <- n_tmp$N_train
-    sumstats_tmp$Direction <-sign(sumstats_tmp$BETA)
-    sumstats_tmp <- sumstats_tmp[, c('Predictor','A1','A2','n','P','Direction'), with = F]
-    fwrite(sumstats_tmp, paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.reformat.txt'), sep=' ')
-
-    score_subset[[populations[i]]][[paste0('subset_', j)]] <- quickprs(
-      sumstats = paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', i, '_rep', j, '_train.reformat.txt'),
-      quickprs_ldref = paste0(opt$quickprs_ldref, '/', populations[i]),
-      quickprs_multi_ldref = paste0(opt$quickprs_multi_ldref, '/', populations[i]),
-      genomic_control = opt$genomic_control,
-      n_cores = opt$n_cores,
-      ref_subset = '2',
-      prs_model = opt$prs_model)
-
-    fwrite(score_subset[[populations[i]]][[paste0('subset_', j)]],
-           paste0(tmp_dir, '/LEOPARD/post_targ_', populations[i], '/output_', j, '_', populations[i], '_Post.txt'),
-           sep=' ')
-  }
-}
-
-#####
-# Estimating the linear combination weights
-#####
-
-log_add(log_file = log_file, message = 'Estimating the linear combination weights.')
-
-for(targ_pop in populations){
-  dir.create(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop), recursive = T)
-  quickprs_multi_ldref_pop_i <- paste0(opt$quickprs_multi_ldref, '/', targ_pop)
-  ref_files <- list.files(quickprs_multi_ldref_pop_i)
-  ref_files <- gsub('.bed', '', ref_files[grepl('subset_3.bed', ref_files)])
-
-  for(j in 1:4){
-    targ_gwas_valid_n<-fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_train_valid_N.txt'))$N_valid
-
-    sumstats_tmp <- fread(paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_valid.txt'))
-    sumstats_tmp$SNP<-sumstats_tmp$Predictor
-    sumstats_tmp <- sumstats_tmp[, c('SNP','CHR','BP','A1','A2','BETA','P'), with = F]
-    fwrite(sumstats_tmp, paste0(tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_valid.reformat.txt'), sep=' ')
-
-    system(paste0(
-      'Rscript ', opt$xwing_repo, '/LEOPARD_Weights.R ',
-      '--beta_file ', tmp_dir, '/LEOPARD/post_targ_', populations[1], '/output_', j, '_', populations[1], '_Post.txt',',',tmp_dir, '/LEOPARD/post_targ_', populations[2], '/output_', j, '_', populations[2], '_Post.txt ',
-      '--valid_file ', tmp_dir, '/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', j, '_valid.reformat.txt ',
-      '--n_valid ', targ_gwas_valid_n ,' ',
-      '--ref_prefix ', quickprs_multi_ldref_pop_i, '/', ref_files, ' ',
-      '--out ', tmp_dir,'/LEOPARD/weights_', targ_pop,'/output_LEOPARD_weights_rep', j, '.txt'
-    ))
-  }
-}
-
-# Average weights across repeats
-mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = paste0(tmp_dir,'/LEOPARD'), log_file = log_file)
-
-####
-# Adjust weights to correspond to PGS with SD of 1
-####
-
-# Read in the scale files for the original score files
-scale_all <- NULL
-for(i in 1:length(score_files)){
-  scale_file <- gsub('.score.gz', '', score_files[i])
-  scale_file <- paste0(scale_file, '-', populations[i], '.scale')
-  scale_file <- fread(scale_file)
-  
-  scale_all <- rbind(
-    scale_all, 
-    data.table(
-      SD = scale_file$SD,
-      Discovery = populations[i]
-    )
-  )
-}
-
-# Adjust weights to correspond to PGS with SD of 1
-log_add(log_file = log_file, message = '------------------------')
-for(i in populations){
-  mix_weights[[i]] <- adjust_weights(weights = mix_weights[[i]], pgs_sd = scale_all$SD)
-  
-  log_add(log_file = log_file, message = paste0("Adjusted LEOPARD weights - ", i, " target: "))
-  for(j in populations){
-    log_add(log_file = log_file, message = paste0(j, ' = ', mix_weights[[i]][which(populations == j)]))
-  }
-  log_add(log_file = log_file, message = '------------------------')
-}
-
-####
-# Combine score files
-####
-
-log_add(log_file = log_file, message = 'Creating score file.')
-
-# Combine the scores from each population
-score_all <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE), score_full)
-score_all[is.na(score_all)]<-0
-
-# Read in reference SNP and population data
-ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
-pop_data <- read_pop_data(opt$pop_data)
-
-# Subset PGS to SNPs in ref (useful when testing)
-score_all <- score_all[score_all$SNP %in% ref$SNP,]
-
-# Calculate linear combination of scores using mixing weights for each target population
-score_weighted <- score_all
-for(targ_pop in populations){
-  # Read in the .freq file for target population
-  freq_data <- read_frq(freq_dir = opt$ref_freq_chr, population = targ_pop, chr = CHROMS)
-  
-  # Centre SNP-weights for target population
-  score_i <- centre_weights(score = score_all, freq = freq_data, ref = ref)
-  
-  ###
-  # Scale weights to give PGS SD of 1 in target population
-  ###
-  
-  # Calculate scores in reference, and scale weights accordingly
-  fwrite(score_i, paste0(tmp_dir,'/tmp.',targ_pop,'.score'), col.names=T, sep=' ', quote=F)
-  
-  # Calc score in target sample
-  ref_pgs <- plink_score(pfile = opt$ref_plink_chr, keep = pop_data[pop_data$POP == targ_pop, c('FID'), with=F], chr = CHROMS, plink2 = opt$plink2, score = paste0(tmp_dir,'/tmp.',targ_pop,'.score'), threads = opt$n_cores)
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs)
-  
-  # Rescale SNP-weights according to PGS SD in target
-  for(i in populations){
-    scaling_factor <- 1 / ref_pgs_scale_i$SD[ref_pgs_scale_i$Param == paste0('SCORE_targ_', i)]
-    score_i[[paste0('SCORE_targ_', i)]] <- score_i[[paste0('SCORE_targ_', i)]] * scaling_factor
-  }
-  
-  # Linearly combine scores using mixing weights for target population
-  score_weighted[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <-
-    calculate_weighted_scores(score = score_i,
-                              mix_weights = mix_weights,
-                              targ_pop = targ_pop)
-}
-
-# Only retain weighted score columns
-score_weighted <- score_weighted[, c('SNP','A1','A2', names(score_weighted)[grepl('_weighted$', names(score_weighted))]), with=F]
-
-# Reduce number of significant figures to save space
-score_weighted[, (4:ncol(score_all)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_all)]
-
-fwrite(score_weighted, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
-
-if(file.exists(paste0(opt$output,'.score.gz'))){
-  system(paste0('rm ',opt$output,'.score.gz'))
-}
-
-system(paste0('gzip ',opt$output,'.score'))
-
-# Record end time of test
-if(!is.na(opt$test)){
-  test_finish(log_file = log_file, test_start.time = test_start.time)
-}
-
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
-end.time <- Sys.time()
-time.taken <- end.time - start.time
-sink(file = log_file, append = T)
-cat('Analysis finished at', as.character(end.time),'\n')
-cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
-sink()
diff --git a/Scripts/pgs_methods/sbayesr.R b/Scripts/pgs_methods/sbayesr.R
index 981ae7ae..a360775e 100644
--- a/Scripts/pgs_methods/sbayesr.R
+++ b/Scripts/pgs_methods/sbayesr.R
@@ -222,29 +222,6 @@ for(par in names(parRes_mcmc[[i]])){
 write.table(parRes, paste0(opt$output_dir,'/GWAS_sumstats_SBayesR.GW.parRes'), col.names=T, row.names=F, quote=F)
 log_add(log_file = log_file, message = paste0('SNP-heritability estimate is ',parRes[parRes$Par == 'hsq', names(parRes) == 'Mean']," (SD=",parRes[parRes$Par == 'hsq', names(parRes) == 'SD'],")."))
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/sbayesrc.R b/Scripts/pgs_methods/sbayesrc.R
index ddb1c5b5..1768d0c7 100644
--- a/Scripts/pgs_methods/sbayesrc.R
+++ b/Scripts/pgs_methods/sbayesrc.R
@@ -245,29 +245,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/tlprs.R b/Scripts/pgs_methods/tlprs.R
index e1531ac4..9c020264 100644
--- a/Scripts/pgs_methods/tlprs.R
+++ b/Scripts/pgs_methods/tlprs.R
@@ -268,29 +268,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/xwing.R b/Scripts/pgs_methods/xwing.R
index 24e17d1e..4d1f4084 100644
--- a/Scripts/pgs_methods/xwing.R
+++ b/Scripts/pgs_methods/xwing.R
@@ -386,29 +386,6 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
-####
-# Calculate mean and sd of polygenic scores
-####
-
-log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
-
-# Calculate scores in the full reference
-ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
-
-if(!is.null(opt$ref_pcs)){
-  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
-  # Derive trans-ancestry PGS models and estimate PGS residual scale
-  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
-}
-
-# Calculate scale within each reference population
-pop_data <- read_pop_data(opt$pop_data)
-
-for(pop_i in unique(pop_data$POP)){
-  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
-  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 7fbe4bb3..026f3aaa 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -6376,7 +6376,8 @@ config<-c(
   "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_meta.yaml",
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_meta.txt",
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
-  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
+  "pgs_methods: ['quickprs','sbayesrc','ldpred2','ptclump','dbslmm']",
+#  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
   "cores_prep_pgs: 10",
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
@@ -6410,10 +6411,370 @@ snakemake \
 
 ## Run model_builder
 
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_meta.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in gwas_list
+gwas_list<-read_param(config = config, param = 'gwas_list')
+
+# Create column containing the phenotypes corresponding to each GWAS
+gwas_list$pheno<-tolower(gwas_list$label)
+gwas_list$pheno[gwas_list$pheno == 'breast cancer']<-'bc'
+gwas_list$pheno[gwas_list$pheno == 'prostate cancer']<-'pc'
+gwas_list$pheno[gwas_list$pheno == 'egfr']<-'egfr,ckd'
+gwas_list$pheno[gwas_list$pheno == 'urate levels']<-'urate,gout'
+gwas_list$pheno[gwas_list$pheno == 'rheumatoid arthritis']<-'ra'
+
+bin_phenos <- c('bc', 'ckd', 'gout', 'ibd', 'pc', 'ra', 'stroke', 't1d', 't2d')
+con_phenos <- c('height','bmi','egfr','hba1c','urate','hdl')
+phenos<-c(bin_phenos, con_phenos)
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Create files for EUR target
+for(trait_i in phenos){
+  scores_i <- scores[scores$name == gwas_list$name[grepl(paste0('^', trait_i, '$','|', ',', trait_i, '$','|', '^', trait_i, ','), gwas_list$pheno)],]
+
+  dir.create(
+    paste0(
+      '/users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/', trait_i
+    ),
+    recursive = T
+  )
+  
+  scores_i$predictor <- paste0(
+    outdir,
+    '/ukb/pgs/TRANS/',
+    scores_i$method,
+    '/',
+    scores_i$name,
+    '/ukb-',
+    scores_i$name,
+    '-TRANS.profiles'
+  )
+  
+  scores_i$multi<-scores_i$method
+  scores_i$top1 <- 'EUR'
+
+  # Extract the pseudo score for each method and specify as a separate group
+  for(i in 1:nrow(scores_i)) {
+    param <- find_pseudo(
+      config = config,
+      gwas = scores_i$name[i],
+      pgs_method = scores_i$method[i],
+      target_pop = 'EUR'
+    )
+    
+    score_header <-
+      fread(scores_i$predictor[i], nrows = 1)
+    score_cols <-
+      which(names(score_header) %in% c('FID', 'IID', paste0(scores_i$name[i], '_', param)))
+    
+    system(
+      paste0(
+        "cut -d' ' -f ", 
+        paste0(score_cols, collapse=','),
+        " ", 
+        scores_i$predictor[i], 
+        " > ", 
+        gsub('.profiles',
+             paste0('.EUR_pseudo.profiles'),
+             scores_i$predictor[i])
+      )
+    )
+  }
+  
+  scores_i_pseudo <- scores_i
+  scores_i_pseudo$multi <- paste0(scores_i_pseudo$multi, '.pseudo')
+
+  scores_i_pseudo$predictor <- gsub('.profiles', 
+                                paste0('.EUR_pseudo.profiles'),
+                                scores_i_pseudo$predictor)
+
+
+  predictors_i<- do.call(rbind, list(
+    scores_i, scores_i_pseudo
+  ))
+  
+  predictors_i <- predictors_i[, c('predictor', 'top1','multi'), with=F]
+  
+  write.table(
+    predictors_i,
+    paste0(
+      '/users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/', trait_i, '/predictor_list.txt'
+    ),
+    col.names = T,
+    row.names = F,
+    quote = F
+  )
+}
+
+########
+# Prepare phenotype data
+########
+
+# Read in list of EUR in UKB
+eur_keep <- fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/pcs/within_sample/ukb.outlier_detection.EUR.keep')
+names(eur_keep)<-c('FID','IID')
+
+# Update row number IDs to project specific IDs
+psam<-fread('/scratch/prj/ukbiobank/recovered/ukb82087/imputed/ukb82087_imp_chr1_MAF1_INFO4_v1.psam')
+psam$rn<-1:nrow(psam)
+psam<-psam[,c('IID','rn'), with = F]
+
+bin_phenos <- c('bc', 'ckd', 'gout', 'ibd', 'pc', 'ra', 'stroke', 't1d', 't2d')
+con_phenos <- c('height','bmi','egfr','hba1c','urate','hdl')
+phenos<-c(bin_phenos, con_phenos)
+
+dir.create('~/oliverpainfel/Data/ukb/phenotypes/benchmark')
+
+for(i in phenos){
+  # Read in pheno data
+  pheno_i <- fread(paste0(
+      '/scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/phenotypes/',
+      i,
+      '.unrel.txt'
+    ))
+  
+  names(pheno_i)<-c('IID','PHENO')
+  
+  # Update to row number based IDs
+  pheno_i<-merge(psam[,c('IID','rn'), with=F], pheno_i, by='IID')
+  pheno_i$IID<-pheno_i$rn
+  pheno_i$rn<-NULL
+  pheno_i$FID<-pheno_i$IID
+  pheno_i<-pheno_i[, c('FID','IID','PHENO'), with=F]
+  
+  # Restrict to EUR
+  pheno_i <- pheno_i[pheno_i$FID %in% eur_keep$FID,]
+  
+  # Write file
+  write.table(pheno_i, paste0('~/oliverpainfel/Data/ukb/phenotypes/benchmark/', i, '.unrel.eur.txt'), row.names = F, quote = F)
+}
+
+```
+
+</details>
+
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_*.disc_EUR_*/*/res*
+
+for pheno in $(echo bc ckd gout ibd pc ra stroke t1d t2d height bmi egfr hba1c urate hdl); do
+  if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/${pheno}/res.pred_comp.txt" ]; then
+    sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+      --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/benchmark/${pheno}.unrel.eur.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/${pheno}/predictor_list.txt \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/${pheno}/res \
+      --n_core 5"
+  fi
+done
+
+```
+</details>
+
 ***
 
 ## Plot results
 
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+bin_phenos <- c('bc', 'ckd', 'gout', 'ibd', 'pc', 'ra', 'stroke', 't1d', 't2d')
+con_phenos <- c('height','bmi','egfr','hba1c','urate','hdl')
+phenos<-c(bin_phenos, con_phenos)
+
+# Calculate correlation between all phenotypes in each target population
+pheno_pop_i <- list()
+for(pheno_i in phenos){
+  pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/benchmark/', pheno_i, '.unrel.eur.txt'))
+  names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+}
+
+pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+cors <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+cors[is.na(cors)]<-0
+
+# Read in results
+res_eval <- list()
+for(pheno_i in phenos){
+  eval_i <-
+    fread(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/', pheno_i,'/res.pred_eval.txt'
+      )
+    )
+
+  eval_i$Ncase <- NULL
+  eval_i$Ncont <- NULL
+  eval_i$R2l <- NULL
+  eval_i$R2o <- NULL
+  
+  eval_i <- eval_i[!grepl('\\.multi', eval_i$Group),]
+  
+  eval_i$Method<-sub('\\..*','',eval_i$Group)
+  eval_i$Method<-gsub('-.*','', eval_i$Method)
+  
+  eval_i$Model[grepl('top1$', eval_i$Group) &
+                   !grepl('pseudo', eval_i$Group)]<-'IndivTune'
+  eval_i$Model[grepl('top1$', eval_i$Group) &
+                   grepl('pseudo', eval_i$Group)]<-'SumStatTune'
+
+  eval_i$Method<-factor(eval_i$Method, levels=unique(eval_i$Method))
+  eval_i$Model<-factor(eval_i$Model, levels=c('IndivTune','SumStatTune'))
+
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  eval_i <- eval_i[
+    !(eval_i$Method %in%  c('quickprs','sbayesrc') & 
+      eval_i$Model %in% c('IndivTune')),]
+  
+  # Remove pseudo model for methods that don't really have one 
+  eval_i <- eval_i[
+    !(eval_i$Method %in%  c('ptclump') & 
+      eval_i$Model %in% c('SumStatTune')),]
+  
+  res_eval[[pheno_i]]<-eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC") 
+
+res_eval_simp <- NULL
+for(pheno_i in phenos){
+  tmp <- res_eval[[pheno_i]]
+  tmp$Trait <- pheno_i
+  
+  # Insert nice PGS method names
+  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+  tmp$label <- factor(tmp$label, levels = model_order)
+
+  res_eval_simp <- rbind(res_eval_simp, tmp)
+}
+
+# Plot results for each phenotype separately
+dir.create('~/oliverpainfel/Analyses/crosspop/plots_meta')
+
+ggplot(res_eval_simp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x=NULL, fill = NULL) +
+    facet_wrap(Trait ~ ., scales = 'free_y') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval <- NULL
+
+# Average res_evalults for each test across phenotypes
+# Use MAd to account for correlation between them
+res_eval_simp$Sample<-'A'
+
+for(group_i in unique(res_eval_simp$Group)){
+  res_eval_group_i <- res_eval_simp[res_eval_simp$Group == group_i,]
+  missing_pheno <- colnames(cors)[!(colnames(cors) %in% unique(res_eval_simp$Trait))]
+  
+  if (!all(colnames(cors) %in% unique(res_eval_simp$Trait))) {
+    print(paste0(
+      'res_evalults missing for ',
+      targ_pop_i,
+      ' ',
+      group_i,
+      ' ',
+      paste0(missing_pheno, collapse = ' ')
+    ))
+  }
+  
+  cors_i <- cors[unique(res_eval_group_i$Trait), unique(res_eval_group_i$Trait)]
+  
+  meta_res_eval_simp <-
+    agg(
+      id = Sample,
+      es = R,
+      var = SE ^ 2,
+      cor = cors_i,
+      method = "BHHR",
+      mod = NULL,
+      data = res_eval_group_i
+    )
+  
+  tmp <- data.table(Group = group_i,
+                    Method = res_eval_group_i$Method[1],
+                    Model = res_eval_group_i$Model[1],
+                    R = meta_res_eval_simp$es,
+                    SE = sqrt(meta_res_eval_simp$var))
+  
+  meta_res_eval <- rbind(meta_res_eval, tmp)
+}
+
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTune','SumStatTune'))
+
+write.csv(meta_res_eval, '~/oliverpainfel/Analyses/crosspop/meta/r_eval.csv', row.names = F)
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp <- meta_res_eval
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label <- factor(tmp$label, levels = model_order)
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots_meta/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+# Result is consistent. SBayesRC is slightly better than QuickPRS on average.
+
+```
+
 ***
 
 # Run methods using downsampled UKB sumstats
@@ -6521,7 +6882,7 @@ for(n in c(5, 15, 45, 135)){
       gwas_list_tmp$pheno,
       '.',
       n,
-      '.GW.txt'
+      '.GW.txt.gz'
     )
   gwas_list_tmp$label <-
     paste0(
@@ -6546,7 +6907,8 @@ config<-c(
   "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml",
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt",
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
-  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
+  "pgs_methods: ['quickprs','sbayesrc','ptclump','ldpred2']",
+#  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
   "cores_prep_pgs: 10",
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
@@ -6559,6 +6921,162 @@ config<-c(
 write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml', col.names = F, row.names = F, quote = F)
 ```
 
+***
+
+## Run pipeline
+
+<details><summary>Show code</summary>
+
+```{bash}
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml \
+  target_pgs -n
+```
+
+</details>
+
+***
+
+### Create predictor lists
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in list of outcomes 
+selected_traits<-c('TC', 'HT', 'HB')
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Create files for EAS and AFR targets
+for(trait_i in selected_traits){
+  
+  dir.create(
+    paste0(
+      '/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/', trait_i
+    ),
+    recursive = T
+  )
+  
+  scores_i <- scores[grepl('^HT_', scores$name),]
+
+  scores_i$predictor <- paste0(
+    outdir,
+    '/ukb/pgs/TRANS/',
+    scores_i$method,
+    '/',
+    scores_i$name,
+    '/ukb-',
+    scores_i$name,
+    '-TRANS.profiles'
+  )
+  
+  scores_i$top1 <- paste0(scores_i$method,'.',gsub('.*_', '', scores_i$name))
+
+  ####
+  # Make groups containing pseudo scores for single source methods
+  ####
+
+  # Extract the pseudo score for each method and specify as a separate group
+  # This can be skipped as it was done before
+  for(i in 1:nrow(scores_i)) {
+    param <- find_pseudo(
+      config = config,
+      gwas = scores_i$name[i],
+      pgs_method = scores_i$method[i],
+      target_pop = 'EUR'
+    )
+    
+    score_header <-
+      fread(scores_i$predictor[i], nrows = 1)
+    score_cols <-
+      which(names(score_header) %in% c('FID', 'IID', paste0(scores_i$name[i], '_', param)))
+    
+    system(
+      paste0(
+        "cut -d' ' -f ", 
+        paste0(score_cols, collapse=','),
+        " ", 
+        scores_i$predictor[i], 
+        " > ", 
+        gsub('.profiles',
+             paste0('.EUR_pseudo.profiles'),
+             scores_i$predictor[i])
+      )
+    )
+  }
+  
+  scores_i_pseudo <- scores_i
+  scores_i_pseudo$top1<-paste0(scores_i_pseudo$top1,'.pseudo')
+
+  scores_i_pseudo$predictor <- gsub('.profiles', 
+                                paste0('.EUR_pseudo.profiles'),
+                                scores_i_pseudo$predictor)
+
+  
+  ####
+  # Combine the different predictor groups
+  ####
+  predictors_i<- do.call(rbind, list(
+    scores_i, 
+    scores_i_pseudo
+  ))
+  predictors_i$multi <- 'All'
+  predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
+      
+  write.table(
+    predictors_i,
+    paste0(
+      '/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/', trait_i, '/predictor_list.txt'
+    ),
+    col.names = T,
+    row.names = F,
+    quote = F
+  )
+}
+
+```
+</details>
+
+***
+
+### Run model_builder
+
+<details><summary>Show code</summary>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/targ_*.disc_AFR_EAS_EUR/*/res*
+
+for pheno in $(echo TC HT HB); do
+  if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/res.pred_comp.txt" ]; then
+    sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+      --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.EUR_test.row_number.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/predictor_list.txt \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/res \
+      --n_core 5"
+  fi
+done
+
+```
+</details>
+
+
 ***
 
 Additional things we could check:
diff --git a/docs/prep_quickprs_ref.Rmd b/docs/prep_quickprs_ref.Rmd
index 6871e09e..396ed363 100644
--- a/docs/prep_quickprs_ref.Rmd
+++ b/docs/prep_quickprs_ref.Rmd
@@ -220,3 +220,22 @@ for(pop in c('EUR','EAS','AFR')){
 }
 
 ```
+
+***
+
+# Package for sharing online
+
+We should provide the quickprs reference using full sample, and split for LEOPARD.
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3
+for pop in $(echo EUR EAS AFR); do
+  tar -czvf ldak_quickprs_hm3_${pop}.tar.gz ${pop}
+done
+
+cd /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset
+for pop in $(echo EUR EAS AFR); do
+  tar -czvf ldak_quickprs_hm3_subset_${pop}.tar.gz ${pop}
+done
+
+```
\ No newline at end of file
diff --git a/functions/model_builder.R b/functions/model_builder.R
index d978ce3c..f78b883e 100644
--- a/functions/model_builder.R
+++ b/functions/model_builder.R
@@ -168,8 +168,8 @@ eval_pred <- function(obs, pred, family){
       mod_sum,
       R2l = h2l_R2(
         opt$outcome_pop_prev,
-        coef(Indep_mod)[2, 1] ^ 2,
-        sum(cv_dat$test$y == 'CASE') / length(cv_dat$test$y)
+        coef(mod_sum)[2, 1] ^ 2,
+        sum(obs == 'CASE') / length(obs)
       ),
       N = length(obs),
       Ncase = sum(obs == 'CASE'),
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 1acc5401..0a88ff10 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -319,12 +319,12 @@ if (config["leopard_methods"] and config["leopard_methods"] != "NA") or "quickpr
     quickprs_ldref=f"{resdir}/data/quickprs"
     
     # Check if gwas_list contains invalid populations
-    valid_pops = {'EUR', 'EAS', 'AFR', 'SAS'}
+    valid_pops = {'EUR', 'EAS', 'AFR'}
     invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
   
     if invalid_pops:
       raise ValueError(
-        f"Default quickprs reference data is only available for EUR, EAS, AFR and SAS populations. For other populations, please provide your own quickprs reference data using the quickprs_ldref parameter."
+        f"Default quickprs reference data is only available for EUR, EAS, and AFR populations. For other populations, please provide your own quickprs reference data using the quickprs_ldref parameter."
       )
   else:
     quickprs_ldref=config['quickprs_ldref']
@@ -339,20 +339,34 @@ if (config["leopard_methods"] and config["leopard_methods"] != "NA") or "quickpr
         raise FileNotFoundError(f"Required file not found: {cors_file}. quickprs reference data must include .cors.bin for all populations when quickprs_ldref is specified.")
 
 # Set quickprs_multi reference path
-quickprs_multi_ldref=config['quickprs_multi_ldref']
-if config["leopard_methods"] and config["leopard_methods"] != "NA":
-  missing_files = []
-  for pop in gwas_list_df['population'].unique():
-    path = f"{quickprs_multi_ldref}/{pop}"
-    # Check if required files exists
-    if not os.path.exists(f"{path}/{pop}.subset_1.bed"):
-      missing_files.append(f"{path}/{pop}.subset_1.bed")
-    if not os.path.exists(f"{path}/{pop}.subset_2.bed"):
-      missing_files.append(f"{path}/{pop}.subset_2.bed")
-    if not os.path.exists(f"{path}/{pop}.subset_3.bed"):
-      missing_files.append(f"{path}/{pop}.subset_3.bed")
-    if missing_files:
-      raise FileNotFoundError(f"The following quickprs_multi reference data are missing: {', '.join(missing_files)}")
+if (config["leopard_methods"] and config["leopard_methods"] != "NA"):
+  if config['quickprs_multi_ldref'] == 'NA':
+    quickprs_multi_ldref=f"{resdir}/data/quickprs"
+    
+    # Check if gwas_list contains invalid populations
+    valid_pops = {'EUR', 'EAS', 'AFR'}
+    invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
+  
+    if invalid_pops:
+      raise ValueError(
+        f"Default quickprs reference data is only available for EUR, EAS, and AFR populations. For other populations, please provide your own quickprs reference data using the quickprs_multi_ldref parameter."
+      )
+  else:
+    quickprs_multi_ldref=config['quickprs_multi_ldref']
+  
+    # Check the quickprs ldref data is present for the required populations in the gwas_list
+    missing_files = []
+    for pop in gwas_list_df['population'].unique():
+      path = f"{quickprs_multi_ldref}/{pop}"
+      # Check if required files exists
+      if not os.path.exists(f"{path}/{pop}.subset_1.bed"):
+        missing_files.append(f"{path}/{pop}.subset_1.bed")
+      if not os.path.exists(f"{path}/{pop}.subset_2.bed"):
+        missing_files.append(f"{path}/{pop}.subset_2.bed")
+      if not os.path.exists(f"{path}/{pop}.subset_3.bed"):
+        missing_files.append(f"{path}/{pop}.subset_3.bed")
+      if missing_files:
+        raise FileNotFoundError(f"The following quickprs_multi reference data are missing: {', '.join(missing_files)}")
 
 # Set sbayesrc reference path
 if config['sbayesrc_ldref'] == 'NA':
@@ -927,6 +941,7 @@ rule download_ldpred2_ref:
       rm -r {resdir}/data/ldpred2_ref/EUR/ldref
     }} > {log} 2>&1
     """
+    
 # Download LDAK
 rule download_ldak:
   output:
@@ -946,6 +961,7 @@ rule download_ldak:
       rm {resdir}/software/ldak/ldak5.1.linux_.zip
     }} > {log} 2>&1
     """
+    
 # Download LDAK map data
 rule download_ldak_map:
   output:
@@ -964,6 +980,7 @@ rule download_ldak_map:
       rm {resdir}/data/ldak_map/genetic_map_b37.zip
     }} > {log} 2>&1
     """
+    
 # Download LDAK bld snp annotations
 rule download_ldak_bld:
   output:
@@ -982,6 +999,7 @@ rule download_ldak_bld:
       rm {resdir}/data/ldak_bld/bld.zip
     }} > {log} 2>&1
     """
+    
 # Download LDAK high ld regions file
 rule download_ldak_highld:
   output:
@@ -1018,8 +1036,37 @@ rule download_ldak5_2:
     }} > {log} 2>&1
     """
 
+# Download QuickPRS reference data
 # NOTE. This doesn't currently work as the reference data on LDAK website isn't in the right format for LDAK 5.1, 5.2, or 6
-# In due course we will update this to download from our own repo. For the timebeing. The user must specify the reference data themselves
+#rule download_quickprs_ref:
+#  output:
+#    f"{resdir}/data/quickprs/{{population}}/{{population}}.cors.bin"
+#  benchmark:
+#    f"{resdir}/data/benchmarks/download_quickprs_ref-{{population}}.txt"
+#  log:
+#    f"{resdir}/data/logs/download_quickprs_ref-{{population}}.log"
+#  params:
+#    pop_code=lambda wildcards: {'EUR': 'gbr', 'SAS': 'sas', 'EAS': 'eas', 'AFR': 'afr'}[wildcards.population]
+#  shell:
+#    """
+#    {{
+#      mkdir -p {resdir}/data/quickprs; \
+#      rm -r -f {resdir}/data/quickprs/{wildcards.population}; \
+#      wget --no-check-certificate -O {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz https://genetics.ghpc.au.dk/doug/{params.pop_code}.hapmap.tar.gz; \
+#      tar -zxvf {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz -C {resdir}/data/quickprs/; \
+#      mv {resdir}/data/quickprs/{params.pop_code}.hapmap {resdir}/data/quickprs/{wildcards.population}; \
+#      find {resdir}/data/quickprs/{wildcards.population} -type f -name '*{params.pop_code}*' -exec bash -c 'mv \"$0\" \"${{0//{params.pop_code}/{wildcards.population}}}\"' {{}} \; \
+#      find {resdir}/data/quickprs/{wildcards.population} -type f -name '*hapmap*' -exec bash -c 'mv \"$0\" \"${{0//.hapmap./.}}\"' {{}} \; \
+#      rm {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz
+#    }} > {log} 2>&1
+#    """
+
+quickprs_ref_gdrive = {
+    'EUR': '10fuqn6X23dA9WKjQd9xs7xUDiFjTwfz9',
+    'EAS': '1m1OI9HpHbVcX88YvtIt80-1zonSWrZP_',
+    'AFR': '11NoeBLOC-YsxrnRPa0TOPsxywZXCWbP3'
+}
+
 rule download_quickprs_ref:
   output:
     f"{resdir}/data/quickprs/{{population}}/{{population}}.cors.bin"
@@ -1028,24 +1075,52 @@ rule download_quickprs_ref:
   log:
     f"{resdir}/data/logs/download_quickprs_ref-{{population}}.log"
   params:
-    pop_code=lambda wildcards: {'EUR': 'gbr', 'SAS': 'sas', 'EAS': 'eas', 'AFR': 'afr'}[wildcards.population]
+    id=lambda w: quickprs_ref_gdrive.get(w.population)
   shell:
     """
     {{
       mkdir -p {resdir}/data/quickprs; \
       rm -r -f {resdir}/data/quickprs/{wildcards.population}; \
-      wget --no-check-certificate -O {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz https://genetics.ghpc.au.dk/doug/{params.pop_code}.hapmap.tar.gz; \
-      tar -zxvf {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz -C {resdir}/data/quickprs/; \
-      mv {resdir}/data/quickprs/{params.pop_code}.hapmap {resdir}/data/quickprs/{wildcards.population}; \
-      find {resdir}/data/quickprs/{wildcards.population} -type f -name '*{params.pop_code}*' -exec bash -c 'mv \"$0\" \"${{0//{params.pop_code}/{wildcards.population}}}\"' {{}} \; \
-      find {resdir}/data/quickprs/{wildcards.population} -type f -name '*hapmap*' -exec bash -c 'mv \"$0\" \"${{0//.hapmap./.}}\"' {{}} \; \
-      rm {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz
+      gdown {params.id} -O {resdir}/data/quickprs/ldak_quickprs_hm3_{wildcards.population}.tar.gz; \
+      tar -zxvf {resdir}/data/quickprs/ldak_quickprs_hm3_{wildcards.population}.tar.gz -C {resdir}/data/quickprs/; \
+      rm {resdir}/data/quickprs/ldak_quickprs_hm3_{wildcards.population}.tar.gz
     }} > {log} 2>&1
     """
 
 rule download_quickprs_ref_all:
   input:
-    lambda w: expand(f"{resdir}/data/quickprs/{{population}}/{{population}}.cors.bin", population=['EUR', 'SAS', 'EAS', 'AFR'])
+    lambda w: expand(f"{resdir}/data/quickprs/{{population}}/{{population}}.cors.bin", population=['EUR', 'EAS', 'AFR'])
+
+# Download QuickPRS reference data that has been subset for LEOPARD
+quickprs_leopard_ref_gdrive = {
+    'EUR': '1basMTYv6VEIRDZ3qRdJ04hlVwNmzVpmU',
+    'EAS': '1OGELjphyPbe9Qu9ZjRavhhnM9REuUhoc',
+    'AFR': '1fWQ77dYKaYIcHJFLii-0Aic_9F96VnQK'
+}
+
+rule download_quickprs_leopard_ref:
+  output:
+    f"{resdir}/data/quickprs_leopard/{{population}}/{{population}}.cors.bin"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_quickprs_leopard_ref-{{population}}.txt"
+  log:
+    f"{resdir}/data/logs/download_quickprs_leopard_ref-{{population}}.log"
+  params:
+    id=lambda w: quickprs_leopard_ref_gdrive.get(w.population)
+  shell:
+    """
+    {{
+      mkdir -p {resdir}/data/quickprs_leopard; \
+      rm -r -f {resdir}/data/quickprs_leopard/{wildcards.population}; \
+      gdown {params.id} -O {resdir}/data/quickprs_leopard/ldak_quickprs_hm3_{wildcards.population}.tar.gz; \
+      tar -zxvf {resdir}/data/quickprs_leopard/ldak_quickprs_hm3_{wildcards.population}.tar.gz -C {resdir}/data/quickprs_leopard/; \
+      rm {resdir}/data/quickprs_leopard/ldak_quickprs_hm3_{wildcards.population}.tar.gz
+    }} > {log} 2>&1
+    """
+
+rule download_quickprs_leopard_ref_all:
+  input:
+    lambda w: expand(f"{resdir}/data/quickprs_leopard/{{population}}/{{population}}.cors.bin", population=['EUR', 'EAS', 'AFR'])
 
 # Download preprocessed reference data (1KG+HGDP HapMap3)
 rule download_default_ref:

From 56461dc3037e11111b3f51299a7574e084a573ed Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 28 Mar 2025 18:18:53 +0000
Subject: [PATCH 125/153] Updates

---
 docs/CrossPop.Rmd                             | 3141 +++---
 docs/CrossPop.html                            | 8527 ++++++++++++++---
 docs/Images/CrossPop_2025/avengeme_h2.png     |  Bin 0 -> 36154 bytes
 .../CrossPop_2025/avengeme_polygenicity.png   |  Bin 0 -> 36479 bytes
 .../CrossPop_2025/average_r.perc_improv.png   |  Bin 0 -> 740441 bytes
 .../CrossPop_2025/average_r_downsample.png    |  Bin 0 -> 70150 bytes
 .../average_r_eur.perc_improv.png             |  Bin 0 -> 548477 bytes
 .../CrossPop_2025/average_r_leopard.png       |  Bin 0 -> 154616 bytes
 docs/Images/CrossPop_2025/average_r_meta.png  |  Bin 0 -> 20897 bytes
 .../CrossPop_2025/average_r_three_gwas.png    |  Bin 0 -> 264198 bytes
 docs/Images/CrossPop_2025/average_r_tlprs.png |  Bin 0 -> 216587 bytes
 .../comparison_to_main_result.png             |  Bin 0 -> 344634 bytes
 docs/Images/CrossPop_2025/ldsc_h2.png         |  Bin 0 -> 29419 bytes
 docs/Images/CrossPop_2025/leopard_weights.png |  Bin 0 -> 142153 bytes
 docs/Images/CrossPop_2025/popcorn_rg.png      |  Bin 0 -> 28733 bytes
 docs/Images/CrossPop_2025/ptclump_assoc.png   |  Bin 0 -> 64685 bytes
 docs/miraclestrial.Rmd                        |   67 +
 17 files changed, 9020 insertions(+), 2715 deletions(-)
 create mode 100644 docs/Images/CrossPop_2025/avengeme_h2.png
 create mode 100644 docs/Images/CrossPop_2025/avengeme_polygenicity.png
 create mode 100644 docs/Images/CrossPop_2025/average_r.perc_improv.png
 create mode 100644 docs/Images/CrossPop_2025/average_r_downsample.png
 create mode 100644 docs/Images/CrossPop_2025/average_r_eur.perc_improv.png
 create mode 100644 docs/Images/CrossPop_2025/average_r_leopard.png
 create mode 100644 docs/Images/CrossPop_2025/average_r_meta.png
 create mode 100644 docs/Images/CrossPop_2025/average_r_three_gwas.png
 create mode 100644 docs/Images/CrossPop_2025/average_r_tlprs.png
 create mode 100644 docs/Images/CrossPop_2025/comparison_to_main_result.png
 create mode 100644 docs/Images/CrossPop_2025/ldsc_h2.png
 create mode 100644 docs/Images/CrossPop_2025/leopard_weights.png
 create mode 100644 docs/Images/CrossPop_2025/popcorn_rg.png
 create mode 100644 docs/Images/CrossPop_2025/ptclump_assoc.png
 create mode 100644 docs/miraclestrial.Rmd

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 026f3aaa..ffc66b00 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1,5 +1,5 @@
 ---
-title: Cross-population evaluation of polygenic scores
+title: Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations
 output: 
   html_document:
     theme: cosmo
@@ -15,6 +15,9 @@ output:
 
 ```{r setup, include=FALSE}
 knitr::opts_chunk$set(eval = FALSE)
+library(knitr)
+library(kableExtra)
+library(data.table)
 ```
 
 ```{css, echo=F}
@@ -30,26 +33,55 @@ pre code, pre, code {
 
 # Introduction
 
-European (EUR) GWAS are typically the largest in sample size, or even the only GWAS for certain outcomes. Here we will evaluate approaches for calculating polygenic scores (PGS) across populations. We will include single- and multi-source PGS methods, using EUR GWAS alone, or using EUR GWAS in combination with GWAS from other populations.
+Genome-wide association studies (GWAS) from multiple ancestral populations are increasingly available, offering opportunities to improve the accuracy and equity of polygenic scores (PGS). Several methods now aim to leverage multiple GWAS sources, but predictive performance and computational efficiency across contexts remain unclear, especially in the absence of individual-level tuning data. This study evaluates a comprehensive set of PGS methods across African (AFR), East Asian (EAS), and European (EUR) ancestry groups.
+
+This study evaluated the performance of PGS methods and modelling approaches across diverse populations using GWAS and target datasets of EUR, EAS, and AFR ancestry (Figure 2). EUR GWAS summary statistics were generated within a training subset of European ancestry individuals in the UK Biobank sample (UKB)(Bycroft et al., 2018). Publicly available EAS and AFR GWAS were obtained from Biobank Japan (BBJ) (Sakaue et al., 2021) and the Ugandan Genome Resource (UGR) (Gurdasani et al., 2019), respectively. An independent target sample of EUR, EAS and AFR ancestry individuals within UKB were used to evaluate the predictive performance of the PGS. To compare PGS methods, 10 traits available in UKB, BBJ, and UGR were selected to represent a range of genetic architectures.
+
+*** 
+
+**Document overview**:
+
+* Data preparation
+  * UKB
+    * Ancestry inference
+    * Phenotype extraction
+    * Sample splitting
+  * GWAS sumstats
+    * UK Biobank GWAS
+    * Download BBJ and UGR sumstats
+  * Heritability and polygenicity estimation
+* Main analysis 
+  * PGS calculation
+  * PGS evaluation
+  * LEOPARD+QuickPRS
+  * Computational resources
+* TL-PRS analysis
+* Sensitivity analyses
+  * Using 1KG reference
+  * Using three GWAS
+  * Using external GWAS sumstats
+  * Using downsampled GWAS
 
 ***
 
-# Derive GWAS in UKB
-
-To avoid sample overlap between the EUR GWAS and the EUR target sample for evaluation, we will split EUR individuals in UKB into training and testing subsets. The GWAS will be performed in the training subset, and the PGS evaluation will occur in the testing subset.
+# Data Preparation
 
 ***
 
-## Perform ancestry inference in UKB
+## UKB
 
-### Create target_list
+This section will describe the preparation of the UKB data for this study. We will need to separate UKB participants into ancestral groups (AFR, EAS, and EUR). Then we will need to prepare phenotype data for traits that overlap with the BBJ and UGR samples. Then we will need to split EUR UKB participants into training and testing subsets. We will then perform GWAS in the training subset, and evaluate PGS in the testing subset.
 
-<div class="shallow-break"></div>
+***
+
+### Ancestry inference
+
+We will perform this using the GenoPred pipeline. We will need to prepare the configuration files before running the pipeline.
 
 <details><summary>Show code</summary>
 <br/>
 
-<h3>Create symlinks</h3>
+<h4>Create symlinks</h4>
 
 We will create symlinks to the imputed genotype data for UKB. We will use the pgen format data for computationl efficiency and those restricted to MAF >= 1% and INFO >= 0.4. We are using genetic data that is not application specific, so the data doesn't need to be reprocessed for each application. Therefore we will use row number IDs for the .psam file so they can be connected to application specific data downstream.
 
@@ -81,7 +113,7 @@ done
 
 ***
 
-<h3>Create list of unrelated individuals</h3>
+<h4>Create list of unrelated individuals</h4>
 
 ```{r}
 library(ukbkings)
@@ -111,7 +143,7 @@ write.table(psam_unrel_all$rn, '/users/k1806347/oliverpainfel/Data/ukb/phenotype
 
 ***
 
-<h3>Create target_list</h3>
+<h4>Create target_list</h4>
 
 ```{bash}
 mkdir -p /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic
@@ -129,13 +161,9 @@ target_list <- data.frame(
 write.table(target_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt', col.names=T, row.names=F, quote=F)
 ```
 
-</details>
-
 ***
 
-### Create configfile
-
-<details><summary>Show code</summary>
+<h4>Create configfile</h4>
 
 ```{r}
 # Create config file
@@ -148,13 +176,10 @@ conf <- c(
 write.table(conf, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/config.yaml', col.names = F, row.names = F, quote = F)
 ```
 
-</details>
-
 ***
 
-### Run pipeline
+<h4>Run pipeline</h4>
 
-<details><summary>Show code</summary>
 ```{bash}
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 git describe --tags
@@ -170,9 +195,9 @@ snakemake \
 
 ***
 
-## Collect phenotype data
+### Phenotype extraction
 
-We will use the same 33 quantitative traits that were used in the PRS-CSx paper (Supp Table 10). 
+We will use the same 33 quantitative traits that were used in the PRS-CSx paper (Supp Table 10 of PRS-CSx paper). We will use ukbkings to extract the phenotypes, then remove related individuals, split EUR into training and testing subsets, and adjust EUR training phenotypes for covariates.
 
 <details><summary>Show code</summary>
 ```{bash}
@@ -470,10 +495,16 @@ for(i in 1:nrow(prscsx_dat)){
 
 ***
 
-## Run GWAS
+### GWAS sumstats
+
+We will generate EUR GWAS using the EUR training subset of UKB. BBJ will be used for EAS GWAS, and UGR will be used for AFR GWAS.
+
+***
+
+#### UKB GWAS
 
 <details><summary>Show code</summary>
-```{bash, eval=F, echo=T}
+```{bash}
 for pheno in $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
   mkdir -p /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}
   for chr in $(seq 1 22); do
@@ -508,7 +539,7 @@ rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*/*chr*
 
 ***
 
-# Download relevant BBJ sumstats
+#### Download BBJ sumstats
 
 <details><summary>Show code</summary>
 
@@ -576,7 +607,7 @@ done
 
 ***
 
-# Download relevant UGR sumstats
+#### Download UGR sumstats
 
 <details><summary>Show code</summary>
 
@@ -703,19 +734,23 @@ furrr::future_map_dfr(1:nrow(ugr_data), function(i) {
 
 ***
 
-# Estimate SNP-h2, polygenicity and rG across populations
+## Heritability and polygenicity estimation
 
 We will estimate SNP-h2 using LD-score regression, and the rG using POPCORN.
-POPCORN can estimate the SNP-h2, but it will vary according to the other GWAS included due to SNP overlap.
-Use the sumstats QC'd by GenoPred.
-To estimate polygenicity, lets use AVENGEME based on ptclump score association results. Lets generate those using GenoPred.
+POPCORN can estimate the SNP-h2, but it will vary according to the other GWAS included due to SNP overlap. Use the sumstats QC'd by GenoPred. To estimate polygenicity, lets use AVENGEME based on ptclump score association results. Lets generate those using GenoPred.
 
 ***
 
-## Prepare configuration for GenoPred
+### QC GWAS sumstats
+
+Use GenoPred for this.
 
 <details><summary>Show code</summary>
 
+<div class="shallow-break"></div>
+
+<h4>Prepare configuration</h4>
+
 ```{r}
 ######
 # gwas_list
@@ -802,13 +837,10 @@ config<-c(
 write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml', col.names = F, row.names = F, quote = F)
 
 ```
-</details>
 
 ***
 
-## Run pipeline
-
-<details><summary>Show code</summary>
+<h4>Run pipeline</h4>
 
 ```{bash}
 snakemake \
@@ -818,13 +850,10 @@ snakemake \
   target_pgs -n
 ```
 
-</details>
-
 ***
 
-## Reformat for LDSC and POPCORN
+<h4>Reformat for LDSC and POPCORN</h4>
 
-<details><summary>Show code</summary>
 ```{r}
 library(data.table)
 dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats', recursive = T)
@@ -872,7 +901,7 @@ for(i in 1:nrow(gwas_list)){
 
 ***
 
-## Run LDSC
+### LDSC
 
 <details><summary>Show code</summary>
 ```{bash}
@@ -928,10 +957,14 @@ done
 
 ***
 
-## Calculate CSCOREs
+### POPCORN
 
 <details><summary>Show code</summary>
 
+<div class="shallow-break"></div>
+
+<h4>Calculate CSCOREs</h4>
+
 ```{bash}
 
 # Subset the reference data into relevant populations
@@ -967,13 +1000,10 @@ rm -r /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp
 rm /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_*_CSCORES/*chr*.txt
 ```
 
-</details>
-
 ***
 
-## Run POPCORN
+<h4>Run POPCORN</h4>
 
-<details><summary>Show code</summary>
 ```{bash}
 conda activate popcorn
 for pop in $(echo EAS AFR);do
@@ -1002,7 +1032,7 @@ done
 
 ***
 
-## Plot the LDSC and POPCORN results
+### Plot LDSC and POPCORN results
 
 <details><summary>Show code</summary>
 
@@ -1057,13 +1087,18 @@ for(pop in c('AFR','EAS', 'EUR')){
 
 write.csv(h2_res, '/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results.csv', row.names = F, quote = F)
 
+png('~/oliverpainfel/Analyses/crosspop/plots/ldsc_h2.png', res = 100, width = 700, height = 300, units = 'px')
 ggplot(h2_res, aes(x = Phenotype, y = h2_est, fill = Population)) +
   geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
   geom_errorbar(aes(ymin=h2_est-h2_se, ymax=h2_est+h2_se), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
-  labs(y="SNP-based Heritability (SE)") +
+  labs(y="SNP-based Heritability (SE)", fill = NULL) +
   theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = "top",
+        legend.key.spacing.x = unit(2, "cm"),
+        legend.justification = "center") +
   background_grid(major = 'y', minor = 'y')
+dev.off()
 
 # Plot rG estimates
 rg_res <- NULL
@@ -1083,23 +1118,61 @@ rg_res$Comparison <- paste0(rg_res$Population_1, ' vs. ', rg_res$Population_2)
 
 write.csv(rg_res, '/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results.csv', row.names = F, quote = F)
 
+png('~/oliverpainfel/Analyses/crosspop/plots/popcorn_rg.png', res = 100, width = 700, height = 300, units = 'px')
 ggplot(rg_res, aes(x = Phenotype, y = Estimate, fill = Comparison)) +
   geom_bar(stat="identity", position=position_dodge(), width = 0.7) +
   geom_errorbar(aes(ymin=Estimate-SE, ymax=Estimate+SE), width=.2, position=position_dodge(width = 0.7)) +
-  labs(y="SNP-based\nGenetic Correlation (SE)") +
+  labs(y="SNP-based\nGenetic Correlation (SE)", fill = NULL) +
   theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = "top",
+        legend.key.spacing.x = unit(2, "cm"),
+        legend.justification = "center") +
   background_grid(major = 'y', minor = 'y')
+dev.off()
+
+```
+</details>
+
+```{bash, eval=T, echo=F}
+mkdir -p /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025
+
+cp ~/oliverpainfel/Analyses/crosspop/plots/ldsc_h2.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+
+cp ~/oliverpainfel/Analyses/crosspop/plots/popcorn_rg.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
 ```
+
+<details><summary>Show LDSC SNP-heritability</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/ldsc_h2.png">
+</div>
+</div>
+
+</details>
+
+<details><summary>Show POPCORN genetic correlation estimates</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/ldsc_h2.png">
+</div>
+</div>
+
 </details>
 
-## AVENGEME
+***
 
-### Create predictor lists
+### AVENGEME
 
 <details><summary>Show code</summary>
 
+<div class="shallow-break"></div>
+
+<h4>Create predictor list</h4>
+
 ```{r}
 
 setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -1183,13 +1256,10 @@ for(trait_i in pheno_intersect){
 }
 
 ```
-</details>
 
 ***
 
-### Run model_builder
-
-<details><summary>Show code</summary>
+<h4>Run model_builder</h4>
 
 ```{bash}
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
@@ -1214,13 +1284,10 @@ for pop in $(echo EUR EAS AFR); do
 done
 
 ```
-</details>
 
 ***
 
-### Plot assoc results
-
-<details><summary>Show code</summary>
+<h4>Plot pT+clump association results</h4>
 
 ```{r}
 setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -1265,6 +1332,7 @@ for(pheno_i in pheno_intersect){
 res_all$pT <- gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.', '', res_all$Predictor))
 res_all$pT <- factor(res_all$pT, levels = unique(res_all$pT))
 
+png('~/oliverpainfel/Analyses/crosspop/plots/ptclump_assoc.png', res = 100, width = 900, height = 500, units = 'px')
 ggplot(res_all, aes(x = Phenotype, y = BETA, fill = pT)) +
   geom_hline(yintercept = 0, colour = 'darkgrey') +
   geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.8) +
@@ -1273,17 +1341,20 @@ ggplot(res_all, aes(x = Phenotype, y = BETA, fill = pT)) +
   theme_half_open() +
   background_grid() +
   panel_border() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = "top",
+        legend.key.spacing.x = unit(1, "cm"),
+        legend.justification = "center") +
   background_grid(major = 'y', minor = 'y') +
   scale_fill_manual(values = colorRampPalette(c("lightblue", "darkblue"))(length(unique(res_all$pT)))) +
   facet_grid(Population ~.)
+dev.off()
 
 ```
-</details>
 
-### Run AVENGEME
+***
 
-<details><summary>Show code</summary>
+<h4>Run AVENGEME</h4>
 
 ```{r}
 
@@ -1393,36 +1464,34 @@ for(pop_i in pop){
 dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme')
 write.csv(mod_res_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv', row.names = F, quote = F)
 
+mod_res_all<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv')
+
+png('~/oliverpainfel/Analyses/crosspop/plots/avengeme_h2.png', res = 100, width = 900, height = 500, units = 'px')
 ggplot(mod_res_all, aes(x = Phenotype, y = vg_est, fill = Population)) +
   geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
   geom_errorbar(aes(ymin=vg_lowCI, ymax=vg_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
-  labs(y="SNP-based Heritability (95%CI)") +
+  labs(y="SNP-based Heritability (95%CI)", fill = NULL) +
   theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = "top",
+        legend.key.spacing.x = unit(1, "cm"),
+        legend.justification = "center") +
   background_grid(major = 'y', minor = 'y')
+dev.off()
 
+png('~/oliverpainfel/Analyses/crosspop/plots/avengeme_polygenicity.png', res = 100, width = 900, height = 500, units = 'px')
 ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) +
   geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
   geom_errorbar(aes(ymin=1 - pi0_lowCI, ymax=1 - pi0_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
-  labs(y="Proporition non-zero\neffects (95%CI)") +
-  theme_half_open() +
-  coord_cartesian(ylim = c(0, 0.15)) + 
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  background_grid(major = 'y', minor = 'y')
-
-ggplot(mod_res_all, aes(x = Phenotype, y = max_r2, fill = Population)) +
-  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
-  labs(y="Max R2") +
+  labs(y="Proporition non-zero\neffects (95%CI)", fill = NULL) +
   theme_half_open() +
   coord_cartesian(ylim = c(0, 0.15)) + 
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = "top",
+        legend.key.spacing.x = unit(1, "cm"),
+        legend.justification = "center") +
   background_grid(major = 'y', minor = 'y')
-
-# 
-hist(mod_res_all$max_r2)
-hist(mod_res_all$max_r2[mod_res_all$Population == 'EUR'])
-hist(mod_res_all$max_r2[mod_res_all$Population == 'EAS'])
-hist(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])
+dev.off()
 
 summary(mod_res_all$max_r2)
 summary(mod_res_all$max_r2[mod_res_all$Population == 'EUR'])
@@ -1433,9 +1502,36 @@ summary(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])
 
 </details>
 
+```{bash, eval=T, echo=F}
+cp ~/oliverpainfel/Analyses/crosspop/plots/avengeme_h2.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+
+cp ~/oliverpainfel/Analyses/crosspop/plots/avengeme_polygenicity.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+
+```
+
+<details><summary>Show AVENGEME results</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/avengeme_h2.png">
+</div>
+</div>
+
+<br>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/avengeme_polygenicity.png">
+</div>
+</div>
+
+</details>
+
 ***
 
-### Select GWAS for downstream analyses
+### Select traits
+
+Here we will identify a list of traits that fulfill our selection criteria, and that represent a range of heritability and polygenicity combinations.
 
 <details><summary>Show code</summary>
 
@@ -1521,7 +1617,9 @@ round(sqrt(max(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])), 2)
 
 ***
 
-# Make a descriptives table of GWAS
+### GWAS descriptives
+
+Make a table showing GWAS information for the manuscript.
 
 <details><summary>Show code</summary>
 
@@ -1641,14 +1739,36 @@ for(i in unique(info_all_selected$`GWAS Sample`)){
 
 </details>
 
+<details><summary>Show descriptives table</summary>
+
+```{r, eval = T, echo = F}
+info_all<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+
+kable(info_all, "html") %>%
+  kable_styling(bootstrap_options = c("striped", "hover"),
+                full_width = F) %>%
+  scroll_box(width = "100%", height = "500px")
+
+```
+
+</details>
+
+***
+
+# Main analysis
+
 ***
 
-# Run GenoPred
+## PGS calculation
 
-## Prepare configuration for GenoPred
+We will do this using GenoPred. 
 
 <details><summary>Show code</summary>
 
+<div class="shallow-break"></div>
+
+<h4>Prepare configuration</h4>
+
 ```{r}
 ######
 # gwas_list
@@ -1707,13 +1827,10 @@ config<-c(
 write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml', col.names = F, row.names = F, quote = F)
 
 ```
-</details>
 
 ***
 
-## Run pipeline
-
-<details><summary>Show code</summary>
+<h4>Run pipeline</h4>
 
 ```{bash}
 snakemake \
@@ -1727,14 +1844,10 @@ snakemake \
 
 ***
 
-# Evaluate PGS
+## PGS evaluation
 
 Lets use the model builder script which implements nested 10 fold cross validation. Similar set up to previous paper, evaluating a model containing the best PGS selected by 10-fold cross validation, a model containing the PGS selected by pseudovalidation (if available), and an elastic net model containing all PGS from a given method. We will need to update the model builder script to achieve this
 
-***
-
-## Compare all methods
-
 We want to see:
 - Performance of pseudo and top1 models for single-source methods
 - Performance of pseudo and top1 models for multi-source methods
@@ -1745,11 +1858,11 @@ We want to see:
 
 To achieve this. Will need to define groups of predictors for step 1 modelling, and groups that should then be linearly combined.
 
-***
+<details><summary>Show code</summary>
 
-### Create predictor lists
+<div class="shallow-break"></div>
 
-<details><summary>Show code</summary>
+<h4>Create predictor list</h4>
 
 ```{r}
 
@@ -2084,13 +2197,10 @@ for(trait_i in selected_traits){
 }
 
 ```
-</details>
 
 ***
 
-### Run model_builder
-
-<details><summary>Show code</summary>
+<h4>Run model_builder</h4>
 
 ```{bash}
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
@@ -2131,13 +2241,10 @@ for targ_pop in $(echo EUR EAS AFR); do
 done
 
 ```
-</details>
 
 ***
 
-### Plot results
-
-<details><summary>Show code</summary>
+<h4>Plot results</h4>
 
 ```{r}
 setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -3391,9 +3498,49 @@ dev.off()
 ```
 </details>
 
+```{bash, eval=T, echo=F}
+
+cp ~/oliverpainfel/Analyses/crosspop/plots/average_r.perc_improv.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+
+cp ~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.perc_improv.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+
+cp ~/oliverpainfel/Analyses/crosspop/plots/average_r_leopard.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+
+```
+
+<details><summary>Show average improvement in AFR + EAS</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/average_r.perc_improv.png">
+</div>
+</div>
+
+</details>
+
+<details><summary>Show average improvement in EUR</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/average_r_eur.perc_improv.png">
+</div>
+</div>
+
+</details>
+
+<details><summary>Show LEOPARD comparison</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/average_r_leopard.png">
+</div>
+</div>
+
+</details>
+
 ***
 
-### Check LEOPARD weights
+## LEOPARD+QuickPRS
 
 Here we will compare the LEOPARD estimated weights for population specific PGS, to the weights estimated using observed data in the UKB target sample.
 
@@ -3621,9 +3768,27 @@ dev.off()
 
 </details>
 
+```{bash, eval=T, echo=F}
+
+cp ~/oliverpainfel/Analyses/crosspop/plots/leopard_weights.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+
+```
+
+<details><summary>Show observed and LEOPARD PGS weights</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/leopard_weights.png">
+</div>
+</div>
+
+</details>
+
 ***
 
-# Computational resoures
+## Computational resoures
+
+Here we will read in the benchmark data for PGS methods and create a table for the manuscript.
 
 <details><summary>Show code</summary>
 
@@ -3789,11 +3954,24 @@ write.csv(method_avg, '~/oliverpainfel/Analyses/crosspop/time_memory.csv', row.n
 
 </details>
 
+<details><summary>Show computational resources table</summary>
+
+```{r, eval = T, echo = F}
+method_avg<-fread('~/oliverpainfel/Analyses/crosspop/time_memory.csv')
+
+kable(method_avg, "html") %>%
+  kable_styling(bootstrap_options = c("striped", "hover"),
+                full_width = F)
+
+```
+
+</details>
+
 ***
 
-# Sensitivity analysis using 1KG reference
+# TL-PRS
 
-PRS-CS, PRS-CSx and X-Wing all use the 1KG reference sample, whereas the other methods are using the 1KG+HGDP reference sample. We should check whether this difference is impacting our conclusions.
+Run using AFR and EAS subset in UKB to make it quicker to run. This is the main interest when running TL-PRS anyway. 
 
 ***
 
@@ -3844,51 +4022,20 @@ done
 
 ***
 
-## Create 1KG only GenoPred reference data
+## PGS calculation
 
-Subset the 1KG+HGDP reference data to include only 1KG individuals.
+To save time, run using PGS methods that do not need pre-processed LD matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary from the 1KG+HGDP results, then expand to other methods (LDpred2, SBayesRC, QuickPRS).
 
 <details><summary>Show code</summary>
 
-```{bash}
-mkdir -p ~/oliverpainfel/Data/1kg/genopred/
-cp -r ~/oliverpainfel/Data/hgdp_1kg/genopred/ref ~/oliverpainfel/Data/1kg/genopred/
-rm ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr*.p*
-```
-
-```{r}
-library(data.table)
-
-ref<- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr1.psam')
-ref<-ref[ref$Project == 'gnomAD_1kG',]
-
-write.table(ref[,1, drop = F], '~/oliverpainfel/Data/1kg/1kg.keep', col.names=F, row.names=F, quote=F)
-```
-
-```{bash}
-for chr in $(seq 1 22); do
-  ~/oliverpainfel/Software/plink2 \
-    --pfile ~/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} \
-    --keep ~/oliverpainfel/Data/1kg/1kg.keep \
-    --make-pgen \
-    --out ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr${chr}
-done
-```
-
-</details>
-
-***
-
-## Run pipeline
-
-To save time, run using PGS methods that do not need pre-processed LD matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary from the 1KG+HGDP results, then expand to other methods (LDpred2, SBayesRC, QuickPRS).
+<div class="shallow-break"></div>
 
-<details><summary>Show code</summary>
+<h4>Prepare configuration</h4>
 
 ```{r}
 library(data.table)
 
-dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only')
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs')
 
 ######
 # target_list
@@ -3908,28 +4055,37 @@ write.table(target_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/config
 ######
 
 config<-c(
-  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_1kgref",
-  "refdir: /users/k1806347/oliverpainfel/Data/1kg/genopred/ref",
-  "resdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/resdir_1kgref",
-  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml",
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_tlprs",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml",
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt",
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt",
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
-  "pgs_methods: ['ptclump','dbslmm','lassosum','megaprs']",
-#  "leopard_methods: ['ptclump','dbslmm','lassosum','megaprs']",
+  "pgs_methods: ['quickprs','dbslmm','ldpred2','sbayesrc']",
+  "tlprs_methods: ['quickprs','dbslmm','ldpred2','sbayesrc']",
   "cores_prep_pgs: 10",
-  "cores_target_pgs: 10"
+  "cores_target_pgs: 50",
+  "prscs_phi: ['auto']",
+  "ldpred2_model: ['auto']",
+  "ldpred2_inference: F",
+  "dbslmm_h2f: ['1']",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
 )
 
-write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml', col.names = F, row.names = F, quote = F)
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml', col.names = F, row.names = F, quote = F)
 
 ```
 
+***
+
+<h4>Run pipeline</h4>
+
 ```{bash}
 snakemake \
   --profile slurm \
   --use-conda \
-  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml \
   output_all -n
 ```
 
@@ -3937,10 +4093,14 @@ snakemake \
 
 ***
 
-## Evaluate PGS
+## PGS evaluation
 
 <details><summary>Show code</summary>
 
+<div class="shallow-break"></div>
+
+<h4>Create predictor list</h4>
+
 ```{r}
 
 setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -3949,8 +4109,7 @@ source_all('../functions')
 library(data.table)
 
 # Get some key variables from config
-config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml'
-pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml'
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
@@ -3963,9 +4122,9 @@ scores <- list_score_files(config)
 targ_pop <- c('EAS','AFR')
 for(trait_i in selected_traits){
   scores_i <- scores[grepl(trait_i, scores$name),]
-  scores_i$multi <- scores_i$method
-  
+
   for(targ_pop_i in targ_pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
     if(targ_pop_i == 'EAS'){
       disc_pop <- 'BBJ'
     }
@@ -3986,7 +4145,7 @@ for(trait_i in selected_traits){
 
       dir.create(
         paste0(
-          '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_',
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_',
           targ_pop_i,
           '.disc_EUR_',
           disc_pop_j_2,
@@ -4000,7 +4159,6 @@ for(trait_i in selected_traits){
         (grepl('UKB$', scores_i$name, ignore.case = F) | 
          grepl(paste0(disc_pop_j, '$'), scores_i$name, ignore.case = T)),]
 
-      # Insert path to score file
       scores_i_j$predictor <- paste0(
         outdir,
         '/ukb/pgs/TRANS/',
@@ -4012,250 +4170,70 @@ for(trait_i in selected_traits){
         '-TRANS.profiles'
       )
       
-      ####
-      # Make groups single source methods
-      ####
+      #####
+      # List single-source PGS
+      #####
+      # These are actually pseudoval scores (as per the config)
+      scores_i_j_single <- scores_i_j[!grepl('tlprs', scores_i_j$method),]
       
-      scores_i_j_single_top1 <-
-        scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &
-                     !grepl('_multi$', scores_i_j$method), ]
-
-      # Create top1 column indicating which predictors top1 models should be derived
-      scores_i_j_single_top1$top1[grepl('UKB', scores_i_j_single_top1$name, ignore.case = F)] <- 'EUR'
-      scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] <- disc_pop_j_2
+      scores_i_j_single$top1[grepl('UKB', scores_i_j_single$name, ignore.case = F)] <- 'EUR'
+      scores_i_j_single$top1[grepl(disc_pop_j, scores_i_j_single$name, ignore.case = F)] <- disc_pop_j_2
+      scores_i_j_single$multi <- paste0(scores_i_j_single$method,'.pseudo')
       
-      ####
-      # Make groups containing pseudo scores for single source methods
-      ####
-
-      # Extract the pseudo score for each method and specify as a separate group
-      for(i in 1:nrow(scores_i_j_single_top1)) {
-        param <- find_pseudo(
-          config = config,
-          gwas = scores_i_j_single_top1$name[i],
-          pgs_method = scores_i_j_single_top1$method[i],
-          target_pop = targ_pop_i
-        )
-        
-        score_header <-
-          fread(scores_i_j_single_top1$predictor[i], nrows = 1)
-        score_cols <-
-          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_single_top1$name[i], '_', param)))
+      #####
+      # List tlprs scores (split by target population)
+      #####
+      scores_i_j_tlprs <- scores_i_j[grepl('tlprs', scores_i_j$method),]
+      scores_i_j_tlprs$multi <- scores_i_j_tlprs$method
+      
+      scores_i_j_tlprs_pop<-NULL
+      for(i in 1:nrow(scores_i_j_tlprs)){
+        score_header<-fread(scores_i_j_tlprs$predictor[i], nrow = 1)
         
-        system(
-          paste0(
-            "cut -d' ' -f ", 
-            paste0(score_cols, collapse=','),
-            " ", 
-            scores_i_j_single_top1$predictor[i], 
-            " > ", 
-            gsub('.profiles',
-                 paste0('.', targ_pop_i, '_pseudo.profiles'),
-                 scores_i_j_single_top1$predictor[i])
+        for(pop in c('EUR', disc_pop_j_2)){
+          score_cols <- which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_'), names(score_header)))
+
+          system(
+            paste0(
+              "cut -d' ' -f ", 
+              paste0(score_cols, collapse=','),
+              " ", 
+              scores_i_j_tlprs$predictor[i], 
+              " > ", 
+              gsub('.profiles',
+                   paste0('.targ_', pop, '.profiles'),
+                   scores_i_j_tlprs$predictor[i])
+            )
           )
-        )
+          
+          tmp <- scores_i_j_tlprs[i,]
+          tmp$multi <- paste0(tmp$multi, '.pop')
+          tmp$top1 <- pop
+          tmp$predictor <-
+              gsub('.profiles',
+                   paste0('.targ_', pop, '.profiles'),
+                   scores_i_j_tlprs$predictor[i])
+          
+          scores_i_j_tlprs_pop <- rbind(scores_i_j_tlprs_pop, tmp)
+        }
       }
-      
-      scores_i_j_single_pseudo <- scores_i_j_single_top1
-      scores_i_j_single_pseudo$multi <- paste0(scores_i_j_single_pseudo$multi, '.pseudo')
 
-      scores_i_j_single_pseudo$predictor <- gsub('.profiles', 
-                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
-                                    scores_i_j_single_pseudo$predictor)
-
-#      ####
-#      # Make groups for multi-single-source pseudo scores
-#      ####
-#      
-#      scores_i_j_multi_single_pseudo <- scores_i_j[grepl('_multi$', scores_i_j$method),]
-#
-#      # Extract the pseudo score for each method and specify as a separate group
-#      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
-#        param <- find_pseudo(
-#          config = config,
-#          gwas = scores_i_j_multi_single_pseudo$name[i],
-#          pgs_method = scores_i_j_multi_single_pseudo$method[i],
-#          target_pop = targ_pop_i
-#        )
-#        
-#        score_header <-
-#          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
-#        score_cols <-
-#          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi_single_pseudo$name[i], '_', param)))
-#        
-#        system(
-#          paste0(
-#            "cut -d' ' -f ", 
-#            paste0(score_cols, collapse=','),
-#            " ", 
-#            scores_i_j_multi_single_pseudo$predictor[i], 
-#            " > ", 
-#            gsub('.profiles',
-#                 paste0('.', targ_pop_i, '_pseudo.profiles'),
-#                 scores_i_j_multi_single_pseudo$predictor[i])
-#          )
-#        )
-#      }
-#      
-#      scores_i_j_multi_single_pseudo$multi <- paste0(scores_i_j_multi_single_pseudo$multi, '.pseudo')
-#
-#      scores_i_j_multi_single_pseudo$predictor <- gsub('.profiles', 
-#                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
-#                                    scores_i_j_multi_single_pseudo$predictor)
-#      
-#      scores_i_j_multi_single_pseudo$top1<-paste0('EUR_', disc_pop_j_2)
-#
-#      ####
-#      # Make groups for the Multi-Source methods
-#      ####
-#      
-#      scores_i_j_multi <- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
-#
-#      # Split top1 scores by target population
-#      # This doesn't apply to xwing because it only has pop-specific pseudo scores
-#      scores_i_j_multi_top1<-NULL
-#      for(i in 1:which(scores_i_j_multi$method %in% c('prscsx'))){
-#        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
-#        
-#        for(pop in c('EUR', disc_pop_j_2)){
-#          
-#          if(scores_i_j_multi$method[i] == 'prscsx'){
-#            score_cols <-
-#              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi'), names(score_header)))
-#          }
-#          if(scores_i_j_multi$method[i] == 'xwing'){
-#            score_cols <-
-#              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst'), names(score_header)))
-#          }
-#          
-#          system(
-#            paste0(
-#              "cut -d' ' -f ", 
-#              paste0(score_cols, collapse=','),
-#              " ", 
-#              scores_i_j_multi$predictor[i], 
-#              " > ", 
-#              gsub('.profiles',
-#                   paste0('.', pop, '_grid.profiles'),
-#                   scores_i_j_multi$predictor[i])
-#            )
-#          )
-#          
-#          tmp <- scores_i_j_multi[i,]
-#          tmp$multi <- paste0(tmp$multi, '.grid')
-#          tmp$top1 <- pop
-#          tmp$predictor <-
-#              gsub('.profiles',
-#                   paste0('.', pop, '_grid.profiles'),
-#                   scores_i_j_multi$predictor[i])
-#          
-#          scores_i_j_multi_top1 <- rbind(scores_i_j_multi_top1, tmp)
-#        }
-#      }
-#
-#      # Split pop-specific pseudo scores by target population
-#      scores_i_j_multi_pop_pseudo<-NULL
-#      for(i in 1:nrow(scores_i_j_multi)){
-#        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
-#        
-#        for(pop in c('EUR', disc_pop_j_2)){
-#          if(scores_i_j_multi$method[i] == 'prscsx'){
-#            score_cols <-
-#              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi_auto'), names(score_header)))
-#          }
-#          if(scores_i_j_multi$method[i] == 'xwing'){
-#            score_cols <-
-#              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst_', pop), names(score_header)))
-#          }
-#          
-#          system(
-#            paste0(
-#              "cut -d' ' -f ", 
-#              paste0(score_cols, collapse=','),
-#              " ", 
-#              scores_i_j_multi$predictor[i], 
-#              " > ", 
-#              gsub('.profiles',
-#                   paste0('.', pop, '_pseudo.profiles'),
-#                   scores_i_j_multi$predictor[i])
-#            )
-#          )
-#          
-#          tmp <- scores_i_j_multi[i,]
-#          tmp$multi <- paste0(tmp$multi, '.pop_pseudo')
-#          tmp$top1 <- pop
-#          tmp$predictor <-
-#              gsub('.profiles',
-#                   paste0('.', pop, '_pseudo.profiles'),
-#                   scores_i_j_multi$predictor[i])
-#          
-#          scores_i_j_multi_pop_pseudo <- rbind(scores_i_j_multi_pop_pseudo, tmp)
-#        }
-#      }
-#      
-#      # Create pseudo score for multi-source methods
-#      scores_i_j_multi_pseudo<-NULL
-#      for(i in 1:nrow(scores_i_j_multi)) {
-#        param <- find_pseudo(
-#          config = config,
-#          gwas = scores_i_j_multi$name[i],
-#          pgs_method = scores_i_j_multi$method[i],
-#          target_pop = targ_pop_i
-#        )
-#        
-#        score_header <-
-#          fread(scores_i_j_multi$predictor[i], nrows = 1)
-#        score_cols <-
-#          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
-#
-#        system(
-#          paste0(
-#            "cut -d' ' -f ", 
-#            paste0(score_cols, collapse=','),
-#            " ", 
-#            scores_i_j_multi$predictor[i], 
-#            " > ", 
-#            gsub('.profiles',
-#                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
-#                 scores_i_j_multi$predictor[i])
-#          )
-#        )
-#        
-#        tmp <- scores_i_j_multi[i,]
-#        tmp$multi <- paste0(tmp$multi, '.pseudo')
-#        tmp$top1 <- paste0('EUR_', disc_pop_j_2)
-#        tmp$predictor <-
-#            gsub('.profiles',
-#                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
-#                 scores_i_j_multi$predictor[i])
-#        
-#        scores_i_j_multi_pseudo <- rbind(scores_i_j_multi_pseudo, tmp)
-#      }
-      
-      ####
-      # Combine the different predictor groups
-      ####
       predictors_i<- do.call(rbind, list(
-        scores_i_j_single_top1, 
-        scores_i_j_single_pseudo#, 
-#        scores_i_j_multi_single_pseudo,
-#        scores_i_j_multi_top1,
-#        scores_i_j_multi_pop_pseudo,
-#        scores_i_j_multi_pseudo
+        scores_i_j_single, scores_i_j_tlprs_pop
       ))
       
-      predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
+      predictors_i <- predictors_i[, c('predictor', 'top1','multi'), with=F]
       
       write.table(
         predictors_i,
         paste0(
-          '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_',
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_',
           targ_pop_i,
           '.disc_EUR_',
           disc_pop_j_2,
           '/',
           trait_i,
-          '/predictor_list.txt'
+          '/predictor_list.tlprs.txt'
         ),
         col.names = T,
         row.names = F,
@@ -4266,19 +4244,16 @@ for(trait_i in selected_traits){
 }
 
 ```
-</details>
 
 ***
 
-### Run model_builder
-
-<details><summary>Show code</summary>
+<h4>Run model_builder</h4>
 
 ```{bash}
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-#rm /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_*.disc_EUR_*/*/res*
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_*.disc_EUR_*/*/res*
 
 for targ_pop in $(echo EAS AFR); do
   if [ "$targ_pop" == "EUR" ]; then
@@ -4288,7 +4263,7 @@ for targ_pop in $(echo EAS AFR); do
   fi
   
   if [ "$targ_pop" == "EUR" ]; then
-    disc_pop=$(echo EAS AFR)
+    disc_pop=$(echo AFR EAS)
   fi
   
   if [ "$targ_pop" == "EAS" ]; then
@@ -4301,65 +4276,365 @@ for targ_pop in $(echo EAS AFR); do
   
   for disc_pop_i in ${disc_pop}; do
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
-      if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt" ]; then
+      if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs.pred_comp.txt" ]; then
         sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
           --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
-          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
-          --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
           --n_core 5"
       fi
     done
   done
 done
+```
+
+***
+
+<h4>Plot results</h4>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+
+# Calculate correlation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop = c('EAS','AFR')
+res_eval <- list()
+for(pheno_i in selected_traits){
+  res_eval_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.tlprs.pred_eval.txt'
+          )
+        )
+      eval_i$Target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_eval_i<-rbind(res_eval_i, eval_i)
+    }
+  }
+  
+  res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
+  res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
+  
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTune'
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTune'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTune'
+  
+  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTune'
+  
+  res_eval_i$Source<-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
+    !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
+  
+  res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
+  res_eval_i$Discovery[grepl('EAS', res_eval_i$Group)] <- 'EAS'
+  res_eval_i$Discovery[grepl('AFR', res_eval_i$Group)] <- 'AFR'
+  res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
+  
+  res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+  res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
+      res_eval_i$Model %in% c('IndivTune','Multi-IndivTune')),]
+  
+  # Remove pseudo model for methods that don't really have one 
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
+      res_eval_i$Model %in% c('SumStatTune','Multi-SumStatTune')),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing, TL-*
+  res_eval_i <- res_eval_i[
+    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method)) & 
+      grepl('top1', res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
+    "Target", "Method", "Model", "Source", "Discovery","gwas_group"
+  )]),]
+  
+  res_eval[[pheno_i]]<-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi","TL-DBSLMM","TL-LDpred2","TL-QuickPRS","TL-SBayesRC", "PRS-CSx", "X-Wing", "All") 
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_eval for each scenario
+    res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x <- res_eval[[i]]
+      x$pheno <- names(res_eval)[i]
+      x <- x[x$Target == targ_pop_i]
+      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
+    }))
+    
+    # Average res_evalults for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_eval_i$Sample<-'A'
+  
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno <-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+      
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          'res_evalults missing for ',
+          targ_pop_i,
+          ' ',
+          group_i,
+          ' ',
+          paste0(missing_pheno, collapse = ' ')
+        ))
+      }
+      
+      cors_i <- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i <-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_eval_group_i
+        )
+      
+      tmp <- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval <- rbind(meta_res_eval, tmp)
+    }
+  }
+}
 
-```
-</details>
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-***
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All' & !grepl('^tlprs_', tmp$Method)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All' & !grepl('^tlprs_', tmp$Method)], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
-### Plot results
+dir.create('~/oliverpainfel/Analyses/crosspop/tlprs/plots')
 
-<details><summary>Show code</summary>
+# Plot unidirectional TL-PRS (as it was intended), comparing the unadjusted EUR PGS to the EUR PGS that has been adjusted according to the target-matched GWAS
+tmp_tlprs_uni <- tmp[grepl('tlprs', tmp$Method) & !grepl('pop-EUR.top1', tmp$Group) & tmp$Source == 'Single', ]
+tmp_tlprs_uni$Type <- 'TL-PRS'
+tmp_unadj <- tmp[!grepl('tlprs', tmp$Method) & tmp$Discovery == 'EUR', ]
+tmp_unadj$Type <- 'Original'
+tmp_both <- rbind(tmp_unadj, tmp_tlprs_uni)
+tmp_both$label<-gsub('TL-','',tmp_both$label)
+tmp_both$Type<-factor(tmp_both$Type, levels = c('Original','TL-PRS'))
 
-```{r}
-setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+png(paste0('~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.png'), res=300, width = 2000, height = 1600, units = 'px')
+ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ ., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
 
-library(data.table)
-library(ggplot2)
-library(cowplot)
+tmp_tlprs_uni <- tmp[grepl('tlprs', tmp$Method) & grepl('pop-EUR.top1', tmp$Group) & tmp$Source == 'Single', ]
+tmp_tlprs_uni$Type <- 'TL-PRS'
+tmp_unadj <- tmp[!grepl('tlprs', tmp$Method) & tmp$Discovery == 'EUR', ]
+tmp_unadj$Type <- 'Original'
+tmp_both <- rbind(tmp_unadj, tmp_tlprs_uni)
+tmp_both$label<-gsub('TL-','',tmp_both$label)
+tmp_both$Type<-factor(tmp_both$Type, levels = c('Original','TL-PRS'))
 
-source('../functions/misc.R')
-source_all('../functions')
+png(paste0('~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.targ_EUR.png'), res=300, width = 2000, height = 1600, units = 'px')
+ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ ., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
 
-# Read in list of outcomes 
-selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
-info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+# Have one column per method, but fill according to Original EUR PGS, Original matched-PGS, TL-PRS EUR Target, TL-PRS non-EUR Target, TL-PRS Multi, and Original-Multi
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$Type <- NA
+tmp$Type[grepl('tlprs', tmp$Method) & grepl('pop-EUR.top1', tmp$Group)]<-"TL-PRS (EUR PGS tuned to target)"
+tmp$Type[grepl('tlprs', tmp$Method) & !grepl('pop-EUR.top1', tmp$Group) & tmp$Source == 'Single']<-"TL-PRS (Target-matched PGS tuned to EUR)"
+tmp$Type[!grepl('tlprs', tmp$Method) & tmp$Discovery == 'EUR']<-"Original (EUR PGS)"
+tmp$Type[!grepl('tlprs', tmp$Method) & tmp$Discovery != 'EUR' & tmp$Source == 'Single']<-"Original (Target-matched PGS)"
+tmp$Type[grepl('tlprs', tmp$Method) & grepl('multi', tmp$Group)]<-"TL-PRS-multi"
+tmp$Type[!grepl('tlprs', tmp$Method) & grepl('multi', tmp$Group)]<-"Original-multi"
+tmp <- tmp[!is.na(tmp$Type),]
+tmp$Type<-factor(tmp$Type, levels=c("Original (EUR PGS)", "Original (Target-matched PGS)", "TL-PRS (EUR PGS tuned to target)", "TL-PRS (Target-matched PGS tuned to EUR)", "Original-multi", "TL-PRS-multi"))
+tmp$label<-gsub('TL-','',tmp$label)
 
-# Calculate correlation between all phenotypes in each target population
-cors <- list()
-for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
-  if(pop_i == 'EUR'){
-    pop_i_2 <- 'EUR_test'
-  } else {
-    pop_i_2 <- pop_i
-  }
-  pheno_pop_i <- list()
-  for(pheno_i in selected_traits){
-    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
-    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
-  }
-  
-  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+png(paste0('~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png'), res=300, width = 4000, height = 2200, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Type)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ ., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
 
-  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
-  cors[[pop_i]] <- cors_i
-}
+#########################
+# Check significance of differences between TL-PRS and unadjusted approaches
+########################
+
+####
+# Create heatmap showing difference between all methods and models
+####
 
+# Create a function to mirror pred_comp results
+mirror_comp<-function(x){
+  x_sym <- x
+  x_sym$Model_1 <- x$Model_2
+  x_sym$Model_2 <- x$Model_1
+  x_sym$Model_1_R <- x$Model_2_R
+  x_sym$Model_2_R <- x$Model_1_R
+  x_sym$R_diff <- -x_sym$R_diff
+  x_mirrored <- rbind(x, x_sym)
+  x_diag<-data.frame(
+      Model_1=unique(x_mirrored$Model_1),
+      Model_2=unique(x_mirrored$Model_1),
+      Model_1_R=x_mirrored$Model_1_R,
+      Model_2_R=x_mirrored$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp<-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
 # Read in results
-targ_pop = c('EAS','AFR')
-res_eval <- list()
+targ_pop=c('EAS','AFR')
+res_comp <- list()
 for(pheno_i in selected_traits){
-  res_eval_i<-NULL
+  res_comp_i<-NULL
   for(targ_pop_i in targ_pop){
     if(targ_pop_i == 'EAS'){
       disc_pop <- 'EAS'
@@ -4371,108 +4646,107 @@ for(pheno_i in selected_traits){
       disc_pop <- c('EAS','AFR')
     }
     for(disc_pop_i in disc_pop){
-      eval_i <-
+      comp_i <-
         fread(
           paste0(
-            '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/',
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/',
             'targ_',
             targ_pop_i,
             '.disc_EUR_',
             disc_pop_i,
             '/',
             pheno_i,
-            '/res.pred_eval.txt'
+            '/res.tlprs.pred_comp.txt'
           )
         )
-      eval_i$Target<-targ_pop_i
-      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
-      res_eval_i<-rbind(res_eval_i, eval_i)
+      comp_i<-mirror_comp(comp_i)
+      comp_i$Target<-targ_pop_i
+      comp_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_comp_i<-rbind(res_comp_i, comp_i)
     }
   }
   
-  res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
-  res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
-  
-  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
-                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTune'
-  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
-                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTune'
-  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
-                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTune'
-  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
-                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTune'
-  
-  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTune'
-  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTune'
-  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTune'
-  
-  res_eval_i$Source<-ifelse(
-    res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
-    !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
-  
-  res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
-  res_eval_i$Discovery[grepl('EAS', res_eval_i$Group)] <- 'EAS'
-  res_eval_i$Discovery[grepl('AFR', res_eval_i$Group)] <- 'AFR'
-  res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
-  
-  res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
-  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
-  res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
-
-  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
-  res_eval_i <- res_eval_i[
-    !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
-      res_eval_i$Model %in% c('IndivTune','Multi-IndivTune')),]
-  
-  # Remove pseudo model for methods that don't really have one 
-  res_eval_i <- res_eval_i[
-    !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
-      res_eval_i$Model %in% c('SumStatTune','Multi-SumStatTune')),]
+  res_comp[[pheno_i]]<-res_comp_i
+}
 
-  # Remove top1 models for *-Multi, PRS-CSx, X-wing
-  res_eval_i <- res_eval_i[
-    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method)) & 
-      grepl('top1', res_eval_i$Group)),]
-  
-  # Remove any duplicate models
-  res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
-    "Target", "Method", "Model", "Source", "Discovery","gwas_group"
-  )]),]
-  
-  res_eval[[pheno_i]]<-res_eval_i
+res_comp_all <- do.call(rbind, lapply(names(res_comp), function(name) {
+  x <- res_comp[[name]]
+  x$pheno <- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Annotate tests to get order correct
+res_comp_all$Method1<-sub('\\..*','',res_comp_all$Model_1)
+res_comp_all$Method1<-gsub('-.*','', res_comp_all$Method1)
+res_comp_all$Method2<-sub('\\..*','',res_comp_all$Model_2)
+res_comp_all$Method2<-gsub('-.*','', res_comp_all$Method2)
+
+find_model<-function(x){
+  mod <- x
+  mod[grepl('top1$', x) & !grepl('pseudo', x)] <- 'IndivTune'
+  mod[grepl('top1$', x) & grepl('pseudo', x)] <- 'SumStatTune'
+  mod[grepl('multi$', x) & !grepl('pseudo', x)] <- 'Multi-IndivTune'
+  mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTune'
+  mod[grepl('_multi', x)] <- 'SumStatTune'
+  mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTune'
+  mod[x == 'xwing.pseudo.multi'] <- 'SumStatTune'
   
+  return(mod)
 }
 
-# Create vector defining or of methods in plots
-model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi", "PRS-CSx", "X-Wing", "All") 
+res_comp_all$Model1<-find_model(res_comp_all$Model_1)
+res_comp_all$Model2<-find_model(res_comp_all$Model_2)
 
-res_eval_simp <- NULL
-for(pheno_i in selected_traits){
-  tmp <- res_eval[[pheno_i]]
-  tmp$Trait <- pheno_i
-  
-  # Insert nice PGS method names
-  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
-  tmp$label[is.na(tmp$label)] <- 'All'
-  tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
-  tmp$label <- factor(tmp$label, levels = model_order)
-  
-  # Simplify result to either SumStatTune or IndivTune
-  tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
-  tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
-  tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery','Model'), with=F]),]
+res_comp_all$Source1<-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method1) | !grepl('AFR|EAS|EUR', res_comp_all$Model_1), 'Multi', 'Single')
+res_comp_all$Source2<-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method2) | !grepl('AFR|EAS|EUR', res_comp_all$Model_2), 'Multi', 'Single')
   
-  res_eval_simp <- rbind(res_eval_simp, tmp)
+for(i in c('EUR','EAS','AFR')){
+  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] <- i
+  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] <- i
 }
+res_comp_all$Discovery1[res_comp_all$Source1 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source1 == 'Multi']
+res_comp_all$Discovery2[res_comp_all$Source2 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source2 == 'Multi']
 
-####
-# Average results across phenotypes
-####
+res_comp_all$Method1<-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
+res_comp_all$Method2<-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
+res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+res_comp_all$Discovery1<-factor(res_comp_all$Discovery1, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
+res_comp_all$Discovery2<-factor(res_comp_all$Discovery2, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+# Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method1 %in%  c('quickprs','sbayesrc') & 
+  res_comp_all$Model1 %in% c('IndivTune','Multi-IndivTune')),]
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method2 %in%  c('quickprs','sbayesrc') & 
+  res_comp_all$Model2 %in% c('IndivTune','Multi-IndivTune')),]
+
+# Remove pseudo model for methods that don't really have one 
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method1 %in%  c('ptclump','ptclump_multi') & 
+  res_comp_all$Model1 %in% c('SumStatTune','Multi-SumStatTune')),]
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method2 %in%  c('ptclump','ptclump_multi') & 
+  res_comp_all$Model2 %in% c('SumStatTune','Multi-SumStatTune')),]
+
+# Remove top1 models for PRS-CSx
+res_comp_all <- res_comp_all[
+!(grepl('prscsx|xwing|_multi', res_comp_all$Method1) & 
+  grepl('top1', res_comp_all$Model_1)),]
+res_comp_all <- res_comp_all[
+!(grepl('prscsx|xwing|_multi', res_comp_all$Method2) & 
+  grepl('top1', res_comp_all$Model_2)),]
+
+# Remove any comparisons
+res_comp_all <- res_comp_all[!duplicated(res_comp_all[, c("Target", "Method1", "Model1", "Source1", "Discovery1", "Method2", "Model2", "Source2", "Discovery2",'pheno')]),]
+
+###########
 
 library(MAd)
 
 # Average R across phenotypes
-meta_res_eval <- NULL
+meta_res_comp <- NULL
 for(targ_pop_i in targ_pop){
   if(targ_pop_i == 'EAS'){
     disc_pop <- 'EAS'
@@ -4485,230 +4759,310 @@ for(targ_pop_i in targ_pop){
   }
   for(disc_pop_i in disc_pop){
   
-    # Subset res_eval for each scenario
-    res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
-      x <- res_eval[[i]]
-      x$pheno <- names(res_eval)[i]
-      x <- x[x$Target == targ_pop_i]
-      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
-    }))
-    
-    # Average res_evalults for each test across phenotypes
+    # Subset res_comp for each scenario
+    res_comp_i <- res_comp_all[res_comp_all$Target == targ_pop_i & res_comp_all$gwas_group == paste0('EUR+', disc_pop_i)]
+  
+    # Calculate diff SE based on p-value
+    res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] <- 1-0.001
+    res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]<-1-0.001
+    res_comp_i$R_diff_z<-qnorm(res_comp_i$R_diff_pval/2)
+    res_comp_i$R_diff_SE<-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
+        
+    # Average results for each test across phenotypes
     # Use MAd to account for correlation between them
-    res_eval_i$Sample<-'A'
+    res_comp_i$Sample<-'A'
+    res_comp_i$Group <- paste0(res_comp_i$Model_1, '_vs_', res_comp_i$Model_2)
   
-    for(group_i in unique(res_eval_i$Group)){
-      res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
-      missing_pheno <-
-        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+    for(group_i in unique(res_comp_i$Group)){
+      res_comp_group_i <- res_comp_i[res_comp_i$Group == group_i,]
+      cors_i <- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
       
-      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
-        print(paste0(
-          'res_evalults missing for ',
-          targ_pop_i,
-          ' ',
-          group_i,
-          ' ',
-          paste0(missing_pheno, collapse = ' ')
-        ))
+      if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
+        
+        meta_res_comp_i <-
+          agg(
+            id = Sample,
+            es = R_diff,
+            var = R_diff_SE ^ 2,
+            cor = cors_i,
+            method = "BHHR",
+            mod = NULL,
+            data = res_comp_group_i
+          )
+        
+        tmp <- res_comp_group_i[1,]
+        tmp$pheno <- NULL
+        tmp$Model_1_R <-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &
+                            meta_res_eval$Target == targ_pop_i &
+                            meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i)]
+        tmp$Model_2_R <-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &
+                            meta_res_eval$Target == targ_pop_i &
+                            meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i)]
+        tmp$R_diff <- meta_res_comp_i$es
+        tmp$R_diff_SE <- sqrt(meta_res_comp_i$var)
+        tmp$R_diff_z <- tmp$R_diff / tmp$R_diff_SE
+        tmp$R_diff_p <- 2*pnorm(-abs(tmp$R_diff_z))
+      } else {
+        tmp <- res_comp_group_i[1,]
+        tmp$pheno <- NULL
+        tmp$R_diff <- NA
+        tmp$R_diff_SE <- NA
+        tmp$R_diff_z <- NA
+        tmp$R_diff_p <- NA
       }
-      
-      cors_i <- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
-      
-      meta_res_eval_i <-
-        agg(
-          id = Sample,
-          es = R,
-          var = SE ^ 2,
-          cor = cors_i,
-          method = "BHHR",
-          mod = NULL,
-          data = res_eval_group_i
-        )
-      
-      tmp <- data.table(Group = group_i,
-                        Method = res_eval_group_i$Method[1],
-                        Model = res_eval_group_i$Model[1],
-                        Source = res_eval_group_i$Source[1],
-                        Discovery = res_eval_group_i$Discovery[1],
-                        gwas_group = res_eval_group_i$gwas_group[1],
-                        Target = targ_pop_i,
-                        R = meta_res_eval_i$es,
-                        SE = sqrt(meta_res_eval_i$var))
-      
-      meta_res_eval <- rbind(meta_res_eval, tmp)
+      meta_res_comp <- rbind(meta_res_comp, tmp)
     }
   }
 }
 
-meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
-meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+meta_res_comp$R_diff_perc <- meta_res_comp$R_diff / meta_res_comp$Model_2_R
+  
+# Compare IndivTune SBayesRC-multi to TL-SBayesRC-multi
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_2 == 'sbayesrc.pseudo.multi' & 
+                                meta_res_comp$Model_1 == 'tlprs_sbayesrc.pop.multi' &
+                    meta_res_comp$Target == 'AFR',]
+round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
+tmp_sbayesrc$R_diff_p
+
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_2 == 'sbayesrc.pseudo.multi' & 
+                                meta_res_comp$Model_1 == 'tlprs_sbayesrc.pop.multi' &
+                    meta_res_comp$Target == 'EAS',]
+round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
+tmp_sbayesrc$R_diff_p
+
+```
+
+</details>
+
+
+```{bash, eval=T, echo=F}
+
+cp ~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_tlprs.png
+
+```
+
+<details><summary>Show TLPRS results</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/average_r_tlprs.png">
+</div>
+</div>
+
+</details>
+
+***
+
+## Computational resoures
+
+<details><summary>Show code</summary>
+
+```{r}
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in configuration specific benchmark files
+bm_files_i <- list.files(paste0(outdir, '/reference/benchmarks/'), full.names = T)
+
+# Subset benchmarks for pgs_methods
+bm_files_i <- bm_files_i[grepl('prep_pgs_tlprs', bm_files_i)]
+
+# Read in benchmark files
+bm_dat_all <- do.call(rbind, lapply(bm_files_i, function(file) {
+  tmp <- fread(file)
+  tmp$file <- basename(file)
+  return(tmp)
+}))
+
+# Create rule column
+bm_dat_all$rule <- gsub('-.*','',bm_dat_all$file)
+
+# Create method column
+bm_dat_all$method <-
+  gsub('_i', '', gsub('prep_pgs_', '', bm_dat_all$rule))
+
+#############
+# Time
+#############
+
+# Calculate average time taken for each method
+method_avg <- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg <- rbind(
+    method_avg,
+    data.frame(
+      Method = i,
+      Time = mean(bm_dat_all$s[bm_dat_all$method == i])
+    )
+  )
+}
+
+# Convert time in seconds to hours
+method_avg$Time_hour <- method_avg$Time / 60/60
+method_avg$Time_hour <- round(method_avg$Time_hour, 2)
 
-# Plot average performance across phenotypes for AFR and EAS targets
-tmp <- meta_res_eval
-tmp <- tmp[tmp$Target != 'EUR',]
-tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
-tmp$label[is.na(tmp$label)] <- 'All'
-tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
-tmp$label <- factor(tmp$label, levels = model_order)
-tmp$Discovery_clean <- as.character(tmp$Discovery)
-tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
-tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
-tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
-tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
-                              levels = c('Target-matched GWAS',
-                                         'EUR GWAS',
-                                         'Both'))
-tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
-tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
-tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
+#This is for bidirectional TL-PRS
 
-png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid(major = 'y', minor = 'y') + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
-          legend.position = "top",
-          legend.key.spacing.x = unit(1, "cm"),
-          legend.justification = "center")
-dev.off()
+#############
+# Memory
+#############
 
-# The results look very similar to when using 1KG+HGDP. 
+# Calculate average max_rss for each method
+method_avg_mem <- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg_mem <- rbind(
+    method_avg_mem,
+    data.frame(
+      Method = i,
+      Memory = mean(bm_dat_all$max_rss[bm_dat_all$method == i])
+    )
+  )
+}
 
-###################
-# Plot a comparison between the runs using different references
+# Format the Memory nicely
+method_avg_mem$Memory_clean <-
+  paste0(round(method_avg_mem$Memory/1000, 2), ' Gb')
 
-# Read in results using 1KG+HGDP reference
-main_results<-fread('~/oliverpainfel/Analyses/crosspop/r_eval.csv')
-sens_results<-meta_res_eval
+ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
+  geom_bar(stat = "identity", position="dodge") +
+  geom_text(aes(label = Memory_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
+  labs(x = "PGS Method", y = "Memory (Mb)") +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
 
-tmp <- main_results
-tmp <- tmp[tmp$Target != 'EUR',]
-tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
-tmp$label[is.na(tmp$label)] <- 'All'
-tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
-tmp$label <- factor(tmp$label, levels = model_order)
-tmp$Discovery_clean <- as.character(tmp$Discovery)
-tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
-tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
-tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
-tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
-                              levels = c('Target-matched GWAS',
-                                         'EUR GWAS',
-                                         'Both'))
-tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
-tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
-main_results <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
+method_avg_mem$Memory_gb <- method_avg_mem$Memory/1000
+method_avg_mem <- method_avg_mem[, c('Method','Memory_gb')]
+method_avg_mem$Memory_gb <- round(method_avg_mem$Memory_gb, 2)
+names(method_avg_mem)<-c('Method',"Memory (Gb)")
 
-tmp <- sens_results
-tmp <- tmp[tmp$Target != 'EUR',]
-tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
-tmp$label[is.na(tmp$label)] <- 'All'
-tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
-tmp$label <- factor(tmp$label, levels = model_order)
-tmp$Discovery_clean <- as.character(tmp$Discovery)
-tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
-tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
-tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
-tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
-                              levels = c('Target-matched GWAS',
-                                         'EUR GWAS',
-                                         'Both'))
-tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
-tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
-sens_results <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
+method_avg<-merge(method_avg, method_avg_mem, by = 'Method')
 
-main_results<-main_results[main_results$Method %in% sens_results$Method,]
-main_results<-main_results[main_results$Target %in% sens_results$Target,]
+write.csv(method_avg, '~/oliverpainfel/Analyses/crosspop/time_memory_tlprs.csv', row.names=F)
+```
 
-sens_results$Reference <- '1KG'
-main_results$Reference <- '1KG+HGDP'
+</details>
 
-both_results <- rbind(main_results, sens_results)
+<details><summary>Show computational resources table</summary>
+
+```{r, eval = T, echo = F}
+method_avg<-fread('~/oliverpainfel/Analyses/crosspop/time_memory_tlprs.csv')
+
+kable(method_avg, "html") %>%
+  kable_styling(bootstrap_options = c("striped", "hover"),
+                full_width = F)
 
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/comparison_to_main_result.png', units = 'px', res = 300, width=4000, height=2500)
-ggplot(both_results, aes(x=label, y=R , fill = Model)) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(Target ~ Discovery_clean + Reference, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid(major = 'y', minor = 'y') + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
-          legend.position = "top",
-          legend.key.spacing.x = unit(1, "cm"),
-          legend.justification = "center")
-dev.off()
-  
 ```
+
 </details>
 
 ***
 
-# TL-PRS
+# Sensitivity analyses
 
-Run using AFR and EAS subset in UKB to make it quicker to run. This is the main interest when running TL-PRS anyway. 
+***
+
+## Using 1KG reference
+
+PRS-CS, PRS-CSx and X-Wing all use the 1KG reference sample, whereas the other methods are using the 1KG+HGDP reference sample. We should check whether this difference is impacting our conclusions.
+
+To make this quicker, focus on evaluating the PGS methods in the AFR and EAS target individuals in UKB. This will avoid reprocessing the full UKB data.
+
+***
+
+### Create 1KG only GenoPred reference data
+
+Subset the 1KG+HGDP reference data to include only 1KG individuals.
+
+<details><summary>Show code</summary>
+
+```{bash}
+mkdir -p ~/oliverpainfel/Data/1kg/genopred/
+cp -r ~/oliverpainfel/Data/hgdp_1kg/genopred/ref ~/oliverpainfel/Data/1kg/genopred/
+rm ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr*.p*
+```
+
+```{r}
+library(data.table)
+
+ref<- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr1.psam')
+ref<-ref[ref$Project == 'gnomAD_1kG',]
+
+write.table(ref[,1, drop = F], '~/oliverpainfel/Data/1kg/1kg.keep', col.names=F, row.names=F, quote=F)
+```
+
+```{bash}
+for chr in $(seq 1 22); do
+  ~/oliverpainfel/Software/plink2 \
+    --pfile ~/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} \
+    --keep ~/oliverpainfel/Data/1kg/1kg.keep \
+    --make-pgen \
+    --out ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr${chr}
+done
+```
+
+</details>
 
 ***
 
-## Run pipeline
+### PGS calculation
 
 To save time, run using PGS methods that do not need pre-processed LD matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary from the 1KG+HGDP results, then expand to other methods (LDpred2, SBayesRC, QuickPRS).
 
 <details><summary>Show code</summary>
 
+<div class="shallow-break"></div>
+
+<h4>Prepare configuration</h4>
+
 ```{r}
 library(data.table)
 
-dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs')
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only')
 
 ######
 # config
 ######
 
 config<-c(
-  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_tlprs",
-  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml",
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_1kgref",
+  "refdir: /users/k1806347/oliverpainfel/Data/1kg/genopred/ref",
+  "resdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/resdir_1kgref",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml",
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt",
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt",
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
-  "pgs_methods: ['quickprs','dbslmm','ldpred2','sbayesrc']",
-  "tlprs_methods: ['quickprs','dbslmm','ldpred2','sbayesrc']",
+  "pgs_methods: ['ptclump','dbslmm','lassosum','megaprs']",
+#  "leopard_methods: ['ptclump','dbslmm','lassosum','megaprs']",
   "cores_prep_pgs: 10",
-  "cores_target_pgs: 50",
-  "prscs_phi: ['auto']",
-  "ldpred2_model: ['auto']",
-  "ldpred2_inference: F",
-  "dbslmm_h2f: ['1']",
-  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
-  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3",
-  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
+  "cores_target_pgs: 10"
 )
 
-write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml', col.names = F, row.names = F, quote = F)
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml', col.names = F, row.names = F, quote = F)
 
 ```
 
+***
+
+<h4>Run pipeline</h4>
+
 ```{bash}
 snakemake \
   --profile slurm \
   --use-conda \
-  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \
   output_all -n
 ```
 
@@ -4716,10 +5070,14 @@ snakemake \
 
 ***
 
-## Create predictor lists
+### PGS evaluation
 
 <details><summary>Show code</summary>
 
+<div class="shallow-break"></div>
+
+<h4>Create predictor list</h4>
+
 ```{r}
 
 setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -4728,7 +5086,8 @@ source_all('../functions')
 library(data.table)
 
 # Get some key variables from config
-config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml'
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
@@ -4741,9 +5100,9 @@ scores <- list_score_files(config)
 targ_pop <- c('EAS','AFR')
 for(trait_i in selected_traits){
   scores_i <- scores[grepl(trait_i, scores$name),]
-
+  scores_i$multi <- scores_i$method
+  
   for(targ_pop_i in targ_pop){
-    # Subset GWAS based on EUR and/or targ_pop_i
     if(targ_pop_i == 'EAS'){
       disc_pop <- 'BBJ'
     }
@@ -4764,7 +5123,7 @@ for(trait_i in selected_traits){
 
       dir.create(
         paste0(
-          '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_',
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_',
           targ_pop_i,
           '.disc_EUR_',
           disc_pop_j_2,
@@ -4778,6 +5137,7 @@ for(trait_i in selected_traits){
         (grepl('UKB$', scores_i$name, ignore.case = F) | 
          grepl(paste0(disc_pop_j, '$'), scores_i$name, ignore.case = T)),]
 
+      # Insert path to score file
       scores_i_j$predictor <- paste0(
         outdir,
         '/ukb/pgs/TRANS/',
@@ -4789,70 +5149,250 @@ for(trait_i in selected_traits){
         '-TRANS.profiles'
       )
       
-      #####
-      # List single-source PGS
-      #####
-      # These are actually pseudoval scores (as per the config)
-      scores_i_j_single <- scores_i_j[!grepl('tlprs', scores_i_j$method),]
-      
-      scores_i_j_single$top1[grepl('UKB', scores_i_j_single$name, ignore.case = F)] <- 'EUR'
-      scores_i_j_single$top1[grepl(disc_pop_j, scores_i_j_single$name, ignore.case = F)] <- disc_pop_j_2
-      scores_i_j_single$multi <- paste0(scores_i_j_single$method,'.pseudo')
-      
-      #####
-      # List tlprs scores (split by target population)
-      #####
-      scores_i_j_tlprs <- scores_i_j[grepl('tlprs', scores_i_j$method),]
-      scores_i_j_tlprs$multi <- scores_i_j_tlprs$method
+      ####
+      # Make groups single source methods
+      ####
       
-      scores_i_j_tlprs_pop<-NULL
-      for(i in 1:nrow(scores_i_j_tlprs)){
-        score_header<-fread(scores_i_j_tlprs$predictor[i], nrow = 1)
-        
-        for(pop in c('EUR', disc_pop_j_2)){
-          score_cols <- which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_'), names(score_header)))
+      scores_i_j_single_top1 <-
+        scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &
+                     !grepl('_multi$', scores_i_j$method), ]
 
-          system(
-            paste0(
-              "cut -d' ' -f ", 
-              paste0(score_cols, collapse=','),
-              " ", 
-              scores_i_j_tlprs$predictor[i], 
-              " > ", 
-              gsub('.profiles',
-                   paste0('.targ_', pop, '.profiles'),
-                   scores_i_j_tlprs$predictor[i])
-            )
+      # Create top1 column indicating which predictors top1 models should be derived
+      scores_i_j_single_top1$top1[grepl('UKB', scores_i_j_single_top1$name, ignore.case = F)] <- 'EUR'
+      scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] <- disc_pop_j_2
+      
+      ####
+      # Make groups containing pseudo scores for single source methods
+      ####
+
+      # Extract the pseudo score for each method and specify as a separate group
+      for(i in 1:nrow(scores_i_j_single_top1)) {
+        param <- find_pseudo(
+          config = config,
+          gwas = scores_i_j_single_top1$name[i],
+          pgs_method = scores_i_j_single_top1$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header <-
+          fread(scores_i_j_single_top1$predictor[i], nrows = 1)
+        score_cols <-
+          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_single_top1$name[i], '_', param)))
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            scores_i_j_single_top1$predictor[i], 
+            " > ", 
+            gsub('.profiles',
+                 paste0('.', targ_pop_i, '_pseudo.profiles'),
+                 scores_i_j_single_top1$predictor[i])
           )
-          
-          tmp <- scores_i_j_tlprs[i,]
-          tmp$multi <- paste0(tmp$multi, '.pop')
-          tmp$top1 <- pop
-          tmp$predictor <-
-              gsub('.profiles',
-                   paste0('.targ_', pop, '.profiles'),
-                   scores_i_j_tlprs$predictor[i])
-          
-          scores_i_j_tlprs_pop <- rbind(scores_i_j_tlprs_pop, tmp)
-        }
+        )
       }
+      
+      scores_i_j_single_pseudo <- scores_i_j_single_top1
+      scores_i_j_single_pseudo$multi <- paste0(scores_i_j_single_pseudo$multi, '.pseudo')
+
+      scores_i_j_single_pseudo$predictor <- gsub('.profiles', 
+                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
+                                    scores_i_j_single_pseudo$predictor)
 
+#      ####
+#      # Make groups for multi-single-source pseudo scores
+#      ####
+#      
+#      scores_i_j_multi_single_pseudo <- scores_i_j[grepl('_multi$', scores_i_j$method),]
+#
+#      # Extract the pseudo score for each method and specify as a separate group
+#      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
+#        param <- find_pseudo(
+#          config = config,
+#          gwas = scores_i_j_multi_single_pseudo$name[i],
+#          pgs_method = scores_i_j_multi_single_pseudo$method[i],
+#          target_pop = targ_pop_i
+#        )
+#        
+#        score_header <-
+#          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
+#        score_cols <-
+#          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi_single_pseudo$name[i], '_', param)))
+#        
+#        system(
+#          paste0(
+#            "cut -d' ' -f ", 
+#            paste0(score_cols, collapse=','),
+#            " ", 
+#            scores_i_j_multi_single_pseudo$predictor[i], 
+#            " > ", 
+#            gsub('.profiles',
+#                 paste0('.', targ_pop_i, '_pseudo.profiles'),
+#                 scores_i_j_multi_single_pseudo$predictor[i])
+#          )
+#        )
+#      }
+#      
+#      scores_i_j_multi_single_pseudo$multi <- paste0(scores_i_j_multi_single_pseudo$multi, '.pseudo')
+#
+#      scores_i_j_multi_single_pseudo$predictor <- gsub('.profiles', 
+#                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
+#                                    scores_i_j_multi_single_pseudo$predictor)
+#      
+#      scores_i_j_multi_single_pseudo$top1<-paste0('EUR_', disc_pop_j_2)
+#
+#      ####
+#      # Make groups for the Multi-Source methods
+#      ####
+#      
+#      scores_i_j_multi <- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
+#
+#      # Split top1 scores by target population
+#      # This doesn't apply to xwing because it only has pop-specific pseudo scores
+#      scores_i_j_multi_top1<-NULL
+#      for(i in 1:which(scores_i_j_multi$method %in% c('prscsx'))){
+#        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
+#        
+#        for(pop in c('EUR', disc_pop_j_2)){
+#          
+#          if(scores_i_j_multi$method[i] == 'prscsx'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi'), names(score_header)))
+#          }
+#          if(scores_i_j_multi$method[i] == 'xwing'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst'), names(score_header)))
+#          }
+#          
+#          system(
+#            paste0(
+#              "cut -d' ' -f ", 
+#              paste0(score_cols, collapse=','),
+#              " ", 
+#              scores_i_j_multi$predictor[i], 
+#              " > ", 
+#              gsub('.profiles',
+#                   paste0('.', pop, '_grid.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#            )
+#          )
+#          
+#          tmp <- scores_i_j_multi[i,]
+#          tmp$multi <- paste0(tmp$multi, '.grid')
+#          tmp$top1 <- pop
+#          tmp$predictor <-
+#              gsub('.profiles',
+#                   paste0('.', pop, '_grid.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#          
+#          scores_i_j_multi_top1 <- rbind(scores_i_j_multi_top1, tmp)
+#        }
+#      }
+#
+#      # Split pop-specific pseudo scores by target population
+#      scores_i_j_multi_pop_pseudo<-NULL
+#      for(i in 1:nrow(scores_i_j_multi)){
+#        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
+#        
+#        for(pop in c('EUR', disc_pop_j_2)){
+#          if(scores_i_j_multi$method[i] == 'prscsx'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi_auto'), names(score_header)))
+#          }
+#          if(scores_i_j_multi$method[i] == 'xwing'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst_', pop), names(score_header)))
+#          }
+#          
+#          system(
+#            paste0(
+#              "cut -d' ' -f ", 
+#              paste0(score_cols, collapse=','),
+#              " ", 
+#              scores_i_j_multi$predictor[i], 
+#              " > ", 
+#              gsub('.profiles',
+#                   paste0('.', pop, '_pseudo.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#            )
+#          )
+#          
+#          tmp <- scores_i_j_multi[i,]
+#          tmp$multi <- paste0(tmp$multi, '.pop_pseudo')
+#          tmp$top1 <- pop
+#          tmp$predictor <-
+#              gsub('.profiles',
+#                   paste0('.', pop, '_pseudo.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#          
+#          scores_i_j_multi_pop_pseudo <- rbind(scores_i_j_multi_pop_pseudo, tmp)
+#        }
+#      }
+#      
+#      # Create pseudo score for multi-source methods
+#      scores_i_j_multi_pseudo<-NULL
+#      for(i in 1:nrow(scores_i_j_multi)) {
+#        param <- find_pseudo(
+#          config = config,
+#          gwas = scores_i_j_multi$name[i],
+#          pgs_method = scores_i_j_multi$method[i],
+#          target_pop = targ_pop_i
+#        )
+#        
+#        score_header <-
+#          fread(scores_i_j_multi$predictor[i], nrows = 1)
+#        score_cols <-
+#          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
+#
+#        system(
+#          paste0(
+#            "cut -d' ' -f ", 
+#            paste0(score_cols, collapse=','),
+#            " ", 
+#            scores_i_j_multi$predictor[i], 
+#            " > ", 
+#            gsub('.profiles',
+#                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
+#                 scores_i_j_multi$predictor[i])
+#          )
+#        )
+#        
+#        tmp <- scores_i_j_multi[i,]
+#        tmp$multi <- paste0(tmp$multi, '.pseudo')
+#        tmp$top1 <- paste0('EUR_', disc_pop_j_2)
+#        tmp$predictor <-
+#            gsub('.profiles',
+#                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
+#                 scores_i_j_multi$predictor[i])
+#        
+#        scores_i_j_multi_pseudo <- rbind(scores_i_j_multi_pseudo, tmp)
+#      }
+      
+      ####
+      # Combine the different predictor groups
+      ####
       predictors_i<- do.call(rbind, list(
-        scores_i_j_single, scores_i_j_tlprs_pop
+        scores_i_j_single_top1, 
+        scores_i_j_single_pseudo#, 
+#        scores_i_j_multi_single_pseudo,
+#        scores_i_j_multi_top1,
+#        scores_i_j_multi_pop_pseudo,
+#        scores_i_j_multi_pseudo
       ))
       
-      predictors_i <- predictors_i[, c('predictor', 'top1','multi'), with=F]
+      predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
       
       write.table(
         predictors_i,
         paste0(
-          '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_',
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_',
           targ_pop_i,
           '.disc_EUR_',
           disc_pop_j_2,
           '/',
           trait_i,
-          '/predictor_list.tlprs.txt'
+          '/predictor_list.txt'
         ),
         col.names = T,
         row.names = F,
@@ -4864,19 +5404,15 @@ for(trait_i in selected_traits){
 
 ```
 
-</details>
-
 ***
 
-## Run model_builder
-
-<details><summary>Show code</summary>
+<h4>Run model_builder</h4>
 
 ```{bash}
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-#rm /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_*.disc_EUR_*/*/res*
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_*.disc_EUR_*/*/res*
 
 for targ_pop in $(echo EAS AFR); do
   if [ "$targ_pop" == "EUR" ]; then
@@ -4886,7 +5422,7 @@ for targ_pop in $(echo EAS AFR); do
   fi
   
   if [ "$targ_pop" == "EUR" ]; then
-    disc_pop=$(echo AFR EAS)
+    disc_pop=$(echo EAS AFR)
   fi
   
   if [ "$targ_pop" == "EAS" ]; then
@@ -4899,368 +5435,62 @@ for targ_pop in $(echo EAS AFR); do
   
   for disc_pop_i in ${disc_pop}; do
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
-      if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs.pred_comp.txt" ]; then
+      if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt" ]; then
         sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
           --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
-          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
-          --out /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
           --n_core 5"
       fi
     done
   done
 done
-```
-</details>
-
-***
-
-## Plot results
-
-<details><summary>Show code</summary>
-
-```{r}
-setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
-
-library(data.table)
-library(ggplot2)
-library(cowplot)
-
-source('../functions/misc.R')
-source_all('../functions')
-
-# Read in list of outcomes 
-selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
-info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
-
-# Calculate correlation between all phenotypes in each target population
-cors <- list()
-for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
-  if(pop_i == 'EUR'){
-    pop_i_2 <- 'EUR_test'
-  } else {
-    pop_i_2 <- pop_i
-  }
-  pheno_pop_i <- list()
-  for(pheno_i in selected_traits){
-    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
-    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
-  }
-  
-  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
-
-  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
-  cors[[pop_i]] <- cors_i
-}
-
-# Read in results
-targ_pop = c('EAS','AFR')
-res_eval <- list()
-for(pheno_i in selected_traits){
-  res_eval_i<-NULL
-  for(targ_pop_i in targ_pop){
-    if(targ_pop_i == 'EAS'){
-      disc_pop <- 'EAS'
-    }
-    if(targ_pop_i == 'AFR'){
-      disc_pop <- 'AFR'
-    }
-    if(targ_pop_i == 'EUR'){
-      disc_pop <- c('EAS','AFR')
-    }
-    for(disc_pop_i in disc_pop){
-      eval_i <-
-        fread(
-          paste0(
-            '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/',
-            'targ_',
-            targ_pop_i,
-            '.disc_EUR_',
-            disc_pop_i,
-            '/',
-            pheno_i,
-            '/res.tlprs.pred_eval.txt'
-          )
-        )
-      eval_i$Target<-targ_pop_i
-      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
-      res_eval_i<-rbind(res_eval_i, eval_i)
-    }
-  }
-  
-  res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
-  res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
-  
-  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
-                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTune'
-  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
-                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTune'
-  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
-                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTune'
-  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
-                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTune'
-  
-  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTune'
-  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTune'
-  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTune'
-  
-  res_eval_i$Source<-ifelse(
-    res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
-    !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
-  
-  res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
-  res_eval_i$Discovery[grepl('EAS', res_eval_i$Group)] <- 'EAS'
-  res_eval_i$Discovery[grepl('AFR', res_eval_i$Group)] <- 'AFR'
-  res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
-  
-  res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
-  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
-  res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
-
-  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
-  res_eval_i <- res_eval_i[
-    !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
-      res_eval_i$Model %in% c('IndivTune','Multi-IndivTune')),]
-  
-  # Remove pseudo model for methods that don't really have one 
-  res_eval_i <- res_eval_i[
-    !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
-      res_eval_i$Model %in% c('SumStatTune','Multi-SumStatTune')),]
-
-  # Remove top1 models for *-Multi, PRS-CSx, X-wing, TL-*
-  res_eval_i <- res_eval_i[
-    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method)) & 
-      grepl('top1', res_eval_i$Group)),]
-  
-  # Remove any duplicate models
-  res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
-    "Target", "Method", "Model", "Source", "Discovery","gwas_group"
-  )]),]
-  
-  res_eval[[pheno_i]]<-res_eval_i
-  
-}
-
-# Create vector defining or of methods in plots
-model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi","TL-DBSLMM","TL-LDpred2","TL-QuickPRS","TL-SBayesRC", "PRS-CSx", "X-Wing", "All") 
-
-####
-# Average results across phenotypes
-####
-
-library(MAd)
-
-# Average R across phenotypes
-meta_res_eval <- NULL
-for(targ_pop_i in targ_pop){
-  if(targ_pop_i == 'EAS'){
-    disc_pop <- 'EAS'
-  }
-  if(targ_pop_i == 'AFR'){
-    disc_pop <- 'AFR'
-  }
-  if(targ_pop_i == 'EUR'){
-    disc_pop <- c('EAS','AFR')
-  }
-  for(disc_pop_i in disc_pop){
-  
-    # Subset res_eval for each scenario
-    res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
-      x <- res_eval[[i]]
-      x$pheno <- names(res_eval)[i]
-      x <- x[x$Target == targ_pop_i]
-      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
-    }))
-    
-    # Average res_evalults for each test across phenotypes
-    # Use MAd to account for correlation between them
-    res_eval_i$Sample<-'A'
-  
-    for(group_i in unique(res_eval_i$Group)){
-      res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
-      missing_pheno <-
-        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
-      
-      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
-        print(paste0(
-          'res_evalults missing for ',
-          targ_pop_i,
-          ' ',
-          group_i,
-          ' ',
-          paste0(missing_pheno, collapse = ' ')
-        ))
-      }
-      
-      cors_i <- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
-      
-      meta_res_eval_i <-
-        agg(
-          id = Sample,
-          es = R,
-          var = SE ^ 2,
-          cor = cors_i,
-          method = "BHHR",
-          mod = NULL,
-          data = res_eval_group_i
-        )
-      
-      tmp <- data.table(Group = group_i,
-                        Method = res_eval_group_i$Method[1],
-                        Model = res_eval_group_i$Model[1],
-                        Source = res_eval_group_i$Source[1],
-                        Discovery = res_eval_group_i$Discovery[1],
-                        gwas_group = res_eval_group_i$gwas_group[1],
-                        Target = targ_pop_i,
-                        R = meta_res_eval_i$es,
-                        SE = sqrt(meta_res_eval_i$var))
-      
-      meta_res_eval <- rbind(meta_res_eval, tmp)
-    }
-  }
-}
-
-meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
-meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
-
-# Plot average performance across phenotypes for AFR and EAS targets
-tmp <- meta_res_eval
-tmp <- tmp[tmp$Target != 'EUR',]
-tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
-tmp$label[is.na(tmp$label)] <- 'All'
-tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All' & !grepl('^tlprs_', tmp$Method)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All' & !grepl('^tlprs_', tmp$Method)], '-multi')
-tmp$label <- factor(tmp$label, levels = model_order)
-tmp$Discovery_clean <- as.character(tmp$Discovery)
-tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
-tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
-tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
-tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
-                              levels = c('Target-matched GWAS',
-                                         'EUR GWAS',
-                                         'Both'))
-tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
-tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
-tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
-
-dir.create('~/oliverpainfel/Analyses/crosspop/tlprs/plots')
 
-# Plot unidirectional TL-PRS (as it was intended), comparing the unadjusted EUR PGS to the EUR PGS that has been adjusted according to the target-matched GWAS
-tmp_tlprs_uni <- tmp[grepl('tlprs', tmp$Method) & !grepl('pop-EUR.top1', tmp$Group) & tmp$Source == 'Single', ]
-tmp_tlprs_uni$Type <- 'TL-PRS'
-tmp_unadj <- tmp[!grepl('tlprs', tmp$Method) & tmp$Discovery == 'EUR', ]
-tmp_unadj$Type <- 'Original'
-tmp_both <- rbind(tmp_unadj, tmp_tlprs_uni)
-tmp_both$label<-gsub('TL-','',tmp_both$label)
-tmp_both$Type<-factor(tmp_both$Type, levels = c('Original','TL-PRS'))
+```
 
-png(paste0('~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.png'), res=300, width = 2000, height = 1600, units = 'px')
-ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(Target ~ ., scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid(major = 'y', minor = 'y') + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
-          legend.position = "top",
-          legend.key.spacing.x = unit(1, "cm"),
-          legend.justification = "center")
-dev.off()
+***
 
-tmp_tlprs_uni <- tmp[grepl('tlprs', tmp$Method) & grepl('pop-EUR.top1', tmp$Group) & tmp$Source == 'Single', ]
-tmp_tlprs_uni$Type <- 'TL-PRS'
-tmp_unadj <- tmp[!grepl('tlprs', tmp$Method) & tmp$Discovery == 'EUR', ]
-tmp_unadj$Type <- 'Original'
-tmp_both <- rbind(tmp_unadj, tmp_tlprs_uni)
-tmp_both$label<-gsub('TL-','',tmp_both$label)
-tmp_both$Type<-factor(tmp_both$Type, levels = c('Original','TL-PRS'))
+<h4>Plot results</h4>
 
-png(paste0('~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.targ_EUR.png'), res=300, width = 2000, height = 1600, units = 'px')
-ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(Target ~ ., scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid(major = 'y', minor = 'y') + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
-          legend.position = "top",
-          legend.key.spacing.x = unit(1, "cm"),
-          legend.justification = "center")
-dev.off()
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 
-# Have one column per method, but fill according to Original EUR PGS, Original matched-PGS, TL-PRS EUR Target, TL-PRS non-EUR Target, TL-PRS Multi, and Original-Multi
-tmp <- meta_res_eval
-tmp <- tmp[tmp$Target != 'EUR',]
-tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
-tmp$Type <- NA
-tmp$Type[grepl('tlprs', tmp$Method) & grepl('pop-EUR.top1', tmp$Group)]<-"TL-PRS (EUR PGS tuned to target)"
-tmp$Type[grepl('tlprs', tmp$Method) & !grepl('pop-EUR.top1', tmp$Group) & tmp$Source == 'Single']<-"TL-PRS (Target-matched PGS tuned to EUR)"
-tmp$Type[!grepl('tlprs', tmp$Method) & tmp$Discovery == 'EUR']<-"Original (EUR PGS)"
-tmp$Type[!grepl('tlprs', tmp$Method) & tmp$Discovery != 'EUR' & tmp$Source == 'Single']<-"Original (Target-matched PGS)"
-tmp$Type[grepl('tlprs', tmp$Method) & grepl('multi', tmp$Group)]<-"TL-PRS-multi"
-tmp$Type[!grepl('tlprs', tmp$Method) & grepl('multi', tmp$Group)]<-"Original-multi"
-tmp <- tmp[!is.na(tmp$Type),]
-tmp$Type<-factor(tmp$Type, levels=c("Original (EUR PGS)", "Original (Target-matched PGS)", "TL-PRS (EUR PGS tuned to target)", "TL-PRS (Target-matched PGS tuned to EUR)", "Original-multi", "TL-PRS-multi"))
-tmp$label<-gsub('TL-','',tmp$label)
+library(data.table)
+library(ggplot2)
+library(cowplot)
 
-png(paste0('~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png'), res=300, width = 4000, height = 2200, units = 'px')
-ggplot(tmp, aes(x=label, y=R , fill = Type)) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(Target ~ ., scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid(major = 'y', minor = 'y') + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-dev.off()
+source('../functions/misc.R')
+source_all('../functions')
 
-#########################
-# Check significance of differences between TL-PRS and unadjusted approaches
-########################
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
 
-####
-# Create heatmap showing difference between all methods and models
-####
+# Calculate correlation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
 
-# Create a function to mirror pred_comp results
-mirror_comp<-function(x){
-  x_sym <- x
-  x_sym$Model_1 <- x$Model_2
-  x_sym$Model_2 <- x$Model_1
-  x_sym$Model_1_R <- x$Model_2_R
-  x_sym$Model_2_R <- x$Model_1_R
-  x_sym$R_diff <- -x_sym$R_diff
-  x_mirrored <- rbind(x, x_sym)
-  x_diag<-data.frame(
-      Model_1=unique(x_mirrored$Model_1),
-      Model_2=unique(x_mirrored$Model_1),
-      Model_1_R=x_mirrored$Model_1_R,
-      Model_2_R=x_mirrored$Model_1_R,
-      R_diff=NA,
-      R_diff_pval=NA
-    )
-  x_comp<-rbind(x_mirrored, x_diag)
-  return(x_comp)
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
 }
-  
+
 # Read in results
-targ_pop=c('EAS','AFR')
-res_comp <- list()
+targ_pop = c('EAS','AFR')
+res_eval <- list()
 for(pheno_i in selected_traits){
-  res_comp_i<-NULL
+  res_eval_i<-NULL
   for(targ_pop_i in targ_pop){
     if(targ_pop_i == 'EAS'){
       disc_pop <- 'EAS'
@@ -5272,107 +5502,108 @@ for(pheno_i in selected_traits){
       disc_pop <- c('EAS','AFR')
     }
     for(disc_pop_i in disc_pop){
-      comp_i <-
+      eval_i <-
         fread(
           paste0(
-            '/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/',
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/',
             'targ_',
             targ_pop_i,
             '.disc_EUR_',
             disc_pop_i,
             '/',
             pheno_i,
-            '/res.tlprs.pred_comp.txt'
+            '/res.pred_eval.txt'
           )
         )
-      comp_i<-mirror_comp(comp_i)
-      comp_i$Target<-targ_pop_i
-      comp_i$gwas_group<-paste0('EUR+', disc_pop_i)
-      res_comp_i<-rbind(res_comp_i, comp_i)
+      eval_i$Target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_eval_i<-rbind(res_eval_i, eval_i)
     }
   }
   
-  res_comp[[pheno_i]]<-res_comp_i
-}
-
-res_comp_all <- do.call(rbind, lapply(names(res_comp), function(name) {
-  x <- res_comp[[name]]
-  x$pheno <- name  # Add a new column with the name of the element
-  x  # Return the updated dataframe
-}))
-
-# Annotate tests to get order correct
-res_comp_all$Method1<-sub('\\..*','',res_comp_all$Model_1)
-res_comp_all$Method1<-gsub('-.*','', res_comp_all$Method1)
-res_comp_all$Method2<-sub('\\..*','',res_comp_all$Model_2)
-res_comp_all$Method2<-gsub('-.*','', res_comp_all$Method2)
-
-find_model<-function(x){
-  mod <- x
-  mod[grepl('top1$', x) & !grepl('pseudo', x)] <- 'IndivTune'
-  mod[grepl('top1$', x) & grepl('pseudo', x)] <- 'SumStatTune'
-  mod[grepl('multi$', x) & !grepl('pseudo', x)] <- 'Multi-IndivTune'
-  mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTune'
-  mod[grepl('_multi', x)] <- 'SumStatTune'
-  mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTune'
-  mod[x == 'xwing.pseudo.multi'] <- 'SumStatTune'
+  res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
+  res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
   
-  return(mod)
-}
-
-res_comp_all$Model1<-find_model(res_comp_all$Model_1)
-res_comp_all$Model2<-find_model(res_comp_all$Model_2)
-
-res_comp_all$Source1<-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method1) | !grepl('AFR|EAS|EUR', res_comp_all$Model_1), 'Multi', 'Single')
-res_comp_all$Source2<-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method2) | !grepl('AFR|EAS|EUR', res_comp_all$Model_2), 'Multi', 'Single')
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTune'
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTune'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTune'
   
-for(i in c('EUR','EAS','AFR')){
-  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] <- i
-  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] <- i
-}
-res_comp_all$Discovery1[res_comp_all$Source1 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source1 == 'Multi']
-res_comp_all$Discovery2[res_comp_all$Source2 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source2 == 'Multi']
-
-res_comp_all$Method1<-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
-res_comp_all$Method2<-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
-res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
-res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
-res_comp_all$Discovery1<-factor(res_comp_all$Discovery1, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
-res_comp_all$Discovery2<-factor(res_comp_all$Discovery2, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
-
-# Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
-res_comp_all <- res_comp_all[
-!(res_comp_all$Method1 %in%  c('quickprs','sbayesrc') & 
-  res_comp_all$Model1 %in% c('IndivTune','Multi-IndivTune')),]
-res_comp_all <- res_comp_all[
-!(res_comp_all$Method2 %in%  c('quickprs','sbayesrc') & 
-  res_comp_all$Model2 %in% c('IndivTune','Multi-IndivTune')),]
+  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTune'
+  
+  res_eval_i$Source<-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
+    !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
+  
+  res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
+  res_eval_i$Discovery[grepl('EAS', res_eval_i$Group)] <- 'EAS'
+  res_eval_i$Discovery[grepl('AFR', res_eval_i$Group)] <- 'AFR'
+  res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
+  
+  res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+  res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-# Remove pseudo model for methods that don't really have one 
-res_comp_all <- res_comp_all[
-!(res_comp_all$Method1 %in%  c('ptclump','ptclump_multi') & 
-  res_comp_all$Model1 %in% c('SumStatTune','Multi-SumStatTune')),]
-res_comp_all <- res_comp_all[
-!(res_comp_all$Method2 %in%  c('ptclump','ptclump_multi') & 
-  res_comp_all$Model2 %in% c('SumStatTune','Multi-SumStatTune')),]
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
+      res_eval_i$Model %in% c('IndivTune','Multi-IndivTune')),]
+  
+  # Remove pseudo model for methods that don't really have one 
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
+      res_eval_i$Model %in% c('SumStatTune','Multi-SumStatTune')),]
 
-# Remove top1 models for PRS-CSx
-res_comp_all <- res_comp_all[
-!(grepl('prscsx|xwing|_multi', res_comp_all$Method1) & 
-  grepl('top1', res_comp_all$Model_1)),]
-res_comp_all <- res_comp_all[
-!(grepl('prscsx|xwing|_multi', res_comp_all$Method2) & 
-  grepl('top1', res_comp_all$Model_2)),]
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
+  res_eval_i <- res_eval_i[
+    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method)) & 
+      grepl('top1', res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
+    "Target", "Method", "Model", "Source", "Discovery","gwas_group"
+  )]),]
+  
+  res_eval[[pheno_i]]<-res_eval_i
+  
+}
 
-# Remove any comparisons
-res_comp_all <- res_comp_all[!duplicated(res_comp_all[, c("Target", "Method1", "Model1", "Source1", "Discovery1", "Method2", "Model2", "Source2", "Discovery2",'pheno')]),]
+# Create vector defining or of methods in plots
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi", "PRS-CSx", "X-Wing", "All") 
 
-###########
+res_eval_simp <- NULL
+for(pheno_i in selected_traits){
+  tmp <- res_eval[[pheno_i]]
+  tmp$Trait <- pheno_i
+  
+  # Insert nice PGS method names
+  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+  tmp$label[is.na(tmp$label)] <- 'All'
+  tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+  tmp$label <- factor(tmp$label, levels = model_order)
+  
+  # Simplify result to either SumStatTune or IndivTune
+  tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+  tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+  tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery','Model'), with=F]),]
+  
+  res_eval_simp <- rbind(res_eval_simp, tmp)
+}
+
+####
+# Average results across phenotypes
+####
 
 library(MAd)
 
 # Average R across phenotypes
-meta_res_comp <- NULL
+meta_res_eval <- NULL
 for(targ_pop_i in targ_pop){
   if(targ_pop_i == 'EAS'){
     disc_pop <- 'EAS'
@@ -5385,197 +5616,211 @@ for(targ_pop_i in targ_pop){
   }
   for(disc_pop_i in disc_pop){
   
-    # Subset res_comp for each scenario
-    res_comp_i <- res_comp_all[res_comp_all$Target == targ_pop_i & res_comp_all$gwas_group == paste0('EUR+', disc_pop_i)]
-  
-    # Calculate diff SE based on p-value
-    res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] <- 1-0.001
-    res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]<-1-0.001
-    res_comp_i$R_diff_z<-qnorm(res_comp_i$R_diff_pval/2)
-    res_comp_i$R_diff_SE<-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
-        
-    # Average results for each test across phenotypes
+    # Subset res_eval for each scenario
+    res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x <- res_eval[[i]]
+      x$pheno <- names(res_eval)[i]
+      x <- x[x$Target == targ_pop_i]
+      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
+    }))
+    
+    # Average res_evalults for each test across phenotypes
     # Use MAd to account for correlation between them
-    res_comp_i$Sample<-'A'
-    res_comp_i$Group <- paste0(res_comp_i$Model_1, '_vs_', res_comp_i$Model_2)
+    res_eval_i$Sample<-'A'
   
-    for(group_i in unique(res_comp_i$Group)){
-      res_comp_group_i <- res_comp_i[res_comp_i$Group == group_i,]
-      cors_i <- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno <-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
       
-      if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
-        
-        meta_res_comp_i <-
-          agg(
-            id = Sample,
-            es = R_diff,
-            var = R_diff_SE ^ 2,
-            cor = cors_i,
-            method = "BHHR",
-            mod = NULL,
-            data = res_comp_group_i
-          )
-        
-        tmp <- res_comp_group_i[1,]
-        tmp$pheno <- NULL
-        tmp$Model_1_R <-
-          meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &
-                            meta_res_eval$Target == targ_pop_i &
-                            meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i)]
-        tmp$Model_2_R <-
-          meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &
-                            meta_res_eval$Target == targ_pop_i &
-                            meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i)]
-        tmp$R_diff <- meta_res_comp_i$es
-        tmp$R_diff_SE <- sqrt(meta_res_comp_i$var)
-        tmp$R_diff_z <- tmp$R_diff / tmp$R_diff_SE
-        tmp$R_diff_p <- 2*pnorm(-abs(tmp$R_diff_z))
-      } else {
-        tmp <- res_comp_group_i[1,]
-        tmp$pheno <- NULL
-        tmp$R_diff <- NA
-        tmp$R_diff_SE <- NA
-        tmp$R_diff_z <- NA
-        tmp$R_diff_p <- NA
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          'res_evalults missing for ',
+          targ_pop_i,
+          ' ',
+          group_i,
+          ' ',
+          paste0(missing_pheno, collapse = ' ')
+        ))
       }
-      meta_res_comp <- rbind(meta_res_comp, tmp)
+      
+      cors_i <- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i <-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_eval_group_i
+        )
+      
+      tmp <- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval <- rbind(meta_res_eval, tmp)
     }
   }
 }
 
-meta_res_comp$R_diff_perc <- meta_res_comp$R_diff / meta_res_comp$Model_2_R
-  
-# Compare IndivTune SBayesRC-multi to TL-SBayesRC-multi
-tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_2 == 'sbayesrc.pseudo.multi' & 
-                                meta_res_comp$Model_1 == 'tlprs_sbayesrc.pop.multi' &
-                    meta_res_comp$Target == 'AFR',]
-round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
-tmp_sbayesrc$R_diff_p
-
-tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_2 == 'sbayesrc.pseudo.multi' & 
-                                meta_res_comp$Model_1 == 'tlprs_sbayesrc.pop.multi' &
-                    meta_res_comp$Target == 'EAS',]
-round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
-tmp_sbayesrc$R_diff_p
-
-```
-
-</details>
-
-***
-
-## Computational resoures
-
-<details><summary>Show code</summary>
-
-```{r}
-library(data.table)
-library(ggplot2)
-library(cowplot)
-
-setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
-source('../functions/misc.R')
-source_all('../functions')
-
-# Get some key variables from config
-config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml'
-pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
-outdir <- read_param(config = config, param = 'outdir', return_obj = F)
-
-# Read in configuration specific benchmark files
-bm_files_i <- list.files(paste0(outdir, '/reference/benchmarks/'), full.names = T)
-
-# Subset benchmarks for pgs_methods
-bm_files_i <- bm_files_i[grepl('prep_pgs_tlprs', bm_files_i)]
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
 
-# Read in benchmark files
-bm_dat_all <- do.call(rbind, lapply(bm_files_i, function(file) {
-  tmp <- fread(file)
-  tmp$file <- basename(file)
-  return(tmp)
-}))
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
-# Create rule column
-bm_dat_all$rule <- gsub('-.*','',bm_dat_all$file)
+png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
 
-# Create method column
-bm_dat_all$method <-
-  gsub('_i', '', gsub('prep_pgs_', '', bm_dat_all$rule))
+# The results look very similar to when using 1KG+HGDP. 
 
-#############
-# Time
-#############
+###################
+# Plot a comparison between the runs using different references
 
-# Calculate average time taken for each method
-method_avg <- NULL
-for(i in unique(bm_dat_all$method)){
-  method_avg <- rbind(
-    method_avg,
-    data.frame(
-      Method = i,
-      Time = mean(bm_dat_all$s[bm_dat_all$method == i])
-    )
-  )
-}
+# Read in results using 1KG+HGDP reference
+main_results<-fread('~/oliverpainfel/Analyses/crosspop/r_eval.csv')
+sens_results<-meta_res_eval
 
-# Convert time in seconds to hours
-method_avg$Time_hour <- method_avg$Time / 60/60
-method_avg$Time_hour <- round(method_avg$Time_hour, 2)
+tmp <- main_results
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+main_results <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
-#This is for bidirectional TL-PRS
+tmp <- sens_results
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+sens_results <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
 
-#############
-# Memory
-#############
+main_results<-main_results[main_results$Method %in% sens_results$Method,]
+main_results<-main_results[main_results$Target %in% sens_results$Target,]
 
-# Calculate average max_rss for each method
-method_avg_mem <- NULL
-for(i in unique(bm_dat_all$method)){
-  method_avg_mem <- rbind(
-    method_avg_mem,
-    data.frame(
-      Method = i,
-      Memory = mean(bm_dat_all$max_rss[bm_dat_all$method == i])
-    )
-  )
-}
+sens_results$Reference <- '1KG'
+main_results$Reference <- '1KG+HGDP'
 
-# Format the Memory nicely
-method_avg_mem$Memory_clean <-
-  paste0(round(method_avg_mem$Memory/1000, 2), ' Gb')
+both_results <- rbind(main_results, sens_results)
 
-ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
-  geom_bar(stat = "identity", position="dodge") +
-  geom_text(aes(label = Memory_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
-  labs(x = "PGS Method", y = "Memory (Mb)") +
-  theme_half_open() +
-  background_grid() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position="none")
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/comparison_to_main_result.png', units = 'px', res = 300, width=4000, height=2500)
+ggplot(both_results, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean + Reference, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+  
+```
+</details>
 
-method_avg_mem$Memory_gb <- method_avg_mem$Memory/1000
-method_avg_mem <- method_avg_mem[, c('Method','Memory_gb')]
-method_avg_mem$Memory_gb <- round(method_avg_mem$Memory_gb, 2)
-names(method_avg_mem)<-c('Method',"Memory (Gb)")
+```{bash, eval=T, echo=F}
 
-method_avg<-merge(method_avg, method_avg_mem, by = 'Method')
+cp ~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/comparison_to_main_result.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
-write.csv(method_avg, '~/oliverpainfel/Analyses/crosspop/time_memory.csv', row.names=F)
 ```
 
+<details><summary>Show results</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/comparison_to_main_result.png">
+</div>
+</div>
+
 </details>
 
 ***
 
-# With 2+ GWAS
+## Using three GWAS
 
 Extend analysis to include gwas_groups including AFR EAS and EUR GWAS. Only some multi-source methods should be applicable here, including LEOPARD, PRS-CSx, and X-Wing. Given X-Wing with LEOPARD is slow, limit X-Wing analysis to the IndivTune model alone.
 
 ***
 
-## Prepare configuration for GenoPred
+### PGS calculation
 
 <details><summary>Show code</summary>
 
+<div class="shallow-break"></div>
+
+<h4>Prepare configuration</h4>
+
 ```{r}
 
 library(data.table)
@@ -5623,13 +5868,10 @@ config<-c(
 write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml', col.names = F, row.names = F, quote = F)
 
 ```
-</details>
 
 ***
 
-## Run pipeline
-
-<details><summary>Show code</summary>
+<h4>Run pipeline</h4>
 
 ```{bash}
 snakemake \
@@ -5641,10 +5883,16 @@ snakemake \
 
 </details>
 
-### Create predictor lists
+***
+
+### PGS evaluation
 
 <details><summary>Show code</summary>
 
+<div class="shallow-break"></div>
+
+<h4>Create predictor list</h4>
+
 ```{r}
 
 setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -5994,13 +6242,10 @@ for(trait_i in selected_traits){
 }
 
 ```
-</details>
 
 ***
 
-### Run model_builder
-
-<details><summary>Show code</summary>
+<h4>Run model_builder</h4>
 
 ```{bash}
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
@@ -6027,13 +6272,10 @@ for targ_pop in $(echo EAS AFR); do
 done
 
 ```
-</details>
 
 ***
 
-### Plot results
-
-<details><summary>Show code</summary>
+<h4>Plot results</h4>
 
 ```{r}
 setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -6337,17 +6579,39 @@ dev.off()
 ```
 </details>
 
-Relative performance of methods is unchanged. Slight absolute improvement for the AFR target, but not for the EAS target.
+```{bash, eval=T, echo=F}
+
+cp ~/oliverpainfel/Analyses/crosspop/plots_three_pop/average_r.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_three_gwas.png
+
+```
+
+<details><summary>Show results</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/average_r_three_gwas.png">
+</div>
+</div>
+
+</details>
 
 ***
 
-# Using GWAS in GenoPred pipeline paper
+## Using external GWAS sumstats
 
-This is to provide more confidence in the performance of SBayesRC and QuickPRS relative to other methods, when using GWAS that are not from single samples, typically large meta-analyses. This is a common scenario and some methods might be more robust to mispecification than others.
+Here we will use GWAS sumtats that were used in the original GenoPred paper. These GWAS are from a range of sources, often large meta-analyses, which can lead to greater mispecification in the sumstats, which can impact the performance of some PGS methods. This is to provide more confidence in the performance of SBayesRC and QuickPRS relative to other methods.
 
 ***
 
-## Prepare configuration
+### PGS calculation
+
+We will do this using GenoPred. 
+
+<details><summary>Show code</summary>
+
+<div class="shallow-break"></div>
+
+<h4>Prepare configuration</h4>
 
 We can use the gwas_list from the GenoPred pipeline paper. Just make the new configuration file.
 
@@ -6393,9 +6657,7 @@ write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/cro
 
 ***
 
-## Run pipeline
-
-<details><summary>Show code</summary>
+<h4>Run pipeline</h4>
 
 ```{bash}
 snakemake \
@@ -6409,10 +6671,14 @@ snakemake \
 
 ***
 
-## Run model_builder
+### PGS evaluation
 
 <details><summary>Show code</summary>
 
+<div class="shallow-break"></div>
+
+<h4>Create predictor list</h4>
+
 ```{r}
 
 setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -6565,16 +6831,15 @@ for(i in phenos){
 
 ```
 
-</details>
-
+***
 
-<details><summary>Show code</summary>
+<h4>Run model_builder</h4>
 
 ```{bash}
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-#rm /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_*.disc_EUR_*/*/res*
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/*/res*
 
 for pheno in $(echo bc ckd gout ibd pc ra stroke t1d t2d height bmi egfr hba1c urate hdl); do
   if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/${pheno}/res.pred_comp.txt" ]; then
@@ -6587,11 +6852,10 @@ for pheno in $(echo bc ckd gout ibd pc ra stroke t1d t2d height bmi egfr hba1c u
 done
 
 ```
-</details>
 
 ***
 
-## Plot results
+<h4>Plot results</h4>
 
 ```{r}
 setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -6754,7 +7018,7 @@ tmp <- meta_res_eval
 tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
 tmp$label <- factor(tmp$label, levels = model_order)
 
-png(paste0('~/oliverpainfel/Analyses/crosspop/plots_meta/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots_meta/average_r.png'), res=100, width = 500, height = 300, units = 'px')
 ggplot(tmp, aes(x=label, y=R , fill = Model)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
@@ -6771,19 +7035,38 @@ ggplot(tmp, aes(x=label, y=R , fill = Model)) +
           legend.justification = "center")
 dev.off()
 
-# Result is consistent. SBayesRC is slightly better than QuickPRS on average.
+# Results and conclusions remain consistent.
+
+```
+</details>
+
+```{bash, eval=T, echo=F}
+
+cp ~/oliverpainfel/Analyses/crosspop/plots_meta/average_r.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_meta.png
 
 ```
 
+<details><summary>Show results</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 50%;">
+<img src="Images/CrossPop_2025/average_r_meta.png">
+</div>
+</div>
+
+</details>
+
 ***
 
-# Run methods using downsampled UKB sumstats
+## Run methods using downsampled UKB sumstats
 
 It seems the performance of methods varies across EAS and AFR datasets. This could be due to the difference in sample size. To explore this, lets run the methods on EUR GWAS generated using UKB, using a range of sample sizes.
 
 ***
 
-## Downsample GWAS
+### Downsample GWAS
+
+<details><summary>Show code</summary>
 
 ```{r}
 library(data.table)
@@ -6851,9 +7134,17 @@ rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*_subsampled/*chr*
 
 ```
 
+</details>
+
 ***
 
-## Prepare configuration
+### PGS calculation
+
+<details><summary>Show code</summary>
+
+<div class="shallow-break"></div>
+
+<h4>Prepare configuration</h4>
 
 ```{r}
 ######
@@ -6923,9 +7214,7 @@ write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/cro
 
 ***
 
-## Run pipeline
-
-<details><summary>Show code</summary>
+<h4>Run pipeline</h4>
 
 ```{bash}
 snakemake \
@@ -6939,10 +7228,14 @@ snakemake \
 
 ***
 
-### Create predictor lists
+### PGS evaluation
 
 <details><summary>Show code</summary>
 
+<div class="shallow-break"></div>
+
+<h4>Create predictor list</h4>
+
 ```{r}
 
 setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
@@ -6971,7 +7264,7 @@ for(trait_i in selected_traits){
     recursive = T
   )
   
-  scores_i <- scores[grepl('^HT_', scores$name),]
+  scores_i <- scores[grepl(paste0('^', trait_i,'_'), scores$name),]
 
   scores_i$predictor <- paste0(
     outdir,
@@ -7049,50 +7342,158 @@ for(trait_i in selected_traits){
 }
 
 ```
-</details>
 
 ***
 
-### Run model_builder
-
-<details><summary>Show code</summary>
+<h4>Run model_builder</h4>
 
 ```{bash}
 cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-#rm /users/k1806347/oliverpainfel/Analyses/crosspop/targ_*.disc_AFR_EAS_EUR/*/res*
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/*/res*
 
 for pheno in $(echo TC HT HB); do
   if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/res.pred_comp.txt" ]; then
-    sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+    sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
       --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.EUR_test.row_number.txt \
       --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/predictor_list.txt \
       --out /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/res \
-      --n_core 5"
+      --n_core 1"
   fi
 done
 
 ```
-</details>
-
 
 ***
 
-Additional things we could check:
+<h4>Plot results</h4>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+phenos<-c('TC', 'HT', 'HB')
+
+# Calculate correlation between all phenotypes in each target population
+pheno_pop_i <- list()
+for(pheno_i in phenos){
+  pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.EUR_test.row_number.txt'))
+  names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+}
+
+pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+cors <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+
+# Read in results
+res_eval <- list()
+for(pheno_i in phenos){
+  eval_i <-
+    fread(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/', pheno_i,'/res.pred_eval.txt'
+      )
+    )
+
+  eval_i$Ncase <- NULL
+  eval_i$Ncont <- NULL
+  eval_i$R2l <- NULL
+  eval_i$R2o <- NULL
+  
+  eval_i <- eval_i[!grepl('\\.multi', eval_i$Group),]
+  
+  eval_i$Method<-sub('\\..*','',eval_i$Group)
+  eval_i$Method<-gsub('.*-','', eval_i$Method)
+  
+  eval_i$GWAS_N <- gsub('K\\..*','',eval_i$Group)
+  eval_i$GWAS_N <- gsub('.*\\.','',eval_i$GWAS_N)
+  eval_i$GWAS_N <- paste0(eval_i$GWAS_N,'K')
+  
+  eval_i$Model[grepl('top1$', eval_i$Group) &
+                   !grepl('pseudo', eval_i$Group)]<-'IndivTune'
+  eval_i$Model[grepl('top1$', eval_i$Group) &
+                   grepl('pseudo', eval_i$Group)]<-'SumStatTune'
+
+  eval_i$Method<-factor(eval_i$Method, levels=unique(eval_i$Method))
+  eval_i$Model<-factor(eval_i$Model, levels=c('IndivTune','SumStatTune'))
+
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  eval_i <- eval_i[
+    !(eval_i$Method %in%  c('quickprs','sbayesrc') & 
+      eval_i$Model %in% c('IndivTune')),]
+  
+  # Remove pseudo model for methods that don't really have one 
+  eval_i <- eval_i[
+    !(eval_i$Method %in%  c('ptclump') & 
+      eval_i$Model %in% c('SumStatTune')),]
+  
+  res_eval[[pheno_i]]<-eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC") 
+
+res_eval_simp <- NULL
+for(pheno_i in phenos){
+  tmp <- res_eval[[pheno_i]]
+  tmp$Trait <- pheno_i
+  
+  # Insert nice PGS method names
+  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+  tmp$label <- factor(tmp$label, levels = model_order)
+
+  res_eval_simp <- rbind(res_eval_simp, tmp)
+}
+
+# Plot results for each phenotype separately
+dir.create('~/oliverpainfel/Analyses/crosspop/plots_downsample')
+
+tmp<-res_eval_simp
+tmp$GWAS_N <- paste0('GWAS N = ', tmp$GWAS_N)
+tmp$GWAS_N <-factor(tmp$GWAS_N, levels = unique(tmp$GWAS_N))
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png'), res=100, width = 1000, height = 600, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x=NULL, fill = NULL) +
+    facet_grid(Trait ~ GWAS_N, scales = 'free_y') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+```
 
-- Performance when using PAGE GWAS - It is a mixture of non-EUR populations, mainly AFR and AMR. The same size is larger than UGR. It was used in SBayesRC and PRS-CSx paper
-- Downsample BBJ GWAS
-- Larger reference sample size
-- Larger SNP set
-- Performance of single source methods using GWAS that were in original GenoPred pipeline (meta-analyses)
+```{bash, eval=T, echo=F}
 
-All of these additional tests could be restricted to UKB GWAS sumstats. They have been done to some extent already in the SBayesRC paper. 
-I think the most interesting points are:
-- Looking at performance using downsampled UKB sumstats
-- Looking at performance using GWAS from GenoPred pipe study
+cp ~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_downsample.png
 
+```
 
+<details><summary>Show results</summary>
 
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 50%;">
+<img src="Images/CrossPop_2025/average_r_downsample.png">
+</div>
+</div>
 
+</details>
 
+***
diff --git a/docs/CrossPop.html b/docs/CrossPop.html
index 1550d0e4..5ac30d91 100644
--- a/docs/CrossPop.html
+++ b/docs/CrossPop.html
@@ -11,7 +11,7 @@
 
 
 
-<title>Cross-population evaluation of polygenic scores</title>
+<title>Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations</title>
 
 <script src="site_libs/header-attrs-2.26/header-attrs.js"></script>
 <script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
@@ -35,6 +35,8 @@
 <script src="site_libs/navigation-1.1/tabsets.js"></script>
 <link href="site_libs/highlightjs-9.12.0/default.css" rel="stylesheet" />
 <script src="site_libs/highlightjs-9.12.0/highlight.js"></script>
+<script src="site_libs/kePrint-0.0.1/kePrint.js"></script>
+<link href="site_libs/lightable-0.0.1/lightable.css" rel="stylesheet" />
 <link href="site_libs/font-awesome-6.4.2/css/all.min.css" rel="stylesheet" />
 <link href="site_libs/font-awesome-6.4.2/css/v4-shims.min.css" rel="stylesheet" />
 <link rel="shortcut icon" href="Images/logo/favicon.ico">
@@ -422,8 +424,8 @@
 
 
 
-<h1 class="title toc-ignore">Cross-population evaluation of polygenic
-scores</h1>
+<h1 class="title toc-ignore">Leveraging Global Genetics Resources to
+Enhance Polygenic Prediction Across Ancestrally Diverse Populations</h1>
 
 </div>
 
@@ -431,35 +433,86 @@ <h1 class="title toc-ignore">Cross-population evaluation of polygenic
 <hr />
 <div id="introduction" class="section level1">
 <h1>Introduction</h1>
-<p>European (EUR) GWAS are typically the largest in sample size, or even
-the only GWAS for certain outcomes. Here we will evaluate approaches for
-calculating polygenic scores (PGS) across populations. We will include
-single- and multi-source PGS methods, using EUR GWAS alone, or using EUR
-GWAS in combination with GWAS from other populations.</p>
+<p>Genome-wide association studies (GWAS) from multiple ancestral
+populations are increasingly available, offering opportunities to
+improve the accuracy and equity of polygenic scores (PGS). Several
+methods now aim to leverage multiple GWAS sources, but predictive
+performance and computational efficiency across contexts remain unclear,
+especially in the absence of individual-level tuning data. This study
+evaluates a comprehensive set of PGS methods across African (AFR), East
+Asian (EAS), and European (EUR) ancestry groups.</p>
+<p>This study evaluated the performance of PGS methods and modelling
+approaches across diverse populations using GWAS and target datasets of
+EUR, EAS, and AFR ancestry (Figure 2). EUR GWAS summary statistics were
+generated within a training subset of European ancestry individuals in
+the UK Biobank sample (UKB)(Bycroft et al., 2018). Publicly available
+EAS and AFR GWAS were obtained from Biobank Japan (BBJ) (Sakaue et al.,
+2021) and the Ugandan Genome Resource (UGR) (Gurdasani et al., 2019),
+respectively. An independent target sample of EUR, EAS and AFR ancestry
+individuals within UKB were used to evaluate the predictive performance
+of the PGS. To compare PGS methods, 10 traits available in UKB, BBJ, and
+UGR were selected to represent a range of genetic architectures.</p>
 <hr />
-</div>
-<div id="derive-gwas-in-ukb" class="section level1">
-<h1>Derive GWAS in UKB</h1>
-<p>To avoid sample overlap between the EUR GWAS and the EUR target
-sample for evaluation, we will split EUR individuals in UKB into
-training and testing subsets. The GWAS will be performed in the training
-subset, and the PGS evaluation will occur in the testing subset.</p>
+<p><strong>Document overview</strong>:</p>
+<ul>
+<li>Data preparation
+<ul>
+<li>UKB
+<ul>
+<li>Ancestry inference</li>
+<li>Phenotype extraction</li>
+<li>Sample splitting</li>
+</ul></li>
+<li>GWAS sumstats
+<ul>
+<li>UK Biobank GWAS</li>
+<li>Download BBJ and UGR sumstats</li>
+</ul></li>
+<li>Heritability and polygenicity estimation</li>
+</ul></li>
+<li>Main analysis
+<ul>
+<li>PGS calculation</li>
+<li>PGS evaluation</li>
+<li>LEOPARD+QuickPRS</li>
+<li>Computational resources</li>
+</ul></li>
+<li>TL-PRS analysis</li>
+<li>Sensitivity analyses
+<ul>
+<li>Using 1KG reference</li>
+<li>Using three GWAS</li>
+<li>Using external GWAS sumstats</li>
+<li>Using downsampled GWAS</li>
+</ul></li>
+</ul>
 <hr />
-<div id="perform-ancestry-inference-in-ukb" class="section level2">
-<h2>Perform ancestry inference in UKB</h2>
-<div id="create-target_list" class="section level3">
-<h3>Create target_list</h3>
-<div class="shallow-break">
-
 </div>
+<div id="data-preparation" class="section level1">
+<h1>Data Preparation</h1>
+<hr />
+<div id="ukb" class="section level2">
+<h2>UKB</h2>
+<p>This section will describe the preparation of the UKB data for this
+study. We will need to separate UKB participants into ancestral groups
+(AFR, EAS, and EUR). Then we will need to prepare phenotype data for
+traits that overlap with the BBJ and UGR samples. Then we will need to
+split EUR UKB participants into training and testing subsets. We will
+then perform GWAS in the training subset, and evaluate PGS in the
+testing subset.</p>
+<hr />
+<div id="ancestry-inference" class="section level3">
+<h3>Ancestry inference</h3>
+<p>We will perform this using the GenoPred pipeline. We will need to
+prepare the configuration files before running the pipeline.</p>
 <details>
 <summary>
 Show code
 </summary>
 <p><br/></p>
-<h3>
+<h4>
 Create symlinks
-</h3>
+</h4>
 <p>We will create symlinks to the imputed genotype data for UKB. We will
 use the pgen format data for computationl efficiency and those
 restricted to MAF &gt;= 1% and INFO &gt;= 0.4. We are using genetic data
@@ -485,9 +538,9 @@ <h3>
   ln -s /users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/rownumber.psam /users/k1806347/oliverpainfel/Data/ukb/ukb_symlinks/ukb_imp_maf1_info4.chr${chr}.psam
 done</code></pre>
 <hr />
-<h3>
+<h4>
 Create list of unrelated individuals
-</h3>
+</h4>
 <pre class="r"><code>library(ukbkings)
 library(data.table)
 
@@ -512,9 +565,9 @@ <h3>
 write.table(psam_unrel_all$IID, &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.txt&#39;, row.names=F, col.names=F, quote=F)
 write.table(psam_unrel_all$rn, &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt&#39;, row.names=F, col.names=F, quote=F)</code></pre>
 <hr />
-<h3>
+<h4>
 Create target_list
-</h3>
+</h4>
 <pre class="bash"><code>mkdir -p /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic</code></pre>
 <pre class="r"><code>target_list &lt;- data.frame(
   name=&#39;ukb&#39;,
@@ -525,15 +578,10 @@ <h3>
 )
 
 write.table(target_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&#39;, col.names=T, row.names=F, quote=F)</code></pre>
-</details>
 <hr />
-</div>
-<div id="create-configfile" class="section level3">
-<h3>Create configfile</h3>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Create configfile
+</h4>
 <pre class="r"><code># Create config file
 conf &lt;- c(
   &#39;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&#39;,
@@ -542,15 +590,10 @@ <h3>Create configfile</h3>
 )
 
 write.table(conf, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/config.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
-</details>
 <hr />
-</div>
-<div id="run-pipeline" class="section level3">
-<h3>Run pipeline</h3>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Run pipeline
+</h4>
 <pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 git describe --tags
 #v2.2.2-213-g2f05853
@@ -563,11 +606,13 @@ <h3>Run pipeline</h3>
 </details>
 <hr />
 </div>
-</div>
-<div id="collect-phenotype-data" class="section level2">
-<h2>Collect phenotype data</h2>
+<div id="phenotype-extraction" class="section level3">
+<h3>Phenotype extraction</h3>
 <p>We will use the same 33 quantitative traits that were used in the
-PRS-CSx paper (Supp Table 10).</p>
+PRS-CSx paper (Supp Table 10 of PRS-CSx paper). We will use ukbkings to
+extract the phenotypes, then remove related individuals, split EUR into
+training and testing subsets, and adjust EUR training phenotypes for
+covariates.</p>
 <details>
 <summary>
 Show code
@@ -860,8 +905,13 @@ <h2>Collect phenotype data</h2>
 </details>
 <hr />
 </div>
-<div id="run-gwas" class="section level2">
-<h2>Run GWAS</h2>
+<div id="gwas-sumstats" class="section level3">
+<h3>GWAS sumstats</h3>
+<p>We will generate EUR GWAS using the EUR training subset of UKB. BBJ
+will be used for EAS GWAS, and UGR will be used for AFR GWAS.</p>
+<hr />
+<div id="ukb-gwas" class="section level4">
+<h4>UKB GWAS</h4>
 <details>
 <summary>
 Show code
@@ -898,9 +948,8 @@ <h2>Run GWAS</h2>
 </details>
 <hr />
 </div>
-</div>
-<div id="download-relevant-bbj-sumstats" class="section level1">
-<h1>Download relevant BBJ sumstats</h1>
+<div id="download-bbj-sumstats" class="section level4">
+<h4>Download BBJ sumstats</h4>
 <details>
 <summary>
 Show code
@@ -962,8 +1011,8 @@ <h1>Download relevant BBJ sumstats</h1>
 </details>
 <hr />
 </div>
-<div id="download-relevant-ugr-sumstats" class="section level1">
-<h1>Download relevant UGR sumstats</h1>
+<div id="download-ugr-sumstats" class="section level4">
+<h4>Download UGR sumstats</h4>
 <details>
 <summary>
 Show code
@@ -1079,21 +1128,30 @@ <h1>Download relevant UGR sumstats</h1>
 </details>
 <hr />
 </div>
-<div id="estimate-snp-h2-polygenicity-and-rg-across-populations"
-class="section level1">
-<h1>Estimate SNP-h2, polygenicity and rG across populations</h1>
+</div>
+</div>
+<div id="heritability-and-polygenicity-estimation"
+class="section level2">
+<h2>Heritability and polygenicity estimation</h2>
 <p>We will estimate SNP-h2 using LD-score regression, and the rG using
 POPCORN. POPCORN can estimate the SNP-h2, but it will vary according to
 the other GWAS included due to SNP overlap. Use the sumstats QC’d by
 GenoPred. To estimate polygenicity, lets use AVENGEME based on ptclump
 score association results. Lets generate those using GenoPred.</p>
 <hr />
-<div id="prepare-configuration-for-genopred" class="section level2">
-<h2>Prepare configuration for GenoPred</h2>
+<div id="qc-gwas-sumstats" class="section level3">
+<h3>QC GWAS sumstats</h3>
+<p>Use GenoPred for this.</p>
 <details>
 <summary>
 Show code
 </summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Prepare configuration
+</h4>
 <pre class="r"><code>######
 # gwas_list
 ######
@@ -1177,29 +1235,19 @@ <h2>Prepare configuration for GenoPred</h2>
 )
 
 write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
-</details>
 <hr />
-</div>
-<div id="run-pipeline-1" class="section level2">
-<h2>Run pipeline</h2>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Run pipeline
+</h4>
 <pre class="bash"><code>snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml \
   target_pgs -n</code></pre>
-</details>
 <hr />
-</div>
-<div id="reformat-for-ldsc-and-popcorn" class="section level2">
-<h2>Reformat for LDSC and POPCORN</h2>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Reformat for LDSC and POPCORN
+</h4>
 <pre class="r"><code>library(data.table)
 dir.create(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats&#39;, recursive = T)
 gwas_list&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt&#39;)
@@ -1243,8 +1291,8 @@ <h2>Reformat for LDSC and POPCORN</h2>
 </details>
 <hr />
 </div>
-<div id="run-ldsc" class="section level2">
-<h2>Run LDSC</h2>
+<div id="ldsc" class="section level3">
+<h3>LDSC</h3>
 <details>
 <summary>
 Show code
@@ -1298,12 +1346,18 @@ <h2>Run LDSC</h2>
 </details>
 <hr />
 </div>
-<div id="calculate-cscores" class="section level2">
-<h2>Calculate CSCOREs</h2>
+<div id="popcorn" class="section level3">
+<h3>POPCORN</h3>
 <details>
 <summary>
 Show code
 </summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Calculate CSCOREs
+</h4>
 <pre class="bash"><code>
 # Subset the reference data into relevant populations
 for pop in $(echo EUR EAS AFR); do
@@ -1336,15 +1390,10 @@ <h2>Calculate CSCOREs</h2>
 
 rm -r /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp
 rm /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_*_CSCORES/*chr*.txt</code></pre>
-</details>
 <hr />
-</div>
-<div id="run-popcorn" class="section level2">
-<h2>Run POPCORN</h2>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Run POPCORN
+</h4>
 <pre class="bash"><code>conda activate popcorn
 for pop in $(echo EAS AFR);do
   if [ &quot;$pop&quot; == &quot;EAS&quot; ]; then
@@ -1369,8 +1418,8 @@ <h2>Run POPCORN</h2>
 </details>
 <hr />
 </div>
-<div id="plot-the-ldsc-and-popcorn-results" class="section level2">
-<h2>Plot the LDSC and POPCORN results</h2>
+<div id="plot-ldsc-and-popcorn-results" class="section level3">
+<h3>Plot LDSC and POPCORN results</h3>
 <details>
 <summary>
 Show code
@@ -1424,13 +1473,18 @@ <h2>Plot the LDSC and POPCORN results</h2>
 
 write.csv(h2_res, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results.csv&#39;, row.names = F, quote = F)
 
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/ldsc_h2.png&#39;, res = 100, width = 700, height = 300, units = &#39;px&#39;)
 ggplot(h2_res, aes(x = Phenotype, y = h2_est, fill = Population)) +
   geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.7) +
   geom_errorbar(aes(ymin=h2_est-h2_se, ymax=h2_est+h2_se), width=.2, position=position_dodge(width = 0.7, preserve = &quot;single&quot;)) +
-  labs(y=&quot;SNP-based Heritability (SE)&quot;) +
+  labs(y=&quot;SNP-based Heritability (SE)&quot;, fill = NULL) +
   theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(2, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;) +
   background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
+dev.off()
 
 # Plot rG estimates
 rg_res &lt;- NULL
@@ -1450,23 +1504,53 @@ <h2>Plot the LDSC and POPCORN results</h2>
 
 write.csv(rg_res, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results.csv&#39;, row.names = F, quote = F)
 
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/popcorn_rg.png&#39;, res = 100, width = 700, height = 300, units = &#39;px&#39;)
 ggplot(rg_res, aes(x = Phenotype, y = Estimate, fill = Comparison)) +
   geom_bar(stat=&quot;identity&quot;, position=position_dodge(), width = 0.7) +
   geom_errorbar(aes(ymin=Estimate-SE, ymax=Estimate+SE), width=.2, position=position_dodge(width = 0.7)) +
-  labs(y=&quot;SNP-based\nGenetic Correlation (SE)&quot;) +
+  labs(y=&quot;SNP-based\nGenetic Correlation (SE)&quot;, fill = NULL) +
   theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)</code></pre>
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(2, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
+dev.off()</code></pre>
+</details>
+<details>
+<summary>
+Show LDSC SNP-heritability
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/ldsc_h2.png"></p>
+</div>
+</div>
+</details>
+<details>
+<summary>
+Show POPCORN genetic correlation estimates
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/ldsc_h2.png"></p>
+</div>
+</div>
 </details>
+<hr />
 </div>
-<div id="avengeme" class="section level2">
-<h2>AVENGEME</h2>
-<div id="create-predictor-lists" class="section level3">
-<h3>Create predictor lists</h3>
+<div id="avengeme" class="section level3">
+<h3>AVENGEME</h3>
 <details>
 <summary>
 Show code
 </summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Create predictor list
+</h4>
 <pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 source(&#39;../functions/misc.R&#39;)
 source_all(&#39;../functions&#39;)
@@ -1546,15 +1630,10 @@ <h3>Create predictor lists</h3>
     )
   }
 }</code></pre>
-</details>
 <hr />
-</div>
-<div id="run-model_builder" class="section level3">
-<h3>Run model_builder</h3>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Run model_builder
+</h4>
 <pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
@@ -1576,15 +1655,10 @@ <h3>Run model_builder</h3>
   done
 done
 </code></pre>
-</details>
 <hr />
-</div>
-<div id="plot-assoc-results" class="section level3">
-<h3>Plot assoc results</h3>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Plot pT+clump association results
+</h4>
 <pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 
 library(data.table)
@@ -1627,6 +1701,7 @@ <h3>Plot assoc results</h3>
 res_all$pT &lt;- gsub(&#39;e.&#39;,&#39;e-&#39;, gsub(&#39;.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.&#39;, &#39;&#39;, res_all$Predictor))
 res_all$pT &lt;- factor(res_all$pT, levels = unique(res_all$pT))
 
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/ptclump_assoc.png&#39;, res = 100, width = 900, height = 500, units = &#39;px&#39;)
 ggplot(res_all, aes(x = Phenotype, y = BETA, fill = pT)) +
   geom_hline(yintercept = 0, colour = &#39;darkgrey&#39;) +
   geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.8) +
@@ -1635,18 +1710,18 @@ <h3>Plot assoc results</h3>
   theme_half_open() +
   background_grid() +
   panel_border() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(1, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;) +
   background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) +
   scale_fill_manual(values = colorRampPalette(c(&quot;lightblue&quot;, &quot;darkblue&quot;))(length(unique(res_all$pT)))) +
-  facet_grid(Population ~.)</code></pre>
-</details>
-</div>
-<div id="run-avengeme" class="section level3">
-<h3>Run AVENGEME</h3>
-<details>
-<summary>
-Show code
-</summary>
+  facet_grid(Population ~.)
+dev.off()</code></pre>
+<hr />
+<h4>
+Run AVENGEME
+</h4>
 <pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 
 library(data.table)
@@ -1753,46 +1828,63 @@ <h3>Run AVENGEME</h3>
 dir.create(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme&#39;)
 write.csv(mod_res_all, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv&#39;, row.names = F, quote = F)
 
+mod_res_all&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv&#39;)
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/avengeme_h2.png&#39;, res = 100, width = 900, height = 500, units = &#39;px&#39;)
 ggplot(mod_res_all, aes(x = Phenotype, y = vg_est, fill = Population)) +
   geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.7) +
   geom_errorbar(aes(ymin=vg_lowCI, ymax=vg_highCI), width=.2, position=position_dodge(width = 0.7, preserve = &quot;single&quot;)) +
-  labs(y=&quot;SNP-based Heritability (95%CI)&quot;) +
+  labs(y=&quot;SNP-based Heritability (95%CI)&quot;, fill = NULL) +
   theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(1, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;) +
   background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
+dev.off()
 
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/avengeme_polygenicity.png&#39;, res = 100, width = 900, height = 500, units = &#39;px&#39;)
 ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) +
   geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.7) +
   geom_errorbar(aes(ymin=1 - pi0_lowCI, ymax=1 - pi0_highCI), width=.2, position=position_dodge(width = 0.7, preserve = &quot;single&quot;)) +
-  labs(y=&quot;Proporition non-zero\neffects (95%CI)&quot;) +
-  theme_half_open() +
-  coord_cartesian(ylim = c(0, 0.15)) + 
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
-
-ggplot(mod_res_all, aes(x = Phenotype, y = max_r2, fill = Population)) +
-  geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.7) +
-  labs(y=&quot;Max R2&quot;) +
+  labs(y=&quot;Proporition non-zero\neffects (95%CI)&quot;, fill = NULL) +
   theme_half_open() +
   coord_cartesian(ylim = c(0, 0.15)) + 
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(1, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;) +
   background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
-
-# 
-hist(mod_res_all$max_r2)
-hist(mod_res_all$max_r2[mod_res_all$Population == &#39;EUR&#39;])
-hist(mod_res_all$max_r2[mod_res_all$Population == &#39;EAS&#39;])
-hist(mod_res_all$max_r2[mod_res_all$Population == &#39;AFR&#39;])
+dev.off()
 
 summary(mod_res_all$max_r2)
 summary(mod_res_all$max_r2[mod_res_all$Population == &#39;EUR&#39;])
 summary(mod_res_all$max_r2[mod_res_all$Population == &#39;EAS&#39;])
 summary(mod_res_all$max_r2[mod_res_all$Population == &#39;AFR&#39;])</code></pre>
 </details>
+<details>
+<summary>
+Show AVENGEME results
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/avengeme_h2.png"></p>
+</div>
+</div>
+<p><br></p>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/avengeme_polygenicity.png"></p>
+</div>
+</div>
+</details>
 <hr />
 </div>
-<div id="select-gwas-for-downstream-analyses" class="section level3">
-<h3>Select GWAS for downstream analyses</h3>
+<div id="select-traits" class="section level3">
+<h3>Select traits</h3>
+<p>Here we will identify a list of traits that fulfill our selection
+criteria, and that represent a range of heritability and polygenicity
+combinations.</p>
 <details>
 <summary>
 Show code
@@ -1874,10 +1966,9 @@ <h3>Select GWAS for downstream analyses</h3>
 </details>
 <hr />
 </div>
-</div>
-</div>
-<div id="make-a-descriptives-table-of-gwas" class="section level1">
-<h1>Make a descriptives table of GWAS</h1>
+<div id="gwas-descriptives" class="section level3">
+<h3>GWAS descriptives</h3>
+<p>Make a table showing GWAS information for the manuscript.</p>
 <details>
 <summary>
 Show code
@@ -1993,16 +2084,3892 @@ <h1>Make a descriptives table of GWAS</h1>
   )
 }</code></pre>
 </details>
+<details>
+<summary>
+Show descriptives table
+</summary>
+<div
+style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:500px; overflow-x: scroll; width:100%; ">
+<table class="table table-striped table-hover" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
+<thead>
+<tr>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Trait Label
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Trait Description
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Ancestry
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+GWAS Sample
+</th>
+<th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;">
+GWAS N
+</th>
+<th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;">
+Target N
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+SNP-h2 (AVENGEME)
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+pi0 (AVENGEME)
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+SNP-h2 (LDSC)
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Intercept (LDSC)
+</th>
+<th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;">
+Lambda
+</th>
+<th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;">
+UKB Field
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+URL
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Selected
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+ALB
+</td>
+<td style="text-align:left;">
+Albumin
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+13125
+</td>
+<td style="text-align:right;">
+5868
+</td>
+<td style="text-align:left;">
+0.14 (95%CI = 0.1 - 0.19)
+</td>
+<td style="text-align:left;">
+0.98 (95%CI = 0 - 0.99)
+</td>
+<td style="text-align:left;">
+0.07 (SE = 0.05)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0135
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009048"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009048</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+ALB
+</td>
+<td style="text-align:left;">
+Albumin
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+120539
+</td>
+<td style="text-align:right;">
+1780
+</td>
+<td style="text-align:left;">
+0.06 (95%CI = 0.04 - 0.07)
+</td>
+<td style="text-align:left;">
+0.98 (95%CI = 0.97 - 0.99)
+</td>
+<td style="text-align:left;">
+0.06 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.03 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1459
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.Alb.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.Alb.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+ALB
+</td>
+<td style="text-align:left;">
+Albumin
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+268088
+</td>
+<td style="text-align:right;">
+67382
+</td>
+<td style="text-align:left;">
+0.13 (95%CI = 0.13 - 0.13)
+</td>
+<td style="text-align:left;">
+0.94 (95%CI = 0.94 - 0.94)
+</td>
+<td style="text-align:left;">
+0.12 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.13 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.4281
+</td>
+<td style="text-align:right;">
+30600
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+ALP
+</td>
+<td style="text-align:left;">
+Alkaline phosphatase
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+9322
+</td>
+<td style="text-align:right;">
+6328
+</td>
+<td style="text-align:left;">
+0.03 (95%CI = 0.02 - 0.06)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 1 - 1)
+</td>
+<td style="text-align:left;">
+0.09 (SE = 0.07)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0255
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009049"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009049</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+ALP
+</td>
+<td style="text-align:left;">
+Alkaline phosphatase
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+118886
+</td>
+<td style="text-align:right;">
+1955
+</td>
+<td style="text-align:left;">
+0.12 (95%CI = 0.1 - 0.14)
+</td>
+<td style="text-align:left;">
+0.98 (95%CI = 0.97 - 0.99)
+</td>
+<td style="text-align:left;">
+0.12 (SE = 0.03)
+</td>
+<td style="text-align:left;">
+1.09 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.2005
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.ALP.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.ALP.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+ALP
+</td>
+<td style="text-align:left;">
+Alkaline phosphatase
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+292054
+</td>
+<td style="text-align:right;">
+73314
+</td>
+<td style="text-align:left;">
+0.16 (95%CI = 0.16 - 0.16)
+</td>
+<td style="text-align:left;">
+0.94 (95%CI = 0.94 - 0.94)
+</td>
+<td style="text-align:left;">
+0.21 (SE = 0.03)
+</td>
+<td style="text-align:left;">
+1.15 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.5144
+</td>
+<td style="text-align:right;">
+30610
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+ALT
+</td>
+<td style="text-align:left;">
+Alanine aminotransferase
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+9401
+</td>
+<td style="text-align:right;">
+6326
+</td>
+<td style="text-align:left;">
+0.07 (95%CI = 0.03 - 0.12)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 1 - 1)
+</td>
+<td style="text-align:left;">
+0.11 (SE = 0.07)
+</td>
+<td style="text-align:left;">
+1 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0165
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009047"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009047</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+ALT
+</td>
+<td style="text-align:left;">
+Alanine aminotransferase
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+150545
+</td>
+<td style="text-align:right;">
+1954
+</td>
+<td style="text-align:left;">
+0.03 (95%CI = 0.02 - 0.04)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.98 - 1)
+</td>
+<td style="text-align:left;">
+0.06 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.06 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1459
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.ALT.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.ALT.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+ALT
+</td>
+<td style="text-align:left;">
+Alanine aminotransferase
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+291932
+</td>
+<td style="text-align:right;">
+73278
+</td>
+<td style="text-align:left;">
+0.09 (95%CI = 0.08 - 0.09)
+</td>
+<td style="text-align:left;">
+0.95 (95%CI = 0.95 - 0.95)
+</td>
+<td style="text-align:left;">
+0.11 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.09 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.4069
+</td>
+<td style="text-align:right;">
+30620
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+AST
+</td>
+<td style="text-align:left;">
+Aspartate transaminase
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+8995
+</td>
+<td style="text-align:right;">
+6289
+</td>
+<td style="text-align:left;">
+0.03 (95%CI = 0 - 0.09)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 0 - 1)
+</td>
+<td style="text-align:left;">
+0.12 (SE = 0.07)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0165
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009046"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009046</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+AST
+</td>
+<td style="text-align:left;">
+Aspartate transaminase
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+150068
+</td>
+<td style="text-align:right;">
+1940
+</td>
+<td style="text-align:left;">
+0.01 (95%CI = 0 - 0.02)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 0.99 - 1)
+</td>
+<td style="text-align:left;">
+0.07 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.06 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1459
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.AST.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.AST.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+AST
+</td>
+<td style="text-align:left;">
+Aspartate transaminase
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+291016
+</td>
+<td style="text-align:right;">
+73049
+</td>
+<td style="text-align:left;">
+0.08 (95%CI = 0.07 - 0.08)
+</td>
+<td style="text-align:left;">
+0.95 (95%CI = 0.95 - 0.95)
+</td>
+<td style="text-align:left;">
+0.12 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.11 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.3964
+</td>
+<td style="text-align:right;">
+30650
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+BAS
+</td>
+<td style="text-align:left;">
+Basophil
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2681
+</td>
+<td style="text-align:right;">
+6353
+</td>
+<td style="text-align:left;">
+0 (95%CI = 0 - 0.02)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 0.99 - 1)
+</td>
+<td style="text-align:left;">
+-0.37 (SE = 0.25)
+</td>
+<td style="text-align:left;">
+1.02 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0046
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009039"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009039</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+BAS
+</td>
+<td style="text-align:left;">
+Basophil
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+91908
+</td>
+<td style="text-align:right;">
+1997
+</td>
+<td style="text-align:left;">
+0 (95%CI = 0 - 0.02)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 0.81 - 1)
+</td>
+<td style="text-align:left;">
+0.07 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.06 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0957
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.BAS.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.BAS.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+BAS
+</td>
+<td style="text-align:left;">
+Basophil
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+296570
+</td>
+<td style="text-align:right;">
+74442
+</td>
+<td style="text-align:left;">
+0.04 (95%CI = 0.04 - 0.04)
+</td>
+<td style="text-align:left;">
+0.98 (95%CI = 0.98 - 0.98)
+</td>
+<td style="text-align:left;">
+0.04 (SE = 0)
+</td>
+<td style="text-align:left;">
+1.06 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1811
+</td>
+<td style="text-align:right;">
+30160
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+BMI
+</td>
+<td style="text-align:left;">
+Body mass index
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+13976
+</td>
+<td style="text-align:right;">
+6646
+</td>
+<td style="text-align:left;">
+0.13 (95%CI = 0.09 - 0.18)
+</td>
+<td style="text-align:left;">
+0.97 (95%CI = 0 - 0.99)
+</td>
+<td style="text-align:left;">
+0.11 (SE = 0.05)
+</td>
+<td style="text-align:left;">
+1.02 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0315
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009057"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009057</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+BMI
+</td>
+<td style="text-align:left;">
+Body mass index
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+163835
+</td>
+<td style="text-align:right;">
+2046
+</td>
+<td style="text-align:left;">
+0.17 (95%CI = 0.15 - 0.18)
+</td>
+<td style="text-align:left;">
+0.91 (95%CI = 0.88 - 0.93)
+</td>
+<td style="text-align:left;">
+0.18 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.14 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.4926
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.BMI.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.BMI.v1.zip</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+BMI
+</td>
+<td style="text-align:left;">
+Body mass index
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+304506
+</td>
+<td style="text-align:right;">
+76393
+</td>
+<td style="text-align:left;">
+0.22 (95%CI = 0.21 - 0.22)
+</td>
+<td style="text-align:left;">
+0.85 (95%CI = 0.85 - 0.86)
+</td>
+<td style="text-align:left;">
+0.23 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.14 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+2.0302
+</td>
+<td style="text-align:right;">
+21001
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+BWT
+</td>
+<td style="text-align:left;">
+Body weight
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+14005
+</td>
+<td style="text-align:right;">
+6659
+</td>
+<td style="text-align:left;">
+0.11 (95%CI = 0.08 - 0.15)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.92 - 0.99)
+</td>
+<td style="text-align:left;">
+0.13 (SE = 0.05)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0255
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009056"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009056</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+BWT
+</td>
+<td style="text-align:left;">
+Body weight
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+165419
+</td>
+<td style="text-align:right;">
+2046
+</td>
+<td style="text-align:left;">
+0.14 (95%CI = 0.12 - 0.16)
+</td>
+<td style="text-align:left;">
+0.91 (95%CI = 0.89 - 0.94)
+</td>
+<td style="text-align:left;">
+0.22 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.17 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.6259
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.BW.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.BW.v1.zip</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+BWT
+</td>
+<td style="text-align:left;">
+Body weight
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+304622
+</td>
+<td style="text-align:right;">
+76415
+</td>
+<td style="text-align:left;">
+0.2 (95%CI = 0.2 - 0.2)
+</td>
+<td style="text-align:left;">
+0.87 (95%CI = 0.87 - 0.88)
+</td>
+<td style="text-align:left;">
+0.25 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.16 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+2.0556
+</td>
+<td style="text-align:right;">
+21002
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+DBP
+</td>
+<td style="text-align:left;">
+Diastolic blood pressure
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+13618
+</td>
+<td style="text-align:right;">
+6658
+</td>
+<td style="text-align:left;">
+0.23 (95%CI = 0.19 - 0.27)
+</td>
+<td style="text-align:left;">
+0.96 (95%CI = 0.74 - 0.98)
+</td>
+<td style="text-align:left;">
+-0.02 (SE = 0.05)
+</td>
+<td style="text-align:left;">
+1.02 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0165
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009052"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009052</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+DBP
+</td>
+<td style="text-align:left;">
+Diastolic blood pressure
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+145515
+</td>
+<td style="text-align:right;">
+1976
+</td>
+<td style="text-align:left;">
+0.09 (95%CI = 0.07 - 0.1)
+</td>
+<td style="text-align:left;">
+0.93 (95%CI = 0.89 - 0.96)
+</td>
+<td style="text-align:left;">
+0.05 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.07 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1459
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.DBP.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.DBP.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+DBP
+</td>
+<td style="text-align:left;">
+Diastolic blood pressure
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+289698
+</td>
+<td style="text-align:right;">
+72640
+</td>
+<td style="text-align:left;">
+0.13 (95%CI = 0.12 - 0.13)
+</td>
+<td style="text-align:left;">
+0.89 (95%CI = 0.89 - 0.9)
+</td>
+<td style="text-align:left;">
+0.12 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.08 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.5548
+</td>
+<td style="text-align:right;">
+4079
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+EOS
+</td>
+<td style="text-align:left;">
+Eosinophil
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2671
+</td>
+<td style="text-align:right;">
+6353
+</td>
+<td style="text-align:left;">
+0 (95%CI = 0 - 0.02)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 0.99 - 1)
+</td>
+<td style="text-align:left;">
+-0.26 (SE = 0.25)
+</td>
+<td style="text-align:left;">
+1.02 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0195
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009041"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009041</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+EOS
+</td>
+<td style="text-align:left;">
+Eosinophil
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+93063
+</td>
+<td style="text-align:right;">
+1997
+</td>
+<td style="text-align:left;">
+0.08 (95%CI = 0.07 - 0.1)
+</td>
+<td style="text-align:left;">
+0.98 (95%CI = 0.97 - 0.99)
+</td>
+<td style="text-align:left;">
+0.09 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.06 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1459
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.EOS.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.EOS.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+EOS
+</td>
+<td style="text-align:left;">
+Eosinophil
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+296570
+</td>
+<td style="text-align:right;">
+74442
+</td>
+<td style="text-align:left;">
+0.15 (95%CI = 0.15 - 0.16)
+</td>
+<td style="text-align:left;">
+0.93 (95%CI = 0.93 - 0.94)
+</td>
+<td style="text-align:left;">
+0.19 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.17 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.4962
+</td>
+<td style="text-align:right;">
+30150
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+GGT
+</td>
+<td style="text-align:left;">
+γ-glutamyl transpeptidase
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+8995
+</td>
+<td style="text-align:right;">
+6322
+</td>
+<td style="text-align:left;">
+0.05 (95%CI = 0.03 - 0.08)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 1 - 1)
+</td>
+<td style="text-align:left;">
+0.23 (SE = 0.07)
+</td>
+<td style="text-align:left;">
+0.99 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0075
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009050"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009050</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+GGT
+</td>
+<td style="text-align:left;">
+γ-glutamyl transpeptidase
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+133471
+</td>
+<td style="text-align:right;">
+1952
+</td>
+<td style="text-align:left;">
+0.08 (95%CI = 0.06 - 0.09)
+</td>
+<td style="text-align:left;">
+0.97 (95%CI = 0.95 - 0.98)
+</td>
+<td style="text-align:left;">
+0.15 (SE = 0.05)
+</td>
+<td style="text-align:left;">
+1.07 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.2005
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.GGT.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.GGT.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+GGT
+</td>
+<td style="text-align:left;">
+γ-glutamyl transpeptidase
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+291885
+</td>
+<td style="text-align:right;">
+73270
+</td>
+<td style="text-align:left;">
+0.1 (95%CI = 0.1 - 0.1)
+</td>
+<td style="text-align:left;">
+0.95 (95%CI = 0.95 - 0.95)
+</td>
+<td style="text-align:left;">
+0.18 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.16 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.5696
+</td>
+<td style="text-align:right;">
+30730
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HB
+</td>
+<td style="text-align:left;">
+Hemoglobin
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2741
+</td>
+<td style="text-align:right;">
+6375
+</td>
+<td style="text-align:left;">
+0.25 (95%CI = 0.16 - 0.36)
+</td>
+<td style="text-align:left;">
+0.97 (95%CI = 0 - 1)
+</td>
+<td style="text-align:left;">
+0.22 (SE = 0.25)
+</td>
+<td style="text-align:left;">
+1 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0105
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009034"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009034</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HB
+</td>
+<td style="text-align:left;">
+Hemoglobin
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+152447
+</td>
+<td style="text-align:right;">
+1999
+</td>
+<td style="text-align:left;">
+0.06 (95%CI = 0.05 - 0.08)
+</td>
+<td style="text-align:left;">
+0.95 (95%CI = 0.89 - 0.97)
+</td>
+<td style="text-align:left;">
+0.07 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.09 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.2005
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.Hb.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.Hb.v1.zip</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HB
+</td>
+<td style="text-align:left;">
+Hemoglobin
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+297083
+</td>
+<td style="text-align:right;">
+74575
+</td>
+<td style="text-align:left;">
+0.13 (95%CI = 0.13 - 0.13)
+</td>
+<td style="text-align:left;">
+0.92 (95%CI = 0.92 - 0.92)
+</td>
+<td style="text-align:left;">
+0.15 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.16 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.5217
+</td>
+<td style="text-align:right;">
+30020
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HCT
+</td>
+<td style="text-align:left;">
+Hematocrit
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2744
+</td>
+<td style="text-align:right;">
+6375
+</td>
+<td style="text-align:left;">
+0.26 (95%CI = 0.16 - 0.36)
+</td>
+<td style="text-align:left;">
+0.88 (95%CI = 0 - 1)
+</td>
+<td style="text-align:left;">
+0.18 (SE = 0.26)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0105
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009033"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009033</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HCT
+</td>
+<td style="text-align:left;">
+Hematocrit
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+153015
+</td>
+<td style="text-align:right;">
+1999
+</td>
+<td style="text-align:left;">
+0.05 (95%CI = 0.03 - 0.06)
+</td>
+<td style="text-align:left;">
+0.97 (95%CI = 0.94 - 0.98)
+</td>
+<td style="text-align:left;">
+0.07 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.09 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.2005
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.Ht.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.Ht.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HCT
+</td>
+<td style="text-align:left;">
+Hematocrit
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+297084
+</td>
+<td style="text-align:right;">
+74575
+</td>
+<td style="text-align:left;">
+0.13 (95%CI = 0.13 - 0.13)
+</td>
+<td style="text-align:left;">
+0.92 (95%CI = 0.92 - 0.93)
+</td>
+<td style="text-align:left;">
+0.14 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.16 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.5035
+</td>
+<td style="text-align:right;">
+30030
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HDL
+</td>
+<td style="text-align:left;">
+HDL-cholesterol
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+13114
+</td>
+<td style="text-align:right;">
+5863
+</td>
+<td style="text-align:left;">
+0.06 (95%CI = 0.04 - 0.09)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 1 - 1)
+</td>
+<td style="text-align:left;">
+0.02 (SE = 0.05)
+</td>
+<td style="text-align:left;">
+1.02 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0225
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009044"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009044</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HDL
+</td>
+<td style="text-align:left;">
+HDL-cholesterol
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+74970
+</td>
+<td style="text-align:right;">
+1783
+</td>
+<td style="text-align:left;">
+0.15 (95%CI = 0.13 - 0.17)
+</td>
+<td style="text-align:left;">
+0.98 (95%CI = 0.97 - 0.99)
+</td>
+<td style="text-align:left;">
+0.17 (SE = 0.03)
+</td>
+<td style="text-align:left;">
+1.09 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.2005
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.HDLC.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.HDLC.v1.zip</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HDL
+</td>
+<td style="text-align:left;">
+HDL-cholesterol
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+267973
+</td>
+<td style="text-align:right;">
+67346
+</td>
+<td style="text-align:left;">
+0.2 (95%CI = 0.2 - 0.2)
+</td>
+<td style="text-align:left;">
+0.92 (95%CI = 0.92 - 0.93)
+</td>
+<td style="text-align:left;">
+0.23 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.15 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.6524
+</td>
+<td style="text-align:right;">
+30760
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HT
+</td>
+<td style="text-align:left;">
+Height
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+14126
+</td>
+<td style="text-align:right;">
+6658
+</td>
+<td style="text-align:left;">
+0.23 (95%CI = 0.19 - 0.26)
+</td>
+<td style="text-align:left;">
+0.98 (95%CI = 0.96 - 0.99)
+</td>
+<td style="text-align:left;">
+0.12 (SE = 0.06)
+</td>
+<td style="text-align:left;">
+1.02 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0345
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009055"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009055</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HT
+</td>
+<td style="text-align:left;">
+Height
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+165056
+</td>
+<td style="text-align:right;">
+2048
+</td>
+<td style="text-align:left;">
+0.2 (95%CI = 0.19 - 0.22)
+</td>
+<td style="text-align:left;">
+0.93 (95%CI = 0.92 - 0.95)
+</td>
+<td style="text-align:left;">
+0.41 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.33 (SE = 0.03)
+</td>
+<td style="text-align:right;">
+1.7648
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.Hei.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.Hei.v1.zip</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HT
+</td>
+<td style="text-align:left;">
+Height
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+304826
+</td>
+<td style="text-align:right;">
+76470
+</td>
+<td style="text-align:left;">
+0.24 (95%CI = 0.24 - 0.25)
+</td>
+<td style="text-align:left;">
+0.91 (95%CI = 0.91 - 0.91)
+</td>
+<td style="text-align:left;">
+0.45 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.39 (SE = 0.03)
+</td>
+<td style="text-align:right;">
+2.3412
+</td>
+<td style="text-align:right;">
+50
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HbA1c
+</td>
+<td style="text-align:left;">
+HbA1c
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+6116
+</td>
+<td style="text-align:right;">
+5405
+</td>
+<td style="text-align:left;">
+0 (95%CI = 0 - 0.05)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 0.9 - 1)
+</td>
+<td style="text-align:left;">
+0.42 (SE = 0.15)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0405
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009054"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009054</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HbA1c
+</td>
+<td style="text-align:left;">
+HbA1c
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+71221
+</td>
+<td style="text-align:right;">
+1949
+</td>
+<td style="text-align:left;">
+0.08 (95%CI = 0.06 - 0.1)
+</td>
+<td style="text-align:left;">
+0.98 (95%CI = 0.96 - 0.99)
+</td>
+<td style="text-align:left;">
+0.07 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.05 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0957
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.HbA1c.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.HbA1c.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HbA1c
+</td>
+<td style="text-align:left;">
+HbA1c
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+292134
+</td>
+<td style="text-align:right;">
+73182
+</td>
+<td style="text-align:left;">
+0.14 (95%CI = 0.13 - 0.14)
+</td>
+<td style="text-align:left;">
+0.93 (95%CI = 0.93 - 0.94)
+</td>
+<td style="text-align:left;">
+0.2 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.17 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.5659
+</td>
+<td style="text-align:right;">
+30750
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+LDL
+</td>
+<td style="text-align:left;">
+LDL-cholesterol
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+13086
+</td>
+<td style="text-align:right;">
+6313
+</td>
+<td style="text-align:left;">
+0.07 (95%CI = 0.07 - 0.08)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 1 - 1)
+</td>
+<td style="text-align:left;">
+0.03 (SE = 0.05)
+</td>
+<td style="text-align:left;">
+1.03 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0285
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009043"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009043</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+LDL
+</td>
+<td style="text-align:left;">
+LDL-cholesterol
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+72866
+</td>
+<td style="text-align:right;">
+1953
+</td>
+<td style="text-align:left;">
+0.05 (95%CI = 0.03 - 0.06)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 0.99 - 1)
+</td>
+<td style="text-align:left;">
+0.07 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.05 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0957
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.LDLC.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.LDLC.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+LDL
+</td>
+<td style="text-align:left;">
+LDL-cholesterol
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+291538
+</td>
+<td style="text-align:right;">
+73172
+</td>
+<td style="text-align:left;">
+0.12 (95%CI = 0.11 - 0.12)
+</td>
+<td style="text-align:left;">
+0.97 (95%CI = 0.97 - 0.97)
+</td>
+<td style="text-align:left;">
+0.1 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.06 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.2697
+</td>
+<td style="text-align:right;">
+30780
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+LYM
+</td>
+<td style="text-align:left;">
+Lymphocyte
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2681
+</td>
+<td style="text-align:right;">
+6353
+</td>
+<td style="text-align:left;">
+0 (95%CI = 0 - 0.06)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 0 - 1)
+</td>
+<td style="text-align:left;">
+0.08 (SE = 0.28)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0135
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009037"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009037</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+LYM
+</td>
+<td style="text-align:left;">
+Lymphocyte
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+95717
+</td>
+<td style="text-align:right;">
+1997
+</td>
+<td style="text-align:left;">
+0.16 (95%CI = 0.14 - 0.17)
+</td>
+<td style="text-align:left;">
+0.94 (95%CI = 0.91 - 0.96)
+</td>
+<td style="text-align:left;">
+0.08 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.05 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1459
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.LYM.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.LYM.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+LYM
+</td>
+<td style="text-align:left;">
+Lymphocyte
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+296570
+</td>
+<td style="text-align:right;">
+74442
+</td>
+<td style="text-align:left;">
+0.08 (95%CI = 0.08 - 0.08)
+</td>
+<td style="text-align:left;">
+0.94 (95%CI = 0.94 - 0.94)
+</td>
+<td style="text-align:left;">
+0.18 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.18 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.6070
+</td>
+<td style="text-align:right;">
+30120
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MCH
+</td>
+<td style="text-align:left;">
+Mean corpuscular hemoglobin
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2742
+</td>
+<td style="text-align:right;">
+6375
+</td>
+<td style="text-align:left;">
+0.21 (95%CI = 0.18 - 0.27)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 1 - 1)
+</td>
+<td style="text-align:left;">
+0.26 (SE = 0.22)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0225
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009063"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009063</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MCH
+</td>
+<td style="text-align:left;">
+Mean corpuscular hemoglobin
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+128028
+</td>
+<td style="text-align:right;">
+1999
+</td>
+<td style="text-align:left;">
+0.12 (95%CI = 0.1 - 0.13)
+</td>
+<td style="text-align:left;">
+0.98 (95%CI = 0.97 - 0.98)
+</td>
+<td style="text-align:left;">
+0.19 (SE = 0.03)
+</td>
+<td style="text-align:left;">
+1.11 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.2005
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MCH.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MCH.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MCH
+</td>
+<td style="text-align:left;">
+Mean corpuscular hemoglobin
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+297080
+</td>
+<td style="text-align:right;">
+74575
+</td>
+<td style="text-align:left;">
+0.19 (95%CI = 0.18 - 0.19)
+</td>
+<td style="text-align:left;">
+0.94 (95%CI = 0.94 - 0.95)
+</td>
+<td style="text-align:left;">
+0.25 (SE = 0.03)
+</td>
+<td style="text-align:left;">
+1.12 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.4781
+</td>
+<td style="text-align:right;">
+30050
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MCHC
+</td>
+<td style="text-align:left;">
+Mean corpuscular hemoglobin concentration
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2744
+</td>
+<td style="text-align:right;">
+6375
+</td>
+<td style="text-align:left;">
+0.05 (95%CI = 0.04 - 0.16)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 1 - 1)
+</td>
+<td style="text-align:left;">
+0.01 (SE = 0.23)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0225
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009064"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009064</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MCHC
+</td>
+<td style="text-align:left;">
+Mean corpuscular hemoglobin concentration
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+135482
+</td>
+<td style="text-align:right;">
+1999
+</td>
+<td style="text-align:left;">
+0.04 (95%CI = 0.03 - 0.05)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.98 - 1)
+</td>
+<td style="text-align:left;">
+0.07 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.06 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1459
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MCHC.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MCHC.v1.zip</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MCHC
+</td>
+<td style="text-align:left;">
+Mean corpuscular hemoglobin concentration
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+297079
+</td>
+<td style="text-align:right;">
+74573
+</td>
+<td style="text-align:left;">
+0.05 (95%CI = 0.05 - 0.05)
+</td>
+<td style="text-align:left;">
+0.97 (95%CI = 0.97 - 0.97)
+</td>
+<td style="text-align:left;">
+0.06 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.04 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1843
+</td>
+<td style="text-align:right;">
+30060
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MCV
+</td>
+<td style="text-align:left;">
+Mean corpuscular volume
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2742
+</td>
+<td style="text-align:right;">
+6375
+</td>
+<td style="text-align:left;">
+0.23 (95%CI = 0.18 - 0.29)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 1 - 1)
+</td>
+<td style="text-align:left;">
+0.41 (SE = 0.23)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0225
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009065"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009065</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MCV
+</td>
+<td style="text-align:left;">
+Mean corpuscular volume
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+129832
+</td>
+<td style="text-align:right;">
+1999
+</td>
+<td style="text-align:left;">
+0.12 (95%CI = 0.1 - 0.13)
+</td>
+<td style="text-align:left;">
+0.98 (95%CI = 0.97 - 0.98)
+</td>
+<td style="text-align:left;">
+0.22 (SE = 0.03)
+</td>
+<td style="text-align:left;">
+1.1 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.2531
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MCV.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MCV.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MCV
+</td>
+<td style="text-align:left;">
+Mean corpuscular volume
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+297083
+</td>
+<td style="text-align:right;">
+74574
+</td>
+<td style="text-align:left;">
+0.22 (95%CI = 0.21 - 0.22)
+</td>
+<td style="text-align:left;">
+0.94 (95%CI = 0.93 - 0.94)
+</td>
+<td style="text-align:left;">
+0.25 (SE = 0.03)
+</td>
+<td style="text-align:left;">
+1.13 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.5144
+</td>
+<td style="text-align:right;">
+30040
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MON
+</td>
+<td style="text-align:left;">
+Monocyte
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2681
+</td>
+<td style="text-align:right;">
+6353
+</td>
+<td style="text-align:left;">
+0.15 (95%CI = 0.06 - 0.24)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 0 - 1)
+</td>
+<td style="text-align:left;">
+-0.25 (SE = 0.22)
+</td>
+<td style="text-align:left;">
+1.02 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0135
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009038"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009038</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MON
+</td>
+<td style="text-align:left;">
+Monocyte
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+95119
+</td>
+<td style="text-align:right;">
+1997
+</td>
+<td style="text-align:left;">
+0.1 (95%CI = 0.09 - 0.12)
+</td>
+<td style="text-align:left;">
+0.98 (95%CI = 0.97 - 0.99)
+</td>
+<td style="text-align:left;">
+0.08 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.09 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1459
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MON.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MON.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MON
+</td>
+<td style="text-align:left;">
+Monocyte
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+296570
+</td>
+<td style="text-align:right;">
+74442
+</td>
+<td style="text-align:left;">
+0.13 (95%CI = 0.13 - 0.14)
+</td>
+<td style="text-align:left;">
+0.95 (95%CI = 0.95 - 0.95)
+</td>
+<td style="text-align:left;">
+0.19 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.2 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.5144
+</td>
+<td style="text-align:right;">
+30130
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+NEU
+</td>
+<td style="text-align:left;">
+Neutrophil
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2671
+</td>
+<td style="text-align:right;">
+6353
+</td>
+<td style="text-align:left;">
+0.11 (95%CI = 0.1 - 0.15)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 1 - 1)
+</td>
+<td style="text-align:left;">
+0.1 (SE = 0.23)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0105
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009040"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009040</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+NEU
+</td>
+<td style="text-align:left;">
+Neutrophil
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+82810
+</td>
+<td style="text-align:right;">
+1997
+</td>
+<td style="text-align:left;">
+0.07 (95%CI = 0.05 - 0.09)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.98 - 0.99)
+</td>
+<td style="text-align:left;">
+0.11 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.05 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1459
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.NEU.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.NEU.v1.zip</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+NEU
+</td>
+<td style="text-align:left;">
+Neutrophil
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+296570
+</td>
+<td style="text-align:right;">
+74442
+</td>
+<td style="text-align:left;">
+0.14 (95%CI = 0.14 - 0.15)
+</td>
+<td style="text-align:left;">
+0.93 (95%CI = 0.93 - 0.94)
+</td>
+<td style="text-align:left;">
+0.15 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.15 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.5401
+</td>
+<td style="text-align:right;">
+30140
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+PLT
+</td>
+<td style="text-align:left;">
+Platelet
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2723
+</td>
+<td style="text-align:right;">
+6375
+</td>
+<td style="text-align:left;">
+0.11 (95%CI = 0.02 - 0.21)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0 - 1)
+</td>
+<td style="text-align:left;">
+0.26 (SE = 0.25)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0225
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009036"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009036</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+PLT
+</td>
+<td style="text-align:left;">
+Platelet
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+148623
+</td>
+<td style="text-align:right;">
+1999
+</td>
+<td style="text-align:left;">
+0.15 (95%CI = 0.14 - 0.17)
+</td>
+<td style="text-align:left;">
+0.95 (95%CI = 0.94 - 0.96)
+</td>
+<td style="text-align:left;">
+0.18 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.13 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.3101
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.PLT.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.PLT.v1.zip</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+PLT
+</td>
+<td style="text-align:left;">
+Platelet
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+297082
+</td>
+<td style="text-align:right;">
+74575
+</td>
+<td style="text-align:left;">
+0.24 (95%CI = 0.24 - 0.24)
+</td>
+<td style="text-align:left;">
+0.94 (95%CI = 0.93 - 0.94)
+</td>
+<td style="text-align:left;">
+0.27 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.2 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.6108
+</td>
+<td style="text-align:right;">
+30080
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+RBC
+</td>
+<td style="text-align:left;">
+Red blood cell
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2744
+</td>
+<td style="text-align:right;">
+6375
+</td>
+<td style="text-align:left;">
+0.32 (95%CI = 0.24 - 0.42)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.97 - 1)
+</td>
+<td style="text-align:left;">
+0.54 (SE = 0.24)
+</td>
+<td style="text-align:left;">
+1 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0165
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009062"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009062</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+RBC
+</td>
+<td style="text-align:left;">
+Red blood cell
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+153512
+</td>
+<td style="text-align:right;">
+1999
+</td>
+<td style="text-align:left;">
+0.08 (95%CI = 0.07 - 0.1)
+</td>
+<td style="text-align:left;">
+0.97 (95%CI = 0.96 - 0.98)
+</td>
+<td style="text-align:left;">
+0.12 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.12 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.2531
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.RBC.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.RBC.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+RBC
+</td>
+<td style="text-align:left;">
+Red blood cell
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+297084
+</td>
+<td style="text-align:right;">
+74575
+</td>
+<td style="text-align:left;">
+0.17 (95%CI = 0.17 - 0.18)
+</td>
+<td style="text-align:left;">
+0.93 (95%CI = 0.93 - 0.93)
+</td>
+<td style="text-align:left;">
+0.2 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.19 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.6108
+</td>
+<td style="text-align:right;">
+30010
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+SBP
+</td>
+<td style="text-align:left;">
+Sytolic blood pressure
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+13613
+</td>
+<td style="text-align:right;">
+6658
+</td>
+<td style="text-align:left;">
+0.17 (95%CI = 0.13 - 0.22)
+</td>
+<td style="text-align:left;">
+0.94 (95%CI = 0 - 0.98)
+</td>
+<td style="text-align:left;">
+0.06 (SE = 0.06)
+</td>
+<td style="text-align:left;">
+1.02 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0285
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009053"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009053</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+SBP
+</td>
+<td style="text-align:left;">
+Sytolic blood pressure
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+145505
+</td>
+<td style="text-align:right;">
+1976
+</td>
+<td style="text-align:left;">
+0.11 (95%CI = 0.09 - 0.12)
+</td>
+<td style="text-align:left;">
+0.94 (95%CI = 0.9 - 0.96)
+</td>
+<td style="text-align:left;">
+0.07 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.08 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.2005
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.SBP.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.SBP.v1.zip</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+SBP
+</td>
+<td style="text-align:left;">
+Sytolic blood pressure
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+289696
+</td>
+<td style="text-align:right;">
+72639
+</td>
+<td style="text-align:left;">
+0.12 (95%CI = 0.12 - 0.12)
+</td>
+<td style="text-align:left;">
+0.89 (95%CI = 0.88 - 0.9)
+</td>
+<td style="text-align:left;">
+0.13 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.1 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.5995
+</td>
+<td style="text-align:right;">
+4080
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+TC
+</td>
+<td style="text-align:left;">
+Total cholesterol
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+13116
+</td>
+<td style="text-align:right;">
+6324
+</td>
+<td style="text-align:left;">
+0.05 (95%CI = 0.05 - 0.06)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 1 - 1)
+</td>
+<td style="text-align:left;">
+0.02 (SE = 0.05)
+</td>
+<td style="text-align:left;">
+1.03 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0285
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009042"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009042</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+TC
+</td>
+<td style="text-align:left;">
+Total cholesterol
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+135808
+</td>
+<td style="text-align:right;">
+1955
+</td>
+<td style="text-align:left;">
+0.08 (95%CI = 0.07 - 0.09)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.99 - 1)
+</td>
+<td style="text-align:left;">
+0.07 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.07 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1459
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.TC.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.TC.v1.zip</a>
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+TC
+</td>
+<td style="text-align:left;">
+Total cholesterol
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+292046
+</td>
+<td style="text-align:right;">
+73305
+</td>
+<td style="text-align:left;">
+0.12 (95%CI = 0.12 - 0.12)
+</td>
+<td style="text-align:left;">
+0.96 (95%CI = 0.96 - 0.97)
+</td>
+<td style="text-align:left;">
+0.11 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.07 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.3068
+</td>
+<td style="text-align:right;">
+30690
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+TRUE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+TG
+</td>
+<td style="text-align:left;">
+Triglycerides
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+13115
+</td>
+<td style="text-align:right;">
+6323
+</td>
+<td style="text-align:left;">
+0.11 (95%CI = 0.08 - 0.15)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 0.99 - 1)
+</td>
+<td style="text-align:left;">
+0.08 (SE = 0.05)
+</td>
+<td style="text-align:left;">
+1.02 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0315
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009045"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009045</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+TG
+</td>
+<td style="text-align:left;">
+Triglycerides
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+111667
+</td>
+<td style="text-align:right;">
+1954
+</td>
+<td style="text-align:left;">
+0.07 (95%CI = 0.06 - 0.09)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.99 - 1)
+</td>
+<td style="text-align:left;">
+0.13 (SE = 0.03)
+</td>
+<td style="text-align:left;">
+1.05 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.1459
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.TG.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.TG.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+TG
+</td>
+<td style="text-align:left;">
+Triglycerides
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+291813
+</td>
+<td style="text-align:right;">
+73241
+</td>
+<td style="text-align:left;">
+0.16 (95%CI = 0.16 - 0.16)
+</td>
+<td style="text-align:left;">
+0.94 (95%CI = 0.93 - 0.94)
+</td>
+<td style="text-align:left;">
+0.18 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.14 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.5696
+</td>
+<td style="text-align:right;">
+30870
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+WBC
+</td>
+<td style="text-align:left;">
+White blood cell
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+UGR
+</td>
+<td style="text-align:right;">
+2741
+</td>
+<td style="text-align:right;">
+6375
+</td>
+<td style="text-align:left;">
+0.03 (95%CI = 0.03 - 0.05)
+</td>
+<td style="text-align:left;">
+1 (95%CI = 1 - 1)
+</td>
+<td style="text-align:left;">
+0.03 (SE = 0.26)
+</td>
+<td style="text-align:left;">
+1.01 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.0105
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009061"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009061</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+WBC
+</td>
+<td style="text-align:left;">
+White blood cell
+</td>
+<td style="text-align:left;">
+EAS
+</td>
+<td style="text-align:left;">
+BBJ
+</td>
+<td style="text-align:right;">
+154355
+</td>
+<td style="text-align:right;">
+1999
+</td>
+<td style="text-align:left;">
+0.12 (95%CI = 0.11 - 0.14)
+</td>
+<td style="text-align:left;">
+0.93 (95%CI = 0.91 - 0.95)
+</td>
+<td style="text-align:left;">
+0.11 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.1 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.2531
+</td>
+<td style="text-align:right;">
+NA
+</td>
+<td style="text-align:left;">
+<a
+href="https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.WBC.v1.zip"
+class="uri">https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.WBC.v1.zip</a>
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+WBC
+</td>
+<td style="text-align:left;">
+White blood cell
+</td>
+<td style="text-align:left;">
+EUR
+</td>
+<td style="text-align:left;">
+UKB
+</td>
+<td style="text-align:right;">
+297079
+</td>
+<td style="text-align:right;">
+74575
+</td>
+<td style="text-align:left;">
+0.14 (95%CI = 0.14 - 0.14)
+</td>
+<td style="text-align:left;">
+0.92 (95%CI = 0.92 - 0.92)
+</td>
+<td style="text-align:left;">
+0.17 (SE = 0.01)
+</td>
+<td style="text-align:left;">
+1.18 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.6334
+</td>
+<td style="text-align:right;">
+30000
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+FALSE
+</td>
+</tr>
+</tbody>
+</table>
+</div>
+</details>
 <hr />
 </div>
-<div id="run-genopred" class="section level1">
-<h1>Run GenoPred</h1>
-<div id="prepare-configuration-for-genopred-1" class="section level2">
-<h2>Prepare configuration for GenoPred</h2>
+</div>
+</div>
+<div id="main-analysis" class="section level1">
+<h1>Main analysis</h1>
+<hr />
+<div id="pgs-calculation" class="section level2">
+<h2>PGS calculation</h2>
+<p>We will do this using GenoPred.</p>
 <details>
 <summary>
 Show code
 </summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Prepare configuration
+</h4>
 <pre class="r"><code>######
 # gwas_list
 ######
@@ -2047,7 +6014,6 @@ <h2>Prepare configuration for GenoPred</h2>
   &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&quot;,
   &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt&quot;,
   &quot;pgs_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;,&#39;prscsx&#39;,&#39;xwing&#39;]&quot;,
-  &quot;tlprs_methods: [&#39;dbslmm&#39;]&quot;,
   &quot;leopard_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
   &quot;cores_prep_pgs: 10&quot;, # xwing run with 20 cores
   &quot;cores_target_pgs: 50&quot;,
@@ -2059,15 +6025,10 @@ <h2>Prepare configuration for GenoPred</h2>
 )
 
 write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
-</details>
 <hr />
-</div>
-<div id="run-pipeline-2" class="section level2">
-<h2>Run pipeline</h2>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Run pipeline
+</h4>
 <pre class="bash"><code>snakemake \
   --profile slurm \
   --use-conda \
@@ -2076,18 +6037,14 @@ <h2>Run pipeline</h2>
 </details>
 <hr />
 </div>
-</div>
-<div id="evaluate-pgs" class="section level1">
-<h1>Evaluate PGS</h1>
+<div id="pgs-evaluation" class="section level2">
+<h2>PGS evaluation</h2>
 <p>Lets use the model builder script which implements nested 10 fold
 cross validation. Similar set up to previous paper, evaluating a model
 containing the best PGS selected by 10-fold cross validation, a model
 containing the PGS selected by pseudovalidation (if available), and an
 elastic net model containing all PGS from a given method. We will need
 to update the model builder script to achieve this</p>
-<hr />
-<div id="compare-all-methods" class="section level2">
-<h2>Compare all methods</h2>
 <p>We want to see: - Performance of pseudo and top1 models for
 single-source methods - Performance of pseudo and top1 models for
 multi-source methods - Performance of multi-source methods: - Using
@@ -2095,13 +6052,16 @@ <h2>Compare all methods</h2>
 2 - Using pseudoval for tuning step 1 and crossval for tuning step 2</p>
 <p>To achieve this. Will need to define groups of predictors for step 1
 modelling, and groups that should then be linearly combined.</p>
-<hr />
-<div id="create-predictor-lists-1" class="section level3">
-<h3>Create predictor lists</h3>
 <details>
 <summary>
 Show code
 </summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Create predictor list
+</h4>
 <pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 source(&#39;../functions/misc.R&#39;)
 source_all(&#39;../functions&#39;)
@@ -2431,15 +6391,10 @@ <h3>Create predictor lists</h3>
     }
   }
 }</code></pre>
-</details>
 <hr />
-</div>
-<div id="run-model_builder-1" class="section level3">
-<h3>Run model_builder</h3>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Run model_builder
+</h4>
 <pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
@@ -2467,7 +6422,7 @@ <h3>Run model_builder</h3>
   for disc_pop_i in ${disc_pop}; do
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
       if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt&quot; ]; then
-        sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
+        sbatch --mem 10G -n 5 --exclude=erc-hpc-comp058 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
           --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
           --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
           --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
@@ -2477,15 +6432,10 @@ <h3>Run model_builder</h3>
   done
 done
 </code></pre>
-</details>
 <hr />
-</div>
-<div id="plot-results" class="section level3">
-<h3>Plot results</h3>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Plot results
+</h4>
 <pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 
 library(data.table)
@@ -2558,17 +6508,17 @@ <h3>Plot results</h3>
   res_eval_i$Method&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_eval_i$Method)
   
   res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
-                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTrain&#39;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTune&#39;
   res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
-                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
   res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
-                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTrain&#39;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTune&#39;
   res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
-                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTrain&#39;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTune&#39;
   
-  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
-  res_eval_i$Model[res_eval_i$Group == &#39;prscsx.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
-  res_eval_i$Model[res_eval_i$Group == &#39;xwing.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
+  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;prscsx.pseudo.multi&#39;]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;xwing.pseudo.multi&#39;]&lt;-&#39;SumStatTune&#39;
   
   res_eval_i$Source&lt;-ifelse(
     res_eval_i$Method %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_eval_i$Method) | 
@@ -2580,18 +6530,18 @@ <h3>Plot results</h3>
   res_eval_i$Discovery[res_eval_i$Source == &#39;Multi&#39;] &lt;- res_eval_i$gwas_group[res_eval_i$Source == &#39;Multi&#39;]
   
   res_eval_i$Method&lt;-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
-  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
   res_eval_i$Discovery&lt;-factor(res_eval_i$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
 
-  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
   res_eval_i &lt;- res_eval_i[
     !(res_eval_i$Method %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
-      res_eval_i$Model %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+      res_eval_i$Model %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
   
   # Remove pseudo model for methods that don&#39;t really have one 
   res_eval_i &lt;- res_eval_i[
     !(res_eval_i$Method %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
-      res_eval_i$Model %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+      res_eval_i$Model %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
 
   # Remove top1 models for *-Multi, PRS-CSx, X-wing
   res_eval_i &lt;- res_eval_i[
@@ -2621,9 +6571,9 @@ <h3>Plot results</h3>
   tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
   tmp$label &lt;- factor(tmp$label, levels = model_order)
   
-  # Simplify result to either SumStatTrain or IndivTrain
-  tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-  tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+  # Simplify result to either SumStatTune or IndivTune
+  tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+  tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
   tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery&#39;,&#39;Model&#39;), with=F]),]
   
   res_eval_simp &lt;- rbind(res_eval_simp, tmp)
@@ -2747,29 +6697,6 @@ <h3>Plot results</h3>
   dev.off()
 }
 
-# Compare performance of each method in each target population across values of GWAS sample size
-# Because we cannot hold all other things constant, this plot is confusing. I think relative performance might be easier to read.
-for(targ_pop_i in targ_pop){
-    tmp &lt;- res_eval_simp[res_eval_simp$Target == targ_pop_i,]
-    for(disc_pop_i in unique(tmp$Discovery)){
-      tmp2 &lt;- tmp[tmp$Discovery == disc_pop_i,]
-      
-      ggplot(tmp2, aes(x=label, y=R , fill = Model)) +
-        geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                      width = 0,
-                      position = position_dodge(width = 1)) +
-        geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
-        geom_vline(xintercept = seq(1.5, length(unique(tmp2$Trait))), linetype=&quot;dotted&quot;) +
-        labs(y = &quot;R (SE)&quot;, x=NULL, fill = NULL, title = info_all$`Trait Description`[info_all$`Trait Label` == pheno_i]) +
-        facet_grid(. ~ Trait, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
-        theme_half_open() +
-        background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
-        panel_border() + 
-        theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-      
-    }
-}
-
 ####
 # Average results across phenotypes
 ####
@@ -2846,7 +6773,7 @@ <h3>Plot results</h3>
   }
 }
 
-meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
 meta_res_eval$Discovery&lt;-factor(meta_res_eval$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
 
 write.csv(meta_res_eval, &#39;~/oliverpainfel/Analyses/crosspop/r_eval.csv&#39;, row.names = F)
@@ -2867,8 +6794,8 @@ <h3>Plot results</h3>
                                          &#39;EUR GWAS&#39;,
                                          &#39;Both&#39;))
 tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
-tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
 tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
 
 png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r.png&#39;), res=300, width = 3200, height = 2000, units = &#39;px&#39;)
@@ -2899,8 +6826,8 @@ <h3>Plot results</h3>
 tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
 tmp$Discovery_clean &lt;- paste0(tmp$Discovery_clean, &#39; GWAS&#39;)
 tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
-tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
 tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
 
 png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.png&#39;), res=300, width = 4000, height = 1500, units = &#39;px&#39;)
@@ -2928,10 +6855,10 @@ <h3>Plot results</h3>
 tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods)] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods)], &#39;-multi&#39;)
 tmp$label &lt;- factor(tmp$label, levels = unique(tmp$label[order(!(grepl(&#39;Multi&#39;, tmp$label)), tmp$label)]))
 tmp&lt;-tmp[grepl(&#39;multi&#39;, tmp$label),]
-tmp &lt;- tmp[tmp$Model != &#39;Multi-IndivTrain&#39;,]
+tmp &lt;- tmp[tmp$Model != &#39;Multi-IndivTune&#39;,]
 tmp$Model&lt;-as.character(tmp$Model)
-tmp$Model[tmp$Model != &#39;SumStatTrain&#39;]&lt;-&#39;IndivTrain&#39;
-tmp$Model[tmp$Model == &#39;SumStatTrain&#39;]&lt;-&#39;LEOPARD&#39;
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;]&lt;-&#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;]&lt;-&#39;LEOPARD&#39;
 tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
 
 png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r_leopard.png&#39;), res=300, width = 1500, height = 2000, units = &#39;px&#39;)
@@ -3030,13 +6957,13 @@ <h3>Plot results</h3>
 
 find_model&lt;-function(x){
   mod &lt;- x
-  mod[grepl(&#39;top1$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;IndivTrain&#39;
-  mod[grepl(&#39;top1$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;SumStatTrain&#39;
-  mod[grepl(&#39;multi$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-IndivTrain&#39;
-  mod[grepl(&#39;multi$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-SumStatTrain&#39;
-  mod[grepl(&#39;_multi&#39;, x)] &lt;- &#39;SumStatTrain&#39;
-  mod[x == &#39;prscsx.pseudo.multi&#39;] &lt;- &#39;SumStatTrain&#39;
-  mod[x == &#39;xwing.pseudo.multi&#39;] &lt;- &#39;SumStatTrain&#39;
+  mod[grepl(&#39;top1$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;IndivTune&#39;
+  mod[grepl(&#39;top1$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;SumStatTune&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-IndivTune&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-SumStatTune&#39;
+  mod[grepl(&#39;_multi&#39;, x)] &lt;- &#39;SumStatTune&#39;
+  mod[x == &#39;prscsx.pseudo.multi&#39;] &lt;- &#39;SumStatTune&#39;
+  mod[x == &#39;xwing.pseudo.multi&#39;] &lt;- &#39;SumStatTune&#39;
   
   return(mod)
 }
@@ -3056,26 +6983,26 @@ <h3>Plot results</h3>
 
 res_comp_all$Method1&lt;-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
 res_comp_all$Method2&lt;-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
-res_comp_all$Model1&lt;-factor(res_comp_all$Model1, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
-res_comp_all$Model2&lt;-factor(res_comp_all$Model2, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+res_comp_all$Model1&lt;-factor(res_comp_all$Model1, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+res_comp_all$Model2&lt;-factor(res_comp_all$Model2, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
 res_comp_all$Discovery1&lt;-factor(res_comp_all$Discovery1, levels=rev(c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;)))
 res_comp_all$Discovery2&lt;-factor(res_comp_all$Discovery2, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
 
-# Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+# Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
 res_comp_all &lt;- res_comp_all[
 !(res_comp_all$Method1 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
-  res_comp_all$Model1 %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+  res_comp_all$Model1 %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
 res_comp_all &lt;- res_comp_all[
 !(res_comp_all$Method2 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
-  res_comp_all$Model2 %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+  res_comp_all$Model2 %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
 
 # Remove pseudo model for methods that don&#39;t really have one 
 res_comp_all &lt;- res_comp_all[
 !(res_comp_all$Method1 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
-  res_comp_all$Model1 %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+  res_comp_all$Model1 %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
 res_comp_all &lt;- res_comp_all[
 !(res_comp_all$Method2 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
-  res_comp_all$Model2 %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+  res_comp_all$Model2 %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
 
 # Remove top1 models for PRS-CSx
 res_comp_all &lt;- res_comp_all[
@@ -3086,7 +7013,7 @@ <h3>Plot results</h3>
   grepl(&#39;top1&#39;, res_comp_all$Model_2)),]
 
 # Remove any comparisons
-res_comp_all &lt;- res_comp_all[!duplicated(res_comp_all[, c(&quot;Target&quot;, &quot;Method1&quot;, &quot;Model1&quot;, &quot;Source1&quot;, &quot;Discovery1&quot;, &quot;Method2&quot;, &quot;Model2&quot;, &quot;Source2&quot;, &quot;Discovery2&quot;,&#39;pheno&#39;)]),]
+res_comp_all &lt;- res_comp_all[!duplicated(res_comp_all[, c(&quot;Target&quot;, &quot;gwas_group&quot;, &quot;Method1&quot;, &quot;Model1&quot;, &quot;Source1&quot;, &quot;Discovery1&quot;, &quot;Method2&quot;, &quot;Model2&quot;, &quot;Source2&quot;, &quot;Discovery2&quot;,&#39;pheno&#39;)]),]
 
 res_comp_all$r_diff_rel &lt;- res_comp_all$R_diff / res_comp_all$Model_2_R
 
@@ -3129,7 +7056,7 @@ <h3>Plot results</h3>
 #####
 # Export a csv containing difference results for all traits
 #####
-# Simplify to contain only IndivTrain or SumStatTrain result
+# Simplify to contain only IndivTune or SumStatTune result
 tmp &lt;- res_comp_all
 tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
 tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
@@ -3141,10 +7068,10 @@ <h3>Plot results</h3>
 tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;] &lt;- paste0(tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;], &#39;-multi&#39;)
 tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;] &lt;- paste0(tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;], &#39;-multi&#39;)
 
-tmp$Model1[tmp$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp$Model1[tmp$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
-tmp$Model2[tmp$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp$Model2[tmp$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp$Model1[tmp$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model1[tmp$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp$Model2[tmp$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model2[tmp$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
 
 tmp&lt;-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
 tmp&lt;-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
@@ -3332,7 +7259,7 @@ <h3>Plot results</h3>
 #####
 # Export a csv containing difference results for all traits
 #####
-# Simplify to contain only IndivTrain or SumStatTrain result
+# Simplify to contain only IndivTune or SumStatTune result
 tmp &lt;- meta_res_comp
 tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
 tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
@@ -3344,10 +7271,10 @@ <h3>Plot results</h3>
 tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;] &lt;- paste0(tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;], &#39;-multi&#39;)
 tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;] &lt;- paste0(tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;], &#39;-multi&#39;)
 
-tmp$Model1[tmp$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp$Model1[tmp$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
-tmp$Model2[tmp$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp$Model2[tmp$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp$Model1[tmp$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model1[tmp$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp$Model2[tmp$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model2[tmp$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
 
 tmp&lt;-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
 tmp&lt;-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
@@ -3410,10 +7337,10 @@ <h3>Plot results</h3>
     tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;] &lt;- paste0(tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;], &#39;-multi&#39;)
     tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;] &lt;- paste0(tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;], &#39;-multi&#39;)
     
-    tmp$Model1[tmp$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-    tmp$Model1[tmp$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
-    tmp$Model2[tmp$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-    tmp$Model2[tmp$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+    tmp$Model1[tmp$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+    tmp$Model1[tmp$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+    tmp$Model2[tmp$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+    tmp$Model2[tmp$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
     
     tmp&lt;-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
     tmp&lt;-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
@@ -3423,8 +7350,8 @@ <h3>Plot results</h3>
 
     tmp&lt;-tmp[order(tmp$label1, tmp$label2),]
     
-    tmp$label1 &lt;- paste0(tmp$label1,&quot; (&quot;, ifelse(tmp$Model1 == &#39;SumStatTrain&#39;, &#39;ST&#39;, &#39;IT&#39;), &quot;)&quot;)
-    tmp$label2 &lt;- paste0(tmp$label2,&quot; (&quot;, ifelse(tmp$Model2 == &#39;SumStatTrain&#39;, &#39;ST&#39;, &#39;IT&#39;), &quot;)&quot;)
+    tmp$label1 &lt;- paste0(tmp$label1,&quot; (&quot;, ifelse(tmp$Model1 == &#39;SumStatTune&#39;, &#39;ST&#39;, &#39;IT&#39;), &quot;)&quot;)
+    tmp$label2 &lt;- paste0(tmp$label2,&quot; (&quot;, ifelse(tmp$Model2 == &#39;SumStatTune&#39;, &#39;ST&#39;, &#39;IT&#39;), &quot;)&quot;)
 
     tmp$label1 &lt;- factor(tmp$label1, levels = unique(tmp$label1))
     tmp$label2 &lt;- factor(tmp$label2, levels = unique(tmp$label2))
@@ -3467,7 +7394,7 @@ <h3>Plot results</h3>
 ####
 # Plot relative improvement of methods
 ####
-# Use ptclump IndivTrain using EUR GWAS as the reference, as provides an interpretable scale
+# Use ptclump IndivTune using EUR GWAS as the reference, as provides an interpretable scale
 
 meta_res_comp_ptclump_top1&lt;-meta_res_comp[meta_res_comp$Method2 == &#39;all&#39; &amp; meta_res_comp$Source2 == &#39;Multi&#39;,]
 meta_res_comp_ptclump_top1$reference_point&lt;-F
@@ -3498,7 +7425,7 @@ <h3>Plot results</h3>
                                          &#39;EUR GWAS&#39;,
                                          &#39;Both&#39;))
 meta_tmp$Target &lt;- paste0(meta_tmp$Target, &#39; Target&#39;)
-meta_tmp$Model1 &lt;- factor(meta_tmp$Model1, levels = names(model_palette))
+meta_tmp$Model1 &lt;- factor(meta_tmp$Model1, levels = c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
 
 meta_tmp_ref &lt;- meta_res_comp_reference
 meta_tmp_ref &lt;- merge(meta_tmp_ref, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
@@ -3514,7 +7441,7 @@ <h3>Plot results</h3>
                                          &#39;EUR GWAS&#39;,
                                          &#39;Both&#39;))
 meta_tmp_ref$Target &lt;- paste0(meta_tmp_ref$Target, &#39; Target&#39;)
-meta_tmp_ref$Model1 &lt;- factor(meta_tmp_ref$Model1, levels = names(model_palette))
+meta_tmp_ref$Model1 &lt;- factor(meta_tmp_ref$Model1, levels = c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
 
 tmp &lt;- res_comp_all_ptclump_top1
 tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
@@ -3530,7 +7457,7 @@ <h3>Plot results</h3>
                                          &#39;EUR GWAS&#39;,
                                          &#39;Both&#39;))
 tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
-tmp$Model1 &lt;- factor(tmp$Model1, levels = names(model_palette))
+tmp$Model1 &lt;- factor(tmp$Model1, levels = c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
 
 ggplot(meta_tmp, aes(x=label, y=R_diff , fill = Model1)) +
     geom_point(
@@ -3624,32 +7551,38 @@ <h3>Plot results</h3>
 
 # Simplify results showing results only with or without training data
 meta_tmp_simple &lt;- meta_tmp
-meta_tmp_simple$Model1[meta_tmp_simple$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-meta_tmp_simple$Model1[meta_tmp_simple$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
-meta_tmp_simple$Model2[meta_tmp_simple$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-meta_tmp_simple$Model2[meta_tmp_simple$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
 meta_tmp_simple&lt;-meta_tmp_simple[meta_tmp_simple$Model_1 %in% res_eval_simp$Group,]
 meta_tmp_simple&lt;-meta_tmp_simple[meta_tmp_simple$Model_2 %in% res_eval_simp$Group,]
 
 meta_tmp_ref_simple &lt;- meta_tmp_ref
-meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
-meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
 meta_tmp_ref_simple&lt;-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_1 %in% res_eval_simp$Group,]
 meta_tmp_ref_simple&lt;-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_2 %in% res_eval_simp$Group,]
 
 tmp_simple &lt;- tmp
-tmp_simple$Model1[tmp_simple$Model1 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp_simple$Model1[tmp_simple$Model1 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
-tmp_simple$Model2[tmp_simple$Model2 != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp_simple$Model2[tmp_simple$Model2 == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp_simple$Model1[tmp_simple$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp_simple$Model1[tmp_simple$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp_simple$Model2[tmp_simple$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp_simple$Model2[tmp_simple$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
 tmp_simple&lt;-tmp_simple[tmp_simple$Model_1 %in% res_eval_simp$Group,]
 tmp_simple&lt;-tmp_simple[tmp_simple$Model_2 %in% res_eval_simp$Group,]
 
 # Export plot for manuscript
 png(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r.perc_improv.png&#39;, width = 3200, height = 2000, res= 300, units = &#39;px&#39;)
 ggplot(meta_tmp_simple[meta_tmp_simple$Target != &#39;EUR Target&#39;,], aes(x=label, y=R_diff_perc , fill = Model1)) +
+#    geom_boxplot(
+#      data = tmp_simple[tmp_simple$Target != &#39;EUR Target&#39;,],
+#        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+#        position = position_dodge(0.7),
+#        alpha = 0.3
+#      ) +
     geom_point(
         data = tmp_simple[tmp_simple$Target != &#39;EUR Target&#39;,],
         mapping = aes(x=label, y=R_diff_perc, colour=Model1),
@@ -3751,10 +7684,40 @@ <h3>Plot results</h3>
     )
 dev.off()</code></pre>
 </details>
+<details>
+<summary>
+Show average improvement in AFR + EAS
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/average_r.perc_improv.png"></p>
+</div>
+</div>
+</details>
+<details>
+<summary>
+Show average improvement in EUR
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/average_r_eur.perc_improv.png"></p>
+</div>
+</div>
+</details>
+<details>
+<summary>
+Show LEOPARD comparison
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/average_r_leopard.png"></p>
+</div>
+</div>
+</details>
 <hr />
 </div>
-<div id="check-leopard-weights" class="section level3">
-<h3>Check LEOPARD weights</h3>
+<div id="leopardquickprs" class="section level2">
+<h2>LEOPARD+QuickPRS</h2>
 <p>Here we will compare the LEOPARD estimated weights for population
 specific PGS, to the weights estimated using observed data in the UKB
 target sample.</p>
@@ -3914,6 +7877,7 @@ <h3>Check LEOPARD weights</h3>
                      direction = &quot;wide&quot;)
 
 names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
 
 cor_matrix_EAS &lt;- cor(tmp_wide[tmp_wide$Target == &#39;EAS&#39;, -1:-2])
 cor_matrix_AFR &lt;- cor(tmp_wide[tmp_wide$Target == &#39;AFR&#39;, -1:-2])
@@ -3950,6 +7914,7 @@ <h3>Check LEOPARD weights</h3>
                      direction = &quot;wide&quot;)
 
 names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
 
 cor_matrix_EAS &lt;- cor(tmp_wide[tmp_wide$Discovery == &#39;EAS&#39;, -1:-2])
 cor_matrix_AFR &lt;- cor(tmp_wide[tmp_wide$Discovery == &#39;AFR&#39;, -1:-2])
@@ -3977,12 +7942,22 @@ <h3>Check LEOPARD weights</h3>
   labs(fill = &quot;Correlation&quot;)
 dev.off()</code></pre>
 </details>
-<hr />
+<details>
+<summary>
+Show observed and LEOPARD PGS weights
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/leopard_weights.png"></p>
 </div>
 </div>
+</details>
+<hr />
 </div>
-<div id="computational-resoures" class="section level1">
-<h1>Computational resoures</h1>
+<div id="computational-resoures" class="section level2">
+<h2>Computational resoures</h2>
+<p>Here we will read in the benchmark data for PGS methods and create a
+table for the manuscript.</p>
 <details>
 <summary>
 Show code
@@ -4145,14 +8120,156 @@ <h1>Computational resoures</h1>
 
 write.csv(method_avg, &#39;~/oliverpainfel/Analyses/crosspop/time_memory.csv&#39;, row.names=F)</code></pre>
 </details>
+<details>
+<summary>
+Show computational resources table
+</summary>
+<table class="table table-striped table-hover" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
+<thead>
+<tr>
+<th style="text-align:left;">
+Method
+</th>
+<th style="text-align:right;">
+Time (hrs)
+</th>
+<th style="text-align:right;">
+Memory (Gb)
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+DBSLMM
+</td>
+<td style="text-align:right;">
+0.15
+</td>
+<td style="text-align:right;">
+1.15
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+lassosum
+</td>
+<td style="text-align:right;">
+0.08
+</td>
+<td style="text-align:right;">
+7.30
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+LDpred2
+</td>
+<td style="text-align:right;">
+0.38
+</td>
+<td style="text-align:right;">
+20.54
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+LEOPARD (QuickPRS)
+</td>
+<td style="text-align:right;">
+0.23
+</td>
+<td style="text-align:right;">
+8.22
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MegaPRS
+</td>
+<td style="text-align:right;">
+0.85
+</td>
+<td style="text-align:right;">
+12.51
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+PRS-CS
+</td>
+<td style="text-align:right;">
+4.40
+</td>
+<td style="text-align:right;">
+10.57
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+PRS-CSx
+</td>
+<td style="text-align:right;">
+6.84
+</td>
+<td style="text-align:right;">
+15.18
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+pT+clump
+</td>
+<td style="text-align:right;">
+0.02
+</td>
+<td style="text-align:right;">
+0.83
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+QuickPRS
+</td>
+<td style="text-align:right;">
+0.06
+</td>
+<td style="text-align:right;">
+4.87
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+SBayesRC
+</td>
+<td style="text-align:right;">
+0.38
+</td>
+<td style="text-align:right;">
+4.40
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+X-Wing
+</td>
+<td style="text-align:right;">
+34.12
+</td>
+<td style="text-align:right;">
+48.70
+</td>
+</tr>
+</tbody>
+</table>
+</details>
 <hr />
 </div>
-<div id="sensitivity-analysis-using-1kg-reference"
-class="section level1">
-<h1>Sensitivity analysis using 1KG reference</h1>
-<p>PRS-CS, PRS-CSx and X-Wing all use the 1KG reference sample, whereas
-the other methods are using the 1KG+HGDP reference sample. We should
-check whether this difference is impacting our conclusions.</p>
+</div>
+<div id="tl-prs" class="section level1">
+<h1>TL-PRS</h1>
+<p>Run using AFR and EAS subset in UKB to make it quicker to run. This
+is the main interest when running TL-PRS anyway.</p>
 <hr />
 <div id="subset-afr-and-eas-individuals-in-ukb-data"
 class="section level2">
@@ -4197,36 +8314,8 @@ <h2>Subset AFR and EAS individuals in UKB data</h2>
 </details>
 <hr />
 </div>
-<div id="create-1kg-only-genopred-reference-data"
-class="section level2">
-<h2>Create 1KG only GenoPred reference data</h2>
-<p>Subset the 1KG+HGDP reference data to include only 1KG
-individuals.</p>
-<details>
-<summary>
-Show code
-</summary>
-<pre class="bash"><code>mkdir -p ~/oliverpainfel/Data/1kg/genopred/
-cp -r ~/oliverpainfel/Data/hgdp_1kg/genopred/ref ~/oliverpainfel/Data/1kg/genopred/
-rm ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr*.p*</code></pre>
-<pre class="r"><code>library(data.table)
-
-ref&lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr1.psam&#39;)
-ref&lt;-ref[ref$Project == &#39;gnomAD_1kG&#39;,]
-
-write.table(ref[,1, drop = F], &#39;~/oliverpainfel/Data/1kg/1kg.keep&#39;, col.names=F, row.names=F, quote=F)</code></pre>
-<pre class="bash"><code>for chr in $(seq 1 22); do
-  ~/oliverpainfel/Software/plink2 \
-    --pfile ~/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} \
-    --keep ~/oliverpainfel/Data/1kg/1kg.keep \
-    --make-pgen \
-    --out ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr${chr}
-done</code></pre>
-</details>
-<hr />
-</div>
-<div id="run-pipeline-3" class="section level2">
-<h2>Run pipeline</h2>
+<div id="pgs-calculation-1" class="section level2">
+<h2>PGS calculation</h2>
 <p>To save time, run using PGS methods that do not need pre-processed LD
 matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary
 from the 1KG+HGDP results, then expand to other methods (LDpred2,
@@ -4235,9 +8324,15 @@ <h2>Run pipeline</h2>
 <summary>
 Show code
 </summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Prepare configuration
+</h4>
 <pre class="r"><code>library(data.table)
 
-dir.create(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only&#39;)
+dir.create(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs&#39;)
 
 ######
 # target_list
@@ -4257,42 +8352,56 @@ <h2>Run pipeline</h2>
 ######
 
 config&lt;-c(
-  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_1kgref&quot;,
-  &quot;refdir: /users/k1806347/oliverpainfel/Data/1kg/genopred/ref&quot;,
-  &quot;resdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/resdir_1kgref&quot;,
-  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml&quot;,
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_tlprs&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&quot;,
   &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&quot;,
   &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt&quot;,
   &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt&quot;,
-  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;]&quot;,
-#  &quot;leopard_methods: [&#39;ptclump&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;]&quot;,
+  &quot;pgs_methods: [&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
+  &quot;tlprs_methods: [&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
   &quot;cores_prep_pgs: 10&quot;,
-  &quot;cores_target_pgs: 10&quot;
-)
+  &quot;cores_target_pgs: 50&quot;,
+  &quot;prscs_phi: [&#39;auto&#39;]&quot;,
+  &quot;ldpred2_model: [&#39;auto&#39;]&quot;,
+  &quot;ldpred2_inference: F&quot;,
+  &quot;dbslmm_h2f: [&#39;1&#39;]&quot;,
+  &quot;ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3&quot;,
+  &quot;quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3&quot;,
+  &quot;sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3&quot;
+)
 
-write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<hr />
+<h4>
+Run pipeline
+</h4>
 <pre class="bash"><code>snakemake \
   --profile slurm \
   --use-conda \
-  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml \
   output_all -n</code></pre>
 </details>
 <hr />
 </div>
-<div id="evaluate-pgs-1" class="section level2">
-<h2>Evaluate PGS</h2>
+<div id="pgs-evaluation-1" class="section level2">
+<h2>PGS evaluation</h2>
 <details>
 <summary>
 Show code
 </summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Create predictor list
+</h4>
 <pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 source(&#39;../functions/misc.R&#39;)
 source_all(&#39;../functions&#39;)
 library(data.table)
 
 # Get some key variables from config
-config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml&#39;
-pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&#39;
 outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
 
 # Read in list of outcomes 
@@ -4305,9 +8414,9 @@ <h2>Evaluate PGS</h2>
 targ_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
 for(trait_i in selected_traits){
   scores_i &lt;- scores[grepl(trait_i, scores$name),]
-  scores_i$multi &lt;- scores_i$method
-  
+
   for(targ_pop_i in targ_pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
     if(targ_pop_i == &#39;EAS&#39;){
       disc_pop &lt;- &#39;BBJ&#39;
     }
@@ -4328,7 +8437,7 @@ <h2>Evaluate PGS</h2>
 
       dir.create(
         paste0(
-          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_&#39;,
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_&#39;,
           targ_pop_i,
           &#39;.disc_EUR_&#39;,
           disc_pop_j_2,
@@ -4342,7 +8451,6 @@ <h2>Evaluate PGS</h2>
         (grepl(&#39;UKB$&#39;, scores_i$name, ignore.case = F) | 
          grepl(paste0(disc_pop_j, &#39;$&#39;), scores_i$name, ignore.case = T)),]
 
-      # Insert path to score file
       scores_i_j$predictor &lt;- paste0(
         outdir,
         &#39;/ukb/pgs/TRANS/&#39;,
@@ -4354,270 +8462,86 @@ <h2>Evaluate PGS</h2>
         &#39;-TRANS.profiles&#39;
       )
       
-      ####
-      # Make groups single source methods
-      ####
+      #####
+      # List single-source PGS
+      #####
+      # These are actually pseudoval scores (as per the config)
+      scores_i_j_single &lt;- scores_i_j[!grepl(&#39;tlprs&#39;, scores_i_j$method),]
       
-      scores_i_j_single_top1 &lt;-
-        scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &amp;
-                     !grepl(&#39;_multi$&#39;, scores_i_j$method), ]
-
-      # Create top1 column indicating which predictors top1 models should be derived
-      scores_i_j_single_top1$top1[grepl(&#39;UKB&#39;, scores_i_j_single_top1$name, ignore.case = F)] &lt;- &#39;EUR&#39;
-      scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] &lt;- disc_pop_j_2
+      scores_i_j_single$top1[grepl(&#39;UKB&#39;, scores_i_j_single$name, ignore.case = F)] &lt;- &#39;EUR&#39;
+      scores_i_j_single$top1[grepl(disc_pop_j, scores_i_j_single$name, ignore.case = F)] &lt;- disc_pop_j_2
+      scores_i_j_single$multi &lt;- paste0(scores_i_j_single$method,&#39;.pseudo&#39;)
       
-      ####
-      # Make groups containing pseudo scores for single source methods
-      ####
-
-      # Extract the pseudo score for each method and specify as a separate group
-      for(i in 1:nrow(scores_i_j_single_top1)) {
-        param &lt;- find_pseudo(
-          config = config,
-          gwas = scores_i_j_single_top1$name[i],
-          pgs_method = scores_i_j_single_top1$method[i],
-          target_pop = targ_pop_i
-        )
-        
-        score_header &lt;-
-          fread(scores_i_j_single_top1$predictor[i], nrows = 1)
-        score_cols &lt;-
-          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_single_top1$name[i], &#39;_&#39;, param)))
+      #####
+      # List tlprs scores (split by target population)
+      #####
+      scores_i_j_tlprs &lt;- scores_i_j[grepl(&#39;tlprs&#39;, scores_i_j$method),]
+      scores_i_j_tlprs$multi &lt;- scores_i_j_tlprs$method
+      
+      scores_i_j_tlprs_pop&lt;-NULL
+      for(i in 1:nrow(scores_i_j_tlprs)){
+        score_header&lt;-fread(scores_i_j_tlprs$predictor[i], nrow = 1)
         
-        system(
-          paste0(
-            &quot;cut -d&#39; &#39; -f &quot;, 
-            paste0(score_cols, collapse=&#39;,&#39;),
-            &quot; &quot;, 
-            scores_i_j_single_top1$predictor[i], 
-            &quot; &gt; &quot;, 
-            gsub(&#39;.profiles&#39;,
-                 paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
-                 scores_i_j_single_top1$predictor[i])
+        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
+          score_cols &lt;- which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_&#39;), names(score_header)))
+
+          system(
+            paste0(
+              &quot;cut -d&#39; &#39; -f &quot;, 
+              paste0(score_cols, collapse=&#39;,&#39;),
+              &quot; &quot;, 
+              scores_i_j_tlprs$predictor[i], 
+              &quot; &gt; &quot;, 
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.targ_&#39;, pop, &#39;.profiles&#39;),
+                   scores_i_j_tlprs$predictor[i])
+            )
           )
-        )
+          
+          tmp &lt;- scores_i_j_tlprs[i,]
+          tmp$multi &lt;- paste0(tmp$multi, &#39;.pop&#39;)
+          tmp$top1 &lt;- pop
+          tmp$predictor &lt;-
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.targ_&#39;, pop, &#39;.profiles&#39;),
+                   scores_i_j_tlprs$predictor[i])
+          
+          scores_i_j_tlprs_pop &lt;- rbind(scores_i_j_tlprs_pop, tmp)
+        }
       }
+
+      predictors_i&lt;- do.call(rbind, list(
+        scores_i_j_single, scores_i_j_tlprs_pop
+      ))
       
-      scores_i_j_single_pseudo &lt;- scores_i_j_single_top1
-      scores_i_j_single_pseudo$multi &lt;- paste0(scores_i_j_single_pseudo$multi, &#39;.pseudo&#39;)
+      predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;top1&#39;,&#39;multi&#39;), with=F]
+      
+      write.table(
+        predictors_i,
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_j_2,
+          &#39;/&#39;,
+          trait_i,
+          &#39;/predictor_list.tlprs.txt&#39;
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}</code></pre>
+<hr />
+<h4>
+Run model_builder
+</h4>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
 
-      scores_i_j_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
-                                    paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
-                                    scores_i_j_single_pseudo$predictor)
-
-#      ####
-#      # Make groups for multi-single-source pseudo scores
-#      ####
-#      
-#      scores_i_j_multi_single_pseudo &lt;- scores_i_j[grepl(&#39;_multi$&#39;, scores_i_j$method),]
-#
-#      # Extract the pseudo score for each method and specify as a separate group
-#      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
-#        param &lt;- find_pseudo(
-#          config = config,
-#          gwas = scores_i_j_multi_single_pseudo$name[i],
-#          pgs_method = scores_i_j_multi_single_pseudo$method[i],
-#          target_pop = targ_pop_i
-#        )
-#        
-#        score_header &lt;-
-#          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
-#        score_cols &lt;-
-#          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_multi_single_pseudo$name[i], &#39;_&#39;, param)))
-#        
-#        system(
-#          paste0(
-#            &quot;cut -d&#39; &#39; -f &quot;, 
-#            paste0(score_cols, collapse=&#39;,&#39;),
-#            &quot; &quot;, 
-#            scores_i_j_multi_single_pseudo$predictor[i], 
-#            &quot; &gt; &quot;, 
-#            gsub(&#39;.profiles&#39;,
-#                 paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
-#                 scores_i_j_multi_single_pseudo$predictor[i])
-#          )
-#        )
-#      }
-#      
-#      scores_i_j_multi_single_pseudo$multi &lt;- paste0(scores_i_j_multi_single_pseudo$multi, &#39;.pseudo&#39;)
-#
-#      scores_i_j_multi_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
-#                                    paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
-#                                    scores_i_j_multi_single_pseudo$predictor)
-#      
-#      scores_i_j_multi_single_pseudo$top1&lt;-paste0(&#39;EUR_&#39;, disc_pop_j_2)
-#
-#      ####
-#      # Make groups for the Multi-Source methods
-#      ####
-#      
-#      scores_i_j_multi &lt;- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
-#
-#      # Split top1 scores by target population
-#      # This doesn&#39;t apply to xwing because it only has pop-specific pseudo scores
-#      scores_i_j_multi_top1&lt;-NULL
-#      for(i in 1:which(scores_i_j_multi$method %in% c(&#39;prscsx&#39;))){
-#        score_header&lt;-fread(scores_i_j_multi$predictor[i], nrow = 1)
-#        
-#        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
-#          
-#          if(scores_i_j_multi$method[i] == &#39;prscsx&#39;){
-#            score_cols &lt;-
-#              which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi&#39;), names(score_header)))
-#          }
-#          if(scores_i_j_multi$method[i] == &#39;xwing&#39;){
-#            score_cols &lt;-
-#              which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst&#39;), names(score_header)))
-#          }
-#          
-#          system(
-#            paste0(
-#              &quot;cut -d&#39; &#39; -f &quot;, 
-#              paste0(score_cols, collapse=&#39;,&#39;),
-#              &quot; &quot;, 
-#              scores_i_j_multi$predictor[i], 
-#              &quot; &gt; &quot;, 
-#              gsub(&#39;.profiles&#39;,
-#                   paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
-#                   scores_i_j_multi$predictor[i])
-#            )
-#          )
-#          
-#          tmp &lt;- scores_i_j_multi[i,]
-#          tmp$multi &lt;- paste0(tmp$multi, &#39;.grid&#39;)
-#          tmp$top1 &lt;- pop
-#          tmp$predictor &lt;-
-#              gsub(&#39;.profiles&#39;,
-#                   paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
-#                   scores_i_j_multi$predictor[i])
-#          
-#          scores_i_j_multi_top1 &lt;- rbind(scores_i_j_multi_top1, tmp)
-#        }
-#      }
-#
-#      # Split pop-specific pseudo scores by target population
-#      scores_i_j_multi_pop_pseudo&lt;-NULL
-#      for(i in 1:nrow(scores_i_j_multi)){
-#        score_header&lt;-fread(scores_i_j_multi$predictor[i], nrow = 1)
-#        
-#        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
-#          if(scores_i_j_multi$method[i] == &#39;prscsx&#39;){
-#            score_cols &lt;-
-#              which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi_auto&#39;), names(score_header)))
-#          }
-#          if(scores_i_j_multi$method[i] == &#39;xwing&#39;){
-#            score_cols &lt;-
-#              which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst_&#39;, pop), names(score_header)))
-#          }
-#          
-#          system(
-#            paste0(
-#              &quot;cut -d&#39; &#39; -f &quot;, 
-#              paste0(score_cols, collapse=&#39;,&#39;),
-#              &quot; &quot;, 
-#              scores_i_j_multi$predictor[i], 
-#              &quot; &gt; &quot;, 
-#              gsub(&#39;.profiles&#39;,
-#                   paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
-#                   scores_i_j_multi$predictor[i])
-#            )
-#          )
-#          
-#          tmp &lt;- scores_i_j_multi[i,]
-#          tmp$multi &lt;- paste0(tmp$multi, &#39;.pop_pseudo&#39;)
-#          tmp$top1 &lt;- pop
-#          tmp$predictor &lt;-
-#              gsub(&#39;.profiles&#39;,
-#                   paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
-#                   scores_i_j_multi$predictor[i])
-#          
-#          scores_i_j_multi_pop_pseudo &lt;- rbind(scores_i_j_multi_pop_pseudo, tmp)
-#        }
-#      }
-#      
-#      # Create pseudo score for multi-source methods
-#      scores_i_j_multi_pseudo&lt;-NULL
-#      for(i in 1:nrow(scores_i_j_multi)) {
-#        param &lt;- find_pseudo(
-#          config = config,
-#          gwas = scores_i_j_multi$name[i],
-#          pgs_method = scores_i_j_multi$method[i],
-#          target_pop = targ_pop_i
-#        )
-#        
-#        score_header &lt;-
-#          fread(scores_i_j_multi$predictor[i], nrows = 1)
-#        score_cols &lt;-
-#          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_multi$name[i], &#39;_&#39;, param)))
-#
-#        system(
-#          paste0(
-#            &quot;cut -d&#39; &#39; -f &quot;, 
-#            paste0(score_cols, collapse=&#39;,&#39;),
-#            &quot; &quot;, 
-#            scores_i_j_multi$predictor[i], 
-#            &quot; &gt; &quot;, 
-#            gsub(&#39;.profiles&#39;,
-#                 paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
-#                 scores_i_j_multi$predictor[i])
-#          )
-#        )
-#        
-#        tmp &lt;- scores_i_j_multi[i,]
-#        tmp$multi &lt;- paste0(tmp$multi, &#39;.pseudo&#39;)
-#        tmp$top1 &lt;- paste0(&#39;EUR_&#39;, disc_pop_j_2)
-#        tmp$predictor &lt;-
-#            gsub(&#39;.profiles&#39;,
-#                 paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
-#                 scores_i_j_multi$predictor[i])
-#        
-#        scores_i_j_multi_pseudo &lt;- rbind(scores_i_j_multi_pseudo, tmp)
-#      }
-      
-      ####
-      # Combine the different predictor groups
-      ####
-      predictors_i&lt;- do.call(rbind, list(
-        scores_i_j_single_top1, 
-        scores_i_j_single_pseudo#, 
-#        scores_i_j_multi_single_pseudo,
-#        scores_i_j_multi_top1,
-#        scores_i_j_multi_pop_pseudo,
-#        scores_i_j_multi_pseudo
-      ))
-      
-      predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;multi&#39;,&#39;top1&#39;), with=F]
-      
-      write.table(
-        predictors_i,
-        paste0(
-          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_&#39;,
-          targ_pop_i,
-          &#39;.disc_EUR_&#39;,
-          disc_pop_j_2,
-          &#39;/&#39;,
-          trait_i,
-          &#39;/predictor_list.txt&#39;
-        ),
-        col.names = T,
-        row.names = F,
-        quote = F
-      )
-    }
-  }
-}</code></pre>
-</details>
-<hr />
-<div id="run-model_builder-2" class="section level3">
-<h3>Run model_builder</h3>
-<details>
-<summary>
-Show code
-</summary>
-<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
-conda activate model_builder
-
-#rm /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_*.disc_EUR_*/*/res*
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_*.disc_EUR_*/*/res*
 
 for targ_pop in $(echo EAS AFR); do
   if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
@@ -4627,7 +8551,7 @@ <h3>Run model_builder</h3>
   fi
   
   if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
-    disc_pop=$(echo EAS AFR)
+    disc_pop=$(echo AFR EAS)
   fi
   
   if [ &quot;$targ_pop&quot; == &quot;EAS&quot; ]; then
@@ -4640,26 +8564,20 @@ <h3>Run model_builder</h3>
   
   for disc_pop_i in ${disc_pop}; do
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
-      if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt&quot; ]; then
+      if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs.pred_comp.txt&quot; ]; then
         sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
           --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
-          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
-          --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
           --n_core 5&quot;
       fi
     done
   done
-done
-</code></pre>
-</details>
+done</code></pre>
 <hr />
-</div>
-<div id="plot-results-1" class="section level3">
-<h3>Plot results</h3>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Plot results
+</h4>
 <pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 
 library(data.table)
@@ -4712,14 +8630,14 @@ <h3>Plot results</h3>
       eval_i &lt;-
         fread(
           paste0(
-            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/&#39;,
+            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/&#39;,
             &#39;targ_&#39;,
             targ_pop_i,
             &#39;.disc_EUR_&#39;,
             disc_pop_i,
             &#39;/&#39;,
             pheno_i,
-            &#39;/res.pred_eval.txt&#39;
+            &#39;/res.tlprs.pred_eval.txt&#39;
           )
         )
       eval_i$Target&lt;-targ_pop_i
@@ -4732,17 +8650,17 @@ <h3>Plot results</h3>
   res_eval_i$Method&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_eval_i$Method)
   
   res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
-                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTrain&#39;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTune&#39;
   res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
-                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
   res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
-                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTrain&#39;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTune&#39;
   res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
-                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTrain&#39;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTune&#39;
   
-  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
-  res_eval_i$Model[res_eval_i$Group == &#39;prscsx.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
-  res_eval_i$Model[res_eval_i$Group == &#39;xwing.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
+  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;prscsx.pseudo.multi&#39;]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;xwing.pseudo.multi&#39;]&lt;-&#39;SumStatTune&#39;
   
   res_eval_i$Source&lt;-ifelse(
     res_eval_i$Method %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_eval_i$Method) | 
@@ -4754,20 +8672,20 @@ <h3>Plot results</h3>
   res_eval_i$Discovery[res_eval_i$Source == &#39;Multi&#39;] &lt;- res_eval_i$gwas_group[res_eval_i$Source == &#39;Multi&#39;]
   
   res_eval_i$Method&lt;-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
-  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
   res_eval_i$Discovery&lt;-factor(res_eval_i$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
 
-  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
   res_eval_i &lt;- res_eval_i[
     !(res_eval_i$Method %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
-      res_eval_i$Model %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+      res_eval_i$Model %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
   
   # Remove pseudo model for methods that don&#39;t really have one 
   res_eval_i &lt;- res_eval_i[
     !(res_eval_i$Method %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
-      res_eval_i$Model %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+      res_eval_i$Model %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
 
-  # Remove top1 models for *-Multi, PRS-CSx, X-wing
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing, TL-*
   res_eval_i &lt;- res_eval_i[
     !((res_eval_i$Method %in%  c(&#39;prscsx&#39;, &#39;xwing&#39;) | grepl(&#39;_multi$&#39;, res_eval_i$Method)) &amp; 
       grepl(&#39;top1&#39;, res_eval_i$Group)),]
@@ -4782,26 +8700,7 @@ <h3>Plot results</h3>
 }
 
 # Create vector defining or of methods in plots
-model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;, &quot;DBSLMM-multi&quot;, &quot;lassosum-multi&quot;, &quot;LDpred2-multi&quot;, &quot;MegaPRS-multi&quot;, &quot;PRS-CS-multi&quot;, &quot;pT+clump-multi&quot;, &quot;QuickPRS-multi&quot;, &quot;SBayesRC-multi&quot;, &quot;PRS-CSx&quot;, &quot;X-Wing&quot;, &quot;All&quot;) 
-
-res_eval_simp &lt;- NULL
-for(pheno_i in selected_traits){
-  tmp &lt;- res_eval[[pheno_i]]
-  tmp$Trait &lt;- pheno_i
-  
-  # Insert nice PGS method names
-  tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
-  tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
-  tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
-  tmp$label &lt;- factor(tmp$label, levels = model_order)
-  
-  # Simplify result to either SumStatTrain or IndivTrain
-  tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-  tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
-  tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery&#39;,&#39;Model&#39;), with=F]),]
-  
-  res_eval_simp &lt;- rbind(res_eval_simp, tmp)
-}
+model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;, &quot;DBSLMM-multi&quot;, &quot;lassosum-multi&quot;, &quot;LDpred2-multi&quot;, &quot;MegaPRS-multi&quot;, &quot;PRS-CS-multi&quot;, &quot;pT+clump-multi&quot;, &quot;QuickPRS-multi&quot;, &quot;SBayesRC-multi&quot;,&quot;TL-DBSLMM&quot;,&quot;TL-LDpred2&quot;,&quot;TL-QuickPRS&quot;,&quot;TL-SBayesRC&quot;, &quot;PRS-CSx&quot;, &quot;X-Wing&quot;, &quot;All&quot;) 
 
 ####
 # Average results across phenotypes
@@ -4879,7 +8778,7 @@ <h3>Plot results</h3>
   }
 }
 
-meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
+meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
 meta_res_eval$Discovery&lt;-factor(meta_res_eval$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
 
 # Plot average performance across phenotypes for AFR and EAS targets
@@ -4887,7 +8786,7 @@ <h3>Plot results</h3>
 tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
 tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
 tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
-tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39; &amp; !grepl(&#39;^tlprs_&#39;, tmp$Method)] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39; &amp; !grepl(&#39;^tlprs_&#39;, tmp$Method)], &#39;-multi&#39;)
 tmp$label &lt;- factor(tmp$label, levels = model_order)
 tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
 tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
@@ -4898,19 +8797,30 @@ <h3>Plot results</h3>
                                          &#39;EUR GWAS&#39;,
                                          &#39;Both&#39;))
 tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
-tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
 tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
 
-png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/average_r.png&#39;), res=300, width = 3200, height = 2000, units = &#39;px&#39;)
-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+dir.create(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots&#39;)
+
+# Plot unidirectional TL-PRS (as it was intended), comparing the unadjusted EUR PGS to the EUR PGS that has been adjusted according to the target-matched GWAS
+tmp_tlprs_uni &lt;- tmp[grepl(&#39;tlprs&#39;, tmp$Method) &amp; !grepl(&#39;pop-EUR.top1&#39;, tmp$Group) &amp; tmp$Source == &#39;Single&#39;, ]
+tmp_tlprs_uni$Type &lt;- &#39;TL-PRS&#39;
+tmp_unadj &lt;- tmp[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery == &#39;EUR&#39;, ]
+tmp_unadj$Type &lt;- &#39;Original&#39;
+tmp_both &lt;- rbind(tmp_unadj, tmp_tlprs_uni)
+tmp_both$label&lt;-gsub(&#39;TL-&#39;,&#39;&#39;,tmp_both$label)
+tmp_both$Type&lt;-factor(tmp_both$Type, levels = c(&#39;Original&#39;,&#39;TL-PRS&#39;))
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.png&#39;), res=300, width = 2000, height = 1600, units = &#39;px&#39;)
+ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
                   position = position_dodge(width = 1)) +
     geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
     geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
     labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
-    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
     theme_half_open() +
     background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
     panel_border() + 
@@ -4920,70 +8830,23 @@ <h3>Plot results</h3>
           legend.justification = &quot;center&quot;)
 dev.off()
 
-# The results look very similar to when using 1KG+HGDP. 
-
-###################
-# Plot a comparison between the runs using different references
-
-# Read in results using 1KG+HGDP reference
-main_results&lt;-fread(&#39;~/oliverpainfel/Analyses/crosspop/r_eval.csv&#39;)
-sens_results&lt;-meta_res_eval
-
-tmp &lt;- main_results
-tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
-tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
-tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
-tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
-tmp$label &lt;- factor(tmp$label, levels = model_order)
-tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
-tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
-tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
-tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
-tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
-                              levels = c(&#39;Target-matched GWAS&#39;,
-                                         &#39;EUR GWAS&#39;,
-                                         &#39;Both&#39;))
-tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
-tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
-main_results &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
-
-tmp &lt;- sens_results
-tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
-tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
-tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
-tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
-tmp$label &lt;- factor(tmp$label, levels = model_order)
-tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
-tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
-tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
-tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
-tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
-                              levels = c(&#39;Target-matched GWAS&#39;,
-                                         &#39;EUR GWAS&#39;,
-                                         &#39;Both&#39;))
-tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
-tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
-sens_results &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
-
-main_results&lt;-main_results[main_results$Method %in% sens_results$Method,]
-main_results&lt;-main_results[main_results$Target %in% sens_results$Target,]
-
-sens_results$Reference &lt;- &#39;1KG&#39;
-main_results$Reference &lt;- &#39;1KG+HGDP&#39;
-
-both_results &lt;- rbind(main_results, sens_results)
+tmp_tlprs_uni &lt;- tmp[grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;pop-EUR.top1&#39;, tmp$Group) &amp; tmp$Source == &#39;Single&#39;, ]
+tmp_tlprs_uni$Type &lt;- &#39;TL-PRS&#39;
+tmp_unadj &lt;- tmp[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery == &#39;EUR&#39;, ]
+tmp_unadj$Type &lt;- &#39;Original&#39;
+tmp_both &lt;- rbind(tmp_unadj, tmp_tlprs_uni)
+tmp_both$label&lt;-gsub(&#39;TL-&#39;,&#39;&#39;,tmp_both$label)
+tmp_both$Type&lt;-factor(tmp_both$Type, levels = c(&#39;Original&#39;,&#39;TL-PRS&#39;))
 
-png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/comparison_to_main_result.png&#39;, units = &#39;px&#39;, res = 300, width=4000, height=2500)
-ggplot(both_results, aes(x=label, y=R , fill = Model)) +
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.targ_EUR.png&#39;), res=300, width = 2000, height = 1600, units = &#39;px&#39;)
+ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
                   position = position_dodge(width = 1)) +
     geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
     geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
     labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
-    facet_grid(Target ~ Discovery_clean + Reference, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
     theme_half_open() +
     background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
     panel_border() + 
@@ -4991,221 +8854,1675 @@ <h3>Plot results</h3>
           legend.position = &quot;top&quot;,
           legend.key.spacing.x = unit(1, &quot;cm&quot;),
           legend.justification = &quot;center&quot;)
-dev.off()</code></pre>
-</details>
-<hr />
-</div>
-</div>
-</div>
-<div id="tl-prs" class="section level1">
-<h1>TL-PRS</h1>
-<p>Run using AFR and EAS subset in UKB to make it quicker to run. This
-is the main interest when running TL-PRS anyway.</p>
-<hr />
-<div id="run-pipeline-4" class="section level2">
-<h2>Run pipeline</h2>
-<p>To save time, run using PGS methods that do not need pre-processed LD
-matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary
-from the 1KG+HGDP results, then expand to other methods (LDpred2,
-SBayesRC, QuickPRS).</p>
-<details>
-<summary>
-Show code
-</summary>
-<pre class="r"><code>library(data.table)
+dev.off()
 
-dir.create(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs&#39;)
+# Have one column per method, but fill according to Original EUR PGS, Original matched-PGS, TL-PRS EUR Target, TL-PRS non-EUR Target, TL-PRS Multi, and Original-Multi
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$Type &lt;- NA
+tmp$Type[grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;pop-EUR.top1&#39;, tmp$Group)]&lt;-&quot;TL-PRS (EUR PGS tuned to target)&quot;
+tmp$Type[grepl(&#39;tlprs&#39;, tmp$Method) &amp; !grepl(&#39;pop-EUR.top1&#39;, tmp$Group) &amp; tmp$Source == &#39;Single&#39;]&lt;-&quot;TL-PRS (Target-matched PGS tuned to EUR)&quot;
+tmp$Type[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery == &#39;EUR&#39;]&lt;-&quot;Original (EUR PGS)&quot;
+tmp$Type[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;]&lt;-&quot;Original (Target-matched PGS)&quot;
+tmp$Type[grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;multi&#39;, tmp$Group)]&lt;-&quot;TL-PRS-multi&quot;
+tmp$Type[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;multi&#39;, tmp$Group)]&lt;-&quot;Original-multi&quot;
+tmp &lt;- tmp[!is.na(tmp$Type),]
+tmp$Type&lt;-factor(tmp$Type, levels=c(&quot;Original (EUR PGS)&quot;, &quot;Original (Target-matched PGS)&quot;, &quot;TL-PRS (EUR PGS tuned to target)&quot;, &quot;TL-PRS (Target-matched PGS tuned to EUR)&quot;, &quot;Original-multi&quot;, &quot;TL-PRS-multi&quot;))
+tmp$label&lt;-gsub(&#39;TL-&#39;,&#39;&#39;,tmp$label)
 
-######
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png&#39;), res=300, width = 4000, height = 2200, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Type)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+#########################
+# Check significance of differences between TL-PRS and unadjusted approaches
+########################
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Create a function to mirror pred_comp results
+mirror_comp&lt;-function(x){
+  x_sym &lt;- x
+  x_sym$Model_1 &lt;- x$Model_2
+  x_sym$Model_2 &lt;- x$Model_1
+  x_sym$Model_1_R &lt;- x$Model_2_R
+  x_sym$Model_2_R &lt;- x$Model_1_R
+  x_sym$R_diff &lt;- -x_sym$R_diff
+  x_mirrored &lt;- rbind(x, x_sym)
+  x_diag&lt;-data.frame(
+      Model_1=unique(x_mirrored$Model_1),
+      Model_2=unique(x_mirrored$Model_1),
+      Model_1_R=x_mirrored$Model_1_R,
+      Model_2_R=x_mirrored$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp&lt;-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
+# Read in results
+targ_pop=c(&#39;EAS&#39;,&#39;AFR&#39;)
+res_comp &lt;- list()
+for(pheno_i in selected_traits){
+  res_comp_i&lt;-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;EAS&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;AFR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+    }
+    for(disc_pop_i in disc_pop){
+      comp_i &lt;-
+        fread(
+          paste0(
+            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/&#39;,
+            &#39;targ_&#39;,
+            targ_pop_i,
+            &#39;.disc_EUR_&#39;,
+            disc_pop_i,
+            &#39;/&#39;,
+            pheno_i,
+            &#39;/res.tlprs.pred_comp.txt&#39;
+          )
+        )
+      comp_i&lt;-mirror_comp(comp_i)
+      comp_i$Target&lt;-targ_pop_i
+      comp_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
+      res_comp_i&lt;-rbind(res_comp_i, comp_i)
+    }
+  }
+  
+  res_comp[[pheno_i]]&lt;-res_comp_i
+}
+
+res_comp_all &lt;- do.call(rbind, lapply(names(res_comp), function(name) {
+  x &lt;- res_comp[[name]]
+  x$pheno &lt;- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Annotate tests to get order correct
+res_comp_all$Method1&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_1)
+res_comp_all$Method1&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method1)
+res_comp_all$Method2&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_2)
+res_comp_all$Method2&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method2)
+
+find_model&lt;-function(x){
+  mod &lt;- x
+  mod[grepl(&#39;top1$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;IndivTune&#39;
+  mod[grepl(&#39;top1$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;SumStatTune&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-IndivTune&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-SumStatTune&#39;
+  mod[grepl(&#39;_multi&#39;, x)] &lt;- &#39;SumStatTune&#39;
+  mod[x == &#39;prscsx.pseudo.multi&#39;] &lt;- &#39;SumStatTune&#39;
+  mod[x == &#39;xwing.pseudo.multi&#39;] &lt;- &#39;SumStatTune&#39;
+  
+  return(mod)
+}
+
+res_comp_all$Model1&lt;-find_model(res_comp_all$Model_1)
+res_comp_all$Model2&lt;-find_model(res_comp_all$Model_2)
+
+res_comp_all$Source1&lt;-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method1) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_1), &#39;Multi&#39;, &#39;Single&#39;)
+res_comp_all$Source2&lt;-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method2) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_2), &#39;Multi&#39;, &#39;Single&#39;)
+  
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)){
+  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] &lt;- i
+  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] &lt;- i
+}
+res_comp_all$Discovery1[res_comp_all$Source1 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source1 == &#39;Multi&#39;]
+res_comp_all$Discovery2[res_comp_all$Source2 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source2 == &#39;Multi&#39;]
+
+res_comp_all$Method1&lt;-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
+res_comp_all$Method2&lt;-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
+res_comp_all$Model1&lt;-factor(res_comp_all$Model1, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+res_comp_all$Model2&lt;-factor(res_comp_all$Model2, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+res_comp_all$Discovery1&lt;-factor(res_comp_all$Discovery1, levels=rev(c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;)))
+res_comp_all$Discovery2&lt;-factor(res_comp_all$Discovery2, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+# Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method1 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+  res_comp_all$Model1 %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method2 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+  res_comp_all$Model2 %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
+
+# Remove pseudo model for methods that don&#39;t really have one 
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method1 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+  res_comp_all$Model1 %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method2 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+  res_comp_all$Model2 %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
+
+# Remove top1 models for PRS-CSx
+res_comp_all &lt;- res_comp_all[
+!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method1) &amp; 
+  grepl(&#39;top1&#39;, res_comp_all$Model_1)),]
+res_comp_all &lt;- res_comp_all[
+!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method2) &amp; 
+  grepl(&#39;top1&#39;, res_comp_all$Model_2)),]
+
+# Remove any comparisons
+res_comp_all &lt;- res_comp_all[!duplicated(res_comp_all[, c(&quot;Target&quot;, &quot;Method1&quot;, &quot;Model1&quot;, &quot;Source1&quot;, &quot;Discovery1&quot;, &quot;Method2&quot;, &quot;Model2&quot;, &quot;Source2&quot;, &quot;Discovery2&quot;,&#39;pheno&#39;)]),]
+
+###########
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_comp &lt;- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == &#39;EAS&#39;){
+    disc_pop &lt;- &#39;EAS&#39;
+  }
+  if(targ_pop_i == &#39;AFR&#39;){
+    disc_pop &lt;- &#39;AFR&#39;
+  }
+  if(targ_pop_i == &#39;EUR&#39;){
+    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_comp for each scenario
+    res_comp_i &lt;- res_comp_all[res_comp_all$Target == targ_pop_i &amp; res_comp_all$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+  
+    # Calculate diff SE based on p-value
+    res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] &lt;- 1-0.001
+    res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]&lt;-1-0.001
+    res_comp_i$R_diff_z&lt;-qnorm(res_comp_i$R_diff_pval/2)
+    res_comp_i$R_diff_SE&lt;-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
+        
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_comp_i$Sample&lt;-&#39;A&#39;
+    res_comp_i$Group &lt;- paste0(res_comp_i$Model_1, &#39;_vs_&#39;, res_comp_i$Model_2)
+  
+    for(group_i in unique(res_comp_i$Group)){
+      res_comp_group_i &lt;- res_comp_i[res_comp_i$Group == group_i,]
+      cors_i &lt;- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
+      
+      if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
+        
+        meta_res_comp_i &lt;-
+          agg(
+            id = Sample,
+            es = R_diff,
+            var = R_diff_SE ^ 2,
+            cor = cors_i,
+            method = &quot;BHHR&quot;,
+            mod = NULL,
+            data = res_comp_group_i
+          )
+        
+        tmp &lt;- res_comp_group_i[1,]
+        tmp$pheno &lt;- NULL
+        tmp$Model_1_R &lt;-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &amp;
+                            meta_res_eval$Target == targ_pop_i &amp;
+                            meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+        tmp$Model_2_R &lt;-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &amp;
+                            meta_res_eval$Target == targ_pop_i &amp;
+                            meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+        tmp$R_diff &lt;- meta_res_comp_i$es
+        tmp$R_diff_SE &lt;- sqrt(meta_res_comp_i$var)
+        tmp$R_diff_z &lt;- tmp$R_diff / tmp$R_diff_SE
+        tmp$R_diff_p &lt;- 2*pnorm(-abs(tmp$R_diff_z))
+      } else {
+        tmp &lt;- res_comp_group_i[1,]
+        tmp$pheno &lt;- NULL
+        tmp$R_diff &lt;- NA
+        tmp$R_diff_SE &lt;- NA
+        tmp$R_diff_z &lt;- NA
+        tmp$R_diff_p &lt;- NA
+      }
+      meta_res_comp &lt;- rbind(meta_res_comp, tmp)
+    }
+  }
+}
+
+meta_res_comp$R_diff_perc &lt;- meta_res_comp$R_diff / meta_res_comp$Model_2_R
+  
+# Compare IndivTune SBayesRC-multi to TL-SBayesRC-multi
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_2 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                                meta_res_comp$Model_1 == &#39;tlprs_sbayesrc.pop.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;AFR&#39;,]
+round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
+tmp_sbayesrc$R_diff_p
+
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_2 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                                meta_res_comp$Model_1 == &#39;tlprs_sbayesrc.pop.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;EAS&#39;,]
+round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
+tmp_sbayesrc$R_diff_p</code></pre>
+</details>
+<details>
+<summary>
+Show TLPRS results
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/average_r_tlprs.png"></p>
+</div>
+</div>
+</details>
+<hr />
+</div>
+<div id="computational-resoures-1" class="section level2">
+<h2>Computational resoures</h2>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+library(ggplot2)
+library(cowplot)
+
+setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&#39;
+pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Read in configuration specific benchmark files
+bm_files_i &lt;- list.files(paste0(outdir, &#39;/reference/benchmarks/&#39;), full.names = T)
+
+# Subset benchmarks for pgs_methods
+bm_files_i &lt;- bm_files_i[grepl(&#39;prep_pgs_tlprs&#39;, bm_files_i)]
+
+# Read in benchmark files
+bm_dat_all &lt;- do.call(rbind, lapply(bm_files_i, function(file) {
+  tmp &lt;- fread(file)
+  tmp$file &lt;- basename(file)
+  return(tmp)
+}))
+
+# Create rule column
+bm_dat_all$rule &lt;- gsub(&#39;-.*&#39;,&#39;&#39;,bm_dat_all$file)
+
+# Create method column
+bm_dat_all$method &lt;-
+  gsub(&#39;_i&#39;, &#39;&#39;, gsub(&#39;prep_pgs_&#39;, &#39;&#39;, bm_dat_all$rule))
+
+#############
+# Time
+#############
+
+# Calculate average time taken for each method
+method_avg &lt;- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg &lt;- rbind(
+    method_avg,
+    data.frame(
+      Method = i,
+      Time = mean(bm_dat_all$s[bm_dat_all$method == i])
+    )
+  )
+}
+
+# Convert time in seconds to hours
+method_avg$Time_hour &lt;- method_avg$Time / 60/60
+method_avg$Time_hour &lt;- round(method_avg$Time_hour, 2)
+
+#This is for bidirectional TL-PRS
+
+#############
+# Memory
+#############
+
+# Calculate average max_rss for each method
+method_avg_mem &lt;- NULL
+for(i in unique(bm_dat_all$method)){
+  method_avg_mem &lt;- rbind(
+    method_avg_mem,
+    data.frame(
+      Method = i,
+      Memory = mean(bm_dat_all$max_rss[bm_dat_all$method == i])
+    )
+  )
+}
+
+# Format the Memory nicely
+method_avg_mem$Memory_clean &lt;-
+  paste0(round(method_avg_mem$Memory/1000, 2), &#39; Gb&#39;)
+
+ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
+  geom_bar(stat = &quot;identity&quot;, position=&quot;dodge&quot;) +
+  geom_text(aes(label = Memory_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
+  labs(x = &quot;PGS Method&quot;, y = &quot;Memory (Mb)&quot;) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position=&quot;none&quot;)
+
+method_avg_mem$Memory_gb &lt;- method_avg_mem$Memory/1000
+method_avg_mem &lt;- method_avg_mem[, c(&#39;Method&#39;,&#39;Memory_gb&#39;)]
+method_avg_mem$Memory_gb &lt;- round(method_avg_mem$Memory_gb, 2)
+names(method_avg_mem)&lt;-c(&#39;Method&#39;,&quot;Memory (Gb)&quot;)
+
+method_avg&lt;-merge(method_avg, method_avg_mem, by = &#39;Method&#39;)
+
+write.csv(method_avg, &#39;~/oliverpainfel/Analyses/crosspop/time_memory_tlprs.csv&#39;, row.names=F)</code></pre>
+</details>
+<details>
+<summary>
+Show computational resources table
+</summary>
+<table class="table table-striped table-hover" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
+<thead>
+<tr>
+<th style="text-align:left;">
+Method
+</th>
+<th style="text-align:right;">
+Time
+</th>
+<th style="text-align:right;">
+Time_hour
+</th>
+<th style="text-align:right;">
+Memory (Gb)
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+tlprs
+</td>
+<td style="text-align:right;">
+2614.237
+</td>
+<td style="text-align:right;">
+0.73
+</td>
+<td style="text-align:right;">
+31.38
+</td>
+</tr>
+</tbody>
+</table>
+</details>
+<hr />
+</div>
+</div>
+<div id="sensitivity-analyses" class="section level1">
+<h1>Sensitivity analyses</h1>
+<hr />
+<div id="using-1kg-reference" class="section level2">
+<h2>Using 1KG reference</h2>
+<p>PRS-CS, PRS-CSx and X-Wing all use the 1KG reference sample, whereas
+the other methods are using the 1KG+HGDP reference sample. We should
+check whether this difference is impacting our conclusions.</p>
+<p>To make this quicker, focus on evaluating the PGS methods in the AFR
+and EAS target individuals in UKB. This will avoid reprocessing the full
+UKB data.</p>
+<hr />
+<div id="create-1kg-only-genopred-reference-data"
+class="section level3">
+<h3>Create 1KG only GenoPred reference data</h3>
+<p>Subset the 1KG+HGDP reference data to include only 1KG
+individuals.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>mkdir -p ~/oliverpainfel/Data/1kg/genopred/
+cp -r ~/oliverpainfel/Data/hgdp_1kg/genopred/ref ~/oliverpainfel/Data/1kg/genopred/
+rm ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr*.p*</code></pre>
+<pre class="r"><code>library(data.table)
+
+ref&lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr1.psam&#39;)
+ref&lt;-ref[ref$Project == &#39;gnomAD_1kG&#39;,]
+
+write.table(ref[,1, drop = F], &#39;~/oliverpainfel/Data/1kg/1kg.keep&#39;, col.names=F, row.names=F, quote=F)</code></pre>
+<pre class="bash"><code>for chr in $(seq 1 22); do
+  ~/oliverpainfel/Software/plink2 \
+    --pfile ~/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr} \
+    --keep ~/oliverpainfel/Data/1kg/1kg.keep \
+    --make-pgen \
+    --out ~/oliverpainfel/Data/1kg/genopred/ref/ref.chr${chr}
+done</code></pre>
+</details>
+<hr />
+</div>
+<div id="pgs-calculation-2" class="section level3">
+<h3>PGS calculation</h3>
+<p>To save time, run using PGS methods that do not need pre-processed LD
+matrix data (ptclump, dbslmm, megaprs, lassosum). If the results vary
+from the 1KG+HGDP results, then expand to other methods (LDpred2,
+SBayesRC, QuickPRS).</p>
+<details>
+<summary>
+Show code
+</summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Prepare configuration
+</h4>
+<pre class="r"><code>library(data.table)
+
+dir.create(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only&#39;)
+
+######
+# config
+######
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_1kgref&quot;,
+  &quot;refdir: /users/k1806347/oliverpainfel/Data/1kg/genopred/ref&quot;,
+  &quot;resdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/resdir_1kgref&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt&quot;,
+  &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt&quot;,
+  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;]&quot;,
+#  &quot;leopard_methods: [&#39;ptclump&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;]&quot;,
+  &quot;cores_prep_pgs: 10&quot;,
+  &quot;cores_target_pgs: 10&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<hr />
+<h4>
+Run pipeline
+</h4>
+<pre class="bash"><code>snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \
+  output_all -n</code></pre>
+</details>
+<hr />
+</div>
+<div id="pgs-evaluation-2" class="section level3">
+<h3>PGS evaluation</h3>
+<details>
+<summary>
+Show code
+</summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Create predictor list
+</h4>
+<pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+library(data.table)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml&#39;
+pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+# Create files for EAS and AFR targets
+targ_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+for(trait_i in selected_traits){
+  scores_i &lt;- scores[grepl(trait_i, scores$name),]
+  scores_i$multi &lt;- scores_i$method
+  
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;BBJ&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;UGR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;BBJ&#39;,&#39;UGR&#39;)
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == &#39;BBJ&#39;){
+        disc_pop_j_2 &lt;- &#39;EAS&#39;
+      }
+      if(disc_pop_j == &#39;UGR&#39;){
+        disc_pop_j_2 &lt;- &#39;AFR&#39;
+      }
+
+      dir.create(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_j_2,
+          &#39;/&#39;,
+          trait_i
+        ),
+        recursive = T
+      )
+      
+      scores_i_j &lt;- scores_i[
+        (grepl(&#39;UKB$&#39;, scores_i$name, ignore.case = F) | 
+         grepl(paste0(disc_pop_j, &#39;$&#39;), scores_i$name, ignore.case = T)),]
+
+      # Insert path to score file
+      scores_i_j$predictor &lt;- paste0(
+        outdir,
+        &#39;/ukb/pgs/TRANS/&#39;,
+        scores_i_j$method,
+        &#39;/&#39;,
+        scores_i_j$name,
+        &#39;/ukb-&#39;,
+        scores_i_j$name,
+        &#39;-TRANS.profiles&#39;
+      )
+      
+      ####
+      # Make groups single source methods
+      ####
+      
+      scores_i_j_single_top1 &lt;-
+        scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &amp;
+                     !grepl(&#39;_multi$&#39;, scores_i_j$method), ]
+
+      # Create top1 column indicating which predictors top1 models should be derived
+      scores_i_j_single_top1$top1[grepl(&#39;UKB&#39;, scores_i_j_single_top1$name, ignore.case = F)] &lt;- &#39;EUR&#39;
+      scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] &lt;- disc_pop_j_2
+      
+      ####
+      # Make groups containing pseudo scores for single source methods
+      ####
+
+      # Extract the pseudo score for each method and specify as a separate group
+      for(i in 1:nrow(scores_i_j_single_top1)) {
+        param &lt;- find_pseudo(
+          config = config,
+          gwas = scores_i_j_single_top1$name[i],
+          pgs_method = scores_i_j_single_top1$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header &lt;-
+          fread(scores_i_j_single_top1$predictor[i], nrows = 1)
+        score_cols &lt;-
+          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_single_top1$name[i], &#39;_&#39;, param)))
+        
+        system(
+          paste0(
+            &quot;cut -d&#39; &#39; -f &quot;, 
+            paste0(score_cols, collapse=&#39;,&#39;),
+            &quot; &quot;, 
+            scores_i_j_single_top1$predictor[i], 
+            &quot; &gt; &quot;, 
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                 scores_i_j_single_top1$predictor[i])
+          )
+        )
+      }
+      
+      scores_i_j_single_pseudo &lt;- scores_i_j_single_top1
+      scores_i_j_single_pseudo$multi &lt;- paste0(scores_i_j_single_pseudo$multi, &#39;.pseudo&#39;)
+
+      scores_i_j_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                    paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                                    scores_i_j_single_pseudo$predictor)
+
+#      ####
+#      # Make groups for multi-single-source pseudo scores
+#      ####
+#      
+#      scores_i_j_multi_single_pseudo &lt;- scores_i_j[grepl(&#39;_multi$&#39;, scores_i_j$method),]
+#
+#      # Extract the pseudo score for each method and specify as a separate group
+#      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
+#        param &lt;- find_pseudo(
+#          config = config,
+#          gwas = scores_i_j_multi_single_pseudo$name[i],
+#          pgs_method = scores_i_j_multi_single_pseudo$method[i],
+#          target_pop = targ_pop_i
+#        )
+#        
+#        score_header &lt;-
+#          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
+#        score_cols &lt;-
+#          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_multi_single_pseudo$name[i], &#39;_&#39;, param)))
+#        
+#        system(
+#          paste0(
+#            &quot;cut -d&#39; &#39; -f &quot;, 
+#            paste0(score_cols, collapse=&#39;,&#39;),
+#            &quot; &quot;, 
+#            scores_i_j_multi_single_pseudo$predictor[i], 
+#            &quot; &gt; &quot;, 
+#            gsub(&#39;.profiles&#39;,
+#                 paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+#                 scores_i_j_multi_single_pseudo$predictor[i])
+#          )
+#        )
+#      }
+#      
+#      scores_i_j_multi_single_pseudo$multi &lt;- paste0(scores_i_j_multi_single_pseudo$multi, &#39;.pseudo&#39;)
+#
+#      scores_i_j_multi_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+#                                    paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+#                                    scores_i_j_multi_single_pseudo$predictor)
+#      
+#      scores_i_j_multi_single_pseudo$top1&lt;-paste0(&#39;EUR_&#39;, disc_pop_j_2)
+#
+#      ####
+#      # Make groups for the Multi-Source methods
+#      ####
+#      
+#      scores_i_j_multi &lt;- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
+#
+#      # Split top1 scores by target population
+#      # This doesn&#39;t apply to xwing because it only has pop-specific pseudo scores
+#      scores_i_j_multi_top1&lt;-NULL
+#      for(i in 1:which(scores_i_j_multi$method %in% c(&#39;prscsx&#39;))){
+#        score_header&lt;-fread(scores_i_j_multi$predictor[i], nrow = 1)
+#        
+#        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
+#          
+#          if(scores_i_j_multi$method[i] == &#39;prscsx&#39;){
+#            score_cols &lt;-
+#              which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi&#39;), names(score_header)))
+#          }
+#          if(scores_i_j_multi$method[i] == &#39;xwing&#39;){
+#            score_cols &lt;-
+#              which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst&#39;), names(score_header)))
+#          }
+#          
+#          system(
+#            paste0(
+#              &quot;cut -d&#39; &#39; -f &quot;, 
+#              paste0(score_cols, collapse=&#39;,&#39;),
+#              &quot; &quot;, 
+#              scores_i_j_multi$predictor[i], 
+#              &quot; &gt; &quot;, 
+#              gsub(&#39;.profiles&#39;,
+#                   paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
+#                   scores_i_j_multi$predictor[i])
+#            )
+#          )
+#          
+#          tmp &lt;- scores_i_j_multi[i,]
+#          tmp$multi &lt;- paste0(tmp$multi, &#39;.grid&#39;)
+#          tmp$top1 &lt;- pop
+#          tmp$predictor &lt;-
+#              gsub(&#39;.profiles&#39;,
+#                   paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
+#                   scores_i_j_multi$predictor[i])
+#          
+#          scores_i_j_multi_top1 &lt;- rbind(scores_i_j_multi_top1, tmp)
+#        }
+#      }
+#
+#      # Split pop-specific pseudo scores by target population
+#      scores_i_j_multi_pop_pseudo&lt;-NULL
+#      for(i in 1:nrow(scores_i_j_multi)){
+#        score_header&lt;-fread(scores_i_j_multi$predictor[i], nrow = 1)
+#        
+#        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
+#          if(scores_i_j_multi$method[i] == &#39;prscsx&#39;){
+#            score_cols &lt;-
+#              which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi_auto&#39;), names(score_header)))
+#          }
+#          if(scores_i_j_multi$method[i] == &#39;xwing&#39;){
+#            score_cols &lt;-
+#              which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst_&#39;, pop), names(score_header)))
+#          }
+#          
+#          system(
+#            paste0(
+#              &quot;cut -d&#39; &#39; -f &quot;, 
+#              paste0(score_cols, collapse=&#39;,&#39;),
+#              &quot; &quot;, 
+#              scores_i_j_multi$predictor[i], 
+#              &quot; &gt; &quot;, 
+#              gsub(&#39;.profiles&#39;,
+#                   paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
+#                   scores_i_j_multi$predictor[i])
+#            )
+#          )
+#          
+#          tmp &lt;- scores_i_j_multi[i,]
+#          tmp$multi &lt;- paste0(tmp$multi, &#39;.pop_pseudo&#39;)
+#          tmp$top1 &lt;- pop
+#          tmp$predictor &lt;-
+#              gsub(&#39;.profiles&#39;,
+#                   paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
+#                   scores_i_j_multi$predictor[i])
+#          
+#          scores_i_j_multi_pop_pseudo &lt;- rbind(scores_i_j_multi_pop_pseudo, tmp)
+#        }
+#      }
+#      
+#      # Create pseudo score for multi-source methods
+#      scores_i_j_multi_pseudo&lt;-NULL
+#      for(i in 1:nrow(scores_i_j_multi)) {
+#        param &lt;- find_pseudo(
+#          config = config,
+#          gwas = scores_i_j_multi$name[i],
+#          pgs_method = scores_i_j_multi$method[i],
+#          target_pop = targ_pop_i
+#        )
+#        
+#        score_header &lt;-
+#          fread(scores_i_j_multi$predictor[i], nrows = 1)
+#        score_cols &lt;-
+#          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_multi$name[i], &#39;_&#39;, param)))
+#
+#        system(
+#          paste0(
+#            &quot;cut -d&#39; &#39; -f &quot;, 
+#            paste0(score_cols, collapse=&#39;,&#39;),
+#            &quot; &quot;, 
+#            scores_i_j_multi$predictor[i], 
+#            &quot; &gt; &quot;, 
+#            gsub(&#39;.profiles&#39;,
+#                 paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
+#                 scores_i_j_multi$predictor[i])
+#          )
+#        )
+#        
+#        tmp &lt;- scores_i_j_multi[i,]
+#        tmp$multi &lt;- paste0(tmp$multi, &#39;.pseudo&#39;)
+#        tmp$top1 &lt;- paste0(&#39;EUR_&#39;, disc_pop_j_2)
+#        tmp$predictor &lt;-
+#            gsub(&#39;.profiles&#39;,
+#                 paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
+#                 scores_i_j_multi$predictor[i])
+#        
+#        scores_i_j_multi_pseudo &lt;- rbind(scores_i_j_multi_pseudo, tmp)
+#      }
+      
+      ####
+      # Combine the different predictor groups
+      ####
+      predictors_i&lt;- do.call(rbind, list(
+        scores_i_j_single_top1, 
+        scores_i_j_single_pseudo#, 
+#        scores_i_j_multi_single_pseudo,
+#        scores_i_j_multi_top1,
+#        scores_i_j_multi_pop_pseudo,
+#        scores_i_j_multi_pseudo
+      ))
+      
+      predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;multi&#39;,&#39;top1&#39;), with=F]
+      
+      write.table(
+        predictors_i,
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_j_2,
+          &#39;/&#39;,
+          trait_i,
+          &#39;/predictor_list.txt&#39;
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}</code></pre>
+<hr />
+<h4>
+Run model_builder
+</h4>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_*.disc_EUR_*/*/res*
+
+for targ_pop in $(echo EAS AFR); do
+  if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
+      targ_pop2=&quot;EUR_test&quot;
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
+    disc_pop=$(echo EAS AFR)
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;EAS&quot; ]; then
+    disc_pop=&quot;EAS&quot;
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;AFR&quot; ]; then
+    disc_pop=&quot;AFR&quot;
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+      if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt&quot; ]; then
+        sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
+          --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
+          --n_core 5&quot;
+      fi
+    done
+  done
+done
+</code></pre>
+<hr />
+<h4>
+Plot results
+</h4>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+info_all &lt;- fread(&#39;~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv&#39;)
+
+# Calculate correlation between all phenotypes in each target population
+cors &lt;- list()
+for(pop_i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;CSA&#39;,&#39;AMR&#39;)){
+  if(pop_i == &#39;EUR&#39;){
+    pop_i_2 &lt;- &#39;EUR_test&#39;
+  } else {
+    pop_i_2 &lt;- pop_i
+  }
+  pheno_pop_i &lt;- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] &lt;- fread(paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;, pheno_i, &#39;.unrel.&#39;, pop_i_2, &#39;.row_number.txt&#39;))
+    names(pheno_pop_i[[pheno_i]])[3] &lt;- pheno_i
+  }
+  
+  pheno_pop_i_merged &lt;- merged_df &lt;- Reduce(function(x, y) merge(x, y, all = TRUE, by = c(&#39;FID&#39;,&#39;IID&#39;)), pheno_pop_i)
+
+  cors_i &lt;- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use=&#39;p&#39;))
+  cors[[pop_i]] &lt;- cors_i
+}
+
+# Read in results
+targ_pop = c(&#39;EAS&#39;,&#39;AFR&#39;)
+res_eval &lt;- list()
+for(pheno_i in selected_traits){
+  res_eval_i&lt;-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;EAS&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;AFR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i &lt;-
+        fread(
+          paste0(
+            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/&#39;,
+            &#39;targ_&#39;,
+            targ_pop_i,
+            &#39;.disc_EUR_&#39;,
+            disc_pop_i,
+            &#39;/&#39;,
+            pheno_i,
+            &#39;/res.pred_eval.txt&#39;
+          )
+        )
+      eval_i$Target&lt;-targ_pop_i
+      eval_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
+      res_eval_i&lt;-rbind(res_eval_i, eval_i)
+    }
+  }
+  
+  res_eval_i$Method&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_eval_i$Group)
+  res_eval_i$Method&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_eval_i$Method)
+  
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTune&#39;
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTune&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTune&#39;
+  
+  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;prscsx.pseudo.multi&#39;]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;xwing.pseudo.multi&#39;]&lt;-&#39;SumStatTune&#39;
+  
+  res_eval_i$Source&lt;-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_eval_i$Method) | 
+    !grepl(&#39;EUR|EAS|AFR&#39;, res_eval_i$Group), &#39;Multi&#39;, &#39;Single&#39;)
+  
+  res_eval_i$Discovery[grepl(&#39;EUR&#39;, res_eval_i$Group)] &lt;- &#39;EUR&#39;
+  res_eval_i$Discovery[grepl(&#39;EAS&#39;, res_eval_i$Group)] &lt;- &#39;EAS&#39;
+  res_eval_i$Discovery[grepl(&#39;AFR&#39;, res_eval_i$Group)] &lt;- &#39;AFR&#39;
+  res_eval_i$Discovery[res_eval_i$Source == &#39;Multi&#39;] &lt;- res_eval_i$gwas_group[res_eval_i$Source == &#39;Multi&#39;]
+  
+  res_eval_i$Method&lt;-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+  res_eval_i$Discovery&lt;-factor(res_eval_i$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
+  
+  # Remove pseudo model for methods that don&#39;t really have one 
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
+  res_eval_i &lt;- res_eval_i[
+    !((res_eval_i$Method %in%  c(&#39;prscsx&#39;, &#39;xwing&#39;) | grepl(&#39;_multi$&#39;, res_eval_i$Method)) &amp; 
+      grepl(&#39;top1&#39;, res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i &lt;- res_eval_i[!duplicated(res_eval_i[, c(
+    &quot;Target&quot;, &quot;Method&quot;, &quot;Model&quot;, &quot;Source&quot;, &quot;Discovery&quot;,&quot;gwas_group&quot;
+  )]),]
+  
+  res_eval[[pheno_i]]&lt;-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;, &quot;DBSLMM-multi&quot;, &quot;lassosum-multi&quot;, &quot;LDpred2-multi&quot;, &quot;MegaPRS-multi&quot;, &quot;PRS-CS-multi&quot;, &quot;pT+clump-multi&quot;, &quot;QuickPRS-multi&quot;, &quot;SBayesRC-multi&quot;, &quot;PRS-CSx&quot;, &quot;X-Wing&quot;, &quot;All&quot;) 
+
+res_eval_simp &lt;- NULL
+for(pheno_i in selected_traits){
+  tmp &lt;- res_eval[[pheno_i]]
+  tmp$Trait &lt;- pheno_i
+  
+  # Insert nice PGS method names
+  tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+  tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+  tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+  tmp$label &lt;- factor(tmp$label, levels = model_order)
+  
+  # Simplify result to either SumStatTune or IndivTune
+  tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+  tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+  tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery&#39;,&#39;Model&#39;), with=F]),]
+  
+  res_eval_simp &lt;- rbind(res_eval_simp, tmp)
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval &lt;- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == &#39;EAS&#39;){
+    disc_pop &lt;- &#39;EAS&#39;
+  }
+  if(targ_pop_i == &#39;AFR&#39;){
+    disc_pop &lt;- &#39;AFR&#39;
+  }
+  if(targ_pop_i == &#39;EUR&#39;){
+    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_eval for each scenario
+    res_eval_i &lt;- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x &lt;- res_eval[[i]]
+      x$pheno &lt;- names(res_eval)[i]
+      x &lt;- x[x$Target == targ_pop_i]
+      x &lt;- x[x$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+    }))
+    
+    # Average res_evalults for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_eval_i$Sample&lt;-&#39;A&#39;
+  
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i &lt;- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno &lt;-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+      
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          &#39;res_evalults missing for &#39;,
+          targ_pop_i,
+          &#39; &#39;,
+          group_i,
+          &#39; &#39;,
+          paste0(missing_pheno, collapse = &#39; &#39;)
+        ))
+      }
+      
+      cors_i &lt;- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i &lt;-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = &quot;BHHR&quot;,
+          mod = NULL,
+          data = res_eval_group_i
+        )
+      
+      tmp &lt;- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval &lt;- rbind(meta_res_eval, tmp)
+    }
+  }
+}
+
+meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+meta_res_eval$Discovery&lt;-factor(meta_res_eval$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/average_r.png&#39;), res=300, width = 3200, height = 2000, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+# The results look very similar to when using 1KG+HGDP. 
+
+###################
+# Plot a comparison between the runs using different references
+
+# Read in results using 1KG+HGDP reference
+main_results&lt;-fread(&#39;~/oliverpainfel/Analyses/crosspop/r_eval.csv&#39;)
+sens_results&lt;-meta_res_eval
+
+tmp &lt;- main_results
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+main_results &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+
+tmp &lt;- sens_results
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+sens_results &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+
+main_results&lt;-main_results[main_results$Method %in% sens_results$Method,]
+main_results&lt;-main_results[main_results$Target %in% sens_results$Target,]
+
+sens_results$Reference &lt;- &#39;1KG&#39;
+main_results$Reference &lt;- &#39;1KG+HGDP&#39;
+
+both_results &lt;- rbind(main_results, sens_results)
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/comparison_to_main_result.png&#39;, units = &#39;px&#39;, res = 300, width=4000, height=2500)
+ggplot(both_results, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ Discovery_clean + Reference, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()</code></pre>
+</details>
+<details>
+<summary>
+Show results
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/comparison_to_main_result.png"></p>
+</div>
+</div>
+</details>
+<hr />
+</div>
+</div>
+<div id="using-three-gwas" class="section level2">
+<h2>Using three GWAS</h2>
+<p>Extend analysis to include gwas_groups including AFR EAS and EUR
+GWAS. Only some multi-source methods should be applicable here,
+including LEOPARD, PRS-CSx, and X-Wing. Given X-Wing with LEOPARD is
+slow, limit X-Wing analysis to the IndivTune model alone.</p>
+<hr />
+<div id="pgs-calculation-3" class="section level3">
+<h3>PGS calculation</h3>
+<details>
+<summary>
+Show code
+</summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Prepare configuration
+</h4>
+<pre class="r"><code>library(data.table)
+
+# Subset original gwas_list to include selected traits
+gwas_list&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt&#39;)
+pheno&lt;-gsub(&#39;_.*&#39;,&#39;&#39;, gwas_list$name)
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+gwas_list&lt;-gwas_list[pheno %in% selected_traits,]
+gwas_list$label&lt;-paste0(&#39;&quot;&#39;, gwas_list$label, &#39;&quot;&#39;)
+
+######
+# gwas_groups
+######
+
+gwas_groups_three_pop&lt;-data.frame(
+  name=paste0(selected_traits, &#39;_UKB_BBJ_UGR&#39;),
+  gwas=sapply(selected_traits, function(x) paste0(x,&#39;_UKB,&#39;,x,&#39;_BBJ,&#39;,x,&#39;_UGR&#39;)),
+  label=paste0(&#39;&quot;&#39;, selected_traits, &quot; (UKB+BBJ+UGR)&quot;, &#39;&quot;&#39;)
+)
+
+write.table(gwas_groups_three_pop, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_three_pop.txt&#39;, col.names = T, row.names = F, quote = F)
+
+######
 # config
 ######
 
 config&lt;-c(
-  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_tlprs&quot;,
-  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&quot;,
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml&quot;,
   &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&quot;,
-  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/target_list.txt&quot;,
-  &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt&quot;,
-  &quot;pgs_methods: [&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
-  &quot;tlprs_methods: [&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
-  &quot;cores_prep_pgs: 10&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&quot;,
+  &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_three_pop.txt&quot;,
+  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;,&#39;prscsx&#39;]&quot;, # xwing removed for time sake
+  &quot;leopard_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
+  &quot;cores_prep_pgs: 10&quot;, # xwing run with 20 cores
   &quot;cores_target_pgs: 50&quot;,
-  &quot;prscs_phi: [&#39;auto&#39;]&quot;,
-  &quot;ldpred2_model: [&#39;auto&#39;]&quot;,
   &quot;ldpred2_inference: F&quot;,
-  &quot;dbslmm_h2f: [&#39;1&#39;]&quot;,
   &quot;ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3&quot;,
   &quot;quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3&quot;,
+  &quot;quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset&quot;,
   &quot;sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3&quot;
 )
 
-write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<hr />
+<h4>
+Run pipeline
+</h4>
 <pre class="bash"><code>snakemake \
   --profile slurm \
   --use-conda \
-  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml \
-  output_all -n</code></pre>
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml \
+  target_pgs  -n</code></pre>
 </details>
 <hr />
 </div>
-<div id="create-predictor-lists-2" class="section level2">
-<h2>Create predictor lists</h2>
+<div id="pgs-evaluation-3" class="section level3">
+<h3>PGS evaluation</h3>
 <details>
 <summary>
 Show code
 </summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Create predictor list
+</h4>
 <pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 source(&#39;../functions/misc.R&#39;)
 source_all(&#39;../functions&#39;)
 library(data.table)
 
 # Get some key variables from config
-config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&#39;
-outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+config_2_pop&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml&#39;
+config_3_pop&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml&#39;
+pgs_methods &lt;- read_param(config = config_3_pop, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config_3_pop, param = &#39;outdir&#39;, return_obj = F)
 
 # Read in list of outcomes 
 selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
 
 # Get a list of score files
-scores &lt;- list_score_files(config)
+scores_2_pop &lt;- list_score_files(config_2_pop)
+scores_3_pop &lt;- list_score_files(config_3_pop)
+scores &lt;- rbind(scores_2_pop, scores_3_pop)
+scores &lt;- scores[!duplicated(scores),]
+
+# Remove xwing 
+scores &lt;- scores[scores$method != &#39;xwing&#39;, ]
 
 # Create files for EAS and AFR targets
 targ_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
 for(trait_i in selected_traits){
-  scores_i &lt;- scores[grepl(trait_i, scores$name),]
-
+  scores_j &lt;- scores[grepl(trait_i, scores$name),]
+  scores_j$multi &lt;- scores_j$method
+  
   for(targ_pop_i in targ_pop){
-    # Subset GWAS based on EUR and/or targ_pop_i
-    if(targ_pop_i == &#39;EAS&#39;){
-      disc_pop &lt;- &#39;BBJ&#39;
-    }
-    if(targ_pop_i == &#39;AFR&#39;){
-      disc_pop &lt;- &#39;UGR&#39;
-    }
-    if(targ_pop_i == &#39;EUR&#39;){
-      disc_pop &lt;- c(&#39;BBJ&#39;,&#39;UGR&#39;)
-    }
-    
-    for(disc_pop_j in disc_pop){
-      if(disc_pop_j == &#39;BBJ&#39;){
-        disc_pop_j_2 &lt;- &#39;EAS&#39;
-      }
-      if(disc_pop_j == &#39;UGR&#39;){
-        disc_pop_j_2 &lt;- &#39;AFR&#39;
-      }
-
-      dir.create(
-        paste0(
-          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_&#39;,
-          targ_pop_i,
-          &#39;.disc_EUR_&#39;,
-          disc_pop_j_2,
-          &#39;/&#39;,
-          trait_i
-        ),
-        recursive = T
-      )
-      
-      scores_i_j &lt;- scores_i[
-        (grepl(&#39;UKB$&#39;, scores_i$name, ignore.case = F) | 
-         grepl(paste0(disc_pop_j, &#39;$&#39;), scores_i$name, ignore.case = T)),]
+    disc_pop_j &lt;- c(&#39;UGR&#39;,&#39;BBJ&#39;,&#39;UKB&#39;)
+    disc_pop_j_2 &lt;- c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;)
 
-      scores_i_j$predictor &lt;- paste0(
-        outdir,
-        &#39;/ukb/pgs/TRANS/&#39;,
-        scores_i_j$method,
+    dir.create(
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/targ_&#39;,
+        targ_pop_i,
+        &#39;.disc_&#39;, paste(disc_pop_j_2, collapse = &#39;_&#39;),
         &#39;/&#39;,
-        scores_i_j$name,
-        &#39;/ukb-&#39;,
-        scores_i_j$name,
-        &#39;-TRANS.profiles&#39;
+        trait_i
+      ),
+      recursive = T
+    )
+    
+    # Insert path to score file
+    scores_i &lt;- scores_j[!grepl(paste0(&#39;UKB_&#39;, disc_pop_j[!(disc_pop_j_2 %in% c(&#39;EUR&#39;, targ_pop_i))], &#39;$&#39;), scores_j$name),]
+    
+    scores_i$predictor &lt;- paste0(
+      outdir,
+      &#39;/ukb/pgs/TRANS/&#39;,
+      scores_i$method,
+      &#39;/&#39;,
+      scores_i$name,
+      &#39;/ukb-&#39;,
+      scores_i$name,
+      &#39;-TRANS.profiles&#39;
+    )
+    
+    ####
+    # Make groups single source methods
+    ####
+    
+    scores_i_single_top1 &lt;-
+      scores_i[!(scores_i$method %in% pgs_group_methods) &amp;
+                   !grepl(&#39;_multi$&#39;, scores_i$method), ]
+
+    # Create top1 column indicating which predictors top1 models should be derived
+    scores_i_single_top1$top1[grepl(&#39;UKB&#39;, scores_i_single_top1$name, ignore.case = F)] &lt;- &#39;EUR&#39;
+    scores_i_single_top1$top1[grepl(&#39;BBJ&#39;, scores_i_single_top1$name, ignore.case = F)] &lt;- &#39;EAS&#39;
+    scores_i_single_top1$top1[grepl(&#39;UGR&#39;, scores_i_single_top1$name, ignore.case = F)] &lt;- &#39;AFR&#39;
+        
+    ####
+    # Make groups containing pseudo scores for single source methods
+    ####
+
+    # Extract the pseudo score for each method and specify as a separate group
+    # This can be skipped as it was done before
+    for(i in 1:nrow(scores_i_single_top1)) {
+      param &lt;- find_pseudo(
+        config = ifelse(scores_i_single_top1$name[i] %in% scores_2_pop$name, config_2_pop, config_3_pop),
+        gwas = scores_i_single_top1$name[i],
+        pgs_method = scores_i_single_top1$method[i],
+        target_pop = targ_pop_i
       )
       
-      #####
-      # List single-source PGS
-      #####
-      # These are actually pseudoval scores (as per the config)
-      scores_i_j_single &lt;- scores_i_j[!grepl(&#39;tlprs&#39;, scores_i_j$method),]
+      score_header &lt;-
+        fread(scores_i_single_top1$predictor[i], nrows = 1)
+      score_cols &lt;-
+        which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_single_top1$name[i], &#39;_&#39;, param)))
       
-      scores_i_j_single$top1[grepl(&#39;UKB&#39;, scores_i_j_single$name, ignore.case = F)] &lt;- &#39;EUR&#39;
-      scores_i_j_single$top1[grepl(disc_pop_j, scores_i_j_single$name, ignore.case = F)] &lt;- disc_pop_j_2
-      scores_i_j_single$multi &lt;- paste0(scores_i_j_single$method,&#39;.pseudo&#39;)
+      system(
+        paste0(
+          &quot;cut -d&#39; &#39; -f &quot;, 
+          paste0(score_cols, collapse=&#39;,&#39;),
+          &quot; &quot;, 
+          scores_i_single_top1$predictor[i], 
+          &quot; &gt; &quot;, 
+          gsub(&#39;.profiles&#39;,
+               paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+               scores_i_single_top1$predictor[i])
+        )
+      )
+    }
+    
+    scores_i_single_pseudo &lt;- scores_i_single_top1
+    scores_i_single_pseudo$multi &lt;- paste0(scores_i_single_pseudo$multi, &#39;.pseudo&#39;)
+
+    scores_i_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                  paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                                  scores_i_single_pseudo$predictor)
+
+    ####
+    # Make groups for multi-single-source pseudo scores
+    ####
+    
+    scores_i_multi_single_pseudo &lt;- scores_i[grepl(&#39;_multi$&#39;, scores_i$method),]
+
+    # Extract the pseudo score for each method and specify as a separate group
+    for(i in 1:nrow(scores_i_multi_single_pseudo)) {
+      param &lt;- find_pseudo(
+        config = ifelse(scores_i_multi_single_pseudo$name[i] %in% scores_2_pop$name, config_2_pop, config_3_pop),
+        gwas = scores_i_multi_single_pseudo$name[i],
+        pgs_method = scores_i_multi_single_pseudo$method[i],
+        target_pop = targ_pop_i
+      )
       
-      #####
-      # List tlprs scores (split by target population)
-      #####
-      scores_i_j_tlprs &lt;- scores_i_j[grepl(&#39;tlprs&#39;, scores_i_j$method),]
-      scores_i_j_tlprs$multi &lt;- scores_i_j_tlprs$method
+      score_header &lt;-
+        fread(scores_i_multi_single_pseudo$predictor[i], nrows = 1)
+      score_cols &lt;-
+        which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_multi_single_pseudo$name[i], &#39;_&#39;, param)))
       
-      scores_i_j_tlprs_pop&lt;-NULL
-      for(i in 1:nrow(scores_i_j_tlprs)){
-        score_header&lt;-fread(scores_i_j_tlprs$predictor[i], nrow = 1)
-        
-        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
-          score_cols &lt;- which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_&#39;), names(score_header)))
+      system(
+        paste0(
+          &quot;cut -d&#39; &#39; -f &quot;, 
+          paste0(score_cols, collapse=&#39;,&#39;),
+          &quot; &quot;, 
+          scores_i_multi_single_pseudo$predictor[i], 
+          &quot; &gt; &quot;, 
+          gsub(&#39;.profiles&#39;,
+               paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+               scores_i_multi_single_pseudo$predictor[i])
+        )
+      )
+    }
+    
+    scores_i_multi_single_pseudo$multi &lt;- paste0(scores_i_multi_single_pseudo$multi, &#39;.pseudo&#39;)
+
+    scores_i_multi_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                  paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                                  scores_i_multi_single_pseudo$predictor)
+    
+    scores_i_multi_single_pseudo$top1&lt;-paste(disc_pop_j_2, collapse = &#39;_&#39;)
+
+    ####
+    # Make groups for the Multi-Source methods
+    ####
+    
+    scores_i_multi &lt;- scores_i[(scores_i$method %in% pgs_group_methods),]
 
-          system(
-            paste0(
-              &quot;cut -d&#39; &#39; -f &quot;, 
-              paste0(score_cols, collapse=&#39;,&#39;),
-              &quot; &quot;, 
-              scores_i_j_tlprs$predictor[i], 
-              &quot; &gt; &quot;, 
-              gsub(&#39;.profiles&#39;,
-                   paste0(&#39;.targ_&#39;, pop, &#39;.profiles&#39;),
-                   scores_i_j_tlprs$predictor[i])
-            )
-          )
-          
-          tmp &lt;- scores_i_j_tlprs[i,]
-          tmp$multi &lt;- paste0(tmp$multi, &#39;.pop&#39;)
-          tmp$top1 &lt;- pop
-          tmp$predictor &lt;-
-              gsub(&#39;.profiles&#39;,
-                   paste0(&#39;.targ_&#39;, pop, &#39;.profiles&#39;),
-                   scores_i_j_tlprs$predictor[i])
-          
-          scores_i_j_tlprs_pop &lt;- rbind(scores_i_j_tlprs_pop, tmp)
+    # Split top1 scores by target population
+    # This doesn&#39;t apply to xwing because it only has pop-specific pseudo scores
+    scores_i_multi_top1&lt;-NULL
+    for(i in which(scores_i_multi$method %in% c(&#39;prscsx&#39;))){
+      score_header&lt;-fread(scores_i_multi$predictor[i], nrow = 1)
+      
+      pops &lt;- gsub(paste0(trait_i, &#39;_&#39;),&#39;&#39;, scores_i_multi$name[i])
+      pops &lt;- unlist(strsplit(pops, &#39;_&#39;))
+      pops &lt;- disc_pop_j_2[disc_pop_j %in% pops]
+
+      for(pop in pops){
+        
+        if(scores_i_multi$method[i] == &#39;prscsx&#39;){
+          score_cols &lt;-
+            which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi&#39;), names(score_header)))
+        }
+        if(scores_i_multi$method[i] == &#39;xwing&#39;){
+          score_cols &lt;-
+            which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst&#39;), names(score_header)))
         }
+        
+        system(
+          paste0(
+            &quot;cut -d&#39; &#39; -f &quot;, 
+            paste0(score_cols, collapse=&#39;,&#39;),
+            &quot; &quot;, 
+            scores_i_multi$predictor[i], 
+            &quot; &gt; &quot;, 
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
+                 scores_i_multi$predictor[i])
+          )
+        )
+        
+        tmp &lt;- scores_i_multi[i,]
+        tmp$multi &lt;- paste0(tmp$multi, &#39;.grid&#39;)
+        tmp$top1 &lt;- pop
+        tmp$predictor &lt;-
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
+                 scores_i_multi$predictor[i])
+        
+        scores_i_multi_top1 &lt;- rbind(scores_i_multi_top1, tmp)
       }
+    }
 
-      predictors_i&lt;- do.call(rbind, list(
-        scores_i_j_single, scores_i_j_tlprs_pop
-      ))
+    # Split pop-specific pseudo scores by target population
+    scores_i_multi_pop_pseudo&lt;-NULL
+    for(i in 1:nrow(scores_i_multi)){
+      score_header&lt;-fread(scores_i_multi$predictor[i], nrow = 1)
       
-      predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;top1&#39;,&#39;multi&#39;), with=F]
+      pops &lt;- gsub(paste0(trait_i, &#39;_&#39;),&#39;&#39;, scores_i_multi$name[i])
+      pops &lt;- unlist(strsplit(pops, &#39;_&#39;))
+      pops &lt;- disc_pop_j_2[disc_pop_j %in% pops]
+
+      for(pop in pops){
+        if(scores_i_multi$method[i] == &#39;prscsx&#39;){
+          score_cols &lt;-
+            which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi_auto&#39;), names(score_header)))
+        }
+        if(scores_i_multi$method[i] == &#39;xwing&#39;){
+          score_cols &lt;-
+            which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst_&#39;, pop), names(score_header)))
+        }
+        
+        system(
+          paste0(
+            &quot;cut -d&#39; &#39; -f &quot;, 
+            paste0(score_cols, collapse=&#39;,&#39;),
+            &quot; &quot;, 
+            scores_i_multi$predictor[i], 
+            &quot; &gt; &quot;, 
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
+                 scores_i_multi$predictor[i])
+          )
+        )
+        
+        tmp &lt;- scores_i_multi[i,]
+        tmp$multi &lt;- paste0(tmp$multi, &#39;.pop_pseudo&#39;)
+        tmp$top1 &lt;- pop
+        tmp$predictor &lt;-
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
+                 scores_i_multi$predictor[i])
+        
+        scores_i_multi_pop_pseudo &lt;- rbind(scores_i_multi_pop_pseudo, tmp)
+      }
+    }
+    
+    # Create pseudo score for multi-source methods
+    scores_i_multi_pseudo&lt;-NULL
+    for(i in 1:nrow(scores_i_multi)) {
+      param &lt;- find_pseudo(
+        config = ifelse(scores_i_multi$name[i] %in% scores_2_pop$name, config_2_pop, config_3_pop),
+        gwas = scores_i_multi$name[i],
+        pgs_method = scores_i_multi$method[i],
+        target_pop = targ_pop_i
+      )
       
-      write.table(
-        predictors_i,
+      score_header &lt;-
+        fread(scores_i_multi$predictor[i], nrows = 1)
+      score_cols &lt;-
+        which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_multi$name[i], &#39;_&#39;, param)))
+
+      system(
         paste0(
-          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_&#39;,
-          targ_pop_i,
-          &#39;.disc_EUR_&#39;,
-          disc_pop_j_2,
-          &#39;/&#39;,
-          trait_i,
-          &#39;/predictor_list.tlprs.txt&#39;
-        ),
-        col.names = T,
-        row.names = F,
-        quote = F
+          &quot;cut -d&#39; &#39; -f &quot;, 
+          paste0(score_cols, collapse=&#39;,&#39;),
+          &quot; &quot;, 
+          scores_i_multi$predictor[i], 
+          &quot; &gt; &quot;, 
+          gsub(&#39;.profiles&#39;,
+               paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
+               scores_i_multi$predictor[i])
+        )
       )
+      
+      tmp &lt;- scores_i_multi[i,]
+      tmp$multi &lt;- paste0(tmp$multi, &#39;.pseudo&#39;)
+      tmp$top1 &lt;- paste(disc_pop_j_2, collapse = &#39;_&#39;)
+      tmp$predictor &lt;-
+          gsub(&#39;.profiles&#39;,
+               paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
+               scores_i_multi$predictor[i])
+      
+      scores_i_multi_pseudo &lt;- rbind(scores_i_multi_pseudo, tmp)
     }
+    
+    ####
+    # Combine the different predictor groups
+    ####
+    predictors_i&lt;- do.call(rbind, list(
+      scores_i_single_top1, 
+      scores_i_single_pseudo, 
+      scores_i_multi_single_pseudo,
+      scores_i_multi_top1,
+      scores_i_multi_pop_pseudo,
+      scores_i_multi_pseudo
+    ))
+    
+    ####
+    # Make a group that will combined all population specific PGS
+    ####
+    
+    predictors_i_all &lt;- predictors_i[predictors_i$top1 %in% c(&#39;EUR&#39;,&#39;AFR&#39;,&#39;EAS&#39;),]
+    predictors_i_all$multi &lt;- &#39;all&#39;
+    predictors_i&lt;-rbind(predictors_i, predictors_i_all)
+    
+    ####
+    # Split into pairwise groups (2 pop groups)
+    ####
+    
+    afr_eur &lt;- predictors_i[!grepl(&#39;BBJ&#39;, predictors_i$name),]
+    afr_eur$multi &lt;- paste0(afr_eur$multi, &#39;.EUR_AFR&#39;)
+    afr_eur$top1[afr_eur$top1 == &#39;AFR_EAS_EUR&#39;] &lt;- &#39;AFR_EUR&#39;
+    
+    eas_eur &lt;- predictors_i[!grepl(&#39;UGR&#39;, predictors_i$name),]
+    eas_eur$multi &lt;- paste0(eas_eur$multi, &#39;.EUR_EAS&#39;)
+    eas_eur$top1[eas_eur$top1 == &#39;AFR_EAS_EUR&#39;] &lt;- &#39;EAS_EUR&#39;
+
+    one_or_three &lt;- predictors_i[!grepl(&#39;UKB_BBJ$&#39;, predictors_i$name) &amp;
+                                   !grepl(&#39;UKB_UGR$&#39;, predictors_i$name),]
+    
+    predictors_clean &lt;- do.call(rbind, list(
+      afr_eur, eas_eur, one_or_three
+    ))
+    predictors_clean &lt;- predictors_clean[, c(&#39;predictor&#39;, &#39;multi&#39;,&#39;top1&#39;), with=F]
+
+        
+    write.table(
+      predictors_clean,
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/targ_&#39;,
+        targ_pop_i,
+        &#39;.disc_&#39;, paste(disc_pop_j_2, collapse = &#39;_&#39;),
+        &#39;/&#39;,
+        trait_i,
+        &#39;/predictor_list.txt&#39;
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
   }
 }</code></pre>
-</details>
 <hr />
-</div>
-<div id="run-model_builder-3" class="section level2">
-<h2>Run model_builder</h2>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Run model_builder
+</h4>
 <pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
 conda activate model_builder
 
-#rm /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_*.disc_EUR_*/*/res*
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/targ_*.disc_AFR_EAS_EUR/*/res*
 
 for targ_pop in $(echo EAS AFR); do
   if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
@@ -5214,39 +10531,21 @@ <h2>Run model_builder</h2>
       targ_pop2=$targ_pop
   fi
   
-  if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
-    disc_pop=$(echo AFR EAS)
-  fi
-  
-  if [ &quot;$targ_pop&quot; == &quot;EAS&quot; ]; then
-    disc_pop=&quot;EAS&quot;
-  fi
-  
-  if [ &quot;$targ_pop&quot; == &quot;AFR&quot; ]; then
-    disc_pop=&quot;AFR&quot;
-  fi
-  
-  for disc_pop_i in ${disc_pop}; do
-    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
-      if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs.pred_comp.txt&quot; ]; then
-        sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
-          --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
-          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.tlprs.txt \
-          --out /users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.tlprs \
-          --n_core 5&quot;
-      fi
-    done
+  for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+    if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_AFR_EAS_EUR/${pheno}/res.pred_comp.txt&quot; ]; then
+      sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
+        --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_AFR_EAS_EUR/${pheno}/predictor_list.txt \
+        --out /users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_AFR_EAS_EUR/${pheno}/res \
+        --n_core 1&quot;
+    fi
   done
-done</code></pre>
-</details>
+done
+</code></pre>
 <hr />
-</div>
-<div id="plot-results-2" class="section level2">
-<h2>Plot results</h2>
-<details>
-<summary>
-Show code
-</summary>
+<h4>
+Plot results
+</h4>
 <pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 
 library(data.table)
@@ -5292,69 +10591,68 @@ <h2>Plot results</h2>
     if(targ_pop_i == &#39;AFR&#39;){
       disc_pop &lt;- &#39;AFR&#39;
     }
-    if(targ_pop_i == &#39;EUR&#39;){
-      disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
-    }
-    for(disc_pop_i in disc_pop){
-      eval_i &lt;-
-        fread(
-          paste0(
-            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/&#39;,
-            &#39;targ_&#39;,
-            targ_pop_i,
-            &#39;.disc_EUR_&#39;,
-            disc_pop_i,
-            &#39;/&#39;,
-            pheno_i,
-            &#39;/res.tlprs.pred_eval.txt&#39;
-          )
+    eval_i &lt;-
+      fread(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/&#39;,
+          &#39;targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_AFR_EAS_EUR/&#39;,
+          pheno_i,
+          &#39;/res.pred_eval.txt&#39;
         )
-      eval_i$Target&lt;-targ_pop_i
-      eval_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
-      res_eval_i&lt;-rbind(res_eval_i, eval_i)
-    }
+      )
+    eval_i$Target&lt;-targ_pop_i
+    eval_i$gwas_group &lt;- &#39;EUR+AFR+EAS&#39;
+    res_eval_i&lt;-rbind(res_eval_i, eval_i)
   }
   
   res_eval_i$Method&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_eval_i$Group)
   res_eval_i$Method&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_eval_i$Method)
   
   res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
-                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTrain&#39;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTune&#39;
   res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
-                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
   res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
-                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTrain&#39;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTune&#39;
   res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
-                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTrain&#39;
-  
-  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTrain&#39;
-  res_eval_i$Model[res_eval_i$Group == &#39;prscsx.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
-  res_eval_i$Model[res_eval_i$Group == &#39;xwing.pseudo.multi&#39;]&lt;-&#39;SumStatTrain&#39;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTune&#39;
   
+  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[grepl(&#39;prscsx.pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+
   res_eval_i$Source&lt;-ifelse(
     res_eval_i$Method %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_eval_i$Method) | 
-    !grepl(&#39;EUR|EAS|AFR&#39;, res_eval_i$Group), &#39;Multi&#39;, &#39;Single&#39;)
+    !grepl(&#39;-EUR|-EAS|-AFR&#39;, res_eval_i$Group), &#39;Multi&#39;, &#39;Single&#39;)
   
-  res_eval_i$Discovery[grepl(&#39;EUR&#39;, res_eval_i$Group)] &lt;- &#39;EUR&#39;
-  res_eval_i$Discovery[grepl(&#39;EAS&#39;, res_eval_i$Group)] &lt;- &#39;EAS&#39;
-  res_eval_i$Discovery[grepl(&#39;AFR&#39;, res_eval_i$Group)] &lt;- &#39;AFR&#39;
+  res_eval_i$Group &lt;- gsub(&#39;\\.multi&#39;, &#39;-multi&#39;, res_eval_i$Group)
+  res_eval_i$Group_short &lt;- gsub(&#39;.*\\.&#39;, &#39;&#39;, gsub(&#39;-.*&#39;, &#39;&#39;, res_eval_i$Group)) 
+  res_eval_i$n_gwas &lt;- 3
+  res_eval_i$n_gwas[grepl(&#39;EUR_&#39;, res_eval_i$Group_short)] &lt;- 2
+  res_eval_i$n_gwas[res_eval_i$Source == &#39;Single&#39;] &lt;- 1
+
+  res_eval_i$Discovery[grepl(&#39;-EUR&#39;, res_eval_i$Group)] &lt;- &#39;EUR&#39;
+  res_eval_i$Discovery[grepl(&#39;-EAS&#39;, res_eval_i$Group)] &lt;- &#39;EAS&#39;
+  res_eval_i$Discovery[grepl(&#39;-AFR&#39;, res_eval_i$Group)] &lt;- &#39;AFR&#39;
   res_eval_i$Discovery[res_eval_i$Source == &#39;Multi&#39;] &lt;- res_eval_i$gwas_group[res_eval_i$Source == &#39;Multi&#39;]
+  res_eval_i$Discovery[res_eval_i$n_gwas == 2] &lt;- gsub(&#39;_&#39;, &#39;+&#39;, res_eval_i$Group_short[res_eval_i$n_gwas == 2])
   
   res_eval_i$Method&lt;-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
-  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
-  res_eval_i$Discovery&lt;-factor(res_eval_i$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+  res_eval_i$Discovery&lt;-factor(res_eval_i$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;,&#39;EUR+AFR+EAS&#39;))
 
-  # Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
   res_eval_i &lt;- res_eval_i[
     !(res_eval_i$Method %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
-      res_eval_i$Model %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+      res_eval_i$Model %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
   
   # Remove pseudo model for methods that don&#39;t really have one 
   res_eval_i &lt;- res_eval_i[
     !(res_eval_i$Method %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
-      res_eval_i$Model %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+      res_eval_i$Model %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
 
-  # Remove top1 models for *-Multi, PRS-CSx, X-wing, TL-*
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
   res_eval_i &lt;- res_eval_i[
     !((res_eval_i$Method %in%  c(&#39;prscsx&#39;, &#39;xwing&#39;) | grepl(&#39;_multi$&#39;, res_eval_i$Method)) &amp; 
       grepl(&#39;top1&#39;, res_eval_i$Group)),]
@@ -5369,127 +10667,58 @@ <h2>Plot results</h2>
 }
 
 # Create vector defining or of methods in plots
-model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;, &quot;DBSLMM-multi&quot;, &quot;lassosum-multi&quot;, &quot;LDpred2-multi&quot;, &quot;MegaPRS-multi&quot;, &quot;PRS-CS-multi&quot;, &quot;pT+clump-multi&quot;, &quot;QuickPRS-multi&quot;, &quot;SBayesRC-multi&quot;,&quot;TL-DBSLMM&quot;,&quot;TL-LDpred2&quot;,&quot;TL-QuickPRS&quot;,&quot;TL-SBayesRC&quot;, &quot;PRS-CSx&quot;, &quot;X-Wing&quot;, &quot;All&quot;) 
-
-####
-# Average results across phenotypes
-####
-
-library(MAd)
+model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;, &quot;DBSLMM-multi&quot;, &quot;lassosum-multi&quot;, &quot;LDpred2-multi&quot;, &quot;MegaPRS-multi&quot;, &quot;PRS-CS-multi&quot;, &quot;pT+clump-multi&quot;, &quot;QuickPRS-multi&quot;, &quot;SBayesRC-multi&quot;, &quot;PRS-CSx&quot;, &quot;X-Wing&quot;, &quot;All&quot;) 
 
-# Average R across phenotypes
-meta_res_eval &lt;- NULL
-for(targ_pop_i in targ_pop){
-  if(targ_pop_i == &#39;EAS&#39;){
-    disc_pop &lt;- &#39;EAS&#39;
-  }
-  if(targ_pop_i == &#39;AFR&#39;){
-    disc_pop &lt;- &#39;AFR&#39;
-  }
-  if(targ_pop_i == &#39;EUR&#39;){
-    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
-  }
-  for(disc_pop_i in disc_pop){
-  
-    # Subset res_eval for each scenario
-    res_eval_i &lt;- do.call(rbind, lapply(seq_along(res_eval), function(i) {
-      x &lt;- res_eval[[i]]
-      x$pheno &lt;- names(res_eval)[i]
-      x &lt;- x[x$Target == targ_pop_i]
-      x &lt;- x[x$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
-    }))
-    
-    # Average res_evalults for each test across phenotypes
-    # Use MAd to account for correlation between them
-    res_eval_i$Sample&lt;-&#39;A&#39;
+res_eval_simp &lt;- NULL
+for(pheno_i in selected_traits){
+  tmp &lt;- res_eval[[pheno_i]]
+  tmp$Trait &lt;- pheno_i
   
-    for(group_i in unique(res_eval_i$Group)){
-      res_eval_group_i &lt;- res_eval_i[res_eval_i$Group == group_i,]
-      missing_pheno &lt;-
-        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
-      
-      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
-        print(paste0(
-          &#39;res_evalults missing for &#39;,
-          targ_pop_i,
-          &#39; &#39;,
-          group_i,
-          &#39; &#39;,
-          paste0(missing_pheno, collapse = &#39; &#39;)
-        ))
-      }
-      
-      cors_i &lt;- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
-      
-      meta_res_eval_i &lt;-
-        agg(
-          id = Sample,
-          es = R,
-          var = SE ^ 2,
-          cor = cors_i,
-          method = &quot;BHHR&quot;,
-          mod = NULL,
-          data = res_eval_group_i
-        )
-      
-      tmp &lt;- data.table(Group = group_i,
-                        Method = res_eval_group_i$Method[1],
-                        Model = res_eval_group_i$Model[1],
-                        Source = res_eval_group_i$Source[1],
-                        Discovery = res_eval_group_i$Discovery[1],
-                        gwas_group = res_eval_group_i$gwas_group[1],
-                        Target = targ_pop_i,
-                        R = meta_res_eval_i$es,
-                        SE = sqrt(meta_res_eval_i$var))
-      
-      meta_res_eval &lt;- rbind(meta_res_eval, tmp)
-    }
-  }
+  # Insert nice PGS method names
+  tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+  tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+  tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+  tmp$label &lt;- factor(tmp$label, levels = model_order)
+  
+  # Simplify result to either SumStatTune or IndivTune
+  tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+  tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+  tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery&#39;,&#39;Model&#39;), with=F]),]
+  
+  res_eval_simp &lt;- rbind(res_eval_simp, tmp)
 }
 
-meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
-meta_res_eval$Discovery&lt;-factor(meta_res_eval$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
-
-# Plot average performance across phenotypes for AFR and EAS targets
-tmp &lt;- meta_res_eval
-tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
-tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
-tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
-tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39; &amp; !grepl(&#39;^tlprs_&#39;, tmp$Method)] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39; &amp; !grepl(&#39;^tlprs_&#39;, tmp$Method)], &#39;-multi&#39;)
-tmp$label &lt;- factor(tmp$label, levels = model_order)
-tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
-tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
-tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
-tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
-tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
-                              levels = c(&#39;Target-matched GWAS&#39;,
-                                         &#39;EUR GWAS&#39;,
-                                         &#39;Both&#39;))
-tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
-tmp$Model[tmp$Model != &#39;SumStatTrain&#39;] &lt;- &#39;IndivTrain&#39;
-tmp$Model[tmp$Model == &#39;SumStatTrain&#39;] &lt;- &#39;SumStatTrain&#39;
-tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+# Plot results for each phenotype separately
+dir.create(&#39;~/oliverpainfel/Analyses/crosspop/plots_three_pop&#39;)
 
-dir.create(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots&#39;)
+for(pheno_i in selected_traits){
+  tmp &lt;- res_eval_simp[res_eval_simp$Trait == pheno_i,]
 
-# Plot unidirectional TL-PRS (as it was intended), comparing the unadjusted EUR PGS to the EUR PGS that has been adjusted according to the target-matched GWAS
-tmp_tlprs_uni &lt;- tmp[grepl(&#39;tlprs&#39;, tmp$Method) &amp; !grepl(&#39;pop-EUR.top1&#39;, tmp$Group) &amp; tmp$Source == &#39;Single&#39;, ]
-tmp_tlprs_uni$Type &lt;- &#39;TL-PRS&#39;
-tmp_unadj &lt;- tmp[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery == &#39;EUR&#39;, ]
-tmp_unadj$Type &lt;- &#39;Original&#39;
-tmp_both &lt;- rbind(tmp_unadj, tmp_tlprs_uni)
-tmp_both$label&lt;-gsub(&#39;TL-&#39;,&#39;&#39;,tmp_both$label)
-tmp_both$Type&lt;-factor(tmp_both$Type, levels = c(&#39;Original&#39;,&#39;TL-PRS&#39;))
+  # Remove single GWAS results
+  tmp &lt;- tmp[tmp$n_gwas != 1,]
+  
+  # Restrict to target matched + EUR and All GWAS
+  tmp &lt;- tmp[!(tmp$Target == &#39;AFR&#39; &amp; tmp$Discovery == &#39;EUR+EAS&#39;),]
+  tmp &lt;- tmp[!(tmp$Target == &#39;EAS&#39; &amp; tmp$Discovery == &#39;EUR+AFR&#39;),]
+  tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+  tmp$Discovery_clean[(tmp$Target == &#39;EAS&#39; &amp; tmp$Discovery == &#39;EUR+EAS&#39;)]&lt;-&#39;Target-matched + EUR GWAS&#39;
+  tmp$Discovery_clean[(tmp$Target == &#39;AFR&#39; &amp; tmp$Discovery == &#39;EUR+AFR&#39;)]&lt;-&#39;Target-matched + EUR GWAS&#39;
+  tmp$Discovery_clean[tmp$Discovery == &#39;EUR+AFR+EAS&#39;]&lt;-&#39;AFR + EAS + EUR GWAS&#39;
+  tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, levels = c(
+    &#39;Target-matched + EUR GWAS&#39;, &#39;AFR + EAS + EUR GWAS&#39;
+  ))
+  
+  tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
 
-png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.png&#39;), res=300, width = 2000, height = 1600, units = &#39;px&#39;)
-ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
+  png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots_three_pop/&#39;, pheno_i,&#39;.png&#39;), res=300, width = 3400, height = 2000, units = &#39;px&#39;)
+  plot_tmp&lt;-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
                   position = position_dodge(width = 1)) +
     geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
     geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
-    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
-    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    labs(y = &quot;R (SE)&quot;, x=NULL, fill = NULL, title = info_all$`Trait Description`[info_all$`Trait Label` == pheno_i]) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
     theme_half_open() +
     background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
     panel_border() + 
@@ -5497,25 +10726,115 @@ <h2>Plot results</h2>
           legend.position = &quot;top&quot;,
           legend.key.spacing.x = unit(1, &quot;cm&quot;),
           legend.justification = &quot;center&quot;)
-dev.off()
+  print(plot_tmp)
+  dev.off()
+}
 
-tmp_tlprs_uni &lt;- tmp[grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;pop-EUR.top1&#39;, tmp$Group) &amp; tmp$Source == &#39;Single&#39;, ]
-tmp_tlprs_uni$Type &lt;- &#39;TL-PRS&#39;
-tmp_unadj &lt;- tmp[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery == &#39;EUR&#39;, ]
-tmp_unadj$Type &lt;- &#39;Original&#39;
-tmp_both &lt;- rbind(tmp_unadj, tmp_tlprs_uni)
-tmp_both$label&lt;-gsub(&#39;TL-&#39;,&#39;&#39;,tmp_both$label)
-tmp_both$Type&lt;-factor(tmp_both$Type, levels = c(&#39;Original&#39;,&#39;TL-PRS&#39;))
+####
+# Average results across phenotypes
+####
 
-png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots/unidirectional_r.targ_EUR.png&#39;), res=300, width = 2000, height = 1600, units = &#39;px&#39;)
-ggplot(tmp_both, aes(x=label, y=R , fill = Type)) +
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval &lt;- NULL
+for(targ_pop_i in targ_pop){
+  # Subset res_eval for each scenario
+  res_eval_i &lt;- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+    x &lt;- res_eval[[i]]
+    x$pheno &lt;- names(res_eval)[i]
+    x &lt;- x[x$Target == targ_pop_i]
+  }))
+  
+  # Average res_evalults for each test across phenotypes
+  # Use MAd to account for correlation between them
+  res_eval_i$Sample&lt;-&#39;A&#39;
+
+  for(group_i in unique(res_eval_i$Group)){
+    res_eval_group_i &lt;- res_eval_i[res_eval_i$Group == group_i,]
+    missing_pheno &lt;-
+      colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+    
+    if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+      print(paste0(
+        &#39;res_evalults missing for &#39;,
+        targ_pop_i,
+        &#39; &#39;,
+        group_i,
+        &#39; &#39;,
+        paste0(missing_pheno, collapse = &#39; &#39;)
+      ))
+    }
+    
+    cors_i &lt;- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+    
+    meta_res_eval_i &lt;-
+      agg(
+        id = Sample,
+        es = R,
+        var = SE ^ 2,
+        cor = cors_i,
+        method = &quot;BHHR&quot;,
+        mod = NULL,
+        data = res_eval_group_i
+      )
+    
+    tmp &lt;- data.table(Group = group_i,
+                      Method = res_eval_group_i$Method[1],
+                      Model = res_eval_group_i$Model[1],
+                      Source = res_eval_group_i$Source[1],
+                      Discovery = res_eval_group_i$Discovery[1],
+                      gwas_group = res_eval_group_i$gwas_group[1],
+                      n_gwas = res_eval_group_i$n_gwas[1],
+                      Target = targ_pop_i,
+                      R = meta_res_eval_i$es,
+                      SE = sqrt(meta_res_eval_i$var))
+    
+    meta_res_eval &lt;- rbind(meta_res_eval, tmp)
+  }
+}
+
+meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+meta_res_eval$Discovery&lt;-factor(meta_res_eval$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;,&#39;EUR+AFR+EAS&#39;))
+
+write.csv(meta_res_eval, &#39;~/oliverpainfel/Analyses/crosspop/r_eval_three_pop.csv&#39;, row.names = F)
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery&#39;,&#39;Model&#39;), with=F]),]
+
+# Remove single GWAS results
+tmp &lt;- tmp[tmp$n_gwas != 1,]
+
+# Restrict to target matched + EUR and All GWAS
+tmp &lt;- tmp[!(tmp$Target == &#39;AFR&#39; &amp; tmp$Discovery == &#39;EUR+EAS&#39;),]
+tmp &lt;- tmp[!(tmp$Target == &#39;EAS&#39; &amp; tmp$Discovery == &#39;EUR+AFR&#39;),]
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[(tmp$Target == &#39;EAS&#39; &amp; tmp$Discovery == &#39;EUR+EAS&#39;)]&lt;-&#39;Target-matched + EUR GWAS&#39;
+tmp$Discovery_clean[(tmp$Target == &#39;AFR&#39; &amp; tmp$Discovery == &#39;EUR+AFR&#39;)]&lt;-&#39;Target-matched + EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR+AFR+EAS&#39;]&lt;-&#39;AFR + EAS + EUR GWAS&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, levels = c(
+  &#39;Target-matched + EUR GWAS&#39;, &#39;AFR + EAS + EUR GWAS&#39;
+))
+
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots_three_pop/average_r.png&#39;), res=300, width = 3200, height = 2000, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
                   position = position_dodge(width = 1)) +
     geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
     geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
     labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
-    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
     theme_half_open() +
     background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
     panel_border() + 
@@ -5523,369 +10842,887 @@ <h2>Plot results</h2>
           legend.position = &quot;top&quot;,
           legend.key.spacing.x = unit(1, &quot;cm&quot;),
           legend.justification = &quot;center&quot;)
-dev.off()
+dev.off()</code></pre>
+</details>
+<details>
+<summary>
+Show results
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/average_r_three_gwas.png"></p>
+</div>
+</div>
+</details>
+<hr />
+</div>
+</div>
+<div id="using-external-gwas-sumstats" class="section level2">
+<h2>Using external GWAS sumstats</h2>
+<p>Here we will use GWAS sumtats that were used in the original GenoPred
+paper. These GWAS are from a range of sources, often large
+meta-analyses, which can lead to greater mispecification in the
+sumstats, which can impact the performance of some PGS methods. This is
+to provide more confidence in the performance of SBayesRC and QuickPRS
+relative to other methods.</p>
+<hr />
+<div id="pgs-calculation-4" class="section level3">
+<h3>PGS calculation</h3>
+<p>We will do this using GenoPred.</p>
+<details>
+<summary>
+Show code
+</summary>
+<div class="shallow-break">
 
-# Have one column per method, but fill according to Original EUR PGS, Original matched-PGS, TL-PRS EUR Target, TL-PRS non-EUR Target, TL-PRS Multi, and Original-Multi
-tmp &lt;- meta_res_eval
-tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
-tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
-tmp$Type &lt;- NA
-tmp$Type[grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;pop-EUR.top1&#39;, tmp$Group)]&lt;-&quot;TL-PRS (EUR PGS tuned to target)&quot;
-tmp$Type[grepl(&#39;tlprs&#39;, tmp$Method) &amp; !grepl(&#39;pop-EUR.top1&#39;, tmp$Group) &amp; tmp$Source == &#39;Single&#39;]&lt;-&quot;TL-PRS (Target-matched PGS tuned to EUR)&quot;
-tmp$Type[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery == &#39;EUR&#39;]&lt;-&quot;Original (EUR PGS)&quot;
-tmp$Type[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;]&lt;-&quot;Original (Target-matched PGS)&quot;
-tmp$Type[grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;multi&#39;, tmp$Group)]&lt;-&quot;TL-PRS-multi&quot;
-tmp$Type[!grepl(&#39;tlprs&#39;, tmp$Method) &amp; grepl(&#39;multi&#39;, tmp$Group)]&lt;-&quot;Original-multi&quot;
-tmp &lt;- tmp[!is.na(tmp$Type),]
-tmp$Type&lt;-factor(tmp$Type, levels=c(&quot;Original (EUR PGS)&quot;, &quot;Original (Target-matched PGS)&quot;, &quot;TL-PRS (EUR PGS tuned to target)&quot;, &quot;TL-PRS (Target-matched PGS tuned to EUR)&quot;, &quot;Original-multi&quot;, &quot;TL-PRS-multi&quot;))
-tmp$label&lt;-gsub(&#39;TL-&#39;,&#39;&#39;,tmp$label)
+</div>
+<h4>
+Prepare configuration
+</h4>
+<p>We can use the gwas_list from the GenoPred pipeline paper. Just make
+the new configuration file.</p>
+<pre class="r"><code>######
+# gwas_list
+######
 
-png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png&#39;), res=300, width = 4000, height = 2200, units = &#39;px&#39;)
-ggplot(tmp, aes(x=label, y=R , fill = Type)) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
-    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
-    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
-    theme_half_open() +
-    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
-dev.off()
+library(data.table)
+gwas_list &lt;- fread(&#39;/scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/benchmark/gwas_list.txt&#39;)
 
-#########################
-# Check significance of differences between TL-PRS and unadjusted approaches
-########################
+gwas_list$path &lt;- gsub(&#39;/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/gwas_sumstats/&#39;,
+                       &#39;/users/k1806347/oliverpainfel/Data/GWAS_sumstats/genopred_pipeline_paper/&#39;,
+                       gwas_list$path)
 
-####
-# Create heatmap showing difference between all methods and models
-####
+gwas_list$label=paste0(&#39;&quot;&#39;, gwas_list$label, &#39;&quot;&#39;)
 
-# Create a function to mirror pred_comp results
-mirror_comp&lt;-function(x){
-  x_sym &lt;- x
-  x_sym$Model_1 &lt;- x$Model_2
-  x_sym$Model_2 &lt;- x$Model_1
-  x_sym$Model_1_R &lt;- x$Model_2_R
-  x_sym$Model_2_R &lt;- x$Model_1_R
-  x_sym$R_diff &lt;- -x_sym$R_diff
-  x_mirrored &lt;- rbind(x, x_sym)
-  x_diag&lt;-data.frame(
-      Model_1=unique(x_mirrored$Model_1),
-      Model_2=unique(x_mirrored$Model_1),
-      Model_1_R=x_mirrored$Model_1_R,
-      Model_2_R=x_mirrored$Model_1_R,
-      R_diff=NA,
-      R_diff_pval=NA
-    )
-  x_comp&lt;-rbind(x_mirrored, x_diag)
-  return(x_comp)
-}
-  
-# Read in results
-targ_pop=c(&#39;EAS&#39;,&#39;AFR&#39;)
-res_comp &lt;- list()
-for(pheno_i in selected_traits){
-  res_comp_i&lt;-NULL
-  for(targ_pop_i in targ_pop){
-    if(targ_pop_i == &#39;EAS&#39;){
-      disc_pop &lt;- &#39;EAS&#39;
-    }
-    if(targ_pop_i == &#39;AFR&#39;){
-      disc_pop &lt;- &#39;AFR&#39;
-    }
-    if(targ_pop_i == &#39;EUR&#39;){
-      disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
-    }
-    for(disc_pop_i in disc_pop){
-      comp_i &lt;-
-        fread(
-          paste0(
-            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/tlprs/&#39;,
-            &#39;targ_&#39;,
-            targ_pop_i,
-            &#39;.disc_EUR_&#39;,
-            disc_pop_i,
-            &#39;/&#39;,
-            pheno_i,
-            &#39;/res.tlprs.pred_comp.txt&#39;
-          )
-        )
-      comp_i&lt;-mirror_comp(comp_i)
-      comp_i$Target&lt;-targ_pop_i
-      comp_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
-      res_comp_i&lt;-rbind(res_comp_i, comp_i)
-    }
+write.table(gwas_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_meta.txt&#39;, col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_meta.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_meta.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&quot;,
+  &quot;pgs_methods: [&#39;quickprs&#39;,&#39;sbayesrc&#39;,&#39;ldpred2&#39;,&#39;ptclump&#39;,&#39;dbslmm&#39;]&quot;,
+#  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
+  &quot;cores_prep_pgs: 10&quot;,
+  &quot;cores_target_pgs: 50&quot;,
+  &quot;ldpred2_inference: F&quot;,
+  &quot;ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3&quot;,
+  &quot;quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3&quot;,
+  &quot;quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset&quot;,
+  &quot;sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_meta.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<hr />
+<h4>
+Run pipeline
+</h4>
+<pre class="bash"><code>snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_meta.yaml \
+  target_pgs -n</code></pre>
+</details>
+<hr />
+</div>
+<div id="pgs-evaluation-4" class="section level3">
+<h3>PGS evaluation</h3>
+<details>
+<summary>
+Show code
+</summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Create predictor list
+</h4>
+<pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+library(data.table)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_meta.yaml&#39;
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Read in gwas_list
+gwas_list&lt;-read_param(config = config, param = &#39;gwas_list&#39;)
+
+# Create column containing the phenotypes corresponding to each GWAS
+gwas_list$pheno&lt;-tolower(gwas_list$label)
+gwas_list$pheno[gwas_list$pheno == &#39;breast cancer&#39;]&lt;-&#39;bc&#39;
+gwas_list$pheno[gwas_list$pheno == &#39;prostate cancer&#39;]&lt;-&#39;pc&#39;
+gwas_list$pheno[gwas_list$pheno == &#39;egfr&#39;]&lt;-&#39;egfr,ckd&#39;
+gwas_list$pheno[gwas_list$pheno == &#39;urate levels&#39;]&lt;-&#39;urate,gout&#39;
+gwas_list$pheno[gwas_list$pheno == &#39;rheumatoid arthritis&#39;]&lt;-&#39;ra&#39;
+
+bin_phenos &lt;- c(&#39;bc&#39;, &#39;ckd&#39;, &#39;gout&#39;, &#39;ibd&#39;, &#39;pc&#39;, &#39;ra&#39;, &#39;stroke&#39;, &#39;t1d&#39;, &#39;t2d&#39;)
+con_phenos &lt;- c(&#39;height&#39;,&#39;bmi&#39;,&#39;egfr&#39;,&#39;hba1c&#39;,&#39;urate&#39;,&#39;hdl&#39;)
+phenos&lt;-c(bin_phenos, con_phenos)
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+# Create files for EUR target
+for(trait_i in phenos){
+  scores_i &lt;- scores[scores$name == gwas_list$name[grepl(paste0(&#39;^&#39;, trait_i, &#39;$&#39;,&#39;|&#39;, &#39;,&#39;, trait_i, &#39;$&#39;,&#39;|&#39;, &#39;^&#39;, trait_i, &#39;,&#39;), gwas_list$pheno)],]
+
+  dir.create(
+    paste0(
+      &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/&#39;, trait_i
+    ),
+    recursive = T
+  )
+  
+  scores_i$predictor &lt;- paste0(
+    outdir,
+    &#39;/ukb/pgs/TRANS/&#39;,
+    scores_i$method,
+    &#39;/&#39;,
+    scores_i$name,
+    &#39;/ukb-&#39;,
+    scores_i$name,
+    &#39;-TRANS.profiles&#39;
+  )
+  
+  scores_i$multi&lt;-scores_i$method
+  scores_i$top1 &lt;- &#39;EUR&#39;
+
+  # Extract the pseudo score for each method and specify as a separate group
+  for(i in 1:nrow(scores_i)) {
+    param &lt;- find_pseudo(
+      config = config,
+      gwas = scores_i$name[i],
+      pgs_method = scores_i$method[i],
+      target_pop = &#39;EUR&#39;
+    )
+    
+    score_header &lt;-
+      fread(scores_i$predictor[i], nrows = 1)
+    score_cols &lt;-
+      which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i$name[i], &#39;_&#39;, param)))
+    
+    system(
+      paste0(
+        &quot;cut -d&#39; &#39; -f &quot;, 
+        paste0(score_cols, collapse=&#39;,&#39;),
+        &quot; &quot;, 
+        scores_i$predictor[i], 
+        &quot; &gt; &quot;, 
+        gsub(&#39;.profiles&#39;,
+             paste0(&#39;.EUR_pseudo.profiles&#39;),
+             scores_i$predictor[i])
+      )
+    )
   }
   
-  res_comp[[pheno_i]]&lt;-res_comp_i
+  scores_i_pseudo &lt;- scores_i
+  scores_i_pseudo$multi &lt;- paste0(scores_i_pseudo$multi, &#39;.pseudo&#39;)
+
+  scores_i_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                paste0(&#39;.EUR_pseudo.profiles&#39;),
+                                scores_i_pseudo$predictor)
+
+
+  predictors_i&lt;- do.call(rbind, list(
+    scores_i, scores_i_pseudo
+  ))
+  
+  predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;top1&#39;,&#39;multi&#39;), with=F]
+  
+  write.table(
+    predictors_i,
+    paste0(
+      &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/&#39;, trait_i, &#39;/predictor_list.txt&#39;
+    ),
+    col.names = T,
+    row.names = F,
+    quote = F
+  )
 }
 
-res_comp_all &lt;- do.call(rbind, lapply(names(res_comp), function(name) {
-  x &lt;- res_comp[[name]]
-  x$pheno &lt;- name  # Add a new column with the name of the element
-  x  # Return the updated dataframe
-}))
+########
+# Prepare phenotype data
+########
 
-# Annotate tests to get order correct
-res_comp_all$Method1&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_1)
-res_comp_all$Method1&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method1)
-res_comp_all$Method2&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_2)
-res_comp_all$Method2&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method2)
+# Read in list of EUR in UKB
+eur_keep &lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/pcs/within_sample/ukb.outlier_detection.EUR.keep&#39;)
+names(eur_keep)&lt;-c(&#39;FID&#39;,&#39;IID&#39;)
 
-find_model&lt;-function(x){
-  mod &lt;- x
-  mod[grepl(&#39;top1$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;IndivTrain&#39;
-  mod[grepl(&#39;top1$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;SumStatTrain&#39;
-  mod[grepl(&#39;multi$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-IndivTrain&#39;
-  mod[grepl(&#39;multi$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-SumStatTrain&#39;
-  mod[grepl(&#39;_multi&#39;, x)] &lt;- &#39;SumStatTrain&#39;
-  mod[x == &#39;prscsx.pseudo.multi&#39;] &lt;- &#39;SumStatTrain&#39;
-  mod[x == &#39;xwing.pseudo.multi&#39;] &lt;- &#39;SumStatTrain&#39;
+# Update row number IDs to project specific IDs
+psam&lt;-fread(&#39;/scratch/prj/ukbiobank/recovered/ukb82087/imputed/ukb82087_imp_chr1_MAF1_INFO4_v1.psam&#39;)
+psam$rn&lt;-1:nrow(psam)
+psam&lt;-psam[,c(&#39;IID&#39;,&#39;rn&#39;), with = F]
+
+bin_phenos &lt;- c(&#39;bc&#39;, &#39;ckd&#39;, &#39;gout&#39;, &#39;ibd&#39;, &#39;pc&#39;, &#39;ra&#39;, &#39;stroke&#39;, &#39;t1d&#39;, &#39;t2d&#39;)
+con_phenos &lt;- c(&#39;height&#39;,&#39;bmi&#39;,&#39;egfr&#39;,&#39;hba1c&#39;,&#39;urate&#39;,&#39;hdl&#39;)
+phenos&lt;-c(bin_phenos, con_phenos)
+
+dir.create(&#39;~/oliverpainfel/Data/ukb/phenotypes/benchmark&#39;)
+
+for(i in phenos){
+  # Read in pheno data
+  pheno_i &lt;- fread(paste0(
+      &#39;/scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/phenotypes/&#39;,
+      i,
+      &#39;.unrel.txt&#39;
+    ))
   
-  return(mod)
+  names(pheno_i)&lt;-c(&#39;IID&#39;,&#39;PHENO&#39;)
+  
+  # Update to row number based IDs
+  pheno_i&lt;-merge(psam[,c(&#39;IID&#39;,&#39;rn&#39;), with=F], pheno_i, by=&#39;IID&#39;)
+  pheno_i$IID&lt;-pheno_i$rn
+  pheno_i$rn&lt;-NULL
+  pheno_i$FID&lt;-pheno_i$IID
+  pheno_i&lt;-pheno_i[, c(&#39;FID&#39;,&#39;IID&#39;,&#39;PHENO&#39;), with=F]
+  
+  # Restrict to EUR
+  pheno_i &lt;- pheno_i[pheno_i$FID %in% eur_keep$FID,]
+  
+  # Write file
+  write.table(pheno_i, paste0(&#39;~/oliverpainfel/Data/ukb/phenotypes/benchmark/&#39;, i, &#39;.unrel.eur.txt&#39;), row.names = F, quote = F)
+}</code></pre>
+<hr />
+<h4>
+Run model_builder
+</h4>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/*/res*
+
+for pheno in $(echo bc ckd gout ibd pc ra stroke t1d t2d height bmi egfr hba1c urate hdl); do
+  if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/${pheno}/res.pred_comp.txt&quot; ]; then
+    sbatch --mem 10G -n 5 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
+      --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/benchmark/${pheno}.unrel.eur.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/${pheno}/predictor_list.txt \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/${pheno}/res \
+      --n_core 5&quot;
+  fi
+done
+</code></pre>
+<hr />
+<h4>
+Plot results
+</h4>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+bin_phenos &lt;- c(&#39;bc&#39;, &#39;ckd&#39;, &#39;gout&#39;, &#39;ibd&#39;, &#39;pc&#39;, &#39;ra&#39;, &#39;stroke&#39;, &#39;t1d&#39;, &#39;t2d&#39;)
+con_phenos &lt;- c(&#39;height&#39;,&#39;bmi&#39;,&#39;egfr&#39;,&#39;hba1c&#39;,&#39;urate&#39;,&#39;hdl&#39;)
+phenos&lt;-c(bin_phenos, con_phenos)
+
+# Calculate correlation between all phenotypes in each target population
+pheno_pop_i &lt;- list()
+for(pheno_i in phenos){
+  pheno_pop_i[[pheno_i]] &lt;- fread(paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/benchmark/&#39;, pheno_i, &#39;.unrel.eur.txt&#39;))
+  names(pheno_pop_i[[pheno_i]])[3] &lt;- pheno_i
 }
 
-res_comp_all$Model1&lt;-find_model(res_comp_all$Model_1)
-res_comp_all$Model2&lt;-find_model(res_comp_all$Model_2)
+pheno_pop_i_merged &lt;- merged_df &lt;- Reduce(function(x, y) merge(x, y, all = TRUE, by = c(&#39;FID&#39;,&#39;IID&#39;)), pheno_pop_i)
 
-res_comp_all$Source1&lt;-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method1) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_1), &#39;Multi&#39;, &#39;Single&#39;)
-res_comp_all$Source2&lt;-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method2) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_2), &#39;Multi&#39;, &#39;Single&#39;)
+cors &lt;- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use=&#39;p&#39;))
+cors[is.na(cors)]&lt;-0
+
+# Read in results
+res_eval &lt;- list()
+for(pheno_i in phenos){
+  eval_i &lt;-
+    fread(
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/&#39;, pheno_i,&#39;/res.pred_eval.txt&#39;
+      )
+    )
+
+  eval_i$Ncase &lt;- NULL
+  eval_i$Ncont &lt;- NULL
+  eval_i$R2l &lt;- NULL
+  eval_i$R2o &lt;- NULL
+  
+  eval_i &lt;- eval_i[!grepl(&#39;\\.multi&#39;, eval_i$Group),]
+  
+  eval_i$Method&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,eval_i$Group)
+  eval_i$Method&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, eval_i$Method)
+  
+  eval_i$Model[grepl(&#39;top1$&#39;, eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, eval_i$Group)]&lt;-&#39;IndivTune&#39;
+  eval_i$Model[grepl(&#39;top1$&#39;, eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+
+  eval_i$Method&lt;-factor(eval_i$Method, levels=unique(eval_i$Method))
+  eval_i$Model&lt;-factor(eval_i$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;))
+
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  eval_i &lt;- eval_i[
+    !(eval_i$Method %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+      eval_i$Model %in% c(&#39;IndivTune&#39;)),]
+  
+  # Remove pseudo model for methods that don&#39;t really have one 
+  eval_i &lt;- eval_i[
+    !(eval_i$Method %in%  c(&#39;ptclump&#39;) &amp; 
+      eval_i$Model %in% c(&#39;SumStatTune&#39;)),]
+  
+  res_eval[[pheno_i]]&lt;-eval_i
   
-for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)){
-  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] &lt;- i
-  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] &lt;- i
 }
-res_comp_all$Discovery1[res_comp_all$Source1 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source1 == &#39;Multi&#39;]
-res_comp_all$Discovery2[res_comp_all$Source2 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source2 == &#39;Multi&#39;]
 
-res_comp_all$Method1&lt;-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
-res_comp_all$Method2&lt;-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
-res_comp_all$Model1&lt;-factor(res_comp_all$Model1, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
-res_comp_all$Model2&lt;-factor(res_comp_all$Model2, levels=c(&#39;IndivTrain&#39;,&#39;SumStatTrain&#39;,&#39;Multi-IndivTrain&#39;,&#39;Multi-SumStatTrain&#39;))
-res_comp_all$Discovery1&lt;-factor(res_comp_all$Discovery1, levels=rev(c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;)))
-res_comp_all$Discovery2&lt;-factor(res_comp_all$Discovery2, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+# Create vector defining or of methods in plots
+model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;) 
 
-# Remove IndivTrain and Multi-IndivTrain model for groups that contain one score (aka QuickPRS and SBayesRC)
-res_comp_all &lt;- res_comp_all[
-!(res_comp_all$Method1 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
-  res_comp_all$Model1 %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
-res_comp_all &lt;- res_comp_all[
-!(res_comp_all$Method2 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
-  res_comp_all$Model2 %in% c(&#39;IndivTrain&#39;,&#39;Multi-IndivTrain&#39;)),]
+res_eval_simp &lt;- NULL
+for(pheno_i in phenos){
+  tmp &lt;- res_eval[[pheno_i]]
+  tmp$Trait &lt;- pheno_i
+  
+  # Insert nice PGS method names
+  tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+  tmp$label &lt;- factor(tmp$label, levels = model_order)
 
-# Remove pseudo model for methods that don&#39;t really have one 
-res_comp_all &lt;- res_comp_all[
-!(res_comp_all$Method1 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
-  res_comp_all$Model1 %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
-res_comp_all &lt;- res_comp_all[
-!(res_comp_all$Method2 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
-  res_comp_all$Model2 %in% c(&#39;SumStatTrain&#39;,&#39;Multi-SumStatTrain&#39;)),]
+  res_eval_simp &lt;- rbind(res_eval_simp, tmp)
+}
 
-# Remove top1 models for PRS-CSx
-res_comp_all &lt;- res_comp_all[
-!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method1) &amp; 
-  grepl(&#39;top1&#39;, res_comp_all$Model_1)),]
-res_comp_all &lt;- res_comp_all[
-!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method2) &amp; 
-  grepl(&#39;top1&#39;, res_comp_all$Model_2)),]
+# Plot results for each phenotype separately
+dir.create(&#39;~/oliverpainfel/Analyses/crosspop/plots_meta&#39;)
 
-# Remove any comparisons
-res_comp_all &lt;- res_comp_all[!duplicated(res_comp_all[, c(&quot;Target&quot;, &quot;Method1&quot;, &quot;Model1&quot;, &quot;Source1&quot;, &quot;Discovery1&quot;, &quot;Method2&quot;, &quot;Model2&quot;, &quot;Source2&quot;, &quot;Discovery2&quot;,&#39;pheno&#39;)]),]
+ggplot(res_eval_simp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=NULL, fill = NULL) +
+    facet_wrap(Trait ~ ., scales = &#39;free_y&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
 
-###########
+####
+# Average results across phenotypes
+####
 
 library(MAd)
 
 # Average R across phenotypes
-meta_res_comp &lt;- NULL
-for(targ_pop_i in targ_pop){
-  if(targ_pop_i == &#39;EAS&#39;){
-    disc_pop &lt;- &#39;EAS&#39;
-  }
-  if(targ_pop_i == &#39;AFR&#39;){
-    disc_pop &lt;- &#39;AFR&#39;
-  }
-  if(targ_pop_i == &#39;EUR&#39;){
-    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+meta_res_eval &lt;- NULL
+
+# Average res_evalults for each test across phenotypes
+# Use MAd to account for correlation between them
+res_eval_simp$Sample&lt;-&#39;A&#39;
+
+for(group_i in unique(res_eval_simp$Group)){
+  res_eval_group_i &lt;- res_eval_simp[res_eval_simp$Group == group_i,]
+  missing_pheno &lt;- colnames(cors)[!(colnames(cors) %in% unique(res_eval_simp$Trait))]
+  
+  if (!all(colnames(cors) %in% unique(res_eval_simp$Trait))) {
+    print(paste0(
+      &#39;res_evalults missing for &#39;,
+      targ_pop_i,
+      &#39; &#39;,
+      group_i,
+      &#39; &#39;,
+      paste0(missing_pheno, collapse = &#39; &#39;)
+    ))
   }
-  for(disc_pop_i in disc_pop){
   
-    # Subset res_comp for each scenario
-    res_comp_i &lt;- res_comp_all[res_comp_all$Target == targ_pop_i &amp; res_comp_all$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+  cors_i &lt;- cors[unique(res_eval_group_i$Trait), unique(res_eval_group_i$Trait)]
   
-    # Calculate diff SE based on p-value
-    res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] &lt;- 1-0.001
-    res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]&lt;-1-0.001
-    res_comp_i$R_diff_z&lt;-qnorm(res_comp_i$R_diff_pval/2)
-    res_comp_i$R_diff_SE&lt;-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
-        
-    # Average results for each test across phenotypes
-    # Use MAd to account for correlation between them
-    res_comp_i$Sample&lt;-&#39;A&#39;
-    res_comp_i$Group &lt;- paste0(res_comp_i$Model_1, &#39;_vs_&#39;, res_comp_i$Model_2)
+  meta_res_eval_simp &lt;-
+    agg(
+      id = Sample,
+      es = R,
+      var = SE ^ 2,
+      cor = cors_i,
+      method = &quot;BHHR&quot;,
+      mod = NULL,
+      data = res_eval_group_i
+    )
   
-    for(group_i in unique(res_comp_i$Group)){
-      res_comp_group_i &lt;- res_comp_i[res_comp_i$Group == group_i,]
-      cors_i &lt;- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
-      
-      if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
-        
-        meta_res_comp_i &lt;-
-          agg(
-            id = Sample,
-            es = R_diff,
-            var = R_diff_SE ^ 2,
-            cor = cors_i,
-            method = &quot;BHHR&quot;,
-            mod = NULL,
-            data = res_comp_group_i
-          )
-        
-        tmp &lt;- res_comp_group_i[1,]
-        tmp$pheno &lt;- NULL
-        tmp$Model_1_R &lt;-
-          meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &amp;
-                            meta_res_eval$Target == targ_pop_i &amp;
-                            meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
-        tmp$Model_2_R &lt;-
-          meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &amp;
-                            meta_res_eval$Target == targ_pop_i &amp;
-                            meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
-        tmp$R_diff &lt;- meta_res_comp_i$es
-        tmp$R_diff_SE &lt;- sqrt(meta_res_comp_i$var)
-        tmp$R_diff_z &lt;- tmp$R_diff / tmp$R_diff_SE
-        tmp$R_diff_p &lt;- 2*pnorm(-abs(tmp$R_diff_z))
-      } else {
-        tmp &lt;- res_comp_group_i[1,]
-        tmp$pheno &lt;- NULL
-        tmp$R_diff &lt;- NA
-        tmp$R_diff_SE &lt;- NA
-        tmp$R_diff_z &lt;- NA
-        tmp$R_diff_p &lt;- NA
-      }
-      meta_res_comp &lt;- rbind(meta_res_comp, tmp)
-    }
+  tmp &lt;- data.table(Group = group_i,
+                    Method = res_eval_group_i$Method[1],
+                    Model = res_eval_group_i$Model[1],
+                    R = meta_res_eval_simp$es,
+                    SE = sqrt(meta_res_eval_simp$var))
+  
+  meta_res_eval &lt;- rbind(meta_res_eval, tmp)
+}
+
+meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;))
+
+write.csv(meta_res_eval, &#39;~/oliverpainfel/Analyses/crosspop/meta/r_eval.csv&#39;, row.names = F)
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp &lt;- meta_res_eval
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots_meta/average_r.png&#39;), res=100, width = 500, height = 300, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+# Results and conclusions remain consistent.</code></pre>
+</details>
+<details>
+<summary>
+Show results
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 50%;">
+<p><img src="Images/CrossPop_2025/average_r_meta.png"></p>
+</div>
+</div>
+</details>
+<hr />
+</div>
+</div>
+<div id="run-methods-using-downsampled-ukb-sumstats"
+class="section level2">
+<h2>Run methods using downsampled UKB sumstats</h2>
+<p>It seems the performance of methods varies across EAS and AFR
+datasets. This could be due to the difference in sample size. To explore
+this, lets run the methods on EUR GWAS generated using UKB, using a
+range of sample sizes.</p>
+<hr />
+<div id="downsample-gwas" class="section level3">
+<h3>Downsample GWAS</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+
+# Read in phenotype file
+subsample_n&lt;-c(5, 15, 45, 135)
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+set.seed(1)
+
+dir.create(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/subsample&#39;)
+for(i in selected_traits){
+  pheno_i_dat &lt;- fread(
+    paste0(
+      &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;,
+      i,
+      &#39;.unrel.EUR_train.norm_resid_scale.row_number.txt&#39;
+    )
+  )
+  
+  for(n in subsample_n){
+    tmp &lt;- pheno_i_dat[sample(1:nrow(pheno_i_dat), size = n*1000),]
+    fwrite(tmp, paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/subsample/&#39;, i, &#39;.&#39;,n,&#39;.txt&#39;),
+           sep=&#39; &#39;,
+           na=&#39;NA&#39;,
+           quote=F)
   }
-}
+} </code></pre>
+<pre class="bash"><code>for pheno in $(echo TC HT HB); do
+  for n in $(echo 5 15 45 135); do
+    mkdir -p /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled
+    for chr in $(seq 1 22); do
+      if [ ! -f &quot;/users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}.outcome.glm.linear&quot; ]; then
+        sbatch -p interruptible_cpu --wrap=&quot;/users/k1806347/oliverpainfel/Software/plink2 \
+          --pfile /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
+          --pheno /users/k1806347/oliverpainfel/Data/ukb/phenotypes/subsample/${pheno}.${n}.txt \
+          --linear omit-ref cols=+a1freq,+ax \
+          --maf 0.01 \
+          --geno 0.05 \
+          --out /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}&quot;
+      fi
+    done
+  done
+done
 
-meta_res_comp$R_diff_perc &lt;- meta_res_comp$R_diff / meta_res_comp$Model_2_R
+# Once complete, merge results across chromosomes
+for pheno in $(echo TC HT HB); do
+  for n in $(echo 5 15 45 135); do
+    head -n 1 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr1.outcome.glm.linear &gt; /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
+      for chr in $(seq 1 22); do
+        tail -n +2 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}.outcome.glm.linear &gt;&gt; /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
+      done
+      
+      # Remove REF and ALT columns and rename AX column to A2
+      cut -f 4,5 --complement /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt | awk &#39;BEGIN{FS=OFS=&quot;\t&quot;} NR==1 {$5=&quot;A2&quot;} 1&#39; &gt; temp.txt &amp;&amp; mv temp.txt /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
   
-# Compare IndivTrain SBayesRC-multi to TL-SBayesRC-multi
-tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_2 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
-                                meta_res_comp$Model_1 == &#39;tlprs_sbayesrc.pop.multi&#39; &amp;
-                    meta_res_comp$Target == &#39;AFR&#39;,]
-round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
-tmp_sbayesrc$R_diff_p
+      gzip /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
+  done
+done
 
-tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_2 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
-                                meta_res_comp$Model_1 == &#39;tlprs_sbayesrc.pop.multi&#39; &amp;
-                    meta_res_comp$Target == &#39;EAS&#39;,]
-round(min(tmp_sbayesrc$R_diff_perc)*100, 1)
-tmp_sbayesrc$R_diff_p</code></pre>
+# Delete per chromosome files
+rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*_subsampled/*chr*
+</code></pre>
 </details>
 <hr />
 </div>
-<div id="computational-resoures-1" class="section level2">
-<h2>Computational resoures</h2>
+<div id="pgs-calculation-5" class="section level3">
+<h3>PGS calculation</h3>
 <details>
 <summary>
 Show code
 </summary>
-<pre class="r"><code>library(data.table)
-library(ggplot2)
-library(cowplot)
+<div class="shallow-break">
 
-setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+</div>
+<h4>
+Prepare configuration
+</h4>
+<pre class="r"><code>######
+# gwas_list
+######
+
+gwas_list&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt&#39;)
+pheno&lt;-gsub(&#39;_.*&#39;,&#39;&#39;, gwas_list$name)
+gwas_list&lt;-gwas_list[pheno %in% c(&#39;TC&#39;, &#39;HT&#39;, &#39;HB&#39;),]
+gwas_list&lt;-gwas_list[gwas_list$population == &#39;EUR&#39;,]
+gwas_list$pheno&lt;-gsub(&#39;_UKB&#39;,&#39;&#39;,gwas_list$name)
+
+gwas_list_subsampled &lt;- NULL
+for(n in c(5, 15, 45, 135)){
+  gwas_list_tmp&lt;-gwas_list
+  
+  gwas_list_tmp$name &lt;-
+    paste0(
+      gwas_list_tmp$name, &#39;_&#39;, n, &#39;K&#39;
+    )
+  gwas_list_tmp$path &lt;-
+    paste0(
+      &#39;/users/k1806347/oliverpainfel/Data/ukb/gwas/&#39;,
+      gwas_list_tmp$pheno,
+      &#39;_subsampled/ukb.eur_train.&#39;,
+      gwas_list_tmp$pheno,
+      &#39;.&#39;,
+      n,
+      &#39;.GW.txt.gz&#39;
+    )
+  gwas_list_tmp$label &lt;-
+    paste0(
+      gsub(&quot;\\)&quot;, paste0(&#39; - &#39;, n,&quot;K)&quot;), gwas_list_tmp$label)
+    )
+  
+  gwas_list_subsampled &lt;- rbind(gwas_list_subsampled, gwas_list_tmp)
+}
+
+gwas_list_subsampled$pheno&lt;-NULL
+
+gwas_list_subsampled$label&lt;-paste0(&#39;&quot;&#39;, gwas_list_subsampled$label, &#39;&quot;&#39;)
+
+write.table(gwas_list_subsampled, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt&#39;, col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&quot;,
+  &quot;pgs_methods: [&#39;quickprs&#39;,&#39;sbayesrc&#39;,&#39;ptclump&#39;,&#39;ldpred2&#39;]&quot;,
+#  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
+  &quot;cores_prep_pgs: 10&quot;,
+  &quot;cores_target_pgs: 50&quot;,
+  &quot;ldpred2_inference: F&quot;,
+  &quot;ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3&quot;,
+  &quot;quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3&quot;,
+  &quot;quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset&quot;,
+  &quot;sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<hr />
+<h4>
+Run pipeline
+</h4>
+<pre class="bash"><code>snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml \
+  target_pgs -n</code></pre>
+</details>
+<hr />
+</div>
+<div id="pgs-evaluation-5" class="section level3">
+<h3>PGS evaluation</h3>
+<details>
+<summary>
+Show code
+</summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Create predictor list
+</h4>
+<pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 source(&#39;../functions/misc.R&#39;)
 source_all(&#39;../functions&#39;)
+library(data.table)
 
 # Get some key variables from config
-config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml&#39;
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml&#39;
 pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
 outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
 
-# Read in configuration specific benchmark files
-bm_files_i &lt;- list.files(paste0(outdir, &#39;/reference/benchmarks/&#39;), full.names = T)
+# Read in list of outcomes 
+selected_traits&lt;-c(&#39;TC&#39;, &#39;HT&#39;, &#39;HB&#39;)
 
-# Subset benchmarks for pgs_methods
-bm_files_i &lt;- bm_files_i[grepl(&#39;prep_pgs_tlprs&#39;, bm_files_i)]
+# Get a list of score files
+scores &lt;- list_score_files(config)
 
-# Read in benchmark files
-bm_dat_all &lt;- do.call(rbind, lapply(bm_files_i, function(file) {
-  tmp &lt;- fread(file)
-  tmp$file &lt;- basename(file)
-  return(tmp)
-}))
+# Create files for EAS and AFR targets
+for(trait_i in selected_traits){
+  
+  dir.create(
+    paste0(
+      &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/&#39;, trait_i
+    ),
+    recursive = T
+  )
+  
+  scores_i &lt;- scores[grepl(paste0(&#39;^&#39;, trait_i,&#39;_&#39;), scores$name),]
+
+  scores_i$predictor &lt;- paste0(
+    outdir,
+    &#39;/ukb/pgs/TRANS/&#39;,
+    scores_i$method,
+    &#39;/&#39;,
+    scores_i$name,
+    &#39;/ukb-&#39;,
+    scores_i$name,
+    &#39;-TRANS.profiles&#39;
+  )
+  
+  scores_i$top1 &lt;- paste0(scores_i$method,&#39;.&#39;,gsub(&#39;.*_&#39;, &#39;&#39;, scores_i$name))
+
+  ####
+  # Make groups containing pseudo scores for single source methods
+  ####
+
+  # Extract the pseudo score for each method and specify as a separate group
+  # This can be skipped as it was done before
+  for(i in 1:nrow(scores_i)) {
+    param &lt;- find_pseudo(
+      config = config,
+      gwas = scores_i$name[i],
+      pgs_method = scores_i$method[i],
+      target_pop = &#39;EUR&#39;
+    )
+    
+    score_header &lt;-
+      fread(scores_i$predictor[i], nrows = 1)
+    score_cols &lt;-
+      which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i$name[i], &#39;_&#39;, param)))
+    
+    system(
+      paste0(
+        &quot;cut -d&#39; &#39; -f &quot;, 
+        paste0(score_cols, collapse=&#39;,&#39;),
+        &quot; &quot;, 
+        scores_i$predictor[i], 
+        &quot; &gt; &quot;, 
+        gsub(&#39;.profiles&#39;,
+             paste0(&#39;.EUR_pseudo.profiles&#39;),
+             scores_i$predictor[i])
+      )
+    )
+  }
+  
+  scores_i_pseudo &lt;- scores_i
+  scores_i_pseudo$top1&lt;-paste0(scores_i_pseudo$top1,&#39;.pseudo&#39;)
 
-# Create rule column
-bm_dat_all$rule &lt;- gsub(&#39;-.*&#39;,&#39;&#39;,bm_dat_all$file)
+  scores_i_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                paste0(&#39;.EUR_pseudo.profiles&#39;),
+                                scores_i_pseudo$predictor)
 
-# Create method column
-bm_dat_all$method &lt;-
-  gsub(&#39;_i&#39;, &#39;&#39;, gsub(&#39;prep_pgs_&#39;, &#39;&#39;, bm_dat_all$rule))
+  
+  ####
+  # Combine the different predictor groups
+  ####
+  predictors_i&lt;- do.call(rbind, list(
+    scores_i, 
+    scores_i_pseudo
+  ))
+  predictors_i$multi &lt;- &#39;All&#39;
+  predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;multi&#39;,&#39;top1&#39;), with=F]
+      
+  write.table(
+    predictors_i,
+    paste0(
+      &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/&#39;, trait_i, &#39;/predictor_list.txt&#39;
+    ),
+    col.names = T,
+    row.names = F,
+    quote = F
+  )
+}</code></pre>
+<hr />
+<h4>
+Run model_builder
+</h4>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
 
-#############
-# Time
-#############
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/*/res*
 
-# Calculate average time taken for each method
-method_avg &lt;- NULL
-for(i in unique(bm_dat_all$method)){
-  method_avg &lt;- rbind(
-    method_avg,
-    data.frame(
-      Method = i,
-      Time = mean(bm_dat_all$s[bm_dat_all$method == i])
-    )
-  )
-}
+for pheno in $(echo TC HT HB); do
+  if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/res.pred_comp.txt&quot; ]; then
+    sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
+      --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.EUR_test.row_number.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/predictor_list.txt \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/res \
+      --n_core 1&quot;
+  fi
+done
+</code></pre>
+<hr />
+<h4>
+Plot results
+</h4>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 
-# Convert time in seconds to hours
-method_avg$Time_hour &lt;- method_avg$Time / 60/60
-method_avg$Time_hour &lt;- round(method_avg$Time_hour, 2)
+library(data.table)
+library(ggplot2)
+library(cowplot)
 
-#This is for bidirectional TL-PRS
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
 
-#############
-# Memory
-#############
+phenos&lt;-c(&#39;TC&#39;, &#39;HT&#39;, &#39;HB&#39;)
 
-# Calculate average max_rss for each method
-method_avg_mem &lt;- NULL
-for(i in unique(bm_dat_all$method)){
-  method_avg_mem &lt;- rbind(
-    method_avg_mem,
-    data.frame(
-      Method = i,
-      Memory = mean(bm_dat_all$max_rss[bm_dat_all$method == i])
+# Calculate correlation between all phenotypes in each target population
+pheno_pop_i &lt;- list()
+for(pheno_i in phenos){
+  pheno_pop_i[[pheno_i]] &lt;- fread(paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;, pheno_i, &#39;.unrel.EUR_test.row_number.txt&#39;))
+  names(pheno_pop_i[[pheno_i]])[3] &lt;- pheno_i
+}
+
+pheno_pop_i_merged &lt;- merged_df &lt;- Reduce(function(x, y) merge(x, y, all = TRUE, by = c(&#39;FID&#39;,&#39;IID&#39;)), pheno_pop_i)
+
+cors &lt;- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use=&#39;p&#39;))
+
+# Read in results
+res_eval &lt;- list()
+for(pheno_i in phenos){
+  eval_i &lt;-
+    fread(
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/&#39;, pheno_i,&#39;/res.pred_eval.txt&#39;
+      )
     )
-  )
+
+  eval_i$Ncase &lt;- NULL
+  eval_i$Ncont &lt;- NULL
+  eval_i$R2l &lt;- NULL
+  eval_i$R2o &lt;- NULL
+  
+  eval_i &lt;- eval_i[!grepl(&#39;\\.multi&#39;, eval_i$Group),]
+  
+  eval_i$Method&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,eval_i$Group)
+  eval_i$Method&lt;-gsub(&#39;.*-&#39;,&#39;&#39;, eval_i$Method)
+  
+  eval_i$GWAS_N &lt;- gsub(&#39;K\\..*&#39;,&#39;&#39;,eval_i$Group)
+  eval_i$GWAS_N &lt;- gsub(&#39;.*\\.&#39;,&#39;&#39;,eval_i$GWAS_N)
+  eval_i$GWAS_N &lt;- paste0(eval_i$GWAS_N,&#39;K&#39;)
+  
+  eval_i$Model[grepl(&#39;top1$&#39;, eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, eval_i$Group)]&lt;-&#39;IndivTune&#39;
+  eval_i$Model[grepl(&#39;top1$&#39;, eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+
+  eval_i$Method&lt;-factor(eval_i$Method, levels=unique(eval_i$Method))
+  eval_i$Model&lt;-factor(eval_i$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;))
+
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  eval_i &lt;- eval_i[
+    !(eval_i$Method %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+      eval_i$Model %in% c(&#39;IndivTune&#39;)),]
+  
+  # Remove pseudo model for methods that don&#39;t really have one 
+  eval_i &lt;- eval_i[
+    !(eval_i$Method %in%  c(&#39;ptclump&#39;) &amp; 
+      eval_i$Model %in% c(&#39;SumStatTune&#39;)),]
+  
+  res_eval[[pheno_i]]&lt;-eval_i
+  
 }
 
-# Format the Memory nicely
-method_avg_mem$Memory_clean &lt;-
-  paste0(round(method_avg_mem$Memory/1000, 2), &#39; Gb&#39;)
+# Create vector defining or of methods in plots
+model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;) 
 
-ggplot(method_avg_mem, aes(x = Method, y = Memory, fill = Method)) +
-  geom_bar(stat = &quot;identity&quot;, position=&quot;dodge&quot;) +
-  geom_text(aes(label = Memory_clean), vjust = -0.5, position = position_dodge(width = 0.9)) +
-  labs(x = &quot;PGS Method&quot;, y = &quot;Memory (Mb)&quot;) +
-  theme_half_open() +
-  background_grid() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position=&quot;none&quot;)
+res_eval_simp &lt;- NULL
+for(pheno_i in phenos){
+  tmp &lt;- res_eval[[pheno_i]]
+  tmp$Trait &lt;- pheno_i
+  
+  # Insert nice PGS method names
+  tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+  tmp$label &lt;- factor(tmp$label, levels = model_order)
 
-method_avg_mem$Memory_gb &lt;- method_avg_mem$Memory/1000
-method_avg_mem &lt;- method_avg_mem[, c(&#39;Method&#39;,&#39;Memory_gb&#39;)]
-method_avg_mem$Memory_gb &lt;- round(method_avg_mem$Memory_gb, 2)
-names(method_avg_mem)&lt;-c(&#39;Method&#39;,&quot;Memory (Gb)&quot;)
+  res_eval_simp &lt;- rbind(res_eval_simp, tmp)
+}
 
-method_avg&lt;-merge(method_avg, method_avg_mem, by = &#39;Method&#39;)
+# Plot results for each phenotype separately
+dir.create(&#39;~/oliverpainfel/Analyses/crosspop/plots_downsample&#39;)
 
-write.csv(method_avg, &#39;~/oliverpainfel/Analyses/crosspop/time_memory.csv&#39;, row.names=F)</code></pre>
+tmp&lt;-res_eval_simp
+tmp$GWAS_N &lt;- paste0(&#39;GWAS N = &#39;, tmp$GWAS_N)
+tmp$GWAS_N &lt;-factor(tmp$GWAS_N, levels = unique(tmp$GWAS_N))
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png&#39;), res=100, width = 1000, height = 600, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=NULL, fill = NULL) +
+    facet_grid(Trait ~ GWAS_N, scales = &#39;free_y&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()</code></pre>
+<details>
+<summary>
+Show results
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 50%;">
+<p><img src="Images/CrossPop_2025/average_r_downsample.png"></p>
+</div>
+</div>
 </details>
+<hr />
+</div>
 </div>
 </div>
 
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z2}gE(JRla|`iDr8W?VD~uC#|2?oKhrg;>zj|GNC$i`!9*%LK7ZG#X?9wXGvs8seEx
zAg1dd7{GZJ=)a24R7IraZZ`h)d74An%l@KnNIAh`f6)7e;BLw?_!{ura<H`i&w+q{
xA0clLUdPFWL=fDq<U&BCUP-P@GzFjeuqI$A=AhEcu1WIe-Bz}iRpuTM{{!E$<23*P

literal 0
HcmV?d00001

diff --git a/docs/miraclestrial.Rmd b/docs/miraclestrial.Rmd
new file mode 100644
index 00000000..fe29d0c1
--- /dev/null
+++ b/docs/miraclestrial.Rmd
@@ -0,0 +1,67 @@
+---
+title: Genetic analysis of Mirocals trial
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+```{css, echo=F}
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+```
+
+***
+
+# Proposal
+
+The Mirocals trial tested the efficacy of low dose IL-2 for treatment of ALS. The trial showed IL-2 + riluzole treatment provided a significant improvement in survival compared to those only receiving placebo + riluzole. Low dose IL-2 has been shown to increase the number of regulatory T-cells (Tregs).
+
+This trial has collected whole genome sequence data and methylation data (across several time points). This presents an opportunity to identify genetic predictors/biomarkers of response to IL-2 treatment. The sample size of the trial is limited (N drug = 110, N placebo = 110), which means we need to test specific hypotheses and leverage larger external datasets.
+
+***
+
+## Outcome
+
+There are two key outcome variables from the trial, including survival and regulatory T-cell numbers. It seems survival is the more meaningful outcome for patients, but perhaps there are benefits to using T-cell numbers that I am unaware of. If we are using survival, then we should using a cox regression, but if use T cell numbers linear regression.
+
+***
+
+## Polygenic scores (PGS)
+
+List of polygenic scores that might predict response to IL-2:
+
+* ALS risk, survival, age at onset
+* Risk of other neurodegenerative diseases
+* Interleukin-2 receptor subunit alpha (IL2RA) protein levels
+* Regulatory T-cell molecule levels
+* Autoimmune disease risk
+* Other inflammatory markers (CRP, IL-6, etc.)
+
+I would suggest using SBayesRC as the method to generate the PGS. If GWAS from multiple populations are available we can using SBayesRC-multi.
+
+We could also further stratify the polygenic scores by functional annotations, such as pathway membership. This could be particularly useful, as it may be that only certain pathways are relevant to IL-2 efficacy. For example, ALS risk mediated by inflammatory pathways may be particularly relevant for IL-2 efficacy. 
+
+***
+
+## Methylation scores
+
+We can use previously published EWAS summary statistics to do the same thing. Alternatively, we can use 
+
+
+

From 14990a87ba8a28bb090d4f6179be5fdabb3f63ac Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 31 Mar 2025 15:14:44 +0100
Subject: [PATCH 126/153] Updates

---
 docs/CrossPop.Rmd                             |  97 ++++---
 docs/CrossPop.html                            | 137 ++++++----
 .../pipeline_readme/input_schematic_wide.png  | Bin 615907 -> 43513 bytes
 .../pipeline_readme/pipeline_schematic.png    | Bin 4703497 -> 0 bytes
 .../pipeline_schematic_groups_lowdef.png      | Bin 320065 -> 374063 bytes
 .../pipeline_schematic_lowdef.png             | Bin 305401 -> 0 bytes
 docs/pipeline_readme.Rmd                      |  51 ++--
 docs/pipeline_readme.html                     | 141 +++++-----
 docs/pipeline_technical.Rmd                   |  93 +++++--
 docs/pipeline_technical.html                  | 241 ++++++++++++++----
 pipeline/envs/pipeline.yaml                   |   1 -
 11 files changed, 509 insertions(+), 252 deletions(-)
 delete mode 100644 docs/Images/pipeline_readme/pipeline_schematic.png
 delete mode 100644 docs/Images/pipeline_readme/pipeline_schematic_lowdef.png

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index ffc66b00..f877b95b 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -20,17 +20,35 @@ library(kableExtra)
 library(data.table)
 ```
 
-```{css, echo=F}
+<style>
 pre code, pre, code {
   white-space: pre !important;
   overflow-x: scroll !important;
   word-break: keep-all !important;
   word-wrap: initial !important;
 }
-```
+
+ul li p, ol li p {
+  margin: 0 !important;
+}
+</style>
+
+***
+
+# Preprint
+
+This document provides code and summarises the findings reported in the following preprint:
+
+<div style="border-left: 4px solid #4A90E2; padding-left: 10px; margin: 10px 0;">
+
+**Citation:**  
+Pain, O. (2025). *Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations*. **medRxiv.** [https://doi.org/10.1101/2025.03.27.25324773](https://doi.org/10.1101/2025.03.27.25324773)
+
+</div>
 
 ***
 
+
 # Introduction
 
 Genome-wide association studies (GWAS) from multiple ancestral populations are increasingly available, offering opportunities to improve the accuracy and equity of polygenic scores (PGS). Several methods now aim to leverage multiple GWAS sources, but predictive performance and computational efficiency across contexts remain unclear, especially in the absence of individual-level tuning data. This study evaluates a comprehensive set of PGS methods across African (AFR), East Asian (EAS), and European (EUR) ancestry groups.
@@ -41,27 +59,30 @@ This study evaluated the performance of PGS methods and modelling approaches acr
 
 **Document overview**:
 
-* Data preparation
-  * UKB
-    * Ancestry inference
-    * Phenotype extraction
-    * Sample splitting
-  * GWAS sumstats
-    * UK Biobank GWAS
-    * Download BBJ and UGR sumstats
-  * Heritability and polygenicity estimation
-* Main analysis 
-  * PGS calculation
-  * PGS evaluation
-  * LEOPARD+QuickPRS
-  * Computational resources
-* TL-PRS analysis
-* Sensitivity analyses
-  * Using 1KG reference
-  * Using three GWAS
-  * Using external GWAS sumstats
-  * Using downsampled GWAS
-
+- [Data preparation](#data-preparation)  
+  - [UKB](#ukb)  
+    - [Ancestry inference](#ancestry-inference)  
+    - [Phenotype extraction](#phenotype-extraction)  
+  - [GWAS sumstats](#gwas-sumstats)  
+    - [UK Biobank GWAS](#uk-biobank-gwas)  
+    - [Download BBJ sumstats](#download-bbj-sumstats)  
+    - [Download UGR sumstats](#download-ugr-sumstats)  
+  - [Heritability and polygenicity estimation](#heritability-and-polygenicity-estimation)  
+
+- [Main analysis](#main-analysis)  
+  - [PGS calculation](#pgs-calculation)  
+  - [PGS evaluation](#pgs-evaluation)  
+  - [LEOPARD+QuickPRS](#leopardquickprs)  
+  - [Computational resources](#computational-resources)  
+
+- [TL-PRS analysis](#tl-prs-analysis)  
+
+- [Sensitivity analyses](#sensitivity-analyses)  
+  - [Using 1KG reference](#using-1kg-reference)  
+  - [Using three GWAS](#using-three-gwas)  
+  - [Using external GWAS sumstats](#using-external-gwas-sumstats)  
+  - [Using downsampled GWAS](#using-downsampled-gwas)  
+  
 ***
 
 # Data Preparation
@@ -495,13 +516,13 @@ for(i in 1:nrow(prscsx_dat)){
 
 ***
 
-### GWAS sumstats
+## GWAS sumstats
 
 We will generate EUR GWAS using the EUR training subset of UKB. BBJ will be used for EAS GWAS, and UGR will be used for AFR GWAS.
 
 ***
 
-#### UKB GWAS
+### UKB GWAS
 
 <details><summary>Show code</summary>
 ```{bash}
@@ -539,7 +560,7 @@ rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*/*chr*
 
 ***
 
-#### Download BBJ sumstats
+### Download BBJ sumstats
 
 <details><summary>Show code</summary>
 
@@ -607,7 +628,7 @@ done
 
 ***
 
-#### Download UGR sumstats
+### Download UGR sumstats
 
 <details><summary>Show code</summary>
 
@@ -734,14 +755,14 @@ furrr::future_map_dfr(1:nrow(ugr_data), function(i) {
 
 ***
 
-## Heritability and polygenicity estimation
+# Heritability and polygenicity estimation
 
 We will estimate SNP-h2 using LD-score regression, and the rG using POPCORN.
 POPCORN can estimate the SNP-h2, but it will vary according to the other GWAS included due to SNP overlap. Use the sumstats QC'd by GenoPred. To estimate polygenicity, lets use AVENGEME based on ptclump score association results. Lets generate those using GenoPred.
 
 ***
 
-### QC GWAS sumstats
+## QC GWAS sumstats
 
 Use GenoPred for this.
 
@@ -901,7 +922,7 @@ for(i in 1:nrow(gwas_list)){
 
 ***
 
-### LDSC
+## LDSC
 
 <details><summary>Show code</summary>
 ```{bash}
@@ -957,7 +978,7 @@ done
 
 ***
 
-### POPCORN
+## POPCORN
 
 <details><summary>Show code</summary>
 
@@ -1032,7 +1053,7 @@ done
 
 ***
 
-### Plot LDSC and POPCORN results
+## Plot LDSC and POPCORN results
 
 <details><summary>Show code</summary>
 
@@ -1165,7 +1186,7 @@ cp ~/oliverpainfel/Analyses/crosspop/plots/popcorn_rg.png /scratch_tmp/prj/olive
 
 ***
 
-### AVENGEME
+## AVENGEME
 
 <details><summary>Show code</summary>
 
@@ -1529,7 +1550,7 @@ cp ~/oliverpainfel/Analyses/crosspop/plots/avengeme_polygenicity.png /scratch_tm
 
 ***
 
-### Select traits
+## Select traits
 
 Here we will identify a list of traits that fulfill our selection criteria, and that represent a range of heritability and polygenicity combinations.
 
@@ -1617,7 +1638,7 @@ round(sqrt(max(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])), 2)
 
 ***
 
-### GWAS descriptives
+## GWAS descriptives
 
 Make a table showing GWAS information for the manuscript.
 
@@ -7058,7 +7079,7 @@ cp ~/oliverpainfel/Analyses/crosspop/plots_meta/average_r.png /scratch_tmp/prj/o
 
 ***
 
-## Run methods using downsampled UKB sumstats
+## Using downsampled GWAS
 
 It seems the performance of methods varies across EAS and AFR datasets. This could be due to the difference in sample size. To explore this, lets run the methods on EUR GWAS generated using UKB, using a range of sample sizes.
 
@@ -7480,6 +7501,8 @@ dev.off()
 
 ```
 
+</details>
+
 ```{bash, eval=T, echo=F}
 
 cp ~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_downsample.png
@@ -7489,7 +7512,7 @@ cp ~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png /scratch_tmp
 <details><summary>Show results</summary>
 
 <div class="centered-container">
-<div class="rounded-image-container" style="width: 50%;">
+<div class="rounded-image-container" style="width: 100%;">
 <img src="Images/CrossPop_2025/average_r_downsample.png">
 </div>
 </div>
diff --git a/docs/CrossPop.html b/docs/CrossPop.html
index 5ac30d91..087b8ee7 100644
--- a/docs/CrossPop.html
+++ b/docs/CrossPop.html
@@ -430,7 +430,33 @@ <h1 class="title toc-ignore">Leveraging Global Genetics Resources to
 </div>
 
 
+<style>
+pre code, pre, code {
+  white-space: pre !important;
+  overflow-x: scroll !important;
+  word-break: keep-all !important;
+  word-wrap: initial !important;
+}
+
+ul li p, ol li p {
+  margin: 0 !important;
+}
+</style>
+<hr />
+<div id="preprint" class="section level1">
+<h1>Preprint</h1>
+<p>This document provides code and summarises the findings reported in
+the following preprint:</p>
+<div
+style="border-left: 4px solid #4A90E2; padding-left: 10px; margin: 10px 0;">
+<p><strong>Citation:</strong><br />
+Pain, O. (2025). <em>Leveraging Global Genetics Resources to Enhance
+Polygenic Prediction Across Ancestrally Diverse Populations</em>.
+<strong>medRxiv.</strong> <a
+href="https://doi.org/10.1101/2025.03.27.25324773">https://doi.org/10.1101/2025.03.27.25324773</a></p>
+</div>
 <hr />
+</div>
 <div id="introduction" class="section level1">
 <h1>Introduction</h1>
 <p>Genome-wide association studies (GWAS) from multiple ancestral
@@ -455,35 +481,48 @@ <h1>Introduction</h1>
 <hr />
 <p><strong>Document overview</strong>:</p>
 <ul>
-<li>Data preparation
+<li><p><a href="#data-preparation">Data preparation</a></p>
 <ul>
-<li>UKB
+<li><a href="#ukb">UKB</a>
 <ul>
-<li>Ancestry inference</li>
-<li>Phenotype extraction</li>
-<li>Sample splitting</li>
+<li><a href="#ancestry-inference">Ancestry inference</a><br />
+</li>
+<li><a href="#phenotype-extraction">Phenotype extraction</a><br />
+</li>
 </ul></li>
-<li>GWAS sumstats
+<li><a href="#gwas-sumstats">GWAS sumstats</a>
 <ul>
-<li>UK Biobank GWAS</li>
-<li>Download BBJ and UGR sumstats</li>
+<li><a href="#uk-biobank-gwas">UK Biobank GWAS</a><br />
+</li>
+<li><a href="#download-bbj-sumstats">Download BBJ sumstats</a><br />
+</li>
+<li><a href="#download-ugr-sumstats">Download UGR sumstats</a><br />
+</li>
 </ul></li>
-<li>Heritability and polygenicity estimation</li>
+<li><a href="#heritability-and-polygenicity-estimation">Heritability and
+polygenicity estimation</a></li>
 </ul></li>
-<li>Main analysis
+<li><p><a href="#main-analysis">Main analysis</a></p>
 <ul>
-<li>PGS calculation</li>
-<li>PGS evaluation</li>
-<li>LEOPARD+QuickPRS</li>
-<li>Computational resources</li>
+<li><a href="#pgs-calculation">PGS calculation</a><br />
+</li>
+<li><a href="#pgs-evaluation">PGS evaluation</a><br />
+</li>
+<li><a href="#leopardquickprs">LEOPARD+QuickPRS</a><br />
+</li>
+<li><a href="#computational-resources">Computational resources</a></li>
 </ul></li>
-<li>TL-PRS analysis</li>
-<li>Sensitivity analyses
+<li><p><a href="#tl-prs-analysis">TL-PRS analysis</a></p></li>
+<li><p><a href="#sensitivity-analyses">Sensitivity analyses</a></p>
 <ul>
-<li>Using 1KG reference</li>
-<li>Using three GWAS</li>
-<li>Using external GWAS sumstats</li>
-<li>Using downsampled GWAS</li>
+<li><a href="#using-1kg-reference">Using 1KG reference</a><br />
+</li>
+<li><a href="#using-three-gwas">Using three GWAS</a><br />
+</li>
+<li><a href="#using-external-gwas-sumstats">Using external GWAS
+sumstats</a><br />
+</li>
+<li><a href="#using-downsampled-gwas">Using downsampled GWAS</a></li>
 </ul></li>
 </ul>
 <hr />
@@ -905,13 +944,14 @@ <h3>Phenotype extraction</h3>
 </details>
 <hr />
 </div>
-<div id="gwas-sumstats" class="section level3">
-<h3>GWAS sumstats</h3>
+</div>
+<div id="gwas-sumstats" class="section level2">
+<h2>GWAS sumstats</h2>
 <p>We will generate EUR GWAS using the EUR training subset of UKB. BBJ
 will be used for EAS GWAS, and UGR will be used for AFR GWAS.</p>
 <hr />
-<div id="ukb-gwas" class="section level4">
-<h4>UKB GWAS</h4>
+<div id="ukb-gwas" class="section level3">
+<h3>UKB GWAS</h3>
 <details>
 <summary>
 Show code
@@ -948,8 +988,8 @@ <h4>UKB GWAS</h4>
 </details>
 <hr />
 </div>
-<div id="download-bbj-sumstats" class="section level4">
-<h4>Download BBJ sumstats</h4>
+<div id="download-bbj-sumstats" class="section level3">
+<h3>Download BBJ sumstats</h3>
 <details>
 <summary>
 Show code
@@ -1011,8 +1051,8 @@ <h4>Download BBJ sumstats</h4>
 </details>
 <hr />
 </div>
-<div id="download-ugr-sumstats" class="section level4">
-<h4>Download UGR sumstats</h4>
+<div id="download-ugr-sumstats" class="section level3">
+<h3>Download UGR sumstats</h3>
 <details>
 <summary>
 Show code
@@ -1131,16 +1171,16 @@ <h4>Download UGR sumstats</h4>
 </div>
 </div>
 <div id="heritability-and-polygenicity-estimation"
-class="section level2">
-<h2>Heritability and polygenicity estimation</h2>
+class="section level1">
+<h1>Heritability and polygenicity estimation</h1>
 <p>We will estimate SNP-h2 using LD-score regression, and the rG using
 POPCORN. POPCORN can estimate the SNP-h2, but it will vary according to
 the other GWAS included due to SNP overlap. Use the sumstats QC’d by
 GenoPred. To estimate polygenicity, lets use AVENGEME based on ptclump
 score association results. Lets generate those using GenoPred.</p>
 <hr />
-<div id="qc-gwas-sumstats" class="section level3">
-<h3>QC GWAS sumstats</h3>
+<div id="qc-gwas-sumstats" class="section level2">
+<h2>QC GWAS sumstats</h2>
 <p>Use GenoPred for this.</p>
 <details>
 <summary>
@@ -1291,8 +1331,8 @@ <h4>
 </details>
 <hr />
 </div>
-<div id="ldsc" class="section level3">
-<h3>LDSC</h3>
+<div id="ldsc" class="section level2">
+<h2>LDSC</h2>
 <details>
 <summary>
 Show code
@@ -1346,8 +1386,8 @@ <h3>LDSC</h3>
 </details>
 <hr />
 </div>
-<div id="popcorn" class="section level3">
-<h3>POPCORN</h3>
+<div id="popcorn" class="section level2">
+<h2>POPCORN</h2>
 <details>
 <summary>
 Show code
@@ -1418,8 +1458,8 @@ <h4>
 </details>
 <hr />
 </div>
-<div id="plot-ldsc-and-popcorn-results" class="section level3">
-<h3>Plot LDSC and POPCORN results</h3>
+<div id="plot-ldsc-and-popcorn-results" class="section level2">
+<h2>Plot LDSC and POPCORN results</h2>
 <details>
 <summary>
 Show code
@@ -1539,8 +1579,8 @@ <h3>Plot LDSC and POPCORN results</h3>
 </details>
 <hr />
 </div>
-<div id="avengeme" class="section level3">
-<h3>AVENGEME</h3>
+<div id="avengeme" class="section level2">
+<h2>AVENGEME</h2>
 <details>
 <summary>
 Show code
@@ -1880,8 +1920,8 @@ <h4>
 </details>
 <hr />
 </div>
-<div id="select-traits" class="section level3">
-<h3>Select traits</h3>
+<div id="select-traits" class="section level2">
+<h2>Select traits</h2>
 <p>Here we will identify a list of traits that fulfill our selection
 criteria, and that represent a range of heritability and polygenicity
 combinations.</p>
@@ -1966,8 +2006,8 @@ <h3>Select traits</h3>
 </details>
 <hr />
 </div>
-<div id="gwas-descriptives" class="section level3">
-<h3>GWAS descriptives</h3>
+<div id="gwas-descriptives" class="section level2">
+<h2>GWAS descriptives</h2>
 <p>Make a table showing GWAS information for the manuscript.</p>
 <details>
 <summary>
@@ -5953,7 +5993,6 @@ <h3>GWAS descriptives</h3>
 <hr />
 </div>
 </div>
-</div>
 <div id="main-analysis" class="section level1">
 <h1>Main analysis</h1>
 <hr />
@@ -11303,9 +11342,8 @@ <h4>
 <hr />
 </div>
 </div>
-<div id="run-methods-using-downsampled-ukb-sumstats"
-class="section level2">
-<h2>Run methods using downsampled UKB sumstats</h2>
+<div id="using-downsampled-gwas" class="section level2">
+<h2>Using downsampled GWAS</h2>
 <p>It seems the performance of methods varies across EAS and AFR
 datasets. This could be due to the difference in sample size. To explore
 this, lets run the methods on EUR GWAS generated using UKB, using a
@@ -11711,12 +11749,13 @@ <h4>
           legend.key.spacing.x = unit(1, &quot;cm&quot;),
           legend.justification = &quot;center&quot;)
 dev.off()</code></pre>
+</details>
 <details>
 <summary>
 Show results
 </summary>
 <div class="centered-container">
-<div class="rounded-image-container" style="width: 50%;">
+<div class="rounded-image-container" style="width: 100%;">
 <p><img src="Images/CrossPop_2025/average_r_downsample.png"></p>
 </div>
 </div>
diff --git a/docs/Images/pipeline_readme/input_schematic_wide.png b/docs/Images/pipeline_readme/input_schematic_wide.png
index f7475ee46ac7c3eb8ce864922684e23fb0d9e155..45938395d5cb38230fd85ccc7451520b78b27a10 100644
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diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 56d53fc4..50f7fe9e 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -38,17 +38,23 @@ For a technical details of the GenoPred pipeline, click [here](pipeline_technica
 
 ***
 
-**Citations**
+<h3>Citations</h4>
 
-Please cite our preprint when using the pipeline:
+Please cite the following relevant papers when using the pipeline:
 
-- "Pain, O. et al. "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." Bioinformatics 2024. https://doi.org/10.1093/bioinformatics/btae551
+**Pipeline overview:**
+- Pain, O. et al. (2024). *The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring*. Bioinformatics.  
+  https://doi.org/10.1093/bioinformatics/btae551
 
-If relevant, please also cite our paper comparing polygenic scoring methods and describing the reference-standardised approach:
+**Comparison of single-source PGS methods:**
+- Pain, O. et al. (2021). *Evaluation of polygenic prediction methodology within a reference-standardized framework*. PLoS Genetics.  
+  https://doi.org/10.1371/journal.pgen.1009021
 
-- Pain, O. et al. "Evaluation of polygenic prediction methodology within a reference-standardized framework." PLoS genetics. https://doi.org/10.1371/journal.pgen.1009021
+**Integration and comparison of multi-source PGS methods:**
+- Pain, O. (2025). *Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations*. medRxiv. 
+  https://doi.org/10.1101/2025.03.27.25324773
 
-Please also cite the relevant studies for the tools and data used by the GenoPred pipeline.
+Please also cite the relevant studies for the tools and data used by the GenoPred pipeline - Details provided in the [Technical Documentation](pipeline_technical.html).
 
 ***
 
@@ -118,31 +124,38 @@ git clone https://github.com/opain/GenoPred.git
 
 ***
 
-## Step 2: Create conda environment for pipeline
+## Step 2: Create software environment for pipeline
 
-Conda is a software environment management system which is great way for easily downloading and storing software. We will use conda to create an environment that the GenoPred pipeline will run in.
+Conda is a software environment management system that simplifies installing and managing dependencies. We recommend using Miniforge — a minimal conda installer that comes with Mamba, a fast drop-in replacement for conda.
 
-If you don't already have conda installed, we will install it using miniconda.
+If you don't already have Miniforge installed, you can install it using the commands below:
 
 ```{bash}
-wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
-sh Miniconda3-latest-Linux-x86_64.sh
+# Download and install Miniforge (for Linux)
+wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
+bash Miniforge3-Linux-x86_64.sh
 ```
 
-I would say `yes` to the default options. You may then need to refresh your workspace to initiate conda, by running `source ~/.bashrc`. You should see `(base)` written in the bottom left of the terminal. Once miniconda installation is complete, you can then delete the `Miniconda3-latest-Linux-x86_64.sh` file.
+Accept the default installation options. Once installed, you may need to run source ~/.bashrc or restart your terminal. You should see (base) appear at the beginning of your terminal prompt.
 
-Now create the conda environment based on the `GenoPred/pipeline/envs/pipeline.yaml` file. This will create an environment called `genopred` with some essential packages installed.
+Now, create the GenoPred environment using Mamba:
 
 ```{bash}
 conda env create -f GenoPred/pipeline/envs/pipeline.yaml
 ```
 
-Now activate the new `genopred` environment.
+Activate the new `genopred` environment:
 
 ```{bash}
 conda activate genopred
 ```
 
+<div class="note-box">
+
+**Note:** If you are working on an HPC, check whether mamba is an available module. Using the centrally installed version of mamba may avoid issues down the road.
+
+</div>
+
 ***
 
 ## Step 3: Download dependencies
@@ -273,7 +286,7 @@ config <- list(
     pgs_methods = list(
       description = 'List of polygenic scoring methods to run',
       example = "`['ptclump','dbslmm']`",
-      note = "Options are: `ptclump`, `dbslmm`, `prscs`, `sbayesr`, `sbayesrc`, `lassosum`, `ldpred2`, `megaprs`, `quickprs`, `prscsx`, `xwing`. **Note.** by default, `quickprs`, `sbayesr`, `sbayesrc` and `ldpred2` are only implemented for GWAS of EUR ancestry. To be applicable to other populations, the user must indicate where the relevent LD reference data is stored (See [here](#specifying-alternative-reference-data-for-pgs-methods. **Note.** when using multi-source methods (`prscsx`, `xwing`), the `gwas_groups` parameter must also be provided. See [here](#multi-source-pgs-methods) for more information."
+      note = "Options are: `ptclump`, `dbslmm`, `prscs`, `sbayesr`, `sbayesrc`, `lassosum`, `ldpred2`, `megaprs`, `quickprs`, `prscsx`, `xwing`. **Note.** by default, `sbayesr` and `ldpred2` are only implemented for EUR ancestry GWAS, and SBayesRC is restricted to AFR, EAS, and EUR ancestry GWAS. To be applicable to other populations, the user must indicate where the relevent LD reference data is stored (See [here](#specifying-alternative-reference-data-for-pgs-methods. **Note.** when using multi-source methods (`prscsx`, `xwing`), the `gwas_groups` parameter must also be provided. See [here](#multi-source-pgs-methods) for more information."
     ),
     testing = list(
       description = 'Controls testing mode',
@@ -308,7 +321,7 @@ print(kable(config_df, 'markdown'))
 
 <div class="note-box">
 
-**Note:** If you do not provide a target_list, then only rules that do not require a target_list can be performed, such as GWAS sumstat QC. Similarly, if you do not provide a gwas_list or score_list, only rules that do not require these files can be performed, such as target sample ancestry inference.
+**Note:** If you do not provide a target_list, then only rules that do not require a `target_list` can be performed, such as GWAS sumstat QC. Similarly, if you do not provide a `gwas_list` or `score_list`, only rules that do not require these files can be performed, such as target sample ancestry inference.
 
 </div>
 
@@ -1399,9 +1412,11 @@ kable(gwas_groups_df, 'markdown')
 
 </details>
 
-Another multi-source PGS approach is TL-PRS, which tunes an existing PGS model based on GWAS summary statistics from another population. GenoPred can automate this process. The user must specify which PGS methods they want TL-PRS to be applied to, using the `tlprs_methods` parameter in the `configfile`. For example: `tlprs_methods: ['lassosum','dbslmm']`. Currently, TL-PRS is only applied to the PGS from a given method selected using pseudovalidation. TL-PRS can currently only be applied to the following PGS methods `ptclump`, `dbslmm`, `prscs`, `sbayesrc`, `lassosum`, `ldpred2`, `megaprs`, and `quickprs`.
+<div class="shallow-break"></div>
+
+Another multi-source PGS approach is TL-PRS, which tunes an existing PGS model based on GWAS summary statistics from another population. GenoPred can automate this process. The user must specify which PGS methods they want TL-PRS to be applied to, using the `tlprs_methods` parameter in the `configfile`. For example: `tlprs_methods: ['lassosum','dbslmm']`. TL-PRS is only applied to the PGS from a given method selected using pseudovalidation.
 
-An alternative multi-source PGS approach, is to linearly combine population-specific PGS derived using single-source methods. GenoPred provides use a method called LEOPARD to estimate the optimal linear combination population-specific PGS using summary statistics alone. To calculate PGS based on this estimated linear combination using GenoPred, the user must specify which methods they wish the PGS to be combined using the `leopard_methods` parameter in the `configfile`. For example: `leopard_methods: ['lassosum','dbslmm']`. Currently, this LEOPARD weighting is only applied to the PGS from a given method selected using pseudovalidation. LEOPARD can currently only be applied to the following PGS methods `ptclump`, `dbslmm`, `prscs`, `sbayesrc`, `lassosum`, `ldpred2`, `megaprs`, and `quickprs`.
+An alternative multi-source PGS approach is to linearly combine population-specific PGS derived using single-source methods. GenoPred implements an approach called `LEOPARD+QuickPRS`, which estimates the optimal linear combination weights for population-specific PGS using summary statistics alone. Using the `leopard_methods` parameter in the `configfile`, the user can specify which single-source PGS should be linearly combined using the LEOPARD+QuickPRS estimated weights. For example: `leopard_methods: ['lassosum', 'dbslmm']`. LEOPARD weighting is only applied to the PGS from a given method selected using pseudovalidation.
 
 ***
 
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index d4f0942c..e7aa4c42 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -450,24 +450,30 @@ <h1 class="title toc-ignore">GenoPred Pipeline - Instructions</h1>
 href="pipeline_overview.html">here</a>. For a technical details of the
 GenoPred pipeline, click <a href="pipeline_technical.html">here</a>.</p>
 <hr />
-<p><strong>Citations</strong></p>
-<p>Please cite our preprint when using the pipeline:</p>
-<ul>
-<li>“Pain, O. et al. ”The GenoPred Pipeline: A Comprehensive and
-Scalable Pipeline for Polygenic Scoring.” Bioinformatics 2024. <a
-href="https://doi.org/10.1093/bioinformatics/btae551"
-class="uri">https://doi.org/10.1093/bioinformatics/btae551</a></li>
-</ul>
-<p>If relevant, please also cite our paper comparing polygenic scoring
-methods and describing the reference-standardised approach:</p>
-<ul>
-<li>Pain, O. et al. “Evaluation of polygenic prediction methodology
-within a reference-standardized framework.” PLoS genetics. <a
-href="https://doi.org/10.1371/journal.pgen.1009021"
-class="uri">https://doi.org/10.1371/journal.pgen.1009021</a></li>
-</ul>
+<h3>
+Citations
+</h4>
+<p>Please cite the following relevant papers when using the
+pipeline:</p>
+<p><strong>Pipeline overview:</strong> - Pain, O. et al. (2024). <em>The
+GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic
+Scoring</em>. Bioinformatics.<br />
+<a href="https://doi.org/10.1093/bioinformatics/btae551"
+class="uri">https://doi.org/10.1093/bioinformatics/btae551</a></p>
+<p><strong>Comparison of single-source PGS methods:</strong> - Pain, O.
+et al. (2021). <em>Evaluation of polygenic prediction methodology within
+a reference-standardized framework</em>. PLoS Genetics.<br />
+<a href="https://doi.org/10.1371/journal.pgen.1009021"
+class="uri">https://doi.org/10.1371/journal.pgen.1009021</a></p>
+<p><strong>Integration and comparison of multi-source PGS
+methods:</strong> - Pain, O. (2025). <em>Leveraging Global Genetics
+Resources to Enhance Polygenic Prediction Across Ancestrally Diverse
+Populations</em>. medRxiv. <a
+href="https://doi.org/10.1101/2025.03.27.25324773"
+class="uri">https://doi.org/10.1101/2025.03.27.25324773</a></p>
 <p>Please also cite the relevant studies for the tools and data used by
-the GenoPred pipeline.</p>
+the GenoPred pipeline - Details provided in the <a
+href="pipeline_technical.html">Technical Documentation</a>.</p>
 <hr />
 <div id="video-tutorials" class="section level1">
 <h1>Video tutorials</h1>
@@ -524,29 +530,30 @@ <h2>Step 1: Download GenoPred repository</h2>
 <pre class="bash"><code>git clone https://github.com/opain/GenoPred.git</code></pre>
 <hr />
 </div>
-<div id="step-2-create-conda-environment-for-pipeline"
+<div id="step-2-create-software-environment-for-pipeline"
 class="section level2">
-<h2>Step 2: Create conda environment for pipeline</h2>
-<p>Conda is a software environment management system which is great way
-for easily downloading and storing software. We will use conda to create
-an environment that the GenoPred pipeline will run in.</p>
-<p>If you don’t already have conda installed, we will install it using
-miniconda.</p>
-<pre class="bash"><code>wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
-sh Miniconda3-latest-Linux-x86_64.sh</code></pre>
-<p>I would say <code>yes</code> to the default options. You may then
-need to refresh your workspace to initiate conda, by running
-<code>source ~/.bashrc</code>. You should see <code>(base)</code>
-written in the bottom left of the terminal. Once miniconda installation
-is complete, you can then delete the
-<code>Miniconda3-latest-Linux-x86_64.sh</code> file.</p>
-<p>Now create the conda environment based on the
-<code>GenoPred/pipeline/envs/pipeline.yaml</code> file. This will create
-an environment called <code>genopred</code> with some essential packages
-installed.</p>
+<h2>Step 2: Create software environment for pipeline</h2>
+<p>Conda is a software environment management system that simplifies
+installing and managing dependencies. We recommend using Miniforge — a
+minimal conda installer that comes with Mamba, a fast drop-in
+replacement for conda.</p>
+<p>If you don’t already have Miniforge installed, you can install it
+using the commands below:</p>
+<pre class="bash"><code># Download and install Miniforge (for Linux)
+wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
+bash Miniforge3-Linux-x86_64.sh</code></pre>
+<p>Accept the default installation options. Once installed, you may need
+to run source ~/.bashrc or restart your terminal. You should see (base)
+appear at the beginning of your terminal prompt.</p>
+<p>Now, create the GenoPred environment using Mamba:</p>
 <pre class="bash"><code>conda env create -f GenoPred/pipeline/envs/pipeline.yaml</code></pre>
-<p>Now activate the new <code>genopred</code> environment.</p>
+<p>Activate the new <code>genopred</code> environment:</p>
 <pre class="bash"><code>conda activate genopred</code></pre>
+<div class="note-box">
+<p><strong>Note:</strong> If you are working on an HPC, check whether
+mamba is an available module. Using the centrally installed version of
+mamba may avoid issues down the road.</p>
+</div>
 <hr />
 </div>
 <div id="step-3-download-dependencies" class="section level2">
@@ -670,9 +677,9 @@ <h2><code>configfile</code></h2>
 <table>
 <colgroup>
 <col width="2%" />
-<col width="10%" />
+<col width="9%" />
 <col width="5%" />
-<col width="81%" />
+<col width="82%" />
 </colgroup>
 <thead>
 <tr class="header">
@@ -737,11 +744,11 @@ <h2><code>configfile</code></h2>
 <code>prscs</code>, <code>sbayesr</code>, <code>sbayesrc</code>,
 <code>lassosum</code>, <code>ldpred2</code>, <code>megaprs</code>,
 <code>quickprs</code>, <code>prscsx</code>, <code>xwing</code>.
-<strong>Note.</strong> by default, <code>quickprs</code>,
-<code>sbayesr</code>, <code>sbayesrc</code> and <code>ldpred2</code> are
-only implemented for GWAS of EUR ancestry. To be applicable to other
-populations, the user must indicate where the relevent LD reference data
-is stored (See
+<strong>Note.</strong> by default, <code>sbayesr</code> and
+<code>ldpred2</code> are only implemented for EUR ancestry GWAS, and
+SBayesRC is restricted to AFR, EAS, and EUR ancestry GWAS. To be
+applicable to other populations, the user must indicate where the
+relevent LD reference data is stored (See
 [here](#specifying-alternative-reference-data-for-pgs-methods.
 <strong>Note.</strong> when using multi-source methods
 (<code>prscsx</code>, <code>xwing</code>), the <code>gwas_groups</code>
@@ -760,10 +767,11 @@ <h2><code>configfile</code></h2>
 </table>
 <div class="note-box">
 <p><strong>Note:</strong> If you do not provide a target_list, then only
-rules that do not require a target_list can be performed, such as GWAS
-sumstat QC. Similarly, if you do not provide a gwas_list or score_list,
-only rules that do not require these files can be performed, such as
-target sample ancestry inference.</p>
+rules that do not require a <code>target_list</code> can be performed,
+such as GWAS sumstat QC. Similarly, if you do not provide a
+<code>gwas_list</code> or <code>score_list</code>, only rules that do
+not require these files can be performed, such as target sample ancestry
+inference.</p>
 </div>
 </details>
 <hr />
@@ -2193,33 +2201,28 @@ <h2>Multi-source PGS methods</h2>
 </tbody>
 </table>
 </details>
+<div class="shallow-break">
+
+</div>
 <p>Another multi-source PGS approach is TL-PRS, which tunes an existing
 PGS model based on GWAS summary statistics from another population.
 GenoPred can automate this process. The user must specify which PGS
 methods they want TL-PRS to be applied to, using the
 <code>tlprs_methods</code> parameter in the <code>configfile</code>. For
-example: <code>tlprs_methods: ['lassosum','dbslmm']</code>. Currently,
-TL-PRS is only applied to the PGS from a given method selected using
-pseudovalidation. TL-PRS can currently only be applied to the following
-PGS methods <code>ptclump</code>, <code>dbslmm</code>,
-<code>prscs</code>, <code>sbayesrc</code>, <code>lassosum</code>,
-<code>ldpred2</code>, <code>megaprs</code>, and
-<code>quickprs</code>.</p>
-<p>An alternative multi-source PGS approach, is to linearly combine
+example: <code>tlprs_methods: ['lassosum','dbslmm']</code>. TL-PRS is
+only applied to the PGS from a given method selected using
+pseudovalidation.</p>
+<p>An alternative multi-source PGS approach is to linearly combine
 population-specific PGS derived using single-source methods. GenoPred
-provides use a method called LEOPARD to estimate the optimal linear
-combination population-specific PGS using summary statistics alone. To
-calculate PGS based on this estimated linear combination using GenoPred,
-the user must specify which methods they wish the PGS to be combined
-using the <code>leopard_methods</code> parameter in the
-<code>configfile</code>. For example:
-<code>leopard_methods: ['lassosum','dbslmm']</code>. Currently, this
-LEOPARD weighting is only applied to the PGS from a given method
-selected using pseudovalidation. LEOPARD can currently only be applied
-to the following PGS methods <code>ptclump</code>, <code>dbslmm</code>,
-<code>prscs</code>, <code>sbayesrc</code>, <code>lassosum</code>,
-<code>ldpred2</code>, <code>megaprs</code>, and
-<code>quickprs</code>.</p>
+implements an approach called <code>LEOPARD+QuickPRS</code>, which
+estimates the optimal linear combination weights for population-specific
+PGS using summary statistics alone. Using the
+<code>leopard_methods</code> parameter in the <code>configfile</code>,
+the user can specify which single-source PGS should be linearly combined
+using the LEOPARD+QuickPRS estimated weights. For example:
+<code>leopard_methods: ['lassosum', 'dbslmm']</code>. LEOPARD weighting
+is only applied to the PGS from a given method selected using
+pseudovalidation.</p>
 <hr />
 </div>
 <div id="select-ancestry-adjustment-approach" class="section level2">
diff --git a/docs/pipeline_technical.Rmd b/docs/pipeline_technical.Rmd
index 66d361b3..29d07053 100644
--- a/docs/pipeline_technical.Rmd
+++ b/docs/pipeline_technical.Rmd
@@ -24,15 +24,25 @@ library(data.table)
 
 This document provides technical details of the GenoPred pipeline. The GenoPred pipeline automates the process of calculating polygenic scores. The pipeline aims to implement the current practises for polygenic scoring. See [here](pipeline_overview.html) more general information regarding the GenoPred pipeline.
 
-Please cite our preprint when using the pipeline:
+***
+
+<h3>Citations</h4>
 
-- "Pain, O. et al. "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." Bioinformatics 2024. https://doi.org/10.1093/bioinformatics/btae551
+Please cite the following relevant papers when using the pipeline:
 
-If relevant, please also cite our paper comparing polygenic scoring methods and describing the reference-standardised approach:
+**Pipeline overview:**
+- Pain, O. et al. (2024). *The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring*. Bioinformatics.  
+  https://doi.org/10.1093/bioinformatics/btae551
 
-- Pain, O. et al. "Evaluation of polygenic prediction methodology within a reference-standardized framework." PLoS genetics. https://doi.org/10.1371/journal.pgen.1009021
+**Comparison of single-source PGS methods:**
+- Pain, O. et al. (2021). *Evaluation of polygenic prediction methodology within a reference-standardized framework*. PLoS Genetics.  
+  https://doi.org/10.1371/journal.pgen.1009021
 
-Please also cite the relevant studies for the tools and data used by the GenoPred pipeline.
+**Integration and comparison of multi-source PGS methods:**
+- Pain, O. (2025). *Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations*. medRxiv. 
+  https://doi.org/10.1101/2025.03.27.25324773
+
+Please also cite the relevant studies for the tools and data used by the GenoPred pipeline - Details provided in the [Technical Documentation](pipeline_technical.html).
 
 ***
 
@@ -49,9 +59,10 @@ The pipeline can be separated into two halves, reference data preparation and ta
 See [here](pipeline_readme.html#pipeline-configuration) for more information on pipeline configuration.
 
 ***
+
 <div class="centered-container">
 <div class="rounded-image-container">
-![Figure 1. GenoPred pipeline schematic. Shows input files, processes, outputs, and rules.](Images/pipeline_readme/pipeline_schematic_lowdef.png)
+![Figure 1. GenoPred pipeline schematic. Shows input files, processes, outputs, and rules.](Images/pipeline_readme/pipeline_schematic_groups_lowdef.png)
 </div>
 </div>
 
@@ -122,21 +133,23 @@ The GenoPred pipeline implements a range of PGS methods intended for GWAS based
 ```{r, eval = T, echo = F}
 
 methods_table <- data.frame(
-  Method = c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "SBayesR"),
-  Software = c("DBSLMM", "lassosum R package", "bigsnpr R package", "LDAK", "PRS-CS", "PLINK", "GCTB"),
-  PubMedID = c("32330416", "28480976", "33326037", "34234142", "30992449", "25722852", "31704910"),
-  PseudoValidationOption = c("Yes (only option)", "Yes", "Yes", "Yes", "Yes", "No", "Yes (only option)"),
-  Parameters = c("SNP-heritability estimated using LD Score Regression (on liability scale for binary outcomes)",
+  Method = c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesR", "SBayesRC"),
+  Software = c("DBSLMM", "lassosum R package", "bigsnpr R package", "LDAK", "PRS-CS", "PLINK", "LDAK", "GCTB", "SBayesRC R package"),
+  PubMedID = c("32330416", "28480976", "33326037", "34234142", "30992449", "25722852", "34234142", "31704910", "38689000"),
+  PseudoValidationOption = c("Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes (only option)", "Yes (only option)", "Yes (only option)"),
+  Parameters = c("SNP-heritability estimated using LD Score Regression (on liability scale for binary outcomes), multiplied by 0.8, 1, and 1.2",
                  "s = 0.2, 0.5, 0.9, 1; lambda = exp(seq(log(0.001), log(0.1), length.out=20))",
                  "Grid includes 126 combinations of heritability and non-zero effect fractions (p).",
                  "Fits lasso, ridge, bolt and BayesR models, with a total of 148 sets of hyperparameters",
                  "phi = 1e-6, 1e-4, 1e-2, 1, auto",
                  "p-value thresholds: 1e-8, 1e-6, 1e-4, 1e-2, 0.1, 0.2, 0.3, 0.4, 0.5, 1; Clumping: r2 = 0.1; window = 250kb",
+                 "Fits lasso, ridge, bolt and BayesR models, with a total of 148 sets of hyperparameters",
+                 "NA",
                  "NA"),
-  MHCRegion = c("Not excluded", "Not excluded", "Not excluded", "Not excluded", "Not excluded", "Only top variant retained", "Excluded (as recommended)"),
-  LDReference = c("Population-matched 1KG+HGDP", "Population-matched 1KG+HGDP", "EUR UKB (LDpred2-provided)", "Population-matched 1KG+HGDP", "Population-matched UKB (PRS-CS provided)", "Population-matched 1KG+HGDP", "EUR UKB (GCTB-provided)"),
-  CPUUsage = c("40 seconds", "10 seconds", "3 minutes", "5 minutes", "35 minutes", "5 seconds", "3 minutes"),
-  MemoryUsage = c("450 Mb", "400 Mb", "500 Mb", "450Mb", "350 Mb", "100 Mb", "500 Mb")
+  MHCRegion = c("Not excluded", "Not excluded", "Not excluded", "Not excluded", "Not excluded", "Only top variant retained", "Not excluded", "Excluded (as recommended)", "Not excluded"),
+  LDReference = c("Population-matched 1KG+HGDP", "Population-matched 1KG+HGDP", "EUR UKB (LDpred2-provided)", "Population-matched 1KG+HGDP", "Population-matched UKB (PRS-CS provided)", "Population-matched 1KG+HGDP", "Population-matched 1KG+HGDP", "EUR UKB (GCTB-provided)", "AFR/EAS/EUR UKB HapMap3 (SBayesRC provided)"),
+  CPUUsage = c("0.15 hrs", "0.08 hrs", "0.38 hrs", "0.85 hrs", "4.40 hrs", "0.02 hrs", "0.06 hrs", "0.38 hrs", "0.38 hrs"),
+  MemoryUsage = c("1.2 GB", "7.3 GB", "20.5 GB", "12.5 GB", "10.6 GB", "0.8 GB", "4.9 GB", "4.4 GB", "4.4 GB")
 )
 
 names(methods_table) <- c('Method','Software','PubMed ID', 'Pseudovalidation Option','Parameters','MHC Region','LD Reference','CPU usage*','Memory Usage*')
@@ -148,7 +161,9 @@ kable(methods_table, "html", caption = "Table 1 provides a summary of each appro
 
 ```
 
-\* = Based on chromosome 22 using single core. See [here](pipeline_benchmark.html) for full benchmark information.
+\* = Using genome-wide GWAS with 10 cores. See [here](crosspop.html#computational-resources) for full benchmark information.
+
+Note.This is the default behaviour of the GenoPred pipeline. Certain aspects can be modified using the configfile ([see here](pipeline_readme.html#additional-parameters)).
 
 ***
 
@@ -166,7 +181,7 @@ lassosum, an R package, applies the LASSO method for constructing PGS, is implem
 
 #### LDpred2
 
-LDpred2, part of the bigsnpr R package, operates in 'inf', 'grid', and 'auto' modes. The 'inf' mode is better suited for highly polygenic traits, whereas 'grid' and 'auto' adjust effect sizes using various hyperparameters, including SNP heritability. In GenoPred, LDpred2 is run using the script [pgs_methods/ldpred2](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/ldpred2.R).R. GenoPred uses bigsnpr v1.12.2, with the default LDpred2 grid search, and recommended GWAS quality control checks. GenoPred employs LDpred2's precomputed LD matrices based on the European individuals from the UK Biobank, and it is applied only to GWAS based on a EUR population. If the SNP-h2 estimated using LDSC is <0.05, the SNP-heritability used by LDpred2 is set to 0.05.
+LDpred2, part of the bigsnpr R package, operates in 'inf', 'grid', and 'auto' modes. The 'inf' mode is better suited for highly polygenic traits, whereas 'grid' and 'auto' adjust effect sizes using various hyperparameters, including SNP heritability. In GenoPred, LDpred2 is run using the script [pgs_methods/ldpred2](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/ldpred2.R).R. GenoPred uses bigsnpr v1.12.2, with the default LDpred2 grid search, and recommended GWAS quality control checks. GenoPred employs LDpred2's precomputed LD matrices based on the European individuals from the UK Biobank, and it is applied only to GWAS based on a EUR population.  The user can specify alternative LD reference data ([see here](pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods)). If the SNP-h2 estimated using LDSC is <0.05, the SNP-heritability used by LDpred2 is set to 0.05.
 
 ***
 
@@ -188,15 +203,33 @@ In GenoPred, pT+clump is run using the script [pgs_methods/ptclump.R](https://gi
 
 ***
 
+#### QuickPRS
+
+In GenoPred, QuickPRS is run using the script [pgs_methods/quickprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/quickprs.R). QuickPRS is a faster implementation of MegaPRS, sped up by using precomputed LD and annotation data. It only inlcudes the model selected using pseudovalidation. GenoPred employs the 1KG+HGDP reference for LD estimation, matching the population of the GWAS sample.
+
+***
+
 #### SBayesR
 
 In GenoPred, SBayesR is run using the script [pgs_methods/sbayesr.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/sbayesr.R). Implemented with GCTB software, SBayesR is a Bayesian method that uses GWAS summary statistics to estimate key parameters. GenoPred uses GCTB v2.03 with LD matrices from the GCTB authors, calculated using European individuals in the UK Biobank, restricting its application to GWAS based on EUR populations. GenoPred uses the robust parameterisation option in SBayesR.
 
 ***
 
+#### SBayesRC
+
+In GenoPred, SBayesRC is implemented via pgs_[pgs_methods/sbayesrc.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/sbayesrc.R). This method extends SBayesR to integrate functional annotations into the Bayesian framework for estimating SNP effects from GWAS summary statistics. By default SBayesRC will use the LD matrices released by the developers, restricted to GWAS from EUR, EAS, and AFR populations. The user can specify alternative LD reference data ([see here](pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods))
+
+***
+
 ### Multi-ancestry Methods 
 
-The GenoPred pipeline implements a range of PGS methods intended for multiple GWAS from different ancestral populations. 
+The GenoPred pipeline implements a range of PGS methods intended for analysis of multiple GWAS from different ancestral populations. Multi-ancestry / Multi-source approaches can be catagorised as jointly optimised or independently optimised.
+
+***
+
+#### Jointly optimised
+
+Jointly optimised methods, modelling all GWAS simultaneously when generating SNP-weights, are shown below:
 
 ```{r, eval = T, echo = F}
 
@@ -210,8 +243,8 @@ methods_table <- data.frame(
                  "Block learning rates = 1, 10, 100, 1000. Iterations (k) = "),
   MHCRegion = c("Not excluded", "Not excluded", "Dependant on the PGS method used to generate the score file input to TL-PRS"),
   LDReference = c("Population-matched UKB (PRS-CS provided)", "Population-matched UKB (PRS-CS provided)", "Population-matched 1KG+HGDP"),
-  CPUUsage = c(NA),
-  MemoryUsage = c(NA)
+  CPUUsage = c("6.84 hrs", "34.12 hrs", "0.33 hrs"),
+  MemoryUsage = c("15.2 GB", " 48.7 GB", "31.0 GB")
 )
 
 names(methods_table) <- c('Method','Software','PubMed ID', 'Pseudovalidation Option','Parameters','MHC Region','LD Reference','CPU usage*','Memory Usage*')
@@ -223,29 +256,41 @@ kable(methods_table, "html", caption = "Table 2 provides a summary of each appro
 
 ```
 
+\* = Time per GWAS/population, using genome-wide data with 10 cores. See [here](crosspop.html#computational-resources) for full benchmark information.
+
+Note.This is the default behaviour of the GenoPred pipeline. Certain aspects can be modified using the configfile ([see here](pipeline_readme.html#additional-parameters)).
+
 ***
 
-#### PRS-CSx
+##### PRS-CSx
 
 PRS-CSx, an extension of PRS-CS, integrates GWAS summary statistics from multiple ancestry groups using a Bayesian framework. The method is implemented in GenoPred via the script [pgs_methods/prscsx.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscsx.R). We use summary statistics from the populations most closely matching the ancestry of the GWAS data to estimate posterior effect sizes. The LD information is derived from population-matched reference panels (e.g., 1KG). GenoPred clones PRS-CSx from GitHub (29a1148875f6ae3f2594b25579f40d4b587c5691) and uses the provided precompiled PRS-CSx binary to estimate SNP effect sizes. By default, GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the auto model, but the user can modify this behaviour using the prscs_phi parameter in the configfile. By default, GenoPred uses the PRS-CS provided 1KG-derived LD matrix data, matching the population of the GWAS sample. The user can select the UKB-derived LD matrix data to be used using the `prscs_ldref` parameter in the `configfile`. 1KG is used by default as PGS based on Yengo et al. sumstats performed significantly better in the OpenSNP target sample, when using the 1KG reference data (this may differ for other GWAS).
 
 ***
 
-#### X-Wing
+##### X-Wing
 
 X-Wing is a polygenic scoring method designed to enhance genetic prediction accuracy across ancestrally diverse populations by leveraging a cross-population weighting framework. The method operates through a combination of several software tools: LOGODetect, PANTHER, and LEOPARD. GenoPred integrates this method using the script [pgs_methods/xwing.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/xwing.R). GenoPred downloads X-WING software from GitHub (e9fcc264266e0e884323311816bfe20053fd3f7a). The LD information derived from the 1KG dataset is provided by the developers of X-Wing. 
 
 ***
 
-#### TL-PRS
+##### TL-PRS
 
 TL-PRS (Transfer Learning Polygenic Risk Score) is implemented using the script [pgs_methods/tlprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/tlprs.R). The method combines summary statistics from target GWAS and a reference dataset via transfer learning, iteratively updating SNP effect sizes. GenoPred uses a modified version of TL-PRS to improve efficiency when being applied to many score files (https://github.com/opain/TLPRS). No modifications to the parameters used has been altered from the original version of TL-PRS (https://github.com/ZhangchenZhao/TLPRS/commit/6c8d8905f91ce902f0cca8d5465f9952b0b3e274). Multiple learning rates (factors of 1, 10, 100, 1000) are tested in an iterative approach with 15 steps per factor to update the SNP effect sizes. GenoPred uses the 1KG+HGDP reference for LD estimation, matching the population of the GWAS sample.
 
 ***
 
+#### Independently optimised
+
+The independentdently optimised multi-source approach describes the process of applying single-source PGS methods to each GWAS separately, and then linearly combining the population-specific PGS for the target population of interest. This linear combination can be estimated using individual-level data (simply by fitting a linear model containing all population-specific PGS), or using summary-statistics alone (using an approach called LEOPARD, from the Xwing software). In the GenoPred pipeline, we have adapted LEOPARD to use the super fast PGS method QuickPRS, to quickly and accurately estimate the optimal weights for linearly combining the population-specific PGS. These weights can be used to linearly combine population-specific PGS from single-source PGS method. We decribe and evaluate this process in detail in [this paper](https://doi.org/10.1101/2025.03.27.25324773).
+
+The script used to estimate the weights for population-specific PGS is [pgs_methods/leopard_quickprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/leopard_quickprs.R), and the script used to linearly combine the PGS using these weights is [pgs_methods/apply_leopard_weights.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/apply_leopard_weights.R).
+
+***
+
 ### Pseudovalidation
 
-Several polygenic scoring methods use a range of 'tuning' parameters when adjusting the GWAS effect sizes for polygenic scoring, resulting in multiple polygenic scores from a given GWAS. Traditionally, the user would then evaluate the predictive utility of polygenic scores derived using these various parameters, to select the best performing polygenic score, using cross-validation or bootstrapping to estimating the effect of the polygenic score without overfitting. However, several methods offer a pseudovalidation approach, whereby the optimal tuning parameter is estimated based on the GWAS summary statistics alone, convieniently avoiding the need for a tuning sample. The performance of pseudovalidation relative to formal validation varies between methods. See our previous [polygenic scoring methods comparison](comparison_of_methods_summary.html) for more information. 
+Several polygenic scoring methods use a range of 'tuning' parameters when adjusting the GWAS effect sizes for polygenic scoring, resulting in multiple polygenic scores from a given GWAS. Traditionally, the user would then evaluate the predictive utility of polygenic scores derived using these various parameters, to select the best performing polygenic score, using cross-validation or bootstrapping to estimating the effect of the polygenic score without overfitting. However, several methods offer a pseudovalidation approach, whereby the optimal tuning parameter is estimated based on the GWAS summary statistics alone, conveniently avoiding the need for a tuning sample. The performance of pseudovalidation relative to formal validation varies between methods. See our previous [polygenic scoring methods comparison](https://doi.org/10.1101/2025.03.27.25324773) for more information. 
 
 ***
 
diff --git a/docs/pipeline_technical.html b/docs/pipeline_technical.html
index c50d522c..08815ee2 100644
--- a/docs/pipeline_technical.html
+++ b/docs/pipeline_technical.html
@@ -437,23 +437,31 @@ <h1>Introduction</h1>
 scores. The pipeline aims to implement the current practises for
 polygenic scoring. See <a href="pipeline_overview.html">here</a> more
 general information regarding the GenoPred pipeline.</p>
-<p>Please cite our preprint when using the pipeline:</p>
-<ul>
-<li>“Pain, O. et al. ”The GenoPred Pipeline: A Comprehensive and
-Scalable Pipeline for Polygenic Scoring.” Bioinformatics 2024. <a
-href="https://doi.org/10.1093/bioinformatics/btae551"
-class="uri">https://doi.org/10.1093/bioinformatics/btae551</a></li>
-</ul>
-<p>If relevant, please also cite our paper comparing polygenic scoring
-methods and describing the reference-standardised approach:</p>
-<ul>
-<li>Pain, O. et al. “Evaluation of polygenic prediction methodology
-within a reference-standardized framework.” PLoS genetics. <a
-href="https://doi.org/10.1371/journal.pgen.1009021"
-class="uri">https://doi.org/10.1371/journal.pgen.1009021</a></li>
-</ul>
+<hr />
+<h3>
+Citations
+</h4>
+<p>Please cite the following relevant papers when using the
+pipeline:</p>
+<p><strong>Pipeline overview:</strong> - Pain, O. et al. (2024). <em>The
+GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic
+Scoring</em>. Bioinformatics.<br />
+<a href="https://doi.org/10.1093/bioinformatics/btae551"
+class="uri">https://doi.org/10.1093/bioinformatics/btae551</a></p>
+<p><strong>Comparison of single-source PGS methods:</strong> - Pain, O.
+et al. (2021). <em>Evaluation of polygenic prediction methodology within
+a reference-standardized framework</em>. PLoS Genetics.<br />
+<a href="https://doi.org/10.1371/journal.pgen.1009021"
+class="uri">https://doi.org/10.1371/journal.pgen.1009021</a></p>
+<p><strong>Integration and comparison of multi-source PGS
+methods:</strong> - Pain, O. (2025). <em>Leveraging Global Genetics
+Resources to Enhance Polygenic Prediction Across Ancestrally Diverse
+Populations</em>. medRxiv. <a
+href="https://doi.org/10.1101/2025.03.27.25324773"
+class="uri">https://doi.org/10.1101/2025.03.27.25324773</a></p>
 <p>Please also cite the relevant studies for the tools and data used by
-the GenoPred pipeline.</p>
+the GenoPred pipeline - Details provided in the <a
+href="pipeline_technical.html">Technical Documentation</a>.</p>
 <hr />
 </div>
 <div id="software" class="section level1">
@@ -483,7 +491,7 @@ <h1>Workflow</h1>
 <div class="centered-container">
 <div class="rounded-image-container">
 <div class="figure">
-<img src="Images/pipeline_readme/pipeline_schematic_lowdef.png"
+<img src="Images/pipeline_readme/pipeline_schematic_groups_lowdef.png"
 alt="" />
 <p class="caption">Figure 1. GenoPred pipeline schematic. Shows input
 files, processes, outputs, and rules.</p>
@@ -660,11 +668,11 @@ <h3>Single Ancestry Methods</h3>
 32330416
 </td>
 <td style="text-align:left;">
-Yes (only option)
+Yes
 </td>
 <td style="text-align:left;">
 SNP-heritability estimated using LD Score Regression (on liability scale
-for binary outcomes)
+for binary outcomes), multiplied by 0.8, 1, and 1.2
 </td>
 <td style="text-align:left;">
 Not excluded
@@ -673,10 +681,10 @@ <h3>Single Ancestry Methods</h3>
 Population-matched 1KG+HGDP
 </td>
 <td style="text-align:left;">
-40 seconds
+0.15 hrs
 </td>
 <td style="text-align:left;">
-450 Mb
+1.2 GB
 </td>
 </tr>
 <tr>
@@ -703,10 +711,10 @@ <h3>Single Ancestry Methods</h3>
 Population-matched 1KG+HGDP
 </td>
 <td style="text-align:left;">
-10 seconds
+0.08 hrs
 </td>
 <td style="text-align:left;">
-400 Mb
+7.3 GB
 </td>
 </tr>
 <tr>
@@ -733,10 +741,10 @@ <h3>Single Ancestry Methods</h3>
 EUR UKB (LDpred2-provided)
 </td>
 <td style="text-align:left;">
-3 minutes
+0.38 hrs
 </td>
 <td style="text-align:left;">
-500 Mb
+20.5 GB
 </td>
 </tr>
 <tr>
@@ -763,10 +771,10 @@ <h3>Single Ancestry Methods</h3>
 Population-matched 1KG+HGDP
 </td>
 <td style="text-align:left;">
-5 minutes
+0.85 hrs
 </td>
 <td style="text-align:left;">
-450Mb
+12.5 GB
 </td>
 </tr>
 <tr>
@@ -792,10 +800,10 @@ <h3>Single Ancestry Methods</h3>
 Population-matched UKB (PRS-CS provided)
 </td>
 <td style="text-align:left;">
-35 minutes
+4.40 hrs
 </td>
 <td style="text-align:left;">
-350 Mb
+10.6 GB
 </td>
 </tr>
 <tr>
@@ -822,10 +830,40 @@ <h3>Single Ancestry Methods</h3>
 Population-matched 1KG+HGDP
 </td>
 <td style="text-align:left;">
-5 seconds
+0.02 hrs
 </td>
 <td style="text-align:left;">
-100 Mb
+0.8 GB
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+QuickPRS
+</td>
+<td style="text-align:left;">
+LDAK
+</td>
+<td style="text-align:left;">
+34234142
+</td>
+<td style="text-align:left;">
+Yes (only option)
+</td>
+<td style="text-align:left;">
+Fits lasso, ridge, bolt and BayesR models, with a total of 148 sets of
+hyperparameters
+</td>
+<td style="text-align:left;">
+Not excluded
+</td>
+<td style="text-align:left;">
+Population-matched 1KG+HGDP
+</td>
+<td style="text-align:left;">
+0.06 hrs
+</td>
+<td style="text-align:left;">
+4.9 GB
 </td>
 </tr>
 <tr>
@@ -851,18 +889,50 @@ <h3>Single Ancestry Methods</h3>
 EUR UKB (GCTB-provided)
 </td>
 <td style="text-align:left;">
-3 minutes
+0.38 hrs
 </td>
 <td style="text-align:left;">
-500 Mb
+4.4 GB
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+SBayesRC
+</td>
+<td style="text-align:left;">
+SBayesRC R package
+</td>
+<td style="text-align:left;">
+38689000
+</td>
+<td style="text-align:left;">
+Yes (only option)
+</td>
+<td style="text-align:left;">
+NA
+</td>
+<td style="text-align:left;">
+Not excluded
+</td>
+<td style="text-align:left;">
+AFR/EAS/EUR UKB HapMap3 (SBayesRC provided)
+</td>
+<td style="text-align:left;">
+0.38 hrs
+</td>
+<td style="text-align:left;">
+4.4 GB
 </td>
 </tr>
 </tbody>
 </table>
 </div>
-<p>* = Based on chromosome 22 using single core. See <a
-href="pipeline_benchmark.html">here</a> for full benchmark
+<p>* = Using genome-wide GWAS with 10 cores. See <a
+href="crosspop.html#computational-resources">here</a> for full benchmark
 information.</p>
+<p>Note.This is the default behaviour of the GenoPred pipeline. Certain
+aspects can be modified using the configfile (<a
+href="pipeline_readme.html#additional-parameters">see here</a>).</p>
 <hr />
 <div id="dblsmm" class="section level4">
 <h4>DBLSMM</h4>
@@ -910,9 +980,11 @@ <h4>LDpred2</h4>
 GenoPred uses bigsnpr v1.12.2, with the default LDpred2 grid search, and
 recommended GWAS quality control checks. GenoPred employs LDpred2’s
 precomputed LD matrices based on the European individuals from the UK
-Biobank, and it is applied only to GWAS based on a EUR population. If
-the SNP-h2 estimated using LDSC is &lt;0.05, the SNP-heritability used
-by LDpred2 is set to 0.05.</p>
+Biobank, and it is applied only to GWAS based on a EUR population. The
+user can specify alternative LD reference data (<a
+href="pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods">see
+here</a>). If the SNP-h2 estimated using LDSC is &lt;0.05, the
+SNP-heritability used by LDpred2 is set to 0.05.</p>
 <hr />
 </div>
 <div id="megaprs" class="section level4">
@@ -965,6 +1037,16 @@ <h4>pT+clump</h4>
 GWAS sample.</p>
 <hr />
 </div>
+<div id="quickprs" class="section level4">
+<h4>QuickPRS</h4>
+<p>In GenoPred, QuickPRS is run using the script <a
+href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/quickprs.R">pgs_methods/quickprs.R</a>.
+QuickPRS is a faster implementation of MegaPRS, sped up by using
+precomputed LD and annotation data. It only inlcudes the model selected
+using pseudovalidation. GenoPred employs the 1KG+HGDP reference for LD
+estimation, matching the population of the GWAS sample.</p>
+<hr />
+</div>
 <div id="sbayesr" class="section level4">
 <h4>SBayesR</h4>
 <p>In GenoPred, SBayesR is run using the script <a
@@ -977,11 +1059,31 @@ <h4>SBayesR</h4>
 SBayesR.</p>
 <hr />
 </div>
+<div id="sbayesrc" class="section level4">
+<h4>SBayesRC</h4>
+<p>In GenoPred, SBayesRC is implemented via pgs_<a
+href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/sbayesrc.R">pgs_methods/sbayesrc.R</a>.
+This method extends SBayesR to integrate functional annotations into the
+Bayesian framework for estimating SNP effects from GWAS summary
+statistics. By default SBayesRC will use the LD matrices released by the
+developers, restricted to GWAS from EUR, EAS, and AFR populations. The
+user can specify alternative LD reference data (<a
+href="pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods">see
+here</a>)</p>
+<hr />
+</div>
 </div>
 <div id="multi-ancestry-methods" class="section level3">
 <h3>Multi-ancestry Methods</h3>
 <p>The GenoPred pipeline implements a range of PGS methods intended for
-multiple GWAS from different ancestral populations.</p>
+analysis of multiple GWAS from different ancestral populations.
+Multi-ancestry / Multi-source approaches can be catagorised as jointly
+optimised or independently optimised.</p>
+<hr />
+<div id="jointly-optimised" class="section level4">
+<h4>Jointly optimised</h4>
+<p>Jointly optimised methods, modelling all GWAS simultaneously when
+generating SNP-weights, are shown below:</p>
 <div
 style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:500px; overflow-x: scroll; width:100%; ">
 <table class="table table-striped table-hover" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
@@ -1044,10 +1146,10 @@ <h3>Multi-ancestry Methods</h3>
 Population-matched UKB (PRS-CS provided)
 </td>
 <td style="text-align:left;">
-NA
+6.84 hrs
 </td>
 <td style="text-align:left;">
-NA
+15.2 GB
 </td>
 </tr>
 <tr>
@@ -1073,10 +1175,10 @@ <h3>Multi-ancestry Methods</h3>
 Population-matched UKB (PRS-CS provided)
 </td>
 <td style="text-align:left;">
-NA
+34.12 hrs
 </td>
 <td style="text-align:left;">
-NA
+48.7 GB
 </td>
 </tr>
 <tr>
@@ -1103,18 +1205,24 @@ <h3>Multi-ancestry Methods</h3>
 Population-matched 1KG+HGDP
 </td>
 <td style="text-align:left;">
-NA
+0.33 hrs
 </td>
 <td style="text-align:left;">
-NA
+31.0 GB
 </td>
 </tr>
 </tbody>
 </table>
 </div>
+<p>* = Time per GWAS/population, using genome-wide data with 10 cores.
+See <a href="crosspop.html#computational-resources">here</a> for full
+benchmark information.</p>
+<p>Note.This is the default behaviour of the GenoPred pipeline. Certain
+aspects can be modified using the configfile (<a
+href="pipeline_readme.html#additional-parameters">see here</a>).</p>
 <hr />
-<div id="prs-csx" class="section level4">
-<h4>PRS-CSx</h4>
+<div id="prs-csx" class="section level5">
+<h5>PRS-CSx</h5>
 <p>PRS-CSx, an extension of PRS-CS, integrates GWAS summary statistics
 from multiple ancestry groups using a Bayesian framework. The method is
 implemented in GenoPred via the script <a
@@ -1137,8 +1245,8 @@ <h4>PRS-CSx</h4>
 GWAS).</p>
 <hr />
 </div>
-<div id="x-wing" class="section level4">
-<h4>X-Wing</h4>
+<div id="x-wing" class="section level5">
+<h5>X-Wing</h5>
 <p>X-Wing is a polygenic scoring method designed to enhance genetic
 prediction accuracy across ancestrally diverse populations by leveraging
 a cross-population weighting framework. The method operates through a
@@ -1150,8 +1258,8 @@ <h4>X-Wing</h4>
 from the 1KG dataset is provided by the developers of X-Wing.</p>
 <hr />
 </div>
-<div id="tl-prs" class="section level4">
-<h4>TL-PRS</h4>
+<div id="tl-prs" class="section level5">
+<h5>TL-PRS</h5>
 <p>TL-PRS (Transfer Learning Polygenic Risk Score) is implemented using
 the script <a
 href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/tlprs.R">pgs_methods/tlprs.R</a>.
@@ -1171,6 +1279,30 @@ <h4>TL-PRS</h4>
 <hr />
 </div>
 </div>
+<div id="independently-optimised" class="section level4">
+<h4>Independently optimised</h4>
+<p>The independentdently optimised multi-source approach describes the
+process of applying single-source PGS methods to each GWAS separately,
+and then linearly combining the population-specific PGS for the target
+population of interest. This linear combination can be estimated using
+individual-level data (simply by fitting a linear model containing all
+population-specific PGS), or using summary-statistics alone (using an
+approach called LEOPARD, from the Xwing software). In the GenoPred
+pipeline, we have adapted LEOPARD to use the super fast PGS method
+QuickPRS, to quickly and accurately estimate the optimal weights for
+linearly combining the population-specific PGS. These weights can be
+used to linearly combine population-specific PGS from single-source PGS
+method. We decribe and evaluate this process in detail in <a
+href="https://doi.org/10.1101/2025.03.27.25324773">this paper</a>.</p>
+<p>The script used to estimate the weights for population-specific PGS
+is <a
+href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/leopard_quickprs.R">pgs_methods/leopard_quickprs.R</a>,
+and the script used to linearly combine the PGS using these weights is
+<a
+href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/apply_leopard_weights.R">pgs_methods/apply_leopard_weights.R</a>.</p>
+<hr />
+</div>
+</div>
 <div id="pseudovalidation" class="section level3">
 <h3>Pseudovalidation</h3>
 <p>Several polygenic scoring methods use a range of ‘tuning’ parameters
@@ -1181,11 +1313,12 @@ <h3>Pseudovalidation</h3>
 score, using cross-validation or bootstrapping to estimating the effect
 of the polygenic score without overfitting. However, several methods
 offer a pseudovalidation approach, whereby the optimal tuning parameter
-is estimated based on the GWAS summary statistics alone, convieniently
+is estimated based on the GWAS summary statistics alone, conveniently
 avoiding the need for a tuning sample. The performance of
 pseudovalidation relative to formal validation varies between methods.
-See our previous <a href="comparison_of_methods_summary.html">polygenic
-scoring methods comparison</a> for more information.</p>
+See our previous <a
+href="https://doi.org/10.1101/2025.03.27.25324773">polygenic scoring
+methods comparison</a> for more information.</p>
 <hr />
 </div>
 </div>
diff --git a/pipeline/envs/pipeline.yaml b/pipeline/envs/pipeline.yaml
index e2f80b0e..6cd3f68a 100644
--- a/pipeline/envs/pipeline.yaml
+++ b/pipeline/envs/pipeline.yaml
@@ -5,7 +5,6 @@ channels:
   - defaults
 dependencies:
   - python=3.8
-  - mamba=1.5.6
   - snakemake-minimal=7.32.3
   - pandas=2.0.3
   - ghostscript=10.02.1

From f8430005bb377af907fa3a69b89323d76ecb9ea2 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sun, 6 Apr 2025 15:41:06 +0100
Subject: [PATCH 127/153] setting up test data for multi-source methods, and
 fixed bug in quickprs_leopard

---
 Scripts/pgs_methods/leopard_quickprs.R        |  28 +++-
 Scripts/pgs_methods/quickprs.R                |  24 +++
 docs/CrossPop.Rmd                             |  64 +++++++-
 docs/miraclestrial.Rmd                        | 144 ++++++++++++++++++
 .../example_input/config.multisource.yaml     |  11 ++
 .../example_input/gwas_groups.multisource.txt |   2 +
 pipeline/example_input/gwas_groups.txt        |   2 -
 .../example_input/gwas_list.multisource.txt   |   3 +
 pipeline/misc/dev/test_setup.Rmd              | 126 ++++++++++++++-
 pipeline/rules/dependencies.smk               |   6 +-
 pipeline/rules/pgs_methods.smk                |   2 +
 11 files changed, 399 insertions(+), 13 deletions(-)
 create mode 100644 pipeline/example_input/config.multisource.yaml
 create mode 100644 pipeline/example_input/gwas_groups.multisource.txt
 delete mode 100644 pipeline/example_input/gwas_groups.txt
 create mode 100644 pipeline/example_input/gwas_list.multisource.txt

diff --git a/Scripts/pgs_methods/leopard_quickprs.R b/Scripts/pgs_methods/leopard_quickprs.R
index 7de31a18..0134bad2 100644
--- a/Scripts/pgs_methods/leopard_quickprs.R
+++ b/Scripts/pgs_methods/leopard_quickprs.R
@@ -6,6 +6,12 @@ suppressMessages(library("optparse"))
 option_list = list(
   make_option("--output", action="store", default='NA', type='character',
               help="Path for output files [required]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+              help="Path PLINK v2 software binary [optional]"),
+  make_option("--ref_plink_chr", action="store", default=NA, type='character',
+              help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--pop_data", action="store", default=NULL, type='character',
+              help="File containing the population code and location of the keep file [required]"),
   make_option("--sumstats", action="store", default=NULL, type='character',
               help="Comma-seperated list of GWAS summary statistics [required]"),
   make_option("--scores", action="store", default=NULL, type='character',
@@ -39,6 +45,12 @@ source('../functions/misc.R')
 source_all('../functions')
 
 # Check required inputs
+if(is.null(opt$ref_plink_chr)){
+  stop('--ref_plink_chr must be specified.\n')
+}
+if(is.null(opt$pop_data)){
+  stop('--pop_data must be specified.\n')
+}
 if(is.null(opt$sumstats)){
   stop('--sumstats must be specified.\n')
 }
@@ -222,17 +234,21 @@ mix_weights <- calculate_avg_weights(populations = populations, leopard_dir = pa
 # Adjust weights to correspond to PGS with SD of 1
 ####
 
-# Read in the scale files for the original score files
+# Calculate scale of polygenic scores
+# This is done later by the ref_pgs rule, but we need it now
+pop_data <- read_pop_data(opt$pop_data)
 scale_all <- NULL
 for(i in 1:length(score_files)){
-  scale_file <- gsub('.score.gz', '', score_files[i])
-  scale_file <- paste0(scale_file, '-', populations[i], '.scale')
-  scale_file <- fread(scale_file)
-  
+  # Calculate scores in the full reference
+  ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = score_files[i], threads = opt$n_cores)
+
+  # Calculate scale within GWAS population
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == populations[i], c('FID','IID'), with=F])
+
   scale_all <- rbind(
     scale_all, 
     data.table(
-      SD = scale_file$SD,
+      SD = ref_pgs_scale_i$SD,
       Discovery = populations[i]
     )
   )
diff --git a/Scripts/pgs_methods/quickprs.R b/Scripts/pgs_methods/quickprs.R
index d200b093..764d4a3a 100644
--- a/Scripts/pgs_methods/quickprs.R
+++ b/Scripts/pgs_methods/quickprs.R
@@ -190,6 +190,30 @@ if(!is.na(opt$test)){
   test_finish(log_file = log_file, test_start.time = test_start.time)
 }
 
+
+####
+# Calculate mean and sd of polygenic scores
+####
+
+log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.')
+
+# Calculate scores in the full reference
+ref_pgs <- plink_score(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, score = paste0(opt$output,'.score.gz'), threads = opt$n_cores)
+
+if(!is.null(opt$ref_pcs)){
+  log_add(log_file = log_file, message = 'Deriving trans-ancestry PGS models...')
+  # Derive trans-ancestry PGS models and estimate PGS residual scale
+  model_trans_pgs(scores=ref_pgs, pcs=opt$ref_pcs, output=opt$output)
+}
+
+# Calculate scale within each reference population
+pop_data <- read_pop_data(opt$pop_data)
+
+for(pop_i in unique(pop_data$POP)){
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs, keep = pop_data[pop_data$POP == pop_i, c('FID','IID'), with=F])
+  fwrite(ref_pgs_scale_i, paste0(opt$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA')
+}
+
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index f877b95b..f84e669a 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1794,7 +1794,9 @@ We will do this using GenoPred.
 ######
 # gwas_list
 ######
+
 library(data.table)
+
 # Subset original gwas_list to include selected traits
 gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt')
 pheno<-gsub('_.*','', gwas_list$name)
@@ -1802,7 +1804,11 @@ selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_su
 gwas_list<-gwas_list[pheno %in% selected_traits,]
 gwas_list$label<-paste0('"', gwas_list$label, '"')
 
-write.table(gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt', col.names = T, row.names = F, quote = F)
+write.table(
+  gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt', 
+  col.names = T, 
+  row.names = F, 
+  quote = F)
 
 ######
 # gwas_groups
@@ -2711,6 +2717,62 @@ ggplot(tmp, aes(x=label, y=R , fill = Model)) +
           legend.justification = "center")
 dev.off()
 
+# Make simplified plot
+# Just show performance when using IndivTrain (or SumStat), and Remove 'All' model, with both GWAS.
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- tmp[tmp$Method != 'all',]
+tmp <- tmp[tmp$Source  == 'Multi',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
+tmp<-tmp[tmp$Model == 'IndivTune',]
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_simple.png'), res=300, width = 3200, height = 2000, units = 'px')
+ggplot(tmp, aes(x=label, y=R)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23, fill = 'black') +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ ., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+tmp<-tmp[tmp$Method %in% c('ldpred2','prscsx','xwing'),]
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_simple_ldpred2.png'), res=300, width = 500, height = 500, units = 'px')
+ggplot(tmp, aes(x=label, y=R)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+  #  geom_point(stat="identity", position=position_dodge(1), fill = '#3399FF') +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23, fill = '#3399FF') +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ ., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+
 ####
 # Create heatmap showing difference between all methods and models
 ####
diff --git a/docs/miraclestrial.Rmd b/docs/miraclestrial.Rmd
index fe29d0c1..206775d5 100644
--- a/docs/miraclestrial.Rmd
+++ b/docs/miraclestrial.Rmd
@@ -63,5 +63,149 @@ We could also further stratify the polygenic scores by functional annotations, s
 
 We can use previously published EWAS summary statistics to do the same thing. Alternatively, we can use 
 
+***
+
+# Calculate PGS
+
+```{bash}
+mkdir -p /users/k1806347/oliverpainfel/Data/miraclestrial/original
+
+# Create index for each batch
+module add bcftools/1.12-gcc-13.2.0-python-3.11.6
+
+####
+# Format for GenoPred
+####
+# Split into per chromosome files
+
+##
+# Batch 1
+## 
+for chr in $(seq 1 22);do
+    sbatch --mem 10G -n 1 -p interruptible_cpu -t 1:00:00 --wrap="/users/k1806347/oliverpainfel/Software/plink2 \
+        --vcf /scratch/prj/mirocalstrial/recovered/DNAscan/variantcalling/merged_strelka_stringent.vcf.gz \
+        --chr ${chr} \
+        --out /users/k1806347/oliverpainfel/Data/miraclestrial/processed/miracles_batch1.chr${chr} \
+        --allow-extra-chr \
+        --export vcf bgz"
+done
+
+# The file is truncated mid chr16, and missing chr 17-22
+
+# Recreate merged dataset 
+module add bcftools/1.12-gcc-13.2.0-python-3.11.6
+
+bcftools merge \
+  -l sample_vcf_list.txt \
+  --missing-to-ref \
+  -o /users/k1806347/oliverpainfel/Data/miraclestrial/original/miracles_batch1.vcf.gz
+
+# Replace corrupt version
+cp /users/k1806347/oliverpainfel/Data/miraclestrial/original/miracles_batch1.vcf.gz /scratch/prj/mirocalstrial/recovered/DNAscan/variantcalling/merged_strelka_stringent_fixed.vcf.gz
+
+# Split by chromosome
+for chr in $(seq 1 22);do
+    sbatch --mem 10G -n 1 -p interruptible_cpu -t 1:00:00 --wrap="/users/k1806347/oliverpainfel/Software/plink2 \
+        --vcf /users/k1806347/oliverpainfel/Data/miraclestrial/original/miracles_batch1.vcf.gz \
+        --chr ${chr} \
+        --out /users/k1806347/oliverpainfel/Data/miraclestrial/processed/miracles_batch1.chr${chr} \
+        --allow-extra-chr \
+        --export vcf bgz"
+done
+
+##
+# Batch 2
+## 
+for chr in $(seq 1 22);do
+    sbatch --mem 10G -n 1 -p interruptible_cpu -t 1:00:00 --wrap="/users/k1806347/oliverpainfel/Software/plink2 \
+        --vcf /scratch/prj/mirocalstrial/recovered/DNAscan_batch2/variantcalling/merged_strelka_stringent.vcf.gz \
+        --chr ${chr} \
+        --out /users/k1806347/oliverpainfel/Data/miraclestrial/processed/miracles_batch2.chr${chr} \
+        --allow-extra-chr \
+        --export vcf bgz"
+done
+
+# Batch 2 seems fine.
+
+```
+
+```{r}
+library(data.table)
+
+dir.create('/users/k1806347/oliverpainfel/Data/miraclestrial/GenoPred/output', recursive = T)
+dir.create('/users/k1806347/oliverpainfel/Data/miraclestrial/GenoPred/config', recursive = T)
+
+# Create target_list
+target_list <- NULL
+target_list <- rbind(target_list, data.table(
+  name = 'miracles_batch1',
+  path = '/users/k1806347/oliverpainfel/Data/miraclestrial/processed/miracles_batch1',
+  type = 'vcf',
+  indiv_report = F))
+
+target_list <- rbind(target_list, data.table(
+  name = 'miracles_batch2',
+  path = '/users/k1806347/oliverpainfel/Data/miraclestrial/processed/miracles_batch2',
+  type = 'vcf',
+  indiv_report = F))
+
+write.table(
+  target_list,
+  '/users/k1806347/oliverpainfel/Data/miraclestrial/GenoPred/config/target_list.txt',
+  col.names = T,
+  row.names = F,
+  quote = F,
+  sep = ' '
+)
+
+# Create gwas_list
+gwas_list <- data.table(
+  name='yengo_eur',
+  path = '/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eur.txt',
+  population = 'EUR',
+  n = NA,
+  sampling = NA,
+  prevalence = NA,
+  mean = NA,
+  sd = NA,
+  label = "\"Yengo 2022 Height EUR\"")
+
+write.table(
+  gwas_list,
+  '/users/k1806347/oliverpainfel/Data/miraclestrial/GenoPred/config/gwas_list.txt',
+  col.names = T,
+  row.names = F,
+  quote = F,
+  sep = ' '
+)
+
+# Create config file
+conf <- c(
+  'outdir: /users/k1806347/oliverpainfel/Data/miraclestrial/GenoPred/output',
+  'config_file: /users/k1806347/oliverpainfel/Data/miraclestrial/GenoPred/config/config.yaml',
+  'target_list: /users/k1806347/oliverpainfel/Data/miraclestrial/GenoPred/config/target_list.txt',
+  'gwas_list: /users/k1806347/oliverpainfel/Data/miraclestrial/GenoPred/config/gwas_list.txt',
+  "pgs_methods: ['dbslmm']"
+)
+
+write.table(
+  conf,
+  '/users/k1806347/oliverpainfel/Data/miraclestrial/GenoPred/config/config.yaml',
+  col.names = F,
+  row.names = F,
+  quote = F
+)
 
+```
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate genopred
+
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/miraclestrial/GenoPred/config/config.yaml \
+  output_all -n 
+```
 
diff --git a/pipeline/example_input/config.multisource.yaml b/pipeline/example_input/config.multisource.yaml
new file mode 100644
index 00000000..e31a175e
--- /dev/null
+++ b/pipeline/example_input/config.multisource.yaml
@@ -0,0 +1,11 @@
+outdir: test_data/output/test1
+config_file: example_input/config.multisource.yaml
+gwas_list: example_input/gwas_list.multisource.txt
+gwas_groups: example_input/gwas_groups.multisource.txt
+target_list: example_input/target_list.txt
+pgs_methods: ['lassosum']
+leopard_methods: ['lassosum']
+testing: chr22
+cores_prep_pgs: 5
+cores_target_pgs: 5
+mem_target_pgs: 2000
diff --git a/pipeline/example_input/gwas_groups.multisource.txt b/pipeline/example_input/gwas_groups.multisource.txt
new file mode 100644
index 00000000..36d4a0fa
--- /dev/null
+++ b/pipeline/example_input/gwas_groups.multisource.txt
@@ -0,0 +1,2 @@
+name gwas label
+height height_eur,height_eas "Height EUR+EAS"
diff --git a/pipeline/example_input/gwas_groups.txt b/pipeline/example_input/gwas_groups.txt
deleted file mode 100644
index 213fc7ff..00000000
--- a/pipeline/example_input/gwas_groups.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-name gwas label
-height yengo_eur,yengo_eas "Height (EUR+EAS)"
diff --git a/pipeline/example_input/gwas_list.multisource.txt b/pipeline/example_input/gwas_list.multisource.txt
new file mode 100644
index 00000000..61569ac7
--- /dev/null
+++ b/pipeline/example_input/gwas_list.multisource.txt
@@ -0,0 +1,3 @@
+name path population n sampling prevalence mean sd label
+height_eur test_data/reference/gwas_sumstats/height_eur.txt.gz EUR NA NA NA NA NA "Height EUR"
+height_eas test_data/reference/gwas_sumstats/height_eas.txt.gz EAS NA NA NA NA NA "Height EAS"
diff --git a/pipeline/misc/dev/test_setup.Rmd b/pipeline/misc/dev/test_setup.Rmd
index 63736f58..55de6b1a 100644
--- a/pipeline/misc/dev/test_setup.Rmd
+++ b/pipeline/misc/dev/test_setup.Rmd
@@ -7,7 +7,131 @@ output: html_document
 knitr::opts_chunk$set(echo = TRUE)
 ```
 
-I want to set up a series of tests that can be quickly run to check everything is working as it should. We can test every step all the time, so lets focus on testing the following scenario:
+# Test data
+
+I previously created some test data for users to test out the pipeline. I would like to update this test data to include height GWAS from EUR and EAS populations to test out the multi-source functionality.
+  
+```{r}
+library(data.table)
+
+yengo_eur<-fread('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eur.txt')
+yengo_eas<-fread('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eas.txt')
+
+ref <- NULL
+for(i in 1:22){
+  ref <-
+    rbind(
+      ref,
+      readRDS(
+        paste0(
+          '~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr', i, '.rds'
+      )))
+}
+
+yengo_eur <- yengo_eur[yengo_eur$variant_id %in% ref$SNP,]
+yengo_eas <- yengo_eas[yengo_eas$variant_id %in% ref$SNP,]
+
+# Save subset GWAS sumstats
+dir.create('~/test_data/reference/gwas_sumstats', recursive = T)
+fwrite(yengo_eur, '~/oliverpainfel/Software/MyGit/GenoPred/pipeline/test_data/reference/gwas_sumstats/height_eur.txt.gz', quote=F, sep=' ', na='NA')
+fwrite(yengo_eas, '~/oliverpainfel/Software/MyGit/GenoPred/pipeline/test_data/reference/gwas_sumstats/height_eas.txt.gz', quote=F, sep=' ', na='NA')
+
+```
+
+Test the pipeline using the example config.
+
+```{bash}
+cd ~/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate genopred
+snakemake --profile slurm --configfile=example_input/config.yaml --use-conda output_all -n
+```
+
+Make a test config that uses the EUR and EAS height GWAS.
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline')
+library(data.table)
+
+# Create gwas_list
+gwas_list <- NULL
+gwas_list<-rbind(gwas_list, 
+                 data.table(name='height_eur',
+                            path = 'test_data/reference/gwas_sumstats/height_eur.txt.gz',
+                            population = 'EUR',
+                            n = NA,
+                            sampling = NA,
+                            prevalence = NA,
+                            mean = NA,
+                            sd = NA,
+                            label = "\"Height EUR\""))
+
+gwas_list<-rbind(gwas_list, 
+                 data.table(name='height_eas',
+                            path = 'test_data/reference/gwas_sumstats/height_eas.txt.gz',
+                            population = 'EAS',
+                            n = NA,
+                            sampling = NA,
+                            prevalence = NA,
+                            mean = NA,
+                            sd = NA,
+                            label = "\"Height EAS\""))
+
+write.table(gwas_list, 'example_input/gwas_list.multisource.txt', col.names = T, row.names = F, quote = F, sep = ' ')
+
+# Create gwas_groups
+gwas_groups <- data.frame(
+  name = 'height',
+  gwas = 'height_eur,height_eas',
+  label = "\"Height EUR+EAS\""
+)
+
+write.table(gwas_groups, 'example_input/gwas_groups.multisource.txt', col.names = T, row.names = F, quote = F, sep = ' ')
+
+# Create config file
+conf <- c(
+  'outdir: test_data/output/test1',
+  'config_file: example_input/config.multisource.yaml',
+  'gwas_list: example_input/gwas_list.multisource.txt',
+  'gwas_groups: example_input/gwas_groups.multisource.txt',
+  'target_list: example_input/target_list.txt',
+  "pgs_methods: ['lassosum']",
+  "leopard_methods: ['lassosum']",
+  'testing: chr22',
+  'cores_prep_pgs: 5',
+  'cores_target_pgs: 5',
+  'mem_target_pgs: 2000'
+)
+
+write.table(conf, 'example_input/config.multisource.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+Now test it using the multi-source set up.
+
+```{bash}
+cd ~/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate genopred
+snakemake --profile slurm --configfile=example_input/config.multisource.yaml --use-conda output_all -n
+
+# There was an error when running leopard_quickprs - running interactively to understand.
+```
+
+Now compress the test data, upload to Zenodo, and update the documentation.
+
+```{bash}
+cd ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/test_data
+tar -czvf archive_name.tar.gz folder_name/
+```
+
+***
+
+
+
+some target data (in various formats), GWAS summary statistics for two t
+
+I want to set up a series of tests that can be quickly run to check everything is working as it should. 
+
+We can test every step all the time, so lets focus on testing the following scenario:
 
 - Target sample data: Same as GenoPred test data, but restricted to subset of SNPs
 - GWAS sumstats: Same as GenoPred test data, but restricted to subset of SNPs
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 0a88ff10..4e9bfd5b 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -341,7 +341,7 @@ if (config["leopard_methods"] and config["leopard_methods"] != "NA") or "quickpr
 # Set quickprs_multi reference path
 if (config["leopard_methods"] and config["leopard_methods"] != "NA"):
   if config['quickprs_multi_ldref'] == 'NA':
-    quickprs_multi_ldref=f"{resdir}/data/quickprs"
+    quickprs_multi_ldref=f"{resdir}/data/quickprs_leopard"
     
     # Check if gwas_list contains invalid populations
     valid_pops = {'EUR', 'EAS', 'AFR'}
@@ -1100,7 +1100,7 @@ quickprs_leopard_ref_gdrive = {
 
 rule download_quickprs_leopard_ref:
   output:
-    f"{resdir}/data/quickprs_leopard/{{population}}/{{population}}.cors.bin"
+    f"{resdir}/data/quickprs_leopard/{{population}}/{{population}}.subset_1.bed"
   benchmark:
     f"{resdir}/data/benchmarks/download_quickprs_leopard_ref-{{population}}.txt"
   log:
@@ -1120,7 +1120,7 @@ rule download_quickprs_leopard_ref:
 
 rule download_quickprs_leopard_ref_all:
   input:
-    lambda w: expand(f"{resdir}/data/quickprs_leopard/{{population}}/{{population}}.cors.bin", population=['EUR', 'EAS', 'AFR'])
+    lambda w: expand(f"{resdir}/data/quickprs_leopard/{{population}}/{{population}}.subset_1.bed", population=['EUR', 'EAS', 'AFR'])
 
 # Download preprocessed reference data (1KG+HGDP HapMap3)
 rule download_default_ref:
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 31dc2c41..ec6e95fa 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -602,6 +602,8 @@ rule leopard_quickprs_i:
     testing=config["testing"]
   shell:
     "Rscript ../Scripts/pgs_methods/leopard_quickprs.R \
+      --ref_plink_chr {refdir}/ref.chr \
+      --pop_data {refdir}/ref.pop.txt \
       --sumstats {params.sumstats} \
       --scores {params.scores} \
       --populations {params.populations} \

From 329f78fb933207d91ee64e10b899afa241ea01c9 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sun, 6 Apr 2025 16:39:20 +0100
Subject: [PATCH 128/153] Updates

---
 Scripts/pgs_methods/apply_leopard_weights.R   |  5 ---
 Scripts/pgs_methods/leopard_quickprs.R        |  1 +
 .../pipeline_reports/indiv_report_creator.Rmd |  7 +++--
 docs/pipeline_readme.Rmd                      |  2 +-
 docs/pipeline_readme.html                     |  2 +-
 .../example_input/gwas_list.multisource.txt   |  4 +--
 .../misc/dev/test_data/config/config.yaml     |  1 +
 pipeline/misc/dev/test_data/ref/ref.keep.list |  6 ----
 pipeline/misc/dev/test_setup.Rmd              | 31 ++++++-------------
 9 files changed, 20 insertions(+), 39 deletions(-)
 delete mode 100644 pipeline/misc/dev/test_data/ref/ref.keep.list

diff --git a/Scripts/pgs_methods/apply_leopard_weights.R b/Scripts/pgs_methods/apply_leopard_weights.R
index e79f6e72..52967fb5 100644
--- a/Scripts/pgs_methods/apply_leopard_weights.R
+++ b/Scripts/pgs_methods/apply_leopard_weights.R
@@ -169,11 +169,6 @@ if(file.exists(paste0(opt$output,'.score.gz'))){
 
 system(paste0('gzip ',opt$output,'.score'))
 
-# Record end time of test
-if(!is.na(opt$test)){
-  test_finish(log_file = log_file, test_start.time = test_start.time)
-}
-
 end.time <- Sys.time()
 time.taken <- end.time - start.time
 sink(file = log_file, append = T)
diff --git a/Scripts/pgs_methods/leopard_quickprs.R b/Scripts/pgs_methods/leopard_quickprs.R
index 0134bad2..1170693f 100644
--- a/Scripts/pgs_methods/leopard_quickprs.R
+++ b/Scripts/pgs_methods/leopard_quickprs.R
@@ -255,6 +255,7 @@ for(i in 1:length(score_files)){
 }
 
 # Adjust weights to correspond to PGS with SD of 1
+# Note. It might be more appropriate to use the SD of the score in the target population, rather than PGS SD in GWAS population. Unlikely to make a big difference.
 log_add(log_file = log_file, message = '------------------------')
 for(i in populations){
   mix_weights[[i]] <- adjust_weights(weights = mix_weights[[i]], pgs_sd = scale_all$SD)
diff --git a/Scripts/pipeline_reports/indiv_report_creator.Rmd b/Scripts/pipeline_reports/indiv_report_creator.Rmd
index 5155a349..e1ec8499 100644
--- a/Scripts/pipeline_reports/indiv_report_creator.Rmd
+++ b/Scripts/pipeline_reports/indiv_report_creator.Rmd
@@ -12,7 +12,6 @@ output:
     toc_depth: 3
     toc_float: true
     fig_caption: yes
-
 ---
 
 ```{r setup, include=FALSE}
@@ -487,8 +486,10 @@ cat0("## Target Polygenic Profile {.tabset .tabset-fade} \n\n")
 
 ```{r, include = F, eval = pgs_incl}
 
-# Read in prs
-pgs <- read_pgs(config = params$config, name = params$name, pop = 'TRANS', pseudo_only=T)[[1]]
+# Read in PGS
+# Exclude PGS from multi-source methods as no estimate of R is available 
+single_source_methods <- pgs_methods_list[!(pgs_methods_list %in% pgs_group_methods) & !(grepl('_multi|tlprs_', pgs_methods_list))]
+pgs <- read_pgs(config = params$config, name = params$name, pop = 'TRANS', pseudo_only=T, pgs_method = single_source_methods)[[1]]
 
 # Structure PGS for target individual
 pgs_dat <- NULL
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 50f7fe9e..cc5266a6 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -536,7 +536,7 @@ First, we need to download and decompress the test data. Do this within the `Gen
 ```{bash}
 # Download from Google Drive
 conda activate genopred
-gdown --id 1C4AwDnY_hJ4ilGneMlAjwEKghzss5PeG -O test_data.tar.gz
+gdown 1C4AwDnY_hJ4ilGneMlAjwEKghzss5PeG -O test_data.tar.gz
 
 # Decompress
 tar -xf test_data.tar.gz
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index e7aa4c42..aa485950 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -1041,7 +1041,7 @@ <h2>Step 1: Download the test data</h2>
 within the <code>GenoPred/pipeline</code> folder.</p>
 <pre class="bash"><code># Download from Google Drive
 conda activate genopred
-gdown --id 1C4AwDnY_hJ4ilGneMlAjwEKghzss5PeG -O test_data.tar.gz
+gdown 1C4AwDnY_hJ4ilGneMlAjwEKghzss5PeG -O test_data.tar.gz
 
 # Decompress
 tar -xf test_data.tar.gz
diff --git a/pipeline/example_input/gwas_list.multisource.txt b/pipeline/example_input/gwas_list.multisource.txt
index 61569ac7..92087fbb 100644
--- a/pipeline/example_input/gwas_list.multisource.txt
+++ b/pipeline/example_input/gwas_list.multisource.txt
@@ -1,3 +1,3 @@
 name path population n sampling prevalence mean sd label
-height_eur test_data/reference/gwas_sumstats/height_eur.txt.gz EUR NA NA NA NA NA "Height EUR"
-height_eas test_data/reference/gwas_sumstats/height_eas.txt.gz EAS NA NA NA NA NA "Height EAS"
+height_eur test_data/reference/gwas_sumstats/height_eur.txt.gz EUR NA NA NA 0 1 "Height EUR"
+height_eas test_data/reference/gwas_sumstats/height_eas.txt.gz EAS NA NA NA 0 1 "Height EAS"
diff --git a/pipeline/misc/dev/test_data/config/config.yaml b/pipeline/misc/dev/test_data/config/config.yaml
index e6ecfa53..024d9313 100644
--- a/pipeline/misc/dev/test_data/config/config.yaml
+++ b/pipeline/misc/dev/test_data/config/config.yaml
@@ -9,3 +9,4 @@ pgs_methods: ['ptclump','lassosum']
 testing: chr22
 cores_target_pgs: 1
 mem_target_pgs: 2000
+pgs_scaling: ['continuous', 'discrete]
diff --git a/pipeline/misc/dev/test_data/ref/ref.keep.list b/pipeline/misc/dev/test_data/ref/ref.keep.list
deleted file mode 100644
index 4bfb5db1..00000000
--- a/pipeline/misc/dev/test_data/ref/ref.keep.list
+++ /dev/null
@@ -1,6 +0,0 @@
-EUR resources/data/ref/keep_files/EUR.keep
-EAS resources/data/ref/keep_files/EAS.keep
-AMR resources/data/ref/keep_files/AMR.keep
-CSA resources/data/ref/keep_files/CSA.keep
-AFR resources/data/ref/keep_files/AFR.keep
-MID resources/data/ref/keep_files/MID.keep
diff --git a/pipeline/misc/dev/test_setup.Rmd b/pipeline/misc/dev/test_setup.Rmd
index 55de6b1a..e11258a6 100644
--- a/pipeline/misc/dev/test_setup.Rmd
+++ b/pipeline/misc/dev/test_setup.Rmd
@@ -61,8 +61,8 @@ gwas_list<-rbind(gwas_list,
                             n = NA,
                             sampling = NA,
                             prevalence = NA,
-                            mean = NA,
-                            sd = NA,
+                            mean = 0,
+                            sd = 1,
                             label = "\"Height EUR\""))
 
 gwas_list<-rbind(gwas_list, 
@@ -72,8 +72,8 @@ gwas_list<-rbind(gwas_list,
                             n = NA,
                             sampling = NA,
                             prevalence = NA,
-                            mean = NA,
-                            sd = NA,
+                            mean = 0,
+                            sd = 1,
                             label = "\"Height EAS\""))
 
 write.table(gwas_list, 'example_input/gwas_list.multisource.txt', col.names = T, row.names = F, quote = F, sep = ' ')
@@ -119,29 +119,18 @@ snakemake --profile slurm --configfile=example_input/config.multisource.yaml --u
 Now compress the test data, upload to Zenodo, and update the documentation.
 
 ```{bash}
-cd ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/test_data
-tar -czvf archive_name.tar.gz folder_name/
+cd ~/oliverpainfel/Software/MyGit/GenoPred/pipeline
+tar -czvf test_data.tar.gz test_data
 ```
 
 ***
 
+# Create smaller test data
 
-
-some target data (in various formats), GWAS summary statistics for two t
-
-I want to set up a series of tests that can be quickly run to check everything is working as it should. 
-
-We can test every step all the time, so lets focus on testing the following scenario:
-
-- Target sample data: Same as GenoPred test data, but restricted to subset of SNPs
-- GWAS sumstats: Same as GenoPred test data, but restricted to subset of SNPs
-- External sumstats: Same as GenoPred test data, but restricted to subset of SNPs
-- Reference data: Same as GenoPred test data, but restricted to subset of SNPs
+This is for rapid testing of the pipeline.
 
 ***
 
-# Create test data
-
 ## Reference data
 
 ```{r}
@@ -157,7 +146,6 @@ dir.create('misc/dev/test_data/ref')
 saveRDS(ref_rds[ref_rds$SNP %in% test_snps,], 'misc/dev/test_data/ref/ref.chr22.rds')
 
 system('cp ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.pop.txt misc/dev/test_data/ref/')
-system('cp ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.keep.list misc/dev/test_data/ref/')
 system('cp -r ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files misc/dev/test_data/ref/')
 
 pops<-list.files('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/freq_files')
@@ -275,7 +263,8 @@ config<-c(
   "pgs_methods: ['ptclump','lassosum']",
   "testing: chr22",
   "cores_target_pgs: 1",
-  "mem_target_pgs: 2000"
+  "mem_target_pgs: 2000",
+  "pgs_scaling: ['continuous', 'discrete]"
   )
 
 write.table(config, 'misc/dev/test_data/config/config.yaml', col.names = F, row.names = F, quote = F)

From d2f2a91fc119b3052418edd751525bfcaebbf93d Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sun, 6 Apr 2025 17:05:54 +0100
Subject: [PATCH 129/153] preparing test_data

---
 pipeline/misc/dev/test_data/config/config.yaml | 2 +-
 pipeline/misc/dev/test_setup.Rmd               | 3 +--
 pipeline/rules/dependencies.smk                | 2 +-
 3 files changed, 3 insertions(+), 4 deletions(-)

diff --git a/pipeline/misc/dev/test_data/config/config.yaml b/pipeline/misc/dev/test_data/config/config.yaml
index 024d9313..e693aa64 100644
--- a/pipeline/misc/dev/test_data/config/config.yaml
+++ b/pipeline/misc/dev/test_data/config/config.yaml
@@ -9,4 +9,4 @@ pgs_methods: ['ptclump','lassosum']
 testing: chr22
 cores_target_pgs: 1
 mem_target_pgs: 2000
-pgs_scaling: ['continuous', 'discrete]
+pgs_scaling: ['continuous', 'discrete']
diff --git a/pipeline/misc/dev/test_setup.Rmd b/pipeline/misc/dev/test_setup.Rmd
index e11258a6..2c296713 100644
--- a/pipeline/misc/dev/test_setup.Rmd
+++ b/pipeline/misc/dev/test_setup.Rmd
@@ -264,7 +264,7 @@ config<-c(
   "testing: chr22",
   "cores_target_pgs: 1",
   "mem_target_pgs: 2000",
-  "pgs_scaling: ['continuous', 'discrete]"
+  "pgs_scaling: ['continuous', 'discrete']"
   )
 
 write.table(config, 'misc/dev/test_data/config/config.yaml', col.names = F, row.names = F, quote = F)
@@ -295,7 +295,6 @@ snakemake \
 -j1 \
 --use-conda \
 output_all \
-pc_projection \
 misc/dev/test_data/output/reference/target_checks/example_plink2/indiv_report-4_EAS.4_EAS-report.done \
 --configfile=misc/dev/test_data/config/config.yaml
 
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 4e9bfd5b..a04266f0 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -416,7 +416,7 @@ else:
     # Check frequency files for populations in ref.pop.txt and TRANS
     freq_dir = os.path.join(refdir, "freq_files")
     for pop in list(populations) + ['TRANS']:
-        for i in range(1, 23):  # Chromosomes 1-22
+        for i in get_chr_range(testing=config['testing']):
             freq_file = os.path.join(freq_dir, pop, f"ref.{pop}.chr{i}.afreq")
             ref_input.append(freq_file)
     

From a27fd43ec29cc83257e49fc211cb2d29a65c0cd1 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sun, 6 Apr 2025 17:52:28 +0100
Subject: [PATCH 130/153] Updating test_data

---
 .../ancestry/example_plink2.Ancestry.log      |   42 +-
 .../example_plink2/ancestry/keep_list.txt     |    8 +-
 ...example_plink2.ref.chr22.format_target.log |   32 +-
 .../geno/example_plink2.ref.chr22.log         |   33 +-
 .../pcs/projected/AFR/example_plink2-AFR.log  |   27 -
 .../projected/AFR/example_plink2-AFR.profiles |    3 -
 .../pcs/projected/CSA/example_plink2-CSA.log  |   27 -
 .../projected/CSA/example_plink2-CSA.profiles |    2 -
 .../pcs/projected/EAS/example_plink2-EAS.log  |   27 -
 .../projected/EAS/example_plink2-EAS.profiles |    3 -
 .../pcs/projected/EUR/example_plink2-EUR.log  |   27 -
 .../projected/EUR/example_plink2-EUR.profiles |    6 -
 .../projected/TRANS/example_plink2-TRANS.log  |   27 +
 .../TRANS/example_plink2-TRANS.profiles       |   13 +
 .../output/example_plink2/pgs/AFR.log         |   28 -
 .../pgs/AFR_2025-04-06_17-34-20.log           |   29 +
 .../output/example_plink2/pgs/CSA.log         |   28 -
 .../pgs/CSA_2025-04-06_17-34-18.log           |   29 +
 .../output/example_plink2/pgs/EAS.log         |   28 -
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 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-CSA.log
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-EAS.log
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-EUR.log
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.AFR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.AMR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.CSA.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.EAS.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.EUR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.MID.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.eigenvec.var.gz
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.AFR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.AMR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.CSA.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.EAS.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.EUR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.MID.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.eigenvec.var.gz
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.log
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.AFR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.AMR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.CSA.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.EAS.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.EUR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.MID.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.eigenvec.var.gz
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.log
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.AFR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.AMR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.CSA.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.EAS.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.EUR.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.MID.scale
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.eigenvec.var.gz
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.log
 delete mode 100644 pipeline/misc/dev/test_data/output/resources/last_version.txt

diff --git a/pipeline/misc/dev/test_data/output/example_plink2/ancestry/example_plink2.Ancestry.log b/pipeline/misc/dev/test_data/output/example_plink2/ancestry/example_plink2.Ancestry.log
index 8ece18bc..f521c519 100644
--- a/pipeline/misc/dev/test_data/output/example_plink2/ancestry/example_plink2.Ancestry.log
+++ b/pipeline/misc/dev/test_data/output/example_plink2/ancestry/example_plink2.Ancestry.log
@@ -3,27 +3,27 @@
 # For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
 #################################################################
 # Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
+# Version (tag): v2.2.2-258-gd2f2a91
 ---------------
- Parameter        Value                                                                    
- target_plink_chr misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr     
- ref_plink_chr    misc/dev/test_data/ref/ref.chr                                           
- maf              0.05                                                                     
- geno             0.02                                                                     
- hwe              1e-06                                                                    
- n_pcs            6                                                                        
- plink2           plink2                                                                   
- output           misc/dev/test_data/output/example_plink2/ancestry/example_plink2.Ancestry
- pop_data         misc/dev/test_data/ref/ref.pop.txt                                       
- model_method     glmnet                                                                   
- sd_rule          FALSE                                                                    
- prob_thresh      0.95                                                                     
- test             chr22                                                                    
- memory           5000                                                                     
- help             FALSE                                                                    
- out_dir          misc/dev/test_data/output/example_plink2/ancestry/                       
+ Parameter        Value                                                                                           
+ target_plink_chr /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr     
+ ref_plink_chr    misc/dev/test_data/ref/ref.chr                                                                  
+ maf              0.05                                                                                            
+ geno             0.02                                                                                            
+ hwe              1e-06                                                                                           
+ n_pcs            6                                                                                               
+ plink2           plink2                                                                                          
+ output           /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/ancestry/example_plink2.Ancestry
+ pop_data         misc/dev/test_data/ref/ref.pop.txt                                                              
+ model_method     glmnet                                                                                          
+ sd_rule          FALSE                                                                                           
+ prob_thresh      0.95                                                                                            
+ test             chr22                                                                                           
+ memory           5000                                                                                            
+ help             FALSE                                                                                           
+ out_dir          /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/ancestry/                       
 ---------------
-Analysis started at 2024-07-25 17:38:49
+Analysis started at 2025-04-06 17:33:43
 Lowering prob_thresh parameter to 0.5 for testing.
 Target sample size is <100 so only checking genotype missingness.
 587 variants match between target and reference after QC.
@@ -47,5 +47,5 @@ N per group based on model:
  MID        0
  Unassigned 2
 ----------
-Analysis finished at 2024-07-25 17:39:05 
-Analysis duration was 15.96 secs 
+Analysis finished at 2025-04-06 17:34:08 
+Analysis duration was 25.34 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/ancestry/keep_list.txt b/pipeline/misc/dev/test_data/output/example_plink2/ancestry/keep_list.txt
index c4d780d2..808ed465 100644
--- a/pipeline/misc/dev/test_data/output/example_plink2/ancestry/keep_list.txt
+++ b/pipeline/misc/dev/test_data/output/example_plink2/ancestry/keep_list.txt
@@ -1,5 +1,5 @@
 POP file
-AFR misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/AFR.keep
-CSA misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/CSA.keep
-EAS misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/EAS.keep
-EUR misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/EUR.keep
+AFR /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/ancestry/keep_files/model_based/AFR.keep
+CSA /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/ancestry/keep_files/model_based/CSA.keep
+EAS /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/ancestry/keep_files/model_based/EAS.keep
+EUR /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/ancestry/keep_files/model_based/EUR.keep
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.format_target.log b/pipeline/misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.format_target.log
index c7bfc263..ce5a3a1a 100644
--- a/pipeline/misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.format_target.log
+++ b/pipeline/misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.format_target.log
@@ -3,28 +3,28 @@
 # For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
 #################################################################
 # Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
+# Version (tag): v2.2.2-258-gd2f2a91
 ---------------
- Parameter Value                                                                 
- target    misc/dev/test_data/target/example.chr22                               
- ref       misc/dev/test_data/ref/ref.chr22                                      
- format    plink2                                                                
- plink     plink                                                                 
- plink2    plink2                                                                
- output    misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
- help      FALSE                                                                 
- out_dir   misc/dev/test_data/output/example_plink2/geno/                        
+ Parameter Value                                                                                        
+ target    misc/dev/test_data/target/example.chr22                                                      
+ ref       misc/dev/test_data/ref/ref.chr22                                                             
+ format    plink2                                                                                       
+ plink     plink                                                                                        
+ plink2    plink2                                                                                       
+ output    /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22
+ help      FALSE                                                                                        
+ out_dir   /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/                        
 ---------------
-Analysis started at 2024-07-25 17:38:45
+Analysis started at 2025-04-06 17:33:41
 Reading in reference SNP data.
 Reference data contains 1000 variants.
 Reading in target SNP data.
 Target data contains 1000 variants.
-GRCh36 match: 0%
-GRCh37 match: 100%
-GRCh38 match: 0%
+GRCh36 match: 0% (Target), 0% (Ref)
+GRCh37 match: 100% (Target), 100% (Ref)
+GRCh38 match: 0% (Target), 0% (Ref)
 Target contains 1000 reference variants.
 Removing 0 duplicate variants - May have IUPAC NA.
 Inserting missing reference variants.
-Analysis finished at 2024-07-25 17:38:46 
-Analysis duration was 0.58 secs 
+Analysis finished at 2025-04-06 17:33:42 
+Analysis duration was 1.09 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.log b/pipeline/misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.log
index 4ea95aa3..5a5d945b 100644
--- a/pipeline/misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.log
+++ b/pipeline/misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.log
@@ -1,35 +1,34 @@
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)
 Options in effect:
-  --bfile /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ
+  --bfile /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ
   --make-pgen
-  --memory 5000
-  --out misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
-  --remove /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.psam
+  --out /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22
+  --remove /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.psam
   --threads 1
 
-Hostname: erc-hpc-comp179
+Hostname: erc-hpc-comp017
 Working directory: /tools/GenoPred/pipeline
-Start time: Thu Jul 25 17:38:46 2024
+Start time: Sun Apr  6 17:33:42 2025
 
-Random number seed: 1721925526
-1031702 MiB RAM detected, ~1018519 available; reserving 5000 MiB for main
+Random number seed: 1743957222
+1031753 MiB RAM detected, ~924320 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 3325 samples (1573 females, 1752 males; 3325 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ.fam.
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ.fam.
 1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ.bim.
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ.bim.
 Note: No phenotype data present.
 --remove: 12 samples remaining.
 12 samples (5 females, 7 males; 12 founders) remaining after main filters.
 Writing
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam ...
-done.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.psam
+... done.
 Writing
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar ...
-done.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pvar
+... done.
 Writing
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pgen ...
-done.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pgen
+... done.
 
-End time: Thu Jul 25 17:38:46 2024
+End time: Sun Apr  6 17:33:42 2025
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/AFR/example_plink2-AFR.log b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/AFR/example_plink2-AFR.log
deleted file mode 100644
index 6639ff35..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/AFR/example_plink2-AFR.log
+++ /dev/null
@@ -1,27 +0,0 @@
-#################################################################
-# target_scoring.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-# Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
----------------
- Parameter        Value                                                                                      
- target_plink_chr misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr                       
- target_keep      misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/AFR.keep          
- ref_score        misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.eigenvec.var.gz
- ref_freq_chr     misc/dev/test_data/ref/freq_files/AFR/ref.AFR.chr                                          
- plink2           plink2                                                                                     
- output           misc/dev/test_data/output/example_plink2/pcs/projected/AFR/example_plink2-AFR              
- ref_scale        misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.AFR.scale      
- n_cores          1                                                                                          
- test             chr22                                                                                      
- memory           5000                                                                                       
- help             FALSE                                                                                      
- output_dir       misc/dev/test_data/output/example_plink2/pcs/projected/AFR/                                
----------------
-Analysis started at 2024-07-25 17:39:07
-Calculating polygenic scores in the target sample.
-Scaling target polygenic scores to the reference.
-Saved polygenic scores to: misc/dev/test_data/output/example_plink2/pcs/projected/AFR/example_plink2-AFR.profiles.
-Analysis finished at 2024-07-25 17:39:07 
-Analysis duration was 0.27 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/AFR/example_plink2-AFR.profiles b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/AFR/example_plink2-AFR.profiles
deleted file mode 100644
index 4cbe407a..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/AFR/example_plink2-AFR.profiles
+++ /dev/null
@@ -1,3 +0,0 @@
-FID IID PC1 PC2 PC3 PC4 PC5 PC6
-9_AFR 9_AFR 0.327 -0.746 2.616 -0.22 -1.106 -0.708
-10_AFR 10_AFR -0.488 0.242 -0.664 -0.149 0.055 1.339
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/CSA/example_plink2-CSA.log b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/CSA/example_plink2-CSA.log
deleted file mode 100644
index 6e3f8fb1..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/CSA/example_plink2-CSA.log
+++ /dev/null
@@ -1,27 +0,0 @@
-#################################################################
-# target_scoring.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-# Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
----------------
- Parameter        Value                                                                                      
- target_plink_chr misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr                       
- target_keep      misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/CSA.keep          
- ref_score        misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.eigenvec.var.gz
- ref_freq_chr     misc/dev/test_data/ref/freq_files/CSA/ref.CSA.chr                                          
- plink2           plink2                                                                                     
- output           misc/dev/test_data/output/example_plink2/pcs/projected/CSA/example_plink2-CSA              
- ref_scale        misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.CSA.scale      
- n_cores          1                                                                                          
- test             chr22                                                                                      
- memory           5000                                                                                       
- help             FALSE                                                                                      
- output_dir       misc/dev/test_data/output/example_plink2/pcs/projected/CSA/                                
----------------
-Analysis started at 2024-07-25 17:39:26
-Calculating polygenic scores in the target sample.
-Scaling target polygenic scores to the reference.
-Saved polygenic scores to: misc/dev/test_data/output/example_plink2/pcs/projected/CSA/example_plink2-CSA.profiles.
-Analysis finished at 2024-07-25 17:39:26 
-Analysis duration was 0.27 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/CSA/example_plink2-CSA.profiles b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/CSA/example_plink2-CSA.profiles
deleted file mode 100644
index 29d5a72d..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/CSA/example_plink2-CSA.profiles
+++ /dev/null
@@ -1,2 +0,0 @@
-FID IID PC1 PC2 PC3 PC4 PC5 PC6
-7_CSA 7_CSA -1.523 0.467 -1.36 -2.592 1.272 -1.152
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EAS/example_plink2-EAS.log b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EAS/example_plink2-EAS.log
deleted file mode 100644
index ed0008c1..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EAS/example_plink2-EAS.log
+++ /dev/null
@@ -1,27 +0,0 @@
-#################################################################
-# target_scoring.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-# Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
----------------
- Parameter        Value                                                                                      
- target_plink_chr misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr                       
- target_keep      misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/EAS.keep          
- ref_score        misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.eigenvec.var.gz
- ref_freq_chr     misc/dev/test_data/ref/freq_files/EAS/ref.EAS.chr                                          
- plink2           plink2                                                                                     
- output           misc/dev/test_data/output/example_plink2/pcs/projected/EAS/example_plink2-EAS              
- ref_scale        misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.EAS.scale      
- n_cores          1                                                                                          
- test             chr22                                                                                      
- memory           5000                                                                                       
- help             FALSE                                                                                      
- output_dir       misc/dev/test_data/output/example_plink2/pcs/projected/EAS/                                
----------------
-Analysis started at 2024-07-25 17:39:10
-Calculating polygenic scores in the target sample.
-Scaling target polygenic scores to the reference.
-Saved polygenic scores to: misc/dev/test_data/output/example_plink2/pcs/projected/EAS/example_plink2-EAS.profiles.
-Analysis finished at 2024-07-25 17:39:10 
-Analysis duration was 0.24 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EAS/example_plink2-EAS.profiles b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EAS/example_plink2-EAS.profiles
deleted file mode 100644
index bbecd0ef..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EAS/example_plink2-EAS.profiles
+++ /dev/null
@@ -1,3 +0,0 @@
-FID IID PC1 PC2 PC3 PC4 PC5 PC6
-3_EAS 3_EAS -0.093 -0.155 -0.274 0.916 -0.733 0.736
-4_EAS 4_EAS -0.137 -0.887 -0.52 1.711 0.49 0.371
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EUR/example_plink2-EUR.log b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EUR/example_plink2-EUR.log
deleted file mode 100644
index 5dcbdb3c..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EUR/example_plink2-EUR.log
+++ /dev/null
@@ -1,27 +0,0 @@
-#################################################################
-# target_scoring.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-# Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
----------------
- Parameter        Value                                                                                      
- target_plink_chr misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr                       
- target_keep      misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/EUR.keep          
- ref_score        misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.eigenvec.var.gz
- ref_freq_chr     misc/dev/test_data/ref/freq_files/EUR/ref.EUR.chr                                          
- plink2           plink2                                                                                     
- output           misc/dev/test_data/output/example_plink2/pcs/projected/EUR/example_plink2-EUR              
- ref_scale        misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.EUR.scale      
- n_cores          1                                                                                          
- test             chr22                                                                                      
- memory           5000                                                                                       
- help             FALSE                                                                                      
- output_dir       misc/dev/test_data/output/example_plink2/pcs/projected/EUR/                                
----------------
-Analysis started at 2024-07-25 17:39:26
-Calculating polygenic scores in the target sample.
-Scaling target polygenic scores to the reference.
-Saved polygenic scores to: misc/dev/test_data/output/example_plink2/pcs/projected/EUR/example_plink2-EUR.profiles.
-Analysis finished at 2024-07-25 17:39:27 
-Analysis duration was 0.27 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EUR/example_plink2-EUR.profiles b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EUR/example_plink2-EUR.profiles
deleted file mode 100644
index 22912c9b..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/EUR/example_plink2-EUR.profiles
+++ /dev/null
@@ -1,6 +0,0 @@
-FID IID PC1 PC2 PC3 PC4 PC5 PC6
-1_EUR 1_EUR -0.941 -0.209 0.316 1.022 0.346 -0.875
-6_AMR 6_AMR 0.589 -0.541 1.249 0.345 1.103 -0.206
-8_CSA 8_CSA 0.875 -0.66 -0.909 0.369 -1.035 0.821
-11_MID 11_MID -0.275 1.046 -0.342 0.489 -0.323 0.039
-12_MID 12_MID -0.005 -0.672 -0.287 -1.475 -0.222 0.476
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/TRANS/example_plink2-TRANS.log b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/TRANS/example_plink2-TRANS.log
new file mode 100644
index 00000000..998b6226
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/TRANS/example_plink2-TRANS.log
@@ -0,0 +1,27 @@
+#################################################################
+# target_scoring.R
+# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
+#################################################################
+# Repository: GenoPred
+# Version (tag): v2.2.2-258-gd2f2a91
+---------------
+ Parameter        Value                                                                                                        
+ target_plink_chr /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr                  
+ target_keep      NA                                                                                                           
+ ref_score        /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pc_score_files/TRANS/ref-TRANS-pcs.eigenvec.var.gz
+ ref_freq_chr     misc/dev/test_data/ref/freq_files/TRANS/ref.TRANS.chr                                                        
+ plink2           plink2                                                                                                       
+ output           /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/pcs/projected/TRANS/example_plink2-TRANS     
+ ref_scale        /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pc_score_files/TRANS/ref-TRANS-pcs.TRANS.scale    
+ n_cores          1                                                                                                            
+ test             chr22                                                                                                        
+ memory           5000                                                                                                         
+ help             FALSE                                                                                                        
+ output_dir       /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/pcs/projected/TRANS/                         
+---------------
+Analysis started at 2025-04-06 17:34:09
+Calculating polygenic scores in the target sample.
+Scaling target polygenic scores to the reference.
+Saved polygenic scores to: /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/pcs/projected/TRANS/example_plink2-TRANS.profiles.
+Analysis finished at 2025-04-06 17:34:09 
+Analysis duration was 0.57 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/TRANS/example_plink2-TRANS.profiles b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/TRANS/example_plink2-TRANS.profiles
new file mode 100644
index 00000000..e956416b
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/example_plink2/pcs/projected/TRANS/example_plink2-TRANS.profiles
@@ -0,0 +1,13 @@
+FID IID PC1 PC2 PC3 PC4 PC5 PC6
+1_EUR 1_EUR -0.309 1.643 -1.269 0.629 -0.411 1.314
+2_EUR 2_EUR -0.36 0.793 -0.621 -0.809 1.446 0.092
+3_EAS 3_EAS -0.527 -1.167 -0.919 0.716 -1.465 -0.476
+4_EAS 4_EAS -0.031 -2.111 -0.869 0.616 -0.105 0.812
+5_AMR 5_AMR -0.179 1.135 0.612 -0.506 1.221 -1.994
+6_AMR 6_AMR -0.108 1.68 -0.222 0.524 0.736 -0.663
+7_CSA 7_CSA -0.528 -0.86 1.629 -0.208 1.829 0.649
+8_CSA 8_CSA -0.792 0.513 -0.639 0.586 0.396 0.734
+9_AFR 9_AFR 1.24 -0.847 0.888 -0.45 -0.996 0.877
+10_AFR 10_AFR 2.209 -0.389 -0.459 0.429 -0.123 1.062
+11_MID 11_MID -0.558 1.005 -0.066 0.619 -0.224 -0.261
+12_MID 12_MID -0.048 1.239 -0.189 0.45 1.303 0.357
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/AFR.log b/pipeline/misc/dev/test_data/output/example_plink2/pgs/AFR.log
deleted file mode 100644
index 49dffe90..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pgs/AFR.log
+++ /dev/null
@@ -1,28 +0,0 @@
-#################################################################
-# target_scoring_pipeline.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-# Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
----------------
- Parameter  Value                                           
- config     misc/dev/test_data/config/config.yaml           
- name       example_plink2                                  
- population AFR                                             
- plink2     plink2                                          
- n_cores    1                                               
- test       chr22                                           
- memory     5000                                            
- help       FALSE                                           
- output_dir misc/dev/test_data/output/example_plink2/pgs/AFR
----------------
-Analysis started at 2024-07-25 17:39:14
-Processing 3 score files.
-Aggregating score files in 1 batches.
-Aggregating batched score files.
-Reading in scale files.
-Calculating polygenic scores in the target sample.
-Scaling target polygenic scores to the reference.
-Saved polygenic scores.
-Analysis finished at 2024-07-25 17:39:15 
-Analysis duration was 0.37 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/AFR_2025-04-06_17-34-20.log b/pipeline/misc/dev/test_data/output/example_plink2/pgs/AFR_2025-04-06_17-34-20.log
new file mode 100644
index 00000000..29f58dc5
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/example_plink2/pgs/AFR_2025-04-06_17-34-20.log
@@ -0,0 +1,29 @@
+#################################################################
+# target_scoring_pipeline.R
+# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
+#################################################################
+# Repository: GenoPred
+# Version (tag): v2.2.2-258-gd2f2a91
+---------------
+ Parameter  Value                                                                  
+ config     /scratch_tmp/prj/oliverpainfel/tmp/config.yaml                         
+ name       example_plink2                                                         
+ population AFR                                                                    
+ plink2     plink2                                                                 
+ n_cores    1                                                                      
+ test       chr22                                                                  
+ memory     5000                                                                   
+ help       FALSE                                                                  
+ output     /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/pgs/AFR
+---------------
+Analysis started at 2025-04-06 17:34:20
+After checking timestamps, 3/3 score files will be used for target scoring.
+########################
+Processing chromosome 22:
+Aggregating score files in 1 batches.
+Aggregating batched score files.
+Reading in scale files.
+Scaling target polygenic scores to the reference.
+Saved polygenic scores.
+Analysis finished at 2025-04-06 17:34:20 
+Analysis duration was 0.66 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/CSA.log b/pipeline/misc/dev/test_data/output/example_plink2/pgs/CSA.log
deleted file mode 100644
index 0090e52d..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pgs/CSA.log
+++ /dev/null
@@ -1,28 +0,0 @@
-#################################################################
-# target_scoring_pipeline.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-# Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
----------------
- Parameter  Value                                           
- config     misc/dev/test_data/config/config.yaml           
- name       example_plink2                                  
- population CSA                                             
- plink2     plink2                                          
- n_cores    1                                               
- test       chr22                                           
- memory     5000                                            
- help       FALSE                                           
- output_dir misc/dev/test_data/output/example_plink2/pgs/CSA
----------------
-Analysis started at 2024-07-25 17:39:13
-Processing 3 score files.
-Aggregating score files in 1 batches.
-Aggregating batched score files.
-Reading in scale files.
-Calculating polygenic scores in the target sample.
-Scaling target polygenic scores to the reference.
-Saved polygenic scores.
-Analysis finished at 2024-07-25 17:39:13 
-Analysis duration was 0.37 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/CSA_2025-04-06_17-34-18.log b/pipeline/misc/dev/test_data/output/example_plink2/pgs/CSA_2025-04-06_17-34-18.log
new file mode 100644
index 00000000..ebcff29b
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/example_plink2/pgs/CSA_2025-04-06_17-34-18.log
@@ -0,0 +1,29 @@
+#################################################################
+# target_scoring_pipeline.R
+# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
+#################################################################
+# Repository: GenoPred
+# Version (tag): v2.2.2-258-gd2f2a91
+---------------
+ Parameter  Value                                                                  
+ config     /scratch_tmp/prj/oliverpainfel/tmp/config.yaml                         
+ name       example_plink2                                                         
+ population CSA                                                                    
+ plink2     plink2                                                                 
+ n_cores    1                                                                      
+ test       chr22                                                                  
+ memory     5000                                                                   
+ help       FALSE                                                                  
+ output     /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/pgs/CSA
+---------------
+Analysis started at 2025-04-06 17:34:17
+After checking timestamps, 3/3 score files will be used for target scoring.
+########################
+Processing chromosome 22:
+Aggregating score files in 1 batches.
+Aggregating batched score files.
+Reading in scale files.
+Scaling target polygenic scores to the reference.
+Saved polygenic scores.
+Analysis finished at 2025-04-06 17:34:18 
+Analysis duration was 0.66 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/EAS.log b/pipeline/misc/dev/test_data/output/example_plink2/pgs/EAS.log
deleted file mode 100644
index 1195b163..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pgs/EAS.log
+++ /dev/null
@@ -1,28 +0,0 @@
-#################################################################
-# target_scoring_pipeline.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-# Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
----------------
- Parameter  Value                                           
- config     misc/dev/test_data/config/config.yaml           
- name       example_plink2                                  
- population EAS                                             
- plink2     plink2                                          
- n_cores    1                                               
- test       chr22                                           
- memory     5000                                            
- help       FALSE                                           
- output_dir misc/dev/test_data/output/example_plink2/pgs/EAS
----------------
-Analysis started at 2024-07-25 17:39:14
-Processing 3 score files.
-Aggregating score files in 1 batches.
-Aggregating batched score files.
-Reading in scale files.
-Calculating polygenic scores in the target sample.
-Scaling target polygenic scores to the reference.
-Saved polygenic scores.
-Analysis finished at 2024-07-25 17:39:14 
-Analysis duration was 0.35 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/EAS_2025-04-06_17-34-17.log b/pipeline/misc/dev/test_data/output/example_plink2/pgs/EAS_2025-04-06_17-34-17.log
new file mode 100644
index 00000000..492c74ae
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/example_plink2/pgs/EAS_2025-04-06_17-34-17.log
@@ -0,0 +1,29 @@
+#################################################################
+# target_scoring_pipeline.R
+# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
+#################################################################
+# Repository: GenoPred
+# Version (tag): v2.2.2-258-gd2f2a91
+---------------
+ Parameter  Value                                                                  
+ config     /scratch_tmp/prj/oliverpainfel/tmp/config.yaml                         
+ name       example_plink2                                                         
+ population EAS                                                                    
+ plink2     plink2                                                                 
+ n_cores    1                                                                      
+ test       chr22                                                                  
+ memory     5000                                                                   
+ help       FALSE                                                                  
+ output     /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/pgs/EAS
+---------------
+Analysis started at 2025-04-06 17:34:16
+After checking timestamps, 3/3 score files will be used for target scoring.
+########################
+Processing chromosome 22:
+Aggregating score files in 1 batches.
+Aggregating batched score files.
+Reading in scale files.
+Scaling target polygenic scores to the reference.
+Saved polygenic scores.
+Analysis finished at 2025-04-06 17:34:17 
+Analysis duration was 0.65 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/EUR.log b/pipeline/misc/dev/test_data/output/example_plink2/pgs/EUR.log
deleted file mode 100644
index 0e5e12aa..00000000
--- a/pipeline/misc/dev/test_data/output/example_plink2/pgs/EUR.log
+++ /dev/null
@@ -1,28 +0,0 @@
-#################################################################
-# target_scoring_pipeline.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-# Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
----------------
- Parameter  Value                                           
- config     misc/dev/test_data/config/config.yaml           
- name       example_plink2                                  
- population EUR                                             
- plink2     plink2                                          
- n_cores    1                                               
- test       chr22                                           
- memory     5000                                            
- help       FALSE                                           
- output_dir misc/dev/test_data/output/example_plink2/pgs/EUR
----------------
-Analysis started at 2024-07-25 17:39:12
-Processing 3 score files.
-Aggregating score files in 1 batches.
-Aggregating batched score files.
-Reading in scale files.
-Calculating polygenic scores in the target sample.
-Scaling target polygenic scores to the reference.
-Saved polygenic scores.
-Analysis finished at 2024-07-25 17:39:13 
-Analysis duration was 0.38 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/EUR_2025-04-06_17-34-19.log b/pipeline/misc/dev/test_data/output/example_plink2/pgs/EUR_2025-04-06_17-34-19.log
new file mode 100644
index 00000000..1ec37a61
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/example_plink2/pgs/EUR_2025-04-06_17-34-19.log
@@ -0,0 +1,29 @@
+#################################################################
+# target_scoring_pipeline.R
+# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
+#################################################################
+# Repository: GenoPred
+# Version (tag): v2.2.2-258-gd2f2a91
+---------------
+ Parameter  Value                                                                  
+ config     /scratch_tmp/prj/oliverpainfel/tmp/config.yaml                         
+ name       example_plink2                                                         
+ population EUR                                                                    
+ plink2     plink2                                                                 
+ n_cores    1                                                                      
+ test       chr22                                                                  
+ memory     5000                                                                   
+ help       FALSE                                                                  
+ output     /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/pgs/EUR
+---------------
+Analysis started at 2025-04-06 17:34:18
+After checking timestamps, 3/3 score files will be used for target scoring.
+########################
+Processing chromosome 22:
+Aggregating score files in 1 batches.
+Aggregating batched score files.
+Reading in scale files.
+Scaling target polygenic scores to the reference.
+Saved polygenic scores.
+Analysis finished at 2025-04-06 17:34:19 
+Analysis duration was 0.66 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS/external/PGS002804/example_plink2-PGS002804-TRANS.profiles b/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS/external/PGS002804/example_plink2-PGS002804-TRANS.profiles
new file mode 100644
index 00000000..a7385684
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS/external/PGS002804/example_plink2-PGS002804-TRANS.profiles
@@ -0,0 +1,13 @@
+FID IID PGS002804_external
+1_EUR 1_EUR 0.214
+2_EUR 2_EUR -0.794
+3_EAS 3_EAS -0.738
+4_EAS 4_EAS -1.111
+5_AMR 5_AMR -0.224
+6_AMR 6_AMR -0.143
+7_CSA 7_CSA -0.773
+8_CSA 8_CSA 0.425
+9_AFR 9_AFR 0.57
+10_AFR 10_AFR 1.289
+11_MID 11_MID 0.622
+12_MID 12_MID -0.527
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS/lassosum/BODY04/example_plink2-BODY04-TRANS.profiles b/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS/lassosum/BODY04/example_plink2-BODY04-TRANS.profiles
new file mode 100644
index 00000000..8dc6f00a
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS/lassosum/BODY04/example_plink2-BODY04-TRANS.profiles
@@ -0,0 +1,13 @@
+FID IID BODY04_s0.2_lambda0.001 BODY04_s0.2_lambda0.00127427498570313 BODY04_s0.2_lambda0.00162377673918872 BODY04_s0.2_lambda0.00206913808111479 BODY04_s0.2_lambda0.00263665089873036 BODY04_s0.2_lambda0.00335981828628378 BODY04_s0.2_lambda0.00428133239871939 BODY04_s0.2_lambda0.00545559478116852 BODY04_s0.2_lambda0.00695192796177561 BODY04_s0.2_lambda0.00885866790410083 BODY04_s0.2_lambda0.0112883789168469 BODY04_s0.2_lambda0.0143844988828766 BODY04_s0.2_lambda0.0183298071083244 BODY04_s0.2_lambda0.0233572146909012 BODY04_s0.2_lambda0.0297635144163132 BODY04_s0.2_lambda0.0379269019073225 BODY04_s0.2_lambda0.0483293023857176 BODY04_s0.2_lambda0.0615848211066027 BODY04_s0.2_lambda0.0784759970351462 BODY04_s0.2_lambda0.1 BODY04_s0.5_lambda0.001 BODY04_s0.5_lambda0.00127427498570313 BODY04_s0.5_lambda0.00162377673918872 BODY04_s0.5_lambda0.00206913808111479 BODY04_s0.5_lambda0.00263665089873036 BODY04_s0.5_lambda0.00335981828628378 BODY04_s0.5_lambda0.00428133239871939 BODY04_s0.5_lambda0.00545559478116852 BODY04_s0.5_lambda0.00695192796177561 BODY04_s0.5_lambda0.00885866790410083 BODY04_s0.5_lambda0.0112883789168469 BODY04_s0.5_lambda0.0143844988828766 BODY04_s0.5_lambda0.0183298071083244 BODY04_s0.5_lambda0.0233572146909012 BODY04_s0.5_lambda0.0297635144163132 BODY04_s0.5_lambda0.0379269019073225 BODY04_s0.5_lambda0.0483293023857176 BODY04_s0.5_lambda0.0615848211066027 BODY04_s0.5_lambda0.0784759970351462 BODY04_s0.5_lambda0.1 BODY04_s0.9_lambda0.001 BODY04_s0.9_lambda0.00127427498570313 BODY04_s0.9_lambda0.00162377673918872 BODY04_s0.9_lambda0.00206913808111479 BODY04_s0.9_lambda0.00263665089873036 BODY04_s0.9_lambda0.00335981828628378 BODY04_s0.9_lambda0.00428133239871939 BODY04_s0.9_lambda0.00545559478116852 BODY04_s0.9_lambda0.00695192796177561 BODY04_s0.9_lambda0.00885866790410083 BODY04_s0.9_lambda0.0112883789168469 BODY04_s0.9_lambda0.0143844988828766 BODY04_s0.9_lambda0.0183298071083244 BODY04_s0.9_lambda0.0233572146909012 BODY04_s0.9_lambda0.0297635144163132 BODY04_s0.9_lambda0.0379269019073225 BODY04_s0.9_lambda0.0483293023857176 BODY04_s0.9_lambda0.0615848211066027 BODY04_s0.9_lambda0.0784759970351462 BODY04_s0.9_lambda0.1 BODY04_s1_lambda0.001 BODY04_s1_lambda0.00127427498570313 BODY04_s1_lambda0.00162377673918872 BODY04_s1_lambda0.00206913808111479 BODY04_s1_lambda0.00263665089873036 BODY04_s1_lambda0.00335981828628378 BODY04_s1_lambda0.00428133239871939 BODY04_s1_lambda0.00545559478116852 BODY04_s1_lambda0.00695192796177561 BODY04_s1_lambda0.00885866790410083 BODY04_s1_lambda0.0112883789168469 BODY04_s1_lambda0.0143844988828766 BODY04_s1_lambda0.0183298071083244 BODY04_s1_lambda0.0233572146909012 BODY04_s1_lambda0.0297635144163132 BODY04_s1_lambda0.0379269019073225 BODY04_s1_lambda0.0483293023857176 BODY04_s1_lambda0.0615848211066027 BODY04_s1_lambda0.0784759970351462 BODY04_s1_lambda0.1
+1_EUR 1_EUR -0.002 -0.047 0 0.227 0.359 0.345 0.112 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.101 -0.085 0.088 0.351 0.392 0.393 0.11 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.373 -0.249 -0.015 0.326 0.486 0.452 0.108 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.707 -0.595 -0.267 0.243 0.517 0.488 0.108 0 0 0 0 0 0 0 0 0 0 0 0 0
+2_EUR 2_EUR -0.742 -0.638 -0.563 -0.589 -0.473 -0.313 -0.151 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.864 -0.828 -0.757 -0.651 -0.499 -0.307 -0.149 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.88 -0.892 -0.845 -0.736 -0.602 -0.339 -0.146 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.583 -0.636 -0.668 -0.684 -0.644 -0.367 -0.146 0 0 0 0 0 0 0 0 0 0 0 0 0
+3_EAS 3_EAS -1.57 -1.273 -0.757 -0.217 -0.098 -0.038 -0.012 0 0 0 0 0 0 0 0 0 0 0 0 0 -1.34 -1.102 -0.638 -0.158 -0.112 -0.069 -0.014 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.972 -0.773 -0.378 0.04 -0.033 -0.086 -0.016 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.459 -0.342 -0.157 0.122 0.075 -0.08 -0.017 0 0 0 0 0 0 0 0 0 0 0 0 0
+4_EAS 4_EAS 0.058 0.129 0.196 0.204 -0.029 -0.105 -0.046 0 0 0 0 0 0 0 0 0 0 0 0 0 0.321 0.408 0.42 0.256 -0.104 -0.169 -0.047 0 0 0 0 0 0 0 0 0 0 0 0 0 0.321 0.38 0.45 0.345 -0.035 -0.251 -0.05 0 0 0 0 0 0 0 0 0 0 0 0 0 0.467 0.489 0.407 0.356 0.025 -0.265 -0.05 0 0 0 0 0 0 0 0 0 0 0 0 0
+5_AMR 5_AMR -0.043 0.06 0.217 0.204 0.119 -0.088 -0.125 0 0 0 0 0 0 0 0 0 0 0 0 0 0.123 0.129 0.277 0.262 0.182 -0.105 -0.123 0 0 0 0 0 0 0 0 0 0 0 0 0 0.786 0.809 0.769 0.646 0.377 -0.1 -0.121 0 0 0 0 0 0 0 0 0 0 0 0 0 1.138 1.204 1.2 1.06 0.614 -0.104 -0.121 0 0 0 0 0 0 0 0 0 0 0 0 0
+6_AMR 6_AMR 0.515 0.415 0.315 0.233 0.162 -0.179 -0.141 0 0 0 0 0 0 0 0 0 0 0 0 0 0.795 0.713 0.575 0.489 0.358 -0.104 -0.138 0 0 0 0 0 0 0 0 0 0 0 0 0 1.591 1.563 1.446 1.247 0.83 0.012 -0.135 0 0 0 0 0 0 0 0 0 0 0 0 0 1.588 1.576 1.545 1.486 1.15 0.058 -0.134 0 0 0 0 0 0 0 0 0 0 0 0 0
+7_CSA 7_CSA 3.188 2.966 2.553 1.815 1.041 0.358 0.016 0 0 0 0 0 0 0 0 0 0 0 0 0 3.47 3.198 2.746 2.03 1.174 0.415 0.015 0 0 0 0 0 0 0 0 0 0 0 0 0 3.868 3.788 3.435 2.736 1.545 0.55 0.014 0 0 0 0 0 0 0 0 0 0 0 0 0 3.757 3.689 3.46 2.86 1.777 0.606 0.014 0 0 0 0 0 0 0 0 0 0 0 0 0
+8_CSA 8_CSA -0.222 -0.14 -0.09 -0.033 0.007 0.061 0.006 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.044 -0.039 -0.046 -0.04 -0.025 0.025 0.005 0 0 0 0 0 0 0 0 0 0 0 0 0 0.094 0.046 0.012 -0.001 -0.031 -0.005 0.003 0 0 0 0 0 0 0 0 0 0 0 0 0 0.236 0.169 0.073 -0.014 -0.018 -0.006 0.002 0 0 0 0 0 0 0 0 0 0 0 0 0
+9_AFR 9_AFR -1.897 -1.691 -1.205 -0.74 -0.36 -0.233 -0.039 0 0 0 0 0 0 0 0 0 0 0 0 0 -1.491 -1.348 -1.093 -0.797 -0.516 -0.297 -0.039 0 0 0 0 0 0 0 0 0 0 0 0 0 -1.304 -1.28 -1.212 -1.124 -0.87 -0.45 -0.038 0 0 0 0 0 0 0 0 0 0 0 0 0 -1.167 -1.174 -1.185 -1.167 -1.097 -0.519 -0.038 0 0 0 0 0 0 0 0 0 0 0 0 0
+10_AFR 10_AFR 0.237 0.091 0.064 0.176 0.131 -0.105 -0.012 0 0 0 0 0 0 0 0 0 0 0 0 0 0.555 0.43 0.217 0.127 0.077 -0.132 -0.011 0 0 0 0 0 0 0 0 0 0 0 0 0 0.714 0.651 0.504 0.188 -0.004 -0.182 -0.009 0 0 0 0 0 0 0 0 0 0 0 0 0 0.736 0.692 0.622 0.294 0.032 -0.222 -0.009 0 0 0 0 0 0 0 0 0 0 0 0 0
+11_MID 11_MID 1.13 0.898 0.637 0.29 -0.085 -0.21 -0.081 0 0 0 0 0 0 0 0 0 0 0 0 0 1.045 0.871 0.634 0.233 -0.121 -0.235 -0.078 0 0 0 0 0 0 0 0 0 0 0 0 0 0.83 0.706 0.482 0.196 -0.214 -0.289 -0.075 0 0 0 0 0 0 0 0 0 0 0 0 0 0.631 0.531 0.373 0.198 -0.264 -0.317 -0.074 0 0 0 0 0 0 0 0 0 0 0 0 0
+12_MID 12_MID -0.81 -0.841 -0.864 -0.426 0.135 0.245 0.043 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.765 -0.761 -0.74 -0.294 0.191 0.304 0.041 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.932 -0.869 -0.773 -0.314 0.342 0.399 0.037 0 0 0 0 0 0 0 0 0 0 0 0 0 -1.003 -0.989 -0.815 -0.322 0.444 0.449 0.036 0 0 0 0 0 0 0 0 0 0 0 0 0
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS/ptclump/BODY04/example_plink2-BODY04-TRANS.profiles b/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS/ptclump/BODY04/example_plink2-BODY04-TRANS.profiles
new file mode 100644
index 00000000..72dec512
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS/ptclump/BODY04/example_plink2-BODY04-TRANS.profiles
@@ -0,0 +1,13 @@
+FID IID BODY04_0_0.1 BODY04_0_0.2 BODY04_0_0.3 BODY04_0_0.4 BODY04_0_0.5 BODY04_0_1
+1_EUR 1_EUR 1.638 1.012 1.755 1.225 1.402 1.76
+2_EUR 2_EUR -1.111 -1.421 -1.37 -1.315 -0.891 -0.538
+3_EAS 3_EAS 0.181 0.687 -0.599 -1.853 -2.19 -2.32
+4_EAS 4_EAS 0.132 1.014 2.093 2.159 1.641 1.27
+5_AMR 5_AMR 0.738 -0.498 -0.279 -0.939 -1.159 -1.385
+6_AMR 6_AMR -0.878 0.047 0.055 0.537 0.718 0.61
+7_CSA 7_CSA 2.27 2.805 2.686 2.858 3.041 2.883
+8_CSA 8_CSA 0.287 0.504 0.49 0.625 0.584 0.144
+9_AFR 9_AFR -1.658 -1.712 -1.905 -1.894 -1.863 -1.922
+10_AFR 10_AFR -0.757 0.917 -0.634 -0.543 -0.654 -0.332
+11_MID 11_MID -0.613 1.466 1.785 1.714 1.812 1.701
+12_MID 12_MID 0.477 -0.341 -0.892 -0.419 -0.555 -0.422
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS_2025-04-06_17-34-15.log b/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS_2025-04-06_17-34-15.log
new file mode 100644
index 00000000..4a0a07aa
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/example_plink2/pgs/TRANS_2025-04-06_17-34-15.log
@@ -0,0 +1,30 @@
+#################################################################
+# target_scoring_pipeline.R
+# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
+#################################################################
+# Repository: GenoPred
+# Version (tag): v2.2.2-258-gd2f2a91
+---------------
+ Parameter  Value                                                                    
+ config     /scratch_tmp/prj/oliverpainfel/tmp/config.yaml                           
+ name       example_plink2                                                           
+ population TRANS                                                                    
+ plink2     plink2                                                                   
+ n_cores    1                                                                        
+ test       chr22                                                                    
+ memory     5000                                                                     
+ help       FALSE                                                                    
+ output     /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/pgs/TRANS
+---------------
+Analysis started at 2025-04-06 17:34:14
+After checking timestamps, 3/3 score files will be used for target scoring.
+########################
+Processing chromosome 22:
+Aggregating score files in 1 batches.
+Aggregating batched score files.
+Reading in ancestry adjustment models.
+Reading in target reference-projected PCs.
+Adjusting target PGS for ancestry.
+Saved polygenic scores.
+Analysis finished at 2025-04-06 17:34:16 
+Analysis duration was 1.44 secs 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/reports/example_plink2-report.html b/pipeline/misc/dev/test_data/output/example_plink2/reports/example_plink2-report.html
index 8d905216..e98371a1 100644
--- a/pipeline/misc/dev/test_data/output/example_plink2/reports/example_plink2-report.html
+++ b/pipeline/misc/dev/test_data/output/example_plink2/reports/example_plink2-report.html
@@ -3978,8 +3978,8 @@ <h1>Ancestry</h1>
 reference population with a probability &gt;50%.</li>
 <li>The number of individuals assigned to each population were:</li>
 </ul>
-<div id="htmlwidget-2fe9e5a73527aefc8a1d" style="width:35%;height:auto;" class="datatables html-widget "></div>
-<script type="application/json" data-for="htmlwidget-2fe9e5a73527aefc8a1d">{"x":{"filter":"none","vertical":false,"data":[["AFR","AMR","CSA","EAS","EUR","MID","Unassigned"],[2,0,1,2,5,0,2]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Population<\/th>\n      <th>N<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<div id="htmlwidget-3f823656ed6503236917" style="width:35%;height:auto;" class="datatables html-widget "></div>
+<script type="application/json" data-for="htmlwidget-3f823656ed6503236917">{"x":{"filter":"none","vertical":false,"data":[["AFR","AMR","CSA","EAS","EUR","MID","Unassigned"],[2,0,1,2,5,0,2]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Population<\/th>\n      <th>N<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
 <p></br></p>
 <details>
 <summary>
@@ -3997,54 +3997,91 @@ <h1>Ancestry</h1>
 <h1>Polygenic Scores</h1>
 <ul>
 <li>1 GWAS summary statistics were provided for polygenic scoring.</li>
+<li>0 GWAS groups were specified.</li>
 <li>2 PGS methods were applied, including lassosum, ptclump.</li>
 <li>1 external score files were provided for polygenic scoring.</li>
 </ul>
 <hr />
 <div id="gwas-summary-statistics" class="section level2">
 <h2>GWAS summary statistics</h2>
-<div id="htmlwidget-bb72d67c748b523c0386" style="width:100%;height:auto;" class="datatables html-widget "></div>
-<script type="application/json" data-for="htmlwidget-bb72d67c748b523c0386">{"x":{"filter":"none","vertical":false,"data":[["BODY04"],["Body Mass Index"],["EUR"],[885],[841]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Name<\/th>\n      <th>Label<\/th>\n      <th>Population<\/th>\n      <th>NSNP Original<\/th>\n      <th>NSNP Final<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<div id="htmlwidget-563055388d27e3360d91" style="width:100%;height:auto;" class="datatables html-widget "></div>
+<script type="application/json" data-for="htmlwidget-563055388d27e3360d91">{"x":{"filter":"none","vertical":false,"data":[["BODY04"],["Body Mass Index"],["EUR"],[885],[841]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Name<\/th>\n      <th>Label<\/th>\n      <th>Population<\/th>\n      <th>NSNP Original<\/th>\n      <th>NSNP Final<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<hr />
+<details>
+<summary>
+Show GWAS header interpretations
+</summary>
+<p><strong>BODY04</strong></p>
+<div id="htmlwidget-79219dfd19cc06805c04" class="datatables html-widget" style="width:50%;height:auto;">
+
+</div>
+<script type="application/json" data-for="htmlwidget-79219dfd19cc06805c04">{"x":{"filter":"none","vertical":false,"data":[["SNP","A1","A2","P","BETA","SE","N"],["RSID for variant","Allele 1 (effect allele)","Allele 2","P-value of association","BETA effect size","Standard error of log(OR) or BETA","Total sample size"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Header<\/th>\n      <th>Interpretation<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<hr />
+<p><strong>Note.</strong> Columns that were dropped are not shown
+here.</p>
+</details>
 <hr />
 </div>
 <div id="score-files" class="section level2">
 <h2>Score files</h2>
-<div id="htmlwidget-9bf71a5c77e283510f2d" style="width:100%;height:auto;" class="datatables html-widget "></div>
-<script type="application/json" data-for="htmlwidget-9bf71a5c77e283510f2d">{"x":{"filter":"none","vertical":false,"data":[["PGS002804"],["Height Yengo EUR"],[708],[708],[true]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Name<\/th>\n      <th>Label<\/th>\n      <th>NSNP Original<\/th>\n      <th>NSNP Final<\/th>\n      <th>Pass<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<div id="htmlwidget-ed05371d5a78b927cf83" style="width:100%;height:auto;" class="datatables html-widget "></div>
+<script type="application/json" data-for="htmlwidget-ed05371d5a78b927cf83">{"x":{"filter":"none","vertical":false,"data":[["PGS002804"],["Height Yengo EUR"],[708],[708],[true]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Name<\/th>\n      <th>Label<\/th>\n      <th>NSNP Original<\/th>\n      <th>NSNP Final<\/th>\n      <th>Pass<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
 <p><strong>Note.</strong> The <code>Pass</code> column indicates whether
 a sufficient number of variants within the score file were present in
 the reference data.</p>
 <hr />
 </div>
+<div id="pgs-correlation" class="section level2">
+<h2>PGS correlation</h2>
+<p>This section shows the correlation between PGS in the reference
+sample. This is intended as a sanity check that the PGS correlations are
+in the expected direction, thereby confirming the GWAS alleles are being
+interpreted correctly.</p>
+<details>
+<summary>
+Show reference PGS correlation matrix
+</summary>
+<p><img 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" /></p>
+<p><strong>Note.</strong> Plot shows correlation between pseudovalidated
+PGS across all populations, adjusted for ancestry.</p>
+</details>
+<hr />
+</div>
 <div id="pgs-distribution" class="section level2 tabset tabset-fade">
 <h2 class="tabset tabset-fade">PGS Distribution</h2>
+<p>This section shows the distribution of PGS in the target sample. PGS
+are shown scaled according to each ancestry-match reference population.
+TRANS includes PGS across all populations after continuous adjustment
+for ancestry.</p>
 <div id="lassosum" class="section level3">
 <h3>lassosum</h3>
 <div class="float">
-<img 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" alt="Density plot of PGS from lassosum" />
+<img 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" alt="Density plot of PGS from lassosum" />
 <div class="figcaption">Density plot of PGS from lassosum</div>
 </div>
 </div>
 <div id="ptclump" class="section level3">
 <h3>ptclump</h3>
 <div class="float">
-<img 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" alt="Density plot of PGS from ptclump" />
+<img 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" alt="Density plot of PGS from ptclump" />
 <div class="figcaption">Density plot of PGS from ptclump</div>
 </div>
 </div>
 <div id="external" class="section level3">
 <h3>external</h3>
 <div class="float">
-<img 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alt="Density plot of PGS from external" />
+<img 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eHTp09v86Gnp+eCBQv8/f1nzpxJRO+//76R+I67u7uvb9vaYCqVqq6uji3zeDx2T+nm5mbLrptMp9OxL3Q+n++U10TUemFwYge0Wq1Op+PxeE4JunMdoDvjz8DKuD5OGXKBU4Z1wOmnjBM7wP0Z2PvvkDuQxf/XrnPKWNABNsUJEfmEB7f/v/aNbBm0lZ+fHx8fb6SdTk8ZlrxDRL6Roe0P5BMezBaKiooSExPN+AVM7oC9Of0q47AOcH9v5uL+a9gpcyd8vXTaAWf9xTq9A+QCVxmHdcD6U4b70VZdraqqYgsiP18jbYoC5RVX80tLS/l8vlmnTEVFBVsQB8oNtc8X8L2koqaausrKyk6bdfo5Sy5wlXH6naHDOmDxKaPT6TIzMzMyMs6ePVtVVVVTU+Pp6enj49OtW7eUlJShQ4faY0L0NhDfMebxxx9fvHhxdnZ2VlZWUVFRaGhoh5sNGTKksrKyzYe//PILV05JJBKxWE+3bt08PDzs2ucONTU13bx5k4iCg4P105Qc6fLly0Tk6+vr5+fX6cb2cOPGjebmZpFIZOj/0d6Ki4sVCoW7u7sjM/T0VVVVlZWVEVF0dLRdL04ajebq1atEZGQwRYe4FHHulImJiXHKNay+vp49c4aFhZn7W9iEWq3Oy8sjIn9/fx8fH8d3gIiuXr2q0WikUmlQUJBTOlBYWFhfX+/p6WnBrBk2UV5eXllZyePx7D2tRnNz840bN4jIy8vLrB0FAgFbEAqF9fX1fD7fWTOAKBQKVr/AgstcWloaW4iIj5W0e1ni2TuOLdTW1hr57bh/QyOXuS1btrCFyKQ4UbsDeSS1HKi+vt6yf0bXucyFhXVQycgBHHaZa2hoYKPgPT09zdqR297b27uhocHDwyMqKsrm3TNFZWVleXk5EUVHRzvlSQmXOSLKy8tTq9V3wmWO+4o29/uZuyp5eXk1NTV5eXlFRHRQv8wC7BubiAIjw9q/Juf4hgbdJKqsrJTJZFVVVaZf5riH87Ae0V5Sg88+4gB5U01dY2Njp81ac5mzCe4yFxQUJJFIHN8B0rvM+fsbrGpkV+wyJxQK7X2Z4x6azHLw4MFly5a1L+R38+bN7OzstLQ0gUAwZcqUZ555RiqV2qinHbDBFYWrtXFbGj58OFu4du2ac3sCAABw+2GXV3dPATeViT6h3MdTLCQi9ihoDdaCQOgt8u/gWULk7+sh9CJc7gEA7I+rn9LhFzKHTZFeVlZmbmI4mzzL3cvTSHCHiMT+vmT/Kntw29Nqtf/+978XLFhgfJYGpVK5YcOGGTNm2HUyBxvk7yxcuDAjI+PJJ5+8//77nVKhza64IovOHRsMAABwW7p+/ToRycKC2s9ZzvhEhJRcvGqr+I5vt5AO1/J4PJ+woLLcG9xbZQAAsBNuAg2x0fgOm91co9GUl5dzKaumaJkcPcBY49QaXUJ8B6y0dOnSdevWseX4+Phx48YlJSWxhOLm5maFQnH58uWMjIydO3c2NjYWFhY+99xza9asCQwMtEdnbJC/o1Kptm3bNnny5IiIiL///e8sceu2wYaZEFFAQIBzewIAAHD7YfEUnzCD4yPkkaGkdzm2GIvv+ER0HN+h1hI8LN4EAAD2w0IqPD5P6Cszshk3e7q5IZiWaRkNT47e0r6/3ILGAfSdOHHim2++ISKZTPbJJ5+sWbPm0UcfTU5ODgwMFAqFvr6+kZGR99133+uvv56WljZy5EgiqqioePPNN+3UH1uO+C0uLv7www/j4uLuueee1atXO70gv/VKS0t37txJRGKxOCEhwdndAQAAuN2w+I7McHzHNyKEiPLz860p3K7VatmBDOXvcH1A/g4AgL2x4ibePjKem7GnUUnruF0u38dELGTDhYcMEfrJiKihoeE2eG4FZ1m+fDkReXt7s0m3jWwpk8k+/PDD++67j4gyMzPT09Pt0R8bxHf+8pe/fPLJJ/qTTRw+fHjWrFkhISFPPfXU8ePHrT+E9aqrq9PS0tLS0jIyMvQ/b25uPn36dIe71NTUTJ06lRV5feSRR5xSSQsAAOA2plAo2Cwq3PRV7bFVKpWKldS1TGFhIZsC2TfC8IHCgoiotra2/ZwJAABgQyxeI/LvpLQ2G59F5qfYsPFZks7iOyK/lg5YUEwXgIjy8vKys7OJaNasWXFxcZ1uz+Px3njjDVYhm5v2wbZsEN/x9/d/6aWXzp07d/z48aeeeoorB11bW7tixYrBgwcnJiZ++umnNjxtSkpKrrdi2XfscNf1qFQq/V1ycnImTpw4ceLEt99+W//zxsbGlJSUIUOGLFq0aM+ePefPn79y5crRo0cXLVqUlJT022+/EVFwcHCbvQAAAMB6XLKMkfFZPuEtq6wZOcVVTTYyPksW2jISns04CQAAdtIS3/HrLL7TGqAxK3+nrq6OvaHvNH9H5OdrQfsAHJY74ubmNnXqVBN3EYvFEydOJKJTp07ZY6IqW47PGjRo0PLly4uLi7/99tvhw4dzsxpfuHDhpZdeCgsLe+CBB3bs2GFNfjUzffr06FZjxoxhH65fvz5aj1kD9Y8dO/b666+PGTMmKSkpNjb27rvvfv3119l7wpiYmN27d4eEGLwdBAAAAMvk5+ezBWmowSJ3XGqPNfEdbl8uWtQe4jsAAI5hYnxHIPQWCL3JzPwdlrxDJsV3fPT7A2Cu3NxcIkpISBCLjc3U1saAAQOIqLKysry83OZdsmV8h/H29p4xY8aBAweuXLnyxhtvhIeHs89VKtXmzZvHjx8fGRn55ptvWl8o0SZEItFbb701cuRIoVCo/zmPx0tMTPzwww+zsrKSk5Od1T0AAIDbGBdJkYUYnEXCJvGd3xOFDA8E42oAcVEnAACwh9b4TifzW1HrEC2z4i9cfEcS5Gd8S26AGMZngWWqq6uJiIt4mIjbnu1uW3aczjwmJua9995755139uzZs2rVqm3btrGh74WFhe+///7ChQuHDx8+e/bsBx54wNvb26yWFy9ezIbrG9GtWzf9HxMSEvbv309Efn5/OM89PDz+9a9/EZFGoyktLS0rK6utrZVIJN26dfPx6SSiDAAAANZgkRSeG18aYnCWEw+hl1Du01BZbU3lYxYb8vaVslfBHZIE+vHd3LQaDeI7AAD209TUxMZPCeXGJs9ixAHyyuuFZsV3uGQfkdHJ14lIJEf+DliloaGBiFg9nTZu3LhRX18vl8uDg9u+WOK2Z7vblh3jOwyfzx87duzYsWMrKiqWL1/+9ttvsyiPTqc7cODAgQMH/vrXvz755JMvvfSS6WOg+vTpY243pFKp8XLWbm5uISEhGIcFAADgMCySIvb35bsbuyHxCQtqqKy2Pn/HSPIOEfHc+OJAueJWmTWFnAEAwDguWUZkWnyH7DY+SyAWunm4a1Rq5O+AZdiwLLVa3X7V+++/n5mZOXXq1FdeeaXNKi6sIxKJbN4l24/P6tDRo0dfffXVDz74gAV39FVVVS1evDgmJuaDDz6wR4UhAAAAcE0sviM1PDiLYUVzrCmL0xLfMVzFmZGGBBAR4jsAAPbDJct0Wn+HWqdINyu+U1xcTEQ8Pq/T9nk8HsshQnwHLOPr60tE5pbR4f7e2gwtsgn75u/cunVrzZo1q1atunTpkv7nffr0mTt3rlgs/vrrr9kcVU1NTa+99lpxcfFnn31m1y4BAACAi2CRFBZVMUIWHkRE+fn5Wq2Wzzf71ZROp2OBJFmn8Z3gAEL9HQAAe+IehoUmxHfEAb7UOqSLm6bZOBbfEcp9+O5unW4s8vOtLalAfAcsEx8fT0RtYh2dOnPmDBEFBQWx8JBt2SV/R6VSbdmyZeLEiREREa+++ir3C0skkrlz56anp585c+a5556bOXPmgQMHcnJyHn30UbbBkiVLrly5Yo8uAQAAgEvR6XQsviPrNL4TGkhESqWSy7o3S3FxcXNzM+nNkGUIizQVFhYioRgAwE64YIrQ19TxWWROigSL73Q6OKulD8jfASv07duXiEpKSs6ePWv6XgcPHiSifv362aNLNs7fOX/+/KpVq9auXdumSNXAgQPnzp07bdq09jOHxcXFrVu3LiQkZPHixTqdbu/evT169LBtrwAAAMDVVFRUNDU1EZEk2GBxZYYbV3Xz5s2wsDBzD/T7LF2dxneC/Imoubm5rKwsMLCTjQEAwAK/19/xMyO+U1ZWFhMTY0r7ZsV32BguxHfAMpGRkQkJCRcuXHj33Xfj4uL0VxkqGpidnX3+/HkiGjt2rD26ZJv4Tk1Nzffff79q1ar09HT9z318fB577LG5c+d2OsX43LlzFy9eTKheDgAAcGcoLCxkCyyqYoQs9Pf4zuDBg8090O/xnbBOQjZcpKmgoADxHQAAe2DBFJ4b31vW+Xgr/fiOie23xndMGvnC8nfMrZ8CwJk2bdo///nPvLy8vLy8TjfWarWsHE1MTExqaqo9+mOD+M633377zDPPNDY26n84bNiwOXPmPPTQQ15eXqY0EhTUcuum0Wis7xIAAAC4OK6Mcef1d1rzbiwrscxNrG5ifWUiKiwsTElJseBYAABgHHudL/SR8tw6LxUiDjRvfJZGo2HtSzp7c8Cw+E59fX1DQ4NQKDRlFwB948aN+/HHH00cn3XlypUePXr06NFjzJgxFtQTNIUN4jt5eXlccCcgIGDmzJlz5sxpk57UKT6fzzKuTaybBQAAAF1aUVERW2DToxjh7Sv1EHqpGposq3zMAknuXp7endV64DKJuNwiAACwrYqKCmoNrHRKJPfhufF1Gq2J+Tvl5eUsXcCUyblY+2yhrKwsMjLSlF0A9PF4vP/+9781NTUdrm1TnaZnz56vv/66Xftjm/FZPB5v1KhRc+fOnTx5skAgsKAFqVSK6UgBAADuHL/Hd4I6nx9UFhpYfuWmZfk7bC9ZSACPxzO+pSTIj3g80um4vgEAgG2xSI0pxZWJiOfGF8l96soqTczfYYOzyMz6yoT4DlhBJBKJRCJn96KFDeI748ePf+KJJ6KioqxvCgAAAO4QLIbiJZO4e3l2urEsxPL4Dsv6kXZWXJmI3AQeQh9pQ1UN4jsAAHbCIjWmTI7OiAPkdWWVJubvmBvf4dJ8UIIHbg82iO8MGDDA+kYAAADgjsLGQHVaXJmRhgZQa6TGXKz+TqfFdxhJkB/iOwAA9tOav2NqUQ5WKdn8/B3T6iu3phEhvgO3BxvEdzZs2LBhw4aQkJDPP//clO2bmpoee+wxIpo6derUqVOt7wCAw+h0uq+//nrnzp0BAQEvvvhiz549ndWThoaGTz75JD09vX///vPnz5fJTMpxBQBwHSyGwtXONE4WEkhE5eXl5pbAbGpqYnft0s5mYWckQX4lOXmovwMAYA9qtbqqqoqIRH4mxV+oNRPHxPwd0yu7MUJ5S5gJU6SDZY4dO3bixAkimjJliqEhTcePHz958qRCofDx8enfv/+AAQM6HTBuMRvEdy5cuLBp06bu3bubuL1ard60aRMRJSUlWX90AEd6+umnV6xYwZbXr1+/b98+p0ywUl1dPXLkyNOnTxPRzz///OOPPx4+fNjX19TLJACAK7h16xaZVnyH9KbQys/PN2sOh4KCAp1OR6aNz6LWKVdY3wAAwLbKy8vZdzIXWOmUONCPiCorK02ZZ5nl77h7eXpKTKqH4i2TsvrNyN8By6xYsSI7OzswMPC5555rv7a+vv5vf/tbeno698mqVatSUlI++ugjHx9ThyiaxS6TcgHcllatWsWCO/4yKY/Hq6mpeeCBB+rq6hzfkxdeeIEFd/ykEiK6cOHCs88+6/huAABYTK1WmzWFrVQvvmPWgbiSPTIT4zuBfkRUXl6uVCrNOhAAAHSKTZ5FREIfU3PPxf6+RKTVaisrKzvduKSkhEwenEVEPDe+t1RCyN8Bi1RXV7OZ0R966CEPD4/2GyxcuFA/uMNkZmY+//zzpsQrLeCE+I5Wq2ULbm5ujj86gGXq6+vZbHYhcnnm8qXvzZpJRNevX1+8eLGDe3L8+PE1a9YQ0aiUvje/Xztu4AAi+uGHHw4ePOjgngAAWKy4uJjdD5iavxMSwBbMnW3T3PgOGy+m0+m4Ig4AAGArXJqM0M/k+E5rpWRTQjAs+9LE4sotPZHLCPV3wCKZmZksH23MmDHt116+fHn37t1EJBKJXn755W+++Wbx4sWJiYlEdPHiRTakyeacEN9h7+uISCKROP7oAJZZuXIleyHw/uwnQuTylx9+MDkmmog+++yz2tpaR/bk3Xff1el0Anf3ZS/8ReDuvvSF57wEAiJatGiRI7sBAGANc0skSEMszN/htpe2RoiM4/KJUGIZAMDmuBiNSG7y/FmBZsR32Fe3iZmhDOI7YLGcnBwiCg8PDw0Nbb82LS2NLbz11lvTpk1LTEwcPnz4F1980a1bNyLaunWrPbrkhPgOSz0goujoaMcfHcACOp1u6dKlRBQdEvzovX8iIj6P9/r0aURUXV3N/Uk7wNWrV3fu3ElEj913b3RIMBFFBATMuO9eItq9e/fVq1cd1hMAAGtwBW5MjO94eHt6+0rJ/PgOy/fxkkkEQm9Ttpe0PkigBA8AgM1xMRoWVTEFd5kwJb7DUi8lyN8Bh2D3GL169epw7fHjx4nIz89v+PDh3Ife3t5ssqnc3Nz6+nqbd8mS+sp79uzZs2cP9+PRo0eJqKKi4uWXXzayl1arVSgUWVlZJ0+eJCIPD48hQ4ZYcHQAxzt69Ojly5eJ6JkJ4934LVHRKXcP6RYYcLO0bNWqVfPmzXNMT1avXs2SAP8yeSL34bMTx6/4eadOp/vmm2/eeecdx/QEAMAaXPTExPmziEgWEthYpbAsf0dmWvIOtRbyJMR3AADs4PfxWabPj956meBGgRhSW1vLKmOaNz7LF/EdsJBCoSCiDme5qauru3btGhH179+fz/9DVs1dd91FRDqdrqioKDY21rZdsiS+c/To0fY1R6qrq80qiJ9B8gAAIABJREFURDJ79uyAAFNvtgCc64cffiAiNz7/kXtHcB+68fnTR41ctP6HU6dOXbp0yaz5XCyj0+nWrVtHRCmxPXrH/J7+1jsmum+P7qevXP3uu+8Q3wGALqFl9BOPZ/pduDTYv/jCFa6ejonY9iYW3yH2VMDjkU6H+A4AgM2xGske3p7uXp4m7iIQeguEXsqGpk7zd7i6aRbU36msrNRqtW2ewwGMY+/dOywrfOnSJbaWFdzR5+/fMn6woaHB5l1ywl+wSCSaP3/+Z5995vhDA1hAp9Nt3ryZiIb2TgyR/+FqMe1PLbl2GzdudEBPTp06xcLAj4wc0WbV1BHDiOjKlSvZ2dkO6AkAgJVY9MTbR+LuKTBxF1lYEFlaf8fEydGJyM3DXegjJeTvAADYAUuTYSkzphMFyMmE/B0LMkOJiH3nazSaqqoqs3oFIBaLyUDy1/nz59lC+/iOWq1mCx1OuWUlS/J3xo4dqz9b+65du3bv3u3v7//GG28YO5K7u0QiiY6O7tevn0gksuC4AE5x6tSpwsJCIpo8ZHCbVQmRkb26ReTczN+yZYvxv3+bYFW4eDzen4fe3WbVpCGpr331NRFt3749OTnZ3j0BALASe8tq1itWViC5tra2pqZGJjPp2UChULDcaWmwGSnD4kB5Q1UN4jsAADbXEt8xufgOIwqQV90o6jR/R7+ym06nu7z3WEHmBZ/w4N6TRwpEQkN7cZ0pLy/38zOpJJxl1Gr1xo0bs7KyoqKiHn/8cW9vk6rCgSsLCwsjouzsbJ1Ox+Px9FcdPnyYiDw8POLj49vsxf0l6wdVbMWS+E5qampqair3Y3V19e7du2Uy2Ysvvmi7jgG4ClbPmIgmpA5qv3bSkNScm/mZmZlFRUUdFk63oZ9//pmIkmOiI4PavojuGR4eGx6WW1C4a9cuB0SaAACsZEEJTG4CrJs3b/bu3duUXbhkH1moGfEdSaBf6aVrmB8dAMDm2JOt0OTJsxiWj9Np/g57I0tE3nLZD0/989KvR9mPR5Z/P+Pbj3wjO75R5yoBlZeX26/eQm1t7aRJkw4cOMB+/Pzzz3fu3BkREWGnw4FjJCUlEVFpaenOnTvvv/9+7vOzZ89mZmYSUd++fQWCtnnKFy5cICIPD4/g4GCbd8kG47NSUlJmz549depU65sCcEG//PILEfUMD2fzVbUxbuAAItLpdLt377ZrN27dupWVlUVEYwf073CDMf37EdHx48dramrs2hMAAOtZMIUtV0PH9CFaevEdU8dnUWtWEfJ3AABsrjV/x9TiyozY35eISktLWUETQ9j3Nt/N7bf/fMsFd4io6uatdbNeUzY0driXfv6OWb0yy6OPPsqCO3wen4jOnz8/efJkpVJpvyOCAwwZMkQoFBLRwoULN27cWF1dXVlZuWvXrgULFrANJkyY0H4vltoTFxdnj3pPNmhx0qRJX3311cKFC61vCsDV1NfXnzhxgohG9u3T4Qap8b2kQiG1hoHs55dffmGXtDED+nW4wah+fYlIrVb/9ttvdu0JAICVtFotew0rDuhgyglDZCGWx3ekIebEd1pfFGs0GtP3AgCATlVUVBCRt8y8+A6rv6NSqVh5ZkNY3qWXTHz6hx1EFNe9z/ovjk8eO5OIKq4V7F/8dYd7ccWAWN/sYf369WlpaUR0b99RBT+UzBzzJBGdPn36448/ttMRwTG8vLxmz55NRE1NTR988MGoUaNGjx795ptvslpOcXFxY8eObbOLQqE4duwYEfXr1/EznZVQIRzAmGPHjrHI+oi7Oi5q4+7mNrxPbyLav3+/8VcKVtq7dy8RSby9U+N7dbjBPb2T3N3ciIjL/AQAcE3l5eWsuKDI34zxWZJgfx6fR+bEdwoKCoiIeDxpsBmJQix/R6PRdFrrAQAATNfU1MTmLze3/g5XL7ll7kUD2FpVYzMReXl6v/KXTyVin9nTX43vmUJEGWu2Ved3MPDW3vk7SqXytddeIyJ/WcDXr6yRCKWLn/msV7d4Ivrwww/tF1QCx5gxY0aHSTrh4eEff/xx+wydK1eujBgxYvTo0WPGjLFHfxDfATCG5cLweLxhyQZrPYzsexcRFRcXs7GUdrJ//34iGto7ycO947JZUqGwT/cYIjp06JD9ugEAYD2utI3EnClO3DzcWTzI3PwdkZ+Pm8CMKSokgX5t+gkAANbjAihcyRsTmRnfaSKiKeNmBQaEERGPx589/VUi0qjUh7/4rv1eArGQ7+5OdovvrFmz5ubNm0T02iNv+krkROTp4fnerEVEVFtb+7///c8eBwWH4fP5b7311qeffjpy5MjIyMjg4OA+ffr85S9/WbduXYe1WVNSUhYuXLhw4cKePXvaoz/m1VdOS0tjqWVE9OSTTw4cOLDNh2aZMGFCh7EuANfBhkf26hYRYHiulhF9WlJ7fvvtt/YT4NlEXl4ee0phuUKGDE1KPHU598yZM/X19ZilDgBcFhc3MWv+LCKShQbWlVawG2VTsC1l5gzOIr1RY4jvAADYkF58x8z8nYCWsLsp8R0iEgi8Jo6ZwX0e171PSvLQzOzD2Vt+GfXqXC+pWH8vHo8n9JXWlVXaKWfz888/J6IQecjMMbO4D8cMGJcU1fvc9bNffPHF3//+d3cDr2+hqxg2bNiwYcOc3Qsic+M7J0+eXL58OVseOnQoi+/of2iW4OBgxHfAlalUqvT0dCK6OzHByGYJUZF+UkmFovbgwYPz5s2zR0+4lJx7eicZ2WxIYsJ/Nm9Vq9UnTpwYOXKkPXoCAGA97hZcbE7+DhHJQgIKz1w0PX/nxo0bRCQLDzLrKFzUCSWWAQBsiIvveNshf6eurk6hULDlYan3SyV/qO82cfSMzOzDqsbms1v3Dnh8cpt9vX2ldWWV9hgqlZ6eziZIeWLsbE8PT/1VT0145q//fa6wsHDnzp0TJ060+aHhzoTxWQAGZWVlNTQ0ENHghHgjm/F5vCGJCUR05MgRO/WEpRGJvLz69uhuZLPBiS39ZFW7AABcEzfHLZsSxXSysEAiKigo0Gq1nW6s0+nYXLlmFd8hvfhOSUmJWTsCAIARXADF3PFZApG3QOhNejOgt6e/avSIB9usTUke6u8XQkRnNnYw4y3LJ7LH+KzVq1cTEZ/Hf3z0rDarHhr+sLfAm4jWrVtn8+PCHcu8/J1JkyaFh4ez5cGDB7f/0CwpKSkW7AXgMGzmLCIaZKCkMWdIYsL2YycKCgpu3LgRGRlp856w+M7AXnGGiu8wIXJ5ZFDQjZKS48eP27wPAAC2wvJi3D0FXjKJWTuyabCam5tLSkpCQkKMb1xWVtbY2Ejmj8/ylIg8vD1Vjc3I3wEAsCFuAJRI7mPuvqJAufJ6oZH4TktBfSJ/3+BesX3brOXz3UbePWnDT8uLzl6qvFEojwzTX8viTTaP76jV6o0bNxLR8D4jwv3bPi+LvSXjBo3ffGjjzz//3NjY6O3tbdujg+NptdqcnJy8vLyCgoL6+vrGxkZ3d3ehUOjn5xcdHd2rVy+53Ly0ZQuYF99JSUlpH5Tp8EOA2wAbnCUTiXqGhxnfkkvwOXr0qM3jO5WVlZcuXSKi1IROwkxENKhX3I2SEtZzAADXxOraiMxM3iEiWWhLpObmzZudxne4Mj0s68csIn/f6vxi1N8BALAhi8dnEZEoUF5lNL5z/vx5tjC47708Hq/9BvcMHr/hp+VEdGHHwaHPPqK/iuXv2Hx81m+//cZCWg8Om9rhBn8e+sDmQxvr6up27949ZcoU2x4dHOnWrVurV6/eu3dvdXW1oW34fH7v3r0feuihMWPGdPgnahMYnwVgUEZGBhH16xnb6RmYEhsrcHcn+wyMOnHiBJt5PTXe2DAxpn9cLBGVlpayqhMAAC6IxU24aapMx0VqTCmx/Ht8J9Ts+A4booX4DgCADVVWVhKRh9DL3VNg7r6sBI+R+M6BAwfYwpBBHU87HRXRMyw4iohydh9us0oolxJRdXW1RqMxt2NGbNmyhYjc3dwnDJ7U4Qb39RvtJfAiop9++smGxwUH27Bhw//93/9t2rTJSHCHiLRabVZW1ptvvvnkk0/ao9gTg0rdAB2rra1lWTMD4jqfu87bU9Cne0zGpcvckC4bYm3yeLxOh4kRUb+esWzh5MmT9hgpBgBgPVbXxqL8nZZKyYjvAAB0OSx/R+hjdvIOtcZ3SktLVSqVh4dH+w3OnDlDREIPYWJCf0ONDOp37+afVxZmX6otqZAE/f6OwdtHRkRarbaioiIw0OxLhiHbt28noiGJd8slHb/PEHmJhyeP2H1y186dO3U6nf1yOsB+NmzY8NFHH7Hl0NDQ1NTUXr16hYSEiMViLy8vtVrd1NRUVlZ28+bN06dPnzx5UqPRnD17ds6cOevWrRMKhTbvj+PiO9XV1TweT2Z4kmmzVFZWnjx5MiMj4+TJk1VVVUQ0adKkBQsWWNNmfX391q1b9+3bV1RUJBaL4+LiHn744d69jU1HDbex06dPs/qdKbE9TNl+YHxcxqXLWVlZzc3Nnp6ene9gMhbf6R4a4iftvFBF39gefB5Pq9NlZmY+8MADNuwGAICtsLgJNw256YRymbuXp7qp2fT4jpvAQ+Rv9nB3Ft9BfWUAABtqie/4mV18h4jEQf5EpNVqi4qK2r/CVCqVbGrF7v5xPJ7BESoDU/60+eeVpNNdOZDe9+Fx3Ocsf4eIbBjfOXv2LLsMjRtobMLo/2fvvOObKr8/fm6Spk2adM9073TS0hZaoFD2BkGmIktQcADqVwUVRPyioFIZKvAVlI0MGSIooywpFDsYLR1009IWSkl30mb9/njKpb+2pMm9NwnV5/3ij0vy5HlO0uSOzz3nc4ZGjTid+mdlZeXNmzcjItrbBmGec6qqqjZs2AAANjY2S5cujY+PZ7E0FUhVVVWtX7/+9OnTpaWlW7ZsoSlfdAoz+s7jx49Rup1IJGqnQqnV6nXr1m3cuBH95Nzc3N5666333nuPzWZTW6u8vDwuLq6wsLDd42Jx16kNGvjrr79mzJjR7nxx9erVCxYsWL9+PbOX65huQXp6Otro6a+VvhMdEABworm5+ebNm71792YqDLVajcx0tEkjAgAhj+fr4nK3rCwtLY2pGDAYDIZBZDIZujFDtqnSHoIgLJ3tq4vKtNF3iouLAcBS5ECwdL4jimKrra1tamrSx+01DAaD+RfSqu9YU7nfL3iSblNaWtpR30lOTlYoFAAQ4BqiYZJAvwihwLK+oTbv0v/Xd56kFDFosfznn3+ijeFRIzQMG9JzKNo4e/Ys1ne6HadPn25ubuZyuVu2bPH29u5yvL29/erVq9ls9qlTp06ePLl48WLKqsizYMZ/Z968eX5+fqGhoaiZdFvefPPN999/H4k7AFBaWvrhhx9Onz4d+YlQoKmpqa24w0hCUFpa2siRI9HJ4pAhQ5YtW7ZgwQKk3W7ZsmXOnDmUo8V0X27cuAEA1gKBh3YqfnRAa2EUcu1hioKCAiSeaqnvAEBPPx94Ej8Gg8E8b1Bujo6wdHEEXeqz0HhdIXOLcAoPBoPBMEWrvmNDrT7rqb7T8dlz586hjWAvTW1/WCx2j+A+AFCUlK5SPLXa4dtYto2QEU6fPg0A7o4efq6azuF9RL7uDu4AcP78eaaWxhiM7OxsABg4cKA24g7Jq6++CgC1tbXl5eWMh8SAvqNQKBITEwHghRdesLOza/tUcnLy5s2byf9yua1OWocOHfrpp5+oLcflcpEE8+uvv5aUlKC+0XRQqVSzZs1qbGxksViHDh06e/bsF198sXnz5ry8vLi4OADYv3//wYMHaa6C6XagIt4ePt5alsL6ubhYmpsDQGpqKoNhkGpRlNb6Tg8fbwB4+PChBgs6DAaDMRakqQ2F/B14YrGMcnM0g2zmrSjqO62xYQseDAaDYQrkKcu3opS/46RJ30FXo3Y8ezuHLlor9gztCwCyuobyjFzyQTKliCl9p6mpKSkpCQAGRwztcnD/sIEAcOXKlZaWFkZWxxiM2tpaAHBx6aLVcjtcXV3RBkpnZhYG9J3U1NS6ujoAGDlyZLunUDUaAEyaNKmqqkoqle7evRvlIK1Zs4bacu7u7kiCmThxoru7O43AWzl06BBqp/fWW29NmjSJfNzCwmL//v08Hg8APv30U/oLYboRzc3NSI5FWok2EASBnHqY1XfQbBw2O9zHR8uXhPu2jrx16xaDkWAwGAwjVFRUoA1klqkrSK95/PhxfX29hmENDQ3oNJ1q/g7WdzAYDIZJpFIpKvWglr9jZiEw4ZlCZ/mbUqkU3RB1E3qYCs01z9MjpA/aKEp6mupO5u8w1dUoKSlJJpMBwMDwQV0O7h82AAAaGxuZvYjAGABUS6RrGg55D97amkois2YY0HfKysrQRo8ePdo+3tLSgjq92dra7ty5087OjsVizZgxY9asWQCQn5+fmZlJf3X6HDhwAG2888477Z5ycXGZOnUqAOTm5qJsDsy/hKysLLlcDrroO/CkN3lOTk5DQwNTkaAdfbCnB0/rRpJh3l5oA+s7GAzmOYRm/o6VqxPa0JzCQz5r5UpJ33lSO4b1HQwGg2EEUjqh5r8DAEJne+hM30lOTkbn7a5CNzOhQPMkDnYiZ0d3ACi69lTf4Qr4bBMOMKfvoHwiFsFC2o1m+obEoY3Lly8zsjrGYAQGBgLAhQsXUMqwlvz8888AYGlp6ezcRboZBRjQd6qqqtBGu/iuX7+ONNpJkya19SacPn062ngeFBO1Wo3KNcVisaenZ8cBZFISKqHE/EsglRGd9J2efn4AoFQqmfpuq1QqZKOjZQ8vhIOVlbONDWB9B4PBPJeQjjbmtlRuW+mu7zhRWEVgbwMEAdh/B4PBYBiCLH0ik2V0Bek7HS+kSb8OV6G7WVf5OwAQGtgbAErTs5QtcvQIQRAoKqbqsy5evAgAIV6hthZ2XY0Fdwd3N3s3aPNGMN2F4cOHc7nc5ubmhQsXavPnk0gkq1atOnHiBACMGjWKw2G+mzkDM5JlY2ZmZm0fR02dAWD48OFtHydllOfhnKmkpAQleD/LrjwyMhJtoBouzL+E27dvAwCXwxG7u2n/qij/Vovl1NTUfv360Q8jLy8PfT8jddF3ACDU27Pi8eOMjAz6MWAwGAyzoKO/qYCPMu11xcqtVa8pKirSMIymvsPmmphZCGS19Th/B4PBYBjhqb5DOX/HyQ4603eQ042VqbW5iXmX9VkAEBbU+8zFQwpZ8/1bOe7RoehBnpVF/YNqRvSdhoYG1Me235PEnC6JDe5bevGXq1evqtVqLa0/Mc8D9vb2ixcv/vrrrx8+fLhkyRJ3d/fY2NiAgABnZ2ehUMjlcpVKpUwmq6qqKi0tvXnz5tNcM1fXhQsX6iMkBvQd0jW5trbWwuJpOSWpYLW70CU7XkmlUvqr06SgoABtdGyzh3BzcyMIQq1WkyMx/wZQ5kuAuxtXF1XVw9HBWiCQNDSQvdVpQs4Toau+4+V1JjX97t27MpmsnfCKwWAwxgX575CdUHRF6GDH5pooW+Sa83eQ+sPicCwcu7532ikCe2tZbf3zcC8Kg8Fg/gGQNR8083dqa2slEglpXKJSqa5duwYAIoEL15zH4nTdbTokMBptFF+/Reo75rZWwFD+zrVr11Cz9n6h/bV8SUxg7MGLv0gkkuzs7KCgIPoxYAzG1KlT1Wr1+vXrFQrFvXv3tOnvGRQUlJCQ0LbCiUEY0HfIsqyMjAw3t9ZkB5lMhnq8BQYG2tvbtx1fU1ODNgSCLsojDQByhoZnmxtxuVw+n9/Y2IjMsTulqKho69at7R5se95JeqFLJBIWi5me9DqBdjEAUFdXh7y+jEVTU5Oxms2jD6GlpUWbHTfK3wl0c0U1htoT6u15+XZmSkpKx1Wam5sBQKVSaX/kQLcjOGy2j5OjTpH4u4gAQKFQXL16NSwsDD1I/umrq6v1+j0k/8RIn9YelUqFNsifDFNV0LpCRl5bW9vY2Gj4AMiPoqGhgfz9GiUGmUzGYK9QnUBfA4VCYawAyJsQ+g5AqWxt0arrT4Z8Ifqs1Gq1cf9YoN1hDtn28W0tdd3Bklg420tKynNzc8n3S34U9fX1aF+Xm5sLAJYie2kzxaMez9oSRavTp9pdDnP6gMJhjhrkL0XX3SM5Hs2gVCqNvnuprq42yr16fJiDf9NhDv02oc2uUkva/dbkcjmdUMm8G8LMRNf9P4rEQuSA/nvjxg3y/DY7Oxtd0LkIXE2F5tocSS0ENk4ObpUPS4uu3YiaOxE9yLUwB4CHDx92+h51Osz98ccfAEAQRIR3Ty3faQ+v1lKSM2fOODg4dBzQ9jBH/kGNQlNTk7F+MgY7zOl6vTxt2rR+/fr99NNPFy9eJOWFjhAEERgYOGXKlFGjRunvWowBfYesYPrf//43atQotL17925UVzJoUHvP8JycHLRBNgYzIuQhTUOOA4/Ha2xs1HDwKy0tXbt2rYZVyD0CqW0ZCwZ9f6khlUqNm7fV0tLy+PFjzWMePXqE9hp+zs66Rhvi7n75dmZeXl55eXmnXyqVStVlACQot9NPJAKlUqdIfJxa/URTUlI6/tAM9j3U9Yyto76j/WelJzTsow1DU1MT5WtgRmhubjbuaYRCoTDu10CtVhssAF1/Mu2EIUOG+iy02b2giidTSyHlw4FQZC8pKS8oKOj4fsmmWih/R+BkR3kVM2sLAKioqNDpUzX6YU4ulxv3a6DTYY4mul6sdtR3jP6T0UdzXJ3Ah7l/1WGOPNHSEvIngzZohkr2DFKbmlDbT1qIWpMG7ty5Q57fXrp0CW2IBK5cAV/LI6nYN6LyYen9mzmNDQ0sNhsATAR8AHj06JHm96jNYQ5Vsfg4+/I4fC3fqae9l7mZeaOsMSkpibyg7hSjX83JZDLjpgsY/TDXKa6uritWrPj444+zsrIKCgrKysoaGhpkMhmHw+Hz+TY2Nl5eXkFBQe0SX/QBA/qOWCwODQ3NyMg4fvz4Sy+9NGHChNzc3NWrV6NnSTdlEpRBB0/spo0L6WmkYV+AnjIxMTFQTBhjk5eXhzYC3XWWIMO8PAFAqVTm5ua26yinK2q1OisrCwBCvTovHtSAv0jEYbMVSiX5XjAYDOY5AQnofFsryjOgW7ilpaUaxqBnLVw6uQuqJXxbSzBeFiEGg8H8w0BqJtechzpVUcDCpfX+Zdv9PzJV4LK5djx7bcx3EIF+ERev/iZvkj3KLXYI8gEAnpUQAOrr6xUKBR3XW7lcjhwwe/n31v5VbBY71LNHcs5V3B2lW8Nms0NDQ0NDQ40YAzOOzWvWrBk9ejQA7N+/f//+/eTjQ4YM6du3b7vBx44dAwALC4vnQd8RCoVoQ4O2iu4qaKgm69+/f8dk7LNnzw4bNgxtCwQCJLV6eXkZRSeSyWSoFFAkEhmrLO7u3bsAYGtra2tL0XOBJiUlJc3NzQKBQCQSaR75559/oo2Y0FBdo42LaNV0qqqq/P392z5VWVlZV1fH4XC8vbXqyVVYWIjuqsUEB1H40HxdRDn3Su/fv0+GIZFIUOWzj48Pm911cTJllEol8qvi8Xg6vZA8mpI/GT8/P6Mkrjc2NqK7TG5ubrq+C0ZQKBSFhYUA4ODgYGVF/TKYDgUFBUql0tLS0tGRSntp+ty/f7+xsdHMzMzd3d0oAaCbeARB+Pn56XWh5uZmlLWu65eN9L8zNzdvbGxksVi+vrp5dTFFXV0dysrp8jBXX1+PDrh2biLKhwPnAO9MgKamJvL7SX6G6DD36NEjlMjj7O9NeRU7NxEAyGQyR0dH0jpQA8/JYc7c3NzFxcUoAeh6mKNMU1MTqvIzNdXNopvMq+Xz+U1NTVwut9PeqQbg8ePHSOj09fU1SuU+PswBQGFhoUKh+Dcc5shdtK6XITweDzlUmJmZyWQyHo9HenFQAN0yN7ezprCTbGpqkkqlfBtLU6F5c31jXV0deX6LSkMc+U4EQVjY22j5fe4REoM26vLLAuN6AYCtixMAqNVqGxsbJ6f2xvzaH+aSk5NRekt8z0E6vdOY4NjknKuFhYXImrfds+RhrtNnDQM6zNnY2NjZUTS2o4nBDnPkRVN3hJkjyqhRo7Zs2dLu5xQREbF79+52I5OTk7OzswFg0KBBRjmetYPcoZeXl3c6oLq6GpWKGGvXjzE8mZmZACDk8dwddM6g83VxEfB4AID6mtOBsrkyItjDA3DfNwwG85xBtqMS2FNpjo6wdms1/kPXhx0hH7d2d6a8isDeBm3gFloYDAZDn9bkTSuLLkdqwMbTBdrk2jc0NKBLSydzEQCYWWorfIicPC0tbAGgNK31VJns6kXT2wW5ZwJAbFAfnV4Y6R8FAEqlkqk+LZh/J4x1XH/99dfHjh177NixwsJCLpfbu3fvMWPGdMwRSElJQZk+c+fOZWppOojFYtQe61llLOTj2Mn83wPSRAI93ClkjrAIooePd1LmHfq7ZqQQsQgizNuLwsuDPT1+/etKWVlZbW2tNneeMRgMxgC00XdsKE+Czu8BoKCgIDY2tuMAsuWltXsXCZsaENi1KlCVlZUBAQGU58FgMBgMkPoO1eZZCBsPUUXGXZRIAgDp6enIh8tZIAIAntb6DkEQYr/w62mJT/UdG2b0natXrwKAk42Tp5NuJ/CR/q1dvVJSUgYMGEAnBoNRXl5eUVEhEonIhkvdAnTtb2dnZ2ND/VSkU1QqVU5OTmFhYVlZWWNjo1QqRf47tra2Xl5eYrGY8RU7wpi+AwAikeiNN97QPObtt99+++23GVyUJgKBQCwWZ2dnX79+vdNiS7LLe3R0tMGjwxgH5HoT5KGz6w0i3Nc7KfPOnTt3WlpayOoJCiB9x1vkbEGpeV6Qpzs8MfHp9PoHg8FgDA9qjg409R0PFyAIUKvz8/M7HUCe+tt6UU/hxvk7GAwGwyCt+o41LX3HztvGXFkBAAAgAElEQVQNAO7fv19fXy8UClNTU9HjjnxnNteEa65DsaHYN/x6WmJdZVVt+UNLkQOz+Tu9A3U+93azd3OwcnhY8zAlJYVOAIbh77///s9//vPXX3+h//br1y8hIaFbXC/v3Llz5cqVxcXF27Zte/XVV5matqKiYseOHYmJiRocuFksVmho6OTJk4cPH64/AwrjV0gZnfHjxwNAXV3d77//3vHZPXv2AACXy9XsZI75x3D//n30swz2pFgLHeHjAwDNzc1IJ6IM0nd6UnXTCHpSy00zDAwGg2GQhw8fog0yO4YCJjxToYMNtMmxbQfSfQT2NlxzKvo4gqwge/DgAeVJMBgMBoN4kr9Dqz7Lzqf1/mVubi4AIH1HYCq0NLXUVTkK9G9tSY5SeBjJ3ykoKECHjF7iGAovj/CLhCf9c59nduzY0a9fP1LcAYArV6707dv3p59+MmJUXSKXy2fPnj179uzi4mJo00ebPgcPHpw4ceKvv/6qub2aSqW6devWJ598MnfuXP11b2Ayf+d5pqamBmXiODo6tlMW58+fv27dOrlc/tFHHw0fPryti9COHTuQh/mMGTMsLGjtjDDdBdKwhkb+jg/auHHjRnh4OLVJKioq0B1jcjZd8XURmXA4coUC6zsYDOb5Ae3ZCBZhbkfLUdXG07X+QTWZp9OOVg9IT1r+i3wbK4JFqFVqnL+DwWAwNKmvr0d96Pk2tHb+dr5P719GRUUhPwRHvhMAmNvqpu/4eYVw2ByFUlGWnhUydqA5E/pOcnIy2ogJpKTv+PY8nfJHYWGhRCKxtqZ+F0SvnDhxYt68eSqVisvhLBg3uk9w0NU7WZt/OymXy+fNm8fj8Tq2z35OmD9//s6dOwHAycb63UkTmcreOHjw4FdffYW2RSJRTEyMWCx2dnYWCARmZmYKhUImk1VVVd27d+/GjRupqalKpTIjI2PevHl79+7lU6rS0Ey31HcePHhAtrsifZHr6+uRFIdwcXFp622ek5MzduxYABg9enS7PB1vb+9FixatW7cuOzt70KBB69ati4qKqqmp2bFjxyeffAIAVlZWn332mZ7fE+Z5gdR3Aj0oNggI8nA343JlLS3p6elz5syhNkkbc2WK+o4Jh+PrIsouuYf1HQwG8/yA6rP41pYsGt1nAcDWy7Xk+q3c3Fy1Wt0xyRnd10W3eSnD4rD5NlaNjyRY38FgMBiakMmb5vT0HXtfDxaHo1IoMjIy6uvrURanPc8BAMx1TAvlcs28PALzCjNK0+8AAMfM1IRvJm+S0dd3TE1Me/hEUHh5T79IAFCr1enp6YMHD6Ychv4oLCx86623VCqVgMc7/vmn/cNCAeDFuH4T+vUd/8nKuqamV199NSgoqEePHsaOtD0bN25E4k4PH+/fV69ysrHmMNFytKqqasOGDQBgY2OzdOnS+Ph4zS2kqqqq1q9ff/r06dLS0i1btrz77rv0Y2gHw/pORUXFxYsXMzMzq6urGxsbO3YNb8vEiRMnTpxIYZWXX345MTGx3YP79u3bt28f+d/s7GyxWKzlhGvWrMnNzf3999+Tk5PbNXQ3Nzc/cuSIq6srhTgx3RHUZNGCz3el2vnPhMMJ8nBPz8un00Lr5s2baIOauTIi2MMd6zsYDOa5AiWu63oW3hGk3dTX1yNnx7ZPVVRUoIa+yKaBDgJ7G6zvYDAYDH3IahSeNa2SCDbXxNbbtepu8a1bt27evKlSqYDM39H9yCL2C88rzKjMKlDImjlmpnxry9omGZ3G2NeuXQOAMJ9wUxNTCi8nVaHnU99RqVTLli1rampiEcTuZe8jcQfRLyT4l+XLxn78qVQqnTFjRlpaGh0TUsbJzs7+8MMPAcDVzg6JO0zNfPr06ebmZi6Xu2XLFm9v7y7H29vbr169ms1mnzp16uTJk4sXL+7YkIomjOk7paWl77777tGjR5GHuTb4+vpS03cYh8PhHD9+/Lvvvtu4cSPZdIPL5Y4ePXrt2rV+fn7GDQ9jSJAaInZ3o+N6Fe7rk56Xf+vWLZVKpVnEfRYof8fVzs7BivpdDrG7GwCUlpYiCzrK82AwGAxTIH2HjvkOws6nVbvJzs5up+8gmb7tGMoI7G0eZBdgfQeDwWBoQibFmNPrnwUATkE+VXeL09PTSZ8aB74TAAh1t+0X+4afOL1bpVCUZ+a5R4WY21jV3n9AOX+nqanp9u3bANAroBe1GUS2ImSx/Hy2SD9w4AC6A/3mC+PGxPRu9+zQyJ4fTJv85b4DmZmZ33zzzUcffWSMGDtBrVYvWLBAJpOxCGLXsvcZFHcAIDs7GwAGDhyojbhD8uqrr546daq2tra8vNzNje65SjuY0Xfy8vLi4uIMZkC4bt06iUSieYy7+//Lyg4KCrpw4QIA2NradjqexWItWrRo0aJFJSUlFRUV5ubmXl5eAoGAqZgx3QX0K0XKCGUifH0AoKGhIS8vj1pXXZT7Q9l8BxHo0WpBl5OT0y0M7TEYzD8epJXQaZ6FsPdrtUi7c+dOu5ucZJmtvZ8nzVWQxTLWdzAYDIYmpGhCsz86AIhCAzKOJVZXV1+6dAkAzLnmlqaWZhYCjpnOKTNi31ajzLIbWe5RIcj7mXL+TmpqqlwuB6rmyogePuFn087QKQLQE/X19Zs2bQIAFzvbVbNf6XTMxy9PP3rlas690i+++GLOnDnPSdP0Q4cOXb58GQBeGzMqLjSE2clRvrCLi25+f2RtkEQieU71nblz55LiTnR09IgRI7y9vc3NzTVnQAQFBVFbjkJFn4WFRXx8vDYjPTw8PKga62K6Ow8ePEDpo0EetFwbItpYLFPQdyQSCTKT6ulHqy408Mn+IisrC+s7GAzG6KjV6tb8Hdr6jqWLI5fPa2mSdixBRY+Y8M0sXRxproLirKqqopyMicFgMBhoI5rQ7I8OAK7hgWgDNc+y5zsCgIWzPYWpHOxdrC3tJLWPym5kk7FRzt8hzZWjxe1zW7QnzDv8bNqZvLy85y37fsOGDegqaeUrLwt4nfehNzUxSVj42qhlyxsbG1etWrV582bDxtgJcrn8448/BgBbC+GqOTMZn9/S0hLaOAJryf3799GGPly0GdB3MjIyUGsqExOT3bt3T506lf6cGIxRQMk7QDt/J8zbm8NmK5TK9PT0adOm6fryGzduIO8qyubKCD9XFzaLpVSpyPeFwWAwRqS6uhrd26Sv7xAEYe/vef9mdkZGRrunMjMzAcDex4NgUS+zRaA4FQpFVVWVoyNdtQiDwWD+tSDRhGCzzCzpahaiMH9khIwuku3M7AHAUuRAbbYA3/DktHNlN7IAgG9rBTT0HWS+42zj7GZP/Tqih084AKhUqtu3b7ezhTUiDQ0N69evB4BAN7dJ/ftpGDk0sufQyJ5n09K3b9++dOlSo6dN7NmzJz8/HwCWvTTNWg+lOYGBgadPn75w4UJJSYn2b/bnn38GAEtLS32kODFwMwp9lQFg0aJFWNzBdGtIHSSQXqYcz5Qb4OYKbdpg6QRZThxBz9fdjMv1cnaCNu8Lg8FgjAhqngUAAge6+g4AOIq9ASAjI6NtMwe1Wo0UH8cgHSrhnwWpQ+ESLQwGg6EDyt/hW1nQV95ZHI5nTDgAoJ2/A98RAKw9RF287BkE+PYAgPrKR3UVVSh/RyqVNjQ0UJgK5e/0otQZnSTMu7VOhey18jywfft2lLyzZMI4VlcWpZ/NfgUA5HI52TXcWCiVyi+//BIAnG1sXh/DTDf0dgwfPpzL5TY3Ny9cuBClvGhGIpGsWrXqxIkTADBq1CgOvV6incLAjKQd+vjx4+nPhsEYEaSD8Ey5ns5ONKeK9PO9U1ySnp7eae9ezSBVyNHaysWuc7so7Ql0d8u/X471HQwG8zxAlnLT91cGAMdAbwCor68vKCggy9dLS0tramoAwMGfevNBkrb6znPY7RWDwWC6Cygphr75DiJ49IC8863FUA58R4G9DY9qWpDYr9WCpzT9jrlta3iPHj3S1Ya1uLgY3QmIDqBenAUAXs7eAp6wQVp/69YtOvMwiFKpRC3APR0dxvTu2vAhOsB/SM+Ic+k3fv75508//dTBgWJqFX2OHj2al5cHAO9Onmimn35e9vb2ixcv/vrrrx8+fLhkyRJ3d/fY2NiAgABnZ2ehUMjlcpVKpUwmq6qqKi0tvXnzZnJyMkpkdnV1XbhwoT5CYkDfIcvGrGg0+sFgngdQ1xU/F5culekuifDz3XU2USKRFBYW+vjoVmaF8ndoJu8gxO5uJ65dLyoqam5upj8bBoPB0OFp/g7t+iwAcA5u7W6ZlpZG6jtk/qNzCAO9L5G/MuD8HQwGg6FHa/6ODTNXi8GjBpz8eL1c1sxhcWzMbF16iClP5ecdymazlUpl2Y1sj+hQMlpPT0+d5iHNd3qJKTbPQrAIVohXaHLW1ecnf+fkyZNFRUUAMG/EMLZ2VnTvT510Lv2GVCrdunXr8uXL9RzgM/n2228BwFogmDdqhP5WmTp1qlqtXr9+vUKhuHfv3r1797p8SVBQUEJCAp/P10c8DNRnkTbJpFEQBtNNQfqOmAkbc9IambzY0JKamhpUJkrTXBmB3otCoUDqNQaDwRgRUiURONBNTgQApyBfgs2CJxabCLTLJdgshvSdVh2KVKYwGAwGQwFm83c4ZqaWLg4AYMezt/VwcQqiblhpyjXzdAsAgLL0LDI8ChY8SN8x4ZiE+/akHAwi1CsMADIzMxUKBc2pGGHr1q0AIODxpg6I0/IlgyLCQ729AGDLli0oXcXwpKamXr16FQBeHTXiWYbQTDFt2rTDhw+PGzfOwsJCwzCCIIKCglauXLljxw47Ozs9BcNA/k5cXJy3t3dhYeFvv/02YoQetTEMRq/U19eXlZUBQIC7K/3Zevh4I2/j1NTUKVOmaP9CVNIFAD39mcnfQRvZ2dm6tu7DYDAYZkEqCZtrYmbJgMehCc/UUexdeSc/KSmJfBCdYTv4e3H5DJzMoYa7Clkzzt/BYDAYOqD8HXOG9B21Wl3/oBoA/MXhoeOHAL28e7FfREFxVsWdPFNBaz4FhRbpyJE2zKsHj0v36BPmHQYAUqn07t27lPtNM0Vpaenp06cBYNrAAUJdVJK3Xxj3WsKG8vLyI0eOGMWi94cffgAANou1cNwYAyzn6uq6YsWKjz/+OCsrq6CgoKysrKGhQSaTcTgcPp9vY2Pj5eUVFBRkb0+l0ZtOMKDvEASxcePGsWPHbt++fe7cuVFRUfTnxGAMT05ODhJWaDbPQpibmYnd3e4Ul7S9sawN5PhIfwZuPvu7tmpV2dnZQ4YMoT8hBoPBUAapJAJ7G11dyZ6Fe1Ro5Z38tLS0xsZGAJDJZCih3T06hJH5AUBgb11TWonzdzAYDIYycrkcOaOZM2G+BgA19yqaG5oAIDAkir5hs9g3/OTZvcoWOdKMQHd9RyqVoqNPNL3iLATK3wGAW7duGV3f2blzp1KpBIC5I4fr9MJpA+M//N92SUPD1q1bDa/vSCSSX375BQBGx/Ryd9C7pELCZrNDQ0NDQ0MNtmJHGKjPAoDRo0dv2LBBqVQOHz78l19+UalUjEyLwRgSVJwFDNVnwROBJi0tTadfBNJ3nGysXZlI27MSmDvb2ECbd4fBYDDGAqkkjJjvIDx7hwFAS0sLalqRmpqKvMY8ezPmhSx0sAXsv4PBYDA0qKqqQvdQzRny33mQU4g2vNwD6M9GWiw/zCtmsdmgu76Tmpra0tICAL3EtJpnIQI9gtksNgAY3WJZrVbv3LkTAII9PaID/HV6Lc+UO2PoYAC4ePHi3bt39RLfs9mzZ49UKgWA+aNHGnhpo8NA/s7ly5cvX74MAAMGDDh//vz06dOXLl0aHx/v6upqZmam4YX9+/fv378//QAwGEZACgiLIPwYqmOKDvDfdeZcXV1dbm5uYGCglq/6+++/ASDKX7d9qAYC3FwrHj/Ozc1lakIMBoOhBlJJhEw0R0d4xkYQbJZaqfrzzz/FYjE6GyFYhFdsBFNLIDUK5+9gMBgMZUi5hG/LTH1WZXYBABAE4enGwAmzk4ObtaWdpPZRWXoWz9qi8ZFEV30HFWcBQO/AWPrx8E35PiLfu2W5Rtd3kpOTkSvozKFUigDmjRq+6ehxtVq9ffv2tWvXMh2dJrZt2wYAHo4Ow6IiDbnu8wAD+s758+c/++yzto+UlJQgqU8zn376KdZ3MM8PqIm4h5Mjz5SZ/nlRTwqsUlJStNR3Hj58WFJSAgBRAQwUZyHE7m4Xb93Ozc1Fd04wGAzGWLTm7zBhrozgWQndo0JLrt86duzYG2+8cebMGQBw6xnMs9ZkcKgTAgcbwPk7GAwGQwPSrphvxczOGeXv2NuJzPnMTCj2i7iWerY0Pcvc1qrxkeThw4c6vRz5+IpsRe4O7ozEE+oVdrcs9/bt24zMRpl9+/YBAJvFmjYonsLLgzw8YoLEyVk5u3fvXr16NYfDgPKgDWlpaeijmzVsKP2eyJq5cOFCVVWVhYWF9jbE6enpMpmMy+XqydaGmfosDOYfAMrfCWTCfAcR5uNtxuXCk5QcbSBHRgcwkG6KQOVmjY2NyD0ag8FgjEJTU1NtbS0wWp8FACFjBwJARUXFf/7zH6QfhYwbxOD8QntbAKivr29oaGBwWgwGg/n3QMolTPnvPMguAAAvN8bOlgP9IgCg4WG1qTkfdKzPUqvVSN+JCerDVDwhXqEAUF5eTsHpmSmUSuWhQ4cAYECPMJEtxQP3rGFDAaCiogKZNBuGHTt2AACLIF4ZNljfa+3fv/+rr7765JNPEhIStHzJ+fPnFy1atGDBAm06qVOAARUtPj7ewC/EYBhHLpej/EPSkJg+XA6nh4/39eyc69eva/kSNJIgiGgxc/VZT9qB3b17NywsjKlpMRgMRifIEicG67MAIOyFwRfW/dwkqUXnjjxrix4vDmNw/rYt0v38GMusxGAwmH8PDx48QBvmtgz47zQ3NElKKwHAy0Nb94MuCfRvbWqOst11UlXu3r2LxscEMqbvkBbLt2/fHjxYXyKFUqnMysq6fft2QUFBTU1NY2OjtbW1k5NTUFBQr169UlNT0R9uitZt0TsyeUDcu5u3Sptbdu7cOXr0aOZifyYtLS3IWXlAjzBPR0cDrIjYt2+fQqH44IMPuhw5c+bMgwcPqlSq06dPz58/n/FImNF3sFKD6e4UFBTI5XIAELsxpu8AQO/AgOvZObdu3WpqatJmPJL//V1drAUMNA9GBLhhfQeDwRgfUt9hsD4LALjm/JGfvf3r4tWgVgNBDP14ASOd0UkET9SoyspKrO9gMBgMBZD8QbAIHhP1WQ9zCkGtBgBGzHcQPp5BXBPTFnlzS6MU2iQcaUNSUhLa6B3IgLkyItirtQGTPvQdqVR6/PjxI0eOnDt3TiKRdDqGzWajTt4mHM4L/fpSXsvS3Hx8n9hfLlw6ceJETU2NlRUzBtsaOHXqFKoHfGWo3pN32nHw4EEA6FLicXBwiI2NTUpKSkpK0oe+g+uzMBiAts2zmKvPAoDegWIAkMvl2nRJVyqVqD4rJoix2xEA4GpnJ+DxAMDw3vUYDAZD8jR/h9H6LAAIGTvwpZ+/6Dlz/Nj1S4PHDmR2cuETNaq8vJzZmTEYDOZfApJLeFaWLA6b/mxtmmeJ6c+GMDHh+vuEAUBjtQQAGhoatLw1CwCogaO5mSDMm7HWja52rtZCGwBg1oKnqKhoyZIlIpFo+vTphw4daivucPkCMwtrFrs1+UOpVCLjObVa/d2x4w1SKeVFZwwZDAAymQxVe+mb3bt3A4C5mdmEfoylU3XJoEGDIiIiAODgwYNr167t0vO0b9++AJCdnY3arjEL1ncwGID/p+8w44uGiHliq0xK+xq4ffs28neIDWTscAUABEGgFJ68vDwGp8VgMBidIPURoSOT+TsI736RfRa97NGXsbZZJGT+Dm6hhcFgMNRA+TvmDDXPQvqOmSnP2ZHJm7JB/j0BoPFxDfqv9ik8SN/pJe7FYTPpHxzsEQLM6Tv5+fmzZs3y9/ffsGFDTU0NAJgKLIJHThqz8vvXfr3+UbpkWdrjD68/WJ7Z9M6Foumbjwa+MAt4QgBQKJX/3bM/eO5rR69cpbb04J7hjtZWALBnzx5G3osGJBLJyZMnAeCFvrHo9rZh4PP5GzduRH7Jhw4d+vLLLzVLPGKxGACUSqU+3FH1qO9IpdLq6mrttU8Mxoig5ln2lpa2FkIGp3V3sHd3sAft9B1yTN+QYAZjgCdFZ1jfwWAwRgTpIwSbZW7LjL+mYTC3tWax2YD1HQwGg6EK0nf4NszU5iB9x8PVnyCYvJINFkcDADy5KtfSgqeiogKdYPcNoW5S0ykhXiEAkJWVpVAo6MxTXV29aNGioKCgXbt2oak8ew2YlLD3P1fKJiXsi5w63zkowoRnTo63cHLxjx8t7D8Rxr1N9Jvk4BUAAOXVj6esWv32D1sbZTJdA+Cw2VPiBwDAlStX9OQoTPLrr782NzcDwEuDmey0oA08Hm/Dhg29evUCgCNHjvz3v/9VqVTPGkzWqdXX1zMeCcP6zv3791esWBEbG8vn8/l8vp2d3VdffdV2wJ9//rlt27Zt27bp481gMJRB+k4Ao+Y7CCTWJCUlafiRI/766y8AsLUQMh5GgJsbAFRVVaHmNRgMBmN4UP6OwM6aYHen3GGCRZjbWwOuz8JgMBiqIJteRsyV1Wr1g5wiAPB0Z6x5FiLQP4LNflo+pmX+zqVLl9BG35B+zMYT4hkKADKZjLLBgkql2rJli7+//6ZNm+RyORCEePC41w4nz9p5NnjkZI6pmYbX5uTkAEG4R8a9tunwmEWfmQksAODQX0mjV6wqfuKWrT0vDYpH8ezfv5/ae9GSvXv3AoCjtdWgCMZq5bTH1NR0/fr1ffr0AYDjx4+vXLnyWVd/pBLC00OSEWPnWGq1es2aNV5eXp9//nlycrL0GUV6ZWVl8+fPnz9/PvK1xmCeB9RqdW5uLgAEMGq+g4gLDQGAmpoaJCFpAOk7fUOCCYJgNgbSVKiwsJDZmTEYDEZLUCU/s83RDQNqkY7zdzAYDIYaSN9hZP9fU1rZ0tgEetB3eGbmPp5PM+gfaKdiIH3HjGsW5d+L2XhC2lgsU3h5RkZG3759Fy5c+PjxYwDwiO4//0DS1O8OOwf37PK15eXldXV1ACB2cyMIVsTwSQt+OOYRGg0AOaVlQz74OO2ubjUBUQH+fq4uAKBXfef+/fuXL18GgEkD4jhsBpyeKMDlcr/55pv+/fsDwKlTp957771Oi5muXbsGAARBiEQixmNgTN9ZuHDhsmXLUAciABAKhezOPtZXXnnF0tIS9PzXxWB0os1ejPn8nf5hrXtn1BvrWeTm5qKLB3I8gwQ+0XcKCgoYnxyDwWC0AeW/CBltnmUYkGEQzt/BYDAYCkilUuQvyYj/DmmuzGDzLJLQwKcajZb5OxcuXACAKP9eZlxN6TAUCPQIZrPYoLu+09LSsmLFisjIyOTkZACwcHKZ/O3+2bvOiUKjtJwB3fYGAP8nV0ZCW8cZq7f3HDUdAKpqa4d98NHVO1k6RTVt4AAAuHXrVmZmpk4v1J5ffvkF5ctMHxivpyW0gcvlfvXVV6jr2V9//TVnzpx2119FRUXIBDogIEDAXMdkEmb0nT179mzduhUATExMli1bVlhYWFdX5+rayaWyqanpuHHjACApKamxsZGR1TEYmpDmyvqoz/J3dXGysYauLHgSExPRxsBw5vMJfUTOJhwOYH0Hg8EYj/v37wOA0MnO2IHoDNZ3MBgMhjJkIoy5HQPma22aZzGcvwMAoUG9yW1t9J2ysjIkhQzoEc94MHxTvpezN+io76Snp0dFRX3++edyuZxgs2NmLnrzZEbQiBd1Whq9KUdrKyvBU18eFps9aM57g+a8BwRR19Q05qMVyVk52s9Jai76S/JAM3uLnHuJmf9u6ASHw/nyyy9Hjx4NAAUFBS+//PKqVavOnz//999/b9u2be7cuUgGmTJlij5WZ0DfUavVK1asAAAOh/P7779/8cUXXl5eGsajhKWWlhZm+71hMJQh9Z1ARptnIQiCQJLN1atXNRiknTt3DgDsLS1DvDwZj8GEw/FycgJcn4XBYIyEVCpFDTu6Y/6OwMEWAGpra/F9KQwGg9EVUigxZ8JfGek79rbO5nwL+rO1IzggisMxaV1Ii/os8u5sfI+BjAcDTyx4bt26pc1guVy+cuXKmJiYjIwMAHDwD5n3y5Xhy77h8nXLEKmtrUXv3b+zXI2eo6aPfHMFQbDqpdJxn3x6p7hEy2n9XF0i/f0AYP/+/V22D6dAbm5uWloaAEyLH8C40wUFWCzWypUr58yZQxCEQqH47bffPvjggzfeeGPLli3IfKdXr15jx47Vy9L0p7hx40ZRUREAvP7668OGDetyfFhYGNogL6oxGOOCnHH4pqbujg76mH9IZE8AqK+vv3HjRqcD5HL5+fPnAWBQRA+WfnZJqEQL5+9gMBijoNfm6PqGjBlb8GAwGIyuMJy/k10AejDfQZiZ8sR+4Whbm/yds2fPAoCQbxEVwLD5DiLUKwwAysrKkIeOBrKysmJjYz/77DO5XM5ic/ov/Oi1w8mikEgKiz4tznJ16XRAjyETRr25HAhC0tAw9uNP7z+q1nLmqfH9AaCoqOj69esUAtPMvn370AYqBHseIAjizTff/P777z08PNo9NWzYsISEBD3pUBz6U6Snp6ON6dOnazPe1rb1PKm6WttvAwajV5C+4+/mqidtZWhkBEEQarX60qVLsbGxHQdcvXoVdbYaFq1tZayuBLi7wlW4f/8+6hqIwWAwhoRURgTdMH+HzDm6f/++r6+vcYPBYDAI1BwjJSUlMzMzPz+/vLy8urpaqVQCgFAodHFx8fLyCg8Pj42NDQ4O7nI2jP4g9R36/spyabPkXkko+3oAACAASURBVDkAeLnpqwAnIrRfZnYKAJSWlmoeqVKpkL4TF9qfw2bgmrojbS2WO72CQGF8++23n3zyiUwmAwB7n8AX1vxETdlBoHZdAh7P2faZx+ueI6c01Uku7NpYWlU18dNVFxK+4puadjnzlPgBS3/8SaVW79+/PyYmhnKEnYJ6N4V5ewV6MF+NoYHw8HChUCgWi581oFevXgcPHkxJSbl9+3ZjY6OtrW2fPn30ei7BwHexqqoKbWguy3q6JKd1UdKMGYMxLkjfCdRD8yyEs41NhK9Pel7+hQsXli5d2nHA8ePHAYBFEMMiu/a0p4bYzQ0AVCpVcXFxUFCQnlbBYDCYTikrK0MbFo7dz3/H4olnELbgwWCMjkQiOXny5MmTJxMTE8lrkI60LWlxdXWdOHHizJkzIyOpX/RiKPNU36Gdv1NdcE+tUoPe8ncAoGdov90HvwUtdvjp6ekox2dIz6F6CgbVZ8Gz9Z3CwsLZs2ejDrwEixU7e8nARSs1Nz7XjFwuLy4uBgA/F5Hmu979pr5eU3n/xplf0/PyF3y7cdfS97uc3MXOtn9Y6MVbtw8cOLBu3TpSE6BPamoqkqWmGdxZ+Y033uhyDJvNjomJYVzSehYM1GeZPpHrWlpatBmPOqQCgI1N92uSivnnUVNTg24si/Wm7wDA2NgYAMjPz+/ogKNWq48ePQoAvQPFyIlZHwQ9EbNxiRYGgzE8yFwZuqm/8pOYyXeBwWAMjEwmO3DgwJgxY5ycnF555ZVffvmFFHdYbI6Nu49HdP+gES+if779Rzj4h3DMeGhAWVnZxo0bo6KiYmJijh07pg/vD4wGkL5jwjcz4dPtMPXobqvbi/70HR/PQAu+FQA0NDRo8M0EgN9//x1tDI8aqadg3BzcLcwtoTOLZbVa/d1334WFhSFxx9rNa/auc0PfX0NH3AGAwsJClATn94zirLaMenOFe0gUAOw/f3HT0ePazD99UDwAPHjwgLQuYoS9e/cCAEEQU5+b4iwjwoBs5uTkhDays7M9PT27HH/lyhW04a4HL1sMRldQ8g48yXDRExP69fls1x4A+P3334cMGdL2qZSUFKSUT4zrq78A/N1cUY1Yfn6+/lbBYDCYTkE3QlkcDt+agf64BoZnZcEx5SqaW7C+g8EYnry8vC1btuzYsaOtBYmpwMIrZqBX73jX8BhH/xA2t5PaELVS+TDvTtH1CzmJJ+6lXVGrVNevX58wYUJUVFRCQkJcXJwB38S/GpTkYm7LgLlydV4JAHC5Zi5OnvRn6xSCYIX6RCVlnFOr1cXFxRrqaFD2fZBHsLtje3cV5oIhgj1CrmUltdN3CgoK5s2bd/HiRTQocsq8YR+s1dVHuVPy8vIAgM1ieT25wNcAi8OZtCzhx0WT66sfLP3xp5igwOiALprWT4jru+i7zc1y+d69e4cPH04/YABQKpWoOKtvcJC7gz0jc3ZrGMjf6dOnD9o4ePBgl4OVSiXqpM5ms/v21ePVLAajJVlZWWhDf/VZABDs6RHs4Q4AHW8c7d69GwBYBDFpgB5PNYQ8nqudLeD8HQwGYwyQviN0tCVYxu9qoSsEQQgcbADXZ2EwhiUxMXH06NFisTghIQGJO6ZCy4gXZ8/YdvKDaxVTNx3qNeNNUUhkp+IOABBstqM4LGbW4tm7zi0+e7ff/A/MhFYAkJqaOmDAgNmzZyPrQ4y+QaUb9M13AODR3WIAcHfxZbHY9Gd7FpHB/dDGkSNHnjWmqKjo5s2bADA6Ri8tkEiQBU9mZiZKq1EqlRs3bgwLC0PijoWT64wffx+z8ntGxB14Yr7j4ejINdEqC8TcynbSsgQWh9OiULz8xdq6pibN460FghHRUQBw9OhRplpSJiYmou/YS4P10sWs28GAvuPp6RkREQEAu3fvRhliGnjvvfeQLjhy5EgLC+bb2mEwuoLyd0w4HF8XkV4Xmj5wAACUlJSgVlmIhoaGPXv2AMCAHmGudvotWwhwcwWs72AwGGOAMl+6Y/MsBLINIl2EMBiM/lAqlQcOHIiMjBwyZMipU6dUKhUAeETFTfxq538u3xv33//59B3KetLEWkssRe6D3/3v4sS7ca8v5Zjx1Gr1zp07R44cee3aNf28CcxTGNN31OpHeSUA4OXxTC9bRggP6WvC4kKbCqyOHD58GG2M6/OCXoMJ9QoFAKlUmpeXl5GRMWnSpE8++aSpqQml7bxx4qZPX8bcfyorK1Hrbl8XZ+1f5RoYPvCVRQBQVFG5aNMPXY6fPjgeABoaGlACFH3QlRSXw3mxfz9GJqRGU1PTuXPntm7d+u233+7bt6/ThF+5XC6VSvUdCQP6DgCsWrUKAJRK5ejRo/fu3dtpXWtxcfHkyZM3bNgAAARBLF++nJGlMRiaoPwdH5GzCXMuX50yLX4A0sK/+eYb8sEff/yxpqYGAF4fO0qvq8OTArTi4mLN5cQYDAbDOKgRiaWzg7EDoYiFsz1gfQeD0TMtLS3bt28PDAycNm0a6s/LMTXrOWnuwuPps3cnho6dTlrqUMNMaDVoyao3fmu9JH7w4MHcuXPXrVuHHXn0ClP6Tl1FVUujFAC83PWr75hbWvha+wHA9evX6+rqOh2DDF+8nL3DfSL0GgxqkQ4A77333pQpU9Bli427z8yf/hzz2Q+mAiazJVASBgD4uXRtvtOW2BfneIXHAMDexAuHLnWR7TG6dy8rgTk80WVo0tjYiGxMR/WOthEK6U9IjT/++GPs2LFLly798ccf9+7dm5CQMGHChG3btpEDzp49O3Xq1L59+8bFxb3wwgv79u1DyrU+YEbfGTNmzMKFCwGgvr5+xowZnp6ec+bMQVetV69efe+99/r16+fr60sqnStXruzVqxcjS2MwNEE7yiD999KztRBO6x8HAH/++ee5c+cAoKamZu3atQDgIxK90LePvgMQu7kCgFwuxxY8GAzGkKjVamRjb+Hc/cyVEchiuaKiAmXIYzAYZpHJZN99952fn9+8efPQRSbP0qb/wo+XJOaP/XyLg38Ig2tZu3m9/OPvo1ds4piaqVSqr7/+euLEiUyVimDaIZPJUB0c/eZZpLmy/pqjI7h8XqBNMAC0tLQgY5d23Lx5EzVomxo/Xa+RAIDYLZAgWACQmJioUqlYHJPYV/+z8Hi6VwzztUjop2ctFNpY6CaUEARr/Ltf8ISWALDoux8qH0s0DDbjcif1jwOAs2fPonMDOhw5cqShoQEAXh4ymOZUlDl79uyKFSva1XuqVKotW7agEr99+/YtW7asoKAAaTplZWUJCQkrV67UUzzM6DsAsGnTpjfffBNt37t3b8eOHehNnj17NiEhISkpiTwl+vDDD3HyDuY5oaGh4d69ewAQaBC373cmjueZcgFg3rx5d+7cmTlzJuopsPyVl9gsxn6Mz4JsEEZaSmMwGIwBqKqqam5uBgChQ3fVd1B9llwu19CPGYPBUKCpqenbb7/19vZ+++230SmZ0MF52IdfLTmfP3DRp+a2ekn6IwgiavrrU378w9LVCwCOHTsWHx9PtvHGMAj5qdL3V0bmOwRB6K95FoJjyvW28TM3EQDA//73v44DUF4GQRAvDZqh10jy8vJ27dhtbdoqjblE9Jm241z8opU0E9k6RSqVopIiP0qGFUJbxxELPwGAR7V1b2/6XvPgV4YOBgCFQkE/hWfnzp0AYGshHNU7muZU1Ghpafnqq69QDuCQIUO+/PLLzZs3L1++PDAwEAA2bdp079699evXA4CVlVVUVJS3tzeLxQKAU6dOnTlzRh8hMXZJyWazv/vuu1OnTsXGxj5rTGRk5B9//LFmzRqC6H72iph/JNnZ2egHGaj//B0AcLaxWTZ1MgCUlJSEhIScOHECAIZF9XxpULwBVkf5O9DGUhqDwWAMQLdujo5A9VmAS7QwGOZoaGjYunVrjx493n33XXQb38rFY/Sn3y0+ezd29hKm/GI1YOcbNOXHP9x7DQCA1NTUfv36lZSU6HvRfxuoOAsABA50/deQ+Y69rbPAXO8Wrqbm/FD7HgCQlpaWlJTU9imJRLJr1y4AiO8x0MvZW08BlJaW7ty5c9++fVVVVfZ8BwAws7CesOlXG88uGlRRprCwEGWXUDYkDRkwStxnCAAcS7qmuUorNigQrbJjxw5qayGKi4svXLgAAFMHxnP17LPxLC5duiSRSABg8eLFa9asGTp0aHR09Pjx43fs2BEXF1dfX7906VKVSjVx4sQ//vhjy5YtBw8e3LZtm7W1NQDs27dPHyEx/EGMHDly5MiRubm5ly9fzs7Orq6uVigUtra2vr6+AwcODA0NZXCtxsbGY8eOnT9/vry8XCAQBAQETJ06lcIS+fn58+bN0zAgKCjohx+6NovCdEdIpSNIn82z2vL6qBFV9Q2bjrY6ikUH+O/56EPDKJ6W5uZO1taVEgnO38FgMIYE3ZMHAEvn7tq41MLpqb4TFRVl3GAwmO5OVVXV+vXrv/vuO9LcxMbDN+61D8PGvaSrcTJNTIWWL244mPjlO7d/25efn9+/f//z58/7+PgYMoZ/NmTbQfr++o9yi0H/5jsIU3N+uENkSuV1pUqxatWq06dPk09t2LABmRC/OX6RPpa+f//+pUuXSCsctgk3wD8mNzlbVieRSh7xrPV1mwStaMJmezhST5ob9eaKkoxUaX3NOz9sGdwz/FmGOARBzBo2ZPnPu7Kysq5du6YhO0QzP//8M9KkZg9nzGRaV1ClnoeHx4wZ/y+Zi81mv//++1euXLl7965IJHr//fdNTFp3bmFhYa+//vqaNWuysrKampr4fD6zIelF6AoICAgI0G/i3F9//TVjxgzylBGxevXqBQsWrF+/3tS080aJndLQ0HDp0iUNA2QyGcUoMc89SN9hs1j+T3Jb9A1BEAkLX3sxru+1rGxPJ6fxfWL07evcFn9XUaVEcufOHYOtiMFgMGTOi4Woe/srwxOjaAwGQ43i4uKEhITt27c3PemjbOcT2P/1pSGjphBsPXa81gDbhPvCmp95ljbXd3937969gQMHXrp0ycvLyyjB/PMgDVaE9PJ3musb6yqqAMDLI5CBsLqCa84TmAiiXKKvl147c+bMyZMn4+LiAKC8vBw1C4rw7TksagSzixYXF1+5coVsdEuw2KKQSI+ofmZ5f/2W/DMAVOXdQelm+gC5c3o6OXJo/BLNrWyHzf/geMJHDyQ1H/7vpx/fW/yska8MHbJy5x6lSrVt2zZq+o5Sqfz5558BINzXJ8LXaJosUjB79+7d8W69SCRyd3cvKSnp3bs3Ke4g+vbtCwAqlaqsrMzfn+GcLOMkMtEkLS1t5MiRyAhtyJAh0dHREonkyJEjDx8+3LJlS21t7d69eykkRMTHx3cq2GMV/x9MZmYmAPiIRKYmBr1f1DckuG9IsCFXRPi7uFzOuHP37l2FQsExUh4jBoP5t4HqswgWIXSg3R/XSAjsbQg2S61UddrxFIPBdElKSkpCQsLhw4fJJp4OAWGRMxeHjniRx/Tta10hCGLERwlsrunV7etKS0sHDx585coVkYhilQqmLa36DkGY0/NXfphTBGo1AHjruTk6gmvOA4DeTn3uPMpskNbPmzfv9OnTarX6nXfeQVegn81ezVT2vUqlys7Ovnr1KpnrRLBYTuIeHlFxpkJLAPB274Eef5SXqSd9p6KiAr0vysVZJGGDxt0+f6Lo5rWdZ87OHDY4LrRzc3QXO9sR0VEnr/994MCBhIQES0tLXRf6888/0R2XV0cOpxkzHVAiiJVV5/ZSVlZWJSUl9vbtk5fJR0ilm0G63wWeSqWaNWtWY2Mji8U6cODApEmT0ONr164dM2bMX3/9tX///vHjx0+dOlXXmV999dV2iVWYfzwokyXY0xDmO88DAa4uANDc3Jyfny8WG+IAicFgMCh/R2Bvw+q2sjKLwxba29ZVVmH/HQxGJxQKxdGjRzdu3HjlyhXyQa+YgX1efc86MBIACP33l9CSof/5Uq1UXtuxvqioaNiwYZcvX7ax6a6S9PMD8t/hWQnZJrT2/w9zCtGGt6HydwCAC6afz179zuZFlZWVcXFxPB4P2UXPGj53YPgg+qtIpdL09PSUlBSy9RKLzXEKDHfv2QcpOwgbK2dLC/vauqpHefpKwCfLwXzpy5oEMfqtT7e88YKiRfbmxu9TN296ljPO/DEjT17/u7GxcdeuXW+//bau62zevBkAzM3Mpg+KpxcxLczMzAAA9Q3vCPrLduzMQD7CeHEWMOWvfOvWrfXr17/44oshISEuLi48Ho/P57u6uoaGhk6ePHnjxo0oS4IRDh06hK7J33rrLVLcAQALC4v9+/fzeDwA+PTTT5laDvMPpr6+HpX4BXl4GDsWA0FaLDP4k8RgMBjNoD2tpcjR2IHQwkJkD7g+C4PRmoqKiv/+979eXl5TpkxB4g6LzQkeOXn+oWszfz7t3WeIsQPshKEfrI2cOh8A7ty5M27cOKlUauyIuj0oJ4VmcRYAPMwuBABzvtDBzoWBsLrC1Lz1qnti7OQlL74HAHV1dUjcGRg++JvXv6U5f2Vl5YkTJxISEs6dO4ckAI6pmXtkv94z3/YbMLKtuINAKTxV+fo6e0f6jq2FhZWQAV9za2e3flPnA0B2yb0Nvx571rAR0VEejo4A8MMPP6B2N9pTUFDwxx9/AMC0QfGW5ub04qWFs7MzAKSkpHR86sGDB8iy/fr162TeIuLq1asAQBCEiwvz32e6+k5iYmJ8fHx4ePg777xz5MiRO3fulJeXy2Qy1GItMzPz8OHDixcvDg0NHTJkiGabGy05cOAA2njnnXfaPeXi4oLSdnJzc2/evEl/Lcw/m8zMTLQ3Cfb8t+g7Aa4uKJsU6zsYDMZgPNF3uqv5DsLS2QGwvoPBdIVSqfzjjz9efPFFDw+P5cuXo5Q3M6FVn7nvLjqTMylhrygk0tgxPhOCIEYv3xg4bAIAJCUlzZo1C7m3YiiDalotaDdPRPk7nm4BhulJgvJ3AKC+vv7zOV/88snhMTHj4sMGrZ615vCnx8y4ZtSmVSgUt2/f3r59+9atW9PT09E1P8/K1m/AyNjZi71iBj6rbZy3exgA1NwrVMiY1xzJzuj0i7NI+rw419bFEwBW791f2iF7BcFmsRaMHQUAOTk5bR2stWHTpk3ot/nGuDF0Y6VHjx49AKCoqIjUKBAqleqbb75Rq9V+fn7l5eXff/89uTMpLCzcunUrAIjFYnM9iFPUM+VUKtVHH320du1aLccnJiYmJiYuX778s88+o/zLVKvV586dAwCxWOzp6dlxwMiRI1GjtdOnT4eHh1NbBfMvgbQZDvnX6DsWfL6zjXV59WOs72AwGMOgUqnQ/VsL5+7aHB2B8nfu37+vUqlYz01FCQbz/JCbm7tr165du3a1LWN08A+Jnr4gbNxLBuh3zggEmz3xq527qx/eS0s6dOiQj4/Pl19+aeygujGt+TuOtPb/iuaWRwWlAODppt8GPiSmgtb8HdQqa3TM2CERwxoaGgCAw6Zy+SyRSFJTU2/cuPE0KYwgbD18XUKjrd29Abq4NEb5O2qV8lH+HWt7hm+W5OfnoxveDOo7bBPuiAUf710+v1Em+2Drtv2fLOt02NyRw/+7Z3+jTLZu3boRI7T1q66trd2+fTsAxPcIC/M2shV6fHy8lZVVTU3N119/nZWVNWTIEEtLy/Ly8oMHD96+fZvH461du3bKlCm7d+9OTEwMCAioq6u7desWkvamTZumj5Co6zvz5s1DntUIFosVFBQUHR3t4OBgbW2tUqlqa2srKytTUlJycnJIverzzz9/9OgR5XbjJSUl6GcWERHR6YDIyNZ7AhQ6BCUmJl6+fPnevXtcLlckEsXGxk6YMMHCwoJaqJjnH6RxmJqYMLg7e/4JdHPD+g4GgzEYDx48aG5uBgAL526evyNyAICWlpbKykpsvIrBkDx48ODAgQN79+79+++/yQc5ZrygYROjps5369nHiLFRg2NqNu27w9un968uzluzZk1QUNArr7xi7KC6JXK5vLq6GgAE9Mz1H94tUikUAODlbgjzHfj/+Tt05lGr1fn5+SkpKaSGAgAmPL5zYIRzSE8zYeemvB3x8WzNWniUd8cvdiCdkDqCOmdxORwPByYP0949+wT2G5Z95czhy1deu3lrYHiPjmNshMKZw4Zs/u33c+fOpaWlkRfymtm5cyfS2pZMmsBgwNTgcrkffPDBRx99BAAnT548efJk22cXLlzo7u7+7rvvfvXVV+Xl5aSFNgAMGjRo1KhR+giJor7zyy+/kOKOlZXVkiVLFixY4OjYeWl9ZWXl5s2b169fX1dXBwCbN28eOnTohAlU/h5kxziPZximuLm5EQShVqvJkdqDEn9Itm7dunjx4pUrVy5ZskT3SDHdgNu3bwNAgJurITuUGx2xm2vizVv5+flSqRT5VWEwGIz+QMXnAGDl0r39d0j/oHv37mF9B4Oprq4+evTogQMHLly4oFQqycdFIZHhE2eFjpmm/bXrcwjPynb65mPbpvaT1Ulee+21gICAXr16GTuo7kdFRQW6x0/Tf6cis9X918vdQPk7JjwzgsVSq1SU9R25XH7z5s3k5OTHjx+TD1o6u4lCoux9xISOSUAiR1+emUAqa3h4N4NaPM8CKVAA4OnkyGYznJo6dN4H+SmX5c2yd37Ymrp5U6ed19+dNPHHk38olMpVq1YdP368yzkbGxt//PFHAAjy8BjVK5rZgKkxbNgwuVy+bt06pHUgCIKYOXPmSy+9BABTpkyxtrb+8ccfi4qK1Gq1g4PD5MmTZ86cqadiQyqXtS0tLYsWLULb4eHhR44c8fLSlBnl5OT02WefzZw5c8KECRkZGQDw5ptvjhkzxkT3jtTkp2Zt3XmPPS6Xy+fzGxsbSR9yLeFyuYGBgW5ubjY2Ng8fPkxOTq6pqamtrX3nnXfy8vK+//57Da8tKipCRXRtKS4uJrdbWlrQhkQiMUpSN2npVFdXh7q4GYumpiZdDbSYAn0ILS0tjx49Qo8gfUfs5qqP1nQdQWc/arXaMMt1BH0CyGJZqVReu3YtLCxMHwuRf2K5XK7TC8lEP/Ing277GB4y8traWtQw0sCQH0VDQ0M7SzYDxyCTycifjIFBXwOFQmGsAMgkan0HQF4a6fqTIV+IPiu1Wm3cPxZ0dpgj02lNbS31t/cjdzstLS16+smY2bYaXmZmZvr6+j5r2HN1mDMwKFFLpVLpOwDyl6Lr35ocj2ZQKpVG371UV1cbxk+kHZQPc48fPz558uSJEyeuXLnSdpcldHQJGjk5aNQ0Ox8xAKi66vvb9jdrLI8bFINCoeg0VJ6Dy9g1P/369mSZTPbCCy8kJiZ2bHJME4Md5tBvE9ocNbSk3W9NLpfrFOrT/b+NBZ39f9mtHAAw5ZqJnL10PVBShmvOa65vlEgkKHLyo5NKpZp/s1KpNC0t7ebNm+TPnMUxsfcLdg7uybdxAACFSg0qnd+Fl1tYVt7Vqtzbzc3NDB7mysvL0Rv0dnLS8rNVKVVy0Gok38ou5sW5f+374U5xyaYjx14fPbLjGAcL4dQBcXvPXzxx4sSZM2d69uypYUKFQrFz506JRAIA/5k0gaYDesuzv8y6Xi+PHj16wIABSUlJ+fn5TU1NDg4OAwYMaGsmM3To0KFDhzY3N7e0tAiFQsoxawMVfefw4cOop5ejo+PJkye1vIvl4+Nz6tSpnj17VlVVVVRUHDt2bPLkybouTR6BUCuyTuHxeI2Njdofq+zs7Pbs2TN+/HiB4GlhcHNz8/fff79s2bKWlpYffvghPj5eQ7SlpaWafYjIE99n9U4zGCiZzYhIpVLjNiNoaWlBOnpVVRXSDgJcRIYMSa1WG/cTCPZobQb/999/u7q66nUtXQ8/HfWdtjc9jEJbJd4oNDU1GUsQRDQ3N5PnhUZBoVAY92ugVqsNFoCuP5l2wpAhQ30WHQ9zd+/eRRtca6EB9n76+7qaWLeeJNy9e1fD52z0w5xcLjfu10ClUhksAF0vVjvqO0b/yaALFSOi5WGuqqrq7Nmzp0+fTklJafux86xsfQeO9RvygnNoNOp0ruv3Xy6XG+yKvVMUCsWz9r2OYTF93lh+ZdOnFRUVs2bN2rlzJ7uzBAT6ARjse6irlEZ+MmhD11DJrtsmlgI6O0aUv+Pu6sdmsQ1208uEb9Zc31hbW9sucg1X/k1NTenp6ZmZmWSQXIGFY2CEvX8ox9QUABQK6l91T7fQrLyrj4typI0NbBMu5XnaQR6jPRz+j737DmyifB8A/mTP7r33hJaWUigbSqFALRuZsmX81KIo4sCBIiqIInsoIijIBkGmTFmljELpAkoppYPulTZpxv3+eDHfCqU0aZJL2ufz11Eud0/G5e6evO/z2CnkzXptVSpV8z9IEXETbv99oLIo/6vtO+M7d7ISN1KHK2HoK7vOX5ArFAsXLvztt9+a2FpJSclPP/0EAEFubgMjwlt4tq3U6XEnFotjY2NjY2ObWIfH4/F4PB3utFHajCVRz2P65ptvNBqi7Orqqi5R1rB2T/Ox/51H08SxTf6r+YODXF1dJ0yYIP7vp43H482bN4+M/gKAr776SotokTHLzMwkC+qW4W2En7MTmY+mfgUQQkh/yGxzNp/Ht9TvD1b6xrcw4woFANCwdixCrVt+fv6WLVvGjRvXq1evRYsWXblyhSR3BJY27YZMHLpi57Q/b/V+92vnDl0YrbToeNiYmQEDRgBAUlLSsmXL6A7HxBQVFZEFkW3jEy+aQ6VQltzLAQAfz3a6Cat5SAmeZo4YkMlkly5d2rp1a3JyMrkVFdrY+/R5pcOo6U4hndi6uKX3dg8DAKVcXvogo+VbUyMzTuwszM30U7SBzeX1mfQ2AFRKJN/u2tvoOh729pNjogEgKSnp8OHDTWxt2bJl5B35m+3J0QAAIABJREFUZPwYJh0jH02CxuN3KIpKTEwEADs7u3Hjxmn68AkTJnzwwQclJSWXL1+mKErTIanq4UxNpOvIb93ixrKDmpo0adLy5ctv375969at/Pz8FyWzevXq9fxg7JMnTw4YMIAsi8ViMnDGy8tLi1lpLSeVSkl7WmdnZ528Mlog6WEbGxsbmxZNwdVaTk6OTCYTi8XkfTx06BD5e/cOHQwTUk1NjUwmYzKZL5pdqG9SqVQikXDY7EA315Tsh7m5uf7+/vrYkVKpJAWwNK3vo07gqg8ZPz8/WgauSyQS0irSzc2NlipFCoXiwYMHAGBvb29pSU8Jg6ysLKVSaWFh8aLaavqWl5cnkUj4fL67uzstAZSUlJSVlTEYDD8/P73uSCaTkSI1mn7YuNynv+CJRCKJRMJkMpuYN6RXVVVVhYWF0NhpjozosXJz1Os3rVKpJDsyMzNTvyw6Z+nqWHQ3u7KystEvTyM5zYlEIhcXF1oCKCwsrKqqYrPZ3t7eet1RbW0tybJp+kOoevS3UCisra3lcrmNNmM1gLKyMjLPxdfXl5aZ+02f5nJzc3fv3r1r166rV682vMQV2zkFxQwNGjDcM7IXo2UjWVQqFRm7JBKJmhiVr1fl5eUqlYrP5zfdonjkt5t/zrn3JDNly5YtcXFxI0eO1FUABjvNqb+iNb0NEQgEpOQFn8+XSqUCgcDNza35DycDKpkslpufN0Pb2i6FafeV9XIA8HIPBM1PlFoTWpiXAUgkEvKVLpPJyKWplZVVw2NWqVQmJSWdP39efXNq4eTmEdnTys1Ht/EE+j6tNVP1MCOom25KLFdXV5NJOf5ubgLhy1/Yuto6AGBz2Bp9kEL7xiUf352TkvTb6bNvjx7ZrrG2xUtmTj9w+UpJZdU333wzYcKERq88jxw5cuDAAQDo3zF8dD8dvAIOL74PKi8vL35BW3fjp3F+JzMzk4zk7NOnjxbji/h8fu/evffu3VtRUXH//n1NL5rVb3bD6tMNlZaWkpkduroh6d27N6nSkp2djfUUW5Pk5GQAsDE3c7Gl50KcRiHeXinZD8krgBBCekV+G7R0daQ7EB2wcHUoupvdsL4eQq1JcXHxrl27/vjjj0uXLjWcf2Hh7B7Uf3jwgOGuYVGtdZxOEzh84as/7tw4uqusunL69OlhYWE+Pjq+dW+tSB7WzMFG6+QOAOTffjre3MczWDdhNQ9pka5UKiUSyYsygFlZWUePHlUXizRzcPGK6mPlqpcEt5tzACmxXJimswt49QQ6Pz23Eo6d9dGmhFEKpfLd9RuPfdPItBgrsfi72TOnfPtdcXHxxIkTjx49yv5v95vc3NypU6cCgFjAX/b6NL1G2xIqlSojI+PBgwePHz+WSCR1dXVsNlsoFNrY2Hh5eQUGBlpbt6iXXHNonN9JT08nC126dNFul1FRUXv37gWA1NRUTfM7gYGBpD2W+uP4DPXfg4N18xWgPp7pnU6PdI6k7UL1/BujcQrx8gSA0tLSJkalIYSQTpChSRYm3jyLsHJ1BICHDx9qMQAZIaMlk8kOHz7866+/Hjt2rGEpHEtXz+DYkcEDRjiHdGrjH3hrD9+hizfuentsZWXlq6++evHiRbrGHJkWkt8xd2pRXer8lLsAwOPyXZwMetHOM3t6D1hVVfV8fkcikRw7duzOnTvkn3wLK5+u/Wx9AgH0daQwGExP15D0+5cL7lzX1TbJsFMBj+tqZ6urbTbKwcs/PHbkjaO7Tt1IPnQ5Mb5rI2mECf36Hrlydde583///feMGTN+/vlndbmrwsLCQYMGkel+iydNdLfXcaVznSgoKNiyZcupU6eaqLfLZDJDQkJGjx4dGxurvy9VjZOp6jpwWo/+Vd9PalFsWCwWBwYGAkBiYmKjJXguXLhAFiIjddMvTd1nXec18xGNZDIZyVSG+bbFX2DUWS0cwoMQ0quSkhLSXLZ1jN8hz6K2tlZdVAIhk5aVlbVgwQJXV9dRo0YdOnSIJHfMHV26Tnl7xs6Lc0/e7f/e1y6hkW08uUMEDRje5bU3AeDGjRvvvvsu3eGYhqf5HceW5XduZwKAl0cQi6n74tZN4JkJycLzNcjv3LmzZs0aktxhsjneXaM7j59j6xOkv+QO4e0RBgDFWekKqQ6GHahLAfg6OxvgGO/72ls8kRkAvL/xp/oXFNJdPy8h1NsLAH799deYmJjExMTS0tLdu3d37tyZ9GKbFtt/bJ9e+g5VC7t27RoxYgSZotTEaiqV6tatWwsXLpw2bZr+egRrPH5H3Xdc65IQ6gdq2sKcGDp0aHp6elVV1eHDh4cNG/bM/5Ka21wud/DgwdqF11BRUdHRo0cBQCwW62pAEDIGqamp5CKGjGRpazr4eJGF5ORknRwpCCHUqOzsbLJg5e5EbyQ6Yen2NEv18OFDuupSIdRyCoVi//79q1atunr1qvqPXJFZcOyI0Pjxnp17t8FJWM3R/72vc29ezk+5tnbt2n79+o0YMYLuiIyaSqUqKCgAAHMn7ceGKGT1TzKyAcDXq73OImsenvjpmJ2Gd6wymWzv3r1paWnknzZe/n69BvHE5oYJydcjHABUCnlhxi3XsKgWbi07O5vcDfm5GqJkm9DCuufYWX///N39vPy1Bw+9PXL48+uYCQR/Lfly0IcL72Q/PHv2bFTUf57jpP79vp42mdKwB5wB7Nq1a+nSpWTZ2dk5KioqMDDQyclJLBbz+XyFQiGVSouLix89enTz5s1r164plcqUlJQZM2b8/vvvQqFQ5/FonN9RN9jWelyi+mlo13j49ddfX758uVwu/+ijj2JjYxsW2dqyZcutW7cAYOLEiebm/znSKioqyNAeBweHhkN7ZDJZWlpaeHj48zsiIzDJ8x03bhwtdZGRnty8eZMshPm0xflZ9paWzjbW+aVlOH4HIaRX6vyOpWtryIZYuT3NUj148EDrWeoI0aiqqmrjxo2rVq0ibTcAABgMj4ge4aOmBseO4PB1f6fRmrA43FHLf98wsrOsunLGjBkREREeHo1UikVEYWEhqa9s4az9939h2n2VQgEAfobP74gEDCaTUqnUd6wFBQUnTpwgg1I5fKFf74F2vgZt6eXj2ZEs5KVca3l+h0zOYjKZPoaq1dB5yMQbR3eV5T/66vcdE2Ki7Swsnl/H0drqnxXfvbfhpy3HTij/TeVYikWLJr82Z8grVVVVCiPL7xQXF//4448AYG1t/cEHH/Tp06fpkvnFxcUrVqw4fvx4bm7u+vXr582bp/OQNM7vkLaIOqHdpry9vRMSEpYvX56enh4dHb18+fJOnTpVVFRs2bJl4cKFAGBpablo0aJnHpWRkREfHw8AcXFxDfuu1dXVdezYsWvXrvHx8RERES4uLjwer6io6Ny5c2vXriWjCh0dHZ/fIDJpN27cAAA+lxvorkEXgNYkzNcnv7SMvA4IIaQn/xu/49qqxu+QMe0ImZDi4uIffvhh7dq16sEIPLF54OAxXSf+n4OfQe9RTZqVm1f8onV75o0vLy8fP378uXPnnqkCi9TUOUQLZ3utN5J362kvcF9vQ+d3gMHgigSyagk5ZK5evXrmzBnSTs7awzewXzxHYOiWxA62nmYi62pJWV5KUsu3lpmZCQAe9vZ8roEGMbDYnJjp83d9+VZFjWTR1t9Xv/V/ja4mFgjWv/3WR+PH/n39RmlVtbez44BOEXpq395yx48fl8lkXC53/fr1zekdaWdn99VXX7FYrCNHjvz1119z585ltawX4fNM8ivpm2++yczMPHz48JUrV7p3797wv0Qi0b59+1xdXTXa4OXLly9fvtzof3l7e+/fv9/JqTVcmCI1Mm6lvZcnp62elcN8fI4kJj148KCystKisfQ5Qgi1HKlhxzcXC6wMNHxdr/jmYr6FmbSyWp23Qsj4FRcXL126dN26dRKJhPzF2sM3cuIbnn3iOQIRXgNoqt2gUdlXTl/f9dOlS5c+//zzxYsX0x2RkdJNfic5AwDMxJaOdm46HGTQTHwzkaxaUl5evnv3bjIni8Fkenft5xrWRd+ldhrFYDB8PMOTU0/l3br68rWblJ+fTwYiBbhpdtfcQgFR0Z4dujy8lfjzkWNz4uMa7ZVOuNvbTRsUa8jYtEMquvbt27c5yR216dOnHzlypLKyMj8/381Nx6MNTHJ6LZvNPnjw4I8//tiwPSGXyx0+fPjNmzf79u3b/E2JRKLPP/88Ojr6mclvDAajXbt233777a1bt0JDQ3UWOjICSqWSzOOL8NesfVtr0tHfFwAoilJPVUMIIZ0j+R0rj9bTp8/a3Rlw/A4yEdXV1Z999pm3t/d3331HkjsuoZFjVu1+88idiDEzOYLGWz6jlxr44XI732AA+Prrr8+cOUN3OEaKNE8EAAsX7fM7j2+mAYC/TygtRb555iIAKCgoIMkdrsgsKG6cSwd6kjuEn1cnACjLfVBbXtKS7WRkPB0YZZjiOw0NeP0DBpNFeqUbeNf6QIZ3adp4Sj0YRd26Soe0H7ygURpF55hMZkJCQkJCQk5OTkFBgUgk8vLyEotfOEwuODiYfP/a2Ng0/DuHw/nss88AQKlUFhUVFRcXV1dXm5mZubu7a11AGhm5jIwMcpUT3iaL7xAd/XzJwvXr1/v06UNrLAihVovkd0hOpHWwcnfKT8lU99ZEyDgpFIqNGzcuWrRI3evNLbxr7zcW+nTv/3QNgw+FaE3YfMGo5b9terWbQiZ97bXXkpOTbW31217aFD18+BAAOEK+0FrLWypJaUX5owIACPTtoMPAmo8UUlGpVABg4eLh0zuOzad5lpCfZwQAAEU9vpXo3ydO6+2Q/I6DlZWl2NB5Xgcv//ABI24c291Er3QTQoZA5ufna/SovLw8smBlZaXzkExy/E5DHh4eUVFRISEhTSR3AMDc3LxPnz59+vQJCQlpdAUWi+Xk5BQaGtq9e/fQ0FBM7rRi165dIwsd2/D4HTc7O3tLS2jwaiCEkG7JZDJSxs7as/Xkd8hzefz4cV2dDtrTIqQPp06dCgsLe+ONN0hyxzEobMKGP6dtP/e/5A5qMXv/9gMWLAWAvLy8adOmkbIsqCEyfsfS1VHrLTy+kUoWAnzDdBOTJkqzHz/JfDoV1z4orH3cWNqTOwDg5xXBYDAB4HFyotYbKSsrKy4uBoBAw07OUus7KYEvMgOA+Rt+ksnltMSgK0FBQQBw5swZ9YC15vjll18AwMLCQh9FYEw+v4OQpkhGg8fhNDHnsy0g09Mwv4MQ0pOsrCxSLsHak54rSH0gz0WlUmEJHmSE8vLyXn311ZiYmNTUVACwcHYfsfTXmXuu+PYaSHdorVCnsbMC+sUDwKFDh9asWUN3OEaHjN+xdNG+eVbujTQAYDCY/j6GrpVRmHo/9dAZSqkCAHDydgzvwWiyKZLBiISWrk7+AJB7s/HSsc1BSsYAAF19ZoQW1j3HzQGArPz8VfsP0hKDrsTGxnK5XJlMNmfOHNKtu2nl5eVffPHFoUOHAGDw4MH6KNCu8RanTJmiq9kcnp6eOtkOQhq5evUqAIR4e3HbanFlIsLf9+jVpKysrLKyMmtra7rDQQi1Nvfv3ycL1p6Gnt6vPzb/Ppe7d+8GBwfTGwxCaiqVas2aNQsXLiS9nDkCUY/X53eb+o4xjDhorRgMxtDFG9fdiah+kj9//vxevXphyU41iqKe5ndaMH4n93oqAHi4+YmEZnIDjvLIvZ764MJ1AGCwWJRLAJjbSKVSc3MzgwXQtEDfqNz8jLyUayqlgsnS5l6G5HeszMT2VrRNWImMH3/j6K7SvIdLfv9jQky0k8neidjZ2c2dO3fZsmVFRUVvv/22u7t7165dAwICnJyczMzMuFyuUqmUSqXFxcW5ubnJyclXrlwhH2ZXV9c5c+boIySNPxOenp6Yl0GmSy6Xk+LKkQH+dMdCs04B/gBAUVRSUlJsrAkUqEcImZa7d++SBZtWlN+x9no6Fkn97BCiXXp6+vTp09WtYIP6D4v9cLmFEz2/zLcpAkubEUt/3TptoFQqHTduXFJS0jMNW9qsoqIiUuzSyk3L6SfKenn+7UwACPIL12VkL/PwcnLO1dsAwOTy7GLinjwqBgCpVGrIGJoW5Bt18vwWeZ2kMC3ZOaSTpg+vrKwkxV+C3N31EF1zsdicAa8v2PH5nOq6uo9/3rJ5/jwag2mhMWPGUBS1YsUKhULx6NEjdee4JgQHB3///fd6+rowipFmCBlMamqqTCYDgM6BAXTHQrNO/k8zXGRAE0II6RbJgPDMRCJb3ZcPpIvIxpJvLgaAe/fu0R0LQqBUKpcuXdqxY0eS3LFwdh+3bv+rK3dhcsdgPDv37vH6+wCQlpb29ttv0x2OsVA3GbR003L8Tv7tTIWsHgCC/DvqLKyXyfrnGknusARCh7jhfCdnFpcDAFKpERVcC/TtShYe3bykxcPVk7OCaJqcpeYb2cuvc28A+O3v05fT0ukNpoXGjh27Z8+eIUOGmJubN7Eag8EIDg7+/PPPt2zZor+i7G16fgpqg27cuEEWurT5/I6DlaWno8PDwidXrlyhOxaEUCtE8js2raj4DmHj5Zp3K0PdXBYhumRlZU2aNOnSpUsAwGAyI8fP6ffOl1xhU/1GkD70efOTh1fP5d64tGnTpn79+o0ZM4buiOinrlBm5a7l+J2cpBSy0C5A4yEq2rl/LikvOR0AWCKx46BhbAtLAGDzeMp6eV2dEY3fcbL3tjS3r6gqenTtQtSkBE0f/rQ4l0jk/N+m0rQY8PoHD25eVsrr565ed3n1CpZxFDnSjqur66effvrxxx+npaVlZWU9fvy4pqZGKpWy2WyhUGhtbe3l5RUcHGxnZ6fvSDC/g9qW69evA4C1mZmvS+vp56K1qKBAkt+hKIrBYNAdDkKoVSEZEBvv1pbfsfVxx/wOot3mzZvnzp1bU1MDANbuPkOX/OQe0Z3uoNooJos9ctnW9cMjpVXls2bNioyM9Pb2pjsomv2v/pqHltfbZByNva2zva0hrtjVyR222Mxh8HC22dNRGBw+V1YNRtUwkcFgtA/ocSFpX871i5pewFdUVJC+lsEe7mAEF/7Wzu5dR0y5sHPjzftZm/46Ojte+47vRoLFYoWEhLyoYbdhmHCSDCEtkPxOl6BATGcAQFRQEACUlZVlZmbSHQtCqFWpqKh48uQJANj60jnDXx9svN0AoKSkpLS0lO5YUFtUUVHx6quvTp8+vaamBhiMTuNmzT5wDZM79LJwdh+yeAMAVFZWjh07tr6+nu6IaJaVlQUAIlsrrkibCiOUUpV77Q4AtAuM1HFkjck6f+1pcsfM3CFuhDq5AwBsPg8AFAq5QqEwQCTN1C6gBwDUlhUX30/T6IFk8A4AGE8T4R6vzjS3cwSAT3/ZWlRRQXc4rQHmd1AbUlRUlJubCwBdg4PojsUoRAUHkgUyuhshhHRFPcPf1qe15Xfs/s1YqZ8jQgZz+fLl8PDw3bt3A4DIxmH8+gNxn67iCER0x4UgqP+wyHGzASApKWnBggV0h0Mzkt/RevBOQdo9WU0tAIToP7/z4ML1xzfTQD1yR/yfPlkcPo8sGNUUrfaBPclCTtJ5jR54584dALA2M3OyMZZ+VRy+IHbmhwBQXlPz4abNdIfTUuXl5UVFRUVFRRRF0RUD5ndQG0IG7wBAt3bY1BYAoIOPt1ggAICLFy/SHQtCqFVJS3v6o6Jd68vv+D392VP9HBEyAIqili1b1rt3b9J22rfXwDkHr/n1GkR3XOh/BixY6hQcDgA//vjj/v376Q6HTqT+mrWHls0Tsy8lk4X2QZ11FlNjHl65Rbqws0RmDnEjnknuAACbzyULRjVFy8OlnYWZLQBkJ55t/qOKi4sLCwsBoL2XsQzeIQK7xfhG9gKAbX+fPn87he5wWmTBggWDBw8ePHjwzJkzm9NISx8wv4PakGvXrgEAl82ODGzrzdEJNotF+oj9888/dMeCEGpVyI+ETDZb3VC81bByf9pRBfM7yGDKysqGDBny/vvvy+VyFofbf/4349cfFNk40B0X+g82jz/qh+08sTlFUdOmTSNjWNqg8vLy4uJi+Hc2qxayL90EAHtbZycHPf5CkHs9NSfxFgCwhCLHuGHPJ3fgP+N3jCi/w2AwQgJ7AcDDq+cplaqZj7p9+zZZaO/lqZ+4tDdw9sdsLp+iqDdWrpHJ5XSHowM3b94cO3bsL7/8olQqDbxrzO+gNoTkdzr6+wp5PLpjMRY9Q9oDwL179/Lz8+mOBSHUepBJ/rbebixOa+vkwGSzbL3d4N8cFkL6du3atYiIiMOHDwOApYvH1N/OdJs2D8sIGidrd58hX24AgIqKilGjRkmlRjSpx2DI4B3Qtr6+sl7+KOk2AHRo11WXYf1X/u3MBxeuAwCTL3AYPIxtZtHoagwmkyT0jSq/AwAdgvsCQF1FaUHazeasT1EUye8429jYNNnDmxZWjq49x84CgIxHud/+sYvucFqqe/fuXbp0qa+vX7NmzWuvvWbghgyY30FtRUVFBTnfkIwGInqFPn01zp07R28kCKHWhFxHOgS1ziYy9gFe0OC3UIT0Z8OGDT169CBzsvz7xs3ad9Ul1BAVZ5HWggeO7PLamwCQnJz8xhtv0B0ODdR3s7Y+2ozfeZSUIq+TAUBY+266DKuBJ+lZ984mAgCTy3MYNJRjYdXEyhw+HwDq6mr1FIx2wtr3IwsPLv3dnPWzs7OrqqoAINTbS49htUC3kdPsPfwAYOkfu9Nz6JnZpCsODg5r1qz54osvrKys7t69O2nSpJUrVxqs7Drmd1BbcfXqVZVKBQC9QulsWWdsOgcG8LlcwPwOQkh3iouLSfMse39PumPRC5LfUT9NhPShrq5u2rRps2fPlslkTBY75t2vxq7Zxzdv6kYUGYn+879xDYsCgM2bN2/YsIHucAyN1J5nsJg2ntqM38n65xoAMBhMPY3fKbn/KPPkJaCAweHYDxzCtbZten2OgAcAdXV19BXMbYSjnZeTvTcAZF1sVn7n5s2bAMBiMo1wchbBZLNfSVjEYDBlcvnr3/+obPa8M6M1ePDgvXv3DhkyRKVSbd26dezYsTdu3DDAfjG/g9oKUkKYw2J1b9+O7liMCJ/L7RIUAACnT5+mOxaEUCuRnPy0NKZDYOscv6N+Xrdu3aI3EtRaZWdn9+jR45dffgEAkY3Daz8f6T5jPs7JMhUsDvfVFX+QAkkJCQltrUspqU1m5eZEZjZp6t7ZqwDg4xlkYa77Hk/lOflpR89TFMVgse37v8Kze3kRK46ADwAqlaq+XqbzeFoivH0MAOTevCyrqWp6zbq6OjKoKsDNVcDjGiI4rbgEdogcMgEAEtMzftx7gO5wdMDc3PzTTz/dsGGDu7v7o0ePZs2atWTJEolEotedYn4HtRUkv9PRz9dMIKA7FuMSHR4GAPfu3aOrzDtCqJUhvxMCgGOwL72R6In6eamfKUI6dOLEicjISPJLr1t411l7Ez279KE7KKQZMwfn0Su2M9mc+vr6UaNG5eXl0R2R4ZD6a3Z+nlo8tqqguCgzGwAiOvTSbVQAUJlfdOfwGUqlAibTrt9AvlOz2nuR8TsAUFtrXCV4IkIHAIBSXv/g8kt+o719+7ZCoQCAcF8fQ0TWAtGT5lo5uQHA51u3ZeY+pjsc3YiIiPjjjz9mzJjBZrP37ds3evTo8+c1a2yvEczvoDYhNzf3/v37ANCnA07OelZ0WBhZOH78OL2RIIRaBzKqRWhtaeb4knHvJsrMwUZkawUNRiohpBMURS1ZsmTw4MGlpaUAEDl+zuRfT5o5ONMdF9KGR6eesR8sA4CCgoJhw4YZW4FePZFIJKRclL2/Nk24756+QhYiw/roLigAgOqi0pSDp1QKJTAYdn0GCNw8m/lArpBPFoztHQwN6sPl8AHg7tm/ml6TdJixFIu9nJwMEVkLcPiCIW8vZjCYdbL6KUuXKwzefEpPuFzu7Nmzt2/fHhYWVlRUNG/evA8//LCsrEwf+8L8DmoT1JmL6A6h9EZihCID/a3EYsD8DkJIR8ilpHOIH92B6JFTez8AuH79Ot2BoNajsrJy5MiRH3/8sVKpZPMFw7/9ZfAnP7I4xjuZAr1U5wn/13HUNAC4du3a5MmTKaOq4KIfqamppN6lfYA283PvnroMABbmNv4+uvxFVlJacXv/38p6OQDY9IgWemkwtpTJZjPZbDC+Est8nig0qA8A3Dt3lHpxHiQ7O7ukpAQAIvx8TWKKp3v7TmSW1rXMu4t/20F3OLrk5eW1adOmjz76yMzM7OTJk6NGjcrJydH5Xlpb11KEGnXs2DEAsBSLOvq1zskCLcFiMvtHdNx17vzff/8tl8s5HG0mSyOEEFFVVUXGS5IMSGvl3N7//tmr9+/fr6ystLBovLEuQs2Xmpo6YsQI0ujT0tVzzKrdjoEd6A4K6cDgT1eWZN99dP3C7t27/fz8vvrqK7oj0i91VTLHII2nAtVLarMv3QSAzuF9GAydjUKoK6+6te+EQioDAOuuvcT+QZpugSsUSKuqa2uNK78DAF06vnLt9jFJaVFu8hX3iO6NrnP16lUAYLNYYUY/OUut35S3s29eKn6U9c2Onf0jwo25duqSJUtIQXGC5GvOnDnT8I/PMzMzq66urqqqKikp8fDQZqRbE3D8Dmr96uvrT5w4AQB9QkNYTPzMNyK2cycAqKysvHDhAt2xIIRM27Vr18iPt84hAXTHokfOHQIAgKKopKQkumNBJm/Hjh1RUVEkuePbM3bmniuY3Gk1WBzumFW7rN19AGDJkiU//fQT3RHp1+3btwGAzeNaezaruk1Dd09dUcjqASAqIkZX8dRVVifvOyGvlQKAZaeuZsHaDOTnigQAUFcnVamMawRWl/BXSCIs/cT+RlcoLy/PzMwEgHYe7kI+z6DBtQCbyx97zJ9SAAAgAElEQVQ2fymLw1WqVJO+WVZWXU13RC/08OHD9AZIErC8vDy9Sfn5+foLCe91Uet35syZ6upqAOgfHkZ3LEZqYGQEk8EAgEOHDtEdC0LItF258rR6gkuYxr+RmhCXDoFkITExkd5IkEmrr6+fO3fu+PHja2pqgMHoNefj8esPCix03zYI0UhoZTt+/UGBpQ0AzJkz5/Dhw3RHpEekLrh9gBeTzdL0sWlHzwOAUCAOC+mmk2CkVTW39p6or6kFAIvwSIsOEdpthyskvVkoYxvCY2XhEOQXBQBpJ/Y2OvvvypUr5O9dggINHVzLOHoH9pvyDgA8Kiqe8d0PRju3kcViMRsgf2QwGMzm0UdXRJyfhVq/AwcOAACHxYoJx1/DGmdvadklKPByWvqBAwe+//57usNBCJmwy5cvA4CFs72Zgw3dseiR2M7a0tWx4nFhW+t8jHSooKBg8uTJJCXKN7ca/s1m/75xdAeF9MLGy3/s6j3bpg9SyKRjxow5fvx4jx496A5K9xQKBak67xzir+ljZTW1984kAkCXjtFcjg5GmkirapL3HJdVSwDAPLSjZccuWm+KJ37ae1cikdjaGlffgB6RI9LuXqoqzMu9cemZKVq1tbWkyaOXo6ODtRVNAWqvy9DXHt5OvJt49tDlxO927Z0/ZhTdETVi3bp1Df85c+bMGzduDB8+/KOPPqIrJBy/g1o5lUpF8jvd2gVZiER0h2O8hnXvBgDZ2dnkhxeEENKCSqUi+Q63Tu3pjkXv3Dq1A4DLly+T+WgIaeTcuXPDhg0jyR3HwA4zd1/G5E7r5h7RfeR32xgsVm1tbXx8fKu83EpNTSUjXLSYn5t+7B8yOatn18Etj0RaWf2/5E5IuFVkiwYEsfk8BosFABKJpOWx6Vb3yJFMJgsAUv7645n/unLlilwuB4Bu7YNpiKzlGIwh7yyxdHABgE9++fXUDWxY2SyY30Gt3Pnz5wsLCwEgvktnumMxaiN6dSdDBHft2kV3LAghU5WWlkb6fbq3gfyOe6cQACgvL09JSaE7FmRK5HL5okWLZs2aVVFRAQAdR02bvuO8lbs2zYaQaQmMGRq/aB0wGBUVFbGxsRkZGXRHpGOklC8AuIRpPBvo1r4TAGBuZhUe0tKRTbXllTfVyZ32YVadGy88rBGeSAAAEklNyzelW9aWjqSLVurRPcp6mfrvdXV15O1wtrHxdnKkK7wWEphZjPrwBzaXp1SpJiz5JrvwCd0RvYRQKBSLxXw+n8YYML+DWrnt27cDAJvFio/C/E5TPB0cIgP8AeCPP/4w2jmuCCEjd+7cObLg2aX1z4f16Py0TufZs2dpDQSZkgcPHvTs2XPlypUURXH4wmHfbI7/cj2bL6A7LmQg4SOnxH6wDABKSkrGjBlDat+2GmT8JlcktPPVrCVQRW5hTuItAOjdNY7NalH9EElJefKe46TmjnlIR6suupkHxxUJAaCuTqpQyHWyQR2K7j4BAOoqSjNO/a+M5sWLF2UyGQD0CjXtn1uc/NrFvfkZAJRWVY//Zlml8Q2hamjFihVnz56dN28ejTFgfge1ZlKpdPfu3QAQ0zHcxtyc7nCM3di+vQEgJydHfYeGEEIaOX36NACIbCxt/XTc79MI2fq6i+2s4d9njdBLbdu2LTw8nNTktvEJevXn46FDJtAdFDK0qEkJMe9+BQClpaWTJk0iDadah4sXLwKAa3gQg6XZPeaNXUcoFQUAMb1HtiSAyvyi5D3HSbcsi/BIq866qdMMADwzIQAAUFVVVbrapq506zRUJLAAgBu7fyZ/qa6uJoN3XGxt/Fw1bmRmbEL7DY0aMQUA7uXlT/1+pUxudCk2o4L5HdSaHThwgAx+nhATTXcsJmBM394cNhsAfvnlF7pjQQiZHoVCQbLDXt076qMlhLFhMBhe3cIB4OzZs/X19XSHg4xaWVnZmDFjJk2aRG4Ow0dPH73xiJWHL91xIXp0nzE/5r0lAFBRUTFu3LjWMQYwPz//3r17AOAeGaLRA5Vyxc2dRwHAzzvE20P7xoulD3Jv7z9JivhYde7WkoLKz+OJnxbxrKys1OFmdYLHFfbuNhYAHlw5XfrwHgCcOnWKVN6Jbi29g2OmzguIigaAS2nps1euUWLZuxfD/A5qzTZu3AgAVmLx0G5d6Y7FBNhbWsZ1iQSA3bt3k7wYQgg1340bN8i9q2+vSLpjMRDf3pEAUFVVRX61RqhRhw8fbt++PSlvJ7S2G7t6b/8Pv2fz6CzQgGjXffp7fd/9GhiMmpqaQYMGtYLqh+oslWeXUI0eeOfQ6ZriMgCIixmv9d7zbmXcOXxWpVACg2HTM9o8pKPWm2oUk83iCPgAYJxXyIP7vg4AQFGJ21bn5eWRQWF+Li6ejg40R6YjDCZr+PvLnP1DAODgpStzVqzCahIvgvkd1GqlpqaSM82EmGgBj0t3OKZh+uCBAFBXV4dDeBBCmiLTlBhMBsl6tAU+vSLJNISDBw/SHQsyRmQCTnx8fEFBAQD49Ro05+D1gH7xdMeFjEL4mJkxH//IZHOkUunYsWO//vpruiNqkRMnTgAAR8BzDdegWxNFUZd/2gMAFuY22nXOolTU/bNX75+9ChTFYLPtYwaL/fXSLopnLgKA6uoaIyzB4+HaLqxdNAAk79/65+4/KIpiMZn9O4XTHZcucXj8UR+vtHXzBoBfjp14c9VaTPE0CvM7qNVasWIFRVEMBmPOEGw42lwDOkX4ODsDwMqVKxUKBd3hIIRMBkVRJ0+eBADX8GCRrRXd4RiIyMbSLTwYAA4cOIAXmqghiqK2bdsWFBS0bds2AOCJzeO/XD9+w0Gxrak2skH6EDhw9JDvfuOKzCiK+uijjyZMmED6i5sclUp17NgxAPCMCmNxOc1/4N1Tl5+kZwHAKwMmcDk8Tfcrl8puH/g771YGALAEQofBwwXuXppupJl45mIAAKBIm0hjM2LQOwAgr5MUXfgLAKKCg1pf7VG+2GL0J2utnNwBYOPhI69//yNO1Hoe5ndQ65Sfn0+uqAZ17uTv6kp3OCaDyWAkDB8CAA8fPty5cyfd4SCETMadO3ceP34MAEGxPemOxaCCBvUCgJycnNZUJBW1UGpqanR09KRJk4qLiwHAt2fsnD9vdhw1je64kDHyjIqe9tsZCyc3ANi+fXtUVNTdu3fpDkpjV69effLkCQD49dGg6g2los7+8CsACAXiuBiNa41XF5Zc3364IrcAADjWNo5DRvPs9DgdiScWMphMACgtLdXfXrQW3j7G2z0MAOBukhWH2SvEtNtmvYjIynbcl5usnT0A4NfjJ8d8saROhvXv/gPzO6h1+vrrr0lTwPljRtMdi4mZMnCAnYUFACxevFipVNIdDkLINDydoMRgBA/uRXcsBhU8qBeDyQCAQ4cOvXRl1OqVlpYmJCSEhYWRGeIiG/sRS3+dsPEQuXtHqFEOgaGv777k0aknAKSkpERERJjcNPn9+/cDADAYAf01aFl158/ThWn3AWDooClmYgsN9kdB7o3Um7uPyaolACD08nGKH8UWm2kUs6YYTCbfXAwApaVlKuMbNqJSKbuEjgMAkMscC9PYbBbdEemLmbX95KVb7T39AeDgpcv93/+wsKyc7qCMCOZ3UCv04MEDUlm5T4fQHu3b0R2OiRHyePNGjwCAjIwMk7u8QAjRQiqV/vnnnwDg0TnUwqWVVHNsJnMnO48uHQDgzz//lEqldIeDaFNXV/ftt9/6+vquWrVKoVAwWKxOY2e+8dftkPhxdIeGTIDIxmHSL8e6Tn2HVFyeNm3a8OHDSdkm40dRFKkP7RoWZO5k18xH1dfW/f3tJgCwMLcZPnhq83cnq5bc2n/iwT/XKZUKmEyrzt3togcx2BpMCtMa38ocAJRKRVmZ0Q3hyczMdLELdXVsDwCZZw4W59ynOyI9ElvZTv72V/f2nQAgMT2j65tvJ6Zn0B2UsTDh/I5EIvn999+nT58+aNCg0aNHL1y4MCUlxag2iOgyb968+vp6BoOxeNpkumMxSf83NN7F1gYAFi5caJxtAhBCRmXPnj2kZWzHsdpUxzR1EWPjAKCysvLw4cN0x4JoIJVKV61a5evr+8EHH5CTpkdkr5m7L8d9tlpgYU13dMhkMNmcAe9/O379AZGNAwAcOHAgODh4/fr1RjhU5Blnzpx5+PAhALQf0rf5jzq19OeqwmIAeG30XAFf1KzHUJB3KyNp258VuYUAwDYzd4wbYR5iuCrCfAszMkWrsLDQYDttjocPHxYVFQFAfO+3mUyWSqE4vOozStWah+HzxeYTF28K7TcUAB6XlES/u+D73ftUWAjPdPM7//zzT3Bw8MSJEzdv3nzs2LE9e/Z89dVXoaGhc+bMIbNyaN8gosvevXvJNIFxfft0CQqkOxyTJOTxFk+bAgBPnjyZP38+3eEghIwaRVFr1qwBAKGNZdDAtlV8hwga1EtsbwMAmzdvNv47MaRDlZWVS5cu9fLySkhIyM/PBwBb74Axq3ZP2fq3Y1AY3dEhk+TXa9D//XkjOHYEAFRUVMyZMycyMvLcuXN0x9WUdevWAQCLww4ZEt3Mhzy4cD1p2wEACPQLG9BnVHMeUlNUmnrg1P2zV5VyOQCI/YKch4/l2Ru0YDmTxRTZWAJAaWmZ8QzYzMvLy8l5CAA8DjemU/+4HpMB4HF68qW9rXwYPovDHTpvyYDXFzDZ7HqFYsGmn2Pf/yi7wLhSb4Znkvmd69evDxo06NGjRwAQExPz4Ycfzp49297eHgDWr18/depUTXtY6HyDiC6PHz+ePXs2AFiJxd/OnE53OCZsQr++fTqEAsDPP/+MfX8RQk3YtWtXWloaAISOGaRR25RWg8VhR00dAQBZWVkHDhygOxxkCJmZmQkJCW5ubgsWLCC/5Fu6eg79atOcP28GxgylOzpk2oTWdqNX/DFm1W5zRxcAuHHjRp8+feLi4pKSkugOrRH3798nxXfaxfURWls25yFVhcX73vmaUlE8Ln/u60sYjJfckNZVVt89eSntwGlJcRkAsERm9gPibXr1Y3C4LY9fU2YONgAAQJGWArR7/Dj3/v17AMBmsUK8PDls1muDFzjaegDA2W0rc1KM8TOjW12GTZr87a+WDq4AcPbW7bCZ//f19p3S+rZbdNn08jsqlWry5MkSiYTJZO7evfvkyZNLlixZt27dvXv3evbsCQA7duwgU0Dp2iCii1QqHTVqVElJCQD88MZsR+u20qBXHxgMxqZ33zYXCimKmjJlSnp6Ot0RIYSMUW1t7YIFCwCAb2kWOmYg3eHQJvK1IXxLMwD48ssvJRIJ3eEgfZFIJFu3bu3bt29QUNCqVauqq6sBwNY7YOiSn946mho2YjKTxaY7RtRKBMYMffPInZ6zPmDzBQBw5MiRzp07Dxgw4Pjx40b1w/MXX3yhVCqBwej6erNamkirarZP/UhSUg4AMyd97Ors3cTKteWVmScvJv16oOReDgAAk2ke2tFl1ASBm4cuYtcGVyQkVZYLCvLpneRBUdT9+/eysrLgaXLHS8jnAQCfJ5r/2loOm6tSKnd/Nbf4URaNQRqGa2DYzNV7w2NHAYNRK5N9umVr++mzf/v7dNvsnm56+Z3du3enpqYCwJtvvjlq1P+G85mbm+/YsUMgEADAZ599RuMGES3q6+tHjx6dmJgIAFNiB0zop8EEYNQoT0eH9e8kMBiMioqKgQMHkpnVCCHU0EcffZSTkwMAXWa+yhUJ6A6HNlyRsMusVwEgNzf3gw8+oDscpGN1dXX79++fMGGCg4PD5MmTz549S1EUMBheUX3Hrtn3f4duhQ2fxDRIeVfUpnAEoui3v3jraGrHUdPIB+zkyZMDBw4MCgr67rvvjKEEzOXLl3///XcACOzfzTHY96Xr15ZXbpv4/pOMBwAwqN/YAX1ekBKioOxhXsqfp5O2HSxMyyL5LIGHt8PQMVaR3RhsmrOoFi6OAKBSqR48oC11IpVKk5OT8/LyAIDDZod6e5sJ/3cK9nPvMGf01wBQV13528fTix7epStOg+EJxa8kLHptyWZbN28AyHnyZOrS5SEzZv985Fhba6BuevmdnTt3koV33nnnmf9ycXEZM2YMAGRmZiYnJ9O1QWR4ZWVlgwcPJoUte4a0X53wf3RH1EqM7t1z4cRxAPDo0aMePXrcvHmT7ogQQkZkx44dK1euBACn0ID2o/rTHQ7N2o8c4BjiBwBr1qzZvn073eEgHbh3797atWuHDBlia2s7YsSI7du3k8FZAkubLpPeeuPQrUm/HA+IfoXUW0VIT8wdXeO/XP/W0dRO42Zx+EIAyMzMnD9/vqur64ABAzZt2kRjoic1NZXNZrO4nJgFr7905SfpWT8Pfys/JRMAunbqP2vSJ8+vIykpz75088rmPSkHT5VlPwYKAEDo4eUwZLR1nwFsc016qOsN31wksLIAgKKioqKiJwbeu0qlfPToUVJSUlVVJQCI+PxwX1+xgP/MajGdx0wYNB8AasqKt7w/KfPKaQPHSQvP0M6zVu8fOPtjkaUNANx7nDd7xSrP8ZPmrdt4K+sB3dEZiIkNIqUo6u+//waAwMBAT0/P51cYNGjQli1bAOD48eNhYS+vbKfzDSLDO378+KxZs57+gBwUuP+LT3kc/A1NZz59bUJ1bd2Kvfvz8vK6deu2ePHiuXPnsun+5QQhRLtdu3ZNmTKFoiiemSh+6Xt4i8tgMvp/8dauSR/KqiVTpkxhsVjkJyJkWm7evHnx4sXLly+fO3eO/DauxuYL/HsPCnllnF/vQSw6Cn+gtszS1TPu01XRCZ/f3Lvl+u6fy3LuK5XKkydPnjx5ksFghIeHT548OSEhwcBRzZgx45qTqiD1no23WxOryetklzbu/GftdmW9HAB6dY17Z9Y3LBaL/K9SLq98/KTsUUFpdq60skb9KAabLfLxN28XxrGyVqlURlW93sbTJb+6RqVQZmZmcrk8S8tmFR5qIYVCWVxc/OTJk/r6p/PCnKytfZydmUxGo+uPGTCXxWRtO/KtTFK968u3OsQMi57yjtjK1gCh0ojJZkfGjw/rP/z6kZ1XDmytLn1SVl29av/BVfsP+ru6Du0eNahzZFRQIKf13suY2BPLyckhU53DwxvvhBcREUEWyJQrw28QGYxKpTp16tTy5cuPHz9O/jKse9ctC94T8Z9NYKMWWjZrhoOV5cLNv0ql0vfee2/jxo0LFiwYO3asUCikOzSEEA0qKio++eSTNWvWUBTF4rBfXfe5tadLTU3Nyx/Z2lm4OQ5b8eGeOYvk9fJx48adP39+8eLFVlZYDM6UvPPOO8/0KhLZ2Pv2GODf9xW/XrEcQfO6OCOkHwJLm27T3+06bV7ujYsph/9IO7G/tqyYoqgbN250796dlpAc2/k6tnvhzKyqguLkvceTth6sKS4DACaTNXbYnFfjZ9WVVdUUl1cXlVYVFtcUl8N/Kwpxbe3FfoEi3wAml6f3J6AVFpdj6+NRlJmtUqlSUm77+Pg6OzsBNJ5naSG5XF5RUV5cXFxSUkpRT5NcAh7Pz8XZUixu+rGjYt50tPVYvfP9Wmn1rb8PpF04HtZ/eHjsaAcvf32Eajw4fEHUiCmRQyakXzx54+iunDvXgKLuPn68bOeeZTv3iAWCqKDAaSWV48aNoztS3TOx/A6pIAUAHh6NV9Vyc3NjMBgURanXNPAGkV7JZDIyV+6ff/45evSo+oc1EZ//5dRJbw4bwmDo5YsVvffqqM6BATOWr8guKLx79+706dPnzZuXl5cnEuGVLkJtRWVl5ZUrVw4ePPj7779XVVUBAFckHL3mU+/uHemtMWlUPLqGjdv05a7/+6JeUrt27dpt27ZNmDBh6NCh3bp1Mzc3pzs69HL13h3h3Dm+uZV7x24ekT29ovo6BnbA4WnIqDAYDPeIHu4RPQYv/DE3+fK9c8fuXzhRHGgUpSeV9fKKx4VPMrNzr93JvnSzKDNbXQ3aycr1ldDRDpV2F9buaOSRDAbP3lHo7iX08mGbGcU8rKYJLM1sfdxKsnJVKtW9e3fz8/OcnZ2tra35/BaVolMqFTKZrK5OWldXK5FIqqurJZJagP/lvwRcrpu9vYOVVTNvenqExfu5dVi356MbGWfl0rqkQ9uTDm23dvbwDu/mEhBi7xVg4+zBaVnMRovF5rTvPbh978HlhY/Tzh9Nv3Sy4H4aUFRNXd3fN272y8mhO0C9MLH8DrmgBIAX/RrG5XKFQqFEIqmsrDTYBnNzc5+faX/v3j31cv2/HdoqKirUYxGJzMzMY8eONSfUllCpVFKpFAB4PN4zARhMbW0tAHA4HM5zk6eUSuXzP/zW1NQoFAqpVFpbW1tRUVFeXl5cXFxUVPRMywAehzMhus/8MaNcbGzIc3wRMqpTqVTW1dXp4PloTqlUAgBFUXQFoFAoyEJdXZ0WibBIP9/ElT9s/OvoqoN/PimvCHFzkclkjd7Uqd8juVyu0S7UI2/Vh0x5ebmmceqEOoCqqipa3i/1SyGRSOgakEz2K5PJysrKaAmAfH4UCgVdAai/UvQdgPrYVC80k/qzQV4riqKeD/XixYvXrl1r5garqqrUx69Kpaqqqqqvr6+srCwtLc3Nzc3Pz2+4skvH4EGL51p7utTV1ZHvNwCQSqW05Nkbfnuog6GFXC53jmw/ec+Kox+veHwjrbq6ev369evXrwcAZ2dnV1dXGxsbCwsLHo9nYWGhfq0YDEbLsz9SqVSlUrFYLB6Pnl+8ZTKZUqlkMBikN0ULhYSEREdHN/pf6pOLpu+1+hAjW1Aqlc8fMqEjpwXHj7fx8IN/3x2pHtKX6kiavnTRH/VLJ5PJND1Z60TDSwW6ukGR/SoUCnqvDFUqVUsCsAuOsAuO6DbnY3jx2Up9tab1IUMWGj0jr457vSq3kFKqVAolpVBSjV202ArsIx27BNu0Z9QypNDgmp/JYllasW3sWLYObDt7BoerAqhRUlBR8fxGyPtF40+5jQTAZojc7GsLSiiFUiKRkLs/JpPJZnOYTCaTyWQwgPmf7DCDwWCoT1gUpaIoUKlUFEWpnlK+6HBgMBgCLk8s4At4vHq5PLeoSJPY+dOGfd/9YeKJK7/cfZhEAVWWn1OWn3Ptr6eJNo5QzBVbsoViNl/I5HDZfBGDwWByuM+UjacoFZPNZXG4QNO7QFEUUAAMLT8GAp8OHq7+tSWF0rIn0orisrKyFx0ydH0n6ISJ5XfUPUf5L56DIxAIJBJJM7uT6mSD2dnZTTfLaOJm9dKlS1988UVzQkUN8bncSH+/QZ06DusWZW1mBv/mj15KqVQ2c009oSiK3gCgZd9ZM2JjJvfrc+JGsoDHJa3om6D1zar6kHnpLvStmZli/Wn+t5meSKVSum4/CIVCQe/HgKIogwWg6V2W+hAjh0yjoR46dGjTpk06CY9gMJmuke1Dxwz06hkBDMYzX2i0XxLRPpJILpfL5XKeneWwDZ9l/3MjZdex3Ksp5IYnPz//mRwZasLYsWNDQ0ObXkfTs8zz+Z3nDxmOpS0HoNZQn2TaLwno/YYHgPr6evVJnxYKhULTD5JuqVQqXX0MSkqqXrovjTaoPiuRl0gulz9/yJQ/zFPVNvLFywCGndDew9zL3zrQSeQMAMDhAF8IAiEIxSA2A7EZiMyUDMbTnFNtHYBp3lRbi6FWBrX1oFIBgEqlUtfH0SGKompl0lqZ9gcsT+AZ33dRRVV+xsPzDx9fLyjJVCqfvr/y2hp5bZubZJ2Wlkb7jYY+mFh+R13VtYkvYvJfzw8SMcwGkU4IBALygpuZmXG5XLFYbGVlZW1t7ezs7Obm5u/vHxAQoH5HaL7/bpN6dKZnjjdCyGCEQiGbzRYKhSKRyMrKysnJyd3dPTg4ODIy0sLCBEbOG4W4MIibVllZee3atbS0tJycnPz8/IqKColEUltbK5fLaU+Hoee9ZfeSO2SEUEMCBlsCjY3mBqqo9klR7ZOkwiuGjwqhNsvE8jtmZmZkoYlLIpICF7+s3JQON9irV6/nh9KdPHlywIABZFksFpP5R15eXs/kid5999133323OaG2hFQqffToEQA4Ozs385XRubt37wKAjY2NjY0NLQHk5OTIZDKxWOzs7ExLAIWFhVVVVWw229vbm5YAyBw3APDx8dHrND2lUknqVWk6RF+db1UfMn5+frQMxJVIJKS6k5ubm04mGmhKoVA8ePAAAOzt7Q3TlOF5WVlZSqXSwsLCwcGBlgDy8vIkEgmfz3d3d6clgJKSkrKyMgaD4efnp9cdyWQy0gFQ0w8bl/u0g49IJJJIJEwm09f32SKXGzdu3Lhxo07ibEJVVRVp0Pv8ac4w1K+hcZ7mIiMjDRAAOc2JRCIXFxcD7O55BjvN1dbWPn78GAA0nYmmHqwtFApra2u5XG6jvVMNoKysjPxu7Ovry6SjuA+e5gDgwYMHCoWiLZzm1F/Rmn4/CwQCMpCZz+dLpVKBQODm9myfLMMU1y8uLi4vL2/0NGcYxnOac3JyUt/AGhg5zVlbW9va0tOEy2CnOfVNkykysXJx6u/fF41wLi0tJYM8m/lNrfMNIoQQQgghhBBCCBmYieV3AgMDyY/5DasXN6T+e3BwMC0bRAghhBBCCCGEEDIwE8vviMXiwMBAAEhMTGy0Ys6FCxfIQjPHQut8gwghhBBCCCGEEEIGZmL5HQAYOnQoAFRVVR0+fPj5//3tt98AgMvlDh48mK4NIoQQQgghhBBCCBmS6eV3Xn/9dVLU6qOPPnqmKPKWLVtu3boFABMnTjQ3N2/4XxUVFYcPHz58+HBSUpJONogQQgghhBBCCCFkJEysfxYAeHt7JyQkLF++PD09PTo6evny5Z06daqoqNiyZcvChQsBwNLSctGiRc88KiMjIz4+HgDi4uKeGaej3QY1IpVKyUJRUREtXRKUSiVZKC8vrz/k9ykAACAASURBVK6uNnwAajU1NaReteGR+XdSqbSgoICWAMjHQKVS0RWA+pV/8uSJXptSqVSqFm5BfciQVgWGp56tWVpaqtdeYy+ibslXVVVFVwdl8j7W1tbS9YmVyWQAIJfL6Q0AAPQdgPorWmvkkKEoiq7XSi6XkwW6TnPqrx08zclkslZ/mmt0Qr1GyHukUChoPyMXFhbS0iYST3PQlk5z6q/oFm6hvr6e3tcKT3MAUFFRYZieZS9SU1PT8k+Udgx2mqPrPK4TppffAYBvvvkmMzPz8OHDV65c6d69e8P/EolE+/btc3V1pXeDAEDaXgLAmTNnsrOzNX04Qq2AmZlZUFBQbGxsc1a+d+/eqVOnTLcZIUItZ2FhER4e3rNnz+asfPv27QsXLpSWluo7KoSMlpWVVVRUVKdOnZqzcmJi4tWrVysqKvQdFUJGy8bGpnfv3u3atWvOymfOnElJSSH90RFqm+rr6zt37uzs7Ex3IBowyfwOm80+ePDg6tWrV65cmZWVRf7I5XLj4uK+/fZbPz8/2jcIAI8ePSILmzdv1uLhCLUOQ4cObWZ+JzExcc6cOfqOByEjN2vWrGbmd06ePPnee+/pOx6EjNynn37azPzO3r17ly1bpu94EDJyq1evbmZ+Z8uWLVu3btV3PAgZue+//97Ly4uuvbPZGqdrTDK/AwBMJjMhISEhISEnJ6egoEAkEnl5eYnF4hetHxwcfObMGQCwsbHRyQYRQgghhBBCCCHUWrm7u7dv357uKDTAUE9/RbpVVlaWkpICAPb29gcPHvzwww8B4OzZs25uboYP5s6dO6RN2Lp16wYMGGD4AJRKpb+/PwAkJCTMnTvX8AEAQFxcXEZGRkxMzIYNG2gJ4L333tu/f7+zs/M///xDSwA///zzkiVLAOD69euWlpaG2alQKHR0dGzOmjU1NUVFRWR57dq1y5cvB4A7d+4IBAI9xvcCZ8+enT59OgDs3r27Y8eOhg/gyZMn3bp1A4BFixZNnDjR8AEAQOfOnUtLS8eOHfvVV1/REsDUqVPPnz8fGhq6f/9+WgJYunTphg0b2Gx2ZmamwXZqbm5ua2vbnDUrKyvVk7O+/PLLLVu2iESi27dv6zO6F9qzZ8+CBQsA4MyZM+7u7oYPIC0tjVTZW7NmzcCBAw0fAEVRvr6+APDmm2++8847hg8AAF555ZX09PR+/fpt3LiRlgDmz5+/b98+JyenCxcuGGynVlZWVlZWzVmzrKxMPTkrISHhr7/+8vT0PHXqlD6je6F169Z99913AJCSkiIUCg0fwPnz56dOnQoAO3fubOYAKN0qLi6OiooCgM8++2zSpEmGDwAAoqKiiouLx4wZQ66ODI+c5kJCQg4cOGCwndra2jazaUxRUZG6zsv48eMTExM7deq0c+dOfUb3QosXL/7ll1+EQiG5vTK8vXv3vv/++wBw+vRpDw8PwwegPs2tXr160KBBhg8AAHx9fSmKeuONN+bNm0dLAPHx8WlpaX379v3pp58MtlMHBweRSGSw3bWcqY7fMX7W1ta9e/cmy5cuXSIL7u7utIzvKi8vJwsODg7e3t6GD0BdPdTKyoqWAACAy+UCgFAopCsAMzMzAGCz2XQFoB685unpaW1tTUsMTRCLxeoRc+rwvLy8aLnwzcjIIAvOzs60vF98Pp8s2Nra0vWBIRU3zc3N6QqApPZ4PB5dAZA0KIPBoCuApllYWFhYWKiXAYDJZNIVqp2dHVlwc3OjJQZ1hQi6TnPqX8vwNEfjaa5p1tbW6pMLOd1wOBy6Qm14mqPlzuHu3btkga7TnPrkTvtpzszMjK4AyItA42muafb29vb29mSZnJH5fD5doeJprqqqiizQdZpTs7S0pCsAHo8HtJ7mTILp9UdHCCGEEEIIIYQQQg1hfgchhBBCCCGEEELItGF+ByGEEEIIIYQQQsi0YX4HIYQQQgghhBBCyLRhfgchhBBCCCGEEELItGF/dEOQyWS1tbUAYGFhwWTSkFNTKpWk6LpYLOZwOIYPAP7t4cXn82lpdw0AVVVVSqWSw+GomzQZmEQiqa+vZzKZ6pY3BiaVSuvq6gDA0tKSwWDQEkMz0R6qXC4nPUHNzMzYbBr6DKpUKtIPSCgUkmYBhldRUUFRFI/Ho6WFGQDU1NTI5XI2m02a8hheXV2dVCoFgGZ2X6ZRbW2tTCZjMBik55fh1dfXSyQSMILTnEgkIm2kDA9Pc7Sf5pqP9lDxNGcMp7nKykqVSkX7aY7FYjWzYTmNqqurFQoFjWdkPM3haQ6M4DRnEjC/gxBCCCGEEEIIIWTacH4WQgghhBBCCCGEkGnD/A5CCCGEEEIIIYSQacP8DkIIIYQQQgghhJBpw/wOQgghhBBCCCGEkGnD/A5CCCGEEEIIIYSQaaOhIWIro1Qq09PTk5KSrl27lpGRoVQqAWDfvn3W1tZabG3hwoUXLlxoYoWlS5d27tz5mT/m5ORcu3YtKSkpOTmZdPP99NNPo6OjtQhAraCg4I8//rh+/XpxcbGdnV1ERMTYsWOdnJxetL5EIjlw4MDp06fz8/PFYnFAQMCYMWNCQkI03e/9+/dnzJjRxArBwcFr164FAJVKderUqT///DMnJwcAPD094+Pj+/Xrp3XPQi02mJKSsnPnzszMzJqaGmdn5+jo6GHDholEIk13rcX7rtsPnpqunlET8JAhDH/I6GqPWj8FHR6zr7zyCuns+yInTpx4pnsoHjKE6R4ymq7/Inv27Fm9enUTK4wfP37mzJmAh4zJHjJ1dXXJyckk8tzcXIqi3N3dt27dqt3WTPela4OHjOFPrA3p9oNH6Pxat1F4yBCGP2R0tUetnwIeMq0ThVpg4cKFjX5NFBQUaLfBoUOHNv1+HT16tOH6f/75p52d3fOr7dixoyXPa926dQKB4JltCoXCdevWNbr++fPn3d3dnw9j9uzZUqlUo13fvHmz6VegS5cuFEXl5ub27Nnz+f/t1asX+YLQlKYblEqls2bNen59d3f3f/75R9O9a/q+6/yDp/Nn9CJ4yBCGP2R0uEftnoJuj1kLC4umn3VdXV3D9fGQUTPRQ0bT9Zvwww8/NP0KLFiwgMJDxmQPmZiYGDb72R8vAwICtN6gib50bfCQMfyJtSGdf/AoPVzrNgoPGcLwh4wO96jdU8BDprXC8Tstkp2dLZFIyDKbzeZyubW1tS3frLm5+ejRoxv9Lzc3t4b/fPLkSXFxccMHVlVVtXDvGzZsmDNnDgDw+fxhw4b5+Pg8ePBg//79tbW1c+bMYTAYz3xjXr9+fdCgQeR1iImJiYyMLC8v37dvX1FR0fr16ysrK3///XcGg6FpGH369PHx8Xn+7z4+PhUVFf3798/IyACAdu3axcbGAsCJEyfu3Llz/vz5AQMGXLp0ydLSsvn70nSDFEVNnTp1x44dAGBvbz9ixAgrK6ukpKRTp049evRo4MCB58+f79ixo6ZPufnvu84/eHp6Rs/DQwboOGR0vkdNN6jzY5YICAjo0aNHo//1zEUDHjLPMK1DRtP1m2ncuHFCofD5v3fu3BkPGdM9ZNLT0xUKBVkWi8W1tbUqlarlmzWtl64NHjKGP7E+8xedf/D09CXwPDxkgI5DRud7NP5r0Wf+YrqHjAmgM7lk+j755JNJkyatWrXq8uXLdXV1I0eOJK9qC39Z9fHxaeb6e/fuHT58+JIlS/6/vfuOi+La/wZ+lt5UinRRAQuIDQEVS8TeUDFRQdRo7BV7S3ItN5rotSQmmhgrQVBsYDeoiBqxAFJUFAWki/QmSN/nj/O78+zd5nZ24fP+w9c4c+aU2fkuu2fPnHPr1q3i4uLffvuNVkDiX1YzMjK0tbUJIRYWFomJicz+V69eWVhYEEK0tbUzMjKY/Q0NDU5OToQQNTW18+fPM/vLysqYDtTg4GDRK8B0AJ86dUpQmsWLF9M0K1eubGxspDsbGxtXrVpF9y9evFiMNoufYXBwMN0/aNCgsrIyZv+FCxfo8D8nJ6eGhgbRKyDu6y7zG0/mLVJYzREynw0ZmZcoQYYyj1n68+C8efNETI+QYahcyIib/rOYX1YF/ZqHkGGrcsh4eXktW7bM39//5cuXDQ0NJiYmRBaDEVTo0rXAkGmSz6JcZH7jyfxNQGE1R8h8NmRkXqLyfxblpboho/zQvyNLiv/kzUX6T95MbISEhHAdCgkJoYcWLVrE7GTeQ/38/LjSZ2dn01GCYsXqZ98gsrKyaMe/s7NzfX0956H6+npnZ2dCiIaGRmZmpoglSpChg4MDIURXVzc7O5srNz8/PwleAilfd+lvPJm3SEQIGU5yChmZlyhuhjKPWbb4Hx+5IGRUKGTETf9Zn/3kjZDhpboho/gvq1wUf+laYMgo/g/rZ0l548njTUBECBlOcgoZmZeo/J9FP0t1Q0YJtaSphuBzGhoaLly4QAixtbX18vLiOurl5WVnZ0cIuXDhAp35jBBy9uxZurF69Wqu9NbW1t7e3oSQN2/exMfHy6qSISEhdDjfypUr1dXVOQ+pq6uvXLmSEFJfX3/x4kU5Zfj8+XM69s/b29va2porN+Y6MG+dyq/5tUhhVCJkZF6iuBnKPGabHEJGYuKGjAQhJj2EjMwhZCQm7qVrmSGj+D+s8tb83gQURvlDRuYlqsRnUXlDyHBC/w78f3FxcYWFhYSQ0aNH8z5yyWKx6KOMRUVFMTExhBA2m33nzh1CiIODQ8eOHXkzHDt2LN0ICwuTVSVv3bpFN2hluIwZM0bcEsXNkEnPHOLUsWNHR0dHQsidO3dk8gCzAjS/FimM8oeMzEuUIEOZx2yTQ8hITNyQETe99BAy8oCQkZi4l64FhkyTfBaVt+b3JqAwyh8yMi9R+T+LKgBChhPmV1ZGHz9+/OGHH+Lj4ysqKoyNjbt16zZmzBjeBWtl7uXLl3SDDmPj5eLiQjcSExP79euXkZFRUVEhYnpxKxMeHv7gwYPMzEwtLS0rKyt3d/fJkye3bt2aVtLS0pI+UMrF3Ny8Xbt22dnZopcoboaiXKXXr19XVlamp6fTLnMRKfPrLlmLFEaZLx3daKqQKS4ulm2JEjRB5jHLSE5OXrt2bXJycn19fdu2bV1dXSdOnMj3w4psIWQkJm7IiJterMocO3YsOTm5oKCgVatWtra2Hh4eY8eOzcrKQsjIXDMIGVW5dC0wZJrks6i4+YhLfm8CCoOQIQJCRuYlKv9nUYSMgqF/Rxnl5eVt2bKFc8/WrVuHDBly8uRJW1tb+ZWbmppKNzp06MA3AbOfpvxsehsbGxaLxWazmZSi8/f35/zvn3/+uXLlyi1btmRmZgopkR7Kzs7Oysqqra3V0tISXkp9fb24GTJt4bumIPnfqyTW59Qmf91l3iKFafJLp7Qh4+vrK9sSxW2CBCEmSjWoBw8ePHjwgPnvqVOn1qxZM3fu3J9//pnvYquygpCRmMxDhiu9WLZv387533379tna2i5dulR4iQgZCTSDkFGVS9cCQ0bxf1i3bdvGzNgqD3J9E1AYhAwREDIDBgyQbYnK/1kUIaNg6N9RRlZWVvb29u3atWtsbHz9+vXz588JIffv33d1dX306FHXrl3lVC6z6q2RkRHfBMyqcmVlZaKk19LS0tPTq6yspOlFp6Wl5ejoaGNjY2xsnJ+f/+TJk9LS0rKysrVr1wovkbOS5eXlbdu2FV5QRUUFm80WK0Paal1dXR0dHeHpxW11077u8miRwiBkBIXMH3/8IdsSxW2CBCEmSjUIISwWq3Pnzu3bt7e0tPz48eOzZ88yMzMbGhqOHj0aHx9/7949vouSygRCRmIyDxmJL3WbNm26du1qY2Ojo6OTkZERHR1dU1OTlpa2YcMG4SUiZCSg6iGjQpeuBYaM4v+wrl69Ojk5+dChQ2LlJjr5vQkoDEKGCA6Z9PR02Zao/J9FETIKhv4d5eLr67tjx47u3btz7kxMTJw3b97Tp0+Li4unTp0aHx9Pl/eTucrKSroh6D2RTqjOpPxsenpKZWUlk/Kz2rZtGxgYOGnSJAMDA2ZnTU3NoUOHNm/eXFtbK0qJTCU/G8AiNoEzQ3qKiOmFl85Qhtddti1SGGW4dET1Q0b0EsVtggQhJko1fvrpJy8vL0tLS86d165dW7hwYW5ubnR09Pr16+X3SQIhIzGZh4wEl9rV1fXOnTseHh6cUzDm5+dv3rz5xIkT9DOikBIJQkZ8Kh0yqnXpEDLSV4CI8If1999/9/DwmDp1qogZikVObwIKg5CRMmTELVElPosiZBQJ8ysL1NjYWM2PXAudNm0a58duWqK9vf3169fp/hcvXly+fFlOpdOF5QghdAZyQkhtbS1n25n40dTU5JueFz1E04uiXbt2M2bM4Hx3IIRoa2uvWbPm6NGjXNkKKVHEQkVvAvnfVsuqAhTX6045OTmFh4f36NGDEPLixYsrV66ImJu4ZNUihAxR5ZARsURxmyBBiIliyZIlXJ8dCSGenp53796lvwoePXo0Pz9fxNzEhZCRGO/9wNV2cUNGgptn0KBBw4cP51pfw8zM7Pjx43PnzuXNWVChCBnRySpk6uvreeOF6cWWE85Lx8TsiBEjbt682SSXjqv5nz59ovsRMgr+w7pz504RcxOXDN8EEDJEBUNG3BLFbQJCRk7VUB7o3xHo9OnTuvx8+PBBMRXIzs5mCm3bti0ze5b8+rxbtWpFN5j3vm7dunG2nZnliwYwb3peVVVVTHopff3118zXktLSUuElilio6E0g/9tq0dNLQ19fn3l29+bNm1LmJoisWoSQIcoXMsxchkI+S4lVorhNkCDEpOHg4DB//nxCSF1dXXh4uJS5CYKQkRjX/cBZAclCRoY3DyFk165dzMAlWQUpQobILmQWL17MGy89e/aUbW2F4IzZ3r1702rX1dXJb81drkvHGzLr16+nhxAyCvvDSm+5hISE9+/fS58hLxm+CSBklDBkmCWuZFWiuE1AyMijDkoF/TuqR3593ubm5nTjs+FHU342fVFREf2VgEkppaFDh9KNrKwsQWloZfT09JhoF0JfX5/GuZAmc2VI21JfX19QUCAkPZFRq4cMGUI33r17J31ufCm4RYrXkkOG+bb89u1bmZQobhMkCDEpIWSkp0IhI9tLbWpqykzuK6sgRciQFhAydGpPefjspeNK2QJDRvF/WJmQSUtLk0mGXBT/JqB4LTlkmAWeZFWiuE1AyMijDkoF8+8INHLkyIiICN79xsbGiqmAqakpZwXq6upGjRpFOB4glDlHR0e6kZycTDeCgoI4u0JPnz5NR9l169aNEOLg4EDnV2fSc2H20/TSY6bcz83Nra+vZ8bjMRoaGuh7B62bKHk6ODjExMSkpaWJmKGjo+P169cJIcnJyaamprwZMq1mrqc0mCYL6ZOWkqxahJAhyhcy1tbWdEPQZylxS5SgCeKGmJQQMqJr8pDhqgDhCRneEOMi85BhZmcU1CWKkJGArEJm/fr1M2fO5Nopv4laeXHFbHR0NDO7sJxwXTrekNmwYUN0dDT576VrgSHThJ9F5RcysnoTQMgoYci0adMmNzeXEJKUlCSTEsVtAkJGTnVQHujfEcjc3Lxpf0fS1tb28PBg/vvq1Su6YWVlJacSXVxcaMBHRkbSPcyP/9TevXsJISwWy8XFhRBiYGDg4ODw+vXrp0+f8g2nhw8f0g03NzeZ1JBZqK+mpiYuLo4327i4ODp9g+glurm5xcTEfPr0ScQMmY3IyEhmjUNGQ0PDkydPCCFdu3Zt3bq1iHUQgmky38/EMiGrFiFkiPKFDDPSLSkpSSYlStAEcUNMSggZ0TV5yHBVgPCEDG+IcaH3G5NeSmw2m+kJjYqKQsjIiqxCpmvXrvJb3E0UXDHLhAzzg7zMcV06rpBpaGigvSrMpWuBIdOEn0XlGjIyeRNAyChhyBQVFdFt+r4nfYniNgEh0+zh+SyV4e/vTze4vkDKkIWFBc38zp07vOPz8/Pzb9++TQjp27cv8+l/0qRJhJDy8vJr167xZhgYGEgI0dLSGjdunPTVy8/P55yDJigoSFCJhBAvLy8Rs6VNED3DsWPHamlpCUp/9epVupqg6BUQTgGvu4JbpDAIGc6QqayslFWJ4jZB3BCTBpvN/uuvv+g2QkZcShgyEoSYNG7evJmXl0e3ZRikCJlmGTLKeelacsgo+A+rgYGBrEY38FLkm4DCIGQIITdv3mQeHJNViaryWRQhozhskJ2vvvqKXtXc3FwhyZ4+fXr16tWrV6+WlpZy7k9ISKisrOR7yoULF2j3qpaW1rt37wTl/Ntvv9EKnDlzRkgFUlJSaAVSU1O5DgUEBNAcZ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" alt="Density plot of PGS from external" />
 <div class="figcaption">Density plot of PGS from external</div>
 </div>
 </div>
 </div>
 <div id="section" class="section level2 unnumbered">
 <h2 class="unnumbered"></h2>
+<p><strong>Note.</strong> Plot shows distribution of pseudovalidated
+PGS.</p>
 <hr />
 <hr />
-<p>This report was created using GenoPred (v2.2.2-110-gb4e52b5).</p>
+<p>This report was created using GenoPred (v2.2.2-258-gd2f2a91).</p>
 </div>
 </div>
 
diff --git a/pipeline/misc/dev/test_data/output/example_plink2/reports/individual/example_plink2-4_EAS.4_EAS-report.html b/pipeline/misc/dev/test_data/output/example_plink2/reports/individual/example_plink2-4_EAS.4_EAS-report.html
index 755d8961..db28e385 100644
--- a/pipeline/misc/dev/test_data/output/example_plink2/reports/individual/example_plink2-4_EAS.4_EAS-report.html
+++ b/pipeline/misc/dev/test_data/output/example_plink2/reports/individual/example_plink2-4_EAS.4_EAS-report.html
@@ -3994,25 +3994,56 @@ <h1>Ancestry</h1>
 <h1>Polygenic Scores</h1>
 <ul>
 <li>1 GWAS summary statistics were provided for polygenic scoring.</li>
-<li>2 PGS methods were applied, including pT+clump, lassosum.</li>
+<li>0 GWAS groups were specified.</li>
+<li>2 PGS methods were applied, including lassosum, pT+clump.</li>
 <li>1 external score files were provided for polygenic scoring.</li>
 </ul>
 <hr />
 <div id="gwas-summary-statistics" class="section level2">
 <h2>GWAS summary statistics</h2>
-<div id="htmlwidget-6fc2e017e460e48705ed" style="width:100%;height:auto;" class="datatables html-widget "></div>
-<script type="application/json" data-for="htmlwidget-6fc2e017e460e48705ed">{"x":{"filter":"none","vertical":false,"data":[["BODY04"],["Body Mass Index"],["EUR"],[885],[841]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Name<\/th>\n      <th>Label<\/th>\n      <th>Population<\/th>\n      <th>NSNP Original<\/th>\n      <th>NSNP Final<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<div id="htmlwidget-f25650ed65f8442e3866" style="width:100%;height:auto;" class="datatables html-widget "></div>
+<script type="application/json" data-for="htmlwidget-f25650ed65f8442e3866">{"x":{"filter":"none","vertical":false,"data":[["BODY04"],["Body Mass Index"],["EUR"],[885],[841]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Name<\/th>\n      <th>Label<\/th>\n      <th>Population<\/th>\n      <th>NSNP Original<\/th>\n      <th>NSNP Final<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<hr />
+<details>
+<summary>
+Show GWAS header interpretations
+</summary>
+<p><strong>BODY04</strong></p>
+<div id="htmlwidget-32d84a85a55cf832a50e" class="datatables html-widget" style="width:50%;height:auto;">
+
+</div>
+<script type="application/json" data-for="htmlwidget-32d84a85a55cf832a50e">{"x":{"filter":"none","vertical":false,"data":[["SNP","A1","A2","P","BETA","SE","N"],["RSID for variant","Allele 1 (effect allele)","Allele 2","P-value of association","BETA effect size","Standard error of log(OR) or BETA","Total sample size"]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Header<\/th>\n      <th>Interpretation<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<hr />
+<p><strong>Note.</strong> Columns that were dropped are not shown
+here.</p>
+</details>
 <hr />
 </div>
 <div id="score-files" class="section level2">
 <h2>Score files</h2>
-<div id="htmlwidget-c34f37720f3841afe242" style="width:100%;height:auto;" class="datatables html-widget "></div>
-<script type="application/json" data-for="htmlwidget-c34f37720f3841afe242">{"x":{"filter":"none","vertical":false,"data":[["PGS002804"],["Height Yengo EUR"],[708],[708],[true]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Name<\/th>\n      <th>Label<\/th>\n      <th>NSNP Original<\/th>\n      <th>NSNP Final<\/th>\n      <th>Pass<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
+<div id="htmlwidget-196605182c8b85934d98" style="width:100%;height:auto;" class="datatables html-widget "></div>
+<script type="application/json" data-for="htmlwidget-196605182c8b85934d98">{"x":{"filter":"none","vertical":false,"data":[["PGS002804"],["Height Yengo EUR"],[708],[708],[true]],"container":"<table class=\"display\">\n  <thead>\n    <tr>\n      <th>Name<\/th>\n      <th>Label<\/th>\n      <th>NSNP Original<\/th>\n      <th>NSNP Final<\/th>\n      <th>Pass<\/th>\n    <\/tr>\n  <\/thead>\n<\/table>","options":{"dom":"t","ordering":false,"columnDefs":[{"className":"dt-center","targets":"_all"}],"scrollX":true,"order":[],"autoWidth":false,"orderClasses":false}},"evals":[],"jsHooks":[]}</script>
 <p><strong>Note.</strong> The <code>Pass</code> column indicates whether
 a sufficient number of variants within the score file were present in
 the reference data.</p>
 <hr />
 </div>
+<div id="pgs-correlation" class="section level2">
+<h2>PGS correlation</h2>
+<p>This section shows the correlation between PGS in the reference
+sample. This is intended as a sanity check that the PGS correlations are
+in the expected direction, thereby confirming the GWAS alleles are being
+interpreted correctly.</p>
+<details>
+<summary>
+Show reference PGS correlation matrix
+</summary>
+<p><img 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" /></p>
+<p><strong>Note.</strong> Plot shows correlation between pseudovalidated
+PGS across all populations, adjusted for ancestry.</p>
+</details>
+<hr />
+</div>
 <div id="target-polygenic-profile" class="section level2 tabset tabset-fade">
 <h2 class="tabset tabset-fade">Target Polygenic Profile</h2>
 <div id="method-lassosum" class="section level3 tabset tabset-fade">
@@ -4092,7 +4123,7 @@ <h3 class="tabset tabset-fade">Method: lassosum</h3>
 1
 </td>
 <td style="text-align:center;">
-0.994
+0.058
 </td>
 <td style="text-align:center;">
 NA
@@ -4114,11 +4145,11 @@ <h3 class="tabset tabset-fade">Method: lassosum</h3>
 <div id="gwas-body-mass-index" class="section level4 tabset tabset-fade">
 <h4 class="tabset tabset-fade">GWAS: Body Mass Index</h4>
 <center>
-<p><img 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" 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50KVlZWrVq1Uj2tS6j18srqfWOT1xYNT5BGZWJicubMmTVr1qgm7ik0adJk5cqVf//9N/vvPTNnzlR9MHTp0kWxfLUmGxubiIiIgQMHqkr8/f3PnTun+pbJMIxqhqBAIDh58uSmTZsqr9vfsmXLGTNmjB49mmVg1eLxeEeOHPn+++91FnI2NzfXmeVe2Zw5cxYvXty9e3edJQI5HE5wcPDp06fHjRtX0wPV44vScJfR29t77ty5sbGxqoU6DJswYYKvr6/OeLytre2MGTN0fqFj27ZtqvGC+rVx48aFCxeqhmzMzc2XLFmybt06VQWBQBAeHq76zQgAXbt2vXTpkuoOVR01DbUeX1lCFBg5zYYldZOVlaUabHZycmrTpg3LHZOSklSL4Tdt2rRyhYSEhGfPniked+rUSWcCYFFRkYuLi2Jwd/v27R9//HFVB0pNTU1ISHB0dGzRokVycnLbtm0VU9I6d+5ceX4DgOfPnyclJZWXlzs5Obm6uqoWgTAc1ePHj1NSUhSPu3XrpvhzbPgUAGRnZyckJJSUlDg4OHh5eRlY3U+HVCpNSEjIzMwsLy+3tLRs0aKF6sNJL5YHquOLosnwZdRL870EgMvlmpiY2NjYuLu7Gzg7AyEVFhamp6dnZ2fLZDJHR8dmzZpV1QORlJSUlJSkWgopODiYw+EYPlm9WysXisXihw8fymSyNm3aVDWb8smTJ8+fP3d3d1d8ukdHR2dlZSk2Ve4qqEWoqMMrq/eNTV5blDSQl0xpaem9e/cKCwu3bNmiWGrQ1tY2KSlJ74dK165dAwMDAwMDFWP/MTExW7ZsUU2K3LVr14QJExo1ekIIeZnRREjykklKSgoMDNQsWbZsWVVfQ1NSUm7cuKF305QpUyhjIISQGqE5DeQlZmlpuWLFiunTp1dVYciQIToLJnK53J49ex4+fPi7775r+AAJIeSVQsMThBBCCGGFehoIIYQQwgolDYQQQghhhZIGQgghhLBCSQMhhBBCWKGkgRBCCCGsUNJACCGEEFYoaSCEEEIIK5Q0EEIIIYQVShoIIYQQwgolDYSQ2tuzZ4+dnZ1EIjF2IISQxkBJAyGk9kpLS3Nzc40dBSGkkVDSQAipPW9v71GjRjEMY+xACCGNgX6wihBCCCGsUE8DeV3IJMg4h/y7xo6jahlnkXfL2EEQTRIJzv2KuxeNHUcVWIZ3dj9unav/ZslriWfsAMjLIe8mSvN1C83cYemjWyhOgjgePEtYtQOH3ftLJkHRM5Q+B98S5i3AM9NTpyQT4qew8IXAptIRU1AQC8fu4AgNHkWM5APg28Hfn1VUjS/5IHiWsOlo7DgaQl4m/v0bAAL6w8qhojAb/57WX9+1GVp3q6Ip1ntlpuDpHfgGwsZRt2ZKHGKvo3sohPrebSriPBxYDzsX+AcbqmYsLMM7uA6WdujYt56bJa8lShoIKwn7ICnULTTzROv/Uz4WpyB+J4qTAdV4FwPLVmg1q5r+rHvzUZanVSJ0RfMpELqqSx6uQtEz5WPbDvCeplX/0XpwOHBm/SeRGMHaychKAQBLBwT2VxY+vIYD6/XX92pbZdLAcq9VE/EsWvm4Qx9MW6tVc/1kcDjo+z77M3iZMeDQpBNSPyhpIDXg0EPrqWUr9WPRHRQngWMCsybg26EsG+I4FMTi4Ur4LjDUpqQYAjuYOENghfIiFD1BSRpilqPDBmVHReoxFD2DQw80eQ+PNyH3NnKiYBeg3D3xV0iL0HxO/Z4oqVd//oSsFHC5kEq1ypv7o0eobuXrJyCRoMewKltjs9ex7/AsGj1C8d5cbPoUt88j6hQCBiq3/roaRSLM2VH7M3q5bP/H2BGQVwclDaQGPD+ocpN5M3hOhMMb6pKCx3i8HkWJKMuGwL7KHTt+q/1chjtzISlE5kVl50HuTTAc5aFbhuHWp8i6rEwaynKRFQFLH1i2rNmJyEqQFw3IYNMBHEHlzSiKQ3GankEWiRjZ1yB0gnU73V0yzoNvBbtAjTIJRNGQFMLUA+ZeNYtQfUQRRDEAYNUGPCt1eeYF8C1h01l9RplXweHAsbdGnYvgCmHXhVVIMgkyL0LoAGt/yEoguo/yAq3W9Dp58uQ333zz119/cblc/TVEWTi+HQ7ukEmRk661ybEJPvhKqyQtDlf+AJeP4BFVHpLNXjfPgsNVVgv7Hz4NxuU/lElDbgYiDsMnAC07VXNuOkrEiL4EmRwdekNQaSRMJkHcPaQlwNIG7YLA03jTiEW4dgJOTdFOO+mWyXD+IKzs1V0vACQSRF9FYRY8fOHVpn7CO7sfdk66wxOJMUh4BM9WaNoGuRm4dQ7e/vBqW7OzNhywRIKLv8HBHf7BKBHjfiQKctB7VA1Oirx4KGkg9cOyNSx1SlqCZ4XyfBQ+hl3VSYMuDoSOKCyEpEBZIC0BU/G5zhECjHrT080ABy2m1yzUlN+Rfko5jMIwcB8BZ40/2g+XoyhJa5DF/g14TagIQIDkg2A46LRVq82M80g+CPNm6qQhbhtyb2uclgm8J8O6JtMpZCWIXQtxsrrEvClazVcmMSmHIS1H54qkIfMKkg8CgIU3TJsCQFk2En+BiaM6aTAckmrOh91DZJxTXoFqk4aUlJS///7b0E1YG2ZALsMn32JDWPXn/PsWAPCp4cd55b1KCiEwUT4WmoHhoqBiDGzzp+BwMX1dDQ+xGad2Qy4DAIbBiJnor5FBLx+LpEdQXQSGwRtvYcIi5VOBGQ7+DxwutkZqtXn+IA6uQ7N26qRh21zcvqB+85mYY/Iy+Pesa3g6cxrysrFyPPKeK5/aOCH4bRzbDr8gzNhQg2arDVg1N+LhDZw7oLw+lDS85OjuCVIDdz7HrTDcmomYr1HwuPr6sjIAMHGorp6GnGsojAcA24oBCL4VZKXKpkoyATmEDsqa4mS4hVYz/1GHRISMU7DwgmMfWLSAXI7kQ1qTKkqew8wTDj3hHgr7rmB4yL6K1GPKrRwehG6QS5FzQ6vZ7EsA4FLxxz92LXJvg28Nx15wHQZLX8jK8HQrStJqEOr9JRAng2cBl4FwGQieOYoSEbNEudXEDZCj4IHyaf4d5YPMqxUPLgGARUUfDMuQJPnIOAuhGxyCYKXTm1ILF35DyhMEvQNnT1b1YyIBIJRFemF4Lyt7lBajrAQAMpMgl8LBDQCu/YnkJwidCqFFDdoXZePULni1Rp/30KI95HIc2oS8bHWF58nwbIueIxA6HV2HgCfA1eM49p1yK48HN29Iy3HjL61mLx0GgP5jlU/X/ge3z8PaHr1GYtgU+AaiTIyts5H2DIZVG56O5WOQ9xz2rgidioHjUVKEEz/VplmWAedn4+wBuDVH0HDdvhbyEqKeBlID0hIwHMjKUZyMR+vh/jZcBlZZOecGpMXgCGHeovqWb38CuQxyifLbiMsQmLkrN7kNxZMtuL8YdgHIugIAbsMBGRL3QWAHl4GQlSDnH/DMYNOx+jRYLoHrMLiFKJ/GrkfhI6T/habvKUs6bNSq7zESd+fj+Tm4VQyXO/VG4i/IOKv+Bi8rQ3EaGJ5ysKDgAQqfwMwDrVWd6CHIOIPkQ0jYB5/Pq78aALKuoCwHHBO0X6s8KddhuPMZSp8rp3RYt4U4HtnXYdkaAMTJ4JlDIkbhQ2ULikEN+241C0kuhWNvNK2XCYLiQhz8BubWGPclq/rX/kR5Gcyta9Ytr3evoVOw5TMsHoWAAbhyFACGh0Emwb4VsHPGwAkoEeOf0zCzRMc+4FT3ppGUY9gUhHykfLp+Ch7dxF878V7FVJqNEVr1R36C+UNw7gCGTVWW9H4Xv6zA2V/RZZCypKwEafHgCdC5PwA8uIYnd+DRAl/tV1YIAc7sxqFvsW8VPt9ep/A0XTwMUTYsbLHiuLJk0CR83l9PTcPNsg9YWo7eo/D+fEOnQF4elDQQVtyGwrqD8nZHWRmeboMoBilHYNdVzz2QAMqyEf8zAHiOZ9U+wwekkEsBOUzd1N0MAKz94TESqceRcRocUzSbBKErnv0AWRl85iDvFuJ2KDtQuUK0WaI/HvWBeOqMAYBdIAofoThFq45MgrwoFKdBWgoAHD6kxeqtjkFI/BXFiYBM+XGefgrQ+E6f9hcACGyRfEj36CUpuiVVyboMAI7B6jSIw4N9d2ReQGYE7ALg0ANpJ5T9PbISSItg6YvSTJRkVhwrDeAo74llHxLDQdPRbIOsxrczIC3XvW3BgDO7Aag/Vuuyl39PjPwEx3/E6d0wtcCkpXBthh8WoKwEc37ArXPY8QVkMgAQmmPJIT33ZGriCdSfnQACB+LRTaRod7VJyhB1BmnPUFoCAHwBigvUW4OG49c1SHwAmUyZo5zaDcjRsoOywl8/A4CtMw5pDxCAQcqTaq4Am/BUIsMBoP8YdYmZFXwC8OB6zZplHzCHi9FzqzkF8vKgpIGwojm2zRGg5ae49yXKcpD+J5qO0a0sEeH+EsglcOyjvs3BsPYV99Dl3ED8TjxchjZLIaz4S+7cX2vaQUkacqJg2x6mTRG7HgwPbRaiJBlPv0fcdvga/EqjswiE4hCKsQ+F5IPqEX2tkxJX7MuBuReK4pAZAcc+AJB9DQBcBytrlj4HgLx7wD3dRmTlhmLTVJ4DQNmLoGLVGpkXUJYFAAJ7cATKatk3AMC2A4qeIfs6xCngmUNWBoFdjUPimtVs0NLExMTW1lbPhqhTeBaNdj3YzjcsESPlKRgGw6ZVX5nNXv0/0BqAT4tD1Gm0742mvlj/MXh8LNyH5Ef4/v+wfR7m7zR0FDPt6TqObgCUYx8KB9fh3EFl6qpJXAgzCwDgcODVGnH3EHEIfd4FgGvhADD4Q2XN54kAcO8K7l3RbaS8TLekpuFpUsxF9e2iVdi0lZ6kwXCz7AM2s6i+L4e8PChpILVk1RpZV1CcqlsuESH6v5CVwv4NdZ8/e3ZdUPgUmReQuAetZuuv82QLGB68PkJRHKQlcOwFoTOEzjA9DnF8dQfQ+5avSBFybiDjLDg8uIbAqj0E1gAHD5agLA/Q+B1Hl354ugOZF+HYBxIxyrLAFajXuZJJAKDVLJjX8J4OTYqvwXwrrUK+DQD1Z5OJE4qTUfBYuY6k/RsQOCP7OnKugmcOABYVA0PsQ2Iq30ti0Pjx48eP19ebtHcVGA5adca5X5UlJUUAcP8yCrLQLQRm2ud2fDvkcrg0U37KssR+ry2fgSfAR6sQdw8lYvQaCWdPOHvi+PeIj6lmXx5f+zkH0HgZbvyFs/vBEyBkMtoHw9oBHB6WjEJeJiQlQEVg/cZhx3xcPIw+70IsQlYaBKbwqcipJeUAMGuruu9B53C1Dk+HTAoAlnZaheb60j7DzbIPWGCqPxLycqKkgdSSXPEhqr1mjKQQ9xdBWgy7AHhNrGXLZl4AUJapf2vGWZRmwnM8ODyIEwDArGKOncABxWmQSdiuRFnZ83MA0GSc1r2jmmMTCjadweErj5UeDgBWGuPpPFNIRBDF6vYT1AhXCIkI4mSYNVEXihOV7StYt0VxMnIiIU4E1xQcIazbgGGQ/wBcIQD1rRz1ElLNSEogl+HIt7rlkScQeQKuzdFa+8vutT8BoN9Y3fqGsdzr7H5kpmD8/4HHQ8J9APCsuBAObkiLg6QMvBqmSyrn9gPAuC/xxlB1oebYhELnN8HnI+0ZJGUI/x6Qo43G6lWmFhDlIPam7mWpd0JziHKQ8hj2GqunKboNaqTRAiYvGOo1IrWUfx8AzDUmxUvEiP4KEjGs26PZR1XtVz3RbQDg6vt+IitDyhEIXeAQBFR8EZeK1VsZpvYZAyrGKUyd1SUFscqZDTrMWwByPD+NnH8AwOUt9SZLXwDIulT7MABYNNPTiOIeDXNv5VP7HgAgioWkEKYV80b5tijLQEkKwKhvp6yXkGpmwAT0GqH1T7Fgc5uu6DUC7t5alZMeoTAXXD56Dq/BIVjuVVaCI9/CxRNB7wCAlT0AiCsWRS8rBoPaZwyo6LF39lKXxEYpZzboaNEJchlO78U/pwHgLY3/JIrxAsX9FA1KsRLDxSgYIloAACAASURBVCNahTGReusa0mgBkxcMJQ2kellX8PhbFMUDMgAoSUPMYkgKwXDgMkRZRyLG/YWQimHVztDCCbFrcXeu+nbNzPN4uhnFFd9zZCVI2K1cS8BR37LQT7+DXILmM5RPLXzAMMiJUj4tSqjZ7ZeVmboBQOIB5ZmWZSPuO/01XUMA4Pl5lIvANdPqD/B4BxweJIV4tEE5LgBAIkL8T0j5g20kbm8DDMRxyhmRADLPoygRDAP3io9IoTMYnnKKgyo/sPCGTAJpCQTW9RxSzQybijFfav1TjEd0H44xX6p/fkLh6GYA8K1i/sva/2DuQDyu9FtehvdS+W4uJOXqFQh8AsEAUWeVTxMeQGhZ1a6suDUHgAPrlENK2Sn4rooFSkP+AwDnD0CUAzNrNNFYUfWdT8DjoTAPG6ZDUjEnQJSDn77CH1v1NFVrw6aBYRB9BfevKUv+/Ak5GTVup9ECJi8YGp4g1SvLhug+RPcBAEzFDAAOvCap5xWm/A5JEQAUPMC/2kmD2zC4VMxtL0mFRIzyinURSrORdw959wCAYdSr49h2UPYlaCp6AtF92HdXT5DkWcCiFQpiEbMEkiLISuBRt5VjmoxB3i2I43EzDFwBpCXgCiCw0f11DACWLcExQbkIAGy0R3U5QnhPx9MtKHiAW2HgmEAug7wcAOy7so1EYAv3t5FyBAl7kLgfgLIF91Fa60IKnZTTSlTjKbaByizKrHk9h9SAHkYBwPAqpkCmPoW4EHlZNdtL4ckd3I9E96FwrEjrLGzQqjNib2LJaBTloUSMUZ/VKfgx83HrLOLvI6w7BCYoEUMghI0j8ioNsLXsBBNTiLIBoIP2b0EJzTB9A7bMwoMbCOsBEzPIJCgvBYCuQ3TbqQtHd/QbizN78e0MCM0hk6KsBF5tEX8f3Jp8HDRawOQFQz0NpHoOwXB+E6Ye4NuAZwaBHewC4bdSa31iE1vwzMAzA9ek0j+Nb/98B/DMwK+YHOb2FtxDYeYFgQ24ZhDYwLI1Ws3W/UkqhdSTMHGEl/asu1azYNcFEhEYBu7vwLlf1afBA89MnXAoy8zBM4PQqeKpGdouh6UP+JbgCGHbAe1Ww8QVPDM9CbZ9N/DMwDWF21u6m6zbwn8d7N+AwB4cHniWMPdG07HVzPPgmsNEIzyXgfCdC/Pm4AnBM4F5c/h+Cec3tXaxCwTPDEIXdSZh4w++BXhmcND+vafqQ9J3fap19+7d1atXy2RVTLvT5OgBMwtYWOuWP74FgQAeLdC0iuUZHNz17FjtXgonf4KjO8ZrLzs9axu6DIIoBwwX78w0NCWCJ4CZBRw9tArNLWBmAaemyqdmVlh+HD6dYWkNoRk69Mbqk3D1hpmFnlGPbiEws4CpJd76WHdT225Y9zfeCIG9C3g8WNrC2w9j/w8TF9cpPADmVnB0Vz8dOQuTlsLFEzwebJ0wcRGa+wGAVcXsSJbNVhuw3nbIS44xtP4rIYQY9MMPP3z00Ufl5eU8HnVbvrS+fg/JTzB5pdavYBCiD/U0EELI6yTqFBI17jL9YyuSn4AnoIyBsEFfDggh5HVy+Q88uAGeAAJTlBZBKgHDYLzBH7AnpAIlDYQQ8jp5awo4XKQ9Q2kxLKzh0gyjZmvdykFI1ShpIISQ10mL9vhkk7GDIC8rmghJCKm9rKyshISETp06MQxTfW1CyEuOkgZCCCGEsEJ3TxBCCCGEFUoaCCGEEMIKJQ2EEEIIYYWSBkJI7WVlZd28eZOmRhHymqCkgbwcMs4ir9LPHL4gXuTYGtrRo0cDAgKkUmlDHeDsftw611CN19GLHBshDYPWaSAvh+SD4FnCpqOx49DnRY7tpXdwHSzt0FHfD6Ub3YscGyENg3oaCKkHDP1Peh0x4NDqFOT1Qj0NhNRV5+3GjoAYxfZ/jB0BIY2NkgbSsGQSZF6E0AHW/pAUQhQNDh82HfV3cklEEMUAgFUb8Kz0NygRI/uaskHtIyHjPExsYNNZXVaciKIEmHvCtCnKcpF7CxbeMPfSjVAUDUkhTD30bFIFLytBXjQgg00HcARa1TLOwsSu0vCEDPnRKBfB1AXm3tV06slKUBCH8myYOMG8OTiV/18abK2q66YTv+g+ygvg2Lv6E2cblQESCS7+Bgd3+AejMA/RV8Hno2Mf/a2IshBzHQDavAErO/0NikW4dgIOHvDvqR2lBOcPwcYenTV+pDExBgmP4NkKTdsgNwO3zsHbH15tdSOMvorCLHj4wqtNlcGXiBF9CTI5OvSGQKhV7ex+2DnpDk/IJIiOhCgTLs3g3R4cgy98SSHi7iE7A04eaO4PnkC3guHWqrpuOvHfj0RBDnqPqv7EWUZFXmOUNJCGJRMj+QD4drC6heyrykKGgybvwrGPRrUSxK6FOFldYt4Urebr+YjhCZHyG8Cg02atz870M0g5AouWyqShLA8PV6I8T7mVbwOHYKQdg7UfWsxQ7xW3Dbm31U85JvCerE5HVMHbP0X6KUAOAAwD9xFw1viEqjynIW4bcu8o6yvO1/0drV00Pd4I0QN1ZQB2AWj2EavWDF83Vfx2D5FxTtmCImkwfOJsolLo0qXLqlWrOJU/GsV5OLAedi64dQFXj1Uchot3P0Of99TVSsRY+yGSn1Q8Z9C0Febv1PNBJTTDb/8Dw2BzpNZn55lfcGQTWnZUJg152Vg5HnnPlVttnBD8No5th18QZmxQ77VtLm5fUJ+eiTkmL1OnI6rgn97Fqd2QywCAYTBiJvp/oG6k8pyGbXNw56KyPgAOB+/M0NpF08YZeHAdmjeeBPTDR6tYtWb4uqnif3gD5w4oD6FIGgyfOJuoyOuNkgbSGCR5yL4KGz9Y+KDgMfLvIHE/hC6wbK2scH8JynLAs4BDDwDIuoyiRMQsQbuvK7XFgbk3Cp8g4wKcNf5cZ0YAgFuo8umD5ZCIILCHYxCkJciMQPoJ3ZZi16LwCfjWsOkAnjUKH6EgFk+3os0iCF01ghch4xQsvGDqheIkFD5B8iHYBkJgo/9kH6yEOB4cPmwDIHRCSSZEMShO1V858VeIYsA1hV03mNijNBMFT1BexLY1NtdNko+MsxC6w6IZyvJYnXi1Uan4+/v7+/vr2aCQl4mrx+DXAz6d8fgu7lzA/nVw8ULrbsoKS0YjJw0WNugxFAAu/4HEWCwZja9/122Kw4O3P57cxoUD6Pu+ujziEACETlM+XT4GomzYuyIoFCVFiDiCEz/pNrX2P3hyB9YO6NAb1vZ49C9io7B1NhYdhGszdTVRNk7tgldbePkh6QGe3MGhTQgMgY29/pNdOQHx98EXIKA/nDyRmYSY60h9pr/yr6sRcw2mlugWAnsnZKbiyW0Uidi2xua65Wfj7AG4N0ezdsjLZHXi1UZFXnuUNJDGIJfB+U14vAsAzv2R8jvS/0L8XvgtB4CsKyjLAccE7dcqOw9ch+HOZyh9jpwo2AXotuY2HI/WIfOcOmkoy0VZNnhmsGwJAJkXIRGBbwG/FcoKzoNw93OtRgoeoPAJzDzQ+quKohBknEHyISTsg49GZbkErsPgFqJ8GrsehY+Q/heavofK8u9CHA+GC7814JlVf2UUwwptFutPQQy3xvK6yaVw7I2mFZ+zbE7ccFQ1IJPizffw7hwA6A/8vhl/7cLeFVh+DACu/IGcNJiYY+1fyr6RYdPwWV88T0LUKQQM1G1teBjWfYRzGklDbgay02BmgZadAODiYYiyYWGLFceVFQZNwufaPTwPruHJHXi0wFf7VaePM7tx6FvsW4XPNeanSMoxbApCKnpX1k/Bo5v4ayfem6PnTO9eRPx9cPlY8xfMqhha0xRzDQAWH4SNY41bY3ndpOXoPQrvz6/BiRuOihC6e4I0Eg48RqqfuYeC4aIsG5ABQNZlAHAMVr8fOTzYdwcq+g90WLYE1xylWZBUfAVKPgIANp2UT7MjAcBJ48OCZwYLH61G0v4CAIEtkg+p/5XnA0BJilZNhqfOGADYBQJAsXYdlfQzAOAQxCpjAMA1BYDsi7VpjeV1YzhoOlr9lM2JG46qBjgcjJylfho6HVwestMgkwHA5aMAEPyOehSKJ0D3oQAQcVhPay07wtwKWSkQZSlLjmwGgE79lE8jwwGg/xj1LmZW8NHOOv/6GQBsnXFog/pffg7AIOWJVk2eQJ0xAAgcCAApj/Wf6Zm9ABAUyipjAGBqAQAXD9WmNZbXjcPF6Lnqp2xO3HBUhFBPA2kcXFPtBJUDrjkkIojTYOaO8hwA6qEKBavWyLyAsizoZReIzAtIOQrPDwAg/w4YBu4VYxPKBn21djFvioIH6qelzwEg7x5wT7dxWbnWU50PbKEjAMjK9AdWlgkA1u30b63MLRRPNiH1BNJPw9QVVv5w6Q9OxXw7w62xvG5cM62Lz+bEDUdVA6YWWtNSOByYW0OUjbR4uHsjJwMAWnfV2qX1G7jwG7LS9DcYOBAXfsPRrfjgvwBw5wIYIHS6cmtOOgD4dtHapWkrPLiufvo8EQDuXcG9K7qNl2u/qGaWWk8d3QCgrER/YJkpANCuh/6tlYWGYdNMnPgRp/fAtRn8e6H/OAgr3mqGW2N53cwstCZ/sDlxw1ERQkkDaRwcbqUSPgDISwEov3bytb9T8W0AqCeB6XALRWYEcv+F5wfIj4asFEJn9Y0Dyga1/+ZzzbWeyiQA0GoWzFtWF73e/yVVrJsskwKoQa++dVu0+T8kHoA4EUWJKEpE2gk0HQPH4OpbY3ndGO05hWxO3HBUNcDl65bwBQBQWqQORed2CcWMAcWmykLDEHEI/57HB/9F9BWUFsO5qboFxfWy1G7Q3FbrqaQcAGZtRcsOlVrX7nnl8fVsreodKZUAqEGvfttu+L89OLAOiQ+RGIvEWJz4EWPmI/id6ltjed0EploV2Jy44agIoeEJ0jiklb6eyYoBwMQBALhCAFq3AAAQJwIAzxR68cxg6gZpMQoeIz0cAJz6qbcqGtQZQSh9rvVU0bIoFhyenn+1puiWKIyvwS6mTeEzFx03wW8F7AIBOZIqBp0Nt1aL6wbWJ24gKk179uyxs7OTSKr4jC8R65YUFwCAgzsACC0AIFm7wz8xFqjoJ6/MzAJuzVFcgMf/InwHAPQbq94qNAcqjSAovmGrT8wCAGJvgieo9K8OL7yZBQDEx9Rgl6a+mPsDNl3GiuMIHAi5FPtXs2qtFtcNrE/cQFSEUNJAGoesDEXx6qclaZCIwfCVfQMWzQAg65LWLtmXAMDcu8o2XQYBQMrvKIwHh6f1JVix6kCm9pC8SPsvsGLwQuegdWfpAwCZ52uzr8AezSaDbwO5FGXZ1bdWu+tW0xOvHJWm0tLS3NzcKncuK9b65EuLg7gQfBPlt+Rm7QDgkvb0BcVT76rvyBg0EQB+34L4B+DxEDxCvUmxEsPFI1r1YyK1nioGLy7pmzNRF4qZE+cP1GZfe1dMXg4bR0ilyE6pvrXaXbeannjlqAihpIE0mqfbIBEDgKwEj74FANuKhQ3c3gYYiOOUM/sAZJ5HUSIYBu7Dq2zQrgs4AhQ9BeS6kxxdhwEMRNHIv68sSf8TZTladTzeAYcHSSEebVD36UpEiP8JKX/U/jQ93gHDRXEqEvepC/PvIu+m/vrPvkeWxhCzOAkSEcCAZ119a7W7bmxO3HBUNbPtc4gLAaBEjG8/AYCOFQt0vB0GhkHcXVyu+Jg/vx+JsWAYDA+rssEugyAwwdM7kMvgE6i1adg0MAyir+D+NWXJnz8pZwCovPMJeDwU5mHDdEgqxvJFOfjpK/yxteanp2p2JrhcpD7FPo0lDe5exM0z+ut//yWuaLzPkh5BlAOGgbVj9a3V7rqxOXHDURFCcxpI4+BZQVqEO7PBFUJaAsjBt0azScqtAlu4v42UI0jYg8T9ACAvBwD3UVWuC6lg5af8+PTQ/owUOsK5HzLO4Mm34Aohl0FWBjMviOPVsys4QnhPx9MtKHiAW2HgmEAuUx7XXnuGWY1whPD+CE93IDMCWZfA8CGXQC6FXRetpSpV8mOQE4WEPeDyAQbSUgBwCFaOFBhurXbXjc2JG46qBqzsUJSP2X0gNEeJGHIZrO0xabFyq60z3p6BI5uwZwX2rweA8lKAwajPq1wXUsGvJ27+DQDDp2mVO7qj31ic2YtvZ0BoDpkUZSXwaov4++BWhC40w/QN2DILD24grAdMzCCToLwUALoOqeHpaRBa4KOV2PEFIg7h0hHwTSEpgVSKLoO0lqpUibmBqDPYswx8IRg5SksBOYLfUa7OZLi12l03NiduOCpCKGkgjYPhoe0yxO9CcQr4fFi1htdErX4ul4GwbI6kIyh7DsghaIom72qtbcw1h0mlbztOPZF3E3xLmDbV3eQxEmYeSDsJSSH45nAejJIkiOPB1fg0tW4L/3VIPoiCR5CVgDGBwAb2b8AxqKIGDzwz5e0SKjxz8MwgdKoyNpuO8FuJxH0QJ0AuAdcKFi3Vq07paD4dWREQJ0IqBrgQusE9VOuGCMOtVXPd9MXP5sSrjYotngDLjmPXf5HyGHwTtO6Kif/VSj0GTkBzfxzZhOeJkDNo6ot3Z2ut92xuBUd33WZ7voObf8PSBk0rrYI8chY8WuHkjyjMg7k9Bk9CUizi72t9mrbthnV/4+BaPPoXJWKYWMLGEW8MRVBF7skTwMwCjh5aLZtbwMwCThpvNZ3YOvbFyhPYtxoJMZCUwcoWLTupV53SMX0dIg4h8QHEInAFcHNC6HS07sK2NcPXTW/8bE682qjIa4+Ry6uYBU5IfZCIcGcu+HbwX1n/jSvWQnbuB49R1Vd+8DXEyWg2WbnQAqkX586d++6773799VcuV/sOGVEW5g6CnQtWhtf/UbfNwe0L6DcWoz6rvvLX7yH5CSavRGAV63gTQlijOQ3kZSURIe8eGAZuQ/VszYlCscas+ZQ/IE4Gh0cZQz3r27fvwYMHdTOGBiXKwb1LYDgYOkXP1qhTSNSYevnHViQ/AU9AGQMh9YKGJ8jLJ+cfJO2HpAiQw76r/kWHsi6j4AE4PDACyEohlwIMPMc3eqykHv1zBvtXoagQcim6Dta/6NDlP/DgBngCCExRWgSpBAyD8QsaPVZCXk2UNJAGVsWwep1IARn4lrD2rzIPcH8LqRwUp0FeCp4FhC7wGAWzJvUaBjGgqmH1upCWQyaFpQ38e2L8Qv113poCDhdpz1BaDAtruDTDqNlo0qo+wyDkNUZzGgghhBDCCs1pIIQQQggrlDQQQmrv3Llz7777rlQqNXYghJDGQEkDIaT24uLifvvtNxrlJOQ1QUkDIYQQQlihpIEQQgghrFDSQAghhBBWKGkghNSeiYmJra2tsaMghDQSWqeBEEIIIaxQTwMhhBBCWKGkgRBCCCGsUNJACCGEEFYoaSCEEEIIK5Q0EEJq7+HDhzt27JDJZMYOhBDSGChpIITU3uXLl6dMmUJJAyGvCUoaCCGEEMIKJQ2EEEIIYYWSBkIIIYSwwjN2AISQl8yzZ8+io6MtLCw6dOhg7FgIIY2qBstISySS69evJyQk+Pn5+fn5NWhYhJAXx6NHjy5evHjnzp07d+7cvXu3r+to1aZ/Cv/09vYODg5u3759//79ra2tjRgnIaShsU0aLl++PH78+Pj4eAArV6784osvAPj5+XXo0GHPnj0NGiIhxFiSk5O/+uqr3bt3D/f5yNHR0cPDo0mTJh4eHu7u7sXFxcnJySkpKSVJG5OTkwsKCg5nyhcuXDh9+nSBQGDswAkhDYLV8ERiYuKQIUMcHBw2b968adMmVfn48eNXrlwpkUh4PBrmIOSVIhKJVq1atWHDhhFtpv6y8HRQUJCJiYlOHQ8PDwCOYABkZWV1P3EicseK1ps2rVy5ctSoUQzDGCFuQkhDYjURcvv27QzDXL16NSwszMbGRlUeGBiYl5eXnJzcYOERQhpbeXn5jh07WrVq9eR4/ppJO5ctW/bmm29Wzhh0ODg4TJgwYdGiRfPfaHl2UVjXrl0vXLjQKPESQhoPqx6C27dv9+nTx8XFRadc8T0jPT3dy8ur3iMjhDS+pKSkkJCQlpLuswevDA0NtbOzq9Hurq6uH3/88YMHDw4fPtynT59p06Zt2rSJy+U2ULSEkEbGqqdBLpfrHYBIT08HIBQK6zkoQogx3L9/v3v37l0sQ7788stJkyaxyRgKxQVxqRk6U6Nat269YMGCc4tnyCMOjxgxori4uMFCJoQ0KlZJg4+Pz7Vr10pLS3XKjxw5IhAIfHx8GiAwQkijunbtWq9evUJbTZ43b56npyfLvY6eO9D8/enSSstIMwyj6Glo8uyfPn36ZGVl1Xe8hBAjYJU0TJgwITU1derUqWKxWFEilUo3bty4efPm0aNHm5qaNmSEhJAG98cff/Tt23dyjy/nzp1ra2tbX836+/t//vnnQbLnwcHBiYmJ9dUsIcRY2N5yuXr16i+++EKRH9ja2ubn5xcVFbVo0eLq1auOjo4NHCQhpAH99NNPf67/x9/ff/LkyXw+v0b77g3/Yfbaj8rPHeRVPXEhMzNz48aNPz3J+/PPP2k9KEJeamyXkZ4/f/6pU6f69Oljbm6em5vr6uo6Z86c69evU8ZAyEttyZIl4WuvBwcHT506taYZA0uOjo5z5syZ1ta5T58+V69ebYhDEEIaRw3WVxgwYMCAAQMaLhRCSCPbtWvXnf2pQ4cOfeuttxr0QDY2NnPmzDHZtGn48OE3btyg+60IeUnRD1YR8pqKjIycOnVqt27d6pIxdGrdZdWUcRym+r8kpqamYWFhH/s6hISEiESiWh+REGJEVc5pOHbsWGpqKpsmhg0b5ubmVq9REUIaVmpqapcuXd5rP/Pzzz+v44qujjjCvnJaWtqqVasSvQIOHz7M4dCXFkJeMlUmDb17946IiGDTxPnz53v37l2fQRFCGlJxcXGvXr3acft8+eWXmmu81k6NkgYAt2/f/u6771zenb548eI6HpoQ0siqTBpu3ryZm5vLponOnTvX4z1ahJAGJZfLx44dK422mzNnTr3MLahp0gAgPDw8PDy8z6JNo0ePrr42IeSFUWW3ZOfOnRszDkJI41ixYkXpbav//GeCEWcjhoSEpKenT5o0qXnz5gEBAcYKgxBSUzSmSMhr5OTJk4sWLRo8eHBgYKARw2AYZsKECQuD24wYMeL58+dGjIQQUiNsF3cCEB8fv3fv3nv37hUXF7u5uQ0YMCA0NJR+ioaQl0VycnK7du0+G7Ri2rRp9fW71X+cP7hyxyePftnMrfmsxpycnBUrVjxwbH3ixIl6CYYQ0tDYzpretm3brFmzysrKVCXbt2/v0qVLeHg4re9EyEvh448/Hu7z0YQJE+orYwBQUCSq/INVLNnZ2X3wwQdbtmzZtWvXxIkT6yskQkjDYfXl4NKlS9OnTw8ODj5z5kx6enpubu7NmzenT58eFRU1duzYhg6REFJ3O3fuFD5rMmbMGHNzc2PHoubv7x8YGDhr1qzk5GRjx0IIqR6rnoYff/wxKCjo5MmTqvu5O3Xq1KlTJ3d39wULFiQmJjZt2rQhgySE1Elqaurnn38+b+jajh07GjsWXe+//35sbOzUqVPDw8ONHQshpBqsehpSU1NHjhxZeQUYxe1SLNeAIoQYS1hY2NAWH7777rvGDkQPc3PzcePGuT+9vnv3bmPHQgipBqukwd3dXe+aDXl5eQzDUDcDIS+yn3/+mXnoNGbMGEtLy3pv3NXRvV9n/zpOkmjfvn1AQMAnn3xCgxSEvOBY3T1x9uzZcePGXb9+XTM/KCsre+eddwQCwZEjNV7ahRDSONLS0tq1azcnZPXkyZMb6BC1WNypssLCwiVLljx0anP8+PG6t0YIaSCs5jQwDOPj49OmTZvx48f7+fmZmJjExcXt2bOntLR02bJl+/fvV9Xs27evk5NTg0VLCKmZsLCwEO+JL/7CixYWFuPGjdu6devevXvHjRtn7HAIIfqx6mmg36Eg5GW0Z8+e31dcnjJlSqdOnRruKPXS06CwY8eO+SeuR0dHu7u711ebhJB6xCppiIiIyMzMZNNcr169aNkGQl4EeXl5rVq1+nTAso8//rhBD1SPSUNBQcHixYvTW3X/5Zdf6qtNQkg9qsGKkISQl8js2bNTz5YvXbq0oX9Prh6TBgCXLl3q9dXGixcvBgUF1WOzhJB6UbOVX3Nyci5dunT69Ono6GiZTNZAMRFC6ujp06dbt24dPHhwQ2cM1+9dnrLuO5ms3r57BAUFLRvQadasWfQXhpAXENuk4fnz56NGjXJ0dAwODh44cKCfn5+npyd1IRLyYpo1a9bYTrP69evX0Ad6nPBwx/EzMnm9fcAzDDNy5MjORQn79u2rrzYJIfWF1d0TxcXFffr0iYmJ6dy5c48ePaytrePi4o4ePTpu3DipVPrBBx80dJSEEPbOnTvHf+I2YvIIgUBg7Fhqo1WrVh07dvziiy/efvvtF2rRa0IIq56Gn376KSYmZtOmTVFRURs3bly6dOnevXufPn3aunXrefPmUS8iIS8OqVQ6a9Ysb2/vgIAAY8dSeyNHjgy1la5du9bYgRBCtLBKGs6fPx8YGDhjxgzNQmdn57Vr12ZkZDx48KBhYiOE1Nj27dtbSrqPHj26Hn/KsvE5ODj07dt3zZo1CQkJxo6FEKLGKmkQiUTNmjWrXK4oFIlE9RwUIaRW8vLyFi1a1K1bNy8vL2PHUlchISFhfq4LFiwwdiCEEDVWSYOHh8eNGzfKysp0yi9evKjYWv9xEUJqbunSpW+6vRcaGtpoRxz31mR5xGEel1vvLQuFwmHDhu3bt+/y5cv13jghpHZYJQ0jRoyIj48fO3as6gct5XL5oUOH5s6dGxgY2KRJk4aMkBDCiuI2y0GDBjX0bZaNRnH75Zw5c2g5GUJeEGwXd/rwww937tzJ4/E8PT2trKwSEhJy3NEB1QAAIABJREFUcnJsbGwuXLjQvn37ho6SEFKt0NBQx6w2S5cu5fP5jXnc+l3cScfDhw//97//BS/cMHbs2IY7CiGEJbbrNPz000+7d+/u3r17Tk7OgwcPbGxspk6devv2bcoYCHkRXLt27dixY6GhoY2cMTQ0X19ff3//RYsWlZeXGzsWQggtI03IK6F///4tyt9YvHgxh1OzZV7rrkF7GgCkpKR8/fXXAbNXNNyvexNCWKrB3xeJRHLlypV9+/bdu3ev4QIihNTU5cuX//7779DQ0MbPGBLTnv124WqDfvdwd3fv2LHj119/XVpa2nBHIYSwwfZPzOXLl1u2bBkUFDR27NgTJ04oCv38/MaPH99gsRFCWFm4cOGMN5c06O9fV+XizbPvLlovbeAV3oYOHTrYvPiHH35o0KMQQqrFKmlITEwcMmQIwzCbN2/28fFRlY8fPz48PFwikTRYeISQapw5c8Yuwyc0NPSlXs3JMDc3t8DAwOXLl4vFYmPHQshrjVXSsH37doZhrl69GhYWZmNjoyoPDAzMy8tLTk5usPAIIdX473//6+np6e/vb+xAGtbQoUNDbaXfffedsQMh5LXGKmm4fft2nz59XFxcdMoVyzqlp6fXf1yEEBbCw8Nd8/xf7W4GBScnp27duq1cubKgoMDYsRDy+mKVNMjlch5Pz+9hKtIFoVBYz0ERQliQy+VLly5t3rx527ZtjR1LY3jrrbfedeFt2bLF2IEQ8vpilTT4+Phcu3at8tTlI0eOCAQCzVkOhJBGc+TIEY+Cjo25aHRlb3YbfOabRdxGuWvD3t6+e/fuq1evzsvLa4TDEUIqY/VffcKECampqVOnTlXNQpJKpRs3bty8efPo0aNNTU0bMkJCiB4ymWzp0qUtW7Y0btbu6uDer7N/ow2OhISEjG1iunHjxsY5HCFEB9vFnVavXv3FF18o8gNbW9v8/PyioqIWLVpcvXrV0dGxgYMkhOj69ddff1t6Yc6cOS1btjRuJA29uJOOAwcOTPnlVFxcnJ2dXWMelxAC9us0zJ8//9SpU3369DE3N8/NzXV1dZ0zZ87169cpYyCk8Uml0iVLlrRt29boGUPjGzx48HhP8/Xr1xs7EEJeR7SMNCEvnwMHDhxYfG7evHnNmzc3diyN3dMA4ODBgx/tOZmQkKB5BzghpBE09qKzhJC6W7NmTatWrV6EjKGsvDS3oLCRD9q/f/9xTc22bt3ayMclhNQgaYiPj1+2bNno0aOHDRs2derUI0eOSKXShouMEKLXiRMnPMUBQ4YMMXYgAHDw1B67tyZIGvdPga2tbbdu3b755puioqLGPC4hRM/qC3pt27Zt1qxZZWVlqpLt27d36dIlPDycpjUQ0phWrVrVqclAX19fYwdiTIMGDbp69eqPP/74ySefGDsWQl4jrHoaLl26NH369ODg4DNnzqSnp+fm5t68eXP69OlRUVFjx45t6BAJISoRERGXL18OCQl55ZeANMzR0bFz585r167V/CZDCGlorHoafvzxx6CgoJMnT6rWhezUqVOnTp3c3d0XLFiQmJjYtGnThgySEKK0cuXK6X0WdejQwdiBGN+gQYOioqL27t374YcfGjsWQl4XrHoaUlNTR44cWXkl6dGjRyu21n9chJBKbt++ffr06UGDBr3m3QwKHh4efn5+K1asoMlVhDQaVkmDu7t7bm5u5fK8vDyGYaibgZDGsWzZssk9vuzSpYuxA1Fr26L9/DFvcxjj3Ic1ZMiQN/n5hw8fNsrRCXkNsVqn4ezZs+PGjbt+/bpmflBWVvbOO+8IBIIjRxr7Lm1CXkMPHz5s27btgcXnevXqZexYtDT+Og2a1q9fvyeh6NatW9T7QkgjYDWngWEYHx+fNm3ajB8/3s/Pz8TEJC4ubs+ePaWlpcuWLdu/f7+qZt++fZ2cnBosWkJeXytXrhwfMLt79+7GDuTFMnjw4EcbN/75558hISHGjoWQVx+rnobevXtHRESwae78+fO9e/eua1CEEG2JiYktWrTYOS98wIABxo5Fl3F7GgCsXLnyWB4vMjLSuGEQ8jpglTRERERkZmayaa5Xr160bAMh9S4sLEx03WTFihVCodDYsegyetJw69atTrOWXbhw4UUbuCHk1UO/PUHIiy4zM9PT03Nz2IGhQ4caOxY9jJ40yOXyJUuW/GPa5MSJE8aNhJBXHv32BCEvus2bNw9r+Z8Xc+Dv5KWjAR/Pk8pkRoyBYZgBAwacPHnyzp07RgyDkNcBJQ2EvNDEYvHWrVu7d+9uaWlp7Fj0yM7Puhn71Ogdll27dp3XrcWGDRuMGwYhrzxKGgh5oe3cuTPYccSbb75p7EBeaFwut3fv3r/88ktSUpKxYyHkVUZJAyEvLqlUumHDho4dO9KdzNXq3bv39HYuW7ZsMXYghLzKKGkg5MX1+++/+/H69u/f39iBvASEQmGPHj22bduWn59v7FgIeWVR0kDIi+ubb75p0aKFt7e3sQOpkr21Q2ef5i/Iaoz9+vUb28T0xx9/NHYghLyyqkwaxo4du2TJEsXjY8eORUVFNVZIhBAAuHTpUmRk5Au4mpOmwT2HR+1Yw+W8EF8/bG1tO3fu/L///a+8vNzYsRDyaqryv3paWtrz588Vj7/55pvw8PDGCokQAgDr1q2b2usrf39/YwfyMhk4cGCIZenBgweNHQghr6YqkwYXF5fHjx83ZiiEEJXY2Njw8PD/Z+++w5rI3reBn9BCF1SKooB0REEBEQGpgqAiIHaxoCAorGKva8W6dl0LimXtiq66uPbeewMrCgpKlS6d5P0jv++8WZoDApNyf669vJIzJfckO+TJmZkz7u7uAtLzLyzatWtnYmKyevVqxq8CBRBJtd6wytPTc/To0VZWVrq6uvHx8RkZGXFxcTXOuXjxYjMzsyZLCCCO1qxZE2A1xdbWlukgwsfd3f3Nm02XL1/GCaQAja7WYaS5XO7KlSv37t375cuXkpISFoslUcthyytXrmDId4BGlJGRoaOjs33yiT59+jCd5ecYH0a6usjIyFsstQsXLjAdBEDU1Hp4gsVizZkz5927d8XFxU5OTgsWLKioBSoGgMa1adMmH6MgR0dHpoP83INXt0PWbOdwBOtYQK9evS5evPjs2TOmgwCIGlrnPPv5+dnY2DR1FAAghBQVFW3fvt3Ozk5RUZHpLD/34fPbqH8ucbhM3nuium7dus3qYbh+/XqmgwCIGlpFw+TJk3ndpNnZ2bdu3bp48WJcXByH0VvUAIiqPXv2OKkPxLjRv0JSUtLFxeXIkSMpKSlMZwEQKXSvrs7IyBg0aJCampqjo2Pv3r07d+6so6Nz8ODBJg0HIG64XO6WLVu6dOmCcaN/kZOT0wQzja1btzIdBECk1Hr1BL/i4mIXF5fXr19bWVnZ29u3aNHi06dPp06dCggIqKysHDVqVFOnBBATZ86cMSWOvXr1YjqI0JOVlbWzs5uwffvcuXOF4kAPgFCgVTTs3r379evXmzdvDg8PpxrT09NdXFxmzpwZEBBQ24UVAFAv69ev76rjYWBgwHQQUeDm5jbk+vV9+/aFhYUxnQVARNR6ySW/gQMHfvny5eHDh1Xaz549269fv7i4OIzTAPDrnjx5Ym1tfXbNw27dujGdha6y8lL5kqOqSgL6U3779u1/PPj4/v17SUlJprMAiAJaPQT5+fkdOnSo3s5rzM/Pb+RQAGJp3bp1Y3vMsrS0ZDpIPchIswW2YiCEuLu795LJ/+eff5gOAiAiaBUN7dq1e/jwYVlZWZX2mzdv8qY2fi4AMfP169fjx4+7ubnhN3Ej0tfX19PTw7WXAI2FVtHg7++flJQ0YsSIb9++8Vq4XG5MTMyMGTO6devWvn37pkwIIBY2bdo00GyCg4MD00FEjZub282bN6sfXQWABqB1TgMhZOzYsXv27JGSktLR0VFWVv78+XN2draKisr169ctLCyaOiWAaPvx44e2tvbS4dsHDRrEdJZ6E8BhpPlxOJzff/89SdsSl4gD/Dq6Vz3s3r37r7/+srOzy87OfvPmjYqKSmho6PPnz1ExAPy66OhoF83BLi4uTAepty+picev3xXkW0pKSEi4uLgcO3bsy5cvTGcBEHp0exoAoIlUVlYaGxuP7TErODiY6Sz1diB219Q/gsuvHpMS4FMxSkpKZs+eLdNr6OrVq5nOAiDcML4CAMNOnTplLu2GcaObjqysrL29/Y4dO3CpF8AvQtEAwLANGzbo6enp6ekxHUSUubq6Dm8nu2fPHqaDAAg3FA0ATHr48OHt27fd3d2ZDiLiWrVqZWlpuWnTpsrKSqazAAgxFA0ATFq7dm2Q/ZwuXbowHUT08QZ6OnXqFNNBAIQYigYAxnz+/PnkyZNubm7Ce/cWRyu3Y4unSQpDfl1dXX19/XXr1jEdBECICcGuDiCqNm3aNKjTRHt7e6aDNJx2mw6DnO1YLBbTQWjp1avX3bt379+/z3QQAGFFq2hYvnz53r17mzgJgHgpKCiIjo52dHSUlZVlOou46NKly7yephhVGqDBaBUN169f//r1a1NHARArO3fu7KU1TBgHdBJevIGeTpw48enTJ6azAAglWkVDhw4dUlJSmjoKgPiorKzcsmWLlZVVy5Ytmc4iXuzt7X+z0Przzz+ZDgIglGgVDSNHjjx69GhiYmJTpwEQEzExMV3Y7r169WI6yK8qKy/NKShkOkU9yMrKOjg47Ny5My8vj+ksAMJHis5MZWVl1tbWFhYWgYGBZmZmysrK/FNdXV3V1dWbJh6AaNqwYYOjob+uri7TQX7VsQv7BX8Y6Src3NwCrlyJjo6eOnUq01kAhAytomHJkiU3btwghGzatKn61GvXrqFoAKDvzp079+/fX7JxG9NBxJSKioqlpWXkpk2TJk2SkqL1NxAAeGjtMIsXL87MzKxtqpmZWePlARB969atC3aYa25uznQQ8eXu7v7o0aMTJ04MGTKE6SwAwoRW0eDk5NTUOQDERGJi4unTpw8v+E14B3QSATo6OoaGhmvWrEHRAFAv9fizVVFRcefOnUOHDr169arpAgGItg0bNgy1+K1Hjx5MBxF3vXr1evz48Z07d5gOAiBM6BYNt2/fNjQ0dHBwGDFixNmzZ3mNnTt3HjlyZJNlAxA1OTk5e/bscXR0ZLPZTGdpHIY6JuO93SVYwtdrYmFhMd+xI0aVBqgXWrv6ly9f+vTpw2KxtmzZYmxsTLWPHDkyNja2oqKiyeIBiJTt27d76owUpQGdund22DE9VEJCOIaR5sdisdzc3E6dOvX27VumswAIDVpFw44dO1gs1t27d8PCwlRUVKj2bt265ebmYtwnADpKS0s3b95sY2PDvxMBg+zs7KZY6dR4URgA1IhW0fD8+XMXFxdNTc0q7e3atSOEpKWlNX4uAJFz8ODBHir93d3dmQ4C/0dGRsbJyWnPnj3p6elMZwEQDrSKBi6XW+PVzLxyAbfbAfgpLpe7bt06MzOztm3bMp0F/j9nZ+dxhqrbt29nOgiAcKBVNBgbG9+/f7+0tLRK+8mTJ2VkZPjPcgCAGv37779GlfYeHh5MB4H/UFJS6tGjx+bNm3/8+MF0FgAhQKtoGD169Ldv30JDQ4uKingtlZWVGzdu3LJly5AhQ+Tk5JoyIYAoWLNmjY6OjuhV2OdunbIeP7OSw2E6SMO5u7sPVJfYv38/00EAhACLy+XSmW/VqlWzZ8/m1Qeqqqp5eXk/fvwwMDC4e/eumppaE4cEEG5PnjyxtrY+u+Zht27dmM7SyA7E7hK6e09Ut23btjUPP71//15SmLcCoBnQvbp61qxZFy5ccHFxUVBQyMnJadOmzfTp0x88eICKAeCn1qxZE2Q/x8rKiukgUDMPD49eMvlnzpxhOgiAoKvHzVo8PDxwRBagvpKSkmJiYvbPPY9xowWWvr6+vr7+ypUr/fz8mM4CINDwVwygaW3YsGGIebiDgwPTQaAu7u7uDx8+vHv3LtNBAARaPXoakpKSDhw48OrVq+Li4rZt23p4ePj4+OAQIEAdcnJyoqOj1wXvF5lxo6tQUlDWa6vBYgnfiJBVdOnSZb5jxzVr1pw8eZLpLACCi+6JkNu2bYuIiCgrK+NvtLGxiY2NxWkNALVZvnz588Nfly1bJsKjQKoREfmWvXHjhuuiLfHx8SYmJkxnARBQtA5P3Lp1a+LEiYaGhjExMcnJyXl5eS9evIiIiHj06NGIESOaOiKAkCopKdmyZQvGjRYWvFGlN2zYwHQQAMFF6/BEdHS0mpratWvXqE4Fc3Pz9evXKysrL1my5MuXL9ra2k0ZEkAo7du3D+NGCxFpaWlnZ+eBm/YuWLAAA3cC1IhWT0Nqaqqzs3P1wxCDBw8mhHz79q3xcwEIucrKyrVr13bp0gVfP0LExcUlxFRt48aNTAcBEFC0igYdHZ2cnJzq7Tk5OSwWC90MANUdO3ask6QLrlIWLgoKCj179ty2bVtubi7TWQAEEa2iISQk5Pbt29euXeNvLC8vX7x4sa+vL35IAVS3Zs0aExMTPT09poM0rWdvH83ecYDDoXU+tVDw8PAYpau4detWpoMACKJaz2m4evVqRkYG9dTf39/d3X3QoEG2trbKyspJSUn79+8vKChYvnx5RkaGurp6s6QFEA7//vvv06dP148/wHSQJhef8GLVob8jg4ZJEBG5+lpZWdnW1jZi3brJkycrKCgwHQdAsNR6yaWzs/ONGzforOLatWvOzs6NGQpAyPXs2dNSrve8efNEYACDuonGvSeqyMzMXLBggdmE38PDw5nOAiBYau1pWLx4cWZmJp1VmJmZNV4eAKF3//7927dv/75hk8hXDKJKTU3N0tJy6erVISEh0tLSTMcBECC1Fg1OTk7NmQNAZERGRoY6/d6lSxemg0DDeXl5PXny5MiRIyNHjmQ6C4AAwb0nABrTy5cv//33X09PT3QzCLV27dp17NhxxYoVHA6H6SwAAoTuMNKEkMTExBcvXqSmplZWVvK3+/n5aWlpNUE2AOEzfPhwuSTtyMhIKal63NhFeBUWFXBzD3Rooy56RdL79+/Xrl3bZ+UuHx8fprMACApaf9fKysrGjx+/f//+GovuTp06oWgAIIQkJiYeP358/9zzYlIxEEIU5ZXU5DWYTtEkjIyM9PX1ly9fjqIBgELrT9vGjRv37dvn5eU1cuRIDQ0NCYn/HNTAsVsAnj/++GNYl0m4C7bI8PT0nPXb4mvXrrm4uDCdBUAg0Do80adPn8+fP7969apKuQAAlK9fvxoYGGybFNO3b1+mszQrkbnLZXVcLnfp0qU3uK2uXLnCdBYAgUCrCJCTkzM0NETFAFCHVatWDTANwU9SUcJisfr27Xv16tWbN28ynQVAINCqA9zd3Z88eVJaWtrUaQCEVGpq6q5du3r16iUvL890lmaVmvX18pOX9M+nFjqWlpaL3SyWLl3KdBAAgUCraAgKCurUqVNAQMDXr1+bOhCAMFq5cqWfyXhXV1emgzS3K/fPuU9dXCm61yWyWKw+ffpcvnz51q1bTGcBYB6tokFKSuqPP/64fv16u3btWrVq1e6/7t2719QpAQRZWlrazp073dzcxK2bQUxYWVktdrOIjIxkOggA82hdPREfH29nZ1dQUKCtrV396gk2m9002QCEw6pVq3yNg93c3JgOAk2CxWJ5eXktnL7y9u3buDQGxBytomHDhg3l5eVXr17FSV4AVaSlpUVFRW2acBjdDCLM2tp6kat5ZGTk+fPnmc4CwCRahyeSk5NdXFxQMQBUt3r1ah+jIHQziDZeZ8OFCxfu3LnDdBYAJtEqGnR1dYuKipo6CoDQSU9P37Fjh6urq4KCAtNZmNHd3GHH9FAJluhfj21tbb3AudOyZcuYDgLAJFq7+oQJEx4/fvz48eOmTgMgXFavXt3fcJw4dzMYapuM93aXkBC1G09UJyEh0adPn3Pnzj148IDpLACMoXVOw/fv34cMGeLo6BgcHNylSxc5OTn+qa6ururq6k0TD0BwZWVlRUVFrR9/QFFRkeks0By6deu2wLnTkiVLzp49y3QWAGbQGkba2dn5xo0btU29du2as7NzY4YCEAYzZsxIuVS6bNkyJSUlprMwSYSHka7uwYMHtjNXP3jwwMbGhuksAAygVTTcuHEjMzOztqlOTk5qamqNmgpA0H379s3Q0HBDyEFfX1+mszBMrIoGDoezaNGiB2ytCxcuMJ0FgAG0igYAqCI0NLTwkVxkZKTYngJJEauigRDy+PHjbtNWXL58WZzPZQGxJfrnPAM0ug8fPuzevdvT0xMVw4HYXSwn/4rKSqaDNB8rK6sVXt3mzJmDX1wghmgVDWVlZSW144jusPMANZo/f/4IywiMXCKeWCxW//79Hz16dPKkeHWxABCaRYOHh4dc7XDTWBArL168iImJ8fb2lpGRYToLMKNjx47rfB3mzp1bUVHBdBaAZkXrkktPT09dXV3qKZfLTUlJuXfvnqqqqru7u6amZlOlAxA8M2bMCHaYa2dnx3QQYNKAAQPevVuxb9++cePGMZ0FoPnQKhpmz55dvTEpKalXr17du3c3MTFp7FQAAurGjRuXLl36d+1ySUlJprMAk3R0dLp27bpgwYJhw4bhtiMgPhp+IqSurm5kZOSsWbMwwjSIj9mzZ//Wa4mVlRXTQQSFTlu9Qc52LJbojwhZna+vb3+Viq1btzIdBKD5/NLVE6ampgUFBa9fv26sNACC7MSJE/fv3x84cKB4fkfWqKel67HF0yQlxPE6LA0NDTs7u+XLl+fk5DCdBaCZ/NKu/vTpU0KIlBStYxwAQq2ysnLBggWz+q8xNTVlOgsICm9v7xHt5dauXct0EIBmQuv7/tWrV3l5efwtP378ePz48erVq9u0adOpU6emyQYgQPbs2WPMcfDz82M6CAgQFRUVZ2fn0Rs2hIeH45RwEAe/dO8JHR2dffv2OTk5NUEwAAHy48cPExOT8T3nhYaGMp1F4IjbiJBV/PjxY968eSU2fbZv3850FoAmR6toiImJSUlJ4W9RUlLS1dXt2bMnLlUHcbBgwYK3f39fsGCBhoYG01kEjpgXDYSQixcveq3Y+fDhQ5whCyIP954A+Ink5GQTE5N1wfsHDBjAdBaBc+XBueijs8+v+V08z4XkqaioWLJkybVK1Zs3b+IkWRBt4rufA9A0bdq0QZ0menl5MR1EEKVmfr385KWY//aQkpIaOHDg7du3T5w4wXQWgKaFogGgLnfu3ImJifHz85OTk2M6Cwguc3PzTQOdp06dinFrQLTVVTQ4OzuzaLh+/XpzpQVoVhwOZ/LkyZPcl/bo0YPpLCDoBg0a1E+5DJdfgmir65LLzp0713E7lrKyssePH4t5tySItp07d+oUWQ+eMBgHquGnNDU1XVxcRq9YMXr0aG1tbabjADSJuoqGzZs319jO5XKPHTs2d+5cLpdrZGSko6PTNNkAmJSfn79o0aKI3ssMDQ2ZziK42DJsVSVFplMICm9v77CHD+fOnXvgwAGmswA0iXqf03Dz5k1bW9uhQ4cWFhb++eef8fHxHTp0aIpkAMxatGiRo5q/r68v00EE2iCPkdmx+6Rw+y5CCCGysrLe3t6HDh26desW01kAmkQ9ioY3b974+Pg4OTnFxcXNnz8/ISFh4sSJGEMaRNLbt2+3bNni5eXVsmVLprOAMOnZs2ekh2VERASHw2E6C0Djo1U0pKWlhYaGdu7c+ezZs+PGjfvw4cPSpUuVlJSaOhwAU6ZMmTLaZrqHhwfTQUDIsFisgQMHWhd92bt3L9NZABrfT4qGHz9+LF682MDAYMeOHb17937x4sWuXbvatm3bPOEAGHHkyJHz588PGjRIWlqa6SwgfIyMjLp16zZz5syMjAymswA0srqKhkOHDhkYGCxatMjExOTq1atnz541MzNrtmQAjMjOzo6IiFgwaHPXrl2ZzgLCaujQoaM7KE2ZMoXpIACNrK5hpHn3qdLS0vLx8anjkrPJkyfj9HIQGYGBgaXPlRctWqSqqsp0FiHw+uPL+w/+mDHUV0ICV6X+x507dxzmrjtz5oy3tzfTWQAazc+Lhp+u4tq1a87Ozo0ZCoAh169fd3V1Pbb4Gu7dStOB2F1T/wguv3oMF1BUweVyN27cuP7J5/j4eEVFXJUKIqKuax927dpVWFj401UYGBg0Xh4AxhQXFwcHB0/1WuHo6Mh0FhB6LBZr+PDhCQlLfv/99/Xr1zMdB6Bx1FU0oBoAsbJ48eKush6jRo3C+I/QKNTV1fv27dtv9abBgwdjJHIQDbhhFQAhhLx8+XLdunVeXl5t2rRhOguIjt69ey92swgJCSkvL2c6C0AjQNEAQDgcTkhIyDi72b1792Y6C4gUCQmJgIAA+8p03MgKRAOKBgCyYcOGtnkWo0ePxsAM9eXrOuTj4a2SEvhLUitdXV0XF5clS5Z8+PCB6SwAvwq7Ooi7169fz58/38nJSV9fn+kswkdRXkmvrQbOAqmbj49PhKX26NGj67hvMIBQQNEAYq20tHTEiBGjuk3z8/NjOguILDabPXbsWIu8j5GRkUxnAfglKBpArM2dO1e/zHbs2LGysrJMZwFRZmBg4OnpuXTpUjqD3wAILBQNIL4uXbq0YcMGX19f3N4dmoG3t/dyT+vAwMD8/HymswA0EIoGEFNZWVljxoyZ3neVu7s701mEWHZe1pN3H+sYWBYoEhIS48aN825RHhYWxnQWgAZC0QBiauLEiS6ag8eOHSuBM/9/wb+3TlmPn1nJ4TAdRDi0bt16yJAhBw4cOHToENNZABoCfy5BHO3YseP48eMjRoxo2bIl01lAvNjZ2e0c6TVx4sSkpCSmswDUG4oGEDsJCQnTp09fM26ftbU101lAHA0fPnxiJ82AgIDKykqmswDUD4oGEC/FxcXwGWmuAAAgAElEQVSDBw8eYRkxZMgQprOAmJKXlx8zZkyn7PeLFy9mOgtA/aBoAPESHBxsWGEXHBzMZrOZzgLiy8jIqF+/fpGRkadOnWI6C0A9oGgAMfLHH38cPHhw+PDh2traTGcREd3NHXZMD5Vg4S9JvfXt23f7cI+RI0fGxcUxnQWALhaulQIxcfnyZS8vr80Tj/j7+zOdRaSokZNMRxBWpaWlK1eu3JdY8PDhw9atWzMdB+Dn8PsAxEJSUtKwYcNmev+B4aJBcLDZ7LCwML9W3OHDh+OkSBAKKBpA9BUWFnp7e/saBwcFBWFUBhAorVu3DgoK0v/6Yu7cuUxnAfg5/AEFEcflcseNG9dJ0iU8PFxRUZHpOABVmZqa+vr6rl69+vDhw0xnAfgJFA0g4pYuXVrxSjUwMLBt27ZMZwGomYeHx86RXkFBQU+fPmU6C0BdUDSAKIuOjl60aFHfvn27du3KdBbRdPzi/pb9RlfgePyvYbFYo0aNmtfTtG/fvgkJCUzHAagVigYQWadPnw4NDV01Zne/fv2YziKySstKcwoKmU4hCmRkZH777bfxJq1dXV2/fPnCdByAmqFoANF07dq1oUOH/j5w06hRo1gsFtNxAH5OSUkpIiIiQFu+T58+2dnZTMcBqAGKBhBBjx498vHxmeK5PDg4GJdLgBBRVVWNiIjwlC/y8vIqLEQXDggc/D0FUfPhw4d+/foF2c+ZOHGilJQU03EA6kddXT08PNy27Juvr29paSnTcQD+A0UDiJSUlBR3d/dBnSZOmjQJd5doBjpt9QY52+EAUOPS1dWdMGGCScbroUOHYtAnECgYRhpER1JSkru7u7PGoJkzZ6qoqDAdR1xgGOkm8vTp0507d/6w6r179270mYGAQE8DiIjXr187ODi4aA6eOnUqKgYQAZaWlmPGjFF4csHf37+kpITpOACEoGgA0fD48WMnJycfo6Dp06fjxj8gMrp37z5hwoT2iY88PT3z8/OZjgOAogGE3/Xr193c3AKspkyfPl1VVZXpOACNydzcfPLkyV3yP7m6umZmZjIdB8QdigYQbv/884+Xl9cE5wVTp07FrSVAJBkaGk6dOtVZItvR0TE5OZnpOCDWUDSAENu9e7efn99cv/WTJ0+WlZVlOo44uvX06uCFays5HKaDiDhtbe3p06f7qFb27Nnz3bt3TMcB8YWiAYRSRUVFRETEuHHjVo3ZPX78eJxbzpTP3z4dv34XF2E1A01NzZkzZ47uoNS9e/czZ84wHQfEFIoGED5ZWVmenp4pl0r3zz0/atQojPkIYqJly5Zz5sxZ2cfG19d39uzZHHTwQLPDX1sQMk+fPrW2tm6bZzF58uTevXszHQegWcnKyoaEhJybE5x7Zne/fv1ycnKYTgTiBUUDCJO//vrLwcHB1zh47ty5JiYmTMcBYACLxerdu3d4eLhxeny3bt1evnzJdCIQIygaQDjk5+ePHTt29OjRywKiZs2a1apVK6YTASGEsGXYqkq4aIUBnTp1mjNnzihdRTs7uz179jAdB8QFhpEGIXDx4sXg4GA7VZ/Bgwc7ODgwHQf+A8NIM6i0tPTQoUP3799P7tAtKiqqXbt2TCcCEYeeBhBo+fn5ISEhnp6egzuHzZs3DxUDAD82mx0YGPjbb791zvlgZmYWFRWF34HQpNDTAILrwoUL48eP76HSv1+/fh4eHriVomBCT4MgKCoqOnny5Midf/fu3TsqKkpbW5vpRCCa0NMAgujbt29jxozx8vIa3nXywoULe/fujYoBoA7y8vIBAQHPNy0wz00wNzffvHlzeXk506FABKGnAQRLYWHh6tWr161b52cy3sfHx9HREeWCgENPg0ApKio6fvz4vXv3rlaorFixwt/fn+lEIFJQNICg4HA4Bw4cmDVrlkMrPzs7Ox8fHyUlJaZDwU98+PI27sXGoL69JCRQ2wmQ1NTUEydO/Hb8mo2NzZo1a3r27Ml0IhARKBqAeRwO5/Tp0/PmzTMljtbW1n5+friiUlgciN019Y/g8qvHpCQlmc4CVcXFxZ08eXLR1Zf+/v5LliwxNTVlOhEIPYzYD0wqKSn566+/1q1b15Hl1N9onL+/v66uLtOhAEREp06dOnbs6OZ278yZM2ZmZn379p0+fbqTkxPTuUCIoWgAZmRlZUVHR2/atMm2hfdQi99cXV3xMwig0UlISNjb23fv3t3v8eMLFy44Ozt36dJlypQpw4cPx23eoAFweAKaFZfLvXv37r59+w4cOOBtMLZHjx69evXS0NBgOhc0EA5PCBEulxsXF3fp0qV3795dLFEcP358QEBA+/btmc4FwgSVJjSTT58+7d+/f//+/R8/fhzbY9af4cecnJxwqiNAs2GxWJ07d+7cufPnz5+7X7ny9PBm3fnznZ2dR40aNWDAAOyMQAd6GqBpffny5ezZs4cOHbpz584Q8/CuXbva2tqamJjgQkrRkJ2XVZi2x9JIDx+o0CkuLn769Om9e/cSEhL2f/7h5+c3aNCgXr16KSgoMB0NBBeKBmh8lZWV9+/fP3v27NmzZ1++fOnfMdTY2NjW1rZr165sNpvpdNDIME6DsPv+/fv9+/cfPHiQnp6+L7HA2dm5b9++ffv21dPTYzoaCBwUDdA4KisrX716dfv27du3b1++fPn79+8BVlM6duxobm5uZmaG3y4iDEWDyEhLS3v58mVcXFxCQsK2+HRTU1M3Nzd7e3sHBwfcCgt4UDRAw2VmZj5//vzevXt37ty5d+9eQUGBf8dQbW1tExOTzp076+npSUhgnHLRh6JB9BQXF8fHx8fFxb1///779+9Rb7O0tbUdHBzs7e1tbGzMzMzk5OSYzgjMQNEAdP348SMhISE+Pv7FixcvXrx4+fJlamqqn8l4WVlZfX19PT09AwODDh064ACEuEHRINpyc3MTEhI+fvz48ePH5OTk7a8zJCUlDQ0Nzc3Nu3TpYm5ubmpqqq2tjQs4xQSKBqiqrKzs27dvKSkpycnJSUlJCQkJCQkJHz58SE1NJYT4mYxXVVXV0tJq165d+/bttbS0NDQ00KMgtgqLCri5Bzq0UceJkOKgtLT069evKf/z9evXkpKSqLdZ0tLSurq6BgYGBgYGhoaGOjo6WlpaWlpampqaTEeGRoaiQbyUlJTk5ubm5eXl5uZm/U96enpmZmZWVlZqaurXr1/T0tIIIX4m4wkhcnJy6urqampq1L9t2rTBCQpAwTgN4ozL5X7//j09PT0jIyMzM5P3l+T79+8VFRVRb7MIITIyMm3btm3Xrp26urqGhkbr1q1bt27N+0uiqqqq8j/41SFE0KHEsIqKioKCgiqNBQUFFRUV1NP8/PzKykre47y8PA6HQwjJzc3lcrmVlZX5+flUO68xJyenvLy8sLAwPz+/qKioqKgoNze3sLAwNze3tLSUtx5eTUAIkZGRUVRUVFU2aq9o6dylhaqLqoqKiqqqasuWLVu0aIH6AABqw2KxeHWAmZkZ1cjhcPLz82dlZ+fl5eXk5OTk5OTl5eXmphe+/1j4tPBNQUE8h0MI4VUVPEpKSioqKoqKivLy8ioqKnJycnJycqqqqjIyMgoKCmw2W15envevpKSksrIyIYT3lP8BIURWVpb/ZAveUvyBJSQkWrRo0ZRviegTyqIhMTHRysqK6RS0FBYWNuJd7alv+urYbLakpCTvX1nZdgpSUm3k5GRUZGTUZWRlZdlsNm93kpeXl/sfJSUlRUVFGRmZxooHACAhIcHrP6hthh8/fhQWFs4uKiouLi7i+7ekpKSsrKykpKS0tLCsNLv086eysrLy8vKisrL8ioqysjL+n1JV8Jcgv0hBQUEo/ipaWFhcu3at+V9XKIsGZWXl8eNr/frkEZyKksVi1bH/EELk5ORkZWX5W6SkpPhHZ+Mvn6mZqf+zW7Rogc49YIrSe0IIIT39CM6DA3oUCGlwB2ZJSUlxcTHh+z3G64jlfR9QHbGEEA6Hk5eXV2Vx/l7b6mrs92UE1YVcBy0treYJU4VQ7uetWrVauXIl0ykAAKBZycrK8n41qaqqMp1FTOEXKgA0nI2NzcqVK9HXBSAmcPUEAAAA0ILfBwAAAEALigYAAACgBUUDAAAA0IKiAQAAAGhB0QAADXfs2DF9ff06rn0HAFGCogEAGi4/P//Tp0+4CAtATKBoAAAAAFpQNAAAAAAtKBoAAACAliYcEbJHjx7v3r1rijVXVFSwWCxJScmmWDk0g4qKCgkJCYw9LLyoT7C8vLy0tFRRUZHpRFA/2AeFXZN+grNmzZo1a1aNk5rwhlVBQUFZWY12u1J+69atMzQ09Pb2boqVQzNYs2aNqalp3759mQ4CDbR69erOnTt7eXkxHQQaaMWKFVZWVh4eHkwHgQZatmxZt27devXq1RQrt7e3r22SUN57omPHjh4eHhs2bGA6CDSQkZFR//7916xZw3QQaCA9Pb3BgwfjZrPCS1tbe9SoUZGRkUwHgQZq27bt+PHjFy1a1Myvi74pAAAAoAVFAwAAANDShOc0NJ3169draGgwnQIabuPGjW3btmU6BTTcli1b2rdvz3QKaLht27bp6OgwnQIabseOHXp6es3/ukJ5TgMAAAA0PxyeAAAAAFpQNAAAAAAtAn1OQ3FxcfULwU+ePNmyZcvaFjl//vz+/fuzs7PNzc2nTZumrq7exBmhLqWlpRcvXrx48eKnT59UVVXt7OyCgoJkZGRqnLkBHzc0qXrtTdj1BAp2PWEnsF9/At3TUFlZeePGDQkJiU58pKWla5t/8+bNXl5eOTk5pqamhw4dsra2Tk9Pb87AUEVkZKSPj8/9+/fV1dUzMjLCw8Otra3z8/NrnLm+Hzc0qXrtTdj1BA12PWEnuF9/XAFWUFBACNm5cyedmTMyMuTl5QMDA3lPU1JSlJSUQkNDmzIg/MTly5ffv39PPT1y5AghZPny5TXOXK+PG5pUvfYm7HoCCLuesBPYrz+B7mmolzNnzhQVFU2ZMoX3VEtLa+DAgUePHuVwOMwGE2dubm6GhobU0yFDhigrK8fFxTEYCeio196EXU8AYdcTK825DwpB0RAVFWVra+vq6rpo0aLs7OzaZnv27JmsrGynTp2oFmtr65ycnMTExGaJCT+XnZ1dVFRU9wgNND9uaFL12puw6wk+7HpCSgC//gS9aGjbtm3Hjh2dnJxUVFQiIyOtrKxSU1NrnDMtLU1NTY3FYlEtvAGgcGxVcPz+++8sFisoKKi2Geh/3NCk6rU3YdcTfNj1hJFgfv0J9NUTCgoKycnJ1K0/r1y54uHhsXDhwqioqOozV1RUSEn9Z3N4T8vLy5shKvzU7t27t27dumHDBmNj4xpnqNfHDU2qXnsTdj0Bh11PGAns158AFQ22trYVFRW8x4cOHTIyMmKxWPylk5ubW8+ePS9fvlzj4kpKSoWFhfwtvKdKSkpNFhn+o/onSE2KiYkZP3787NmzJ0+eXNvi9fq4oUnVa2/CrifIsOsJKYH9+hOgoqF///7UWRstWrSocR5VVdW3b9/WOMnAwODAgQN5eXnUsgkJCYQQfX39JggLNajtE4yNjR0+fHhoaOiKFSvqtcI6Pm5oUvXam7DrCSzseqJEUL7+muKSjCaSl5enoaHh6OhY49SbN28SQg4dOkS12NjYWFtbN1c6qNmlS5fYbPbo0aM5HE69Fqz744YmVa+9CbueYMKuJ0oE5+tPoIuG7du3r1279uHDh4mJiZcuXbKzsyOE/P3337ypBQUFTk5OGzZsoObv3r17+/btHz58mJeXt2TJEkLI0aNHGcoOXC6Xe/v2bXl5eXd397S0tMz/ycvL402t8gnW/XFDM6tjb8KuJ/iw6wk7gf36E+iiYePGjWw2m+oU0dDQ2L17NzU1JyeHEBIWFka1fPnyxcbGhjezjIxMZGQkE6nh/wsJCaneueXm5sabWuUTrPvjhmZWx96EXU/wYdcTdgL79Sfot8YuLS39+PFjTk6OqqqqsbGxpKQkNamiouL27dtaWlr8Y5gQQt69e5eTk2NiYqKiotLseeE/3r9//+3btyqNqqqqFhYWpKZPsI6PGxhR496EXU/wYdcTAYL59SfoRQMAAAAICEEf3AkAAAAEBIoGAAAAoAVFAwAAANCCogEAAABoQdEAIOgSExOvX79eVFTEdJCqBDYYADQRARpGGkBUpaSk8EZ1JYSwWCwlJSVDQ0P6w8JHR0cvW7bszZs3JiYmTZaxIRolGIfDSUtLS05OlpeXb9OmTevWrRsxIQA0LhQNAE0uJiZmypQp/C1SUlIDBgz4888/hfo7sn379t27d5eTk2vY4lwud+vWratWrUpOTqYa9fT0IiIifvvtt0bKCACNCUUDQDOZP3++j48Pl8tNSUn5888/jx07lpaWduPGDaZzNVxISEiNIw/SFBkZuWDBAgsLi8jISGNj46KiooSEhNjY2BcvXjRiSABoRCgaAJqJjo6OtbU1IaRbt259+/Y1MjK6efPmp0+f9PT0CCFcLvfdu3fZ2dlt27bV1dWtbSVv3rxJT0/v0aMH/xCzhJD4+PjMzEwHBwcpqf/bqYuLi+Pi4mRkZMzMzKSkpB48eCAlJWVlZVVlhcnJySkpKYqKih07duQfcu7Dhw9fv361s7OTkZH58OFDVlZWhw4dNDU1+ZdNTEz8/PmzjY2NvLw8f3tqaurnz5/l5eU7dOhQx1GYzZs3Kyoq3rhxg7o1n4uLS3BwMHWvVJrrrOOt498K3iCJ+vr67du3r3vbAaBWTTQ8NQBQ1q9fTwjZuXMnf2Pv3r0JIXfv3uVyuceOHWvXrh21V1pYWDx8+JCac968eYSQN2/ecLncHTt2EEIOHDjAv6ry8nJNTc3OnTtTLX/++aeioiJvbZqamrGxsfr6+lZWVvxL3b17l7+GaN269a5du6ipYWFhhJB///23c+fOMjIy0tLSLBaL941eYzBqnd26daPWKS0tPXHixBrfEw6HIy0tbWBg8NN3r+511v3W8bbi77//NjQ0ZLFYEhISx44d++m2A0BtcPUEAAOys7OfPHlCCNHV1T19+vTgwYPl5OROnz79+vXrXbt2JSUlubq6vnv3rvqCI0aMUFZWjoqK4m88c+ZMWlra+PHjeU+PHj0aFhZmbGx8+fLlt2/fLliwYNSoURkZGfyLPHr0yMXFpaysLCYm5u3bt7du3erevXtQUNCRI0f4ZwsMDFywYEFhYeGPHz9mzpy5c+fOQ4cO1bZRd+/edXZ2/vTp07Zt2168ePHs2bNdu3ZRvQhVsFgsa2vrhISEFStWFBQUNGydNN+6cePGzZw5My8vr7i4uFevXjS3HQBqwHTVAiD6eD0NgYGBe/bs2b1799KlS/X19QkhI0aM4HK5JiYmbDY7OTmZmv+ff/4hhAwfPpz3tMoPet6v57dv31Lze3p6ysvL5+bm8p4aGhoqKyvn5ORQM/CKDP6ehh49eqirq2dmZlItZWVlpqamJiYm/K+ydu1a/hlUVVU9PT2plirBrKysJCUlX758SfNtefHiBe94h6SkZJcuXUaPHh0dHU1tBZ11/vSt423FvHnz+Jf66bYDQG1QNAA0OV7RQGGxWIaGhkuXLi0tLf3y5QshxNfXt8oiWlparVq14j2u8t386tUrQsj06dN5Tz9//iwhITFmzBjqKSEkICCAf20lJSUSEhJU0ZCenk4IcXZ2PvdfXl5ehBBetcH7uq3ybW1lZcX/zcof7OvXr4SQPn361OudKSws3L179/Dhw42NjVksFiFERUXln3/+4U2te5103jreVjx48ICaSmfbAaA2OBESoJls2rRp5MiRLBZLXl5eWlqa18i7f3GHDh2qzKynp3fr1q2ysjIZGZkqkzp16uTg4LBv375ly5bJyMjs2rWLw+FQVzGkpqYSQrS1tfkXYbPZGhoa1FNeYXHr1q179+5VWTmbzc7Pz6fuq6umpsY/VVZWNi0trcatS0lJIYQYGRnV+R5UpaCgEBgYGBgYSAjJysrau3fvnDlzAgIC3r59q6mpWfc66b91Wlpa1FT62w4A1eGcBoBmIicnp6Ki0qJFC6pi4DUSQvLz86vMnJeXJykpyT8nv9DQ0MzMzFOnTlVWVu7Zs6dz5862tra8SbKysoSQ6mcJ8LfwrrwYM2ZMSU2qFBz0t44Qkpub24BleVq3bj19+vSxY8fm5eXdvHnzp+uk/9bxXxnRFNsOID5QNAAwycjIiM1m379/n8vlUo1ZWVnv37/v3Lkzr8e+uoEDB7Zu3ToqKurff/9NSUnhHyzByMhIRkbm0aNH/PO/efOmsLCQempsbKygoHD16tWysrLG2hBjY2NFRcVbt26Vl5f/ynqUlZXJ/0qButfZsLeuKbYdQHygaABgkqys7IgRI+Lj46nzHsrLyyMiIkpKSoKCgmpbis1mjx079urVq4sWLZKXlw8ICKAmycnJDR48+P79+wcPHuS1lJSUTJs2rcriYWFhiYmJ4eHhJSUlVHtWVta5c+catiEyMjKhoaEfP36cPn06/3f8x48fa1tkwoQJz58/52959uzZvn37JCQkHB0df7rOBr91jb7tAGKE6ZMqAERfjeM0ULKzs7t27UoIsbS0HDJkCO/CisGDB1dWVvJmqD4cApfL/fjxI+/HdGBgYJUVpqWl8VbSs2fPYcOG6erquri46OjodOvWjZqnrKxs8ODBhBBNTc1+/foNGTKke/fuMjIybm5uvBl4pxCmpqbyr9ne3l5LS4t6WiVYSUlJnz59CCHa2tp+fn4DBgwwMDCwt7ev7W3hnXOgpaXl7u7er1+/Ll26EEIkJSX5L9moe50/fetq3IqfbjsA1AY9DQBNrl27dk5OTm3atKlxqqqq6r1793bs2KGvr5+dnW1vb3/y5MkjR45ISPzf7tmhQwcnJ6cqoy7q6em5uLgQQqoP5KyhofH48ePFixcrKioWFRVFREScP38+JyeHfyBFaWnpo0ePXrlyZeDAgRUVFYWFhebm5tu3bz98+DBvBkNDQycnpyqnYXbt2rVHjx7U0yrB2Gx2bGzsqVOn3NzceEdDBg0atHnz5treltTU1CNHjvj7+ysrK5eUlOjr6y9cuDAuLm7q1KnUPHWv86dvXY1b8dNtB4DasLh8hwMBQFhUVlZ26NBBVVWVzp0aPn78aGBgMHHixD///LMZsgGAqEJPA4BQOnjwYHJycnh4ePVJXC732bNn1NMfP35MnjyZEMLrkwcAaDD0NAAImRUrVsTFxf399986OjovXryoPpBDRUWFtLS0lZWVmZlZUVHR3bt3v337NmnSpI0bNzISGABEBnoaAIRMUlJSenr6uHHjLl++XL1iIIRISUnt3bvX0tLy+/fv+fn5ffv2PX/+PCoGAPh16GkAAAAAWtDTAAAAALSgaAAAAABaUDQAAAAALSgaAAAAgBYUDQAAAEALigYAAACgBUUDAAAA0IKiAQAAAGhB0QAAAAC0oGgAAAAAWlA0AAAAAC0oGgAAAIAWFA0AAABAC4oGAAAAoAVFAwAAANCCogEAAABoQdEAAAAAtEgxHQBABG3YsOHUqVPU0ylTpvj4+FSZZ//+/dHR0bzHW7du7dixY/PlE4wAdbt9+/b8+fP5W1gslpycnIaGhqWl5cCBA9u0acM/9Vc2R8DfCgDBgaIBoJFxOJy1a9empKRQLWw2u3rR8Pnz5xs3bvAe5+fnN1++xgvw9evXESNG8B4PGTJkwoQJjRaOkKysLCpeFXv37p01a9bWrVvHjBlDNf7K5jD+WQAICxQNAI3sxo0b/BUDIeTKlStpaWmamppMRWoixcXF1HettbV1M790cHBwjx49jI2Nm/N1AcQczmkAaGQHDx6s0lJZWXnkyBFGwogGT0/PV69ePXnyZM+ePa1ateI1VlRUxMTEUPMMHTr0n/8xMTFhKCmAiENPA0BjKi0tPXHiBO+xg4NDQkJCWloaIeTgwYMRERF1LPj9+/eoqKgHDx5UVlba2NhMnDiR+nbk+fLly5EjR549e/b9+3dpaelWrVrp6upaW1vb2dm1bt2af87i4uJjx47dunXr27dvhJA2bdo4ODgMGTJEXl7+p/nXrVt35swZ3uPjx4+rqanxHs+fP//27du8x9evXyeEeHh45OXlUQsePXr08ePHvMfz5s1zd3en3pCYmJgbN258/fqVw+Ho6Oh4enr2799fQqIev1hatGjRqVMnQoilpeW3b9/mzZvHa09OTqbmuXfvHnVegp6enoqKCu8x/fetCl5nBq/TiMVizZkzx8PDg35mANHEBYDGQ1UMhJDNmzdPnDiRevru3Tv+OZcuXUpN2rlzZ5XT+jQ0NF6+fEnNfOrUKTabXeMuPG3aNP7VXrhwQUNDo/psampq//77b20B7t27x2sMCQmhGlNSUqiZ+c/J4LXUlocQsn//ft48165da9u2bfUZLC0tk5KS6n4n//77b2r+IUOGUO1UZUAImTNnTt2bQ/N9q75saWlp7969qcYVK1bUnRZATODwBEBjoo5NSEhI+Pv7Dxo0qPqk6sLDw1NTU1ksFtWSnp7u5+dXVFRECKmsrAwJCSktLSWESElJde3a1draWl1dvfp67t275+3tnZ6eTmWgftBnZmb6+vrevXuX/rbw56lORUVFSUmJespms1v8j4yMDCHk8ePHXl5evN4OFotlZmZmZmbGm/np06ceHh4FBQX0w1Bu3rxJPXZ0dKxjTvrvWxUVFRWDBw++cOEC7+m6detmz57dgKgAogdFA0CjycvLO3v2LO+xvb19mzZtHB0dqd/9dRQN0tLSJ06cKC4ujo+Pt7Cw4DV+/Pjxr7/+IoSkpKRQdcDVq1efPn366NGj9PT09PT0qKgo/jMQJ0+eXFZWxns8b968vLy8/Px86sLFsrKyug+RVFH3EYS0tLSnT59ST8PDw3P/Z/DgwRNvgREAACAASURBVISQSZMmlZSUEELk5ORu3rwZFxcXFxdHnYXw/v37DRs20Ezy8uXL2bNnT5061c3Nbd++fbzG/v37e3p61rEU/feNX2VlZUBAwOnTpwkhLBZry5YtU6ZMoZkTQPQx3dUBIDp27dpF7VmbNm3iNfJfiEh1m3P/2yU+a9Ysqp2/M6BPnz5cLrewsJD60R8UFHT9+vW0tLTqr56UlEQtaG5uzj/J3NycmpSYmFg9QI2HJ1JTU6k1VD88weVyP3z4QDVWOUry+fNnalJgYGABH1NTU157p06d6ngz+Q9PVKGoqLhq1aqysjL++atvDs33rcqyVDHBYrGioqLqSAgghtDTANBoqL4EFovl7+/Pe0znCIW9vT31uHv37lJS/3eGMu9bWUFBgTqvcNeuXc7Ozpqami1btuzTp8/evXsrKyt5k16/fk2txMHBobb1v337tiHbVk/x8fHU4z179ijxefPmDZWEy+U2YOWFhYXnz5//6dENmu9bFdTpnJs2bQoODm5APAARhqIBoHF8/fqVGrRAXl5+5syZAQEBAQEBUVFR1DxHjx6tqKioviz/yQESEhLUZQ687n1CyF9//eXj48N/vCAnJ+fcuXOBgYFBQUFVZq6ywipPi4uLaW4Rh8OhHtd3yCM6r1JRUcE7aeOnXFxcrl279tdff/GuoSCEXLt2bfTo0T9dkM77VpsjR47QjAcgPnDJJUDjOHz4MPUt++PHjxo7FTIzMy9evNinT58q7fxHFnJzc6lvaOqsPQ0NjVOnTmVlZd29e/ft27fv378/e/Ys72LOvXv3Lly4UFdXl/+iCf4VEkL4DxbUeG0FRVJSknr848cP3gMOh/Pu3bs6lqqO/1UCAgLGjRtX42yysrJ01qauru7s7EwI8fT07NixY1ZWFiEkNjb2/PnzdZ/WQOd9q7KIs7Mz75rSO3fuDBgw4MyZM7zzOgGAoKcBoLHUcZ7jT2eLiooqLy/nPd62bRvV3r17d94D3oAErVu37t+//8yZM3ft2sW/Ht5RDCsrK6qL4p9//uFdtkAI+fbt2z///MN7LC8vb2lpWUe8li1bUo+p6xSioqKotfHj/zYtLCzkn2Rtba2goMB7/OzZM1tbW+f/0tXVlZaW5q9R6FBTU/v999+pp4sXL657fjrvWxXLly8fNmwY7/GFCxcCAgL4e1wAxBx6GgAawevXr58/f857bGtru2LFiiozjBw5kjdM0OnTp3/8+EF9ofLcu3evR48eHh4enz59OnbsGK+RxWJRP9D19fVtbGy8vb1NTExatGiRnZ29ZcsWanHeEExsNjssLOyPP/4ghBQVFdnY2IwePZrFYu3bt4/qZp8wYULdP+75rykICwu7dOlSTk7OpUuXapxZQ0NDSkqKd8Dl6NGjmpqavOGSJkyYwGazw8PDV61aRQiJj4/v2bPnhAkTdHV1i4qKXr9+ffHixevXr8+fP5//ZAuagoODIyMjMzMzCSH379+/evWqq6trbTPTed+qYLFYe/fuzczMvHz5MiHk+PHjLVq02LlzZ31zAogmps/EBBAFc+fOpfapP//8s/oMkyZNomY4cOAA979n7FtZWVXfN6dOnUotXscvckdHR2q2kpISXjd+bXMWFxdTM9d49URpaam+vn6VBZWVlY2MjGr8o+Hl5VX9hXivUlpaWvcQigsXLqzjLa1tcCcul7ts2TJqkpubWx2bQ/N9q75sfn5+165dqcYq14YAiC0cngD4VVwu99ChQ7zHEhISAwYMqD4PdTEFIeTAgQNVpm7cuHH+/PlU94OCgsLixYvXrFlDzTB69GgTE5Mqoy2pqqqGh4fHxsZSLWw2+9KlS6tXr9bR0eGfs3379itWrLh8+fJPzyGQkZGJjY3t0qUL1dK9e/dbt25R10lWsW3bNuoYSvVVnTt3bvPmzdXvBGFoaBgeHj5kyJC6w9QmLCysRYsWvMdXrlx58OBBbXPSfN+qU1JSOnfunJ6eHu/p2rVrIyMjG5YWQJSwuA265AkAKGVlZdTgCrKysra2ttXn4XA41CkCUlJSDg4Onz9/TkxM5LVYWloqKysXFRW9ffuWw+F07NixxvtEFBYWpqWlff/+ncPhqKmpdejQoY5f0snJydS9J7S1tavPUD0A/9SEhISMjAwtLS1e/REXF8c7/ZAQUr0zIzk5OTk5mRpXytHRscrAUBkZGcnJyeXl5erq6m3atJGTk6stNiUrKysuLo73WF1dvWPHjvxT+fPo6Oh06NChjs356ftW27KpqanUGaAsFsve3p66GhZAPKFoAAAAAFpweAIAAABoQdEAAAAAtKBoAAAAAFpQNAAAAAAtKBoAAACAFhQNAAAAQAuKBgAAAKCluQcqef78+bp16xq27KBBg7y9vRs3DwAAANDU3EVDcnLy/v37TS1MpKXr99Lxz98YGxujaAAAAGAKM0Oirv9rlXqbGu4vVwcXY88mCgMAAAB04JwGAAAAoEUEi4aCgoKtW7eOGDHCw8PD19d37ty5jx49YjpUzaKjo2u8I+Iv2rJly9ChQ+m8aBMFAAAAkSRqRcONGzf09fWnTZuWk5NjYGCgrKx8/PhxGxubGTNmMB2tBomJidTdERtRQkLC/fv36bxoEwUAAACRJFK3ef348aO3t7exsfGpU6e0tLSo9tjY2EuXLjEYTGDNnTt36tSpTKcAAADhIFI9DcuXLy8uLj5+/Dh/xUAI6dev3/r166mn+fn5f/zxh4+PT58+fZYuXZqXl0dN4nXsp6amTps2zcvLKyQk5PXr1/yr+umyycnJ06ZN8/T03LFjByEkMzNz69ato0aN8vLyGj9+/Llz52huC29tnz59+u2333r37j1u3LhXr15RU5csWfLbb7/xzx8YGFjlWtb09HRekirL8jt8+HBQUBD1tKCgYO3atX5+fn369AkPD79z5w7NtAAAIA5EqmiIjY11cnLS1dWtPklC4v+2ND093crKauXKldra2h07dty2bZu9vX1hYSFvakJCwsWLF11dXVVVVUeNGpWcnGxjY/P161f6yzo6OsrKyo4YMcLIyIgQEhMTc+jQIVVVVVtb28LCQl9f3yVLltDZloSEhAsXLri4uOjo6ISGhubk5PTo0ePFixe8qa9fv3727Bn//I8ePXr//j31tLi4uGfPnqmpqdbW1g8ePOBflh//4YnU1NSuXbsuXry4devWVlZW6enpa9asoRMVAADEhOgcnsjLy8vIyDA1Na17tunTp2dlZb18+bJ9+/aEkIiICFNT0/Xr1//++++8GXJycvbt28cbEMLHx0dLSys6OnrBggU0l928efOIESOolwsNDZ0wYQL1dM+ePWFhYdOmTVNQUPjpFuXm5m7cuHHUqFGEEF9fX1tb29mzZ9Psq8jIyIiIiJgzZw4hZM6cOV27dv3pslOmTElPT3/+/Lm+vj6vpby8nM5rAQCAmBCdnobS0lJCCJvNplp4v7N57O3tCSEVFRUnT54MCAjgfesTQtq1a+ft7c3/bSonJ9evXz/eY3l5eVNT04SEBJrLSklJDRkyhD8Vi8V68ODBlClT/P39PT09o6Oji4uL4+Pj6WyRpKQkdREEi8UaPnz4xYsXy8rK6CzLYrGo4xcKCgrjxo2re9ny8vLTp08HBgZSFQMhRFpams5rAQCAmBCdngZVVVVJScn09HSqRV5e3tfXlxASGxv75MkTQkhGRkZRUdHly5c9Pf//UFHv3r3jcrnUU3V1dRaLxb+S/Px8mstqampKSf3nLd2+ffvEiRMHDhxoa2urqKiYnp5+586dgoICOlukpqYmIyNDPdXS0uJwOF++fDEwMKCzrKKiIvVUT0+v7mUzMzNLSkpMTEzoBAMAAPEkOkWDtLS0paXlzZs3ORwO7wyGFi1azJ8/nxCSlpb2/PlzQoicnBwhxNzc3MXFhX9Z/u/X2tBZVlJSsspSkZGRwcHBvJMiCSHXr19fvHgxf51Rh9zcXP6nOTk5vI3ivVCVlfA6WijUmRY8vLqHt2yN5OXlq78iAAAAP9EpGgghISEhQUFB27ZtCwsLq3EGVVVVIyOjioqK0NDQ+q68ActWVlampqZaWFhQLbdu3aL/iiUlJXfv3rWzs+M9vXr1qrq6upqaGiGkbdu2Dx48oOYsKCj48uUL/7JFRUUPHz60sbHhPb1+/Tq1bI1UVFSMjIxiY2Pnzp1LPyEAAIgV0TmngRAyduzY/v37T5o0adKkSY8ePcrMzPz8+fO5c+fu3btHXT2xaNGikydPzpo1Kzk5uaKiIjU19e+//46JiaGz/vouKykp2blz5zNnzlRUVBBCnj59unHjRvqbw2azJ0yY8Pr16+Li4p07dx4/fpwaosrV1fXjx4+8wSfKy8sjIiKqnLQoIyMTEhLy5s2b4uLi6OjoI0eO/HR4q3nz5t27dy8kJOTz589lZWXx8fGHDh2inxYAAESeSBUNLBbrxIkTS5YsOXLkiI2Njbq6uq6ubv/+/TU1Na9fv86bZ9iwYdHR0Xv37tXW1paWlm7btu3EiROLiororL8By+7YsePVq1d6enpWVlbe3t7UdRZ0aGpqjh49ukuXLgoKCuPHjx8+fPjkyZN5k7y8vEJDQ728vMzMzLS0tFq3bt2xY0f+Zdu0aRMQEGBhYaGgoBAUFDRs2DBq2dqMGjVq8+bNR48e1dXVZbPZnTt3xjgNAADAj0Xz+Hpj+eeff/r373/+xekG3OVyxrSZ8+bNozMzl8v98OFDZmamgoKCkZER74A9v8rKyjdv3hQUFGhqauro6FD9EAkJCVlZWba2ttScL168kJSU7NSpUwOW5SkpKXnz5g0hpFOnTuXl5Q8fPrSwsFBVVSWEJCYmpqamUgcg+EVERJw6dSopKen79+8fPnxo27attrZ2lXnS09MTExP19fXV1NQePXrUokUL3uAQVJIal+V/0eoBysrK4uLiKisrdXV16zicAQAAYkg0iwYRQBUNTAcBAAD4P8ycCPni4UuVlir1WqSiorKJwgAAHR8+fLhz5058fPzXr1+zs7OlpaVbtmypr69vYWHh6OjI6zwDANHGTNEwK7geh/bFU3h4eB23twZoNikpKTt37jx8+PCHDx9YLFbb9m00tNSVWigVFlckxSWe/udUXk6+pKSko6PjqFGjhg4dKisry3RkAGgqzX144t27dydOnGjYsg4ODo6Ojo2bBwBq8/nz50WLFh04cECGLe3Wz8W5j6NVj67KKkpVZktOTLl/4+Gl01ef3H2mpqY2c+bMsLAwlA4AIomZcxoatmxkZKT4nNMAwKDS0tLly5evXr1aXlFuVPgI/5E+Cko/v1tKUsKXvZv3nz1+Xru99ubNm/v27dsMUQGgOTFzeGL5sacqrTTqtcgMv04/nwkAftnLly+HDx/+7v27gNChQVMD5RXkaC6oa6C9aOO80WEjVs1Z169fvzFjxmzatElJqWrPBAAIL2bGaZCUlJSUkq7Xf4zkBBA3u3fv7t69e2ll8f4L0ZN+n0i/YqB0MNLdFrPx93Wzjx0/am1tTfP2bAAgFERqcCcAaDAOhzN16tRx48Z5+Lntvxht3MmwwatisVh+Af0PXNxdwSq3s7M7f/58I+YEAAaJVNGwZs0aFotlYWFhbW2tr68vISFhamqakpLSpC86f/58TU3NX1yQ/3FERISurm5jxQOgo6ysbOjQoRs3bZy+dPKijfPYsuyfL/MzuoY6+87t7NzNzNvbe9++fb++QgBgnEgVDTxnz559/Pjxx48fL1y48OHDh2nTpjGd6OeGDRu2a9cuplOAmCopKfH19T19+tSqnZHDQ4Y04poVFOU37F/dZ1DvwMDAbdu2NeKaAYARInWXyyrc3d2dnJz4byz58OHDO3fulJeX9+jRo2fPnvwzczicCxcuvHz5UlZW1s3NjX/c6PPnz799+zYsLOzMmTPv379v166dv79/9aGp6bxKbdLT09++fduvX7/qkzIyMrZv325mZubv709//Q8ePDh//vyMGTP4cx46dOjHjx/BwcF1b29MTEx+fv7YsWOplq1bt2pra9cYD4RdWVnZgAEDrly9su6vVXauVQdB/3WSUpILN8yVlZMNCwuTkJAICQlp9JcAgGYjgj0N/OTl5YuLiwkh5eXlQ4YM6d69++7du48dO+bi4jJy5EjqctO8vDwHB4f+/fsfP35806ZN5ubm8+fPp1Zy/vz5VatWOTg47NmzJy0tbeHChVZWVpmZmdVfru5XqcPVq1fXrFlTvT0pKcnBweHSpUtubm71Wr+6uvqSJUuOHj1KtRQUFIwfPz4tLe2n2xsTE7N7927+tW3dujU2NvanWwFCh8PhBAQEXL5yed2+lU1RMfCwWKxZK6YOHO03ceLEw4cPN9GrAEAzEOWi4du3b7du3TI3NyeEREZGnjhxIjY29tWrV48fP/73338PHjy4f/9+3pyzZ89++vTp9evXecc1ZsyYsWzZssuXL1OrSktLc3V1jY2N3bhx45MnT4qKihYuXFj9Fet+lfp69eqVvb29sbHxxYsXVVRU6rX+Dh069O7dOyoqimo5ePBgSUkJr//gp9sLYiIiIuLEyRPLti1quoqBh8VizVo51cPXbcyYMVevXm3S1wKApiOCRcOiRYsiIiLGjBljYWFRXFy8cuVKQsiOHTsGDx5MjTbj4eHh6el58OBBQgiHwzl8+PCwYcPs7e15U5csWdK6deu//vqLf7UzZszgPVBVVR03btyePXs4HE6Vl67jVerr9u3bjo6O7u7uf//9t5ycXAPWHxoaev/+/VevXvGe7ty5s1+/flpaWjS3F0Texo0bN2/ePCMyope3SzO8nISExOJN87t0N/f39+fd9xUAhI4IntMQHx/PZrNbtGgxZsyY0NBQfX39zMzM9PT03NxcXgHBk5eXl56eTgjJyMjIy8vr2rUrNYnNZnfq1OnDhw9US+vWrVu2bEk9NTY2LikpSUlJ4b/ldN2vUi+ZmZkeHh7+/v579uxhsVgNW3/fvn3bt28fFRW1efP/Y+/O46Fq3z+Anxk72SL7Hqmk9Ki0qCipVChr2QrZW5CQFqWVNtmylJJKKm1oT6KdIklFsmXf1wxmfn/M8/P4Co2aM2eM6/2XuefMuT/mVTOXc+4lKCMj4927d35+fhT+voDhPXjwwN3dfZ2dsYmNIc06ZWFlCTh7cP0KO11d3Tdv3sAeVwCMOAxYNFy9elVCQqJvC/mSQE1NTVZWVm+jpKSkiopK78N+IwNIJFLvtzWCIAQCoe+znZ2dCIL0PYDCXijEx8enoqKSnJyclZXV++0+3PMzMTHZ2dkdO3bM398/MjJSSkpq2bJlfX/Bvgf3/X37/V69XQOG8f3797Vr185UV3X13UTjrrl5x5yIOWK5zHbdunVJSUl4PANe7ASAgTFg0fArISEhQUHBOXPmnDp1asBneXl5371719vS2dmZm5vbd+X85ubmwsJCOTk58sOsrCx2dnZxcXHKexkWFhaWhISENWvWaGlpPXr0iFw3/MH5bW1t9+3bFx0dffnyZQ8PD/IH9G9/X0FBwY8fP/Y+SyQSS0tL//I3AvSjs7PTyMiInYvtUMReJmYm2geQkpPcH+q71dxj//79u3fvpn0AAMAfGxVlPg6Hc3R0jIiIuHv3bm9j75/seDze3Nw8Li6ud3Lmrl27amtrLS0t+55k+/bt3d3dCILk5OScPXvW2tq63x9JQ/cyXGxsbAkJCbNnz9bS0nr//v2fnV9ERERfX3/btm3t7e29Uyh/+/uqqKh8/vy5pKSE/PD06dOtra1/8CsA+uTu7p6T88H/zAFefl6sMqhrzbFxtdq3b19KSgpWGQAAf2BUXGlAEGT37t1FRUU6OjpycnJiYmLV1dXfvn3bsWMH+dr+wYMH379/r6mpqays3NDQUFJSsnv37kWLFvW+XEpKikQiSUtLS0pKZmVlycjIDDh7YuhehotcN/S93vAH53d0dLx69aqurm7f6yJD/77m5ubBwcHz5s3T1dUtKSlpaGiQlZX9g/yADt26dSskJMTjgOtklYnYJrHfZvPuZZa5uXl2dragoCC2YQAAFGKoomHu3Ll79uzh4eH59SlmZuaYmJjt27c/ffq0paVFRERk9uzZkyZNIj/Lw8OTlpZ2//797OxsdnZ2LS2tvosdIQiCw+GuXr2alJSUm5vr5ORkaGjYu2jSokWLyPMhf9tLP31f2PfnZcuW9S4jTa4bTp48mZ6erqKiMqzzk3FxcSEIYmdn17dx6N+XjY3t5cuXt27d+v79++LFi3V1dSMiIvoO+QQjVGVlpa2t7Xzteaa2tBv8OBg8E35/6B5TTUs7O7uEhASs4wAAKIKjZOkhKrpz546uru6R69l8gqLDeqHbKkXv7e4+Pj4oBRvC1q1bb968WVRURPuu/56RkdG7d+/y8/NhxBlYsWLFqzcv45/FjhWkl2kLD28/8bTdGR0dvX79eqyzAAB+D5srDSQikUjswaTr0cPb2/vx48dv376Nj4+HigGcPXs2OTn5eMwR+qkYEARZorvoqYH21q1btbS0+k16AgDQIWyKBi+j6b8/iG70vV8wgigqKvLw8Bw/flxdXR3rLABjZWVlbm5uOoZLNZZRtBkKLW0/6GaUbmZvb5+UlIR1FgDAb9D69kRJScn9+/f/7LUzZ878sxGFAIxyurq6L149v5Z+EcMZE0NISU51X+8dExNjYWGBdRYAwFBoXTQAAGjs2rVrRkZGR6L2L9Fd9PujMbLdxifrZU5eXh7MpACAnkHRAAAja25unjRp0nglmcCLA2yjSj9qq+rWzFtrZGDUb4dVAABdgfFxADCy3bt3NzQ2eB3ZhnWQ3xAUFnDxcTh37lx6ejrWWQAAg4IrDQAwrOzs7BkzZjh42lpvsfz90Vgj9hAtltmykFgzMzOZmRlqCRkAGAZcaQCAMZFIpM2bN4tLi1k4rsU6C0XwTHjvI+4fP34MCwvDOgsAYGBQNADAmK5cufLs2bNt+7ewsLJgnYVSU/5RWmm8fM+ePXV1dVhnAQAMAG5PAMCAOjo6Jk6cKK0oQefjH39VV12nP8fUysIqJCQE6ywAgP7gSgMADOj48ePlFeWuezdjHWTYBIQErLdYRkREfPr0CessAID+4EoDAIymqqpKQUFhpekyjwOuWGf5E4ROwpq5a6dP/ScxMRHrLACA/wFXGgBgNL6+vgieZLfNGusgf4iVjdXFxyEpKenJkydYZwEA/A+40gAAQ/ny5cuUKVOcvO3WbzLHOsufI5FIFkttOJnHvHnzBofDYR0HAPAvuNIAAEPZsWOHgLDA2o1GWAf5Kzgcbssu54yMjPj4eKyzAAD+A1caAGAcb9++VVNT23XcS99sFdZZqMDZxLW2rP7Tp0+w1hMAdAKuNADAOHbs2CE9XnKViQ7WQajDZYd9QUFBdHQ01kEAAP+CogEABvH06dNHjx45etkxMTNhnYU6Jk2buHilhp+f38+fP7HOAgBAECgaAGAYO3fuVJyioLVKE+sg1OSw3fZH+Y+IiAisgwAAEASKBgAYw/37958/f+6w3ZbB5hrIKcouW73k0KFD7e3tWGcBAEDRAABD8PX1VZo+acFSdayDUJ/dNuua2prTp09jHQQAAEUDACPf/fv3X716ZbfNhsEuM5BJyUnqGCwNCAiAiw0AYA6KBgBGvL179ypNnzR/yVysg6DFxtWqprYGRjYAgDkoGgAY2R4/fvzy5cuNbhuwDoIiKTnJZauXBAQEwDQKALAFRQMAI5ufn5/iFIX52vOwDoIum61WlZWVZ8+exToIAKMaFA0AjGDp6empqam2bhsYcjRDXzIK0otXafr7+3d1dWGdBYDRC4oGAEawgwcPyk6Q0dRZgHUQWrDZalVSUhIbG4t1EABGLygaABip3r9/f/fu3fWbzPH4UfEfeYKSvLrWnCNHjhCJRKyzADBKjYrPGgAY0uHDh8UkRZev0cY6CO1Yb7X68uVLQkIC1kEAGKWgaABgRCooKLh+/bq5oykzyyjaAXLaTOXps6f5+/tjHQSAUQqKBgBGpGPHjvHwcTPGFtjDsn6Txdu3b588eYJ1EABGIygaABh5qqqqzp07Z2prxM7BjnUWWlPXmiM/aTxcbAAAE1A0ADDyBAcH4/A4Y+s1WAfBAA6Hs3Ixe/DgQXZ2NtZZABh1oGgAYIRpa2sLDQ3VXbuCl58X6yzYWKqvJSQ67tixY1gHAWDUgaIBgBHm7NmzjU2N5g6mWAfBDDML81o747i4uLKyMqyzADC6QNEAwEjS09Nz8uTJxSs0xKXFsM6CJQMLPVZ21lOnTmEdBIDRBYoGAEaSW7duFRYWWjitxToIxri4uVab60ZGRra0tGCdBYBRZBhFQ3d39/Pnzy9dupSTk4NeIADAEI4dO6Yya+qUf5SwDoK9tRuNWlpbYAsrAGiJ0qIhPT1dQUFBXV3dzMwsKSmJ3KisrGxhYYFaNgDA/3j9+vWLFy/MRvFohr5EJUQWr9Q8depUT08P1lkAGC0oKhpKSkp0dHRwOFxwcLCiomJvu4WFRWJiYnd3N2rxAAD/OXnypLiUmObyUbE9FSXM7E0KCwtv376NdRAARguKiobw8HAcDvfixQtnZ2c+Pr7e9pkzZzY2NsIAZgBooLS09Pr166a2hngmGIr0L2VVJWVVpZMnT2IdBIDRgqJPn6ysLE1NTRERkX7tEhISCIJUVlZSPxcA4H+FhISwsLGMwnWjh2Zmb/Ls2bN3795hHQSAUYGiooFEIjEzD7ApDrlcYGcfdQvZAkBj7e3tUVFRemtXcHFzYZ2FvixeqSksLgxzLwGgDYqKBkVFxVevXnV2dvZrT0hIYGVl7TvKAQCAhosXLzY0NJjaGmEdhO4wMTMZb1gTFxdXVVWFdRYAGB9FRYOVlVV5ebmDg0N7ezu5paenJzAwMDg42MTEhIODXIonQAAAIABJREFUA82EAADk1KlT87TmSMpKYB2EHq2x0EVwSHh4ONZBAGB8OBKJRMlxR44c8fLyItcH/Pz8TU1NbW1t8vLyL168GDduHMohARjVnjx5snjx4pD4k3M0ZmGdhU75uR1+9fhtUVERKysr1lkAYGSUDsP29PS8f/++pqYmFxdXQ0ODqKjotm3bXr9+DRUDAGgLCgqSUZCevXAm1kHol4mNYUVFxbVr17AOAgCDo/RKAwAAE8XFxePHj/c4sNXY2gDrLHTNVs+JDeF48eIF1kEAYGQw4RsAuhYWFsbOyb7CeDnWQeidqa3Ry5cvYe4lAKga9ErD7du3y8vLKTmFrq6umNio3nAPAJT8/PlTUlJSS19z+0FXrLPQu57unpUzDHSWroDdKABAzwCrL5AdP348NTWVklNMnDgRigYA0BAXF1dXVwc3JijBxMxkYKkfHXghICBAQEAA6zgAMKZBrzRkZmY2NDRQcgpVVVV+fn6qpgIAIAiCzJo1i4kTF3oVlkmmSF1Nvc701Qf2H/Dw8MA6CwCMCQZCAkCn3rx5o6amduz8YdihinI+jr5f3hfk5+fj8TBgCwDqg/9XANCp0NBQEQnhhdrqWAcZSYw2rCksLLx79y7WQQBgTMO40lBUVBQbG5uTk9PR0SEmJqatra2np8fExIRqPgBGp9raWklJSRs3K5utVlhnGWHWLrIaL6WQlJSEdRAAGNCgAyH7CQsL27p1K4FA6G0JDw+fNWtWYmIirO8EANVFR0f3EHtgT8s/YLR+zcHtAYWFhXJyclhnAYDRUHR7Ii0tzcnJacGCBQ8fPqysrGxoaMjMzHRycsrIyDAzM0M7IgCjDYlEioiI0FqlKTBuLNZZRp5lBtqcYzhhKwoA0EDR7Yn169d/+/YtJSWl3wbZBw8e9PHxKS4ulpKSQi0hAKPO3bt3dXR0ztwOmz57GtZZRqQj3scf33paVlbGxsaGdRYAGApFVxrKy8sNDQ37VQwIgpiYmJCfpX4uAEax8PBw+YlyUDH8MaP1q2tra2ErCgCojqKiQVxcfMA1GxobG3E4HFxmAICKSktLExMTDaz0sQ4ygskpyv4zR+X06dNYBwGA0VBUNJibm4eHh5eUlPRtJBAIe/bs0dfXh+UgAaCiyMhINnbWFUbLsA4yshlarU5PT8/JycE6CAAMhaLZEzgcTlFRcfLkyRYWFsrKymxsbIWFhRcuXOjs7Ny/f39cXFzvkYsWLRISEkItLQAMrru7+8yZM8vWLBnDMwbrLCPb4pUaYwX5w8PDg4ODsc4CAOOgaCCkhoYGhftQpKSkaGho/G0oAEar69evGxoaXnoUPXGqItZZRrxTfqE3Yu78+PGDi4sL6ywAMAiKiobU1NSamhpKTrdw4UJYtgGAP6atrV1eW3bh/hmsgzCCH8XlemrG4eHhtra2WGcBgEHA3hMA0IuCgoIJEybsPuGtt24l1lkYhLOJK6G5OyMjA+sgADCI4e09UV9fn5aW9uDBg48fPxKJRJQyATA6RUVFcXFzLV2thXUQxmFopZ+ZmZmZmYl1EAAYBKVFQ3V1tZGR0bhx4xYsWLB06VJlZWVpaemLFy+iGg6A0YNAIERHR+sYLmXnYMc6C+NYoK0+TkQQVocEgFoomj3R0dGhqan56dMnVVXVefPm8fLyFhYW3rx509zcvKenx9LSEu2UADC8GzduVFdXG8LyDFTFxMykb7bqcvjlo0eP8vDwYB0HgBGPojENISEhLi4uQUFBLi4uvY1VVVWampr19fXl5eWwdT0Af2nx4sV1LTXRSfA3MZVVllWtnGkQEhzi4OCAdRYARjyKvuxTUlJmzpzZt2JAEERYWDggIKCqqiovLw+dbACMFvn5+SkpKWss9bAOwoBEJITnaqpFRkZiHQQARkBR0dDc3CwrK/trO7mxubmZyqEAGGUiIiLG8HAt0V2EdRDGtMZS/927dzCHAoC/R1HRICEh8ebNGwKB0K/92bNn5GepnwuAUYNAIJw/f36F0XIYAomS+VpzhUTHwXBIAP4eRUWDgYFBUVGRmZlZ74aWJBLp2rVrHh4eM2fOlJSURDMhAAzuxo0bNTU1ayx0sQ7CsJiYmfTWrYyLi2tpacE6CwAjG6WLO1lbW0dHRzMzM0tLS/Pw8BQXF9fX1/Px8T19+nTaNNjAF4A/p6WlVdNUdS45AusgjKyirHLVDMPQ0FB7e3usswAwglE66+Hs2bMxMTFz586tr6/Py8vj4+NzcHDIysqCigGAv5Gfn//kyRMYAok2UQmRuYvUIiKgMgPgr8Ay0gBgycvLKyQs5EHObRjQgLaUu8/crbwyMjJUVVWxzgLASDWM9RW6u7ufP39+6dIl2KIeAKro6uo6d+6cjoE2VAw0sGDJPEFhAZh7CcDfoLRoSE9PV1BQUFdXNzMzS0pKIjcqKytbWFiglg0ABnfr1q2qqqo1lrAKJC0wMTPprV156dKl1tZWrLMAMFJRVDSUlJTo6OjgcLjg4GBFRcXedgsLi8TExO7ubtTiAcDIIiMjp/wzeYKSPNZBRgt981VtbW1XrlzBOggAIxVFRUN4eDgOh3vx4oWzszMfH19v+8yZMxsbG8vKylCLBwDD+v79+6NHj/TNYKYl7YhLiaktmAF3KAD4YxQVDVlZWZqamiIiIv3aycs6VVZWUj8XAIzuzJkz7JzssBE2jembrXr9+vWHDx+wDgLAiERR0UAikZiZB9gPk1wusLPDGC4Ahqe7uzs6OnrZai2uMZxYZxldNJYvGCvIT5uLDe7u7suXL6dBRwDQDEVbYysqKl69erWzs5ONja1ve0JCAisra99RDgAASiQnJ5eXl682h+UZaI2FlWWViU5sbKy/vz8HBwe5cd++fU+ePPn1YCYmpsePH1Ny2k2bNn3//j0xMbFvY3Z29qtXr/4+85/1jq27d+9euXKlsrJSXFzc3NxcU1MT60SAOii60mBlZVVeXu7g4NDe3k5u6enpCQwMDA4ONjEx6f2PBwCgUGRk5AQleaXpk7AOMhrpma1qamq6du1ab8unT59SU1O/fv1a9L+Ki4spPOf79+/T09P7NW7YsGH37t1Uyz3M3jHk7u6uo6Nz/fr1ysrKuLi4RYsWHThwAOtQgDooXdzpyJEjXl5e5PqAn5+/qampra1NXl7+xYsX48aNQzkkAAylrKxMRkbG48BWY2sDrLOMUrZ6TlzM3OQt9xAEMTU1vXLlyvv371VUVP7shOrq6h8/fmxsbKRexhHTez/JyckrVqyYO3duUlISHx9fTU3NkiVLsrOzX79+PWvWLKzTgb9F0e0JBEE8PT2nT58eGBj45s2bhoYGcXFxfX19b2/vsWPHopoPAMYTHR3Nwsqy3GAp1kFGrzUWejud9n7+/HnixIkUvqS7u/vSpUspKSkVFRW8vLzjx49fuXLl3LlzEQTR0NDIyclpa2vT0NAgH+zm5qarq+vu7v7p06e7d++SG+3t7WtqahISEuLj469fv97e3q6hoeHi4sLGxtbQ0BAcHPzmzRsODg5ra+tly5b19tvU1JSUlPTs2bOysjIcDjdt2jQ7OzspKaneAwbrHUEQIpF49erVpKSkyspKAQGBJUuWWFhYsLCw/OW7N7STJ08iCBIeHk6eajdu3LigoKAFCxYEBgZevHgR1a4BDVBaNCAIoq2tra2tjV4UAEYDIpF49uxZrVWa3LxjsM4yei1eqeG/43hUVNTRo0cpOb6np0dTUzM9PV1MTExOTq6srCwxMTE5OTkrKwtBEBkZmby8vI6ODhkZGfLx3NzcyC9jGjIzM8vKytzc3AIDA+Xk5KqqqhITE1+9ehUcHDx//vzy8nJxcfFv375du3bt9u3bK1euJL9qy5YtMTExoqKiEhISjY2NSUlJQUFBDx8+7P2rfbDeW1padHV1nz59KioqKikp+fHjx7i4uKioqIcPH3JxcVHjXRxAV1fX06dP5eXlp0yZ0tuorq4uKCj44MEDlDoFtDSMogEA8PcePnxYVFS0O8gT6yCjGhs7m47hspiYmIMHD7KyspIbb9++TS4CeklKSi5evBhBkAcPHqSnp2/bts3f3x+HwyEI0t7e3lsQnDt3jnyD4Ny5c0P3W1NTc+/evfz8fDk5uba2tiVLlly7dq2goGDJkiUnT55kYWF5//79nDlzfH19e4sGS0tLX1/f3oIgKytr6dKldnZ2vVEH633Lli0vXryIi4szNjbG4XAkEunkyZNubm6+vr4BAQGDJczOzv7tRgFLly4d7K50QUFBV1fX5MmT+zbicLhJkyalpaXV1tYKCgoOfXJA54ZRNBQVFcXGxubk5HR0dIiJiWlra+vp6TExMaEXDgDGExkZKTtBRkUNtofF2GrzVXFRV2/evGlsbExu2bNnT79jVqxYQS4aamtrEQTR1NQkVwwIgnByci5atGi4nRKJxNDQUDk5OQRBuLi43N3dDQ0Nm5qaAgMDydPap0+frqOjc+PGjfb2dk5OTgRB+vWioqLi4uKye/fuoqKi3kriV+Xl5TExMZs3bzYxMSG34HA4V1fXy5cvX758eYii4erVq78dtJiWljZY0VBXV4cgiICAQL92ckt9fT0UDSMdpUVDWFjY1q1bCQRCb0t4ePisWbMSExNhICQAFKqurr59+/amnQ5YBwGIwmT5Kf8oRUZG9hYNkZGR8vL/s6R375ffrFmzmJiYnJ2dvby8li9f3ndIwbAwMzPPnz+/9+H48eMRBJk3b17fhXDIJUVpaWnvbPYbN27Ex8fn5+e3tbWRSKSmpiYEQQoLC4coGp49e9bT0xMfH9/vpkBpaWlzczOBQOi9vtKPjY2NltZvFhxTVlYe7CnyrgK/rutDbunq6hr6zID+UVQ0pKWlOTk5aWlpeXp6Kisrs7GxFRYWnjlz5vTp02ZmZnCnCgAKnT9/HsEhK4xhwR+6sMZCd7/7ke/fv5MfzpgxY7DZE4qKiufPn3dzc3NwcEAQRE5OztjY2MPDY7gjwQUEBPpenSWvjCcsLNz3GHJjR0cH+aGzs3NoaKi8vDx5ZAATE9OHDx/u3r3be8CAKioqEARRU1ObNGl403plZWVlZWWH9ZK+yKMleifn92pra0MQZMwYGMcz4lFUNJw5c0ZdXf3u3bu99eM///zzzz//iIuL+/j4lJSU/HHdDcDoQSKRoqKiNHUW8gvw/f5ogD5tfa2juwKjoqIoOdjMzMzU1PTFixepqak3b948fPhwYmJiRkZGvyXvqOvz58+hoaErVqy4ffs2Hv/vsjqHDx++e/fu0LPlyRcSZs2a5ek5vNEz79+/z87OHvoYHR0dISGhAZ+SlpZGEKSkpKRfe2lpKTMzs5iY2LDCADpEUdFQXl5uaGj46xUnExMTHx+f8vJyKBoA+K1nz559/frV9aAz1kHAvzi5OJatWUIeSEjJ8UxMTPPnz58/f/7OnTttbW3PnDnz4sUL8lqH5JGGVE9IHu1obGzcWzH0Nvb1a+/koYgpKSnDLRquX79OyZiGwYoGISEhaWnpd+/e/fz5s3eHgYaGhry8PBUVFbRnewIaoKhoEBcXb2ho+LW9sbERh8NBxQAAJaKiosSlxWbNn4F1EPCf1Wa6CTG3ysvLhz6submZh4enb8uECRPI7eSH3Nzcra2tXV1d1P1eJA8b7L2BgiBIbm5u37UsB+tdXV1dSkrq/v37Dx486DdVfugRlH85pgFBEBMTE39//6ioKBcXF3JLcHBwT0+Pqanp0KcFIwJFRYO5ubm5ubm1tXXf+oBAIOzZs0dfXx+uOAHwW/X19deuXbNxs+odfg/ogdL0SROU5AsLC5GBplwiCLJ69WpeXt5Tp05dvXp13bp1EyZM4ObmzsrKOnDggKCgYO96StOnT797966Xl5eenh4rK6ucnNxgf4sPi5qamoiIiL+/Pzs7+4wZM758+eLn5zdp0qSPHz/2PWzA3iMiIlauXKmvr79p06aFCxficLjc3Nz4+Hg5Obm4uLjBevzLMQ0Ignh4eMTGxrq7u1dXV6uqqj5//vz48eMKCgpOTk5/c1pAJygqGnA4nKKi4uTJky0sLHoHQl64cKGzs3P//v19//0tWrSIKv9VAGAwFy5c6Oru0jVdgXUQ0N9qc13/HSeQgaZcIggye/ZsXl5eZWXl2NhYLy+v3nYNDY0TJ07w8vKSH27evDk5Ofn48ePHjx9HECQsLIw8ZPIvcXNzx8fHW1hYkLtmZ2d3dXWVlZW1s7Pre9iAvS9duvTJkydubm7+/v7+/v7kI6dNm6anh+42aYKCgo8ePbKysvLz8yO3LFiw4Pz58+QZpGCko+g+nIaGRmpqKiWnS0lJ6S29AQC9pk6dKiDOdzzmCNZBQH8tTa3ayqvWrV1naWn567OzZs3q/barra0tKyvr7u6WlpYecKp5fn5+ZWVlT0/PhAkTxMTEsrOzm5ube+dYZmZmdnZ2kleeJmtvb3/z5o2kpCR57iXZ9+/fi4uLZ8yY0TvXoLu7mzzfcsKECTw8PBUVFV++fFFWVu63HEK/3nvby8vLS0pKODg4JCQkfl1BAT2FhYVVVVXi4uJwC5uRUFQ0pKam1tTUUHK6hQsXwrINAPTz6tWrOXPmBMYGzNeeh3UWMIDdLn4f3+R9+/at73hDAMCvUBnxCwDoy8bGJuleYlJmAp4JvpPo0ftX2Ta6jvfv34ftdQAYGnyEAYCu5ubmK1eu6K1bCRUD3Zo+e5rsBJmIiAisgwBA7+BTDAB0Xb58uaOjQ2/dSqyDgKGsMde9fft2VVUV1kEAoGtQNACArsjIyNkas0QlRLAOAoaywngZDoecP38e6yAA0DUoGgBA0bt37zIzM9dY6GIdBPwG31g+zRULo6KiYJgXAEOAogEAFEVGRgoKCyzQpmiVYoCtNeZ6+fn5KSkpWAcBgH5B0QAAWtra2i5fvqxruoKZhdI96AGGZqj/IykrQeH+VQCMToMWDWZmZnv37iX/fPv27YyMDFpFAoBBxMfHNzc365utwjoIoAgOh1ttrpuQkFBXV4d1FgDo1KB/AFVUVPDx/buB7/HjxzU0NGbMgI12ABiGiIiIWfNnSMiIYx0E/F5OKT7qKf594/pOc3Plvbk7Vk900hDC99kn5PC9Su8bP/q9SlWaM2PHpN6HX6p+Ol8qeVPULsrLsn2psM08wb4HX8losIr+/nGPkrwQRbtpV7d0n3pSnZzTVFjb2dFFFOFhmTd+jI264OKJ3OQDTCMLr2Q0vN85SUWS1is051X8jH1dl1HcnlHcXt/WrTeN76bT+N+/DEFeFbbtT6548a21o4ukIMRmN1+w3/tM9rXq54Hkykefm+tau4W4WWbLce1aIaoszgHhMTdo0SAiIpKfn0/LKAAwkpycnFevXh2J9MM6CPi9tM94t0vMeDyyUJH4+W1mdafUprjSDz86Isyl+x2pM4V3HPd/H5uygv99/f/sImqfzBfnY7nuIJfypcU2pliYm2Xl1H83p2j52eN2tdRzmQiFFcP93GaTyMKmjh4ZAdblU3i52fE/GrrufGi8/Lb+gL74juUYT8a5/6n54N1KNmac3Di2+rZuCl+V+KFpddg3PB7Rnco3jpv56deWAd/n+7nN+mHfunpIc+S4Fipw17R2P8pr1pnCS63v3REdHnODFg3Lli2zsrJSVVWVkZHJzc2trq7ut69ar7179yopKaGWEIARKSIiYqwgv8byBVgHAb/RQ0QO3GYiIUikTfdUSeJL0R5nU4M5e55HptWunTlWU5G778G7VojOluMa8Dz3PzWX1BNuO8tPk+BYMonn3sfmiLSa3qJh1+1yDha89zKKvuw/lHWsDvuGIEjMBhlzNYHejVHbOoknHlfVtlL6PYcenSk88+UnTZXg+FbTOWlPLiUv6SaSHC4WkxDkqbviHDkuBEG6ekirw771e5/LG7tMIgsFuJiSNylMlfj3i7aHSGojECE8PRi0aLCwsPjx48e5c+eSk5N//vxZV1f39evXAY/ctGkTavEAGJHa29tjY2P1zFeysLJgnQX8xudyXHUzTn0CcaokEUEQtQUzxCWF+AqvImymkWm1/YqGIRTXERAEmSjCTn44WYz9Q1kH+efsso7glJo7zuPZWSgae+52tbSji3jGUtpi9v/sL8XFht+pI9rWif030ARh9uG+5H1J+4/GLp0pvHP+v+piYcLtXimalNPU930++rCqqaPngrVs75cugiBMeBwPOxNVkiMjPDzmBi0acDict7e3t7c3giAaGhoaGhq+vr60ywXASHb16tWmpqbV5jAEcgSob8MhCCLG/+/yDHg8frW5bmRwLGJimprf0u/guIz68y/rWJhwUyU4jFT5eTn++zIQ4mZGEKSyqUtagJX8gwgPC4IgJBLicLFYX4Vv+RReSvKUNRAef26R5GddP1dwwAO42EbkrLfqlm4EQWQEWfs2ygqwIQjS932+ltkgOIZ5hTLvw7zmV4VtrMw4dfkx88aPoXHafkZ0eOqiaCbY6tWrFRQU0I4CAMOIiIiYMW+6lJwk1kHA7/FzkRAEqWj8bzybrumKsLPPEQQpb+xq6yT2/ZIOfFzd+/P262VX7OSWTOIhP1w8kYebnWn37fLjxhJp+a0pX1qCTKUQBIlKr/34o+OqnRyFeV59b0MQZOGEMb+OsPsDN7MaEz80DX2MF8UjLf4GeSwI+XpMr6K6TqTP+1zb2l3aQFigMEY/tOBOn9i60/jiNspyUHadBg0jOjx1UVQ0bNmyhfxDfX19bm5uR0eHmJjY5MmTYRtZAH6Vl5f34sWLwxH7sA4CKDJRlDSWi/Q8H/+xDDdFgoQgCP84Af4l22oRBEGQ5p895KJhqgTHBWvZBQpjhLiZS+oJp5/VBj6uWh32LXePEvnSwjhu5os2suvPFcW8qkMQxFZd0G6BYG1rt9eNH76rxMT5WHfdKg9NrWnr7Fk5lS/SQpqfc+BL1lXN3QiCiPOxDvjscGUUt595Xjv0MevnCtCgaJguySnEzXwvt/n19zY1WS4EQbqJJL+kCvKz5PeZPFzj+bc2Hnb8VXs57ck8pfUEl8ult7MbvW/8OGmMWRU+osNTF6VrzlRXVzs7OyckJBCJ/95Ok5CQOHz4sJmZGWrZABiRwsLC+AX4NHUWYh0EUISZCXHX6dl5jdk2knmREomPE8koxLWOmYg0ViJjRHr/3Nfpc3NhgjD7cSMJZjwS8KAqLLXm8Jp/Z9WumspbdXRqYU2nMA8L+c6Fx/UyMV6WLYuFzjyvPXSvMsBAXFaQzeFiifOlkku2skOkwlHjMgOCIPv1xPbrif3xy6+/a0graO19aKEmoCr9h9M7WZhwJ4wlzc9+X3j0y5rp/IJjmFO+tBTXE6TGspbUE8jvM5GEIAjSQyQdM5I0/IcfQRAlMY7rDnJyPh/Dn9UeXi0+4KCQlp89v440xCGIMA/VRhShF37Eoaho6Ojo0NTU/PTpk6qq6rx583h5eQsLC2/evGlubt7T02NpaYl2SgBGivb29gsXLuiZrYAhkCPI8mlEPq7uc8/wzz7jSAhORYroZ9hjcZTUg5D4OQf9kLSaIxDwoOptcVvfRmY8rnecXXpBa8zLutRtisx4XFBKte40XlctYQRBfjR2bY4rOWkiKcQ9wMlFeJgRBClrIPz6FO2lfGkJeVrT+3CGNNcfFw0IgqybNVZwDPPhe5V3PjSSEGTe+DEXbWQNwr8x4XHk97l3jMhK5f9KtLFczHPHj7n7selz5c8BV6TwvvGjb0gyNmbcz5B//jgqzcKPOBQVDWfPnv306VNQUJCLi0tvY1VVlaam5vbt283NzeE+BQBk8fHxTU1Nq2GHqpFmjjxxjvx/f602tiPdnCK4+oKWJlkBAYEBX0L+kujsGniDq24iyfFiieUcAXX5MQiC5Fd1Gqnyk59SleIkkpD86p9C3AMMkZstNwZBkKdfW4kk5O+HNSS8b7yd3Tj0MT46ogqD3J4IXisVvFbqb0P0oT2ZR3syT+/D2tbubzWdU8U5WJlxCIKI8bJwseHbOok8HP9z74b8VncM8lY7awitnMrXr5EJhS8lNMKPOBQVDSkpKTNnzuxbMSAIIiwsHBAQsHLlyry8PFinAQCy8PDwmeqqMARypLuRwYQgOFzxk/Pny93c3AY8Jr2gFUEQWcGBBx+cfFRd3tTlv0YCQZB+G2eSHw1WD4jzsWhN4nmU13z2ea2t+gATKFp+9nBTPIXvXUn7+Ze/WRXbVl1wsKIBbVHptSQSYvj/5RQOh8yXH3MvtzmvoqPv3+WfKjoQBJEaO/BbPUmUfZLosGdR/j2qhB9xKCoampubZWUHuP1GbmxubqZyKABGpuzs7FevXh2J2o91EDA8tS04Hk6Elenf7/aX+fjIFCaBMcgUsR8REc9cXV27epDEnCbdabzM//+3f1Zpu9vVMgRBTGaM/fWEZQ2EvYnlx4wkyaPucThEQZgto6id/GxmcRseh8gLDfo9d8xQQu3w501xpcx4nNWc/xZ3au0kHntY1djefYLiUXV/Oabhb7wqbHv1vW3hhDHT//8btKKpaywXMxvzv7/P/dxmv6QKYR4Wp4Xjel9lN3/cvdzm3bfLrzuMZ2HCIQgS97b+Q1nHbDkucT7a3fIb0eFRRVHRICEhkZKSQiAQWFn/p1Z69uwZ+VlUogEw0oSHhwuMG6uxbD7WQcDwPMjBh6fgJ4sh/Fyk0npcbhmOiw0JWNdFKF7mYLA5NTV1xpwFBqe/jeNmnizKIcLDXFJPeFPU3kMkbZwv2LvmY19brpQqiXFs7HOdwEVDyOFiccCDKllB1n1JFSYzxg44oIFsqgTHTcfxJpGFG84X7blTPluOawwbU1kDIb2gtZ1APLIG+91MfjR2+dz8gSBIU0cPgiDvStrXnytCEERFknPrYiHyMfdym/YmVpwwluz93r2S0bA3sXyGNNe4MczF3l4FAAAgAElEQVQFNZ1vi9q42ZmuO8iN5frvrVg9nc9Ilf9qZoOK36eFE7jLGgiJOU3c7Exh66h2l2REh8ccRUWDgYFBdHS0mZlZYGCgmJgYgiAkEun69eseHh4zZ86UlIQrsQAgra2tFy9eNNygz6hDIAmErury6rqa+p8dP5sbW1hYWTg42Xn5eASEBASExo7ogU1TpYgq0rhPZfjmn7ixXIi+KtFWs0eMj0SSVJWUlTh9+nTsgoXey0WefW39XPnzeUE3Nzt+4YQxG9UFTWcOcJnh7semW9lNb3dM7DsDwlZdsLSBEPCgsq2TuHIqb+jvvkWWKvF89ZsS+Ljq7sfmex+bO7qIorwsa6bz2c0fN18B+8WCGtq7+971KG0gkB/Wtnb3fu/+au54LnX5MRlF7fXt3ULcLDbzBHeuEJUR6H/d/pKt7EwZrrPPa888r+VmYzL8h3/vKjEq3oAY0eExhyORKBqdYW1tHR0dzczMLC0tzcPDU1xcXF9fz8fH9/Tp02nTpqGdEgD6FxER4ejoeOtNvLgUNleDqa6L0JWTmfvuZVZuVl7Bp28VZZW9M677YWVjlVWQnqCkoDxDacbcf2QU+u/zNHLFhF4KPRhRUlIiLCyMdRYAsEdp0YAgyIULF6KionJycsiLO2lra3t5eUlLM86nAwB/Q1VVlYOfLejyMayD/K2fHT+f3k17nJjyMuV1e1sHO+cYmYnTJeSniEiNHysswTtWmJ1zjM/amQiC+F163dbU0FRXVVP+vaI4v+RL9o/CPCKxR1RCRGP5/CV6i6fNVMZRa8EBjDTWNy6bpr/Xd6+XlxfWWQDA3jCKBgDAYN68eaOmpnY85siIHtDwNbcgPvr6vYSH7a3tUhOmqqgvV1JbJDVhKh7ff6y+/UIhBEHCU6v7tf9sb/2a9Tzn1aOstLvN9dWSshJrLHT1zVbx8lO07QJ92um0Ny/za0FBwYi+BQMAVUDRAAAVWFtbJz9ITsq4jkdjejj63qRlnD0Z8yYtg1dAeJ7OutlLjYUlxw9x/GBFQy8isefL++cvki+/S73DzIzXXbvCysVMVIKijaHpTdbrbOtVjsnJycuXL8c6CwAYg6IBgL/V0NAgISFhtclso/sGrLMMW/bbnFN+oe9fZUvIK2mbOs/Q1GNi/v1Azt8WDb2aG2pSb0Sn3DhD+Nm62mzVxm3WAuMGGDxI54wXWkxWULp16xbWQQDA2Ij8qwgAunLu3LlOQqe+2QjbCLuirNLTdueGFfbV1QSnAzE7o56oLTGkpGIYFh7+caustx+8kqljuS3x6mO9WUZnA2MIhC7q9oI2Qyv9pKSkkpISrIMAgDEoGgD4KyQSKTw8XHP5gnEiAyzeR5+6u7qjT10wmLfudfpHK6/AXWdSpqkvQ3XEIjvnGB0L1/2X36gtNQs9HGW8wPxtWiZ63VHdCqNlbOysERERWAcBAGNQNADwV548efLlyxej9WuwDkKpr7kFFkttQg5GzFth6Rf7cu7ytThaje/j4uE33XJwZ9RjFi5RB8PNfm6HW5tbf/8yOsDFzbXccOmZM2cIBLrYRwoArEDRAMBfCQsLk1GQnqFOzf30UEIkEs8GxlgstWlpw3uGJhlv2s/OxU37GOJykzyC7phuOZR8/bGJhmXmi/e0z/AHjDesqaysvHHjBtZBAMASRUXDwYMHz507h3ISAEaeHz9+3Lp1y2j9GvpfjaCmstbRcHPIoQiN1Rt3RDySmYRllYPD4TRWW+8++5STT9LeYNNp/yhiz8DLRtEPhcnyKrOmhoWFYR0EACxRVDQ8ffr0x48faEcB4LfwDu9w9nR0LzwiIoKFlWWVCb3PxHublrl2kdWXT2VbjsYbOvmysGKzpWE/gmLS24Ju65i7RR0/72i0ua6mHutEv2G0YU1qaurHjx+xDvIvJqd3OPt37K5fsQ4CRhGKigZZWdmysjK0owDwezg6miHc1dUVGRm53FB7DA/2ewEMISb0kqPxFkGJybvOPJmkugDrOP8Dj2daZb19y9H4z7klZlobPr77hHWioWit0hQYNzY0NBTrIP8i9eAQhETsHBnjQgBjoKhosLCwuHLlyvfv39FOA8AIkpCQUFFRQc9DIAmdhJ1Oe0/6Bi82dHA9fpVn7KCb8WBroup8n8hHnHyStnpOSVfvYR1nUCysLPpmq2JjY5ubm7HOAgA2KNrlkkAgzJgxY9q0aRs2bFBSUuLh4en77KJFi4SE6PTDCAD0hISEqMyaqjhFAesgA2uoa3Sz9PyU/dXaJ1RN2xDrOL/BP05s26lbsQFuu5z3FRWUOHltpM9hIgaW+tFBF2JiYlxcXLDOAgAGKCoa9u3bl5qaiiDIqVOnfn02JSUFigYw2nz48CEtLe1Q+D6sgwys9HuZs4lrU+NP1xMJ46fMxDoORVhY2Tb4hAhLyZ89ebiitGLPyR10uMm4iITwwqXzQ0NDnZ2d6bOsAQBVFBUNe/furampGexZJSUl6uUBYEhEHILQxbCGkJAQASGBRSsWYh1kAJ+yPm9a68bCMdYz7MY4MRms4wyPjoXrODHpc4c211XXHz13iGsMJ9aJ+jOxMXAw2Pz48WMtLS2Mo+BICAnBEeliWCsYJWDvCQCGraGhQVJS0szRxGG7LdZZ+nuTluFm5SUkOdHl8EVuPgGUeqF874k/8/ldWtjO9XIK4sFxx/nG8qHUy58hkUiG882mTpp28+ZNrLMAQGvDWNypu7v7+fPnly5dysnJQS8QAPQvOjq6k9BpYKmPdZD+Uu+lbV7rLq04w+3EdfQqBhqY+M98txMJpUU1NrpONZW1WMf5HzgczsTaIDExsaioCOssANAapUVDenq6goKCurq6mZlZUlISuVFZWdnCwgK1bADQIyKRGBoaumiFBr1tNvHw1mMPG59Jsxa7HLnExsGFdZy/Ja04zf3UrYb6Thtdx4qySqzj/I8Vxss5uDhgoScwClFUNJSUlOjo6OBwuODgYEVFxd52CwuLxMTE7u5u1OIBQHeSkpK+fftmamOAdZD/cff6A2+HPSrzVzrsO8vMwop1HOoQlZ7gEXS7owO3Uc+5vLQC6zj/4RrDucpkeVRUVHt7O9ZZAKApioqG8PBwHA734sULZ2dnPr7/7i/OnDmzsbER1n0Co0pQUNDEqYoqatOwDvKfewkPd7nsm7l4jc2uMDwTRaObRwpBMWn3oNuEHlZ6qxtMbAwbGxsvXbqEdRAAaIqioiErK0tTU1NERKRfu4SEBIIglZX0deUQMDAm+yy8wzsMA3z+/PnRo0drNxphmKGfh7efkCuG9d5BeDwT1nGoT0BYwv3kja4eVvvVm6p+VGEd519ScpJzF6kFBQVhmIHZIRPvkDnG8RuGGcBoQ1HRQCKRmJkH+POFXC6ws7NTORQAgyDhe7Cd73Pq1Cl+Ab6lq5dgmKGv1HtpPo6+0xesYtSKgWyssITbyRsdnXh7g821VXVYx/mXqa3Rhw8fnj59ilUAIglPIiEEXBNWAcAoRFHRoKio+OrVq87Ozn7tCQkJrKysfUc5AMDAGhoaYmJiDKz0Welj0aFXqW88bXdOmb3EZmcoA1cMZAIikq4nrjc3dzkabm5qoIuvyTmaajIK0oGBgVgHAYB2KCoarKysysvLHRwcekf99PT0BAYGBgcHm5iYcHBwoJkQAHpx5syZTkKnodVqrIMgCIJ8yPjobuUtP23exj2RDDaOYTBC4rJbjl6tqmxyMXVra8V+BCIOh1u70ejOnTuFhYVYZwGARigqGlRUVA4dOnTu3DlBQcEPHz4EBQXx8vJu3bpVRkbm2LFjaEcEgB709PQEBwdr6y2mh5mWBZ8LN69zF5Ob4rD/HMPMlaCEmKzi5oC4b19K3aw8CYQurOMgK4yWc47hCAkJwToIADRC6ToNnp6e9+/f19TU5OLiamhoEBUV3bZt2+vXr8eNG4dqPgDoxI0bN4qLi9duNMY6CFJRVuli4so9VsLl8EU2drpbZRlt0ooqTgdj3r/K2enoS+whYhuGk4tjjbnemTNnWlpasE0CAG0MY0VIbW3tpKSkmpqa9vb2/Pz8gICAsWPHopcMgF8xs3Li8cP4R0tFJ0+eVJk1VWn6JEx679XU0ORs4tpDYt8ccIWTm77WV6YZxenqNrtOP0l65r/jONZZEBMbw9a21ujoaNp3zYrjwCNMgkL957UBgB7YewKA33vz5o2ampr/mQNaqzQxjNH5s9N+zaZvX394hiQJS8ljmATtvSco8fRm9OUTns477G22WmEYA0EQT9udhbnFX758YWJi8OGoAAxj/FRRUVFsbGxOTk5HR4eYmJi2traenh78JwGjwYkTJ8QkRRfpYLmnJZFI3GG/Jy+nwO1EArYVA53Q0N/QWFMReihQVEJEx3AphknMHEzX69jdvn179Wq6GCQLAHoovdIQFha2detWAoHQt3HWrFmJiYkwrAEwtpKSkvHjx2/Z7WTmYIphjKM7T8ZFXbffd1Zl/nIMY5DRw5UGBEFIJNK5gy6ZT28Gx52Yqf4Phkmslm/k5eBPS0vDMAMANEDR7eG0tDQnJycFBYVr166VlpY2NTVlZ2dv3br17du3ZmZmaEcEAFvBwcHsnOz6ZqswzHApIv5SRLyxix89VAz0A4fDWWw/MX7KbA9r7+9fizBMYu64Nj09/e3btxhmAIAGKLrSsH79+uTk5Nzc3H4XFfbs2bNv377i4mIpKSnUEgLwH9YteT2Enz1h02nWY0tLi6SkpK6ZjqvvJpp12s+z++lu670012w0dvHDKkM/dHKlgay9tcnfeSWe2HL+XuRYQX5MMhB7iHpqxupz5sfFxdGsU3aHz12kn8KiQuW+YjTrFIxyFF1pqKio0NDQ+PU2hLGxMYIg5eXl1M8FwEC6Ce1EIk1n2UVFRbW1t621xWym5ZeP+Tsc9kxRW2Lo5ItVBjrHOYbX5fDF5haCm6UnoZPw+xegAM+EX2tnfP369aKiIpp1SiB1EJGe2mrY/QfQDkVFg7S0dENDw6/tDQ0NOBwOLjMARtXd3R0YGLhEd5GIhDAmAWqr6raaewiIjrfdfZrhF4r+G4KiUk4HYvI+5O/dehCrGWH6Zqs4uNhhVWnA2CgqGuzt7dPT01NSUvo2dnV17d27V19fX0wMrowBxhQfH19cXGzhuBaT3jt/drpabv/ZiXM+HMvGwYVJhhFETmmGlVfgvYSHUcfPYRKAk4vDwFI/KipqwD+xAGAMg065fPLkSXX1fzcsDQwMlixZYmRkNHv2bB4enqKiogsXLrS0tBw8eLC6ulpISIgmaQGgqaNHj86aP2PiVAy2ZCORSHs2H/j66fu2U7f4x0FdTpGZi9dUFOef9j8uO0EGkxU11m40vhR+5fTp097e3rTvHQAaGLRo2LdvX2pqar/GuLi4fsN87OzsFBQUoGgAjOfRo0fv378PuXICk97PnDj/8NZjm12nZSbSbtQnA1i1YXtF8dfdLn4SMuITlSfQuPdxIoLLDZeeOnXKzc2NjY2Nxr0DQAODFg179+6tqamh5BRKSkrUywPAUHBEJgRHo4GQ/v7+E5TkZ2vMok13faUkp572j1pu4TpzMSwWNDw4HG69d1CAyypXS8/YB2cExtF6qXtLp3V34pIvXLhga2uLdl94HJGIIKwkXrQ7AqAXLCMNwADevXunqqq6P3QP7ZcazP9UsGGF/YR/NBz2ReMw2mjjt+hqyuWv6qvKDtkvlVMQDU8IYmFloXHvrpaeFYVVeXl5WO2TAgB64N80AAPw9/cXkxRdqq9F436bGprcLL34hWWsfULptmKgf2OFJRz8oj++/3zY6xjte9+w2eLr1683btygfdcAoG0Ye098//49Ozu7oqKip6enb/vq1avFxcWpHQwAzBQUFFy7ds3dbzMTM01nOfZ092y33dnY2LEj/BZMl/hL45VnmW49FBvgrjhFwdjagJZdT50xZfrsaYcPHzYwoGm/ANAARUUDgUCws7O7cOHCgOvqTJkyBYoGwEgCAgJ4+Xlov270Cd+gzBfZWwKuCIpJ07hrhjR/pUVZ/sejuwLHT5RTnUvT8aQbNltuXuf+6NEjLS1aX6wCAFUUFQ2BgYHnz59fvny5hYWFsLBwvxt1Kioq6GQDAAPl5eXnz5+3cbVi52CnZb+J8XcvRcQbOe+bqDqflv0yNuNN+398/7zdxufiw2hartA1b/HsCUryhw8fhqIBMBiKBkLq6OgUFxfn5OTAuB6ALbz9OxJCIoWroteFu7t7eGR4UmYCDx83er308ynrs/Uqh38W6m3wCaFZp3+DzgdC9tXSUHvAbomIKPfZO2Fs7LSbBnn/xiNv+90vX76cPXs2Sl3gHd6RSCRWHHvnaZjCBmiEoiKAg4NDQUEBKgaAPTy6k33q6urCw8ON1q+mZcXQUNe4zdpbRHqi+TYMRu0xPG5+Qcf90QV53/e7H6Flv0t0F0nJSR46dAjFPkg4BEFI+E4UuwDgf1FUByxZsiQzM7OzE/5pAgYXGBjY3dNt5mBKsx57uns8bXe2tXY77j/HwkbTGyKjh7Siipn70aSr9y5HXqVZp3gm/IbNFnfu3Pnw4QPNOgUAbRQVDba2tlOmTDE3N//x4wfagQDASlNTU1BQ0BoLXVquCHRyX8i7Vx82+kaOFZagWaej0JxlJpprbE74Br1/lU2zTnUMlwqLCx04cIBmPQKANoqKBmZm5oCAgKdPn0pISAgICEj8r5cvX6KdEgAaCAoKam9vs3Q2o1mPd68/uHg6zsBhl+J0dZp1OmoZOe+TnTRju41PdQVFa93+PRZWlvWbLK5du/b582fa9AgA2igqGnJzc+fOnVtbWyslJTV+/Ph+RQMssQ5oiAnB4dA4b0tLy8mTJ3XXrhQWo9FGKl9zC/zcDs9cvEbL2JE2PY5yTMwsdnujekhsHtY7CIQu2nSqv26lgNBYlC424JhJCIIwscAnMKAdimZPbNy4MTY2Njk5WVMTg43jAKCBw4cP796z++arK6ISIjTorrmxxWzJBhwz3/bQZDZ2Thr0SF0jaPZEP4WfMo9t1tNdu3znUU/a9HgpIv7EnqC8vDwFBQXa9AgAeii60lBaWqqpqQkVA2BUra2tx44dW2WynDYVA5FI9HH0bahvd9h/biRWDCOa3GRVk80HEmJu3bx4hzY9Gljq8Qnw7d+/nzbdAYAqiooGGRmZ9vZ2tKMAgJWQkJDGpsYNWyxp0114wJkXKa9tdoaOE5OhTY+grwW6VvN01h32Opb7Po8G3bGxs613Mbt06VJ+fj4NugMAVRQVDY6OjhkZGRkZGWinAYD2Wlpajh49uspER1xKjAbdpd5Lizp+buX6bVNmw1qBmFnrekRUZrKHzY6GukYadGdgpc87ltfPz48GfQGAKoqWka6rqzMxMVmwYMHGjRtVVFQ4ODj6Prto0SIhIRqNHQOA6oKDg5uam2xcrWjQV/G3kl0ufspztFdYuNGgOzAYFlY2e7+zBzdqedntCosPxDOhu3IdOwf7hs0WJ/YE+fj4KCoqotoXAKiiaCCkhoZGamrqYM+mpKRoaGhQMxQAg8A7ZpFIRNLpf6h1wubmZllZWc1VC3wCtlPrnINpb+uwWmbb0obbEfGAg4sH7e5QNXIHQvb1OTMtcJuxhaPplj3OaPdF6CSsmmmopbnk0qVL1Donk/M7YjeJnZ29IxCWkQY0QtGVhr1799bUDDqzWUkJ/r0CmulBqLqQ9PHjx1vbWm1d11PzpAMhkUh7tx4sK6n2Crs70isGhjFRdb6+nU9MqJ/SP5O1VqE70JuVjdXGdb2/93Fvb29lZWWqnJPUjUMQUk8XrNULaIeiomHhwoVo5wCA9urq6k6cOGFgqUeDtRliw+Ie3nq8cU+EmOxEtPsClNM2dS7Ke+e75YDcBBk5RVlU+1pttup8UOyePXsSEhJQ7QgA9MAeVGD0OnLkSFd3lzX6kybepr87tT9Uy9hxxiJ9tPsCw4LD4ay8TvEKSm7b4N3W0oZqXyysLHbbrG/evPn27VtUOwIAPRQVDQQC4efgiEQi2ikBoLry8vKQkJC1G40EhARQ7ajqR5W33S555TkGDrtQ7Qj8GXbOMY5+0ZUVDbuc91EyxutvrDReLj1e0sfHB9VeAEAPRUWDtrY2x+CePXuGdkoA/kWk2hrSfn5+zKxMaO80QegkbLPeQcJxbNwTgWei6G4goD1hKfkN3sGp99PPnDiPakdMzEyOXnYPHz588uQJFU6HIyEIgiPCMtKAdij6FFu2bJmMjEzvQxKJVFZW9vLlS35+/iVLloiI0GIRPQAQBCGGU2feRH5+/pkzZxw8bXn4uKlywsEc8jz65eM3j6Db3PyCqHYE/pLK/OXLzbee9g+cNE1x3uI56HWktUpz0rSJ3t7er169wv3dRipE6k0jAoBCFBUNXl5evzYWFRVpaWmpqalNnAgDu8AIs3PnTj4B3rUbjVDt5dr5G7cuJVp4HJeZBB/uI4CutWfxl2wfR9/YB2clZMRR6gWHw23e5ehouOX69euGhoYo9QIASv58IKSMjMz+/fs9PT1hhWkwsrx9+/bq1av2HrbsHOzo9ZL9NifA5+QCXSv1lebo9QKoCIfH2+4+zcYp4Gbl1dHegV5Hagtmzl44y8fHp6uLRpttAkAtfzV7YtKkSS0tLZ8+faJWGgBowMPDQ0ZBWm/tCvS6qK2q87DeIakwzWQzKnsiA5RwcvM57D9X+r3Cd8tBVAdFbt7lWFBQEBkZiV4XAKDhr4qGd+/eIQjCzAzDu8CIcefOndTU1M07HZmYmVDqoovQtW2Dd1c3s/2+s8wsrCj1AlAiMX6ypefJR7efxIRcRK+XiVMVl61Zsnfv3paWFvR6AYDqKPq+z8nJaWpq6tvS1taWkZHh7+8vKio6ZcoUdLIB0B+TQyYJwf3x+K/u7m5PT0/VudMXLptP3WB9Hdlx/FP2V7cTCXyCMEZ4RJqxSL/4a3bQgbAJSgpzNNVQ6sV5h/3qO6aHDx8+cOAPL0cxu3wkdndxi7A2+cKyvIBGKCoaNm3aNODeE9LS0ufPn4crDYBmSDiERPzzi8YRERFfvny5cD+KipH6uXb+RkLMLTP3gPHKs9DrBaBttd3O0vyPOxz2xNyLkpSVQKMLUQkRM3uTEydO2NvbS0lJ/cEZiF1dJITYUQ3LSAPaoWjDqmvXrpWVlfVt4ebmlpGRmT9/PisrXH0FtIN3zCQREVK46h+8trGxUUFBQU1zhl/IbqoHI8t6nW1vsHn20rXm246i1AWdYIwNq4bW1txw0F6bj4fp3N1ITi6O37/gD7poadNTM16mvfzixT+5FYK3f09CiCxMOEIoTM8BNELRRQKYFwQYgJ+fX2tbq4uPA0rnr/pR5WHtI6U43XTLQZS6ALTExcPvdCDmiJPObhe/gLMH/nJNhYG74OZy9Nx4cHuAi4vLnDkoLg4BALXA3hNgVPjy5UtwcLCVixlKe1N1/ux03+BNRNgdYPAjAxGXm7TeOyglOTXi6FmUulhtris/afzWrVthPX4wIkDRAEYFNze3sUL8Vi6oLBpNIpH2uR7KzytyPHCOZyzqG2YCWvpn4coVlu4RR88+SXqKxvnxTPhtflvevHlz4cIFNM4PAHUNVTRoaGjgKPD06VNapQWjHTMrJx4/7Eo3KSkpOTnZdY8LSqs5nQuKvXv9gYXHcWlFFTTOD7C1coOHivry3S5++Z8K0Dj/zPmqWqs0vby8mpubh/VCVhwHHmESFIJJOoB2hhoIuWnTpvfv3w/2LIFAyMjIIJFIKSkpGhoaqKQD4K91dnZOmTKFT5gn4kYwGud/dj/dzcprianzGvtRtInlaBgI2VdnR5u/8woioeHCgzP8AnxUP39FWeWauWudnZyPHTtG9ZMDQEUUzZ7oh0QixcfH79ixo7CwcMKECffu3ZOVlUUjHAB/b//+/b57fS8/OS8/UY7qJy/4XLhex05+6nynA+dxw78EMnKNtqIBQZC6ytJD9tryEyVOXzvFwspC9fNHHD175sT59+/fKynBoguAfg37Y+7Zs2ezZ882NTVtbW0NCQnJzc2FigHQraKiokOHDpnaGqFRMTTUNbqab+cbJ2WzK2xUVQyjk4CIpL1fdE5m3gEPfzTOv36TubC4kIuLC6rLVwPwl4bxSZeXl6enp7dw4cKPHz/u3LmzoKDAyckJVnYC9Gzz5s1cPJwOHjZUPzOB0OW+3ru5udP5UCw75xiqnx/QIYWps9e5+d++nBQTeonqJ2dlY/U85Pb06dPY2FiqnxwAaqGoaKisrHRwcFBWVk5KSrKxscnPz/fz8+Pm5kY7HAD9cHp+YHbMpPDgGzdu3Llzx33fZi5uLqon2e92+OO7PIf95wRF/2QtPzBCzdNZp23qdGpfaOq9NOqffPGcxSs1PDw8GhoaKDl+jOM35k3vlXxzqZ4EgMH8pmhoa2vbu3evvLx8eHj40qVLs7Ozo6KixMTEaBMOgH5+Nnf1UDabvaWlZfPmzXM01bT1tage48yJc4nxdy08jssro7UxAaBba+x3K8/R9nH0/Zzzleon37Z/a0tbi6enJyUHtxObewjEfFhGGtDQUEXDpUuX5OXlfX19J06c+OTJk6SkJBihA0aKnTt31tbV7vD3oPqZH956HHo4UsfCdfZSY6qfHNA/HB5vs/u0gJi8q8X26ooa6p5cWEzI2ds+KioqLY36VzIA+HtDzZ7Q0NBITU0VFxfX09MbYgnVLVu2KCgooBMPgP9B4d4Tr1+/njdvnouPvZWLOXUDfMj4aLfaZeo8Hdvd4WisKzxSjMLZE/001lYcsl8mIsoddTuMujtTEHuI61fYdbcTs7Ky2NjYhjgS9p4AtPf7ouG3p4B1GgDNUFI0EAgEVVXVbhzhwv0zTMxMVOy9rOiH1fKNY0UVXE8ksLAO9WnO8KBoQBCktOBjgMuqWbUd5ncAACAASURBVOrTjp8/Qt1/afmfCsyWWHtu99y/f/8Qh0HRAGhvqLkPUVFRra2tvz2FvLw89fIA8LcOHDiQ9znvwr0o6n6ON9Y3blrrzsLO73TwwiivGACZpPwUO9/IkB2WR7yP7wig5o0whcny6zeZ+/v7GxoaqqjAMqOAjgxVNEA1AOgNjsiE4IYaCZmdnX3o0KH1LuYTpypSsd/On52uFp51ta2eocljeMdS8cxgRJsyW2vt1kMXj3mISops2GxBxTPbum1ISUrdsGHDmzdvWFgGXksKjyMSEYSVxEvFfgEY2p+sCAkAfSIQCGpqas3/1959xjV19n0APxmEEUbYS1BZgjgQUVRARKW3OHCDiooFKihaFW0RVMCJqyp1VRzVWmfrVhR3FTeIioogQxDZEAIEQsh4XuR++HhbRyAHCPD7viInV/7556MHfrnOOdep5Ry9cZBB3pp9IqHoJ7/whJuPF205Zd6jH1ll2zQcnvjY2b1rrxz5ddWOFaMmjyCx7Kvk1FmjZq9YviIyMpLEsgCywDJ20H6sWbMm5WXKqu0rSEwMBEFsCNvyT/y9gBV7kBjgs8YGhDu6T1q5cN2D249JLGvbx8Y32Gft2rVPnz4lsSyALBAaoJ149OhRdHS034KZ3e2sSSy795ff/zp4esrCaDsXDxLLQntCoVBmhm6zsnP+yS/89bM3JFae/ZN/F8vOM2fO5PF4JJYFaDKEBmgPuFzuzJkzLW0tfgj5nsSyp/84t3vD3lG+i13HziKxLLQ/NLpC0OrfdY2tfpy2OCczl6yyDIbC6p0R6W/Tw8PDyaoJIAuEBmgPQkJCcnJz1uyMoCuQdjOUGxdvrwvdNNjT19NPquX5oINTVGbO33hMQVkr2GsRiYs+WdlazA2bHRMTc+3aNbJqAjQZQgO0JdQ5SZTAT4/vnjt3LjY2dlHUvK5WXch6o4f/PF4WFGnnMmrqovVk1YR2T42lvWDzyZpaIthrIYfNIavsjDlT7QfazZo1q7S09OPt1KCnlMAkxfkpZL0RwDchNECb8z/X++Tl5fn7+w/+j/Pk7yeQ9QbPn6Qs9g2z6O3kv3w3lUrmYg/Q7mkbmCz85WRRIWfelBBuFZeUmlQqdfXOCG5ttb+///9c7yamEAQhFtaT8i4A0kBogDZMIBD4+PjQGNSomHCyFnV+k5L+47TFRuY9g9YcpCswSKkJHYphl24/bjqemZ63cMbPvFpyTmDUN9KLjFl24cKFmJgYUgoCNA1CA7RhUVFRCfcS1uyKZGmxSCmYlZYd7LVQU99s3vojikoqpNSEDqhzN7t50X+mPH2zeFYYn0/OTMCQES7e/pNCQ0OfPHlCSkGAJkBogLbqypUr0dHRQT8FODiRs/B+TmZu0KQfldX1F2w+qaKKVfZAJpa9B85Zc+hJQnJowPJ6knLDwqh5Ft3NvLy82Gw2KQUBGguhAdoU8X+PQeTk5EyfPt3RtZ/fwpmkFH6fnRc08UeaouaiLaewUDSQonu/IYGrDty78SgsMFJQL5C9IIOhsGHvmvKKshkzZohEIgpNTBAUqqKq7JUBpIRlpKHt4fF4zs7OH4ryjt04qKFJwpRA3rsPP4yfJ6YwF8ecZekYyF6w3cMy0tJ7dvdybFSAm4dz9J5VpNxE7e7Ve4tmhkZGRkZERMheDaBRMNMAbU9gYGDKy5TNB6JJSQzvs/N+GBcspjBDtp1GYgDS2bl4/BC199blhNAfVpAy3+DynZP/It+VK1deuHBB9moAjYLQAG3Mtm3b/vjjj/BNP5GyXPS7tzkBY+cSNLXFMWc0dY1kLwjwb31cRs6O2nfn6v2f/ZeRcl5k4E/+TsMHTp8+PTU1VfZqANJDaIC2JD4+fsmSJdNme3lOGSV7tYzUzB/GBdMVtUK2nWHpGMpeEOBL7Fw8AlftT7jxaLHv0jpenYzVqFTq2l2R2gZanp6e5eXlpHQIIA2EBmgzXr9+7e3t3X+ww6Ko+bJXe5Wc+sO4YGV1A5zHAC2j16D/zF13+Mm9Z/OnLuZW18hYTVVdddvhjSVlxZMmTeLz+aR0CPBNCA3QNhQXF48ePbrS6/oDy52yn032JOFp4MT5mgYWIdvOqGnqkNIhwDfZ9nf7cePxl8/eBk2cL/s60+MPmHIm30iwiAwKCiKlPYBvQmiANoDL5Y4ZM6aymkOQsabzrct35k8NMbXqG7L1FFNdk4SKAFKz7D0wZOupnKwif8+5RfkyXX4iFtMJgkLQqQcPHly1ahVZHQJ8BUIDyDuBQODt7Z3yMmXbn5tkr3b68Pmf/MJtHd3nbTiqqMyUvSBAY3XuZrdk+/mKivrvRwdmpWXLXI8+e4lfVFTU/v37SWgO4KsQGkCuicXigICAK/FXNuxbY9vHRsZSezbtX7N4vfOoGbNX7lNgKJLVJEBjGXa2+nnnRaoCy99zzrNHz2WsNnuJ3zifMUFBQefOnSOlPYAvQWgAuRYSEvLHH39Ebgt3Hj5QljqCesHKhetiNx/w9Av1WbwJ966EVqepa/TT9gt6prZBkxZcO3dDllIUCmXZpp9d3AdNmTLl1q1bZHUI8G8IDSC/IiMjt23btnj1gtFeHrLUqeJUz5sScumvq75Lfx3lu5is9gBkpKLGWvjLX72cRi6dHfH7r4dlKUWlUaNjV/XsZzt27NiHDx+S1SHAJ7CMNMip6Ojo8PDwuUtnB4TMkqXO++y8BdN/Ki6oCFx9wNrehaTuOjosI00isVh8bt+6y3/GjPEeueyXUAZDocmlari1cycvyM3Iu379et++fUlsEkACMw0gj9avXx8eHh4QMkvGxPD4buLMEQE1NdTQ3XFIDCCfKBTKuB+WzQrbfvn09aAJ88tKmr5YkwpTecfxLcZdDd3d3ZOSkkhsEkACoQHkzurVq8PCwvwX+s5dOluWOkf3nAj2XmRo1nvpb1cMTC3Jag+gOQwc4b1o66mstwXTv/N//exNk+uoqqvuOhlj1MVg+PDhjx49IrFDAAKhAeRNWFhYRETE7CV+weGBTS5SW1O7fO7KzStihowPWLDpBBZjgDbBoqdjeOxVJVVDvzFB549danIdNQ3VXSdjTMyN3d3d//nnHxI7BEBoAHkhEonmzJmzYcOGBRFzg34OaHKddxm5vh6zr1+847dsl9e81VQancQmAZqVpp7xTzsuOAydELVg7eqQ9U2+S4Wahuquv2K69bL08PC4ePEiuU1CR4bQAHKBx+N5e3vH7o1dtuln33nTvzSs33J63+VfCwFXTl+b/p1fZZU4dNdlx+8mNUOnAM1LgaHou/TX6Us2XzwZ7+vxQ05m7pdG9l/F6LtCwW3t559lqqrsOL6lv6vD+PHjDxw40FztQgeD0ACtr6yszN3d/fyF8xv3rZkwc+xXRoqoFPEX/tPyanmrQ9aHB0XaOAxfFnutk3n35mkWoCW4jJkZuiuuokLo4+4X93f8Z8cI+YRYTKn58s2qGIqMzQfWjZkyMiAgICoqCtfKgewwcwutLD09ffTo0cWlxb+d+tWuf6+mFUl9/mbZ3JUfcot8Fm8a7OlLbocArcLEsueyvdePbPl5+dyV9248WLp+iZqGamOL0Oi0FVuW6hvprVq1KjMzc+/evUpKSs3RLXQQmGmA1nT16tUBAwbwRXWHLsc2LTGIhKL92w75jpwtEDHD91xDYoD2RImp5r9i96yw7bcu3/d2m/Ek4WnT6sxe4rd6Z8TJkyeGDh1aWFhIbpPQoSA0QOsQi8WbNm0aOXKkdW/LQ5f3mpqZNKHIu7c5348O3LV+79CJs8P2XDXq2o30PgFa3cAR3iv232Jqdp4z6cdNy7byanlNKDJy0n9iz+5Mz0x3cHDAkpHQZAgN0Ao4HM6kSZNCQ0N9Ar23H9uizlJrbAWhQPj7r4enDPUtKqxZHHN24pwougKjOVoFkAc6Rp2X/HpufGDE34fOe7k2ccqhl0OPI9cOaOqzXF1dd+zYQXqT0BEgNEBLS0xM7Nu3b/y1+A371iyMmkelNeI/IU2hnkIlXiWnTv/Ob+e62MFj/VYcuG3Za0DzdQsgJ6hU2ndT5i7ff1NRzTho4vyVC9fRlcQUipBFaUQRPUPdfed3jfMZPX/+/MmTJ1dUVDRbv9A+4d4T0HKEQuHmzZsjIiLMrLuuj13VhEMSlRVVu6L3/H3orFFX6+lLtnTtbt8cfcI34d4TrUgsEv1z/tCZ2DXKSrQFEcFjpoykUBoTHAiCIIirZ6+vWbJRW1P78OHDLi5YYR2khZkGaCFZWVlubm5hYWHeARMPxcU2NjGIRKIzf54fP9D73PH48YER4bHXkBigY6JQqUPGfb/yj3vmvYdELVj7/ajAJiw7/d244cduHtTQVRsyZEhoaGhdXRNXkYKOBjMN0OxEItHOnTvDwsLUNFWjYpb1d3FobIUnCU+3Rv6a9vKtw9BxE+dEauoaNUefID3MNMiJN0l3T2wPL8x5O2ryiODwQD1D3Ua9XHJuUOwvB6wsrfbv3z9gAI70wTcgNEDzevHiRVBQ0MOHD8fP8FwUOY+pxmzUyzPeZG1fs/vu1XtdrPtMnrfKoqdjM/UJjYLQID9EQsE/5w5d+H2jsL7WJ2iKb/A0VfXGLeeQ/iojasHa9Jdvg4ODV69eraGh0UytQjuA0ADNhcPhrFy5cvv27Z26GC3bHNp3UJ9Gvfx9dt6eTfuvnL6mpd9pbEBYv2ETKBRK2E2Cy2H/Oh43oGplCA3yIOpmfTmneoCn5jQaUVPNufJnzM3T+1RUFH3n+Xj7T1RWUZa+lFAg/HPP8dhNB1garI0bN/r4+DThPAnoCBAagHxCoXD//v0rVqyorKr0WzBz5jwfBkNB+pfnZOYe2PZH3Kl4prr2yOkLXTxnNlxOGXQsVywi9viYNk/jIC2EBnkQdCRPTIgUaJQdU/57hhC7JP/SoS33Lx/TYKnOCJ42edYEFWYjokNBXuHmZdtuXb4zaNCgLVu2ODpiYg8+hRMhgWTnzp3r1atXUFCQvXPvM/ePBYTMkj4xvHmRtnR2xETnaf9cfTJ+9oq1x564TQzAAgwAUtLUNZq+ZPOqw/dt+nvsWBs7yn78nk372WXSXldp2Mngl0Prd/21rbi8cODAgd7e3unp6c3aMLQ5CA1AmsuXLzs6Oo4bN06ZpXjocuy631bqG+tL80KRSHT36r3AifOnDf8+6cEbr3lr151Icveey1BqxJckAJDQMeo8M3Tb6iMP+7hOPPDrkZF9xq/7adO7tzlSvnyAa//jt/5Y/kvo7bu3bG1t/fz8srKymrVhaENweAJkJRKJzp07Fx0d/eTJkx723eeE/jDQTdpZTQ678sLxS38dPPM+O69zt97DvYIc3MZSaV+8jxoOT8gJHJ6QB/8+PPFvVRVlt88c+Ofc79UVZQNc+032mzjY3UnKFdXqeHUnD5w+uP1wFad66tSpoaGhtra25LUPbRJCAzQdl8s9dOhQTExMenq6Xf9e/ot8nYYNlOaFYrE46X7y2SMXrl+4JRSK+wwe5TbBX5orIxAa5ARCgzyQJjRI1PPrntw4ffvMgZy05/pGep5TR3lOHWVsKtWly7U1tacOnT28+1hpUZmHh8fChQuHDx+O0yQ7LIQGaIrU1NQ9e/YcOnSosrLSdYTLjLlTpbxH5buM3Cunr146eeVDbr6OUWfnUT5OI6epa+lJ+b5zjuaKxAgNrQ+hQR7MOZorElNU1LW2jpH2SuZ3qU/vXDicePMsn1djP9ButJfH0FFDpLnjNp9ff/nv+CO/Hc94k2VjYzNnzpwZM2awWCzZPgG0PQgN0AgcDuevv/46ePDgvXv3NDQ1xk4dNdlvgjTfV/Lefbhx8Vb82RtvXqQpqaj2GTxqkMcUy96D8H2ljUJoaNPqarlJty88uHLi7YsHCnSa07CBwz2HDv7O6ZvLqIjF4icJSSf2n7pzNYGhwJgwYYKvr+/QoUNpNFrLdA6tDqEBvq2mpubSpUvHjx+Pi4vj8/n9nPuOnTZ66ChXhuLXrmsQi8Wpz9PuXE24fflO+qsMBYairePQfkPH9xr0H5zh2NYhNLQP7OIPT26cTbx1NiftOYOh0M/FYcgIF2f3QfpG35j8KyksPX/s4vnjce+z84yMjLy8vLy9vR0dHfE1oN1DaIAvKi4ujouLO3/+fHx8fE1NjZWtxYgJ33lMcP/6NRFlJeWP7yQ+vP34/s2HZSXlSky1Ho7D+riM7DFguJJK4xaqA7mF0NDOlBbkJt+5+Ozu5axXiSKR0MrWYtDQAQOG9Lfr3+sr3w3EYvHzJylXTl29dv4mu6yiU6dOY8eO9fT0dHV1VVRUbMn+ocUgNMD/4PP5Dx48uH79+tWrVxMTE8VicXc7a7eRrkNHDeli8cUzCUqLypIfPU9++DwxISkzLVssFht17Wbbf1gPx2EWvRyx0EL7g9DQXlVzyl89uvny0Y3UxNtVFWWKSoq9+vXoO7CP/UC7HvbdlZSVPvsqkVCUeP/pzYu3/4lPKMovZjKZw4YNc3d3HzZsmI2NTQt/BGhWCA1AVFZWPnr06N69e3fv3n348GFNTY06S62/i8OgoQOchw/S0df+90vqeHVpL9++Sk59lfz6+ZOUDzn5BEFoG5hY2Q2ytnex7uvC0jFs8c8BLQehod0Ti0TvM16+eZqQlpyQkfKIx62iK9Cte1r1sLftYd+9u52NqVknKvXTSzfFYnFaSvq9Gw/u3XyUkvRSKBAaGhoOHjzY2dnZycmpZ8+edPoXL6iGNgGhoSOqrKxMSUlJTk5OSkpKTExMTU0VCoWq6qp2/Xv2HdTHwamvTa9un1zJXZRfnJX+7u3rjPSXb9Nfvs3OyBEKhDS6golFj67d7c1sHSx7DdDUM27uzoOO5YpFlD0+jbutNpAOoUEeBB19LxaLGQzG9skGzfpGIpEwL+NVZsrjrNdJWa8TS/NzCIJQUVWxsrXs1sPSsruFZXdzM6sun5xHya2uefrgWeK9pOSHz1NfpAkFQiaTaW9v7+DgYG9vb2dnZ21tjQzR5iA0tH+lpaXp6elpaWlv3rxJTU19+fLlu3fvxGIxXYFu3q1rdzubHvbdezr0MLPqIvnewGFX5ufmv8/Oe5+d9y4jNyczN/ttDreKSxAEQ0nZqIu1iUUPE6ueppa9TCx7tPChB6zTICcQGuSB9Os0kKuaU56T9iw3/UXu25S8jFcl+e/EIhFBEPpGel0sOnc2NzE1NzU1M+nU2cjI1FBySkRtTe3rZ29Skl69Sn79+tmbgrxCgiAUFRVtbGy6d+9ua2trZWVlZWVlaWmprIyzpOUaQkM7IRQKCwsLP3z48OHDh9zc3JycnJycnOzs7KysLA6HIxmjb6xvZtnZ3MbcwtrM0MRAnaXOYXNKCkuL8ouLC0oKPxQVvC/Mf18gyQcEQaioauh1MtM3NTfsbGVgamlsZqNj1JlKbc1rqxAa5ARCgzxordDwCT6vtiAnLT87rSAnveh9RlFuZkn+O0E9nyAICoWiradlZGJo0MnAwEhPz0hP30hPR19bUUmxrLgsJyM3401WRmrWu4yc6spqSTVjY+OuXbt27dq1c+fOpqamnTp1MjU1NTAw0Nb+zHFSaHkIDfKOz+dXVlZWVFRUVFSw2ezy8vLy8vKysrKysrKSkpKSkpLCwsLi4uLi4mKRSCR5CV2BrqWjydLSUGOpM5nKikqKNDpNLBJXVVaXl7LZpWx2WQW/jt/wFkpMNS09I01dYy39TtoGnXQMTXUMu+gad1HV0GqlD/1FCA1yAqFBHshJaPg3kUjILs4vyX9Xmp9TVvi+rOh9eWEeuyS/orRQEiYkVJjKOvo6mtosljZLSVmREBNCobC2pra6ilvJrmKXsTnsyoa/UIqKivr6+kZGRrq6ujo6Orq6urq6ulpaWlpaWpqamiwWS0NDQ0NDQ1NTs5U+dEeB40nSqqurq6mp+WRjfX19dXV1w0M+n19YWNjwv7zhK35paSmPxyMIora2tr6+vr6+vqysjCAILpcrFArZbLZQKKyqquLz+Vwut66ujsfj1dTU8Hg8Ho8nEAj+3YyCggKNTqPRqBQqVSwWi4QiOp3G5/83NAjqBcUFJcUFJQRBUKhUphqLqaGlqq6pqqGt3cmsay9dNU1dNZY2S8dQTVNHS89YUVna5eQAAL6JSqVpG5hoG5gQ9i6fPFXJLqksL6koLagsL+GUFVVzyqoqSkvLy6s5pdWcMm5VBY9b9e+CFAqFQiXKykvL2GUUCiESiQX1AqFAKBQK/z1YUVFRSUmJwWCoqakpKioqKCiwWCwFBQUlJSVlZWUmkyl5lk6n6+joEAShrKysoKBAEISmpiaTySQIgsFgNBwlkYSVhvUnKBTKZ9fBZLFYHWSNirYaGhgMxmf/mjaN3E63UGl0hrIqhSCUmOoUKkWZqU6j0ZnqGjQaXZmpTmcwGIoqKqrqCgwlRWWmsqqakoqqorKqsqq6MlNdmammoqahzFRv7Q9BLipBiFq7ByAIguggvyLlGkVMEAS1Tf07qGvqqmvqdjLv/qUBIpGwpopTy62sqeLUVlfW1lTxuFV1vJq6mupabiW/jsfn1dZyK4X1fF5NNb+OV8er4fNq+XU8QT2/vq5WTBB1tdV1dXUEQZSUlLTgJ/s2cneZfv36PXr0iMSCUmqrhycmT57M5XLJrclgML6+GCqDwVBX//RvsIKCgiSlSqioqDSMYTAYkqeoVKqRkRFBEDQaTRJgNTQ0Gq5WYjKZDAZDMkxDQ0OyEZNsAABN1jANLBAIqqr+O3vB5XL5/P8eH5FMHovFYsmfkrq6uoaQwePxGuYwSkpKPp7PqK2t/eSPpkgkqqioaDg6/Fl8Pv+zkyKycHV1DQ0NJbemNNpqaAAAAIAWJtVd1QEAAAAQGgAAAEAqCA0AAAAgFYQGAAAAkApCAwAAAEgFoQEAAACkgtAAAAAAUkFoAAAAAKkgNAAAAIBUEBoAAABAKggNAAAAIJVmvMvlwIED09LSmq8+yAmRSCQUCj++axe0ewKBgEqlNtx0Ddo97OYdSmho6JfuhtWMoSEgIKC0tLT56oOcePz48enTp6Ojo3Gv5I5j48aNPXv29PDwaO1GoIU8f/782LFjkZGRkvv0Qvvm5OT0padwl0uQVWxsbGBgoFAoxPfOjsPMzMzLy2v9+vWt3Qi0kOPHj0+dOpXNZrNYrNbuBVoTfssDAACAVBAaAAAAQCrNeE4DdBDDhw8/efIkTmjoUHbs2GFiYtLaXUDLcXJyOnnyJJPJbO1GoJXhnAYAAACQCg5PAAAAgFQQGgAAAEAqOKcBGiEzM/PUqVPPnj2rrKw0NzcPCAjo2bPnlwbv3r37xIkTH2/x9PQMCQlp/jaBHFeuXDl8+HB5eXmvXr0WL16sp6dH1mCQQ9i7QRqYaQBpCYVCCwuLjRs31tbWampqnjlzxs7Obt++fV8an5mZmZiY2OMjxsbGLdkwyGL79u0eHh5sNtvGxubo0aMODg5FRUWkDAY5hL0bpCUGkI5QKPz999/r6uokD7lcrr29vZqaGo/H++z4xYsXGxsbt2CDQJri4mIVFZXvv/9e8jAvL09NTS0oKEj2wSCfsHeDlDDTANKiUqmzZs1iMBiShyoqKj4+PlVVVbm5ua3bGJDu/PnzNTU1ixYtkjw0NjaeNGnSiRMnRCKRjINBPmHvBikhNEDTffjwgUaj6evrf2lARUWFh4eHo6PjlClT4uPjW7I3kEVycrKSklKPHj0atjg4OLDZ7OzsbBkHQ1uBvRs+C6EBmigrKys2NtbHx0ddXf2zA+h0uqOjo7W1tYODQ2Ji4ogRI6Kjo1u4SWiawsJCXV3djxfskvzx+OyZCo0aDG0C9m74Elw9AU1RUVExbtw4PT29rVu3fmnM2rVraTSa5Gc+nz969OiIiAgfHx9TU9OWahOaSCAQ0On/88tB8rC+vl7GwSD/sHfDV2CmAT7vwIEDDv9vyZIlHz/F5XJHjRpVVlZ27do1LS2tL1Vo+J1CEASDwQgPDxcIBLdv326+noEsampq1dXVH2+RPFRTU5NxMMg57N3wdZhpgM+zsrIaN26c5Odu3bo1bOfxeGPGjElPT79z546ZmZn0BTU1NQmCqKmpIbdPaA4WFhZ//vknh8PR0NCQbMnIyCAIwtzcXMbBIM+wd8M3YaYBPs/Z2Xn5/5s8ebJkI5/PnzBhwtOnT+Pj421sbBpV8MqVK8T/5g+QW0OHDiUIIi4urmFLXFycg4NDQyxo8mCQW9i7QSqtfc0ntBkCgWDChAkMBuPcuXMlH6mvr5cM2L59u6ura0VFhVgsrqurmzJlyqVLl9LS0l6+fBkdHa2kpGRvby8UClv1Q4C0HB0dTUxMHj9+zOFwVq1aRRDEiRMnJE9VVVW5urpu27ZNmsHQJmDvBikhNIC0CgoKPps77969KxmwYMECgiBKSkrEYnFdXd3Hp0RRKJQxY8YUFBS06ieARsjNze3fv7/kn4/BYKxZs6bhKTabTRBEcHCwNIOhTcDeDVLCrbFBWnw+//79+//e3qdPH8lEdEZGRl5enpOTk4KCguSpgoKCvLw8oVBoaWmpra3dou0CGdLS0thstrW1NYvFatgoEAgSEhKMjY0tLS2/ORjaBOzdICWEBgAAAJAKToQEAAAAqSA0AAAAgFQQGgAAAEAqCA0AAAAgFYQGAAAAkAqWkQbooMrKylJSUr4+5uNL7KS3fPnytWvXpqamWltbN7U7AJBHCA0AHdSDBw/GjBnz9TElJSU6OjqNrWxiYuLo6KisrCx5GBsbe/To0ePHjxsYGDSlUQCQG1inAaCDYrPZr169anj422+/HTlyZN26dS4uLg0bBwwY8Mltr5tg6dKlGzZsyM7O7tKli4ylAKB1YaYBoIPS1NR0dnZueHjx4kWCIGxsbBo2vn37NiEhYdCgQQwGIz09PT8/39zc3MTERPJs1LeWtAAAAsZJREFUaWnp+/fv+Xy+ubn5J7MR2dnZOTk5/fv3V1FRefbsWW5uLkEQDx8+fPfuHUEQJiYmuAEmQBuFEyEB4PNiYmLc3Nzi4uKsrKysra2HDRv28OFDgiBu3rxpa2urq6trb28/YMAAPT09T0/PkpKShhfu37/fzc1NkhUWLlx47NgxgiCmTp3q5ubm5ua2e/fu1vpEACAjhAYA+Bp/f/+ff/6Zw+HU1tYOHz6cIIjCwkIXF5dLly6lpqYmJSVFRERcvnzZy8vrsy+/fft2aGgoQRDZ2dmSG95s3ry5RT8AAJAHhycA4GvmzJkTEBAg+ZnBYBAEMW3atGnTpjUMsLe3r62t3bhx46tXr2xtbVunSwBoEQgNAPA1np6en91eUlKSnp7O5XJFIpGKigpBEC9fvkRoAGjfEBoA4GuMjY0/2ZKdnR0QEHDr1i2xWEyj0eh0ukgkIgiiurq6NRoEgJaDcxoA4GtoNNonW8aNG5ecnPz333+z2WyBQMDj8f7++2+CIHD9NkC7h9AAAI2Qn5//4sWLqVOnTpgwgcViSTYmJiZ+5SVUKpUgCMlsBAC0aQgNANAIWlpadDr9+fPnQqFQsiUxMXHr1q1feYlkFQfJIg0A0KYhNABAIygpKQUHB9+7d8/W1tbX19fd3d3Z2flL11tKeHh40On0GTNmzJo1Kygo6NSpUy3WLQCQCydCAgBBEISZmZmrq+vHaztaWlq6urpKLrP82NatW/v16xcXF1dUVGRhYbFu3ToqlZqZmWloaCgZ0LVrV1dXV8klFQRB2NjYXL9+/dChQ+/fv6+vr+/du3fLfCIAIB3uPQEAAABSweEJAAAAkApCAwAAAEgFoQEAAACkgtAAAAAAUkFoAAAAAKkgNAAAAIBUEBoAAABAKggNAAAAIBWEBgAAAJAKQgMAAABIBaEBAAAApILQAAAAAFL5PzRdCff/pjdbAAAAAElFTkSuQmCC" width="600" /></p>
 </center>
 <ul>
-<li><p>Your PGS Z-score for Body Mass IndexBody Mass Index is 0.994
-which is higher than 84% of other EAS individuals.</p></li>
+<li><p>Your PGS Z-score for Body Mass IndexBody Mass Index is 0.058
+which is higher than 52.3% of other EAS individuals.</p></li>
 <li><p>Assuming the PGS explains 0% of the variance in Body Mass Index,
 and the mean and SD of Body Mass Index in the general population is 0
 and 1 respectively, on average people with your PGS have a Body Mass
@@ -4207,7 +4238,7 @@ <h3 class="tabset tabset-fade">Method: pT+clump</h3>
 1
 </td>
 <td style="text-align:center;">
-1.486
+1.27
 </td>
 <td style="text-align:center;">
 NA
@@ -4229,11 +4260,11 @@ <h3 class="tabset tabset-fade">Method: pT+clump</h3>
 <div id="gwas-body-mass-index-1" class="section level4 tabset tabset-fade">
 <h4 class="tabset tabset-fade">GWAS: Body Mass Index</h4>
 <center>
-<p><img 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" 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" width="600" /></p>
 </center>
 <ul>
-<li><p>Your PGS Z-score for Body Mass IndexBody Mass Index is 1.486
-which is higher than 93.1% of other EAS individuals.</p></li>
+<li><p>Your PGS Z-score for Body Mass IndexBody Mass Index is 1.27 which
+is higher than 89.8% of other EAS individuals.</p></li>
 <li><p>Assuming the PGS explains 0% of the variance in Body Mass Index,
 and the mean and SD of Body Mass Index in the general population is 0
 and 1 respectively, on average people with your PGS have a Body Mass
@@ -4245,119 +4276,12 @@ <h4 class="tabset tabset-fade">GWAS: Body Mass Index</h4>
 </ul>
 </div>
 </div>
-<div id="method-external" class="section level3 tabset tabset-fade">
-<h3 class="tabset tabset-fade">Method: External</h3>
-<table class="table table-striped table-hover table-responsive" style="margin-left: auto; margin-right: auto;">
-<thead>
-<tr>
-<th style="empty-cells: hide;border-bottom:hidden;" colspan="1">
-</th>
-<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="2">
-<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
-PGS Descriptives
-</div>
-</th>
-<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="3">
-<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
-Distribution in General<br>Population
-</div>
-</th>
-<th style="border-bottom:hidden;padding-bottom:0; padding-left:3px;padding-right:3px;text-align: center; " colspan="4">
-<div style="border-bottom: 1px solid #ddd; padding-bottom: 5px; ">
-Distribution in People<br>Like You
-</div>
-</th>
-</tr>
-<tr>
-<th style="text-align:center;">
-Outcome
-</th>
-<th style="text-align:center;">
-PGS R-squared
-</th>
-<th style="text-align:center;">
-PGS AUC
-</th>
-<th style="text-align:center;">
-Prevelance
-</th>
-<th style="text-align:center;">
-Mean
-</th>
-<th style="text-align:center;">
-SD
-</th>
-<th style="text-align:center;">
-PGS Z-score
-</th>
-<th style="text-align:center;">
-Prevalence
-</th>
-<th style="text-align:center;">
-Mean
-</th>
-<th style="text-align:center;">
-SD
-</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td style="text-align:center;">
-Height Yengo EUR
-</td>
-<td style="text-align:center;">
-NA
-</td>
-<td style="text-align:center;">
-NA
-</td>
-<td style="text-align:center;">
-NA
-</td>
-<td style="text-align:center;">
-NA
-</td>
-<td style="text-align:center;">
-NA
-</td>
-<td style="text-align:center;">
--0.711
-</td>
-<td style="text-align:center;">
-NA
-</td>
-<td style="text-align:center;">
-NA
-</td>
-<td style="text-align:center;">
-NA
-</td>
-</tr>
-</tbody>
-</table>
-<p><strong>Note.</strong> PGS R-squared/AUC cannot be estimated when
-using externally derived score files. To specify parameters, check out
-our <a href="https://opain.github.io/GenoPred/PRS_to_Abs_tool.html">interactive
-tool</a> for converting polygenic scores to the absolute scale.</p>
-<hr />
-<div id="gwas-height-yengo-eur" class="section level4 tabset tabset-fade">
-<h4 class="tabset tabset-fade">GWAS: Height Yengo EUR</h4>
-<center>
-<p><img 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" width="600" /></p>
-</center>
-<ul>
-<li>Your PGS Z-score for Height Yengo EUR is -0.711 which is higher than
-23.9% of other EAS individuals.</li>
-</ul>
-</div>
-</div>
 </div>
 <div id="section" class="section level2 unnumbered">
 <h2 class="unnumbered"></h2>
 <hr />
 <hr />
-<p>This report was created using GenoPred (v2.2.2-110-gb4e52b5).</p>
+<p>This report was created using GenoPred (v2.2.2-258-gd2f2a91).</p>
 </div>
 </div>
 
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/ancestry_inference_i-example_plink2.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/ancestry_inference_i-example_plink2.txt
index 207c59ca..08162dd6 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/ancestry_inference_i-example_plink2.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/ancestry_inference_i-example_plink2.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-16.3677	0:00:16	452.86	4615.30	449.12	451.21	0.00	1.07	71.43	12.44
+25.8500	0:00:25	468.83	4636.66	466.46	468.56	0.00	1.09	54.48	14.68
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/ancestry_reporter-example_plink2.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/ancestry_reporter-example_plink2.txt
index df6a3842..9f00461d 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/ancestry_reporter-example_plink2.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/ancestry_reporter-example_plink2.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.3205	0:00:00	2.00	4.53	0.13	1.24	0.00	0.00	0.00	0.00
+0.3930	0:00:00	2.00	4.36	0.14	1.12	0.00	0.00	0.00	0.00
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/format_target_i-example_plink2-22.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/format_target_i-example_plink2-22.txt
index 1e0135b3..f976dfb4 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/format_target_i-example_plink2-22.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/format_target_i-example_plink2-22.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.9387	0:00:00	70.86	158.98	67.72	69.58	0.00	0.00	0.00	0.27
+1.5335	0:00:01	73.84	163.89	70.66	72.61	0.00	0.00	31.70	0.78
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/indiv_report_i-example_plink2-4_EAS.4_EAS.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/indiv_report_i-example_plink2-4_EAS.4_EAS.txt
index 314b2985..0be46676 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/indiv_report_i-example_plink2-4_EAS.4_EAS.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/indiv_report_i-example_plink2-4_EAS.4_EAS.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-6.5071	0:00:06	373.91	765.55	369.68	371.98	0.00	0.21	64.05	4.42
+8.3089	0:00:08	374.77	823.55	371.79	373.84	0.00	0.22	80.65	7.04
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-AFR.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-AFR.txt
deleted file mode 100644
index d7bf3b1d..00000000
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-AFR.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.6233	0:00:00	70.41	232.98	67.00	68.88	0.00	0.03	0.00	0.27
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-CSA.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-CSA.txt
deleted file mode 100644
index 4c4a095b..00000000
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-CSA.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.6211	0:00:00	69.86	232.98	66.98	68.82	0.00	0.02	0.00	0.26
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-EAS.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-TRANS.txt
similarity index 54%
rename from pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-EAS.txt
rename to pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-TRANS.txt
index 0cf9f6ce..bb3a0a52 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-EAS.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-TRANS.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.5834	0:00:00	70.77	222.45	67.59	69.47	0.00	0.03	0.00	0.28
+0.9522	0:00:00	68.84	213.21	66.34	68.22	0.00	0.01	0.00	0.34
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_external_i-PGS002804.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_external_i-PGS002804.txt
index 2d30d0fb..2661c22a 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_external_i-PGS002804.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_external_i-PGS002804.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-2.2334	0:00:02	80.87	516135.57	77.45	79.49	0.00	0.09	5.99	0.38
+1.4603	0:00:01	84.83	356.72	82.44	84.34	0.00	0.29	32.67	0.82
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_lassosum_i-BODY04.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_lassosum_i-BODY04.txt
index ee6514bb..5178b8fe 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_lassosum_i-BODY04.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_lassosum_i-BODY04.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-2.1087	0:00:02	253.93	534.92	240.67	247.77	0.00	0.26	39.57	1.12
+2.8474	0:00:02	237.92	390.75	224.36	231.55	0.00	0.27	44.52	1.58
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_ptclump_i-BODY04.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_ptclump_i-BODY04.txt
index b9cb1ddf..6dfa7797 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_ptclump_i-BODY04.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/prep_pgs_ptclump_i-BODY04.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.8102	0:00:00	70.01	213.62	66.91	68.80	0.00	0.06	0.00	0.26
+0.8680	0:00:00	68.92	213.68	66.74	68.64	0.00	0.01	0.00	0.33
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/indiv_report_i-example_plink2-2_EUR.2_EUR.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/ref_pca_i-TRANS.txt
similarity index 52%
rename from pipeline/misc/dev/test_data/output/reference/benchmarks/indiv_report_i-example_plink2-2_EUR.2_EUR.txt
rename to pipeline/misc/dev/test_data/output/reference/benchmarks/ref_pca_i-TRANS.txt
index e9c2e324..702224c8 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/indiv_report_i-example_plink2-2_EUR.2_EUR.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/ref_pca_i-TRANS.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-4.5052	0:00:04	330.16	630.03	324.82	325.57	0.00	1.12	36.65	1.96
+11.3433	0:00:11	193.84	4344.80	190.40	192.54	0.00	0.51	81.34	9.56
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/indiv_report_i-example_plink2-12_MID.12_MID.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/ref_pgs.txt
similarity index 52%
rename from pipeline/misc/dev/test_data/output/reference/benchmarks/indiv_report_i-example_plink2-12_MID.12_MID.txt
rename to pipeline/misc/dev/test_data/output/reference/benchmarks/ref_pgs.txt
index e497f2b3..164705ac 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/indiv_report_i-example_plink2-12_MID.12_MID.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/ref_pgs.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-9.5935	0:00:09	368.09	797.20	360.48	361.50	0.00	1.61	35.83	3.69
+3.5964	0:00:03	155.64	433.56	151.59	153.52	0.00	10.65	62.67	2.62
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/sample_report_i-example_plink2.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/sample_report_i-example_plink2.txt
index 42c4f3c0..eaa2f520 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/sample_report_i-example_plink2.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/sample_report_i-example_plink2.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-2.6543	0:00:02	199.87	457.83	196.50	198.66	0.00	0.12	55.70	1.74
+6.9834	0:00:06	285.29	1049441.62	277.92	280.47	0.00	3.36	53.56	4.10
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/score_reporter.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/score_reporter.txt
index dbca80ce..12f4fa98 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/score_reporter.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/score_reporter.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.4226	0:00:00	2.00	4.53	0.13	1.24	0.00	0.00	0.00	0.00
+0.6003	0:00:00	2.00	4.36	0.16	1.16	0.00	0.00	0.00	0.00
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/sumstat_prep_i-BODY04.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/sumstat_prep_i-BODY04.txt
index ac401e0b..8f5117b0 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/sumstat_prep_i-BODY04.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/sumstat_prep_i-BODY04.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.6972	0:00:00	65.26	146.04	62.62	64.50	0.00	0.00	0.00	0.14
+0.8624	0:00:00	56.83	136.68	52.32	54.13	0.00	0.01	0.00	0.18
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-AFR.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-AFR.txt
index 29defcaa..b38f992e 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-AFR.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-AFR.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.7326	0:00:00	70.86	216.51	67.04	69.10	0.00	0.02	0.00	0.25
+1.0597	0:00:01	68.36	213.05	66.17	68.05	0.00	0.00	0.00	0.34
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-CSA.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-CSA.txt
index 9aafcaaf..55b34e73 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-CSA.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-CSA.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.7210	0:00:00	70.86	216.51	66.91	69.04	0.00	0.02	0.00	0.26
+1.0467	0:00:01	69.59	213.21	66.30	67.88	0.00	0.00	0.00	0.34
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-EAS.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-EAS.txt
index 111afc71..1d025f1d 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-EAS.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-EAS.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.7284	0:00:00	69.27	213.75	66.91	68.78	0.00	0.00	0.00	0.24
+1.0513	0:00:01	68.59	213.21	66.23	68.08	0.00	0.00	0.00	0.35
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-EUR.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-EUR.txt
index 721509e2..21be74ff 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-EUR.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-EUR.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.7317	0:00:00	70.19	213.75	66.89	68.80	0.00	0.02	0.00	0.25
+1.0549	0:00:01	69.27	213.21	66.21	67.80	0.00	0.00	0.00	0.34
diff --git a/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-EUR.txt b/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-TRANS.txt
similarity index 53%
rename from pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-EUR.txt
rename to pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-TRANS.txt
index 0252350a..8f2eb89b 100644
--- a/pipeline/misc/dev/test_data/output/reference/benchmarks/pc_projection_i-example_plink2-EUR.txt
+++ b/pipeline/misc/dev/test_data/output/reference/benchmarks/target_pgs_i-example_plink2-TRANS.txt
@@ -1,2 +1,2 @@
 s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.6145	0:00:00	70.47	232.98	67.01	68.84	0.00	0.02	0.00	0.26
+1.8437	0:00:01	100.91	244.70	98.12	99.99	0.00	0.75	53.60	1.34
diff --git a/pipeline/misc/dev/test_data/output/reference/gwas_sumstat/BODY04/BODY04-cleaned.log b/pipeline/misc/dev/test_data/output/reference/gwas_sumstat/BODY04/BODY04-cleaned.log
index a787e8cf..c623a3e6 100644
--- a/pipeline/misc/dev/test_data/output/reference/gwas_sumstat/BODY04/BODY04-cleaned.log
+++ b/pipeline/misc/dev/test_data/output/reference/gwas_sumstat/BODY04/BODY04-cleaned.log
@@ -3,20 +3,20 @@
 # For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
 #################################################################
 ---------------
- Parameter  Value                                                                 
- sumstats   misc/dev/test_data/gwas/BODY04.gz                                     
- ref_chr    misc/dev/test_data/ref/ref.chr                                        
- population EUR                                                                   
- sampling   <NA>                                                                  
- n          NaN                                                                   
- info       0.9                                                                   
- maf        0.01                                                                  
- maf_diff   0.2                                                                   
- output     misc/dev/test_data/output/reference/gwas_sumstat/BODY04/BODY04-cleaned
- test       chr22                                                                 
- help       FALSE                                                                 
+ Parameter  Value                                                                                        
+ sumstats   misc/dev/test_data/gwas/BODY04.gz                                                            
+ ref_chr    misc/dev/test_data/ref/ref.chr                                                               
+ population EUR                                                                                          
+ sampling   <NA>                                                                                         
+ n          NaN                                                                                          
+ info       0.9                                                                                          
+ maf        0.01                                                                                         
+ maf_diff   0.2                                                                                          
+ output     /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/gwas_sumstat/BODY04/BODY04-cleaned
+ test       chr22                                                                                        
+ help       FALSE                                                                                        
 ---------------
-Analysis started at 2024-07-25 17:38:46
+Analysis started at 2025-04-06 17:28:42
 Reading in sumstats.
 GWAS contains 885 variants.
 ---------------
@@ -42,5 +42,5 @@ After removal of SNPs with duplicate IDs, 871 variants remain.
 After removal of SNPs with N > 334358.59618899 or < 126741.40381101, 841 variants remain.
 Genomic control was not detected.
 After removal of SNPs with SE == 0, 841 variants remain.
-Analysis finished at 2024-07-25 17:38:47
-Analysis duration was 0.21secs
+Analysis finished at 2025-04-06 17:28:43
+Analysis duration was 0.29secs
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/ancestry_inference_i-example_plink2.log b/pipeline/misc/dev/test_data/output/reference/logs/ancestry_inference_i-example_plink2.log
index 260557b4..42998869 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/ancestry_inference_i-example_plink2.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/ancestry_inference_i-example_plink2.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -74,45 +73,45 @@ The following object is masked from ‘package:pROC’:
 
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265d6e241373.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd40b2c7ed.log.
 Options in effect:
   --geno 0.02
   --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265d6e241373
-  --pfile misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd40b2c7ed
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22
   --threads 1
   --write-snplist
 
-Start time: Thu Jul 25 17:38:51 2024
-1031702 MiB RAM detected, ~1018306 available; reserving 4000 MiB for main
+Start time: Sun Apr  6 17:33:47 2025
+1031753 MiB RAM detected, ~923883 available; reserving 4000 MiB for main
 workspace.
 Using 1 compute thread.
 12 samples (5 females, 7 males; 12 founders) loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.psam.
 1000 variants loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pvar.
 Note: No phenotype data present.
 Calculating allele frequencies... 0%done.
 --geno: 10 variants removed due to missing genotype data.
 990 variants remaining after main filters.
 --write-snplist: Variant IDs written to
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265d6e241373.snplist .
-End time: Thu Jul 25 17:38:51 2024
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd40b2c7ed.snplist .
+End time: Sun Apr  6 17:33:47 2025
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265d2f206710.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd6c73cf0e.log.
 Options in effect:
   --geno 0.02
   --hwe 1e-06
   --maf 0.05
   --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265d2f206710
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd6c73cf0e
   --pfile misc/dev/test_data/ref/ref.chr22
   --threads 1
   --write-snplist
 
-Start time: Thu Jul 25 17:38:51 2024
-1031702 MiB RAM detected, ~1018306 available; reserving 4000 MiB for main
+Start time: Sun Apr  6 17:33:47 2025
+1031753 MiB RAM detected, ~923883 available; reserving 4000 MiB for main
 workspace.
 Using 1 compute thread.
 3313 samples (1568 females, 1745 males; 3313 founders) loaded from
@@ -126,21 +125,21 @@ Calculating allele frequencies... 0%done.
 (--maf/--max-maf/--mac/--max-mac).
 590 variants remaining after main filters.
 --write-snplist: Variant IDs written to
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265d2f206710.snplist .
-End time: Thu Jul 25 17:38:51 2024
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd6c73cf0e.snplist .
+End time: Sun Apr  6 17:33:47 2025
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265d18659ffd.chr22.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd19c4ad11.chr22.log.
 Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265d521053ad
+  --extract /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd3b35c7ba
   --indep-pairwise 1000 5 0.2
   --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265d18659ffd.chr22
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd19c4ad11.chr22
   --pfile misc/dev/test_data/ref/ref.chr22
   --threads 1
 
-Start time: Thu Jul 25 17:38:52 2024
-1031702 MiB RAM detected, ~1018301 available; reserving 4000 MiB for main
+Start time: Sun Apr  6 17:33:47 2025
+1031753 MiB RAM detected, ~923891 available; reserving 4000 MiB for main
 workspace.
 Using 1 compute thread.
 3313 samples (1568 females, 1745 males; 3313 founders) loaded from
@@ -152,22 +151,24 @@ Calculating allele frequencies... 0%done.
 587 variants remaining after main filters.
 --indep-pairwise (1 compute thread): 0%50%415/587 variants removed.
 Writing...
Variant lists written to
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265d18659ffd.chr22.prune.in and
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265d18659ffd.chr22.prune.out .
-End time: Thu Jul 25 17:38:52 2024
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd19c4ad11.chr22.prune.in
+and
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd19c4ad11.chr22.prune.out
+.
+End time: Sun Apr  6 17:33:47 2025
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_subset_chr22.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_subset_chr22.log.
 Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/file1265df4106ed
+  --extract /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/file43afd3f933712
   --make-pgen
   --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_subset_chr22
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_subset_chr22
   --pfile misc/dev/test_data/ref/ref.chr22
   --threads 1
 
-Start time: Thu Jul 25 17:38:52 2024
-1031702 MiB RAM detected, ~1018295 available; reserving 4000 MiB for main
+Start time: Sun Apr  6 17:33:47 2025
+1031753 MiB RAM detected, ~923891 available; reserving 4000 MiB for main
 workspace.
 Using 1 compute thread.
 3313 samples (1568 females, 1745 males; 3313 founders) loaded from
@@ -176,81 +177,82 @@ misc/dev/test_data/ref/ref.chr22.psam.
 3 categorical phenotypes loaded.
 --extract: 172 variants remaining.
 172 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_subset_chr22.psam ...
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_subset_chr22.psam ...
 done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_subset_chr22.pvar ...
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_subset_chr22.pvar ...
 0%0%1%1%2%2%3%4%4%5%5%6%6%7%8%8%9%9%10%11%11%12%12%13%13%14%15%15%16%16%17%18%18%19%19%20%20%21%22%22%23%23%24%25%25%26%26%27%27%28%29%29%30%30%31%31%32%33%33%34%34%35%36%36%37%37%38%38%39%40%40%41%41%42%43%43%44%44%45%45%46%47%47%48%48%49%50%50%51%51%52%52%53%54%54%55%55%56%56%57%58%58%59%59%60%61%61%62%62%63%63%64%65%65%66%66%67%68%68%69%69%70%70%71%72%72%73%73%74%75%75%76%76%77%77%78%79%79%80%80%81%81%82%83%83%84%84%85%86%86%87%87%88%88%89%90%90%91%91%92%93%93%94%94%95%95%96%97%97%98%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_subset_chr22.pgen ...
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_subset_chr22.pgen ...
 0%done.
-End time: Thu Jul 25 17:38:52 2024
+End time: Sun Apr  6 17:33:47 2025
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge.log.
 Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/extract.snplist
+  --extract /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/extract.snplist
   --make-pgen
   --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge
-  --pfile /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_subset_chr22
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_subset_chr22
   --threads 1
 
-Start time: Thu Jul 25 17:38:52 2024
-1031702 MiB RAM detected, ~1018295 available; reserving 4000 MiB for main
+Start time: Sun Apr  6 17:33:47 2025
+1031753 MiB RAM detected, ~923891 available; reserving 4000 MiB for main
 workspace.
 Using 1 compute thread.
 3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_subset_chr22.psam.
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_subset_chr22.psam.
 172 variants loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_subset_chr22.pvar.
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_subset_chr22.pvar.
 3 categorical phenotypes loaded.
 --extract: 172 variants remaining.
 172 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge.psam ... done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge.pvar ... 0%0%1%1%2%2%3%4%4%5%5%6%6%7%8%8%9%9%10%11%11%12%12%13%13%14%15%15%16%16%17%18%18%19%19%20%20%21%22%22%23%23%24%25%25%26%26%27%27%28%29%29%30%30%31%31%32%33%33%34%34%35%36%36%37%37%38%38%39%40%40%41%41%42%43%43%44%44%45%45%46%47%47%48%48%49%50%50%51%51%52%52%53%54%54%55%55%56%56%57%58%58%59%59%60%61%61%62%62%63%63%64%65%65%66%66%67%68%68%69%69%70%70%71%72%72%73%73%74%75%75%76%76%77%77%78%79%79%80%80%81%81%82%83%83%84%84%85%86%86%87%87%88%88%89%90%90%91%91%92%93%93%94%94%95%95%96%97%97%98%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge.pgen ... 0%done.
-End time: Thu Jul 25 17:38:52 2024
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge.psam ... done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge.pvar ... 0%0%1%1%2%2%3%4%4%5%5%6%6%7%8%8%9%9%10%11%11%12%12%13%13%14%15%15%16%16%17%18%18%19%19%20%20%21%22%22%23%23%24%25%25%26%26%27%27%28%29%29%30%30%31%31%32%33%33%34%34%35%36%36%37%37%38%38%39%40%40%41%41%42%43%43%44%44%45%45%46%47%47%48%48%49%50%50%51%51%52%52%53%54%54%55%55%56%56%57%58%58%59%59%60%61%61%62%62%63%63%64%65%65%66%66%67%68%68%69%69%70%70%71%72%72%73%73%74%75%75%76%76%77%77%78%79%79%80%80%81%81%82%83%83%84%84%85%86%86%87%87%88%88%89%90%90%91%91%92%93%93%94%94%95%95%96%97%97%98%98%99%done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge.pgen ... 0%done.
+End time: Sun Apr  6 17:33:47 2025
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge.log.
 Options in effect:
   --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge
   --pca 6 biallelic-var-wts
-  --pfile /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge
   --threads 1
 
-Start time: Thu Jul 25 17:38:52 2024
-1031702 MiB RAM detected, ~1018296 available; reserving 4000 MiB for main
+Start time: Sun Apr  6 17:33:47 2025
+1031753 MiB RAM detected, ~923891 available; reserving 4000 MiB for main
 workspace.
 Using 1 compute thread.
 3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge.psam.
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge.psam.
 172 variants loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge.pvar.
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge.pvar.
 3 categorical phenotypes loaded.
 Calculating allele frequencies... 0%done.
 Constructing GRM: 0%83%done.
 Correcting for missingness... 0%done.
 Extracting eigenvalues and eigenvectors... done.
 --pca: Variant weights written to
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge.eigenvec.var .
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge.eigenvec.var .
 --pca: Eigenvectors written to
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge.eigenvec , and eigenvalues
-written to /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref_merge.eigenval .
-End time: Thu Jul 25 17:38:58 2024
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge.eigenvec , and
+eigenvalues written to
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref_merge.eigenval .
+End time: Sun Apr  6 17:33:58 2025
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/profiles.chr22.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/profiles.chr22.log.
 Options in effect:
   --chr 22
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/profiles.chr22
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/profiles.chr22
   --pfile misc/dev/test_data/ref/ref.chr22
-  --score /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref.eigenvec.var header-read no-mean-imputation cols=+scoresums,-scoreavgs
+  --score /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref.eigenvec.var header-read no-mean-imputation cols=+scoresums,-scoreavgs
   --score-col-nums 4-9
   --threads 1
 
-Start time: Thu Jul 25 17:38:58 2024
-1031702 MiB RAM detected, ~1018302 available; reserving 515851 MiB for main
+Start time: Sun Apr  6 17:33:58 2025
+1031753 MiB RAM detected, ~923612 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 3313 samples (1568 females, 1745 males; 3313 founders) loaded from
@@ -259,34 +261,34 @@ misc/dev/test_data/ref/ref.chr22.psam.
 3 categorical phenotypes loaded.
 
--score: 172 variants processed.
 --score: Results written to
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/profiles.chr22.sscore .
-End time: Thu Jul 25 17:38:58 2024
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/profiles.chr22.sscore .
+End time: Sun Apr  6 17:33:58 2025
 Warning message:
 In nominalTrainWorkflow(x = x, y = y, wts = weights, info = trainInfo,  :
   There were missing values in resampled performance measures.
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/profiles.chr22.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/profiles.chr22.log.
 Options in effect:
   --chr 22
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/profiles.chr22
-  --pfile misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
-  --score /scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/ref.eigenvec.var header-read no-mean-imputation cols=+scoresums,-scoreavgs
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/profiles.chr22
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22
+  --score /scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/ref.eigenvec.var header-read no-mean-imputation cols=+scoresums,-scoreavgs
   --score-col-nums 4-9
   --threads 1
 
-Start time: Thu Jul 25 17:39:03 2024
-1031702 MiB RAM detected, ~1018277 available; reserving 515851 MiB for main
+Start time: Sun Apr  6 17:34:06 2025
+1031753 MiB RAM detected, ~924043 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 12 samples (5 females, 7 males; 12 founders) loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.psam.
 1000 variants loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pvar.
 Note: No phenotype data present.
 
--score: 172 variants processed.
 --score: Results written to
-/scratch/prj/oliverpainfel/tmp/Rtmp1Ggj2C/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:03 2024
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpMb1cWD/profiles.chr22.sscore .
+End time: Sun Apr  6 17:34:06 2025
 null device 
           1 
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/ancestry_reporter-example_plink2.log b/pipeline/misc/dev/test_data/output/reference/logs/ancestry_reporter-example_plink2.log
index 6d5f4104..46b320de 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/ancestry_reporter-example_plink2.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/ancestry_reporter-example_plink2.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/format_target_i-example_plink2-22.log b/pipeline/misc/dev/test_data/output/reference/logs/format_target_i-example_plink2-22.log
index 6d610c67..4f76c730 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/format_target_i-example_plink2-22.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/format_target_i-example_plink2-22.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -8,17 +7,16 @@ During startup - Warning messages:
 6: Setting LC_MEASUREMENT failed, using "C" 
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.log.
 Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/extract_list_1.txt
+  --extract /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/extract_list_1.txt
   --make-pgen pvar-cols=
-  --memory 5000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset
+  --out /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset
   --pfile misc/dev/test_data/target/example.chr22
   --threads 1
 
-Start time: Thu Jul 25 17:38:46 2024
-1031702 MiB RAM detected, ~1018530 available; reserving 5000 MiB for main
+Start time: Sun Apr  6 17:33:42 2025
+1031753 MiB RAM detected, ~924361 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 12 samples (5 females, 7 males; 12 founders) loaded from
@@ -27,51 +25,49 @@ misc/dev/test_data/target/example.chr22.psam.
 Note: No phenotype data present.
 --extract: 1000 variants remaining.
 1000 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.psam ... done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.pvar ... 0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.pgen ... 0%done.
-End time: Thu Jul 25 17:38:46 2024
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.psam ... done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.pvar ... 0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.pgen ... 0%done.
+End time: Sun Apr  6 17:33:42 2025
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.log.
 Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/extract_list_2.txt
+  --extract /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/extract_list_2.txt
   --make-pgen
-  --memory 5000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset
-  --pfile /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset
+  --out /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset
   --threads 1
 
-Start time: Thu Jul 25 17:38:46 2024
-1031702 MiB RAM detected, ~1018527 available; reserving 5000 MiB for main
+Start time: Sun Apr  6 17:33:42 2025
+1031753 MiB RAM detected, ~924361 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 Warning: --make-pgen input and output filenames match.  Appending '~' to input
 filenames.
 12 samples (5 females, 7 males; 12 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.psam~.
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.psam~.
 1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.pvar~.
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.pvar~.
 Note: No phenotype data present.
 --extract: 1000 variants remaining.
 1000 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.psam ... done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.pvar ... 0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.pgen ... 0%done.
-End time: Thu Jul 25 17:38:46 2024
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.psam ... done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.pvar ... 0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.pgen ... 0%done.
+End time: Sun Apr  6 17:33:42 2025
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.log.
 Options in effect:
   --make-pgen
-  --memory 5000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF
+  --out /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF
   --pfile misc/dev/test_data/ref/ref.chr22
   --threads 1
-  --update-ids /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_ID_update.txt
+  --update-ids /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_ID_update.txt
 
-Start time: Thu Jul 25 17:38:46 2024
-1031702 MiB RAM detected, ~1018526 available; reserving 5000 MiB for main
+Start time: Sun Apr  6 17:33:42 2025
+1031753 MiB RAM detected, ~924360 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 3313 samples (1568 females, 1745 males; 3313 founders) loaded from
@@ -79,79 +75,79 @@ misc/dev/test_data/ref/ref.chr22.psam.
 1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
 3 categorical phenotypes loaded.
 --update-ids: 3313 samples updated.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.psam ... done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.pvar ... 0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.pgen ... 0%done.
-End time: Thu Jul 25 17:38:46 2024
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.psam ... done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.pvar ... 0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.pgen ... 0%done.
+End time: Sun Apr  6 17:33:42 2025
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.log.
 Options in effect:
   --make-bed
-  --memory 5000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset
-  --pfile /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset
+  --out /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset
   --threads 1
 
-Start time: Thu Jul 25 17:38:46 2024
-1031702 MiB RAM detected, ~1018519 available; reserving 5000 MiB for main
+Start time: Sun Apr  6 17:33:42 2025
+1031753 MiB RAM detected, ~924352 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 12 samples (5 females, 7 males; 12 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.psam.
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.psam.
 1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.pvar.
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.pvar.
 Note: No phenotype data present.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.fam ... done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.bim ... done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.bed ... 0%done.
-End time: Thu Jul 25 17:38:46 2024
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.fam ... done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.bim ... done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.bed ... 0%done.
+End time: Sun Apr  6 17:33:42 2025
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.log.
 Options in effect:
   --make-bed
-  --memory 5000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF
-  --pfile /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF
+  --out /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF
   --threads 1
 
-Start time: Thu Jul 25 17:38:46 2024
-1031702 MiB RAM detected, ~1018519 available; reserving 5000 MiB for main
+Start time: Sun Apr  6 17:33:42 2025
+1031753 MiB RAM detected, ~924352 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.psam.
-1000 variants loaded from /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.pvar.
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.psam.
+1000 variants loaded from
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.pvar.
 3 categorical phenotypes loaded.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.fam ... done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.bim ... done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.bed ... 0%done.
-End time: Thu Jul 25 17:38:46 2024
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.fam ... done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.bim ... done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.bed ... 0%done.
+End time: Sun Apr  6 17:33:42 2025
 PLINK v1.90b6.21 64-bit (19 Oct 2020)          www.cog-genomics.org/plink/1.9/
 (C) 2005-2020 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ.log.
 Options in effect:
   --allow-no-sex
-  --bfile /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset
-  --bmerge /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF
+  --bfile /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset
+  --bmerge /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF
   --make-bed
-  --out /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ
+  --out /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ
 
-1031702 MB RAM detected; reserving 515851 MB for main workspace.
-12 people loaded from /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.fam.
+1031753 MB RAM detected; reserving 515876 MB for main workspace.
+12 people loaded from /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.fam.
 3313 people to be merged from
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.fam.
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.fam.
 Of these, 3313 are new, while 0 are present in the base dataset.
-1000 markers loaded from /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/subset.bim.
+1000 markers loaded from
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/subset.bim.
 1000 markers to be merged from
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.bim.
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.bim.
 Of these, 0 are new, while 1000 are present in the base dataset.
 Performing single-pass merge (3325 people, 1000 variants).
 
Pass 1: fileset #1 complete.
                                              
Merged fileset written to
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ-merge.bed +
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ-merge.bim +
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ-merge.fam .
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ-merge.bed +
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ-merge.bim +
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ-merge.fam .
 1000 variants loaded from .bim file.
 3325 people (1752 males, 1573 females) loaded from .fam.
 Using 1 thread (no multithreaded calculations invoked).
@@ -160,38 +156,37 @@ Calculating allele frequencies... 0%1%2%3%4%5%6%7%8%9%10%
 Total genotyping rate is 0.998018.
 1000 variants and 3325 people pass filters and QC.
 Note: No phenotypes present.
---make-bed to /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ.bed +
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ.bim +
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ.fam ... 0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
+--make-bed to /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ.bed +
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ.bim +
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ.fam ... 0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.log.
 Options in effect:
-  --bfile /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ
+  --bfile /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ
   --make-pgen
-  --memory 5000
-  --out misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
-  --remove /scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/REF.psam
+  --out /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22
+  --remove /scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/REF.psam
   --threads 1
 
-Start time: Thu Jul 25 17:38:46 2024
-1031702 MiB RAM detected, ~1018519 available; reserving 5000 MiB for main
+Start time: Sun Apr  6 17:33:42 2025
+1031753 MiB RAM detected, ~924320 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 3325 samples (1573 females, 1752 males; 3325 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ.fam.
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ.fam.
 1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmpjx5MIY/ref_targ.bim.
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp6bFDWL/ref_targ.bim.
 Note: No phenotype data present.
 --remove: 12 samples remaining.
 12 samples (5 females, 7 males; 12 founders) remaining after main filters.
 Writing
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam ...
-done.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.psam
+... done.
 Writing
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar ...
-0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pvar
+... 0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
 Writing
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pgen ...
-0%done.
-End time: Thu Jul 25 17:38:46 2024
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pgen
+... 0%done.
+End time: Sun Apr  6 17:33:42 2025
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/indiv_report_i-example_plink2-12_MID.12_MID.log b/pipeline/misc/dev/test_data/output/reference/logs/indiv_report_i-example_plink2-12_MID.12_MID.log
deleted file mode 100644
index 9931ac1e..00000000
--- a/pipeline/misc/dev/test_data/output/reference/logs/indiv_report_i-example_plink2-12_MID.12_MID.log
+++ /dev/null
@@ -1,43 +0,0 @@
-
-
-processing file: indiv_report_creator.Rmd
-1/35                   
-2/35 [setup]           
-3/35                   
-4/35 [unnamed-chunk-1] 
-5/35                   
-6/35 [unnamed-chunk-2] 
-7/35                   
-8/35 [unnamed-chunk-3] 
-9/35                   
-10/35 [unnamed-chunk-4] 
-11/35                   
-12/35 [unnamed-chunk-5] 
-13/35                   
-14/35 [unnamed-chunk-6] 
-15/35                   
-16/35 [unnamed-chunk-7] 
-17/35                   
-18/35 [unnamed-chunk-8] 
-19/35                   
-20/35 [unnamed-chunk-9] 
-21/35                   
-22/35 [unnamed-chunk-10]
-23/35                   
-24/35 [unnamed-chunk-11]
-25/35                   
-26/35 [unnamed-chunk-12]
-27/35                   
-28/35 [unnamed-chunk-13]
-29/35                   
-30/35 [unnamed-chunk-14]
-31/35                   
-32/35 [unnamed-chunk-15]
-33/35                   
-34/35 [unnamed-chunk-16]
-35/35                   
-output file: indiv_report_creator.knit.md
-
-/scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/.snakemake/conda/aea4ef260655c3bcc12fec8909f5f6dd_/bin/pandoc +RTS -K512m -RTS indiv_report_creator.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/misc/dev/test_data/output/example_plink2/reports/individual/example_plink2-12_MID.12_MID-report.html --lua-filter /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/.snakemake/conda/aea4ef260655c3bcc12fec8909f5f6dd_/lib/R/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/.snakemake/conda/aea4ef260655c3bcc12fec8909f5f6dd_/lib/R/library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 3 --variable toc_float=1 --variable toc_selectors=h1,h2,h3 --variable toc_collapsed=1 --variable toc_smooth_scroll=1 --variable toc_print=1 --template /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/.snakemake/conda/aea4ef260655c3bcc12fec8909f5f6dd_/lib/R/library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=cosmo --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /scratch/prj/oliverpainfel/tmp/Rtmp2fqC8E/rmarkdown-str626d6c4a4812.html 
-
-Output created: misc/dev/test_data/output/example_plink2/reports/individual/example_plink2-12_MID.12_MID-report.html
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/indiv_report_i-example_plink2-2_EUR.2_EUR.log b/pipeline/misc/dev/test_data/output/reference/logs/indiv_report_i-example_plink2-2_EUR.2_EUR.log
deleted file mode 100644
index 9886a150..00000000
--- a/pipeline/misc/dev/test_data/output/reference/logs/indiv_report_i-example_plink2-2_EUR.2_EUR.log
+++ /dev/null
@@ -1,43 +0,0 @@
-
-
-processing file: indiv_report_creator.Rmd
-1/35                   
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-35/35                   
-output file: indiv_report_creator.knit.md
-
-/scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/.snakemake/conda/aea4ef260655c3bcc12fec8909f5f6dd_/bin/pandoc +RTS -K512m -RTS indiv_report_creator.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/misc/dev/test_data/output/example_plink2/reports/individual/example_plink2-2_EUR.2_EUR-report.html --lua-filter /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/.snakemake/conda/aea4ef260655c3bcc12fec8909f5f6dd_/lib/R/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/.snakemake/conda/aea4ef260655c3bcc12fec8909f5f6dd_/lib/R/library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 3 --variable toc_float=1 --variable toc_selectors=h1,h2,h3 --variable toc_collapsed=1 --variable toc_smooth_scroll=1 --variable toc_print=1 --template /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/.snakemake/conda/aea4ef260655c3bcc12fec8909f5f6dd_/lib/R/library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=cosmo --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /scratch/prj/oliverpainfel/tmp/RtmphCGhPS/rmarkdown-str62f1ac089d3.html 
-
-Output created: misc/dev/test_data/output/example_plink2/reports/individual/example_plink2-2_EUR.2_EUR-report.html
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/indiv_report_i-example_plink2-4_EAS.4_EAS.log b/pipeline/misc/dev/test_data/output/reference/logs/indiv_report_i-example_plink2-4_EAS.4_EAS.log
index 9ce979c3..fada32ff 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/indiv_report_i-example_plink2-4_EAS.4_EAS.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/indiv_report_i-example_plink2-4_EAS.4_EAS.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -9,9 +8,49 @@ During startup - Warning messages:
 
 
 processing file: indiv_report_creator.Rmd
-
  |                                                         
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+41/41                   
 output file: indiv_report_creator.knit.md
 
-/tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/bin/pandoc +RTS -K512m -RTS indiv_report_creator.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tools/GenoPred/pipeline/misc/dev/test_data/output/example_plink2/reports/individual/example_plink2-4_EAS.4_EAS-report.html --lua-filter /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 3 --variable toc_float=1 --variable toc_selectors=h1,h2,h3 --variable toc_collapsed=1 --variable toc_smooth_scroll=1 --variable toc_print=1 --template /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=cosmo --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /scratch/prj/oliverpainfel/tmp/Rtmp9coZyb/rmarkdown-str12b3c2427cfba.html 
+/tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/bin/pandoc +RTS -K512m -RTS indiv_report_creator.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/reports/individual/example_plink2-4_EAS.4_EAS-report.html --lua-filter /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 3 --variable toc_float=1 --variable toc_selectors=h1,h2,h3 --variable toc_collapsed=1 --variable toc_smooth_scroll=1 --variable toc_print=1 --template /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=cosmo --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /scratch_tmp/prj/oliverpainfel/tmp/RtmpvLTo6g/rmarkdown-str43f6a3fdb2cfd.html 
 
-Output created: misc/dev/test_data/output/example_plink2/reports/individual/example_plink2-4_EAS.4_EAS-report.html
+Output created: /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/reports/individual/example_plink2-4_EAS.4_EAS-report.html
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-AFR.log b/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-AFR.log
deleted file mode 100644
index d5ee13b8..00000000
--- a/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-AFR.log
+++ /dev/null
@@ -1,38 +0,0 @@
-WARNING: ignoring environment value of R_HOME
-During startup - Warning messages:
-1: Setting LC_COLLATE failed, using "C" 
-2: Setting LC_TIME failed, using "C" 
-3: Setting LC_MESSAGES failed, using "C" 
-4: Setting LC_MONETARY failed, using "C" 
-5: Setting LC_PAPER failed, using "C" 
-6: Setting LC_MEASUREMENT failed, using "C" 
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpXybnEP/profiles.chr22.log.
-Options in effect:
-  --chr 22
-  --keep misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/AFR.keep
-  --out /scratch/prj/oliverpainfel/tmp/RtmpXybnEP/profiles.chr22
-  --pfile misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
-  --read-freq misc/dev/test_data/ref/freq_files/AFR/ref.AFR.chr22.afreq
-  --score misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.eigenvec.var.gz header-read cols=+scoresums,-scoreavgs
-  --score-col-nums 4-9
-  --threads 1
-
-Start time: Thu Jul 25 17:39:07 2024
-1031702 MiB RAM detected, ~1018553 available; reserving 515851 MiB for main
-workspace.
-Using 1 compute thread.
-12 samples (5 females, 7 males; 12 founders) loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam.
-1000 variants loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar.
-Note: No phenotype data present.
---keep: 2 samples remaining.
-2 samples (0 females, 2 males; 2 founders) remaining after main filters.
---read-freq: PLINK 2 --freq file detected.
-
--read-freq: Frequencies for 1000 variants loaded.
-
--score: 275 variants processed.
---score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpXybnEP/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:07 2024
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-CSA.log b/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-CSA.log
deleted file mode 100644
index 927a3694..00000000
--- a/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-CSA.log
+++ /dev/null
@@ -1,38 +0,0 @@
-WARNING: ignoring environment value of R_HOME
-During startup - Warning messages:
-1: Setting LC_COLLATE failed, using "C" 
-2: Setting LC_TIME failed, using "C" 
-3: Setting LC_MESSAGES failed, using "C" 
-4: Setting LC_MONETARY failed, using "C" 
-5: Setting LC_PAPER failed, using "C" 
-6: Setting LC_MEASUREMENT failed, using "C" 
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpjNoYq2/profiles.chr22.log.
-Options in effect:
-  --chr 22
-  --keep misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/CSA.keep
-  --out /scratch/prj/oliverpainfel/tmp/RtmpjNoYq2/profiles.chr22
-  --pfile misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
-  --read-freq misc/dev/test_data/ref/freq_files/CSA/ref.CSA.chr22.afreq
-  --score misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.eigenvec.var.gz header-read cols=+scoresums,-scoreavgs
-  --score-col-nums 4-9
-  --threads 1
-
-Start time: Thu Jul 25 17:39:26 2024
-1031702 MiB RAM detected, ~1018540 available; reserving 515851 MiB for main
-workspace.
-Using 1 compute thread.
-12 samples (5 females, 7 males; 12 founders) loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam.
-1000 variants loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar.
-Note: No phenotype data present.
---keep: 1 sample remaining.
-1 sample (0 females, 1 male; 1 founder) remaining after main filters.
---read-freq: PLINK 2 --freq file detected.
-
--read-freq: Frequencies for 1000 variants loaded.
-
--score: 138 variants processed.
---score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpjNoYq2/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:26 2024
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-EAS.log b/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-EAS.log
deleted file mode 100644
index 9e88eaa2..00000000
--- a/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-EAS.log
+++ /dev/null
@@ -1,38 +0,0 @@
-WARNING: ignoring environment value of R_HOME
-During startup - Warning messages:
-1: Setting LC_COLLATE failed, using "C" 
-2: Setting LC_TIME failed, using "C" 
-3: Setting LC_MESSAGES failed, using "C" 
-4: Setting LC_MONETARY failed, using "C" 
-5: Setting LC_PAPER failed, using "C" 
-6: Setting LC_MEASUREMENT failed, using "C" 
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpIDG1xk/profiles.chr22.log.
-Options in effect:
-  --chr 22
-  --keep misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/EAS.keep
-  --out /scratch/prj/oliverpainfel/tmp/RtmpIDG1xk/profiles.chr22
-  --pfile misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
-  --read-freq misc/dev/test_data/ref/freq_files/EAS/ref.EAS.chr22.afreq
-  --score misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.eigenvec.var.gz header-read cols=+scoresums,-scoreavgs
-  --score-col-nums 4-9
-  --threads 1
-
-Start time: Thu Jul 25 17:39:10 2024
-1031702 MiB RAM detected, ~1018551 available; reserving 515851 MiB for main
-workspace.
-Using 1 compute thread.
-12 samples (5 females, 7 males; 12 founders) loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam.
-1000 variants loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar.
-Note: No phenotype data present.
---keep: 2 samples remaining.
-2 samples (1 female, 1 male; 2 founders) remaining after main filters.
---read-freq: PLINK 2 --freq file detected.
-
--read-freq: Frequencies for 1000 variants loaded.
-
--score: 133 variants processed.
---score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpIDG1xk/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:10 2024
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-EUR.log b/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-EUR.log
deleted file mode 100644
index 57538ba6..00000000
--- a/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-EUR.log
+++ /dev/null
@@ -1,38 +0,0 @@
-WARNING: ignoring environment value of R_HOME
-During startup - Warning messages:
-1: Setting LC_COLLATE failed, using "C" 
-2: Setting LC_TIME failed, using "C" 
-3: Setting LC_MESSAGES failed, using "C" 
-4: Setting LC_MONETARY failed, using "C" 
-5: Setting LC_PAPER failed, using "C" 
-6: Setting LC_MEASUREMENT failed, using "C" 
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpPGWm8i/profiles.chr22.log.
-Options in effect:
-  --chr 22
-  --keep misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/EUR.keep
-  --out /scratch/prj/oliverpainfel/tmp/RtmpPGWm8i/profiles.chr22
-  --pfile misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
-  --read-freq misc/dev/test_data/ref/freq_files/EUR/ref.EUR.chr22.afreq
-  --score misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.eigenvec.var.gz header-read cols=+scoresums,-scoreavgs
-  --score-col-nums 4-9
-  --threads 1
-
-Start time: Thu Jul 25 17:39:27 2024
-1031702 MiB RAM detected, ~1018539 available; reserving 515851 MiB for main
-workspace.
-Using 1 compute thread.
-12 samples (5 females, 7 males; 12 founders) loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam.
-1000 variants loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar.
-Note: No phenotype data present.
---keep: 5 samples remaining.
-5 samples (2 females, 3 males; 5 founders) remaining after main filters.
---read-freq: PLINK 2 --freq file detected.
-
--read-freq: Frequencies for 1000 variants loaded.
-
--score: 144 variants processed.
---score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpPGWm8i/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:27 2024
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-TRANS.log b/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-TRANS.log
new file mode 100644
index 00000000..51775204
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/logs/pc_projection_i-example_plink2-TRANS.log
@@ -0,0 +1,34 @@
+During startup - Warning messages:
+1: Setting LC_COLLATE failed, using "C" 
+2: Setting LC_TIME failed, using "C" 
+3: Setting LC_MESSAGES failed, using "C" 
+4: Setting LC_MONETARY failed, using "C" 
+5: Setting LC_PAPER failed, using "C" 
+6: Setting LC_MEASUREMENT failed, using "C" 
+PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
+(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpEyxCEs/profiles.chr22.log.
+Options in effect:
+  --chr 22
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpEyxCEs/profiles.chr22
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22
+  --read-freq misc/dev/test_data/ref/freq_files/TRANS/ref.TRANS.chr22.afreq
+  --score /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pc_score_files/TRANS/ref-TRANS-pcs.eigenvec.var.gz header-read cols=+scoresums,-scoreavgs
+  --score-col-nums 4-9
+  --threads 1
+
+Start time: Sun Apr  6 17:34:09 2025
+1031753 MiB RAM detected, ~924627 available; reserving 515876 MiB for main
+workspace.
+Using 1 compute thread.
+12 samples (5 females, 7 males; 12 founders) loaded from
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.psam.
+1000 variants loaded from
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pvar.
+Note: No phenotype data present.
+--read-freq: PLINK 2 --freq file detected.
+
--read-freq: Frequencies for 1000 variants loaded.
+
--score: 172 variants processed.
+--score: Results written to
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpEyxCEs/profiles.chr22.sscore .
+End time: Sun Apr  6 17:34:09 2025
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_external_i-PGS002804.log b/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_external_i-PGS002804.log
index 58e68479..41e3f734 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_external_i-PGS002804.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_external_i-PGS002804.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -6,22 +5,22 @@ During startup - Warning messages:
 4: Setting LC_MONETARY failed, using "C" 
 5: Setting LC_PAPER failed, using "C" 
 6: Setting LC_MEASUREMENT failed, using "C" 
-GRCh36 match: 0%
-GRCh37 match: 100%
-GRCh38 match: 0%
+GRCh36 match: 0% (Target), 0% (Ref)
+GRCh37 match: 100% (Target), 70.8% (Ref)
+GRCh38 match: 0% (Target), 0% (Ref)
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpykqA2X/profiles.chr22.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/Rtmp5KMDED/profiles.chr22.log.
 Options in effect:
   --chr 22
-  --out /scratch/prj/oliverpainfel/tmp/RtmpykqA2X/profiles.chr22
+  --out /scratch_tmp/prj/oliverpainfel/tmp/Rtmp5KMDED/profiles.chr22
   --pfile misc/dev/test_data/ref/ref.chr22
-  --score misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804.score.gz header-read no-mean-imputation cols=+scoresums,-scoreavgs
+  --score /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pgs_score_files/external/PGS002804/ref-PGS002804.score.gz header-read no-mean-imputation cols=+scoresums,-scoreavgs
   --score-col-nums 4
   --threads 1
 
-Start time: Thu Jul 25 17:38:44 2024
-1031702 MiB RAM detected, ~1018562 available; reserving 515851 MiB for main
+Start time: Sun Apr  6 17:33:40 2025
+1031753 MiB RAM detected, ~925668 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 3313 samples (1568 females, 1745 males; 3313 founders) loaded from
@@ -30,5 +29,5 @@ misc/dev/test_data/ref/ref.chr22.psam.
 3 categorical phenotypes loaded.
 
--score: 1000 variants processed.
 --score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpykqA2X/profiles.chr22.sscore .
-End time: Thu Jul 25 17:38:44 2024
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmp5KMDED/profiles.chr22.sscore .
+End time: Sun Apr  6 17:33:40 2025
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_lassosum_i-BODY04.log b/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_lassosum_i-BODY04.log
index 7be53d27..33aadb93 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_lassosum_i-BODY04.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_lassosum_i-BODY04.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -15,18 +14,18 @@ During startup - Warning messages:
 6: Setting LC_MEASUREMENT failed, using "C" 
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpIJCTmD/ref_merge.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpAJ00tp/ref_merge.log.
 Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/RtmpIJCTmD/extract.snplist
+  --extract /scratch_tmp/prj/oliverpainfel/tmp/RtmpAJ00tp/extract.snplist
   --keep misc/dev/test_data/ref/keep_files/EUR.keep
   --make-bed
   --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpIJCTmD/ref_merge
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpAJ00tp/ref_merge
   --pfile misc/dev/test_data/ref/ref.chr22
   --threads 1
 
-Start time: Thu Jul 25 17:38:48 2024
-1031702 MiB RAM detected, ~1018357 available; reserving 4000 MiB for main
+Start time: Sun Apr  6 17:28:56 2025
+1031753 MiB RAM detected, ~924526 available; reserving 4000 MiB for main
 workspace.
 Using 1 compute thread.
 3313 samples (1568 females, 1745 males; 3313 founders) loaded from
@@ -38,10 +37,10 @@ misc/dev/test_data/ref/ref.chr22.psam.
 665 samples (335 females, 330 males; 665 founders) remaining after main
 filters.
 841 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpIJCTmD/ref_merge.fam ... done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpIJCTmD/ref_merge.bim ... done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpIJCTmD/ref_merge.bed ... 0%done.
-End time: Thu Jul 25 17:38:48 2024
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpAJ00tp/ref_merge.fam ... done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpAJ00tp/ref_merge.bim ... done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpAJ00tp/ref_merge.bed ... 0%done.
+End time: Sun Apr  6 17:28:56 2025
 Coordinating summary stats with reference panel...
 Splitting genome by LD blocks ...
 Running lassosum ...
@@ -57,26 +56,3 @@ Step 3... compute p-values and estimate empirical PDF/CDF
 Step 4... compute q-values and local fdr
 
 Performing pseudovalidation ...
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpIJCTmD/profiles.chr22.log.
-Options in effect:
-  --chr 22
-  --out /scratch/prj/oliverpainfel/tmp/RtmpIJCTmD/profiles.chr22
-  --pfile misc/dev/test_data/ref/ref.chr22
-  --score misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04.score.gz header-read no-mean-imputation cols=+scoresums,-scoreavgs
-  --score-col-nums 4-83
-  --threads 1
-
-Start time: Thu Jul 25 17:38:49 2024
-1031702 MiB RAM detected, ~1018324 available; reserving 515851 MiB for main
-workspace.
-Using 1 compute thread.
-3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-misc/dev/test_data/ref/ref.chr22.psam.
-1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
-3 categorical phenotypes loaded.
-
--score: 1000 variants processed.
---score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpIJCTmD/profiles.chr22.sscore .
-End time: Thu Jul 25 17:38:49 2024
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_ptclump_i-BODY04.log b/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_ptclump_i-BODY04.log
index 607ea55e..ecabefc0 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_ptclump_i-BODY04.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/prep_pgs_ptclump_i-BODY04.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -10,21 +9,21 @@ Warning message:
 In min(hla$P) : no non-missing arguments to min; returning Inf
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmptx4xxe/file1290d73bebeb4.chr22.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/Rtmpr43qvd/file43c341a62c73.chr22.log.
 Options in effect:
-  --clump /scratch/prj/oliverpainfel/tmp/Rtmptx4xxe/file1290d3e51275e
+  --clump /scratch_tmp/prj/oliverpainfel/tmp/Rtmpr43qvd/file43c341159aa6a
   --clump-kb 250
   --clump-p1 1
   --clump-p2 1
   --clump-r2 0.1
   --keep misc/dev/test_data/ref/keep_files/EUR.keep
   --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmptx4xxe/file1290d73bebeb4.chr22
+  --out /scratch_tmp/prj/oliverpainfel/tmp/Rtmpr43qvd/file43c341a62c73.chr22
   --pfile misc/dev/test_data/ref/ref.chr22
   --threads 1
 
-Start time: Thu Jul 25 17:39:12 2024
-1031702 MiB RAM detected, ~1018551 available; reserving 4000 MiB for main
+Start time: Sun Apr  6 17:34:10 2025
+1031753 MiB RAM detected, ~924681 available; reserving 4000 MiB for main
 workspace.
 Using 1 compute thread.
 3313 samples (1568 females, 1745 males; 3313 founders) loaded from
@@ -35,28 +34,5 @@ misc/dev/test_data/ref/ref.chr22.psam.
 665 samples (335 females, 330 males; 665 founders) remaining after main
 filters.
 
--clump: 0/841 index candidates processed.
--clump: 148 clumps formed from 841 index candidates.  
-Results written to /scratch/prj/oliverpainfel/tmp/Rtmptx4xxe/file1290d73bebeb4.chr22.clumps .
-End time: Thu Jul 25 17:39:12 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmptx4xxe/profiles.chr22.log.
-Options in effect:
-  --chr 22
-  --out /scratch/prj/oliverpainfel/tmp/Rtmptx4xxe/profiles.chr22
-  --pfile misc/dev/test_data/ref/ref.chr22
-  --score misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04.score.gz header-read no-mean-imputation cols=+scoresums,-scoreavgs
-  --score-col-nums 4-9
-  --threads 1
-
-Start time: Thu Jul 25 17:39:12 2024
-1031702 MiB RAM detected, ~1018546 available; reserving 515851 MiB for main
-workspace.
-Using 1 compute thread.
-3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-misc/dev/test_data/ref/ref.chr22.psam.
-1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
-3 categorical phenotypes loaded.
-
--score: 1000 variants processed.
---score: Results written to
-/scratch/prj/oliverpainfel/tmp/Rtmptx4xxe/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:12 2024
+Results written to /scratch_tmp/prj/oliverpainfel/tmp/Rtmpr43qvd/file43c341a62c73.chr22.clumps .
+End time: Sun Apr  6 17:34:10 2025
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/ref_pca_i-TRANS.log b/pipeline/misc/dev/test_data/output/reference/logs/ref_pca_i-TRANS.log
new file mode 100644
index 00000000..3e8b72cf
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/logs/ref_pca_i-TRANS.log
@@ -0,0 +1,173 @@
+During startup - Warning messages:
+1: Setting LC_COLLATE failed, using "C" 
+2: Setting LC_TIME failed, using "C" 
+3: Setting LC_MESSAGES failed, using "C" 
+4: Setting LC_MONETARY failed, using "C" 
+5: Setting LC_PAPER failed, using "C" 
+6: Setting LC_MEASUREMENT failed, using "C" 
+PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
+(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/file43854955df6d.log.
+Options in effect:
+  --geno 0.02
+  --hwe 1e-06
+  --maf 0.05
+  --memory 4000
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/file43854955df6d
+  --pfile misc/dev/test_data/ref/ref.chr22
+  --threads 1
+  --write-snplist
+
+Start time: Sun Apr  6 17:28:43 2025
+1031753 MiB RAM detected, ~924691 available; reserving 4000 MiB for main
+workspace.
+Using 1 compute thread.
+3313 samples (1568 females, 1745 males; 3313 founders) loaded from
+misc/dev/test_data/ref/ref.chr22.psam.
+1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
+3 categorical phenotypes loaded.
+Calculating allele frequencies... 0%done.
+--geno: 12 variants removed due to missing genotype data.
+--hwe: 350 variants removed due to Hardy-Weinberg exact test (founders only).
+48 variants removed due to allele frequency threshold(s)
+(--maf/--max-maf/--mac/--max-mac).
+590 variants remaining after main filters.
+--write-snplist: Variant IDs written to
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/file43854955df6d.snplist .
+End time: Sun Apr  6 17:28:43 2025
+PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
+(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/file4385441e053bf.chr22.log.
+Options in effect:
+  --extract /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/file4385478cdf3f9
+  --indep-pairwise 1000 5 0.2
+  --memory 4000
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/file4385441e053bf.chr22
+  --pfile misc/dev/test_data/ref/ref.chr22
+  --threads 1
+
+Start time: Sun Apr  6 17:28:44 2025
+1031753 MiB RAM detected, ~924688 available; reserving 4000 MiB for main
+workspace.
+Using 1 compute thread.
+3313 samples (1568 females, 1745 males; 3313 founders) loaded from
+misc/dev/test_data/ref/ref.chr22.psam.
+1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
+3 categorical phenotypes loaded.
+--extract: 590 variants remaining.
+Calculating allele frequencies... 0%done.
+590 variants remaining after main filters.
+--indep-pairwise (1 compute thread): 0%50%418/590 variants removed.
+Writing...
Variant lists written to
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/file4385441e053bf.chr22.prune.in
+and
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/file4385441e053bf.chr22.prune.out
+.
+End time: Sun Apr  6 17:28:44 2025
+PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
+(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_subset_chr22.log.
+Options in effect:
+  --extract /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/file438545f66c0a4
+  --make-pgen
+  --memory 4000
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_subset_chr22
+  --pfile misc/dev/test_data/ref/ref.chr22
+  --threads 1
+
+Start time: Sun Apr  6 17:28:44 2025
+1031753 MiB RAM detected, ~924691 available; reserving 4000 MiB for main
+workspace.
+Using 1 compute thread.
+3313 samples (1568 females, 1745 males; 3313 founders) loaded from
+misc/dev/test_data/ref/ref.chr22.psam.
+1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
+3 categorical phenotypes loaded.
+--extract: 172 variants remaining.
+172 variants remaining after main filters.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_subset_chr22.psam ...
+done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_subset_chr22.pvar ...
+0%0%1%1%2%2%3%4%4%5%5%6%6%7%8%8%9%9%10%11%11%12%12%13%13%14%15%15%16%16%17%18%18%19%19%20%20%21%22%22%23%23%24%25%25%26%26%27%27%28%29%29%30%30%31%31%32%33%33%34%34%35%36%36%37%37%38%38%39%40%40%41%41%42%43%43%44%44%45%45%46%47%47%48%48%49%50%50%51%51%52%52%53%54%54%55%55%56%56%57%58%58%59%59%60%61%61%62%62%63%63%64%65%65%66%66%67%68%68%69%69%70%70%71%72%72%73%73%74%75%75%76%76%77%77%78%79%79%80%80%81%81%82%83%83%84%84%85%86%86%87%87%88%88%89%90%90%91%91%92%93%93%94%94%95%95%96%97%97%98%98%99%done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_subset_chr22.pgen ...
+0%done.
+End time: Sun Apr  6 17:28:44 2025
+PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
+(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge.log.
+Options in effect:
+  --extract /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/extract.snplist
+  --make-pgen
+  --memory 4000
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_subset_chr22
+  --threads 1
+
+Start time: Sun Apr  6 17:28:44 2025
+1031753 MiB RAM detected, ~924691 available; reserving 4000 MiB for main
+workspace.
+Using 1 compute thread.
+3313 samples (1568 females, 1745 males; 3313 founders) loaded from
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_subset_chr22.psam.
+172 variants loaded from
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_subset_chr22.pvar.
+3 categorical phenotypes loaded.
+--extract: 172 variants remaining.
+172 variants remaining after main filters.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge.psam ... done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge.pvar ... 0%0%1%1%2%2%3%4%4%5%5%6%6%7%8%8%9%9%10%11%11%12%12%13%13%14%15%15%16%16%17%18%18%19%19%20%20%21%22%22%23%23%24%25%25%26%26%27%27%28%29%29%30%30%31%31%32%33%33%34%34%35%36%36%37%37%38%38%39%40%40%41%41%42%43%43%44%44%45%45%46%47%47%48%48%49%50%50%51%51%52%52%53%54%54%55%55%56%56%57%58%58%59%59%60%61%61%62%62%63%63%64%65%65%66%66%67%68%68%69%69%70%70%71%72%72%73%73%74%75%75%76%76%77%77%78%79%79%80%80%81%81%82%83%83%84%84%85%86%86%87%87%88%88%89%90%90%91%91%92%93%93%94%94%95%95%96%97%97%98%98%99%done.
+Writing /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge.pgen ... 0%done.
+End time: Sun Apr  6 17:28:44 2025
+PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
+(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge.log.
+Options in effect:
+  --memory 4000
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge
+  --pca 6 biallelic-var-wts
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge
+  --threads 1
+
+Start time: Sun Apr  6 17:28:44 2025
+1031753 MiB RAM detected, ~924691 available; reserving 4000 MiB for main
+workspace.
+Using 1 compute thread.
+3313 samples (1568 females, 1745 males; 3313 founders) loaded from
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge.psam.
+172 variants loaded from
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge.pvar.
+3 categorical phenotypes loaded.
+Calculating allele frequencies... 0%done.
+Constructing GRM: 0%83%done.
+Correcting for missingness... 0%done.
+Extracting eigenvalues and eigenvectors... done.
+--pca: Variant weights written to
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge.eigenvec.var .
+--pca: Eigenvectors written to
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge.eigenvec , and
+eigenvalues written to
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/ref_merge.eigenval .
+End time: Sun Apr  6 17:28:54 2025
+PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
+(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/profiles.chr22.log.
+Options in effect:
+  --chr 22
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/profiles.chr22
+  --pfile misc/dev/test_data/ref/ref.chr22
+  --score /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pc_score_files/TRANS/ref-TRANS-pcs.eigenvec.var.gz header-read no-mean-imputation cols=+scoresums,-scoreavgs
+  --score-col-nums 4-9
+  --threads 1
+
+Start time: Sun Apr  6 17:28:54 2025
+1031753 MiB RAM detected, ~924685 available; reserving 515876 MiB for main
+workspace.
+Using 1 compute thread.
+3313 samples (1568 females, 1745 males; 3313 founders) loaded from
+misc/dev/test_data/ref/ref.chr22.psam.
+1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
+3 categorical phenotypes loaded.
+
--score: 172 variants processed.
+--score: Results written to
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpCzUObV/profiles.chr22.sscore .
+End time: Sun Apr  6 17:28:54 2025
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/ref_pgs.log b/pipeline/misc/dev/test_data/output/reference/logs/ref_pgs.log
new file mode 100644
index 00000000..a872e2bc
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/logs/ref_pgs.log
@@ -0,0 +1,33 @@
+During startup - Warning messages:
+1: Setting LC_COLLATE failed, using "C" 
+2: Setting LC_TIME failed, using "C" 
+3: Setting LC_MESSAGES failed, using "C" 
+4: Setting LC_MONETARY failed, using "C" 
+5: Setting LC_PAPER failed, using "C" 
+6: Setting LC_MEASUREMENT failed, using "C" 
+Loading required package: iterators
+Loading required package: parallel
+gwas_groups parameter is not present in user specified config file, so will use value in default config file.
+PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
+(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpeWmerQ/profiles.chr22.log.
+Options in effect:
+  --chr 22
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpeWmerQ/profiles.chr22
+  --pfile misc/dev/test_data/ref/ref.chr22
+  --score /scratch_tmp/prj/oliverpainfel/tmp/RtmpeWmerQ/all_score.txt header-read no-mean-imputation cols=+scoresums,-scoreavgs
+  --score-col-nums 4-90
+  --threads 1
+
+Start time: Sun Apr  6 17:34:12 2025
+1031753 MiB RAM detected, ~924710 available; reserving 515876 MiB for main
+workspace.
+Using 1 compute thread.
+3313 samples (1568 females, 1745 males; 3313 founders) loaded from
+misc/dev/test_data/ref/ref.chr22.psam.
+1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
+3 categorical phenotypes loaded.
+
--score: 1000 variants processed.
+--score: Results written to
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpeWmerQ/profiles.chr22.sscore .
+End time: Sun Apr  6 17:34:12 2025
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/sample_report_i-example_plink2.log b/pipeline/misc/dev/test_data/output/reference/logs/sample_report_i-example_plink2.log
index 7f10004a..2470b35c 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/sample_report_i-example_plink2.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/sample_report_i-example_plink2.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -9,9 +8,37 @@ During startup - Warning messages:
 
 
 processing file: samp_report_creator.Rmd
-
  |                                                         
  |                                                   |   0%
  |                                                         
  |..                                                 |   4%                   
  |                                                         
  |....                                               |   9% [setup]           
  |                                                         
  |.......                                            |  13%                   
  |                                                         
  |.........                                          |  17% [unnamed-chunk-1] 
  |                                                         
  |...........                                        |  22%                   
  |                                                         
  |.............                                      |  26% [unnamed-chunk-2] 
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  |                                                         
  |........................                           |  48%                   
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  |...........................                        |  52% [unnamed-chunk-5] 
  |                                                         
  |.............................                      |  57%                   
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  |...............................                    |  61% [unnamed-chunk-6] 
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  |.................................                  |  65%                   
  |                                                         
  |...................................                |  70% [unnamed-chunk-7] 
  |                                                         
  |......................................             |  74%                   
  |                                                         
  |........................................           |  78% [unnamed-chunk-8] 
  |                                                         
  |..........................................         |  83%                   
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  |............................................       |  87% [unnamed-chunk-9] 
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  |...............................................    |  91%                   
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  |.................................................  |  96% [unnamed-chunk-10]
  |                                                         
  |...................................................| 100%                   
                                                                                                             
+1/29                   
+2/29 [setup]           
+3/29                   
+4/29 [unnamed-chunk-1] 
+5/29                   
+6/29 [unnamed-chunk-2] 
+7/29                   
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+9/29                   
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+27/29                   
+28/29 [unnamed-chunk-13]
+29/29                   
 output file: samp_report_creator.knit.md
 
-/tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/bin/pandoc +RTS -K512m -RTS samp_report_creator.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /tools/GenoPred/pipeline/misc/dev/test_data/output/example_plink2/reports/example_plink2-report.html --lua-filter /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 3 --variable toc_float=1 --variable toc_selectors=h1,h2,h3 --variable toc_collapsed=1 --variable toc_smooth_scroll=1 --variable toc_print=1 --template /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=cosmo --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /scratch/prj/oliverpainfel/tmp/RtmpPPJU3F/rmarkdown-str12ade2ea41f34.html 
+/tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/bin/pandoc +RTS -K512m -RTS samp_report_creator.knit.md --to html4 --from markdown+autolink_bare_uris+tex_math_single_backslash --output /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/reports/example_plink2-report.html --lua-filter /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmarkdown/lua/pagebreak.lua --lua-filter /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmarkdown/lua/latex-div.lua --embed-resources --standalone --variable bs3=TRUE --section-divs --table-of-contents --toc-depth 3 --variable toc_float=1 --variable toc_selectors=h1,h2,h3 --variable toc_collapsed=1 --variable toc_smooth_scroll=1 --variable toc_print=1 --template /tools/GenoPred/pipeline/.snakemake/conda/4e4fa1f1bb8db14f9a36511ee76ae12c_/lib/R/library/rmarkdown/rmd/h/default.html --no-highlight --variable highlightjs=1 --variable theme=cosmo --mathjax --variable 'mathjax-url=https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML' --include-in-header /scratch_tmp/prj/oliverpainfel/tmp/RtmpYA1D8c/rmarkdown-str43ecf3fab081d.html 
 
-Output created: misc/dev/test_data/output/example_plink2/reports/example_plink2-report.html
+Output created: /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/reports/example_plink2-report.html
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/score_reporter.log b/pipeline/misc/dev/test_data/output/reference/logs/score_reporter.log
index 84bbe778..c21ac848 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/score_reporter.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/score_reporter.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -6,4 +5,4 @@ During startup - Warning messages:
 4: Setting LC_MONETARY failed, using "C" 
 5: Setting LC_PAPER failed, using "C" 
 6: Setting LC_MEASUREMENT failed, using "C" 
-[1] "misc/dev/test_data/output"
+[1] "/scratch_tmp/prj/oliverpainfel/tmp/genopred_test"
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/sumstat_prep_i-BODY04.log b/pipeline/misc/dev/test_data/output/reference/logs/sumstat_prep_i-BODY04.log
index 6d5f4104..46b320de 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/sumstat_prep_i-BODY04.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/sumstat_prep_i-BODY04.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-AFR.log b/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-AFR.log
index 7e4b47cb..6e0758de 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-AFR.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-AFR.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -8,29 +7,28 @@ During startup - Warning messages:
 6: Setting LC_MEASUREMENT failed, using "C" 
 Loading required package: iterators
 Loading required package: parallel
-[1] 0 0 0
-[1] 0
+gwas_groups parameter is not present in user specified config file, so will use value in default config file.
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpmQRlVu/profiles.chr22.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpySbygY/profiles.chr22.log.
 Options in effect:
   --chr 22
-  --keep misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/AFR.keep
-  --out /scratch/prj/oliverpainfel/tmp/RtmpmQRlVu/profiles.chr22
-  --pfile misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
+  --keep /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/ancestry/keep_files/model_based/AFR.keep
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpySbygY/profiles.chr22
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22
   --read-freq misc/dev/test_data/ref/freq_files/AFR/ref.AFR.chr22.afreq
-  --score /scratch/prj/oliverpainfel/tmp/RtmpmQRlVu/all_score.txt header-read cols=+scoresums,-scoreavgs
+  --score /scratch_tmp/prj/oliverpainfel/tmp/RtmpySbygY/all_score.txt header-read cols=+scoresums,-scoreavgs
   --score-col-nums 4-90
   --threads 1
 
-Start time: Thu Jul 25 17:39:15 2024
-1031702 MiB RAM detected, ~1018546 available; reserving 515851 MiB for main
+Start time: Sun Apr  6 17:34:20 2025
+1031753 MiB RAM detected, ~924855 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 12 samples (5 females, 7 males; 12 founders) loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.psam.
 1000 variants loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pvar.
 Note: No phenotype data present.
 --keep: 2 samples remaining.
 2 samples (0 females, 2 males; 2 founders) remaining after main filters.
@@ -38,5 +36,5 @@ Note: No phenotype data present.
 
--read-freq: Frequencies for 1000 variants loaded.
 
--score: 1000 variants processed.
 --score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpmQRlVu/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:15 2024
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpySbygY/profiles.chr22.sscore .
+End time: Sun Apr  6 17:34:20 2025
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-CSA.log b/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-CSA.log
index a54eeb29..83f2a917 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-CSA.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-CSA.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -8,29 +7,28 @@ During startup - Warning messages:
 6: Setting LC_MEASUREMENT failed, using "C" 
 Loading required package: iterators
 Loading required package: parallel
-[1] 0 0 0
-[1] 0
+gwas_groups parameter is not present in user specified config file, so will use value in default config file.
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpcrwG98/profiles.chr22.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpVPDLic/profiles.chr22.log.
 Options in effect:
   --chr 22
-  --keep misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/CSA.keep
-  --out /scratch/prj/oliverpainfel/tmp/RtmpcrwG98/profiles.chr22
-  --pfile misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
+  --keep /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/ancestry/keep_files/model_based/CSA.keep
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpVPDLic/profiles.chr22
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22
   --read-freq misc/dev/test_data/ref/freq_files/CSA/ref.CSA.chr22.afreq
-  --score /scratch/prj/oliverpainfel/tmp/RtmpcrwG98/all_score.txt header-read cols=+scoresums,-scoreavgs
+  --score /scratch_tmp/prj/oliverpainfel/tmp/RtmpVPDLic/all_score.txt header-read cols=+scoresums,-scoreavgs
   --score-col-nums 4-90
   --threads 1
 
-Start time: Thu Jul 25 17:39:13 2024
-1031702 MiB RAM detected, ~1018546 available; reserving 515851 MiB for main
+Start time: Sun Apr  6 17:34:18 2025
+1031753 MiB RAM detected, ~924852 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 12 samples (5 females, 7 males; 12 founders) loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.psam.
 1000 variants loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pvar.
 Note: No phenotype data present.
 --keep: 1 sample remaining.
 1 sample (0 females, 1 male; 1 founder) remaining after main filters.
@@ -38,5 +36,5 @@ Note: No phenotype data present.
 
--read-freq: Frequencies for 1000 variants loaded.
 
--score: 1000 variants processed.
 --score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpcrwG98/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:13 2024
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpVPDLic/profiles.chr22.sscore .
+End time: Sun Apr  6 17:34:18 2025
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-EAS.log b/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-EAS.log
index 06b72277..27207be2 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-EAS.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-EAS.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -8,29 +7,28 @@ During startup - Warning messages:
 6: Setting LC_MEASUREMENT failed, using "C" 
 Loading required package: iterators
 Loading required package: parallel
-[1] 0 0 0
-[1] 0
+gwas_groups parameter is not present in user specified config file, so will use value in default config file.
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp6pxz9R/profiles.chr22.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/Rtmpx4Y2G4/profiles.chr22.log.
 Options in effect:
   --chr 22
-  --keep misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/EAS.keep
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp6pxz9R/profiles.chr22
-  --pfile misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
+  --keep /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/ancestry/keep_files/model_based/EAS.keep
+  --out /scratch_tmp/prj/oliverpainfel/tmp/Rtmpx4Y2G4/profiles.chr22
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22
   --read-freq misc/dev/test_data/ref/freq_files/EAS/ref.EAS.chr22.afreq
-  --score /scratch/prj/oliverpainfel/tmp/Rtmp6pxz9R/all_score.txt header-read cols=+scoresums,-scoreavgs
+  --score /scratch_tmp/prj/oliverpainfel/tmp/Rtmpx4Y2G4/all_score.txt header-read cols=+scoresums,-scoreavgs
   --score-col-nums 4-90
   --threads 1
 
-Start time: Thu Jul 25 17:39:14 2024
-1031702 MiB RAM detected, ~1018546 available; reserving 515851 MiB for main
+Start time: Sun Apr  6 17:34:17 2025
+1031753 MiB RAM detected, ~924835 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 12 samples (5 females, 7 males; 12 founders) loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.psam.
 1000 variants loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pvar.
 Note: No phenotype data present.
 --keep: 2 samples remaining.
 2 samples (1 female, 1 male; 2 founders) remaining after main filters.
@@ -38,5 +36,5 @@ Note: No phenotype data present.
 
--read-freq: Frequencies for 1000 variants loaded.
 
--score: 1000 variants processed.
 --score: Results written to
-/scratch/prj/oliverpainfel/tmp/Rtmp6pxz9R/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:14 2024
+/scratch_tmp/prj/oliverpainfel/tmp/Rtmpx4Y2G4/profiles.chr22.sscore .
+End time: Sun Apr  6 17:34:17 2025
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-EUR.log b/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-EUR.log
index 859aadfe..97aea426 100644
--- a/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-EUR.log
+++ b/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-EUR.log
@@ -1,4 +1,3 @@
-WARNING: ignoring environment value of R_HOME
 During startup - Warning messages:
 1: Setting LC_COLLATE failed, using "C" 
 2: Setting LC_TIME failed, using "C" 
@@ -8,29 +7,28 @@ During startup - Warning messages:
 6: Setting LC_MEASUREMENT failed, using "C" 
 Loading required package: iterators
 Loading required package: parallel
-[1] 0 0 0
-[1] 0
+gwas_groups parameter is not present in user specified config file, so will use value in default config file.
 PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
 (C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpIBxqLH/profiles.chr22.log.
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpJ5TuyM/profiles.chr22.log.
 Options in effect:
   --chr 22
-  --keep misc/dev/test_data/output/example_plink2/ancestry/keep_files/model_based/EUR.keep
-  --out /scratch/prj/oliverpainfel/tmp/RtmpIBxqLH/profiles.chr22
-  --pfile misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22
+  --keep /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/ancestry/keep_files/model_based/EUR.keep
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpJ5TuyM/profiles.chr22
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22
   --read-freq misc/dev/test_data/ref/freq_files/EUR/ref.EUR.chr22.afreq
-  --score /scratch/prj/oliverpainfel/tmp/RtmpIBxqLH/all_score.txt header-read cols=+scoresums,-scoreavgs
+  --score /scratch_tmp/prj/oliverpainfel/tmp/RtmpJ5TuyM/all_score.txt header-read cols=+scoresums,-scoreavgs
   --score-col-nums 4-90
   --threads 1
 
-Start time: Thu Jul 25 17:39:13 2024
-1031702 MiB RAM detected, ~1018550 available; reserving 515851 MiB for main
+Start time: Sun Apr  6 17:34:19 2025
+1031753 MiB RAM detected, ~924852 available; reserving 515876 MiB for main
 workspace.
 Using 1 compute thread.
 12 samples (5 females, 7 males; 12 founders) loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.psam.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.psam.
 1000 variants loaded from
-misc/dev/test_data/output/example_plink2/geno/example_plink2.ref.chr22.pvar.
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pvar.
 Note: No phenotype data present.
 --keep: 5 samples remaining.
 5 samples (2 females, 3 males; 5 founders) remaining after main filters.
@@ -38,5 +36,5 @@ Note: No phenotype data present.
 
--read-freq: Frequencies for 1000 variants loaded.
 
--score: 1000 variants processed.
 --score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpIBxqLH/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:13 2024
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpJ5TuyM/profiles.chr22.sscore .
+End time: Sun Apr  6 17:34:19 2025
diff --git a/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-TRANS.log b/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-TRANS.log
new file mode 100644
index 00000000..be11e793
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/logs/target_pgs_i-example_plink2-TRANS.log
@@ -0,0 +1,127 @@
+During startup - Warning messages:
+1: Setting LC_COLLATE failed, using "C" 
+2: Setting LC_TIME failed, using "C" 
+3: Setting LC_MESSAGES failed, using "C" 
+4: Setting LC_MONETARY failed, using "C" 
+5: Setting LC_PAPER failed, using "C" 
+6: Setting LC_MEASUREMENT failed, using "C" 
+Loading required package: iterators
+Loading required package: parallel
+gwas_groups parameter is not present in user specified config file, so will use value in default config file.
+PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
+(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
+Logging to /scratch_tmp/prj/oliverpainfel/tmp/RtmpF339sT/profiles.chr22.log.
+Options in effect:
+  --chr 22
+  --out /scratch_tmp/prj/oliverpainfel/tmp/RtmpF339sT/profiles.chr22
+  --pfile /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22
+  --read-freq misc/dev/test_data/ref/freq_files/TRANS/ref.TRANS.chr22.afreq
+  --score /scratch_tmp/prj/oliverpainfel/tmp/RtmpF339sT/all_score.txt header-read cols=+scoresums,-scoreavgs
+  --score-col-nums 4-90
+  --threads 1
+
+Start time: Sun Apr  6 17:34:15 2025
+1031753 MiB RAM detected, ~924756 available; reserving 515876 MiB for main
+workspace.
+Using 1 compute thread.
+12 samples (5 females, 7 males; 12 founders) loaded from
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.psam.
+1000 variants loaded from
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/example_plink2/geno/example_plink2.ref.chr22.pvar.
+Note: No phenotype data present.
+--read-freq: PLINK 2 --freq file detected.
+
--read-freq: Frequencies for 1000 variants loaded.
+
--score: 1000 variants processed.
+--score: Results written to
+/scratch_tmp/prj/oliverpainfel/tmp/RtmpF339sT/profiles.chr22.sscore .
+End time: Sun Apr  6 17:34:15 2025
+[1] 1
+[1] 2
+[1] 3
+Processing: score_file_1.s0.2_lambda0.001 
+Processing: score_file_1.s0.2_lambda0.00127427498570313 
+Processing: score_file_1.s0.2_lambda0.00162377673918872 
+Processing: score_file_1.s0.2_lambda0.00206913808111479 
+Processing: score_file_1.s0.2_lambda0.00263665089873036 
+Processing: score_file_1.s0.2_lambda0.00335981828628378 
+Processing: score_file_1.s0.2_lambda0.00428133239871939 
+Processing: score_file_1.s0.2_lambda0.00545559478116852 
+Processing: score_file_1.s0.2_lambda0.00695192796177561 
+Processing: score_file_1.s0.2_lambda0.00885866790410083 
+Processing: score_file_1.s0.2_lambda0.0112883789168469 
+Processing: score_file_1.s0.2_lambda0.0143844988828766 
+Processing: score_file_1.s0.2_lambda0.0183298071083244 
+Processing: score_file_1.s0.2_lambda0.0233572146909012 
+Processing: score_file_1.s0.2_lambda0.0297635144163132 
+Processing: score_file_1.s0.2_lambda0.0379269019073225 
+Processing: score_file_1.s0.2_lambda0.0483293023857176 
+Processing: score_file_1.s0.2_lambda0.0615848211066027 
+Processing: score_file_1.s0.2_lambda0.0784759970351462 
+Processing: score_file_1.s0.2_lambda0.1 
+Processing: score_file_1.s0.5_lambda0.001 
+Processing: score_file_1.s0.5_lambda0.00127427498570313 
+Processing: score_file_1.s0.5_lambda0.00162377673918872 
+Processing: score_file_1.s0.5_lambda0.00206913808111479 
+Processing: score_file_1.s0.5_lambda0.00263665089873036 
+Processing: score_file_1.s0.5_lambda0.00335981828628378 
+Processing: score_file_1.s0.5_lambda0.00428133239871939 
+Processing: score_file_1.s0.5_lambda0.00545559478116852 
+Processing: score_file_1.s0.5_lambda0.00695192796177561 
+Processing: score_file_1.s0.5_lambda0.00885866790410083 
+Processing: score_file_1.s0.5_lambda0.0112883789168469 
+Processing: score_file_1.s0.5_lambda0.0143844988828766 
+Processing: score_file_1.s0.5_lambda0.0183298071083244 
+Processing: score_file_1.s0.5_lambda0.0233572146909012 
+Processing: score_file_1.s0.5_lambda0.0297635144163132 
+Processing: score_file_1.s0.5_lambda0.0379269019073225 
+Processing: score_file_1.s0.5_lambda0.0483293023857176 
+Processing: score_file_1.s0.5_lambda0.0615848211066027 
+Processing: score_file_1.s0.5_lambda0.0784759970351462 
+Processing: score_file_1.s0.5_lambda0.1 
+Processing: score_file_1.s0.9_lambda0.001 
+Processing: score_file_1.s0.9_lambda0.00127427498570313 
+Processing: score_file_1.s0.9_lambda0.00162377673918872 
+Processing: score_file_1.s0.9_lambda0.00206913808111479 
+Processing: score_file_1.s0.9_lambda0.00263665089873036 
+Processing: score_file_1.s0.9_lambda0.00335981828628378 
+Processing: score_file_1.s0.9_lambda0.00428133239871939 
+Processing: score_file_1.s0.9_lambda0.00545559478116852 
+Processing: score_file_1.s0.9_lambda0.00695192796177561 
+Processing: score_file_1.s0.9_lambda0.00885866790410083 
+Processing: score_file_1.s0.9_lambda0.0112883789168469 
+Processing: score_file_1.s0.9_lambda0.0143844988828766 
+Processing: score_file_1.s0.9_lambda0.0183298071083244 
+Processing: score_file_1.s0.9_lambda0.0233572146909012 
+Processing: score_file_1.s0.9_lambda0.0297635144163132 
+Processing: score_file_1.s0.9_lambda0.0379269019073225 
+Processing: score_file_1.s0.9_lambda0.0483293023857176 
+Processing: score_file_1.s0.9_lambda0.0615848211066027 
+Processing: score_file_1.s0.9_lambda0.0784759970351462 
+Processing: score_file_1.s0.9_lambda0.1 
+Processing: score_file_1.s1_lambda0.001 
+Processing: score_file_1.s1_lambda0.00127427498570313 
+Processing: score_file_1.s1_lambda0.00162377673918872 
+Processing: score_file_1.s1_lambda0.00206913808111479 
+Processing: score_file_1.s1_lambda0.00263665089873036 
+Processing: score_file_1.s1_lambda0.00335981828628378 
+Processing: score_file_1.s1_lambda0.00428133239871939 
+Processing: score_file_1.s1_lambda0.00545559478116852 
+Processing: score_file_1.s1_lambda0.00695192796177561 
+Processing: score_file_1.s1_lambda0.00885866790410083 
+Processing: score_file_1.s1_lambda0.0112883789168469 
+Processing: score_file_1.s1_lambda0.0143844988828766 
+Processing: score_file_1.s1_lambda0.0183298071083244 
+Processing: score_file_1.s1_lambda0.0233572146909012 
+Processing: score_file_1.s1_lambda0.0297635144163132 
+Processing: score_file_1.s1_lambda0.0379269019073225 
+Processing: score_file_1.s1_lambda0.0483293023857176 
+Processing: score_file_1.s1_lambda0.0615848211066027 
+Processing: score_file_1.s1_lambda0.0784759970351462 
+Processing: score_file_1.s1_lambda0.1 
+Processing: score_file_2.0_0.1 
+Processing: score_file_2.0_0.2 
+Processing: score_file_2.0_0.3 
+Processing: score_file_2.0_0.4 
+Processing: score_file_2.0_0.5 
+Processing: score_file_2.0_1 
+Processing: score_file_3.external 
diff --git a/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.AFR.scale b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.AFR.scale
new file mode 100644
index 00000000..198cf4f9
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.AFR.scale
@@ -0,0 +1,7 @@
+Param Mean SD
+PC1 -18.9814274796512 9.89335661955237
+PC2 -8.64323169752907 8.91053381719754
+PC3 -20.2934528876453 8.70324201268448
+PC4 15.1359213466279 9.18958054750852
+PC5 -35.9123294476744 9.85571044936291
+PC6 21.8108191271802 10.2461364791139
diff --git a/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.AMR.scale b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.AMR.scale
new file mode 100644
index 00000000..1e4217f1
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.AMR.scale
@@ -0,0 +1,7 @@
+Param Mean SD
+PC1 -64.8996417475728 13.101042352565
+PC2 -0.158530183495146 17.1715308694982
+PC3 -35.2209976747573 13.8829333986314
+PC4 3.47204956140777 16.8111775623253
+PC5 -36.1835395606796 13.5218172228499
+PC6 21.2077585485437 13.0009565912436
diff --git a/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.CSA.scale b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.CSA.scale
new file mode 100644
index 00000000..97a6e757
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.CSA.scale
@@ -0,0 +1,7 @@
+Param Mean SD
+PC1 -63.0708591111111 9.0098231969311
+PC2 3.01485634740741 10.6319271181925
+PC3 -6.79859343674074 14.4613153016269
+PC4 8.21018499644445 10.4947208001446
+PC5 -34.2205163674074 14.0859835418051
+PC6 21.7388748174815 14.3322286137219
diff --git a/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.EAS.scale b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.EAS.scale
new file mode 100644
index 00000000..54a9fd95
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.EAS.scale
@@ -0,0 +1,7 @@
+Param Mean SD
+PC1 -70.4234268656717 8.01414231470619
+PC2 -23.5980969090909 9.28339340278281
+PC3 -23.0809556078697 10.2482722136741
+PC4 22.2072509402985 10.588171306603
+PC5 -34.749115807327 12.6428122178264
+PC6 19.5809987354138 10.5939730071615
diff --git a/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale
new file mode 100644
index 00000000..0194bf8c
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale
@@ -0,0 +1,7 @@
+Param Mean SD
+PC1 -61.1520145864662 8.67251444285097
+PC2 19.2920513669173 9.35083521308838
+PC3 -22.5249495428571 11.4880873353148
+PC4 20.7228436921504 10.0463218941429
+PC5 -35.7022403578947 13.7706474025973
+PC6 20.2459927774436 14.0955692530143
diff --git a/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.MID.scale b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.MID.scale
new file mode 100644
index 00000000..06f2447f
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.MID.scale
@@ -0,0 +1,7 @@
+Param Mean SD
+PC1 -55.8225382352941 9.31254685000243
+PC2 15.7909285294118 8.45499858376908
+PC3 -22.7928469485294 10.979082405182
+PC4 22.6671036911765 10.6259485254891
+PC5 -43.9140660735294 12.5250035045814
+PC6 24.3972792647059 11.9341488817171
diff --git a/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.TRANS.scale b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.TRANS.scale
new file mode 100644
index 00000000..abdc7358
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.TRANS.scale
@@ -0,0 +1,7 @@
+Param Mean SD
+PC1 -55.0952987944461 21.1310735797769
+PC2 -1.92932290733474 18.3900575588274
+PC3 -20.570956870661 14.3053191696195
+PC4 15.2780061209357 13.0825771501909
+PC5 -35.6289015034712 12.9073338797417
+PC6 21.0172024836402 12.5068503549219
diff --git a/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.eigenvec.var.gz b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.eigenvec.var.gz
new file mode 100644
index 0000000000000000000000000000000000000000..d0b64990a0d64a5a370315db128342ea3ec615a7
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literal 0
HcmV?d00001

diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.log b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.log
similarity index 50%
rename from pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.log
rename to pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.log
index f93ed484..3c93bdf2 100644
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.log
+++ b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.log
@@ -3,28 +3,28 @@
 # For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
 #################################################################
 # Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
+# Version (tag): v2.2.2-258-gd2f2a91
 ---------------
- Parameter     Value                                                                      
- ref_plink_chr misc/dev/test_data/ref/ref.chr                                             
- ref_keep      misc/dev/test_data/ref/keep_files/AFR.keep                                 
- maf           0.05                                                                       
- geno          0.02                                                                       
- hwe           1e-06                                                                      
- n_pcs         6                                                                          
- plink2        plink2                                                                     
- output        misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs
- pop_data      misc/dev/test_data/ref/ref.pop.txt                                         
- memory        5000                                                                       
- test          chr22                                                                      
- help          FALSE                                                                      
- output_dir    misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/           
+ Parameter     Value                                                                                        
+ ref_plink_chr misc/dev/test_data/ref/ref.chr                                                               
+ ref_keep      NA                                                                                           
+ maf           0.05                                                                                         
+ geno          0.02                                                                                         
+ hwe           1e-06                                                                                        
+ n_pcs         6                                                                                            
+ plink2        plink2                                                                                       
+ output        /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pc_score_files/TRANS/ref-TRANS-pcs
+ pop_data      misc/dev/test_data/ref/ref.pop.txt                                                           
+ memory        5000                                                                                         
+ test          chr22                                                                                        
+ help          FALSE                                                                                        
+ output_dir    /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pc_score_files/TRANS/             
 ---------------
-Analysis started at 2024-07-25 17:39:06
+Analysis started at 2025-04-06 17:28:43
 Identifying LD independent SNPs based on reference data.
-805 variants after removal of LD high regions.
-275 independent variants retained.
+590 variants after removal of LD high regions.
+172 independent variants retained.
 Performing PCA based on reference.
 Computing reference PCs.
-Analysis finished at 2024-07-25 17:39:07 
-Analysis duration was 0.56 secs 
+Analysis finished at 2025-04-06 17:28:54 
+Analysis duration was 10.94 secs 
diff --git a/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles
new file mode 100644
index 00000000..9ed88a4c
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles
@@ -0,0 +1,3314 @@
+FID IID PC1 PC2 PC3 PC4 PC5 PC6
+HG00096 HG00096 -0.309 1.643 -1.269 0.629 -0.411 1.314
+HG00097 HG00097 -0.36 0.793 -0.621 -0.809 1.446 0.092
+HG00099 HG00099 -0.364 0.854 -0.047 -0.462 0.015 0.537
+HG00100 HG00100 -0.223 1.785 -0.58 1.414 0.279 0.349
+HG00101 HG00101 -0.689 0.335 0.266 -0.408 1.075 2.607
+HG00102 HG00102 -0.354 1.003 -0.632 -0.298 2.08 1.223
+HG00103 HG00103 -0.16 0.65 1.02 0.721 -0.372 0.55
+HG00105 HG00105 -0.65 1.013 -0.505 0.344 1.393 1.398
+HG00106 HG00106 -0.26 1.969 0.799 0.767 -0.116 -0.414
+HG00107 HG00107 0.332 0.997 -2.191 -1.004 0.887 -0.882
+HG00108 HG00108 -0.004 1.414 0.072 -0.849 0.411 -0.282
+HG00109 HG00109 -0.903 0.048 0.297 1.014 0.375 0.906
+HG00110 HG00110 -0.507 1.268 0.19 1.407 -0.491 0.426
+HG00111 HG00111 -0.297 1.356 -2.005 0.203 -0.711 0.861
+HG00112 HG00112 -0.348 2.069 -0.382 0.507 0.793 -2.058
+HG00113 HG00113 -0.575 1.673 0.095 -1.416 1.185 -2.763
+HG00114 HG00114 0.012 1.812 -0.453 0.074 -2.084 -0.179
+HG00115 HG00115 -0.321 0.684 1.069 -0.521 1.379 -1.889
+HG00116 HG00116 -0.453 1.155 -0.688 -0.512 2.23 -0.634
+HG00117 HG00117 -0.574 0.442 0.144 0.97 -0.324 -0.513
+HG00118 HG00118 -0.628 1.107 -0.583 -0.263 -0.095 0.801
+HG00119 HG00119 -0.136 1.755 0.57 1.144 0.173 1.295
+HG00121 HG00121 0.549 1.453 -0.286 -0.09 1.865 2.39
+HG00122 HG00122 -0.2 1.16 -0.182 -0.936 -0.202 -0.934
+HG00123 HG00123 0.204 1.288 -1.699 0.18 1.743 -1.885
+HG00125 HG00125 -0.359 1.042 0.005 0.646 -0.381 -0.735
+HG00126 HG00126 -0.588 1.32 -0.216 0.26 1.64 -1.364
+HG00127 HG00127 0.506 1.411 -0.42 -0.318 0.227 0.318
+HG00128 HG00128 -0.857 1.85 1.181 -0.132 1.438 0.954
+HG00129 HG00129 -0.921 0.273 -1.478 0.245 -0.68 -0.076
+HG00130 HG00130 -0.859 1.922 0.437 -0.243 0.168 0.539
+HG00131 HG00131 0.035 0.943 -1.387 0.533 -0.521 -0.508
+HG00132 HG00132 -0.182 1.379 0.08 0.128 -1.132 -0.129
+HG00133 HG00133 -0.272 1.352 -1.538 -0.596 -0.431 0.327
+HG00136 HG00136 -0.606 0.748 0.285 0.247 1.136 -0.244
+HG00137 HG00137 -0.669 1.435 0.465 -0.382 1.856 1.06
+HG00138 HG00138 -0.903 1.112 1.572 -0.646 -0.843 0.299
+HG00139 HG00139 -0.784 0.938 -0.075 -0.207 -0.802 0.138
+HG00140 HG00140 -0.384 0.777 0.142 0.574 -0.119 -1.003
+HG00141 HG00141 -0.616 0.318 -1.334 0.167 1.066 1.257
+HG00142 HG00142 -0.435 1.289 -1.518 -0.448 0.575 -0.72
+HG00143 HG00143 -0.43 1.707 -1.669 -0.186 0.199 1.525
+HG00145 HG00145 -0.099 0.655 -0.307 0.087 -0.95 0.993
+HG00146 HG00146 -0.201 1.674 -0.166 0.183 -0.439 1.257
+HG00148 HG00148 -0.488 1.398 0.99 0.52 0.779 0.097
+HG00149 HG00149 -0.456 1.495 -0.057 1.489 -0.344 -0.809
+HG00150 HG00150 0.059 1.715 -0.343 -0.236 -1.115 -0.674
+HG00151 HG00151 -0.564 1.237 1.127 0.424 -1.189 -0.181
+HG00155 HG00155 -0.813 0.738 -1.307 -0.258 0.687 0.35
+HG00157 HG00157 -0.225 2.097 -0.384 1.121 -0.768 0.072
+HG00158 HG00158 -0.124 1.641 0.211 0.874 -0.637 0.846
+HG00159 HG00159 -0.082 0.284 0.493 -0.321 -1.607 -1.262
+HG00160 HG00160 -0.218 1.721 -1.841 -0.133 1.405 -0.229
+HG00171 HG00171 -0.314 1.111 -0.723 -0.27 1.754 -1.94
+HG00173 HG00173 -1.022 -0.323 -0.86 -0.465 0.96 0.17
+HG00174 HG00174 -0.266 0.542 -0.074 0.365 0.539 -0.087
+HG00176 HG00176 -0.686 0.661 -0.026 1.687 -0.829 0.599
+HG00177 HG00177 -1.179 0.896 1.29 1.216 -0.358 -0.185
+HG00178 HG00178 -0.314 0.45 -0.411 -0.147 -0.832 0.39
+HG00179 HG00179 -0.283 0.446 0.896 -1.16 0.023 -0.42
+HG00180 HG00180 -0.058 0.849 -0.322 1.741 -1.415 -0.012
+HG00181 HG00181 -0.171 1.177 -0.685 -0.103 -0.449 1.424
+HG00182 HG00182 0.129 0.053 0.508 0.258 -0.8 -0.462
+HG00183 HG00183 -0.827 1.195 0.716 1.05 -0.369 0.094
+HG00185 HG00185 -0.591 1.256 0.639 0.43 1.055 -1.309
+HG00186 HG00186 0.067 1.288 -1.087 0.683 0.017 -1.151
+HG00187 HG00187 0.26 1.486 -0.646 0.395 -1.747 0.934
+HG00188 HG00188 -1.149 0.975 -0.473 -0.047 -1.514 -0.992
+HG00189 HG00189 -0.102 1.057 0.696 1.358 0.265 0.9
+HG00190 HG00190 -0.443 1.858 -0.044 0.777 0.444 -1.73
+HG00231 HG00231 -0.041 1.013 1.489 -0.166 0.032 0.147
+HG00232 HG00232 -0.36 1.772 -0.997 0.381 -2.456 0.809
+HG00233 HG00233 0.467 1.312 -0.477 0.282 -0.87 0.266
+HG00234 HG00234 0.074 0.315 0.864 0.642 2.367 -2.119
+HG00235 HG00235 -0.506 0.894 -0.344 1.55 0.067 0.552
+HG00236 HG00236 -1.115 0.985 -0.049 0.428 0.115 0.812
+HG00237 HG00237 -0.675 0.949 1.125 0.901 0.882 0.24
+HG00239 HG00239 -0.499 0.679 2.081 0.134 1.315 -1.196
+HG00240 HG00240 -0.135 2.193 -0.496 0.936 0.343 0.316
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+HG04153 HG04153 -0.581 0.369 2.255 0.005 0.199 0.499
+HG04155 HG04155 -0.518 0.753 1.419 -0.963 -1.104 -0.027
+HG04156 HG04156 0.069 0.021 0.657 1.087 -0.069 -0.491
+HG04158 HG04158 -0.88 -0.395 1.175 0.494 1.001 0.38
+HG04159 HG04159 -0.412 1.325 2.281 -0.754 0.758 -0.669
+HG04161 HG04161 -1.007 0.307 1.165 -0.436 -0.743 1.94
+HG04164 HG04164 -0.171 0.569 1.356 -0.117 -0.436 1.236
+HG04171 HG04171 -0.008 0.944 0.487 -0.01 0.271 -0.879
+HG04173 HG04173 -0.576 -0.489 2.526 0.521 -0.388 -1.029
+HG04174 HG04174 -0.331 -1.009 2.125 0.337 0.919 -0.081
+HG04176 HG04176 -0.415 1.458 1.925 -0.931 0.461 1.388
+HG04177 HG04177 -0.788 -1.107 1.955 -1.131 -0.268 0.871
+HG04180 HG04180 -0.315 0.153 -0.531 -2.089 -0.749 -0.943
+HG04182 HG04182 -0.355 -0.137 0.504 -0.765 -1.481 0.135
+HG04183 HG04183 -0.381 0.777 0.243 -0.43 -1.602 -1.355
+HG04185 HG04185 0.408 0.179 -1.492 -1.003 -0.272 1.133
+HG04186 HG04186 -0.33 -0.179 0.725 0.488 -1.347 -0.764
+HG04188 HG04188 0.636 0.11 1.482 -0.12 -0.333 -1.337
+HG04189 HG04189 -0.003 0.87 1.253 0.039 -1.118 0.346
+HG04191 HG04191 -0.049 -1.166 -0.325 -1.116 2.286 0.794
+HG04192 HG04192 -0.387 -1.036 -0.684 -0.794 1.478 0.63
+HG04194 HG04194 -0.697 -0.284 1.737 -0.556 -0.074 1.603
+HG04195 HG04195 -0.797 0.158 0.383 -0.57 -0.49 0.265
+HG04198 HG04198 -0.89 -0.294 2.979 -0.613 0.34 -0.205
+HG04200 HG04200 -0.924 -0.01 2.138 -0.307 0.544 1.143
+HG04204 HG04204 -0.32 0.655 0.077 0.536 -0.919 1.087
+HG04206 HG04206 -0.435 -0.434 0.8 -1.014 0.063 -0.92
+HG04209 HG04209 -0.325 -0.22 1.743 0.024 -1.46 -0.867
+HG04210 HG04210 -0.521 0.245 0.38 -1.793 -1.542 0.153
+HG04211 HG04211 -0.885 -0.295 1.133 -2.024 -1.442 -0.954
+HG04212 HG04212 -0.844 0.325 1.479 -1.657 0.703 0.182
+HG04214 HG04214 -0.168 0.719 1.52 -1.146 1.942 0.224
+HG04216 HG04216 -0.571 -0.471 2.267 -1.053 -0.684 -0.509
+HG04217 HG04217 -0.868 -0.068 2.625 -1.389 -0.557 1.467
+HG04219 HG04219 -0.662 0.192 0.912 -0.376 1.91 -0.205
+HG04222 HG04222 -0.935 0.767 0.414 0.722 0.969 0.327
+HG04225 HG04225 0.036 0.964 0.923 -1.299 0.898 -1.721
+HG04227 HG04227 -0.692 0.138 0.895 -0.861 -0.646 -0.788
+HG04229 HG04229 -0.743 0.056 1.609 -1.059 0.478 0.558
+HG04235 HG04235 -0.922 0.744 -0.177 -0.275 0.353 1.461
+HG04238 HG04238 -0.12 -0.34 1.342 -0.602 -0.733 -0.342
+HG04239 HG04239 -0.246 -0.024 2.024 0.007 1.643 -2.079
+HGDP00003 HGDP00003 0.167 0.996 0.705 0.427 -0.507 1.387
+HGDP00007 HGDP00007 -0.202 0.247 0.722 0.292 0.015 0.141
+HGDP00011 HGDP00011 -0.298 1.156 0.903 -0.108 1.154 0.065
+HGDP00015 HGDP00015 0.068 0.717 0.186 0.746 -1.982 -0.643
+HGDP00017 HGDP00017 -0.25 0.933 0.79 0.056 -1.358 -0.307
+HGDP00021 HGDP00021 -0.13 0.081 0.043 0.131 0.997 -0.954
+HGDP00023 HGDP00023 -0.1 0.042 0.714 -0.35 -0.894 0.003
+HGDP00025 HGDP00025 -0.129 0.091 -0.71 -1.085 -0.59 -0.671
+HGDP00031 HGDP00031 -0.292 0.314 1.042 -1.079 -0.536 -0.251
+HGDP00033 HGDP00033 -0.345 0.326 1.115 0.483 -0.77 0.704
+HGDP00035 HGDP00035 0.566 0.766 1.047 1.133 -0.36 -1.646
+HGDP00037 HGDP00037 -0.074 0.904 0.024 0.357 -1.241 -0.433
+HGDP00039 HGDP00039 -0.744 0.546 1.094 1.484 0.68 1.46
+HGDP00041 HGDP00041 -0.603 0.811 0.108 -0.586 3.498 1.207
+HGDP00043 HGDP00043 0.297 0.807 0.963 -0.758 -1.404 -0.527
+HGDP00045 HGDP00045 0.03 0.245 0.354 0.64 -0.219 -2.099
+HGDP00047 HGDP00047 0.649 0.906 0.218 1.017 -0.272 0.283
+HGDP00049 HGDP00049 0.198 0.158 -0.811 0.158 0.996 0.231
+HGDP00052 HGDP00052 0.22 -0.084 0.636 -0.96 -0.303 0.814
+HGDP00054 HGDP00054 -0.459 -0.313 2.398 -0.548 -0.009 0.663
+HGDP00060 HGDP00060 -0.622 0.612 1.066 -0.561 0.725 0.899
+HGDP00064 HGDP00064 -0.384 1.216 -1.468 -0.502 -1.032 -0.084
+HGDP00066 HGDP00066 0.417 0.46 -1.243 -1.177 -0.622 -0.017
+HGDP00068 HGDP00068 -0.267 0.736 0.558 0.055 -1.825 -0.644
+HGDP00070 HGDP00070 0.104 0.289 1.525 0.434 0.607 1.57
+HGDP00072 HGDP00072 -0.358 0.185 1.77 0.34 -0.915 -1.02
+HGDP00074 HGDP00074 0.603 0.66 -0.499 -0.296 0.538 -0.793
+HGDP00076 HGDP00076 1.632 0.196 -0.011 -0.193 0.046 -0.162
+HGDP00078 HGDP00078 0.248 0.636 0.12 -0.832 -0.276 0.914
+HGDP00080 HGDP00080 -0.42 0.636 0.855 0.222 0.61 -0.717
+HGDP00082 HGDP00082 -0.309 1.699 -0.328 0.001 -0.583 -1.098
+HGDP00086 HGDP00086 -0.328 0.414 0.289 -0.038 0.816 -0.604
+HGDP00088 HGDP00088 -0.455 1.621 -0.435 -0.019 -0.119 -0.837
+HGDP00092 HGDP00092 -0.232 1.996 -1.491 0.025 3.625 0.833
+HGDP00094 HGDP00094 0.025 0.813 2.315 -0.82 0.274 -0.675
+HGDP00096 HGDP00096 -0.395 1.713 0.471 -0.588 1.258 2.129
+HGDP00098 HGDP00098 -1.058 0.463 -1.477 -0.473 -0.689 0.305
+HGDP00099 HGDP00099 -0.323 0.037 -0.033 -0.086 -1.511 -0.671
+HGDP00100 HGDP00100 -0.991 0.937 0.37 -0.055 -0.69 0.487
+HGDP00102 HGDP00102 -0.603 -0.637 -0.736 0.715 0.486 0.099
+HGDP00103 HGDP00103 -0.292 -0.125 -0.218 0.251 0.213 0.401
+HGDP00105 HGDP00105 -0.642 0.386 -0.428 0.424 0.089 -0.907
+HGDP00106 HGDP00106 -0.64 -0.389 0.985 -2.331 -0.107 -0.873
+HGDP00109 HGDP00109 -0.241 -0.161 0.841 -0.769 1.446 2.356
+HGDP00110 HGDP00110 -0.586 -0.835 -0.394 -0.664 -0.194 1.048
+HGDP00115 HGDP00115 -0.315 0.797 -0.313 -0.815 -0.706 0.602
+HGDP00118 HGDP00118 0.112 1.394 -0.05 0.525 -0.284 -1.001
+HGDP00119 HGDP00119 -1.069 0.692 -0.954 -0.334 -0.532 1.203
+HGDP00120 HGDP00120 -0.564 -0.577 -0.065 0.553 0.699 -0.922
+HGDP00122 HGDP00122 -0.715 -1.951 -0.575 0.942 1.098 -0.299
+HGDP00127 HGDP00127 -0.499 0.374 0.395 1.033 0.54 0.38
+HGDP00129 HGDP00129 -0.389 0.639 -0.932 -0.179 0.331 0.369
+HGDP00131 HGDP00131 -0.601 0.32 0.783 0.215 -0.502 0.443
+HGDP00133 HGDP00133 0.527 0.701 -0.033 1.401 -0.283 -0.685
+HGDP00134 HGDP00134 -0.208 0.486 -0.135 1.115 -0.03 0.022
+HGDP00135 HGDP00135 0.598 0.305 0.866 -0.21 -0.759 0.676
+HGDP00136 HGDP00136 0.321 0.531 -0.712 -0.313 0.694 1.163
+HGDP00137 HGDP00137 0.014 0.619 0.446 0.281 0.541 -1.967
+HGDP00139 HGDP00139 0.155 0.246 0.466 0.386 0.669 -0.939
+HGDP00140 HGDP00140 -0.084 1.169 -0.146 0.136 -0.697 -0.885
+HGDP00141 HGDP00141 -0.409 0.733 -0.645 0.793 -0.792 -1.273
+HGDP00143 HGDP00143 -0.428 0.51 0.665 0.38 0.627 1.739
+HGDP00144 HGDP00144 -0.057 1.492 0.46 0.668 0.61 -1.221
+HGDP00145 HGDP00145 -0.236 1.074 0.285 -0.057 -0.483 -0.146
+HGDP00146 HGDP00146 -0.332 1.318 0.833 1.003 -1.54 -0.133
+HGDP00148 HGDP00148 -0.384 0.492 -0.33 0.404 -0.738 -0.083
+HGDP00149 HGDP00149 -0.092 0.85 2.223 0.368 -0.86 1.791
+HGDP00151 HGDP00151 -0.678 1.142 0.077 0.672 3.042 2.509
+HGDP00153 HGDP00153 0.984 0.31 0.188 -1.794 0.001 -0.172
+HGDP00154 HGDP00154 -0.613 0.299 0.822 -0.528 0.538 0.025
+HGDP00155 HGDP00155 -0.091 0.37 0.324 0.156 -0.8 2.049
+HGDP00158 HGDP00158 0.202 0.396 -0.168 -0.241 -0.126 0.093
+HGDP00161 HGDP00161 -0.308 0.02 2.099 -0.061 0.48 -1.237
+HGDP00167 HGDP00167 -0.07 1.09 -0.714 0.402 1.269 1.114
+HGDP00169 HGDP00169 -0.254 0.744 1.001 0.833 0.668 -1.408
+HGDP00171 HGDP00171 -0.457 0.061 2.27 -0.685 0.438 0.497
+HGDP00175 HGDP00175 0.936 0.015 0.368 -0.04 -1.493 0.523
+HGDP00177 HGDP00177 -0.24 -0.305 1.66 -0.373 -0.159 0.692
+HGDP00179 HGDP00179 0.291 0.659 -0.059 -0.822 0.315 -2.275
+HGDP00181 HGDP00181 -0.129 0.382 0.946 -0.162 -1.057 -0.158
+HGDP00183 HGDP00183 -0.584 1.072 0.828 -0.877 0.322 1.174
+HGDP00187 HGDP00187 0.207 0.748 -0.202 -1.648 -1.362 -0.085
+HGDP00189 HGDP00189 -0.918 1.138 -1.301 0.018 -0.212 -0.029
+HGDP00191 HGDP00191 -0.254 1.549 1.178 0.643 -0.61 0.094
+HGDP00192 HGDP00192 -0.37 0.508 0.696 -0.135 0.44 -0.764
+HGDP00197 HGDP00197 -0.406 0.607 -0.684 -0.779 -1.43 0.432
+HGDP00199 HGDP00199 -0.75 0.494 -1.003 -0.905 3.078 0.816
+HGDP00201 HGDP00201 -0.129 1.627 0.068 0.317 1.888 0.582
+HGDP00205 HGDP00205 -0.545 0.964 0.521 -0.656 -0.929 -1.11
+HGDP00206 HGDP00206 -0.15 1.131 0.21 -1.126 2.049 -1.989
+HGDP00210 HGDP00210 -0.802 0.375 0.644 0.33 -0.711 -0.238
+HGDP00214 HGDP00214 -0.357 -0.004 0.489 2.006 1.441 0.967
+HGDP00222 HGDP00222 -0.635 0.479 1.162 -0.148 -0.08 0.075
+HGDP00224 HGDP00224 -0.946 0.354 -1.384 0.31 1.118 -1.908
+HGDP00228 HGDP00228 -0.271 0.612 1.046 -1.331 1.339 -1.605
+HGDP00230 HGDP00230 -0.645 -0.504 1.063 -0.501 0.495 -0.042
+HGDP00234 HGDP00234 -0.579 0.477 1.937 -1.503 0.09 2.319
+HGDP00237 HGDP00237 -0.272 -0.37 1.648 -0.283 1.349 -1.161
+HGDP00239 HGDP00239 -0.13 -0.476 0.99 -1.216 -1.308 -0.988
+HGDP00241 HGDP00241 -0.117 1.261 -0.326 -1.076 -0.58 0.095
+HGDP00243 HGDP00243 0.011 0.369 0.745 -0.613 -0.573 1.201
+HGDP00244 HGDP00244 -0.908 -0.004 0.463 -0.771 -1.72 0.461
+HGDP00247 HGDP00247 -0.122 0.233 -0.022 -0.021 -1.789 1.049
+HGDP00248 HGDP00248 -0.631 0.983 0.547 0.46 0.583 0.392
+HGDP00251 HGDP00251 -0.535 0.691 2.252 -0.972 2.084 -1.288
+HGDP00254 HGDP00254 -0.722 1.498 -0.534 -0.446 0.491 1.847
+HGDP00258 HGDP00258 -0.488 0.857 0.372 0.116 0.179 0.98
+HGDP00259 HGDP00259 -0.192 0.728 -0.969 -0.207 -1.242 -1.284
+HGDP00262 HGDP00262 -0.781 0.092 1.603 -1.122 -1.223 0.693
+HGDP00264 HGDP00264 0.028 1.493 -0.732 0.89 0.215 1.216
+HGDP00277 HGDP00277 -0.418 0.209 0.216 -0.387 3.286 2.332
+HGDP00281 HGDP00281 0.167 0.49 0.818 -0.962 1.326 0.601
+HGDP00285 HGDP00285 0.252 1.303 -0.053 -0.717 1.666 0.251
+HGDP00290 HGDP00290 -0.693 0.62 -0.127 -0.887 -0.208 0.315
+HGDP00298 HGDP00298 -0.771 -0.264 1.181 -1.288 1.134 1.332
+HGDP00302 HGDP00302 0.156 -0.342 0.111 -0.203 3.367 1.967
+HGDP00304 HGDP00304 0.036 -0.095 -1.069 -0.821 2.595 1.102
+HGDP00307 HGDP00307 0.579 1.228 -0.357 -0.602 1.37 -0.349
+HGDP00309 HGDP00309 0.227 0.686 -0.977 -0.328 1.76 2.362
+HGDP00311 HGDP00311 -0.336 0.317 0.923 -0.977 2.349 1.807
+HGDP00313 HGDP00313 -0.101 -0.007 -1.798 -0.174 1.989 0.988
+HGDP00315 HGDP00315 0.277 0.378 -2.144 0.321 1.611 2.495
+HGDP00321 HGDP00321 0.488 -0.135 -3.393 -0.416 0.77 1.154
+HGDP00323 HGDP00323 -0.718 0.655 -0.193 -0.559 -0.115 1.535
+HGDP00330 HGDP00330 0.785 -0.073 -2.653 -0.458 2.081 1.311
+HGDP00333 HGDP00333 0.213 -0.223 0.369 -1.673 3.007 1.704
+HGDP00346 HGDP00346 -0.065 0.803 0.634 -0.468 0.685 -0.552
+HGDP00351 HGDP00351 -0.515 0.295 0.861 -0.689 -0.702 0.449
+HGDP00356 HGDP00356 -0.348 0.256 0.83 -0.934 -0.099 -0.162
+HGDP00364 HGDP00364 -0.686 0.443 0.04 -0.344 0.63 0.288
+HGDP00371 HGDP00371 -0.712 0.179 0.493 0.921 0.019 -0.903
+HGDP00372 HGDP00372 -0.062 0.775 0.788 -1.555 -1.593 0.538
+HGDP00376 HGDP00376 -0.589 0.883 -0.718 -0.995 -1.309 0.249
+HGDP00382 HGDP00382 -0.115 -0.106 -0.124 -0.079 -1.689 -0.411
+HGDP00388 HGDP00388 -0.821 -0.831 0.042 0.445 0.09 0.638
+HGDP00392 HGDP00392 -0.933 0.271 1.185 1.058 1.506 -0.539
+HGDP00397 HGDP00397 -0.078 0.526 0.819 0.295 -0.02 0.909
+HGDP00402 HGDP00402 -0.319 -0.866 0.266 0.92 0.322 -0.279
+HGDP00407 HGDP00407 -0.708 -0.09 0.406 -0.688 1.083 -0.696
+HGDP00412 HGDP00412 0.202 0.338 0.294 0.979 1.728 2.181
+HGDP00417 HGDP00417 -0.159 -0.823 -0.684 -0.857 -1.164 0.196
+HGDP00423 HGDP00423 -1.017 -0.583 0.572 0.629 0.149 -1.036
+HGDP00433 HGDP00433 0.088 0.101 0.292 -0.979 0.154 2.137
+HGDP00438 HGDP00438 -1.045 0.274 0.106 -1.656 -0.188 0.967
+HGDP00444 HGDP00444 -0.082 0.679 0.457 -1.073 -0.091 -0.869
+HGDP00445 HGDP00445 -0.292 0.206 -0.666 0.062 1.455 0.171
+HGDP00511 HGDP00511 0.054 1.205 -0.434 1.503 0.188 0.133
+HGDP00512 HGDP00512 -0.827 0.807 0.914 -0.961 0.713 0.401
+HGDP00513 HGDP00513 -0.231 2.018 -0.32 1.717 0.764 0.23
+HGDP00514 HGDP00514 0.253 0.898 -0.093 1.4 1.398 1.185
+HGDP00515 HGDP00515 -1.071 0.86 -0.305 1.351 0.917 2.181
+HGDP00516 HGDP00516 0.128 1.742 -1.678 0.6 -0.064 0.156
+HGDP00517 HGDP00517 -0.654 1.097 0.495 -1.03 -1.772 -0.719
+HGDP00518 HGDP00518 -0.809 1.02 -0.432 0.495 0.665 -0.292
+HGDP00519 HGDP00519 -0.054 0.601 -0.812 0.273 -0.45 -1.683
+HGDP00520 HGDP00520 -0.468 2.78 1.027 -0.331 -0.673 1.228
+HGDP00522 HGDP00522 -0.009 0.813 -0.04 1.469 0.245 -0.801
+HGDP00523 HGDP00523 -0.17 1.364 0.187 1.077 0.295 -1.658
+HGDP00524 HGDP00524 -0.549 0.873 0.39 -0.145 0.043 -0.175
+HGDP00525 HGDP00525 0.339 1.792 0.399 1.246 0.485 -1.561
+HGDP00527 HGDP00527 -0.08 0.708 -0.352 -0.439 -0.653 0.163
+HGDP00528 HGDP00528 -0.524 1.214 0.758 0.722 0.794 0.608
+HGDP00529 HGDP00529 -0.528 0.677 0.396 1.754 -0.466 0.607
+HGDP00531 HGDP00531 0.555 1.464 -1.256 -1.23 -0.428 0.483
+HGDP00534 HGDP00534 0.047 1.423 0.237 0.981 -0.235 0.778
+HGDP00535 HGDP00535 -0.032 0.749 0.131 -1.059 -0.114 0.534
+HGDP00536 HGDP00536 0.051 1.061 -0.496 1.254 1.864 -0.729
+HGDP00537 HGDP00537 -0.55 1.626 0.546 1.141 -0.994 -0.57
+HGDP00538 HGDP00538 -0.535 1.136 -0.309 -0.734 0.438 0.431
+HGDP00539 HGDP00539 0.022 0.785 -0.217 0.228 -0.323 0.061
+HGDP00557 HGDP00557 -0.558 1.005 -0.066 0.619 -0.224 -0.261
+HGDP00558 HGDP00558 -0.048 1.239 -0.189 0.45 1.303 0.357
+HGDP00559 HGDP00559 -0.349 1.062 0.479 2.436 0.875 0.61
+HGDP00560 HGDP00560 -0.241 1.784 -0.435 1.303 -0.047 -2.011
+HGDP00561 HGDP00561 -0.482 0.54 -0.41 0.393 1.269 -0.534
+HGDP00562 HGDP00562 0.425 1.241 1.467 0.282 -0.91 1.746
+HGDP00563 HGDP00563 0.625 1.312 1.335 -0.225 -1.284 -0.12
+HGDP00564 HGDP00564 -0.813 1.04 0.138 0.772 -2.214 0.441
+HGDP00565 HGDP00565 -0.966 0.91 -0.626 -0.002 0.885 1.811
+HGDP00566 HGDP00566 -0.295 0.702 0.219 0.854 -1.719 -0.459
+HGDP00568 HGDP00568 0.236 0.639 0.359 0.731 -0.667 2.068
+HGDP00571 HGDP00571 -0.088 0.904 -1.182 1.188 -0.526 0.964
+HGDP00572 HGDP00572 -0.647 0.689 0.027 1.745 0.217 -0.543
+HGDP00573 HGDP00573 -0.469 0.532 0.432 0.153 -0.529 1.375
+HGDP00574 HGDP00574 0.425 0.961 0.049 -0.066 -1.929 0.407
+HGDP00575 HGDP00575 -0.671 0.862 -0.688 2.159 0.102 0.36
+HGDP00579 HGDP00579 0.455 0.481 0.732 1.746 -0.498 -0.545
+HGDP00580 HGDP00580 0.271 1.836 1.499 1.596 0.399 -0.399
+HGDP00582 HGDP00582 -0.121 0.811 -0.715 0.891 -0.428 -1.111
+HGDP00584 HGDP00584 -0.258 1.507 -1.186 0.777 -1.276 -0.32
+HGDP00586 HGDP00586 0.124 0.869 -1.133 0.62 -1.458 -0.578
+HGDP00587 HGDP00587 0.034 0.867 -0.399 0.082 1.072 0.542
+HGDP00588 HGDP00588 -0.244 1.3 0.309 0.567 -2.223 0.284
+HGDP00591 HGDP00591 -0.341 0.699 -0.161 0.686 0.017 0.276
+HGDP00595 HGDP00595 -0.011 0.942 0.896 1.009 -0.377 -0.797
+HGDP00598 HGDP00598 -0.592 1.427 0.177 0.967 0.162 -1.387
+HGDP00599 HGDP00599 0.226 1.138 -0.636 -0.635 -0.533 2.032
+HGDP00600 HGDP00600 -0.487 1.585 0.426 1.082 -0.027 0.115
+HGDP00601 HGDP00601 0.017 1.055 0.707 -0.743 -1.128 0.607
+HGDP00602 HGDP00602 -0.801 0.266 -0.141 0.764 0.997 0.442
+HGDP00606 HGDP00606 0.132 0.427 0.276 1.341 -0.464 -1.124
+HGDP00607 HGDP00607 -0.285 1.197 -0.281 -1.468 -1.304 1.003
+HGDP00608 HGDP00608 -0.083 1.472 1.976 0.135 -0.907 1.963
+HGDP00609 HGDP00609 0.041 1.217 -0.905 0.167 0.479 1.929
+HGDP00610 HGDP00610 0.375 1.711 -0.47 1.316 -1.811 0.763
+HGDP00611 HGDP00611 0.245 -0.064 0.786 -0.303 0.302 1.395
+HGDP00613 HGDP00613 -0.02 0.169 -1.517 1.378 -1.617 -1.948
+HGDP00614 HGDP00614 0.102 0.749 0.34 0.743 -0.729 -1.134
+HGDP00618 HGDP00618 0.086 0.532 0.045 -1.624 -1.051 0.283
+HGDP00619 HGDP00619 -0.173 2.094 0.086 0.574 -0.372 0.292
+HGDP00620 HGDP00620 1.013 0.25 -0.058 1.535 -1.058 -0.533
+HGDP00622 HGDP00622 0.006 1.035 0.485 -1.159 -0.431 0.262
+HGDP00623 HGDP00623 0.058 2.176 0.185 0.433 -2.21 1.376
+HGDP00624 HGDP00624 -0.205 1.369 -1.75 -0.201 -1.264 1.265
+HGDP00625 HGDP00625 0.457 0.211 -0.05 0.995 -0.649 -1.046
+HGDP00626 HGDP00626 0.254 1.549 -1.024 0.9 -0.893 -0.113
+HGDP00627 HGDP00627 -0.007 1.173 -0.147 -0.062 -0.623 0.36
+HGDP00628 HGDP00628 -0.436 0.568 -0.013 0.058 -0.708 -1.563
+HGDP00629 HGDP00629 -0.535 0.745 -1.075 0.92 -0.66 1.324
+HGDP00630 HGDP00630 -0.753 0.52 0.059 -0.703 -2.157 -0.355
+HGDP00631 HGDP00631 -0.609 1.169 -1.031 -0.906 -1.516 0.401
+HGDP00632 HGDP00632 0.171 1.523 0.221 1.869 -2.117 0.484
+HGDP00634 HGDP00634 -0.354 0.793 0.676 1.029 -1.157 -0.32
+HGDP00635 HGDP00635 -0.209 0.89 -0.183 0.923 -1.003 -0.691
+HGDP00636 HGDP00636 -0.412 1.515 -0.622 0.015 -0.846 -0.045
+HGDP00637 HGDP00637 -0.009 0.613 0.515 0.699 -0.71 -0.33
+HGDP00638 HGDP00638 0.122 0.572 -0.405 0.025 -1.148 -0.576
+HGDP00639 HGDP00639 0.533 1.057 0.171 0.285 -1.599 0.391
+HGDP00640 HGDP00640 0.073 0.683 0.87 0.71 -0.252 1.621
+HGDP00641 HGDP00641 -0.131 0.297 0.212 1.407 -2.49 0.065
+HGDP00643 HGDP00643 0.161 0.69 -0.074 1.246 -1.548 0.084
+HGDP00644 HGDP00644 0.726 0.601 -0.253 -0.01 -2.28 -0.414
+HGDP00645 HGDP00645 -0.578 0.64 -0.176 0.254 2.74 -1.913
+HGDP00646 HGDP00646 0.908 0.983 0.733 0.28 -0.518 0.342
+HGDP00647 HGDP00647 0.465 0.962 -0.406 1.265 -0.18 1.42
+HGDP00648 HGDP00648 0.394 0.616 -0.542 1.38 -1.756 -0.22
+HGDP00649 HGDP00649 -0.112 1.111 -0.293 -0.72 0.489 -1.277
+HGDP00651 HGDP00651 -0.712 0.238 0.146 1.227 -0.049 1.378
+HGDP00653 HGDP00653 -0.535 0.76 -0.688 0.804 -1.175 0.859
+HGDP00654 HGDP00654 -0.025 1.169 0.228 -0.935 0.618 0.289
+HGDP00666 HGDP00666 0.18 1.264 -0.613 -0.075 0.747 0.66
+HGDP00667 HGDP00667 0.05 0.056 -0.722 1.633 0.213 0.177
+HGDP00668 HGDP00668 -0.186 0.785 0.669 1.385 -1.549 0.534
+HGDP00669 HGDP00669 0.239 1.746 0.381 0.797 -1.369 0.696
+HGDP00670 HGDP00670 -0.949 1.127 0.245 1.87 -0.892 0.942
+HGDP00671 HGDP00671 -0.052 1.06 0.04 0.547 -0.502 0.681
+HGDP00672 HGDP00672 0.046 0.686 0.704 1.103 -0.135 -0.092
+HGDP00673 HGDP00673 -0.587 0.774 -1.025 0.164 -0.128 -1.069
+HGDP00674 HGDP00674 -0.554 2.184 -0.459 -0.075 -0.785 -0.455
+HGDP00675 HGDP00675 -0.04 1.49 -1.73 -0.881 -0.678 -0.351
+HGDP00676 HGDP00676 -0.517 1.49 -0.319 -0.088 -1.033 0.788
+HGDP00677 HGDP00677 -0.708 0.607 0.73 -0.431 -1.569 1.357
+HGDP00679 HGDP00679 -0.077 0.479 -1.173 -1.208 0.311 1.011
+HGDP00680 HGDP00680 0.953 1.941 0.667 -0.481 -0.286 0.637
+HGDP00682 HGDP00682 -0.21 1.213 0.309 0.561 0.334 0.944
+HGDP00684 HGDP00684 0.62 0.361 -0.248 -0.111 0.085 -0.272
+HGDP00685 HGDP00685 0.446 1.22 0.059 1.995 -0.172 0.409
+HGDP00686 HGDP00686 0.131 0.867 -0.228 1.562 -0.065 2.248
+HGDP00687 HGDP00687 -0.18 0.559 0.099 1.281 -1.609 -0.615
+HGDP00688 HGDP00688 0.328 0.669 -0.633 1.783 -1.413 0.748
+HGDP00689 HGDP00689 -0.291 2.037 -1.358 -0.558 -0.222 1.953
+HGDP00690 HGDP00690 -1.104 0.214 0.319 1.175 -1.218 -0.638
+HGDP00692 HGDP00692 -0.151 1.659 -2.749 1.321 -1.275 0.042
+HGDP00694 HGDP00694 -0.419 1.374 -0.134 1.818 -0.88 0.765
+HGDP00696 HGDP00696 -0.48 0.743 -1.311 0.7 0.676 -0.335
+HGDP00697 HGDP00697 -0.37 0.948 -0.442 0.491 0.785 0.7
+HGDP00698 HGDP00698 -0.329 0.426 0.498 0.196 0.433 -0.489
+HGDP00699 HGDP00699 0.089 0.74 -0.01 0.372 -1.131 -0.513
+HGDP00700 HGDP00700 -0.551 0.575 0.921 -0.302 -1.32 1.246
+HGDP00701 HGDP00701 -0.082 1.168 -0.201 1.562 0.698 1.139
+HGDP00703 HGDP00703 0.109 -1.01 -1.866 -0.919 0.034 -0.64
+HGDP00704 HGDP00704 -1.369 -0.602 -2.241 -1.318 -0.116 -1.189
+HGDP00708 HGDP00708 -0.466 0.825 -1.155 -2.956 0.202 0.515
+HGDP00710 HGDP00710 -0.002 -0.531 -2.156 -2.691 -0.234 -0.064
+HGDP00711 HGDP00711 -0.964 -1.116 0.277 0.201 -0.684 -0.039
+HGDP00712 HGDP00712 -0.534 -1.086 -0.527 -0.127 -1.15 -0.219
+HGDP00714 HGDP00714 -0.977 -0.371 -0.215 -0.123 0.904 0.024
+HGDP00715 HGDP00715 -0.546 -1.146 -0.867 -0.015 0.034 -1.064
+HGDP00716 HGDP00716 -0.822 -1.05 -0.311 1.227 0.804 -0.296
+HGDP00719 HGDP00719 -0.88 -0.809 0.628 -0.31 0.858 -0.118
+HGDP00721 HGDP00721 -0.249 -1.457 -1.173 1.301 0.913 0.182
+HGDP00726 HGDP00726 -0.187 1.379 0.873 0.213 0.255 1.057
+HGDP00729 HGDP00729 -0.388 1.196 -1.761 0.165 -0.04 0.867
+HGDP00731 HGDP00731 0.053 0.596 -1.058 0.005 -0.193 -0.074
+HGDP00732 HGDP00732 -0.847 0.661 -0.657 0.296 1.758 1.672
+HGDP00733 HGDP00733 0.909 0.769 -0.05 0.73 -0.389 2.373
+HGDP00734 HGDP00734 0.308 0.944 -1.237 -0.233 -0.076 0.663
+HGDP00735 HGDP00735 0.118 0.746 0.185 -0.557 -0.488 0.578
+HGDP00736 HGDP00736 -0.64 1.187 -0.176 -0.278 -1.97 0.802
+HGDP00738 HGDP00738 0.053 0.916 -0.812 0.258 -2.229 -0.12
+HGDP00739 HGDP00739 0.562 0.645 -1.081 0.346 0.803 -0.008
+HGDP00740 HGDP00740 -0.635 0.549 -0.519 -0.298 0.662 2.237
+HGDP00741 HGDP00741 -0.221 0.628 -1.145 1.144 -0.884 -0.177
+HGDP00744 HGDP00744 0.187 1.323 0.342 0.455 -1.747 0.413
+HGDP00745 HGDP00745 -0.168 1.791 0.219 0.452 0.388 0.763
+HGDP00746 HGDP00746 0.01 1.056 1.958 0.379 -1.157 0.876
+HGDP00747 HGDP00747 -0.619 -0.857 0.818 2.255 0.677 -0.241
+HGDP00748 HGDP00748 -1.005 -1.424 0.709 1.698 1.549 0.282
+HGDP00750 HGDP00750 -1 -0.793 0.194 0.344 -1.09 0.491
+HGDP00751 HGDP00751 -0.725 -1.377 0.536 0.156 0.25 0.402
+HGDP00752 HGDP00752 -1.041 -0.879 -0.013 0.483 0.044 -0.05
+HGDP00753 HGDP00753 -0.737 -1.987 0.815 0.308 0.856 0.392
+HGDP00754 HGDP00754 -1.386 -0.96 -0.298 0.339 -0.387 -1.178
+HGDP00755 HGDP00755 -0.784 -0.933 0.243 -0.156 1.08 0.21
+HGDP00756 HGDP00756 -1.482 -1.624 0.157 0.1 0.069 -0.915
+HGDP00757 HGDP00757 -0.864 -1.23 -0.308 0.714 1.404 -1.141
+HGDP00758 HGDP00758 -1.038 -1.402 0.023 0.176 0.182 0.873
+HGDP00759 HGDP00759 -0.483 -1.11 0.014 0.522 -1.217 -0.622
+HGDP00760 HGDP00760 -0.249 -1.074 -0.61 2.057 -0.826 -0.95
+HGDP00761 HGDP00761 -0.64 -1.416 0.4 1.727 -0.587 0.449
+HGDP00762 HGDP00762 -0.66 -0.835 -0.777 -0.587 1.008 -0.526
+HGDP00763 HGDP00763 -0.833 -1.443 0.182 0.853 -1.623 -1.197
+HGDP00764 HGDP00764 -0.99 -1.597 -1.317 0.242 -0.307 -1.912
+HGDP00765 HGDP00765 -1.241 -1.113 -0.45 1.198 0.564 0.575
+HGDP00766 HGDP00766 -1.23 -1.289 -1.238 1.12 0.408 0.464
+HGDP00767 HGDP00767 -1.022 -0.807 -0.502 0.514 1.353 -0.461
+HGDP00768 HGDP00768 -1.427 -0.887 -0.323 0.595 -0.937 -0.483
+HGDP00769 HGDP00769 0.354 -1.71 -0.359 0.815 -0.948 -0.839
+HGDP00771 HGDP00771 -0.844 -0.851 -1.295 -1.123 0.48 -0.568
+HGDP00772 HGDP00772 -0.783 -1.999 -0.752 1.546 -1.51 -0.771
+HGDP00774 HGDP00774 -0.606 -1.761 -0.819 0.257 1.557 1.068
+HGDP00776 HGDP00776 -0.087 -1.451 0.506 1.097 0.955 0.745
+HGDP00777 HGDP00777 -0.311 -1.476 0.816 -0.213 0.428 0.016
+HGDP00779 HGDP00779 -0.369 -1.24 0.592 0.528 0.378 -0.307
+HGDP00780 HGDP00780 -0.777 -1.613 -0.671 0.704 0.02 -0.78
+HGDP00781 HGDP00781 -0.294 -1.393 0.468 1.351 0.746 0.676
+HGDP00782 HGDP00782 -0.937 -1.031 -0.984 1.287 1.332 -0.991
+HGDP00784 HGDP00784 -0.515 -1.322 -0.209 2.254 0.359 2.09
+HGDP00786 HGDP00786 -0.55 -0.985 0.333 -0.824 -0.034 1.014
+HGDP00790 HGDP00790 -0.442 -1.386 0.445 1.029 1.533 -0.448
+HGDP00791 HGDP00791 -0.807 -0.623 0.015 2.277 -1.115 -0.413
+HGDP00794 HGDP00794 -0.489 1.052 -0.466 0.393 0.892 1.005
+HGDP00797 HGDP00797 0.598 0.976 -1.013 0.135 1.065 -0.849
+HGDP00799 HGDP00799 0.387 0.97 -0.555 0.688 -1.171 -0.567
+HGDP00800 HGDP00800 -0.621 1.562 -0.113 0.598 -0.267 1.633
+HGDP00802 HGDP00802 -0.752 0.897 0.663 -0.303 1.615 -2.842
+HGDP00803 HGDP00803 0.543 1.469 -0.641 -0.981 1.97 -3.603
+HGDP00804 HGDP00804 -0.245 1.935 0.128 -0.715 0.359 -1.005
+HGDP00805 HGDP00805 0.639 1.134 -1.275 0.317 -0.368 -1.137
+HGDP00807 HGDP00807 -0.395 -0.151 -0.508 -0.748 2.902 1.03
+HGDP00808 HGDP00808 0.045 0.734 -0.604 0.899 2.042 0.767
+HGDP00810 HGDP00810 -0.705 1.16 -0.421 1.476 0.525 0.196
+HGDP00811 HGDP00811 -0.727 -1.266 -0.854 1.221 0.073 -0.785
+HGDP00812 HGDP00812 -0.468 -1.966 -0.477 0.61 -1.631 -0.747
+HGDP00813 HGDP00813 -1.278 -0.576 0.211 -0.32 -1.33 0.353
+HGDP00814 HGDP00814 -0.444 -1.118 0.406 0.301 0.696 1.329
+HGDP00815 HGDP00815 -0.688 -0.638 -0.277 0.962 -0.4 -0.625
+HGDP00817 HGDP00817 -0.686 0.046 -1.217 0.608 0.524 1.901
+HGDP00818 HGDP00818 -0.66 -0.594 0.559 -0.294 -0.667 -0.175
+HGDP00819 HGDP00819 0.071 -1.149 -1.031 1.78 0.831 -1.097
+HGDP00820 HGDP00820 -0.449 -0.802 0.515 2.402 0.835 1.062
+HGDP00821 HGDP00821 -1.258 -0.021 -0.665 -0.393 0.474 0.165
+HGDP00822 HGDP00822 -0.569 -1.141 -0.125 0.261 0.327 0.669
+HGDP00828 HGDP00828 -0.684 -1.158 -0.863 1.345 0.7 -0.039
+HGDP00832 HGDP00832 -0.318 -1.159 -1.986 -3.372 -0.214 -2.413
+HGDP00837 HGDP00837 -0.85 -1.516 -2.32 -1.662 2.195 1.264
+HGDP00838 HGDP00838 -0.844 -1.957 -3.502 -2.019 3.102 0.699
+HGDP00843 HGDP00843 -0.903 -1.497 -2.874 -1.399 0.588 0.261
+HGDP00849 HGDP00849 -1.133 -1.395 -2.226 -1.919 -1.503 -0.633
+HGDP00854 HGDP00854 -0.761 -0.357 -2.043 -1.918 -0.469 -0.165
+HGDP00856 HGDP00856 -1.482 -1.072 -1.403 -1.669 1.355 0.12
+HGDP00858 HGDP00858 -1.087 -0.057 -0.608 1.072 -1.574 -1.157
+HGDP00859 HGDP00859 -0.402 -1.282 -1.14 -1.121 0.147 0.242
+HGDP00860 HGDP00860 -0.691 1.151 -0.652 0.383 0.038 0.341
+HGDP00861 HGDP00861 -1.156 -0.604 -2.582 -3.318 -0.726 -1.986
+HGDP00862 HGDP00862 -0.721 -0.821 -1.072 -2.57 -1.622 -1.381
+HGDP00863 HGDP00863 -1.233 -0.046 -2.266 -1.412 -1.9 0.622
+HGDP00864 HGDP00864 -1.182 -0.214 -0.17 -1.416 -1.273 -0.934
+HGDP00865 HGDP00865 -1.299 -0.551 -1.222 -2.341 -1.811 0.373
+HGDP00869 HGDP00869 -0.746 -0.94 -1.141 -2.02 -0.33 -1.244
+HGDP00870 HGDP00870 -0.828 -0.32 -0.369 -2.051 -0.407 1.271
+HGDP00871 HGDP00871 -1.456 -1.031 -2.226 -2.654 -1.638 -0.46
+HGDP00872 HGDP00872 -1.069 -0.343 -2.37 -2.615 -1.865 0.16
+HGDP00875 HGDP00875 -0.774 -0.44 -1.209 -1.032 -1.76 -0.432
+HGDP00876 HGDP00876 -0.027 0.112 -0.32 -0.329 -1.679 -0.099
+HGDP00877 HGDP00877 -0.037 0.269 -0.66 1.289 1.476 -2.097
+HGDP00879 HGDP00879 -0.528 1.299 0.933 -0.012 -0.627 -1.066
+HGDP00880 HGDP00880 0.047 1.843 -0.389 -0.678 -0.666 0.848
+HGDP00881 HGDP00881 0.122 0.509 -0.134 0.555 -1.76 0.168
+HGDP00882 HGDP00882 -0.694 0.766 -0.592 0.344 -0.753 0.982
+HGDP00883 HGDP00883 -0.628 -0.485 -0.733 0.14 0.811 0.262
+HGDP00884 HGDP00884 -0.375 0.148 -0.68 0.652 -0.994 -1.358
+HGDP00885 HGDP00885 -0.778 1.329 0.041 -0.355 0.442 1.363
+HGDP00886 HGDP00886 0.422 1.089 -0.976 0.726 2.146 1.155
+HGDP00888 HGDP00888 -0.859 0.745 0.281 1.402 -0.279 0.492
+HGDP00889 HGDP00889 0.223 0.3 0.52 0.652 -0.563 -2.098
+HGDP00890 HGDP00890 0.164 0.94 0.502 0.827 1.042 1.561
+HGDP00891 HGDP00891 -0.121 1.06 -0.349 -0.206 -0.853 0.792
+HGDP00892 HGDP00892 -0.069 1.597 -1.262 -0.403 -1.419 -0.749
+HGDP00893 HGDP00893 -0.095 0.856 0.73 -0.317 -1.892 -0.103
+HGDP00894 HGDP00894 -0.148 0.407 -0.419 -0.176 -1.461 -0.425
+HGDP00895 HGDP00895 -0.186 1.03 0.054 -0.491 0.256 2.136
+HGDP00896 HGDP00896 -0.627 1.293 0.855 -0.142 -1.487 0.528
+HGDP00897 HGDP00897 0.015 1.59 0.83 0.933 -1.55 -0.638
+HGDP00898 HGDP00898 0.013 1.414 -0.407 1.21 -1.26 -1.199
+HGDP00899 HGDP00899 -0.337 0.858 -0.581 0.883 0.404 -0.279
+HGDP00900 HGDP00900 0.009 1.244 0.87 -1.338 -1.153 1.68
+HGDP00901 HGDP00901 0.355 2.048 0.455 0.185 -1.029 -0.231
+HGDP00902 HGDP00902 -0.692 0.458 0.416 0.52 -1.599 -0.661
+HGDP00904 HGDP00904 1.35 0.076 -0.196 -0.457 0.162 1.077
+HGDP00905 HGDP00905 1.751 0.227 0.43 -0.172 -1.337 0.911
+HGDP00906 HGDP00906 1.159 -0.477 -0.361 -1.3 -0.008 -0.411
+HGDP00907 HGDP00907 1.915 -1.028 -1.065 0.143 0.843 -0.863
+HGDP00908 HGDP00908 1.559 -0.423 0.812 -0.013 -0.48 0.912
+HGDP00909 HGDP00909 1.602 -0.424 1.005 -0.664 -0.552 0.531
+HGDP00910 HGDP00910 1.935 -1.433 -0.755 -0.31 -0.445 -1.082
+HGDP00911 HGDP00911 1.833 -0.243 1.094 0.15 0.699 -1.01
+HGDP00912 HGDP00912 1.731 -0.59 -0.535 -0.838 -0.187 -0.751
+HGDP00914 HGDP00914 1.85 0.2 -0.321 -1.578 0.272 0.255
+HGDP00917 HGDP00917 2.064 -0.554 -0.372 -0.548 0.069 0.335
+HGDP00918 HGDP00918 2.16 -0.843 -0.353 0.524 -0.623 1.289
+HGDP00919 HGDP00919 1.657 -0.361 -0.319 -0.296 -0.756 0.793
+HGDP00920 HGDP00920 1.5 -0.11 0.445 -0.879 1.15 -0.171
+HGDP00924 HGDP00924 2.007 -0.41 -1.095 -0.595 0.379 -0.386
+HGDP00925 HGDP00925 1.904 -1.238 -0.837 -0.704 -0.113 0.331
+HGDP00926 HGDP00926 1.209 -0.862 0.11 0.273 0.337 -1.27
+HGDP00929 HGDP00929 2.145 -0.524 -0.442 0.399 -0.25 -0.823
+HGDP00930 HGDP00930 1.928 -0.604 0.151 1.003 0.004 -1.683
+HGDP00931 HGDP00931 1.168 -0.636 0.257 0.36 0.009 -0.454
+HGDP00935 HGDP00935 1.487 0.314 -0.269 0.1 0.013 0.358
+HGDP00937 HGDP00937 0.609 0.23 -0.249 -1.08 -0.959 -0.458
+HGDP00938 HGDP00938 1.852 -0.595 -1.158 0.887 -0.457 -0.898
+HGDP00939 HGDP00939 1.561 -0.778 -0.862 0.4 0.03 0.524
+HGDP00940 HGDP00940 1.934 -0.245 0.135 -0.604 0.074 1.229
+HGDP00941 HGDP00941 1.215 -0.533 -1.435 0.473 0.767 0.662
+HGDP00942 HGDP00942 2.311 -0.204 -0.056 0.527 -0.07 -1.194
+HGDP00943 HGDP00943 1.69 -0.788 0.586 -0.726 -0.896 -0.293
+HGDP00944 HGDP00944 1.986 0.026 0.411 -0.807 0.217 -0.527
+HGDP00945 HGDP00945 -0.118 0.041 -1.457 0.966 0.735 0.118
+HGDP00946 HGDP00946 -0.519 0.911 1.785 0.602 -0.837 -0.587
+HGDP00947 HGDP00947 -0.788 -0.314 -0.166 1.069 -1.654 -0.892
+HGDP00949 HGDP00949 -0.327 -0.23 0.679 0.35 -2.054 0.118
+HGDP00950 HGDP00950 -0.727 -0.211 -0.999 -0.28 0.404 -2.361
+HGDP00952 HGDP00952 -0.587 -0.886 -2.134 1.411 1.92 1.2
+HGDP00953 HGDP00953 -0.505 -0.021 -1.961 0.349 -0.684 0.301
+HGDP00954 HGDP00954 -0.9 -1.309 -0.118 -0.104 1.536 -0.57
+HGDP00955 HGDP00955 -0.736 -0.495 -1.087 0.261 -0.084 -1.662
+HGDP00957 HGDP00957 -1.034 -0.422 -0.415 0.923 -1.581 -0.605
+HGDP00958 HGDP00958 -0.643 -0.684 -0.398 1.309 1.17 -1.448
+HGDP00959 HGDP00959 -0.538 -2.06 0.013 -0.309 0.785 0.178
+HGDP00960 HGDP00960 0.16 0.042 -1.677 1.94 -0.002 -0.054
+HGDP00961 HGDP00961 -0.599 -0.113 -1.091 0.169 0.591 0.52
+HGDP00962 HGDP00962 -0.377 -1.007 0.236 1.22 -0.152 -1.786
+HGDP00963 HGDP00963 -0.104 -0.209 0.335 -0.494 1.836 -1.447
+HGDP00964 HGDP00964 -0.101 -0.745 -1.072 0.202 -1.441 0.017
+HGDP00965 HGDP00965 -0.973 -1.406 0.621 0.341 0.557 -1.324
+HGDP00966 HGDP00966 -0.705 -1.107 -0.985 -1.036 -0.768 -1.006
+HGDP00967 HGDP00967 -0.909 -0.141 -1.323 -0.334 -0.989 -1.222
+HGDP00968 HGDP00968 -0.618 0.064 0.3 1.628 -0.785 -0.569
+HGDP00969 HGDP00969 -0.473 -1.986 0 0.855 -0.835 -1.479
+HGDP00971 HGDP00971 -0.143 -1.547 -0.714 1.045 0.619 -0.491
+HGDP00972 HGDP00972 0.014 -0.843 0.334 0.999 0.443 1.545
+HGDP00973 HGDP00973 -1.044 -1.247 -0.145 1.016 1.063 0.616
+HGDP00974 HGDP00974 -1.038 -1.126 -0.001 -0.249 -0.77 -0.567
+HGDP00975 HGDP00975 -0.375 -0.484 0.197 0.777 -0.118 0.361
+HGDP00976 HGDP00976 -0.988 -1.281 -0.297 -0.014 0.341 -0.453
+HGDP00977 HGDP00977 -1.022 -1.379 1.202 2.666 -0.274 0.359
+HGDP00995 HGDP00995 -1.131 -0.542 -2.447 -2.982 0.183 0.095
+HGDP00999 HGDP00999 -0.566 0.053 -2.147 -3.078 0.438 -0.679
+HGDP01001 HGDP01001 -1.453 -1.091 -2.898 -2.186 -1.212 -0.236
+HGDP01006 HGDP01006 -1.275 -0.076 -0.723 -1.98 -2.441 0.364
+HGDP01009 HGDP01009 -1.326 -0.845 -1.602 -2.24 -1.18 -0.89
+HGDP01010 HGDP01010 -1.781 -1.048 -2.59 -2.65 -0.887 -0.568
+HGDP01013 HGDP01013 -1.242 0.083 -1.628 -1.984 -1.859 -0.047
+HGDP01014 HGDP01014 -0.72 -1.405 -1.886 -2.014 -1.926 0.339
+HGDP01021 HGDP01021 -1.06 -1.554 0.152 0.035 -0.13 0.207
+HGDP01023 HGDP01023 -0.908 -1.259 1.658 -0.342 -0.115 0.798
+HGDP01024 HGDP01024 -0.506 -1.213 -0.76 0.553 0.356 -0.636
+HGDP01037 HGDP01037 -0.394 -0.789 -2.102 -0.311 -0.666 -0.278
+HGDP01041 HGDP01041 -0.975 -0.163 -2.26 -2.518 -0.359 0.157
+HGDP01051 HGDP01051 -0.718 -0.694 -1.897 -2.384 -0.113 -0.85
+HGDP01055 HGDP01055 -0.539 -0.721 -2.051 -0.569 -0.698 -0.129
+HGDP01056 HGDP01056 -0.584 -0.482 -2.494 -0.862 0.973 0.058
+HGDP01057 HGDP01057 -0.913 -0.735 -2.134 -2.271 1.31 1.526
+HGDP01058 HGDP01058 -1.215 -0.386 -2.273 -1.342 0.935 -0.915
+HGDP01059 HGDP01059 -1.279 -1.228 -2.328 -1.331 0.796 -0.172
+HGDP01060 HGDP01060 -0.471 -1.104 -2.505 -3.591 -0.603 0.868
+HGDP01062 HGDP01062 -0.882 0.76 0.753 1.386 0.445 1.159
+HGDP01063 HGDP01063 -1.089 1.236 0.514 0.83 1.576 -1.073
+HGDP01064 HGDP01064 -0.421 0.909 0.033 1.315 -0.298 -1.118
+HGDP01065 HGDP01065 -0.737 0.454 -0.501 -0.762 -0.848 -0.045
+HGDP01066 HGDP01066 -0.197 0.95 1.009 1.893 0.703 0.108
+HGDP01067 HGDP01067 -0.429 1.611 1.122 1.642 -0.093 0.743
+HGDP01068 HGDP01068 -0.037 1.439 -0.697 -0.497 -0.183 -0.122
+HGDP01069 HGDP01069 -0.575 1.438 -0.25 0.663 1.365 -0.729
+HGDP01070 HGDP01070 -0.89 0.339 0.8 1.755 -0.925 1.149
+HGDP01071 HGDP01071 -0.485 0.92 -0.02 1.314 -0.507 -1.079
+HGDP01072 HGDP01072 -0.096 1.452 0.439 -0.407 0.721 1.567
+HGDP01073 HGDP01073 -0.105 0.378 0.145 1.74 0.349 -0.686
+HGDP01074 HGDP01074 -0.436 1.157 -0.773 0.758 -0.474 -0.995
+HGDP01075 HGDP01075 -0.992 1.696 -0.829 0.04 -1.808 -0.725
+HGDP01077 HGDP01077 -0.472 0.402 0.131 0.849 0.588 -3.126
+HGDP01096 HGDP01096 -0.449 -1.067 -1.025 -1.364 -1.554 -0.944
+HGDP01099 HGDP01099 -1.02 -0.886 0.403 1.567 0.83 0.209
+HGDP01100 HGDP01100 -1.024 -1.242 -0.964 1.197 0.318 0.076
+HGDP01101 HGDP01101 -1.102 -0.269 -0.553 1.033 -0.929 -0.515
+HGDP01102 HGDP01102 -0.603 -0.774 -0.686 2.167 0.292 -1.694
+HGDP01103 HGDP01103 -0.526 -2.241 0.029 0.99 0.255 0.13
+HGDP01104 HGDP01104 -0.961 -0.84 0.457 1.347 0.685 0.471
+HGDP01149 HGDP01149 -0.654 1.522 -1.562 0.425 -0.784 0.203
+HGDP01151 HGDP01151 -0.075 1.074 -0.525 0.843 -0.324 0.447
+HGDP01152 HGDP01152 -0.035 0.828 0.089 0.141 0.383 -0.987
+HGDP01155 HGDP01155 -0.696 0.814 -0.076 0.128 -1.446 -1.21
+HGDP01156 HGDP01156 -0.078 2.06 -1.307 0.623 0.886 -0.16
+HGDP01161 HGDP01161 0.133 1.691 -0.027 0.642 -1.446 1.051
+HGDP01162 HGDP01162 -1.068 1.037 0.011 0.985 0.945 1.663
+HGDP01164 HGDP01164 -0.714 0.93 -0.644 0.705 -1.07 -0.448
+HGDP01166 HGDP01166 -0.181 1.427 -2.443 -0.061 -0.959 0.558
+HGDP01167 HGDP01167 0.299 0.569 -0.383 0.254 -1.709 0.834
+HGDP01169 HGDP01169 -0.828 0.415 -1.15 1.685 -0.706 0.35
+HGDP01171 HGDP01171 0.484 1.677 -0.054 -0.931 1.35 -0.116
+HGDP01173 HGDP01173 -0.596 1.017 0.229 0.821 0.047 -0.035
+HGDP01174 HGDP01174 0.612 1.119 0.431 -0.255 1.01 0.057
+HGDP01177 HGDP01177 -0.571 1.101 0.203 1.028 0.576 1.025
+HGDP01181 HGDP01181 -1.212 -1.514 -0.525 1.005 -0.533 -0.146
+HGDP01182 HGDP01182 -0.332 -1.2 -0.591 1.674 1.104 -1.944
+HGDP01183 HGDP01183 -0.394 -0.816 -0.375 0.535 -1.883 -0.431
+HGDP01184 HGDP01184 -1.109 -0.911 0.775 0.906 0.284 0.669
+HGDP01185 HGDP01185 -0.576 -1.258 -1.15 0.669 0.108 0.861
+HGDP01186 HGDP01186 -0.166 -1.221 -1.219 0.037 -0.582 -0.256
+HGDP01187 HGDP01187 -1.112 -1.168 -0.464 2.228 0.929 1.018
+HGDP01189 HGDP01189 -0.715 -0.758 -0.764 1.164 -0.74 -1.163
+HGDP01190 HGDP01190 0.125 -0.84 -1.012 0.571 0.04 0.53
+HGDP01192 HGDP01192 -0.509 -1.217 0.024 2.191 -0.436 1.162
+HGDP01193 HGDP01193 -0.826 -1.073 0.883 1.991 0.48 0.07
+HGDP01194 HGDP01194 -0.621 -1.645 0.575 -0.076 1.191 0.053
+HGDP01195 HGDP01195 -0.283 -1.459 -1.152 0.984 0.35 -0.582
+HGDP01196 HGDP01196 -1.348 -1.143 -0.262 1.261 0.131 -0.62
+HGDP01197 HGDP01197 -0.441 -1.458 0.777 -0.376 0.85 0.395
+HGDP01200 HGDP01200 1.543 -0.499 0.348 -0.968 -0.081 0.301
+HGDP01201 HGDP01201 1.31 -0.544 0.241 1.056 0.032 -0.444
+HGDP01202 HGDP01202 1.674 0.008 0.096 -0.039 0.683 0.091
+HGDP01204 HGDP01204 -0.991 -0.898 -0.261 -0.406 0.767 0.058
+HGDP01205 HGDP01205 -0.827 -0.855 0.401 0.6 -0.421 0.688
+HGDP01206 HGDP01206 -0.574 -1.221 0.506 -0.046 0.091 -1.464
+HGDP01207 HGDP01207 -0.661 -1.927 -0.012 0.276 0.914 -0.801
+HGDP01209 HGDP01209 -0.882 -0.265 -1.108 -0.002 -1.3 -0.779
+HGDP01212 HGDP01212 -1.314 -1.157 -0.649 1.145 -1.767 -0.442
+HGDP01213 HGDP01213 -0.69 -0.854 -0.105 0.4 0.022 -0.134
+HGDP01214 HGDP01214 -1.23 -1.64 -1.019 2.401 0.829 0.527
+HGDP01216 HGDP01216 -0.37 -0.418 -1.018 2.086 -0.939 -2.401
+HGDP01217 HGDP01217 -0.729 -0.437 -0.203 0.646 -1.192 0.126
+HGDP01218 HGDP01218 -1.172 -0.369 2.084 -0.353 0.437 1.149
+HGDP01220 HGDP01220 -0.403 -1.633 -0.39 1.493 -0.276 -1.102
+HGDP01221 HGDP01221 -0.926 -1.069 -0.34 0.738 -0.221 -0.262
+HGDP01222 HGDP01222 -0.761 -1.455 -0.545 1.136 1.724 -1.222
+HGDP01224 HGDP01224 -0.443 -1.834 -0.793 0.665 1.308 -1.573
+HGDP01225 HGDP01225 -0.784 0.523 -0.262 0.11 -1.299 -1.255
+HGDP01226 HGDP01226 -0.99 -0.498 -1.321 -0.34 -0.441 1.062
+HGDP01227 HGDP01227 -0.433 -1.014 -0.984 0.303 -0.317 -2.187
+HGDP01229 HGDP01229 -1.199 -0.984 0.155 0.43 0.096 0.557
+HGDP01230 HGDP01230 -0.693 -1.468 0.221 0.593 0.179 0.117
+HGDP01231 HGDP01231 -1.081 -0.565 -0.799 0.44 1.616 -1.45
+HGDP01232 HGDP01232 -0.338 -1.118 -0.093 0.928 -0.819 -1.445
+HGDP01233 HGDP01233 -0.693 -2.603 -0.751 1.722 -0.456 -1.453
+HGDP01236 HGDP01236 -0.599 -1.187 0.492 0.219 1.091 0.167
+HGDP01237 HGDP01237 -0.935 -1.509 0.149 0.147 -1.909 -1.779
+HGDP01238 HGDP01238 -0.688 -1.026 0.693 -0.416 0.359 -0.785
+HGDP01239 HGDP01239 -0.473 -1.215 0.957 0.215 -0.124 0.529
+HGDP01241 HGDP01241 -0.795 -1.665 0.21 2.904 2.682 -0.699
+HGDP01243 HGDP01243 -0.238 -2.069 -1.122 1.053 1.25 0.48
+HGDP01244 HGDP01244 -0.568 -0.96 -0.687 0.405 0.155 0.265
+HGDP01245 HGDP01245 -0.216 -1.872 -1.664 -0.463 0.142 -0.665
+HGDP01247 HGDP01247 -0.564 -0.549 -1.121 1.383 -0.852 0.104
+HGDP01248 HGDP01248 -0.37 -2.176 -0.165 1.178 0.748 -0.037
+HGDP01249 HGDP01249 -0.32 -1.086 -0.73 -0.495 1.003 0.242
+HGDP01251 HGDP01251 0.272 -0.637 -0.34 -0.414 1.136 1.019
+HGDP01254 HGDP01254 0.455 0.96 0.105 -0.025 -1.395 0.994
+HGDP01255 HGDP01255 0.581 0.227 -1.058 1.238 -1.918 -1.017
+HGDP01257 HGDP01257 -0.141 0.752 -0.672 -0.513 -0.911 -0.011
+HGDP01258 HGDP01258 -0.142 1.4 0.195 0.985 -1.649 1.122
+HGDP01259 HGDP01259 0.375 0.627 0.009 0.432 -0.14 0.233
+HGDP01260 HGDP01260 0.73 0.895 0.631 1.571 -1.529 -0.226
+HGDP01261 HGDP01261 0.678 0.269 0.597 0.766 -0.53 -0.656
+HGDP01262 HGDP01262 0.07 0.866 -0.311 1.467 -1.748 0.521
+HGDP01263 HGDP01263 -0.218 0.673 -0.028 0.418 -0.539 -1.528
+HGDP01264 HGDP01264 0.1 1 -1.794 0.131 0.46 0.462
+HGDP01265 HGDP01265 -0.019 0.883 0.002 0.504 -0.136 1.826
+HGDP01266 HGDP01266 0.279 1.632 0.875 1.161 -0.182 -1.285
+HGDP01267 HGDP01267 0.141 0.687 -0.636 0.622 -0.193 -0.727
+HGDP01268 HGDP01268 -0.056 0.955 -0.788 1.59 -0.546 -0.238
+HGDP01269 HGDP01269 0.189 1.325 -0.747 1.415 -0.826 0.31
+HGDP01272 HGDP01272 -0.111 1.442 -0.587 2.171 -0.036 0.221
+HGDP01275 HGDP01275 -0.146 1.576 0.076 0.123 -0.92 -1.811
+HGDP01276 HGDP01276 0.186 0.715 0.161 0.721 -1.781 0.965
+HGDP01277 HGDP01277 -0.17 0.582 -0.933 2.563 0.033 1.027
+HGDP01279 HGDP01279 0.092 1.231 0.273 1.024 -1.573 0.624
+HGDP01280 HGDP01280 1.116 1.367 0.804 0.199 -0.368 -0.35
+HGDP01282 HGDP01282 0.577 -0.043 -0.855 -0.252 -0.607 0.117
+HGDP01283 HGDP01283 1.997 0.125 -0.885 0.315 -0.651 0.86
+HGDP01287 HGDP01287 -0.81 -0.81 -0.148 1.435 1.31 0.295
+HGDP01288 HGDP01288 -0.345 -1.605 -1.461 0.225 -0.4 0.054
+HGDP01289 HGDP01289 -0.504 -0.388 -0.128 -0.47 -1.603 0.111
+HGDP01290 HGDP01290 -0.556 -0.644 -0.528 0.502 0.333 -0.963
+HGDP01291 HGDP01291 -0.919 -1.851 -0.139 1.723 1.681 -0.845
+HGDP01292 HGDP01292 -0.394 -0.911 -0.123 0.685 -0.566 0.538
+HGDP01293 HGDP01293 -0.382 -0.698 -0.991 1.267 -1.522 -1.235
+HGDP01294 HGDP01294 -0.49 -0.923 -0.046 -0.19 -0.267 0.738
+HGDP01295 HGDP01295 -1.301 -1.181 0.945 0.079 -1.249 -0.529
+HGDP01296 HGDP01296 -0.581 -0.686 0.486 0.11 1.573 0.034
+HGDP01299 HGDP01299 0.588 1.144 0.176 -0.448 2.185 -0.244
+HGDP01301 HGDP01301 -0.845 -1.424 0.525 -0.305 1.981 -0.133
+HGDP01302 HGDP01302 -0.705 0.691 -1.069 -1.666 -0.65 -0.701
+HGDP01304 HGDP01304 -0.833 -1.095 1.044 0.64 -0.486 0.653
+HGDP01305 HGDP01305 -1.24 0.214 0.437 0.533 0.687 -0.766
+HGDP01309 HGDP01309 -1.763 -0.687 -0.402 -0.507 -1.048 -1.091
+HGDP01310 HGDP01310 -0.249 -0.822 -0.484 -0.695 -0.297 0.6
+HGDP01311 HGDP01311 -1.717 -1.545 0.891 -0.383 -0.035 0.372
+HGDP01313 HGDP01313 -0.304 -1.476 1.496 -0.901 1.123 0.881
+HGDP01317 HGDP01317 0.251 -1.057 -1.208 1.012 -1.755 -0.222
+HGDP01318 HGDP01318 -0.996 -1.579 1.266 0.951 0.37 0.478
+HGDP01319 HGDP01319 -0.143 -1.429 -1.231 1.15 -0.463 0.184
+HGDP01321 HGDP01321 -0.879 -1.846 0.469 0.816 0.749 0.771
+HGDP01322 HGDP01322 -0.54 0.071 0.829 0.106 -1.628 0.514
+HGDP01326 HGDP01326 -0.808 -1.291 0.928 1.686 -0.1 -0.087
+HGDP01328 HGDP01328 -1.21 -1.222 0.948 0.306 0.328 0.051
+HGDP01329 HGDP01329 -0.381 -1.971 -0.876 0.057 0.073 -0.022
+HGDP01330 HGDP01330 -0.598 -2.321 -0.139 1.421 -0.703 -0.602
+HGDP01331 HGDP01331 -1.121 -1.273 -1.859 -0.23 -0.003 -1.609
+HGDP01332 HGDP01332 -1.253 -1.403 -0.639 -0.171 -0.557 0.466
+HGDP01334 HGDP01334 -0.69 -1.67 -0.817 -0.221 -1.987 -0.677
+HGDP01336 HGDP01336 -0.303 -0.349 -1.057 -1.779 0.245 0.104
+HGDP01337 HGDP01337 -0.508 -1.103 0.207 0.672 0.5 0.173
+HGDP01339 HGDP01339 -0.812 -1.051 -0.54 -0.944 1.282 0.359
+HGDP01340 HGDP01340 -0.806 -1.006 0.773 1.616 1.951 1.534
+HGDP01341 HGDP01341 -0.682 -1.25 -0.078 -1.608 0.887 -0.025
+HGDP01346 HGDP01346 -0.462 -1.1 -0.067 1.464 1.151 0.028
+HGDP01347 HGDP01347 -1.225 -0.901 -0.271 0.451 -0.966 -0.478
+HGDP01348 HGDP01348 -0.805 -1.179 -0.375 0.47 0.002 -1.175
+HGDP01349 HGDP01349 -0.04 -1.514 -2.008 0.231 1.517 -1.058
+HGDP01351 HGDP01351 -0.513 -0.452 -0.295 -0.243 -1.565 1.162
+HGDP01352 HGDP01352 -0.772 -1.021 -0.398 1.245 0.753 -1.935
+HGDP01353 HGDP01353 -0.808 -1.103 0.258 0.369 -0.107 0.607
+HGDP01354 HGDP01354 -0.842 -1.42 0.362 1.016 1.246 0.476
+HGDP01356 HGDP01356 -0.785 -1.2 -1.252 1.522 1.108 -0.791
+HGDP01357 HGDP01357 0.315 2.197 0.688 -0.978 -1.326 1.063
+HGDP01358 HGDP01358 -0.243 1.115 0.904 0.384 -1.103 1.166
+HGDP01359 HGDP01359 -0.213 1.605 -0.538 0.873 0.209 1.229
+HGDP01360 HGDP01360 -0.215 1.893 -0.384 0.601 0.771 0.575
+HGDP01361 HGDP01361 -1.091 0.863 -0.525 0.07 -0.105 -0.485
+HGDP01362 HGDP01362 0.195 1.483 0.832 0.47 1.115 -1.243
+HGDP01363 HGDP01363 -0.055 0.715 -0.799 0.753 -0.874 -1.174
+HGDP01366 HGDP01366 0.03 0.847 -0.433 1.951 -1.251 -0.185
+HGDP01367 HGDP01367 -0.132 1.013 -0.482 -0.123 -0.627 -1.241
+HGDP01369 HGDP01369 -0.082 2.068 -1.849 1.51 0.809 -1.834
+HGDP01370 HGDP01370 -0.29 1.103 -0.34 -0.5 0.991 -1.567
+HGDP01372 HGDP01372 0.113 1.13 -1.326 0.443 0.097 -0.75
+HGDP01373 HGDP01373 -0.277 0.6 -1.064 -0.974 -0.43 1.146
+HGDP01374 HGDP01374 -0.344 1.053 -1.356 -0.374 0.599 -1.811
+HGDP01375 HGDP01375 0.377 1.568 -1.497 -0.236 -2.023 0.197
+HGDP01376 HGDP01376 -0.96 1.607 0.648 0.79 -0.188 1.107
+HGDP01377 HGDP01377 0.364 1.969 -0.605 -0.455 0.368 0.006
+HGDP01378 HGDP01378 -1.266 1.007 0.297 0.716 2.152 1.461
+HGDP01379 HGDP01379 -0.746 1.355 0.358 0.55 -1.12 0.596
+HGDP01380 HGDP01380 -0.37 1.19 -0.897 -0.978 -0.559 2.224
+HGDP01381 HGDP01381 -0.627 1.38 1.782 1.41 0.657 0.943
+HGDP01382 HGDP01382 -1.039 1.63 -0.454 1.167 -1.335 1.028
+HGDP01384 HGDP01384 -1.021 1.326 -0.595 0.681 0.424 0.387
+HGDP01385 HGDP01385 0.258 0.786 -0.385 -0.113 0.87 2.004
+HGDP01386 HGDP01386 -0.896 1.325 0.005 -1.227 -0.158 1.583
+HGDP01387 HGDP01387 0.126 0.6 0.373 0.658 -0.328 -0.025
+HGDP01388 HGDP01388 -0.333 0.569 1.779 -0.252 -0.819 -0.949
+HGDP01396 HGDP01396 0.417 0.924 -0.396 0.474 -1.234 -0.714
+HGDP01397 HGDP01397 -0.7 -0.076 0.07 0.313 1.411 1.617
+HGDP01398 HGDP01398 -0.681 1.235 -0.28 0.636 -1.306 0.246
+HGDP01399 HGDP01399 -0.198 1.625 0.34 -0.712 -1.072 -1.078
+HGDP01400 HGDP01400 -0.755 1.297 -0.312 1.32 0.333 -0.805
+HGDP01403 HGDP01403 -0.042 0.305 -1.201 0.573 1.003 -0.195
+HGDP01404 HGDP01404 0.064 0.735 -1.251 -1.535 -1.025 -0.124
+HGDP01405 HGDP01405 1.876 0.091 -0.513 -0.607 0.141 0.096
+HGDP01408 HGDP01408 1.486 -0.492 -0.374 0.417 1.513 -1.027
+HGDP01413 HGDP01413 1.632 -0.595 0.331 -1.602 -1.206 1.844
+HGDP01418 HGDP01418 2.028 0.091 -0.133 -0.312 -0.459 -0.493
+HGDP01419 HGDP01419 1.341 -0.432 0.128 0.55 -0.222 -0.062
+LP6005441-DNA_A01 LP6005441-DNA_A01 -0.276 1.641 0.749 -0.162 -3.184 0.305
+LP6005441-DNA_A04 LP6005441-DNA_A04 -0.061 -0.549 -1.636 -2.213 1.598 1.574
+LP6005441-DNA_A05 LP6005441-DNA_A05 -0.963 1.026 0.765 1.293 1.67 -1.837
+LP6005441-DNA_A06 LP6005441-DNA_A06 -0.431 1.102 -0.94 0.44 -0.845 0.452
+LP6005441-DNA_A09 LP6005441-DNA_A09 -0.632 -0.99 0.26 2.034 -0.419 -1.183
+LP6005441-DNA_A12 LP6005441-DNA_A12 -1.094 -1.956 -1.79 -2.392 0.343 0.378
+LP6005441-DNA_B02 LP6005441-DNA_B02 1.067 -1.302 0.984 -0.227 0.394 0.542
+LP6005441-DNA_B05 LP6005441-DNA_B05 -0.34 1.379 0.611 0.679 0.051 0.466
+LP6005441-DNA_B06 LP6005441-DNA_B06 0.78 0.739 0.478 0.664 1.668 -2.355
+LP6005441-DNA_B07 LP6005441-DNA_B07 -0.866 -1.664 0.454 -0.325 -1.79 -0.68
+LP6005441-DNA_B09 LP6005441-DNA_B09 -0.72 -0.579 -1.666 -0.429 1.964 0.923
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+LP6005441-DNA_C01 LP6005441-DNA_C01 -0.436 1.009 0.851 1.446 -1.182 -0.694
+LP6005441-DNA_C02 LP6005441-DNA_C02 -0.274 0.34 1.047 1.163 -0.504 -1.295
+LP6005441-DNA_C03 LP6005441-DNA_C03 -1.346 0.602 0.349 0.734 -2.321 -0.668
+LP6005441-DNA_C05 LP6005441-DNA_C05 -0.822 0.022 -0.918 1.242 -0.755 -0.419
+LP6005441-DNA_C06 LP6005441-DNA_C06 -0.037 -1.5 -0.447 1.075 -0.89 -1.218
+LP6005441-DNA_C07 LP6005441-DNA_C07 0.208 0.558 -1.509 -0.703 -0.621 0.145
+LP6005441-DNA_C08 LP6005441-DNA_C08 -1.116 -1.658 -0.39 2.359 -0.658 -1.231
+LP6005441-DNA_C09 LP6005441-DNA_C09 -1.027 -0.468 -0.631 1.212 0.256 -0.746
+LP6005441-DNA_C10 LP6005441-DNA_C10 -0.68 0.158 1.527 -0.075 0.135 0.181
+LP6005441-DNA_C11 LP6005441-DNA_C11 0.076 0.871 0.605 1.919 -0.562 0.223
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-AFR.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-AFR.profiles
new file mode 100644
index 00000000..a9b48cb9
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-AFR.profiles
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-AMR.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-AMR.profiles
new file mode 100644
index 00000000..1faa7ca2
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-AMR.profiles
@@ -0,0 +1,413 @@
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-CSA.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-CSA.profiles
new file mode 100644
index 00000000..14883203
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-CSA.profiles
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-EAS.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-EAS.profiles
new file mode 100644
index 00000000..41876271
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-EAS.profiles
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-EUR.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-EUR.profiles
new file mode 100644
index 00000000..7ca444dd
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-EUR.profiles
@@ -0,0 +1,666 @@
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-MID.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-MID.profiles
new file mode 100644
index 00000000..184c9ed5
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-MID.profiles
@@ -0,0 +1,137 @@
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-TRANS.model.rds b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-TRANS.model.rds
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-TRANS.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-TRANS.profiles
new file mode 100644
index 00000000..38d6fccd
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-TRANS.profiles
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+NA20870 NA20870 0.964148734405956
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+NA20874 NA20874 1.05574791979532
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+NA21135 NA21135 0.68037666482625
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+NA21143 NA21143 0.27235388947128
+NA21144 NA21144 -0.297662422354408
+SS6004468 SS6004468 0.649240264608328
+SS6004470 SS6004470 0.37197913498885
+SS6004475 SS6004475 0.0456880190227679
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-TRANS.scale b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-TRANS.scale
new file mode 100644
index 00000000..2efd7b10
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804-TRANS.scale
@@ -0,0 +1,2 @@
+Param Mean SD
+SCORE_external -8.02517936287103e-06 0.904733024237797
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804.log b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804.log
index 290d8b66..ea9dbf95 100644
--- a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804.log
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804.log
@@ -3,19 +3,20 @@
 # For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
 #################################################################
 # Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
+# Version (tag): v2.2.2-258-gd2f2a91
 ---------------
- Parameter     Value                                                                               
- ref_plink_chr misc/dev/test_data/ref/ref.chr                                                      
- pop_data      misc/dev/test_data/ref/ref.pop.txt                                                  
- plink2        plink2                                                                              
- output        misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804
- score         misc/dev/test_data/ext_score/PGS002804.txt.gz                                       
- test          chr22                                                                               
- help          FALSE                                                                               
- output_dir    misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/             
+ Parameter     Value                                                                                                      
+ ref_plink_chr misc/dev/test_data/ref/ref.chr                                                                             
+ ref_pcs       /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles     
+ pop_data      misc/dev/test_data/ref/ref.pop.txt                                                                         
+ plink2        plink2                                                                                                     
+ output        /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pgs_score_files/external/PGS002804/ref-PGS002804
+ score         misc/dev/test_data/ext_score/PGS002804.txt.gz                                                              
+ test          chr22                                                                                                      
+ help          FALSE                                                                                                      
+ output_dir    /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pgs_score_files/external/PGS002804/             
 ---------------
-Analysis started at 2024-07-25 17:38:43
+Analysis started at 2025-04-06 17:33:39
 Score file contains 708 variants.
 After removal of variants that are not SNPs or are ambiguous, 708 variants remain.
 Merging sumstats with reference using CHR, BP, A1, and A2
@@ -23,5 +24,6 @@ Genome build GRCh37 detected.
 After matching variants to the reference, 708 variants remain.
 0 variants were flipped to match reference.
 Calculating polygenic scores in reference.
-Analysis finished at 2024-07-25 17:38:44 
-Analysis duration was 1.88 secs 
+Deriving trans-ancestry PGS models...
+Analysis finished at 2025-04-06 17:33:40 
+Analysis duration was 1.04 secs 
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804.score.gz b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804.score.gz
index 5050e952cbdb85f7072a0eebe2f568c66435c24b..cbea74ddfbfe6e043909cf07ba7688c0724e6bdc 100644
GIT binary patch
delta 17
Zcmca$e8HGQzMF&N@tRNR8#zu$0sugG2TK3|

delta 17
Ycmca$e8HGQzMF$%O8ug=jT|Q=0XpCYegFUf

diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AFR.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AFR.profiles
new file mode 100644
index 00000000..51d4cf15
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AFR.profiles
@@ -0,0 +1,689 @@
+FID IID SCORE_s0.2_lambda0.001 SCORE_s0.2_lambda0.00127427498570313 SCORE_s0.2_lambda0.00162377673918872 SCORE_s0.2_lambda0.00206913808111479 SCORE_s0.2_lambda0.00263665089873036 SCORE_s0.2_lambda0.00335981828628378 SCORE_s0.2_lambda0.00428133239871939 SCORE_s0.2_lambda0.00545559478116852 SCORE_s0.2_lambda0.00695192796177561 SCORE_s0.2_lambda0.00885866790410083 SCORE_s0.2_lambda0.0112883789168469 SCORE_s0.2_lambda0.0143844988828766 SCORE_s0.2_lambda0.0183298071083244 SCORE_s0.2_lambda0.0233572146909012 SCORE_s0.2_lambda0.0297635144163132 SCORE_s0.2_lambda0.0379269019073225 SCORE_s0.2_lambda0.0483293023857176 SCORE_s0.2_lambda0.0615848211066027 SCORE_s0.2_lambda0.0784759970351462 SCORE_s0.2_lambda0.1 SCORE_s0.5_lambda0.001 SCORE_s0.5_lambda0.00127427498570313 SCORE_s0.5_lambda0.00162377673918872 SCORE_s0.5_lambda0.00206913808111479 SCORE_s0.5_lambda0.00263665089873036 SCORE_s0.5_lambda0.00335981828628378 SCORE_s0.5_lambda0.00428133239871939 SCORE_s0.5_lambda0.00545559478116852 SCORE_s0.5_lambda0.00695192796177561 SCORE_s0.5_lambda0.00885866790410083 SCORE_s0.5_lambda0.0112883789168469 SCORE_s0.5_lambda0.0143844988828766 SCORE_s0.5_lambda0.0183298071083244 SCORE_s0.5_lambda0.0233572146909012 SCORE_s0.5_lambda0.0297635144163132 SCORE_s0.5_lambda0.0379269019073225 SCORE_s0.5_lambda0.0483293023857176 SCORE_s0.5_lambda0.0615848211066027 SCORE_s0.5_lambda0.0784759970351462 SCORE_s0.5_lambda0.1 SCORE_s0.9_lambda0.001 SCORE_s0.9_lambda0.00127427498570313 SCORE_s0.9_lambda0.00162377673918872 SCORE_s0.9_lambda0.00206913808111479 SCORE_s0.9_lambda0.00263665089873036 SCORE_s0.9_lambda0.00335981828628378 SCORE_s0.9_lambda0.00428133239871939 SCORE_s0.9_lambda0.00545559478116852 SCORE_s0.9_lambda0.00695192796177561 SCORE_s0.9_lambda0.00885866790410083 SCORE_s0.9_lambda0.0112883789168469 SCORE_s0.9_lambda0.0143844988828766 SCORE_s0.9_lambda0.0183298071083244 SCORE_s0.9_lambda0.0233572146909012 SCORE_s0.9_lambda0.0297635144163132 SCORE_s0.9_lambda0.0379269019073225 SCORE_s0.9_lambda0.0483293023857176 SCORE_s0.9_lambda0.0615848211066027 SCORE_s0.9_lambda0.0784759970351462 SCORE_s0.9_lambda0.1 SCORE_s1_lambda0.001 SCORE_s1_lambda0.00127427498570313 SCORE_s1_lambda0.00162377673918872 SCORE_s1_lambda0.00206913808111479 SCORE_s1_lambda0.00263665089873036 SCORE_s1_lambda0.00335981828628378 SCORE_s1_lambda0.00428133239871939 SCORE_s1_lambda0.00545559478116852 SCORE_s1_lambda0.00695192796177561 SCORE_s1_lambda0.00885866790410083 SCORE_s1_lambda0.0112883789168469 SCORE_s1_lambda0.0143844988828766 SCORE_s1_lambda0.0183298071083244 SCORE_s1_lambda0.0233572146909012 SCORE_s1_lambda0.0297635144163132 SCORE_s1_lambda0.0379269019073225 SCORE_s1_lambda0.0483293023857176 SCORE_s1_lambda0.0615848211066027 SCORE_s1_lambda0.0784759970351462 SCORE_s1_lambda0.1
+HG01879 HG01879 -1.452 -1.401 -1.082 -0.663 0.116 0.365 0.315 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.132 -1.052 -0.856 -0.555 -0.102 0.31 0.304 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.119 -1.05 -0.948 -0.731 -0.39 0.019 0.29 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.203 -1.171 -1.033 -0.773 -0.521 -0.071 0.286 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG01882 HG01882 0.589 0.451 0.43 0.695 0.942 0.547 0.315 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.926 0.823 0.659 0.644 0.771 0.514 0.304 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.872 0.85 0.801 0.613 0.577 0.404 0.29 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.769 0.731 0.739 0.54 0.563 0.33 0.286 NA NA NA NA NA NA NA NA NA NA NA NA NA
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+NA20362 NA20362 1.081 1.025 0.939 0.559 0.316 0.236 -0.077 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.872 0.861 0.782 0.463 0.27 0.25 -0.058 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.892 0.859 0.688 0.451 0.327 0.182 -0.03 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.716 0.756 0.724 0.538 0.438 0.161 -0.023 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20412 NA20412 -1.114 -1.26 -1.269 -1.304 -1.17 -0.797 -1.118 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.53 -1.611 -1.521 -1.372 -1.092 -0.677 -1.097 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.679 -1.598 -1.527 -1.38 -1.087 -0.373 -1.069 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.509 -1.482 -1.394 -1.411 -1.053 -0.283 -1.062 NA NA NA NA NA NA NA NA NA NA NA NA NA
+SS6004470 SS6004470 2.032 2.186 2.169 1.702 1.472 1.263 0.419 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.944 1.974 1.925 1.68 1.396 1.154 0.39 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.701 1.773 1.705 1.497 1.317 0.98 0.349 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.323 1.387 1.433 1.429 1.205 0.917 0.339 NA NA NA NA NA NA NA NA NA NA NA NA NA
+SS6004475 SS6004475 -1.349 -1.076 -0.584 0.154 0.618 0.161 0.027 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.476 -0.358 -0.051 0.402 0.723 0.11 0.029 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.807 0.82 0.863 1.06 0.936 0.143 0.029 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.537 1.539 1.521 1.482 1.343 0.175 0.029 NA NA NA NA NA NA NA NA NA NA NA NA NA
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AFR.scale b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AFR.scale
index daf4b36b..97678cc2 100644
--- a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AFR.scale
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AFR.scale
@@ -1,11 +1,11 @@
 Param Mean SD
-SCORE_s0.2_lambda0.001 0.0302161877659884 0.0117186172778871
-SCORE_s0.2_lambda0.00127427498570313 0.0277621208953488 0.00971528890690744
-SCORE_s0.2_lambda0.00162377673918872 0.0233562591787791 0.00758146715383442
-SCORE_s0.2_lambda0.00206913808111479 0.015557623693314 0.00547751337994645
-SCORE_s0.2_lambda0.00263665089873036 0.00554612026468023 0.00371637725092927
-SCORE_s0.2_lambda0.00335981828628378 -0.00130599921263081 0.00226390741796456
-SCORE_s0.2_lambda0.00428133239871939 -0.000414858690348837 0.000989599363111022
+SCORE_s0.2_lambda0.001 0.0302161895450581 0.0117186175956569
+SCORE_s0.2_lambda0.00127427498570313 0.0277621197325581 0.00971528808603498
+SCORE_s0.2_lambda0.00162377673918872 0.0233562588880814 0.00758146676023413
+SCORE_s0.2_lambda0.00206913808111479 0.0155576228110465 0.00547751422131493
+SCORE_s0.2_lambda0.00263665089873036 0.00554611974287791 0.00371637750144871
+SCORE_s0.2_lambda0.00335981828628378 -0.00130599954697674 0.00226390738793002
+SCORE_s0.2_lambda0.00428133239871939 -0.000414858559593023 0.000989599202668769
 SCORE_s0.2_lambda0.00545559478116852 0 0
 SCORE_s0.2_lambda0.00695192796177561 0 0
 SCORE_s0.2_lambda0.00885866790410083 0 0
@@ -19,13 +19,13 @@ SCORE_s0.2_lambda0.0483293023857176 0 0
 SCORE_s0.2_lambda0.0615848211066027 0 0
 SCORE_s0.2_lambda0.0784759970351462 0 0
 SCORE_s0.2_lambda0.1 0 0
-SCORE_s0.5_lambda0.001 0.0360115376976744 0.0117383051275316
-SCORE_s0.5_lambda0.00127427498570313 0.031539792130814 0.00991610201938424
-SCORE_s0.5_lambda0.00162377673918872 0.024003686880814 0.00787319720192562
-SCORE_s0.5_lambda0.00206913808111479 0.0165568202034884 0.00579697482250357
-SCORE_s0.5_lambda0.00263665089873036 0.00638605694309593 0.00383586941604448
-SCORE_s0.5_lambda0.00335981828628378 -0.0019150397609593 0.00222338851222943
-SCORE_s0.5_lambda0.00428133239871939 -0.000381718981395349 0.000978075016503672
+SCORE_s0.5_lambda0.001 0.0360115381206395 0.0117383054508365
+SCORE_s0.5_lambda0.00127427498570313 0.0315397897761628 0.0099161022936629
+SCORE_s0.5_lambda0.00162377673918872 0.0240036865741279 0.00787319816615711
+SCORE_s0.5_lambda0.00206913808111479 0.0165568198517442 0.00579697534647564
+SCORE_s0.5_lambda0.00263665089873036 0.00638605690693314 0.00383586952794697
+SCORE_s0.5_lambda0.00335981828628378 -0.00191503966104651 0.00222338869732296
+SCORE_s0.5_lambda0.00428133239871939 -0.000381718723110465 0.000978074215265379
 SCORE_s0.5_lambda0.00545559478116852 0 0
 SCORE_s0.5_lambda0.00695192796177561 0 0
 SCORE_s0.5_lambda0.00885866790410083 0 0
@@ -39,13 +39,13 @@ SCORE_s0.5_lambda0.0483293023857176 0 0
 SCORE_s0.5_lambda0.0615848211066027 0 0
 SCORE_s0.5_lambda0.0784759970351462 0 0
 SCORE_s0.5_lambda0.1 0 0
-SCORE_s0.9_lambda0.001 0.0812895396802326 0.0192044397843143
-SCORE_s0.9_lambda0.00127427498570313 0.0652181370639535 0.0160627649765918
-SCORE_s0.9_lambda0.00162377673918872 0.0474502427325581 0.0123968535256267
-SCORE_s0.9_lambda0.00206913808111479 0.0286866968313954 0.00841756384263304
-SCORE_s0.9_lambda0.00263665089873036 0.0105309374978198 0.00478308490600703
-SCORE_s0.9_lambda0.00335981828628378 -0.00252797010828488 0.00228724145930565
-SCORE_s0.9_lambda0.00428133239871939 -0.000337217720988372 0.000965160068279503
+SCORE_s0.9_lambda0.001 0.0812895422965116 0.0192044378113531
+SCORE_s0.9_lambda0.00127427498570313 0.0652181331395349 0.0160627649983831
+SCORE_s0.9_lambda0.00162377673918872 0.0474502454941861 0.0123968519009599
+SCORE_s0.9_lambda0.00206913808111479 0.0286866989534884 0.00841756279875266
+SCORE_s0.9_lambda0.00263665089873036 0.010530936547093 0.00478308481590678
+SCORE_s0.9_lambda0.00335981828628378 -0.00252797054883721 0.00228724138170211
+SCORE_s0.9_lambda0.00428133239871939 -0.000337217788226744 0.000965160078169858
 SCORE_s0.9_lambda0.00545559478116852 0 0
 SCORE_s0.9_lambda0.00695192796177561 0 0
 SCORE_s0.9_lambda0.00885866790410083 0 0
@@ -59,13 +59,13 @@ SCORE_s0.9_lambda0.0483293023857176 0 0
 SCORE_s0.9_lambda0.0615848211066027 0 0
 SCORE_s0.9_lambda0.0784759970351462 0 0
 SCORE_s0.9_lambda0.1 0 0
-SCORE_s1_lambda0.001 0.159981837354651 0.0309736172813881
-SCORE_s1_lambda0.00127427498570313 0.125162429215116 0.025364090471554
-SCORE_s1_lambda0.00162377673918872 0.0875951808139535 0.0190096126639827
-SCORE_s1_lambda0.00206913808111479 0.0508423688953488 0.0119873185747801
-SCORE_s1_lambda0.00263665089873036 0.0168893534738372 0.00580364862978239
-SCORE_s1_lambda0.00335981828628378 -0.00285867432645349 0.00238783674489107
-SCORE_s1_lambda0.00428133239871939 -0.000325983536918605 0.000962358002943468
+SCORE_s1_lambda0.001 0.159981835901163 0.0309736149882845
+SCORE_s1_lambda0.00127427498570313 0.125162427761628 0.0253640899751656
+SCORE_s1_lambda0.00162377673918872 0.0875951837209303 0.0190096132003907
+SCORE_s1_lambda0.00206913808111479 0.0508423635174419 0.0119873185736593
+SCORE_s1_lambda0.00263665089873036 0.0168893534011628 0.00580364842210851
+SCORE_s1_lambda0.00335981828628378 -0.00285867502325581 0.00238783680429659
+SCORE_s1_lambda0.00428133239871939 -0.000325983522238372 0.000962358010266397
 SCORE_s1_lambda0.00545559478116852 0 0
 SCORE_s1_lambda0.00695192796177561 0 0
 SCORE_s1_lambda0.00885866790410083 0 0
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AMR.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AMR.profiles
new file mode 100644
index 00000000..52ce2528
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AMR.profiles
@@ -0,0 +1,413 @@
+FID IID SCORE_s0.2_lambda0.001 SCORE_s0.2_lambda0.00127427498570313 SCORE_s0.2_lambda0.00162377673918872 SCORE_s0.2_lambda0.00206913808111479 SCORE_s0.2_lambda0.00263665089873036 SCORE_s0.2_lambda0.00335981828628378 SCORE_s0.2_lambda0.00428133239871939 SCORE_s0.2_lambda0.00545559478116852 SCORE_s0.2_lambda0.00695192796177561 SCORE_s0.2_lambda0.00885866790410083 SCORE_s0.2_lambda0.0112883789168469 SCORE_s0.2_lambda0.0143844988828766 SCORE_s0.2_lambda0.0183298071083244 SCORE_s0.2_lambda0.0233572146909012 SCORE_s0.2_lambda0.0297635144163132 SCORE_s0.2_lambda0.0379269019073225 SCORE_s0.2_lambda0.0483293023857176 SCORE_s0.2_lambda0.0615848211066027 SCORE_s0.2_lambda0.0784759970351462 SCORE_s0.2_lambda0.1 SCORE_s0.5_lambda0.001 SCORE_s0.5_lambda0.00127427498570313 SCORE_s0.5_lambda0.00162377673918872 SCORE_s0.5_lambda0.00206913808111479 SCORE_s0.5_lambda0.00263665089873036 SCORE_s0.5_lambda0.00335981828628378 SCORE_s0.5_lambda0.00428133239871939 SCORE_s0.5_lambda0.00545559478116852 SCORE_s0.5_lambda0.00695192796177561 SCORE_s0.5_lambda0.00885866790410083 SCORE_s0.5_lambda0.0112883789168469 SCORE_s0.5_lambda0.0143844988828766 SCORE_s0.5_lambda0.0183298071083244 SCORE_s0.5_lambda0.0233572146909012 SCORE_s0.5_lambda0.0297635144163132 SCORE_s0.5_lambda0.0379269019073225 SCORE_s0.5_lambda0.0483293023857176 SCORE_s0.5_lambda0.0615848211066027 SCORE_s0.5_lambda0.0784759970351462 SCORE_s0.5_lambda0.1 SCORE_s0.9_lambda0.001 SCORE_s0.9_lambda0.00127427498570313 SCORE_s0.9_lambda0.00162377673918872 SCORE_s0.9_lambda0.00206913808111479 SCORE_s0.9_lambda0.00263665089873036 SCORE_s0.9_lambda0.00335981828628378 SCORE_s0.9_lambda0.00428133239871939 SCORE_s0.9_lambda0.00545559478116852 SCORE_s0.9_lambda0.00695192796177561 SCORE_s0.9_lambda0.00885866790410083 SCORE_s0.9_lambda0.0112883789168469 SCORE_s0.9_lambda0.0143844988828766 SCORE_s0.9_lambda0.0183298071083244 SCORE_s0.9_lambda0.0233572146909012 SCORE_s0.9_lambda0.0297635144163132 SCORE_s0.9_lambda0.0379269019073225 SCORE_s0.9_lambda0.0483293023857176 SCORE_s0.9_lambda0.0615848211066027 SCORE_s0.9_lambda0.0784759970351462 SCORE_s0.9_lambda0.1 SCORE_s1_lambda0.001 SCORE_s1_lambda0.00127427498570313 SCORE_s1_lambda0.00162377673918872 SCORE_s1_lambda0.00206913808111479 SCORE_s1_lambda0.00263665089873036 SCORE_s1_lambda0.00335981828628378 SCORE_s1_lambda0.00428133239871939 SCORE_s1_lambda0.00545559478116852 SCORE_s1_lambda0.00695192796177561 SCORE_s1_lambda0.00885866790410083 SCORE_s1_lambda0.0112883789168469 SCORE_s1_lambda0.0143844988828766 SCORE_s1_lambda0.0183298071083244 SCORE_s1_lambda0.0233572146909012 SCORE_s1_lambda0.0297635144163132 SCORE_s1_lambda0.0379269019073225 SCORE_s1_lambda0.0483293023857176 SCORE_s1_lambda0.0615848211066027 SCORE_s1_lambda0.0784759970351462 SCORE_s1_lambda0.1
+HG00551 HG00551 0.601 0.685 0.865 0.901 0.825 -0.196 -1.8 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.724 0.725 0.892 0.951 0.978 -0.176 -1.801 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.252 1.28 1.278 1.249 1.221 0.035 -1.798 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.564 1.634 1.656 1.601 1.43 0.074 -1.797 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00553 HG00553 0.523 0.474 0.444 0.448 0.43 -0.92 -1.8 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.668 0.663 0.649 0.741 0.772 -0.558 -1.801 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.063 1.081 1.106 1.221 1.279 -0.045 -1.798 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.005 1.018 1.046 1.184 1.408 0.121 -1.797 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00554 HG00554 0.187 0.119 0.023 0.053 0.454 1.075 2.643 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.187 0.061 -0.027 -0.107 0.222 1.036 2.674 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.026 -0.084 -0.223 -0.376 -0.31 0.776 2.715 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.288 -0.341 -0.422 -0.495 -0.564 0.633 2.725 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00637 HG00637 0.907 0.907 0.74 0.565 0.48 0.233 0.444 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.036 0.99 0.761 0.568 0.537 0.317 0.406 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.55 1.428 1.219 0.961 0.655 0.476 0.357 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.799 1.662 1.441 1.12 0.765 0.543 0.346 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00638 HG00638 -0.702 -0.75 -0.536 -0.303 0.028 0.289 0.026 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.889 -0.822 -0.604 -0.317 0.108 0.251 0.005 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.631 -0.543 -0.411 -0.14 0.381 0.506 -0.023 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.412 -0.359 -0.268 -0.075 0.485 0.614 -0.031 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00640 HG00640 1.151 1.161 1.162 1.076 1.045 1.03 1.537 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.1 1.082 1.048 0.968 1.073 1.123 1.52 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.949 0.952 0.979 0.957 1.133 1.145 1.495 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.718 0.756 0.853 0.982 1.141 1.152 1.489 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00641 HG00641 0.08 0.059 -0.04 0.034 0.559 0.489 -0.378 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.141 0.072 -0.05 0.105 0.501 0.53 -0.354 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.25 0.203 0.113 0.154 0.417 0.617 -0.321 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.289 0.229 0.142 0.11 0.332 0.627 -0.313 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00731 HG00731 1.039 0.801 0.634 0.412 -0.047 -0.519 -0.707 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.034 0.888 0.753 0.604 0.348 -0.19 -0.688 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.911 0.904 0.847 0.768 0.747 0.344 -0.661 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.702 0.764 0.785 0.718 0.795 0.5 -0.654 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00732 HG00732 0.878 0.896 0.896 0.947 1.304 1.308 1.537 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.707 0.726 0.777 0.827 1.209 1.396 1.52 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.234 0.218 0.14 0.116 0.492 1.44 1.495 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.346 -0.333 -0.367 -0.48 -0.158 1.382 1.489 NA NA NA NA NA NA NA NA NA NA NA NA NA
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+NA19770 NA19770 0.393 0.268 0.104 0.154 0.211 -0.061 -0.125 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.421 0.332 0.23 0.277 0.324 -0.006 -0.12 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.678 0.598 0.556 0.498 0.337 0.071 -0.11 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.856 0.733 0.597 0.472 0.318 0.047 -0.107 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19771 NA19771 0.447 0.517 0.469 0.528 0.769 1.129 1.852 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.552 0.574 0.565 0.727 0.753 1.081 1.896 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.556 0.616 0.689 0.729 0.649 0.875 1.953 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.578 0.619 0.684 0.723 0.604 0.761 1.967 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19773 NA19773 1.489 1.709 1.973 2.094 2.292 2.607 2.807 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.488 1.622 1.861 2.018 2.189 2.584 2.841 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.447 1.514 1.64 1.824 2.02 2.313 2.885 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.477 1.462 1.499 1.626 1.911 2.177 2.896 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19774 NA19774 0.897 0.931 0.9 1.011 0.672 0.248 0.968 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.725 0.763 0.758 0.868 0.769 0.25 0.993 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.201 0.207 0.211 0.303 0.585 0.129 1.027 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.074 -0.079 -0.137 -0.057 0.301 0.029 1.036 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19776 NA19776 1.973 1.881 1.757 1.571 1.199 0.512 -0.138 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.054 1.983 1.918 1.739 1.407 0.898 -0.161 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.953 1.937 1.92 1.829 1.644 1.312 -0.193 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.868 1.87 1.849 1.827 1.669 1.457 -0.201 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19777 NA19777 0.811 0.813 0.971 1.175 1.626 2.145 3.212 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.919 0.995 1.163 1.386 1.78 2.189 3.2 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.108 1.175 1.229 1.433 1.786 2.313 3.183 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.115 1.158 1.162 1.254 1.633 2.351 3.178 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19779 NA19779 -0.916 -1.241 -1.417 -1.73 -2.434 -2.615 -1.965 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.62 -0.831 -1.089 -1.426 -1.995 -2.369 -1.968 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.178 -0.228 -0.344 -0.717 -1.453 -1.825 -1.968 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.036 -0.05 -0.071 -0.275 -0.982 -1.695 -1.967 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19780 NA19780 2.697 2.722 2.731 2.596 2.376 1.251 0.026 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.727 2.774 2.846 2.798 2.558 1.527 0.005 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.669 2.73 2.82 2.815 2.692 2.046 -0.023 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.438 2.511 2.571 2.543 2.506 2.192 -0.031 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19781 NA19781 2.377 2.331 2.291 1.989 1.305 0.293 -0.543 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.466 2.451 2.428 2.157 1.526 0.496 -0.521 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.407 2.451 2.504 2.326 1.695 0.923 -0.491 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.37 2.434 2.495 2.385 1.764 1.002 -0.484 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19782 NA19782 -0.928 -0.786 -0.654 -0.344 0.014 0.028 -0.138 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.793 -0.683 -0.523 -0.218 -0.027 -0.035 -0.161 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.701 -0.591 -0.433 -0.187 0.005 0.053 -0.193 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.676 -0.636 -0.499 -0.307 -0.112 0.102 -0.201 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19783 NA19783 -0.455 -0.295 -0.141 -0.16 -0.367 -0.019 -0.138 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.403 -0.284 -0.188 -0.187 -0.358 -0.21 -0.161 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.492 -0.452 -0.348 -0.253 -0.202 -0.428 -0.193 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.388 -0.327 -0.226 -0.175 -0.125 -0.434 -0.201 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19785 NA19785 -0.39 -0.381 -0.424 -0.535 -0.795 -0.837 -0.707 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.364 -0.347 -0.392 -0.458 -0.705 -0.842 -0.688 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.183 -0.194 -0.23 -0.298 -0.554 -0.757 -0.661 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.243 -0.23 -0.22 -0.277 -0.443 -0.753 -0.654 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19786 NA19786 -0.231 -0.174 0.253 0.684 0.899 1.074 1.132 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.093 0.055 0.367 0.64 0.773 0.854 1.16 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.059 0.026 0.211 0.438 0.684 0.412 1.197 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.003 0.063 0.146 0.309 0.693 0.274 1.206 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19788 NA19788 0.486 0.495 0.384 0.294 0.118 0.153 0.026 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.361 0.335 0.259 0.224 0.145 0.161 0.005 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.512 0.488 0.464 0.454 0.355 0.263 -0.023 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.571 0.58 0.632 0.65 0.555 0.312 -0.031 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19789 NA19789 -0.077 -0.172 -0.276 -0.146 0.12 0.342 0.026 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.023 -0.13 -0.177 -0.107 0.174 0.321 0.005 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.084 0.038 0.021 -0.006 0.156 0.433 -0.023 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.146 0.122 0.092 0.069 0.054 0.501 -0.031 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19792 NA19792 -0.21 -0.377 -0.474 -0.589 -0.585 -1.147 -0.707 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.192 -0.247 -0.34 -0.407 -0.356 -0.965 -0.688 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.188 0.197 0.174 0.051 -0.128 -0.803 -0.661 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.448 0.456 0.5 0.438 0.159 -0.795 -0.654 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19794 NA19794 0.73 0.645 0.494 0.263 -0.077 -0.046 -0.138 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.694 0.63 0.491 0.334 0.144 0.194 -0.161 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.508 0.465 0.361 0.22 0.18 0.476 -0.193 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.281 0.231 0.174 0.067 -0.024 0.549 -0.201 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA19795 NA19795 1.016 0.967 1.031 1.093 1.036 0.564 -0.138 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.079 1.03 1.078 1.117 1.116 0.837 -0.161 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.776 0.764 0.788 0.824 1.063 0.978 -0.193 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.411 0.395 0.397 0.482 0.841 0.989 -0.201 NA NA NA NA NA NA NA NA NA NA NA NA NA
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AMR.scale b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AMR.scale
index 0f4c946c..46814a12 100644
--- a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AMR.scale
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-AMR.scale
@@ -1,11 +1,11 @@
 Param Mean SD
-SCORE_s0.2_lambda0.001 0.0231404219660194 0.0141783680924932
-SCORE_s0.2_lambda0.00127427498570313 0.0227598242135922 0.0112651601851956
-SCORE_s0.2_lambda0.00162377673918872 0.0199635141092233 0.00861909529436071
-SCORE_s0.2_lambda0.00206913808111479 0.0141611804150485 0.00596967120773375
-SCORE_s0.2_lambda0.00263665089873036 0.00647049006553398 0.00365312994527856
-SCORE_s0.2_lambda0.00335981828628378 0.000929607472330097 0.00183509107911492
-SCORE_s0.2_lambda0.00428133239871939 9.42488933009709e-05 0.000682003388557927
+SCORE_s0.2_lambda0.001 0.0231404222160194 0.0141783680489642
+SCORE_s0.2_lambda0.00127427498570313 0.022759823223301 0.0112651590972667
+SCORE_s0.2_lambda0.00162377673918872 0.019963511723301 0.0086190944524776
+SCORE_s0.2_lambda0.00206913808111479 0.0141611803495146 0.00596967127499055
+SCORE_s0.2_lambda0.00263665089873036 0.00647048950485437 0.00365313017557348
+SCORE_s0.2_lambda0.00335981828628378 0.00092960739684466 0.00183509110975282
+SCORE_s0.2_lambda0.00428133239871939 9.42486723300971e-05 0.000682003308636345
 SCORE_s0.2_lambda0.00545559478116852 0 0
 SCORE_s0.2_lambda0.00695192796177561 0 0
 SCORE_s0.2_lambda0.00885866790410083 0 0
@@ -19,13 +19,13 @@ SCORE_s0.2_lambda0.0483293023857176 0 0
 SCORE_s0.2_lambda0.0615848211066027 0 0
 SCORE_s0.2_lambda0.0784759970351462 0 0
 SCORE_s0.2_lambda0.1 0 0
-SCORE_s0.5_lambda0.001 0.0278233012014563 0.0159166114129803
-SCORE_s0.5_lambda0.00127427498570313 0.0253247000558252 0.0130865122918689
-SCORE_s0.5_lambda0.00162377673918872 0.0202089021674757 0.00995353935394114
-SCORE_s0.5_lambda0.00206913808111479 0.0151042716650485 0.00698674943458468
-SCORE_s0.5_lambda0.00263665089873036 0.00711512085291262 0.00426914625020277
-SCORE_s0.5_lambda0.00335981828628378 0.000469380861650485 0.00192581089309087
-SCORE_s0.5_lambda0.00428133239871939 0.00010842705776699 0.000671828450492866
+SCORE_s0.5_lambda0.001 0.0278233024975728 0.0159166114880798
+SCORE_s0.5_lambda0.00127427498570313 0.0253246984514563 0.0130865123363601
+SCORE_s0.5_lambda0.00162377673918872 0.020208902526699 0.0099535395222695
+SCORE_s0.5_lambda0.00206913808111479 0.0151042712259709 0.00698674939836924
+SCORE_s0.5_lambda0.00263665089873036 0.00711512048179612 0.00426914628755349
+SCORE_s0.5_lambda0.00335981828628378 0.000469380960194175 0.00192581087509134
+SCORE_s0.5_lambda0.00428133239871939 0.000108426861650485 0.000671828316820426
 SCORE_s0.5_lambda0.00545559478116852 0 0
 SCORE_s0.5_lambda0.00695192796177561 0 0
 SCORE_s0.5_lambda0.00885866790410083 0 0
@@ -39,13 +39,13 @@ SCORE_s0.5_lambda0.0483293023857176 0 0
 SCORE_s0.5_lambda0.0615848211066027 0 0
 SCORE_s0.5_lambda0.0784759970351462 0 0
 SCORE_s0.5_lambda0.1 0 0
-SCORE_s0.9_lambda0.001 0.0640531914587379 0.0305048178005772
-SCORE_s0.9_lambda0.00127427498570313 0.0521295161699029 0.0251285429739541
-SCORE_s0.9_lambda0.00162377673918872 0.0393153713592233 0.0186702871171218
-SCORE_s0.9_lambda0.00206913808111479 0.0260980555339806 0.0119012137984868
-SCORE_s0.9_lambda0.00263665089873036 0.0114571260412621 0.00618197785041877
-SCORE_s0.9_lambda0.00335981828628378 7.94261785679612e-05 0.00229216887808427
-SCORE_s0.9_lambda0.00428133239871939 0.000127514895145631 0.000660502979821758
+SCORE_s0.9_lambda0.001 0.0640531954805825 0.030504817006457
+SCORE_s0.9_lambda0.00127427498570313 0.0521295126796117 0.0251285419649809
+SCORE_s0.9_lambda0.00162377673918872 0.0393153744902913 0.0186702865537302
+SCORE_s0.9_lambda0.00206913808111479 0.0260980591262136 0.0119012147821459
+SCORE_s0.9_lambda0.00263665089873036 0.0114571256480583 0.00618197773644397
+SCORE_s0.9_lambda0.00335981828628378 7.94259902669903e-05 0.00229216874381501
+SCORE_s0.9_lambda0.00428133239871939 0.000127514708252427 0.000660502985555213
 SCORE_s0.9_lambda0.00545559478116852 0 0
 SCORE_s0.9_lambda0.00695192796177561 0 0
 SCORE_s0.9_lambda0.00885866790410083 0 0
@@ -59,13 +59,13 @@ SCORE_s0.9_lambda0.0483293023857176 0 0
 SCORE_s0.9_lambda0.0615848211066027 0 0
 SCORE_s0.9_lambda0.0784759970351462 0 0
 SCORE_s0.9_lambda0.1 0 0
-SCORE_s1_lambda0.001 0.132905220024272 0.0513052825401618
-SCORE_s1_lambda0.00127427498570313 0.104492764029126 0.0414312250220026
-SCORE_s1_lambda0.00162377673918872 0.0756564511893204 0.0303406085511644
-SCORE_s1_lambda0.00206913808111479 0.0469558159951456 0.0183112305049493
-SCORE_s1_lambda0.00263665089873036 0.0182263763567961 0.0080137428772862
-SCORE_s1_lambda0.00335981828628378 -8.77416019417476e-06 0.00248192026047516
-SCORE_s1_lambda0.00428133239871939 0.000132341183009709 0.000658067584329101
+SCORE_s1_lambda0.001 0.132905217839806 0.0513052823288881
+SCORE_s1_lambda0.00127427498570313 0.104492766213592 0.0414312287556062
+SCORE_s1_lambda0.00162377673918872 0.0756564511893204 0.0303406080561881
+SCORE_s1_lambda0.00206913808111479 0.0469558111165049 0.0183112294018668
+SCORE_s1_lambda0.00263665089873036 0.0182263771893204 0.00801374218881917
+SCORE_s1_lambda0.00335981828628378 -8.77423677184466e-06 0.00248192038816793
+SCORE_s1_lambda0.00428133239871939 0.000132341061165049 0.000658067681521566
 SCORE_s1_lambda0.00545559478116852 0 0
 SCORE_s1_lambda0.00695192796177561 0 0
 SCORE_s1_lambda0.00885866790410083 0 0
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-CSA.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-CSA.profiles
new file mode 100644
index 00000000..2f552d8b
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-CSA.profiles
@@ -0,0 +1,676 @@
+FID IID SCORE_s0.2_lambda0.001 SCORE_s0.2_lambda0.00127427498570313 SCORE_s0.2_lambda0.00162377673918872 SCORE_s0.2_lambda0.00206913808111479 SCORE_s0.2_lambda0.00263665089873036 SCORE_s0.2_lambda0.00335981828628378 SCORE_s0.2_lambda0.00428133239871939 SCORE_s0.2_lambda0.00545559478116852 SCORE_s0.2_lambda0.00695192796177561 SCORE_s0.2_lambda0.00885866790410083 SCORE_s0.2_lambda0.0112883789168469 SCORE_s0.2_lambda0.0143844988828766 SCORE_s0.2_lambda0.0183298071083244 SCORE_s0.2_lambda0.0233572146909012 SCORE_s0.2_lambda0.0297635144163132 SCORE_s0.2_lambda0.0379269019073225 SCORE_s0.2_lambda0.0483293023857176 SCORE_s0.2_lambda0.0615848211066027 SCORE_s0.2_lambda0.0784759970351462 SCORE_s0.2_lambda0.1 SCORE_s0.5_lambda0.001 SCORE_s0.5_lambda0.00127427498570313 SCORE_s0.5_lambda0.00162377673918872 SCORE_s0.5_lambda0.00206913808111479 SCORE_s0.5_lambda0.00263665089873036 SCORE_s0.5_lambda0.00335981828628378 SCORE_s0.5_lambda0.00428133239871939 SCORE_s0.5_lambda0.00545559478116852 SCORE_s0.5_lambda0.00695192796177561 SCORE_s0.5_lambda0.00885866790410083 SCORE_s0.5_lambda0.0112883789168469 SCORE_s0.5_lambda0.0143844988828766 SCORE_s0.5_lambda0.0183298071083244 SCORE_s0.5_lambda0.0233572146909012 SCORE_s0.5_lambda0.0297635144163132 SCORE_s0.5_lambda0.0379269019073225 SCORE_s0.5_lambda0.0483293023857176 SCORE_s0.5_lambda0.0615848211066027 SCORE_s0.5_lambda0.0784759970351462 SCORE_s0.5_lambda0.1 SCORE_s0.9_lambda0.001 SCORE_s0.9_lambda0.00127427498570313 SCORE_s0.9_lambda0.00162377673918872 SCORE_s0.9_lambda0.00206913808111479 SCORE_s0.9_lambda0.00263665089873036 SCORE_s0.9_lambda0.00335981828628378 SCORE_s0.9_lambda0.00428133239871939 SCORE_s0.9_lambda0.00545559478116852 SCORE_s0.9_lambda0.00695192796177561 SCORE_s0.9_lambda0.00885866790410083 SCORE_s0.9_lambda0.0112883789168469 SCORE_s0.9_lambda0.0143844988828766 SCORE_s0.9_lambda0.0183298071083244 SCORE_s0.9_lambda0.0233572146909012 SCORE_s0.9_lambda0.0297635144163132 SCORE_s0.9_lambda0.0379269019073225 SCORE_s0.9_lambda0.0483293023857176 SCORE_s0.9_lambda0.0615848211066027 SCORE_s0.9_lambda0.0784759970351462 SCORE_s0.9_lambda0.1 SCORE_s1_lambda0.001 SCORE_s1_lambda0.00127427498570313 SCORE_s1_lambda0.00162377673918872 SCORE_s1_lambda0.00206913808111479 SCORE_s1_lambda0.00263665089873036 SCORE_s1_lambda0.00335981828628378 SCORE_s1_lambda0.00428133239871939 SCORE_s1_lambda0.00545559478116852 SCORE_s1_lambda0.00695192796177561 SCORE_s1_lambda0.00885866790410083 SCORE_s1_lambda0.0112883789168469 SCORE_s1_lambda0.0143844988828766 SCORE_s1_lambda0.0183298071083244 SCORE_s1_lambda0.0233572146909012 SCORE_s1_lambda0.0297635144163132 SCORE_s1_lambda0.0379269019073225 SCORE_s1_lambda0.0483293023857176 SCORE_s1_lambda0.0615848211066027 SCORE_s1_lambda0.0784759970351462 SCORE_s1_lambda0.1
+HG01583 HG01583 2.102 2.223 2.351 2.405 2.186 1.539 0.365 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.054 2.082 2.15 2.172 1.938 1.521 0.351 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.74 1.76 1.717 1.655 1.48 1.514 0.33 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.516 1.518 1.449 1.253 1.187 1.485 0.325 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG01586 HG01586 -0.556 -0.455 -0.403 -0.28 -0.021 0.408 0.169 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.331 -0.314 -0.319 -0.255 -0.066 0.239 0.151 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.098 -0.137 -0.172 -0.142 -0.009 0.136 0.125 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.094 0.03 -0.053 -0.113 0.033 0.146 0.119 NA NA NA NA NA NA NA NA NA NA NA NA NA
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-CSA.scale b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-CSA.scale
index bee5a1bc..872542ad 100644
--- a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-CSA.scale
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-CSA.scale
@@ -1,11 +1,11 @@
 Param Mean SD
-SCORE_s0.2_lambda0.001 0.039180274637037 0.0112811892717677
-SCORE_s0.2_lambda0.00127427498570313 0.0350865740888889 0.00940384371500489
-SCORE_s0.2_lambda0.00162377673918872 0.0288646337185185 0.00740071465109934
-SCORE_s0.2_lambda0.00206913808111479 0.0193478164148148 0.00521300871078976
-SCORE_s0.2_lambda0.00263665089873036 0.00880219518488889 0.00316801099649956
-SCORE_s0.2_lambda0.00335981828628378 0.00128683611317778 0.00158044995388029
-SCORE_s0.2_lambda0.00428133239871939 1.56760237333333e-05 0.000571437148109952
+SCORE_s0.2_lambda0.001 0.0391802749333333 0.0112811889754635
+SCORE_s0.2_lambda0.00127427498570313 0.0350865724592593 0.00940384268874754
+SCORE_s0.2_lambda0.00162377673918872 0.0288646317777778 0.00740071534552277
+SCORE_s0.2_lambda0.00206913808111479 0.0193478156592593 0.00521300858973182
+SCORE_s0.2_lambda0.00263665089873036 0.00880219432 0.00316801108876661
+SCORE_s0.2_lambda0.00335981828628378 0.00128683616927407 0.00158045006122974
+SCORE_s0.2_lambda0.00428133239871939 1.56758814814815e-05 0.00057143712407357
 SCORE_s0.2_lambda0.00545559478116852 0 0
 SCORE_s0.2_lambda0.00695192796177561 0 0
 SCORE_s0.2_lambda0.00885866790410083 0 0
@@ -19,13 +19,13 @@ SCORE_s0.2_lambda0.0483293023857176 0 0
 SCORE_s0.2_lambda0.0615848211066027 0 0
 SCORE_s0.2_lambda0.0784759970351462 0 0
 SCORE_s0.2_lambda0.1 0 0
-SCORE_s0.5_lambda0.001 0.0450151057333333 0.0127668081175462
-SCORE_s0.5_lambda0.00127427498570313 0.0391236028592593 0.0107889972662434
-SCORE_s0.5_lambda0.00162377673918872 0.030272927837037 0.00850005129196597
-SCORE_s0.5_lambda0.00206913808111479 0.0213530410518519 0.00606253153234693
-SCORE_s0.5_lambda0.00263665089873036 0.0101297824637037 0.00364140791874368
-SCORE_s0.5_lambda0.00335981828628378 0.00102556152796444 0.00164134706139109
-SCORE_s0.5_lambda0.00428133239871939 2.75689668148148e-05 0.0005601976092435
+SCORE_s0.5_lambda0.001 0.0450151069185185 0.0127668075922708
+SCORE_s0.5_lambda0.00127427498570313 0.0391236018074074 0.0107889977502563
+SCORE_s0.5_lambda0.00162377673918872 0.0302729273925926 0.00850005087695943
+SCORE_s0.5_lambda0.00206913808111479 0.0213530407555556 0.00606253150900665
+SCORE_s0.5_lambda0.00263665089873036 0.01012978212 0.00364140802804855
+SCORE_s0.5_lambda0.00335981828628378 0.00102556170992593 0.00164134704441355
+SCORE_s0.5_lambda0.00428133239871939 2.75687807407407e-05 0.000560197530303796
 SCORE_s0.5_lambda0.00545559478116852 0 0
 SCORE_s0.5_lambda0.00695192796177561 0 0
 SCORE_s0.5_lambda0.00885866790410083 0 0
@@ -39,13 +39,13 @@ SCORE_s0.5_lambda0.0483293023857176 0 0
 SCORE_s0.5_lambda0.0615848211066027 0 0
 SCORE_s0.5_lambda0.0784759970351462 0 0
 SCORE_s0.5_lambda0.1 0 0
-SCORE_s0.9_lambda0.001 0.09339848 0.0245431298233682
-SCORE_s0.9_lambda0.00127427498570313 0.0759293271111111 0.0205055975336407
-SCORE_s0.9_lambda0.00162377673918872 0.0564706497777778 0.0158084257101116
-SCORE_s0.9_lambda0.00206913808111479 0.0362225972740741 0.0105953060477051
-SCORE_s0.9_lambda0.00263665089873036 0.0160308777318519 0.00556748658094705
-SCORE_s0.9_lambda0.00335981828628378 0.00104346373656296 0.00193987733830955
-SCORE_s0.9_lambda0.00428133239871939 4.3435737037037e-05 0.000547292083183871
+SCORE_s0.9_lambda0.001 0.0933984899259259 0.0245431310930464
+SCORE_s0.9_lambda0.00127427498570313 0.075929322962963 0.0205055977677868
+SCORE_s0.9_lambda0.00162377673918872 0.0564706531851852 0.0158084245526143
+SCORE_s0.9_lambda0.00206913808111479 0.0362225997925926 0.0105953065511078
+SCORE_s0.9_lambda0.00263665089873036 0.0160308771837037 0.00556748622921677
+SCORE_s0.9_lambda0.00335981828628378 0.00104346361140741 0.00193987742307128
+SCORE_s0.9_lambda0.00428133239871939 4.34355955555556e-05 0.000547292067102755
 SCORE_s0.9_lambda0.00545559478116852 0 0
 SCORE_s0.9_lambda0.00695192796177561 0 0
 SCORE_s0.9_lambda0.00885866790410083 0 0
@@ -59,13 +59,13 @@ SCORE_s0.9_lambda0.0483293023857176 0 0
 SCORE_s0.9_lambda0.0615848211066027 0 0
 SCORE_s0.9_lambda0.0784759970351462 0 0
 SCORE_s0.9_lambda0.1 0 0
-SCORE_s1_lambda0.001 0.175774325777778 0.044168263626137
-SCORE_s1_lambda0.00127427498570313 0.13837983837037 0.0360766317911203
-SCORE_s1_lambda0.00162377673918872 0.0992569860740741 0.0269226437066558
-SCORE_s1_lambda0.00206913808111479 0.0605634105481482 0.0167609418350732
-SCORE_s1_lambda0.00263665089873036 0.0236983494074074 0.00755991112273715
-SCORE_s1_lambda0.00335981828628378 0.00110292199734815 0.00212251559459152
-SCORE_s1_lambda0.00428133239871939 4.74210125925926e-05 0.000544430044619892
+SCORE_s1_lambda0.001 0.175774327259259 0.0441682662876332
+SCORE_s1_lambda0.00127427498570313 0.138379836592593 0.0360766332739692
+SCORE_s1_lambda0.00162377673918872 0.0992569857777778 0.0269226437104152
+SCORE_s1_lambda0.00206913808111479 0.0605634065333333 0.0167609422853315
+SCORE_s1_lambda0.00263665089873036 0.023698349882963 0.00755991096115021
+SCORE_s1_lambda0.00335981828628378 0.0011029218912 0.00212251591593685
+SCORE_s1_lambda0.00428133239871939 4.74208837037037e-05 0.000544430061275447
 SCORE_s1_lambda0.00545559478116852 0 0
 SCORE_s1_lambda0.00695192796177561 0 0
 SCORE_s1_lambda0.00885866790410083 0 0
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EAS.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EAS.profiles
new file mode 100644
index 00000000..4f6fc4e0
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EAS.profiles
@@ -0,0 +1,738 @@
+FID IID SCORE_s0.2_lambda0.001 SCORE_s0.2_lambda0.00127427498570313 SCORE_s0.2_lambda0.00162377673918872 SCORE_s0.2_lambda0.00206913808111479 SCORE_s0.2_lambda0.00263665089873036 SCORE_s0.2_lambda0.00335981828628378 SCORE_s0.2_lambda0.00428133239871939 SCORE_s0.2_lambda0.00545559478116852 SCORE_s0.2_lambda0.00695192796177561 SCORE_s0.2_lambda0.00885866790410083 SCORE_s0.2_lambda0.0112883789168469 SCORE_s0.2_lambda0.0143844988828766 SCORE_s0.2_lambda0.0183298071083244 SCORE_s0.2_lambda0.0233572146909012 SCORE_s0.2_lambda0.0297635144163132 SCORE_s0.2_lambda0.0379269019073225 SCORE_s0.2_lambda0.0483293023857176 SCORE_s0.2_lambda0.0615848211066027 SCORE_s0.2_lambda0.0784759970351462 SCORE_s0.2_lambda0.1 SCORE_s0.5_lambda0.001 SCORE_s0.5_lambda0.00127427498570313 SCORE_s0.5_lambda0.00162377673918872 SCORE_s0.5_lambda0.00206913808111479 SCORE_s0.5_lambda0.00263665089873036 SCORE_s0.5_lambda0.00335981828628378 SCORE_s0.5_lambda0.00428133239871939 SCORE_s0.5_lambda0.00545559478116852 SCORE_s0.5_lambda0.00695192796177561 SCORE_s0.5_lambda0.00885866790410083 SCORE_s0.5_lambda0.0112883789168469 SCORE_s0.5_lambda0.0143844988828766 SCORE_s0.5_lambda0.0183298071083244 SCORE_s0.5_lambda0.0233572146909012 SCORE_s0.5_lambda0.0297635144163132 SCORE_s0.5_lambda0.0379269019073225 SCORE_s0.5_lambda0.0483293023857176 SCORE_s0.5_lambda0.0615848211066027 SCORE_s0.5_lambda0.0784759970351462 SCORE_s0.5_lambda0.1 SCORE_s0.9_lambda0.001 SCORE_s0.9_lambda0.00127427498570313 SCORE_s0.9_lambda0.00162377673918872 SCORE_s0.9_lambda0.00206913808111479 SCORE_s0.9_lambda0.00263665089873036 SCORE_s0.9_lambda0.00335981828628378 SCORE_s0.9_lambda0.00428133239871939 SCORE_s0.9_lambda0.00545559478116852 SCORE_s0.9_lambda0.00695192796177561 SCORE_s0.9_lambda0.00885866790410083 SCORE_s0.9_lambda0.0112883789168469 SCORE_s0.9_lambda0.0143844988828766 SCORE_s0.9_lambda0.0183298071083244 SCORE_s0.9_lambda0.0233572146909012 SCORE_s0.9_lambda0.0297635144163132 SCORE_s0.9_lambda0.0379269019073225 SCORE_s0.9_lambda0.0483293023857176 SCORE_s0.9_lambda0.0615848211066027 SCORE_s0.9_lambda0.0784759970351462 SCORE_s0.9_lambda0.1 SCORE_s1_lambda0.001 SCORE_s1_lambda0.00127427498570313 SCORE_s1_lambda0.00162377673918872 SCORE_s1_lambda0.00206913808111479 SCORE_s1_lambda0.00263665089873036 SCORE_s1_lambda0.00335981828628378 SCORE_s1_lambda0.00428133239871939 SCORE_s1_lambda0.00545559478116852 SCORE_s1_lambda0.00695192796177561 SCORE_s1_lambda0.00885866790410083 SCORE_s1_lambda0.0112883789168469 SCORE_s1_lambda0.0143844988828766 SCORE_s1_lambda0.0183298071083244 SCORE_s1_lambda0.0233572146909012 SCORE_s1_lambda0.0297635144163132 SCORE_s1_lambda0.0379269019073225 SCORE_s1_lambda0.0483293023857176 SCORE_s1_lambda0.0615848211066027 SCORE_s1_lambda0.0784759970351462 SCORE_s1_lambda0.1
+HG00403 HG00403 -0.839 -0.708 -0.294 0.331 0.415 -0.12 -0.373 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.441 -0.311 0.047 0.599 0.619 -0.079 -0.378 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.227 0.403 0.721 1.047 0.952 0.191 -0.386 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.759 0.873 1.035 1.222 1.123 0.299 -0.388 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00404 HG00404 0.983 1.043 1.121 1.221 0.782 -0.17 -0.553 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.386 1.485 1.513 1.376 0.742 -0.343 -0.561 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.586 1.642 1.648 1.469 1.008 -0.409 -0.571 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.789 1.816 1.711 1.57 1.131 -0.354 -0.573 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00405 HG00405 0.684 0.677 0.683 0.915 0.622 -0.12 -0.373 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.912 0.908 0.941 1.045 0.722 -0.079 -0.378 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.948 1.015 1.135 1.259 1.082 0.189 -0.386 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.096 1.13 1.146 1.31 1.21 0.325 -0.388 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00406 HG00406 -0.516 -0.662 -0.783 -0.886 -0.826 -0.468 -0.553 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.699 -0.775 -0.866 -0.801 -0.633 -0.167 -0.561 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.872 -0.872 -0.851 -0.824 -0.548 -0.033 -0.571 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.927 -0.908 -0.883 -0.793 -0.563 -0.026 -0.573 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00407 HG00407 0.132 0.299 0.72 1.13 1.101 0.327 -0.373 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.374 0.568 0.942 1.305 1.212 0.466 -0.378 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.832 0.94 1.134 1.401 1.343 0.689 -0.386 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.035 1.069 1.1 1.264 1.338 0.732 -0.388 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00409 HG00409 -1.197 -1.153 -1.162 -1.024 -1.049 -0.568 -0.553 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.105 -1.142 -1.132 -1.044 -0.99 -0.467 -0.561 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.936 -1.031 -1.115 -1.157 -0.949 -0.491 -0.571 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.789 -0.877 -0.982 -1.1 -0.922 -0.494 -0.573 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00410 HG00410 1.183 1.121 1.038 0.914 0.28 -0.12 -0.373 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.105 1.103 1.109 0.963 0.448 -0.079 -0.378 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.982 1.031 1.109 1.045 0.659 0.191 -0.386 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.011 1.025 1.019 1.05 0.795 0.299 -0.388 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00418 HG00418 -0.049 -0.428 -0.822 -0.863 -0.707 -0.3 0.658 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.338 -0.584 -0.784 -0.902 -0.925 -0.431 0.701 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.818 -0.907 -0.998 -1.184 -1.388 -0.535 0.76 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.287 -1.349 -1.408 -1.522 -1.692 -0.637 0.775 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00419 HG00419 -0.757 -0.744 -0.74 -0.668 -0.044 0.236 0.415 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.587 -0.581 -0.562 -0.514 -0.009 0.495 0.426 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.509 -0.512 -0.544 -0.469 -0.039 0.674 0.439 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.63 -0.587 -0.547 -0.43 -0.134 0.736 0.442 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00420 HG00420 1.043 0.926 0.791 0.842 1.546 1.987 2.249 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.868 0.84 0.881 0.84 1.353 2.06 2.263 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.533 0.552 0.593 0.68 1.058 2.064 2.28 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.089 0.078 0.136 0.318 0.729 2.066 2.284 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00421 HG00421 -0.566 -0.614 -0.567 -0.456 -0.267 -0.37 -0.553 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.568 -0.531 -0.487 -0.317 0.039 -0.228 -0.561 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.672 -0.593 -0.593 -0.514 0.042 0.222 -0.571 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.647 -0.619 -0.636 -0.652 -0.218 0.317 -0.573 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00422 HG00422 -2.158 -2.354 -2.503 -2.592 -2.831 -1.957 -1.176 NA NA NA NA NA NA NA NA NA NA NA NA NA -2.62 -2.723 -2.682 -2.752 -2.859 -2.028 -1.136 NA NA NA NA NA NA NA NA NA NA NA NA NA -2.955 -2.936 -2.92 -2.88 -2.834 -2.131 -1.081 NA NA NA NA NA NA NA NA NA NA NA NA NA -2.951 -2.906 -2.912 -2.842 -2.762 -2.111 -1.067 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00428 HG00428 -1.165 -1.113 -0.821 -0.365 0.118 0.261 -0.373 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.025 -1.017 -0.739 -0.298 0.157 0.299 -0.378 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.914 -0.893 -0.784 -0.585 -0.057 0.335 -0.386 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.843 -0.916 -0.931 -0.86 -0.395 0.324 -0.388 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00429 HG00429 0.823 0.727 0.665 0.524 0.396 0.737 1.281 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.594 0.485 0.41 0.281 0.243 0.714 1.276 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.054 -0.08 -0.117 -0.202 -0.178 0.535 1.27 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.379 -0.394 -0.422 -0.486 -0.467 0.429 1.269 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00436 HG00436 -2.504 -2.354 -2.163 -1.682 -1.461 -1.092 -0.553 NA NA NA NA NA NA NA NA NA NA NA NA NA -2.404 -2.323 -2.213 -1.818 -1.603 -1.225 -0.561 NA NA NA NA NA NA NA NA NA NA NA NA NA -2.463 -2.472 -2.394 -2.141 -1.739 -1.387 -0.571 NA NA NA NA NA NA NA NA NA NA NA NA NA -2.303 -2.376 -2.397 -2.21 -1.851 -1.42 -0.573 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00437 HG00437 -0.234 -0.228 -0.309 -0.425 -0.121 0.8 1.281 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.66 -0.597 -0.587 -0.629 -0.389 0.58 1.276 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.253 -1.247 -1.229 -1.083 -0.634 0.33 1.27 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.674 -1.643 -1.598 -1.407 -0.906 0.251 1.269 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00442 HG00442 -1.063 -1.156 -1.173 -1.167 -0.978 -0.687 -0.553 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.967 -1.006 -1.109 -1.105 -1.05 -0.836 -0.561 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.894 -0.848 -0.887 -1.008 -1.156 -0.975 -0.571 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.972 -0.868 -0.812 -0.929 -1.168 -1.01 -0.573 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00443 HG00443 2.288 2.353 2.364 2.326 1.999 1.015 -0.373 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.432 2.461 2.431 2.407 2.219 1.19 -0.378 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.501 2.546 2.524 2.438 2.564 1.804 -0.386 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.532 2.539 2.478 2.38 2.537 1.989 -0.388 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00445 HG00445 0.504 0.441 0.313 0.385 0.52 1.411 1.384 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.386 0.293 0.17 0.122 0.053 1.213 1.413 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.061 -0.189 -0.324 -0.399 -0.488 0.882 1.449 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.654 -0.694 -0.758 -0.731 -0.845 0.814 1.457 NA NA NA NA NA NA NA NA NA NA NA NA NA
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+NA19091 NA19091 -0.426 -0.466 -0.49 -0.29 0.21 1.126 1.461 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.387 -0.336 -0.285 -0.174 0.259 1.271 1.459 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.245 -0.215 -0.196 -0.229 0.113 1.214 1.455 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.167 -0.225 -0.337 -0.342 -0.112 1.181 1.454 NA NA NA NA NA NA NA NA NA NA NA NA NA
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EAS.scale b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EAS.scale
index 643f278d..2e065224 100644
--- a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EAS.scale
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EAS.scale
@@ -1,11 +1,11 @@
 Param Mean SD
-SCORE_s0.2_lambda0.001 0.0262375578290366 0.0112377479815627
-SCORE_s0.2_lambda0.00127427498570313 0.025070122972863 0.00917667695085612
-SCORE_s0.2_lambda0.00162377673918872 0.0217762442062415 0.00707299847319993
-SCORE_s0.2_lambda0.00206913808111479 0.015437680156038 0.00492002897040898
-SCORE_s0.2_lambda0.00263665089873036 0.00705392937991859 0.00295975164465451
-SCORE_s0.2_lambda0.00335981828628378 0.00143720670630936 0.00150315647808008
-SCORE_s0.2_lambda0.00428133239871939 0.000344577005454545 0.000622874959042393
+SCORE_s0.2_lambda0.001 0.0262375583894166 0.011237747829712
+SCORE_s0.2_lambda0.00127427498570313 0.0250701221451832 0.00917667718562329
+SCORE_s0.2_lambda0.00162377673918872 0.0217762423880597 0.0070729981449318
+SCORE_s0.2_lambda0.00206913808111479 0.0154376794056988 0.0049200291228459
+SCORE_s0.2_lambda0.00263665089873036 0.00705392883175034 0.00295975186723439
+SCORE_s0.2_lambda0.00335981828628378 0.00143720678700136 0.00150315654762456
+SCORE_s0.2_lambda0.00428133239871939 0.000344576829036635 0.000622875024978204
 SCORE_s0.2_lambda0.00545559478116852 0 0
 SCORE_s0.2_lambda0.00695192796177561 0 0
 SCORE_s0.2_lambda0.00885866790410083 0 0
@@ -19,13 +19,13 @@ SCORE_s0.2_lambda0.0483293023857176 0 0
 SCORE_s0.2_lambda0.0615848211066027 0 0
 SCORE_s0.2_lambda0.0784759970351462 0 0
 SCORE_s0.2_lambda0.1 0 0
-SCORE_s0.5_lambda0.001 0.0298980652652646 0.0124061899449671
-SCORE_s0.5_lambda0.00127427498570313 0.0270668127747626 0.0103802931639169
-SCORE_s0.5_lambda0.00162377673918872 0.0217265035440977 0.00803485108700583
-SCORE_s0.5_lambda0.00206913808111479 0.0162742341858887 0.00559843145464755
-SCORE_s0.5_lambda0.00263665089873036 0.00750215145922388 0.00334760009673567
-SCORE_s0.5_lambda0.00335981828628378 0.000879412184328358 0.00152750617625268
-SCORE_s0.5_lambda0.00428133239871939 0.000344753711261872 0.000614741629312314
+SCORE_s0.5_lambda0.001 0.0298980674177748 0.0124061899075849
+SCORE_s0.5_lambda0.00127427498570313 0.0270668111872456 0.0103802929744255
+SCORE_s0.5_lambda0.00162377673918872 0.021726502807327 0.00803485152241825
+SCORE_s0.5_lambda0.00206913808111479 0.016274234156038 0.00559843105073014
+SCORE_s0.5_lambda0.00263665089873036 0.00750215079330801 0.00334760031217184
+SCORE_s0.5_lambda0.00335981828628378 0.000879412371804613 0.00152750606258598
+SCORE_s0.5_lambda0.00428133239871939 0.000344753523744912 0.00061474169049051
 SCORE_s0.5_lambda0.00545559478116852 0 0
 SCORE_s0.5_lambda0.00695192796177561 0 0
 SCORE_s0.5_lambda0.00885866790410083 0 0
@@ -39,13 +39,13 @@ SCORE_s0.5_lambda0.0483293023857176 0 0
 SCORE_s0.5_lambda0.0615848211066027 0 0
 SCORE_s0.5_lambda0.0784759970351462 0 0
 SCORE_s0.5_lambda0.1 0 0
-SCORE_s0.9_lambda0.001 0.0631018118046133 0.0231547366032372
-SCORE_s0.9_lambda0.00127427498570313 0.0514414689715061 0.0192521132784244
-SCORE_s0.9_lambda0.00162377673918872 0.0394139959105835 0.014708039411743
-SCORE_s0.9_lambda0.00206913808111479 0.0270345938453189 0.00957177857292377
-SCORE_s0.9_lambda0.00263665089873036 0.0117941534341927 0.0049090768644096
-SCORE_s0.9_lambda0.00335981828628378 0.000381534846350068 0.00174706492243267
-SCORE_s0.9_lambda0.00428133239871939 0.000345733229308005 0.000605645640830852
+SCORE_s0.9_lambda0.001 0.0631018163473541 0.0231547355371502
+SCORE_s0.9_lambda0.00127427498570313 0.0514414645427408 0.019252113688198
+SCORE_s0.9_lambda0.00162377673918872 0.0394139994834464 0.0147080396456632
+SCORE_s0.9_lambda0.00206913808111479 0.0270345960203528 0.00957177830612353
+SCORE_s0.9_lambda0.00263665089873036 0.0117941531628223 0.00490907700484847
+SCORE_s0.9_lambda0.00335981828628378 0.00038153467761194 0.00174706483440314
+SCORE_s0.9_lambda0.00428133239871939 0.000345733050746269 0.000605645709304305
 SCORE_s0.9_lambda0.00545559478116852 0 0
 SCORE_s0.9_lambda0.00695192796177561 0 0
 SCORE_s0.9_lambda0.00885866790410083 0 0
@@ -59,13 +59,13 @@ SCORE_s0.9_lambda0.0483293023857176 0 0
 SCORE_s0.9_lambda0.0615848211066027 0 0
 SCORE_s0.9_lambda0.0784759970351462 0 0
 SCORE_s0.9_lambda0.1 0 0
-SCORE_s1_lambda0.001 0.126334419918589 0.0402824574402884
-SCORE_s1_lambda0.00127427498570313 0.0992975649932157 0.0327546729567063
-SCORE_s1_lambda0.00162377673918872 0.0721905860244233 0.0243334896779594
-SCORE_s1_lambda0.00206913808111479 0.046480625834464 0.0149238740107248
-SCORE_s1_lambda0.00263665089873036 0.018277899243555 0.00660932677778809
-SCORE_s1_lambda0.00335981828628378 0.000298524242941655 0.00187561410596669
-SCORE_s1_lambda0.00428133239871939 0.000346113969470828 0.00060367819220266
+SCORE_s1_lambda0.001 0.126334423731343 0.0402824562819916
+SCORE_s1_lambda0.00127427498570313 0.0992975679918589 0.0327546766073528
+SCORE_s1_lambda0.00162377673918872 0.0721905860379919 0.0243334899609946
+SCORE_s1_lambda0.00206913808111479 0.0464806227137042 0.014923874277032
+SCORE_s1_lambda0.00263665089873036 0.0182779001282225 0.00660932721739627
+SCORE_s1_lambda0.00335981828628378 0.000298524250067843 0.00187561415149651
+SCORE_s1_lambda0.00428133239871939 0.000346113880868385 0.000603678413285567
 SCORE_s1_lambda0.00545559478116852 0 0
 SCORE_s1_lambda0.00695192796177561 0 0
 SCORE_s1_lambda0.00885866790410083 0 0
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EUR.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EUR.profiles
new file mode 100644
index 00000000..14317dd8
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EUR.profiles
@@ -0,0 +1,666 @@
+FID IID SCORE_s0.2_lambda0.001 SCORE_s0.2_lambda0.00127427498570313 SCORE_s0.2_lambda0.00162377673918872 SCORE_s0.2_lambda0.00206913808111479 SCORE_s0.2_lambda0.00263665089873036 SCORE_s0.2_lambda0.00335981828628378 SCORE_s0.2_lambda0.00428133239871939 SCORE_s0.2_lambda0.00545559478116852 SCORE_s0.2_lambda0.00695192796177561 SCORE_s0.2_lambda0.00885866790410083 SCORE_s0.2_lambda0.0112883789168469 SCORE_s0.2_lambda0.0143844988828766 SCORE_s0.2_lambda0.0183298071083244 SCORE_s0.2_lambda0.0233572146909012 SCORE_s0.2_lambda0.0297635144163132 SCORE_s0.2_lambda0.0379269019073225 SCORE_s0.2_lambda0.0483293023857176 SCORE_s0.2_lambda0.0615848211066027 SCORE_s0.2_lambda0.0784759970351462 SCORE_s0.2_lambda0.1 SCORE_s0.5_lambda0.001 SCORE_s0.5_lambda0.00127427498570313 SCORE_s0.5_lambda0.00162377673918872 SCORE_s0.5_lambda0.00206913808111479 SCORE_s0.5_lambda0.00263665089873036 SCORE_s0.5_lambda0.00335981828628378 SCORE_s0.5_lambda0.00428133239871939 SCORE_s0.5_lambda0.00545559478116852 SCORE_s0.5_lambda0.00695192796177561 SCORE_s0.5_lambda0.00885866790410083 SCORE_s0.5_lambda0.0112883789168469 SCORE_s0.5_lambda0.0143844988828766 SCORE_s0.5_lambda0.0183298071083244 SCORE_s0.5_lambda0.0233572146909012 SCORE_s0.5_lambda0.0297635144163132 SCORE_s0.5_lambda0.0379269019073225 SCORE_s0.5_lambda0.0483293023857176 SCORE_s0.5_lambda0.0615848211066027 SCORE_s0.5_lambda0.0784759970351462 SCORE_s0.5_lambda0.1 SCORE_s0.9_lambda0.001 SCORE_s0.9_lambda0.00127427498570313 SCORE_s0.9_lambda0.00162377673918872 SCORE_s0.9_lambda0.00206913808111479 SCORE_s0.9_lambda0.00263665089873036 SCORE_s0.9_lambda0.00335981828628378 SCORE_s0.9_lambda0.00428133239871939 SCORE_s0.9_lambda0.00545559478116852 SCORE_s0.9_lambda0.00695192796177561 SCORE_s0.9_lambda0.00885866790410083 SCORE_s0.9_lambda0.0112883789168469 SCORE_s0.9_lambda0.0143844988828766 SCORE_s0.9_lambda0.0183298071083244 SCORE_s0.9_lambda0.0233572146909012 SCORE_s0.9_lambda0.0297635144163132 SCORE_s0.9_lambda0.0379269019073225 SCORE_s0.9_lambda0.0483293023857176 SCORE_s0.9_lambda0.0615848211066027 SCORE_s0.9_lambda0.0784759970351462 SCORE_s0.9_lambda0.1 SCORE_s1_lambda0.001 SCORE_s1_lambda0.00127427498570313 SCORE_s1_lambda0.00162377673918872 SCORE_s1_lambda0.00206913808111479 SCORE_s1_lambda0.00263665089873036 SCORE_s1_lambda0.00335981828628378 SCORE_s1_lambda0.00428133239871939 SCORE_s1_lambda0.00545559478116852 SCORE_s1_lambda0.00695192796177561 SCORE_s1_lambda0.00885866790410083 SCORE_s1_lambda0.0112883789168469 SCORE_s1_lambda0.0143844988828766 SCORE_s1_lambda0.0183298071083244 SCORE_s1_lambda0.0233572146909012 SCORE_s1_lambda0.0297635144163132 SCORE_s1_lambda0.0379269019073225 SCORE_s1_lambda0.0483293023857176 SCORE_s1_lambda0.0615848211066027 SCORE_s1_lambda0.0784759970351462 SCORE_s1_lambda0.1
+HG00096 HG00096 -0.506 -0.554 -0.457 -0.077 0.031 0.096 0.578 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.524 -0.507 -0.328 -0.013 -0.017 0.112 0.593 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.509 -0.464 -0.335 -0.211 -0.148 0.027 0.613 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.573 -0.527 -0.416 -0.255 -0.235 -0.011 0.618 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00097 HG00097 -0.856 -0.771 -0.77 -1.051 -1.301 -1.57 -1.919 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.962 -0.954 -0.954 -1.009 -1.216 -1.503 -1.926 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.92 -0.931 -0.878 -0.802 -0.975 -1.451 -1.931 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.61 -0.661 -0.681 -0.684 -0.82 -1.441 -1.932 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00099 HG00099 -0.948 -1.172 -1.249 -1.009 -0.583 0.453 1.524 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.046 -1.092 -1.107 -0.992 -0.868 0.173 1.557 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.342 -1.28 -1.175 -1.012 -1.041 -0.277 1.599 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.349 -1.321 -1.296 -1.166 -1.086 -0.414 1.609 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00100 HG00100 0.698 1.039 1.308 1.57 1.897 2.029 1.051 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.888 1.126 1.349 1.616 1.909 2.036 1.028 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.427 1.52 1.608 1.798 1.982 2.246 0.996 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.755 1.772 1.81 1.879 2.017 2.315 0.988 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00101 HG00101 0.733 0.554 0.578 0.922 1.014 0.582 0.726 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.629 0.536 0.677 0.971 1.029 0.521 0.743 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.639 0.627 0.71 0.791 0.713 0.538 0.765 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.613 0.638 0.651 0.619 0.539 0.47 0.771 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00102 HG00102 -0.355 -0.445 -0.593 -0.587 -0.709 -0.576 -0.42 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.225 -0.312 -0.426 -0.522 -0.581 -0.541 -0.448 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.11 0.09 0.036 -0.128 -0.285 -0.437 -0.486 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.239 0.268 0.283 0.189 -0.135 -0.355 -0.495 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00103 HG00103 -0.397 -0.464 -0.462 -0.253 -0.278 -0.182 0.213 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.487 -0.501 -0.362 -0.275 -0.325 -0.193 0.269 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.822 -0.768 -0.65 -0.636 -0.621 -0.266 0.344 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.024 -0.989 -0.982 -0.906 -0.792 -0.356 0.363 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00105 HG00105 -0.621 -0.608 -0.547 -0.281 -0.224 0.448 0.726 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.94 -0.969 -0.849 -0.568 -0.399 0.417 0.743 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.231 -1.173 -1.06 -1.029 -0.803 0.269 0.765 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.418 -1.394 -1.325 -1.257 -1.109 0.165 0.771 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00106 HG00106 -0.313 -0.509 -0.42 -0.129 -0.321 -0.727 -0.784 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.359 -0.417 -0.338 -0.232 -0.397 -0.814 -0.772 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.378 -0.35 -0.272 -0.258 -0.572 -0.915 -0.754 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.401 -0.375 -0.309 -0.292 -0.639 -0.959 -0.75 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00107 HG00107 0.84 0.832 0.906 0.862 0.768 0.798 0.526 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.517 0.577 0.64 0.729 0.798 0.873 0.517 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.156 0.2 0.276 0.511 0.865 1.008 0.5 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.265 0.303 0.391 0.57 0.919 1.082 0.496 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00108 HG00108 -0.844 -0.73 -0.663 -0.818 -0.747 -0.584 -0.42 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.633 -0.587 -0.59 -0.703 -0.709 -0.368 -0.448 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.297 -0.431 -0.527 -0.541 -0.513 -0.281 -0.486 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.122 -0.277 -0.443 -0.447 -0.433 -0.221 -0.495 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00109 HG00109 -0.312 -0.356 -0.436 -0.803 -0.984 -0.975 -0.42 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.151 -0.192 -0.286 -0.702 -1.005 -0.961 -0.448 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.721 -0.723 -0.74 -0.823 -1.05 -1.21 -0.486 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.188 -1.198 -1.178 -1.08 -1.115 -1.246 -0.495 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00110 HG00110 1.141 1.243 1.228 1.08 0.869 0.5 0.726 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.075 1.132 1.05 0.96 0.741 0.303 0.743 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.511 0.493 0.453 0.502 0.325 -0.017 0.765 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.008 -0.014 -0.022 0.069 0.093 -0.151 0.771 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00111 HG00111 0.465 0.28 0.176 0.071 -0.196 -0.304 -0.42 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.275 0.188 0.136 0.065 -0.157 -0.155 -0.448 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.221 0.224 0.201 0.073 -0.105 0.032 -0.486 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.091 0.115 0.139 0.079 -0.119 0.106 -0.495 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00112 HG00112 -1.397 -1.321 -1.239 -1.298 -1.392 -1.568 -0.933 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.344 -1.316 -1.288 -1.267 -1.184 -1.475 -0.922 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.123 -1.114 -1.13 -1.066 -0.892 -1.212 -0.907 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.932 -0.93 -0.888 -0.871 -0.717 -1.131 -0.903 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00113 HG00113 -0.616 -0.469 -0.33 -0.388 -0.277 -0.432 -0.784 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.617 -0.507 -0.36 -0.389 -0.273 -0.493 -0.772 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.313 -0.202 -0.14 -0.187 -0.028 -0.474 -0.754 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.057 0.016 0.094 0.105 0.187 -0.452 -0.75 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00114 HG00114 0.93 0.9 0.856 0.94 0.926 1.184 0.874 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.74 0.644 0.669 0.827 0.859 1.143 0.894 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.825 0.808 0.775 0.767 0.724 1.061 0.918 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.055 1.005 0.886 0.703 0.673 0.98 0.924 NA NA NA NA NA NA NA NA NA NA NA NA NA
+HG00115 HG00115 0.18 0.277 0.49 0.565 0.853 0.836 0.526 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.12 0.279 0.483 0.632 0.853 0.945 0.517 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.372 0.474 0.678 0.882 0.952 1.028 0.5 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.719 0.73 0.848 0.979 1.103 1.058 0.496 NA NA NA NA NA NA NA NA NA NA NA NA NA
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+NA20802 NA20802 -1.621 -1.474 -1.26 -0.718 -0.237 -0.725 -0.784 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.216 -1.126 -0.952 -0.707 -0.467 -0.735 -0.772 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.695 -0.641 -0.58 -0.523 -0.641 -0.901 -0.754 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.445 -0.42 -0.431 -0.426 -0.621 -0.961 -0.75 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20803 NA20803 0.194 0.03 0.015 0.15 0.465 0.596 -0.271 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.488 0.436 0.354 0.398 0.552 0.554 -0.297 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.688 0.674 0.618 0.628 0.726 0.586 -0.333 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.777 0.834 0.851 0.845 0.896 0.622 -0.342 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20804 NA20804 0.108 -0.117 -0.335 -0.546 -0.912 -0.977 -0.42 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.26 0.151 -0.094 -0.475 -1.067 -0.963 -0.448 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.154 -0.025 -0.244 -0.57 -1.249 -1.161 -0.486 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.023 -0.142 -0.438 -0.718 -1.268 -1.172 -0.495 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20805 NA20805 -0.473 -0.52 -0.561 -0.624 -0.897 -1.461 -1.297 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.44 -0.478 -0.477 -0.55 -0.817 -1.413 -1.246 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.612 -0.587 -0.563 -0.627 -0.798 -1.432 -1.175 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.823 -0.796 -0.734 -0.715 -0.79 -1.456 -1.157 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20806 NA20806 -0.059 0.062 0.2 0.325 0.619 0.898 1.091 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.082 0.107 0.159 0.204 0.45 0.877 1.067 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.559 0.545 0.507 0.372 0.447 0.618 1.034 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.903 0.863 0.752 0.631 0.559 0.553 1.025 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20807 NA20807 0.965 0.952 0.906 0.798 0.766 0.528 0.578 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.722 0.694 0.579 0.59 0.664 0.449 0.593 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.197 0.128 0.079 0.085 0.268 0.319 0.613 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.102 -0.088 -0.037 0.094 0.119 0.209 0.618 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20808 NA20808 -1.411 -1.607 -1.777 -1.985 -2.017 -1.734 -1.554 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.371 -1.492 -1.593 -1.706 -1.843 -1.788 -1.602 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.821 -0.87 -0.946 -1.162 -1.32 -1.516 -1.663 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.563 -0.537 -0.507 -0.661 -0.968 -1.353 -1.678 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20809 NA20809 1.231 1.107 1.025 1.162 1.065 0.657 0.578 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.078 1.022 1.025 1.159 1.162 0.723 0.593 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.75 0.8 0.823 0.852 1.076 0.919 0.613 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.544 0.667 0.778 0.809 0.938 0.937 0.618 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20810 NA20810 -0.148 -0.025 0.09 0.303 0.968 1.633 2.602 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.337 -0.227 -0.139 0.113 0.579 1.443 2.582 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.659 -0.607 -0.571 -0.367 -0.003 1.052 2.554 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.969 -0.98 -0.984 -0.747 -0.343 0.915 2.546 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20811 NA20811 1.813 1.732 1.717 1.519 0.571 0.216 0.726 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.689 1.684 1.631 1.423 0.639 0.055 0.743 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.13 1.093 1.076 0.9 0.39 0.059 0.765 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.638 0.601 0.564 0.437 0.105 0.022 0.771 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20812 NA20812 0.175 0.005 -0.046 0.246 0.455 0.52 0.378 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.142 0.124 0.171 0.353 0.526 0.599 0.366 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.228 0.262 0.318 0.311 0.369 0.865 0.348 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.07 0.123 0.125 0.037 0.048 0.959 0.343 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20813 NA20813 -0.702 -0.694 -0.799 -1.11 -1.242 -1.069 -0.933 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.541 -0.625 -0.762 -1.118 -1.286 -1.048 -0.922 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.264 -0.34 -0.491 -0.757 -1.126 -0.928 -0.907 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.07 -0.122 -0.211 -0.476 -0.924 -0.868 -0.903 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20814 NA20814 0.894 0.788 0.764 0.727 0.778 0.282 -0.271 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.722 0.69 0.708 0.75 0.812 0.341 -0.297 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.469 0.554 0.656 0.892 1.014 0.369 -0.333 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.563 0.633 0.761 0.998 1.195 0.398 -0.342 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20815 NA20815 -0.888 -0.89 -0.548 -0.429 -0.446 -0.577 -0.42 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.754 -0.73 -0.444 -0.373 -0.521 -0.56 -0.448 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.515 -0.508 -0.422 -0.356 -0.487 -0.655 -0.486 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.328 -0.38 -0.436 -0.404 -0.429 -0.641 -0.495 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20818 NA20818 -0.77 -0.888 -1.17 -1.647 -2.104 -1.886 -1.554 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.912 -1.001 -1.213 -1.548 -1.89 -1.83 -1.602 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.089 -1.213 -1.316 -1.421 -1.541 -1.709 -1.663 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.158 -1.233 -1.31 -1.364 -1.508 -1.608 -1.678 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20819 NA20819 1.239 1.297 1.367 1.521 1.684 1.19 -0.459 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.149 1.213 1.326 1.458 1.769 1.277 -0.487 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.241 1.211 1.268 1.307 1.774 1.684 -0.524 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.129 1.106 1.113 1.2 1.555 1.822 -0.533 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20821 NA20821 -0.099 -0.161 -0.336 -0.477 -0.521 -1.071 -0.933 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.14 -0.225 -0.403 -0.53 -0.563 -1.037 -0.922 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.262 -0.33 -0.488 -0.622 -0.685 -1 -0.907 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.437 -0.448 -0.554 -0.575 -0.672 -1.008 -0.903 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20822 NA20822 0.052 -0.008 -0.171 -0.304 -0.666 -0.572 -0.784 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.194 0.094 -0.11 -0.306 -0.677 -0.688 -0.772 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.187 0.129 0.061 -0.166 -0.592 -0.812 -0.754 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.009 -0.076 -0.12 -0.138 -0.486 -0.853 -0.75 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20826 NA20826 -1.042 -1.156 -1.478 -1.744 -1.712 -1.073 -0.42 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.373 -1.441 -1.597 -1.811 -1.777 -1.12 -0.448 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.53 -1.562 -1.574 -1.577 -1.763 -1.105 -0.486 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.468 -1.451 -1.355 -1.342 -1.589 -1.069 -0.495 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20827 NA20827 -0.874 -0.983 -0.924 -0.876 -0.867 -0.897 -0.42 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.927 -0.98 -0.944 -0.94 -0.937 -0.939 -0.448 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.888 -0.879 -0.84 -0.868 -0.893 -1.104 -0.486 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.819 -0.849 -0.852 -0.791 -0.831 -1.094 -0.495 NA NA NA NA NA NA NA NA NA NA NA NA NA
+NA20828 NA20828 1.076 1.183 1.389 1.464 1.166 0.554 -0.271 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.353 1.418 1.608 1.646 1.205 0.615 -0.297 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.087 1.083 1.105 1.127 0.931 0.83 -0.333 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.855 0.788 0.652 0.508 0.469 0.889 -0.342 NA NA NA NA NA NA NA NA NA NA NA NA NA
+SS6004468 SS6004468 -0.501 -0.533 -0.576 -0.551 -0.767 -1.375 -1.919 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.3 -0.348 -0.409 -0.567 -0.695 -1.356 -1.926 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.038 -0.02 -0.128 -0.319 -0.549 -1.125 -1.931 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.064 -0.084 -0.149 -0.324 -0.449 -1.006 -1.932 NA NA NA NA NA NA NA NA NA NA NA NA NA
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EUR.scale b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EUR.scale
index 5b23b228..149efe62 100644
--- a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EUR.scale
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-EUR.scale
@@ -1,11 +1,11 @@
 Param Mean SD
-SCORE_s0.2_lambda0.001 0.0353259297744361 0.0104167476568147
-SCORE_s0.2_lambda0.00127427498570313 0.0319768081804511 0.00882349870917587
-SCORE_s0.2_lambda0.00162377673918872 0.0265951582857143 0.00713706378484267
-SCORE_s0.2_lambda0.00206913808111479 0.0184432975037594 0.00534175883636748
-SCORE_s0.2_lambda0.00263665089873036 0.00909741801548872 0.00350887274668635
-SCORE_s0.2_lambda0.00335981828628378 0.00189137119729323 0.00189083416132041
-SCORE_s0.2_lambda0.00428133239871939 0.000317325566285714 0.000756175959366239
+SCORE_s0.2_lambda0.001 0.0353259312781955 0.0104167473330499
+SCORE_s0.2_lambda0.00127427498570313 0.0319768050225564 0.00882349907973428
+SCORE_s0.2_lambda0.00162377673918872 0.0265951569022556 0.00713706385965947
+SCORE_s0.2_lambda0.00206913808111479 0.0184432958947368 0.00534175883973053
+SCORE_s0.2_lambda0.00263665089873036 0.00909741704827068 0.00350887288955337
+SCORE_s0.2_lambda0.00335981828628378 0.00189137112210526 0.00189083419532402
+SCORE_s0.2_lambda0.00428133239871939 0.000317325463609023 0.000756175966658968
 SCORE_s0.2_lambda0.00545559478116852 0 0
 SCORE_s0.2_lambda0.00695192796177561 0 0
 SCORE_s0.2_lambda0.00885866790410083 0 0
@@ -19,13 +19,13 @@ SCORE_s0.2_lambda0.0483293023857176 0 0
 SCORE_s0.2_lambda0.0615848211066027 0 0
 SCORE_s0.2_lambda0.0784759970351462 0 0
 SCORE_s0.2_lambda0.1 0 0
-SCORE_s0.5_lambda0.001 0.0409773052631579 0.0122718175873792
-SCORE_s0.5_lambda0.00127427498570313 0.0358198673533835 0.0104869340123412
-SCORE_s0.5_lambda0.00162377673918872 0.0278592493533835 0.00845023335968455
-SCORE_s0.5_lambda0.00206913808111479 0.0203283608917293 0.00627571466686891
-SCORE_s0.5_lambda0.00263665089873036 0.0104118302508271 0.00398526999129937
-SCORE_s0.5_lambda0.00335981828628378 0.00170118249729323 0.00194612770676521
-SCORE_s0.5_lambda0.00428133239871939 0.000333858764661654 0.00074543665704196
+SCORE_s0.5_lambda0.001 0.0409773067669173 0.0122718179890691
+SCORE_s0.5_lambda0.00127427498570313 0.0358198662706767 0.010486933945207
+SCORE_s0.5_lambda0.00162377673918872 0.0278592490225564 0.00845023392180029
+SCORE_s0.5_lambda0.00206913808111479 0.0203283610421053 0.00627571452414742
+SCORE_s0.5_lambda0.00263665089873036 0.0104118292981955 0.00398526996771495
+SCORE_s0.5_lambda0.00335981828628378 0.00170118258511278 0.00194612756572374
+SCORE_s0.5_lambda0.00428133239871939 0.000333858595639098 0.000745436635924941
 SCORE_s0.5_lambda0.00545559478116852 0 0
 SCORE_s0.5_lambda0.00695192796177561 0 0
 SCORE_s0.5_lambda0.00885866790410083 0 0
@@ -39,13 +39,13 @@ SCORE_s0.5_lambda0.0483293023857176 0 0
 SCORE_s0.5_lambda0.0615848211066027 0 0
 SCORE_s0.5_lambda0.0784759970351462 0 0
 SCORE_s0.5_lambda0.1 0 0
-SCORE_s0.9_lambda0.001 0.087425229443609 0.0252167822278077
-SCORE_s0.9_lambda0.00127427498570313 0.0713223734736842 0.0211778732441637
-SCORE_s0.9_lambda0.00162377673918872 0.0531035732631579 0.0164810330374201
-SCORE_s0.9_lambda0.00206913808111479 0.0348247716240601 0.0112159631390741
-SCORE_s0.9_lambda0.00263665089873036 0.0162046873052632 0.00603878518502766
-SCORE_s0.9_lambda0.00335981828628378 0.0017991035372782 0.00223419749454427
-SCORE_s0.9_lambda0.00428133239871939 0.000356492357278196 0.000733665132603684
+SCORE_s0.9_lambda0.001 0.0874252360601504 0.0252167800984249
+SCORE_s0.9_lambda0.00127427498570313 0.0713223706165414 0.0211778747626317
+SCORE_s0.9_lambda0.00162377673918872 0.0531035758345865 0.0164810325812361
+SCORE_s0.9_lambda0.00206913808111479 0.0348247746917293 0.0112159627933283
+SCORE_s0.9_lambda0.00263665089873036 0.0162046861699248 0.00603878509792498
+SCORE_s0.9_lambda0.00335981828628378 0.00179910333851128 0.00223419753869375
+SCORE_s0.9_lambda0.00428133239871939 0.000356492239699248 0.000733665114283066
 SCORE_s0.9_lambda0.00545559478116852 0 0
 SCORE_s0.9_lambda0.00695192796177561 0 0
 SCORE_s0.9_lambda0.00885866790410083 0 0
@@ -59,13 +59,13 @@ SCORE_s0.9_lambda0.0483293023857176 0 0
 SCORE_s0.9_lambda0.0615848211066027 0 0
 SCORE_s0.9_lambda0.0784759970351462 0 0
 SCORE_s0.9_lambda0.1 0 0
-SCORE_s1_lambda0.001 0.166977661353383 0.0464115718895917
-SCORE_s1_lambda0.00127427498570313 0.131967683578947 0.0379413702565025
-SCORE_s1_lambda0.00162377673918872 0.0956776872932331 0.0284393020701052
-SCORE_s1_lambda0.00206913808111479 0.0593824333533835 0.0178800481130355
-SCORE_s1_lambda0.00263665089873036 0.0239958889233083 0.00816075971468931
-SCORE_s1_lambda0.00335981828628378 0.00188295991219549 0.002433006649983
-SCORE_s1_lambda0.00428133239871939 0.000362282727067669 0.000731170599029691
+SCORE_s1_lambda0.001 0.166977663007519 0.0464115714382328
+SCORE_s1_lambda0.00127427498570313 0.131967683413534 0.0379413706873199
+SCORE_s1_lambda0.00162377673918872 0.0956776887969925 0.0284393041984523
+SCORE_s1_lambda0.00206913808111479 0.0593824292631579 0.0178800486506425
+SCORE_s1_lambda0.00263665089873036 0.0239958890887218 0.00816075949229265
+SCORE_s1_lambda0.00335981828628378 0.00188295988369925 0.00243300696545791
+SCORE_s1_lambda0.00428133239871939 0.000362282792481203 0.000731170839076273
 SCORE_s1_lambda0.00545559478116852 0 0
 SCORE_s1_lambda0.00695192796177561 0 0
 SCORE_s1_lambda0.00885866790410083 0 0
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-MID.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-MID.profiles
new file mode 100644
index 00000000..fa0ccc8a
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-MID.profiles
@@ -0,0 +1,137 @@
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-MID.scale b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-MID.scale
index 76d48357..bcbcae16 100644
--- a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-MID.scale
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-MID.scale
@@ -1,11 +1,11 @@
 Param Mean SD
-SCORE_s0.2_lambda0.001 0.0382106063970588 0.0115518771983474
-SCORE_s0.2_lambda0.00127427498570313 0.0342932080882353 0.00958556874848896
+SCORE_s0.2_lambda0.001 0.0382106063970588 0.0115518769953597
+SCORE_s0.2_lambda0.00127427498570313 0.0342932066176471 0.0095855692375277
 SCORE_s0.2_lambda0.00162377673918872 0.0282581702941176 0.00770398525389342
-SCORE_s0.2_lambda0.00206913808111479 0.0195771773455882 0.00582613475345635
-SCORE_s0.2_lambda0.00263665089873036 0.00959669694852941 0.00388427719681776
-SCORE_s0.2_lambda0.00335981828628378 0.00197344352352941 0.00225174999126843
-SCORE_s0.2_lambda0.00428133239871939 0.00040575575 0.000972431985838741
+SCORE_s0.2_lambda0.00206913808111479 0.0195771773235294 0.00582613482742698
+SCORE_s0.2_lambda0.00263665089873036 0.00959669656617647 0.00388427739797061
+SCORE_s0.2_lambda0.00335981828628378 0.00197344345073529 0.00225175013642661
+SCORE_s0.2_lambda0.00428133239871939 0.000405755720588235 0.000972431883497586
 SCORE_s0.2_lambda0.00545559478116852 0 0
 SCORE_s0.2_lambda0.00695192796177561 0 0
 SCORE_s0.2_lambda0.00885866790410083 0 0
@@ -19,13 +19,13 @@ SCORE_s0.2_lambda0.0483293023857176 0 0
 SCORE_s0.2_lambda0.0615848211066027 0 0
 SCORE_s0.2_lambda0.0784759970351462 0 0
 SCORE_s0.2_lambda0.1 0 0
-SCORE_s0.5_lambda0.001 0.0438671466911765 0.0132227323463227
-SCORE_s0.5_lambda0.00127427498570313 0.0382836575 0.0111431103394537
-SCORE_s0.5_lambda0.00162377673918872 0.0295052241029412 0.00893381474854608
-SCORE_s0.5_lambda0.00206913808111479 0.0213222072794118 0.0067568114384163
-SCORE_s0.5_lambda0.00263665089873036 0.0108178387323529 0.00437423268122104
-SCORE_s0.5_lambda0.00335981828628378 0.00172401672058824 0.00228800311372634
-SCORE_s0.5_lambda0.00428133239871939 0.000417958861764706 0.000959931771183693
+SCORE_s0.5_lambda0.001 0.0438671481617647 0.013222731321483
+SCORE_s0.5_lambda0.00127427498570313 0.0382836560294118 0.0111431106883099
+SCORE_s0.5_lambda0.00162377673918872 0.0295052233529412 0.00893381508343371
+SCORE_s0.5_lambda0.00206913808111479 0.02132220875 0.00675681328317002
+SCORE_s0.5_lambda0.00263665089873036 0.0108178387264706 0.004374232607128
+SCORE_s0.5_lambda0.00335981828628378 0.00172401704705882 0.00228800336654865
+SCORE_s0.5_lambda0.00428133239871939 0.000417958691176471 0.000959931786487918
 SCORE_s0.5_lambda0.00545559478116852 0 0
 SCORE_s0.5_lambda0.00695192796177561 0 0
 SCORE_s0.5_lambda0.00885866790410083 0 0
@@ -39,13 +39,13 @@ SCORE_s0.5_lambda0.0483293023857176 0 0
 SCORE_s0.5_lambda0.0615848211066027 0 0
 SCORE_s0.5_lambda0.0784759970351462 0 0
 SCORE_s0.5_lambda0.1 0 0
-SCORE_s0.9_lambda0.001 0.0902274095588235 0.0260890015495529
-SCORE_s0.9_lambda0.00127427498570313 0.0733857125 0.0216408438697907
-SCORE_s0.9_lambda0.00162377673918872 0.0546046374558824 0.0167513690228694
-SCORE_s0.9_lambda0.00206913808111479 0.0355398259558824 0.011473109629891
-SCORE_s0.9_lambda0.00263665089873036 0.0164110431691176 0.00632593028196536
-SCORE_s0.9_lambda0.00335981828628378 0.00170728671029412 0.0025766215908183
-SCORE_s0.9_lambda0.00428133239871939 0.000435252717867647 0.000945907091498698
+SCORE_s0.9_lambda0.001 0.0902274132352941 0.0260889988020914
+SCORE_s0.9_lambda0.00127427498570313 0.0733857066176471 0.0216408435774825
+SCORE_s0.9_lambda0.00162377673918872 0.0546046441029412 0.0167513685528458
+SCORE_s0.9_lambda0.00206913808111479 0.0355398281617647 0.0114731100533655
+SCORE_s0.9_lambda0.00263665089873036 0.0164110430220588 0.00632593044862199
+SCORE_s0.9_lambda0.00335981828628378 0.00170728642941176 0.00257662144443707
+SCORE_s0.9_lambda0.00428133239871939 0.000435252536029412 0.00094590694136019
 SCORE_s0.9_lambda0.00545559478116852 0 0
 SCORE_s0.9_lambda0.00695192796177561 0 0
 SCORE_s0.9_lambda0.00885866790410083 0 0
@@ -59,13 +59,13 @@ SCORE_s0.9_lambda0.0483293023857176 0 0
 SCORE_s0.9_lambda0.0615848211066027 0 0
 SCORE_s0.9_lambda0.0784759970351462 0 0
 SCORE_s0.9_lambda0.1 0 0
-SCORE_s1_lambda0.001 0.168180941176471 0.0464136924979073
-SCORE_s1_lambda0.00127427498570313 0.133134041176471 0.0380278104787934
+SCORE_s1_lambda0.001 0.168180941911765 0.0464136913526052
+SCORE_s1_lambda0.00127427498570313 0.133134033823529 0.0380278133107437
 SCORE_s1_lambda0.00162377673918872 0.0966560326470588 0.0287052344902583
-SCORE_s1_lambda0.00206913808111479 0.0601501140441177 0.0179921965770521
-SCORE_s1_lambda0.00263665089873036 0.0240308149191177 0.00831983114338193
-SCORE_s1_lambda0.00335981828628378 0.00173813337794118 0.00277361654666737
-SCORE_s1_lambda0.00428133239871939 0.000439782216911765 0.000942865452147183
+SCORE_s1_lambda0.00206913808111479 0.0601501110294118 0.0179921945600315
+SCORE_s1_lambda0.00263665089873036 0.0240308149198529 0.00831983114124743
+SCORE_s1_lambda0.00335981828628378 0.00173813332647059 0.00277361669661318
+SCORE_s1_lambda0.00428133239871939 0.000439782098529412 0.00094286548883021
 SCORE_s1_lambda0.00545559478116852 0 0
 SCORE_s1_lambda0.00695192796177561 0 0
 SCORE_s1_lambda0.00885866790410083 0 0
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-TRANS.model.rds b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-TRANS.model.rds
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-TRANS.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-TRANS.profiles
new file mode 100644
index 00000000..7e296d79
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-TRANS.profiles
@@ -0,0 +1,3314 @@
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-TRANS.scale b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-TRANS.scale
new file mode 100644
index 00000000..063a96d1
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04-TRANS.scale
@@ -0,0 +1,81 @@
+Param Mean SD
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+SCORE_s1_lambda0.00206913808111479 -1.86335045158235e-05 0.935224501749439
+SCORE_s1_lambda0.00263665089873036 1.1048509531313e-06 0.668413209575786
+SCORE_s1_lambda0.00335981828628378 3.28715257596331e-19 0.225616804157675
+SCORE_s1_lambda0.00428133239871939 -1.00621103221903e-17 0.0729513833431342
+SCORE_s1_lambda0.00545559478116852 0 0
+SCORE_s1_lambda0.00695192796177561 0 0
+SCORE_s1_lambda0.00885866790410083 0 0
+SCORE_s1_lambda0.0112883789168469 0 0
+SCORE_s1_lambda0.0143844988828766 0 0
+SCORE_s1_lambda0.0183298071083244 0 0
+SCORE_s1_lambda0.0233572146909012 0 0
+SCORE_s1_lambda0.0297635144163132 0 0
+SCORE_s1_lambda0.0379269019073225 0 0
+SCORE_s1_lambda0.0483293023857176 0 0
+SCORE_s1_lambda0.0615848211066027 0 0
+SCORE_s1_lambda0.0784759970351462 0 0
+SCORE_s1_lambda0.1 0 0
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04.log b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04.log
index ca8a8c18..71a6537a 100644
--- a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04.log
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04.log
@@ -3,36 +3,36 @@
 # For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
 #################################################################
 # Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
+# Version (tag): v2.2.2-258-gd2f2a91
 ---------------
- Parameter     Value                                                                         
- ref_plink_chr misc/dev/test_data/ref/ref.chr                                                
- ref_keep      misc/dev/test_data/ref/keep_files/EUR.keep                                    
- gwas_pop      EUR                                                                           
- pop_data      misc/dev/test_data/ref/ref.pop.txt                                            
- plink2        plink2                                                                        
- output        misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04
- n_cores       1                                                                             
- test          chr22                                                                         
- sumstats      misc/dev/test_data/output/reference/gwas_sumstat/BODY04/BODY04-cleaned.gz     
- help          FALSE                                                                         
- output_dir    misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/          
+ Parameter     Value                                                                                                 
+ ref_plink_chr misc/dev/test_data/ref/ref.chr                                                                        
+ ref_keep      misc/dev/test_data/ref/keep_files/EUR.keep                                                            
+ ref_pcs       /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles
+ gwas_pop      EUR                                                                                                   
+ pop_data      misc/dev/test_data/ref/ref.pop.txt                                                                    
+ plink2        plink2                                                                                                
+ output        /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pgs_score_files/lassosum/BODY04/ref-BODY04 
+ n_cores       1                                                                                                     
+ test          chr22                                                                                                 
+ sumstats      /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/gwas_sumstat/BODY04/BODY04-cleaned.gz      
+ help          FALSE                                                                                                 
+ output_dir    /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pgs_score_files/lassosum/BODY04/           
 ---------------
-Analysis started at 2024-07-25 17:38:47
+Analysis started at 2025-04-06 17:28:54
 Reading in GWAS.
 sumstats contains 841 variants.
 841 variants remain after selecting chromosomes.
 sumstats contains 841 variants with complete data.
 Merging per chromosome reference data.
-Test started at 2024-07-25 17:38:48
+Test started at 2025-04-06 17:28:56
 Running lassosum pipeline.
 Performing pseudovalidation.
 Pseudovalidated parameters:
 s = 0.2
 lambda = 0.001
 value = 0
-Test run finished at 2024-07-25 17:38:49 
-Test duration was 0.36 secs 
-Calculating polygenic scores in reference.
-Analysis finished at 2024-07-25 17:38:49 
-Analysis duration was 1.74 secs 
+Test run finished at 2025-04-06 17:28:57 
+Test duration was 0.52 secs 
+Analysis finished at 2025-04-06 17:28:57 
+Analysis duration was 2.42 secs 
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04.score.gz b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04.score.gz
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-AFR.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-AFR.profiles
new file mode 100644
index 00000000..bd5e7275
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-AFR.profiles
@@ -0,0 +1,689 @@
+FID IID SCORE_0_0.1 SCORE_0_0.2 SCORE_0_0.3 SCORE_0_0.4 SCORE_0_0.5 SCORE_0_1
+HG01879 HG01879 0.226 -0.553 -0.288 -0.363 -0.225 -0.409
+HG01882 HG01882 1.123 1.612 0.977 0.973 0.864 1.183
+HG01883 HG01883 -0.481 -1.381 -1.468 -2.065 -2.455 -2.355
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-AMR.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-AMR.profiles
new file mode 100644
index 00000000..03a07e1b
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-AMR.profiles
@@ -0,0 +1,413 @@
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-CSA.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-CSA.profiles
new file mode 100644
index 00000000..4615db6e
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-CSA.profiles
@@ -0,0 +1,676 @@
+FID IID SCORE_0_0.1 SCORE_0_0.2 SCORE_0_0.3 SCORE_0_0.4 SCORE_0_0.5 SCORE_0_1
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+HGDP00234 HGDP00234 0.278 0.672 0.621 0.774 0.407 0.627
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+HGDP00241 HGDP00241 -3.524 -2.626 -2.129 -2.373 -2.517 -2.426
+HGDP00243 HGDP00243 1.16 0.968 0.877 1.212 1.276 1.431
+HGDP00244 HGDP00244 -0.665 -0.451 -0.773 -1.14 -1.05 -1.168
+HGDP00247 HGDP00247 -1.873 -1.084 -1.299 -1.114 -1.025 -0.967
+HGDP00248 HGDP00248 0.194 -0.571 0.24 0.313 0.555 0.043
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+HGDP00262 HGDP00262 0.436 0.773 1.06 1.062 1.095 0.646
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+HGDP00277 HGDP00277 0.863 0.609 0.72 1.37 1.391 1.021
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+HGDP00302 HGDP00302 0.638 -0.039 0.216 -0.28 -0.028 -0.3
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+HGDP00311 HGDP00311 0.565 0.549 0.525 0.236 0.701 0.673
+HGDP00313 HGDP00313 0.879 0.538 0.231 0.111 0.201 0.284
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+HGDP00376 HGDP00376 2.138 1.264 1.072 0.658 0.839 0.547
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+HGDP00388 HGDP00388 -1.716 -0.642 -0.095 -0.539 -0.389 -0.021
+HGDP00392 HGDP00392 0.172 -0.762 -0.185 -0.492 -0.442 -0.467
+HGDP00397 HGDP00397 0.52 1.2 1.051 1.044 0.55 0.524
+HGDP00402 HGDP00402 -1.193 -1.114 -0.374 -0.586 -0.728 -0.811
+HGDP00407 HGDP00407 -0.643 -0.271 0.141 0.557 0.545 0.561
+HGDP00412 HGDP00412 -0.8 0.56 0.321 0.381 0.427 -0.071
+HGDP00417 HGDP00417 0.357 -0.361 -0.299 -0.676 -1.027 0.046
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+HGDP00433 HGDP00433 -0.199 0.234 0.502 1.02 0.635 0.687
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+HGDP00445 HGDP00445 -0.087 0.421 0.618 0.184 -0.04 0.151
+LP6005441-DNA_C01 LP6005441-DNA_C01 -2.092 -1.522 -1.672 -2.114 -2.411 -2.595
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+LP6005441-DNA_C07 LP6005441-DNA_C07 1.728 1.927 1.796 1.471 1.851 1.541
+LP6005441-DNA_C10 LP6005441-DNA_C10 0.335 1.346 0.857 0.862 0.922 0.859
+LP6005441-DNA_D01 LP6005441-DNA_D01 0.593 0.848 0.109 -0.036 0.176 0.124
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+NA21128 NA21128 0.486 1.316 1.441 1.45 1.313 0.932
+NA21129 NA21129 1.014 0.856 0.231 0.241 0.472 0.185
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+NA21135 NA21135 -0.351 -0.305 -0.063 -0.189 0.088 0.156
+NA21137 NA21137 0.43 1.29 1.22 0.681 0.801 0.694
+NA21141 NA21141 0.548 0.837 0.091 -0.285 -0.118 -0.266
+NA21142 NA21142 0.829 1.062 0.286 0.124 0.226 0.376
+NA21143 NA21143 -0.412 0.421 0.624 0.632 0.472 1.189
+NA21144 NA21144 0.43 0.725 0.414 -0.352 -0.013 -0.557
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-EAS.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-EAS.profiles
new file mode 100644
index 00000000..10980868
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-EAS.profiles
@@ -0,0 +1,738 @@
+FID IID SCORE_0_0.1 SCORE_0_0.2 SCORE_0_0.3 SCORE_0_0.4 SCORE_0_0.5 SCORE_0_1
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+HG00404 HG00404 0.012 1.776 2.286 2.242 2.041 1.481
+HG00405 HG00405 -0.066 1.184 0.285 0.1 0.172 0.051
+HG00406 HG00406 -0.027 -0.888 -1.193 -1.287 -1.415 -1.155
+HG00407 HG00407 0.135 1.329 0.782 0.875 1.03 0.794
+HG00409 HG00409 -0.951 -1.322 -1.456 -1.091 -0.93 -0.642
+HG00410 HG00410 -0.066 0.783 1.427 1.557 1.534 1.628
+HG00418 HG00418 -0.389 -0.53 1.404 1.002 0.657 0.675
+HG00419 HG00419 1.22 -0.18 -0.357 0.122 0.181 0.338
+HG00420 HG00420 2.357 1.551 2.238 2.578 2.635 2.947
+HG00421 HG00421 -0.576 -0.184 -0.65 -0.92 -1.027 -0.827
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+HG00428 HG00428 0.044 -0.449 -0.515 -0.411 -0.428 -0.277
+HG00429 HG00429 0.451 0.702 1.042 0.889 0.721 0.927
+HG00436 HG00436 -1.493 -1.263 -2.688 -2.819 -2.556 -2.365
+HG00437 HG00437 0.535 -0.551 -0.69 -0.45 -0.768 -0.609
+HG00442 HG00442 -0.027 -0.649 -0.939 -1.099 -1.543 -1.756
+HG00443 HG00443 1.833 1.93 1.619 2.107 2.524 1.859
+HG00445 HG00445 3.345 1.815 1.336 1.304 1.601 1.174
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+HG00448 HG00448 0.658 -1.101 -0.373 -0.479 -0.307 -0.351
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+HG00451 HG00451 2.331 2.616 1.706 1.507 1.556 1.359
+HG00452 HG00452 0.936 0.14 -0.438 -0.433 -0.402 -0.381
+HG00453 HG00453 3.293 2.254 1.542 1.88 1.812 2.011
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+HG00464 HG00464 2.24 2.058 2.975 3.089 2.723 2.567
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+HG02525 HG02525 -1.377 -0.261 -0.241 -0.734 -0.949 -0.711
+HGDP00711 HGDP00711 0.102 0.233 0.497 0.696 0.759 0.699
+HGDP00712 HGDP00712 0.089 -0.184 -0.263 -0.134 -0.238 -0.355
+HGDP00714 HGDP00714 -0.815 0.08 -0.899 -1.19 -1.072 -1.222
+HGDP00715 HGDP00715 -1.028 -1.625 -2.306 -2.421 -2.409 -2.469
+HGDP00716 HGDP00716 2.214 1.61 0.062 -0.452 -0.397 -0.233
+HGDP00719 HGDP00719 -0.027 1.418 1.989 2.37 2.902 2.882
+HGDP00721 HGDP00721 0.735 0.698 0.189 0.052 0.084 -0.064
+HGDP00747 HGDP00747 -0.744 -0.892 -0.227 -0.626 -0.656 -0.629
+HGDP00748 HGDP00748 0.503 0.169 0.494 0.088 0.034 -0.485
+HGDP00750 HGDP00750 -0.518 0.148 0.618 0.945 0.64 0.746
+HGDP00751 HGDP00751 -1.732 0.297 0.132 -0.184 -0.202 0.425
+HGDP00752 HGDP00752 0.936 0.229 0.587 0.771 1.092 0.914
+HGDP00753 HGDP00753 0.057 0.988 1.528 1.482 1.484 1.479
+HGDP00754 HGDP00754 0.774 -0.167 -0.492 0.332 0.343 -0.116
+HGDP00755 HGDP00755 0.264 -0.453 -0.105 0.185 0.259 0.907
+HGDP00756 HGDP00756 -0.143 -0.905 -0.594 -0.918 -0.851 -0.055
+HGDP00757 HGDP00757 0.038 -0.551 -1.029 -0.882 -0.516 -0.546
+HGDP00758 HGDP00758 0.677 1.354 1.226 0.691 0.562 1.161
+HGDP00759 HGDP00759 1.536 1.082 1.72 1.782 1.817 1.961
+HGDP00760 HGDP00760 1.866 3.192 3.06 1.943 1.399 1.418
+HGDP00761 HGDP00761 -1.073 -0.218 1.633 1.42 1.499 1.083
+HGDP00762 HGDP00762 2.822 1.423 1.362 1.596 1.28 1.611
+HGDP00763 HGDP00763 -0.841 -1.531 -0.097 0.103 -0.123 -0.288
+HGDP00764 HGDP00764 1.678 0.276 0.039 -0.134 -0.528 -0.379
+HGDP00765 HGDP00765 0.051 -0.551 -0.616 -0.288 -0.107 0.501
+HGDP00766 HGDP00766 0.148 0.199 0.666 0.407 0.312 0.153
+HGDP00767 HGDP00767 -0.072 -0.47 0.268 -0.619 -0.723 -0.777
+HGDP00768 HGDP00768 -0.737 -0.619 -0.568 -0.368 -0.644 -0.638
+HGDP00769 HGDP00769 -1.028 -0.542 0.248 -0.006 0.05 -0.029
+HGDP00771 HGDP00771 -0.498 -1.118 -1.317 -1.292 -1.377 -0.631
+HGDP00772 HGDP00772 2.298 2.041 1.644 0.998 1.042 0.716
+HGDP00774 HGDP00774 -1.06 -0.291 -0.577 -0.476 0.045 0.675
+HGDP00776 HGDP00776 -1.273 -0.922 -0.122 -0.064 0.122 -0.008
+HGDP00777 HGDP00777 0.018 0.08 -0.585 0.156 -0.078 0.223
+HGDP00779 HGDP00779 -0.589 -0.061 0.124 0.298 0.029 -0.04
+HGDP00780 HGDP00780 0.148 -0.304 0.214 0.65 0.963 0.633
+HGDP00781 HGDP00781 0.102 -1.698 -0.851 -1.593 -1.821 -1.515
+HGDP00782 HGDP00782 2.57 1.005 0.169 0.592 0.129 -0.125
+HGDP00784 HGDP00784 0.135 1.478 0.378 0.645 1.028 0.809
+HGDP00786 HGDP00786 -1.06 -1.075 -1.464 -1.489 -1.17 -1.483
+HGDP00790 HGDP00790 0.399 -0.79 -0.015 -0.373 -0.463 -0.147
+HGDP00791 HGDP00791 0.509 0.327 0.768 0.329 0.324 0.06
+HGDP00811 HGDP00811 -0.925 0.182 0.706 0.6 0.288 0.168
+HGDP00812 HGDP00812 0.419 -0.368 -0.187 0.288 -0.002 -0.216
+HGDP00813 HGDP00813 -0.944 -0.615 -0.249 0.539 0.654 1.07
+HGDP00814 HGDP00814 -0.305 -0.27 -0.156 0.686 0.909 0.616
+HGDP00815 HGDP00815 0.567 1.576 0.548 0.626 0.359 0.883
+HGDP00817 HGDP00817 1.879 1.482 1.729 1.832 2.233 1.728
+HGDP00818 HGDP00818 1.091 0.672 -0.221 0.021 0.086 0.371
+HGDP00819 HGDP00819 -1.222 -1.305 -1.538 -1.048 -1.265 -1.198
+HGDP00820 HGDP00820 0.296 0.753 0.613 0.882 1.149 1.361
+HGDP00821 HGDP00821 0.903 0.025 -0.229 -0.387 -0.459 -0.605
+HGDP00822 HGDP00822 -0.059 0.608 0.389 -0.443 -0.613 -0.516
+HGDP00828 HGDP00828 -0.182 0.191 -0.854 -1.19 -0.953 -0.918
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+HGDP00946 HGDP00946 0.832 0.664 0.161 0.607 0.711 0.649
+HGDP00947 HGDP00947 0.051 0.442 0.073 0.288 0.133 0.355
+HGDP00949 HGDP00949 -0.905 -0.747 0.726 1.574 1.51 1.654
+HGDP00950 HGDP00950 -0.305 0.263 0.494 0.665 0.626 0.903
+HGDP00952 HGDP00952 -1.441 -1.135 -0.193 -0.459 -0.485 -0.694
+HGDP00953 HGDP00953 0.135 0.383 0.652 0.831 1.17 1.092
+HGDP00954 HGDP00954 0.399 -0.836 -0.077 0.341 0.547 0.533
+HGDP00955 HGDP00955 1.943 0.621 1.017 0.822 0.759 0.84
+HGDP00957 HGDP00957 -0.124 -0.231 0.098 0.481 0.298 0.549
+HGDP00958 HGDP00958 -0.776 -0.223 -0.73 -0.783 -0.549 -0.742
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+HGDP00964 HGDP00964 -0.072 -0.858 -1.094 -0.534 -0.444 -0.581
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+HGDP00967 HGDP00967 -1.403 -0.802 -0.258 0.549 0.716 1.2
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+HGDP00969 HGDP00969 0.509 0.05 1.441 1.002 1.194 1.322
+HGDP00971 HGDP00971 -0.408 0.438 1.07 0.272 -0.028 -0.09
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+HGDP00974 HGDP00974 -0.705 -0.201 -1.199 -1.965 -1.845 -1.385
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+HGDP00976 HGDP00976 -0.602 -1.101 -1.959 -1.955 -1.879 -1.515
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+HGDP01023 HGDP01023 0.503 1.985 3.156 3.364 3.337 2.717
+HGDP01024 HGDP01024 1.827 1.708 0.228 0.272 0.54 0.688
+HGDP01096 HGDP01096 1.258 1.192 1.186 1.774 1.758 1.915
+HGDP01099 HGDP01099 0.038 1.256 0.469 -0.143 -0.161 0.21
+HGDP01100 HGDP01100 0.451 0.348 0.05 0.216 0.179 -0.279
+HGDP01101 HGDP01101 4.649 3.55 2.794 2.592 2.421 2.172
+HGDP01102 HGDP01102 -0.634 -0.282 -0.342 -0.081 0.324 0.407
+HGDP01103 HGDP01103 -0.841 -0.201 -0.06 -0.512 -0.033 0.338
+HGDP01104 HGDP01104 0.716 0.34 0.358 -0.373 -0.112 -0.416
+HGDP01181 HGDP01181 -0.382 -0.363 0.183 -0.725 -1.103 -1.144
+HGDP01182 HGDP01182 -1.713 -0.956 -0.399 -0.821 -0.692 -0.694
+HGDP01183 HGDP01183 0.399 1.048 1.268 1.048 1.156 1.505
+HGDP01184 HGDP01184 0.755 1.943 1.192 0.761 0.619 0.82
+HGDP01185 HGDP01185 0.057 0.029 0.053 0.062 0.317 0.67
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+HGDP01187 HGDP01187 -0.925 -0.248 -0.902 -1.026 -0.485 -0.805
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+HGDP01194 HGDP01194 0.806 2.267 2.037 1.258 1.113 1.272
+HGDP01195 HGDP01195 -1.202 -2.576 -1.346 -1.118 -1.072 -0.97
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+HGDP01197 HGDP01197 -0.434 -0.099 0.401 0.129 -0.035 0.019
+HGDP01204 HGDP01204 0.987 -0.133 -0.523 -0.387 -0.54 -0.277
+HGDP01205 HGDP01205 0.148 0.259 0.333 0.723 0.474 0.746
+HGDP01206 HGDP01206 -1.842 -0.802 -0.125 0.44 -0.102 0.525
+HGDP01207 HGDP01207 2.331 0.758 0.974 1.012 1.177 1.146
+HGDP01209 HGDP01209 0.47 -0.517 -0.085 0.513 0.036 0.368
+HGDP01212 HGDP01212 -0.305 0.31 -0.325 -0.594 -0.761 -0.433
+HGDP01213 HGDP01213 0.748 0.66 0.271 0.132 0.573 0.029
+HGDP01214 HGDP01214 -0.841 0.148 -0.218 -0.136 -0.211 -0.568
+HGDP01216 HGDP01216 0.477 0.877 0.901 0.238 0.367 0.644
+HGDP01217 HGDP01217 0.49 1.487 1.675 1.355 1.503 1.007
+HGDP01218 HGDP01218 0.864 1.866 0.647 1.193 1.365 1.676
+HGDP01220 HGDP01220 0.367 1.278 0.655 0.436 0.526 1.096
+HGDP01221 HGDP01221 -0.033 -0.713 -0.905 -1.019 -0.899 -0.924
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+HGDP01227 HGDP01227 -1.493 -1.148 -1.168 -1.383 -1.115 -0.362
+HGDP01229 HGDP01229 -0.234 1.15 0.565 0.679 0.787 0.305
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+HGDP01231 HGDP01231 -0.86 -0.973 -0.45 -0.561 -0.559 -0.151
+HGDP01232 HGDP01232 0.148 0.485 1.48 1.466 1.199 1.32
+HGDP01233 HGDP01233 0.193 0.106 0.915 0.395 0.428 0.31
+HGDP01236 HGDP01236 -1.461 -1.689 -0.987 -0.744 -1.189 -1.155
+HGDP01237 HGDP01237 2.034 1.968 1.876 1.169 1.056 1.32
+HGDP01238 HGDP01238 0.729 -0.402 -0.142 0.534 0.447 0.477
+HGDP01239 HGDP01239 -0.091 0.02 0.019 0.083 0.031 0.646
+HGDP01241 HGDP01241 1.711 0.31 0.876 0.819 1.035 0.499
+HGDP01243 HGDP01243 -0.569 -0.257 0.237 0.612 0.345 0.038
+HGDP01244 HGDP01244 -0.175 -0.24 0.324 0.293 -0.031 -0.331
+HGDP01245 HGDP01245 -1.622 -0.879 -0.673 -0.522 -0.837 -0.822
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+HGDP01251 HGDP01251 -0.395 -0.022 0.528 0.322 0.328 0.657
+HGDP01287 HGDP01287 -0.401 -1.131 -0.758 -0.778 -0.851 -0.77
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+HGDP01293 HGDP01293 -0.35 0.276 0.457 0.428 0.571 0.292
+HGDP01294 HGDP01294 0.664 1.772 1.427 1.574 1.625 1.339
+HGDP01295 HGDP01295 0.399 0.668 0.347 -0.112 -0.024 0.292
+HGDP01296 HGDP01296 -0.414 0.464 0.559 0.421 0.573 1.111
+HGDP01299 HGDP01299 -0.763 -0.734 -1.06 -0.638 -0.223 -0.092
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+HGDP01304 HGDP01304 0.574 0.127 0.511 1.179 0.778 0.529
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+HGDP01310 HGDP01310 -1.17 -0.325 -0.331 0.395 0.312 0.216
+HGDP01311 HGDP01311 1.459 1.261 1.198 1.388 1.154 1.533
+HGDP01313 HGDP01313 0.994 0.643 0.562 0.807 0.816 0.998
+HGDP01317 HGDP01317 0.554 1.273 1.446 1.323 1.537 0.838
+HGDP01318 HGDP01318 0.619 0.135 -0.184 0.375 0.528 1.19
+HGDP01319 HGDP01319 0.044 0.587 0.683 0.822 0.935 0.59
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+HGDP01322 HGDP01322 0.193 0.442 -0.258 -0.233 -0.463 -0.514
+HGDP01326 HGDP01326 0.89 1.052 1.37 1.837 1.896 1.885
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+HGDP01330 HGDP01330 0.638 0.344 1.085 0.197 0.114 0.171
+HGDP01331 HGDP01331 -0.234 -1.041 -0.082 -0.409 -0.123 0.601
+HGDP01332 HGDP01332 0.012 0.779 -0.164 -0.11 -0.002 0.56
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+HGDP01339 HGDP01339 0.722 0.221 0.941 0.879 0.754 0.768
+HGDP01340 HGDP01340 0.025 0.05 0.542 0.276 0.355 0.434
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-EUR.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-EUR.profiles
new file mode 100644
index 00000000..506b61ad
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-EUR.profiles
@@ -0,0 +1,666 @@
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-MID.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-MID.profiles
new file mode 100644
index 00000000..696dcae9
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-MID.profiles
@@ -0,0 +1,137 @@
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-TRANS.model.rds b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-TRANS.model.rds
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diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-TRANS.profiles b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-TRANS.profiles
new file mode 100644
index 00000000..1705ff64
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-TRANS.profiles
@@ -0,0 +1,3314 @@
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+NA20821 NA20821 -0.638655972242646 -0.0328051600988691 0.0447272555761443 0.375646087211235 0.554974781868796 0.507928038035702
+NA20822 NA20822 -0.774363846548852 -0.101663397645869 0.426567095646481 0.503751964207121 0.208638623397433 0.0916268473331624
+NA20826 NA20826 -1.08935964183379 -1.74004988682488 -2.03972961286266 -1.15827699963186 -1.34635658892166 -0.933729293468275
+NA20827 NA20827 -0.808291290189862 -0.113099816862062 -0.269421817179438 -1.160762015898 -1.0364637072476 -0.57105749244004
+NA20828 NA20828 0.797353364479481 1.72228836466971 2.35470230503919 1.5772879241939 1.55391766163079 1.43313661321539
+NA20845 NA20845 0.845098150873432 1.29911774441571 1.6935633670678 1.66388148118122 1.62072759779439 1.72283579936905
+NA20846 NA20846 -1.82954300180881 -0.0227016930686818 -0.167809702712216 -0.0849060536533593 -0.0424712661557158 -0.484955341121251
+NA20847 NA20847 0.633200244710417 1.34577717864792 1.51741455242522 1.32324464803314 1.28624389027828 1.43134206572297
+NA20849 NA20849 -0.902983035058407 0.0520451453497846 0.546996460878511 1.33507821682879 1.42624172916255 1.23489003675333
+NA20850 NA20850 0.631478744520186 1.04304945914333 1.12823629120264 0.955941655813228 0.913554920184284 0.593111868793539
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+NA20867 NA20867 0.809452589827073 1.3594131829538 1.20425119716292 1.46403675508037 1.41758002818971 1.34925508790711
+NA20868 NA20868 1.88143733514194 3.02366450897232 2.35583318940694 2.2606610454019 2.19300037336709 1.53683322931769
+NA20869 NA20869 -1.25937238992461 -0.25820635898562 0.228250913774625 -0.469443746863038 -0.450329711857817 -0.297506143031066
+NA20870 NA20870 0.582395334013543 0.577512871795934 1.0467878701746 0.788117943615083 1.00046845448108 1.02216341052839
+NA20872 NA20872 1.950405336323 1.99093850788773 1.58116671452197 1.41134954134566 1.61228443250299 1.74311194256787
+NA20874 NA20874 0.360355353979628 -0.903472167282538 -0.499521946576795 -0.736194040182071 -0.680473784065088 -0.585261328053786
+NA20875 NA20875 0.0402677661425172 0.822808682716626 1.08220499788064 0.602763126322088 0.883455996804887 0.89898820362061
+NA20876 NA20876 -1.36847194860778 0.0797605088652774 -0.129891946555075 -0.36197849643124 -0.48375403673944 -0.818453711195472
+NA20877 NA20877 0.600974935452119 0.287343516820605 -0.466527558694108 -0.78843007973005 -0.694861364538433 -0.286026026281766
+NA20878 NA20878 0.355061100112583 1.66004417365886 1.68114524679171 1.06998440833852 1.0076400580484 0.881584615250154
+NA20881 NA20881 -1.09117840363831 -0.44447365280843 -0.00116185981819946 -0.319228256930842 -0.562630690199832 -0.468058166914204
+NA20882 NA20882 -1.58656294034259 -2.03861299094267 -1.92088312394644 -1.84079308788628 -2.03927310801235 -1.66238346015805
+NA20884 NA20884 0.643067944271779 -0.417484332364113 -0.295326385062725 -0.871224156235915 -0.562154405196141 -0.940517491178376
+NA20885 NA20885 -0.364233161359354 -0.342315539239697 0.251293424320153 0.341609565564765 0.205986979806567 0.110748654168594
+NA20886 NA20886 -0.338461219840464 -0.0905302532152066 0.0694667421086858 0.916412171504739 1.08859639929527 1.50301282258356
+NA20887 NA20887 1.3172148874861 0.456836095972095 0.751337145440095 1.00384573627877 0.946266330414251 0.906367661449649
+NA20888 NA20888 -0.473791302645413 -0.631622050935499 -1.11800266237061 -1.53040562020415 -1.40952601904798 -1.92549084833696
+NA20889 NA20889 0.09323569871982 0.936055683710901 0.724399868228626 0.595733805202644 0.911556666346029 1.28403972031309
+NA20890 NA20890 -1.69954860606479 -2.2392768726767 -2.01342630136653 -1.62016517465755 -1.56941040356691 -1.62972881896709
+NA20892 NA20892 -0.241936721592298 -0.0750014301673916 -0.0904159997573888 0.285414607042043 0.38431463811707 0.438577950458691
+NA20894 NA20894 -0.697155612057922 -1.0185885945379 0.115620604182934 0.112638576021219 0.296751024116023 0.0494968154771063
+NA20895 NA20895 -1.33793180086273 -0.519023867621018 -0.787501065393557 -1.02536208821071 -1.32669558637195 -1.28388850896466
+NA20896 NA20896 0.440324873900052 0.843659611571256 0.65095049735134 0.621467714761482 0.620619601258303 0.467589103474193
+NA20897 NA20897 0.424440512749991 0.493145171840638 0.553292549636054 0.420521765968393 0.530142384856573 0.713502298160586
+NA20899 NA20899 -0.993798693323859 1.09103539270712 0.843302068372931 0.620508608010262 0.653972854537427 0.388198206812041
+NA20901 NA20901 -0.197002093137787 -0.0494256009613981 -0.857500068294387 -1.0915230708609 -0.715042739778676 -0.699782039254174
+NA20902 NA20902 0.130246325218206 0.865272427771065 0.739878029039909 0.76281407177646 0.929411985839312 0.757302937151809
+NA20903 NA20903 1.88463712247718 2.22003960794555 2.65353631004096 2.53168289001761 2.53386933708242 2.43349598613053
+NA20904 NA20904 0.84724607438643 1.68457651944368 1.13075091352969 1.29396012745313 1.3743096993775 1.13260743761727
+NA20905 NA20905 -0.158529336356194 -0.00317388383032997 0.449859497428969 0.33841808601366 0.667432760354067 1.03895658688621
+NA20906 NA20906 -0.688186460701799 -0.241572177838479 -0.0281790940150231 0.289857459815727 0.0658188159299734 0.21089242008461
+NA20908 NA20908 -0.345487390707233 1.15148159226754 0.54989062824638 -0.0917978948244864 -0.361022609992076 -0.208173099658546
+NA20910 NA20910 1.23499470524852 0.37356014285291 0.538261790083202 0.720534823983342 0.653417718664388 0.490881446644888
+NA20911 NA20911 1.33667027880804 1.99220358505442 1.77882376294487 1.30015555294201 1.29909080628747 1.25137497009589
+NA21086 NA21086 -1.26227834892988 0.377574538271857 0.279498206010127 0.497986898399412 0.502922076379319 0.289362451654025
+NA21087 NA21087 0.899556838410332 0.896955045099775 1.08698962607097 1.24827231488485 1.46852111443631 1.77324979705816
+NA21088 NA21088 0.881246739132061 0.693066965661147 0.267707718706624 -0.133754826527985 0.0365666915536869 -0.0917940612029429
+NA21089 NA21089 0.270990530855885 0.227345860993911 0.136626067986847 0.447875299769519 0.480368560748144 1.2002961148859
+NA21090 NA21090 0.396724548222817 -1.35277660862995 -1.70612137665111 -2.25085588888507 -1.6813035412648 -1.67086040727019
+NA21091 NA21091 0.20526447816802 0.490475549096464 1.24233042110734 1.1573896301941 1.25866237879247 1.71335881072706
+NA21092 NA21092 -0.0369311461498188 -0.617055314913923 -0.045557403470879 -0.0263833085074928 -0.0871051983810855 -0.0212133664492725
+NA21093 NA21093 -0.302171975041253 -0.640713586801608 0.171222584956281 -0.0758867643887339 0.0146671619283573 0.106878863252458
+NA21094 NA21094 0.305123640526576 0.0340871303740579 0.345544616444939 0.445367486983248 0.824972615389469 1.04068410528839
+NA21095 NA21095 0.959957009148236 0.100044368130014 0.00846804788346242 0.380817458634586 0.519565848004662 0.425954932569761
+NA21097 NA21097 -1.41402679887313 -1.73259102126384 -1.79821492796212 -2.1392290852358 -1.70056220160924 -1.33177828449486
+NA21098 NA21098 0.288382079532442 0.559106402280279 0.672423978072253 -0.101351973622094 -0.220549767094086 -0.168922836862617
+NA21099 NA21099 -0.254329490120757 0.649916188251461 0.27761087082479 -0.628330665832341 -0.499017485569068 -0.746642940859698
+NA21100 NA21100 0.246290084177588 -0.639223972241332 -1.41734419153413 -1.98421544522316 -1.72748442936284 -1.82431986902642
+NA21101 NA21101 1.78107270252465 1.2810638926544 1.43148053471783 1.2845459592329 1.24921835985702 1.72684146875989
+NA21102 NA21102 -0.183069174762895 0.310455566572866 0.268954173108179 0.761084531214661 0.716964629493744 0.115018148620177
+NA21103 NA21103 0.571649387608038 0.29637001745314 1.23936874434604 0.819981863082101 0.792328179568019 0.760115337329925
+NA21104 NA21104 -0.409373345470362 -0.701061681678606 -0.633420604194879 -0.608601221107135 -0.935922080046228 -0.936343183495203
+NA21105 NA21105 -0.388522254876129 -0.739997089329195 -0.0759691346356824 -0.39616411700267 -0.242634163545343 -0.431321846534544
+NA21106 NA21106 -1.98834444980679 -1.13235398179233 -1.28602874429669 -0.865677305282373 -1.00800142172826 -1.20175467516537
+NA21107 NA21107 0.948721124973161 0.819983521783857 0.507266834187231 0.297505781845941 0.531693280517524 0.922061577510746
+NA21108 NA21108 0.198300632596586 0.182640760822233 0.643584289191189 0.134772289296688 -0.139705366731075 0.0466027124400778
+NA21110 NA21110 0.717212594863377 1.41389062820361 1.59901177398746 1.99781857935063 2.0816781649788 1.85023109747206
+NA21111 NA21111 0.472895758471862 0.114878222528721 0.460075641578122 0.554978412983379 0.674639204733084 0.82138264262588
+NA21112 NA21112 2.30551757486355 1.76749018080871 1.28588783463433 1.432676843998 1.39724469031698 0.973314271156248
+NA21113 NA21113 -0.181959084880367 1.26546175152563 1.07946081265795 0.930479781839195 1.02561199005 1.22931240416978
+NA21114 NA21114 0.0171012365256082 -0.756323480016118 -1.55933713119158 -1.44569060867961 -1.63355336949648 -1.93229838501225
+NA21115 NA21115 0.75836468541544 1.49810273839399 0.977000743701931 1.03652019669952 1.14743412828164 1.19949647306242
+NA21116 NA21116 1.34220202772717 0.71975601261075 0.183118686494208 -0.530244331269072 -0.317468154500805 -0.371112075956054
+NA21117 NA21117 -0.989881736376083 0.5243118143335 0.256770710281386 0.910557797818834 0.818852083052852 0.591120603909339
+NA21118 NA21118 1.02174492059241 1.01944062458818 1.35294496008654 1.41298903339808 1.23426331489012 1.09634129468385
+NA21119 NA21119 0.0757264325878911 -0.122323114250547 -0.406369064752795 0.550068858394553 0.513544280281665 1.14788995369222
+NA21120 NA21120 -0.582596828681977 -0.118910746782901 0.197573711093816 0.411197835914152 -0.00969683002625012 0.0612818767729606
+NA21122 NA21122 0.210947377727829 0.087191968928751 -0.445589111441963 -0.395089531047916 0.0409949211055291 0.927304045645879
+NA21123 NA21123 0.333803634990359 0.553070610134322 0.112825154990522 -0.143681597811362 -0.43953740655046 -0.289072114682774
+NA21124 NA21124 -0.406285438113448 0.31179856318434 0.0991087660500818 0.143255534761427 -0.603319183788636 -0.729250037203203
+NA21125 NA21125 -0.456913519506899 0.603773280242222 0.438096144411293 1.01240855192262 1.17517237761581 1.3002551678701
+NA21126 NA21126 -0.140980797067984 1.36493946271371 1.26857744749334 1.1231022903628 0.949532914199212 1.4706764401748
+NA21127 NA21127 0.788393699116053 0.337834454716295 -0.216990374510555 -0.0660454575247462 -0.176214131071111 -0.177730574285721
+NA21128 NA21128 0.192640020216201 1.41175996754389 1.42695045309732 1.53701512246098 1.40635834808617 1.11541418963752
+NA21129 NA21129 0.99029923995132 0.912350173337993 0.338616412480247 0.248503471917735 0.505047202596827 0.191846109700633
+NA21130 NA21130 1.04627207977696 1.29582610104424 1.36280701871482 1.34288278154614 1.05000200327407 0.715079671002031
+NA21133 NA21133 -0.652522248344604 -0.967595041911917 -1.06150563040195 -0.629434646525643 -0.865106684287979 -0.967759415295957
+NA21135 NA21135 -0.758866381493545 -0.00262646328988201 0.220407513703099 0.00292018505557732 0.287612043171384 0.252720494349651
+NA21137 NA21137 1.15860765968907 1.67077086768664 1.75110571223749 1.25790858852446 1.40737112796369 1.33124949217742
+NA21141 NA21141 -0.120421498806447 1.2712739944929 0.469407107583001 0.163848997534279 0.29446435157382 0.0863978346956311
+NA21142 NA21142 1.11781501631735 1.91353221588236 1.39358685292429 1.12484425346695 1.21594470901163 1.20230551714662
+NA21143 NA21143 -0.307898435681026 0.489196049568793 0.742657911240699 0.651487717793065 0.545951620934231 1.18786734784906
+NA21144 NA21144 0.677075024375586 0.871220297140913 0.684529641706738 -0.10101946238268 0.218287041871246 -0.263711681433357
+SS6004468 SS6004468 -0.63757600979785 0.0892264190277978 0.0108411147551283 0.265894361895966 0.0629540669429945 0.188748458089961
+SS6004470 SS6004470 1.56041633773151 0.983483841382243 1.65615517218756 1.85957242527377 1.62783508296938 1.36632861062254
+SS6004475 SS6004475 1.21165964463484 1.04543258943899 0.806362938308136 0.295483159724946 -0.243378547552975 -0.0605442671659699
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-TRANS.scale b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-TRANS.scale
new file mode 100644
index 00000000..18dbbc5d
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04-TRANS.scale
@@ -0,0 +1,7 @@
+Param Mean SD
+SCORE_0_0.1 -0.000919629841923247 0.967031520014574
+SCORE_0_0.2 -0.000677154331125975 0.988021187850138
+SCORE_0_0.3 -0.000352644734642246 0.994621248004377
+SCORE_0_0.4 -0.000244187675542382 0.996280190462029
+SCORE_0_0.5 -0.000385358132314938 0.99700390538981
+SCORE_0_1 -0.000272064017479299 0.997659066805385
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04.log b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04.log
index f3008544..55509f54 100644
--- a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04.log
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04.log
@@ -3,36 +3,36 @@
 # For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
 #################################################################
 # Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
+# Version (tag): v2.2.2-258-gd2f2a91
 ---------------
- Parameter     Value                                                                        
- ref_plink_chr misc/dev/test_data/ref/ref.chr                                               
- ref_keep      misc/dev/test_data/ref/keep_files/EUR.keep                                   
- pop_data      misc/dev/test_data/ref/ref.pop.txt                                           
- plink2        plink2                                                                       
- output        misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04
- memory        5000                                                                         
- sumstats      misc/dev/test_data/output/reference/gwas_sumstat/BODY04/BODY04-cleaned.gz    
- pTs           5e-8,1e-6,1e-4,1e-2,0.1,0.2,0.3,0.4,0.5,1                                    
- nested        TRUE                                                                         
- test          chr22                                                                        
- top_hla       TRUE                                                                         
- help          FALSE                                                                        
- output_dir    misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/          
+ Parameter     Value                                                                                                 
+ ref_plink_chr misc/dev/test_data/ref/ref.chr                                                                        
+ ref_keep      misc/dev/test_data/ref/keep_files/EUR.keep                                                            
+ ref_pcs       /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles
+ pop_data      misc/dev/test_data/ref/ref.pop.txt                                                                    
+ plink2        plink2                                                                                                
+ output        /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pgs_score_files/ptclump/BODY04/ref-BODY04  
+ memory        5000                                                                                                  
+ sumstats      /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/gwas_sumstat/BODY04/BODY04-cleaned.gz      
+ pTs           5e-8,1e-6,1e-4,1e-2,0.1,0.2,0.3,0.4,0.5,1                                                             
+ nested        TRUE                                                                                                  
+ test          chr22                                                                                                 
+ top_hla       TRUE                                                                                                  
+ help          FALSE                                                                                                 
+ output_dir    /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pgs_score_files/ptclump/BODY04/            
 ---------------
-Analysis started at 2024-07-25 17:39:11
+Analysis started at 2025-04-06 17:34:10
 Reading in GWAS.
 sumstats contains 841 variants.
 841 variants remain after selecting chromosomes.
 sumstats contains 841 variants with complete data.
 Extracted top variant in HLA/MHC region.
 841 variants remain.
-Test started at 2024-07-25 17:39:12
+Test started at 2025-04-06 17:34:10
 Performing LD-based clumping.
 148 variants remain after clumping.
 Creating score file.
-Test run finished at 2024-07-25 17:39:12 
-Test duration was 0.1 secs 
-Calculating polygenic scores in reference.
-Analysis finished at 2024-07-25 17:39:12 
-Analysis duration was 0.46 secs 
+Test run finished at 2025-04-06 17:34:10 
+Test duration was 0.14 secs 
+Analysis finished at 2025-04-06 17:34:10 
+Analysis duration was 0.48 secs 
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04.score.gz b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04.score.gz
index 6df3c14574dc98dbc28380ac59cc92819118d2dd..0a5dc2f32ebf0398469762a91939fced0f6189ce 100644
GIT binary patch
delta 17
YcmeyV{ZpGmzMF%CY3--<jT|3E0X_8w(f|Me

delta 17
YcmeyV{ZpGmzMF$%L;a$(jT|3E0YE+n0RR91

diff --git a/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/indiv_report-12_MID.12_MID-report.done b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ref_pgs.done
similarity index 100%
rename from pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/indiv_report-12_MID.12_MID-report.done
rename to pipeline/misc/dev/test_data/output/reference/pgs_score_files/ref_pgs.done
diff --git a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ref_scoring_2025-04-06_17-34-11.log b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ref_scoring_2025-04-06_17-34-11.log
new file mode 100644
index 00000000..44cc2425
--- /dev/null
+++ b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ref_scoring_2025-04-06_17-34-11.log
@@ -0,0 +1,26 @@
+#################################################################
+# ref_scoring_pipeline.R
+# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
+#################################################################
+# Repository: GenoPred
+# Version (tag): v2.2.2-258-gd2f2a91
+---------------
+ Parameter  Value                                                                                 
+ config     /scratch_tmp/prj/oliverpainfel/tmp/config.yaml                                        
+ continuous TRUE                                                                                  
+ plink2     plink2                                                                                
+ n_cores    1                                                                                     
+ test       chr22                                                                                 
+ memory     5000                                                                                  
+ help       FALSE                                                                                 
+ output     /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/pgs_score_files/ref_scoring
+---------------
+Analysis started at 2025-04-06 17:34:11
+After checking timestamps, 3/3 score files will be used for reference scoring.
+########################
+Processing chromosome 22:
+Aggregating score files in 1 batches.
+Aggregating batched score files.
+Adjusting PGS for ancestry.
+Analysis finished at 2025-04-06 17:34:14 
+Analysis duration was 3.2 secs 
diff --git a/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection-CSA.done b/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection-CSA.done
deleted file mode 100644
index e69de29b..00000000
diff --git a/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection-EAS.done b/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection-EAS.done
deleted file mode 100644
index e69de29b..00000000
diff --git a/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection-EUR.done b/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection-EUR.done
deleted file mode 100644
index e69de29b..00000000
diff --git a/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/indiv_report-2_EUR.2_EUR-report.done b/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection-TRANS.done
similarity index 100%
rename from pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/indiv_report-2_EUR.2_EUR-report.done
rename to pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection-TRANS.done
diff --git a/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection.done b/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection.done
deleted file mode 100644
index e69de29b..00000000
diff --git a/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection-AFR.done b/pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/target_pgs-TRANS.done
similarity index 100%
rename from pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/pc_projection-AFR.done
rename to pipeline/misc/dev/test_data/output/reference/target_checks/example_plink2/target_pgs-TRANS.done
diff --git a/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-AFR.txt b/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-AFR.txt
deleted file mode 100644
index 9f744e1b..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-AFR.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.8930	0:00:00	68.86	213.19	66.14	68.01	0.00	0.18	0.00	0.27
diff --git a/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-CSA.txt b/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-CSA.txt
deleted file mode 100644
index 73d3efce..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-CSA.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.9202	0:00:00	68.91	213.16	66.25	68.12	0.00	0.18	0.00	0.25
diff --git a/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-EAS.txt b/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-EAS.txt
deleted file mode 100644
index 3652dc81..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-EAS.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-2.0246	0:00:02	69.87	213.76	66.98	68.84	0.00	0.39	6.53	0.39
diff --git a/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-EUR.txt b/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-EUR.txt
deleted file mode 100644
index 6b24d852..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/benchmarks/ref_pca_i-EUR.txt
+++ /dev/null
@@ -1,2 +0,0 @@
-s	h:m:s	max_rss	max_vms	max_uss	max_pss	io_in	io_out	mean_load	cpu_time
-0.9712	0:00:00	69.45	213.19	66.16	67.98	0.00	0.18	0.00	0.28
diff --git a/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-AFR.log b/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-AFR.log
deleted file mode 100644
index 3c7bdc44..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-AFR.log
+++ /dev/null
@@ -1,203 +0,0 @@
-WARNING: ignoring environment value of R_HOME
-During startup - Warning messages:
-1: Setting LC_COLLATE failed, using "C" 
-2: Setting LC_TIME failed, using "C" 
-3: Setting LC_MESSAGES failed, using "C" 
-4: Setting LC_MONETARY failed, using "C" 
-5: Setting LC_PAPER failed, using "C" 
-6: Setting LC_MEASUREMENT failed, using "C" 
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22.log.
-Options in effect:
-  --keep misc/dev/test_data/ref/keep_files/AFR.keep
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22
-  --pfile misc/dev/test_data/ref/ref.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:06 2024
-1031702 MiB RAM detected, ~1018554 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-misc/dev/test_data/ref/ref.chr22.psam.
-1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
-3 categorical phenotypes loaded.
---keep: 688 samples remaining.
-688 samples (344 females, 344 males; 688 founders) remaining after main
-filters.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22.psam ...
-done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22.pvar ...
-0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22.pgen ...
-0%done.
-End time: Thu Jul 25 17:39:06 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/file127327a1bf0d3.log.
-Options in effect:
-  --geno 0.02
-  --hwe 1e-06
-  --maf 0.05
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/file127327a1bf0d3
-  --pfile /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22
-  --threads 1
-  --write-snplist
-
-Start time: Thu Jul 25 17:39:06 2024
-1031702 MiB RAM detected, ~1018548 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-688 samples (344 females, 344 males; 688 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
-Calculating allele frequencies... 0%done.
---geno: 12 variants removed due to missing genotype data.
---hwe: 0 variants removed due to Hardy-Weinberg exact test (founders only).
-183 variants removed due to allele frequency threshold(s)
-(--maf/--max-maf/--mac/--max-mac).
-805 variants remaining after main filters.
---write-snplist: Variant IDs written to
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/file127327a1bf0d3.snplist .
-End time: Thu Jul 25 17:39:06 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/file127326c4a60b.chr22.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/file1273224ec9d47
-  --indep-pairwise 1000 5 0.2
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/file127326c4a60b.chr22
-  --pfile /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:06 2024
-1031702 MiB RAM detected, ~1018548 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-688 samples (344 females, 344 males; 688 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 805 variants remaining.
-Calculating allele frequencies... 0%done.
-805 variants remaining after main filters.
---indep-pairwise (1 compute thread): 0%50%530/805 variants removed.
-Writing...
Variant lists written to
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/file127326c4a60b.chr22.prune.in and
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/file127326c4a60b.chr22.prune.out .
-End time: Thu Jul 25 17:39:06 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset_chr22.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/file12732137f3e9d
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset_chr22
-  --pfile /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:06 2024
-1031702 MiB RAM detected, ~1018548 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-688 samples (344 females, 344 males; 688 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 275 variants remaining.
-275 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset_chr22.psam ...
-done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset_chr22.pvar ...
-0%0%1%1%2%2%3%4%4%5%5%6%6%7%8%8%9%9%10%10%11%12%12%13%13%14%14%15%16%16%17%17%18%18%19%20%20%21%21%22%22%23%24%24%25%25%26%26%27%28%28%29%29%30%30%31%32%32%33%33%34%34%35%36%36%37%37%38%38%39%40%40%41%41%42%42%43%44%44%45%45%46%46%47%48%48%49%49%50%50%51%52%52%53%53%54%54%55%56%56%57%57%58%58%59%60%60%61%61%62%62%63%64%64%65%65%66%66%67%68%68%69%69%70%70%71%72%72%73%73%74%74%75%76%76%77%77%78%78%79%80%80%81%81%82%82%83%84%84%85%85%86%86%87%88%88%89%89%90%90%91%92%92%93%93%94%94%95%96%96%97%97%98%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset_chr22.pgen ...
-0%done.
-End time: Thu Jul 25 17:39:06 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/extract.snplist
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge
-  --pfile /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset_chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:06 2024
-1031702 MiB RAM detected, ~1018548 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-688 samples (344 females, 344 males; 688 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset_chr22.psam.
-275 variants loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_subset_chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 275 variants remaining.
-275 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge.psam ... done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge.pvar ... 0%0%1%1%2%2%3%4%4%5%5%6%6%7%8%8%9%9%10%10%11%12%12%13%13%14%14%15%16%16%17%17%18%18%19%20%20%21%21%22%22%23%24%24%25%25%26%26%27%28%28%29%29%30%30%31%32%32%33%33%34%34%35%36%36%37%37%38%38%39%40%40%41%41%42%42%43%44%44%45%45%46%46%47%48%48%49%49%50%50%51%52%52%53%53%54%54%55%56%56%57%57%58%58%59%60%60%61%61%62%62%63%64%64%65%65%66%66%67%68%68%69%69%70%70%71%72%72%73%73%74%74%75%76%76%77%77%78%78%79%80%80%81%81%82%82%83%84%84%85%85%86%86%87%88%88%89%89%90%90%91%92%92%93%93%94%94%95%96%96%97%97%98%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge.pgen ... 0%done.
-End time: Thu Jul 25 17:39:06 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge.log.
-Options in effect:
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge
-  --pca 6 biallelic-var-wts
-  --pfile /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge
-  --threads 1
-
-Start time: Thu Jul 25 17:39:06 2024
-1031702 MiB RAM detected, ~1018548 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-688 samples (344 females, 344 males; 688 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge.psam.
-275 variants loaded from
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge.pvar.
-3 categorical phenotypes loaded.
-Calculating allele frequencies... 0%done.
-Constructing GRM: 0%52%done.
-Correcting for missingness... 0%done.
-Extracting eigenvalues and eigenvectors... done.
---pca: Variant weights written to
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge.eigenvec.var .
---pca: Eigenvectors written to
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge.eigenvec , and eigenvalues
-written to /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/ref_merge.eigenval .
-End time: Thu Jul 25 17:39:06 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/profiles.chr22.log.
-Options in effect:
-  --chr 22
-  --out /scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/profiles.chr22
-  --pfile misc/dev/test_data/ref/ref.chr22
-  --score misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.eigenvec.var.gz header-read no-mean-imputation cols=+scoresums,-scoreavgs
-  --score-col-nums 4-9
-  --threads 1
-
-Start time: Thu Jul 25 17:39:06 2024
-1031702 MiB RAM detected, ~1018553 available; reserving 515851 MiB for main
-workspace.
-Using 1 compute thread.
-3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-misc/dev/test_data/ref/ref.chr22.psam.
-1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
-3 categorical phenotypes loaded.
-
--score: 275 variants processed.
---score: Results written to
-/scratch/prj/oliverpainfel/tmp/Rtmp6lCGgc/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:06 2024
diff --git a/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-CSA.log b/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-CSA.log
deleted file mode 100644
index 68a2932b..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-CSA.log
+++ /dev/null
@@ -1,203 +0,0 @@
-WARNING: ignoring environment value of R_HOME
-During startup - Warning messages:
-1: Setting LC_COLLATE failed, using "C" 
-2: Setting LC_TIME failed, using "C" 
-3: Setting LC_MESSAGES failed, using "C" 
-4: Setting LC_MONETARY failed, using "C" 
-5: Setting LC_PAPER failed, using "C" 
-6: Setting LC_MEASUREMENT failed, using "C" 
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22.log.
-Options in effect:
-  --keep misc/dev/test_data/ref/keep_files/CSA.keep
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22
-  --pfile misc/dev/test_data/ref/ref.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:11 2024
-1031702 MiB RAM detected, ~1018552 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-misc/dev/test_data/ref/ref.chr22.psam.
-1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
-3 categorical phenotypes loaded.
---keep: 675 samples remaining.
-675 samples (262 females, 413 males; 675 founders) remaining after main
-filters.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22.psam ...
-done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22.pvar ...
-0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22.pgen ...
-0%done.
-End time: Thu Jul 25 17:39:11 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/file1289e54a01580.log.
-Options in effect:
-  --geno 0.02
-  --hwe 1e-06
-  --maf 0.05
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/file1289e54a01580
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22
-  --threads 1
-  --write-snplist
-
-Start time: Thu Jul 25 17:39:11 2024
-1031702 MiB RAM detected, ~1018546 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-675 samples (262 females, 413 males; 675 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
-Calculating allele frequencies... 0%done.
---geno: 12 variants removed due to missing genotype data.
---hwe: 3 variants removed due to Hardy-Weinberg exact test (founders only).
-94 variants removed due to allele frequency threshold(s)
-(--maf/--max-maf/--mac/--max-mac).
-891 variants remaining after main filters.
---write-snplist: Variant IDs written to
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/file1289e54a01580.snplist .
-End time: Thu Jul 25 17:39:11 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/file1289e3440e0cd.chr22.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/file1289e4989107e
-  --indep-pairwise 1000 5 0.2
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/file1289e3440e0cd.chr22
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:11 2024
-1031702 MiB RAM detected, ~1018546 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-675 samples (262 females, 413 males; 675 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 891 variants remaining.
-Calculating allele frequencies... 0%done.
-891 variants remaining after main filters.
---indep-pairwise (1 compute thread): 0%50%753/891 variants removed.
-Writing...
Variant lists written to
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/file1289e3440e0cd.chr22.prune.in and
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/file1289e3440e0cd.chr22.prune.out .
-End time: Thu Jul 25 17:39:11 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset_chr22.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/file1289e310dbb49
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset_chr22
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:11 2024
-1031702 MiB RAM detected, ~1018546 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-675 samples (262 females, 413 males; 675 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 138 variants remaining.
-138 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset_chr22.psam ...
-done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset_chr22.pvar ...
-0%0%1%2%2%3%4%5%5%6%7%7%8%9%10%10%11%12%13%13%14%15%15%16%17%18%18%19%20%21%21%22%23%23%24%25%26%26%27%28%28%29%30%31%31%32%33%34%34%35%36%36%37%38%39%39%40%41%42%42%43%44%44%45%46%47%47%48%49%50%50%51%52%52%53%54%55%55%56%57%57%58%59%60%60%61%62%63%63%64%65%65%66%67%68%68%69%70%71%71%72%73%73%74%75%76%76%77%78%78%79%80%81%81%82%83%84%84%85%86%86%87%88%89%89%90%91%92%92%93%94%94%95%96%97%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset_chr22.pgen ...
-0%done.
-End time: Thu Jul 25 17:39:11 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/extract.snplist
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset_chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:11 2024
-1031702 MiB RAM detected, ~1018546 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-675 samples (262 females, 413 males; 675 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset_chr22.psam.
-138 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_subset_chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 138 variants remaining.
-138 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge.psam ... done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge.pvar ... 0%0%1%2%2%3%4%5%5%6%7%7%8%9%10%10%11%12%13%13%14%15%15%16%17%18%18%19%20%21%21%22%23%23%24%25%26%26%27%28%28%29%30%31%31%32%33%34%34%35%36%36%37%38%39%39%40%41%42%42%43%44%44%45%46%47%47%48%49%50%50%51%52%52%53%54%55%55%56%57%57%58%59%60%60%61%62%63%63%64%65%65%66%67%68%68%69%70%71%71%72%73%73%74%75%76%76%77%78%78%79%80%81%81%82%83%84%84%85%86%86%87%88%89%89%90%91%92%92%93%94%94%95%96%97%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge.pgen ... 0%done.
-End time: Thu Jul 25 17:39:11 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge.log.
-Options in effect:
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge
-  --pca 6 biallelic-var-wts
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge
-  --threads 1
-
-Start time: Thu Jul 25 17:39:11 2024
-1031702 MiB RAM detected, ~1018546 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-675 samples (262 females, 413 males; 675 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge.psam.
-138 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge.pvar.
-3 categorical phenotypes loaded.
-Calculating allele frequencies... 0%done.
-Constructing GRM: 0%done.
-Correcting for missingness... 0%done.
-Extracting eigenvalues and eigenvectors... done.
---pca: Variant weights written to
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge.eigenvec.var .
---pca: Eigenvectors written to
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge.eigenvec , and eigenvalues
-written to /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/ref_merge.eigenval .
-End time: Thu Jul 25 17:39:11 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/profiles.chr22.log.
-Options in effect:
-  --chr 22
-  --out /scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/profiles.chr22
-  --pfile misc/dev/test_data/ref/ref.chr22
-  --score misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.eigenvec.var.gz header-read no-mean-imputation cols=+scoresums,-scoreavgs
-  --score-col-nums 4-9
-  --threads 1
-
-Start time: Thu Jul 25 17:39:11 2024
-1031702 MiB RAM detected, ~1018551 available; reserving 515851 MiB for main
-workspace.
-Using 1 compute thread.
-3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-misc/dev/test_data/ref/ref.chr22.psam.
-1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
-3 categorical phenotypes loaded.
-
--score: 138 variants processed.
---score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpfYzGwF/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:11 2024
diff --git a/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-EAS.log b/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-EAS.log
deleted file mode 100644
index e5cac654..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-EAS.log
+++ /dev/null
@@ -1,203 +0,0 @@
-WARNING: ignoring environment value of R_HOME
-During startup - Warning messages:
-1: Setting LC_COLLATE failed, using "C" 
-2: Setting LC_TIME failed, using "C" 
-3: Setting LC_MESSAGES failed, using "C" 
-4: Setting LC_MONETARY failed, using "C" 
-5: Setting LC_PAPER failed, using "C" 
-6: Setting LC_MEASUREMENT failed, using "C" 
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22.log.
-Options in effect:
-  --keep misc/dev/test_data/ref/keep_files/EAS.keep
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22
-  --pfile misc/dev/test_data/ref/ref.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:08 2024
-1031702 MiB RAM detected, ~1018554 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-misc/dev/test_data/ref/ref.chr22.psam.
-1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
-3 categorical phenotypes loaded.
---keep: 737 samples remaining.
-737 samples (331 females, 406 males; 737 founders) remaining after main
-filters.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22.psam ...
-done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22.pvar ...
-0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22.pgen ...
-0%done.
-End time: Thu Jul 25 17:39:08 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/file127e73932ed07.log.
-Options in effect:
-  --geno 0.02
-  --hwe 1e-06
-  --maf 0.05
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/file127e73932ed07
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22
-  --threads 1
-  --write-snplist
-
-Start time: Thu Jul 25 17:39:08 2024
-1031702 MiB RAM detected, ~1018548 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-737 samples (331 females, 406 males; 737 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
-Calculating allele frequencies... 0%done.
---geno: 11 variants removed due to missing genotype data.
---hwe: 2 variants removed due to Hardy-Weinberg exact test (founders only).
-179 variants removed due to allele frequency threshold(s)
-(--maf/--max-maf/--mac/--max-mac).
-808 variants remaining after main filters.
---write-snplist: Variant IDs written to
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/file127e73932ed07.snplist .
-End time: Thu Jul 25 17:39:08 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/file127e77c387314.chr22.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/file127e75ae07e8d
-  --indep-pairwise 1000 5 0.2
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/file127e77c387314.chr22
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:08 2024
-1031702 MiB RAM detected, ~1018548 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-737 samples (331 females, 406 males; 737 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 808 variants remaining.
-Calculating allele frequencies... 0%done.
-808 variants remaining after main filters.
---indep-pairwise (1 compute thread): 0%50%675/808 variants removed.
-Writing...
Variant lists written to
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/file127e77c387314.chr22.prune.in and
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/file127e77c387314.chr22.prune.out .
-End time: Thu Jul 25 17:39:08 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset_chr22.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/file127e737163034
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset_chr22
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:08 2024
-1031702 MiB RAM detected, ~1018548 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-737 samples (331 females, 406 males; 737 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 133 variants remaining.
-133 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset_chr22.psam ...
-done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset_chr22.pvar ...
-0%0%1%2%3%3%4%5%6%6%7%8%9%9%10%11%12%12%13%14%15%15%16%17%18%18%19%20%21%21%22%23%24%24%25%26%27%27%28%29%30%30%31%32%33%33%34%35%36%36%37%38%39%39%40%41%42%42%43%44%45%45%46%47%48%48%49%50%51%51%52%53%54%54%55%56%57%57%58%59%60%60%61%62%63%63%64%65%66%66%67%68%69%69%70%71%72%72%73%74%75%75%76%77%78%78%79%80%81%81%82%83%84%84%85%86%87%87%88%89%90%90%91%92%93%93%94%95%96%96%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset_chr22.pgen ...
-0%done.
-End time: Thu Jul 25 17:39:08 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/extract.snplist
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset_chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:08 2024
-1031702 MiB RAM detected, ~1018547 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-737 samples (331 females, 406 males; 737 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset_chr22.psam.
-133 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_subset_chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 133 variants remaining.
-133 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge.psam ... done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge.pvar ... 0%0%1%2%3%3%4%5%6%6%7%8%9%9%10%11%12%12%13%14%15%15%16%17%18%18%19%20%21%21%22%23%24%24%25%26%27%27%28%29%30%30%31%32%33%33%34%35%36%36%37%38%39%39%40%41%42%42%43%44%45%45%46%47%48%48%49%50%51%51%52%53%54%54%55%56%57%57%58%59%60%60%61%62%63%63%64%65%66%66%67%68%69%69%70%71%72%72%73%74%75%75%76%77%78%78%79%80%81%81%82%83%84%84%85%86%87%87%88%89%90%90%91%92%93%93%94%95%96%96%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge.pgen ... 0%done.
-End time: Thu Jul 25 17:39:08 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge.log.
-Options in effect:
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge
-  --pca 6 biallelic-var-wts
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge
-  --threads 1
-
-Start time: Thu Jul 25 17:39:08 2024
-1031702 MiB RAM detected, ~1018547 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-737 samples (331 females, 406 males; 737 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge.psam.
-133 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge.pvar.
-3 categorical phenotypes loaded.
-Calculating allele frequencies... 0%done.
-Constructing GRM: 0%done.
-Correcting for missingness... 0%done.
-Extracting eigenvalues and eigenvectors... done.
---pca: Variant weights written to
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge.eigenvec.var .
---pca: Eigenvectors written to
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge.eigenvec , and eigenvalues
-written to /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/ref_merge.eigenval .
-End time: Thu Jul 25 17:39:08 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/profiles.chr22.log.
-Options in effect:
-  --chr 22
-  --out /scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/profiles.chr22
-  --pfile misc/dev/test_data/ref/ref.chr22
-  --score misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.eigenvec.var.gz header-read no-mean-imputation cols=+scoresums,-scoreavgs
-  --score-col-nums 4-9
-  --threads 1
-
-Start time: Thu Jul 25 17:39:09 2024
-1031702 MiB RAM detected, ~1018552 available; reserving 515851 MiB for main
-workspace.
-Using 1 compute thread.
-3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-misc/dev/test_data/ref/ref.chr22.psam.
-1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
-3 categorical phenotypes loaded.
-
--score: 133 variants processed.
---score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpbDsaJF/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:09 2024
diff --git a/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-EUR.log b/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-EUR.log
deleted file mode 100644
index c7e3f409..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/logs/ref_pca_i-EUR.log
+++ /dev/null
@@ -1,203 +0,0 @@
-WARNING: ignoring environment value of R_HOME
-During startup - Warning messages:
-1: Setting LC_COLLATE failed, using "C" 
-2: Setting LC_TIME failed, using "C" 
-3: Setting LC_MESSAGES failed, using "C" 
-4: Setting LC_MONETARY failed, using "C" 
-5: Setting LC_PAPER failed, using "C" 
-6: Setting LC_MEASUREMENT failed, using "C" 
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22.log.
-Options in effect:
-  --keep misc/dev/test_data/ref/keep_files/EUR.keep
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22
-  --pfile misc/dev/test_data/ref/ref.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:25 2024
-1031702 MiB RAM detected, ~1018542 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-misc/dev/test_data/ref/ref.chr22.psam.
-1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
-3 categorical phenotypes loaded.
---keep: 665 samples remaining.
-665 samples (335 females, 330 males; 665 founders) remaining after main
-filters.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22.psam ...
-done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22.pvar ...
-0%1%2%3%4%5%6%7%8%9%10%11%12%13%14%15%16%17%18%19%20%21%22%23%24%25%26%27%28%29%30%31%32%33%34%35%36%37%38%39%40%41%42%43%44%45%46%47%48%49%50%51%52%53%54%55%56%57%58%59%60%61%62%63%64%65%66%67%68%69%70%71%72%73%74%75%76%77%78%79%80%81%82%83%84%85%86%87%88%89%90%91%92%93%94%95%96%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22.pgen ...
-0%done.
-End time: Thu Jul 25 17:39:25 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/file12bb443937fc3.log.
-Options in effect:
-  --geno 0.02
-  --hwe 1e-06
-  --maf 0.05
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/file12bb443937fc3
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22
-  --threads 1
-  --write-snplist
-
-Start time: Thu Jul 25 17:39:25 2024
-1031702 MiB RAM detected, ~1018536 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-665 samples (335 females, 330 males; 665 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
-Calculating allele frequencies... 0%done.
---geno: 13 variants removed due to missing genotype data.
---hwe: 2 variants removed due to Hardy-Weinberg exact test (founders only).
-103 variants removed due to allele frequency threshold(s)
-(--maf/--max-maf/--mac/--max-mac).
-882 variants remaining after main filters.
---write-snplist: Variant IDs written to
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/file12bb443937fc3.snplist .
-End time: Thu Jul 25 17:39:25 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/file12bb47defa3b4.chr22.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/file12bb453dc108
-  --indep-pairwise 1000 5 0.2
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/file12bb47defa3b4.chr22
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:25 2024
-1031702 MiB RAM detected, ~1018536 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-665 samples (335 females, 330 males; 665 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 882 variants remaining.
-Calculating allele frequencies... 0%done.
-882 variants remaining after main filters.
---indep-pairwise (1 compute thread): 0%50%738/882 variants removed.
-Writing...
Variant lists written to
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/file12bb47defa3b4.chr22.prune.in and
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/file12bb47defa3b4.chr22.prune.out .
-End time: Thu Jul 25 17:39:25 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset_chr22.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/file12bb477857204
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset_chr22
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:25 2024
-1031702 MiB RAM detected, ~1018536 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-665 samples (335 females, 330 males; 665 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22.psam.
-1000 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset.chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 144 variants remaining.
-144 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset_chr22.psam ...
-done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset_chr22.pvar ...
-0%0%1%2%2%3%4%4%5%6%6%7%8%9%9%10%11%11%12%13%13%14%15%15%16%17%18%18%19%20%20%21%22%22%23%24%25%25%26%27%27%28%29%29%30%31%31%32%33%34%34%35%36%36%37%38%38%39%40%40%41%42%43%43%44%45%45%46%47%47%48%49%50%50%51%52%52%53%54%54%55%56%56%57%58%59%59%60%61%61%62%63%63%64%65%65%66%67%68%68%69%70%70%71%72%72%73%74%75%75%76%77%77%78%79%79%80%81%81%82%83%84%84%85%86%86%87%88%88%89%90%90%91%92%93%93%94%95%95%96%97%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset_chr22.pgen ...
-0%done.
-End time: Thu Jul 25 17:39:25 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge.log.
-Options in effect:
-  --extract /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/extract.snplist
-  --make-pgen
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset_chr22
-  --threads 1
-
-Start time: Thu Jul 25 17:39:25 2024
-1031702 MiB RAM detected, ~1018536 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-665 samples (335 females, 330 males; 665 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset_chr22.psam.
-144 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_subset_chr22.pvar.
-3 categorical phenotypes loaded.
---extract: 144 variants remaining.
-144 variants remaining after main filters.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge.psam ... done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge.pvar ... 0%0%1%2%2%3%4%4%5%6%6%7%8%9%9%10%11%11%12%13%13%14%15%15%16%17%18%18%19%20%20%21%22%22%23%24%25%25%26%27%27%28%29%29%30%31%31%32%33%34%34%35%36%36%37%38%38%39%40%40%41%42%43%43%44%45%45%46%47%47%48%49%50%50%51%52%52%53%54%54%55%56%56%57%58%59%59%60%61%61%62%63%63%64%65%65%66%67%68%68%69%70%70%71%72%72%73%74%75%75%76%77%77%78%79%79%80%81%81%82%83%84%84%85%86%86%87%88%88%89%90%90%91%92%93%93%94%95%95%96%97%97%98%99%done.
-Writing /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge.pgen ... 0%done.
-End time: Thu Jul 25 17:39:25 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge.log.
-Options in effect:
-  --memory 4000
-  --out /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge
-  --pca 6 biallelic-var-wts
-  --pfile /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge
-  --threads 1
-
-Start time: Thu Jul 25 17:39:25 2024
-1031702 MiB RAM detected, ~1018536 available; reserving 4000 MiB for main
-workspace.
-Using 1 compute thread.
-665 samples (335 females, 330 males; 665 founders) loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge.psam.
-144 variants loaded from
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge.pvar.
-3 categorical phenotypes loaded.
-Calculating allele frequencies... 0%done.
-Constructing GRM: 0%done.
-Correcting for missingness... 0%done.
-Extracting eigenvalues and eigenvectors... done.
---pca: Variant weights written to
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge.eigenvec.var .
---pca: Eigenvectors written to
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge.eigenvec , and eigenvalues
-written to /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/ref_merge.eigenval .
-End time: Thu Jul 25 17:39:25 2024
-PLINK v2.00a5.12LM 64-bit Intel (25 Jun 2024)  www.cog-genomics.org/plink/2.0/
-(C) 2005-2024 Shaun Purcell, Christopher Chang   GNU General Public License v3
-Logging to /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/profiles.chr22.log.
-Options in effect:
-  --chr 22
-  --out /scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/profiles.chr22
-  --pfile misc/dev/test_data/ref/ref.chr22
-  --score misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.eigenvec.var.gz header-read no-mean-imputation cols=+scoresums,-scoreavgs
-  --score-col-nums 4-9
-  --threads 1
-
-Start time: Thu Jul 25 17:39:25 2024
-1031702 MiB RAM detected, ~1018540 available; reserving 515851 MiB for main
-workspace.
-Using 1 compute thread.
-3313 samples (1568 females, 1745 males; 3313 founders) loaded from
-misc/dev/test_data/ref/ref.chr22.psam.
-1000 variants loaded from misc/dev/test_data/ref/ref.chr22.pvar.
-3 categorical phenotypes loaded.
-
--score: 144 variants processed.
---score: Results written to
-/scratch/prj/oliverpainfel/tmp/RtmpvBlrR1/profiles.chr22.sscore .
-End time: Thu Jul 25 17:39:25 2024
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.AFR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.AFR.scale
deleted file mode 100644
index b46ad259..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.AFR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 -76.4402155552326 20.9559598250705
-PC2 -51.4902854132268 21.6604058023809
-PC3 19.5642509552326 20.335183116368
-PC4 4.92511246424419 19.8595020863587
-PC5 -12.8707050017442 19.3440961554963
-PC6 -12.9831684428779 19.2405539183396
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.AMR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.AMR.scale
deleted file mode 100644
index 2b8ae08f..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.AMR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 6.9709218526699 20.5913945396173
-PC2 -39.6211314563107 11.9523473326549
-PC3 18.0932638021845 12.0589263976546
-PC4 9.85320759757282 13.0092475789245
-PC5 -20.3365437349515 11.3417471160891
-PC6 -20.3018356 12.7290376953756
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.CSA.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.CSA.scale
deleted file mode 100644
index b62bd248..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.CSA.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 8.84325593125926 14.8382323960666
-PC2 -35.5139174217778 13.0466121763559
-PC3 22.3550145899259 12.6577206785626
-PC4 6.84876517437037 13.8237822968951
-PC5 -16.390364181037 11.6650011802854
-PC6 -11.7665009386667 13.1002962452956
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.EAS.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.EAS.scale
deleted file mode 100644
index 440780ab..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.EAS.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 -1.45306350244233 13.5255385124717
-PC2 -35.9061656580733 11.2824338490913
-PC3 23.9373579920353 11.1526519316067
-PC4 16.3021045550882 12.5830329898224
-PC5 -13.2066671587517 10.4702106572966
-PC6 -21.8346390301221 10.9841110012839
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.EUR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.EUR.scale
deleted file mode 100644
index c86c453a..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.EUR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 21.8588063774436 14.5263051038859
-PC2 -34.1927795368421 12.079489368205
-PC3 18.2633459666165 12.5453651652012
-PC4 6.16133519263158 12.9483932029622
-PC5 -21.1298316983158 12.2040182688793
-PC6 -21.1662189933835 13.0486900663856
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.MID.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.MID.scale
deleted file mode 100644
index c72818b1..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.MID.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 8.74283858823529 19.1771213235669
-PC2 -41.008045 13.1133750308077
-PC3 12.1562800294118 13.3274180326257
-PC4 7.99046556691177 13.4544707268668
-PC5 -18.2125197941176 12.3349958865143
-PC6 -19.299985375 12.7803489382831
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.eigenvec.var.gz b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.eigenvec.var.gz
deleted file mode 100644
index a74ee002a7386655287f92efac07d8fa3f16e659..0000000000000000000000000000000000000000
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diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.AFR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.AFR.scale
deleted file mode 100644
index 5051f3f5..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.AFR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 -11.9953619069767 7.5058593369441
-PC2 -13.7047505824128 7.74622549177608
-PC3 0.511085326889535 9.10075446379788
-PC4 -0.556206992165698 10.1238162565204
-PC5 21.0653800642442 7.48760169228602
-PC6 -20.1963610886628 7.76725191688225
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.AMR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.AMR.scale
deleted file mode 100644
index f8209a82..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.AMR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 -5.0381632611165 8.92295551512415
-PC2 -14.874786948301 11.1608181063202
-PC3 10.1705252014563 9.82363083664274
-PC4 -0.499081746626214 10.8841860930566
-PC5 15.291689813835 9.5922463247749
-PC6 -20.4334251667961 8.98276274643203
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.CSA.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.CSA.scale
deleted file mode 100644
index 6a063bb3..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.CSA.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 -18.7649961022222 14.7901329030541
-PC2 -17.9570692764148 13.3458546368728
-PC3 9.93871155007407 11.9500522100873
-PC4 -8.11470405851852 12.0928917438415
-PC5 18.8432801442963 12.5629622939288
-PC6 -18.727387901037 12.4529853676491
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.EAS.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.EAS.scale
deleted file mode 100644
index b65c81b3..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.EAS.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 -15.6376888597015 8.07790322954779
-PC2 -6.44025773565807 9.9311899228111
-PC3 8.05876814776119 7.33251505853685
-PC4 -0.842866190664858 8.24855009994045
-PC5 21.9417478521031 9.52223785183557
-PC6 -31.751053541384 7.91161510463292
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.EUR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.EUR.scale
deleted file mode 100644
index 16ef9e31..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.EUR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 -3.82723232478196 9.59909882875423
-PC2 -19.766470312782 9.30138486107568
-PC3 11.0508521651128 10.8051216662395
-PC4 -6.86269510466165 10.6220460178841
-PC5 16.2486338590977 9.17140625349148
-PC6 -15.2319814996992 10.3729760551083
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.MID.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.MID.scale
deleted file mode 100644
index 9e13f009..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.MID.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 -4.86936142132353 9.38183751469025
-PC2 -22.040640325 9.79259823588125
-PC3 6.47617943308824 9.35171813158274
-PC4 -6.84161813235294 11.4059008575426
-PC5 20.1893520735294 9.63231840567104
-PC6 -15.2990285367647 10.3634687198268
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.eigenvec.var.gz b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.eigenvec.var.gz
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diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.log b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.log
deleted file mode 100644
index 4998ebaf..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.log
+++ /dev/null
@@ -1,30 +0,0 @@
-#################################################################
-# ref_pca.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-# Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
----------------
- Parameter     Value                                                                      
- ref_plink_chr misc/dev/test_data/ref/ref.chr                                             
- ref_keep      misc/dev/test_data/ref/keep_files/CSA.keep                                 
- maf           0.05                                                                       
- geno          0.02                                                                       
- hwe           1e-06                                                                      
- n_pcs         6                                                                          
- plink2        plink2                                                                     
- output        misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs
- pop_data      misc/dev/test_data/ref/ref.pop.txt                                         
- memory        5000                                                                       
- test          chr22                                                                      
- help          FALSE                                                                      
- output_dir    misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/           
----------------
-Analysis started at 2024-07-25 17:39:10
-Identifying LD independent SNPs based on reference data.
-891 variants after removal of LD high regions.
-138 independent variants retained.
-Performing PCA based on reference.
-Computing reference PCs.
-Analysis finished at 2024-07-25 17:39:11 
-Analysis duration was 0.57 secs 
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.AFR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.AFR.scale
deleted file mode 100644
index d21a7f8b..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.AFR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 16.9013200482558 7.02008897842078
-PC2 4.18618902659884 7.84449075109668
-PC3 -24.2866464680233 6.8378483413182
-PC4 -12.0412870497529 6.98709143562716
-PC5 -2.39970248619186 7.84382384115763
-PC6 -12.349384255814 7.32118533097871
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.AMR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.AMR.scale
deleted file mode 100644
index 3fcb2940..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.AMR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 11.209689975 8.61706065288039
-PC2 2.94689796286408 9.83188195494103
-PC3 -21.5467961893204 8.75140117579763
-PC4 -18.6224946427184 10.241713185591
-PC5 -8.77441882014563 8.52523167432528
-PC6 -14.7587666121359 9.91200741319007
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.CSA.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.CSA.scale
deleted file mode 100644
index 84452c21..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.CSA.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 15.0448044222222 7.81605948822785
-PC2 2.49231196918519 8.89117957001706
-PC3 -22.6793555377778 8.91183262048813
-PC4 -19.9551107136296 8.78828821994744
-PC5 -7.69931161422222 8.48485718030077
-PC6 -18.0186283777778 9.75834221203882
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.EAS.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.EAS.scale
deleted file mode 100644
index 81621353..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.EAS.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 17.5281718378562 12.7292394639403
-PC2 0.460759556037992 11.8871937801815
-PC3 -25.7276028046133 11.4885467681503
-PC4 -10.3596585799186 10.76096653577
-PC5 -11.4253680738128 10.5653809581225
-PC6 -25.617249250882 10.9177516979984
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.EUR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.EUR.scale
deleted file mode 100644
index d99bc1b7..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.EUR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 10.5347050554887 6.53177915883785
-PC2 9.03141830947368 9.0131139960596
-PC3 -21.5004240750376 8.36336385639296
-PC4 -25.3883007067669 8.61964085534471
-PC5 -8.13357044225564 8.94977167241527
-PC6 -15.3754791556391 9.03079872821907
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.MID.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.MID.scale
deleted file mode 100644
index 4010e25b..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.MID.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 13.1392382794118 7.05793782904138
-PC2 6.60982905661765 9.30107172061681
-PC3 -24.0128830147059 8.41476786387786
-PC4 -23.2989644926471 8.92930099813341
-PC5 -6.19709049191177 8.23630701864109
-PC6 -13.4183601617647 8.80498131806655
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.eigenvec.var.gz b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.eigenvec.var.gz
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diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.log b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.log
deleted file mode 100644
index 560e4c0e..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.log
+++ /dev/null
@@ -1,30 +0,0 @@
-#################################################################
-# ref_pca.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-# Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
----------------
- Parameter     Value                                                                      
- ref_plink_chr misc/dev/test_data/ref/ref.chr                                             
- ref_keep      misc/dev/test_data/ref/keep_files/EAS.keep                                 
- maf           0.05                                                                       
- geno          0.02                                                                       
- hwe           1e-06                                                                      
- n_pcs         6                                                                          
- plink2        plink2                                                                     
- output        misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs
- pop_data      misc/dev/test_data/ref/ref.pop.txt                                         
- memory        5000                                                                       
- test          chr22                                                                      
- help          FALSE                                                                      
- output_dir    misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/           
----------------
-Analysis started at 2024-07-25 17:39:08
-Identifying LD independent SNPs based on reference data.
-808 variants after removal of LD high regions.
-133 independent variants retained.
-Performing PCA based on reference.
-Computing reference PCs.
-Analysis finished at 2024-07-25 17:39:09 
-Analysis duration was 1.68 secs 
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.AFR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.AFR.scale
deleted file mode 100644
index 95fd2ecd..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.AFR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 3.31177196889535 9.3782822942612
-PC2 28.2928857267442 8.39898668766677
-PC3 -13.690004387064 8.19561398246036
-PC4 0.0715340090116279 7.40581759081168
-PC5 6.9106801184593 6.83657330542908
-PC6 -11.644166965843 7.42350931791503
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.AMR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.AMR.scale
deleted file mode 100644
index 63e094ee..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.AMR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 -4.44105850995146 12.5723304368786
-PC2 31.0468232038835 9.97690650698573
-PC3 -8.36823727038835 11.0824762536286
-PC4 0.729602858009709 9.40871091061862
-PC5 12.109227038835 9.02704322813327
-PC6 -5.79955231796117 10.6022442823539
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.CSA.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.CSA.scale
deleted file mode 100644
index 89dd3c6e..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.CSA.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 -2.40973557822222 11.1594092819422
-PC2 32.3403470962963 9.06715933760887
-PC3 -2.03986850281482 10.2952535630303
-PC4 -0.21679251562963 9.92754265225643
-PC5 6.94042532844444 9.11948555140549
-PC6 -8.70094643792593 9.30885497984036
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.EAS.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.EAS.scale
deleted file mode 100644
index 8bb46376..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.EAS.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 -18.8219199308005 8.88666189263727
-PC2 31.5599420624152 7.84914031014996
-PC3 -6.91464696635007 8.56521343943766
-PC4 3.66290070013569 8.1263194715924
-PC5 -0.228599129715061 8.02370909523248
-PC6 -4.83527525630936 8.61261779152877
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.EUR.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.EUR.scale
deleted file mode 100644
index a2b32fc5..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.EUR.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 1.76764645759399 13.3006126024489
-PC2 32.394816437594 12.6523515519304
-PC3 -0.687226675639098 12.1362486503253
-PC4 -2.75517181924812 12.361800059068
-PC5 12.9225524891729 12.1526887552601
-PC6 -13.7014834724812 12.0751083354253
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.MID.scale b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.MID.scale
deleted file mode 100644
index 6ee8a493..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.MID.scale
+++ /dev/null
@@ -1,7 +0,0 @@
-Param Mean SD
-PC1 7.31013884558824 11.2044289801662
-PC2 34.9700147058824 8.37437633873383
-PC3 -2.10414260294118 10.2706169069991
-PC4 -2.26550077941177 10.0595338581993
-PC5 8.37756379779412 10.5854039872978
-PC6 -10.3069695669118 8.85922405006279
diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.eigenvec.var.gz b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.eigenvec.var.gz
deleted file mode 100644
index 11c7c8027615358ee04e59b1ab87c57d0cc7e207..0000000000000000000000000000000000000000
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diff --git a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.log b/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.log
deleted file mode 100644
index eda52639..00000000
--- a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.log
+++ /dev/null
@@ -1,30 +0,0 @@
-#################################################################
-# ref_pca.R
-# For questions contact Oliver Pain (oliver.pain@kcl.ac.uk)
-#################################################################
-# Repository: GenoPred
-# Version (tag): v2.2.2-110-gb4e52b5
----------------
- Parameter     Value                                                                      
- ref_plink_chr misc/dev/test_data/ref/ref.chr                                             
- ref_keep      misc/dev/test_data/ref/keep_files/EUR.keep                                 
- maf           0.05                                                                       
- geno          0.02                                                                       
- hwe           1e-06                                                                      
- n_pcs         6                                                                          
- plink2        plink2                                                                     
- output        misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs
- pop_data      misc/dev/test_data/ref/ref.pop.txt                                         
- memory        5000                                                                       
- test          chr22                                                                      
- help          FALSE                                                                      
- output_dir    misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/           
----------------
-Analysis started at 2024-07-25 17:39:25
-Identifying LD independent SNPs based on reference data.
-882 variants after removal of LD high regions.
-144 independent variants retained.
-Performing PCA based on reference.
-Computing reference PCs.
-Analysis finished at 2024-07-25 17:39:25 
-Analysis duration was 0.6 secs 
diff --git a/pipeline/misc/dev/test_data/output/resources/last_version.txt b/pipeline/misc/dev/test_data/output/resources/last_version.txt
deleted file mode 100644
index 61618788..00000000
--- a/pipeline/misc/dev/test_data/output/resources/last_version.txt
+++ /dev/null
@@ -1 +0,0 @@
-2.2
\ No newline at end of file
diff --git a/pipeline/misc/dev/test_setup.Rmd b/pipeline/misc/dev/test_setup.Rmd
index 2c296713..96425425 100644
--- a/pipeline/misc/dev/test_setup.Rmd
+++ b/pipeline/misc/dev/test_setup.Rmd
@@ -279,7 +279,7 @@ Generate the reference data within the latest GenoPred container to ensure perfe
 ```{bash}
 
 singularity shell \
-  --bind /scratch/prj/oliverpainfel:/scratch/prj/oliverpainfel \
+  --bind /scratch_tmp/prj/oliverpainfel:/scratch_tmp/prj/oliverpainfel \
   --writable-tmpfs \
   /users/k1806347/oliverpainfel/Software/singularity/genopred_pipeline_latest.sif
   
@@ -290,19 +290,19 @@ cd /tools/GenoPred/pipeline
 git checkout dev
 git pull
 
-# Run pipeline
+# Had to update the outdir to avoid device limit in container
+# Set outdir to /scratch_tmp/prj/oliverpainfel/tmp/genopred_test
 snakemake \
 -j1 \
 --use-conda \
 output_all \
-misc/dev/test_data/output/reference/target_checks/example_plink2/indiv_report-4_EAS.4_EAS-report.done \
---configfile=misc/dev/test_data/config/config.yaml
+/scratch_tmp/prj/oliverpainfel/tmp/genopred_test/reference/target_checks/example_plink2/indiv_report-4_EAS.4_EAS-report.done \
+--configfile=/scratch_tmp/prj/oliverpainfel/tmp/config.yaml
 
 # Move output outside the container so it can be pushed to the repo
-mkdir /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/misc/dev/test_data/output
-cp -r misc/dev/test_data/output/* /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/misc/dev/test_data/output/
+mkdir /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/misc/dev/test_data/output
+cp -r /scratch_tmp/prj/oliverpainfel/tmp/genopred_test/* /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/pipeline/misc/dev/test_data/output/
 
-# 
 ```
 
 ***
@@ -310,27 +310,3 @@ cp -r misc/dev/test_data/output/* /scratch/prj/oliverpainfel/Software/MyGit/Geno
 This output can now be used as a reference to check whether commits to the repo effects the output.
 
 ***
-
-# Files for cross-population PGS methods
-
-Make mini versions of the Yengo EUR and EAS GWAS sumstats.
-
-```{r}
-library(data.table)
-
-test_snps<-fread('misc/dev/test_data/test_snps.txt', header=F)$V1
-
-for(i in c('eur','eas')){
-  ss<-fread(paste0('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_',i,'.txt'))
-  ss<-ss[ss$variant_id %in% test_snps,]
-  fwrite(ss, paste0('misc/dev/test_data/gwas/yengo_2022_height_',i,'.txt.gz'), quote=F, sep=' ', na='NA')
-}
-
-```
-
-
-
-
-
-
-
diff --git a/pipeline/tests/testthat/test-pipeline.R b/pipeline/tests/testthat/test-pipeline.R
index bed27722..481eac87 100644
--- a/pipeline/tests/testthat/test-pipeline.R
+++ b/pipeline/tests/testthat/test-pipeline.R
@@ -171,7 +171,7 @@ write.table(config, paste0(temp_dir2, '/config9.yaml'), col.names = F, row.names
 # Run pipeline commands inside container
 #################
 
-requested_output <- paste0("output_all pc_projection ", temp_dir, "/reference/target_checks/example_plink2/indiv_report-4_EAS.4_EAS-report.done")
+requested_output <- paste0("output_all ", temp_dir, "/reference/target_checks/example_plink2/indiv_report-4_EAS.4_EAS-report.done")
 
 exit_status <- system(paste0(
   "singularity exec --writable-tmpfs ", sif_file, " bash -c \"
@@ -312,7 +312,7 @@ test_that("Check sumstat_prep_i output", {
 # internal
 ###
 for(i in c('ptclump','lassosum')){
-  for(j in c('.score.gz','-AFR.scale')){
+  for(j in c('.score.gz','-AFR.scale','-TRANS.scale')){
     test_that(paste0("Check prep_pgs_", i,"_i output: ", j), {
       results <- fread(paste0(temp_dir,'/reference/pgs_score_files/', i,'/BODY04/ref-BODY04', j))
       expected<-fread(paste0('misc/dev/test_data/output/reference/pgs_score_files/', i, '/BODY04/ref-BODY04', j))
@@ -321,21 +321,10 @@ for(i in c('ptclump','lassosum')){
   }
 }
 
-###
-# prep_pgs_lassosum_i
-###
-for(i in c('.score.gz','-AFR.scale')){
-  test_that(paste0("Check prep_pgs_ptclump_i output: ", i), {
-    results <- fread(paste0(temp_dir,'/reference/pgs_score_files/ptclump/BODY04/ref-BODY04', i))
-    expected<-fread(paste0('misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04', i))
-    expect_equal(expected, results)
-  })
-}
-
 ###
 # prep_pgs_external_i
 ###
-for(i in c('.score.gz','-AFR.scale')){
+for(i in c('.score.gz','-AFR.scale','-TRANS.scale')){
   test_that(paste0("Check prep_pgs_external_i output: ", i), {
     results<-fread(paste0(temp_dir,'/reference/pgs_score_files/external/PGS002804/ref-PGS002804', i))
     expected<-fread(paste0('misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804', i))
@@ -352,19 +341,23 @@ for(i in c('.score.gz','-AFR.scale')){
 ###
 
 # external
-test_that("Check target_pgs_i output: external", {
-  results <- fread(paste0(temp_dir,'/example_plink2/pgs/AFR/external/PGS002804/example_plink2-PGS002804-AFR.profiles'))
-  expected <- fread('misc/dev/test_data/output/example_plink2/pgs/AFR/external/PGS002804/example_plink2-PGS002804-AFR.profiles')
-  expect_equal(expected, results)
-})
+for(j in c('AFR','TRANS')){
+  test_that("Check target_pgs_i output: external", {
+    results <- fread(paste0(temp_dir,'/example_plink2/pgs/', j, '/external/PGS002804/example_plink2-PGS002804-', j, '.profiles'))
+    expected <- fread('misc/dev/test_data/output/example_plink2/pgs/', j, '/external/PGS002804/example_plink2-PGS002804-', j, '.profiles')
+    expect_equal(expected, results)
+  })
+}
 
 # internal
 for(i in c('ptclump','lassosum')){
-  test_that(paste0("Check target_pgs_i output: ", i), {
-    results <- fread(paste0(temp_dir,'/example_plink2/pgs/AFR/', i, '/BODY04/example_plink2-BODY04-AFR.profiles'))
-    expected <- fread(paste0('misc/dev/test_data/output/example_plink2/pgs/AFR/', i, '/BODY04/example_plink2-BODY04-AFR.profiles'))
-    expect_equal(expected, results)
-  })
+  for(j in c('AFR','TRANS')){
+    test_that(paste0("Check target_pgs_i output: ", i), {
+      results <- fread(paste0(temp_dir,'/example_plink2/pgs/', j, '/', i, '/BODY04/example_plink2-BODY04-', j, '.profiles'))
+      expected <- fread(paste0('misc/dev/test_data/output/example_plink2/pgs/', j, '/', i, '/BODY04/example_plink2-BODY04-', j, '.profiles'))
+      expect_equal(expected, results)
+    })
+  }
 }
 
 #######

From b6167509a0552f309db4482aca40ebe39200252b Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sun, 6 Apr 2025 22:12:56 +0100
Subject: [PATCH 131/153] Updating tests

---
 .github/workflows/run_quick_tests.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.github/workflows/run_quick_tests.yaml b/.github/workflows/run_quick_tests.yaml
index 849024ff..51ced879 100644
--- a/.github/workflows/run_quick_tests.yaml
+++ b/.github/workflows/run_quick_tests.yaml
@@ -45,6 +45,7 @@ jobs:
         run: |
           Rscript -e "testthat::test_dir('pipeline/tests/testthat')"
         env:
+          GITHUB_PAT: ${{ secrets.PAT }}
           SIF_FILE: ${{ env.SIF_FILE }}
           BRANCH_NAME: ${{ env.BRANCH_NAME }}
 

From 8724058810f41e010e05dbc99a0f3674924060d8 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sun, 6 Apr 2025 23:03:27 +0100
Subject: [PATCH 132/153] Update to tests

---
 .github/workflows/run_quick_tests.yaml  | 2 +-
 pipeline/tests/testthat/test-pipeline.R | 5 +++--
 2 files changed, 4 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/run_quick_tests.yaml b/.github/workflows/run_quick_tests.yaml
index 51ced879..9e6a06c4 100644
--- a/.github/workflows/run_quick_tests.yaml
+++ b/.github/workflows/run_quick_tests.yaml
@@ -45,7 +45,7 @@ jobs:
         run: |
           Rscript -e "testthat::test_dir('pipeline/tests/testthat')"
         env:
-          GITHUB_PAT: ${{ secrets.PAT }}
+          GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
           SIF_FILE: ${{ env.SIF_FILE }}
           BRANCH_NAME: ${{ env.BRANCH_NAME }}
 
diff --git a/pipeline/tests/testthat/test-pipeline.R b/pipeline/tests/testthat/test-pipeline.R
index 481eac87..bb58a670 100644
--- a/pipeline/tests/testthat/test-pipeline.R
+++ b/pipeline/tests/testthat/test-pipeline.R
@@ -5,7 +5,7 @@ library(testthat)
 setwd('../../')
 
 tempdir <- function(prefix = "tmpdir") {
-  tmpdir <- tempfile(pattern = prefix)
+  tmpdir <- tempfile(tmpdir = '/tmp', pattern = prefix)
   dir.create(tmpdir)
   return(tmpdir)
 }
@@ -174,7 +174,7 @@ write.table(config, paste0(temp_dir2, '/config9.yaml'), col.names = F, row.names
 requested_output <- paste0("output_all ", temp_dir, "/reference/target_checks/example_plink2/indiv_report-4_EAS.4_EAS-report.done")
 
 exit_status <- system(paste0(
-  "singularity exec --writable-tmpfs ", sif_file, " bash -c \"
+  "singularity exec --bind ", temp_dir, ":", temp_dir ," --writable-tmpfs ", sif_file, " bash -c \"
       # Set to exit if any errors incurred
       set -e &&
       # Initiate conda
@@ -219,6 +219,7 @@ exit_status <- system(paste0(
 
 # Check system command completed successfully
 if (exit_status != 0) {
+  print(readLines(paste0(temp_dir, "/snakemake1.log")))
   stop("The script failed. Check the logs for details.")
 }
 

From ef4f31764b05e4d0ae3028cf3906dbc4e6dc0196 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Sun, 6 Apr 2025 23:33:02 +0100
Subject: [PATCH 133/153] Fixed reshape depracation issue

---
 Scripts/pipeline_reports/indiv_report_creator.Rmd | 2 +-
 Scripts/pipeline_reports/samp_report_creator.Rmd  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/Scripts/pipeline_reports/indiv_report_creator.Rmd b/Scripts/pipeline_reports/indiv_report_creator.Rmd
index e1ec8499..921e640a 100644
--- a/Scripts/pipeline_reports/indiv_report_creator.Rmd
+++ b/Scripts/pipeline_reports/indiv_report_creator.Rmd
@@ -440,7 +440,7 @@ ref_pgs$IID<-NULL
 cormat<-cor(ref_pgs)
                 
 # Melt for plotting
-melted_cormat <- melt(cormat, na.rm = TRUE)
+melted_cormat <- reshape2::melt(cormat, na.rm = TRUE)
 
 plot_obj<-ggplot(melted_cormat, aes(x = Var1, y = Var2, fill = value)) +
     geom_tile() +
diff --git a/Scripts/pipeline_reports/samp_report_creator.Rmd b/Scripts/pipeline_reports/samp_report_creator.Rmd
index 3efa5999..62450640 100644
--- a/Scripts/pipeline_reports/samp_report_creator.Rmd
+++ b/Scripts/pipeline_reports/samp_report_creator.Rmd
@@ -356,7 +356,7 @@ ref_pgs$IID<-NULL
 cormat<-cor(ref_pgs)
                 
 # Melt for plotting
-melted_cormat <- melt(cormat, na.rm = TRUE)
+melted_cormat <- reshape2::melt(cormat, na.rm = TRUE)
 
 plot_obj<-ggplot(melted_cormat, aes(x = Var1, y = Var2, fill = value)) +
     geom_tile() +

From bc97e8811b3f4c48ef4eafa5e60e5da9214c0e2a Mon Sep 17 00:00:00 2001
From: Oliver Pain <43761992+opain@users.noreply.github.com>
Date: Mon, 7 Apr 2025 18:52:00 +0200
Subject: [PATCH 134/153] Update test-pipeline.R

---
 pipeline/tests/testthat/test-pipeline.R | 14 +++++---------
 1 file changed, 5 insertions(+), 9 deletions(-)

diff --git a/pipeline/tests/testthat/test-pipeline.R b/pipeline/tests/testthat/test-pipeline.R
index bb58a670..2740e67d 100644
--- a/pipeline/tests/testthat/test-pipeline.R
+++ b/pipeline/tests/testthat/test-pipeline.R
@@ -174,7 +174,7 @@ write.table(config, paste0(temp_dir2, '/config9.yaml'), col.names = F, row.names
 requested_output <- paste0("output_all ", temp_dir, "/reference/target_checks/example_plink2/indiv_report-4_EAS.4_EAS-report.done")
 
 exit_status <- system(paste0(
-  "singularity exec --bind ", temp_dir, ":", temp_dir ," --writable-tmpfs ", sif_file, " bash -c \"
+  "singularity exec --bind ", temp_dir, ":", temp_dir ," --bind ", temp_dir2, ":", temp_dir2 ," --writable-tmpfs ", sif_file, " bash -c \"
       # Set to exit if any errors incurred
       set -e &&
       # Initiate conda
@@ -187,10 +187,6 @@ exit_status <- system(paste0(
       git checkout ", repo_branch, " &&
       # Fetch latest commits
       git pull &&
-      # Clear output directory
-      rm -r -f ", temp_dir, "/reference &&
-      rm -r -f ", temp_dir, "/example_plink2 &&
-      rm -r -f ", temp_dir, "/resources &&
       # Test 1
       cp ", temp_dir, "/config1.yaml ", temp_dir, "/config.yaml &&
       snakemake -j1 --use-conda ", requested_output, " --configfile=", temp_dir, "/config.yaml > ", temp_dir, "/snakemake1.log 2>&1 &&
@@ -291,10 +287,10 @@ for(i in c('pgen','psam','pvar')){
 # ref_pca_i
 ###
 
-for(i in c('eigenvec.var.gz','AFR.scale')){
+for(i in c('eigenvec.var.gz','TRANS.scale')){
   test_that(paste0("Check ref_pca_i output: ", i), {
-    results<-fread(paste0(temp_dir,'/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.', i))
-    expected<-fread(paste0('misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.', i))
+    results<-fread(paste0(temp_dir,'/reference/pc_score_files/TRANS/ref-TRANS-pcs.', i))
+    expected<-fread(paste0('misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.', i))
     expect_equal(expected, results)
   })
 }
@@ -345,7 +341,7 @@ for(i in c('.score.gz','-AFR.scale','-TRANS.scale')){
 for(j in c('AFR','TRANS')){
   test_that("Check target_pgs_i output: external", {
     results <- fread(paste0(temp_dir,'/example_plink2/pgs/', j, '/external/PGS002804/example_plink2-PGS002804-', j, '.profiles'))
-    expected <- fread('misc/dev/test_data/output/example_plink2/pgs/', j, '/external/PGS002804/example_plink2-PGS002804-', j, '.profiles')
+    expected <- fread(paste0('misc/dev/test_data/output/example_plink2/pgs/', j, '/external/PGS002804/example_plink2-PGS002804-', j, '.profiles'))
     expect_equal(expected, results)
   })
 }

From d7a056e8b286c3145fa16211664938257e76d226 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 10 Apr 2025 09:05:08 +0100
Subject: [PATCH 135/153] Updates

---
 docs/Images/tools_summary/brief_schematic.png | Bin 567111 -> 216731 bytes
 docs/pipeline_readme.Rmd                      |  14 +-
 docs/pipeline_readme.html                     |  12 +-
 docs/running_offline.Rmd                      |   8 +-
 docs/running_offline.html                     |  15 +-
 pipeline/misc/docker/Dockerfile               |   2 +-
 pipeline/rules/dependencies.smk               | 180 +++++++++++-------
 7 files changed, 152 insertions(+), 79 deletions(-)

diff --git a/docs/Images/tools_summary/brief_schematic.png b/docs/Images/tools_summary/brief_schematic.png
index 8e41ab7ce446278df0609cc470189cfef669427f..8e381a057ea211a199b6b8c918c9157257b88062 100644
GIT binary patch
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diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index cc5266a6..7a163fb2 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -1304,7 +1304,7 @@ Users can provide their own reference data using the `refdir` parameter in the `
 
 ```{}
 [refdir]
-  ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data with RSIDs in SNP column)
+  ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data in build GRCh37 with RSIDs in SNP column)
   ├── ref.chr[1-22].rds (SNP data - refer to default ref data for format)
   ├── ref.pop.txt (Population data for reference individuals - with header)
   ├── keep_files
@@ -1328,8 +1328,8 @@ It is possible to alter parameters for certain PGS methods by setting the follow
 
 - `ptclump_pts`: list of p-value thresholds for ptclump
 - `dbslmm_h2f`: list SNP-h2 folds for DBSLMM - use `1` for the default model
-- `prscs_phi`: list phi parameters for PRS-CS - use `auto` for the auto model
-- `prscs_ldef`: Selected whether PRS-CS ld reference is derived from `1kg` (default) or `ukb` (UK Biobank).
+- `prscs_phi`: list phi parameters for PRS-CS and PRS-CSx - use `auto` for the auto model
+- `prscs_ldef`: Selected whether PRS-CS/PRS-CSx LD reference is derived from `1kg` (default) or `ukb` (UK Biobank).
 - `ldpred2_model`: list models for LDpred2 - `grid`, `auto`, `inf`
 
 See the default [configfile](https://github.com/opain/GenoPred/blob/master/pipeline/config.yaml) for examples of these parameters being set.
@@ -1454,7 +1454,15 @@ pgs_scaling: ['discrete']
 
 ## Specifying alternative reference data for PGS methods
 
+PGS methods require linkage disequilibrium (LD) data. The default LD data used for each method is indicated in the technical documentation ([here]()). Several methods use the reference data used for the pipeline in general, so altering the `ref_dir` parameter (as [described above]()) will also alter the reference data used by these PGS methods. However, several PGS methods use pre-computed LD matrices, and the user can specify alternative LD matrices for these methods, using the configfile parameters below:
 
+- PRS-CS
+- PRS-CSx
+- SBayesR
+- SBayesRC
+- LDpred2
+- QuickPRS
+- LEOPARD+QuickPRS
 
 ***
 
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index aa485950..655842ca 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -2068,7 +2068,7 @@ <h2>Using your own reference</h2>
 <code>configfile</code>. The reference data folder must have the
 following structure:</p>
 <pre><code>[refdir]
-  ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data with RSIDs in SNP column)
+  ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data in build GRCh37 with RSIDs in SNP column)
   ├── ref.chr[1-22].rds (SNP data - refer to default ref data for format)
   ├── ref.pop.txt (Population data for reference individuals - with header)
   ├── keep_files
@@ -2092,11 +2092,11 @@ <h2>Altering PGS method parameters</h2>
 ptclump</li>
 <li><code>dbslmm_h2f</code>: list SNP-h2 folds for DBSLMM - use
 <code>1</code> for the default model</li>
-<li><code>prscs_phi</code>: list phi parameters for PRS-CS - use
-<code>auto</code> for the auto model</li>
-<li><code>prscs_ldef</code>: Selected whether PRS-CS ld reference is
-derived from <code>1kg</code> (default) or <code>ukb</code> (UK
-Biobank).</li>
+<li><code>prscs_phi</code>: list phi parameters for PRS-CS and PRS-CSx -
+use <code>auto</code> for the auto model</li>
+<li><code>prscs_ldef</code>: Selected whether PRS-CS/PRS-CSx LD
+reference is derived from <code>1kg</code> (default) or <code>ukb</code>
+(UK Biobank).</li>
 <li><code>ldpred2_model</code>: list models for LDpred2 -
 <code>grid</code>, <code>auto</code>, <code>inf</code></li>
 </ul>
diff --git a/docs/running_offline.Rmd b/docs/running_offline.Rmd
index 4d08f230..6f452928 100644
--- a/docs/running_offline.Rmd
+++ b/docs/running_offline.Rmd
@@ -55,13 +55,17 @@ singularity \
 The resources required by the pipeline depends on the analyses requested by the user. I have provided rules to download required resources for two scenarios:
 
 - `get_key_resources`: Allows for **most** PGS methods (`dbslmm`, `lassosum`, `megaprs`, `ptclump`)
-- `get_all_resources`: Allows for **all** PGS methods (additionally incl. `ldpred2`, `prscs`, `sbayesr`)
+- `get_all_resources`: Allows for **all** PGS methods (additionally incl. `ldpred2`, `prscs`, `prscsx`, `quickprs`, `sbayesr`, `sbayesrc`, `xwing`)
 
-Alternatively, the user can download only the data required for `ldpred2`, `prscs`, `sbayesr`:
+Alternatively, the user can download only the data required for `ldpred2`, `prscs`, `prscsx`, `quickprs`, `sbayesr`, `sbayesrc`, `xwing`:
 
 - `get_ldpred2_resources`: Allows for **ldpred2**
 - `get_prscs_resources`: Allows for **prscs**
+- `get_prscsx_resources`: Allows for **prscsx**
+- `get_quickprs_resources`: Allows for **quickprs**
 - `get_sbayesr_resources`: Allows for **sbayesr**
+- `get_sbayesrc_resources`: Allows for **sbayesrc**
+- `get_xwing_resources`: Allows for **xwing**
 
 **Note.** `23andMe` format target data will only be allowed for if the `download_impute2_data` rule is run, as reference data for imputation is required.
 
diff --git a/docs/running_offline.html b/docs/running_offline.html
index 00233b7b..76d3bbdf 100644
--- a/docs/running_offline.html
+++ b/docs/running_offline.html
@@ -468,17 +468,28 @@ <h1>Downloading pipeline resources</h1>
 <code>megaprs</code>, <code>ptclump</code>)</li>
 <li><code>get_all_resources</code>: Allows for <strong>all</strong> PGS
 methods (additionally incl. <code>ldpred2</code>, <code>prscs</code>,
-<code>sbayesr</code>)</li>
+<code>prscsx</code>, <code>quickprs</code>, <code>sbayesr</code>,
+<code>sbayesrc</code>, <code>xwing</code>)</li>
 </ul>
 <p>Alternatively, the user can download only the data required for
-<code>ldpred2</code>, <code>prscs</code>, <code>sbayesr</code>:</p>
+<code>ldpred2</code>, <code>prscs</code>, <code>prscsx</code>,
+<code>quickprs</code>, <code>sbayesr</code>, <code>sbayesrc</code>,
+<code>xwing</code>:</p>
 <ul>
 <li><code>get_ldpred2_resources</code>: Allows for
 <strong>ldpred2</strong></li>
 <li><code>get_prscs_resources</code>: Allows for
 <strong>prscs</strong></li>
+<li><code>get_prscsx_resources</code>: Allows for
+<strong>prscsx</strong></li>
+<li><code>get_quickprs_resources</code>: Allows for
+<strong>quickprs</strong></li>
 <li><code>get_sbayesr_resources</code>: Allows for
 <strong>sbayesr</strong></li>
+<li><code>get_sbayesrc_resources</code>: Allows for
+<strong>sbayesrc</strong></li>
+<li><code>get_xwing_resources</code>: Allows for
+<strong>xwing</strong></li>
 </ul>
 <p><strong>Note.</strong> <code>23andMe</code> format target data will
 only be allowed for if the <code>download_impute2_data</code> rule is
diff --git a/pipeline/misc/docker/Dockerfile b/pipeline/misc/docker/Dockerfile
index 58fe3196..5561ca87 100644
--- a/pipeline/misc/docker/Dockerfile
+++ b/pipeline/misc/docker/Dockerfile
@@ -34,7 +34,7 @@ RUN /opt/mambaforge/bin/conda init bash && \
     . /opt/mambaforge/etc/profile.d/conda.sh && \
     conda activate genopred && \
     cd /tools/GenoPred/pipeline && \
-    snakemake --restart-times 3 -j1 --use-conda --conda-frontend mamba install_r_packages resources/software/pgscatalog_utils/download_pgscatalog_utils.done && \
+    snakemake --restart-times 3 -j1 --use-conda --conda-frontend mamba install_r_packages resources/software/pgscatalog_utils/download_pgscatalog_utils.done download_xwing_software && \
     mamba clean -a -y
 
 ENTRYPOINT ["/bin/bash"]
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index a04266f0..f2940b31 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -349,7 +349,7 @@ if (config["leopard_methods"] and config["leopard_methods"] != "NA"):
   
     if invalid_pops:
       raise ValueError(
-        f"Default quickprs reference data is only available for EUR, EAS, and AFR populations. For other populations, please provide your own quickprs reference data using the quickprs_multi_ldref parameter."
+        f"Default LEOPARD+QuickPRS reference data is only available for EUR, EAS, and AFR populations. For other populations, please provide your own LEOPARD+QuickPRS reference data using the quickprs_multi_ldref parameter."
       )
   else:
     quickprs_multi_ldref=config['quickprs_multi_ldref']
@@ -371,18 +371,27 @@ if (config["leopard_methods"] and config["leopard_methods"] != "NA"):
 # Set sbayesrc reference path
 if config['sbayesrc_ldref'] == 'NA':
   sbayesrc_ldref=f"{resdir}/data/sbayesrc_ref"
+
+  # Check if gwas_list contains invalid populations
+  valid_pops = {'EUR', 'EAS', 'AFR'}
+  invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
+
+  if invalid_pops:
+    raise ValueError(
+      f"Default sbayesrc reference data is only available for EUR, EAS, and AFR populations. For other populations, please provide your own sbayesrc reference data using the sbayesrc_ldref parameter."
+    )
 else:
   sbayesrc_ldref=config['sbayesrc_ldref']
 
-# Check the sbayesrc ldref data is present for the required populations in the gwas_list
-if 'sbayesrc' in config['pgs_methods']:
-  for pop in gwas_list_df['population'].unique():
-    path = f"{sbayesrc_ldref}/{pop}"
-    # Check if required files exists
-    cors_file = os.path.join(path, f"ldm.info")
-    if not os.path.exists(cors_file):
-      print(f"File not found: {cors_file}")
-      raise FileNotFoundError(f"Required file not found: {cors_file}. sbayesrc reference data must include ldm.info for all populations.")
+  # Check the sbayesrc ldref data is present for the required populations in the gwas_list
+  if 'sbayesrc' in config['pgs_methods']:
+    for pop in gwas_list_df['population'].unique():
+      path = f"{sbayesrc_ldref}/{pop}"
+      # Check if required files exists
+      cors_file = os.path.join(path, f"ldm.info")
+      if not os.path.exists(cors_file):
+        print(f"File not found: {cors_file}")
+        raise FileNotFoundError(f"Required file not found: {cors_file}. sbayesrc reference data must include ldm.info for all populations.")
 
 ####
 # Check reference data
@@ -882,6 +891,38 @@ rule download_sbayesrc_annot:
     }} > {log} 2>&1
     """
 
+# Download SBayesRC reference data
+sbayesrc_ref_urls = {
+    'EUR': 'https://sbayes.pctgplots.cloud.edu.au/data/SBayesRC/resources/v2.0/LD/HapMap3/ukbEUR_HM3.zip',
+    'EAS': 'https://sbayes.pctgplots.cloud.edu.au/data/SBayesRC/resources/v2.0/LD/HapMap3/ukbEAS_HM3.zip',
+    'AFR': 'https://sbayes.pctgplots.cloud.edu.au/data/SBayesRC/resources/v2.0/LD/HapMap3/ukbAFR_HM3.zip'
+}
+
+rule download_sbayesrc_ref:
+  output:
+    f"{resdir}/data/sbayesrc_ref/{{population}}/block148.eigen.bin"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_sbayesrc_ref-{{population}}.txt"
+  log:
+    f"{resdir}/data/logs/download_sbayesrc_ref-{{population}}.log"
+  params:
+    url=lambda w: sbayesrc_ref_urls.get(w.population)
+  shell:
+    """
+    {{
+      mkdir -p {resdir}/data/sbayesrc_ref; \
+      rm -r -f {resdir}/data/sbayesrc_ref/{wildcards.population}; \
+      wget --no-check-certificate -O {resdir}/data/sbayesrc_ref/{wildcards.population}.zip {params.url}; \
+      unzip {resdir}/data/sbayesrc_ref/{wildcards.population}.zip -d {resdir}/data/sbayesrc_ref/{wildcards.population}; \
+      rm {resdir}/data/sbayesrc_ref/{wildcards.population}.zip; \
+      mv {resdir}/data/sbayesrc_ref/{wildcards.population}/ukb{wildcards.population}_HM3/* {resdir}/data/sbayesrc_ref/{wildcards.population}/
+    }} > {log} 2>&1
+    """
+
+rule download_sbayesrc_ref_all:
+  input:
+    lambda w: expand(f"{resdir}/data/sbayesrc_ref/{{population}}/block148.eigen.bin", population=['EUR', 'EAS', 'AFR'])
+
 # Download SBayesRC R package
 rule install_sbayesrc:
   input:
@@ -1037,30 +1078,6 @@ rule download_ldak5_2:
     """
 
 # Download QuickPRS reference data
-# NOTE. This doesn't currently work as the reference data on LDAK website isn't in the right format for LDAK 5.1, 5.2, or 6
-#rule download_quickprs_ref:
-#  output:
-#    f"{resdir}/data/quickprs/{{population}}/{{population}}.cors.bin"
-#  benchmark:
-#    f"{resdir}/data/benchmarks/download_quickprs_ref-{{population}}.txt"
-#  log:
-#    f"{resdir}/data/logs/download_quickprs_ref-{{population}}.log"
-#  params:
-#    pop_code=lambda wildcards: {'EUR': 'gbr', 'SAS': 'sas', 'EAS': 'eas', 'AFR': 'afr'}[wildcards.population]
-#  shell:
-#    """
-#    {{
-#      mkdir -p {resdir}/data/quickprs; \
-#      rm -r -f {resdir}/data/quickprs/{wildcards.population}; \
-#      wget --no-check-certificate -O {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz https://genetics.ghpc.au.dk/doug/{params.pop_code}.hapmap.tar.gz; \
-#      tar -zxvf {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz -C {resdir}/data/quickprs/; \
-#      mv {resdir}/data/quickprs/{params.pop_code}.hapmap {resdir}/data/quickprs/{wildcards.population}; \
-#      find {resdir}/data/quickprs/{wildcards.population} -type f -name '*{params.pop_code}*' -exec bash -c 'mv \"$0\" \"${{0//{params.pop_code}/{wildcards.population}}}\"' {{}} \; \
-#      find {resdir}/data/quickprs/{wildcards.population} -type f -name '*hapmap*' -exec bash -c 'mv \"$0\" \"${{0//.hapmap./.}}\"' {{}} \; \
-#      rm {resdir}/data/quickprs/{wildcards.population}.hapmap.tar.gz
-#    }} > {log} 2>&1
-#    """
-
 quickprs_ref_gdrive = {
     'EUR': '10fuqn6X23dA9WKjQd9xs7xUDiFjTwfz9',
     'EAS': '1m1OI9HpHbVcX88YvtIt80-1zonSWrZP_',
@@ -1200,13 +1217,6 @@ rule install_genoutils:
     }} > {log} 2>&1
     """
 
-# Install R packages (handy function for when conda env updates erroneously)
-rule install_r_packages:
-  input:
-    rules.install_ggchicklet.output,
-    rules.install_lassosum.output,
-    rules.install_genoutils.output
-
 # Download pgscatalog_utils
 rule download_pgscatalog_utils:
   output:
@@ -1433,6 +1443,16 @@ rule download_bridgeprs_software:
     }} > {log} 2>&1
     """
 
+# Install R packages (handy function for when conda env updates erroneously)
+rule install_r_packages:
+  input:
+    rules.install_ggchicklet.output,
+    rules.install_lassosum.output,
+    rules.install_genoutils.output,
+    rules.install_genoutils_sbayesrc.output,
+    rules.install_genoutils_xwing.output,
+    rules.install_tlprs.output
+
 ############
 # Check all dependencies are available
 ############
@@ -1458,29 +1478,6 @@ rule get_dependencies:
 # Rules for preparing offline resources
 ############
 
-rule get_all_resources:
-  input:
-    rules.download_plink.output,
-    rules.download_ldsc.output,
-    rules.download_dbslmm.output,
-    rules.download_prscs_software.output,
-    rules.download_gctb_software.output,
-    rules.download_ldak.output,
-    rules.download_ldscores_panukb.output,
-    rules.download_hm3_snplist.output,
-    rules.download_ld_blocks.output,
-    rules.download_prscs_ref_ukb_all.input,
-    rules.download_prscs_ref_1kg_all.input,
-    rules.download_gctb_ref.output,
-    rules.download_ldpred2_ref.output,
-    rules.download_ldak_map.output,
-    rules.download_ldak_bld.output,
-    rules.download_ldak_highld.output,
-    rules.download_default_ref.output,
-    rules.download_quickprs_ref_all.output
-  output:
-    touch(f"{resdir}/software/get_all_resources.done")
-
 rule get_key_resources:
   input:
     rules.download_plink.output,
@@ -1496,7 +1493,7 @@ rule get_key_resources:
     rules.download_default_ref.output
   output:
     touch(f"{resdir}/software/get_key_resources.done")
-
+    
 rule get_prscs_resources:
   input:
     rules.get_key_resources.output,
@@ -1506,6 +1503,17 @@ rule get_prscs_resources:
   output:
     touch(f"{resdir}/software/get_prscs_resources.done")
 
+rule get_prscsx_resources:
+  input:
+    rules.get_key_resources.output,
+    rules.download_prscs_ref_ukb_all.input,
+    rules.download_prscs_ref_1kg_all.input,
+    rules.download_prscs_snp_data_ukb.output,
+    rules.download_prscs_snp_data_1kg.output,
+    rules.download_prscsx_software.output
+  output:
+    touch(f"{resdir}/software/get_prscsx_resources.done")
+
 rule get_ldpred2_resources:
   input:
     rules.get_key_resources.output,
@@ -1520,3 +1528,45 @@ rule get_sbayesr_resources:
     rules.download_gctb_ref.output
   output:
     touch(f"{resdir}/software/get_sbayesr_resources.done")
+
+rule get_xwing_resources:
+  input:
+    rules.get_key_resources.output,
+    rules.download_logodetect_ref_all.input,
+    rules.download_leopard_ref_all.input,
+    rules.download_leopard_panther_ref_all.input,
+    rules.download_leopard_panther_snp_data.output
+  output:
+    touch(f"{resdir}/software/get_xwing_resources.done")
+
+rule get_sbayesrc_resources:
+  input:
+    rules.get_key_resources.output,
+    rules.download_gctb252_software.output,
+    rules.download_sbayesrc_annot.output,
+    rules.download_sbayesrc_ref_all.input
+  output:
+    touch(f"{resdir}/software/get_sbayesrc_resources.done")
+
+rule get_quickprs_resources:
+  input:
+    rules.get_key_resources.output,
+    rules.download_ldak5_2.output,
+    rules.download_quickprs_ref_all.input,
+    rules.download_quickprs_leopard_ref_all.input
+  output:
+    touch(f"{resdir}/software/get_quickprs_resources.done")
+    
+rule get_all_resources:
+  input:
+    rules.get_key_resources.output,
+    rules.get_prscs_resources.output,
+    rules.get_sbayesr_resources.output,
+    rules.get_ldpred2_resources.output,
+    rules.get_prscsx_resources.output,
+    rules.get_xwing_resources.output,
+    rules.get_sbayesrc_resources.output,
+    rules.get_quickprs_resources.output
+  output:
+    touch(f"{resdir}/software/get_all_resources.done")
+

From 375d011b8175c62295ebc7c647839f32866dbb2c Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 21 Apr 2025 15:11:30 +0100
Subject: [PATCH 136/153] Updates

---
 docs/CrossPop.Rmd      | 220 ++++++++++++++++++++++++++++++++++++++---
 docs/CrossPop.html     |  66 ++++++++++++-
 docs/miraclestrial.Rmd | 107 +++++++++++++++++++-
 3 files changed, 375 insertions(+), 18 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index f84e669a..4ea2c270 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1156,11 +1156,11 @@ dev.off()
 </details>
 
 ```{bash, eval=T, echo=F}
-mkdir -p /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025
+mkdir -p /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025
 
-cp ~/oliverpainfel/Analyses/crosspop/plots/ldsc_h2.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+cp ~/oliverpainfel/Analyses/crosspop/plots/ldsc_h2.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
-cp ~/oliverpainfel/Analyses/crosspop/plots/popcorn_rg.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+cp ~/oliverpainfel/Analyses/crosspop/plots/popcorn_rg.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
 ```
 
@@ -1178,7 +1178,7 @@ cp ~/oliverpainfel/Analyses/crosspop/plots/popcorn_rg.png /scratch_tmp/prj/olive
 
 <div class="centered-container">
 <div class="rounded-image-container" style="width: 100%;">
-<img src="Images/CrossPop_2025/ldsc_h2.png">
+<img src="Images/CrossPop_2025/popcorn_rg.png">
 </div>
 </div>
 
@@ -1524,9 +1524,9 @@ summary(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])
 </details>
 
 ```{bash, eval=T, echo=F}
-cp ~/oliverpainfel/Analyses/crosspop/plots/avengeme_h2.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+cp ~/oliverpainfel/Analyses/crosspop/plots/avengeme_h2.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
-cp ~/oliverpainfel/Analyses/crosspop/plots/avengeme_polygenicity.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+cp ~/oliverpainfel/Analyses/crosspop/plots/avengeme_polygenicity.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
 ```
 
@@ -3583,11 +3583,11 @@ dev.off()
 
 ```{bash, eval=T, echo=F}
 
-cp ~/oliverpainfel/Analyses/crosspop/plots/average_r.perc_improv.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+cp ~/oliverpainfel/Analyses/crosspop/plots/average_r.perc_improv.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
-cp ~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.perc_improv.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+cp ~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.perc_improv.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
-cp ~/oliverpainfel/Analyses/crosspop/plots/average_r_leopard.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+cp ~/oliverpainfel/Analyses/crosspop/plots/average_r_leopard.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
 ```
 
@@ -3847,13 +3847,66 @@ ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
   labs(fill = "Correlation")
 dev.off()
 
+###
+# Check calibration of LEOPARD compared to QuickPRS observed weights
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
+tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
+
+# Calculate metrics
+rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)^2))
+me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)
+
+rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)^2))
+me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)
+
+# Create annotation data.frame
+metrics_df <- data.frame(
+  Discovery = c("AFR", "EAS"),
+  x = c(0.5, 0.5),         # Adjust positions as needed
+  y = c(-0.05, -0.05),
+  label = c(
+    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
+    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
+  )
+)
+
+png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
+  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
+  labs(
+    x = "LEOPARD weight",
+    y = "Observed weight",
+  ) +
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+  ) +
+  facet_grid(. ~ Discovery) +
+  coord_fixed()    
+dev.off()
 ```
 
 </details>
 
 ```{bash, eval=T, echo=F}
 
-cp ~/oliverpainfel/Analyses/crosspop/plots/leopard_weights.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+cp ~/oliverpainfel/Analyses/crosspop/plots/leopard_weights.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
 ```
 
@@ -4922,7 +4975,7 @@ tmp_sbayesrc$R_diff_p
 
 ```{bash, eval=T, echo=F}
 
-cp ~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_tlprs.png
+cp ~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_tlprs.png
 
 ```
 
@@ -5874,7 +5927,7 @@ dev.off()
 
 ```{bash, eval=T, echo=F}
 
-cp ~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/comparison_to_main_result.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
+cp ~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/comparison_to_main_result.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
 ```
 
@@ -6664,7 +6717,7 @@ dev.off()
 
 ```{bash, eval=T, echo=F}
 
-cp ~/oliverpainfel/Analyses/crosspop/plots_three_pop/average_r.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_three_gwas.png
+cp ~/oliverpainfel/Analyses/crosspop/plots_three_pop/average_r.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_three_gwas.png
 
 ```
 
@@ -7125,7 +7178,7 @@ dev.off()
 
 ```{bash, eval=T, echo=F}
 
-cp ~/oliverpainfel/Analyses/crosspop/plots_meta/average_r.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_meta.png
+cp ~/oliverpainfel/Analyses/crosspop/plots_meta/average_r.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_meta.png
 
 ```
 
@@ -7567,7 +7620,7 @@ dev.off()
 
 ```{bash, eval=T, echo=F}
 
-cp ~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png /scratch_tmp/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_downsample.png
+cp ~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_downsample.png
 
 ```
 
@@ -7582,3 +7635,140 @@ cp ~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png /scratch_tmp
 </details>
 
 ***
+
+## Using MVP sumstats
+
+### Download MVP sumstats
+
+```{r}
+library(data.table)
+
+mvp <- fread('~/oliverpainfel/Data/GWAS_sumstats/MVP/MVP_sumstats.txt')
+mvp_afr <- mvp[grepl('Afr', mvp$discoverySampleAncestry),]
+mvp_afr <- mvp_afr[!grepl('Eur|Asi|His', mvp_afr$discoverySampleAncestry),]
+
+# Identify traits in common with UKB, UGR, EAS
+prscsx_dat<-fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv')
+selected_traits <-
+  fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt',
+        header = F)$V1
+
+prscsx_dat <- prscsx_dat[prscsx_dat$labels %in% selected_traits, ]
+
+# Subset MVP to selected traits
+mvp_afr_subset <- mvp_afr[
+  mvp_afr$accessionId %in% 
+    c(
+      'GCST90475361', 'GCST90475375', 'GCST90476298', 'GCST90475155', 'GCST90476462', 'GCST90475457', 'GCST90476423', 'GCST90475528', 'GCST90475351', 'GCST90476402'
+    )
+, ]
+
+# platelet count  has low sample size for AFR (N = 235)
+mvp_afr_subset <- mvp_afr_subset[mvp_afr_subset$efoTraits != 'platelet count',]
+
+# Insert labels
+mvp_afr_subset$labels <- NA
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'body mass index'] <- 'BMI'
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'body weight'] <- 'BWT'
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'high density lipoprotein cholesterol measurement'] <- 'HDL'
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'body height'] <- 'HT'
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'hemoglobin measurement'] <- 'HB'
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'mean corpuscular hemoglobin concentration'] <- 'MCHC'
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'neutrophil count'] <- 'NEU'
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'systolic blood pressure'] <- 'SBP'
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'total cholesterol measurement'] <- 'TC'
+
+mvp_afr_subset$url <-paste0(mvp_afr_subset$summaryStatistics, '/', mvp_afr_subset$accessionId, '.tsv.gz')
+
+dir.create('~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR')
+
+write.table(
+  mvp_afr_subset[, c('url', 'labels'), with = F],
+  '~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/urls.txt',
+  row.names = F,
+  quote = F,
+  col.names = F
+)
+
+```
+
+```{bash}
+for pheno in $(cat ~/oliverpainfel/Data/GWAS_sumstats/MVP/urls.txt | cut -d' ' -f 2); do
+  url=$(awk -v var="$pheno" '$2 == var {print $1}' ~/oliverpainfel/Data/GWAS_sumstats/MVP/urls.txt)
+  sbatch -p interruptible_cpu,neurohack_cpu -t 1:00:00 --wrap="wget -O ~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/${pheno}.txt.gz ${url}"
+done
+```
+
+***
+
+### PGS calculation
+
+Run leading single-source PGS methods using MVP GWAS sumstats. 
+
+<details><summary>Show code</summary>
+
+<div class="shallow-break"></div>
+
+<h4>Prepare configuration</h4>
+
+```{r}
+
+library(data.table)
+
+# Subset original gwas_list to include selected traits
+gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt')
+mvp_sumstats <-fread('~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/urls.txt', header = F)
+gwas_list <- gwas_list[gwas_list$population == 'AFR',]
+gwas_list$labels <- gsub('_UGR', '', gwas_list$name)
+gwas_list <- gwas_list[gwas_list$labels %in% mvp_sumstats$V2,]
+gwas_list$name <- gsub('UGR', 'MVP_AFR', gwas_list$name)
+gwas_list$label <- gsub('UGR', 'MVP_AFR', gwas_list$label)
+gwas_list$label <- paste0('"', gwas_list$label, '"')
+
+gwas_list$path <-
+  paste0('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/',
+         gwas_list$labels,'.txt.gz')
+
+gwas_list$labels <- NULL
+
+write.table(gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt', col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
+#  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','ldpred2','sbayesrc']", 
+  "pgs_methods: ['ptclump','quickprs']", 
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 50",
+  "ldpred2_inference: F",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3",
+  "quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+***
+
+<h4>Run pipeline</h4>
+
+```{bash}
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml \
+  output_all -n
+```
+
+</details>
+
+
diff --git a/docs/CrossPop.html b/docs/CrossPop.html
index 087b8ee7..cea7bcc2 100644
--- a/docs/CrossPop.html
+++ b/docs/CrossPop.html
@@ -1573,7 +1573,7 @@ <h2>Plot LDSC and POPCORN results</h2>
 </summary>
 <div class="centered-container">
 <div class="rounded-image-container" style="width: 100%;">
-<p><img src="Images/CrossPop_2025/ldsc_h2.png"></p>
+<p><img src="Images/CrossPop_2025/popcorn_rg.png"></p>
 </div>
 </div>
 </details>
@@ -6012,7 +6012,9 @@ <h4>
 <pre class="r"><code>######
 # gwas_list
 ######
+
 library(data.table)
+
 # Subset original gwas_list to include selected traits
 gwas_list&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt&#39;)
 pheno&lt;-gsub(&#39;_.*&#39;,&#39;&#39;, gwas_list$name)
@@ -6020,7 +6022,11 @@ <h4>
 gwas_list&lt;-gwas_list[pheno %in% selected_traits,]
 gwas_list$label&lt;-paste0(&#39;&quot;&#39;, gwas_list$label, &#39;&quot;&#39;)
 
-write.table(gwas_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&#39;, col.names = T, row.names = F, quote = F)
+write.table(
+  gwas_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&#39;, 
+  col.names = T, 
+  row.names = F, 
+  quote = F)
 
 ######
 # gwas_groups
@@ -6918,6 +6924,62 @@ <h4>
           legend.justification = &quot;center&quot;)
 dev.off()
 
+# Make simplified plot
+# Just show performance when using IndivTrain (or SumStat), and Remove &#39;All&#39; model, with both GWAS.
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- tmp[tmp$Method != &#39;all&#39;,]
+tmp &lt;- tmp[tmp$Source  == &#39;Multi&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+tmp&lt;-tmp[tmp$Model == &#39;IndivTune&#39;,]
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r_simple.png&#39;), res=300, width = 3200, height = 2000, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23, fill = &#39;black&#39;) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+tmp&lt;-tmp[tmp$Method %in% c(&#39;ldpred2&#39;,&#39;prscsx&#39;,&#39;xwing&#39;),]
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots/average_r_simple_ldpred2.png&#39;), res=300, width = 500, height = 500, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+  #  geom_point(stat=&quot;identity&quot;, position=position_dodge(1), fill = &#39;#3399FF&#39;) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23, fill = &#39;#3399FF&#39;) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+
 ####
 # Create heatmap showing difference between all methods and models
 ####
diff --git a/docs/miraclestrial.Rmd b/docs/miraclestrial.Rmd
index 206775d5..9ecb4e31 100644
--- a/docs/miraclestrial.Rmd
+++ b/docs/miraclestrial.Rmd
@@ -61,7 +61,7 @@ We could also further stratify the polygenic scores by functional annotations, s
 
 ## Methylation scores
 
-We can use previously published EWAS summary statistics to do the same thing. Alternatively, we can use 
+We can use previously published EWAS summary statistics to do the same thing.
 
 ***
 
@@ -209,3 +209,108 @@ snakemake \
   output_all -n 
 ```
 
+The vast majority of the clinical trial participants are of European ancestry (Non-EUR N = 15, EUR N = 267). We should restrict the primary analysis to EUR, and then rerun key analyses in full sample. 
+
+***
+
+Analyses:
+
+1. Replicate primary analysis of clinical trial (is this published/can I see details on this)
+2. Then test for interaction between PGS and IL-22 on primary endpoint
+  - We will have very little power to detect main/interaction effects at statistical significance
+  - Could test PGS for risk as primary analysis, as previous studies have done, and then do a hypothesis-free analysis across many PGS, controlling for multiple testing?
+    - ALS risk GWAS Could build multi-trait and multi-population model. Build elastic net containing many scores using ProjectMine and independent GWAS
+    
+
+What about Jiajing's PGS? seemed to predict risk very highly.
+
+I can generate the PGS and hand them over, or do the analysis? Depends on how Ahmad wants to structure the papers.
+
+***
+
+# Clinical data
+
+I have been sent some preliminary clinical data for the trial. Lets have a look at it:
+
+```{r}
+library(data.table)
+library(readxl)
+
+data <- read_excel("~/oliverpainfel/scratch_tmp/Data/miraclestrial/original/Survival-MIR.xlsx")
+data <- data.table(data)
+
+# Rename variables
+original_names<-names(data)
+names(data) <- c('reference','label','id','c9','unc13a','diagnosis','country','site','reference_2','sex','age_at_diagnosis','age_at_onset','site_of_onset','arm','sex_2','familial','alfrs','svc','age_at_death','age_at_death_rounded_down','randomisation','reference_3','gender','age_c','site_of_onset_2','arm_2')
+
+# Overview of data
+str(data)
+summary(data)
+
+# Check references are identical
+all(data$reference == data$reference_2)
+all(data$reference == data$reference_3) # We can delete reference_3 as identical to reference
+data$reference_3 <- NULL
+data[data$reference != data$reference_2,] # There is an reference value matches different row in reference_2.
+
+# Check sex are identical
+all(data$sex == data$sex_2)
+all(data$sex == data$gender)
+all(data$sex_2 == data$gender) # We can delete gender as identical to sex_2
+data$gender <- NULL
+data[data$sex != data$sex_2,] # The same rows with discordant reference values have discordant sex values.
+
+# Check site of onset
+all(data$site_of_onset == data$site_of_onset_2)
+data[data$site_of_onset != data$site_of_onset_2,] # One individual has different site of onset (also mismatched for reference)
+
+# Check arm
+all(data$arm == data$arm_2, na.rm = T) # They are identical
+data$arm_2 <- NULL
+data$arm
+
+# Check age variables
+# age_c seems to be age_at_diagnosis, just rounded to 1 decimal point, so delete age_c
+data$age_c <- NULL
+
+# Create variable indicating death before end of trial
+data$age_at_death_rounded_down <- NULL
+data$died_during_trial <- ifelse(grepl('Alive at cut off date', data$age_at_death), F, T)
+data$died_during_trial[grepl('NA', data$age_at_death)] <- NA
+data$age_at_death <- as.numeric(data$age_at_death)
+
+# Check site of onset
+all(data$site_of_onset == data$site_of_onset_2)
+
+#####
+# Run cox regression
+#####
+library(survival)
+
+# Subset those in the trial
+trial_data <- data[!is.na(data$arm),]
+trial_data <- trial_data[trial_data$randomisation == 'RN',]
+nrow(trial_data) # 218 - This is two less than the publication
+
+# We need to create a time to event variable
+# We can assume age at diagnosis is the date of enrolment
+# The trial ran for 640 days, so we can insert time_to_event for those that survived
+trial_data$time_to_event <- trial_data$age_at_death - trial_data$age_at_diagnosis
+trial_data$time_to_event[trial_data$died_during_trial == F] <- trial_data$age_at_death[trial_data$died_during_trial == F] - trial_data$age_at_diagnosis[trial_data$died_during_trial == F] + (640/365)
+trial_data$status <- as.numeric(trial_data$died_during_trial)
+trial_data$arm_numeric <- ifelse(trial_data$arm == 'IL2', 1, 0)
+
+cox_model <- coxph(Surv(time_to_event, status) ~ arm_numeric, data = trial_data)
+summary(cox_model)
+
+fit <- survfit(Surv(time_to_event, status) ~ arm_numeric, data = trial_data)
+plot(fit, col = c("blue", "red"), xlab = "Time (years)", ylab = "Survival probability")
+legend("bottomleft", legend = c("Control", "Treatment"), col = c("blue", "red"), lty = 1)
+
+# Shows non-significant increase in survival in those given IL-2
+# The real trial used prognostic covariates which we do not have access to currently.
+```
+
+
+
+

From eee0e43c1ca570ba5cb4526532339bef65d343e4 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Tue, 29 Apr 2025 20:49:20 +0100
Subject: [PATCH 137/153] Updating cross_pop for reviewer comments

---
 docs/CrossPop.Rmd | 1922 +++++++++++++++++++++++++++++++++++++++++++--
 1 file changed, 1842 insertions(+), 80 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 4ea2c270..b9090351 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -3717,11 +3717,56 @@ for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_met
   }
 }
 
+###
+# Estimate weights if using the inverse variance weighting
+###
+
+# Read in GWAS descriptives
+gwas_desc<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+gwas_desc <- gwas_desc[, c('Trait Label','Ancestry','GWAS N'), with=F]
+names(gwas_desc)<-c('trait','ancestry','n')
+gwas_desc<-gwas_desc[gwas_desc$trait %in% selected_traits,]
+
+library(dplyr)
+library(tidyr)
+
+# Reshape GWAS table to wide format
+wide_gwas <- gwas_desc %>%
+  pivot_wider(names_from = ancestry, values_from = n, values_fill = 0)
+
+# Function to create rows for each pair
+make_weights_long <- wide_gwas %>%
+  rowwise() %>%
+  do({
+    trait <- .$trait
+    eur <- .$EUR
+    afr <- .$AFR
+    eas <- .$EAS
+    
+    tibble(
+      Trait = trait,
+      Method = "inverse_var",
+      Target = c("AFR", "AFR", "EUR", "EUR", "EUR", "EAS", "EAS"),
+      Discovery = c("EUR", "AFR", "EUR", "AFR", "EAS", "EUR", "EAS"),
+      Weight = c(
+        eur / (eur + afr), afr / (eur + afr),  # AFR target
+        eur / (eur + afr), afr / (eur + afr),  # EUR target (vs AFR)
+        eas / (eur + eas),                     # EUR target (vs EAS)
+        eur / (eur + eas), eas / (eur + eas)   # EAS target (vs EUR)
+      )
+    )
+  }) %>%
+  bind_rows()
+
 ###
 # Combine and compare
 ###
 
-both <- do.call(rbind, list(obs_weights, leopard_weights))
+both <- do.call(rbind, list(obs_weights, leopard_weights, make_weights_long))
+
+# Remove ptclump as it doesn't have a sumstattune method
+both <- both[both$Method != 'ptclump',]
+
 both<-merge(both, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x=T, sort = F)
 both$label[is.na(both$label)] <- both$Method[is.na(both$label)]
 both$label <- factor(both$label, levels=unique(both$label))
@@ -3731,7 +3776,7 @@ tmp <- both[both$Target != 'EUR',]
 tmp <- tmp[tmp$Discovery != 'EUR',]
 
 # Set LEOPARD to black fill
-default_colors <- hue_pal()(9)
+default_colors <- hue_pal()(10)
 names(default_colors) <- levels(tmp$label)
 default_colors["LEOPARD"] <- "black"
 
@@ -3754,7 +3799,7 @@ tmp <- both[both$Target == 'EUR',]
 tmp <- tmp[tmp$Discovery != 'EUR',]
 
 # Set LEOPARD to black fill
-default_colors <- hue_pal()(9)
+default_colors <- hue_pal()(10)
 names(default_colors) <- levels(tmp$label)
 default_colors["LEOPARD"] <- "black"
 
@@ -3772,46 +3817,11 @@ ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
   ylim(c(0,1))
 dev.off()
 
-# Calculate correlation between weights from each method
-# Start with non-EUR target
-tmp <- both[both$Target != 'EUR',]
-tmp$Discovery<-NULL
-tmp_wide <- reshape(tmp, 
-                     idvar = c("Trait", "Target"), 
-                     timevar = "label", 
-                     direction = "wide")
-
-names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
-tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
-
-cor_matrix_EAS <- cor(tmp_wide[tmp_wide$Target == 'EAS', -1:-2])
-cor_matrix_AFR <- cor(tmp_wide[tmp_wide$Target == 'AFR', -1:-2])
-
-# Convert correlation matrix to long format for ggplot
-cor_df_EAS <- melt(cor_matrix_EAS)
-cor_df_AFR <- melt(cor_matrix_AFR)
-cor_df_EAS$Target <- 'Target = EAS'
-cor_df_AFR$Target <- 'Target = AFR'
-cor_df <- rbind(cor_df_AFR, cor_df_EAS)
-
-# Create ggplot correlation heatmap with text inside
-png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_corr.png', units = 'px', width = 3300, height = 1700, res = 300)
-ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
-  geom_tile(color = "white") +  # Tile plot with white borders
-  geom_text(aes(label = round(value, 2)), color = "black") +  # Add correlation values
-  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +  # Color scale
-  theme_half_open() +
-  panel_border() + 
-  theme(
-    axis.text.x = element_text(angle = 45, hjust = 1),
-    axis.title = element_blank()
-  ) +
-  facet_grid(. ~ Target) +
-  labs(fill = "Correlation")
-dev.off()
+###
+# Check calibration of LEOPARD compared to QuickPRS observed weights
+###
 
-# Start with non-EUR target
-tmp <- both[both$Target == 'EUR',]
+tmp <- both[both$Target != 'EUR',]
 tmp$Target<-NULL
 tmp_wide <- reshape(tmp, 
                      idvar = c("Trait", "Discovery"), 
@@ -3821,34 +3831,48 @@ tmp_wide <- reshape(tmp,
 names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
 tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
 
-cor_matrix_EAS <- cor(tmp_wide[tmp_wide$Discovery == 'EAS', -1:-2])
-cor_matrix_AFR <- cor(tmp_wide[tmp_wide$Discovery == 'AFR', -1:-2])
+tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
+tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
+
+# Calculate metrics
+rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)^2))
+me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)
 
-# Convert correlation matrix to long format for ggplot
-cor_df_EAS <- melt(cor_matrix_EAS)
-cor_df_AFR <- melt(cor_matrix_AFR)
-cor_df_EAS$Target <- 'Discovery = EAS + EUR'
-cor_df_AFR$Target <- 'Discovery = AFR + EUR'
-cor_df <- rbind(cor_df_AFR, cor_df_EAS)
+rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)^2))
+me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)
 
-# Create ggplot correlation heatmap with text inside
-png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_corr_eur.png', units = 'px', width = 3300, height = 1700, res = 300)
-ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
-  geom_tile(color = "white") +  # Tile plot with white borders
-  geom_text(aes(label = round(value, 2)), color = "black") +  # Add correlation values
-  scale_fill_gradient2(low = "blue", mid = "white", high = "red", midpoint = 0) +  # Color scale
+# Create annotation data.frame
+metrics_df <- data.frame(
+  Discovery = c("AFR", "EAS"),
+  x = c(0.5, 0.5),         # Adjust positions as needed
+  y = c(-0.05, -0.05),
+  label = c(
+    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
+    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
+  )
+)
+
+png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
+  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
+  labs(
+    x = "LEOPARD weight",
+    y = "Observed weight",
+  ) +
   theme_half_open() +
   panel_border() + 
   theme(
-    axis.text.x = element_text(angle = 45, hjust = 1),
-    axis.title = element_blank()
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
   ) +
-  facet_grid(. ~ Target) +
-  labs(fill = "Correlation")
+  facet_grid(. ~ Discovery) +
+  coord_fixed()    
 dev.off()
 
 ###
-# Check calibration of LEOPARD compared to QuickPRS observed weights
+# Check calibration of inverse_var compared to QuickPRS observed weights
 ###
 
 tmp <- both[both$Target != 'EUR',]
@@ -3865,11 +3889,11 @@ tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
 tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
 
 # Calculate metrics
-rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)^2))
-me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)
+rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)^2))
+me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)
 
-rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)^2))
-me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)
+rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)^2))
+me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)
 
 # Create annotation data.frame
 metrics_df <- data.frame(
@@ -3882,14 +3906,14 @@ metrics_df <- data.frame(
   )
 )
 
-png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
-ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) +
+png('~/oliverpainfel/Analyses/crosspop/plots/inverse_var_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = inverse_var, y = QuickPRS)) +
   geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
   geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
   geom_point(alpha = 0.7) +
-  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 1.5, size = 3.5) +
   labs(
-    x = "LEOPARD weight",
+    x = "inverse_var weight",
     y = "Observed weight",
   ) +
   theme_half_open() +
@@ -3900,6 +3924,162 @@ ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) +
   facet_grid(. ~ Discovery) +
   coord_fixed()    
 dev.off()
+
+###
+# Check calibration of observed weights across all methods
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp <- tmp[!(tmp$label %in% c('LEOPARD','inverse_var')),]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
+
+metrics <- NULL
+for(i in c('EAS','AFR')){
+  for(j in unique(tmp$label)){
+    for(k in unique(tmp$label)){
+      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
+      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics <- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
+      )
+    }
+  }
+}
+
+png('~/oliverpainfel/Analyses/crosspop/plots/observed_weights_calibration.png', units = 'px', width = 3000, height = 1650, res = 300)
+ggplot(metrics, aes(x = Method1, y = Method2, fill = rmse)) +
+  geom_tile(color = "white") +  # Tile plot with white borders
+  geom_text(aes(label = round(rmse, 2)), color = "black") +  # Add correlation values
+  scale_fill_gradient2(mid = "white", high = "red", midpoint = 0) +  # Color scale
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1),
+    axis.title = element_blank()
+  ) +
+  facet_grid(. ~ Population) +
+  labs(fill = "RMSE")
+dev.off()
+
+# Calculate average RMSE for each method against all other methods
+metrics_unique <- metrics[metrics$Method1 != metrics$Method2, ]
+metrics_unique$Comparison <- NA
+for (i in 1:nrow(metrics_unique)) {
+  metrics_unique$Comparison[i] <-
+    paste0(sort(c(
+      metrics_unique$Method1[i], metrics_unique$Method2[i]
+    )), collapse = ' vs. ')
+}
+metrics_unique <- metrics_unique[!duplicated(paste0(metrics_unique$Population, metrics_unique$Comparison)),]
+
+mean_rmse <- NULL
+for(i in unique(tmp$label)){
+  for(j in c('AFR','EAS')){
+    metrics_unique_tmp <- metrics_unique[metrics_unique$Method1 == i | metrics_unique$Method2 == i,]
+    metrics_unique_tmp <- metrics_unique_tmp[metrics_unique_tmp$Population == j,]
+    mean_rmse <- rbind(
+      mean_rmse, 
+      data.frame(
+        Method = i,
+        Population = j,
+        avg_rmse = mean(metrics_unique_tmp$rmse)
+      )
+    )
+  }
+}
+
+png('~/oliverpainfel/Analyses/crosspop/plots/avg_observed_weight_rmse.png', units = 'px', width = 1500, height = 1500, res = 300)
+ggplot(mean_rmse, aes(x = Method, y = avg_rmse, fill = Method)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
+  geom_text(aes(label = round(avg_rmse, 3)),  # <-- Add this
+          vjust = 1.5,                    # <-- Move the text slightly above the bar
+          size = 3) +                      # <-- Adjust text size
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Population ~ .) +
+  theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  labs(y = 'Average RMSE') +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position="none")
+dev.off()
+
+###
+# Check calibration of estimated (LEOPARD and inverse_var) weights compared to observed QuickPRS weights
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp <- tmp[(tmp$label %in% c('LEOPARD','inverse_var','QuickPRS')),]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
+
+metrics <- NULL
+for(i in c('EAS','AFR')){
+  for(j in unique(tmp$label)){
+    for(k in unique(tmp$label)){
+      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
+      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics <- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
+      )
+    }
+  }
+}
+
+# Plot the rmse for LEOPARD and inverse_var predicting observed QuickPRS weight
+metrics <- metrics[metrics$Method1 == 'QuickPRS',]
+metrics <- metrics[metrics$Method2 != 'QuickPRS',]
+
+png('~/oliverpainfel/Analyses/crosspop/plots/inverse_var_comp_rmse.png', units = 'px', width = 800, height = 1500, res = 300)
+ggplot(metrics, aes(x = Method2, y = rmse, fill = Method2)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
+  geom_text(aes(label = round(rmse, 3)),  # <-- Add this
+          vjust = 1.5,                    # <-- Move the text slightly above the bar
+          size = 3) +                      # <-- Adjust text size
+  facet_grid(Population ~ .) +
+  theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  labs(y = 'RMSE relative to QuickPRS', x = 'Method') +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position="none")
+dev.off()
+
 ```
 
 </details>
@@ -4222,7 +4402,7 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml \
-  output_all -n
+  target_pgs -n
 ```
 
 </details>
@@ -5199,7 +5379,7 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \
-  output_all -n
+  target_pgs -n
 ```
 
 </details>
@@ -7663,9 +7843,6 @@ mvp_afr_subset <- mvp_afr[
     )
 , ]
 
-# platelet count  has low sample size for AFR (N = 235)
-mvp_afr_subset <- mvp_afr_subset[mvp_afr_subset$efoTraits != 'platelet count',]
-
 # Insert labels
 mvp_afr_subset$labels <- NA
 mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'body mass index'] <- 'BMI'
@@ -7675,6 +7852,7 @@ mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'body height'] <- 'HT'
 mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'hemoglobin measurement'] <- 'HB'
 mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'mean corpuscular hemoglobin concentration'] <- 'MCHC'
 mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'neutrophil count'] <- 'NEU'
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'platelet count'] <- 'PLT'
 mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'systolic blood pressure'] <- 'SBP'
 mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'total cholesterol measurement'] <- 'TC'
 
@@ -7690,11 +7868,13 @@ write.table(
   col.names = F
 )
 
+write.table(mvp_afr_subset$labels, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', col.names = F, row.names = F, quote = F)
+
 ```
 
 ```{bash}
-for pheno in $(cat ~/oliverpainfel/Data/GWAS_sumstats/MVP/urls.txt | cut -d' ' -f 2); do
-  url=$(awk -v var="$pheno" '$2 == var {print $1}' ~/oliverpainfel/Data/GWAS_sumstats/MVP/urls.txt)
+for pheno in $(cat ~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/urls.txt | cut -d' ' -f 2); do
+  url=$(awk -v var="$pheno" '$2 == var {print $1}' ~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/urls.txt)
   sbatch -p interruptible_cpu,neurohack_cpu -t 1:00:00 --wrap="wget -O ~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/${pheno}.txt.gz ${url}"
 done
 ```
@@ -7739,11 +7919,10 @@ write.table(gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/
 
 config<-c(
   "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
-  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml",
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt",
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
-#  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','ldpred2','sbayesrc']", 
-  "pgs_methods: ['ptclump','quickprs']", 
+  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','ldpred2','sbayesrc']", 
   "cores_prep_pgs: 10",
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
@@ -7766,9 +7945,1592 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml \
-  output_all -n
+  prep_pgs -n
 ```
 
 </details>
 
+***
+
+### PGS evaluation
+
+Compare the single-source PGS within AFR ancestry target individuals.
+
+<details><summary>Show code</summary>
+
+<div class="shallow-break"></div>
+
+<h4>Create predictor list</h4>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Create files for AFR targets
+targ_pop <- c('AFR')
+for(trait_i in selected_traits){
+  scores_i <- scores[grepl(trait_i, scores$name),]
+  scores_i$multi <- scores_i$method
+  
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'BBJ'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'MVP_AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('BBJ','UGR')
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == 'BBJ'){
+        disc_pop_j_2 <- 'EAS'
+      }
+      if(disc_pop_j == 'MVP_AFR'){
+        disc_pop_j_2 <- 'AFR'
+      }
+
+      dir.create(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i
+        ),
+        recursive = T
+      )
+      
+      scores_i_j <- scores_i[
+        (grepl('UKB$', scores_i$name, ignore.case = F) | 
+         grepl(paste0(disc_pop_j, '$'), scores_i$name, ignore.case = T)),]
+
+      # Insert path to score file
+      scores_i_j$predictor <- paste0(
+        outdir,
+        '/ukb/pgs/TRANS/',
+        scores_i_j$method,
+        '/',
+        scores_i_j$name,
+        '/ukb-',
+        scores_i_j$name,
+        '-TRANS.profiles'
+      )
+      
+      ####
+      # Make groups single source methods
+      ####
+      
+      scores_i_j_single_top1 <-
+        scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &
+                     !grepl('_multi$', scores_i_j$method), ]
+
+      # Create top1 column indicating which predictors top1 models should be derived
+      scores_i_j_single_top1$top1[grepl('UKB', scores_i_j_single_top1$name, ignore.case = F)] <- 'EUR'
+      scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] <- disc_pop_j_2
+      
+      ####
+      # Make groups containing pseudo scores for single source methods
+      ####
+
+      # Extract the pseudo score for each method and specify as a separate group
+      for(i in 1:nrow(scores_i_j_single_top1)) {
+        param <- find_pseudo(
+          config = config,
+          gwas = scores_i_j_single_top1$name[i],
+          pgs_method = scores_i_j_single_top1$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header <-
+          fread(scores_i_j_single_top1$predictor[i], nrows = 1)
+        score_cols <-
+          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_single_top1$name[i], '_', param)))
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            scores_i_j_single_top1$predictor[i], 
+            " > ", 
+            gsub('.profiles',
+                 paste0('.', targ_pop_i, '_pseudo.profiles'),
+                 scores_i_j_single_top1$predictor[i])
+          )
+        )
+      }
+      
+      scores_i_j_single_pseudo <- scores_i_j_single_top1
+      scores_i_j_single_pseudo$multi <- paste0(scores_i_j_single_pseudo$multi, '.pseudo')
+
+      scores_i_j_single_pseudo$predictor <- gsub('.profiles', 
+                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
+                                    scores_i_j_single_pseudo$predictor)
+
+#      ####
+#      # Make groups for multi-single-source pseudo scores
+#      ####
+#      
+#      scores_i_j_multi_single_pseudo <- scores_i_j[grepl('_multi$', scores_i_j$method),]
+#
+#      # Extract the pseudo score for each method and specify as a separate group
+#      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
+#        param <- find_pseudo(
+#          config = config,
+#          gwas = scores_i_j_multi_single_pseudo$name[i],
+#          pgs_method = scores_i_j_multi_single_pseudo$method[i],
+#          target_pop = targ_pop_i
+#        )
+#        
+#        score_header <-
+#          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
+#        score_cols <-
+#          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi_single_pseudo$name[i], '_', param)))
+#        
+#        system(
+#          paste0(
+#            "cut -d' ' -f ", 
+#            paste0(score_cols, collapse=','),
+#            " ", 
+#            scores_i_j_multi_single_pseudo$predictor[i], 
+#            " > ", 
+#            gsub('.profiles',
+#                 paste0('.', targ_pop_i, '_pseudo.profiles'),
+#                 scores_i_j_multi_single_pseudo$predictor[i])
+#          )
+#        )
+#      }
+#      
+#      scores_i_j_multi_single_pseudo$multi <- paste0(scores_i_j_multi_single_pseudo$multi, '.pseudo')
+#
+#      scores_i_j_multi_single_pseudo$predictor <- gsub('.profiles', 
+#                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
+#                                    scores_i_j_multi_single_pseudo$predictor)
+#      
+#      scores_i_j_multi_single_pseudo$top1<-paste0('EUR_', disc_pop_j_2)
+#
+#      ####
+#      # Make groups for the Multi-Source methods
+#      ####
+#      
+#      scores_i_j_multi <- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
+#
+#      # Split top1 scores by target population
+#      # This doesn't apply to xwing because it only has pop-specific pseudo scores
+#      scores_i_j_multi_top1<-NULL
+#      for(i in 1:which(scores_i_j_multi$method %in% c('prscsx'))){
+#        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
+#        
+#        for(pop in c('EUR', disc_pop_j_2)){
+#          
+#          if(scores_i_j_multi$method[i] == 'prscsx'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi'), names(score_header)))
+#          }
+#          if(scores_i_j_multi$method[i] == 'xwing'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst'), names(score_header)))
+#          }
+#          
+#          system(
+#            paste0(
+#              "cut -d' ' -f ", 
+#              paste0(score_cols, collapse=','),
+#              " ", 
+#              scores_i_j_multi$predictor[i], 
+#              " > ", 
+#              gsub('.profiles',
+#                   paste0('.', pop, '_grid.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#            )
+#          )
+#          
+#          tmp <- scores_i_j_multi[i,]
+#          tmp$multi <- paste0(tmp$multi, '.grid')
+#          tmp$top1 <- pop
+#          tmp$predictor <-
+#              gsub('.profiles',
+#                   paste0('.', pop, '_grid.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#          
+#          scores_i_j_multi_top1 <- rbind(scores_i_j_multi_top1, tmp)
+#        }
+#      }
+#
+#      # Split pop-specific pseudo scores by target population
+#      scores_i_j_multi_pop_pseudo<-NULL
+#      for(i in 1:nrow(scores_i_j_multi)){
+#        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
+#        
+#        for(pop in c('EUR', disc_pop_j_2)){
+#          if(scores_i_j_multi$method[i] == 'prscsx'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi_auto'), names(score_header)))
+#          }
+#          if(scores_i_j_multi$method[i] == 'xwing'){
+#            score_cols <-
+#              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst_', pop), names(score_header)))
+#          }
+#          
+#          system(
+#            paste0(
+#              "cut -d' ' -f ", 
+#              paste0(score_cols, collapse=','),
+#              " ", 
+#              scores_i_j_multi$predictor[i], 
+#              " > ", 
+#              gsub('.profiles',
+#                   paste0('.', pop, '_pseudo.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#            )
+#          )
+#          
+#          tmp <- scores_i_j_multi[i,]
+#          tmp$multi <- paste0(tmp$multi, '.pop_pseudo')
+#          tmp$top1 <- pop
+#          tmp$predictor <-
+#              gsub('.profiles',
+#                   paste0('.', pop, '_pseudo.profiles'),
+#                   scores_i_j_multi$predictor[i])
+#          
+#          scores_i_j_multi_pop_pseudo <- rbind(scores_i_j_multi_pop_pseudo, tmp)
+#        }
+#      }
+#      
+#      # Create pseudo score for multi-source methods
+#      scores_i_j_multi_pseudo<-NULL
+#      for(i in 1:nrow(scores_i_j_multi)) {
+#        param <- find_pseudo(
+#          config = config,
+#          gwas = scores_i_j_multi$name[i],
+#          pgs_method = scores_i_j_multi$method[i],
+#          target_pop = targ_pop_i
+#        )
+#        
+#        score_header <-
+#          fread(scores_i_j_multi$predictor[i], nrows = 1)
+#        score_cols <-
+#          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
+#
+#        system(
+#          paste0(
+#            "cut -d' ' -f ", 
+#            paste0(score_cols, collapse=','),
+#            " ", 
+#            scores_i_j_multi$predictor[i], 
+#            " > ", 
+#            gsub('.profiles',
+#                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
+#                 scores_i_j_multi$predictor[i])
+#          )
+#        )
+#        
+#        tmp <- scores_i_j_multi[i,]
+#        tmp$multi <- paste0(tmp$multi, '.pseudo')
+#        tmp$top1 <- paste0('EUR_', disc_pop_j_2)
+#        tmp$predictor <-
+#            gsub('.profiles',
+#                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
+#                 scores_i_j_multi$predictor[i])
+#        
+#        scores_i_j_multi_pseudo <- rbind(scores_i_j_multi_pseudo, tmp)
+#      }
+#      
+      ####
+      # Combine the different predictor groups
+      ####
+      predictors_i<- do.call(rbind, list(
+        scores_i_j_single_top1, 
+        scores_i_j_single_pseudo 
+#        scores_i_j_multi_single_pseudo,
+#        scores_i_j_multi_top1,
+#        scores_i_j_multi_pop_pseudo,
+#        scores_i_j_multi_pseudo
+      ))
+      
+      predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
+      
+      ####
+      # Make a group that will combined all population specific PGS
+      ####
+      
+      predictors_i_all <- predictors_i[predictors_i$top1 %in% c('EUR','AFR','EAS'),]
+      predictors_i_all$multi <- 'all'
+      predictors_i<-rbind(predictors_i, predictors_i_all)
+      
+      write.table(
+        predictors_i,
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i,
+          '/predictor_list.txt'
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}
+
+```
+
+***
+
+<h4>Run model_builder</h4>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_*.disc_EUR_*/*/res*
+
+for targ_pop in $(echo AFR); do
+  if [ "$targ_pop" == "EUR" ]; then
+      targ_pop2="EUR_test"
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ "$targ_pop" == "EUR" ]; then
+    disc_pop=$(echo EAS AFR)
+  fi
+  
+  if [ "$targ_pop" == "EAS" ]; then
+    disc_pop="EAS"
+  fi
+  
+  if [ "$targ_pop" == "AFR" ]; then
+    disc_pop="AFR"
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+      if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt" ]; then
+        sbatch --mem 10G -n 1 --exclude=erc-hpc-comp058 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+          --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
+          --n_core 1"
+      fi
+    done
+  done
+done
+
+```
+
+***
+
+<h4>Plot results</h4>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
+info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+
+# Calculate correlation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop = c('AFR')
+res_eval <- list()
+for(pheno_i in selected_traits){
+  res_eval_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.pred_eval.txt'
+          )
+        )
+      eval_i$Target<-targ_pop_i
+      eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_eval_i<-rbind(res_eval_i, eval_i)
+    }
+  }
+  
+  res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
+  res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
+  
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTune'
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTune'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTune'
+  
+  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTune'
+  
+  res_eval_i$Source<-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
+    !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
+  
+  res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
+  res_eval_i$Discovery[grepl('EAS', res_eval_i$Group)] <- 'EAS'
+  res_eval_i$Discovery[grepl('AFR', res_eval_i$Group)] <- 'AFR'
+  res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
+  
+  res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+  res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
+      res_eval_i$Model %in% c('IndivTune','Multi-IndivTune')),]
+  
+  # Remove pseudo model for methods that don't really have one 
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
+      res_eval_i$Model %in% c('SumStatTune','Multi-SumStatTune')),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
+  res_eval_i <- res_eval_i[
+    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method)) & 
+      grepl('top1', res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
+    "Target", "Method", "Model", "Source", "Discovery","gwas_group"
+  )]),]
+  
+  res_eval[[pheno_i]]<-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi", "PRS-CSx", "X-Wing", "All") 
+
+res_eval_simp <- NULL
+for(pheno_i in selected_traits){
+  tmp <- res_eval[[pheno_i]]
+  tmp$Trait <- pheno_i
+  
+  # Insert nice PGS method names
+  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+  tmp$label[is.na(tmp$label)] <- 'All'
+  tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+  tmp$label <- factor(tmp$label, levels = model_order)
+  
+  # Simplify result to either SumStatTune or IndivTune
+  tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+  tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+  tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery','Model'), with=F]),]
+  
+  res_eval_simp <- rbind(res_eval_simp, tmp)
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_eval for each scenario
+    res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x <- res_eval[[i]]
+      x$pheno <- names(res_eval)[i]
+      x <- x[x$Target == targ_pop_i]
+      x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
+    }))
+    
+    # Average res_evalults for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_eval_i$Sample<-'A'
+  
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno <-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+      
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          'res_evalults missing for ',
+          targ_pop_i,
+          ' ',
+          group_i,
+          ' ',
+          paste0(missing_pheno, collapse = ' ')
+        ))
+      }
+      
+      cors_i <- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i <-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_eval_group_i
+        )
+      
+      tmp <- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval <- rbind(meta_res_eval, tmp)
+    }
+  }
+}
+
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+write.csv(meta_res_eval, '~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/r_eval.csv', row.names = F)
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r.png'), res=300, width = 3200, height = 2000, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+# Plot average performance across phenotypes for EUR using AFR or EAS GWAS
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target == 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean <- paste0(tmp$Discovery_clean, ' GWAS')
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_eur.png'), res=300, width = 4000, height = 1500, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+# Plot performance of -multi models trained using LEOPARD vs using indiv-level data
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)], '-multi')
+tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
+tmp<-tmp[grepl('multi', tmp$label),]
+tmp <- tmp[tmp$Model != 'Multi-IndivTune',]
+tmp$Model<-as.character(tmp$Model)
+tmp$Model[tmp$Model != 'SumStatTune']<-'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune']<-'LEOPARD'
+tmp$Target <- paste0(tmp$Target, ' Target')
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_leopard.png'), res=300, width = 1500, height = 2000, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+# Make simplified plot
+# Just show performance when using IndivTrain (or SumStat), and Remove 'All' model, with both GWAS.
+tmp <- meta_res_eval
+tmp <- tmp[tmp$Target != 'EUR',]
+tmp <- tmp[tmp$Method != 'all',]
+tmp <- tmp[tmp$Source  == 'Multi',]
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
+tmp<-tmp[tmp$Model == 'IndivTune',]
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_simple.png'), res=300, width = 3200, height = 2000, units = 'px')
+ggplot(tmp, aes(x=label, y=R)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23, fill = 'black') +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ ., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+tmp<-tmp[tmp$Method %in% c('ldpred2','prscsx','xwing'),]
+png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_simple_ldpred2.png'), res=300, width = 500, height = 500, units = 'px')
+ggplot(tmp, aes(x=label, y=R)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+  #  geom_point(stat="identity", position=position_dodge(1), fill = '#3399FF') +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23, fill = '#3399FF') +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ ., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Create a function to mirror pred_comp results
+mirror_comp<-function(x){
+  x_sym <- x
+  x_sym$Model_1 <- x$Model_2
+  x_sym$Model_2 <- x$Model_1
+  x_sym$Model_1_R <- x$Model_2_R
+  x_sym$Model_2_R <- x$Model_1_R
+  x_sym$R_diff <- -x_sym$R_diff
+  x_mirrored <- rbind(x, x_sym)
+  x_diag<-data.frame(
+      Model_1=unique(x_mirrored$Model_1),
+      Model_2=unique(x_mirrored$Model_1),
+      Model_1_R=x_mirrored$Model_1_R,
+      Model_2_R=x_mirrored$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp<-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
+# Read in results
+targ_pop=c('EUR','EAS','AFR')
+res_comp <- list()
+for(pheno_i in selected_traits){
+  res_comp_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      comp_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.pred_comp.txt'
+          )
+        )
+      comp_i<-mirror_comp(comp_i)
+      comp_i$Target<-targ_pop_i
+      comp_i$gwas_group<-paste0('EUR+', disc_pop_i)
+      res_comp_i<-rbind(res_comp_i, comp_i)
+    }
+  }
+  
+  res_comp[[pheno_i]]<-res_comp_i
+}
+
+res_comp_all <- do.call(rbind, lapply(names(res_comp), function(name) {
+  x <- res_comp[[name]]
+  x$pheno <- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Annotate tests to get order correct
+res_comp_all$Method1<-sub('\\..*','',res_comp_all$Model_1)
+res_comp_all$Method1<-gsub('-.*','', res_comp_all$Method1)
+res_comp_all$Method2<-sub('\\..*','',res_comp_all$Model_2)
+res_comp_all$Method2<-gsub('-.*','', res_comp_all$Method2)
+
+find_model<-function(x){
+  mod <- x
+  mod[grepl('top1$', x) & !grepl('pseudo', x)] <- 'IndivTune'
+  mod[grepl('top1$', x) & grepl('pseudo', x)] <- 'SumStatTune'
+  mod[grepl('multi$', x) & !grepl('pseudo', x)] <- 'Multi-IndivTune'
+  mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTune'
+  mod[grepl('_multi', x)] <- 'SumStatTune'
+  mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTune'
+  mod[x == 'xwing.pseudo.multi'] <- 'SumStatTune'
+  
+  return(mod)
+}
+
+res_comp_all$Model1<-find_model(res_comp_all$Model_1)
+res_comp_all$Model2<-find_model(res_comp_all$Model_2)
+
+res_comp_all$Source1<-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method1) | !grepl('AFR|EAS|EUR', res_comp_all$Model_1), 'Multi', 'Single')
+res_comp_all$Source2<-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method2) | !grepl('AFR|EAS|EUR', res_comp_all$Model_2), 'Multi', 'Single')
+  
+for(i in c('EUR','EAS','AFR')){
+  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] <- i
+  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] <- i
+}
+res_comp_all$Discovery1[res_comp_all$Source1 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source1 == 'Multi']
+res_comp_all$Discovery2[res_comp_all$Source2 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source2 == 'Multi']
+
+res_comp_all$Method1<-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
+res_comp_all$Method2<-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
+res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+res_comp_all$Discovery1<-factor(res_comp_all$Discovery1, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
+res_comp_all$Discovery2<-factor(res_comp_all$Discovery2, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+# Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method1 %in%  c('quickprs','sbayesrc') & 
+  res_comp_all$Model1 %in% c('IndivTune','Multi-IndivTune')),]
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method2 %in%  c('quickprs','sbayesrc') & 
+  res_comp_all$Model2 %in% c('IndivTune','Multi-IndivTune')),]
+
+# Remove pseudo model for methods that don't really have one 
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method1 %in%  c('ptclump','ptclump_multi') & 
+  res_comp_all$Model1 %in% c('SumStatTune','Multi-SumStatTune')),]
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method2 %in%  c('ptclump','ptclump_multi') & 
+  res_comp_all$Model2 %in% c('SumStatTune','Multi-SumStatTune')),]
+
+# Remove top1 models for PRS-CSx
+res_comp_all <- res_comp_all[
+!(grepl('prscsx|xwing|_multi', res_comp_all$Method1) & 
+  grepl('top1', res_comp_all$Model_1)),]
+res_comp_all <- res_comp_all[
+!(grepl('prscsx|xwing|_multi', res_comp_all$Method2) & 
+  grepl('top1', res_comp_all$Model_2)),]
+
+# Remove any comparisons
+res_comp_all <- res_comp_all[!duplicated(res_comp_all[, c("Target", "gwas_group", "Method1", "Model1", "Source1", "Discovery1", "Method2", "Model2", "Source2", "Discovery2",'pheno')]),]
+
+res_comp_all$r_diff_rel <- res_comp_all$R_diff / res_comp_all$Model_2_R
+
+# Calculate relative improvement for ldpred2-multi vs ldpred2 as example
+tmp_ldpred2 <- res_comp_all[res_comp_all$Model_1 == 'ldpred2.multi' & 
+                    grepl('ldpred2-', res_comp_all$Model_2) &
+                    res_comp_all$Target == 'AFR',]
+tmp_ldpred2 <- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+tmp_ldpred2 <- tmp_ldpred2[!duplicated(tmp_ldpred2$pheno),]
+round(min(tmp_ldpred2$r_diff_rel)*100, 1)
+round(max(tmp_ldpred2$r_diff_rel)*100, 1)
+
+tmp_ldpred2 <- res_comp_all[res_comp_all$Model_1 == 'ldpred2.multi' & 
+                    grepl('ldpred2-', res_comp_all$Model_2) &
+                    res_comp_all$Target == 'EAS',]
+tmp_ldpred2 <- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+tmp_ldpred2 <- tmp_ldpred2[!duplicated(tmp_ldpred2$pheno),]
+round(min(tmp_ldpred2$r_diff_rel)*100, 1)
+round(max(tmp_ldpred2$r_diff_rel)*100, 1)
+
+# Calculate relative improvement for sbayesrc-multi vs sbayesrc in EUR target as example
+tmp_sbayesrc <- res_comp_all[res_comp_all$Model_1 == 'sbayesrc.pseudo.multi' & 
+                    grepl('sbayesrc.pseudo-', res_comp_all$Model_2) &
+                    res_comp_all$Target == 'EUR' &
+                    res_comp_all$Discovery1 == 'EUR+EAS' &
+                    res_comp_all$Discovery2 == 'EUR',]
+tmp_sbayesrc <- tmp_sbayesrc[order(-tmp_sbayesrc$Model_2_R),]
+round(min(tmp_sbayesrc$r_diff_rel)*100, 1)
+round(max(tmp_sbayesrc$r_diff_rel)*100, 1)
+
+tmp_sbayesrc <- res_comp_all[res_comp_all$Model_1 == 'sbayesrc.pseudo.multi' & 
+                    grepl('sbayesrc.pseudo-', res_comp_all$Model_2) &
+                    res_comp_all$Target == 'EUR' &
+                    res_comp_all$Discovery1 == 'EUR+AFR' &
+                    res_comp_all$Discovery2 == 'EUR',]
+tmp_sbayesrc <- tmp_sbayesrc[order(-tmp_sbayesrc$Model_2_R),]
+round(min(tmp_sbayesrc$r_diff_rel)*100, 1)
+round(max(tmp_sbayesrc$r_diff_rel)*100, 1)
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTune or SumStatTune result
+tmp <- res_comp_all
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label1'
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method2', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label2'
+
+tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'], '-multi')
+tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'], '-multi')
+
+tmp$Model1[tmp$Model1 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model1[tmp$Model1 == 'SumStatTune'] <- 'SumStatTune'
+tmp$Model2[tmp$Model2 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model2[tmp$Model2 == 'SumStatTune'] <- 'SumStatTune'
+
+tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` <- paste0(tmp$label1, ' - ', tmp$Model1, ' - ', tmp$Discovery1)
+tmp$`Model 2` <- paste0(tmp$label2, ' - ', tmp$Model2, ' - ', tmp$Discovery2)
+
+tmp <- tmp[, c('Target', 'pheno', 'Model 1', 'Model 2', 'Model_1_R', 'Model_2_R', 'R_diff', 'R_diff_pval'), with=F]
+names(tmp) <- c('Target', 'Trait','Model 1', 'Model 2', "R (Model 1)", "R (Model 2)", "R difference (Model 1 R - Model 2 R)", "R difference p-value")
+
+tmp<-tmp[order(tmp$Target, tmp$Trait, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` <- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` <- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` <- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, '~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/r_diff.csv', row.names=F)
+
+###########
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_comp <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_comp for each scenario
+    res_comp_i <- res_comp_all[res_comp_all$Target == targ_pop_i & res_comp_all$gwas_group == paste0('EUR+', disc_pop_i)]
+  
+    # Calculate diff SE based on p-value
+    res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] <- 1-0.001
+    res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]<-1-0.001
+    res_comp_i$R_diff_z<-qnorm(res_comp_i$R_diff_pval/2)
+    res_comp_i$R_diff_SE<-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
+        
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_comp_i$Sample<-'A'
+    res_comp_i$Group <- paste0(res_comp_i$Model_1, '_vs_', res_comp_i$Model_2)
+  
+    for(group_i in unique(res_comp_i$Group)){
+      res_comp_group_i <- res_comp_i[res_comp_i$Group == group_i,]
+      cors_i <- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
+      
+      if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
+        
+        meta_res_comp_i <-
+          agg(
+            id = Sample,
+            es = R_diff,
+            var = R_diff_SE ^ 2,
+            cor = cors_i,
+            method = "BHHR",
+            mod = NULL,
+            data = res_comp_group_i
+          )
+        
+        tmp <- res_comp_group_i[1,]
+        tmp$pheno <- NULL
+        tmp$Model_1_R <-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &
+                            meta_res_eval$Target == targ_pop_i &
+                            meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i)]
+        tmp$Model_2_R <-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &
+                            meta_res_eval$Target == targ_pop_i &
+                            meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i)]
+        tmp$R_diff <- meta_res_comp_i$es
+        tmp$R_diff_SE <- sqrt(meta_res_comp_i$var)
+        tmp$R_diff_z <- tmp$R_diff / tmp$R_diff_SE
+        tmp$R_diff_p <- 2*pnorm(-abs(tmp$R_diff_z))
+      } else {
+        tmp <- res_comp_group_i[1,]
+        tmp$pheno <- NULL
+        tmp$R_diff <- NA
+        tmp$R_diff_SE <- NA
+        tmp$R_diff_z <- NA
+        tmp$R_diff_p <- NA
+      }
+      meta_res_comp <- rbind(meta_res_comp, tmp)
+    }
+  }
+}
+
+meta_res_comp$R_diff_perc <- meta_res_comp$R_diff / meta_res_comp$Model_2_R
+  
+# Extract average improvement for ldpred2-multi vs ldpred2 as example
+tmp_ldpred2 <- meta_res_comp[meta_res_comp$Model_1 == 'ldpred2.multi' & 
+                    grepl('ldpred2-', meta_res_comp$Model_2) &
+                    meta_res_comp$Target == 'AFR',]
+tmp_ldpred2 <- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+round(min(tmp_ldpred2$R_diff_perc)*100, 1)
+
+tmp_ldpred2 <- meta_res_comp[meta_res_comp$Model_1 == 'ldpred2.multi' & 
+                    grepl('ldpred2-', meta_res_comp$Model_2) &
+                    meta_res_comp$Target == 'EAS',]
+tmp_ldpred2 <- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+round(min(tmp_ldpred2$R_diff_perc)*100, 1)
+
+# Extract average improvement for sbayesrc-multi vs sbayesrc in EUR as example
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_1 == 'sbayesrc.pseudo.multi' & 
+                    grepl('sbayesrc.pseudo', meta_res_comp$Model_2) &
+                    meta_res_comp$Target == 'EUR' &
+                    meta_res_comp$Discovery1 == 'EUR+AFR' &
+                    meta_res_comp$Discovery2 == 'EUR',]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_1 == 'sbayesrc.pseudo.multi' & 
+                    grepl('sbayesrc.pseudo', meta_res_comp$Model_2) &
+                    meta_res_comp$Target == 'EUR' &
+                    meta_res_comp$Discovery1 == 'EUR+EAS' &
+                    meta_res_comp$Discovery2 == 'EUR',]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+
+# Extract average improvement for sbayesrc in EUR compared to all model
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_2 == 'sbayesrc.pseudo-EUR.top1' &
+                    meta_res_comp$Model_1 == 'all-EUR.top1' &
+                    meta_res_comp$Target == 'AFR',]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+tmp_sbayesrc$R_diff_p
+
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$Model_2 == 'sbayesrc.pseudo-EUR.top1' &
+                    meta_res_comp$Model_1 == 'all-EUR.top1' &
+                    meta_res_comp$Target == 'EAS',]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+tmp_sbayesrc$R_diff_p
+
+
+# Compare QuickPRS-Multi vs QuickPRS to evaluate LEOPARD performance
+tmp_quickprs <- meta_res_comp[meta_res_comp$Model_1 == 'quickprs_multi.pseudo.multi' & 
+                                meta_res_comp$Model_2 == 'quickprs.pseudo.multi' &
+                    meta_res_comp$Target == 'AFR',]
+round(min(tmp_quickprs$R_diff_perc)*100, 1)
+
+tmp_quickprs <- meta_res_comp[meta_res_comp$Model_1 == 'quickprs_multi.pseudo.multi' & 
+                                meta_res_comp$Model_2 == 'quickprs.pseudo.multi' &
+                    meta_res_comp$Target == 'EAS',]
+round(min(tmp_quickprs$R_diff_perc)*100, 1)
+
+# Compare all.multi method to next best method
+tmp_all <- meta_res_comp[meta_res_comp$Model_1 == 'all.multi' &
+                    meta_res_comp$Target == 'AFR' &
+                    meta_res_comp$Source2 == 'Multi',]
+tmp_all <- tmp_all[order(tmp_all$R_diff),]
+tmp_all <- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+tmp_all <- meta_res_comp[meta_res_comp$Model_1 == 'all.multi' &
+                    meta_res_comp$Target == 'EAS' &
+                    meta_res_comp$Source2 == 'Multi',]
+tmp_all <- tmp_all[order(tmp_all$R_diff),]
+tmp_all <- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+# Compare all.multi method to next best method
+tmp_all <- meta_res_comp[meta_res_comp$Model_1 == 'all-AFR.top1' &
+                    meta_res_comp$Target == 'AFR' &
+                    meta_res_comp$Discovery1 == 'AFR' &
+                    meta_res_comp$Discovery2 == 'AFR',]
+tmp_all <- tmp_all[order(tmp_all$R_diff),]
+tmp_all <- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+tmp_all <- meta_res_comp[meta_res_comp$Model_1 == 'all-EAS.top1' &
+                    meta_res_comp$Target == 'EAS' &
+                    meta_res_comp$Discovery1 == 'EAS' &
+                    meta_res_comp$Discovery2 == 'EAS',]
+tmp_all <- tmp_all[order(tmp_all$R_diff),]
+tmp_all <- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTune or SumStatTune result
+tmp <- meta_res_comp
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label1'
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method2', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label2'
+
+tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'], '-multi')
+tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'], '-multi')
+
+tmp$Model1[tmp$Model1 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model1[tmp$Model1 == 'SumStatTune'] <- 'SumStatTune'
+tmp$Model2[tmp$Model2 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model2[tmp$Model2 == 'SumStatTune'] <- 'SumStatTune'
+
+tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` <- paste0(tmp$label1, ' - ', tmp$Model1, ' - ', tmp$Discovery1)
+tmp$`Model 2` <- paste0(tmp$label2, ' - ', tmp$Model2, ' - ', tmp$Discovery2)
+
+tmp$`Percentage change (R difference / Model 2 R)` <- paste0(round(tmp$R_diff_perc * 100, 1), '%')
+
+tmp <- tmp[, c('Target', 'Model 1', 'Model 2', 'Model_1_R', 'Model_2_R', 'R_diff',"Percentage change (R difference / Model 2 R)", 'R_diff_p'), with=F]
+names(tmp) <- c('Target','Model 1', 'Model 2', "R (Model 1)", "R (Model 2)", "R difference (Model 1 R - Model 2 R)", "Percentage change (R difference / Model 2 R)", "R difference p-value")
+
+tmp<-tmp[order(tmp$Target, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` <- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` <- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` <- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, '~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/r_diff_average.csv', row.names=F)
+
+############
+
+# Group differences
+meta_res_comp$R_diff_catagory <- cut(
+    meta_res_comp$R_diff,
+    breaks = c(-Inf, -0.08, -0.025, -0.002, 0.002, 0.025, 0.08, Inf),
+    labels = c('< -0.08', '-0.08 - -0.025', '-0.025 - -0.002', '-0.002 - 0.002', '0.002 - 0.025', '0.025 - 0.08', '> 0.08'),
+    right = FALSE
+)
+meta_res_comp$R_diff_catagory <- factor(meta_res_comp$R_diff_catagory, levels = rev(levels(meta_res_comp$R_diff_catagory)))
+
+# Assign significance stars
+meta_res_comp$indep_star<-' '
+meta_res_comp$indep_star[meta_res_comp$R_diff_p < 0.05]<-'*'
+meta_res_comp$indep_star[meta_res_comp$R_diff_p < 1e-3]<-'**'
+# meta_res_comp$indep_star[meta_res_comp$R_diff_p < 1e-6]<-'***'
+
+meta_res_comp<-meta_res_comp[order(meta_res_comp$Discovery1, meta_res_comp$Discovery2, meta_res_comp$Method1),]
+
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+
+    tmp <- meta_res_comp[meta_res_comp$Target == targ_pop_i, ]
+
+    tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+    tmp$label[is.na(tmp$label)] <- 'All'
+    names(tmp)[names(tmp) == 'label'] <- 'label1'
+    tmp <- merge(tmp, pgs_method_labels, by.x = 'Method2', by.y = 'method', all.x = T)
+    tmp$label[is.na(tmp$label)] <- 'All'
+    names(tmp)[names(tmp) == 'label'] <- 'label2'
+    
+    tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'], '-multi')
+    tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'], '-multi')
+    
+    tmp$Model1[tmp$Model1 != 'SumStatTune'] <- 'IndivTune'
+    tmp$Model1[tmp$Model1 == 'SumStatTune'] <- 'SumStatTune'
+    tmp$Model2[tmp$Model2 != 'SumStatTune'] <- 'IndivTune'
+    tmp$Model2[tmp$Model2 == 'SumStatTune'] <- 'SumStatTune'
+    
+    tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+    tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+    tmp$label1 <- factor(tmp$label1, levels = model_order)
+    tmp$label2 <- factor(tmp$label2, levels = model_order)
+
+    tmp<-tmp[order(tmp$label1, tmp$label2),]
+    
+    tmp$label1 <- paste0(tmp$label1," (", ifelse(tmp$Model1 == 'SumStatTune', 'ST', 'IT'), ")")
+    tmp$label2 <- paste0(tmp$label2," (", ifelse(tmp$Model2 == 'SumStatTune', 'ST', 'IT'), ")")
+
+    tmp$label1 <- factor(tmp$label1, levels = unique(tmp$label1))
+    tmp$label2 <- factor(tmp$label2, levels = unique(tmp$label2))
+    
+    tmp <- tmp[tmp$gwas_group == paste0('EUR+', disc_pop_i), ]
+    
+    plot_tmp <- ggplot(data = tmp, aes(label2, label1, fill = R_diff_catagory)) +
+      geom_tile(color = "white", show.legend = TRUE) +
+      labs(y = 'Test', x = 'Comparison', fill = 'R difference', title = paste0('Target: ', targ_pop_i)) +
+      facet_grid(Discovery1 ~ Discovery2, scales = 'free', space = 'free', switch="both") +
+      geom_text(
+        data = tmp,
+        aes(label2, label1, label = indep_star),
+        color = "black",
+        size = 4,
+        angle = 0,
+        vjust = 0.8
+      ) +
+      scale_fill_brewer(
+        breaks = levels(tmp$R_diff_catagory),
+        palette = "RdBu",
+        drop = F,
+        na.value = 'grey'
+      ) +
+      theme_half_open() +
+      background_grid() +
+      panel_border() +
+      theme(axis.text.x = element_text(
+        angle = 45,
+        vjust = 1,
+        hjust = 1
+      ))
+    
+    png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_diff.Discovery_EUR_', disc_pop_i,'.Target_', targ_pop_i, '.png'), res=300, width = 4400, height = 3200, units = 'px')
+      print(plot_tmp)
+    dev.off()
+  }
+}
+
+####
+# Plot relative improvement of methods
+####
+# Use ptclump IndivTune using EUR GWAS as the reference, as provides an interpretable scale
+
+meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Method2 == 'all' & meta_res_comp$Source2 == 'Multi',]
+meta_res_comp_ptclump_top1$reference_point<-F
+meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Method1 == 'all' & meta_res_comp_ptclump_top1$Source1 == 'Multi']<-T
+meta_res_comp_ptclump_top1$R_diff[is.na(meta_res_comp_ptclump_top1$R_diff)]<-0
+meta_res_comp_ptclump_top1$Discovery1 <- factor(meta_res_comp_ptclump_top1$Discovery1, levels=rev(levels(meta_res_comp_ptclump_top1$Discovery1)))
+
+res_comp_all_ptclump_top1<-res_comp_all[res_comp_all$Method2 == 'all' & res_comp_all$Source2 == 'Multi',]
+res_comp_all_ptclump_top1$Discovery1 <-  factor(res_comp_all_ptclump_top1$Discovery1, levels=levels(meta_res_comp_ptclump_top1$Discovery1))
+
+# Create data to plot reference points
+meta_res_comp_reference <- meta_res_comp_ptclump_top1
+meta_res_comp_reference$R_diff[meta_res_comp_ptclump_top1$reference_point == F] <- NA
+meta_res_comp_reference$R_diff_SE [meta_res_comp_ptclump_top1$reference_point == F] <- NA
+res_comp_all_ptclump_top1$reference_point<-F
+
+meta_tmp <- meta_res_comp_ptclump_top1
+meta_tmp <- merge(meta_tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+meta_tmp$label[is.na(meta_tmp$label)] <- 'All'
+meta_tmp$label[grepl('Multi', meta_tmp$Model1) & !(meta_tmp$Method1 %in% pgs_group_methods) & meta_tmp$label != 'All'] <- paste0(meta_tmp$label[grepl('Multi', meta_tmp$Model1) & !(meta_tmp$Method1 %in% pgs_group_methods) & meta_tmp$label != 'All'], '-multi')
+meta_tmp$label <- factor(meta_tmp$label, levels = model_order)
+meta_tmp$Discovery_clean <- as.character(meta_tmp$Discovery1)
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 == 'EUR'] <- 'EUR GWAS'
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Single'] <- 'Target-matched GWAS'
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Multi'] <- 'Both'
+meta_tmp$Discovery_clean <- factor(meta_tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+meta_tmp$Target <- paste0(meta_tmp$Target, ' Target')
+meta_tmp$Model1 <- factor(meta_tmp$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+
+meta_tmp_ref <- meta_res_comp_reference
+meta_tmp_ref <- merge(meta_tmp_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+meta_tmp_ref$label[is.na(meta_tmp_ref$label)] <- 'All'
+meta_tmp_ref$label[grepl('Multi', meta_tmp_ref$Model1) & !(meta_tmp_ref$Method1 %in% pgs_group_methods) & meta_tmp_ref$label != 'All'] <- paste0(meta_tmp_ref$label[grepl('Multi', meta_tmp_ref$Model1) & !(meta_tmp_ref$Method1 %in% pgs_group_methods) & meta_tmp_ref$label != 'All'], '-multi')
+meta_tmp_ref$label <- factor(meta_tmp_ref$label, levels = model_order)
+meta_tmp_ref$Discovery_clean <- as.character(meta_tmp_ref$Discovery1)
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 == 'EUR'] <- 'EUR GWAS'
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != 'EUR' & meta_tmp_ref$Source1 == 'Single'] <- 'Target-matched GWAS'
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != 'EUR' & meta_tmp_ref$Source1 == 'Multi'] <- 'Both'
+meta_tmp_ref$Discovery_clean <- factor(meta_tmp_ref$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+meta_tmp_ref$Target <- paste0(meta_tmp_ref$Target, ' Target')
+meta_tmp_ref$Model1 <- factor(meta_tmp_ref$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+
+tmp <- res_comp_all_ptclump_top1
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery1)
+tmp$Discovery_clean[tmp$Discovery1 == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Single'] <- 'Target-matched GWAS'
+tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('Target-matched GWAS',
+                                         'EUR GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model1 <- factor(tmp$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+
+ggplot(meta_tmp, aes(x=label, y=R_diff , fill = Model1)) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff - R_diff_SE,
+          ymax = R_diff + R_diff_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype="dotted") +
+    labs(y = "R_diff (SE)") +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+
+# Plot as % change
+meta_tmp$R_diff_perc <- meta_tmp$R_diff / meta_tmp$Model_2_R
+meta_tmp_ref$R_diff_perc <- meta_tmp_ref$R_diff / meta_tmp_ref$Model_2_R
+tmp$R_diff_perc <- tmp$R_diff / tmp$Model_2_R
+
+meta_tmp$R_diff_perc_SE <- meta_tmp$R_diff_SE / meta_tmp$Model_2_R
+
+library(scales)
+ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype="dotted") +
+    labs(y = "R diff. (SE)") +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+# Simplify results showing results only with or without training data
+meta_tmp_simple <- meta_tmp
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 == 'SumStatTune'] <- 'SumStatTune'
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 == 'SumStatTune'] <- 'SumStatTune'
+meta_tmp_simple<-meta_tmp_simple[meta_tmp_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_simple<-meta_tmp_simple[meta_tmp_simple$Model_2 %in% res_eval_simp$Group,]
+
+meta_tmp_ref_simple <- meta_tmp_ref
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == 'SumStatTune'] <- 'SumStatTune'
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == 'SumStatTune'] <- 'SumStatTune'
+meta_tmp_ref_simple<-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_ref_simple<-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_2 %in% res_eval_simp$Group,]
+
+tmp_simple <- tmp
+tmp_simple$Model1[tmp_simple$Model1 != 'SumStatTune'] <- 'IndivTune'
+tmp_simple$Model1[tmp_simple$Model1 == 'SumStatTune'] <- 'SumStatTune'
+tmp_simple$Model2[tmp_simple$Model2 != 'SumStatTune'] <- 'IndivTune'
+tmp_simple$Model2[tmp_simple$Model2 == 'SumStatTune'] <- 'SumStatTune'
+tmp_simple<-tmp_simple[tmp_simple$Model_1 %in% res_eval_simp$Group,]
+tmp_simple<-tmp_simple[tmp_simple$Model_2 %in% res_eval_simp$Group,]
+
+# Export plot for manuscript
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r.perc_improv.png', width = 3200, height = 2000, res= 300, units = 'px')
+ggplot(meta_tmp_simple[meta_tmp_simple$Target != 'EUR Target',], aes(x=label, y=R_diff_perc , fill = Model1)) +
+#    geom_boxplot(
+#      data = tmp_simple[tmp_simple$Target != 'EUR Target',],
+#        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+#        position = position_dodge(0.7),
+#        alpha = 0.3
+#      ) +
+    geom_point(
+        data = tmp_simple[tmp_simple$Target != 'EUR Target',],
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target != 'EUR Target',],
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 4,
+        shape = 22,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype="dotted") +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = "top",
+        legend.key.spacing.x = unit(2, "cm"),
+        legend.justification = "center"
+    )
+dev.off()
+
+# Plot for EUR
+meta_tmp_simple$Discovery_clean <- paste0(meta_tmp_simple$Discovery1,' GWAS')
+meta_tmp_ref_simple$Discovery_clean <- paste0(meta_tmp_ref_simple$Discovery1,' GWAS')
+tmp_simple$Discovery_clean <- paste0(tmp_simple$Discovery1,' GWAS')
+
+meta_tmp_simple<-meta_tmp_simple[!duplicated(meta_tmp_simple[, c('label', 'Discovery_clean', 'Model1'), with=F]),]
+meta_tmp_ref_simple<-meta_tmp_ref_simple[!duplicated(meta_tmp_ref_simple[, c('label', 'Discovery_clean', 'Model1'), with=F]),]
+tmp_simple<-tmp_simple[!duplicated(tmp_simple[, c('label', 'Discovery_clean', 'Model1','pheno'), with=F]),]
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_eur.perc_improv.png', width = 4000, height = 1500, res= 300, units = 'px')
+ggplot(meta_tmp_simple[meta_tmp_simple$Target == 'EUR Target',], aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp_simple[tmp_simple$Target == 'EUR Target',],
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target == 'EUR Target',],
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 4,
+        shape = 22,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype="dotted") +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = "top",
+        legend.key.spacing.x = unit(2, "cm"),
+        legend.justification = "center"
+    )
+dev.off()
+
+```
+</details>
 

From 924a93a42e3d9a2a4fa1fcc1bcc6618576c27d68 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 30 Apr 2025 15:24:54 +0100
Subject: [PATCH 138/153] Updates for reviews

---
 docs/CrossPop.Rmd | 170 +++++++++++++++++++++++++++++++++++++++-------
 1 file changed, 145 insertions(+), 25 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index b9090351..b3fbe757 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -7413,12 +7413,13 @@ for(i in selected_traits){
 ```
 
 ```{bash}
-for pheno in $(echo TC HT HB); do
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
   for n in $(echo 5 15 45 135); do
     mkdir -p /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled
     for chr in $(seq 1 22); do
-      if [ ! -f "/users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}.outcome.glm.linear" ]; then
-        sbatch -p interruptible_cpu --wrap="/users/k1806347/oliverpainfel/Software/plink2 \
+      if [ ! -s "/users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}.outcome.glm.linear" ]; then
+      if [ ! -f "/users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt.gz" ]; then
+        sbatch -p interruptible_cpu,cpu,neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/plink2 \
           --pfile /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
           --pheno /users/k1806347/oliverpainfel/Data/ukb/phenotypes/subsample/${pheno}.${n}.txt \
           --linear omit-ref cols=+a1freq,+ax \
@@ -7426,13 +7427,16 @@ for pheno in $(echo TC HT HB); do
           --geno 0.05 \
           --out /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}"
       fi
+      fi
     done
   done
 done
 
 # Once complete, merge results across chromosomes
-for pheno in $(echo TC HT HB); do
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
   for n in $(echo 5 15 45 135); do
+    if [ ! -f "/users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt.gz" ]; then
+
     head -n 1 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr1.outcome.glm.linear > /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
       for chr in $(seq 1 22); do
         tail -n +2 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}.outcome.glm.linear >> /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
@@ -7442,6 +7446,7 @@ for pheno in $(echo TC HT HB); do
       cut -f 4,5 --complement /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt | awk 'BEGIN{FS=OFS="\t"} NR==1 {$5="A2"} 1' > temp.txt && mv temp.txt /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
   
       gzip /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
+    fi
   done
 done
 
@@ -7454,6 +7459,63 @@ rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*_subsampled/*chr*
 
 ***
 
+### Subset EUR test individuals in UKB data
+
+To make this quicker, focus on evaluating the PGS methods in the EUR test subset in UKB. This will avoid reprocessing the full UKB data.
+
+<details><summary>Show code</summary>
+
+```{r}
+library(data.table)
+
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+pheno_long <- NULL
+for(i in selected_traits){
+  pheno_i <- fread(paste0(
+    '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
+    i ,
+    '.unrel.EUR_test.row_number.txt'
+  ))
+  
+  pheno_long <- rbind(
+    pheno_long, 
+    pheno_i
+  )
+}
+
+test_subset <- unique(pheno_long$FID)
+keep <- data.frame(FID = test_subset,
+                   IID = test_subset)
+
+write.table(
+  keep,
+  '~/oliverpainfel/Data/ukb/eur_test.keep',
+  row.names = F,
+  col.names = F,
+  quote = F
+)
+
+```
+
+```{bash}
+mkdir ~/oliverpainfel/Data/ukb/eur_test_subset
+
+for chr in $(seq 1 22); do
+  sbatch -p interruptible_cpu,cpu,neurohack_cpu -n 1 --mem 5G \
+  --wrap="~/oliverpainfel/Software/plink2 \
+    --pfile ~/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
+    --keep ~/oliverpainfel/Data/ukb/eur_test.keep \
+    --make-pgen \
+    --out ~/oliverpainfel/Data/ukb/eur_test_subset/ukb.chr${chr}"
+done
+
+```
+
+</details>
+
+***
+
 ### PGS calculation
 
 <details><summary>Show code</summary>
@@ -7467,9 +7529,8 @@ rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*_subsampled/*chr*
 # gwas_list
 ######
 
-gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt')
+gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt')
 pheno<-gsub('_.*','', gwas_list$name)
-gwas_list<-gwas_list[pheno %in% c('TC', 'HT', 'HB'),]
 gwas_list<-gwas_list[gwas_list$population == 'EUR',]
 gwas_list$pheno<-gsub('_UKB','',gwas_list$name)
 
@@ -7505,17 +7566,31 @@ gwas_list_subsampled$label<-paste0('"', gwas_list_subsampled$label, '"')
 
 write.table(gwas_list_subsampled, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt', col.names = T, row.names = F, quote = F)
 
+######
+# target_list
+######
+target_list <- data.frame(
+  name='ukb',
+  path='/users/k1806347/oliverpainfel/Data/ukb/eur_test_subset/ukb',
+  type='plink2',
+  indiv_report=F,
+  unrel='/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt'
+)
+
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eur_test_only')
+
+write.table(target_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eur_test_only/target_list.txt', col.names=T, row.names=F, quote=F)
+
 ######
 # config
 ######
 
 config<-c(
-  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_eur_test_only",
   "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml",
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt",
-  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
-  "pgs_methods: ['quickprs','sbayesrc','ptclump','ldpred2']",
-#  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eur_test_only/target_list.txt",
+  "pgs_methods: ['quickprs','ptclump']",
   "cores_prep_pgs: 10",
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
@@ -7565,7 +7640,7 @@ pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
-selected_traits<-c('TC', 'HT', 'HB')
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
 
 # Get a list of score files
 scores <- list_score_files(config)
@@ -7669,7 +7744,7 @@ conda activate model_builder
 
 #rm /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/*/res*
 
-for pheno in $(echo TC HT HB); do
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
   if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/res.pred_comp.txt" ]; then
     sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
       --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.EUR_test.row_number.txt \
@@ -7895,24 +7970,37 @@ Run leading single-source PGS methods using MVP GWAS sumstats.
 
 library(data.table)
 
-# Subset original gwas_list to include selected traits
-gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt')
-mvp_sumstats <-fread('~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/urls.txt', header = F)
+# Subset original gwas_list to include AFR traits
+gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt')
 gwas_list <- gwas_list[gwas_list$population == 'AFR',]
-gwas_list$labels <- gsub('_UGR', '', gwas_list$name)
-gwas_list <- gwas_list[gwas_list$labels %in% mvp_sumstats$V2,]
+selected_traits <- gsub('_UGR', '', gwas_list$name)
 gwas_list$name <- gsub('UGR', 'MVP_AFR', gwas_list$name)
 gwas_list$label <- gsub('UGR', 'MVP_AFR', gwas_list$label)
-gwas_list$label <- paste0('"', gwas_list$label, '"')
-
 gwas_list$path <-
   paste0('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/',
-         gwas_list$labels,'.txt.gz')
+         selected_traits,'.txt.gz')
+
+gwas_list_eur<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt')
+gwas_list_eur<-gwas_list_eur[gwas_list_eur$population == 'EUR', ]
 
-gwas_list$labels <- NULL
+gwas_list <- rbind(gwas_list, gwas_list_eur)
+
+gwas_list$label <- paste0('"', gwas_list$label, '"')
 
 write.table(gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt', col.names = T, row.names = F, quote = F)
 
+######
+# gwas_groups
+######
+
+gwas_groups<-data.frame(
+  name=paste0(selected_traits, '_UKB_MVP_AFR'),
+  gwas=sapply(selected_traits, function(x) paste0(x,'_UKB,',x,'_MVP_AFR')),
+  label=paste0('"', selected_traits, " (UKB+MVP_AFR)", '"')
+)
+
+write.table(gwas_groups, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_mvp.txt', col.names = T, row.names = F, quote = F)
+
 ######
 # config
 ######
@@ -7921,9 +8009,11 @@ config<-c(
   "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
   "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml",
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt",
+  "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_mvp.txt",
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
-  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','ldpred2','sbayesrc']", 
-  "cores_prep_pgs: 10",
+  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx']",
+  "leopard_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
+  "cores_prep_pgs: 10", # xwing run with 20 cores
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
   "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
@@ -8328,11 +8418,11 @@ for targ_pop in $(echo AFR); do
   for disc_pop_i in ${disc_pop}; do
     for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
       if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt" ]; then
-        sbatch --mem 10G -n 1 --exclude=erc-hpc-comp058 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+        sbatch --mem 10G -n 5 --exclude=erc-hpc-comp058 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
           --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
           --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
           --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
-          --n_core 1"
+          --n_core 5"
       fi
     done
   done
@@ -8488,6 +8578,36 @@ for(pheno_i in selected_traits){
   res_eval_simp <- rbind(res_eval_simp, tmp)
 }
 
+# Plot results for each phenotype separately
+dir.create('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots')
+
+for(pheno_i in selected_traits){
+  tmp <- res_eval_simp[res_eval_simp$Trait == pheno_i,]
+  #tmp <- tmp[tmp$Target != 'EUR',]
+  tmp$Discovery_clean <- as.character(tmp$Discovery)
+  tmp$Discovery_clean <- paste0(tmp$Discovery_clean, ' GWAS')
+  tmp$Target <- paste0(tmp$Target, ' Target')
+
+  png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/', pheno_i,'.png'), res=300, width = 3400, height = 2000, units = 'px')
+  plot_tmp<-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x=NULL, fill = NULL, title = info_all$`Trait Description`[info_all$`Trait Label` == pheno_i]) +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+  print(plot_tmp)
+  dev.off()
+}
+
 ####
 # Average results across phenotypes
 ####

From 12ab6e5dcfaf0c11be59f480512f723fe5695b0d Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 1 May 2025 16:31:04 +0100
Subject: [PATCH 139/153] Added google cookie notification

---
 docs/CrossPop.Rmd            | 12 ++++-----
 docs/cookie-consent.html     | 35 ++++++++++++++++++++++++
 docs/header.html             | 35 ++++++++++++++++++++++++
 docs/index.html              | 37 ++++++++++++++++++++++++-
 docs/pipeline_overview.html  | 35 ++++++++++++++++++++++++
 docs/pipeline_readme.html    | 52 ++++++++++++++++++++++++++++++++++++
 docs/pipeline_technical.html | 35 ++++++++++++++++++++++++
 docs/running_offline.html    | 35 ++++++++++++++++++++++++
 8 files changed, 269 insertions(+), 7 deletions(-)
 create mode 100644 docs/cookie-consent.html

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index b3fbe757..ac047974 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -7590,7 +7590,7 @@ config<-c(
   "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml",
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt",
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eur_test_only/target_list.txt",
-  "pgs_methods: ['quickprs','ptclump']",
+  "pgs_methods: ['quickprs','ptclump','dbslmm','ldpred2','sbayesrc']",
   "cores_prep_pgs: 10",
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
@@ -7746,7 +7746,7 @@ conda activate model_builder
 
 for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
   if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/res.pred_comp.txt" ]; then
-    sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+    sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu,cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
       --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.EUR_test.row_number.txt \
       --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/predictor_list.txt \
       --out /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/res \
@@ -7770,11 +7770,11 @@ library(cowplot)
 source('../functions/misc.R')
 source_all('../functions')
 
-phenos<-c('TC', 'HT', 'HB')
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
 
 # Calculate correlation between all phenotypes in each target population
 pheno_pop_i <- list()
-for(pheno_i in phenos){
+for(pheno_i in selected_traits){
   pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.EUR_test.row_number.txt'))
   names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
 }
@@ -7785,7 +7785,7 @@ cors <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
 
 # Read in results
 res_eval <- list()
-for(pheno_i in phenos){
+for(pheno_i in selected_traits){
   eval_i <-
     fread(
       paste0(
@@ -7833,7 +7833,7 @@ for(pheno_i in phenos){
 model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC") 
 
 res_eval_simp <- NULL
-for(pheno_i in phenos){
+for(pheno_i in selected_traits){
   tmp <- res_eval[[pheno_i]]
   tmp$Trait <- pheno_i
   
diff --git a/docs/cookie-consent.html b/docs/cookie-consent.html
new file mode 100644
index 00000000..0136187d
--- /dev/null
+++ b/docs/cookie-consent.html
@@ -0,0 +1,35 @@
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "This website uses cookies to ensure you get the best experience.",
+      dismiss: "Accept",
+      link: "Learn more",
+      href: "/privacy.html"  <!-- Replace with your privacy policy URL -->
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+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
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+        gtagScript.setAttribute('async', '');
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+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
diff --git a/docs/header.html b/docs/header.html
index 279c000d..e80725b7 100644
--- a/docs/header.html
+++ b/docs/header.html
@@ -39,3 +39,38 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
+      dismiss: "Accept",
+      link: "Google's privacy info",
+      href: "https://policies.google.com/technologies/partner-sites"
+    },
+    onStatusChange: function(status) {
+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
+        var gtagScript = document.createElement('script');
+        gtagScript.setAttribute('async', '');
+        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
diff --git a/docs/index.html b/docs/index.html
index 6b0820a3..c336a36b 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -13,7 +13,7 @@
 
 <title>GenoPred - Homepage</title>
 
-<script src="site_libs/header-attrs-2.25/header-attrs.js"></script>
+<script src="site_libs/header-attrs-2.26/header-attrs.js"></script>
 <script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
 <meta name="viewport" content="width=device-width, initial-scale=1" />
 <link href="site_libs/bootstrap-3.3.5/css/cosmo.min.css" rel="stylesheet" />
@@ -75,6 +75,41 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
+      dismiss: "Accept",
+      link: "Google's privacy info",
+      href: "https://policies.google.com/technologies/partner-sites"
+    },
+    onStatusChange: function(status) {
+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
+        var gtagScript = document.createElement('script');
+        gtagScript.setAttribute('async', '');
+        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}
diff --git a/docs/pipeline_overview.html b/docs/pipeline_overview.html
index de94f042..b358cffb 100644
--- a/docs/pipeline_overview.html
+++ b/docs/pipeline_overview.html
@@ -75,6 +75,41 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
+      dismiss: "Accept",
+      link: "Google's privacy info",
+      href: "https://policies.google.com/technologies/partner-sites"
+    },
+    onStatusChange: function(status) {
+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
+        var gtagScript = document.createElement('script');
+        gtagScript.setAttribute('async', '');
+        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index 655842ca..bbfc6264 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -78,6 +78,41 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
+      dismiss: "Accept",
+      link: "Google's privacy info",
+      href: "https://policies.google.com/technologies/partner-sites"
+    },
+    onStatusChange: function(status) {
+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
+        var gtagScript = document.createElement('script');
+        gtagScript.setAttribute('async', '');
+        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}
@@ -2275,6 +2310,23 @@ <h3>Continuous Correction:</h3>
 <div id="specifying-alternative-reference-data-for-pgs-methods"
 class="section level2">
 <h2>Specifying alternative reference data for PGS methods</h2>
+<p>PGS methods require linkage disequilibrium (LD) data. The default LD
+data used for each method is indicated in the technical documentation
+(<a href="">here</a>). Several methods use the reference data used for
+the pipeline in general, so altering the <code>ref_dir</code> parameter
+(as <a href="">described above</a>) will also alter the reference data
+used by these PGS methods. However, several PGS methods use pre-computed
+LD matrices, and the user can specify alternative LD matrices for these
+methods, using the configfile parameters below:</p>
+<ul>
+<li>PRS-CS</li>
+<li>PRS-CSx</li>
+<li>SBayesR</li>
+<li>SBayesRC</li>
+<li>LDpred2</li>
+<li>QuickPRS</li>
+<li>LEOPARD+QuickPRS</li>
+</ul>
 <hr />
 </div>
 </div>
diff --git a/docs/pipeline_technical.html b/docs/pipeline_technical.html
index 08815ee2..2bed8b6d 100644
--- a/docs/pipeline_technical.html
+++ b/docs/pipeline_technical.html
@@ -80,6 +80,41 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
+      dismiss: "Accept",
+      link: "Google's privacy info",
+      href: "https://policies.google.com/technologies/partner-sites"
+    },
+    onStatusChange: function(status) {
+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
+        var gtagScript = document.createElement('script');
+        gtagScript.setAttribute('async', '');
+        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}
diff --git a/docs/running_offline.html b/docs/running_offline.html
index 76d3bbdf..1da0db2b 100644
--- a/docs/running_offline.html
+++ b/docs/running_offline.html
@@ -78,6 +78,41 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
+      dismiss: "Accept",
+      link: "Google's privacy info",
+      href: "https://policies.google.com/technologies/partner-sites"
+    },
+    onStatusChange: function(status) {
+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
+        var gtagScript = document.createElement('script');
+        gtagScript.setAttribute('async', '');
+        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}

From 90a53777792249c323e1a413feaf9cc7bfa9f252 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 2 May 2025 17:40:16 +0100
Subject: [PATCH 140/153] Prepping for expanded reference

---
 docs/opensnp_benchmark_dense.Rmd         | 602 +++++++++++++++++++++++
 docs/pipeline_1kg_hgdp_prep.Rmd          | 252 +++++++++-
 pipeline/misc/opensnp/config_dense.yaml  |   6 +
 pipeline/misc/opensnp/config_sparse.yaml |   5 +
 pipeline/misc/opensnp/gwas_groups.txt    |   2 +
 pipeline/misc/opensnp/gwas_list.txt      |   2 -
 6 files changed, 866 insertions(+), 3 deletions(-)
 create mode 100644 docs/opensnp_benchmark_dense.Rmd
 create mode 100644 pipeline/misc/opensnp/config_dense.yaml
 create mode 100644 pipeline/misc/opensnp/config_sparse.yaml
 create mode 100644 pipeline/misc/opensnp/gwas_groups.txt

diff --git a/docs/opensnp_benchmark_dense.Rmd b/docs/opensnp_benchmark_dense.Rmd
new file mode 100644
index 00000000..dc8026dc
--- /dev/null
+++ b/docs/opensnp_benchmark_dense.Rmd
@@ -0,0 +1,602 @@
+---
+title: OpenSNP Benchmark with dense SNP list
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+***
+
+# Overview
+
+We have previously used OpenSNP as a dataset for testing GenoPred. An important constraint within the default GenoPred implementation is its restriction to HapMap3 variants. This is particularly problematic when using externally derived score files, such as from the PGS catalogue, which may include variant variants that are not in the default reference. To remedy this we can use a reference dataset that includes more variant, and that is what we will test here.
+
+We will run GenoPred on the OpenSNP data using the default reference data (restricted to HapMap3) and a denser reference (all variants in the 1KG+HGDP dataset). We will calculate PGS based on height GWAS summary statistics, but also a range of height PGS score files on the PGS catalogue, and see the difference in performance.
+
+# Prepare input
+
+***
+
+## Download genotypes
+
+<details><summary>Show code</summary>
+
+```{bash}
+mkdir -p /users/k1806347/oliverpainfel/Data/OpenSNP/raw
+cd /users/k1806347/oliverpainfel/Data/OpenSNP/raw
+wget https://zenodo.org/records/1442755/files/CrowdAI_v3.tar.gz?download=1 
+mv 'CrowdAI_v3.tar.gz?download=1' CrowdAI_v3.tar.gz
+tar -xvzf CrowdAI_v3.tar.gz
+rm CrowdAI_v3.tar.gz
+
+# Use both training and testing data to maximise sample size for testing.
+```
+
+</details>
+
+***
+
+## Prepare phenotype
+
+<details><summary>Show code</summary>
+
+```{r}
+library(data.table)
+
+# Training 
+train_dat <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/training_set_details.txt')
+train_dat$FID <- train_dat$id
+train_dat$IID <- train_dat$id
+train_dat <- train_dat[, c('FID', 'IID', 'height'), with = F]
+
+# Test 
+test_dat <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/test_set_details_SECRET.txt')
+test_dat$FID <- test_dat$id
+test_dat$IID <- test_dat$id
+test_dat <- test_dat[, c('FID', 'IID', 'height'), with = F]
+
+all_dat<-rbind(train_dat, test_dat)
+dir.create('/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno', recursive = T)
+write.table(
+  all_dat,
+  '/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno/height.txt',
+  col.names = T,
+  row.names = F,
+  quote = F
+)
+```
+
+</details>
+
+***
+
+## Split VCF by chromosome
+
+<details><summary>Show code</summary>
+
+```{bash}
+module add bcftools/1.12-gcc-13.2.0-python-3.11.6
+
+# Create index
+bcftools index /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_train.vcf.gz
+
+bcftools index /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_test.vcf.gz
+
+# Merge the vcfs for training and testing data
+bcftools merge \
+  /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_train.vcf.gz \
+  /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_test.vcf.gz \
+  -o /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_merged.vcf.gz
+
+# Now, split by chromosome using plink2
+# Run on the command line within pipeline conda environment
+mkdir /users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno
+for chr in $(seq 1 22);do
+    /users/k1806347/oliverpainfel/Software/plink2 \
+        --vcf /users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/fullset/genotyping_data_fullset_merged.vcf.gz \
+        --chr ${chr} \
+        --out /users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_merged.chr${chr} \
+        --export vcf bgz
+done
+```
+
+</details>
+
+***
+
+## Download height GWAS
+
+<details><summary>Show code</summary>
+
+```{bash}
+# These are from the Yengo 2022 paper
+mkdir -p /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eur.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245992/GCST90245992_buildGRCh37.tsv
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eas.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245991/GCST90245991_buildGRCh37.tsv
+```
+
+</details>
+
+***
+
+## Create gwas_list, target_list and config
+
+<details><summary>Show code</summary>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline')
+library(data.table)
+
+# Create config file for sparse configuration
+conf <- c(
+  'outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/sparse_test',
+  'config_file: misc/opensnp/config_sparse.yaml',
+#  'gwas_list: misc/opensnp/gwas_list.txt',
+  'score_list: misc/opensnp/score_list.txt',
+  'target_list: misc/opensnp/target_list.txt',
+#  'gwas_groups: misc/opensnp/gwas_groups.txt',
+#  "pgs_methods: ['sbayesr']",
+  'testing: chr22'
+)
+
+dir.create('misc/opensnp/', recursive = T)
+dir.create('/users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred', recursive = T)
+write.table(conf, 'misc/opensnp/config_sparse.yaml', col.names = F, row.names = F, quote = F)
+
+# Create config file for dense configuration
+conf <- c(
+  'outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/dense_test',
+  'refdir: /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref',
+  'config_file: misc/opensnp/config_dense.yaml',
+#  'gwas_list: misc/opensnp/gwas_list.txt',
+  'score_list: misc/opensnp/score_list.txt',
+  'target_list: misc/opensnp/target_list.txt',
+#  'gwas_groups: misc/opensnp/gwas_groups.txt',
+#  "pgs_methods: ['sbayesr']",
+  'testing: chr22'
+)
+
+dir.create('misc/opensnp/', recursive = T)
+dir.create('/users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred', recursive = T)
+write.table(conf, 'misc/opensnp/config_dense.yaml', col.names = F, row.names = F, quote = F)
+
+# Create target_list
+target_list <- fread('example_input/target_list.txt')
+target_list <- rbind(target_list,
+                     data.table(name = 'opensnp',
+                                path = '/users/k1806347/oliverpainfel/Data/OpenSNP/processed/geno/opensnp_merged',
+                                type = 'vcf',
+                                indiv_report = F))
+
+target_list <- target_list[target_list$name == 'opensnp', ]
+
+write.table(target_list, 'misc/opensnp/target_list.txt', col.names = T, row.names = F, quote = F, sep = ' ')
+
+# Create gwas_list
+gwas_list <- fread('example_input/gwas_list.txt')
+gwas_list<-rbind(gwas_list, 
+                 data.table(name='yengo_eur',
+                            path = '/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eur.txt',
+                            population = 'EUR',
+                            n = NA,
+                            sampling = NA,
+                            prevalence = NA,
+                            mean = NA,
+                            sd = NA,
+                            label = "\"Yengo 2022 Height EUR\""))
+
+gwas_list <- rbind(gwas_list,
+                   data.table(name = 'yengo_eas', 
+                              path = '/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eas.txt',
+                              population = 'EAS',
+                              n = NA,
+                              sampling = NA,
+                              prevalence = NA,
+                              mean = NA,
+                              sd = NA,
+                              label = "\"Yengo 2022 Height EAS\""))
+
+
+gwas_list <- gwas_list[gwas_list$name %in% c('yengo_eur', 'yengo_eas'), ]
+
+write.table(gwas_list, 'misc/opensnp/gwas_list.txt', col.names = T, row.names = F, quote = F, sep = ' ')
+
+# Create gwas_groups
+gwas_groups <- data.frame(
+  name = 'height',
+  gwas = 'yengo_eur,yengo_eas',
+  label = "\"Yengo 2022 Height EUR+EAS\""
+)
+
+write.table(gwas_groups, 'misc/opensnp/gwas_groups.txt', col.names = T, row.names = F, quote = F, sep = ' ')
+
+# Create score_list
+score_list <- data.frame(
+  name = 'PGS002804',
+  path = NA,
+  label = "\"Yengo 2022 Height EUR PGSC\""
+)
+
+write.table(score_list, 'misc/opensnp/score_list.txt', col.names=T, row.names=F, quote=F, sep=' ')
+
+```
+
+</details>
+
+***
+
+# Run GenoPred
+
+<details><summary>Show code</summary>
+
+```{bash}
+snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_sparse.yaml output_all -n 
+snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_dense.yaml output_all -n 
+```
+
+</details>
+
+***
+
+## Evaluate PGS
+
+<details><summary>Show code</summary>
+
+```{r}
+# Test correlation between PGS and observed height
+
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in pheno data
+pheno <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno/height.txt')
+
+# Read in PGS
+pgs <- read_pgs(config = 'misc/opensnp/config.yaml', name = 'opensnp')$opensnp
+
+# Estimate correlation between pheno and pgs
+cor <- NULL
+for(pop_i in names(pgs)){
+  for(gwas_i in names(pgs[[pop_i]])){
+    for(pgs_method_i in names(pgs[[pop_i]][[gwas_i]])){
+      pgs_i <- pgs[[pop_i]][[gwas_i]][[pgs_method_i]]
+      pheno_pgs<-merge(pheno, pgs_i, by = c('FID','IID'))
+      
+      for(model_i in names(pgs_i)[-1:-2]){
+        y <- scale(pheno_pgs$height)
+        x <- scale(pheno_pgs[[model_i]])
+        
+        if(all(is.na(x))){
+          next
+        }
+        
+        coef_i <- coef(summary(mod <- lm(y ~ x)))
+        
+        tmp <- data.table(
+          pop = pop_i,
+          gwas = gwas_i,
+          pgs_method = pgs_method_i,
+          name = model_i,
+          r = coef_i[2,1],
+          se = coef_i[2,2],
+          p = coef_i[2,4],
+          n = nobs(mod))
+      cor <- rbind(cor, tmp)
+      
+      }
+    }
+  }
+}
+
+# Save the results
+dir.create('/scratch/prj/oliverpainfel/Data/OpenSNP/assoc')
+write.csv(
+  cor,
+  '/scratch/prj/oliverpainfel/Data/OpenSNP/assoc/genopred-yengo-assoc.csv',
+  row.names = F
+)
+
+# The European sample is the only one large enough for interpretable results. All other populations are <20 individuals
+# Subset EUR results
+cor_eur <- cor[cor$pop == 'EUR', ]
+
+# Restrict to best and and pseudoval only
+cor_eur_subset <- NULL
+for(pop_i in unique(cor_eur$pop)){
+  for(gwas_i in unique(cor_eur$gwas[cor_eur$pop == pop_i])){
+    for(pgs_method_i in unique(cor_eur$pgs_method[cor_eur$pop == pop_i & cor_eur$gwas == gwas_i])){
+      
+      # Subset relevant results
+      cor_eur_i <- cor_eur[
+        cor_eur$pop == pop_i &
+        cor_eur$gwas == gwas_i &
+        cor_eur$pgs_method == pgs_method_i,]
+  
+      # Top R
+      if(pgs_method_i %in% c('ptclump','ldpred2','megaprs','prscs','lassosum','dbslmm')){
+        top_i <- cor_eur_i[which(cor_eur_i$r == max(cor_eur_i$r, na.rm = T))[1],]
+        top_i$model <- 'Top'
+        cor_eur_subset <- rbind(cor_eur_subset, top_i)
+      }
+      if(pgs_method_i %in% c('prscsx')){
+        for(targ_i in unique(gsub('.*_','', gsub('_phi.*','',cor_eur_i$name)))){
+          cor_eur_k<-cor_eur_i[grepl(paste0(targ_i,'_phi_'), cor_eur_i$name),]
+          top_i <- cor_eur_k[which(cor_eur_k$r == max(cor_eur_k$r, na.rm = T))[1],]
+          top_i$model <- 'Top'
+          top_i$pgs_method<-paste0(top_i$pgs_method, " (", targ_i, ")")
+          cor_eur_subset <- rbind(cor_eur_subset, top_i)
+        }
+      }  
+      
+      # PseudoVal
+      if(pgs_method_i %in% c('ptclump','sbayesr','ldpred2','megaprs','prscs','lassosum','dbslmm','prscsx')){
+        pseudo_param <- find_pseudo(config = 'misc/opensnp/config.yaml', gwas = gwas_i, pgs_method = pgs_method_i)
+        pseudo_i <- cor_eur_i[grepl(paste0(pseudo_param,'$'), cor_eur_i$name),]
+        pseudo_i$model <- 'Pseudo'
+        if(pgs_method_i %in% c('prscsx')){
+          pseudo_i$pgs_method<-paste0(pseudo_i$pgs_method, " (", gsub('.*_','', gsub(paste0('_',pseudo_param), '', pseudo_i$name)), ")")
+        }
+        cor_eur_subset <- rbind(cor_eur_subset, pseudo_i)
+      }
+      
+      # External
+      external_tmp<-cor_eur_i[cor_eur_i$pgs_method == 'external',]
+      external_tmp$model <- 'External'
+      cor_eur_subset <- rbind(cor_eur_subset, external_tmp)
+      
+    }
+  }
+}
+
+# Plot the results
+cor_eur_subset$model <- factor(cor_eur_subset$model, levels = c('Top','Pseudo','External'))
+dir.create('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP')
+
+# yengo_eur
+plot_obj_eur <- 
+  ggplot(cor_eur_subset[cor_eur_subset$gwas == 'yengo_eur',], aes(x = pgs_method, y = r, fill = model)) +
+  geom_bar(stat = "identity", position = position_dodge2(preserve = "single"), width = 0.7) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  labs(
+    y = "Correlation (SE)",
+    x = 'PGS Method',
+    fill = 'Model',
+    title = paste0("Yengo - EUR vs OpenSNP - EUR\n(N = ", cor_eur_subset$n[1], ")")
+  ) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12))
+
+png('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP/genopred-yengo_eur.png',
+    units = 'px',
+    width = 2000,
+    height = 1000,
+    res = 300)
+
+  plot_obj_eur
+
+dev.off()
+
+# yengo_eas
+plot_obj_eas <- 
+  ggplot(cor_eur_subset[cor_eur_subset$gwas == 'yengo_eas',], aes(x = pgs_method, y = r, fill = model)) +
+    geom_bar(stat = "identity", position = position_dodge2(preserve = "single"), width = 0.7) +
+    geom_errorbar(
+      aes(ymin = r - se, ymax = r + se),
+      width = .2,
+      position = position_dodge(width = 0.7)
+    ) +
+    labs(
+      y = "Correlation (SE)",
+      x = 'PGS Method',
+      fill = 'Model',
+      title = paste0("Yengo - EAS vs OpenSNP - EUR\n(N = ", cor_eur_subset$n[1], ")")
+    ) +
+    theme_half_open() +
+    background_grid() +
+    theme(axis.text.x = element_text(angle = 45, hjust = 1),
+          plot.title = element_text(hjust = 0.5, size=12))
+
+png('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP/genopred-yengo_eas.png',
+    units = 'px',
+    width = 1200,
+    height = 1000,
+    res = 300)
+
+  plot_obj_eas
+
+dev.off()
+
+# Make a plot comparing the yengo_eur results to the score file from PGS-catalogue
+not_external <- unique(cor_eur_subset$pgs_method[cor_eur_subset$pgs_method != 'external'])
+cor_eur_subset$pgs_method <- factor(cor_eur_subset$pgs_method, levels=c(not_external,'external'))
+
+plot_obj_ext <- 
+  ggplot(cor_eur_subset[cor_eur_subset$gwas == 'yengo_eur' | cor_eur_subset$gwas == 'PGS002804',], aes(x = pgs_method, y = r, fill = model)) +
+  geom_bar(stat = "identity", position = position_dodge2(preserve = "single"), width = 0.7) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  labs(
+    y = "Correlation (SE)",
+    x = 'PGS Method',
+    fill = 'Model',
+    title = paste0("Yengo - EUR vs OpenSNP - EUR\n(N = ", cor_eur_subset$n[1], ")")
+  ) +
+  theme_half_open() +
+  background_grid() +
+  geom_vline(xintercept = 7.5, linetype = 'dashed') +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12))
+
+png('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP/genopred-yengo_eur-external.png',
+    units = 'px',
+    width = 2000,
+    height = 1000,
+    res = 300)
+  
+  plot_obj_ext
+
+dev.off()
+
+# Subset EAS results
+cor_eas <- cor[cor$pop == 'EAS', ]
+# Restrict to best and and pseudoval only
+cor_eas_subset <- NULL
+for(pop_i in unique(cor_eas$pop)){
+  for(gwas_i in unique(cor_eas$gwas[cor_eas$pop == pop_i])){
+    for(pgs_method_i in unique(cor_eas$pgs_method[cor_eas$pop == pop_i & cor_eas$gwas == gwas_i])){
+      
+      # Subset relevant results
+      cor_eas_i <- cor_eas[
+        cor_eas$pop == pop_i &
+        cor_eas$gwas == gwas_i &
+        cor_eas$pgs_method == pgs_method_i,]
+  
+      # Top R
+      if(pgs_method_i %in% c('ptclump','ldpred2','megaprs','prscs','lassosum','dbslmm')){
+        top_i <- cor_eas_i[which(cor_eas_i$r == max(cor_eas_i$r, na.rm = T))[1],]
+        top_i$model <- 'Top'
+        cor_eas_subset <- rbind(cor_eas_subset, top_i)
+      }
+      if(pgs_method_i %in% c('prscsx')){
+        for(targ_i in unique(gsub('.*_','', gsub('_phi.*','',cor_eas_i$name)))){
+          cor_eas_k<-cor_eas_i[grepl(paste0(targ_i,'_phi_'), cor_eas_i$name),]
+          top_i <- cor_eas_k[which(cor_eas_k$r == max(cor_eas_k$r, na.rm = T))[1],]
+          top_i$model <- 'Top'
+          top_i$pgs_method<-paste0(top_i$pgs_method, " (", targ_i, ")")
+          cor_eas_subset <- rbind(cor_eas_subset, top_i)
+        }
+      }
+  
+      # PseudoVal
+      if(pgs_method_i %in% c('ptclump','sbayesr','ldpred2','megaprs','prscs','lassosum','dbslmm','prscsx')){
+        pseudo_param <- find_pseudo(config = 'misc/opensnp/config.yaml', gwas = gwas_i, pgs_method = pgs_method_i)
+        pseudo_i <- cor_eas_i[grepl(paste0(pseudo_param,'$'), cor_eas_i$name),]
+        pseudo_i$model <- 'Pseudo'
+        if(pgs_method_i %in% c('prscsx')){
+          pseudo_i$pgs_method<-paste0(pseudo_i$pgs_method, " (", gsub('.*_','', gsub(paste0('_',pseudo_param), '', pseudo_i$name)), ")")
+        }
+        cor_eas_subset <- rbind(cor_eas_subset, pseudo_i)
+      }
+      
+      # External
+      external_tmp<-cor_eas_i[cor_eas_i$pgs_method == 'external',]
+      external_tmp$model <- 'External'
+      cor_eas_subset <- rbind(cor_eas_subset, external_tmp)
+      
+    }
+  }
+}
+
+# Make a plot including PRS-CSx comparing in EUR and EAS OpenSNP individuals
+cor_eur_eas_subset<-rbind(cor_eur_subset, cor_eas_subset)
+cor_eur_eas_subset$pop<-paste0(cor_eur_eas_subset$pop, " (N = ", cor_eur_eas_subset$n, ")")
+
+plot_obj_prscsx <- 
+  ggplot(cor_eur_eas_subset[cor_eur_eas_subset$gwas %in% c('yengo_eur','yengo_eas','height'),], aes(x = pgs_method, y = r, colour = model)) +
+  geom_point(stat = "identity", size = 4, position = position_dodge(width = 0.7)) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  labs(
+    y = "Correlation (SE)",
+    x = 'PGS Method',
+    colour = 'Model',
+  ) +
+  theme_half_open() +
+  panel_border() +
+  background_grid() +
+  facet_grid(pop ~ gwas, scales = 'free', space = 'free_x') +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12))
+
+png('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP/genopred-prscsx.png',
+    units = 'px',
+    width = 3000,
+    height = 1500,
+    res = 300)
+  
+  plot_obj_prscsx
+
+dev.off()
+
+```
+
+</details>
+
+***
+
+<details><summary>Show results in OpenSNP</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 60%;">
+![Yengo - EUR PGS correlation with height in EUR OpenSNP ](Images/OpenSNP/genopred-yengo_eur.png)
+</div>
+</div>
+
+***
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 60%;">
+![Yengo - EAS PGS correlation with height in EUR OpenSNP ](Images/OpenSNP/genopred-yengo_eas.png)
+</div>
+</div>
+
+</details>
+
+<details><summary>Show results in OpenSNP including score from PGS catalogue</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 60%;">
+![Yengo - EUR PGS correlation with height in EUR OpenSNP - Includes externally derived PGS ](Images/OpenSNP/genopred-yengo_eur-external.png)
+</div>
+</div>
+
+<br/>
+
+Note. 'external' is based on a larger GWAS (incl. 23andMe)
+
+</details>
+
+<details><summary>Show results in OpenSNP when using PRS-CSx</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 60%;">
+![Yengo - EUR/EAS PGS correlation with height in OpenSNP EUR and EAS populations ](Images/OpenSNP/genopred-prscsx.png)
+</div>
+</div>
+
+<br/>
+
+Note. 'external' is based on a larger GWAS (incl. 23andMe)
+
+</details>
+
+***
diff --git a/docs/pipeline_1kg_hgdp_prep.Rmd b/docs/pipeline_1kg_hgdp_prep.Rmd
index ccdb2bda..c663ffe1 100644
--- a/docs/pipeline_1kg_hgdp_prep.Rmd
+++ b/docs/pipeline_1kg_hgdp_prep.Rmd
@@ -231,7 +231,7 @@ write.table(keep_list, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref
 
 ***
 
-# Package for zenodo
+## Package for zenodo
 
 <ref_dir>
 ├── ref.chr<1-22>.<pgen/pvar/psam>
@@ -301,3 +301,253 @@ cd ..
 tar -czvf ld_scores.tar.gz ld_scores
 
 ```
+
+***
+
+# Make dense version of the reference data
+
+We will create a reference dataset that is not restricted to HapMap3 variants, to allow for denser coverage, and broader applicability to the PGS catalogue.
+
+```{r}
+
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+library(GenoUtils)
+
+# Read in variant data for 1KG+HGDP
+pvar <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/GRCh37_HGDP+1kGP_ALL.pvar', nThread=5)
+
+# Extract RSID information
+pvar$RSID <- gsub('.*gnomad_ID=', '', pvar$INFO)
+pvar$RSID[grepl('AF', pvar$RSID)] <- NA
+
+# Remove unneeded data and variant that aren't unambiguous SNPs and don't have RSID
+pvar$INFO<-NULL
+pvar$FILTER<-NULL
+pvar<-pvar[nchar(pvar$REF) == 1,]
+pvar<-pvar[nchar(pvar$ALT) == 1,]
+names(pvar)<-c('CHR','BP','SNP','A1','A2','RSID')
+pvar<-pvar[pvar$CHR %in% 1:22,]
+pvar$CHR<-as.numeric(pvar$CHR)
+pvar<-pvar[!is.na(pvar$RSID),]
+pvar$IUPAC<-snp_iupac(pvar$A1, pvar$A2)
+pvar<-pvar[pvar$IUPAC %in% c('R','Y','K','M'),]
+
+# Liftover data to GRCh38
+make_executable <- function(exe) {
+  Sys.chmod(exe, mode = (file.info(exe)$mode | "111"))
+}
+
+snp_modifyBuild_offline<-function (info_snp, liftOver, chain){
+  tmp_folder<-tempdir()
+
+  if (!all(c("chr", "pos") %in% names(info_snp)))
+    stop2("Please use proper names for variables in 'info_snp'. Expected %s.",
+          "'chr' and 'pos'")
+  liftOver <- normalizePath(liftOver)
+  make_executable(liftOver)
+  BED <- tempfile(fileext = ".BED")
+  info_BED <- with(info_snp, data.frame(paste0("chr", chr),
+                                        pos0 = pos - 1L, pos, id = seq_len(nrow(info_snp))))
+  fwrite(info_BED, BED, col.names = FALSE, sep = " ")
+  lifted<-paste0(tmp_folder,'/tmp.lifted')
+  unmapped<-paste0(tmp_folder,'/tmp.unmapped')
+  system(paste(liftOver, BED, chain, lifted, unmapped))
+  new_pos <- fread(lifted)
+  bad <- grep("^#", readLines(unmapped), value = TRUE, invert = TRUE)
+  print(paste0(length(bad)," variants have not been mapped."))
+  info_snp$pos <- NA
+  info_snp$pos[new_pos$V4] <- new_pos$V3
+  info_snp
+}
+
+names(pvar) <- c('chr','pos','snp','a1','a2','rsid','iupac')
+
+pvar_grch38<-snp_modifyBuild_offline(pvar, liftOver='/users/k1806347/oliverpainfel/Software/MyGit/GenoDisc/pipeline/resources/software/liftover/liftover', chain='/users/k1806347/oliverpainfel/Software/MyGit/GenoDisc/pipeline/resources/data/liftover/hg19ToHg38.over.chain.gz')
+
+pvar_grch36<-snp_modifyBuild_offline(pvar, liftOver='/users/k1806347/oliverpainfel/Software/MyGit/GenoDisc/pipeline/resources/software/liftover/liftover', chain='/users/k1806347/oliverpainfel/Software/MyGit/GenoDisc/pipeline/resources/data/liftover/hg19ToHg18.over.chain.gz')
+
+pvar[pvar_grch36, on=.(snp), pos_grch36 := i.pos]
+pvar[pvar_grch38, on=.(snp), pos_grch38 := i.pos]
+
+rm(pvar_grch36)
+rm(pvar_grch38)
+gc()
+
+pvar <- pvar[, c('chr','snp','rsid','a1','a2','iupac','pos_grch36','pos','pos_grch38'), with=F]
+names(pvar) <- c('CHR','SNP','RSID','A1','A2','IUPAC','BP_GRCh36','BP_GRCh37','BP_GRCh38')
+
+pvar<-pvar[complete.cases(pvar),]
+
+# Save intermediate file
+fwrite(pvar,'/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_snps.txt', quote = F, sep = ' ', na='NA')
+
+pvar<-fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_snps.txt')
+
+# Remove variants with duplicate RSIDs (retaining first instance)
+pvar <- pvar[!duplicated(pvar$RSID),]
+
+# Extract SNPs from plink2 files
+dir.create('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred')
+write.table(pvar$SNP, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/extract.snplist', col.names=F, row.names=F, quote=F)
+system('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/GRCh37_HGDP+1kGP_ALL --make-pgen --extract /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/extract.snplist --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg --threads 5')
+
+# Insert RSIDs into new plink files
+pvar_subset <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.pvar')
+pvar_subset <- pvar_subset[, c('#CHROM','POS','ID','REF','ALT'), with=F]
+pvar$ID <- pvar$SNP
+pvar_subset[pvar, on=.(ID), SNP := i.RSID]
+pvar_subset<-pvar_subset[, c('#CHROM','POS','SNP','REF','ALT'), with=F]
+names(pvar_subset)<-c('#CHROM','POS','ID','REF','ALT')
+fwrite(pvar_subset, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.pvar', col.names=T, row.names=F, quote=F, sep=' ')
+
+# Remove hard filtered individuals
+samp_meta<-fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/gnomad.genomes.v3.1.2.hgdp_1kg_subset_sample_meta.tsv.bgz')
+samp_meta<-samp_meta[grepl("hard_filtered\":false", samp_meta$gnomad_sample_filters), ]
+
+# Remove PCA outliers
+samp_meta<-samp_meta[grepl("outlier\":false", samp_meta$hgdp_tgp_meta), ]
+
+# Remove relatives (we will use the sample meta-data for this - Estimated using PC-relate)
+library(jsonlite)
+kin_dat <- NULL
+for(i in 1:nrow(samp_meta)){
+  tmp <- fromJSON(samp_meta$relatedness_inference[i])
+  if(is.data.frame(tmp$related_samples)){
+    tmp$related_samples$ID<-samp_meta$s[i]
+    kin_dat<-rbind(kin_dat, tmp$related_samples)
+  }
+}
+
+# Restrict table to individuals in the genetic data
+psam <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.psam')
+psam<-psam[psam$`#IID` %in% samp_meta$s,]
+kin_dat<-kin_dat[kin_dat$s %in% psam$`#IID` & kin_dat$ID %in% psam$`#IID`, ]
+relations<-data.frame(id_1=kin_dat$s, id_2=kin_dat$ID, kin=kin_dat$kin)
+
+# Use GreedyRelated to find maximum unrelated set
+relations$Pair <- apply(relations, 1, function(row) {
+  # Construct a unique identifier for each pair regardless of order
+  paste(sort(c(row['id_1'], row['id_2'])), collapse = "_")
+})
+relations$Pair<-as.numeric(factor(relations$Pair))
+relations$Factor<-relations$kin
+relations$ID<-relations$id_1
+relations<-relations[, c('ID', 'Pair', 'Factor')]
+relations<-relations[order(relations$Pair), ]
+fwrite(relations, file='/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations.txt', sep=' ', quote=F)
+
+system('/users/k1806347/oliverpainfel/Software/GreedyRelated/bin/GreedyRelated -r /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations.txt -t 0.05 -s 1 > /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations_remove.txt')
+
+remove<-fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/relations_remove.txt')
+
+# Create keep file of unrelated individuals
+psam_unrel <- psam[!(psam$`#IID` %in% remove$V2),]
+nrow(psam_unrel) # 3313 - This is 65 less than in the preprint Table S3. Maybe Greedy related is worse?
+
+write.table(psam_unrel[, '#IID', with=F], '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/unrel.keep', col.names=T, row.names=F, quote=F)
+
+# Split by chromosome and retain unrelated individuals
+for(i in 1:22){
+  system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/unrel.keep --chr ',i,' --make-pgen --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr',i))
+}
+
+# Create keep files and calculate allele frequencies for each population
+# Population data is stored withint the psam file
+psam <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr22.psam')
+pop_dat<-psam[, c('#IID','SuperPop')]
+names(pop_dat)<-c('#IID','POP')
+write.table(pop_dat, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/pop.txt', row.names=F, col.names=T, quote=F)
+
+dir.create('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/')
+for(pop in unique(pop_dat$POP)){
+  write.table(pop_dat[pop_dat$POP == pop, '#IID', with=F], paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/',pop,'.keep'), col.names=F, row.names=F, quote=F)
+  dir.create(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/', pop), recursive = T)
+  for(i in 1:22){
+    system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr',i,' --keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/',pop,'.keep --chr ',i,' --freq --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/', pop,'/hgdp_1kg.chr', i))
+  }
+}
+
+# Create frequency files across all reference individuals (TRANS)
+dir.create(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS'), recursive = T)
+for(i in 1:22){
+  system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr',i,' --chr ',i,' --freq --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS/hgdp_1kg.chr', i))
+}
+
+####
+# Create .rds files for SNP stats
+####
+
+info_subset <- pvar[pvar$RSID %in% pvar_subset$ID, ]
+info_subset<-info_subset[,c('CHR','BP_GRCh37','RSID','A1','A2','BP_GRCh36','BP_GRCh38','IUPAC'), with=F]
+names(info_subset)<-c('#CHROM','POS_GRCh37','ID','ALT','REF','POS_GRCh36','POS_GRCh38','IUPAC')
+
+for(chr in 1:22){
+    ref<-info_subset[info_subset$`#CHROM` == chr,]
+    for(pop in unique(pop_dat$POP)){
+        # Read in reference frequency data
+        freq<-fread(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/', pop,'/hgdp_1kg.chr', chr,'.afreq'))
+
+        # The freq files have come from the reference files, so we can assume they are on the same strand
+        freq_match<-merge(ref, freq[,c('ID','ALT','REF','ALT_FREQS'), with=F], by=c('ID','ALT','REF'))
+        freq_swap<-merge(ref, freq[,c('ID','ALT','REF','ALT_FREQS'), with=F], by.x=c('ID','ALT','REF'), by.y=c('ID','REF','ALT'))
+        freq_swap$ALT_FREQS<-1-freq_swap$ALT_FREQS
+        tmp_freq<-rbind(freq_match, freq_swap)
+        tmp_freq<-tmp_freq[match(ref$ID, tmp_freq$ID),]
+
+        ref[[paste0('REF.FRQ.',pop)]]<-tmp_freq$ALT_FREQS
+    }
+
+    ref<-ref[,c("#CHROM","ID","POS_GRCh36","POS_GRCh37","POS_GRCh38","ALT","REF","IUPAC",paste0('REF.FRQ.',unique(pop_dat$POP))), with=F]
+    names(ref)<-c("CHR","SNP","BP_GRCh36","BP_GRCh37","BP_GRCh38","A1","A2","IUPAC",paste0('REF.FRQ.',unique(pop_dat$POP)))
+    saveRDS(ref, file = paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr',chr,'.rds'))
+}
+
+```
+
+## Package for zenodo
+
+<ref_dir>
+├── ref.chr<1-22>.<pgen/pvar/psam>
+├── ref.chr<1-22>.rds
+├── ref.pop.txt (#IID, POP - with header)
+├── ref.keep.list (POP and PATH - without header)
+├── keep_files
+│   └──<POP>.keep (#IID - with header)
+└── freq_files
+    └──<POP>
+        └──ref.<POP>.chr<CHR>.afreq # PLINK2 .afreq format
+
+```{bash}
+
+# Copy over all the relavent files
+mkdir /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref
+
+for chr in $(seq 1 22); do
+  for file in $(echo pgen pvar psam rds); do
+    cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg.chr${chr}.${file} /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.chr${chr}.${file}
+  done
+done
+
+cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/pop.txt /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/ref.pop.txt
+
+mkdir /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/keep_files
+
+cp /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/keep_files/*.keep /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/keep_files/
+
+cp -r /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/
+
+for pop in $(ls /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files); do
+ for chr in $(seq 1 22); do
+  mv /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/hgdp_1kg.chr${chr}.afreq /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/ref.${pop}.chr${chr}.afreq
+  rm /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref/freq_files/${pop}/hgdp_1kg.chr${chr}.log
+ done
+done
+
+# Compress folder and upload to zenodo
+cd /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred
+tar -czvf genopred_1kg_hgdp_dense.tar.gz ref
+
+```
diff --git a/pipeline/misc/opensnp/config_dense.yaml b/pipeline/misc/opensnp/config_dense.yaml
new file mode 100644
index 00000000..db53ae1f
--- /dev/null
+++ b/pipeline/misc/opensnp/config_dense.yaml
@@ -0,0 +1,6 @@
+outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/dense_test
+refdir: /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref
+config_file: misc/opensnp/config_dense.yaml
+score_list: misc/opensnp/score_list.txt
+target_list: misc/opensnp/target_list.txt
+testing: chr22
diff --git a/pipeline/misc/opensnp/config_sparse.yaml b/pipeline/misc/opensnp/config_sparse.yaml
new file mode 100644
index 00000000..521b97e2
--- /dev/null
+++ b/pipeline/misc/opensnp/config_sparse.yaml
@@ -0,0 +1,5 @@
+outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/sparse_test
+config_file: misc/opensnp/config_sparse.yaml
+score_list: misc/opensnp/score_list.txt
+target_list: misc/opensnp/target_list.txt
+testing: chr22
diff --git a/pipeline/misc/opensnp/gwas_groups.txt b/pipeline/misc/opensnp/gwas_groups.txt
new file mode 100644
index 00000000..2cac132c
--- /dev/null
+++ b/pipeline/misc/opensnp/gwas_groups.txt
@@ -0,0 +1,2 @@
+name gwas label
+height yengo_eur,yengo_eas "Yengo 2022 Height EUR+EAS"
diff --git a/pipeline/misc/opensnp/gwas_list.txt b/pipeline/misc/opensnp/gwas_list.txt
index ce104316..ced82a73 100644
--- a/pipeline/misc/opensnp/gwas_list.txt
+++ b/pipeline/misc/opensnp/gwas_list.txt
@@ -1,5 +1,3 @@
 name path population n sampling prevalence mean sd label
 yengo_eur /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eur.txt EUR NA NA NA NA NA "Yengo 2022 Height EUR"
 yengo_eas /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eas.txt EAS NA NA NA NA NA "Yengo 2022 Height EAS"
-HT_UKB /scratch/prj/ukbiobank/recovered/Edinburgh_Data/usr/ollie_pain/gwas/HT/ukb.eur_train.HT.GW.txt.gz EUR NA NA NA 0 1 "Height (UKB)"
-HT_UGR /scratch/prj/oliverpainfel/recovered/Data/GWAS_sumstats/UGR/HT.reformat.txt.gz AFR NA NA NA 0 1 "Height (UGR)"
\ No newline at end of file

From 01c3f7468134da215d08c0ab0c45087b8f0f1f48 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 5 May 2025 13:14:41 +0100
Subject: [PATCH 141/153] bridgeprs test

---
 Scripts/pgs_methods/bridgeprs.R | 127 ++++++++++++++++----------------
 docs/CrossPop.Rmd               |   6 +-
 pipeline/rules/dependencies.smk |   2 +-
 3 files changed, 68 insertions(+), 67 deletions(-)

diff --git a/Scripts/pgs_methods/bridgeprs.R b/Scripts/pgs_methods/bridgeprs.R
index d1371466..8611af49 100644
--- a/Scripts/pgs_methods/bridgeprs.R
+++ b/Scripts/pgs_methods/bridgeprs.R
@@ -12,6 +12,8 @@ option_list = list(
       help="File containing the population code and location of the keep file [required]"),
   make_option("--plink2", action="store", default='plink2', type='character',
       help="Path PLINK v2 software binary [optional]"),
+  make_option("--bridgeprs_repo", action="store", default=NULL, type='character',
+      help="BridgePRS repo path [optional]"),
   make_option("--output", action="store", default=NULL, type='character',
       help="Path for output files [required]"),
   make_option("--memory", action="store", default=5000, type='numeric',
@@ -52,6 +54,9 @@ if(is.null(opt$output)){
 if(is.null(opt$populations)){
   stop('--populations must be specified.\n')
 }
+if(is.null(opt$bridgeprs_repo)){
+  stop('--bridgeprs_repo must be specified.\n')
+}
 
 # Create output directory
 opt$output_dir <- paste0(dirname(opt$output),'/')
@@ -94,6 +99,7 @@ for(i in CHROMS){
 }
 
 pop_data <- read_pop_data(opt$pop_data)
+pop_data$FID <- 0
 for(i in unique(pop_data$POP)){
   fwrite(
     pop_data[pop_data$POP == i, c('FID', 'IID'), with = F],
@@ -120,88 +126,83 @@ for(i in 1:length(sumstats)){
   # Store average sample size
   gwas_N <- c(gwas_N, round(mean(gwas$N), 0))
   gwas$N<NULL
+  
+  # FOR testing
+  gwas <- gwas[sample(1:nrow(gwas), 10000),]
 
   # Write sumstats split by chromosome
   for(j in CHROMS){
     tmp<-gwas[gwas$CHR == j,]
     tmp$CHR<-NULL
-    fwrite(tmp, paste0(tmp_dir, '/GWAS_sumstats_',i,'_temp_chr', j, '.txt'), sep=' ')
+    fwrite(tmp, paste0(tmp_dir, '/GWAS_sumstats_',populations[i],'_temp_chr', j, '.txt'), sep=' ')
   }
+  
+  # Write QC snplist
+  fwrite(gwas[,'SNP'], paste0(tmp_dir, '/GWAS_sumstats_',populations[i],'_temp.qc.snplist'), col.names = F, quote = F)
 
   rm(gwas)
   gc()
 
 }
 
-#####
-# Create fake phenotype data
-#####
-
-fam<-fread(paste0(tmp_dir, '/ref_ld/chr', CHROMS[1], '.fam'))
-names(fam)[1:2]<-c('FID','IID')
-pheno<-fam[, 1:2]
-pheno$y<-rnorm(nrow(pheno))
-write.table(pheno, paste0(tmp_dir, '/fake_pheno.txt'), row.names = F, quote = F)
-
-#####
-# Create config files
-#####
-
-for(i in 1:length(sumstats)){
-  config_i<-c(
-    paste0("POP=", populations[i]),
-    paste0("LDPOP=", populations[i]),
-    paste0("LD_PATH=", tmp_dir, '/ref_ld'),
-    paste0("SUMSTATS_PREFIX=", tmp_dir, '/GWAS_sumstats_', i, '_temp_chr'),
-    "SUMSTATS_SUFFIX=.txt",
-    "SUMSTATS_FIELDS=SNP,A2,A1,P,BETA",
-    paste0("SUMSTATS_SIZE=", gwas_N[i]),
-    paste0("GENOTYPE_PREFIX=",tmp_dir, '/ref_ld/chr'),
-    paste0("PHENOTYPE_FILE=", tmp_dir, '/fake_pheno.txt')
-  )
-
-  writeLines(config_i, paste0(tmp_dir, '/', i, '.config'))
-}
-
-# Record start time for test
-if(!is.na(opt$test)){
-  test_start.time <- test_start(log_file = log_file)
-}
-
 #####
 # Run BridgePRS
 #####
 
-system(paste0(opt$bridgeprs_repo, '/bridgePRS pipeline go -o ', tmp_dir,'/out --config_files ', tmp_dir, '/', 1, '.config ', tmp_dir, '/', 2, '.config --phenotype y'))
-
-#####
-# Process sumstats using PRS-CSx
-#####
-
-# Create a temporary reference bim files for PRS-CSx to match to
-pvar <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
-pvar$POS<-0
-for(i in CHROMS){
-  write.table(pvar[pvar$CHR == i, c('CHR','SNP','POS','BP','A1','A2'), with=F], paste0(tmp_dir,'/ref.chr',i,'.bim'), col.names=F, row.names=F, quote=F)
+# set fst
+if(any(populations == 'EUR') & any(populations == 'AFR')){
+  fst <- 0.15
 }
-
-rm(pvar)
-gc()
-
-# Make a data.frame listing chromosome and phi combinations
-jobs<-NULL
-for(i in rev(CHROMS)){
-  jobs<-rbind(jobs, data.frame(CHR=i, phi=phi_param))
+if(any(populations == 'EUR') & any(populations == 'EAS')){
+  fst <- 0.11
 }
 
-# Run using PRScs auto, and specifying a range of global shrinkage parameters
-log <- foreach(i = 1:nrow(jobs), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% {
-  if(jobs$phi[i] == 'auto'){
-    system(paste0(opt$prscsx_path, ' --ref_dir=', opt$prscsx_ref_path, '/ --bim_prefix=', tmp_dir,'/ref.chr', jobs$CHR[i], ' --pop=', opt$populations, ' --sst_file=', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','),' --n_gwas=', paste(gwas_N, collapse=','), ' --out_dir=', tmp_dir, '/ --out_name=output --chrom=', jobs$CHR[i], ' --meta=True --seed=', opt$seed))
-  } else {
-    system(paste0(opt$prscsx_path, ' --ref_dir=', opt$prscsx_ref_path, '/ --bim_prefix=', tmp_dir,'/ref.chr', jobs$CHR[i], ' --pop=', opt$populations, ' --phi=', jobs$phi[i], ' --sst_file=', paste0(paste0(tmp_dir, '/GWAS_sumstats_', 1:length(sumstats),'_temp.txt'), collapse=','),' --n_gwas=', paste(gwas_N, collapse=','), ' --out_dir=', tmp_dir, '/ --out_name=output --chrom=', jobs$CHR[i], ' --meta=True --seed=', opt$seed))
-  }
-}
+system(paste0('rm -r ', tmp_dir,'/bridge_out'))
+dir.create(paste0(tmp_dir,'/bridge_out'))
+
+system(paste0(
+  opt$bridgeprs_repo, '/src/Bash/BridgePRS_sumstat.sh ', opt$bridgeprs_repo, '/src/Rscripts ',
+  '--strand_check 1 ',
+  '--outdir ', tmp_dir,'/bridge_out ',
+  '--by_chr 1 ',
+  '--by_chr_sumstats .txt ',
+  '--pop1 ', populations[1], ' ',
+  '--pop2 ', populations[2], ' ',
+  '--fst ', fst, ' ',
+  '--N_pop1 ', gwas_N[1], ' ',
+  '--N_pop2 ', gwas_N[2], ' ',
+  '--pop1_sumstats ', tmp_dir, '/GWAS_sumstats_', populations[1], '_temp_chr ',
+  '--pop1_qc_snplist ', tmp_dir, '/GWAS_sumstats_',populations[1],'_temp.qc.snplist ',
+  '--pop2_sumstats ', tmp_dir, '/GWAS_sumstats_', populations[2], '_temp_chr ',
+  '--pop2_qc_snplist ', tmp_dir, '/GWAS_sumstats_',populations[2],'_temp.qc.snplist ',
+  '--pop1_ld_bfile ', tmp_dir, '/ref_ld/chr ',
+  '--pop1_ld_ids ', tmp_dir, '/ref_ld/', populations[1], '_ids.txt ',
+  '--pop2_ld_bfile ', tmp_dir, '/ref_ld/chr ',
+  '--pop2_ld_ids ', tmp_dir, '/ref_ld/', populations[2], '_ids.txt ',
+  '--sumstats_snpID SNP ',
+  '--sumstats_p P ',
+  '--sumstats_beta BETA ',
+  '--sumstats_allele1 A1 ',
+  '--sumstats_allele0 A2 ',
+  '--pheno_name y ',
+  '--n_cores ', opt$n_cores, ' ',
+  '--prop_train 0.6 ',
+  '--prop_test 0.3 ',
+  '--do_block_pop1 1 ',
+  '--do_block_pop2 1 ',
+  '--do_sumstat_pop1 1 ',
+  '--do_sumstat_pop2 1 ',
+  '--do_clump_pop1 1 ',
+  '--do_est_beta_pop1 1 ',
+  '--do_sumstat_ensembl_pop1 1 ',
+  '--do_est_beta_pop1_precision 1 ',
+  '--do_est_beta_InformPrior 1 ',
+  '--do_clump_pop2 1 ',
+  '--do_est_beta_pop2 1 ',
+  '--do_sumstat_ensembl_pop2 1 ',
+  '--n_folds 10 ',
+  ' > ',tmp_dir,'/bridge_log.txt 2>&1'
+))
 
 ####
 # Combine score files
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index ac047974..e0129d64 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1842,7 +1842,7 @@ config<-c(
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
   "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx','xwing']",
   "leopard_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
-  "cores_prep_pgs: 10", # xwing run with 20 cores
+  "cores_prep_pgs: 20", # xwing run with 20 cores
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
   "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
@@ -8013,7 +8013,7 @@ config<-c(
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
   "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx']",
   "leopard_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
-  "cores_prep_pgs: 10", # xwing run with 20 cores
+  "cores_prep_pgs: 20", # xwing run with 20 cores
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
   "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
@@ -8035,7 +8035,7 @@ snakemake \
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml \
-  prep_pgs -n
+  target_pgs -n
 ```
 
 </details>
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index f2940b31..01473da0 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -1439,7 +1439,7 @@ rule download_bridgeprs_software:
       rm -r -f {resdir}/software/bridgeprs; \
       git clone https://github.com/clivehoggart/BridgePRS.git {resdir}/software/bridgeprs; \
       cd {resdir}/software/bridgeprs; \
-      git reset --hard 1e1c9477d42d44ed312759751adbc25d146aeb9f
+      git reset --hard a4b827c1a57c6e3b7a48ed495cac7ebdb5f89d96
     }} > {log} 2>&1
     """
 

From 79febd058c2df683b7892b2f1ad2c63bb3395e9f Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 9 May 2025 17:07:25 +0100
Subject: [PATCH 142/153] Updates for reviews

---
 Scripts/pgs_methods/megaprs.R   |   17 +-
 Scripts/pgs_methods/megaprs6.R  |  234 +++++
 Scripts/pgs_methods/quickprs.R  |   23 +-
 docs/CrossPop.Rmd               | 1430 +++++++++++++++++++++++++------
 functions/pgs.R                 |   29 +-
 pipeline/rules/dependencies.smk |   22 +-
 pipeline/rules/pgs_methods.smk  |   43 +
 7 files changed, 1514 insertions(+), 284 deletions(-)
 create mode 100644 Scripts/pgs_methods/megaprs6.R

diff --git a/Scripts/pgs_methods/megaprs.R b/Scripts/pgs_methods/megaprs.R
index a2749fee..2cb84590 100644
--- a/Scripts/pgs_methods/megaprs.R
+++ b/Scripts/pgs_methods/megaprs.R
@@ -98,7 +98,22 @@ if(!is.na(opt$test)){
 log_add(log_file = log_file, message = 'Reading in GWAS.')
 
 # Read in, check and format GWAS summary statistics
-gwas <- read_sumstats(sumstats = opt$sumstats, chr = CHROMS, log_file = log_file, req_cols = c('CHR','BP','SNP','A1','A2','BETA','SE','N'))
+gwas <- read_sumstats(sumstats = opt$sumstats, chr = CHROMS, log_file = log_file, req_cols = c('CHR','BP','SNP','A1','A2','BETA','SE','N','FREQ','REF.FREQ'))
+
+# Check allele frequency difference
+ref_psam<-fread(paste0(opt$ref_plink_chr, CHROMS[1],'.psam'))
+names(ref_psam)<-gsub('\\#', '', names(ref_psam))
+
+if(!is.null(opt$ref_keep)){
+  ref_keep <- fread(opt$ref_keep, header=F)$V1
+  ref_psam <- ref_psam[ref_psam$IID %in% ref_keep,]
+}
+
+ref_n <- nrow(ref_psam)
+
+gwas$FREQ_LRT_P <- lrt_af_dual(p1 = gwas$FREQ, n1 = gwas$N, p0 = gwas$REF.FREQ, n0 = ref_n)$p
+log_add(log_file = log_file, message = paste0('Removed ', sum(gwas$FREQ_LRT_P < 1e-6), " variants due to significant difference in allele frequency to reference (P < 1e-6)."))
+gwas <- gwas[!(gwas$FREQ_LRT_P < 1e-6),]
 
 # Format for LDAK
 snplist <- gwas$SNP
diff --git a/Scripts/pgs_methods/megaprs6.R b/Scripts/pgs_methods/megaprs6.R
new file mode 100644
index 00000000..ff93443a
--- /dev/null
+++ b/Scripts/pgs_methods/megaprs6.R
@@ -0,0 +1,234 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+library("optparse")
+
+option_list = list(
+  make_option("--ref_plink_chr", action="store", default=NULL, type='character',
+              help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_keep", action="store", default=NULL, type='character',
+              help="Path to keep file for reference [optional]"),
+  make_option("--ref_pcs", action="store", default=NULL, type='character',
+              help="Reference PCs for continuous ancestry correction [optional]"),
+  make_option("--pop_data", action="store", default=NULL, type='character',
+              help="File containing the population code and location of the keep file [required]"),
+  make_option("--plink", action="store", default='plink', type='character',
+              help="Path PLINK v1.9 software binary [optional]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+              help="Path PLINK v2 software binary [optional]"),
+  make_option("--output", action="store", default=NULL, type='character',
+              help="Path for output files [required]"),
+  make_option("--memory", action="store", default=5000, type='numeric',
+              help="Memory limit [optional]"),
+  make_option("--sumstats", action="store", default=NULL, type='character',
+              help="GWAS summary statistics [required]"),
+  make_option("--ldak", action="store", default=NULL, type='character',
+              help="Path to ldak executable [required]"),
+  make_option("--ldak_map", action="store", default=NULL, type='character',
+              help="Path to ldak map [required]"),
+  make_option("--ldak_tag", action="store", default=NULL, type='character',
+              help="Path to ldak tagging data [required]"),
+  make_option("--ldak_highld", action="store", default=NULL, type='character',
+              help="Path to ldak highld data [required]"),
+  make_option("--n_cores", action="store", default=1, type='numeric',
+              help="Number of cores for parallel computing [optional]"),
+  make_option("--prs_model", action="store", default='mega', type='character',
+              help="Model used for deriving SNP-weights [optional]"),
+  make_option("--test", action="store", default=NA, type='character',
+              help="Specify number of SNPs to include [optional]")
+)
+
+opt = parse_args(OptionParser(option_list = option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Check required inputs
+if(is.null(opt$ref_plink_chr)){
+  stop('--ref_plink_chr must be specified.\n')
+}
+if(is.null(opt$sumstats)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$pop_data)){
+  stop('--pop_data must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--output must be specified.\n')
+}
+if(is.null(opt$ldak)){
+  stop('--ldak must be specified.\n')
+}
+if(is.null(opt$ldak_map)){
+  stop('--ldak_map must be specified.\n')
+}
+if(is.null(opt$ldak_tag)){
+  stop('--ldak_tag must be specified.\n')
+}
+if(is.null(opt$ldak_highld)){
+  stop('--ldak_highld must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
+system(paste0('mkdir -p ',opt$output_dir))
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'megaprs.R', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+#####
+# Format the sumstats
+#####
+
+log_add(log_file = log_file, message = 'Reading in GWAS.')
+
+# Read in, check and format GWAS summary statistics
+gwas <- read_sumstats(sumstats = opt$sumstats, chr = CHROMS, log_file = log_file, req_cols = c('CHR','BP','SNP','A1','A2','BETA','SE','N','FREQ'))
+
+# Format for LDAK
+snplist <- gwas$SNP
+gwas$Z <- gwas$BETA / gwas$SE
+gwas$Predictor<-paste0(gwas$CHR, ':', gwas$BP)
+gwas<-gwas[,c('Predictor','A1','A2','N','Z','FREQ')]
+names(gwas)<-c('Predictor','A1','A2','n','Z','A1Freq')
+
+fwrite(gwas, paste0(tmp_dir,'/GWAS_sumstats_temp.txt'), sep=' ')
+
+###
+# Merge the per chromosome reference genetic data and subset opt$ref_keep
+###
+
+log_add(log_file = log_file, message = 'Merging per chromosome reference data.')
+
+# Save in plink1 format for MegaPRS
+plink_merge(pfile = opt$ref_plink_chr, chr = CHROMS, plink2 = opt$plink2, keep = opt$ref_keep, extract = snplist, make_bed =T, out = paste0(tmp_dir, '/ref_merge'))
+
+# Record start time for test
+if(!is.na(opt$test)){
+  test_start.time <- test_start(log_file = log_file)
+}
+
+############
+# Format reference for LDAK
+############
+
+log_add(log_file = log_file, message = 'Formatting reference for LDAK.')
+
+# Insert CHR:BP IDs
+system(paste0("awk < ", tmp_dir, "/ref_merge.bim '{$2=$1\":\"$4;print $0}' > ", tmp_dir, '/tmp.bim; mv ', tmp_dir, '/tmp.bim ', tmp_dir, '/ref_merge.bim'))
+
+# Insert genetic distances
+system(paste0(opt$plink, ' --bfile ', tmp_dir, '/ref_merge --cm-map ', opt$ldak_map,'/genetic_map_chr@_combined_b37.txt --make-bed --out ', tmp_dir, '/map'))
+system(paste0("cat ", tmp_dir, "/map.bim | awk '{print $2, $3}' > ", tmp_dir, '/map.all'))
+system(paste0("awk '(NR==FNR){arr[$1]=$2;next}{print $1, $2, arr[$2], $4, $5, $6}' ", tmp_dir, '/map.all ', tmp_dir, '/ref_merge.bim > ', tmp_dir, '/tmp.bim; mv ', tmp_dir, '/tmp.bim ', tmp_dir, '/ref_merge.bim'))
+system(paste0('rm ', tmp_dir, '/map*'))
+
+############
+# Estimate Per-Predictor Heritabilities
+############
+# We will use the BLD-LDAK Model, as recommended for human SNP data
+
+log_add(log_file = log_file, message = 'Estimating per-predictor heritabilities.')
+
+# Calculate LDAK weights
+system(paste0(opt$ldak, ' --cut-weights ', tmp_dir,'/sections --bfile ', tmp_dir, '/ref_merge --max-threads ', opt$n_cores))
+system(paste0(opt$ldak, ' --calc-weights-all ', tmp_dir,'/sections --bfile ', tmp_dir, '/ref_merge --max-threads ', opt$n_cores))
+system(paste0('mkdir ', tmp_dir, '/bld'))
+system(paste0('cp ', opt$ldak_tag, '/* ', tmp_dir, '/bld/'))
+system(paste0('mv ', tmp_dir, '/sections/weights.short ', tmp_dir,'/bld/bld65'))
+
+# Calculate taggings
+if(length(CHROMS) != 1){
+  system(paste0(opt$ldak, ' --calc-tagging ', tmp_dir, '/bld.ldak --bfile ', tmp_dir, '/ref_merge --ignore-weights YES --power -.25 --annotation-number 65 --annotation-prefix ', tmp_dir, '/bld/bld --window-cm 1 --save-matrix YES --max-threads ', opt$n_cores))
+} else {
+  system(paste0(opt$ldak, ' --calc-tagging ', tmp_dir, '/bld.ldak --bfile ', tmp_dir, '/ref_merge --ignore-weights YES --power -.25 --annotation-number 65 --annotation-prefix ', tmp_dir, '/bld/bld --window-cm 1 --chr ', CHROMS, ' --save-matrix YES --max-threads ', opt$n_cores))
+}
+
+# Calculate Per-Predictor Heritabilities.
+system(paste0(opt$ldak, ' --sum-hers ', tmp_dir, '/bld.ldak --tagfile ', tmp_dir, '/bld.ldak.tagging --summary ', tmp_dir, '/GWAS_sumstats_temp.txt --matrix ', tmp_dir, '/bld.ldak.matrix --max-threads ', opt$n_cores))
+
+ldak_res_her<-fread(paste0(tmp_dir,'/bld.ldak.hers'))
+
+log_add(log_file = log_file, message = paste0('SNP-based heritability estimated to be ',ldak_res_her$Heritability[nrow(ldak_res_her)]," (SD=", ldak_res_her$SD[nrow(ldak_res_her)],")."))
+
+# Identify SNPs in high LD regions
+system(paste0(opt$ldak, ' --cut-genes ', tmp_dir, '/highld --bfile ', tmp_dir, '/ref_merge --genefile ', opt$ldak_highld, ' --max-threads ', opt$n_cores))
+
+###################
+# Run using full reference.
+###################
+
+log_add(log_file = log_file, message = 'Running using full reference.')
+
+# Calculate predictor-predictor correlations
+log_add(log_file = log_file, message = 'Calculating predictor-predictor correlations.')
+full_cors <- ldak_pred_cor(bfile = paste0(tmp_dir, '/ref_merge'), ldak = opt$ldak, n_cores = opt$n_cores, chr = CHROMS)
+
+# Run MegaPRS
+log_add(log_file = log_file, message = paste0('Running MegaPRS: ',opt$prs_model,' model.'))
+system(paste0(opt$ldak, ' --mega-prs ', tmp_dir, '/mega_full --model ', opt$prs_model, ' --bfile ', tmp_dir, '/ref_merge --cors ', full_cors, ' --summary ', tmp_dir, '/GWAS_sumstats_temp.txt --allow-ambiguous YES --power -0.25 --max-threads ', opt$n_cores))
+
+# Save the parameters file
+system(paste0('cp ', tmp_dir, '/mega_full.parameters ', opt$output, '.model_param.txt'))
+
+# Identify the best fitting model
+ldak_res_cors <- fread(paste0(tmp_dir, '/mega_full.cors'), nThread = opt$n_cores)
+best_score <- ldak_res_cors[ldak_res_cors$Correlation == max(ldak_res_cors$Correlation),]
+
+# Save the pseudovalidation results
+system(paste0('cp ', tmp_dir, '/mega_full.cors ', opt$output, '.pseudoval.txt'))
+log_add(log_file = log_file, message = paste0('Model ', gsub('Score_','',best_score$V1[1]),' is identified as the best with correlation of ', best_score$V2))
+
+######
+# Format final score file
+######
+
+# Read in the scores
+score <- fread(paste0(tmp_dir,'/mega_full.effects'), nThread = opt$n_cores)
+
+# Change IDs to RSIDs
+ref_pvar <- read_pvar(dat = opt$ref_plink_chr, chr = CHROMS)
+ref_pvar$Predictor<-paste0(ref_pvar$CHR,':',ref_pvar$BP)
+score<-merge(score, ref_pvar[,c('Predictor','SNP'), with=F], by='Predictor')
+score<-score[, c('SNP', 'A1', 'A2', names(score)[grepl('Model', names(score))]), with=F]
+names(score)[grepl('Model', names(score))]<-paste0('SCORE_ldak_',names(score)[grepl('Model', names(score))])
+
+# Flip effects to match reference alleles
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
+score_new <- map_score(ref = ref, score = score)
+
+fwrite(score_new, paste0(opt$output, '.score'), col.names=T, sep=' ', quote=F)
+
+if(file.exists(paste0(opt$output,'.score.gz'))){
+  system(paste0('rm ',opt$output,'.score.gz'))
+}
+
+system(paste0('gzip ',opt$output,'.score'))
+
+# Record end time of test
+if(!is.na(opt$test)){
+  test_finish(log_file = log_file, test_start.time = test_start.time)
+}
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
+
diff --git a/Scripts/pgs_methods/quickprs.R b/Scripts/pgs_methods/quickprs.R
index 764d4a3a..6913860a 100644
--- a/Scripts/pgs_methods/quickprs.R
+++ b/Scripts/pgs_methods/quickprs.R
@@ -6,6 +6,8 @@ suppressMessages(library("optparse"))
 option_list = list(
   make_option("--ref_plink_chr", action="store", default=NA, type='character',
               help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--ref_keep", action="store", default=NULL, type='character',
+              help="Path to keep file for reference [optional]"),
   make_option("--ref_pcs", action="store", default=NULL, type='character',
               help="Reference PCs for continuous ancestry correction [optional]"),
   make_option("--pop_data", action="store", default=NULL, type='character',
@@ -88,14 +90,29 @@ if(!is.na(opt$test)){
 log_add(log_file = log_file, message = 'Reading in GWAS.')
 
 # Read in, check and format GWAS summary statistics
-gwas <- read_sumstats(sumstats = opt$sumstats, chr = CHROMS, log_file = log_file, req_cols = c('CHR','BP','SNP','A1','A2','BETA','SE','N'))
+gwas <- read_sumstats(sumstats = opt$sumstats, chr = CHROMS, log_file = log_file, req_cols = c('CHR','BP','SNP','A1','A2','BETA','SE','N','FREQ','REF.FREQ'))
+
+# Check allele frequency difference
+ref_psam<-fread(paste0(opt$ref_plink_chr, CHROMS[1],'.psam'))
+names(ref_psam)<-gsub('\\#', '', names(ref_psam))
+
+if(!is.null(opt$ref_keep)){
+  ref_keep <- fread(opt$ref_keep, header=F)$V1
+  ref_psam <- ref_psam[ref_psam$IID %in% ref_keep,]
+}
+
+ref_n <- nrow(ref_psam)
+
+gwas$FREQ_LRT_P <- lrt_af_dual(p1 = gwas$FREQ, n1 = gwas$N, p0 = gwas$REF.FREQ, n0 = ref_n)$p
+log_add(log_file = log_file, message = paste0('Removed ', sum(gwas$FREQ_LRT_P < 1e-6), " variants due to significant difference in allele frequency to reference (P < 1e-6)."))
+gwas <- gwas[!(gwas$FREQ_LRT_P < 1e-6),]
 
 # Format for LDAK
 snplist <- gwas$SNP
 gwas$Z <- gwas$BETA / gwas$SE
 gwas$Predictor<-paste0(gwas$CHR, ':', gwas$BP)
-gwas<-gwas[,c('Predictor','A1','A2','N','Z')]
-names(gwas)<-c('Predictor','A1','A2','n','Z')
+gwas<-gwas[,c('Predictor','A1','A2','N','Z','FREQ')]
+names(gwas)<-c('Predictor','A1','A2','n','Z','A1Freq')
 
 # Check overlap between GWAS and LDAK reference
 ldak_hm3_file <- list.files(opt$quickprs_ldref)
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index e0129d64..25a6c29d 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -1842,7 +1842,7 @@ config<-c(
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.txt",
   "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx','xwing']",
   "leopard_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
-  "cores_prep_pgs: 20", # xwing run with 20 cores
+  "cores_prep_pgs: 10", # xwing run with 20 cores
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
   "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
@@ -7943,6 +7943,14 @@ write.table(
   col.names = F
 )
 
+write.table(
+  mvp_afr_subset,
+  '~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/info.txt',
+  row.names = F,
+  quote = T,
+  col.names = T
+)
+
 write.table(mvp_afr_subset$labels, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', col.names = F, row.names = F, quote = F)
 
 ```
@@ -8011,9 +8019,35 @@ config<-c(
   "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt",
   "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_mvp.txt",
   "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
-  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx']",
+  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx','xwing']",
+  "leopard_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
+  "cores_prep_pgs: 10", # xwing run with 20 cores
+  "cores_target_pgs: 50",
+  "ldpred2_inference: F",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3",
+  "quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml', col.names = F, row.names = F, quote = F)
+
+# Make a version of the gwas_list without NEU
+gwas_list <- gwas_list[!grepl('NEU', gwas_list$name),]
+write.table(gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp_noneu.txt', col.names = T, row.names = F, quote = F)
+
+gwas_groups <- gwas_groups[!grepl('NEU', gwas_groups$name),]
+write.table(gwas_groups, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_mvp_noneu.txt', col.names = T, row.names = F, quote = F)
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp_noneu.txt",
+  "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_mvp_noneu.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
+  "pgs_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc','prscsx','xwing']",
   "leopard_methods: ['ptclump','quickprs','dbslmm','lassosum','megaprs','prscs','ldpred2','sbayesrc']",
-  "cores_prep_pgs: 20", # xwing run with 20 cores
+  "cores_prep_pgs: 10", # xwing run with 20 cores
   "cores_target_pgs: 50",
   "ldpred2_inference: F",
   "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
@@ -8036,6 +8070,15 @@ snakemake \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml \
   target_pgs -n
+  
+# Note. xwing fails for PLT. Remove PLT from gwas_list to get scores for other traits.
+# Save PLT results for other methods to check the pattern is similar.
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml \
+  target_pgs -n
+  
 ```
 
 </details>
@@ -8066,6 +8109,7 @@ outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Read in list of outcomes 
 selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
+selected_traits<-selected_traits[selected_traits != 'NEU']
 
 # Get a list of score files
 scores <- list_score_files(config)
@@ -8174,198 +8218,198 @@ for(trait_i in selected_traits){
                                     paste0('.', targ_pop_i, '_pseudo.profiles'),
                                     scores_i_j_single_pseudo$predictor)
 
-#      ####
-#      # Make groups for multi-single-source pseudo scores
-#      ####
-#      
-#      scores_i_j_multi_single_pseudo <- scores_i_j[grepl('_multi$', scores_i_j$method),]
-#
-#      # Extract the pseudo score for each method and specify as a separate group
-#      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
-#        param <- find_pseudo(
-#          config = config,
-#          gwas = scores_i_j_multi_single_pseudo$name[i],
-#          pgs_method = scores_i_j_multi_single_pseudo$method[i],
-#          target_pop = targ_pop_i
-#        )
-#        
-#        score_header <-
-#          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
-#        score_cols <-
-#          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi_single_pseudo$name[i], '_', param)))
-#        
-#        system(
-#          paste0(
-#            "cut -d' ' -f ", 
-#            paste0(score_cols, collapse=','),
-#            " ", 
-#            scores_i_j_multi_single_pseudo$predictor[i], 
-#            " > ", 
-#            gsub('.profiles',
-#                 paste0('.', targ_pop_i, '_pseudo.profiles'),
-#                 scores_i_j_multi_single_pseudo$predictor[i])
-#          )
-#        )
-#      }
-#      
-#      scores_i_j_multi_single_pseudo$multi <- paste0(scores_i_j_multi_single_pseudo$multi, '.pseudo')
-#
-#      scores_i_j_multi_single_pseudo$predictor <- gsub('.profiles', 
-#                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
-#                                    scores_i_j_multi_single_pseudo$predictor)
-#      
-#      scores_i_j_multi_single_pseudo$top1<-paste0('EUR_', disc_pop_j_2)
-#
-#      ####
-#      # Make groups for the Multi-Source methods
-#      ####
-#      
-#      scores_i_j_multi <- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
-#
-#      # Split top1 scores by target population
-#      # This doesn't apply to xwing because it only has pop-specific pseudo scores
-#      scores_i_j_multi_top1<-NULL
-#      for(i in 1:which(scores_i_j_multi$method %in% c('prscsx'))){
-#        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
-#        
-#        for(pop in c('EUR', disc_pop_j_2)){
-#          
-#          if(scores_i_j_multi$method[i] == 'prscsx'){
-#            score_cols <-
-#              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi'), names(score_header)))
-#          }
-#          if(scores_i_j_multi$method[i] == 'xwing'){
-#            score_cols <-
-#              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst'), names(score_header)))
-#          }
-#          
-#          system(
-#            paste0(
-#              "cut -d' ' -f ", 
-#              paste0(score_cols, collapse=','),
-#              " ", 
-#              scores_i_j_multi$predictor[i], 
-#              " > ", 
-#              gsub('.profiles',
-#                   paste0('.', pop, '_grid.profiles'),
-#                   scores_i_j_multi$predictor[i])
-#            )
-#          )
-#          
-#          tmp <- scores_i_j_multi[i,]
-#          tmp$multi <- paste0(tmp$multi, '.grid')
-#          tmp$top1 <- pop
-#          tmp$predictor <-
-#              gsub('.profiles',
-#                   paste0('.', pop, '_grid.profiles'),
-#                   scores_i_j_multi$predictor[i])
-#          
-#          scores_i_j_multi_top1 <- rbind(scores_i_j_multi_top1, tmp)
-#        }
-#      }
-#
-#      # Split pop-specific pseudo scores by target population
-#      scores_i_j_multi_pop_pseudo<-NULL
-#      for(i in 1:nrow(scores_i_j_multi)){
-#        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
-#        
-#        for(pop in c('EUR', disc_pop_j_2)){
-#          if(scores_i_j_multi$method[i] == 'prscsx'){
-#            score_cols <-
-#              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi_auto'), names(score_header)))
-#          }
-#          if(scores_i_j_multi$method[i] == 'xwing'){
-#            score_cols <-
-#              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst_', pop), names(score_header)))
-#          }
-#          
-#          system(
-#            paste0(
-#              "cut -d' ' -f ", 
-#              paste0(score_cols, collapse=','),
-#              " ", 
-#              scores_i_j_multi$predictor[i], 
-#              " > ", 
-#              gsub('.profiles',
-#                   paste0('.', pop, '_pseudo.profiles'),
-#                   scores_i_j_multi$predictor[i])
-#            )
-#          )
-#          
-#          tmp <- scores_i_j_multi[i,]
-#          tmp$multi <- paste0(tmp$multi, '.pop_pseudo')
-#          tmp$top1 <- pop
-#          tmp$predictor <-
-#              gsub('.profiles',
-#                   paste0('.', pop, '_pseudo.profiles'),
-#                   scores_i_j_multi$predictor[i])
-#          
-#          scores_i_j_multi_pop_pseudo <- rbind(scores_i_j_multi_pop_pseudo, tmp)
-#        }
-#      }
-#      
-#      # Create pseudo score for multi-source methods
-#      scores_i_j_multi_pseudo<-NULL
-#      for(i in 1:nrow(scores_i_j_multi)) {
-#        param <- find_pseudo(
-#          config = config,
-#          gwas = scores_i_j_multi$name[i],
-#          pgs_method = scores_i_j_multi$method[i],
-#          target_pop = targ_pop_i
-#        )
-#        
-#        score_header <-
-#          fread(scores_i_j_multi$predictor[i], nrows = 1)
-#        score_cols <-
-#          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
-#
-#        system(
-#          paste0(
-#            "cut -d' ' -f ", 
-#            paste0(score_cols, collapse=','),
-#            " ", 
-#            scores_i_j_multi$predictor[i], 
-#            " > ", 
-#            gsub('.profiles',
-#                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
-#                 scores_i_j_multi$predictor[i])
-#          )
-#        )
-#        
-#        tmp <- scores_i_j_multi[i,]
-#        tmp$multi <- paste0(tmp$multi, '.pseudo')
-#        tmp$top1 <- paste0('EUR_', disc_pop_j_2)
-#        tmp$predictor <-
-#            gsub('.profiles',
-#                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
-#                 scores_i_j_multi$predictor[i])
-#        
-#        scores_i_j_multi_pseudo <- rbind(scores_i_j_multi_pseudo, tmp)
-#      }
-#      
       ####
-      # Combine the different predictor groups
+      # Make groups for multi-single-source pseudo scores
       ####
-      predictors_i<- do.call(rbind, list(
-        scores_i_j_single_top1, 
-        scores_i_j_single_pseudo 
-#        scores_i_j_multi_single_pseudo,
-#        scores_i_j_multi_top1,
-#        scores_i_j_multi_pop_pseudo,
-#        scores_i_j_multi_pseudo
-      ))
       
-      predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
+      scores_i_j_multi_single_pseudo <- scores_i_j[grepl('_multi$', scores_i_j$method),]
+
+      # Extract the pseudo score for each method and specify as a separate group
+      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
+        param <- find_pseudo(
+          config = config,
+          gwas = scores_i_j_multi_single_pseudo$name[i],
+          pgs_method = scores_i_j_multi_single_pseudo$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header <-
+          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
+        score_cols <-
+          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi_single_pseudo$name[i], '_', param)))
+        
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            scores_i_j_multi_single_pseudo$predictor[i], 
+            " > ", 
+            gsub('.profiles',
+                 paste0('.', targ_pop_i, '_pseudo.profiles'),
+                 scores_i_j_multi_single_pseudo$predictor[i])
+          )
+        )
+      }
+      
+      scores_i_j_multi_single_pseudo$multi <- paste0(scores_i_j_multi_single_pseudo$multi, '.pseudo')
+
+      scores_i_j_multi_single_pseudo$predictor <- gsub('.profiles', 
+                                    paste0('.', targ_pop_i, '_pseudo.profiles'),
+                                    scores_i_j_multi_single_pseudo$predictor)
       
+      scores_i_j_multi_single_pseudo$top1<-paste0('EUR_', disc_pop_j_2)
+
       ####
-      # Make a group that will combined all population specific PGS
+      # Make groups for the Multi-Source methods
       ####
       
-      predictors_i_all <- predictors_i[predictors_i$top1 %in% c('EUR','AFR','EAS'),]
-      predictors_i_all$multi <- 'all'
-      predictors_i<-rbind(predictors_i, predictors_i_all)
-      
-      write.table(
+      scores_i_j_multi <- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
+
+      # Split top1 scores by target population
+      # This doesn't apply to xwing because it only has pop-specific pseudo scores
+      scores_i_j_multi_top1<-NULL
+      for(i in 1:which(scores_i_j_multi$method %in% c('prscsx'))){
+        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
+        
+        for(pop in c('EUR', disc_pop_j_2)){
+          
+          if(scores_i_j_multi$method[i] == 'prscsx'){
+            score_cols <-
+              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi'), names(score_header)))
+          }
+          if(scores_i_j_multi$method[i] == 'xwing'){
+            score_cols <-
+              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst'), names(score_header)))
+          }
+          
+          system(
+            paste0(
+              "cut -d' ' -f ", 
+              paste0(score_cols, collapse=','),
+              " ", 
+              scores_i_j_multi$predictor[i], 
+              " > ", 
+              gsub('.profiles',
+                   paste0('.', pop, '_grid.profiles'),
+                   scores_i_j_multi$predictor[i])
+            )
+          )
+          
+          tmp <- scores_i_j_multi[i,]
+          tmp$multi <- paste0(tmp$multi, '.grid')
+          tmp$top1 <- pop
+          tmp$predictor <-
+              gsub('.profiles',
+                   paste0('.', pop, '_grid.profiles'),
+                   scores_i_j_multi$predictor[i])
+          
+          scores_i_j_multi_top1 <- rbind(scores_i_j_multi_top1, tmp)
+        }
+      }
+
+      # Split pop-specific pseudo scores by target population
+      scores_i_j_multi_pop_pseudo<-NULL
+      for(i in 1:nrow(scores_i_j_multi)){
+        score_header<-fread(scores_i_j_multi$predictor[i], nrow = 1)
+        
+        for(pop in c('EUR', disc_pop_j_2)){
+          if(scores_i_j_multi$method[i] == 'prscsx'){
+            score_cols <-
+              which(grepl(paste0('^FID$|^IID$|_', pop, '_phi_auto'), names(score_header)))
+          }
+          if(scores_i_j_multi$method[i] == 'xwing'){
+            score_cols <-
+              which(grepl(paste0('^FID$|^IID$|_targ_', pop, '_pst_', pop), names(score_header)))
+          }
+          
+          system(
+            paste0(
+              "cut -d' ' -f ", 
+              paste0(score_cols, collapse=','),
+              " ", 
+              scores_i_j_multi$predictor[i], 
+              " > ", 
+              gsub('.profiles',
+                   paste0('.', pop, '_pseudo.profiles'),
+                   scores_i_j_multi$predictor[i])
+            )
+          )
+          
+          tmp <- scores_i_j_multi[i,]
+          tmp$multi <- paste0(tmp$multi, '.pop_pseudo')
+          tmp$top1 <- pop
+          tmp$predictor <-
+              gsub('.profiles',
+                   paste0('.', pop, '_pseudo.profiles'),
+                   scores_i_j_multi$predictor[i])
+          
+          scores_i_j_multi_pop_pseudo <- rbind(scores_i_j_multi_pop_pseudo, tmp)
+        }
+      }
+      
+      # Create pseudo score for multi-source methods
+      scores_i_j_multi_pseudo<-NULL
+      for(i in 1:nrow(scores_i_j_multi)) {
+        param <- find_pseudo(
+          config = config,
+          gwas = scores_i_j_multi$name[i],
+          pgs_method = scores_i_j_multi$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header <-
+          fread(scores_i_j_multi$predictor[i], nrows = 1)
+        score_cols <-
+          which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi$name[i], '_', param)))
+
+        system(
+          paste0(
+            "cut -d' ' -f ", 
+            paste0(score_cols, collapse=','),
+            " ", 
+            scores_i_j_multi$predictor[i], 
+            " > ", 
+            gsub('.profiles',
+                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
+                 scores_i_j_multi$predictor[i])
+          )
+        )
+        
+        tmp <- scores_i_j_multi[i,]
+        tmp$multi <- paste0(tmp$multi, '.pseudo')
+        tmp$top1 <- paste0('EUR_', disc_pop_j_2)
+        tmp$predictor <-
+            gsub('.profiles',
+                 paste0('.pseudo.targ_', targ_pop_i,'.profiles'),
+                 scores_i_j_multi$predictor[i])
+        
+        scores_i_j_multi_pseudo <- rbind(scores_i_j_multi_pseudo, tmp)
+      }
+      
+      ####
+      # Combine the different predictor groups
+      ####
+      predictors_i<- do.call(rbind, list(
+        scores_i_j_single_top1, 
+        scores_i_j_single_pseudo,
+        scores_i_j_multi_single_pseudo,
+        scores_i_j_multi_top1,
+        scores_i_j_multi_pop_pseudo,
+        scores_i_j_multi_pseudo
+      ))
+      
+      predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
+      
+      ####
+      # Make a group that will combined all population specific PGS
+      ####
+      
+      predictors_i_all <- predictors_i[predictors_i$top1 %in% c('EUR','AFR','EAS'),]
+      predictors_i_all$multi <- 'all'
+      predictors_i<-rbind(predictors_i, predictors_i_all)
+      
+      write.table(
         predictors_i,
         paste0(
           '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
@@ -8446,6 +8490,7 @@ source_all('../functions')
 
 # Read in list of outcomes 
 selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
+selected_traits <- selected_traits[selected_traits != 'NEU']
 info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
 
 # Calculate correlation between all phenotypes in each target population
@@ -8727,38 +8772,6 @@ ggplot(tmp, aes(x=label, y=R , fill = Model)) +
           legend.justification = "center")
 dev.off()
 
-# Plot average performance across phenotypes for EUR using AFR or EAS GWAS
-tmp <- meta_res_eval
-tmp <- tmp[tmp$Target == 'EUR',]
-tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
-tmp$label[is.na(tmp$label)] <- 'All'
-tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
-tmp$label <- factor(tmp$label, levels = model_order)
-tmp$Discovery_clean <- as.character(tmp$Discovery)
-tmp$Discovery_clean <- paste0(tmp$Discovery_clean, ' GWAS')
-tmp$Target <- paste0(tmp$Target, ' Target')
-tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
-tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
-tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model'), with=F]),]
-
-png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_eur.png'), res=300, width = 4000, height = 1500, units = 'px')
-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x='Method') +
-    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid(major = 'y', minor = 'y') + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
-          legend.position = "top",
-          legend.key.spacing.x = unit(1, "cm"),
-          legend.justification = "center")
-dev.off()
-
 # Plot performance of -multi models trained using LEOPARD vs using indiv-level data
 tmp <- meta_res_eval
 tmp <- tmp[tmp$Target != 'EUR',]
@@ -8872,7 +8885,7 @@ mirror_comp<-function(x){
 }
   
 # Read in results
-targ_pop=c('EUR','EAS','AFR')
+targ_pop=c('AFR')
 res_comp <- list()
 for(pheno_i in selected_traits){
   res_comp_i<-NULL
@@ -9595,62 +9608,927 @@ ggplot(meta_tmp_simple[meta_tmp_simple$Target != 'EUR Target',], aes(x=label, y=
     )
 dev.off()
 
-# Plot for EUR
-meta_tmp_simple$Discovery_clean <- paste0(meta_tmp_simple$Discovery1,' GWAS')
-meta_tmp_ref_simple$Discovery_clean <- paste0(meta_tmp_ref_simple$Discovery1,' GWAS')
-tmp_simple$Discovery_clean <- paste0(tmp_simple$Discovery1,' GWAS')
+```
+</details>
 
-meta_tmp_simple<-meta_tmp_simple[!duplicated(meta_tmp_simple[, c('label', 'Discovery_clean', 'Model1'), with=F]),]
-meta_tmp_ref_simple<-meta_tmp_ref_simple[!duplicated(meta_tmp_ref_simple[, c('label', 'Discovery_clean', 'Model1'), with=F]),]
-tmp_simple<-tmp_simple[!duplicated(tmp_simple[, c('label', 'Discovery_clean', 'Model1','pheno'), with=F]),]
+***
 
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_eur.perc_improv.png', width = 4000, height = 1500, res= 300, units = 'px')
-ggplot(meta_tmp_simple[meta_tmp_simple$Target == 'EUR Target',], aes(x=label, y=R_diff_perc , fill = Model1)) +
-    geom_point(
-        data = tmp_simple[tmp_simple$Target == 'EUR Target',],
-        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
-        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
-        alpha = 0.3
-      ) +
-    geom_hline(yintercept = 0) +
-      geom_errorbar(
-        aes(
-          ymin = R_diff_perc - R_diff_perc_SE,
-          ymax = R_diff_perc + R_diff_perc_SE
-        ),
-        width = 0,
-        position = position_dodge(width = 0.7)
-      ) +    
-    geom_point(
-      stat = "identity",
-      position = position_dodge(0.7),
-      size = 3,
-      shape = 23
-    ) +
-    geom_point(
-        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target == 'EUR Target',],
-        aes(x = label, y = R_diff_perc, fill = Model1),
-        stat = "identity",
-        position = position_dodge(0.7), # Ensure same dodge as other points
-        size = 4,
-        shape = 22,
-        show.legend=F
-    ) +
-    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype="dotted") +
-    scale_y_continuous(labels = percent_format()) +
-    labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) +
-    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
-    theme_half_open() +
-    background_grid(major = 'y', minor = 'y') + 
-    panel_border() + 
-    theme(
-        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
-        legend.position = "top",
-        legend.key.spacing.x = unit(2, "cm"),
-        legend.justification = "center"
-    )
-dev.off()
+### LEOPARD+QuickPRS
 
-```
-</details>
+Here we will compare the LEOPARD estimated weights for population specific PGS, to the weights estimated using observed data in the UKB target sample.
+
+<details><summary>Show code</summary>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+###
+# Read in weights estimated by LEOPARD (QuickPRS)
+###
+
+leopard_weights<-NULL
+scores_quickprs <- scores$name[scores$method == 'quickprs_multi']
+for(i in selected_traits){
+  scores_i <- scores_quickprs[grepl(paste0('^', i,'_'), scores_quickprs)]
+  for(j in scores_i){
+      weights_file <- readRDS(paste0(outdir, '/reference/pgs_score_files/leopard/', j, '/ref-', j, '.weights.rds'))
+      weights_file <- data.frame(weights_file)
+      
+      weights <-
+        data.table(
+          Target = do.call(c, lapply(names(weights_file), function(x) rep(x, 2))),
+          Discovery = names(weights_file),
+          Weight = do.call(c, lapply(weights_file, function(x) x)),
+          Trait = i,
+          Method = 'LEOPARD'
+        )
+      
+      leopard_weights <- rbind(leopard_weights, weights)
+  }
+}
+
+#####
+# Read in the PGS weights estimated using UKB data
+#####
+# Read in the final model coefficients for multi-source methods
+
+obs_weights<-NULL
+for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_methods)]){
+  scores_method<-scores$name[scores$method == method_i]
+  method_i <- gsub('_multi','', method_i)
+
+  for(i in selected_traits){
+    for(j in c('AFR')){
+      if(j == 'EUR'){
+        pops <- c('EAS','AFR')
+      } else {
+        pops <- j
+      }
+      
+      for(k in pops){
+        model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_', j, '.disc_EUR_', k, '/', i, '/final_models/', method_i, '.pseudo.multi.final_model.txt'))
+        model<-model[-1,]
+        
+        # Set weight to zero if negative, as this is what LEOPARD does
+        if(any(model$V2 < 0)){
+          model$V2[model$V2 < 0] <- 0
+          model$V2[model$V2 > 0] <- 1
+        }
+        
+        names(model) <- c('x', 'BETA')
+        model$Discovery[grepl('UKB', model$x)]<-'EUR'
+        model$Discovery[grepl('BBJ', model$x)]<-'EAS'
+        model$Discovery[grepl('UGR', model$x)]<-'AFR'
+        model$Target <- j
+        model$Weight <- model$BETA/sum(model$BETA)
+        model$Trait <- i
+        model$Method <- method_i
+        model<-model[,c('Target','Discovery','Weight','Method','Trait'), with=F]
+        obs_weights<-rbind(obs_weights, model)
+      }
+    }
+  }
+}
+
+###
+# Estimate weights if using the inverse variance weighting
+###
+
+# Read in GWAS descriptives
+gwas_desc<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+gwas_desc <- gwas_desc[, c('Trait Label','Ancestry','GWAS N'), with=F]
+names(gwas_desc)<-c('trait','ancestry','n')
+gwas_desc<-gwas_desc[gwas_desc$trait %in% selected_traits,]
+
+library(dplyr)
+library(tidyr)
+
+# Reshape GWAS table to wide format
+wide_gwas <- gwas_desc %>%
+  pivot_wider(names_from = ancestry, values_from = n, values_fill = 0)
+
+# Function to create rows for each pair
+make_weights_long <- wide_gwas %>%
+  rowwise() %>%
+  do({
+    trait <- .$trait
+    eur <- .$EUR
+    afr <- .$AFR
+    eas <- .$EAS
+    
+    tibble(
+      Trait = trait,
+      Method = "inverse_var",
+      Target = c("AFR", "AFR", "EUR", "EUR", "EUR", "EAS", "EAS"),
+      Discovery = c("EUR", "AFR", "EUR", "AFR", "EAS", "EUR", "EAS"),
+      Weight = c(
+        eur / (eur + afr), afr / (eur + afr),  # AFR target
+        eur / (eur + afr), afr / (eur + afr),  # EUR target (vs AFR)
+        eas / (eur + eas),                     # EUR target (vs EAS)
+        eur / (eur + eas), eas / (eur + eas)   # EAS target (vs EUR)
+      )
+    )
+  }) %>%
+  bind_rows()
+
+###
+# Combine and compare
+###
+
+both <- do.call(rbind, list(obs_weights, leopard_weights, make_weights_long))
+
+# Remove ptclump as it doesn't have a sumstattune method
+both <- both[both$Method != 'ptclump',]
+
+both<-merge(both, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x=T, sort = F)
+both$label[is.na(both$label)] <- both$Method[is.na(both$label)]
+both$label <- factor(both$label, levels=unique(both$label))
+
+# Plot non-EUR target first
+tmp <- both[both$Target != 'EUR',]
+tmp <- tmp[tmp$Discovery != 'EUR',]
+
+# Set LEOPARD to black fill
+default_colors <- hue_pal()(10)
+names(default_colors) <- levels(tmp$label)
+default_colors["LEOPARD"] <- "black"
+
+# Plot the estimated and observed weights
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_weights.png', units = 'px', res = 300, width = 2500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Target ~ .) +
+  theme_half_open() +
+  labs(title = 'Weight of target ancestry-matched PGS', fill = NULL) +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
+
+# Plot EUR target
+tmp <- both[both$Target == 'EUR',]
+tmp <- tmp[tmp$Discovery != 'EUR',]
+
+# Set LEOPARD to black fill
+default_colors <- hue_pal()(10)
+names(default_colors) <- levels(tmp$label)
+default_colors["LEOPARD"] <- "black"
+
+# Plot the estimated and observed weights
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_weights_eur.png', units = 'px', res = 300, width = 2500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Discovery ~ .) +
+  theme_half_open() +
+  labs(title = 'Weight of non-EUR PGS for EUR Target', fill = NULL) +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
+
+###
+# Check calibration of LEOPARD compared to QuickPRS observed weights
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
+tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
+
+# Calculate metrics
+rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)^2))
+me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)
+
+rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)^2))
+me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)
+
+# Create annotation data.frame
+metrics_df <- data.frame(
+  Discovery = c("AFR", "EAS"),
+  x = c(0.5, 0.5),         # Adjust positions as needed
+  y = c(-0.05, -0.05),
+  label = c(
+    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
+    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
+  )
+)
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
+  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
+  labs(
+    x = "LEOPARD weight",
+    y = "Observed weight",
+  ) +
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+  ) +
+  facet_grid(. ~ Discovery) +
+  coord_fixed()    
+dev.off()
+
+###
+# Check calibration of inverse_var compared to QuickPRS observed weights
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
+tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
+
+# Calculate metrics
+rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)^2))
+me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)
+
+rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)^2))
+me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)
+
+# Create annotation data.frame
+metrics_df <- data.frame(
+  Discovery = c("AFR", "EAS"),
+  x = c(0.5, 0.5),         # Adjust positions as needed
+  y = c(-0.05, -0.05),
+  label = c(
+    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
+    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
+  )
+)
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/inverse_var_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = inverse_var, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
+  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 1.5, size = 3.5) +
+  labs(
+    x = "inverse_var weight",
+    y = "Observed weight",
+  ) +
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+  ) +
+  facet_grid(. ~ Discovery) +
+  coord_fixed()    
+dev.off()
+
+###
+# Check calibration of observed weights across all methods
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp <- tmp[!(tmp$label %in% c('LEOPARD','inverse_var')),]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
+
+metrics <- NULL
+for(i in c('EAS','AFR')){
+  for(j in unique(tmp$label)){
+    for(k in unique(tmp$label)){
+      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
+      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics <- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
+      )
+    }
+  }
+}
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/observed_weights_calibration.png', units = 'px', width = 3000, height = 1650, res = 300)
+ggplot(metrics, aes(x = Method1, y = Method2, fill = rmse)) +
+  geom_tile(color = "white") +  # Tile plot with white borders
+  geom_text(aes(label = round(rmse, 2)), color = "black") +  # Add correlation values
+  scale_fill_gradient2(mid = "white", high = "red", midpoint = 0) +  # Color scale
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1),
+    axis.title = element_blank()
+  ) +
+  facet_grid(. ~ Population) +
+  labs(fill = "RMSE")
+dev.off()
+
+# Calculate average RMSE for each method against all other methods
+metrics_unique <- metrics[metrics$Method1 != metrics$Method2, ]
+metrics_unique$Comparison <- NA
+for (i in 1:nrow(metrics_unique)) {
+  metrics_unique$Comparison[i] <-
+    paste0(sort(c(
+      metrics_unique$Method1[i], metrics_unique$Method2[i]
+    )), collapse = ' vs. ')
+}
+metrics_unique <- metrics_unique[!duplicated(paste0(metrics_unique$Population, metrics_unique$Comparison)),]
+
+mean_rmse <- NULL
+for(i in unique(tmp$label)){
+  for(j in c('AFR','EAS')){
+    metrics_unique_tmp <- metrics_unique[metrics_unique$Method1 == i | metrics_unique$Method2 == i,]
+    metrics_unique_tmp <- metrics_unique_tmp[metrics_unique_tmp$Population == j,]
+    mean_rmse <- rbind(
+      mean_rmse, 
+      data.frame(
+        Method = i,
+        Population = j,
+        avg_rmse = mean(metrics_unique_tmp$rmse)
+      )
+    )
+  }
+}
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avg_observed_weight_rmse.png', units = 'px', width = 1500, height = 1500, res = 300)
+ggplot(mean_rmse, aes(x = Method, y = avg_rmse, fill = Method)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
+  geom_text(aes(label = round(avg_rmse, 3)),  # <-- Add this
+          vjust = 1.5,                    # <-- Move the text slightly above the bar
+          size = 3) +                      # <-- Adjust text size
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Population ~ .) +
+  theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  labs(y = 'Average RMSE') +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position="none")
+dev.off()
+
+###
+# Check calibration of estimated (LEOPARD and inverse_var) weights compared to observed QuickPRS weights
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp <- tmp[(tmp$label %in% c('LEOPARD','inverse_var','QuickPRS')),]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
+
+metrics <- NULL
+for(i in c('EAS','AFR')){
+  for(j in unique(tmp$label)){
+    for(k in unique(tmp$label)){
+      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
+      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics <- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
+      )
+    }
+  }
+}
+
+# Plot the rmse for LEOPARD and inverse_var predicting observed QuickPRS weight
+metrics <- metrics[metrics$Method1 == 'QuickPRS',]
+metrics <- metrics[metrics$Method2 != 'QuickPRS',]
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/inverse_var_comp_rmse.png', units = 'px', width = 800, height = 1500, res = 300)
+ggplot(metrics, aes(x = Method2, y = rmse, fill = Method2)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
+  geom_text(aes(label = round(rmse, 3)),  # <-- Add this
+          vjust = 1.5,                    # <-- Move the text slightly above the bar
+          size = 3) +                      # <-- Adjust text size
+  facet_grid(Population ~ .) +
+  theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  labs(y = 'RMSE relative to QuickPRS', x = 'Method') +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position="none")
+dev.off()
+
+```
+
+</details>
+
+***
+
+### Descriptive statistics
+
+Create a table showing descriptive statistics for the MVP sumstats. This should include LDSC SNP-heritability and AVENGEME results.
+
+***
+
+#### LDSC
+
+<details><summary>Show code</summary>
+```{bash}
+conda activate ldsc
+
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+  mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/sumstats
+
+  sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
+   --sumstats /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR-cleaned.gz \
+   --out /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR"
+done
+
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+  mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/${pheno}/MVP_AFR
+
+  sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \
+   --h2 /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR.sumstats.gz \
+   --ref-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.AFR.rsid \
+   --w-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.AFR.rsid \
+   --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/${pheno}/MVP_AFR/res"
+done
+
+```
+
+```{r}
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+# Read in phenotypes
+pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
+
+# Plot the heritability estimates
+h2_res <- NULL
+
+for(pheno in pheno_intersect){
+  log <-
+    readLines(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/',
+        pheno,
+        '/',
+        'MVP_AFR',
+        '/res.log'
+      )
+    )
+  
+  h2 <- log[grepl('Total Observed scale h2:', log)]
+  h2_est <- as.numeric(gsub(' .*','', gsub('Total Observed scale h2: ', '', h2)))
+  h2_se <- as.numeric(gsub("\\)",'', gsub(".* \\(", '', h2)))
+  int <- log[grepl('Intercept:', log)]
+  int_est <- as.numeric(gsub(' .*','', gsub('Intercept: ', '', int)))
+  int_se <- as.numeric(gsub("\\)",'', gsub(".* \\(", '', int)))
+  lambda <- log[grepl('Lambda GC:', log)]
+  lambda <- as.numeric(gsub('.* ','', lambda))
+  
+  h2_res <- rbind(
+    h2_res,
+    data.table(
+      Population = 'AFR',
+      Phenotype = pheno,
+      h2_est = h2_est,
+      h2_se = h2_se,
+      int_est = int_est,
+      int_se = int_se,
+      lambda = lambda
+    )
+  )
+}
+
+
+write.csv(h2_res, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results.csv', row.names = F, quote = F)
+
+```
+</details>
+
+***
+
+#### AVENGEME
+
+
+<details><summary>Show code</summary>
+
+<div class="shallow-break"></div>
+
+<h4>Create predictor list</h4>
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Read in phenotypes
+pheno_intersect <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
+
+# Create files for EAS and AFR targets
+pop <- c('AFR')
+for(trait_i in pheno_intersect){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
+  scores_i <- scores[grepl(paste0('^', trait_i, '_'), scores$name),]
+  scores_i$group <- scores_i$method
+  
+  for(pop_i in pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    samp_i <- 'MVP_AFR'
+
+    dir.create(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i
+      ),
+      recursive = T
+    )
+    
+    scores_i_j <- scores_i[grepl(samp_i, scores_i$name, ignore.case = T),]
+    scores_i_j <- scores_i_j[!grepl('UKB', scores_i_j$name),]
+    scores_i_j <- scores_i_j[scores_i_j$method == 'ptclump',]
+    scores_i_j$predictor <- paste0(
+      outdir,
+      '/ukb/pgs/TRANS/',
+      scores_i_j$method,
+      '/',
+      scores_i_j$name,
+      '/ukb-',
+      scores_i_j$name,
+      '-TRANS.profiles'
+    )
+    
+    predictors_i <- scores_i_j[, c('predictor', 'group'), with=F]
+    
+    write.table(
+      predictors_i,
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i,
+        '/predictor_list.ptclump.txt'
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}
+
+```
+
+***
+
+<h4>Run model_builder</h4>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+for pop in $(echo AFR); do
+  if [ "$pop" == "EUR" ]; then
+      pop2="EUR_test"
+  else
+      pop2=$pop
+  fi
+  
+  for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${pop2}.row_number.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${pop}.disc_${pop}/${pheno}/res.ptclump \
+      --n_core 5 \
+      --all_model F \
+      --assoc T"
+  done
+done
+
+```
+
+***
+
+<h4>Plot pT+clump association results</h4>
+
+```{r}
+
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+library(avengeme)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+gwas_list <- read_param(config = config, param = 'gwas_list', return_obj = T)
+
+# Read in phenotypes
+pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
+
+pop = c('AFR')
+
+mod_res_all <- NULL
+for(pop_i in pop){
+  for(pheno_i in pheno_intersect){
+    gwas_i<-gwas_list$name[gwas_list$population == pop_i & grepl(paste0('^', pheno_i, '_'),  gwas_list$name)]
+      
+    res_i <-
+      fread(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
+          pop_i,
+          '.disc_',
+          pop_i,
+          '/',
+          pheno_i,
+          '/res.ptclump.assoc.txt'
+        )
+      )
+    
+    res_i$Z <- res_i$BETA / res_i$SE
+    
+    res_i$pT <- as.numeric(gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.|.*MVP.AFR\\.0\\.', '', res_i$Predictor)))
+
+    nsnp_log <-
+      read.table(
+        paste0(
+          outdir,
+          '/reference/pgs_score_files/ptclump/',
+          gwas_i,
+          '/ref-',
+          gwas_i,
+          '.NSNP_per_pT'
+        ),
+        header = T
+      )
+    
+    nsnp<-nsnp_log$NSNP[nrow(nsnp_log)]
+    
+    disc_N <-
+      median(
+        fread(
+          paste0(
+            outdir,
+            '/reference/gwas_sumstat/',
+            gwas_i,
+            '/',
+            gwas_i,
+            '-cleaned.gz'
+          ), nrows = 10000
+        )$N
+      )
+    
+    targ_N <- res_i$N[1]
+    
+    mod_res <- estimatePolygenicModel(
+      p = res_i$Z,
+      nsnp = nsnp,
+      n = c(disc_N, targ_N),
+      pupper = c(0, res_i$pT),
+      fixvg2pi02 = T,
+      alpha = 0.05
+    )
+    
+    mod_res_all <- rbind(
+      mod_res_all,
+      data.frame(
+        Phenotype = pheno_i,
+        Population = pop_i,
+        GWAS = gwas_i,
+        nsnp = nsnp,
+        max_r2 = max(res_i$Obs_R2),
+        n_disc = disc_N,
+        n_targ = targ_N,
+        vg_est = mod_res$vg[1],
+        vg_lowCI = mod_res$vg[2],
+        vg_highCI = mod_res$vg[3],
+        pi0_est = mod_res$pi0[1],
+        pi0_lowCI = mod_res$pi0[2],
+        pi0_highCI = mod_res$pi0[3]
+      )
+    )
+  }
+}
+
+dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme')
+write.csv(mod_res_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv', row.names = F, quote = F)
+
+mod_res_all<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv')
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avengeme_h2.png', res = 100, width = 900, height = 500, units = 'px')
+ggplot(mod_res_all, aes(x = Phenotype, y = vg_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=vg_lowCI, ymax=vg_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="SNP-based Heritability (95%CI)", fill = NULL) +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = "top",
+        legend.key.spacing.x = unit(1, "cm"),
+        legend.justification = "center") +
+  background_grid(major = 'y', minor = 'y')
+dev.off()
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avengeme_polygenicity.png', res = 100, width = 900, height = 500, units = 'px')
+ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=1 - pi0_lowCI, ymax=1 - pi0_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="Proporition non-zero\neffects (95%CI)", fill = NULL) +
+  theme_half_open() +
+  coord_cartesian(ylim = c(0, 0.15)) + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = "top",
+        legend.key.spacing.x = unit(1, "cm"),
+        legend.justification = "center") +
+  background_grid(major = 'y', minor = 'y')
+dev.off()
+
+summary(mod_res_all$max_r2)
+summary(mod_res_all$max_r2[mod_res_all$Population == 'EUR'])
+summary(mod_res_all$max_r2[mod_res_all$Population == 'EAS'])
+summary(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])
+
+```
+
+</details>
+
+***
+
+#### Make table
+
+Make a table showing GWAS information for the manuscript.
+
+<details><summary>Show code</summary>
+
+```{r}
+library(data.table)
+
+#####
+# Trait names, labels, and URLs
+#####
+
+mvp <- fread('~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/info.txt')
+mvp <- mvp[, c('efoTraits','labels','url'), with=F]
+names(mvp) <- c('trait', 'labels','url')
+mvp$sample <- 'MVP'
+mvp$population <- 'AFR'
+
+#####
+# Sample size, SNP-h2 and polygenicity
+#####
+
+# Read in the AVENGEME and LDSC results
+avengeme <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv')
+ldsc <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results.csv')
+both <- merge(avengeme, ldsc, by = c('Population','Phenotype'))
+
+# Format for descriptives table
+both$h2_avengeme<- paste0(
+  round(both$vg_est,2), 
+  " (95%CI = ", 
+  round(both$vg_lowCI, 2), 
+  " - " , 
+  round(both$vg_highCI, 2), ")")
+
+both$pi0_avengeme <- paste0(
+  round(both$pi0_est,2), 
+  " (95%CI = ", 
+  round(both$pi0_lowCI, 2), 
+  " - " , 
+  round(both$pi0_highCI, 2), ")")
+
+both$h2_ldsc <- paste0(
+  round(both$h2_est,2), 
+  " (SE = ", 
+  round(both$h2_se, 2), 
+  ")")
+
+both$int_ldsc <- paste0(
+  round(both$int_est,2), 
+  " (SE = ", 
+  round(both$int_se, 2), 
+  ")")
+
+both<-both[, c('Population','Phenotype','n_disc','n_targ','h2_avengeme','pi0_avengeme','h2_ldsc','int_ldsc','lambda'), with = F]
+names(both)[1:2]<-c('population','labels')
+
+info_all <- merge(mvp, both, by = c('labels','population'))
+info_all$n_disc<-round(info_all$n_disc, 0)
+info_all$n_targ<-round(info_all$n_targ, 0)
+
+info_all<-info_all[, c('labels','trait','population','sample','n_disc','n_targ','h2_avengeme','pi0_avengeme','h2_ldsc','int_ldsc','lambda','url'), with=F]
+names(info_all) <- c('Trait Label', 'Trait Description', 'Ancestry', 'GWAS Sample', 'GWAS N', 'Target N',"SNP-h2 (AVENGEME)","pi0 (AVENGEME)","SNP-h2 (LDSC)","Intercept (LDSC)",'Lambda', 'URL')
+
+write.csv(info_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/gwas_descriptives.csv', row.names=F)
+
+# Estimate the mean and SD of sample size within each population for selected traits
+info_all_selected<-info_all
+n_dat <- NULL
+for(i in unique(info_all_selected$`GWAS Sample`)){
+  n_dat <-rbind(
+    n_dat,
+    data.table(
+      sample = i,
+      gwas_n_median = round(median(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      gwas_n_mean = round(mean(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      gwas_n_sd = round(sd(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_median = round(median(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_mean = round(mean(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_sd = round(sd(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i]))
+    )
+  )
+}
+
+```
+
+</details>
+
+<details><summary>Show descriptives table</summary>
+
+```{r, eval = T, echo = F}
+info_all<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/gwas_descriptives.csv')
+
+kable(info_all, "html") %>%
+  kable_styling(bootstrap_options = c("striped", "hover"),
+                full_width = F) %>%
+  scroll_box(width = "100%", height = "500px")
+
+```
+
+</details>
diff --git a/functions/pgs.R b/functions/pgs.R
index 6059a6a5..ac45a2dd 100644
--- a/functions/pgs.R
+++ b/functions/pgs.R
@@ -286,7 +286,7 @@ read_sumstats<-function(sumstats, chr = 1:22, log_file = NULL, extract = NULL, r
   # If FREQ is missing, use REF.FREQ
   if('FREQ' %in% req_cols){
     if(all(names(gwas) != 'FREQ')){
-      names(gwas)[names(gwas) == 'REF.FREQ']<-'FREQ'
+      gwas$FREQ <- gwas$REF.FREQ
       log_add(log_file = log_file, message = 'REF.FREQ being used as FREQ.')
     }
   }
@@ -765,4 +765,29 @@ adjust_weights <- function(weights, pgs_sd) {
   # Normalize weights so they sum to 1
   normalized_weights <- adjusted_weights * (1 / sum(adjusted_weights))
   return(normalized_weights)
-}
\ No newline at end of file
+}
+
+# Create function to run LRT on allele frequencies
+lrt_af_dual <- function(p1, n1, p0, n0) {
+  # Convert allele frequencies to counts of alternate alleles
+  k1 <- round(2 * n1 * p1)
+  k0 <- round(2 * n0 * p0)
+  N1 <- 2 * n1
+  N0 <- 2 * n0
+  
+  # Estimate common allele frequency under null
+  p_common <- (k1 + k0) / (N1 + N0)
+  
+  # Log-likelihood under null: same freq
+  logL0 <- k1 * log(p_common) + (N1 - k1) * log(1 - p_common) +
+    k0 * log(p_common) + (N0 - k0) * log(1 - p_common)
+  
+  # Log-likelihood under alternative: separate freqs
+  logL1 <- k1 * log(p1) + (N1 - k1) * log(1 - p1) +
+    k0 * log(p0) + (N0 - k0) * log(1 - p0)
+  
+  stat <- 2 * (logL1 - logL0)
+  pval <- pchisq(stat, df = 1, lower.tail = FALSE)
+  
+  return(list(stat = stat, p = pval))
+}
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 01473da0..d9ace889 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -1002,7 +1002,26 @@ rule download_ldak:
       rm {resdir}/software/ldak/ldak5.1.linux_.zip
     }} > {log} 2>&1
     """
-    
+
+# Download LDAK v6
+rule download_ldak_repo:
+  output:
+    f"{resdir}/software/ldak_repo/ldak6.1.linux"
+  benchmark:
+    f"{resdir}/data/benchmarks/download_ldak_repo.txt"
+  log:
+    f"{resdir}/data/logs/download_ldak_repo.log"
+  shell:
+    """
+    {{
+      rm -r -f {resdir}/software/ldak_repo; \
+      git clone https://github.com/dougspeed/LDAK {resdir}/software/ldak_repo; \
+      cd {resdir}/software/ldak_repo; \
+      git reset --hard ecbe591137ebf6e8efd7bbf924b244cef506f7c3; \
+      chmod a+x ldak6.1.linux
+    }} > {log} 2>&1
+    """
+
 # Download LDAK map data
 rule download_ldak_map:
   output:
@@ -1272,7 +1291,6 @@ rule install_genoutils_xwing:
     }} > {log} 2>&1
     """
 
-
 # Download X-wing repo
 rule download_xwing_software:
   input:
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index ec6e95fa..78553977 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -385,6 +385,48 @@ rule prep_pgs_megaprs_i:
 rule prep_pgs_megaprs:
   input: expand(f"{outdir}/reference/pgs_score_files/megaprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=gwas_list_df['name'])
 
+rule prep_pgs_megaprs6_i:
+  resources:
+    mem_mb=20000,
+    time_min=2800
+  threads: config['cores_prep_pgs']
+  input:
+    f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz",
+    rules.download_ldak_highld.output,
+    rules.download_ldak_repo.output,
+    rules.download_ldak_map.output,
+    rules.download_ldak_bld.output,
+    f"{outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.EUR.scale"
+  output:
+    f"{outdir}/reference/pgs_score_files/megaprs6/{{gwas}}/ref-{{gwas}}.score.gz"
+  benchmark:
+    f"{outdir}/reference/benchmarks/prep_pgs_megaprs6_i-{{gwas}}.txt"
+  log:
+    f"{outdir}/reference/logs/prep_pgs_megaprs6_i-{{gwas}}.log"
+  conda:
+    "../envs/analysis.yaml"
+  params:
+    population= lambda w: gwas_list_df.loc[gwas_list_df['name'] == "{}".format(w.gwas), 'population'].iloc[0],
+    testing=config["testing"]
+  shell:
+    "Rscript ../Scripts/pgs_methods/megaprs.R \
+      --ref_plink_chr {refdir}/ref.chr \
+      --ref_keep {refdir}/keep_files/{params.population}.keep \
+      --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
+      --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
+      --ldak {resdir}/software/ldak_repo/ldak6.1.linux \
+      --ldak_map {resdir}/data/ldak_map/genetic_map_b37 \
+      --ldak_tag {resdir}/data/ldak_bld \
+      --ldak_highld {resdir}/data/ldak_highld/highld.txt \
+      --prs_model bayesr \
+      --n_cores {threads} \
+      --output {outdir}/reference/pgs_score_files/megaprs6/{wildcards.gwas}/ref-{wildcards.gwas} \
+      --pop_data {refdir}/ref.pop.txt \
+      --test {params.testing} > {log} 2>&1"
+
+rule prep_pgs_megaprs6:
+  input: expand(f"{outdir}/reference/pgs_score_files/megaprs6/{{gwas}}/ref-{{gwas}}.score.gz", gwas=gwas_list_df['name'])
+
 ##
 # LDAK QuickPRS
 ##
@@ -423,6 +465,7 @@ rule prep_pgs_quickprs_i:
   shell:
     "Rscript ../Scripts/pgs_methods/quickprs.R \
       --ref_plink_chr {refdir}/ref.chr \
+      --ref_keep {refdir}/keep_files/{params.population}.keep \
       --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \
       --sumstats {outdir}/reference/gwas_sumstat/{wildcards.gwas}/{wildcards.gwas}-cleaned.gz \
       --ldak {resdir}/software/ldak5.2/ldak5.2.linux \

From df24ecf09c378ec5a7b9f4663097d45c1d784d07 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Mon, 12 May 2025 13:48:50 +0100
Subject: [PATCH 143/153] Update for reviewer

---
 docs/CrossPop.Rmd | 1774 ++++++++++++++++++++++++++-------------------
 1 file changed, 1018 insertions(+), 756 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 25a6c29d..bd2778b8 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -7851,7 +7851,7 @@ tmp<-res_eval_simp
 tmp$GWAS_N <- paste0('GWAS N = ', tmp$GWAS_N)
 tmp$GWAS_N <-factor(tmp$GWAS_N, levels = unique(tmp$GWAS_N))
 
-png(paste0('~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png'), res=100, width = 1000, height = 600, units = 'px')
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots_downsample/per_trait_r.png'), res=100, width = 1000, height = 6000, units = 'px')
 ggplot(tmp, aes(x=label, y=R , fill = Model)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
@@ -7869,6 +7869,81 @@ ggplot(tmp, aes(x=label, y=R , fill = Model)) +
           legend.justification = "center")
 dev.off()
 
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval <- NULL
+# Subset res_eval for each scenario
+res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+  x <- res_eval[[i]]
+  x$pheno <- names(res_eval)[i]
+  return(x)
+}))
+
+# Average res_evalults for each test across phenotypes
+# Use MAd to account for correlation between them
+res_eval_i$Sample<-'A'
+
+for(group_i in unique(res_eval_i$Group)){
+  res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
+  missing_pheno <-
+    colnames(cors)[!(colnames(cors) %in% unique(res_eval_group_i$pheno))]
+  
+  cors_i <- cors[unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+  
+  meta_res_eval_i <-
+    agg(
+      id = Sample,
+      es = R,
+      var = SE ^ 2,
+      cor = cors_i,
+      method = "BHHR",
+      mod = NULL,
+      data = res_eval_group_i
+    )
+  
+  tmp <- data.table(Group = group_i,
+                    Method = res_eval_group_i$Method[1],
+                    Model = res_eval_group_i$Model[1],
+                    Source = res_eval_group_i$Source[1],
+                    Discovery = 'EUR',
+                    Target = 'EUR',
+                    R = meta_res_eval_i$es,
+                    SE = sqrt(meta_res_eval_i$var),
+                    GWAS_N = res_eval_group_i$GWAS_N[1])
+  
+  meta_res_eval <- rbind(meta_res_eval, tmp)
+}
+
+tmp <- meta_res_eval
+# Insert nice PGS method names
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$GWAS_N <- paste0('GWAS N = ', tmp$GWAS_N)
+tmp$GWAS_N <-factor(tmp$GWAS_N, levels = unique(tmp$GWAS_N))
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png'), res=100, width = 1000, height = 600, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x=NULL, fill = NULL) +
+    facet_grid(~ GWAS_N, scales = 'free_y') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
 ```
 
 </details>
@@ -8626,6 +8701,7 @@ for(pheno_i in selected_traits){
 # Plot results for each phenotype separately
 dir.create('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots')
 
+per_trait_plot <- list()
 for(pheno_i in selected_traits){
   tmp <- res_eval_simp[res_eval_simp$Trait == pheno_i,]
   #tmp <- tmp[tmp$Target != 'EUR',]
@@ -8651,8 +8727,50 @@ for(pheno_i in selected_traits){
           legend.justification = "center")
   print(plot_tmp)
   dev.off()
+  per_trait_plot[[pheno_i]]<-plot_tmp
 }
 
+tmp <- res_eval_simp
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean <- paste0(tmp$Discovery_clean, ' GWAS')
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/per_trait_part1.png'), res=300, width = 3000, height = 3800, units = 'px')
+ggplot(tmp[tmp$Trait %in% selected_traits[order(selected_traits)][1:5],], aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x=NULL, fill = NULL) +
+    facet_grid(Trait ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/per_trait_part2.png'), res=300, width = 3000, height = 3800, units = 'px')
+ggplot(tmp[tmp$Trait %in% selected_traits[order(selected_traits)][6:10],], aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x=NULL, fill = NULL) +
+    facet_grid(Trait ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+
 ####
 # Average results across phenotypes
 ####
@@ -9613,922 +9731,1066 @@ dev.off()
 
 ***
 
-### LEOPARD+QuickPRS
+### Descriptive statistics
 
-Here we will compare the LEOPARD estimated weights for population specific PGS, to the weights estimated using observed data in the UKB target sample.
+Create a table showing descriptive statistics for the MVP sumstats. This should include LDSC SNP-heritability and AVENGEME results.
+
+***
+
+#### LDSC
 
 <details><summary>Show code</summary>
+```{bash}
+conda activate ldsc
+
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+  mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/sumstats
+
+  sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
+   --sumstats /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR-cleaned.gz \
+   --out /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR"
+done
+
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+  mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/${pheno}/MVP_AFR
+
+  sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \
+   --h2 /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR.sumstats.gz \
+   --ref-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.AFR.rsid \
+   --w-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.AFR.rsid \
+   --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/${pheno}/MVP_AFR/res"
+done
+
+```
 
 ```{r}
-setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 
 library(data.table)
 library(ggplot2)
 library(cowplot)
 
-source('../functions/misc.R')
-source_all('../functions')
+# Read in phenotypes
+pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
 
-# Read in list of outcomes 
-selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+# Plot the heritability estimates
+h2_res <- NULL
 
-# Get some key variables from config
-config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml'
-outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+for(pheno in pheno_intersect){
+  log <-
+    readLines(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/',
+        pheno,
+        '/',
+        'MVP_AFR',
+        '/res.log'
+      )
+    )
+  
+  h2 <- log[grepl('Total Observed scale h2:', log)]
+  h2_est <- as.numeric(gsub(' .*','', gsub('Total Observed scale h2: ', '', h2)))
+  h2_se <- as.numeric(gsub("\\)",'', gsub(".* \\(", '', h2)))
+  int <- log[grepl('Intercept:', log)]
+  int_est <- as.numeric(gsub(' .*','', gsub('Intercept: ', '', int)))
+  int_se <- as.numeric(gsub("\\)",'', gsub(".* \\(", '', int)))
+  lambda <- log[grepl('Lambda GC:', log)]
+  lambda <- as.numeric(gsub('.* ','', lambda))
+  
+  h2_res <- rbind(
+    h2_res,
+    data.table(
+      Population = 'AFR',
+      Phenotype = pheno,
+      h2_est = h2_est,
+      h2_se = h2_se,
+      int_est = int_est,
+      int_se = int_se,
+      lambda = lambda
+    )
+  )
+}
 
-# Get a list of score files
-scores <- list_score_files(config)
 
-###
-# Read in weights estimated by LEOPARD (QuickPRS)
-###
+write.csv(h2_res, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results.csv', row.names = F, quote = F)
 
-leopard_weights<-NULL
-scores_quickprs <- scores$name[scores$method == 'quickprs_multi']
-for(i in selected_traits){
-  scores_i <- scores_quickprs[grepl(paste0('^', i,'_'), scores_quickprs)]
-  for(j in scores_i){
-      weights_file <- readRDS(paste0(outdir, '/reference/pgs_score_files/leopard/', j, '/ref-', j, '.weights.rds'))
-      weights_file <- data.frame(weights_file)
-      
-      weights <-
-        data.table(
-          Target = do.call(c, lapply(names(weights_file), function(x) rep(x, 2))),
-          Discovery = names(weights_file),
-          Weight = do.call(c, lapply(weights_file, function(x) x)),
-          Trait = i,
-          Method = 'LEOPARD'
-        )
-      
-      leopard_weights <- rbind(leopard_weights, weights)
-  }
-}
+```
+</details>
 
-#####
-# Read in the PGS weights estimated using UKB data
-#####
-# Read in the final model coefficients for multi-source methods
+***
 
-obs_weights<-NULL
-for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_methods)]){
-  scores_method<-scores$name[scores$method == method_i]
-  method_i <- gsub('_multi','', method_i)
+#### AVENGEME
 
-  for(i in selected_traits){
-    for(j in c('AFR')){
-      if(j == 'EUR'){
-        pops <- c('EAS','AFR')
-      } else {
-        pops <- j
-      }
-      
-      for(k in pops){
-        model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_', j, '.disc_EUR_', k, '/', i, '/final_models/', method_i, '.pseudo.multi.final_model.txt'))
-        model<-model[-1,]
-        
-        # Set weight to zero if negative, as this is what LEOPARD does
-        if(any(model$V2 < 0)){
-          model$V2[model$V2 < 0] <- 0
-          model$V2[model$V2 > 0] <- 1
-        }
-        
-        names(model) <- c('x', 'BETA')
-        model$Discovery[grepl('UKB', model$x)]<-'EUR'
-        model$Discovery[grepl('BBJ', model$x)]<-'EAS'
-        model$Discovery[grepl('UGR', model$x)]<-'AFR'
-        model$Target <- j
-        model$Weight <- model$BETA/sum(model$BETA)
-        model$Trait <- i
-        model$Method <- method_i
-        model<-model[,c('Target','Discovery','Weight','Method','Trait'), with=F]
-        obs_weights<-rbind(obs_weights, model)
-      }
-    }
-  }
-}
 
-###
-# Estimate weights if using the inverse variance weighting
-###
+<details><summary>Show code</summary>
 
-# Read in GWAS descriptives
-gwas_desc<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
-gwas_desc <- gwas_desc[, c('Trait Label','Ancestry','GWAS N'), with=F]
-names(gwas_desc)<-c('trait','ancestry','n')
-gwas_desc<-gwas_desc[gwas_desc$trait %in% selected_traits,]
+<div class="shallow-break"></div>
 
-library(dplyr)
-library(tidyr)
+<h4>Create predictor list</h4>
 
-# Reshape GWAS table to wide format
-wide_gwas <- gwas_desc %>%
-  pivot_wider(names_from = ancestry, values_from = n, values_fill = 0)
+```{r}
 
-# Function to create rows for each pair
-make_weights_long <- wide_gwas %>%
-  rowwise() %>%
-  do({
-    trait <- .$trait
-    eur <- .$EUR
-    afr <- .$AFR
-    eas <- .$EAS
-    
-    tibble(
-      Trait = trait,
-      Method = "inverse_var",
-      Target = c("AFR", "AFR", "EUR", "EUR", "EUR", "EAS", "EAS"),
-      Discovery = c("EUR", "AFR", "EUR", "AFR", "EAS", "EUR", "EAS"),
-      Weight = c(
-        eur / (eur + afr), afr / (eur + afr),  # AFR target
-        eur / (eur + afr), afr / (eur + afr),  # EUR target (vs AFR)
-        eas / (eur + eas),                     # EUR target (vs EAS)
-        eur / (eur + eas), eas / (eur + eas)   # EAS target (vs EUR)
-      )
-    )
-  }) %>%
-  bind_rows()
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
 
-###
-# Combine and compare
-###
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
-both <- do.call(rbind, list(obs_weights, leopard_weights, make_weights_long))
+# Get a list of score files
+scores <- list_score_files(config)
 
-# Remove ptclump as it doesn't have a sumstattune method
-both <- both[both$Method != 'ptclump',]
+# Read in phenotypes
+pheno_intersect <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
 
-both<-merge(both, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x=T, sort = F)
-both$label[is.na(both$label)] <- both$Method[is.na(both$label)]
-both$label <- factor(both$label, levels=unique(both$label))
+# Create files for EAS and AFR targets
+pop <- c('AFR')
+for(trait_i in pheno_intersect){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
+  scores_i <- scores[grepl(paste0('^', trait_i, '_'), scores$name),]
+  scores_i$group <- scores_i$method
+  
+  for(pop_i in pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    samp_i <- 'MVP_AFR'
 
-# Plot non-EUR target first
-tmp <- both[both$Target != 'EUR',]
-tmp <- tmp[tmp$Discovery != 'EUR',]
+    dir.create(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i
+      ),
+      recursive = T
+    )
+    
+    scores_i_j <- scores_i[grepl(samp_i, scores_i$name, ignore.case = T),]
+    scores_i_j <- scores_i_j[!grepl('UKB', scores_i_j$name),]
+    scores_i_j <- scores_i_j[scores_i_j$method == 'ptclump',]
+    scores_i_j$predictor <- paste0(
+      outdir,
+      '/ukb/pgs/TRANS/',
+      scores_i_j$method,
+      '/',
+      scores_i_j$name,
+      '/ukb-',
+      scores_i_j$name,
+      '-TRANS.profiles'
+    )
+    
+    predictors_i <- scores_i_j[, c('predictor', 'group'), with=F]
+    
+    write.table(
+      predictors_i,
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i,
+        '/predictor_list.ptclump.txt'
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}
 
-# Set LEOPARD to black fill
-default_colors <- hue_pal()(10)
-names(default_colors) <- levels(tmp$label)
-default_colors["LEOPARD"] <- "black"
+```
 
-# Plot the estimated and observed weights
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_weights.png', units = 'px', res = 300, width = 2500, height = 1500)
-ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
-  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) +
-  scale_fill_manual(values = default_colors) +
-  facet_grid(Target ~ .) +
-  theme_half_open() +
-  labs(title = 'Weight of target ancestry-matched PGS', fill = NULL) +
-  background_grid(major = 'y', minor = 'y') + 
-  panel_border() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  ylim(c(0,1))
-dev.off()
+***
 
-# Plot EUR target
-tmp <- both[both$Target == 'EUR',]
-tmp <- tmp[tmp$Discovery != 'EUR',]
+<h4>Run model_builder</h4>
 
-# Set LEOPARD to black fill
-default_colors <- hue_pal()(10)
-names(default_colors) <- levels(tmp$label)
-default_colors["LEOPARD"] <- "black"
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
 
-# Plot the estimated and observed weights
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_weights_eur.png', units = 'px', res = 300, width = 2500, height = 1500)
-ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
-  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) +
-  scale_fill_manual(values = default_colors) +
-  facet_grid(Discovery ~ .) +
-  theme_half_open() +
-  labs(title = 'Weight of non-EUR PGS for EUR Target', fill = NULL) +
-  background_grid(major = 'y', minor = 'y') + 
-  panel_border() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
-  ylim(c(0,1))
-dev.off()
+for pop in $(echo AFR); do
+  if [ "$pop" == "EUR" ]; then
+      pop2="EUR_test"
+  else
+      pop2=$pop
+  fi
+  
+  for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${pop2}.row_number.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${pop}.disc_${pop}/${pheno}/res.ptclump \
+      --n_core 5 \
+      --all_model F \
+      --assoc T"
+  done
+done
 
-###
-# Check calibration of LEOPARD compared to QuickPRS observed weights
-###
+```
 
-tmp <- both[both$Target != 'EUR',]
-tmp$Target<-NULL
-tmp_wide <- reshape(tmp, 
-                     idvar = c("Trait", "Discovery"), 
-                     timevar = "label", 
-                     direction = "wide")
+***
 
-names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
-tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+<h4>Plot pT+clump association results</h4>
 
-tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
-tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
+```{r}
 
-# Calculate metrics
-rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)^2))
-me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 
-rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)^2))
-me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)
+library(data.table)
+library(ggplot2)
+library(cowplot)
+library(avengeme)
 
-# Create annotation data.frame
-metrics_df <- data.frame(
-  Discovery = c("AFR", "EAS"),
-  x = c(0.5, 0.5),         # Adjust positions as needed
-  y = c(-0.05, -0.05),
-  label = c(
-    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
-    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
-  )
-)
+source('../functions/misc.R')
+source_all('../functions')
 
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
-ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) +
-  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
-  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
-  geom_point(alpha = 0.7) +
-  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
-  labs(
-    x = "LEOPARD weight",
-    y = "Observed weight",
-  ) +
-  theme_half_open() +
-  panel_border() + 
-  theme(
-    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
-  ) +
-  facet_grid(. ~ Discovery) +
-  coord_fixed()    
-dev.off()
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+gwas_list <- read_param(config = config, param = 'gwas_list', return_obj = T)
 
-###
-# Check calibration of inverse_var compared to QuickPRS observed weights
-###
+# Read in phenotypes
+pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
 
-tmp <- both[both$Target != 'EUR',]
-tmp$Target<-NULL
-tmp_wide <- reshape(tmp, 
-                     idvar = c("Trait", "Discovery"), 
-                     timevar = "label", 
-                     direction = "wide")
+pop = c('AFR')
 
-names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
-tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+mod_res_all <- NULL
+for(pop_i in pop){
+  for(pheno_i in pheno_intersect){
+    gwas_i<-gwas_list$name[gwas_list$population == pop_i & grepl(paste0('^', pheno_i, '_'),  gwas_list$name)]
+      
+    res_i <-
+      fread(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
+          pop_i,
+          '.disc_',
+          pop_i,
+          '/',
+          pheno_i,
+          '/res.ptclump.assoc.txt'
+        )
+      )
+    
+    res_i$Z <- res_i$BETA / res_i$SE
+    
+    res_i$pT <- as.numeric(gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.|.*MVP.AFR\\.0\\.', '', res_i$Predictor)))
 
-tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
-tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
+    nsnp_log <-
+      read.table(
+        paste0(
+          outdir,
+          '/reference/pgs_score_files/ptclump/',
+          gwas_i,
+          '/ref-',
+          gwas_i,
+          '.NSNP_per_pT'
+        ),
+        header = T
+      )
+    
+    nsnp<-nsnp_log$NSNP[nrow(nsnp_log)]
+    
+    disc_N <-
+      median(
+        fread(
+          paste0(
+            outdir,
+            '/reference/gwas_sumstat/',
+            gwas_i,
+            '/',
+            gwas_i,
+            '-cleaned.gz'
+          ), nrows = 10000
+        )$N
+      )
+    
+    targ_N <- res_i$N[1]
+    
+    mod_res <- estimatePolygenicModel(
+      p = res_i$Z,
+      nsnp = nsnp,
+      n = c(disc_N, targ_N),
+      pupper = c(0, res_i$pT),
+      fixvg2pi02 = T,
+      alpha = 0.05
+    )
+    
+    mod_res_all <- rbind(
+      mod_res_all,
+      data.frame(
+        Phenotype = pheno_i,
+        Population = pop_i,
+        GWAS = gwas_i,
+        nsnp = nsnp,
+        max_r2 = max(res_i$Obs_R2),
+        n_disc = disc_N,
+        n_targ = targ_N,
+        vg_est = mod_res$vg[1],
+        vg_lowCI = mod_res$vg[2],
+        vg_highCI = mod_res$vg[3],
+        pi0_est = mod_res$pi0[1],
+        pi0_lowCI = mod_res$pi0[2],
+        pi0_highCI = mod_res$pi0[3]
+      )
+    )
+  }
+}
 
-# Calculate metrics
-rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)^2))
-me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)
+dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme')
+write.csv(mod_res_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv', row.names = F, quote = F)
 
-rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)^2))
-me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)
+mod_res_all<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv')
 
-# Create annotation data.frame
-metrics_df <- data.frame(
-  Discovery = c("AFR", "EAS"),
-  x = c(0.5, 0.5),         # Adjust positions as needed
-  y = c(-0.05, -0.05),
-  label = c(
-    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
-    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
-  )
-)
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avengeme_h2.png', res = 100, width = 900, height = 500, units = 'px')
+ggplot(mod_res_all, aes(x = Phenotype, y = vg_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=vg_lowCI, ymax=vg_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="SNP-based Heritability (95%CI)", fill = NULL) +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = "top",
+        legend.key.spacing.x = unit(1, "cm"),
+        legend.justification = "center") +
+  background_grid(major = 'y', minor = 'y')
+dev.off()
 
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/inverse_var_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
-ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = inverse_var, y = QuickPRS)) +
-  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
-  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
-  geom_point(alpha = 0.7) +
-  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 1.5, size = 3.5) +
-  labs(
-    x = "inverse_var weight",
-    y = "Observed weight",
-  ) +
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avengeme_polygenicity.png', res = 100, width = 900, height = 500, units = 'px')
+ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=1 - pi0_lowCI, ymax=1 - pi0_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="Proporition non-zero\neffects (95%CI)", fill = NULL) +
   theme_half_open() +
-  panel_border() + 
-  theme(
-    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
-  ) +
-  facet_grid(. ~ Discovery) +
-  coord_fixed()    
+  coord_cartesian(ylim = c(0, 0.15)) + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = "top",
+        legend.key.spacing.x = unit(1, "cm"),
+        legend.justification = "center") +
+  background_grid(major = 'y', minor = 'y')
 dev.off()
 
-###
-# Check calibration of observed weights across all methods
-###
+summary(mod_res_all$max_r2)
+summary(mod_res_all$max_r2[mod_res_all$Population == 'EUR'])
+summary(mod_res_all$max_r2[mod_res_all$Population == 'EAS'])
+summary(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])
 
-tmp <- both[both$Target != 'EUR',]
-tmp <- tmp[!(tmp$label %in% c('LEOPARD','inverse_var')),]
-tmp$Target<-NULL
-tmp_wide <- reshape(tmp, 
-                     idvar = c("Trait", "Discovery"), 
-                     timevar = "label", 
-                     direction = "wide")
+```
 
-names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
-tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+</details>
 
-tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
+***
 
-metrics <- NULL
-for(i in c('EAS','AFR')){
-  for(j in unique(tmp$label)){
-    for(k in unique(tmp$label)){
-      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
-      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
-      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
-      
-      metrics <- rbind(
-        metrics,
-        data.frame(
-          Population = i,
-          Method1 = j,
-          Method2 = k,
-          rmse = rmse,
-          me = me
-        )
-      )
-    }
-  }
-}
+#### Make table
 
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/observed_weights_calibration.png', units = 'px', width = 3000, height = 1650, res = 300)
-ggplot(metrics, aes(x = Method1, y = Method2, fill = rmse)) +
-  geom_tile(color = "white") +  # Tile plot with white borders
-  geom_text(aes(label = round(rmse, 2)), color = "black") +  # Add correlation values
-  scale_fill_gradient2(mid = "white", high = "red", midpoint = 0) +  # Color scale
-  theme_half_open() +
-  panel_border() + 
-  theme(
-    axis.text.x = element_text(angle = 45, hjust = 1),
-    axis.title = element_blank()
-  ) +
-  facet_grid(. ~ Population) +
-  labs(fill = "RMSE")
-dev.off()
+Make a table showing GWAS information for the manuscript.
 
-# Calculate average RMSE for each method against all other methods
-metrics_unique <- metrics[metrics$Method1 != metrics$Method2, ]
-metrics_unique$Comparison <- NA
-for (i in 1:nrow(metrics_unique)) {
-  metrics_unique$Comparison[i] <-
-    paste0(sort(c(
-      metrics_unique$Method1[i], metrics_unique$Method2[i]
-    )), collapse = ' vs. ')
-}
-metrics_unique <- metrics_unique[!duplicated(paste0(metrics_unique$Population, metrics_unique$Comparison)),]
+<details><summary>Show code</summary>
 
-mean_rmse <- NULL
-for(i in unique(tmp$label)){
-  for(j in c('AFR','EAS')){
-    metrics_unique_tmp <- metrics_unique[metrics_unique$Method1 == i | metrics_unique$Method2 == i,]
-    metrics_unique_tmp <- metrics_unique_tmp[metrics_unique_tmp$Population == j,]
-    mean_rmse <- rbind(
-      mean_rmse, 
-      data.frame(
-        Method = i,
-        Population = j,
-        avg_rmse = mean(metrics_unique_tmp$rmse)
-      )
-    )
-  }
-}
+```{r}
+library(data.table)
 
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avg_observed_weight_rmse.png', units = 'px', width = 1500, height = 1500, res = 300)
-ggplot(mean_rmse, aes(x = Method, y = avg_rmse, fill = Method)) +
-  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
-  geom_text(aes(label = round(avg_rmse, 3)),  # <-- Add this
-          vjust = 1.5,                    # <-- Move the text slightly above the bar
-          size = 3) +                      # <-- Adjust text size
-  scale_fill_manual(values = default_colors) +
-  facet_grid(Population ~ .) +
-  theme_half_open() +
-  background_grid(major = 'y', minor = 'y') + 
-  panel_border() +
-  labs(y = 'Average RMSE') +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1),
-        legend.position="none")
-dev.off()
+#####
+# Trait names, labels, and URLs
+#####
 
-###
-# Check calibration of estimated (LEOPARD and inverse_var) weights compared to observed QuickPRS weights
-###
+mvp <- fread('~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/info.txt')
+mvp <- mvp[, c('efoTraits','labels','url'), with=F]
+names(mvp) <- c('trait', 'labels','url')
+mvp$sample <- 'MVP'
+mvp$population <- 'AFR'
 
-tmp <- both[both$Target != 'EUR',]
-tmp <- tmp[(tmp$label %in% c('LEOPARD','inverse_var','QuickPRS')),]
-tmp$Target<-NULL
-tmp_wide <- reshape(tmp, 
-                     idvar = c("Trait", "Discovery"), 
-                     timevar = "label", 
-                     direction = "wide")
+#####
+# Sample size, SNP-h2 and polygenicity
+#####
 
-names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
-tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+# Read in the AVENGEME and LDSC results
+avengeme <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv')
+ldsc <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results.csv')
+both <- merge(avengeme, ldsc, by = c('Population','Phenotype'))
 
-tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
+# Format for descriptives table
+both$h2_avengeme<- paste0(
+  round(both$vg_est,2), 
+  " (95%CI = ", 
+  round(both$vg_lowCI, 2), 
+  " - " , 
+  round(both$vg_highCI, 2), ")")
 
-metrics <- NULL
-for(i in c('EAS','AFR')){
-  for(j in unique(tmp$label)){
-    for(k in unique(tmp$label)){
-      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
-      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
-      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
-      
-      metrics <- rbind(
-        metrics,
-        data.frame(
-          Population = i,
-          Method1 = j,
-          Method2 = k,
-          rmse = rmse,
-          me = me
-        )
-      )
-    }
-  }
-}
-
-# Plot the rmse for LEOPARD and inverse_var predicting observed QuickPRS weight
-metrics <- metrics[metrics$Method1 == 'QuickPRS',]
-metrics <- metrics[metrics$Method2 != 'QuickPRS',]
-
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/inverse_var_comp_rmse.png', units = 'px', width = 800, height = 1500, res = 300)
-ggplot(metrics, aes(x = Method2, y = rmse, fill = Method2)) +
-  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
-  geom_text(aes(label = round(rmse, 3)),  # <-- Add this
-          vjust = 1.5,                    # <-- Move the text slightly above the bar
-          size = 3) +                      # <-- Adjust text size
-  facet_grid(Population ~ .) +
-  theme_half_open() +
-  background_grid(major = 'y', minor = 'y') + 
-  panel_border() +
-  labs(y = 'RMSE relative to QuickPRS', x = 'Method') +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1),
-        legend.position="none")
-dev.off()
-
-```
-
-</details>
-
-***
-
-### Descriptive statistics
-
-Create a table showing descriptive statistics for the MVP sumstats. This should include LDSC SNP-heritability and AVENGEME results.
-
-***
-
-#### LDSC
-
-<details><summary>Show code</summary>
-```{bash}
-conda activate ldsc
-
-for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
-  mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/sumstats
-
-  sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
-   --sumstats /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR-cleaned.gz \
-   --out /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR"
-done
-
-for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
-  mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/${pheno}/MVP_AFR
+both$pi0_avengeme <- paste0(
+  round(both$pi0_est,2), 
+  " (95%CI = ", 
+  round(both$pi0_lowCI, 2), 
+  " - " , 
+  round(both$pi0_highCI, 2), ")")
 
-  sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \
-   --h2 /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR.sumstats.gz \
-   --ref-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.AFR.rsid \
-   --w-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.AFR.rsid \
-   --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/${pheno}/MVP_AFR/res"
-done
+both$h2_ldsc <- paste0(
+  round(both$h2_est,2), 
+  " (SE = ", 
+  round(both$h2_se, 2), 
+  ")")
 
-```
+both$int_ldsc <- paste0(
+  round(both$int_est,2), 
+  " (SE = ", 
+  round(both$int_se, 2), 
+  ")")
 
-```{r}
+both<-both[, c('Population','Phenotype','n_disc','n_targ','h2_avengeme','pi0_avengeme','h2_ldsc','int_ldsc','lambda'), with = F]
+names(both)[1:2]<-c('population','labels')
 
-library(data.table)
-library(ggplot2)
-library(cowplot)
+info_all <- merge(mvp, both, by = c('labels','population'))
+info_all$n_disc<-round(info_all$n_disc, 0)
+info_all$n_targ<-round(info_all$n_targ, 0)
 
-# Read in phenotypes
-pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
+info_all<-info_all[, c('labels','trait','population','sample','n_disc','n_targ','h2_avengeme','pi0_avengeme','h2_ldsc','int_ldsc','lambda','url'), with=F]
+names(info_all) <- c('Trait Label', 'Trait Description', 'Ancestry', 'GWAS Sample', 'GWAS N', 'Target N',"SNP-h2 (AVENGEME)","pi0 (AVENGEME)","SNP-h2 (LDSC)","Intercept (LDSC)",'Lambda', 'URL')
 
-# Plot the heritability estimates
-h2_res <- NULL
+write.csv(info_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/gwas_descriptives.csv', row.names=F)
 
-for(pheno in pheno_intersect){
-  log <-
-    readLines(
-      paste0(
-        '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/',
-        pheno,
-        '/',
-        'MVP_AFR',
-        '/res.log'
-      )
-    )
-  
-  h2 <- log[grepl('Total Observed scale h2:', log)]
-  h2_est <- as.numeric(gsub(' .*','', gsub('Total Observed scale h2: ', '', h2)))
-  h2_se <- as.numeric(gsub("\\)",'', gsub(".* \\(", '', h2)))
-  int <- log[grepl('Intercept:', log)]
-  int_est <- as.numeric(gsub(' .*','', gsub('Intercept: ', '', int)))
-  int_se <- as.numeric(gsub("\\)",'', gsub(".* \\(", '', int)))
-  lambda <- log[grepl('Lambda GC:', log)]
-  lambda <- as.numeric(gsub('.* ','', lambda))
-  
-  h2_res <- rbind(
-    h2_res,
+# Estimate the mean and SD of sample size within each population for selected traits
+info_all_selected<-info_all
+n_dat <- NULL
+for(i in unique(info_all_selected$`GWAS Sample`)){
+  n_dat <-rbind(
+    n_dat,
     data.table(
-      Population = 'AFR',
-      Phenotype = pheno,
-      h2_est = h2_est,
-      h2_se = h2_se,
-      int_est = int_est,
-      int_se = int_se,
-      lambda = lambda
+      sample = i,
+      gwas_n_median = round(median(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      gwas_n_mean = round(mean(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      gwas_n_sd = round(sd(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_median = round(median(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_mean = round(mean(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_sd = round(sd(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i]))
     )
   )
 }
 
+```
 
-write.csv(h2_res, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results.csv', row.names = F, quote = F)
+</details>
+
+<details><summary>Show descriptives table</summary>
+
+```{r, eval = T, echo = F}
+info_all<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/gwas_descriptives.csv')
+
+kable(info_all, "html") %>%
+  kable_styling(bootstrap_options = c("striped", "hover"),
+                full_width = F) %>%
+  scroll_box(width = "100%", height = "500px")
 
 ```
+
 </details>
 
 ***
 
-#### AVENGEME
+### LEOPARD+QuickPRS
 
+Here we will compare the LEOPARD estimated weights for population specific PGS, to the weights estimated using observed data in the UKB target sample.
 
 <details><summary>Show code</summary>
 
-<div class="shallow-break"></div>
-
-<h4>Create predictor list</h4>
-
 ```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
 
-setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
 source('../functions/misc.R')
 source_all('../functions')
-library(data.table)
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
 
 # Get some key variables from config
 config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml'
-pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
 outdir <- read_param(config = config, param = 'outdir', return_obj = F)
 
 # Get a list of score files
 scores <- list_score_files(config)
 
-# Read in phenotypes
-pheno_intersect <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
-
-# Create files for EAS and AFR targets
-pop <- c('AFR')
-for(trait_i in pheno_intersect){
-  # Make a group containing both GWAS for each single source method
-  # Make a group for each multisource method
-  scores_i <- scores[grepl(paste0('^', trait_i, '_'), scores$name),]
-  scores_i$group <- scores_i$method
-  
-  for(pop_i in pop){
-    # Subset GWAS based on EUR and/or targ_pop_i
-    samp_i <- 'MVP_AFR'
+###
+# Read in weights estimated by LEOPARD (QuickPRS)
+###
 
-    dir.create(
-      paste0(
-        '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
-        pop_i,
-        '.disc_',
-        pop_i,
-        '/',
-        trait_i
-      ),
-      recursive = T
-    )
-    
-    scores_i_j <- scores_i[grepl(samp_i, scores_i$name, ignore.case = T),]
-    scores_i_j <- scores_i_j[!grepl('UKB', scores_i_j$name),]
-    scores_i_j <- scores_i_j[scores_i_j$method == 'ptclump',]
-    scores_i_j$predictor <- paste0(
-      outdir,
-      '/ukb/pgs/TRANS/',
-      scores_i_j$method,
-      '/',
-      scores_i_j$name,
-      '/ukb-',
-      scores_i_j$name,
-      '-TRANS.profiles'
-    )
-    
-    predictors_i <- scores_i_j[, c('predictor', 'group'), with=F]
-    
-    write.table(
-      predictors_i,
-      paste0(
-        '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
-        pop_i,
-        '.disc_',
-        pop_i,
-        '/',
-        trait_i,
-        '/predictor_list.ptclump.txt'
-      ),
-      col.names = T,
-      row.names = F,
-      quote = F
-    )
+leopard_weights<-NULL
+scores_quickprs <- scores$name[scores$method == 'quickprs_multi']
+for(i in selected_traits){
+  scores_i <- scores_quickprs[grepl(paste0('^', i,'_'), scores_quickprs)]
+  for(j in scores_i){
+      weights_file <- readRDS(paste0(outdir, '/reference/pgs_score_files/leopard/', j, '/ref-', j, '.weights.rds'))
+      weights_file <- data.frame(weights_file)
+      
+      weights <-
+        data.table(
+          Target = do.call(c, lapply(names(weights_file), function(x) rep(x, 2))),
+          Discovery = names(weights_file),
+          Weight = do.call(c, lapply(weights_file, function(x) x)),
+          Trait = i,
+          Method = 'LEOPARD'
+        )
+      
+      leopard_weights <- rbind(leopard_weights, weights)
   }
 }
 
-```
+#####
+# Read in the PGS weights estimated using UKB data
+#####
+# Read in the final model coefficients for multi-source methods
 
-***
+obs_weights<-NULL
+for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_methods)]){
+  scores_method<-scores$name[scores$method == method_i]
+  method_i <- gsub('_multi','', method_i)
 
-<h4>Run model_builder</h4>
+  for(i in selected_traits){
+    for(j in c('AFR')){
+      if(j == 'EUR'){
+        pops <- c('EAS','AFR')
+      } else {
+        pops <- j
+      }
+      
+      for(k in pops){
+        model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_', j, '.disc_EUR_', k, '/', i, '/final_models/', method_i, '.pseudo.multi.final_model.txt'))
+        model<-model[-1,]
+        
+        # Set weight to zero if negative, as this is what LEOPARD does
+        if(any(model$V2 < 0)){
+          model$V2[model$V2 < 0] <- 0
+          model$V2[model$V2 > 0] <- 1
+        }
+        
+        names(model) <- c('x', 'BETA')
+        model$Discovery[grepl('UKB', model$x)]<-'EUR'
+        model$Discovery[grepl('MVP', model$x)]<-'AFR'
+        model$Target <- j
+        model$Weight <- model$BETA/sum(model$BETA)
+        model$Trait <- i
+        model$Method <- method_i
+        model<-model[,c('Target','Discovery','Weight','Method','Trait'), with=F]
+        obs_weights<-rbind(obs_weights, model)
+      }
+    }
+  }
+}
 
-```{bash}
-cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
-conda activate model_builder
+###
+# Estimate weights if using the inverse variance weighting
+###
 
-for pop in $(echo AFR); do
-  if [ "$pop" == "EUR" ]; then
-      pop2="EUR_test"
-  else
-      pop2=$pop
-  fi
-  
-  for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
-    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
-      --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${pop2}.row_number.txt \
-      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
-      --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${pop}.disc_${pop}/${pheno}/res.ptclump \
-      --n_core 5 \
-      --all_model F \
-      --assoc T"
-  done
-done
+# Read in GWAS descriptives
+gwas_desc<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+gwas_desc <- gwas_desc[, c('Trait Label','Ancestry','GWAS N'), with=F]
+names(gwas_desc)<-c('trait','ancestry','n')
+gwas_desc<-gwas_desc[gwas_desc$trait %in% selected_traits,]
 
-```
+gwas_desc <- gwas_desc[gwas_desc$ancestry == 'EUR',]
 
-***
+gwas_desc_mvp <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/gwas_descriptives.csv')
+gwas_desc_mvp <- gwas_desc_mvp[, c('Trait Label','Ancestry','GWAS N'), with=F]
+names(gwas_desc_mvp)<-c('trait','ancestry','n')
+gwas_desc_mvp<-gwas_desc_mvp[gwas_desc_mvp$trait %in% selected_traits,]
 
-<h4>Plot pT+clump association results</h4>
+gwas_desc<-rbind(gwas_desc, gwas_desc_mvp)
 
-```{r}
+library(dplyr)
+library(tidyr)
 
-setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+# Reshape GWAS table to wide format
+wide_gwas <- gwas_desc %>%
+  pivot_wider(names_from = ancestry, values_from = n, values_fill = 0)
 
-library(data.table)
-library(ggplot2)
-library(cowplot)
-library(avengeme)
+# Function to create rows for each pair
+make_weights_long <- wide_gwas %>%
+  rowwise() %>%
+  do({
+    trait <- .$trait
+    eur <- .$EUR
+    afr <- .$AFR
+    eas <- .$EAS
+    
+    tibble(
+      Trait = trait,
+      Method = "inverse_var",
+      Target = c("AFR", "AFR", "EUR", "EUR"),
+      Discovery = c("EUR", "AFR", "EUR", "AFR"),
+      Weight = c(
+        eur / (eur + afr), afr / (eur + afr),  # AFR target
+        eur / (eur + afr), afr / (eur + afr)  # EUR target (vs AFR)
+      )
+    )
+  }) %>%
+  bind_rows()
 
-source('../functions/misc.R')
-source_all('../functions')
+###
+# Combine and compare
+###
 
-# Get some key variables from config
-config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml'
-outdir <- read_param(config = config, param = 'outdir', return_obj = F)
-gwas_list <- read_param(config = config, param = 'gwas_list', return_obj = T)
+both <- do.call(rbind, list(obs_weights, leopard_weights, make_weights_long))
 
-# Read in phenotypes
-pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1
+# Remove ptclump as it doesn't have a sumstattune method
+both <- both[both$Method != 'ptclump',]
 
-pop = c('AFR')
+both<-merge(both, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x=T, sort = F)
+both$label[is.na(both$label)] <- both$Method[is.na(both$label)]
+both$label <- factor(both$label, levels=unique(both$label))
 
-mod_res_all <- NULL
-for(pop_i in pop){
-  for(pheno_i in pheno_intersect){
-    gwas_i<-gwas_list$name[gwas_list$population == pop_i & grepl(paste0('^', pheno_i, '_'),  gwas_list$name)]
-      
-    res_i <-
-      fread(
-        paste0(
-          '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_',
-          pop_i,
-          '.disc_',
-          pop_i,
-          '/',
-          pheno_i,
-          '/res.ptclump.assoc.txt'
-        )
-      )
-    
-    res_i$Z <- res_i$BETA / res_i$SE
-    
-    res_i$pT <- as.numeric(gsub('e.','e-', gsub('.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.|.*MVP.AFR\\.0\\.', '', res_i$Predictor)))
+# Plot non-EUR target first
+tmp <- both[both$Target != 'EUR',]
+tmp <- tmp[tmp$Discovery != 'EUR',]
 
-    nsnp_log <-
-      read.table(
-        paste0(
-          outdir,
-          '/reference/pgs_score_files/ptclump/',
-          gwas_i,
-          '/ref-',
-          gwas_i,
-          '.NSNP_per_pT'
-        ),
-        header = T
-      )
-    
-    nsnp<-nsnp_log$NSNP[nrow(nsnp_log)]
-    
-    disc_N <-
-      median(
-        fread(
-          paste0(
-            outdir,
-            '/reference/gwas_sumstat/',
-            gwas_i,
-            '/',
-            gwas_i,
-            '-cleaned.gz'
-          ), nrows = 10000
-        )$N
-      )
-    
-    targ_N <- res_i$N[1]
-    
-    mod_res <- estimatePolygenicModel(
-      p = res_i$Z,
-      nsnp = nsnp,
-      n = c(disc_N, targ_N),
-      pupper = c(0, res_i$pT),
-      fixvg2pi02 = T,
-      alpha = 0.05
-    )
-    
-    mod_res_all <- rbind(
-      mod_res_all,
-      data.frame(
-        Phenotype = pheno_i,
-        Population = pop_i,
-        GWAS = gwas_i,
-        nsnp = nsnp,
-        max_r2 = max(res_i$Obs_R2),
-        n_disc = disc_N,
-        n_targ = targ_N,
-        vg_est = mod_res$vg[1],
-        vg_lowCI = mod_res$vg[2],
-        vg_highCI = mod_res$vg[3],
-        pi0_est = mod_res$pi0[1],
-        pi0_lowCI = mod_res$pi0[2],
-        pi0_highCI = mod_res$pi0[3]
+# Set LEOPARD to black fill
+default_colors <- hue_pal()(10)
+names(default_colors) <- levels(tmp$label)
+default_colors["LEOPARD"] <- "black"
+
+# Plot the estimated and observed weights
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_weights.png', units = 'px', res = 300, width = 2500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Target ~ .) +
+  theme_half_open() +
+  labs(title = 'Weight of target ancestry-matched PGS', fill = NULL) +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
+
+###
+# Check calibration of LEOPARD compared to QuickPRS observed weights
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
+
+# Calculate metrics
+rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)^2))
+me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)
+
+# Create annotation data.frame
+metrics_df <- data.frame(
+  Discovery = c("AFR"),
+  x = c(0.5),         # Adjust positions as needed
+  y = c(-0.05),
+  label = c(
+    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2))
+  )
+)
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
+  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
+  labs(
+    x = "LEOPARD weight",
+    y = "Observed weight",
+  ) +
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+  ) +
+  coord_fixed()    
+dev.off()
+
+###
+# Check calibration of inverse_var compared to QuickPRS observed weights
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
+
+# Calculate metrics
+rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)^2))
+me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)
+
+
+# Create annotation data.frame
+metrics_df <- data.frame(
+  Discovery = c("AFR"),
+  x = c(0.5),         # Adjust positions as needed
+  y = c(-0.05),
+  label = c(
+    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2))
+  )
+)
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/inverse_var_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = inverse_var, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
+  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 1.5, size = 3.5) +
+  labs(
+    x = "inverse_var weight",
+    y = "Observed weight",
+  ) +
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+  ) +
+  coord_fixed()    
+dev.off()
+
+###
+# Check calibration of observed weights across all methods
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp <- tmp[!(tmp$label %in% c('LEOPARD','inverse_var')),]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('AFR'),]
+
+metrics <- NULL
+for(i in c('AFR')){
+  for(j in unique(tmp$label)){
+    for(k in unique(tmp$label)){
+      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
+      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics <- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
       )
-    )
+    }
   }
 }
 
-dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme')
-write.csv(mod_res_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv', row.names = F, quote = F)
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/observed_weights_calibration.png', units = 'px', width = 2000, height = 1650, res = 300)
+ggplot(metrics, aes(x = Method1, y = Method2, fill = rmse)) +
+  geom_tile(color = "white") +  # Tile plot with white borders
+  geom_text(aes(label = round(rmse, 2)), color = "black") +  # Add correlation values
+  scale_fill_gradient2(mid = "white", high = "red", midpoint = 0) +  # Color scale
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1),
+    axis.title = element_blank()
+  ) +
+  labs(fill = "RMSE")
+dev.off()
 
-mod_res_all<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv')
+# Calculate average RMSE for each method against all other methods
+metrics_unique <- metrics[metrics$Method1 != metrics$Method2, ]
+metrics_unique$Comparison <- NA
+for (i in 1:nrow(metrics_unique)) {
+  metrics_unique$Comparison[i] <-
+    paste0(sort(c(
+      metrics_unique$Method1[i], metrics_unique$Method2[i]
+    )), collapse = ' vs. ')
+}
+metrics_unique <- metrics_unique[!duplicated(paste0(metrics_unique$Population, metrics_unique$Comparison)),]
 
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avengeme_h2.png', res = 100, width = 900, height = 500, units = 'px')
-ggplot(mod_res_all, aes(x = Phenotype, y = vg_est, fill = Population)) +
-  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
-  geom_errorbar(aes(ymin=vg_lowCI, ymax=vg_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
-  labs(y="SNP-based Heritability (95%CI)", fill = NULL) +
+mean_rmse <- NULL
+for(i in unique(tmp$label)){
+  for(j in c('AFR')){
+    metrics_unique_tmp <- metrics_unique[metrics_unique$Method1 == i | metrics_unique$Method2 == i,]
+    metrics_unique_tmp <- metrics_unique_tmp[metrics_unique_tmp$Population == j,]
+    mean_rmse <- rbind(
+      mean_rmse, 
+      data.frame(
+        Method = i,
+        Population = j,
+        avg_rmse = mean(metrics_unique_tmp$rmse)
+      )
+    )
+  }
+}
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avg_observed_weight_rmse.png', units = 'px', width = 1500, height = 800, res = 300)
+ggplot(mean_rmse, aes(x = Method, y = avg_rmse, fill = Method)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
+  geom_text(aes(label = round(avg_rmse, 3)),  # <-- Add this
+          vjust = 1.5,                    # <-- Move the text slightly above the bar
+          size = 3) +                      # <-- Adjust text size
+  scale_fill_manual(values = default_colors) +
   theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  labs(y = 'Average RMSE') +
   theme(axis.text.x = element_text(angle = 45, hjust = 1),
-        legend.position = "top",
-        legend.key.spacing.x = unit(1, "cm"),
-        legend.justification = "center") +
-  background_grid(major = 'y', minor = 'y')
+        legend.position="none")
 dev.off()
 
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avengeme_polygenicity.png', res = 100, width = 900, height = 500, units = 'px')
-ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) +
-  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
-  geom_errorbar(aes(ymin=1 - pi0_lowCI, ymax=1 - pi0_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
-  labs(y="Proporition non-zero\neffects (95%CI)", fill = NULL) +
+###
+# Check calibration of estimated (LEOPARD and inverse_var) weights compared to observed QuickPRS weights
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp <- tmp[(tmp$label %in% c('LEOPARD','inverse_var','QuickPRS')),]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
+
+metrics <- NULL
+for(i in c('AFR')){
+  for(j in unique(tmp$label)){
+    for(k in unique(tmp$label)){
+      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
+      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics <- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
+      )
+    }
+  }
+}
+
+# Plot the rmse for LEOPARD and inverse_var predicting observed QuickPRS weight
+metrics <- metrics[metrics$Method1 == 'QuickPRS',]
+metrics <- metrics[metrics$Method2 != 'QuickPRS',]
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/inverse_var_comp_rmse.png', units = 'px', width = 800, height = 1500, res = 300)
+ggplot(metrics, aes(x = Method2, y = rmse, fill = Method2)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
+  geom_text(aes(label = round(rmse, 3)),  # <-- Add this
+          vjust = 1.5,                    # <-- Move the text slightly above the bar
+          size = 3) +                      # <-- Adjust text size
   theme_half_open() +
-  coord_cartesian(ylim = c(0, 0.15)) + 
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  labs(y = 'RMSE relative to QuickPRS', x = 'Method') +
   theme(axis.text.x = element_text(angle = 45, hjust = 1),
-        legend.position = "top",
-        legend.key.spacing.x = unit(1, "cm"),
-        legend.justification = "center") +
-  background_grid(major = 'y', minor = 'y')
+        legend.position="none")
 dev.off()
 
-summary(mod_res_all$max_r2)
-summary(mod_res_all$max_r2[mod_res_all$Population == 'EUR'])
-summary(mod_res_all$max_r2[mod_res_all$Population == 'EAS'])
-summary(mod_res_all$max_r2[mod_res_all$Population == 'AFR'])
-
 ```
 
 </details>
 
 ***
 
-#### Make table
-
-Make a table showing GWAS information for the manuscript.
-
-<details><summary>Show code</summary>
+# Check genetic distances
 
 ```{r}
-library(data.table)
+# Read in reference PC SNP-weights
+pc_score_file <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.eigenvec.var.gz')
+
+# Read in allele frequencies for each reference population
+ref_pop_freq <- list()
+for(i in c('EUR','EAS','AFR','AMR','MID','CSA')){
+  freq_i <- NULL
+  for(j in 1:22){
+    freq_i <- rbind(freq_i, fread(paste0('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/freq_files/', i,'/ref.', i, '.chr', j, '.afreq')))
+  }
+  ref_pop_freq[[i]] <- freq_i
+}
 
-#####
-# Trait names, labels, and URLs
-#####
+# Calculate PCs for reference populations
+ref_pc_all <- NULL
+for(i in c('EUR','EAS','AFR','AMR','MID','CSA')){
+  # Merge on SNP ID
+  merged <- merge(
+    pc_score_file, 
+    ref_pop_freq[[i]], 
+    by.x = "SNP", 
+    by.y = "ID"
+  )
+  
+  # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ)
+  merged[, A1_FREQ := ifelse(ALT == A1, ALT_FREQS, 1 - ALT_FREQS)]
+  
+  # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC
+  ref_PC <- merged[, lapply(.SD, function(w) sum(w * A1_FREQ, na.rm = TRUE)), 
+                   .SDcols = patterns("^PC")]
+  
+  ref_pc_all <- rbind(ref_pc_all, data.frame(group = i,
+                                             ref_PC))
+}
 
-mvp <- fread('~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/info.txt')
-mvp <- mvp[, c('efoTraits','labels','url'), with=F]
-names(mvp) <- c('trait', 'labels','url')
-mvp$sample <- 'MVP'
-mvp$population <- 'AFR'
+# Read in GWAS allele frequencies for BMI
+gwas_list_main <- fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt')
+gwas_list_main <- gwas_list_main[grepl('BMI', gwas_list_main$name),]
 
-#####
-# Sample size, SNP-h2 and polygenicity
-#####
+gwas_list_mvp <- fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt')
+gwas_list_mvp <- gwas_list_mvp[grepl('BMI', gwas_list_mvp$name),]
 
-# Read in the AVENGEME and LDSC results
-avengeme <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv')
-ldsc <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results.csv')
-both <- merge(avengeme, ldsc, by = c('Population','Phenotype'))
+gwas_list<-rbind(gwas_list_main, gwas_list_mvp)
 
-# Format for descriptives table
-both$h2_avengeme<- paste0(
-  round(both$vg_est,2), 
-  " (95%CI = ", 
-  round(both$vg_lowCI, 2), 
-  " - " , 
-  round(both$vg_highCI, 2), ")")
+outdir <- '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output'
+gwas_freq <- list()
+for(i in 1:nrow(gwas_list)){
+  ss_i <- fread(paste0(
+            outdir,
+            '/reference/gwas_sumstat/',
+            gwas_list$name[i],
+            '/',
+            gwas_list$name[i],
+            '-cleaned.gz'
+          ))
+  
+  gwas_freq[[gwas_list$name[i]]] <- ss_i[, c('SNP','A1','A2','FREQ'), with = F]
+}
 
-both$pi0_avengeme <- paste0(
-  round(both$pi0_est,2), 
-  " (95%CI = ", 
-  round(both$pi0_lowCI, 2), 
-  " - " , 
-  round(both$pi0_highCI, 2), ")")
+gwas_pc_all <- NULL
+for(i in names(gwas_freq)){
+  # Merge on SNP ID
+  merged <- merge(
+    pc_score_file, 
+    gwas_freq[[i]], 
+    by = "SNP"
+  )
+  
+  # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ)
+  merged[, FREQ := ifelse(A1.x == A1.y, FREQ, 1 - FREQ)]
+  
+  # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC
+  ref_PC <- merged[, lapply(.SD, function(w) sum(w * FREQ, na.rm = TRUE)), 
+                   .SDcols = patterns("^PC")]
+  
+  gwas_pc_all <- rbind(gwas_pc_all, data.frame(group = i,
+                                             ref_PC))
+}
 
-both$h2_ldsc <- paste0(
-  round(both$h2_est,2), 
-  " (SE = ", 
-  round(both$h2_se, 2), 
-  ")")
+ref_pc_all$type <- 'Reference'
+gwas_pc_all$type <- 'GWAS'
 
-both$int_ldsc <- paste0(
-  round(both$int_est,2), 
-  " (SE = ", 
-  round(both$int_se, 2), 
-  ")")
+pc_all <- rbind(ref_pc_all, gwas_pc_all)
 
-both<-both[, c('Population','Phenotype','n_disc','n_targ','h2_avengeme','pi0_avengeme','h2_ldsc','int_ldsc','lambda'), with = F]
-names(both)[1:2]<-c('population','labels')
+ggplot(pc_all, aes(x=PC1, y = PC2, colour = group, shape = type)) +
+  geom_point(size = 5)
 
-info_all <- merge(mvp, both, by = c('labels','population'))
-info_all$n_disc<-round(info_all$n_disc, 0)
-info_all$n_targ<-round(info_all$n_targ, 0)
+# There is a big shift in the PCs for GWAS and reference data, probably due to missing variants. Restrict to variant present in all GWAS.
 
-info_all<-info_all[, c('labels','trait','population','sample','n_disc','n_targ','h2_avengeme','pi0_avengeme','h2_ldsc','int_ldsc','lambda','url'), with=F]
-names(info_all) <- c('Trait Label', 'Trait Description', 'Ancestry', 'GWAS Sample', 'GWAS N', 'Target N',"SNP-h2 (AVENGEME)","pi0 (AVENGEME)","SNP-h2 (LDSC)","Intercept (LDSC)",'Lambda', 'URL')
+extract <- Reduce(intersect, list(
+  pc_score_file$SNP,
+  gwas_freq$BMI_UKB$SNP,
+  gwas_freq$BMI_BBJ$SNP,
+  gwas_freq$BMI_UGR$SNP,
+  gwas_freq$BMI_MVP_AFR$SNP
+))
 
-write.csv(info_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/gwas_descriptives.csv', row.names=F)
+pc_score_file_overlapping <- pc_score_file[pc_score_file$SNP %in% extract,]
+
+# Calculate PCs for reference populations
+ref_pc_all <- NULL
+for(i in c('EUR','EAS','AFR','AMR','MID','CSA')){
+  # Merge on SNP ID
+  merged <- merge(
+    pc_score_file_overlapping, 
+    ref_pop_freq[[i]], 
+    by.x = "SNP", 
+    by.y = "ID"
+  )
+  
+  # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ)
+  merged[, A1_FREQ := ifelse(ALT == A1, ALT_FREQS, 1 - ALT_FREQS)]
+  
+  # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC
+  ref_PC <- merged[, lapply(.SD, function(w) sum(w * A1_FREQ, na.rm = TRUE)), 
+                   .SDcols = patterns("^PC")]
+  
+  ref_pc_all <- rbind(ref_pc_all, data.frame(group = i,
+                                             ref_PC))
+}
 
-# Estimate the mean and SD of sample size within each population for selected traits
-info_all_selected<-info_all
-n_dat <- NULL
-for(i in unique(info_all_selected$`GWAS Sample`)){
-  n_dat <-rbind(
-    n_dat,
-    data.table(
-      sample = i,
-      gwas_n_median = round(median(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
-      gwas_n_mean = round(mean(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
-      gwas_n_sd = round(sd(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
-      target_n_median = round(median(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
-      target_n_mean = round(mean(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
-      target_n_sd = round(sd(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i]))
-    )
+gwas_pc_all <- NULL
+for(i in names(gwas_freq)){
+  # Merge on SNP ID
+  merged <- merge(
+    pc_score_file_overlapping, 
+    gwas_freq[[i]], 
+    by = "SNP"
   )
+  
+  # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ)
+  merged[, FREQ := ifelse(A1.x == A1.y, FREQ, 1 - FREQ)]
+  
+  # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC
+  ref_PC <- merged[, lapply(.SD, function(w) sum(w * FREQ, na.rm = TRUE)), 
+                   .SDcols = patterns("^PC")]
+  
+  gwas_pc_all <- rbind(gwas_pc_all, data.frame(group = i,
+                                             ref_PC))
 }
 
-```
+ref_pc_all$Population <- ref_pc_all$group
+ref_pc_all$GWAS <- NA
 
-</details>
+gwas_pc_all$GWAS <- gwas_pc_all$group
+gwas_pc_all$GWAS[gwas_pc_all$GWAS == 'BMI_UKB'] <- "UKB (EUR)"
+gwas_pc_all$GWAS[gwas_pc_all$GWAS == 'BMI_BBJ'] <- "BBJ (EAS)"
+gwas_pc_all$GWAS[gwas_pc_all$GWAS == 'BMI_UGR'] <- "UGR (AFR)"
+gwas_pc_all$GWAS[gwas_pc_all$GWAS == 'BMI_MVP_AFR'] <- "MVP (AFR)"
+gwas_pc_all$Population <- NA
 
-<details><summary>Show descriptives table</summary>
+ref_pc_all$type <- 'Reference'
+gwas_pc_all$type <- 'GWAS'
 
-```{r, eval = T, echo = F}
-info_all<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/gwas_descriptives.csv')
+pc_all <- rbind(ref_pc_all, gwas_pc_all)
 
-kable(info_all, "html") %>%
-  kable_styling(bootstrap_options = c("striped", "hover"),
-                full_width = F) %>%
-  scroll_box(width = "100%", height = "500px")
+png('~/oliverpainfel/Analyses/crosspop/plots/genetic_distance.png', units = 'px', width = 2000, height = 1500, res = 300)
 
-```
+ggplot(pc_all, aes(x=PC1, y = PC2)) +
+  geom_point(data=pc_all[pc_all$type == 'Reference',], aes(x=PC1, y = PC2, fill = Population), size = 5, shape = 21) +
+  geom_point(data=pc_all[pc_all$type == 'GWAS',], aes(x=PC1, y = PC2, colour = GWAS), size = 4, shape = 3, stroke = 1.5) +
+  theme_half_open() +
+  background_grid()
 
-</details>
+dev.off()
+
+# Perfect. Shows UKB match EUR, BBJ match EAS, UGR matches AFR, but MVP is close to AFR.
+
+```

From 21fa31e46e7aef4a5a63403f2c048fbdbd4b3685 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 14 May 2025 18:58:06 +0100
Subject: [PATCH 144/153] Updates for reviewer

---
 Scripts/model_builder/model_builder_top1.R  |   10 +-
 Scripts/pgs_methods/apply_leopard_weights.R |    5 +-
 docs/CrossPop.Rmd                           | 1899 ++++++++++++++++++-
 3 files changed, 1816 insertions(+), 98 deletions(-)

diff --git a/Scripts/model_builder/model_builder_top1.R b/Scripts/model_builder/model_builder_top1.R
index 65e14c1f..14c1378a 100644
--- a/Scripts/model_builder/model_builder_top1.R
+++ b/Scripts/model_builder/model_builder_top1.R
@@ -60,7 +60,7 @@ tmp_dir <- tempdir()
 
 # Create directory for final models to be saved
 if(opt$export_models){
-  system(paste0('mkdir -p ', opt$output_dir, '/final_models'))
+  system(paste0('mkdir -p ', opt$out, '_final_models'))
 }
 
 # Initiate log file
@@ -255,8 +255,8 @@ top1_indep_pred <- foreach(i = 1:length(unique(group_list$group)), .combine = 'c
     
     export_final_model(model = train_mod,
                        group = group_name,
-                       outdir = paste0(opt$output_dir,
-                                       '/final_models'))
+                       outdir = paste0(opt$out,
+                                       '_final_models'))
   }
   
   # Update progress log
@@ -357,8 +357,8 @@ multi <- foreach(i = 1:length(unique(group_list$multi)), .combine = 'c') %dopar%
     
     export_final_model(model = train_mod,
                        group = group_name,
-                       outdir = paste0(opt$output_dir,
-                                       '/final_models'))
+                       outdir = paste0(opt$out,
+                                       '_final_models'))
   }
   
   # Update progress log
diff --git a/Scripts/pgs_methods/apply_leopard_weights.R b/Scripts/pgs_methods/apply_leopard_weights.R
index 52967fb5..1ec6fc9a 100644
--- a/Scripts/pgs_methods/apply_leopard_weights.R
+++ b/Scripts/pgs_methods/apply_leopard_weights.R
@@ -122,6 +122,9 @@ opt$ref_freq_chr <- paste0(refdir, '/freq_files')
 # Subset PGS to SNPs in ref (useful when testing)
 score_all <- score_all[score_all$SNP %in% ref$SNP,]
 
+# Flip effects to match reference alleles
+score_all <- map_score(ref = ref, score = score_all)
+
 # Calculate linear combination of scores using mixing weights for each target population
 score_weighted <- score_all
 for(targ_pop in gwas_list$population){
@@ -159,7 +162,7 @@ for(targ_pop in gwas_list$population){
 score_weighted <- score_weighted[, c('SNP','A1','A2', names(score_weighted)[grepl('_weighted$', names(score_weighted))]), with=F]
 
 # Reduce number of significant figures to save space
-score_weighted[, (4:ncol(score_all)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_all)]
+score_weighted[, (4:ncol(score_weighted)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_weighted)]
 
 fwrite(score_weighted, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
 
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index bd2778b8..5be1f866 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -7601,6 +7601,51 @@ config<-c(
 )
 
 write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml', col.names = F, row.names = F, quote = F)
+
+####
+# Make a second configuration using EAS GWAS and subsample EUR GWAS with QuickPRS and LEOPARD
+####
+
+gwas_groups<-data.frame(
+  name=paste0(gwas_list_subsampled$name, '_BBJ'),
+  gwas=paste0(gwas_list_subsampled$name),
+  label=gsub("\\)", " + BBJ)", gwas_list_subsampled$label)
+)
+
+gwas_groups$trait <- gsub('_.*','',gwas_groups$name)
+gwas_groups$gwas<-paste0(gwas_groups$gwas,',',gwas_groups$trait,'_BBJ')
+
+gwas_groups$trait<-NULL
+
+write.table(gwas_groups, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_subsampled.txt', col.names = T, row.names = F, quote = F)
+
+gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt')
+gwas_list_eas<-gwas_list[gwas_list$population == 'EAS',]
+gwas_list_eas$label<-paste0('"', gwas_list_eas$label, '"')
+gwas_list_subsampled <- rbind(gwas_list_subsampled, gwas_list_eas)
+
+write.table(gwas_list_subsampled, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt', col.names = T, row.names = F, quote = F)
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_eur_test_only",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt",
+  "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_subsampled.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eur_test_only/target_list.txt",
+  "pgs_methods: ['quickprs']",
+  "leopard_methods: ['quickprs']",
+  "cores_prep_pgs: 10",
+  "cores_target_pgs: 50",
+  "ldpred2_inference: F",
+  "ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3",
+  "quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3",
+  "quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset",
+  "sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml', col.names = F, row.names = F, quote = F)
+
+
 ```
 
 ***
@@ -7758,6 +7803,221 @@ done
 
 ***
 
+<h4>Create predictor list</h4>
+
+When using EUR and EAS GWAS. 
+
+```{r}
+
+setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml'
+pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F)
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Create files for EAS and AFR targets
+targ_pop_i <- 'EUR'
+disc_pop_j <-'BBJ'
+disc_pop_j_2 <-'EAS'
+
+for(trait_i in selected_traits){
+  dir.create(paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_',
+        targ_pop_i,
+        '.disc_EUR_',
+        disc_pop_j_2,
+        '/',
+        trait_i))
+        
+  for(n in c(5, 15, 45, 135)){
+    scores_i <- scores[grepl(paste0('^', trait_i,'_'), scores$name),]
+    scores_i <- scores_i[grepl(paste0('_', n, 'K_BBJ|_', n, 'K|',trait_i,'_BBJ'), scores_i$name),]
+  
+    scores_i$predictor <- paste0(
+      outdir,
+      '/ukb/pgs/TRANS/',
+      scores_i$method,
+      '/',
+      scores_i$name,
+      '/ukb-',
+      scores_i$name,
+      '-TRANS.profiles'
+    )
+    
+    scores_i$multi <- scores_i$method
+
+    scores_i_j <- scores_i
+
+    # Insert path to score file
+    scores_i_j$predictor <- paste0(
+      outdir,
+      '/ukb/pgs/TRANS/',
+      scores_i_j$method,
+      '/',
+      scores_i_j$name,
+      '/ukb-',
+      scores_i_j$name,
+      '-TRANS.profiles'
+    )
+    
+    ####
+    # Make groups single source methods
+    ####
+    
+    scores_i_j_single_top1 <-
+      scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &
+                   !grepl('_multi$', scores_i_j$method), ]
+
+    # Create top1 column indicating which predictors top1 models should be derived
+    scores_i_j_single_top1$top1[grepl('UKB', scores_i_j_single_top1$name, ignore.case = F)] <- 'EUR'
+    scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] <- disc_pop_j_2
+    
+    ####
+    # Make groups containing pseudo scores for single source methods
+    ####
+
+    # Extract the pseudo score for each method and specify as a separate group
+    for(i in 1:nrow(scores_i_j_single_top1)) {
+      param <- find_pseudo(
+        config = config,
+        gwas = scores_i_j_single_top1$name[i],
+        pgs_method = scores_i_j_single_top1$method[i],
+        target_pop = targ_pop_i
+      )
+      
+      score_header <-
+        fread(scores_i_j_single_top1$predictor[i], nrows = 1)
+      score_cols <-
+        which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_single_top1$name[i], '_', param)))
+      
+      system(
+        paste0(
+          "cut -d' ' -f ", 
+          paste0(score_cols, collapse=','),
+          " ", 
+          scores_i_j_single_top1$predictor[i], 
+          " > ", 
+          gsub('.profiles',
+               paste0('.', targ_pop_i, '_pseudo.profiles'),
+               scores_i_j_single_top1$predictor[i])
+        )
+      )
+    }
+    
+    scores_i_j_single_pseudo <- scores_i_j_single_top1
+    scores_i_j_single_pseudo$multi <- paste0(scores_i_j_single_pseudo$multi, '.pseudo')
+
+    scores_i_j_single_pseudo$predictor <- gsub('.profiles', 
+                                  paste0('.', targ_pop_i, '_pseudo.profiles'),
+                                  scores_i_j_single_pseudo$predictor)
+
+    ####
+    # Make groups for multi-single-source pseudo scores
+    ####
+    
+    scores_i_j_multi_single_pseudo <- scores_i_j[grepl('_multi$', scores_i_j$method),]
+
+    # Extract the pseudo score for each method and specify as a separate group
+    for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
+      param <- find_pseudo(
+        config = config,
+        gwas = scores_i_j_multi_single_pseudo$name[i],
+        pgs_method = scores_i_j_multi_single_pseudo$method[i],
+        target_pop = targ_pop_i
+      )
+      
+      score_header <-
+        fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
+      score_cols <-
+        which(names(score_header) %in% c('FID', 'IID', paste0(scores_i_j_multi_single_pseudo$name[i], '_', param)))
+      
+      system(
+        paste0(
+          "cut -d' ' -f ", 
+          paste0(score_cols, collapse=','),
+          " ", 
+          scores_i_j_multi_single_pseudo$predictor[i], 
+          " > ", 
+          gsub('.profiles',
+               paste0('.', targ_pop_i, '_pseudo.profiles'),
+               scores_i_j_multi_single_pseudo$predictor[i])
+        )
+      )
+    }
+    
+    scores_i_j_multi_single_pseudo$multi <- paste0(scores_i_j_multi_single_pseudo$multi, '.pseudo')
+
+    scores_i_j_multi_single_pseudo$predictor <- gsub('.profiles', 
+                                  paste0('.', targ_pop_i, '_pseudo.profiles'),
+                                  scores_i_j_multi_single_pseudo$predictor)
+    
+    scores_i_j_multi_single_pseudo$top1<-paste0('EUR_', disc_pop_j_2)
+
+    ####
+    # Combine the different predictor groups
+    ####
+    predictors_i<- do.call(rbind, list(
+      scores_i_j_single_top1, 
+      scores_i_j_single_pseudo, 
+      scores_i_j_multi_single_pseudo
+    ))
+    
+    predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
+    
+    write.table(
+      predictors_i,
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_',
+        targ_pop_i,
+        '.disc_EUR_',
+        disc_pop_j_2,
+        '/',
+        trait_i,
+        '/predictor_list_n', n, '.txt'
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}
+
+```
+
+<h4>Run model_builder</h4>
+
+```{bash}
+cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR_EAS/*/res*
+
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+  for n in $(echo 5 15 45 135); do
+    if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR_EAS/${pheno}/res_n${n}.pred_comp.txt" ]; then
+      sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu,cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+        --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.EUR_test.row_number.txt \
+        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR_EAS/${pheno}/predictor_list_n${n}.txt \
+        --out /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR_EAS/${pheno}/res_n${n} \
+        --n_core 1"
+    fi
+  done
+done
+
+```
+
+***
+
 <h4>Plot results</h4>
 
 ```{r}
@@ -7841,107 +8101,1415 @@ for(pheno_i in selected_traits){
   tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
   tmp$label <- factor(tmp$label, levels = model_order)
 
-  res_eval_simp <- rbind(res_eval_simp, tmp)
+  res_eval_simp <- rbind(res_eval_simp, tmp)
+}
+
+# Plot results for each phenotype separately
+dir.create('~/oliverpainfel/Analyses/crosspop/plots_downsample')
+
+tmp<-res_eval_simp
+tmp$GWAS_N <- paste0('GWAS N = ', tmp$GWAS_N)
+tmp$GWAS_N <-factor(tmp$GWAS_N, levels = unique(tmp$GWAS_N))
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots_downsample/per_trait_r.png'), res=100, width = 1000, height = 6000, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x=NULL, fill = NULL) +
+    facet_grid(Trait ~ GWAS_N, scales = 'free_y') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval <- NULL
+# Subset res_eval for each scenario
+res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+  x <- res_eval[[i]]
+  x$pheno <- names(res_eval)[i]
+  return(x)
+}))
+
+# Average res_evalults for each test across phenotypes
+# Use MAd to account for correlation between them
+res_eval_i$Sample<-'A'
+
+for(group_i in unique(res_eval_i$Group)){
+  res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
+  missing_pheno <-
+    colnames(cors)[!(colnames(cors) %in% unique(res_eval_group_i$pheno))]
+  
+  cors_i <- cors[unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+  
+  meta_res_eval_i <-
+    agg(
+      id = Sample,
+      es = R,
+      var = SE ^ 2,
+      cor = cors_i,
+      method = "BHHR",
+      mod = NULL,
+      data = res_eval_group_i
+    )
+  
+  tmp <- data.table(Group = group_i,
+                    Method = res_eval_group_i$Method[1],
+                    Model = res_eval_group_i$Model[1],
+                    Source = res_eval_group_i$Source[1],
+                    Discovery = 'EUR',
+                    Target = 'EUR',
+                    R = meta_res_eval_i$es,
+                    SE = sqrt(meta_res_eval_i$var),
+                    GWAS_N = res_eval_group_i$GWAS_N[1])
+  
+  meta_res_eval <- rbind(meta_res_eval, tmp)
+}
+
+tmp <- meta_res_eval
+# Insert nice PGS method names
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$GWAS_N <- paste0('GWAS N = ', tmp$GWAS_N)
+tmp$GWAS_N <-factor(tmp$GWAS_N, levels = unique(tmp$GWAS_N))
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png'), res=100, width = 1000, height = 600, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x=NULL, fill = NULL) +
+    facet_grid(~ GWAS_N, scales = 'free_y') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+```
+
+<h4>Plot results</h4>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+
+# Calculate correlation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop_i = 'EUR'
+disc_pop_i = 'EAS'
+res_eval <- list()
+for(pheno_i in selected_traits){
+  res_eval_i <- NULL
+  for (n_i in c(5, 15, 45, 135)) {
+    eval_i <-
+      fread(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/',
+          'targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_i,
+          '/',
+          pheno_i,
+          '/res_n', n_i, '.pred_eval.txt'
+        )
+      )
+    eval_i$Target<-targ_pop_i
+    eval_i$gwas_group<-paste0('EUR+', disc_pop_i)
+    eval_i$UKB_GWAS_N <- paste0(n_i,'k')
+    res_eval_i<-rbind(res_eval_i, eval_i)
+  }
+  
+  res_eval_i$Method<-sub('\\..*','',res_eval_i$Group)
+  res_eval_i$Method<-gsub('-.*','', res_eval_i$Method)
+  
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'IndivTune'
+  res_eval_i$Model[grepl('top1$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   !grepl('pseudo', res_eval_i$Group)]<-'Multi-IndivTune'
+  res_eval_i$Model[grepl('multi$', res_eval_i$Group) &
+                   grepl('pseudo', res_eval_i$Group)]<-'Multi-SumStatTune'
+  
+  res_eval_i$Model[grepl('_multi', res_eval_i$Group)]<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'prscsx.pseudo.multi']<-'SumStatTune'
+  res_eval_i$Model[res_eval_i$Group == 'xwing.pseudo.multi']<-'SumStatTune'
+  
+  res_eval_i$Source<-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl('_multi$', res_eval_i$Method) | 
+    !grepl('EUR|EAS|AFR', res_eval_i$Group), 'Multi', 'Single')
+  
+  res_eval_i$Discovery[grepl('EUR', res_eval_i$Group)] <- 'EUR'
+  res_eval_i$Discovery[grepl('EAS', res_eval_i$Group)] <- 'EAS'
+  res_eval_i$Discovery[grepl('AFR', res_eval_i$Group)] <- 'AFR'
+  res_eval_i$Discovery[res_eval_i$Source == 'Multi'] <- res_eval_i$gwas_group[res_eval_i$Source == 'Multi']
+  
+  res_eval_i$Method<-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model<-factor(res_eval_i$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+  res_eval_i$Discovery<-factor(res_eval_i$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('quickprs','sbayesrc') & 
+      res_eval_i$Model %in% c('IndivTune','Multi-IndivTune')),]
+  
+  # Remove pseudo model for methods that don't really have one 
+  res_eval_i <- res_eval_i[
+    !(res_eval_i$Method %in%  c('ptclump','ptclump_multi') & 
+      res_eval_i$Model %in% c('SumStatTune','Multi-SumStatTune')),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
+  res_eval_i <- res_eval_i[
+    !((res_eval_i$Method %in%  c('prscsx', 'xwing') | grepl('_multi$', res_eval_i$Method)) & 
+      grepl('top1', res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i <- res_eval_i[!duplicated(res_eval_i[, c(
+    "Target", "Method", "Model", "Source", "Discovery","gwas_group", "UKB_GWAS_N"
+  )]),]
+  
+  res_eval[[pheno_i]]<-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC", "DBSLMM-multi", "lassosum-multi", "LDpred2-multi", "MegaPRS-multi", "PRS-CS-multi", "pT+clump-multi", "QuickPRS-multi", "SBayesRC-multi", "PRS-CSx", "X-Wing", "All") 
+
+# Plot results for each phenotype separately
+dir.create('~/oliverpainfel/Analyses/crosspop/subsampled/plots')
+
+res_eval_simp <- NULL
+for(pheno_i in selected_traits){
+  tmp <- res_eval[[pheno_i]]
+  tmp$Trait <- pheno_i
+  
+  # Insert nice PGS method names
+  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+  tmp$label[is.na(tmp$label)] <- 'All'
+  tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+  tmp$label <- factor(tmp$label, levels = model_order)
+  
+  # Simplify result to either SumStatTune or IndivTune
+  tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+  tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+  tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery','Model'), with=F]),]
+  
+  res_eval_simp <- rbind(res_eval_simp, tmp)
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval <- NULL
+for (n_i in c(5, 15, 45, 135)) {
+  # Subset res_eval for each scenario
+  res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+    x <- res_eval[[i]]
+    x$pheno <- names(res_eval)[i]
+    x <- x[x$Target == targ_pop_i]
+    x <- x[x$gwas_group == paste0('EUR+', disc_pop_i)]
+    x <- x[x$UKB_GWAS_N == paste0(n_i,'k')]
+  }))
+  
+  # Average res_evalults for each test across phenotypes
+  # Use MAd to account for correlation between them
+  res_eval_i$Sample<-'A'
+
+  for(group_i in unique(res_eval_i$Group)){
+    res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
+    missing_pheno <-
+      colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+    
+    if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+      print(paste0(
+        'res_evalults missing for ',
+        targ_pop_i,
+        ' ',
+        group_i,
+        ' ',
+        paste0(missing_pheno, collapse = ' ')
+      ))
+    }
+    
+    cors_i <- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+    
+    meta_res_eval_i <-
+      agg(
+        id = Sample,
+        es = R,
+        var = SE ^ 2,
+        cor = cors_i,
+        method = "BHHR",
+        mod = NULL,
+        data = res_eval_group_i
+      )
+    
+    tmp <- data.table(Group = group_i,
+                      Method = res_eval_group_i$Method[1],
+                      Model = res_eval_group_i$Model[1],
+                      Source = res_eval_group_i$Source[1],
+                      Discovery = res_eval_group_i$Discovery[1],
+                      gwas_group = res_eval_group_i$gwas_group[1],
+                      UKB_GWAS_N = paste0(n_i,'k'),
+                      Target = targ_pop_i,
+                      R = meta_res_eval_i$es,
+                      SE = sqrt(meta_res_eval_i$var))
+    
+    meta_res_eval <- rbind(meta_res_eval, tmp)
+  }
+}
+
+meta_res_eval$Model<-factor(meta_res_eval$Model, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+meta_res_eval$Discovery<-factor(meta_res_eval$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+meta_res_eval$UKB_GWAS_N<-factor(meta_res_eval$UKB_GWAS_N, levels=unique(meta_res_eval$UKB_GWAS_N))
+
+write.csv(meta_res_eval, '~/oliverpainfel/Analyses/crosspop/subsampled/r_eval.csv', row.names = F)
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp <- meta_res_eval
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Single'] <- 'EAS GWAS'
+tmp$Discovery_clean[tmp$Discovery != 'EUR' & tmp$Source == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('EUR GWAS',
+                                         'EAS GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model[tmp$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+tmp <- tmp[!duplicated(tmp[, c('label','Target','Discovery_clean','Model','UKB_GWAS_N'), with=F]),]
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/subsampled/plots/average_r.png'), res=300, width = 3500, height = 1200, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(. ~ UKB_GWAS_N + Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+# Plot performance of -multi models trained using LEOPARD vs using indiv-level data
+tmp <- meta_res_eval
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method')
+tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)] <- paste0(tmp$label[grepl('Multi', tmp$Model) & !(tmp$Method %in% pgs_group_methods)], '-multi')
+tmp$label <- factor(tmp$label, levels = unique(tmp$label[order(!(grepl('Multi', tmp$label)), tmp$label)]))
+tmp<-tmp[grepl('multi', tmp$label),]
+tmp <- tmp[tmp$Model != 'Multi-IndivTune',]
+tmp$Model<-as.character(tmp$Model)
+tmp$Model[tmp$Model != 'SumStatTune']<-'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune']<-'LEOPARD'
+tmp$Target <- paste0(tmp$Target, ' Target')
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/subsampled/plots/average_r_leopard.png'), res=300, width = 1500, height = 1200, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid( ~ UKB_GWAS_N, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Create a function to mirror pred_comp results
+mirror_comp<-function(x){
+  x_sym <- x
+  x_sym$Model_1 <- x$Model_2
+  x_sym$Model_2 <- x$Model_1
+  x_sym$Model_1_R <- x$Model_2_R
+  x_sym$Model_2_R <- x$Model_1_R
+  x_sym$R_diff <- -x_sym$R_diff
+  x_mirrored <- rbind(x, x_sym)
+  x_diag<-data.frame(
+      Model_1=unique(x_mirrored$Model_1),
+      Model_2=unique(x_mirrored$Model_1),
+      Model_1_R=x_mirrored$Model_1_R,
+      Model_2_R=x_mirrored$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp<-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
+# Read in results
+res_comp <- list()
+for(pheno_i in selected_traits){
+  res_comp_i<-NULL
+  for (n_i in c(5, 15, 45, 135)) {
+    comp_i <-
+      fread(
+        paste0(
+          '/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/',
+          'targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_i,
+          '/',
+          pheno_i,
+          '/res_n', n_i, '.pred_comp.txt'
+        )
+      )
+    comp_i<-mirror_comp(comp_i)
+    comp_i$Target<-targ_pop_i
+    comp_i$gwas_group<-paste0('EUR+', disc_pop_i)
+    comp_i$UKB_GWAS_N<-paste0(n_i,'k')
+    res_comp_i<-rbind(res_comp_i, comp_i)
+  }
+  
+  res_comp[[pheno_i]]<-res_comp_i
+}
+
+res_comp_all <- do.call(rbind, lapply(names(res_comp), function(name) {
+  x <- res_comp[[name]]
+  x$pheno <- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Annotate tests to get order correct
+res_comp_all$Method1<-sub('\\..*','',res_comp_all$Model_1)
+res_comp_all$Method1<-gsub('-.*','', res_comp_all$Method1)
+res_comp_all$Method2<-sub('\\..*','',res_comp_all$Model_2)
+res_comp_all$Method2<-gsub('-.*','', res_comp_all$Method2)
+
+find_model<-function(x){
+  mod <- x
+  mod[grepl('top1$', x) & !grepl('pseudo', x)] <- 'IndivTune'
+  mod[grepl('top1$', x) & grepl('pseudo', x)] <- 'SumStatTune'
+  mod[grepl('multi$', x) & !grepl('pseudo', x)] <- 'Multi-IndivTune'
+  mod[grepl('multi$', x) & grepl('pseudo', x)] <- 'Multi-SumStatTune'
+  mod[grepl('_multi', x)] <- 'SumStatTune'
+  mod[x == 'prscsx.pseudo.multi'] <- 'SumStatTune'
+  mod[x == 'xwing.pseudo.multi'] <- 'SumStatTune'
+  
+  return(mod)
+}
+
+res_comp_all$Model1<-find_model(res_comp_all$Model_1)
+res_comp_all$Model2<-find_model(res_comp_all$Model_2)
+
+res_comp_all$Source1<-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method1) | !grepl('AFR|EAS|EUR', res_comp_all$Model_1), 'Multi', 'Single')
+res_comp_all$Source2<-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl('_multi$', res_comp_all$Method2) | !grepl('AFR|EAS|EUR', res_comp_all$Model_2), 'Multi', 'Single')
+  
+for(i in c('EUR','EAS','AFR')){
+  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] <- i
+  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] <- i
+}
+res_comp_all$Discovery1[res_comp_all$Source1 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source1 == 'Multi']
+res_comp_all$Discovery2[res_comp_all$Source2 == 'Multi'] <- res_comp_all$gwas_group[res_comp_all$Source2 == 'Multi']
+
+res_comp_all$Method1<-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
+res_comp_all$Method2<-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
+res_comp_all$Model1<-factor(res_comp_all$Model1, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+res_comp_all$Model2<-factor(res_comp_all$Model2, levels=c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+res_comp_all$Discovery1<-factor(res_comp_all$Discovery1, levels=rev(c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')))
+res_comp_all$Discovery2<-factor(res_comp_all$Discovery2, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+# Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method1 %in%  c('quickprs','sbayesrc') & 
+  res_comp_all$Model1 %in% c('IndivTune','Multi-IndivTune')),]
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method2 %in%  c('quickprs','sbayesrc') & 
+  res_comp_all$Model2 %in% c('IndivTune','Multi-IndivTune')),]
+
+# Remove pseudo model for methods that don't really have one 
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method1 %in%  c('ptclump','ptclump_multi') & 
+  res_comp_all$Model1 %in% c('SumStatTune','Multi-SumStatTune')),]
+res_comp_all <- res_comp_all[
+!(res_comp_all$Method2 %in%  c('ptclump','ptclump_multi') & 
+  res_comp_all$Model2 %in% c('SumStatTune','Multi-SumStatTune')),]
+
+# Remove top1 models for PRS-CSx
+res_comp_all <- res_comp_all[
+!(grepl('prscsx|xwing|_multi', res_comp_all$Method1) & 
+  grepl('top1', res_comp_all$Model_1)),]
+res_comp_all <- res_comp_all[
+!(grepl('prscsx|xwing|_multi', res_comp_all$Method2) & 
+  grepl('top1', res_comp_all$Model_2)),]
+
+# Remove any comparisons
+res_comp_all <- res_comp_all[!duplicated(res_comp_all[, c("Target", "gwas_group", "Method1", "Model1", "Source1", "Discovery1", "Method2", "Model2", "Source2", "Discovery2",'pheno','UKB_GWAS_N')]),]
+
+res_comp_all$r_diff_rel <- res_comp_all$R_diff / res_comp_all$Model_2_R
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTune or SumStatTune result
+tmp <- res_comp_all
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label1'
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method2', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label2'
+
+tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'], '-multi')
+tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'], '-multi')
+
+tmp$Model1[tmp$Model1 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model1[tmp$Model1 == 'SumStatTune'] <- 'SumStatTune'
+tmp$Model2[tmp$Model2 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model2[tmp$Model2 == 'SumStatTune'] <- 'SumStatTune'
+
+tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` <- paste0(tmp$label1, ' - ', tmp$Model1, ' - ', tmp$Discovery1)
+tmp$`Model 2` <- paste0(tmp$label2, ' - ', tmp$Model2, ' - ', tmp$Discovery2)
+
+tmp <- tmp[, c('Target', 'pheno', 'Model 1', 'Model 2', 'Model_1_R', 'Model_2_R', 'R_diff', 'R_diff_pval'), with=F]
+names(tmp) <- c('Target', 'Trait','Model 1', 'Model 2', "R (Model 1)", "R (Model 2)", "R difference (Model 1 R - Model 2 R)", "R difference p-value")
+
+tmp<-tmp[order(tmp$Target, tmp$Trait, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` <- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` <- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` <- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, '~/oliverpainfel/Analyses/crosspop/subsampled/r_diff.csv', row.names=F)
+
+###########
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_comp <- NULL
+for (n_i in c(5, 15, 45, 135)) {
+  # Subset res_comp for each scenario
+  res_comp_i <- res_comp_all[res_comp_all$Target == targ_pop_i & res_comp_all$gwas_group == paste0('EUR+', disc_pop_i)]
+  res_comp_i <- res_comp_i[res_comp_i$UKB_GWAS_N == paste0(n_i,'k'), ]
+  
+  # Calculate diff SE based on p-value
+  res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] <- 1-0.001
+  res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]<-1-0.001
+  res_comp_i$R_diff_z<-qnorm(res_comp_i$R_diff_pval/2)
+  res_comp_i$R_diff_SE<-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
+      
+  # Average results for each test across phenotypes
+  # Use MAd to account for correlation between them
+  res_comp_i$Sample<-'A'
+  res_comp_i$Group <- paste0(res_comp_i$Model_1, '_vs_', res_comp_i$Model_2)
+
+  for(group_i in unique(res_comp_i$Group)){
+    res_comp_group_i <- res_comp_i[res_comp_i$Group == group_i,]
+    cors_i <- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
+    
+    if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
+      
+      meta_res_comp_i <-
+        agg(
+          id = Sample,
+          es = R_diff,
+          var = R_diff_SE ^ 2,
+          cor = cors_i,
+          method = "BHHR",
+          mod = NULL,
+          data = res_comp_group_i
+        )
+      
+      tmp <- res_comp_group_i[1,]
+      tmp$pheno <- NULL
+      tmp$Model_1_R <-
+        meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &
+                          meta_res_eval$Target == targ_pop_i &
+                          meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i) & 
+                          meta_res_eval$UKB_GWAS_N == paste0(n_i,'k')]
+      tmp$Model_2_R <-
+        meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &
+                          meta_res_eval$Target == targ_pop_i &
+                          meta_res_eval$gwas_group == paste0('EUR+', disc_pop_i) & 
+                          meta_res_eval$UKB_GWAS_N == paste0(n_i,'k')]
+      tmp$R_diff <- meta_res_comp_i$es
+      tmp$R_diff_SE <- sqrt(meta_res_comp_i$var)
+      tmp$R_diff_z <- tmp$R_diff / tmp$R_diff_SE
+      tmp$R_diff_p <- 2*pnorm(-abs(tmp$R_diff_z))
+    } else {
+      tmp <- res_comp_group_i[1,]
+      tmp$pheno <- NULL
+      tmp$R_diff <- NA
+      tmp$R_diff_SE <- NA
+      tmp$R_diff_z <- NA
+      tmp$R_diff_p <- NA
+    }
+    meta_res_comp <- rbind(meta_res_comp, tmp)
+  }
+}
+
+meta_res_comp$R_diff_perc <- meta_res_comp$R_diff / meta_res_comp$Model_2_R
+  
+# Compare QuickPRS-Multi vs QuickPRS to evaluate LEOPARD performance
+tmp_quickprs <- meta_res_comp[meta_res_comp$Model_1 == 'quickprs_multi.pseudo.multi' & 
+                                meta_res_comp$Model_2 == 'quickprs.pseudo.multi' &
+                    meta_res_comp$Target == 'EUR',]
+
+tmp_quickprs[,c('UKB_GWAS_N', 'R_diff_perc', 'R_diff_p'), with = F]
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTune or SumStatTune result
+tmp <- meta_res_comp
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label1'
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method2', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+names(tmp)[names(tmp) == 'label'] <- 'label2'
+
+tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'] <- paste0(tmp$label1[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label1 != 'All'], '-multi')
+tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'] <- paste0(tmp$label2[grepl('Multi', tmp$Model2) & !(tmp$Method2 %in% pgs_group_methods) & tmp$label2 != 'All'], '-multi')
+
+tmp$Model1[tmp$Model1 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model1[tmp$Model1 == 'SumStatTune'] <- 'SumStatTune'
+tmp$Model2[tmp$Model2 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model2[tmp$Model2 == 'SumStatTune'] <- 'SumStatTune'
+
+tmp<-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp<-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` <- paste0(tmp$label1, ' - ', tmp$Model1, ' - ', tmp$Discovery1)
+tmp$`Model 2` <- paste0(tmp$label2, ' - ', tmp$Model2, ' - ', tmp$Discovery2)
+
+tmp$`Percentage change (R difference / Model 2 R)` <- paste0(round(tmp$R_diff_perc * 100, 1), '%')
+
+tmp <- tmp[, c('UKB_GWAS_N', 'Target', 'Model 1', 'Model 2', 'Model_1_R', 'Model_2_R', 'R_diff',"Percentage change (R difference / Model 2 R)", 'R_diff_p'), with=F]
+names(tmp) <- c('UKB GWAS N', 'Target','Model 1', 'Model 2', "R (Model 1)", "R (Model 2)", "R difference (Model 1 R - Model 2 R)", "Percentage change (R difference / Model 2 R)", "R difference p-value")
+
+tmp<-tmp[order(tmp$Target, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` <- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` <- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` <- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, '~/oliverpainfel/Analyses/crosspop/subsampled/r_diff_average.csv', row.names=F)
+
+####
+# Plot relative improvement of methods
+####
+# Use the QuickPRS-Multi (IndivTune) as the reference for each UKB_GWAS_N
+
+meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$Method2 == 'quickprs' & meta_res_comp$Source2 == 'Multi' & meta_res_comp$Model2 == 'Multi-SumStatTune',]
+meta_res_comp_ptclump_top1$reference_point<-F
+meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Method1 == 'quickprs' & meta_res_comp_ptclump_top1$Source1 == 'Multi' & meta_res_comp_ptclump_top1$Model1 == 'Multi-SumStatTune']<-T
+meta_res_comp_ptclump_top1$R_diff[is.na(meta_res_comp_ptclump_top1$R_diff)]<-0
+meta_res_comp_ptclump_top1$Discovery1 <- factor(meta_res_comp_ptclump_top1$Discovery1, levels=rev(levels(meta_res_comp_ptclump_top1$Discovery1)))
+
+res_comp_all_ptclump_top1<-res_comp_all[res_comp_all$Method2 == 'quickprs' & res_comp_all$Source2 == 'Multi' & res_comp_all$Model2 == 'Multi-SumStatTune',]
+res_comp_all_ptclump_top1$Discovery1 <-  factor(res_comp_all_ptclump_top1$Discovery1, levels=levels(meta_res_comp_ptclump_top1$Discovery1))
+
+# Create data to plot reference points
+meta_res_comp_reference <- meta_res_comp_ptclump_top1
+meta_res_comp_reference$R_diff[meta_res_comp_ptclump_top1$reference_point == F] <- NA
+meta_res_comp_reference$R_diff_SE [meta_res_comp_ptclump_top1$reference_point == F] <- NA
+res_comp_all_ptclump_top1$reference_point<-F
+
+meta_tmp <- meta_res_comp_ptclump_top1
+meta_tmp <- merge(meta_tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+meta_tmp$label[is.na(meta_tmp$label)] <- 'All'
+meta_tmp$label[grepl('Multi', meta_tmp$Model1) & !(meta_tmp$Method1 %in% pgs_group_methods) & meta_tmp$label != 'All'] <- paste0(meta_tmp$label[grepl('Multi', meta_tmp$Model1) & !(meta_tmp$Method1 %in% pgs_group_methods) & meta_tmp$label != 'All'], '-multi')
+meta_tmp$label <- factor(meta_tmp$label, levels = model_order)
+meta_tmp$Discovery_clean <- as.character(meta_tmp$Discovery1)
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 == 'EUR'] <- 'EUR GWAS'
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Single'] <- 'EAS GWAS'
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Multi'] <- 'Both'
+meta_tmp$Discovery_clean <- factor(meta_tmp$Discovery_clean, 
+                              levels = c('EUR GWAS',
+                                         'EAS GWAS',
+                                         'Both'))
+meta_tmp$Target <- paste0(meta_tmp$Target, ' Target')
+meta_tmp$Model1 <- factor(meta_tmp$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+meta_tmp$UKB_GWAS_N <- factor(meta_tmp$UKB_GWAS_N, levels = unique(meta_tmp$UKB_GWAS_N))
+
+meta_tmp_ref <- meta_res_comp_reference
+meta_tmp_ref <- merge(meta_tmp_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+meta_tmp_ref$label[is.na(meta_tmp_ref$label)] <- 'All'
+meta_tmp_ref$label[grepl('Multi', meta_tmp_ref$Model1) & !(meta_tmp_ref$Method1 %in% pgs_group_methods) & meta_tmp_ref$label != 'All'] <- paste0(meta_tmp_ref$label[grepl('Multi', meta_tmp_ref$Model1) & !(meta_tmp_ref$Method1 %in% pgs_group_methods) & meta_tmp_ref$label != 'All'], '-multi')
+meta_tmp_ref$label <- factor(meta_tmp_ref$label, levels = model_order)
+meta_tmp_ref$Discovery_clean <- as.character(meta_tmp_ref$Discovery1)
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 == 'EUR'] <- 'EUR GWAS'
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != 'EUR' & meta_tmp_ref$Source1 == 'Single'] <- 'EAS GWAS'
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != 'EUR' & meta_tmp_ref$Source1 == 'Multi'] <- 'Both'
+meta_tmp_ref$Discovery_clean <- factor(meta_tmp_ref$Discovery_clean, 
+                              levels = c('EUR GWAS',
+                                         'EAS GWAS',
+                                         'Both'))
+meta_tmp_ref$Target <- paste0(meta_tmp_ref$Target, ' Target')
+meta_tmp_ref$Model1 <- factor(meta_tmp_ref$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+meta_tmp_ref$UKB_GWAS_N <- factor(meta_tmp_ref$UKB_GWAS_N, levels = unique(meta_tmp_ref$UKB_GWAS_N))
+
+tmp <- res_comp_all_ptclump_top1
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+tmp$label[is.na(tmp$label)] <- 'All'
+tmp$label[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label != 'All'] <- paste0(tmp$label[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & tmp$label != 'All'], '-multi')
+tmp$label <- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean <- as.character(tmp$Discovery1)
+tmp$Discovery_clean[tmp$Discovery1 == 'EUR'] <- 'EUR GWAS'
+tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Single'] <- 'EAS GWAS'
+tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Multi'] <- 'Both'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('EUR GWAS',
+                                         'EAS GWAS',
+                                         'Both'))
+tmp$Target <- paste0(tmp$Target, ' Target')
+tmp$Model1 <- factor(tmp$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+tmp$UKB_GWAS_N <- factor(tmp$UKB_GWAS_N, levels = unique(tmp$UKB_GWAS_N))
+
+ggplot(meta_tmp, aes(x=label, y=R_diff , fill = Model1)) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff - R_diff_SE,
+          ymax = R_diff + R_diff_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype="dotted") +
+    labs(y = "R_diff (SE)") +
+    facet_grid(UKB_GWAS_N ~ Discovery_clean, scales = 'free_x', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+
+# Plot as % change
+meta_tmp$R_diff_perc <- meta_tmp$R_diff / meta_tmp$Model_2_R
+meta_tmp_ref$R_diff_perc <- meta_tmp_ref$R_diff / meta_tmp_ref$Model_2_R
+tmp$R_diff_perc <- tmp$R_diff / tmp$Model_2_R
+
+meta_tmp$R_diff_perc_SE <- meta_tmp$R_diff_SE / meta_tmp$Model_2_R
+
+library(scales)
+ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype="dotted") +
+    labs(y = "R diff. (SE)") +
+    facet_grid(UKB_GWAS_N ~ Discovery_clean, scales = 'free_x', space = 'free_x') +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+# Simplify results showing results only with or without training data
+meta_tmp_simple <- meta_tmp
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 == 'SumStatTune'] <- 'SumStatTune'
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 == 'SumStatTune'] <- 'SumStatTune'
+meta_tmp_simple<-meta_tmp_simple[meta_tmp_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_simple<-meta_tmp_simple[meta_tmp_simple$Model_2 %in% res_eval_simp$Group,]
+
+meta_tmp_ref_simple <- meta_tmp_ref
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == 'SumStatTune'] <- 'SumStatTune'
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 != 'SumStatTune'] <- 'IndivTune'
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == 'SumStatTune'] <- 'SumStatTune'
+meta_tmp_ref_simple<-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_ref_simple<-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_2 %in% res_eval_simp$Group,]
+
+tmp_simple <- tmp
+tmp_simple$Model1[tmp_simple$Model1 != 'SumStatTune'] <- 'IndivTune'
+tmp_simple$Model1[tmp_simple$Model1 == 'SumStatTune'] <- 'SumStatTune'
+tmp_simple$Model2[tmp_simple$Model2 != 'SumStatTune'] <- 'IndivTune'
+tmp_simple$Model2[tmp_simple$Model2 == 'SumStatTune'] <- 'SumStatTune'
+tmp_simple<-tmp_simple[tmp_simple$Model_1 %in% res_eval_simp$Group,]
+tmp_simple<-tmp_simple[tmp_simple$Model_2 %in% res_eval_simp$Group,]
+
+# Export plot for manuscript
+png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/average_r.perc_improv.png', width = 3200, height = 2000, res= 300, units = 'px')
+ggplot(meta_tmp_simple[meta_tmp_simple$Target == 'EUR Target',], aes(x=label, y=R_diff_perc , fill = Model1)) +
+#    geom_boxplot(
+#      data = tmp_simple[tmp_simple$Target != 'EUR Target',],
+#        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+#        position = position_dodge(0.7),
+#        alpha = 0.3
+#      ) +
+    geom_point(
+        data = tmp_simple[tmp_simple$Target != 'EUR Target',],
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target != 'EUR Target',],
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 4,
+        shape = 22,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype="dotted") +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(UKB_GWAS_N ~ Discovery_clean, scales = 'free_x', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = "top",
+        legend.key.spacing.x = unit(2, "cm"),
+        legend.justification = "center"
+    )
+dev.off()
+
+# Export plot comparing sumstat vs indivtune for QuickPRS
+# Export plot for manuscript
+png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/average_r.perc_improv.png', width = 1500, height = 1200, res= 300, units = 'px')
+ggplot(meta_tmp_simple[meta_tmp_simple$Target == 'EUR Target' & meta_tmp_simple$Discovery_clean == 'Both',], aes(x=label, y=R_diff_perc , fill = Model1)) +
+#    geom_boxplot(
+#      data = tmp_simple[tmp_simple$Target != 'EUR Target',],
+#        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+#        position = position_dodge(0.7),
+#        alpha = 0.3
+#      ) +
+    geom_point(
+        data = tmp_simple[tmp_simple$Target != 'EUR Target',],
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target != 'EUR Target',],
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = "identity",
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 4,
+        shape = 22,
+        show.legend=F
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(. ~ UKB_GWAS_N, scales = 'free_x', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = "top",
+        legend.key.spacing.x = unit(2, "cm"),
+        legend.justification = "center"
+    )
+dev.off()
+
+```
+
+</details>
+
+```{bash, eval=T, echo=F}
+
+cp ~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_downsample.png
+
+```
+
+<details><summary>Show results</summary>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<img src="Images/CrossPop_2025/average_r_downsample.png">
+</div>
+</div>
+
+</details>
+
+
+## LEOPARD+QuickPRS
+
+Here we will compare the LEOPARD estimated weights for population specific PGS, to the weights estimated using observed data in the UKB target sample.
+
+<details><summary>Show code</summary>
+
+```{r}
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in list of outcomes 
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+###
+# Read in weights estimated by LEOPARD (QuickPRS)
+###
+
+leopard_weights<-NULL
+scores_quickprs <- scores$name[scores$method == 'quickprs_multi']
+for(i in selected_traits){
+  for(n in c('5','15','45','135')){
+    scores_i <- scores_quickprs[grepl(paste0('^', i,'_'), scores_quickprs) & grepl(paste0('_', n,'K_'), scores_quickprs)]
+    for(j in scores_i){
+        weights_file <- readRDS(paste0(outdir, '/reference/pgs_score_files/leopard/', j, '/ref-', j, '.weights.rds'))
+        weights_file <- data.frame(weights_file)
+        
+        weights <-
+          data.table(
+            Target = do.call(c, lapply(names(weights_file), function(x) rep(x, 2))),
+            Discovery = names(weights_file),
+            Weight = do.call(c, lapply(weights_file, function(x) x)),
+            UKB_GWAS_N = paste0(n, 'k'),
+            Trait = i,
+            Method = 'LEOPARD'
+          )
+        
+        leopard_weights <- rbind(leopard_weights, weights)
+    }
+  }
+}
+
+leopard_weights<-leopard_weights[leopard_weights$Target == 'EUR' & leopard_weights$Discovery == 'EUR',]
+
+#####
+# Read in the PGS weights estimated using UKB data
+#####
+# Read in the final model coefficients for multi-source methods
+
+obs_weights<-NULL
+for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_methods)]){
+  scores_method<-scores$name[scores$method == method_i]
+  method_i <- gsub('_multi','', method_i)
+
+  for(i in selected_traits){
+    for(j in c('EUR')){
+      if(j == 'EUR'){
+        pops <- c('EAS')
+      } else {
+        pops <- j
+      }
+      
+      for(k in pops){
+          for(n in c('5','15','45','135')){
+          model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/subsampled/targ_', j, '.disc_EUR_', k, '/', i, '/res_n', n, '_final_models/', method_i, '.pseudo.multi.final_model.txt'))
+          model<-model[-1,]
+          
+          # Set weight to zero if negative, as this is what LEOPARD does
+          if(any(model$V2 < 0)){
+            model$V2[model$V2 < 0] <- 0
+            model$V2[model$V2 > 0] <- 1
+          }
+          
+          names(model) <- c('x', 'BETA')
+          model$Discovery[grepl('UKB', model$x)]<-'EUR'
+          model$Discovery[grepl('BBJ', model$x)]<-'EAS'
+          model$Discovery[grepl('UGR', model$x)]<-'AFR'
+          model$UKB_GWAS_N<-paste0(n,'k')
+          model$Target <- j
+          model$Weight <- model$BETA/sum(model$BETA)
+          model$Trait <- i
+          model$Method <- method_i
+          model<-model[,c('Target','Discovery','Weight','Method','UKB_GWAS_N','Trait'), with=F]
+          obs_weights<-rbind(obs_weights, model)
+        }
+      }
+    }
+  }
+}
+
+obs_weights<-obs_weights[obs_weights$Target == 'EUR' & obs_weights$Discovery == 'EUR',]
+
+###
+# Estimate weights if using the inverse variance weighting
+###
+
+# Read in GWAS descriptives
+gwas_desc<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+gwas_desc <- gwas_desc[, c('Trait Label','Ancestry','GWAS N'), with=F]
+names(gwas_desc)<-c('trait','ancestry','n')
+gwas_desc<-gwas_desc[gwas_desc$trait %in% selected_traits,]
+
+gwas_desc_eas <- gwas_desc[gwas_desc$ancestry == 'EAS',]
+gwas_desc_eas$name<-'BBJ'
+
+gwas_desc_eur <- data.frame(
+  trait = gwas_desc_eas$trait,
+  ancestry = 'EUR',
+  n = c(rep(5000, 10), rep(15000, 10), rep(45000, 10), rep(135000, 10))
+)
+
+gwas_desc<-merge(gwas_desc_eas, gwas_desc_eur, by='trait')
+gwas_desc$inverse_var <- gwas_desc$n.y / (gwas_desc$n.y + gwas_desc$n.x)
+
+gwas_desc$Target <- 'EUR'
+gwas_desc$Discovery <- 'EUR'
+gwas_desc$Weight <- gwas_desc$inverse_var
+gwas_desc$Method <- 'inverse_var'
+gwas_desc$UKB_GWAS_N <- paste0(gwas_desc$n.y/1000,'k')
+gwas_desc$Trait <- gwas_desc$trait
+
+gwas_desc <- gwas_desc[, names(obs_weights), with=F]
+
+###
+# Combine and compare
+###
+
+both <- do.call(rbind, list(obs_weights, leopard_weights, gwas_desc))
+
+both<-merge(both, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x=T, sort = F)
+both$label[is.na(both$label)] <- both$Method[is.na(both$label)]
+both$label <- factor(both$label, levels=unique(both$label))
+
+# Plot EUR target
+tmp <- both[both$Target == 'EUR',]
+tmp <- tmp[tmp$Discovery == 'EUR',]
+
+# Set LEOPARD to black fill
+default_colors <- hue_pal()(10)
+names(default_colors) <- levels(tmp$label)
+default_colors["LEOPARD"] <- "black"
+
+tmp$UKB_GWAS_N <- factor(tmp$UKB_GWAS_N, levels = unique(tmp$UKB_GWAS_N))
+
+# Plot the estimated and observed weights
+png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights_eur.png', units = 'px', res = 300, width = 3500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(. ~ UKB_GWAS_N) +
+  theme_half_open() +
+  labs(title = 'Weight of EUR PGS for EUR Target', fill = NULL) +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
+
+###
+# Check calibration of LEOPARD compared to QuickPRS observed weights
+###
+
+####### GOT TO HERE
+
+tmp <- both[both$Target == 'EUR',]
+tmp <- tmp[both$Discovery == 'EUR',]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
+tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
+
+# Calculate metrics
+rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)^2))
+me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)
+
+rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)^2))
+me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)
+
+# Create annotation data.frame
+metrics_df <- data.frame(
+  Discovery = c("AFR", "EAS"),
+  x = c(0.5, 0.5),         # Adjust positions as needed
+  y = c(-0.05, -0.05),
+  label = c(
+    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
+    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
+  )
+)
+
+png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
+  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
+  labs(
+    x = "LEOPARD weight",
+    y = "Observed weight",
+  ) +
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+  ) +
+  facet_grid(. ~ Discovery) +
+  coord_fixed()    
+dev.off()
+
+###
+# Check calibration of inverse_var compared to QuickPRS observed weights
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
+tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
+
+# Calculate metrics
+rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)^2))
+me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)
+
+rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)^2))
+me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)
+
+# Create annotation data.frame
+metrics_df <- data.frame(
+  Discovery = c("AFR", "EAS"),
+  x = c(0.5, 0.5),         # Adjust positions as needed
+  y = c(-0.05, -0.05),
+  label = c(
+    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
+    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
+  )
+)
+
+png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/inverse_var_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = inverse_var, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
+  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 1.5, size = 3.5) +
+  labs(
+    x = "inverse_var weight",
+    y = "Observed weight",
+  ) +
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+  ) +
+  facet_grid(. ~ Discovery) +
+  coord_fixed()    
+dev.off()
+
+###
+# Check calibration of observed weights across all methods
+###
+
+tmp <- both[both$Target != 'EUR',]
+tmp <- tmp[!(tmp$label %in% c('LEOPARD','inverse_var')),]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
+
+metrics <- NULL
+for(i in c('EAS','AFR')){
+  for(j in unique(tmp$label)){
+    for(k in unique(tmp$label)){
+      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
+      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics <- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
+      )
+    }
+  }
+}
+
+png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/observed_weights_calibration.png', units = 'px', width = 3000, height = 1650, res = 300)
+ggplot(metrics, aes(x = Method1, y = Method2, fill = rmse)) +
+  geom_tile(color = "white") +  # Tile plot with white borders
+  geom_text(aes(label = round(rmse, 2)), color = "black") +  # Add correlation values
+  scale_fill_gradient2(mid = "white", high = "red", midpoint = 0) +  # Color scale
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1),
+    axis.title = element_blank()
+  ) +
+  facet_grid(. ~ Population) +
+  labs(fill = "RMSE")
+dev.off()
+
+# Calculate average RMSE for each method against all other methods
+metrics_unique <- metrics[metrics$Method1 != metrics$Method2, ]
+metrics_unique$Comparison <- NA
+for (i in 1:nrow(metrics_unique)) {
+  metrics_unique$Comparison[i] <-
+    paste0(sort(c(
+      metrics_unique$Method1[i], metrics_unique$Method2[i]
+    )), collapse = ' vs. ')
 }
+metrics_unique <- metrics_unique[!duplicated(paste0(metrics_unique$Population, metrics_unique$Comparison)),]
 
-# Plot results for each phenotype separately
-dir.create('~/oliverpainfel/Analyses/crosspop/plots_downsample')
-
-tmp<-res_eval_simp
-tmp$GWAS_N <- paste0('GWAS N = ', tmp$GWAS_N)
-tmp$GWAS_N <-factor(tmp$GWAS_N, levels = unique(tmp$GWAS_N))
+mean_rmse <- NULL
+for(i in unique(tmp$label)){
+  for(j in c('AFR','EAS')){
+    metrics_unique_tmp <- metrics_unique[metrics_unique$Method1 == i | metrics_unique$Method2 == i,]
+    metrics_unique_tmp <- metrics_unique_tmp[metrics_unique_tmp$Population == j,]
+    mean_rmse <- rbind(
+      mean_rmse, 
+      data.frame(
+        Method = i,
+        Population = j,
+        avg_rmse = mean(metrics_unique_tmp$rmse)
+      )
+    )
+  }
+}
 
-png(paste0('~/oliverpainfel/Analyses/crosspop/plots_downsample/per_trait_r.png'), res=100, width = 1000, height = 6000, units = 'px')
-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x=NULL, fill = NULL) +
-    facet_grid(Trait ~ GWAS_N, scales = 'free_y') +
-    theme_half_open() +
-    background_grid(major = 'y', minor = 'y') + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
-          legend.position = "top",
-          legend.key.spacing.x = unit(1, "cm"),
-          legend.justification = "center")
+png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/avg_observed_weight_rmse.png', units = 'px', width = 1500, height = 1500, res = 300)
+ggplot(mean_rmse, aes(x = Method, y = avg_rmse, fill = Method)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
+  geom_text(aes(label = round(avg_rmse, 3)),  # <-- Add this
+          vjust = 1.5,                    # <-- Move the text slightly above the bar
+          size = 3) +                      # <-- Adjust text size
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Population ~ .) +
+  theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  labs(y = 'Average RMSE') +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position="none")
 dev.off()
 
-####
-# Average results across phenotypes
-####
+###
+# Check calibration of estimated (LEOPARD and inverse_var) weights compared to observed QuickPRS weights
+###
 
-library(MAd)
+tmp <- both[both$Target != 'EUR',]
+tmp <- tmp[(tmp$label %in% c('LEOPARD','inverse_var','QuickPRS')),]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery"), 
+                     timevar = "label", 
+                     direction = "wide")
 
-# Average R across phenotypes
-meta_res_eval <- NULL
-# Subset res_eval for each scenario
-res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
-  x <- res_eval[[i]]
-  x$pheno <- names(res_eval)[i]
-  return(x)
-}))
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
 
-# Average res_evalults for each test across phenotypes
-# Use MAd to account for correlation between them
-res_eval_i$Sample<-'A'
+tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
 
-for(group_i in unique(res_eval_i$Group)){
-  res_eval_group_i <- res_eval_i[res_eval_i$Group == group_i,]
-  missing_pheno <-
-    colnames(cors)[!(colnames(cors) %in% unique(res_eval_group_i$pheno))]
-  
-  cors_i <- cors[unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
-  
-  meta_res_eval_i <-
-    agg(
-      id = Sample,
-      es = R,
-      var = SE ^ 2,
-      cor = cors_i,
-      method = "BHHR",
-      mod = NULL,
-      data = res_eval_group_i
-    )
-  
-  tmp <- data.table(Group = group_i,
-                    Method = res_eval_group_i$Method[1],
-                    Model = res_eval_group_i$Model[1],
-                    Source = res_eval_group_i$Source[1],
-                    Discovery = 'EUR',
-                    Target = 'EUR',
-                    R = meta_res_eval_i$es,
-                    SE = sqrt(meta_res_eval_i$var),
-                    GWAS_N = res_eval_group_i$GWAS_N[1])
-  
-  meta_res_eval <- rbind(meta_res_eval, tmp)
+metrics <- NULL
+for(i in c('EAS','AFR')){
+  for(j in unique(tmp$label)){
+    for(k in unique(tmp$label)){
+      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
+      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics <- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
+      )
+    }
+  }
 }
 
-tmp <- meta_res_eval
-# Insert nice PGS method names
-tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
-tmp$label <- factor(tmp$label, levels = model_order)
-tmp$GWAS_N <- paste0('GWAS N = ', tmp$GWAS_N)
-tmp$GWAS_N <-factor(tmp$GWAS_N, levels = unique(tmp$GWAS_N))
+# Plot the rmse for LEOPARD and inverse_var predicting observed QuickPRS weight
+metrics <- metrics[metrics$Method1 == 'QuickPRS',]
+metrics <- metrics[metrics$Method2 != 'QuickPRS',]
 
-png(paste0('~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png'), res=100, width = 1000, height = 600, units = 'px')
-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
-    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
-                  width = 0,
-                  position = position_dodge(width = 1)) +
-    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
-    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype="dotted") +
-    labs(y = "R (SE)", x=NULL, fill = NULL) +
-    facet_grid(~ GWAS_N, scales = 'free_y') +
-    theme_half_open() +
-    background_grid(major = 'y', minor = 'y') + 
-    panel_border() + 
-    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
-          legend.position = "top",
-          legend.key.spacing.x = unit(1, "cm"),
-          legend.justification = "center")
+png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/inverse_var_comp_rmse.png', units = 'px', width = 800, height = 1500, res = 300)
+ggplot(metrics, aes(x = Method2, y = rmse, fill = Method2)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
+  geom_text(aes(label = round(rmse, 3)),  # <-- Add this
+          vjust = 1.5,                    # <-- Move the text slightly above the bar
+          size = 3) +                      # <-- Adjust text size
+  facet_grid(Population ~ .) +
+  theme_half_open() +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  labs(y = 'RMSE relative to QuickPRS', x = 'Method') +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position="none")
 dev.off()
 
 ```
@@ -7950,15 +9518,15 @@ dev.off()
 
 ```{bash, eval=T, echo=F}
 
-cp ~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_downsample.png
+cp ~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
 
 ```
 
-<details><summary>Show results</summary>
+<details><summary>Show observed and LEOPARD PGS weights</summary>
 
 <div class="centered-container">
 <div class="rounded-image-container" style="width: 100%;">
-<img src="Images/CrossPop_2025/average_r_downsample.png">
+<img src="Images/CrossPop_2025/leopard_weights.png">
 </div>
 </div>
 
@@ -10794,3 +12362,150 @@ dev.off()
 # Perfect. Shows UKB match EUR, BBJ match EAS, UGR matches AFR, but MVP is close to AFR.
 
 ```
+
+Use Florian's data to estimate population proportions in GWAS and reference datasets.
+
+```{r}
+library(bigsnpr)
+
+DIR <- "~/oliverpainfel/Data/bigsnpr"  # you can replace by e.g. "data" or "tmp-data"
+
+# /!\ This downloads 850 Mb (each)
+all_freq <- bigreadr::fread2(
+  runonce::download_file("https://figshare.com/ndownloader/files/31620968",
+                         dir = DIR, fname = "ref_freqs.csv.gz"))
+projection <- bigreadr::fread2(
+  runonce::download_file("https://figshare.com/ndownloader/files/31620953",
+                         dir = DIR, fname = "projection.csv.gz"))
+
+# Read in allele frequencies for each reference population
+ref_rds <- NULL
+for(i in 1:22){
+  ref_rds <- rbind(ref_rds, 
+                   readRDS(paste0('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr', i, '.rds')))
+}
+
+ref_pop_freq <- list()
+for(i in c('EUR','EAS','AFR','AMR','MID','CSA')){
+  ref_pop_freq[[i]] <- ref_rds[, c('CHR','SNP','BP_GRCh37','A1','A2',paste0('REF.FRQ.',i)), with=F]
+  names(ref_pop_freq[[i]]) <-c('chr','rsid','pos','a1','a0','freq')
+}
+
+# Read in GWAS allele frequencies for BMI
+gwas_list_main <- fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt')
+gwas_list_main <- gwas_list_main[grepl('BMI', gwas_list_main$name),]
+
+gwas_list_mvp <- fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt')
+gwas_list_mvp <- gwas_list_mvp[grepl('BMI', gwas_list_mvp$name),]
+gwas_list_mvp <- gwas_list_mvp[gwas_list_mvp$population == 'AFR',]
+
+gwas_list<-rbind(gwas_list_main, gwas_list_mvp)
+
+outdir <- '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output'
+gwas_freq <- list()
+for(i in 1:nrow(gwas_list)){
+  ss_i <- fread(paste0(
+            outdir,
+            '/reference/gwas_sumstat/',
+            gwas_list$name[i],
+            '/',
+            gwas_list$name[i],
+            '-cleaned.gz'
+          ))
+  
+  gwas_freq[[gwas_list$name[i]]] <- ss_i[, c('CHR','BP','SNP','A2','A1','FREQ'), with = F]
+  names(gwas_freq[[gwas_list$name[i]]]) <- c("chr", "pos", 'rsid', "a0", "a1", "freq")
+}
+
+# Harmonise the data
+gwas_freq_matched <- list()
+for(i in 1:nrow(gwas_list)){
+  gwas_freq_matched[[gwas_list$name[i]]] <- snp_match(
+    mutate(gwas_freq[[gwas_list$name[i]]], chr = as.integer(chr), beta = 1),
+    all_freq[1:5]
+  ) %>%
+    mutate(freq = ifelse(beta < 0, 1 - freq, freq))
+}
+
+ref_pop_freq_matched <- list()
+for(i in c('EUR','EAS','AFR','AMR','MID','CSA')){
+  ref_pop_freq_matched[[i]] <- snp_match(
+    mutate(ref_pop_freq[[i]], chr = as.integer(chr), beta = 1),
+    all_freq[1:5]
+  ) %>%
+    mutate(freq = ifelse(beta < 0, 1 - freq, freq))
+}
+
+# Infer ancestry with shrinkage (https://privefl.github.io/bigsnpr/articles/ancestry.html)
+correction <- c(1, 1, 1, 1.008, 1.021, 1.034, 1.052, 1.074, 1.099,
+                1.123, 1.15, 1.195, 1.256, 1.321, 1.382, 1.443)
+
+gwas_ancestry <- list()
+for(i in 1:nrow(gwas_list)){
+  res <- snp_ancestry_summary(
+    freq = gwas_freq_matched[[gwas_list$name[i]]]$freq,
+    info_freq_ref = all_freq[gwas_freq_matched[[gwas_list$name[i]]]$`_NUM_ID_`, -(1:5)],
+    projection = projection[gwas_freq_matched[[gwas_list$name[i]]]$`_NUM_ID_`, -(1:5)],
+    correction = correction
+  )
+  
+  # Group similar popualtions
+  group <- colnames(all_freq)[-(1:5)]
+  group[group %in% c("Scandinavia", "United Kingdom", "Ireland")]   <- "Europe (North West)"
+  group[group %in% c("Europe (South East)", "Europe (North East)")] <- "Europe (East)"
+  grp_fct <- factor(group, unique(group))
+  
+  gwas_ancestry[[gwas_list$name[i]]] <- tapply(res, grp_fct, sum)
+}
+
+ref_ancestry <- list()
+for(i in c('EUR','EAS','AFR','AMR','MID','CSA')){
+  res <- snp_ancestry_summary(
+    freq = ref_pop_freq_matched[[i]]$freq,
+    info_freq_ref = all_freq[ref_pop_freq_matched[[i]]$`_NUM_ID_`, -(1:5)],
+    projection = projection[ref_pop_freq_matched[[i]]$`_NUM_ID_`, -(1:5)],
+    correction = correction
+  )
+  
+  # Group similar popualtions
+  group <- colnames(all_freq)[-(1:5)]
+  group[group %in% c("Scandinavia", "United Kingdom", "Ireland")]   <- "Europe (North West)"
+  group[group %in% c("Europe (South East)", "Europe (North East)")] <- "Europe (East)"
+  grp_fct <- factor(group, unique(group))
+  
+  ref_ancestry[[i]] <- tapply(res, grp_fct, sum)
+}
+
+# Plot the results
+gwas_ancestry_table <- do.call(cbind, gwas_ancestry)
+colnames(gwas_ancestry_table) <- c("UKB (EUR)", "BBJ (EAS)", "UGR (AFR)", "MVP (AFR)")
+gwas_ancestry_table_melt <- melt(gwas_ancestry_table)
+
+gwas_ancestry_table_melt<-gwas_ancestry_table_melt[gwas_ancestry_table_melt$value >= 0.01,]
+
+png('~/oliverpainfel/Analyses/crosspop/plots/gwas_ancestry.png', units = 'px', width = 2000, height = 1500, res = 300)
+ggplot(gwas_ancestry_table_melt, aes(x = Var2, y = value, fill = Var1)) +
+  geom_bar(stat = "identity", colour = 'black', size = 0.2) +
+  labs(x = "GWAS", y = "Proportion", fill = 'Population', title = 'Ancestry proportions in BMI GWAS') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+dev.off()
+
+write.csv(gwas_ancestry_table, '~/oliverpainfel/Analyses/crosspop/plots/gwas_ancestry.csv')
+
+ref_ancestry_table <- do.call(cbind, ref_ancestry)
+ref_ancestry_table_melt <- melt(ref_ancestry_table)
+
+ref_ancestry_table_melt<-ref_ancestry_table_melt[ref_ancestry_table_melt$value >= 0.01,]
+
+png('~/oliverpainfel/Analyses/crosspop/plots/ref_ancestry.png', units = 'px', width = 2000, height = 1500, res = 300)
+ggplot(ref_ancestry_table_melt, aes(x = Var2, y = value, fill = Var1)) +
+  geom_bar(stat = "identity", colour = 'black', size = 0.2) +
+  labs(x = "Reference Label", y = "Proportion", fill = 'Population', title = 'Ancestry proportions in BMI GWAS') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+dev.off()
+
+write.csv(ref_ancestry_table, '~/oliverpainfel/Analyses/crosspop/plots/ref_ancestry.csv')
+
+```

From d9633e725ea81ea558ae1a20d74d3f10de19dac9 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 21 May 2025 15:37:36 +0100
Subject: [PATCH 145/153] Updates

---
 docs/CrossPop.Rmd        | 390 ++++++++++++++++++---------------------
 pipeline/envs/xwing.yaml |   2 +
 2 files changed, 177 insertions(+), 215 deletions(-)

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 5be1f866..f3164245 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -9250,42 +9250,42 @@ dev.off()
 # Check calibration of LEOPARD compared to QuickPRS observed weights
 ###
 
-####### GOT TO HERE
-
 tmp <- both[both$Target == 'EUR',]
 tmp <- tmp[both$Discovery == 'EUR',]
 tmp$Target<-NULL
 tmp_wide <- reshape(tmp, 
-                     idvar = c("Trait", "Discovery"), 
+                     idvar = c("Trait", "Discovery","UKB_GWAS_N"), 
                      timevar = "label", 
                      direction = "wide")
 
 names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
 tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
 
-tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
-tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
-
 # Calculate metrics
-rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)^2))
-me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)
-
-rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)^2))
-me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)
+metrics <- NULL
+for(n in c('5','15','45','135')){
+  tmp_wide_n <- tmp_wide[tmp_wide$UKB_GWAS_N == paste0(n,'k'),]
+  metrics<-rbind(metrics, 
+                 data.frame(
+                   n = paste0(n, 'k'),
+                   rmse = sqrt(mean((tmp_wide_n$QuickPRS - tmp_wide_n$LEOPARD)^2)),
+                   me = mean(tmp_wide_n$QuickPRS - tmp_wide_n$LEOPARD)
+                 ))
+}
 
 # Create annotation data.frame
 metrics_df <- data.frame(
-  Discovery = c("AFR", "EAS"),
-  x = c(0.5, 0.5),         # Adjust positions as needed
-  y = c(-0.05, -0.05),
-  label = c(
-    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
-    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
-  )
+  UKB_GWAS_N = metrics$n,
+  x = 0.1,
+  y = 0.15,
+  label = paste0("RMSE = ", round(metrics$rmse, 2), "\nME = ", round(metrics$me, 2))
 )
 
+tmp_wide$UKB_GWAS_N <- factor(tmp_wide$UKB_GWAS_N, levels = unique(tmp_wide$UKB_GWAS_N))
+metrics_df$UKB_GWAS_N <- factor(metrics_df$UKB_GWAS_N, levels = unique(tmp_wide$UKB_GWAS_N))
+
 png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
-ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) +
+ggplot(tmp_wide, aes(x = LEOPARD, y = QuickPRS)) +
   geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
   geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
   geom_point(alpha = 0.7) +
@@ -9299,7 +9299,7 @@ ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) +
   theme(
     axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
   ) +
-  facet_grid(. ~ Discovery) +
+  facet_grid(. ~ UKB_GWAS_N) +
   coord_fixed()    
 dev.off()
 
@@ -9307,43 +9307,46 @@ dev.off()
 # Check calibration of inverse_var compared to QuickPRS observed weights
 ###
 
-tmp <- both[both$Target != 'EUR',]
+tmp <- both[both$Target == 'EUR',]
+tmp <- tmp[both$Discovery == 'EUR',]
 tmp$Target<-NULL
 tmp_wide <- reshape(tmp, 
-                     idvar = c("Trait", "Discovery"), 
+                     idvar = c("Trait", "Discovery","UKB_GWAS_N"), 
                      timevar = "label", 
                      direction = "wide")
 
 names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
 tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
 
-tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
-tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
-
 # Calculate metrics
-rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)^2))
-me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)
-
-rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)^2))
-me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)
+metrics <- NULL
+for(n in c('5','15','45','135')){
+  tmp_wide_n <- tmp_wide[tmp_wide$UKB_GWAS_N == paste0(n,'k'),]
+  metrics<-rbind(metrics, 
+                 data.frame(
+                   n = paste0(n, 'k'),
+                   rmse = sqrt(mean((tmp_wide_n$QuickPRS - tmp_wide_n$inverse_var)^2)),
+                   me = mean(tmp_wide_n$QuickPRS - tmp_wide_n$inverse_var)
+                 ))
+}
 
 # Create annotation data.frame
 metrics_df <- data.frame(
-  Discovery = c("AFR", "EAS"),
-  x = c(0.5, 0.5),         # Adjust positions as needed
-  y = c(-0.05, -0.05),
-  label = c(
-    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
-    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
-  )
+  UKB_GWAS_N = metrics$n,
+  x = 0.3,
+  y = 0.25,
+  label = paste0("RMSE = ", round(metrics$rmse, 2), "\nME = ", round(metrics$me, 2))
 )
 
-png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/inverse_var_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
-ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = inverse_var, y = QuickPRS)) +
+tmp_wide$UKB_GWAS_N <- factor(tmp_wide$UKB_GWAS_N, levels = unique(tmp_wide$UKB_GWAS_N))
+metrics_df$UKB_GWAS_N <- factor(metrics_df$UKB_GWAS_N, levels = unique(tmp_wide$UKB_GWAS_N))
+
+png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights_calibration_inverse_var.png', units = 'px', width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide, aes(x = inverse_var, y = QuickPRS)) +
   geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
   geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
   geom_point(alpha = 0.7) +
-  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 1.5, size = 3.5) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
   labs(
     x = "inverse_var weight",
     y = "Observed weight",
@@ -9353,165 +9356,10 @@ ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = inverse_var, y = QuickPRS
   theme(
     axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
   ) +
-  facet_grid(. ~ Discovery) +
+  facet_grid(. ~ UKB_GWAS_N) +
   coord_fixed()    
 dev.off()
 
-###
-# Check calibration of observed weights across all methods
-###
-
-tmp <- both[both$Target != 'EUR',]
-tmp <- tmp[!(tmp$label %in% c('LEOPARD','inverse_var')),]
-tmp$Target<-NULL
-tmp_wide <- reshape(tmp, 
-                     idvar = c("Trait", "Discovery"), 
-                     timevar = "label", 
-                     direction = "wide")
-
-names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
-tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
-
-tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
-
-metrics <- NULL
-for(i in c('EAS','AFR')){
-  for(j in unique(tmp$label)){
-    for(k in unique(tmp$label)){
-      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
-      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
-      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
-      
-      metrics <- rbind(
-        metrics,
-        data.frame(
-          Population = i,
-          Method1 = j,
-          Method2 = k,
-          rmse = rmse,
-          me = me
-        )
-      )
-    }
-  }
-}
-
-png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/observed_weights_calibration.png', units = 'px', width = 3000, height = 1650, res = 300)
-ggplot(metrics, aes(x = Method1, y = Method2, fill = rmse)) +
-  geom_tile(color = "white") +  # Tile plot with white borders
-  geom_text(aes(label = round(rmse, 2)), color = "black") +  # Add correlation values
-  scale_fill_gradient2(mid = "white", high = "red", midpoint = 0) +  # Color scale
-  theme_half_open() +
-  panel_border() + 
-  theme(
-    axis.text.x = element_text(angle = 45, hjust = 1),
-    axis.title = element_blank()
-  ) +
-  facet_grid(. ~ Population) +
-  labs(fill = "RMSE")
-dev.off()
-
-# Calculate average RMSE for each method against all other methods
-metrics_unique <- metrics[metrics$Method1 != metrics$Method2, ]
-metrics_unique$Comparison <- NA
-for (i in 1:nrow(metrics_unique)) {
-  metrics_unique$Comparison[i] <-
-    paste0(sort(c(
-      metrics_unique$Method1[i], metrics_unique$Method2[i]
-    )), collapse = ' vs. ')
-}
-metrics_unique <- metrics_unique[!duplicated(paste0(metrics_unique$Population, metrics_unique$Comparison)),]
-
-mean_rmse <- NULL
-for(i in unique(tmp$label)){
-  for(j in c('AFR','EAS')){
-    metrics_unique_tmp <- metrics_unique[metrics_unique$Method1 == i | metrics_unique$Method2 == i,]
-    metrics_unique_tmp <- metrics_unique_tmp[metrics_unique_tmp$Population == j,]
-    mean_rmse <- rbind(
-      mean_rmse, 
-      data.frame(
-        Method = i,
-        Population = j,
-        avg_rmse = mean(metrics_unique_tmp$rmse)
-      )
-    )
-  }
-}
-
-png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/avg_observed_weight_rmse.png', units = 'px', width = 1500, height = 1500, res = 300)
-ggplot(mean_rmse, aes(x = Method, y = avg_rmse, fill = Method)) +
-  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
-  geom_text(aes(label = round(avg_rmse, 3)),  # <-- Add this
-          vjust = 1.5,                    # <-- Move the text slightly above the bar
-          size = 3) +                      # <-- Adjust text size
-  scale_fill_manual(values = default_colors) +
-  facet_grid(Population ~ .) +
-  theme_half_open() +
-  background_grid(major = 'y', minor = 'y') + 
-  panel_border() +
-  labs(y = 'Average RMSE') +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1),
-        legend.position="none")
-dev.off()
-
-###
-# Check calibration of estimated (LEOPARD and inverse_var) weights compared to observed QuickPRS weights
-###
-
-tmp <- both[both$Target != 'EUR',]
-tmp <- tmp[(tmp$label %in% c('LEOPARD','inverse_var','QuickPRS')),]
-tmp$Target<-NULL
-tmp_wide <- reshape(tmp, 
-                     idvar = c("Trait", "Discovery"), 
-                     timevar = "label", 
-                     direction = "wide")
-
-names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
-tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
-
-tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
-
-metrics <- NULL
-for(i in c('EAS','AFR')){
-  for(j in unique(tmp$label)){
-    for(k in unique(tmp$label)){
-      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
-      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
-      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
-      
-      metrics <- rbind(
-        metrics,
-        data.frame(
-          Population = i,
-          Method1 = j,
-          Method2 = k,
-          rmse = rmse,
-          me = me
-        )
-      )
-    }
-  }
-}
-
-# Plot the rmse for LEOPARD and inverse_var predicting observed QuickPRS weight
-metrics <- metrics[metrics$Method1 == 'QuickPRS',]
-metrics <- metrics[metrics$Method2 != 'QuickPRS',]
-
-png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/inverse_var_comp_rmse.png', units = 'px', width = 800, height = 1500, res = 300)
-ggplot(metrics, aes(x = Method2, y = rmse, fill = Method2)) +
-  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
-  geom_text(aes(label = round(rmse, 3)),  # <-- Add this
-          vjust = 1.5,                    # <-- Move the text slightly above the bar
-          size = 3) +                      # <-- Adjust text size
-  facet_grid(Population ~ .) +
-  theme_half_open() +
-  background_grid(major = 'y', minor = 'y') + 
-  panel_border() +
-  labs(y = 'RMSE relative to QuickPRS', x = 'Method') +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1),
-        legend.position="none")
-dev.off()
-
 ```
 
 </details>
@@ -11084,12 +10932,12 @@ meta_tmp$label[grepl('Multi', meta_tmp$Model1) & !(meta_tmp$Method1 %in% pgs_gro
 meta_tmp$label <- factor(meta_tmp$label, levels = model_order)
 meta_tmp$Discovery_clean <- as.character(meta_tmp$Discovery1)
 meta_tmp$Discovery_clean[meta_tmp$Discovery1 == 'EUR'] <- 'EUR GWAS'
-meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Single'] <- 'Target-matched GWAS'
-meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Multi'] <- 'Both'
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Single'] <- 'AFR GWAS'
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Multi'] <- 'EUR + AFR GWAS'
 meta_tmp$Discovery_clean <- factor(meta_tmp$Discovery_clean, 
-                              levels = c('Target-matched GWAS',
+                              levels = c('AFR GWAS',
                                          'EUR GWAS',
-                                         'Both'))
+                                         'EUR + AFR GWAS'))
 meta_tmp$Target <- paste0(meta_tmp$Target, ' Target')
 meta_tmp$Model1 <- factor(meta_tmp$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
 
@@ -11100,12 +10948,12 @@ meta_tmp_ref$label[grepl('Multi', meta_tmp_ref$Model1) & !(meta_tmp_ref$Method1
 meta_tmp_ref$label <- factor(meta_tmp_ref$label, levels = model_order)
 meta_tmp_ref$Discovery_clean <- as.character(meta_tmp_ref$Discovery1)
 meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 == 'EUR'] <- 'EUR GWAS'
-meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != 'EUR' & meta_tmp_ref$Source1 == 'Single'] <- 'Target-matched GWAS'
-meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != 'EUR' & meta_tmp_ref$Source1 == 'Multi'] <- 'Both'
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != 'EUR' & meta_tmp_ref$Source1 == 'Single'] <- 'AFR GWAS'
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != 'EUR' & meta_tmp_ref$Source1 == 'Multi'] <- 'EUR + AFR GWAS'
 meta_tmp_ref$Discovery_clean <- factor(meta_tmp_ref$Discovery_clean, 
-                              levels = c('Target-matched GWAS',
+                              levels = c('AFR GWAS',
                                          'EUR GWAS',
-                                         'Both'))
+                                         'EUR + AFR GWAS'))
 meta_tmp_ref$Target <- paste0(meta_tmp_ref$Target, ' Target')
 meta_tmp_ref$Model1 <- factor(meta_tmp_ref$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
 
@@ -11116,12 +10964,12 @@ tmp$label[grepl('Multi', tmp$Model1) & !(tmp$Method1 %in% pgs_group_methods) & t
 tmp$label <- factor(tmp$label, levels = model_order)
 tmp$Discovery_clean <- as.character(tmp$Discovery1)
 tmp$Discovery_clean[tmp$Discovery1 == 'EUR'] <- 'EUR GWAS'
-tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Single'] <- 'Target-matched GWAS'
-tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Multi'] <- 'Both'
+tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Single'] <- 'AFR GWAS'
+tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Multi'] <- 'EUR + AFR GWAS'
 tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
-                              levels = c('Target-matched GWAS',
+                              levels = c('AFR GWAS',
                                          'EUR GWAS',
-                                         'Both'))
+                                         'EUR + AFR GWAS'))
 tmp$Target <- paste0(tmp$Target, ' Target')
 tmp$Model1 <- factor(tmp$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
 
@@ -11241,7 +11089,7 @@ tmp_simple<-tmp_simple[tmp_simple$Model_1 %in% res_eval_simp$Group,]
 tmp_simple<-tmp_simple[tmp_simple$Model_2 %in% res_eval_simp$Group,]
 
 # Export plot for manuscript
-png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r.perc_improv.png', width = 3200, height = 2000, res= 300, units = 'px')
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r.perc_improv.png', width = 3200, height = 1500, res= 300, units = 'px')
 ggplot(meta_tmp_simple[meta_tmp_simple$Target != 'EUR Target',], aes(x=label, y=R_diff_perc , fill = Model1)) +
 #    geom_boxplot(
 #      data = tmp_simple[tmp_simple$Target != 'EUR Target',],
@@ -12367,6 +12215,7 @@ Use Florian's data to estimate population proportions in GWAS and reference data
 
 ```{r}
 library(bigsnpr)
+library(dplyr)
 
 DIR <- "~/oliverpainfel/Data/bigsnpr"  # you can replace by e.g. "data" or "tmp-data"
 
@@ -12481,14 +12330,21 @@ gwas_ancestry_table <- do.call(cbind, gwas_ancestry)
 colnames(gwas_ancestry_table) <- c("UKB (EUR)", "BBJ (EAS)", "UGR (AFR)", "MVP (AFR)")
 gwas_ancestry_table_melt <- melt(gwas_ancestry_table)
 
-gwas_ancestry_table_melt<-gwas_ancestry_table_melt[gwas_ancestry_table_melt$value >= 0.01,]
+gwas_ancestry_table_melt<-gwas_ancestry_table_melt[gwas_ancestry_table_melt$value >= 0.02,]
+
+library(scales)  # for percent_format()
 
 png('~/oliverpainfel/Analyses/crosspop/plots/gwas_ancestry.png', units = 'px', width = 2000, height = 1500, res = 300)
 ggplot(gwas_ancestry_table_melt, aes(x = Var2, y = value, fill = Var1)) +
-  geom_bar(stat = "identity", colour = 'black', size = 0.2) +
-  labs(x = "GWAS", y = "Proportion", fill = 'Population', title = 'Ancestry proportions in BMI GWAS') +
+  geom_bar(stat = "identity", colour = "black", size = 0.2) +
+  geom_text(data = subset(gwas_ancestry_table_melt, value >= 0.02),
+            aes(label = scales::percent(value, accuracy = 1)),
+            position = position_stack(vjust = 0.5),
+            size = 3) +
+  labs(x = "GWAS", y = "Proportion", fill = "Population",
+       title = "Ancestry proportions in BMI GWAS") +
   theme_half_open() +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+  theme(axis.text.x = element_text(angle = -45, hjust = 1))
 dev.off()
 
 write.csv(gwas_ancestry_table, '~/oliverpainfel/Analyses/crosspop/plots/gwas_ancestry.csv')
@@ -12496,16 +12352,120 @@ write.csv(gwas_ancestry_table, '~/oliverpainfel/Analyses/crosspop/plots/gwas_anc
 ref_ancestry_table <- do.call(cbind, ref_ancestry)
 ref_ancestry_table_melt <- melt(ref_ancestry_table)
 
-ref_ancestry_table_melt<-ref_ancestry_table_melt[ref_ancestry_table_melt$value >= 0.01,]
+ref_ancestry_table_melt<-ref_ancestry_table_melt[ref_ancestry_table_melt$value >= 0.02,]
 
 png('~/oliverpainfel/Analyses/crosspop/plots/ref_ancestry.png', units = 'px', width = 2000, height = 1500, res = 300)
 ggplot(ref_ancestry_table_melt, aes(x = Var2, y = value, fill = Var1)) +
   geom_bar(stat = "identity", colour = 'black', size = 0.2) +
-  labs(x = "Reference Label", y = "Proportion", fill = 'Population', title = 'Ancestry proportions in BMI GWAS') +
+  geom_text(data = subset(ref_ancestry_table_melt, value >= 0.02),
+            aes(label = scales::percent(value, accuracy = 1)),
+            position = position_stack(vjust = 0.5),
+            size = 3) +
+  labs(x = "Reference Label", y = "Proportion", fill = 'Population', title = 'Ancestry proportions in 1KG+HGDP reference') +
   theme_half_open() +
   theme(axis.text.x = element_text(angle = 45, hjust = 1))
 dev.off()
 
 write.csv(ref_ancestry_table, '~/oliverpainfel/Analyses/crosspop/plots/ref_ancestry.csv')
 
+gwas_ancestry_table_melt$Type <- 'GWAS'
+ref_ancestry_table_melt$Type <- 'Reference'
+
+both<-rbind(gwas_ancestry_table_melt, ref_ancestry_table_melt)
+png('~/oliverpainfel/Analyses/crosspop/plots/ancestry_composition.png', units = 'px', width = 3000, height = 1500, res = 300)
+
+ggplot(both, aes(x = Var2, y = value, fill = Var1)) +
+  geom_bar(stat = "identity", colour = 'black', size = 0.2) +
+  geom_text(data = subset(both, value >= 0.02),
+            aes(label = scales::percent(value, accuracy = 1)),
+            position = position_stack(vjust = 0.5),
+            size = 3) +
+  labs(x = NULL, y = "Proportion", fill = 'Population') +
+  facet_grid(. ~ Type, scales = 'free_x') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  panel_border()
+dev.off()
+
 ```
+
+***
+
+# BridgePRS
+
+```{r}
+######
+# gwas_list
+######
+
+library(data.table)
+
+# Subset original gwas_list to include selected traits
+gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt')
+pheno<-gsub('_.*','', gwas_list$name)
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+gwas_list<-gwas_list[pheno %in% selected_traits,]
+gwas_list$label<-paste0('"', gwas_list$label, '"')
+
+gwas_list<-gwas_list[grepl('BMI', gwas_list$name),]
+
+write.table(
+  gwas_list, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_bridge.txt', 
+  col.names = T, 
+  row.names = F, 
+  quote = F)
+
+######
+# gwas_groups
+######
+
+gwas_groups_eas<-data.frame(
+  name=paste0(selected_traits, '_UKB_BBJ'),
+  gwas=sapply(selected_traits, function(x) paste0(x,'_UKB,',x,'_BBJ')),
+  label=paste0('"', selected_traits, " (UKB+BBJ)", '"')
+)
+
+gwas_groups_afr<-data.frame(
+  name=paste0(selected_traits, '_UKB_UGR'),
+  gwas=sapply(selected_traits, function(x) paste0(x,'_UKB,',x,'_UGR')),
+  label=paste0('"', selected_traits, " (UKB+UGR)", '"')
+)
+
+gwas_groups<-rbind(gwas_groups_eas, gwas_groups_afr)
+
+gwas_groups<-gwas_groups[grepl('BMI', gwas_groups$name),]
+
+write.table(gwas_groups, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_bridge.txt', col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config<-c(
+  "outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output",
+  "config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_bridgeprs.yaml",
+  "gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_bridge.txt",
+  "target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt",
+  "gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_bridge.txt",
+  "pgs_methods: ['bridgeprs']",
+  "cores_prep_pgs: 10", 
+  "cores_target_pgs: 50"
+)
+
+write.table(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_bridgeprs.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+***
+
+<h4>Run pipeline</h4>
+
+```{bash}
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_bridgeprs.yaml \
+  target_pgs  -n
+```
+
+</details>
\ No newline at end of file
diff --git a/pipeline/envs/xwing.yaml b/pipeline/envs/xwing.yaml
index 8a121f6a..f0061961 100644
--- a/pipeline/envs/xwing.yaml
+++ b/pipeline/envs/xwing.yaml
@@ -1,5 +1,7 @@
 name: xwing
 channels:
+  - dranew
+  - bioconda
   - conda-forge
   - defaults
 dependencies:

From 437fc157c5c92fd2c915fdeec64619630df7579b Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Wed, 21 May 2025 15:42:32 +0100
Subject: [PATCH 146/153] Reverted documentation to released version

---
 docs/pipeline_readme.Rmd     | 207 +++------------
 docs/pipeline_readme.html    | 381 ++++++---------------------
 docs/pipeline_technical.Rmd  | 147 ++---------
 docs/pipeline_technical.html | 495 ++++-------------------------------
 4 files changed, 185 insertions(+), 1045 deletions(-)

diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 7a163fb2..5386231f 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -27,7 +27,6 @@ This document will provides instructions for the GenoPred pipeline. It covers th
 -   [Run using test data](#run-using-test-data)
 -   [A little about Snakemake](#a-little-about-snakemake)
 -   [Running on an HPC](#running-on-an-hpc)
--   [Running on DNAnexus/UKB-RAP](#running-on-dnanexusukb-rap)
 -   [Requesting outputs](#requesting-outputs)
 -   [Computational resources](#computational-resources)
 -   [Additional parameters](#additional-parameters)
@@ -38,23 +37,17 @@ For a technical details of the GenoPred pipeline, click [here](pipeline_technica
 
 ***
 
-<h3>Citations</h4>
+**Citations**
 
-Please cite the following relevant papers when using the pipeline:
+Please cite our preprint when using the pipeline:
 
-**Pipeline overview:**
-- Pain, O. et al. (2024). *The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring*. Bioinformatics.  
-  https://doi.org/10.1093/bioinformatics/btae551
+- "Pain, O. et al. "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." MedRxiv 2024. https://doi.org/10.1101/2024.06.12.24308843
 
-**Comparison of single-source PGS methods:**
-- Pain, O. et al. (2021). *Evaluation of polygenic prediction methodology within a reference-standardized framework*. PLoS Genetics.  
-  https://doi.org/10.1371/journal.pgen.1009021
+If relevant, please also cite our paper comparing polygenic scoring methods and describing the reference-standardised approach:
 
-**Integration and comparison of multi-source PGS methods:**
-- Pain, O. (2025). *Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations*. medRxiv. 
-  https://doi.org/10.1101/2025.03.27.25324773
+- Pain, O. et al. "Evaluation of polygenic prediction methodology within a reference-standardized framework." PLoS genetics. https://doi.org/10.1371/journal.pgen.1009021
 
-Please also cite the relevant studies for the tools and data used by the GenoPred pipeline - Details provided in the [Technical Documentation](pipeline_technical.html).
+Please also cite the relevant studies for the tools and data used by the GenoPred pipeline.
 
 ***
 
@@ -124,38 +117,31 @@ git clone https://github.com/opain/GenoPred.git
 
 ***
 
-## Step 2: Create software environment for pipeline
+## Step 2: Create conda environment for pipeline
 
-Conda is a software environment management system that simplifies installing and managing dependencies. We recommend using Miniforge — a minimal conda installer that comes with Mamba, a fast drop-in replacement for conda.
+Conda is a software environment management system which is great way for easily downloading and storing software. We will use conda to create an environment that the GenoPred pipeline will run in.
 
-If you don't already have Miniforge installed, you can install it using the commands below:
+If you don't already have conda installed, we will install it using miniconda.
 
 ```{bash}
-# Download and install Miniforge (for Linux)
-wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
-bash Miniforge3-Linux-x86_64.sh
+wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+sh Miniconda3-latest-Linux-x86_64.sh
 ```
 
-Accept the default installation options. Once installed, you may need to run source ~/.bashrc or restart your terminal. You should see (base) appear at the beginning of your terminal prompt.
+I would say `yes` to the default options. You may then need to refresh your workspace to initiate conda, by running `source ~/.bashrc`. You should see `(base)` written in the bottom left of the terminal. Once miniconda installation is complete, you can then delete the `Miniconda3-latest-Linux-x86_64.sh` file.
 
-Now, create the GenoPred environment using Mamba:
+Now create the conda environment based on the `GenoPred/pipeline/envs/pipeline.yaml` file. This will create an environment called `genopred` with some essential packages installed.
 
 ```{bash}
 conda env create -f GenoPred/pipeline/envs/pipeline.yaml
 ```
 
-Activate the new `genopred` environment:
+Now activate the new `genopred` environment.
 
 ```{bash}
 conda activate genopred
 ```
 
-<div class="note-box">
-
-**Note:** If you are working on an HPC, check whether mamba is an available module. Using the centrally installed version of mamba may avoid issues down the road.
-
-</div>
-
 ***
 
 ## Step 3: Download dependencies
@@ -220,7 +206,7 @@ singularity \
 
 # Pipeline configuration
 
-The pipeline is configured using a `configfile`, which tells the pipeline what to do, and the location of the input data listed in the `target_list`, `gwas_list`, and `score_list` files.
+The pipeline is configured using a configfile, which tells the pipeline what to do, and the location of the input data listed in the target_list, gwas_list, and score_list files.
 
 <div class="centered-container">
 <div class="rounded-image-container" style="width: 75%;">
@@ -228,12 +214,6 @@ The pipeline is configured using a `configfile`, which tells the pipeline what t
 </div>
 </div>
 
-<div class="note-box">
-
-**Note:** The pipeline also implements a range of multi-source polygenic scoring methods, that can combine GWAS summary statistics from multiple populations. To use these methods an additional `gwas_groups` file must be provided. See [here](#multi-source-pgs-methods) for more information.
-
-</div>
-
 ***
 
 ## `configfile`
@@ -271,7 +251,7 @@ config <- list(
     gwas_list = list(
       description = "Path to `gwas_list` file, listing GWAS sumstats",
       example = '`example_input/gwas_list.txt`',
-      note = "Set to NA if you don't want to include any GWAS sumstats"
+      note = "Set to NA if you don't want to include and GWAS sumstats"
     ),
     score_list = list(
       description = "Path to `score_list` file, listing external score files",
@@ -286,7 +266,7 @@ config <- list(
     pgs_methods = list(
       description = 'List of polygenic scoring methods to run',
       example = "`['ptclump','dbslmm']`",
-      note = "Options are: `ptclump`, `dbslmm`, `prscs`, `sbayesr`, `sbayesrc`, `lassosum`, `ldpred2`, `megaprs`, `quickprs`, `prscsx`, `xwing`. **Note.** by default, `sbayesr` and `ldpred2` are only implemented for EUR ancestry GWAS, and SBayesRC is restricted to AFR, EAS, and EUR ancestry GWAS. To be applicable to other populations, the user must indicate where the relevent LD reference data is stored (See [here](#specifying-alternative-reference-data-for-pgs-methods. **Note.** when using multi-source methods (`prscsx`, `xwing`), the `gwas_groups` parameter must also be provided. See [here](#multi-source-pgs-methods) for more information."
+      note = "Options are: `ptclump`, `dbslmm`, `prscs`, `sbayesr`, `lassosum`, `ldpred2`, `megaprs`. **Note.** `sbayesr` and `ldpred2` are only implemented for GWAS of EUR ancestry."
     ),
     testing = list(
       description = 'Controls testing mode',
@@ -321,7 +301,7 @@ print(kable(config_df, 'markdown'))
 
 <div class="note-box">
 
-**Note:** If you do not provide a target_list, then only rules that do not require a `target_list` can be performed, such as GWAS sumstat QC. Similarly, if you do not provide a `gwas_list` or `score_list`, only rules that do not require these files can be performed, such as target sample ancestry inference.
+**Note:** If you do not provide a target_list, then only rules that do not require a target_list can be performed, such as GWAS sumstat QC. Similarly, if you do not provide a gwas_list or score_list, only rules that do not require these files can be performed, such as target sample ancestry inference.
 
 </div>
 
@@ -534,9 +514,8 @@ Once you have installed GenoPred, we can run the pipeline using the test data.
 First, we need to download and decompress the test data. Do this within the `GenoPred/pipeline` folder.
 
 ```{bash}
-# Download from Google Drive
-conda activate genopred
-gdown 1C4AwDnY_hJ4ilGneMlAjwEKghzss5PeG -O test_data.tar.gz
+# Download from Zenodo
+wget -O test_data.tar.gz https://zenodo.org/records/10640650/files/test_data.tar.gz?download=1
 
 # Decompress
 tar -xf test_data.tar.gz
@@ -828,19 +807,13 @@ Before launching GenoPred, ensure your environment is clean by not loading any u
 
 ***
 
-# Running on DNAnexus/UKB-RAP
-
-See [here](running_on_dnanexus.html) for specific advice when running GenoPred on DNAnexus and the UKB-RAP.
-
-***
-
 # Requesting outputs
 
 The GenoPred pipeline has many potential outputs. Here is a detailed schematic diagram illustrating the inputs, outputs and processes of the GenoPred pipeline.
 
 <div class="centered-container">
 <div class="rounded-image-container">
-<img src="Images/pipeline_readme/pipeline_schematic_groups_lowdef.png">
+<img src="Images/pipeline_readme/pipeline_schematic_lowdef.png">
 </div>
 </div>
 
@@ -1061,19 +1034,21 @@ Outputs specific to the configuration used are stored in the `outdir` specified
       ├── pgs_score_files (score files for polygenic scoring)
       │   └── [pgs method] (pgs for each pgs method)
       │       └── [gwas/score name] (pgs for each GWAS or score file)
-      ├── pc_score_files (score files for reference principal components)
       ├── benchmarks (pipeline benchmark data)
       └── target_checks (pipeline logs)
 ```
 
-Some additional outputs that are independent of the configuration used, are store in the resource directory, which is by default the `GenoPred/pipeline/resources` folder. The location of the resources directory can be altered using the `resdir` parameter in the `configfile`:
+Some additional outputs that are independent of the configuration used, are store in the resources folder of the `GenoPred/pipeline` folder:
 
 ```{}
-[resdir]
-  ├── data (downloaded data)
-  │   ├── target_checks (pipeline logs)
-  │   └── benchmarks (pipeline benchmark data)      
-  └── software (downloaded software)
+GenoPred/pipeline
+  └── resources
+      ├── data (downloaded data)
+      │   ├── target_checks (pipeline logs)
+      │   ├── benchmarks (pipeline benchmark data)      
+      │   └── ref (default reference data)
+      │       └── pc_score_files (score files for reference pcs)
+      └── software (downloaded software)
 ```
 
 ***
@@ -1135,7 +1110,6 @@ Reads in polygenic scores (PGS) based on the provided configuration and filters.
   - `pgs_methods` (optional): A vector of PGS methods to include. Default is NULL.
   - `gwas` (optional): A vector of GWAS to include. Default is NULL.
   - `pop` (optional): A vector of populations to include. Default is NULL.
-  - `pseudo_only` (optional): A logical parameter that determines whether to return only the PGS selected through pseudovalidation. Default is FALSE.
   
 - **Returns**
   - A list containing the filtered PGS data structured by target name, population, GWAS, and PGS method.
@@ -1221,14 +1195,10 @@ Determines the pseudovalidation parameter for a given GWAS and PGS method. See [
   - `config`: Configuration file specifying paths and parameters.
   - `gwas`: A single GWAS identifier.
   - `pgs_method`: A single PGS method identifier.
-  - `target_pop`: Target population parameter required when using multi-ancestry PGS method.
 - **Returns**
   - A string representing the pseudovalidation parameter.
 - **Note**
   - `ptclump` has no pseudovalidation approach, so this function will return the PGS based on a p-value threshold of 1.
-  - `prscsx` returns meta-analysed PGS weights across populations. The target_pop parameter has no effect on the output.
-  - `tlprs` has no pseudovalidation approach, so the function returns the pseudovalidated score for the PGS method used as input to `tlprs`, using strongest reweighting for the target population.
-  - `xwing` and `tlprs` do not have a pseudovalidation approach when using `target_pop = 'TRANS'`, so this function returns the result for the first population in the `gwas_group` file. The function also returns the result for first population in the `gwas_group` file when the gwas_group did not contain a GWAS from the `target_pop`.
 
 <details>
 <summary>See usage</summary>
@@ -1240,12 +1210,6 @@ pseudo_param <-
               gwas = 'COAD01',
               pgs_method = 'ptclump')
 
-pseudo_param <-
-  find_pseudo(config = 'example_input/config.yaml',
-              gwas = 'COAD01',
-              pgs_method = 'prscsx',
-              target_pop = 'EUR')
-
 ```
 
 </details>
@@ -1304,9 +1268,10 @@ Users can provide their own reference data using the `refdir` parameter in the `
 
 ```{}
 [refdir]
-  ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data in build GRCh37 with RSIDs in SNP column)
+  ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data with RSIDs in SNP column)
   ├── ref.chr[1-22].rds (SNP data - refer to default ref data for format)
   ├── ref.pop.txt (Population data for reference individuals - with header)
+  ├── ref.keep.list (lists keep files for each population - columns pop and path - no header)
   ├── keep_files
   │   └──[pop].keep (keep files for each population - no header)
   └── freq_files
@@ -1328,8 +1293,8 @@ It is possible to alter parameters for certain PGS methods by setting the follow
 
 - `ptclump_pts`: list of p-value thresholds for ptclump
 - `dbslmm_h2f`: list SNP-h2 folds for DBSLMM - use `1` for the default model
-- `prscs_phi`: list phi parameters for PRS-CS and PRS-CSx - use `auto` for the auto model
-- `prscs_ldef`: Selected whether PRS-CS/PRS-CSx LD reference is derived from `1kg` (default) or `ukb` (UK Biobank).
+- `prscs_phi`: list phi parameters for PRS-CS - use `auto` for the auto model
+- `prscs_ldef`: Selected whether PRS-CS ld reference is derived from `1kg` (default) or `ukb` (UK Biobank).
 - `ldpred2_model`: list models for LDpred2 - `grid`, `auto`, `inf`
 
 See the default [configfile](https://github.com/opain/GenoPred/blob/master/pipeline/config.yaml) for examples of these parameters being set.
@@ -1362,110 +1327,6 @@ By default, the pipeline allocates 5 cores when running the outlier_detection ru
 
 ***
 
-## Multi-source PGS methods
-
-The pipeline also implements a range of multi-source polygenic scoring methods, that can combine GWAS summary statistics from multiple populations. To use these methods an additional `gwas_groups` file must be provided, indicating which GWAS are to be jointly analysed. The `gwas_groups` file must be specified in the `configfile` using the `gwas_groups` parameter. An example of a gwas_groups file can be found [here](../pipeline/example_input/gwas_groups.txt). 
-
-<details>
-
-<summary>View gwas_groups format</summary>
-
-```{r, eval = T, echo = F, results = 'asis'}
-
-gwas_groups <- list(
-  Column = list(
-    name = list(
-      example = '`height`',
-      description = "ID for the group of GWAS. Cannot contain spaces (' ') or hyphens ('-')"
-    ),
-    gwas = list(
-      example = '`yengo_eur,yengo_eas`',
-      description = "Comma-seperated list of GWAS names, corresponding to the `gwas_list`."
-    ),
-    label = list(
-      example = '`\"Height (EUR+EAS)\"`',
-      description = "A human readable name for the group of GWAS Wrap in double quotes if multiple words."
-    )
-  )
-)
-
-gwas_groups_df <- NULL
-for (column in names(gwas_groups$Column)) {
-  description <- gwas_groups$Column[[column]]$description
-  example <- gwas_groups$Column[[column]]$example
-
-  # Append each parameter's details to the data frame
-  gwas_groups_df <- rbind(
-    gwas_groups_df,
-    data.frame(
-      Column = column,
-      Example = example,
-      Description = description,
-      stringsAsFactors = FALSE
-    )
-  )
-}
-
-kable(gwas_groups_df, 'markdown')
-
-```
-
-</details>
-
-<div class="shallow-break"></div>
-
-Another multi-source PGS approach is TL-PRS, which tunes an existing PGS model based on GWAS summary statistics from another population. GenoPred can automate this process. The user must specify which PGS methods they want TL-PRS to be applied to, using the `tlprs_methods` parameter in the `configfile`. For example: `tlprs_methods: ['lassosum','dbslmm']`. TL-PRS is only applied to the PGS from a given method selected using pseudovalidation.
-
-An alternative multi-source PGS approach is to linearly combine population-specific PGS derived using single-source methods. GenoPred implements an approach called `LEOPARD+QuickPRS`, which estimates the optimal linear combination weights for population-specific PGS using summary statistics alone. Using the `leopard_methods` parameter in the `configfile`, the user can specify which single-source PGS should be linearly combined using the LEOPARD+QuickPRS estimated weights. For example: `leopard_methods: ['lassosum', 'dbslmm']`. LEOPARD weighting is only applied to the PGS from a given method selected using pseudovalidation.
-
-***
-
-## Select ancestry adjustment approach
-
-The GenoPred pipeline offers two approaches for adjusting PGS for ancestry: discrete or continuous.
-
-### Discrete Correction:
-* This approach assigns each target individual to an ancestry-matched reference population.
-* The target individual's PGS is then standardized based on the mean and standard deviation (SD) of the PGS within the corresponding reference population.
-* This is the approach used by GenoPred until v3.0.0.
-
-### Continuous Correction:
-* This method utilizes genetic principal components projected from reference populations to standardize target PGS.
-* It accounts for variation within reference populations and accommodates individuals whose ancestry does not align closely with a single reference population.
-* GenoPred uses the approach described by Linders et al. ([here](https://doi.org/10.1016/j.gim.2023.100006)), which adjust both the mean and variance of the target PGS.
-
-There is a nice explanation of these approaches on the pgsc_calc website ([here](https://pgsc-calc.readthedocs.io/en/latest/explanation/geneticancestry.html)), where they refer to the continuous approach as 'Empirical Methods', and the continuous approach as 'PCA-based methods' (specifically `Z_norm2`).
-
-By default, the GenoPred pipeline applies only the continuous correction for ancestry to scale the target PGS. However, users can specify the desired ancestry adjustment approach using the `pgs_scaling` parameter in the `configfile`. This parameter accepts a list of values, which can include `continuous` and `discrete`.
-
-For example, to produce target PGS using both continuous and discrete correction, specify:
-
-```{yaml}
-pgs_scaling: ['continuous', 'discrete]
-```
-
-Or, to produce target PGS only the discrete correction, specify:
-
-```{yaml}
-pgs_scaling: ['discrete']
-```
-
-***
-
-## Specifying alternative reference data for PGS methods
-
-PGS methods require linkage disequilibrium (LD) data. The default LD data used for each method is indicated in the technical documentation ([here]()). Several methods use the reference data used for the pipeline in general, so altering the `ref_dir` parameter (as [described above]()) will also alter the reference data used by these PGS methods. However, several PGS methods use pre-computed LD matrices, and the user can specify alternative LD matrices for these methods, using the configfile parameters below:
-
-- PRS-CS
-- PRS-CSx
-- SBayesR
-- SBayesRC
-- LDpred2
-- QuickPRS
-- LEOPARD+QuickPRS
-
-***
-
 # Running in offline environment
 
 See [here](running_offline.html) if you would like to run the GenoPred pipeline in an environment that does not have access to the internet. In brief the user must download the resources required by GenoPred, transfer them to their offline environment.
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index bbfc6264..55263a7d 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -78,41 +78,6 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
-<!-- Osano Cookie Consent -->
-<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
-<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
-<script>
-window.addEventListener("load", function(){
-  window.cookieconsent.initialise({
-    palette: {
-      popup: { background: "#000" },
-      button: { background: "#f1d600" }
-    },
-    theme: "classic",
-    position: "bottom-right",
-    content: {
-      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
-      dismiss: "Accept",
-      link: "Google's privacy info",
-      href: "https://policies.google.com/technologies/partner-sites"
-    },
-    onStatusChange: function(status) {
-      if (status === 'allow') {
-        // Now load Google Analytics only if user consents
-        var gtagScript = document.createElement('script');
-        gtagScript.setAttribute('async', '');
-        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
-        document.head.appendChild(gtagScript);
-
-        window.dataLayer = window.dataLayer || [];
-        function gtag(){ dataLayer.push(arguments); }
-        gtag('js', new Date());
-        gtag('config', 'G-YR18ZB3PR3');
-      }
-    }
-  });
-});
-</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}
@@ -473,8 +438,6 @@ <h1 class="title toc-ignore">GenoPred Pipeline - Instructions</h1>
 <li><a href="#a-little-about-snakemake">A little about
 Snakemake</a></li>
 <li><a href="#running-on-an-hpc">Running on an HPC</a></li>
-<li><a href="#running-on-dnanexusukb-rap">Running on
-DNAnexus/UKB-RAP</a></li>
 <li><a href="#requesting-outputs">Requesting outputs</a></li>
 <li><a href="#computational-resources">Computational resources</a></li>
 <li><a href="#additional-parameters">Additional parameters</a></li>
@@ -485,30 +448,24 @@ <h1 class="title toc-ignore">GenoPred Pipeline - Instructions</h1>
 href="pipeline_overview.html">here</a>. For a technical details of the
 GenoPred pipeline, click <a href="pipeline_technical.html">here</a>.</p>
 <hr />
-<h3>
-Citations
-</h4>
-<p>Please cite the following relevant papers when using the
-pipeline:</p>
-<p><strong>Pipeline overview:</strong> - Pain, O. et al. (2024). <em>The
-GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic
-Scoring</em>. Bioinformatics.<br />
-<a href="https://doi.org/10.1093/bioinformatics/btae551"
-class="uri">https://doi.org/10.1093/bioinformatics/btae551</a></p>
-<p><strong>Comparison of single-source PGS methods:</strong> - Pain, O.
-et al. (2021). <em>Evaluation of polygenic prediction methodology within
-a reference-standardized framework</em>. PLoS Genetics.<br />
-<a href="https://doi.org/10.1371/journal.pgen.1009021"
-class="uri">https://doi.org/10.1371/journal.pgen.1009021</a></p>
-<p><strong>Integration and comparison of multi-source PGS
-methods:</strong> - Pain, O. (2025). <em>Leveraging Global Genetics
-Resources to Enhance Polygenic Prediction Across Ancestrally Diverse
-Populations</em>. medRxiv. <a
-href="https://doi.org/10.1101/2025.03.27.25324773"
-class="uri">https://doi.org/10.1101/2025.03.27.25324773</a></p>
+<p><strong>Citations</strong></p>
+<p>Please cite our preprint when using the pipeline:</p>
+<ul>
+<li>“Pain, O. et al. ”The GenoPred Pipeline: A Comprehensive and
+Scalable Pipeline for Polygenic Scoring.” MedRxiv 2024. <a
+href="https://doi.org/10.1101/2024.06.12.24308843"
+class="uri">https://doi.org/10.1101/2024.06.12.24308843</a></li>
+</ul>
+<p>If relevant, please also cite our paper comparing polygenic scoring
+methods and describing the reference-standardised approach:</p>
+<ul>
+<li>Pain, O. et al. “Evaluation of polygenic prediction methodology
+within a reference-standardized framework.” PLoS genetics. <a
+href="https://doi.org/10.1371/journal.pgen.1009021"
+class="uri">https://doi.org/10.1371/journal.pgen.1009021</a></li>
+</ul>
 <p>Please also cite the relevant studies for the tools and data used by
-the GenoPred pipeline - Details provided in the <a
-href="pipeline_technical.html">Technical Documentation</a>.</p>
+the GenoPred pipeline.</p>
 <hr />
 <div id="video-tutorials" class="section level1">
 <h1>Video tutorials</h1>
@@ -565,30 +522,29 @@ <h2>Step 1: Download GenoPred repository</h2>
 <pre class="bash"><code>git clone https://github.com/opain/GenoPred.git</code></pre>
 <hr />
 </div>
-<div id="step-2-create-software-environment-for-pipeline"
+<div id="step-2-create-conda-environment-for-pipeline"
 class="section level2">
-<h2>Step 2: Create software environment for pipeline</h2>
-<p>Conda is a software environment management system that simplifies
-installing and managing dependencies. We recommend using Miniforge — a
-minimal conda installer that comes with Mamba, a fast drop-in
-replacement for conda.</p>
-<p>If you don’t already have Miniforge installed, you can install it
-using the commands below:</p>
-<pre class="bash"><code># Download and install Miniforge (for Linux)
-wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
-bash Miniforge3-Linux-x86_64.sh</code></pre>
-<p>Accept the default installation options. Once installed, you may need
-to run source ~/.bashrc or restart your terminal. You should see (base)
-appear at the beginning of your terminal prompt.</p>
-<p>Now, create the GenoPred environment using Mamba:</p>
+<h2>Step 2: Create conda environment for pipeline</h2>
+<p>Conda is a software environment management system which is great way
+for easily downloading and storing software. We will use conda to create
+an environment that the GenoPred pipeline will run in.</p>
+<p>If you don’t already have conda installed, we will install it using
+miniconda.</p>
+<pre class="bash"><code>wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+sh Miniconda3-latest-Linux-x86_64.sh</code></pre>
+<p>I would say <code>yes</code> to the default options. You may then
+need to refresh your workspace to initiate conda, by running
+<code>source ~/.bashrc</code>. You should see <code>(base)</code>
+written in the bottom left of the terminal. Once miniconda installation
+is complete, you can then delete the
+<code>Miniconda3-latest-Linux-x86_64.sh</code> file.</p>
+<p>Now create the conda environment based on the
+<code>GenoPred/pipeline/envs/pipeline.yaml</code> file. This will create
+an environment called <code>genopred</code> with some essential packages
+installed.</p>
 <pre class="bash"><code>conda env create -f GenoPred/pipeline/envs/pipeline.yaml</code></pre>
-<p>Activate the new <code>genopred</code> environment:</p>
+<p>Now activate the new <code>genopred</code> environment.</p>
 <pre class="bash"><code>conda activate genopred</code></pre>
-<div class="note-box">
-<p><strong>Note:</strong> If you are working on an HPC, check whether
-mamba is an available module. Using the centrally installed version of
-mamba may avoid issues down the road.</p>
-</div>
 <hr />
 </div>
 <div id="step-3-download-dependencies" class="section level2">
@@ -671,22 +627,14 @@ <h1>Containers</h1>
 </div>
 <div id="pipeline-configuration" class="section level1">
 <h1>Pipeline configuration</h1>
-<p>The pipeline is configured using a <code>configfile</code>, which
-tells the pipeline what to do, and the location of the input data listed
-in the <code>target_list</code>, <code>gwas_list</code>, and
-<code>score_list</code> files.</p>
+<p>The pipeline is configured using a configfile, which tells the
+pipeline what to do, and the location of the input data listed in the
+target_list, gwas_list, and score_list files.</p>
 <div class="centered-container">
 <div class="rounded-image-container" style="width: 75%;">
 <p><img src="Images/pipeline_readme/input_schematic_wide.png"></p>
 </div>
 </div>
-<div class="note-box">
-<p><strong>Note:</strong> The pipeline also implements a range of
-multi-source polygenic scoring methods, that can combine GWAS summary
-statistics from multiple populations. To use these methods an additional
-<code>gwas_groups</code> file must be provided. See <a
-href="#multi-source-pgs-methods">here</a> for more information.</p>
-</div>
 <hr />
 <div id="configfile" class="section level2">
 <h2><code>configfile</code></h2>
@@ -711,10 +659,10 @@ <h2><code>configfile</code></h2>
 </summary>
 <table>
 <colgroup>
-<col width="2%" />
-<col width="9%" />
-<col width="5%" />
-<col width="82%" />
+<col width="4%" />
+<col width="19%" />
+<col width="11%" />
+<col width="63%" />
 </colgroup>
 <thead>
 <tr class="header">
@@ -752,7 +700,7 @@ <h2><code>configfile</code></h2>
 <td align="left">Path to <code>gwas_list</code> file, listing GWAS
 sumstats</td>
 <td align="left"><code>example_input/gwas_list.txt</code></td>
-<td align="left">Set to NA if you don’t want to include any GWAS
+<td align="left">Set to NA if you don’t want to include and GWAS
 sumstats</td>
 </tr>
 <tr class="odd">
@@ -776,19 +724,10 @@ <h2><code>configfile</code></h2>
 <td align="left">List of polygenic scoring methods to run</td>
 <td align="left"><code>['ptclump','dbslmm']</code></td>
 <td align="left">Options are: <code>ptclump</code>, <code>dbslmm</code>,
-<code>prscs</code>, <code>sbayesr</code>, <code>sbayesrc</code>,
-<code>lassosum</code>, <code>ldpred2</code>, <code>megaprs</code>,
-<code>quickprs</code>, <code>prscsx</code>, <code>xwing</code>.
-<strong>Note.</strong> by default, <code>sbayesr</code> and
-<code>ldpred2</code> are only implemented for EUR ancestry GWAS, and
-SBayesRC is restricted to AFR, EAS, and EUR ancestry GWAS. To be
-applicable to other populations, the user must indicate where the
-relevent LD reference data is stored (See
-[here](#specifying-alternative-reference-data-for-pgs-methods.
-<strong>Note.</strong> when using multi-source methods
-(<code>prscsx</code>, <code>xwing</code>), the <code>gwas_groups</code>
-parameter must also be provided. See <a
-href="#multi-source-pgs-methods">here</a> for more information.</td>
+<code>prscs</code>, <code>sbayesr</code>, <code>lassosum</code>,
+<code>ldpred2</code>, <code>megaprs</code>. <strong>Note.</strong>
+<code>sbayesr</code> and <code>ldpred2</code> are only implemented for
+GWAS of EUR ancestry.</td>
 </tr>
 <tr class="even">
 <td align="left"><code>testing</code></td>
@@ -802,11 +741,10 @@ <h2><code>configfile</code></h2>
 </table>
 <div class="note-box">
 <p><strong>Note:</strong> If you do not provide a target_list, then only
-rules that do not require a <code>target_list</code> can be performed,
-such as GWAS sumstat QC. Similarly, if you do not provide a
-<code>gwas_list</code> or <code>score_list</code>, only rules that do
-not require these files can be performed, such as target sample ancestry
-inference.</p>
+rules that do not require a target_list can be performed, such as GWAS
+sumstat QC. Similarly, if you do not provide a gwas_list or score_list,
+only rules that do not require these files can be performed, such as
+target sample ancestry inference.</p>
 </div>
 </details>
 <hr />
@@ -1074,9 +1012,8 @@ <h1>Run using test data</h1>
 <h2>Step 1: Download the test data</h2>
 <p>First, we need to download and decompress the test data. Do this
 within the <code>GenoPred/pipeline</code> folder.</p>
-<pre class="bash"><code># Download from Google Drive
-conda activate genopred
-gdown 1C4AwDnY_hJ4ilGneMlAjwEKghzss5PeG -O test_data.tar.gz
+<pre class="bash"><code># Download from Zenodo
+wget -O test_data.tar.gz https://zenodo.org/records/10640650/files/test_data.tar.gz?download=1
 
 # Decompress
 tar -xf test_data.tar.gz
@@ -1549,12 +1486,6 @@ <h2>Managing Module Conflicts</h2>
 <hr />
 </div>
 </div>
-<div id="running-on-dnanexusukb-rap" class="section level1">
-<h1>Running on DNAnexus/UKB-RAP</h1>
-<p>See <a href="running_on_dnanexus.html">here</a> for specific advice
-when running GenoPred on DNAnexus and the UKB-RAP.</p>
-<hr />
-</div>
 <div id="requesting-outputs" class="section level1">
 <h1>Requesting outputs</h1>
 <p>The GenoPred pipeline has many potential outputs. Here is a detailed
@@ -1562,7 +1493,7 @@ <h1>Requesting outputs</h1>
 GenoPred pipeline.</p>
 <div class="centered-container">
 <div class="rounded-image-container">
-<p><img src="Images/pipeline_readme/pipeline_schematic_groups_lowdef.png"></p>
+<p><img src="Images/pipeline_readme/pipeline_schematic_lowdef.png"></p>
 </div>
 </div>
 <div class="note-box">
@@ -1809,19 +1740,19 @@ <h2>Output structure</h2>
       ├── pgs_score_files (score files for polygenic scoring)
       │   └── [pgs method] (pgs for each pgs method)
       │       └── [gwas/score name] (pgs for each GWAS or score file)
-      ├── pc_score_files (score files for reference principal components)
       ├── benchmarks (pipeline benchmark data)
       └── target_checks (pipeline logs)</code></pre>
 <p>Some additional outputs that are independent of the configuration
-used, are store in the resource directory, which is by default the
-<code>GenoPred/pipeline/resources</code> folder. The location of the
-resources directory can be altered using the <code>resdir</code>
-parameter in the <code>configfile</code>:</p>
-<pre><code>[resdir]
-  ├── data (downloaded data)
-  │   ├── target_checks (pipeline logs)
-  │   └── benchmarks (pipeline benchmark data)      
-  └── software (downloaded software)</code></pre>
+used, are store in the resources folder of the
+<code>GenoPred/pipeline</code> folder:</p>
+<pre><code>GenoPred/pipeline
+  └── resources
+      ├── data (downloaded data)
+      │   ├── target_checks (pipeline logs)
+      │   ├── benchmarks (pipeline benchmark data)      
+      │   └── ref (default reference data)
+      │       └── pc_score_files (score files for reference pcs)
+      └── software (downloaded software)</code></pre>
 <hr />
 </div>
 </div>
@@ -1889,9 +1820,6 @@ <h2><code>read_pgs</code></h2>
 is NULL.</li>
 <li><code>pop</code> (optional): A vector of populations to include.
 Default is NULL.</li>
-<li><code>pseudo_only</code> (optional): A logical parameter that
-determines whether to return only the PGS selected through
-pseudovalidation. Default is FALSE.</li>
 </ul></li>
 <li><strong>Returns</strong>
 <ul>
@@ -1985,8 +1913,6 @@ <h2><code>find_pseudo</code></h2>
 parameters.</li>
 <li><code>gwas</code>: A single GWAS identifier.</li>
 <li><code>pgs_method</code>: A single PGS method identifier.</li>
-<li><code>target_pop</code>: Target population parameter required when
-using multi-ancestry PGS method.</li>
 </ul></li>
 <li><strong>Returns</strong>
 <ul>
@@ -1996,18 +1922,6 @@ <h2><code>find_pseudo</code></h2>
 <ul>
 <li><code>ptclump</code> has no pseudovalidation approach, so this
 function will return the PGS based on a p-value threshold of 1.</li>
-<li><code>prscsx</code> returns meta-analysed PGS weights across
-populations. The target_pop parameter has no effect on the output.</li>
-<li><code>tlprs</code> has no pseudovalidation approach, so the function
-returns the pseudovalidated score for the PGS method used as input to
-<code>tlprs</code>, using strongest reweighting for the target
-population.</li>
-<li><code>xwing</code> and <code>tlprs</code> do not have a
-pseudovalidation approach when using <code>target_pop = 'TRANS'</code>,
-so this function returns the result for the first population in the
-<code>gwas_group</code> file. The function also returns the result for
-first population in the <code>gwas_group</code> file when the gwas_group
-did not contain a GWAS from the <code>target_pop</code>.</li>
 </ul></li>
 </ul>
 <details>
@@ -2017,13 +1931,7 @@ <h2><code>find_pseudo</code></h2>
 <pre class="r"><code>pseudo_param &lt;-
   find_pseudo(config = &#39;example_input/config.yaml&#39;,
               gwas = &#39;COAD01&#39;,
-              pgs_method = &#39;ptclump&#39;)
-
-pseudo_param &lt;-
-  find_pseudo(config = &#39;example_input/config.yaml&#39;,
-              gwas = &#39;COAD01&#39;,
-              pgs_method = &#39;prscsx&#39;,
-              target_pop = &#39;EUR&#39;)</code></pre>
+              pgs_method = &#39;ptclump&#39;)</code></pre>
 </details>
 <hr />
 </div>
@@ -2103,9 +2011,10 @@ <h2>Using your own reference</h2>
 <code>configfile</code>. The reference data folder must have the
 following structure:</p>
 <pre><code>[refdir]
-  ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data in build GRCh37 with RSIDs in SNP column)
+  ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data with RSIDs in SNP column)
   ├── ref.chr[1-22].rds (SNP data - refer to default ref data for format)
   ├── ref.pop.txt (Population data for reference individuals - with header)
+  ├── ref.keep.list (lists keep files for each population - columns pop and path - no header)
   ├── keep_files
   │   └──[pop].keep (keep files for each population - no header)
   └── freq_files
@@ -2127,11 +2036,11 @@ <h2>Altering PGS method parameters</h2>
 ptclump</li>
 <li><code>dbslmm_h2f</code>: list SNP-h2 folds for DBSLMM - use
 <code>1</code> for the default model</li>
-<li><code>prscs_phi</code>: list phi parameters for PRS-CS and PRS-CSx -
-use <code>auto</code> for the auto model</li>
-<li><code>prscs_ldef</code>: Selected whether PRS-CS/PRS-CSx LD
-reference is derived from <code>1kg</code> (default) or <code>ukb</code>
-(UK Biobank).</li>
+<li><code>prscs_phi</code>: list phi parameters for PRS-CS - use
+<code>auto</code> for the auto model</li>
+<li><code>prscs_ldef</code>: Selected whether PRS-CS ld reference is
+derived from <code>1kg</code> (default) or <code>ukb</code> (UK
+Biobank).</li>
 <li><code>ldpred2_model</code>: list models for LDpred2 -
 <code>grid</code>, <code>auto</code>, <code>inf</code></li>
 </ul>
@@ -2187,148 +2096,6 @@ <h2>Control computational resources</h2>
 <code>cores_outlier_detection</code> parameters in the config file.</p>
 <hr />
 </div>
-<div id="multi-source-pgs-methods" class="section level2">
-<h2>Multi-source PGS methods</h2>
-<p>The pipeline also implements a range of multi-source polygenic
-scoring methods, that can combine GWAS summary statistics from multiple
-populations. To use these methods an additional <code>gwas_groups</code>
-file must be provided, indicating which GWAS are to be jointly analysed.
-The <code>gwas_groups</code> file must be specified in the
-<code>configfile</code> using the <code>gwas_groups</code> parameter. An
-example of a gwas_groups file can be found <a
-href="../pipeline/example_input/gwas_groups.txt">here</a>.</p>
-<details>
-<summary>
-View gwas_groups format
-</summary>
-<table>
-<colgroup>
-<col width="6%" />
-<col width="19%" />
-<col width="74%" />
-</colgroup>
-<thead>
-<tr class="header">
-<th align="left">Column</th>
-<th align="left">Example</th>
-<th align="left">Description</th>
-</tr>
-</thead>
-<tbody>
-<tr class="odd">
-<td align="left">name</td>
-<td align="left"><code>height</code></td>
-<td align="left">ID for the group of GWAS. Cannot contain spaces (’ ‘)
-or hyphens (’-’)</td>
-</tr>
-<tr class="even">
-<td align="left">gwas</td>
-<td align="left"><code>yengo_eur,yengo_eas</code></td>
-<td align="left">Comma-seperated list of GWAS names, corresponding to
-the <code>gwas_list</code>.</td>
-</tr>
-<tr class="odd">
-<td align="left">label</td>
-<td align="left"><code>"Height (EUR+EAS)"</code></td>
-<td align="left">A human readable name for the group of GWAS Wrap in
-double quotes if multiple words.</td>
-</tr>
-</tbody>
-</table>
-</details>
-<div class="shallow-break">
-
-</div>
-<p>Another multi-source PGS approach is TL-PRS, which tunes an existing
-PGS model based on GWAS summary statistics from another population.
-GenoPred can automate this process. The user must specify which PGS
-methods they want TL-PRS to be applied to, using the
-<code>tlprs_methods</code> parameter in the <code>configfile</code>. For
-example: <code>tlprs_methods: ['lassosum','dbslmm']</code>. TL-PRS is
-only applied to the PGS from a given method selected using
-pseudovalidation.</p>
-<p>An alternative multi-source PGS approach is to linearly combine
-population-specific PGS derived using single-source methods. GenoPred
-implements an approach called <code>LEOPARD+QuickPRS</code>, which
-estimates the optimal linear combination weights for population-specific
-PGS using summary statistics alone. Using the
-<code>leopard_methods</code> parameter in the <code>configfile</code>,
-the user can specify which single-source PGS should be linearly combined
-using the LEOPARD+QuickPRS estimated weights. For example:
-<code>leopard_methods: ['lassosum', 'dbslmm']</code>. LEOPARD weighting
-is only applied to the PGS from a given method selected using
-pseudovalidation.</p>
-<hr />
-</div>
-<div id="select-ancestry-adjustment-approach" class="section level2">
-<h2>Select ancestry adjustment approach</h2>
-<p>The GenoPred pipeline offers two approaches for adjusting PGS for
-ancestry: discrete or continuous.</p>
-<div id="discrete-correction" class="section level3">
-<h3>Discrete Correction:</h3>
-<ul>
-<li>This approach assigns each target individual to an ancestry-matched
-reference population.</li>
-<li>The target individual’s PGS is then standardized based on the mean
-and standard deviation (SD) of the PGS within the corresponding
-reference population.</li>
-<li>This is the approach used by GenoPred until v3.0.0.</li>
-</ul>
-</div>
-<div id="continuous-correction" class="section level3">
-<h3>Continuous Correction:</h3>
-<ul>
-<li>This method utilizes genetic principal components projected from
-reference populations to standardize target PGS.</li>
-<li>It accounts for variation within reference populations and
-accommodates individuals whose ancestry does not align closely with a
-single reference population.</li>
-<li>GenoPred uses the approach described by Linders et al. (<a
-href="https://doi.org/10.1016/j.gim.2023.100006">here</a>), which adjust
-both the mean and variance of the target PGS.</li>
-</ul>
-<p>There is a nice explanation of these approaches on the pgsc_calc
-website (<a
-href="https://pgsc-calc.readthedocs.io/en/latest/explanation/geneticancestry.html">here</a>),
-where they refer to the continuous approach as ‘Empirical Methods’, and
-the continuous approach as ‘PCA-based methods’ (specifically
-<code>Z_norm2</code>).</p>
-<p>By default, the GenoPred pipeline applies only the continuous
-correction for ancestry to scale the target PGS. However, users can
-specify the desired ancestry adjustment approach using the
-<code>pgs_scaling</code> parameter in the <code>configfile</code>. This
-parameter accepts a list of values, which can include
-<code>continuous</code> and <code>discrete</code>.</p>
-<p>For example, to produce target PGS using both continuous and discrete
-correction, specify:</p>
-<pre class="yaml"><code>pgs_scaling: [&#39;continuous&#39;, &#39;discrete]</code></pre>
-<p>Or, to produce target PGS only the discrete correction, specify:</p>
-<pre class="yaml"><code>pgs_scaling: [&#39;discrete&#39;]</code></pre>
-<hr />
-</div>
-</div>
-<div id="specifying-alternative-reference-data-for-pgs-methods"
-class="section level2">
-<h2>Specifying alternative reference data for PGS methods</h2>
-<p>PGS methods require linkage disequilibrium (LD) data. The default LD
-data used for each method is indicated in the technical documentation
-(<a href="">here</a>). Several methods use the reference data used for
-the pipeline in general, so altering the <code>ref_dir</code> parameter
-(as <a href="">described above</a>) will also alter the reference data
-used by these PGS methods. However, several PGS methods use pre-computed
-LD matrices, and the user can specify alternative LD matrices for these
-methods, using the configfile parameters below:</p>
-<ul>
-<li>PRS-CS</li>
-<li>PRS-CSx</li>
-<li>SBayesR</li>
-<li>SBayesRC</li>
-<li>LDpred2</li>
-<li>QuickPRS</li>
-<li>LEOPARD+QuickPRS</li>
-</ul>
-<hr />
-</div>
 </div>
 <div id="running-in-offline-environment" class="section level1">
 <h1>Running in offline environment</h1>
diff --git a/docs/pipeline_technical.Rmd b/docs/pipeline_technical.Rmd
index 29d07053..7aca331b 100644
--- a/docs/pipeline_technical.Rmd
+++ b/docs/pipeline_technical.Rmd
@@ -24,25 +24,15 @@ library(data.table)
 
 This document provides technical details of the GenoPred pipeline. The GenoPred pipeline automates the process of calculating polygenic scores. The pipeline aims to implement the current practises for polygenic scoring. See [here](pipeline_overview.html) more general information regarding the GenoPred pipeline.
 
-***
-
-<h3>Citations</h4>
+Please cite our preprint when using the pipeline:
 
-Please cite the following relevant papers when using the pipeline:
+- "Pain, O. et al. "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." MedRxiv 2024. https://doi.org/10.1101/2024.06.12.24308843
 
-**Pipeline overview:**
-- Pain, O. et al. (2024). *The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring*. Bioinformatics.  
-  https://doi.org/10.1093/bioinformatics/btae551
+If relevant, please also cite our paper comparing polygenic scoring methods and describing the reference-standardised approach:
 
-**Comparison of single-source PGS methods:**
-- Pain, O. et al. (2021). *Evaluation of polygenic prediction methodology within a reference-standardized framework*. PLoS Genetics.  
-  https://doi.org/10.1371/journal.pgen.1009021
+- Pain, O. et al. "Evaluation of polygenic prediction methodology within a reference-standardized framework." PLoS genetics. https://doi.org/10.1371/journal.pgen.1009021
 
-**Integration and comparison of multi-source PGS methods:**
-- Pain, O. (2025). *Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations*. medRxiv. 
-  https://doi.org/10.1101/2025.03.27.25324773
-
-Please also cite the relevant studies for the tools and data used by the GenoPred pipeline - Details provided in the [Technical Documentation](pipeline_technical.html).
+Please also cite the relevant studies for the tools and data used by the GenoPred pipeline.
 
 ***
 
@@ -59,10 +49,9 @@ The pipeline can be separated into two halves, reference data preparation and ta
 See [here](pipeline_readme.html#pipeline-configuration) for more information on pipeline configuration.
 
 ***
-
 <div class="centered-container">
 <div class="rounded-image-container">
-![Figure 1. GenoPred pipeline schematic. Shows input files, processes, outputs, and rules.](Images/pipeline_readme/pipeline_schematic_groups_lowdef.png)
+![Figure 1. GenoPred pipeline schematic. Shows input files, processes, outputs, and rules.](Images/pipeline_readme/pipeline_schematic_lowdef.png)
 </div>
 </div>
 
@@ -83,6 +72,7 @@ Users can provide there own reference data using the `refdir` parameter in the `
   ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data with RSIDs in SNP column)
   ├── ref.chr[1-22].rds (SNP data - refer to default ref data for format)
   ├── ref.pop.txt (Population data for reference individuals - with header)
+  ├── ref.keep.list (lists keep files for each population - columns pop and path - no header)
   ├── keep_files
   │   └──[pop].keep (keep files for each population - no header)
   └── freq_files
@@ -128,28 +118,26 @@ The GenoPred pipeline integrates various polygenic scoring methods for adjusting
 
 ### Single Ancestry Methods 
 
-The GenoPred pipeline implements a range of PGS methods intended for GWAS based on a single ancestral population. When using GWAS based on mixed ancestry individuals, we suggest specifying the population that matches most individuals in the GWAS. See [here](#multi-ancestry-methods) for methods that leverage multiple GWAS from different ancestral populations. 
+The GenoPred pipeline currently only implements PGS methods intended for GWAS based on a single ancestral population. When using GWAS based on mixed ancestry individuals, we suggest specifying the population that matches most individuals in the GWAS. 
 
 ```{r, eval = T, echo = F}
 
 methods_table <- data.frame(
-  Method = c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesR", "SBayesRC"),
-  Software = c("DBSLMM", "lassosum R package", "bigsnpr R package", "LDAK", "PRS-CS", "PLINK", "LDAK", "GCTB", "SBayesRC R package"),
-  PubMedID = c("32330416", "28480976", "33326037", "34234142", "30992449", "25722852", "34234142", "31704910", "38689000"),
-  PseudoValidationOption = c("Yes", "Yes", "Yes", "Yes", "Yes", "No", "Yes (only option)", "Yes (only option)", "Yes (only option)"),
-  Parameters = c("SNP-heritability estimated using LD Score Regression (on liability scale for binary outcomes), multiplied by 0.8, 1, and 1.2",
+  Method = c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "SBayesR"),
+  Software = c("DBSLMM", "lassosum R package", "bigsnpr R package", "LDAK", "PRS-CS", "PLINK", "GCTB"),
+  PubMedID = c(32330416, "28480976", "33326037", "34234142", "30992449", "25722852", "31704910"),
+  PseudoValidationOption = c("Yes (only option)", "Yes", "Yes", "Yes", "Yes", "No", "Yes (only option)"),
+  Parameters = c("SNP-heritability estimated using LD Score Regression (on liability scale for binary outcomes)",
                  "s = 0.2, 0.5, 0.9, 1; lambda = exp(seq(log(0.001), log(0.1), length.out=20))",
                  "Grid includes 126 combinations of heritability and non-zero effect fractions (p).",
                  "Fits lasso, ridge, bolt and BayesR models, with a total of 148 sets of hyperparameters",
                  "phi = 1e-6, 1e-4, 1e-2, 1, auto",
                  "p-value thresholds: 1e-8, 1e-6, 1e-4, 1e-2, 0.1, 0.2, 0.3, 0.4, 0.5, 1; Clumping: r2 = 0.1; window = 250kb",
-                 "Fits lasso, ridge, bolt and BayesR models, with a total of 148 sets of hyperparameters",
-                 "NA",
                  "NA"),
-  MHCRegion = c("Not excluded", "Not excluded", "Not excluded", "Not excluded", "Not excluded", "Only top variant retained", "Not excluded", "Excluded (as recommended)", "Not excluded"),
-  LDReference = c("Population-matched 1KG+HGDP", "Population-matched 1KG+HGDP", "EUR UKB (LDpred2-provided)", "Population-matched 1KG+HGDP", "Population-matched UKB (PRS-CS provided)", "Population-matched 1KG+HGDP", "Population-matched 1KG+HGDP", "EUR UKB (GCTB-provided)", "AFR/EAS/EUR UKB HapMap3 (SBayesRC provided)"),
-  CPUUsage = c("0.15 hrs", "0.08 hrs", "0.38 hrs", "0.85 hrs", "4.40 hrs", "0.02 hrs", "0.06 hrs", "0.38 hrs", "0.38 hrs"),
-  MemoryUsage = c("1.2 GB", "7.3 GB", "20.5 GB", "12.5 GB", "10.6 GB", "0.8 GB", "4.9 GB", "4.4 GB", "4.4 GB")
+  MHCRegion = c("Not excluded", "Not excluded", "Not excluded", "Not excluded", "Not excluded", "Only top variant retained", "Excluded (as recommended)"),
+  LDReference = c("Population-matched 1KG+HGDP", "Population-matched 1KG+HGDP", "EUR UKB (LDpred2-provided)", "Population-matched 1KG+HGDP", "Population-matched UKB (PRS-CS provided)", "Population-matched 1KG+HGDP", "EUR UKB (GCTB-provided)"),
+  CPUUsage = c("40 seconds", "10 seconds", "3 minutes", "5 minutes", "35 minutes", "5 seconds", "3 minutes"),
+  MemoryUsage = c("450 Mb", "400 Mb", "500 Mb", "450Mb", "350 Mb", "100 Mb", "500 Mb")
 )
 
 names(methods_table) <- c('Method','Software','PubMed ID', 'Pseudovalidation Option','Parameters','MHC Region','LD Reference','CPU usage*','Memory Usage*')
@@ -161,9 +149,7 @@ kable(methods_table, "html", caption = "Table 1 provides a summary of each appro
 
 ```
 
-\* = Using genome-wide GWAS with 10 cores. See [here](crosspop.html#computational-resources) for full benchmark information.
-
-Note.This is the default behaviour of the GenoPred pipeline. Certain aspects can be modified using the configfile ([see here](pipeline_readme.html#additional-parameters)).
+\* = Based on chromosome 22 using single core. See [here](pipeline_benchmark.html) for full benchmark information.
 
 ***
 
@@ -181,7 +167,7 @@ lassosum, an R package, applies the LASSO method for constructing PGS, is implem
 
 #### LDpred2
 
-LDpred2, part of the bigsnpr R package, operates in 'inf', 'grid', and 'auto' modes. The 'inf' mode is better suited for highly polygenic traits, whereas 'grid' and 'auto' adjust effect sizes using various hyperparameters, including SNP heritability. In GenoPred, LDpred2 is run using the script [pgs_methods/ldpred2](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/ldpred2.R).R. GenoPred uses bigsnpr v1.12.2, with the default LDpred2 grid search, and recommended GWAS quality control checks. GenoPred employs LDpred2's precomputed LD matrices based on the European individuals from the UK Biobank, and it is applied only to GWAS based on a EUR population.  The user can specify alternative LD reference data ([see here](pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods)). If the SNP-h2 estimated using LDSC is <0.05, the SNP-heritability used by LDpred2 is set to 0.05.
+LDpred2, part of the bigsnpr R package, operates in 'inf', 'grid', and 'auto' modes. The 'inf' mode is better suited for highly polygenic traits, whereas 'grid' and 'auto' adjust effect sizes using various hyperparameters, including SNP heritability. In GenoPred, LDpred2 is run using the script [pgs_methods/ldpred2](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/ldpred2.R).R. GenoPred uses bigsnpr v1.12.2, with the default LDpred2 grid search, and recommended GWAS quality control checks. GenoPred employs LDpred2's precomputed LD matrices based on the European individuals from the UK Biobank, and it is applied only to GWAS based on a EUR population. If the SNP-h2 estimated using LDSC is <0.05, the SNP-heritability used by LDpred2 is set to 0.05.
 
 ***
 
@@ -193,7 +179,7 @@ MegaPRS uses a range of priors (lasso, ridge, bolt, BayesR) for SNP effects, run
 
 #### PRS-CS
 
-PRS-CS, a Bayesian method using a continuous shrinkage prior, specifies a range of global shrinkage parameters (phi), generating multiple sets of genetic effects for polygenic scoring. Its 'auto' model estimates the optimal parameter directly from GWAS summary statistics, negating the need for an external dataset. In GenoPred, PRS-CS is run using the script [pgs_methods/prscs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscs.R). GenoPred clones PRS-CS from GitHub (621fdc80daac56c93d9528eb1a1187f7b1fc9afb) and uses the provided precompiled PRS-CS binary to estimate SNP effect sizes. By default, GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the auto model, but the user can modify this behaviour using the prscs_phi parameter in the configfile. By default, GenoPred uses the PRS-CS provided 1KG-derived LD matrix data, matching the population of the GWAS sample. The user can select the UKB-derived LD matrix data to be used using the `prscs_ldref` parameter in the `configfile`. 1KG is used by default as PGS based on Yengo et al. sumstats performed significantly better in the OpenSNP target sample, when using the 1KG reference data (this may differ for other GWAS).
+PRS-CS, a Bayesian method using a continuous shrinkage prior, specifies a range of global shrinkage parameters (phi), generating multiple sets of genetic effects for polygenic scoring. Its 'auto' model estimates the optimal parameter directly from GWAS summary statistics, negating the need for an external dataset. In GenoPred, PRS-CS is run using the script [pgs_methods/prscs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscs.R). By default, GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the auto model, but the user can modify this behaviour using the prscs_phi parameter in the configfile. By default, GenoPred uses the PRS-CS provided 1KG-derived LD matrix data, matching the population of the GWAS sample. The user can select the UKB-derived LD matrix data to be used using the `prscs_ldref` parameter in the `configfile`. 1KG is used by default as PGS based on Yengo et al. sumstats performed significantly better in the OpenSNP target sample, when using the 1KG reference data (this may differ for other GWAS).
 
 ***
 
@@ -203,94 +189,15 @@ In GenoPred, pT+clump is run using the script [pgs_methods/ptclump.R](https://gi
 
 ***
 
-#### QuickPRS
-
-In GenoPred, QuickPRS is run using the script [pgs_methods/quickprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/quickprs.R). QuickPRS is a faster implementation of MegaPRS, sped up by using precomputed LD and annotation data. It only inlcudes the model selected using pseudovalidation. GenoPred employs the 1KG+HGDP reference for LD estimation, matching the population of the GWAS sample.
-
-***
-
 #### SBayesR
 
 In GenoPred, SBayesR is run using the script [pgs_methods/sbayesr.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/sbayesr.R). Implemented with GCTB software, SBayesR is a Bayesian method that uses GWAS summary statistics to estimate key parameters. GenoPred uses GCTB v2.03 with LD matrices from the GCTB authors, calculated using European individuals in the UK Biobank, restricting its application to GWAS based on EUR populations. GenoPred uses the robust parameterisation option in SBayesR.
 
 ***
 
-#### SBayesRC
-
-In GenoPred, SBayesRC is implemented via pgs_[pgs_methods/sbayesrc.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/sbayesrc.R). This method extends SBayesR to integrate functional annotations into the Bayesian framework for estimating SNP effects from GWAS summary statistics. By default SBayesRC will use the LD matrices released by the developers, restricted to GWAS from EUR, EAS, and AFR populations. The user can specify alternative LD reference data ([see here](pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods))
-
-***
-
-### Multi-ancestry Methods 
-
-The GenoPred pipeline implements a range of PGS methods intended for analysis of multiple GWAS from different ancestral populations. Multi-ancestry / Multi-source approaches can be catagorised as jointly optimised or independently optimised.
-
-***
-
-#### Jointly optimised
-
-Jointly optimised methods, modelling all GWAS simultaneously when generating SNP-weights, are shown below:
-
-```{r, eval = T, echo = F}
-
-methods_table <- data.frame(
-  Method = c("PRS-CSx", "X-Wing", "TL-PRS"),
-  Software = c("PRS-CSx", "LOGODetect, PANTHER, LEOPARD", "TLPRS R package"),
-  PubMedID = c("35513724", "36788230", "36240765"),
-  PseudoValidationOption = c("Yes", "Yes", "No"),
-  Parameters = c("phi = 1e-6, 1e-4, 1e-2, 1, auto",
-                 "phi = auto",
-                 "Block learning rates = 1, 10, 100, 1000. Iterations (k) = "),
-  MHCRegion = c("Not excluded", "Not excluded", "Dependant on the PGS method used to generate the score file input to TL-PRS"),
-  LDReference = c("Population-matched UKB (PRS-CS provided)", "Population-matched UKB (PRS-CS provided)", "Population-matched 1KG+HGDP"),
-  CPUUsage = c("6.84 hrs", "34.12 hrs", "0.33 hrs"),
-  MemoryUsage = c("15.2 GB", " 48.7 GB", "31.0 GB")
-)
-
-names(methods_table) <- c('Method','Software','PubMed ID', 'Pseudovalidation Option','Parameters','MHC Region','LD Reference','CPU usage*','Memory Usage*')
-
-kable(methods_table, "html", caption = "Table 2 provides a summary of each approach and its implementation in GenoPred.") %>%
-  kable_styling(bootstrap_options = c("striped", "hover"),
-                full_width = F) %>%
-  scroll_box(width = "100%", height = "500px")
-
-```
-
-\* = Time per GWAS/population, using genome-wide data with 10 cores. See [here](crosspop.html#computational-resources) for full benchmark information.
-
-Note.This is the default behaviour of the GenoPred pipeline. Certain aspects can be modified using the configfile ([see here](pipeline_readme.html#additional-parameters)).
-
-***
-
-##### PRS-CSx
-
-PRS-CSx, an extension of PRS-CS, integrates GWAS summary statistics from multiple ancestry groups using a Bayesian framework. The method is implemented in GenoPred via the script [pgs_methods/prscsx.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscsx.R). We use summary statistics from the populations most closely matching the ancestry of the GWAS data to estimate posterior effect sizes. The LD information is derived from population-matched reference panels (e.g., 1KG). GenoPred clones PRS-CSx from GitHub (29a1148875f6ae3f2594b25579f40d4b587c5691) and uses the provided precompiled PRS-CSx binary to estimate SNP effect sizes. By default, GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the auto model, but the user can modify this behaviour using the prscs_phi parameter in the configfile. By default, GenoPred uses the PRS-CS provided 1KG-derived LD matrix data, matching the population of the GWAS sample. The user can select the UKB-derived LD matrix data to be used using the `prscs_ldref` parameter in the `configfile`. 1KG is used by default as PGS based on Yengo et al. sumstats performed significantly better in the OpenSNP target sample, when using the 1KG reference data (this may differ for other GWAS).
-
-***
-
-##### X-Wing
-
-X-Wing is a polygenic scoring method designed to enhance genetic prediction accuracy across ancestrally diverse populations by leveraging a cross-population weighting framework. The method operates through a combination of several software tools: LOGODetect, PANTHER, and LEOPARD. GenoPred integrates this method using the script [pgs_methods/xwing.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/xwing.R). GenoPred downloads X-WING software from GitHub (e9fcc264266e0e884323311816bfe20053fd3f7a). The LD information derived from the 1KG dataset is provided by the developers of X-Wing. 
-
-***
-
-##### TL-PRS
-
-TL-PRS (Transfer Learning Polygenic Risk Score) is implemented using the script [pgs_methods/tlprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/tlprs.R). The method combines summary statistics from target GWAS and a reference dataset via transfer learning, iteratively updating SNP effect sizes. GenoPred uses a modified version of TL-PRS to improve efficiency when being applied to many score files (https://github.com/opain/TLPRS). No modifications to the parameters used has been altered from the original version of TL-PRS (https://github.com/ZhangchenZhao/TLPRS/commit/6c8d8905f91ce902f0cca8d5465f9952b0b3e274). Multiple learning rates (factors of 1, 10, 100, 1000) are tested in an iterative approach with 15 steps per factor to update the SNP effect sizes. GenoPred uses the 1KG+HGDP reference for LD estimation, matching the population of the GWAS sample.
-
-***
-
-#### Independently optimised
-
-The independentdently optimised multi-source approach describes the process of applying single-source PGS methods to each GWAS separately, and then linearly combining the population-specific PGS for the target population of interest. This linear combination can be estimated using individual-level data (simply by fitting a linear model containing all population-specific PGS), or using summary-statistics alone (using an approach called LEOPARD, from the Xwing software). In the GenoPred pipeline, we have adapted LEOPARD to use the super fast PGS method QuickPRS, to quickly and accurately estimate the optimal weights for linearly combining the population-specific PGS. These weights can be used to linearly combine population-specific PGS from single-source PGS method. We decribe and evaluate this process in detail in [this paper](https://doi.org/10.1101/2025.03.27.25324773).
-
-The script used to estimate the weights for population-specific PGS is [pgs_methods/leopard_quickprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/leopard_quickprs.R), and the script used to linearly combine the PGS using these weights is [pgs_methods/apply_leopard_weights.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/apply_leopard_weights.R).
-
-***
-
 ### Pseudovalidation
 
-Several polygenic scoring methods use a range of 'tuning' parameters when adjusting the GWAS effect sizes for polygenic scoring, resulting in multiple polygenic scores from a given GWAS. Traditionally, the user would then evaluate the predictive utility of polygenic scores derived using these various parameters, to select the best performing polygenic score, using cross-validation or bootstrapping to estimating the effect of the polygenic score without overfitting. However, several methods offer a pseudovalidation approach, whereby the optimal tuning parameter is estimated based on the GWAS summary statistics alone, conveniently avoiding the need for a tuning sample. The performance of pseudovalidation relative to formal validation varies between methods. See our previous [polygenic scoring methods comparison](https://doi.org/10.1101/2025.03.27.25324773) for more information. 
+Several polygenic scoring methods use a range of 'tuning' parameters when adjusting the GWAS effect sizes for polygenic scoring, resulting in multiple polygenic scores from a given GWAS. Traditionally, the user would then evaluate the predictive utility of polygenic scores derived using these various parameters, to select the best performing polygenic score, using cross-validation or bootstrapping to estimating the effect of the polygenic score without overfitting. However, several methods offer a pseudovalidation approach, whereby the optimal tuning parameter is estimated based on the GWAS summary statistics alone, convieniently avoiding the need for a tuning sample. The performance of pseudovalidation relative to formal validation varies between methods. See our previous [polygenic scoring methods comparison](comparison_of_methods_summary.html) for more information. 
 
 ***
 
@@ -336,17 +243,7 @@ If the user doesn't provide a list of unrelated indiviuals using the `unrel` col
 
 ## Target scoring
 
-This step calculates scores in the target sample, based on scoring files from the PGS methods, and from the reference PCA. It is carried out using the [target_scoring.R script](https://github.com/opain/GenoPred/blob/master/Scripts/target_scoring/target_scoring.R). It is performed separately for each target sample population. Scoring is performed using PLINK2 using the --read-freq function to impute missing genotypes using the ancestry-matched reference allele frequency. The scores are computed for each chromosome, then multiplied by the number of non-missing variants to get the sum score, and then added across chromosomes. Finally, the target sample PGS are adjusted for ancestry, so they can be interpreted in units of SD from the mean in an ancestry-matched population. See [here](#ancestry-adjustment) for more information on ancestry adjustment. 
-
-***
-
-### Ancestry adjustment
-
-The GenoPred pipeline offers discrete or continuous approaches for adjusting PGS for ancestry. Previously described [here](pipeline_readme.html#select-ancestry-adjustment-approach). As of GenoPred v3.0.0, only the continuous approach is applied by default.
-
-The discrete approach involves standardising target sample PGS according the mean and SD of the PGS in the individual's assigned reference population.
-
-The continuous approach uses reference data to model the relationship between genetic principal components and the PGS, and then corrects the mean and SD of the PGS for target individuals according to their reference-projected principal component scores. This approach was used in the study by Linders et al. ([here](https://doi.org/10.1016/j.gim.2023.100006)).
+This step calculates scores in the target sample, based on scoring files from the PGS methods, and from the reference PCA. It is carried out using the [target_scoring.R script](https://github.com/opain/GenoPred/blob/master/Scripts/target_scoring/target_scoring.R). It is performed separately for each target sample population. Scoring is performed using PLINK2 using the --read-freq function to impute missing genotypes using the ancestry-matched reference allele frequency. The scores are computed for each chromosome, then multiplied by the number of non-missing variants to get the sum score, and then added across chromosomes. Finally, the target sample scores are standardised according to the mean and SD of the score in an ancestry-matched reference.
 
 ***
 
diff --git a/docs/pipeline_technical.html b/docs/pipeline_technical.html
index 2bed8b6d..d78d2211 100644
--- a/docs/pipeline_technical.html
+++ b/docs/pipeline_technical.html
@@ -80,41 +80,6 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
-<!-- Osano Cookie Consent -->
-<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
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-<script>
-window.addEventListener("load", function(){
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-    palette: {
-      popup: { background: "#000" },
-      button: { background: "#f1d600" }
-    },
-    theme: "classic",
-    position: "bottom-right",
-    content: {
-      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
-      dismiss: "Accept",
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 <style type="text/css">
   code{white-space: pre-wrap;}
@@ -472,31 +437,23 @@ <h1>Introduction</h1>
 scores. The pipeline aims to implement the current practises for
 polygenic scoring. See <a href="pipeline_overview.html">here</a> more
 general information regarding the GenoPred pipeline.</p>
-<hr />
-<h3>
-Citations
-</h4>
-<p>Please cite the following relevant papers when using the
-pipeline:</p>
-<p><strong>Pipeline overview:</strong> - Pain, O. et al. (2024). <em>The
-GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic
-Scoring</em>. Bioinformatics.<br />
-<a href="https://doi.org/10.1093/bioinformatics/btae551"
-class="uri">https://doi.org/10.1093/bioinformatics/btae551</a></p>
-<p><strong>Comparison of single-source PGS methods:</strong> - Pain, O.
-et al. (2021). <em>Evaluation of polygenic prediction methodology within
-a reference-standardized framework</em>. PLoS Genetics.<br />
-<a href="https://doi.org/10.1371/journal.pgen.1009021"
-class="uri">https://doi.org/10.1371/journal.pgen.1009021</a></p>
-<p><strong>Integration and comparison of multi-source PGS
-methods:</strong> - Pain, O. (2025). <em>Leveraging Global Genetics
-Resources to Enhance Polygenic Prediction Across Ancestrally Diverse
-Populations</em>. medRxiv. <a
-href="https://doi.org/10.1101/2025.03.27.25324773"
-class="uri">https://doi.org/10.1101/2025.03.27.25324773</a></p>
+<p>Please cite our preprint when using the pipeline:</p>
+<ul>
+<li>“Pain, O. et al. ”The GenoPred Pipeline: A Comprehensive and
+Scalable Pipeline for Polygenic Scoring.” MedRxiv 2024. <a
+href="https://doi.org/10.1101/2024.06.12.24308843"
+class="uri">https://doi.org/10.1101/2024.06.12.24308843</a></li>
+</ul>
+<p>If relevant, please also cite our paper comparing polygenic scoring
+methods and describing the reference-standardised approach:</p>
+<ul>
+<li>Pain, O. et al. “Evaluation of polygenic prediction methodology
+within a reference-standardized framework.” PLoS genetics. <a
+href="https://doi.org/10.1371/journal.pgen.1009021"
+class="uri">https://doi.org/10.1371/journal.pgen.1009021</a></li>
+</ul>
 <p>Please also cite the relevant studies for the tools and data used by
-the GenoPred pipeline - Details provided in the <a
-href="pipeline_technical.html">Technical Documentation</a>.</p>
+the GenoPred pipeline.</p>
 <hr />
 </div>
 <div id="software" class="section level1">
@@ -526,7 +483,7 @@ <h1>Workflow</h1>
 <div class="centered-container">
 <div class="rounded-image-container">
 <div class="figure">
-<img src="Images/pipeline_readme/pipeline_schematic_groups_lowdef.png"
+<img src="Images/pipeline_readme/pipeline_schematic_lowdef.png"
 alt="" />
 <p class="caption">Figure 1. GenoPred pipeline schematic. Shows input
 files, processes, outputs, and rules.</p>
@@ -552,6 +509,7 @@ <h2>Reference genetic data</h2>
   ├── ref.chr[1-22].[pgen/pvar/psam] (plink2 genotype data with RSIDs in SNP column)
   ├── ref.chr[1-22].rds (SNP data - refer to default ref data for format)
   ├── ref.pop.txt (Population data for reference individuals - with header)
+  ├── ref.keep.list (lists keep files for each population - columns pop and path - no header)
   ├── keep_files
   │   └──[pop].keep (keep files for each population - no header)
   └── freq_files
@@ -647,12 +605,10 @@ <h2>Adjustment of GWAS effect sizes</h2>
 <hr />
 <div id="single-ancestry-methods" class="section level3">
 <h3>Single Ancestry Methods</h3>
-<p>The GenoPred pipeline implements a range of PGS methods intended for
-GWAS based on a single ancestral population. When using GWAS based on
-mixed ancestry individuals, we suggest specifying the population that
-matches most individuals in the GWAS. See <a
-href="#multi-ancestry-methods">here</a> for methods that leverage
-multiple GWAS from different ancestral populations.</p>
+<p>The GenoPred pipeline currently only implements PGS methods intended
+for GWAS based on a single ancestral population. When using GWAS based
+on mixed ancestry individuals, we suggest specifying the population that
+matches most individuals in the GWAS.</p>
 <div
 style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:500px; overflow-x: scroll; width:100%; ">
 <table class="table table-striped table-hover" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
@@ -703,11 +659,11 @@ <h3>Single Ancestry Methods</h3>
 32330416
 </td>
 <td style="text-align:left;">
-Yes
+Yes (only option)
 </td>
 <td style="text-align:left;">
 SNP-heritability estimated using LD Score Regression (on liability scale
-for binary outcomes), multiplied by 0.8, 1, and 1.2
+for binary outcomes)
 </td>
 <td style="text-align:left;">
 Not excluded
@@ -716,10 +672,10 @@ <h3>Single Ancestry Methods</h3>
 Population-matched 1KG+HGDP
 </td>
 <td style="text-align:left;">
-0.15 hrs
+40 seconds
 </td>
 <td style="text-align:left;">
-1.2 GB
+450 Mb
 </td>
 </tr>
 <tr>
@@ -746,10 +702,10 @@ <h3>Single Ancestry Methods</h3>
 Population-matched 1KG+HGDP
 </td>
 <td style="text-align:left;">
-0.08 hrs
+10 seconds
 </td>
 <td style="text-align:left;">
-7.3 GB
+400 Mb
 </td>
 </tr>
 <tr>
@@ -776,10 +732,10 @@ <h3>Single Ancestry Methods</h3>
 EUR UKB (LDpred2-provided)
 </td>
 <td style="text-align:left;">
-0.38 hrs
+3 minutes
 </td>
 <td style="text-align:left;">
-20.5 GB
+500 Mb
 </td>
 </tr>
 <tr>
@@ -806,10 +762,10 @@ <h3>Single Ancestry Methods</h3>
 Population-matched 1KG+HGDP
 </td>
 <td style="text-align:left;">
-0.85 hrs
+5 minutes
 </td>
 <td style="text-align:left;">
-12.5 GB
+450Mb
 </td>
 </tr>
 <tr>
@@ -835,10 +791,10 @@ <h3>Single Ancestry Methods</h3>
 Population-matched UKB (PRS-CS provided)
 </td>
 <td style="text-align:left;">
-4.40 hrs
+35 minutes
 </td>
 <td style="text-align:left;">
-10.6 GB
+350 Mb
 </td>
 </tr>
 <tr>
@@ -865,40 +821,10 @@ <h3>Single Ancestry Methods</h3>
 Population-matched 1KG+HGDP
 </td>
 <td style="text-align:left;">
-0.02 hrs
+5 seconds
 </td>
 <td style="text-align:left;">
-0.8 GB
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-QuickPRS
-</td>
-<td style="text-align:left;">
-LDAK
-</td>
-<td style="text-align:left;">
-34234142
-</td>
-<td style="text-align:left;">
-Yes (only option)
-</td>
-<td style="text-align:left;">
-Fits lasso, ridge, bolt and BayesR models, with a total of 148 sets of
-hyperparameters
-</td>
-<td style="text-align:left;">
-Not excluded
-</td>
-<td style="text-align:left;">
-Population-matched 1KG+HGDP
-</td>
-<td style="text-align:left;">
-0.06 hrs
-</td>
-<td style="text-align:left;">
-4.9 GB
+100 Mb
 </td>
 </tr>
 <tr>
@@ -924,50 +850,18 @@ <h3>Single Ancestry Methods</h3>
 EUR UKB (GCTB-provided)
 </td>
 <td style="text-align:left;">
-0.38 hrs
-</td>
-<td style="text-align:left;">
-4.4 GB
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-SBayesRC
-</td>
-<td style="text-align:left;">
-SBayesRC R package
-</td>
-<td style="text-align:left;">
-38689000
+3 minutes
 </td>
 <td style="text-align:left;">
-Yes (only option)
-</td>
-<td style="text-align:left;">
-NA
-</td>
-<td style="text-align:left;">
-Not excluded
-</td>
-<td style="text-align:left;">
-AFR/EAS/EUR UKB HapMap3 (SBayesRC provided)
-</td>
-<td style="text-align:left;">
-0.38 hrs
-</td>
-<td style="text-align:left;">
-4.4 GB
+500 Mb
 </td>
 </tr>
 </tbody>
 </table>
 </div>
-<p>* = Using genome-wide GWAS with 10 cores. See <a
-href="crosspop.html#computational-resources">here</a> for full benchmark
+<p>* = Based on chromosome 22 using single core. See <a
+href="pipeline_benchmark.html">here</a> for full benchmark
 information.</p>
-<p>Note.This is the default behaviour of the GenoPred pipeline. Certain
-aspects can be modified using the configfile (<a
-href="pipeline_readme.html#additional-parameters">see here</a>).</p>
 <hr />
 <div id="dblsmm" class="section level4">
 <h4>DBLSMM</h4>
@@ -1015,11 +909,9 @@ <h4>LDpred2</h4>
 GenoPred uses bigsnpr v1.12.2, with the default LDpred2 grid search, and
 recommended GWAS quality control checks. GenoPred employs LDpred2’s
 precomputed LD matrices based on the European individuals from the UK
-Biobank, and it is applied only to GWAS based on a EUR population. The
-user can specify alternative LD reference data (<a
-href="pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods">see
-here</a>). If the SNP-h2 estimated using LDSC is &lt;0.05, the
-SNP-heritability used by LDpred2 is set to 0.05.</p>
+Biobank, and it is applied only to GWAS based on a EUR population. If
+the SNP-h2 estimated using LDSC is &lt;0.05, the SNP-heritability used
+by LDpred2 is set to 0.05.</p>
 <hr />
 </div>
 <div id="megaprs" class="section level4">
@@ -1044,15 +936,12 @@ <h4>PRS-CS</h4>
 negating the need for an external dataset. In GenoPred, PRS-CS is run
 using the script <a
 href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscs.R">pgs_methods/prscs.R</a>.
-GenoPred clones PRS-CS from GitHub
-(621fdc80daac56c93d9528eb1a1187f7b1fc9afb) and uses the provided
-precompiled PRS-CS binary to estimate SNP effect sizes. By default,
-GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the
-auto model, but the user can modify this behaviour using the prscs_phi
-parameter in the configfile. By default, GenoPred uses the PRS-CS
-provided 1KG-derived LD matrix data, matching the population of the GWAS
-sample. The user can select the UKB-derived LD matrix data to be used
-using the <code>prscs_ldref</code> parameter in the
+By default, GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1)
+and the auto model, but the user can modify this behaviour using the
+prscs_phi parameter in the configfile. By default, GenoPred uses the
+PRS-CS provided 1KG-derived LD matrix data, matching the population of
+the GWAS sample. The user can select the UKB-derived LD matrix data to
+be used using the <code>prscs_ldref</code> parameter in the
 <code>configfile</code>. 1KG is used by default as PGS based on Yengo et
 al. sumstats performed significantly better in the OpenSNP target
 sample, when using the 1KG reference data (this may differ for other
@@ -1072,16 +961,6 @@ <h4>pT+clump</h4>
 GWAS sample.</p>
 <hr />
 </div>
-<div id="quickprs" class="section level4">
-<h4>QuickPRS</h4>
-<p>In GenoPred, QuickPRS is run using the script <a
-href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/quickprs.R">pgs_methods/quickprs.R</a>.
-QuickPRS is a faster implementation of MegaPRS, sped up by using
-precomputed LD and annotation data. It only inlcudes the model selected
-using pseudovalidation. GenoPred employs the 1KG+HGDP reference for LD
-estimation, matching the population of the GWAS sample.</p>
-<hr />
-</div>
 <div id="sbayesr" class="section level4">
 <h4>SBayesR</h4>
 <p>In GenoPred, SBayesR is run using the script <a
@@ -1094,249 +973,6 @@ <h4>SBayesR</h4>
 SBayesR.</p>
 <hr />
 </div>
-<div id="sbayesrc" class="section level4">
-<h4>SBayesRC</h4>
-<p>In GenoPred, SBayesRC is implemented via pgs_<a
-href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/sbayesrc.R">pgs_methods/sbayesrc.R</a>.
-This method extends SBayesR to integrate functional annotations into the
-Bayesian framework for estimating SNP effects from GWAS summary
-statistics. By default SBayesRC will use the LD matrices released by the
-developers, restricted to GWAS from EUR, EAS, and AFR populations. The
-user can specify alternative LD reference data (<a
-href="pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods">see
-here</a>)</p>
-<hr />
-</div>
-</div>
-<div id="multi-ancestry-methods" class="section level3">
-<h3>Multi-ancestry Methods</h3>
-<p>The GenoPred pipeline implements a range of PGS methods intended for
-analysis of multiple GWAS from different ancestral populations.
-Multi-ancestry / Multi-source approaches can be catagorised as jointly
-optimised or independently optimised.</p>
-<hr />
-<div id="jointly-optimised" class="section level4">
-<h4>Jointly optimised</h4>
-<p>Jointly optimised methods, modelling all GWAS simultaneously when
-generating SNP-weights, are shown below:</p>
-<div
-style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:500px; overflow-x: scroll; width:100%; ">
-<table class="table table-striped table-hover" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
-<caption>
-Table 2 provides a summary of each approach and its implementation in
-GenoPred.
-</caption>
-<thead>
-<tr>
-<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
-Method
-</th>
-<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
-Software
-</th>
-<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
-PubMed ID
-</th>
-<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
-Pseudovalidation Option
-</th>
-<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
-Parameters
-</th>
-<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
-MHC Region
-</th>
-<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
-LD Reference
-</th>
-<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
-CPU usage*
-</th>
-<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
-Memory Usage*
-</th>
-</tr>
-</thead>
-<tbody>
-<tr>
-<td style="text-align:left;">
-PRS-CSx
-</td>
-<td style="text-align:left;">
-PRS-CSx
-</td>
-<td style="text-align:left;">
-35513724
-</td>
-<td style="text-align:left;">
-Yes
-</td>
-<td style="text-align:left;">
-phi = 1e-6, 1e-4, 1e-2, 1, auto
-</td>
-<td style="text-align:left;">
-Not excluded
-</td>
-<td style="text-align:left;">
-Population-matched UKB (PRS-CS provided)
-</td>
-<td style="text-align:left;">
-6.84 hrs
-</td>
-<td style="text-align:left;">
-15.2 GB
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-X-Wing
-</td>
-<td style="text-align:left;">
-LOGODetect, PANTHER, LEOPARD
-</td>
-<td style="text-align:left;">
-36788230
-</td>
-<td style="text-align:left;">
-Yes
-</td>
-<td style="text-align:left;">
-phi = auto
-</td>
-<td style="text-align:left;">
-Not excluded
-</td>
-<td style="text-align:left;">
-Population-matched UKB (PRS-CS provided)
-</td>
-<td style="text-align:left;">
-34.12 hrs
-</td>
-<td style="text-align:left;">
-48.7 GB
-</td>
-</tr>
-<tr>
-<td style="text-align:left;">
-TL-PRS
-</td>
-<td style="text-align:left;">
-TLPRS R package
-</td>
-<td style="text-align:left;">
-36240765
-</td>
-<td style="text-align:left;">
-No
-</td>
-<td style="text-align:left;">
-Block learning rates = 1, 10, 100, 1000. Iterations (k) =
-</td>
-<td style="text-align:left;">
-Dependant on the PGS method used to generate the score file input to
-TL-PRS
-</td>
-<td style="text-align:left;">
-Population-matched 1KG+HGDP
-</td>
-<td style="text-align:left;">
-0.33 hrs
-</td>
-<td style="text-align:left;">
-31.0 GB
-</td>
-</tr>
-</tbody>
-</table>
-</div>
-<p>* = Time per GWAS/population, using genome-wide data with 10 cores.
-See <a href="crosspop.html#computational-resources">here</a> for full
-benchmark information.</p>
-<p>Note.This is the default behaviour of the GenoPred pipeline. Certain
-aspects can be modified using the configfile (<a
-href="pipeline_readme.html#additional-parameters">see here</a>).</p>
-<hr />
-<div id="prs-csx" class="section level5">
-<h5>PRS-CSx</h5>
-<p>PRS-CSx, an extension of PRS-CS, integrates GWAS summary statistics
-from multiple ancestry groups using a Bayesian framework. The method is
-implemented in GenoPred via the script <a
-href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/prscsx.R">pgs_methods/prscsx.R</a>.
-We use summary statistics from the populations most closely matching the
-ancestry of the GWAS data to estimate posterior effect sizes. The LD
-information is derived from population-matched reference panels (e.g.,
-1KG). GenoPred clones PRS-CSx from GitHub
-(29a1148875f6ae3f2594b25579f40d4b587c5691) and uses the provided
-precompiled PRS-CSx binary to estimate SNP effect sizes. By default,
-GenoPred specifies four phi parameters (1e-6, 1e-4, 1e-2, 1) and the
-auto model, but the user can modify this behaviour using the prscs_phi
-parameter in the configfile. By default, GenoPred uses the PRS-CS
-provided 1KG-derived LD matrix data, matching the population of the GWAS
-sample. The user can select the UKB-derived LD matrix data to be used
-using the <code>prscs_ldref</code> parameter in the
-<code>configfile</code>. 1KG is used by default as PGS based on Yengo et
-al. sumstats performed significantly better in the OpenSNP target
-sample, when using the 1KG reference data (this may differ for other
-GWAS).</p>
-<hr />
-</div>
-<div id="x-wing" class="section level5">
-<h5>X-Wing</h5>
-<p>X-Wing is a polygenic scoring method designed to enhance genetic
-prediction accuracy across ancestrally diverse populations by leveraging
-a cross-population weighting framework. The method operates through a
-combination of several software tools: LOGODetect, PANTHER, and LEOPARD.
-GenoPred integrates this method using the script <a
-href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/xwing.R">pgs_methods/xwing.R</a>.
-GenoPred downloads X-WING software from GitHub
-(e9fcc264266e0e884323311816bfe20053fd3f7a). The LD information derived
-from the 1KG dataset is provided by the developers of X-Wing.</p>
-<hr />
-</div>
-<div id="tl-prs" class="section level5">
-<h5>TL-PRS</h5>
-<p>TL-PRS (Transfer Learning Polygenic Risk Score) is implemented using
-the script <a
-href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/tlprs.R">pgs_methods/tlprs.R</a>.
-The method combines summary statistics from target GWAS and a reference
-dataset via transfer learning, iteratively updating SNP effect sizes.
-GenoPred uses a modified version of TL-PRS to improve efficiency when
-being applied to many score files (<a
-href="https://github.com/opain/TLPRS"
-class="uri">https://github.com/opain/TLPRS</a>). No modifications to the
-parameters used has been altered from the original version of TL-PRS (<a
-href="https://github.com/ZhangchenZhao/TLPRS/commit/6c8d8905f91ce902f0cca8d5465f9952b0b3e274"
-class="uri">https://github.com/ZhangchenZhao/TLPRS/commit/6c8d8905f91ce902f0cca8d5465f9952b0b3e274</a>).
-Multiple learning rates (factors of 1, 10, 100, 1000) are tested in an
-iterative approach with 15 steps per factor to update the SNP effect
-sizes. GenoPred uses the 1KG+HGDP reference for LD estimation, matching
-the population of the GWAS sample.</p>
-<hr />
-</div>
-</div>
-<div id="independently-optimised" class="section level4">
-<h4>Independently optimised</h4>
-<p>The independentdently optimised multi-source approach describes the
-process of applying single-source PGS methods to each GWAS separately,
-and then linearly combining the population-specific PGS for the target
-population of interest. This linear combination can be estimated using
-individual-level data (simply by fitting a linear model containing all
-population-specific PGS), or using summary-statistics alone (using an
-approach called LEOPARD, from the Xwing software). In the GenoPred
-pipeline, we have adapted LEOPARD to use the super fast PGS method
-QuickPRS, to quickly and accurately estimate the optimal weights for
-linearly combining the population-specific PGS. These weights can be
-used to linearly combine population-specific PGS from single-source PGS
-method. We decribe and evaluate this process in detail in <a
-href="https://doi.org/10.1101/2025.03.27.25324773">this paper</a>.</p>
-<p>The script used to estimate the weights for population-specific PGS
-is <a
-href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/leopard_quickprs.R">pgs_methods/leopard_quickprs.R</a>,
-and the script used to linearly combine the PGS using these weights is
-<a
-href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/apply_leopard_weights.R">pgs_methods/apply_leopard_weights.R</a>.</p>
-<hr />
-</div>
 </div>
 <div id="pseudovalidation" class="section level3">
 <h3>Pseudovalidation</h3>
@@ -1348,12 +984,11 @@ <h3>Pseudovalidation</h3>
 score, using cross-validation or bootstrapping to estimating the effect
 of the polygenic score without overfitting. However, several methods
 offer a pseudovalidation approach, whereby the optimal tuning parameter
-is estimated based on the GWAS summary statistics alone, conveniently
+is estimated based on the GWAS summary statistics alone, convieniently
 avoiding the need for a tuning sample. The performance of
 pseudovalidation relative to formal validation varies between methods.
-See our previous <a
-href="https://doi.org/10.1101/2025.03.27.25324773">polygenic scoring
-methods comparison</a> for more information.</p>
+See our previous <a href="comparison_of_methods_summary.html">polygenic
+scoring methods comparison</a> for more information.</p>
 <hr />
 </div>
 </div>
@@ -1500,31 +1135,11 @@ <h2>Target scoring</h2>
 function to impute missing genotypes using the ancestry-matched
 reference allele frequency. The scores are computed for each chromosome,
 then multiplied by the number of non-missing variants to get the sum
-score, and then added across chromosomes. Finally, the target sample PGS
-are adjusted for ancestry, so they can be interpreted in units of SD
-from the mean in an ancestry-matched population. See <a
-href="#ancestry-adjustment">here</a> for more information on ancestry
-adjustment.</p>
-<hr />
-<div id="ancestry-adjustment" class="section level3">
-<h3>Ancestry adjustment</h3>
-<p>The GenoPred pipeline offers discrete or continuous approaches for
-adjusting PGS for ancestry. Previously described <a
-href="pipeline_readme.html#select-ancestry-adjustment-approach">here</a>.
-As of GenoPred v3.0.0, only the continuous approach is applied by
-default.</p>
-<p>The discrete approach involves standardising target sample PGS
-according the mean and SD of the PGS in the individual’s assigned
-reference population.</p>
-<p>The continuous approach uses reference data to model the relationship
-between genetic principal components and the PGS, and then corrects the
-mean and SD of the PGS for target individuals according to their
-reference-projected principal component scores. This approach was used
-in the study by Linders et al. (<a
-href="https://doi.org/10.1016/j.gim.2023.100006">here</a>).</p>
+score, and then added across chromosomes. Finally, the target sample
+scores are standardised according to the mean and SD of the score in an
+ancestry-matched reference.</p>
 <hr />
 </div>
-</div>
 <div id="report-creation" class="section level2">
 <h2>Report creation</h2>
 <hr />

From 5217b82e8b2d29a7de2e00acaafb92c087701c74 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 22 May 2025 17:29:15 +0100
Subject: [PATCH 147/153] Updates

---
 Scripts/pgs_methods/bridgeprs.R |  8 ++++----
 docs/CrossPop.Rmd               | 35 ++++++++++++++++++++++++++++++++-
 pipeline/rules/dependencies.smk |  8 ++++----
 pipeline/rules/pgs_methods.smk  |  2 +-
 4 files changed, 43 insertions(+), 10 deletions(-)

diff --git a/Scripts/pgs_methods/bridgeprs.R b/Scripts/pgs_methods/bridgeprs.R
index 8611af49..876f3e1f 100644
--- a/Scripts/pgs_methods/bridgeprs.R
+++ b/Scripts/pgs_methods/bridgeprs.R
@@ -127,12 +127,12 @@ for(i in 1:length(sumstats)){
   gwas_N <- c(gwas_N, round(mean(gwas$N), 0))
   gwas$N<NULL
   
-  # FOR testing
-  gwas <- gwas[sample(1:nrow(gwas), 10000),]
-
   # Write sumstats split by chromosome
   for(j in CHROMS){
     tmp<-gwas[gwas$CHR == j,]
+    # FOR testing
+    tmp <- tmp[1:500,]
+    gwas<-gwas[!(gwas$CHR == j & !(gwas$SNP %in% tmp$SNP)),]
     tmp$CHR<-NULL
     fwrite(tmp, paste0(tmp_dir, '/GWAS_sumstats_',populations[i],'_temp_chr', j, '.txt'), sep=' ')
   }
@@ -200,7 +200,7 @@ system(paste0(
   '--do_clump_pop2 1 ',
   '--do_est_beta_pop2 1 ',
   '--do_sumstat_ensembl_pop2 1 ',
-  '--n_folds 10 ',
+  '--n_folds 2 ', # FOR testing. Should be 10.
   ' > ',tmp_dir,'/bridge_log.txt 2>&1'
 ))
 
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index f3164245..47ac908c 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -7690,6 +7690,13 @@ selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_su
 # Get a list of score files
 scores <- list_score_files(config)
 
+# Get list of score files using full EUR GWAS
+config_full<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
+outdir_full <- read_param(config = config_full, param = 'outdir', return_obj = F)
+scores_full <- list_score_files(config_full)
+scores_full <- scores_full[grepl('UKB$', scores_full$name),]
+scores_full <- scores_full[scores_full$method %in% pgs_methods,]
+
 # Create files for EAS and AFR targets
 for(trait_i in selected_traits){
   
@@ -7715,6 +7722,22 @@ for(trait_i in selected_traits){
   
   scores_i$top1 <- paste0(scores_i$method,'.',gsub('.*_', '', scores_i$name))
 
+  # Now for full GWAS
+  scores_full_i <- scores_full[grepl(paste0('^', trait_i,'_'), scores_full$name),]
+
+  scores_full_i$predictor <- paste0(
+    outdir_full,
+    '/ukb/pgs/TRANS/',
+    scores_full_i$method,
+    '/',
+    scores_full_i$name,
+    '/ukb-',
+    scores_full_i$name,
+    '-TRANS.profiles'
+  )
+  
+  scores_full_i$top1 <- paste0(scores_full_i$method,'.full')
+
   ####
   # Make groups containing pseudo scores for single source methods
   ####
@@ -7755,13 +7778,22 @@ for(trait_i in selected_traits){
                                 paste0('.EUR_pseudo.profiles'),
                                 scores_i_pseudo$predictor)
 
+  # Now for full GWAS - skip subsetting pseudo as done before
+  scores_full_i_pseudo <- scores_full_i
+  scores_full_i_pseudo$top1<-paste0(scores_full_i_pseudo$top1,'.pseudo')
   
+  scores_full_i_pseudo$predictor <- gsub('.profiles', 
+                                paste0('.EUR_pseudo.profiles'),
+                                scores_full_i_pseudo$predictor)
+
   ####
   # Combine the different predictor groups
   ####
   predictors_i<- do.call(rbind, list(
     scores_i, 
-    scores_i_pseudo
+    scores_full_i, 
+    scores_i_pseudo, 
+    scores_full_i_pseudo
   ))
   predictors_i$multi <- 'All'
   predictors_i <- predictors_i[, c('predictor', 'multi','top1'), with=F]
@@ -8066,6 +8098,7 @@ for(pheno_i in selected_traits){
   eval_i$GWAS_N <- gsub('K\\..*','',eval_i$Group)
   eval_i$GWAS_N <- gsub('.*\\.','',eval_i$GWAS_N)
   eval_i$GWAS_N <- paste0(eval_i$GWAS_N,'K')
+  eval_i$GWAS_N[eval_i$GWAS_N == 'top1K'] <- '297K'
   
   eval_i$Model[grepl('top1$', eval_i$Group) &
                    !grepl('pseudo', eval_i$Group)]<-'IndivTune'
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index d9ace889..192cb189 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -443,7 +443,7 @@ def check_pgs_methods(x):
         return
 
     valid_pgs_methods = {
-        "ptclump", "dbslmm", "prscs", "sbayesr","sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs", "xwing", "prscsx"
+        "ptclump", "dbslmm", "prscs", "sbayesr","sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs", "xwing", "prscsx", "bridgeprs"
     }
 
     invalid_methods = [method for method in x if method not in valid_pgs_methods]
@@ -1455,12 +1455,12 @@ rule download_bridgeprs_software:
     """
     {{
       rm -r -f {resdir}/software/bridgeprs; \
-      git clone https://github.com/clivehoggart/BridgePRS.git {resdir}/software/bridgeprs; \
+      git clone https://github.com/opain/BridgePRS.git {resdir}/software/bridgeprs; \
       cd {resdir}/software/bridgeprs; \
-      git reset --hard a4b827c1a57c6e3b7a48ed495cac7ebdb5f89d96
+      git reset --hard 2a84532ed56140dc76259cb5818ca0cbc6c8750b
     }} > {log} 2>&1
     """
-
+    
 # Install R packages (handy function for when conda env updates erroneously)
 rule install_r_packages:
   input:
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 78553977..0bffd9ed 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -877,7 +877,7 @@ rule prep_pgs_bridgeprs_i:
   output:
     f"{outdir}/reference/pgs_score_files/bridgeprs/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
   conda:
-    "../envs/analysis.yaml"
+    "../envs/bridgeprs.yaml"
   benchmark:
     f"{outdir}/reference/benchmarks/prep_pgs_bridgeprs_i-{{gwas_group}}.txt"
   log:

From b22b4a74b61e9bbb97381cccacbc3450fca917ba Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 6 Jun 2025 17:49:25 +0100
Subject: [PATCH 148/153] updates

---
 Scripts/pgs_methods/bridgeprs.R  |  54 ++-
 Scripts/pgs_methods/pgs_meta.R   | 280 +++++++++++++++
 docs/CrossPop.Rmd                | 583 +++++++++++++++++++++++--------
 docs/pipeline_readme.Rmd         |   4 +-
 pipeline/misc/dev/setup_test.Rmd |  33 ++
 pipeline/rules/dependencies.smk  |   2 +-
 pipeline/rules/pgs_methods.smk   |  38 ++
 7 files changed, 805 insertions(+), 189 deletions(-)
 create mode 100644 Scripts/pgs_methods/pgs_meta.R
 create mode 100644 pipeline/misc/dev/setup_test.Rmd

diff --git a/Scripts/pgs_methods/bridgeprs.R b/Scripts/pgs_methods/bridgeprs.R
index 876f3e1f..fefec7e8 100644
--- a/Scripts/pgs_methods/bridgeprs.R
+++ b/Scripts/pgs_methods/bridgeprs.R
@@ -131,8 +131,8 @@ for(i in 1:length(sumstats)){
   for(j in CHROMS){
     tmp<-gwas[gwas$CHR == j,]
     # FOR testing
-    tmp <- tmp[1:500,]
-    gwas<-gwas[!(gwas$CHR == j & !(gwas$SNP %in% tmp$SNP)),]
+    # tmp <- tmp[1:500,]
+    #gwas<-gwas[!(gwas$CHR == j & !(gwas$SNP %in% tmp$SNP)),]
     tmp$CHR<-NULL
     fwrite(tmp, paste0(tmp_dir, '/GWAS_sumstats_',populations[i],'_temp_chr', j, '.txt'), sep=' ')
   }
@@ -159,11 +159,13 @@ if(any(populations == 'EUR') & any(populations == 'EAS')){
 
 system(paste0('rm -r ', tmp_dir,'/bridge_out'))
 dir.create(paste0(tmp_dir,'/bridge_out'))
+dir.create(paste0(tmp_dir,'/bridge_out/blocks'))
 
 system(paste0(
   opt$bridgeprs_repo, '/src/Bash/BridgePRS_sumstat.sh ', opt$bridgeprs_repo, '/src/Rscripts ',
   '--strand_check 1 ',
   '--outdir ', tmp_dir,'/bridge_out ',
+  '--blockdir ', tmp_dir,'/bridge_out ',
   '--by_chr 1 ',
   '--by_chr_sumstats .txt ',
   '--pop1 ', populations[1], ' ',
@@ -200,45 +202,29 @@ system(paste0(
   '--do_clump_pop2 1 ',
   '--do_est_beta_pop2 1 ',
   '--do_sumstat_ensembl_pop2 1 ',
-  '--n_folds 2 ', # FOR testing. Should be 10.
+  '--do_pool 1 ',
+  '--clean 1 ',
+  '--n_folds 10 ',
   ' > ',tmp_dir,'/bridge_log.txt 2>&1'
 ))
 
 ####
-# Combine score files
+# Read in the score file
 ####
 
-score_all<-NULL
-for(pop_i in c(unlist(strsplit(opt$populations, ',')), 'META')){
-  score_pop<-NULL
-  for(phi_i in phi_param){
-    score_phi<-NULL
-    for(i in CHROMS){
-      score_phi_i<-fread(paste0(tmp_dir,'/output_',pop_i,'_pst_eff_a1_b0.5_phi',phi_i,'_chr',i,'.txt'))
-      score_phi<-rbind(score_phi, score_phi_i)
-    }
-    if(phi_i == phi_param[1]){
-      score_phi<-score_phi[,c('V2', 'V4','V5', 'V6'), with=F]
-      names(score_phi)<-c('SNP', 'A1', 'A2', paste0('SCORE_',pop_i,'_phi_',phi_i))
-    } else {
-      score_phi<-score_phi[,'V6', with=F]
-      names(score_phi)<-paste0('SCORE_',pop_i,'_phi_',phi_i)
-    }
-    score_pop<-cbind(score_pop, score_phi)
-  }
-  if(pop_i == c(unlist(strsplit(opt$populations, ',')), 'META')[1]){
-    score_all<-score_pop
-  } else {
-    score_all<-merge(score_all, score_pop[, !(names(score_pop) %in% c('A1','A2')), with=F], by='SNP', all=T)
-  }
-}
-
-# Replace NA values with 0
-score_all[is.na(score_all)] <- 0
-
-# Flip effects to match reference alleles
 ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F]
-score_new <- map_score(ref = ref, score = score_all)
+
+score_list<-list()
+for(i in populations){
+  score_list[[i]] <- fread(paste0(tmp_dir,'/bridge_out/',i,'/fold2/snp_weights_weighted_model.dat'))
+  names(score_list[[i]]) <- c('SNP','A1','A2',paste0('SCORE_targ_', i))
+  
+  # Replace NA values with 0
+  score_list[[i]][is.na(score_list[[i]])] <- 0
+  
+  # Flip effects to match reference alleles
+  score_list[[i]] <- map_score(ref = ref, score = score_list[[i]])
+}
 
 fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
 
diff --git a/Scripts/pgs_methods/pgs_meta.R b/Scripts/pgs_methods/pgs_meta.R
new file mode 100644
index 00000000..3548ec76
--- /dev/null
+++ b/Scripts/pgs_methods/pgs_meta.R
@@ -0,0 +1,280 @@
+#!/usr/bin/Rscript
+# This script was written by Oliver Pain whilst at King's College London University.
+start.time <- Sys.time()
+suppressMessages(library("optparse"))
+
+option_list = list(
+  make_option("--output", action="store", default='NA', type='character',
+              help="Path for output files [required]"),
+  make_option("--plink2", action="store", default='plink2', type='character',
+              help="Path PLINK v2 software binary [optional]"),
+  make_option("--ref_plink_chr", action="store", default=NA, type='character',
+              help="Path to per chromosome reference PLINK files [required]"),
+  make_option("--pop_data", action="store", default=NULL, type='character',
+              help="File containing the population code and location of the keep file [required]"),
+  make_option("--method", action="store", default=NULL, type='character',
+              help="PGS method [required]"),
+  make_option("--sumstats", action="store", default=NULL, type='character',
+              help="Comma-seperated list of GWAS summary statistics [required]"),
+  make_option("--scores", action="store", default=NULL, type='character',
+              help="Comma-seperated list of score files [required]"),
+  make_option("--populations", action="store", default=NULL, type='character',
+              help="Comma-seperated list of population codes matching GWAS [required]"),
+  make_option("--test", action="store", default=NA, type='character',
+              help="Specify number of SNPs to include [optional]")
+)
+
+opt = parse_args(OptionParser(option_list=option_list))
+
+# Load dependencies
+library(GenoUtils)
+library(data.table)
+source('../functions/misc.R')
+source_all('../functions')
+
+# Check required inputs
+if(is.null(opt$ref_plink_chr)){
+  stop('--ref_plink_chr must be specified.\n')
+}
+if(is.null(opt$pop_data)){
+  stop('--pop_data must be specified.\n')
+}
+if(is.null(opt$sumstats)){
+  stop('--sumstats must be specified.\n')
+}
+if(is.null(opt$method)){
+  stop('--method must be specified.\n')
+}
+if(is.null(opt$scores)){
+  stop('--scores must be specified.\n')
+}
+if(is.null(opt$output)){
+  stop('--output must be specified.\n')
+}
+if(is.null(opt$populations)){
+  stop('--populations must be specified.\n')
+}
+
+# Create output directory
+opt$output_dir <- paste0(dirname(opt$output),'/')
+system(paste0('mkdir -p ',opt$output_dir))
+
+# Create temp directory
+tmp_dir<-tempdir()
+
+# Initiate log file
+log_file <- paste0(opt$output,'.log')
+log_header(log_file = log_file, opt = opt, script = 'pgsmeta', start.time = start.time)
+
+# If testing, change CHROMS to chr value
+if(!is.na(opt$test) && opt$test == 'NA'){
+  opt$test<-NA
+}
+if(!is.na(opt$test)){
+  CHROMS <- as.numeric(gsub('chr','',opt$test))
+}
+
+# Split opt$sumstats
+sumstats<-unlist(strsplit(opt$sumstats, ','))
+log_add(log_file = log_file, message = paste0(length(sumstats), ' sets of GWAS have been provided.'))
+
+# Split opt$populations
+populations<-unlist(strsplit(opt$populations, ','))
+
+# Split opt$scores
+score_files<-unlist(strsplit(opt$scores, ','))
+
+###
+# Read in GWAS sumstats
+###
+
+sumstats_list<-list()
+for(i in 1:length(sumstats)){
+  sumstats_list[[i]] <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('SNP','N'))
+}
+
+###
+# Read in score files
+###
+
+score_list<-list()
+for(i in 1:length(score_files)){
+  param <- find_pseudo(
+    config = opt$config,
+    gwas = gwas_list$name[i],
+    pgs_method = opt$method,
+    target_pop = gwas_list$population[i]
+  )
+  
+  score_header <-
+    fread(score_files[i], nrows = 1)
+  score_cols <-
+    which(names(score_header) %in% c('SNP','A1','A2', paste0('SCORE_', param)))
+  
+  score_full[[gwas_list$population[i]]] <- fread(cmd = 
+                                                   paste0(
+                                                     "zcat ", score_files[i], " | cut -d' ' -f ", 
+                                                     paste0(score_cols, collapse=','))
+  )
+  
+  score_list[[i]] <- fread(score_files[i])
+}
+
+###
+# Merge sumstats and respective scores
+###
+
+both_list<-list()
+for(i in 1:length(sumstats)){
+  both_list[[i]] <- merge(score_list[[i]], sumstats_list[[i]], by = c('SNP'), sort = F, all.x = T)
+}
+
+###
+# Meta analyse SNP effects
+###
+
+# Stopped here as realised we need to extract pseudoval PGS requiring config
+meta_analyse_snp_weights <- function(both_list, score_col = "SCORE_quickprs", weight_col = "N") {
+  # Merge all data frames by SNP (full outer join)
+  merged <- Reduce(function(x, y) merge(x, y, by = "SNP", all = TRUE), score_list)
+  
+  k <- length(both_list)
+  score_cols <- paste0(score_col, "_", 1:k)
+  weight_cols <- paste0(weight_col, "_", 1:k)
+  
+  # Ensure numeric
+  for (i in 1:k) {
+    merged[[score_cols[i]]] <- as.numeric(merged[[score_cols[i]]])
+    merged[[weight_cols[i]]] <- as.numeric(merged[[weight_cols[i]]])
+  }
+  
+  # Meta-analyse each SNP using available weights
+  numerator <- rowSums(sapply(1:k, function(i) {
+    score <- merged[[score_cols[i]]]
+    weight <- merged[[weight_cols[i]]]
+    score * weight * !is.na(score) * !is.na(weight)
+  }), na.rm = TRUE)
+  
+  denominator <- rowSums(sapply(1:k, function(i) {
+    weight <- merged[[weight_cols[i]]]
+    !is.na(merged[[score_cols[i]]]) * !is.na(weight) * weight
+  }), na.rm = TRUE)
+  
+  # Compute meta score only where denominator > 0
+  merged$meta_score <- ifelse(denominator > 0, numerator / denominator, NA)
+  
+  return(merged[, c("SNP", "meta_score")])
+}
+
+#####
+# Read in score files and subset pseudo score
+#####
+
+log_add(log_file = log_file, message = 'Reading in QuickPRS scores when using full sumstats.')
+
+score_full <- list()
+for(i in 1:nrow(gwas_list)){
+  param <- find_pseudo(
+    config = opt$config,
+    gwas = gwas_list$name[i],
+    pgs_method = opt$method,
+    target_pop = gwas_list$population[i]
+  )
+  
+  score_header <-
+    fread(score_files[i], nrows = 1)
+  score_cols <-
+    which(names(score_header) %in% c('SNP','A1','A2', paste0('SCORE_', param)))
+  
+  score_full[[gwas_list$population[i]]] <- fread(cmd = 
+    paste0(
+      "zcat ", score_files[i], " | cut -d' ' -f ", 
+      paste0(score_cols, collapse=','))
+    )
+  
+  names(score_full[[gwas_list$population[i]]])[4] <- paste0('SCORE_targ_', gwas_list$population[i])
+}
+
+####
+# Read in the mixing weights
+####
+
+mix_weights <- readRDS(paste0(outdir, '/reference/pgs_score_files/leopard/', opt$gwas_group, '/ref-', opt$gwas_group, '.weights.rds'))
+
+####
+# Combine score files
+####
+
+log_add(log_file = log_file, message = 'Creating score file.')
+
+# Combine the scores from each population
+score_all <- Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE), score_full)
+score_all[is.na(score_all)]<-0
+
+# Read in reference SNP and population data
+refdir <- read_param(config = opt$config, param = 'refdir', return_obj = F)
+opt$ref_plink_chr <- paste0(refdir, '/ref.chr')
+ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)
+opt$pop_data <- paste0(refdir, '/ref.pop.txt')
+pop_data <- read_pop_data(opt$pop_data)
+opt$ref_freq_chr <- paste0(refdir, '/freq_files')
+
+# Subset PGS to SNPs in ref (useful when testing)
+score_all <- score_all[score_all$SNP %in% ref$SNP,]
+
+# Flip effects to match reference alleles
+score_all <- map_score(ref = ref, score = score_all)
+
+# Calculate linear combination of scores using mixing weights for each target population
+score_weighted <- score_all
+for(targ_pop in gwas_list$population){
+  # Read in the .freq file for target population
+  freq_data <- read_frq(freq_dir = opt$ref_freq_chr, population = targ_pop, chr = CHROMS)
+  
+  # Centre SNP-weights for target population
+  score_i <- centre_weights(score = score_all, freq = freq_data, ref = ref)
+  
+  ###
+  # Scale weights to give PGS SD of 1 in target population
+  ###
+  
+  # Calculate scores in reference, and scale weights accordingly
+  fwrite(score_i, paste0(tmp_dir,'/tmp.',targ_pop,'.score'), col.names=T, sep=' ', quote=F)
+  
+  # Calc score in target sample
+  ref_pgs <- plink_score(pfile = opt$ref_plink_chr, plink2 = 'plink2', keep = pop_data[pop_data$POP == targ_pop, c('FID'), with=F], chr = CHROMS, score = paste0(tmp_dir,'/tmp.',targ_pop,'.score'))
+  ref_pgs_scale_i <- score_mean_sd(scores = ref_pgs)
+  
+  # Rescale SNP-weights according to PGS SD in target
+  for(i in gwas_list$population){
+    scaling_factor <- 1 / ref_pgs_scale_i$SD[ref_pgs_scale_i$Param == paste0('SCORE_targ_', i)]
+    score_i[[paste0('SCORE_targ_', i)]] <- score_i[[paste0('SCORE_targ_', i)]] * scaling_factor
+  }
+  
+  # Linearly combine scores using mixing weights for target population
+  score_weighted[[paste0('SCORE_targ_', targ_pop, '_weighted')]] <-
+    calculate_weighted_scores(score = score_i,
+                              mix_weights = mix_weights,
+                              targ_pop = targ_pop)
+}
+
+# Only retain weighted score columns
+score_weighted <- score_weighted[, c('SNP','A1','A2', names(score_weighted)[grepl('_weighted$', names(score_weighted))]), with=F]
+
+# Reduce number of significant figures to save space
+score_weighted[, (4:ncol(score_weighted)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_weighted)]
+
+fwrite(score_weighted, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F)
+
+if(file.exists(paste0(opt$output,'.score.gz'))){
+  system(paste0('rm ',opt$output,'.score.gz'))
+}
+
+system(paste0('gzip ',opt$output,'.score'))
+
+end.time <- Sys.time()
+time.taken <- end.time - start.time
+sink(file = log_file, append = T)
+cat('Analysis finished at', as.character(end.time),'\n')
+cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n')
+sink()
diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 47ac908c..55cd2d9a 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -3578,6 +3578,146 @@ ggplot(meta_tmp_simple[meta_tmp_simple$Target == 'EUR Target',], aes(x=label, y=
     )
 dev.off()
 
+########
+# Plot relative improvement of LEOPARD over IndivTune of SumStatTune scores
+########
+
+# meta res
+meta_res_comp_ref <- meta_res_comp[meta_res_comp$Model2 == 'Multi-SumStatTune',]
+meta_res_comp_ref <- meta_res_comp_ref[meta_res_comp_ref$Method1 != 'all' & meta_res_comp_ref$Method2 != 'all',]
+meta_res_comp_ref <- meta_res_comp_ref[meta_res_comp_ref$Model1 == 'SumStatTune' & meta_res_comp_ref$Source1 == 'Multi',]
+meta_res_comp_ref <- meta_res_comp_ref[gsub('_multi','', meta_res_comp_ref$Method1) == gsub('_multi','', meta_res_comp_ref$Method2),]
+
+meta_res_comp_ref$R_diff_perc <- meta_res_comp_ref$R_diff / meta_res_comp_ref$Model_2_R
+meta_res_comp_ref$R_diff_perc_SE <- meta_res_comp_ref$R_diff_SE / meta_res_comp_ref$Model_2_R
+
+meta_res_comp_ref$Discovery_clean <- paste0(meta_res_comp_ref$Discovery1,' GWAS')
+meta_res_comp_ref$Discovery_clean[meta_res_comp_ref$Target != 'EUR'] <- 'EUR + Target-matched GWAS'
+
+meta_res_comp_ref <- merge(meta_res_comp_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+meta_res_comp_ref$label[grepl('Multi', meta_res_comp_ref$Model1) & !(meta_res_comp_ref$Method1 %in% pgs_group_methods)] <- paste0(meta_res_comp_ref$label[grepl('Multi', meta_res_comp_ref$Model1) & !(meta_res_comp_ref$Method1 %in% pgs_group_methods)], '-multi')
+meta_res_comp_ref$label <- factor(meta_res_comp_ref$label, levels = model_order)
+
+meta_res_comp_ref$Target_clean <- paste0(meta_res_comp_ref$Target,' Target')
+
+# trait-specific res
+res_comp_all_ref <- res_comp_all[res_comp_all$Model2 == 'Multi-SumStatTune',]
+res_comp_all_ref <- res_comp_all_ref[res_comp_all_ref$Method1 != 'all' & res_comp_all_ref$Method2 != 'all',]
+res_comp_all_ref <- res_comp_all_ref[res_comp_all_ref$Model1 == 'SumStatTune' & res_comp_all_ref$Source1 == 'Multi',]
+res_comp_all_ref <- res_comp_all_ref[gsub('_multi','', res_comp_all_ref$Method1) == gsub('_multi','', res_comp_all_ref$Method2),]
+
+res_comp_all_ref$R_diff_perc <- res_comp_all_ref$R_diff / res_comp_all_ref$Model_2_R
+res_comp_all_ref$R_diff_perc_SE <- res_comp_all_ref$R_diff_SE / res_comp_all_ref$Model_2_R
+
+res_comp_all_ref$Discovery_clean <- paste0(res_comp_all_ref$Discovery1,' GWAS')
+res_comp_all_ref$Discovery_clean[res_comp_all_ref$Target != 'EUR'] <- 'EUR + Target-matched GWAS'
+
+res_comp_all_ref <- merge(res_comp_all_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+res_comp_all_ref$label[grepl('Multi', res_comp_all_ref$Model1) & !(res_comp_all_ref$Method1 %in% pgs_group_methods)] <- paste0(res_comp_all_ref$label[grepl('Multi', res_comp_all_ref$Model1) & !(res_comp_all_ref$Method1 %in% pgs_group_methods)], '-multi')
+res_comp_all_ref$label <- factor(res_comp_all_ref$label, levels = model_order)
+
+res_comp_all_ref$Target_clean <- paste0(res_comp_all_ref$Target,' Target')
+
+tmp_meta<-meta_res_comp_ref
+tmp_all<-res_comp_all_ref
+
+tmp_meta<-tmp_meta[!(tmp_meta$Method1 %in% c('prscsx','xwing')),]
+tmp_meta<-tmp_meta[tmp_meta$Target != 'EUR',]
+
+tmp_all<-tmp_all[!(tmp_all$Method1 %in% c('prscsx','xwing')),]
+tmp_all<-tmp_all[tmp_all$Target != 'EUR',]
+
+library(ggrepel)
+
+# plot
+png('~/oliverpainfel/Analyses/crosspop/plots/leopard_perc_improv.png', width = 1800, height = 1100, res= 300, units = 'px')
+
+ggplot(tmp_meta, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp_meta$label))), linetype="dotted") +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Difference (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(. ~ Target_clean) +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = 'none'
+    )
+dev.off()
+
+# Now compare quickPRS-multi and prs-csx only with trait
+tmp_meta<-meta_res_comp_ref
+tmp_all<-res_comp_all_ref
+
+tmp_meta<- tmp_meta[tmp_meta$Target != 'EUR' & tmp_meta$Method1 %in% c('quickprs_multi','prscsx'),]
+tmp_all<- tmp_all[tmp_all$Target != 'EUR' & tmp_all$Method1 %in% c('quickprs_multi','prscsx'),]
+
+library(ggrepel)
+
+png('~/oliverpainfel/Analyses/crosspop/plots/leopard_perc_improv_restricted.png', width = 1500, height = 1500, res= 300, units = 'px')
+ggplot(tmp_meta, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp_all,
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(. ~ Target_clean) +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = 'none'
+    ) +
+    geom_text_repel(
+      data = tmp_all[
+        tmp_all$R_diff_perc < -0.25,
+      ],
+      aes(label = pheno),  # label as percent with 1 decimal
+      position = position_dodge(width = 0.7),
+      size = 3,
+      min.segment.length = 0,
+      segment.color = NA,
+      show.legend = FALSE
+    )
+dev.off()
+
+# It shows PRS-CSx --meta flag is actually does very well, except when the AFR GWAS is very small (~2700).
+
 ```
 </details>
 
@@ -3717,52 +3857,53 @@ for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_met
   }
 }
 
-###
-# Estimate weights if using the inverse variance weighting
-###
-
-# Read in GWAS descriptives
-gwas_desc<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
-gwas_desc <- gwas_desc[, c('Trait Label','Ancestry','GWAS N'), with=F]
-names(gwas_desc)<-c('trait','ancestry','n')
-gwas_desc<-gwas_desc[gwas_desc$trait %in% selected_traits,]
-
-library(dplyr)
-library(tidyr)
-
-# Reshape GWAS table to wide format
-wide_gwas <- gwas_desc %>%
-  pivot_wider(names_from = ancestry, values_from = n, values_fill = 0)
-
-# Function to create rows for each pair
-make_weights_long <- wide_gwas %>%
-  rowwise() %>%
-  do({
-    trait <- .$trait
-    eur <- .$EUR
-    afr <- .$AFR
-    eas <- .$EAS
-    
-    tibble(
-      Trait = trait,
-      Method = "inverse_var",
-      Target = c("AFR", "AFR", "EUR", "EUR", "EUR", "EAS", "EAS"),
-      Discovery = c("EUR", "AFR", "EUR", "AFR", "EAS", "EUR", "EAS"),
-      Weight = c(
-        eur / (eur + afr), afr / (eur + afr),  # AFR target
-        eur / (eur + afr), afr / (eur + afr),  # EUR target (vs AFR)
-        eas / (eur + eas),                     # EUR target (vs EAS)
-        eur / (eur + eas), eas / (eur + eas)   # EAS target (vs EUR)
-      )
-    )
-  }) %>%
-  bind_rows()
+####
+## Estimate weights if using the inverse variance weighting (realised this doesn't make sense as PGS are standardised whereas SNP effects in PRS-CSx are not)
+####
+#
+## Read in GWAS descriptives
+#gwas_desc<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+#gwas_desc <- gwas_desc[, c('Trait Label','Ancestry','GWAS N'), with=F]
+#names(gwas_desc)<-c('trait','ancestry','n')
+#gwas_desc<-gwas_desc[gwas_desc$trait %in% selected_traits,]
+#
+#library(dplyr)
+#library(tidyr)
+#
+## Reshape GWAS table to wide format
+#wide_gwas <- gwas_desc %>%
+#  pivot_wider(names_from = ancestry, values_from = n, values_fill = 0)
+#
+## Function to create rows for each pair
+#make_weights_long <- wide_gwas %>%
+#  rowwise() %>%
+#  do({
+#    trait <- .$trait
+#    eur <- .$EUR
+#    afr <- .$AFR
+#    eas <- .$EAS
+#    
+#    tibble(
+#      Trait = trait,
+#      Method = "inverse_var",
+#      Target = c("AFR", "AFR", "EUR", "EUR", "EUR", "EAS", "EAS"),
+#      Discovery = c("EUR", "AFR", "EUR", "AFR", "EAS", "EUR", "EAS"),
+#      Weight = c(
+#        eur / (eur + afr), afr / (eur + afr),  # AFR target
+#        eur / (eur + afr), afr / (eur + afr),  # EUR target (vs AFR)
+#        eas / (eur + eas),                     # EUR target (vs EAS)
+#        eur / (eur + eas), eas / (eur + eas)   # EAS target (vs EUR)
+#      )
+#    )
+#  }) %>%
+#  bind_rows()
 
 ###
 # Combine and compare
 ###
 
-both <- do.call(rbind, list(obs_weights, leopard_weights, make_weights_long))
+#both <- do.call(rbind, list(obs_weights, leopard_weights, make_weights_long))
+both <- do.call(rbind, list(obs_weights, leopard_weights))
 
 # Remove ptclump as it doesn't have a sumstattune method
 both <- both[both$Method != 'ptclump',]
@@ -3871,59 +4012,59 @@ ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) +
   coord_fixed()    
 dev.off()
 
-###
-# Check calibration of inverse_var compared to QuickPRS observed weights
-###
-
-tmp <- both[both$Target != 'EUR',]
-tmp$Target<-NULL
-tmp_wide <- reshape(tmp, 
-                     idvar = c("Trait", "Discovery"), 
-                     timevar = "label", 
-                     direction = "wide")
-
-names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
-tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
-
-tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
-tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
-
-# Calculate metrics
-rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)^2))
-me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)
-
-rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)^2))
-me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)
-
-# Create annotation data.frame
-metrics_df <- data.frame(
-  Discovery = c("AFR", "EAS"),
-  x = c(0.5, 0.5),         # Adjust positions as needed
-  y = c(-0.05, -0.05),
-  label = c(
-    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
-    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
-  )
-)
-
-png('~/oliverpainfel/Analyses/crosspop/plots/inverse_var_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
-ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = inverse_var, y = QuickPRS)) +
-  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
-  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
-  geom_point(alpha = 0.7) +
-  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 1.5, size = 3.5) +
-  labs(
-    x = "inverse_var weight",
-    y = "Observed weight",
-  ) +
-  theme_half_open() +
-  panel_border() + 
-  theme(
-    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
-  ) +
-  facet_grid(. ~ Discovery) +
-  coord_fixed()    
-dev.off()
+####
+## Check calibration of inverse_var compared to QuickPRS observed weights (again realised this doesn't make sense)
+####
+#
+#tmp <- both[both$Target != 'EUR',]
+#tmp$Target<-NULL
+#tmp_wide <- reshape(tmp, 
+#                     idvar = c("Trait", "Discovery"), 
+#                     timevar = "label", 
+#                     direction = "wide")
+#
+#names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+#tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+#
+#tmp_wide_eas <- tmp_wide[tmp_wide$Discovery == 'EAS',]
+#tmp_wide_afr <- tmp_wide[tmp_wide$Discovery == 'AFR',]
+#
+## Calculate metrics
+#rmse_afr <- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)^2))
+#me_afr <- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)
+#
+#rmse_eas <- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)^2))
+#me_eas <- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)
+#
+## Create annotation data.frame
+#metrics_df <- data.frame(
+#  Discovery = c("AFR", "EAS"),
+#  x = c(0.5, 0.5),         # Adjust positions as needed
+#  y = c(-0.05, -0.05),
+#  label = c(
+#    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
+#    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
+#  )
+#)
+#
+#png('~/oliverpainfel/Analyses/crosspop/plots/inverse_var_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
+#ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = inverse_var, y = QuickPRS)) +
+#  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
+#  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
+#  geom_point(alpha = 0.7) +
+#  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 1.5, size = 3.5) +
+#  labs(
+#    x = "inverse_var weight",
+#    y = "Observed weight",
+#  ) +
+#  theme_half_open() +
+#  panel_border() + 
+#  theme(
+#    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+#  ) +
+#  facet_grid(. ~ Discovery) +
+#  coord_fixed()    
+#dev.off()
 
 ###
 # Check calibration of observed weights across all methods
@@ -4022,63 +4163,63 @@ ggplot(mean_rmse, aes(x = Method, y = avg_rmse, fill = Method)) +
         legend.position="none")
 dev.off()
 
-###
-# Check calibration of estimated (LEOPARD and inverse_var) weights compared to observed QuickPRS weights
-###
-
-tmp <- both[both$Target != 'EUR',]
-tmp <- tmp[(tmp$label %in% c('LEOPARD','inverse_var','QuickPRS')),]
-tmp$Target<-NULL
-tmp_wide <- reshape(tmp, 
-                     idvar = c("Trait", "Discovery"), 
-                     timevar = "label", 
-                     direction = "wide")
-
-names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
-tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
-
-tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
-
-metrics <- NULL
-for(i in c('EAS','AFR')){
-  for(j in unique(tmp$label)){
-    for(k in unique(tmp$label)){
-      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
-      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
-      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
-      
-      metrics <- rbind(
-        metrics,
-        data.frame(
-          Population = i,
-          Method1 = j,
-          Method2 = k,
-          rmse = rmse,
-          me = me
-        )
-      )
-    }
-  }
-}
-
-# Plot the rmse for LEOPARD and inverse_var predicting observed QuickPRS weight
-metrics <- metrics[metrics$Method1 == 'QuickPRS',]
-metrics <- metrics[metrics$Method2 != 'QuickPRS',]
-
-png('~/oliverpainfel/Analyses/crosspop/plots/inverse_var_comp_rmse.png', units = 'px', width = 800, height = 1500, res = 300)
-ggplot(metrics, aes(x = Method2, y = rmse, fill = Method2)) +
-  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
-  geom_text(aes(label = round(rmse, 3)),  # <-- Add this
-          vjust = 1.5,                    # <-- Move the text slightly above the bar
-          size = 3) +                      # <-- Adjust text size
-  facet_grid(Population ~ .) +
-  theme_half_open() +
-  background_grid(major = 'y', minor = 'y') + 
-  panel_border() +
-  labs(y = 'RMSE relative to QuickPRS', x = 'Method') +
-  theme(axis.text.x = element_text(angle = 45, hjust = 1),
-        legend.position="none")
-dev.off()
+####
+## Check calibration of estimated (LEOPARD and inverse_var) weights compared to observed QuickPRS weights
+####
+#
+#tmp <- both[both$Target != 'EUR',]
+#tmp <- tmp[(tmp$label %in% c('LEOPARD','inverse_var','QuickPRS')),]
+#tmp$Target<-NULL
+#tmp_wide <- reshape(tmp, 
+#                     idvar = c("Trait", "Discovery"), 
+#                     timevar = "label", 
+#                     direction = "wide")
+#
+#names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+#tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+#
+#tmp_wide <- tmp_wide[tmp_wide$Discovery %in% c('EAS','AFR'),]
+#
+#metrics <- NULL
+#for(i in c('EAS','AFR')){
+#  for(j in unique(tmp$label)){
+#    for(k in unique(tmp$label)){
+#      tmp_wide_i <- tmp_wide[tmp_wide$Discovery == i,]
+#      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+#      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+#      
+#      metrics <- rbind(
+#        metrics,
+#        data.frame(
+#          Population = i,
+#          Method1 = j,
+#          Method2 = k,
+#          rmse = rmse,
+#          me = me
+#        )
+#      )
+#    }
+#  }
+#}
+#
+## Plot the rmse for LEOPARD and inverse_var predicting observed QuickPRS weight
+#metrics <- metrics[metrics$Method1 == 'QuickPRS',]
+#metrics <- metrics[metrics$Method2 != 'QuickPRS',]
+#
+#png('~/oliverpainfel/Analyses/crosspop/plots/inverse_var_comp_rmse.png', units = 'px', width = 800, height = 1500, res = 300)
+#ggplot(metrics, aes(x = Method2, y = rmse, fill = Method2)) +
+#  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) +
+#  geom_text(aes(label = round(rmse, 3)),  # <-- Add this
+#          vjust = 1.5,                    # <-- Move the text slightly above the bar
+#          size = 3) +                      # <-- Adjust text size
+#  facet_grid(Population ~ .) +
+#  theme_half_open() +
+#  background_grid(major = 'y', minor = 'y') + 
+#  panel_border() +
+#  labs(y = 'RMSE relative to QuickPRS', x = 'Method') +
+#  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+#        legend.position="none")
+#dev.off()
 
 ```
 
@@ -9185,7 +9326,7 @@ for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_met
       
       for(k in pops){
           for(n in c('5','15','45','135')){
-          model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/subsampled/targ_', j, '.disc_EUR_', k, '/', i, '/res_n', n, '_final_models/', method_i, '.pseudo.multi.final_model.txt'))
+          model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/subsampled/targ_', j, '.disc_EUR_', k, '/', i, '/res', n, '_final_models/', method_i, '.pseudo.multi.final_model.txt'))
           model<-model[-1,]
           
           # Set weight to zero if negative, as this is what LEOPARD does
@@ -11175,6 +11316,144 @@ ggplot(meta_tmp_simple[meta_tmp_simple$Target != 'EUR Target',], aes(x=label, y=
     )
 dev.off()
 
+########
+# Plot relative improvement of LEOPARD over IndivTune of SumStatTune scores
+########
+
+# meta res
+meta_res_comp_ref <- meta_res_comp[meta_res_comp$Model2 == 'Multi-SumStatTune',]
+meta_res_comp_ref <- meta_res_comp_ref[meta_res_comp_ref$Method1 != 'all' & meta_res_comp_ref$Method2 != 'all',]
+meta_res_comp_ref <- meta_res_comp_ref[meta_res_comp_ref$Model1 == 'SumStatTune' & meta_res_comp_ref$Source1 == 'Multi',]
+meta_res_comp_ref <- meta_res_comp_ref[gsub('_multi','', meta_res_comp_ref$Method1) == gsub('_multi','', meta_res_comp_ref$Method2),]
+
+meta_res_comp_ref$R_diff_perc <- meta_res_comp_ref$R_diff / meta_res_comp_ref$Model_2_R
+meta_res_comp_ref$R_diff_perc_SE <- meta_res_comp_ref$R_diff_SE / meta_res_comp_ref$Model_2_R
+
+meta_res_comp_ref$Discovery_clean <- paste0(meta_res_comp_ref$Discovery1,' GWAS')
+meta_res_comp_ref$Discovery_clean[meta_res_comp_ref$Target != 'EUR'] <- 'EUR + Target-matched GWAS'
+
+meta_res_comp_ref <- merge(meta_res_comp_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+meta_res_comp_ref$label[grepl('Multi', meta_res_comp_ref$Model1) & !(meta_res_comp_ref$Method1 %in% pgs_group_methods)] <- paste0(meta_res_comp_ref$label[grepl('Multi', meta_res_comp_ref$Model1) & !(meta_res_comp_ref$Method1 %in% pgs_group_methods)], '-multi')
+meta_res_comp_ref$label <- factor(meta_res_comp_ref$label, levels = model_order)
+
+meta_res_comp_ref$Target_clean <- paste0(meta_res_comp_ref$Target,' Target')
+
+# trait-specific res
+res_comp_all_ref <- res_comp_all[res_comp_all$Model2 == 'Multi-SumStatTune',]
+res_comp_all_ref <- res_comp_all_ref[res_comp_all_ref$Method1 != 'all' & res_comp_all_ref$Method2 != 'all',]
+res_comp_all_ref <- res_comp_all_ref[res_comp_all_ref$Model1 == 'SumStatTune' & res_comp_all_ref$Source1 == 'Multi',]
+res_comp_all_ref <- res_comp_all_ref[gsub('_multi','', res_comp_all_ref$Method1) == gsub('_multi','', res_comp_all_ref$Method2),]
+
+res_comp_all_ref$R_diff_perc <- res_comp_all_ref$R_diff / res_comp_all_ref$Model_2_R
+res_comp_all_ref$R_diff_perc_SE <- res_comp_all_ref$R_diff_SE / res_comp_all_ref$Model_2_R
+
+res_comp_all_ref$Discovery_clean <- paste0(res_comp_all_ref$Discovery1,' GWAS')
+res_comp_all_ref$Discovery_clean[res_comp_all_ref$Target != 'EUR'] <- 'EUR + Target-matched GWAS'
+
+res_comp_all_ref <- merge(res_comp_all_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+res_comp_all_ref$label[grepl('Multi', res_comp_all_ref$Model1) & !(res_comp_all_ref$Method1 %in% pgs_group_methods)] <- paste0(res_comp_all_ref$label[grepl('Multi', res_comp_all_ref$Model1) & !(res_comp_all_ref$Method1 %in% pgs_group_methods)], '-multi')
+res_comp_all_ref$label <- factor(res_comp_all_ref$label, levels = model_order)
+
+res_comp_all_ref$Target_clean <- paste0(res_comp_all_ref$Target,' Target')
+
+tmp_meta<-meta_res_comp_ref
+tmp_all<-res_comp_all_ref
+
+tmp_meta<-tmp_meta[!(tmp_meta$Method1 %in% c('prscsx','xwing')),]
+tmp_meta<-tmp_meta[tmp_meta$Target != 'EUR',]
+
+tmp_all<-tmp_all[!(tmp_all$Method1 %in% c('prscsx','xwing')),]
+tmp_all<-tmp_all[tmp_all$Target != 'EUR',]
+
+library(ggrepel)
+
+# plot
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_perc_improv.png', width = 1800, height = 1100, res= 300, units = 'px')
+
+ggplot(tmp_meta, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp_meta$label))), linetype="dotted") +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Difference (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(. ~ Target_clean) +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = 'none'
+    )
+dev.off()
+
+# Now compare quickPRS-multi and prs-csx only with trait
+tmp_meta<-meta_res_comp_ref
+tmp_all<-res_comp_all_ref
+
+tmp_meta<- tmp_meta[tmp_meta$Target != 'EUR' & tmp_meta$Method1 %in% c('quickprs_multi','prscsx'),]
+tmp_all<- tmp_all[tmp_all$Target != 'EUR' & tmp_all$Method1 %in% c('quickprs_multi','prscsx'),]
+
+library(ggrepel)
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_perc_improv_restricted.png', width = 1500, height = 1500, res= 300, units = 'px')
+ggplot(tmp_meta, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp_all,
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = "identity",
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Difference (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(. ~ Target_clean) +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = 'none'
+    ) +
+    geom_text_repel(
+      data = tmp_all[
+        tmp_all$R_diff_perc < -0.05,
+      ],
+      aes(label = pheno),  # label as percent with 1 decimal
+      position = position_dodge(width = 0.7),
+      size = 3,
+      min.segment.length = 0,
+      segment.color = NA,
+      show.legend = FALSE
+    )
+dev.off()
+
 ```
 </details>
 
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 7a163fb2..6431cfaa 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -141,13 +141,13 @@ Accept the default installation options. Once installed, you may need to run sou
 Now, create the GenoPred environment using Mamba:
 
 ```{bash}
-conda env create -f GenoPred/pipeline/envs/pipeline.yaml
+mamba env create -f GenoPred/pipeline/envs/pipeline.yaml
 ```
 
 Activate the new `genopred` environment:
 
 ```{bash}
-conda activate genopred
+mamba activate genopred
 ```
 
 <div class="note-box">
diff --git a/pipeline/misc/dev/setup_test.Rmd b/pipeline/misc/dev/setup_test.Rmd
new file mode 100644
index 00000000..1d148146
--- /dev/null
+++ b/pipeline/misc/dev/setup_test.Rmd
@@ -0,0 +1,33 @@
+---
+title: "Test Setup"
+output: html_document
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(echo = TRUE)
+```
+
+This document is just for me to remember how I test the installation of the pipeline within a native environment.
+
+```{bash}
+mkdir -p /users/k1806347/oliverpainfel/test/genopred_demo/tmp
+
+singularity shell \
+  --cleanenv \
+  --containall \
+  --home /users/k1806347/oliverpainfel/test/genopred_demo:/home/genouser \
+  --bind /scratch/k1806347:/tmp \
+  /users/k1806347/oliverpainfel/Software/singularity/my_ubuntu_latest.sif
+
+mkdir /users/k1806347/oliverpainfel/test/empty_dir
+rsync -a --delete /users/k1806347/oliverpainfel/test/empty_dir/    /users/k1806347/oliverpainfel/test/genopred_demo/
+
+cd /users/k1806347/oliverpainfel/test/genopred_demo
+
+
+singularity exec \
+  genopred.sif \
+  bash -c "cd /data && ./run_genopred.sh"
+  
+```
+
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 192cb189..3c2c09f0 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -1457,7 +1457,7 @@ rule download_bridgeprs_software:
       rm -r -f {resdir}/software/bridgeprs; \
       git clone https://github.com/opain/BridgePRS.git {resdir}/software/bridgeprs; \
       cd {resdir}/software/bridgeprs; \
-      git reset --hard 2a84532ed56140dc76259cb5818ca0cbc6c8750b
+      git reset --hard aeea807c9640e28f45dac24a9b5d524a3f11f7f2
     }} > {log} 2>&1
     """
     
diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk
index 0bffd9ed..7ec1fa9a 100644
--- a/pipeline/rules/pgs_methods.smk
+++ b/pipeline/rules/pgs_methods.smk
@@ -699,6 +699,44 @@ rule prep_pgs_multi_i:
 rule prep_pgs_multi:
   input: expand(f"{outdir}/reference/pgs_score_files/{{method}}_multi/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'], method = requested_single_source_methods)
 
+####
+# Inverse-variance meta-analysis 
+####
+
+# Estimate weights for population-specific PGS from single-source methods
+rule pgsmeta_i:
+  resources:
+    mem_mb=10000
+  input:
+    lambda w: expand(f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group)),
+    lambda w: expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))
+  output:
+    f"{outdir}/reference/pgs_score_files/{{method}}_meta/{{gwas_group}}/ref-{{gwas_group}}.score.gz"
+  benchmark:
+    f"{outdir}/reference/benchmarks/pgsmeta_i-{{gwas_group}}-{{method}}.txt"
+  log:
+    f"{outdir}/reference/logs/pgsmeta_i-{{gwas_group}}-{{method}}.log"
+  conda:
+    "../envs/analysis.yaml"
+  params:
+    sumstats= lambda w: ",".join(expand(f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", gwas=get_gwas_names(w.gwas_group))),
+    scores= lambda w: ",".join(expand(f"{outdir}/reference/pgs_score_files/quickprs/{{gwas}}/ref-{{gwas}}.score.gz", gwas=get_gwas_names(w.gwas_group))),
+    populations= lambda w: ",".join(get_populations(w.gwas_group)),
+    testing=config["testing"]
+  shell:
+    "Rscript ../Scripts/pgs_methods/pgsmeta.R \
+      --ref_plink_chr {refdir}/ref.chr \
+      --pop_data {refdir}/ref.pop.txt \
+      --sumstats {params.sumstats} \
+      --scores {params.scores} \
+      --populations {params.populations} \
+      --method {wildcards.method} \
+      --output {outdir}/reference/pgs_score_files/{wildcards.method}_meta/{wildcards.gwas_group}/ref-{wildcards.gwas_group} \
+      --test {params.testing} > {log} 2>&1"
+
+rule pgsmeta:
+  input: expand(f"{outdir}/reference/pgs_score_files/{{method}}_meta/{{gwas_group}}/ref-{{gwas_group}}.score.gz", gwas_group=gwas_groups_df['name'], method = requested_single_source_methods)
+
 #########
 # Jointly optimised methods
 #########

From 0baf762ac4ae09348f3d275e064fbeddd4f6ea81 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 6 Jun 2025 20:16:00 +0100
Subject: [PATCH 149/153] Updating documentation

---
 docs/pipeline_readme.Rmd                      | 149 +++++++++++-
 docs/pipeline_readme.html                     | 227 ++++++++++++++++--
 docs/pipeline_technical.Rmd                   |  10 +-
 .../example_input/config.multisource.yaml     |  23 +-
 pipeline/misc/dev/setup_test.Rmd              |  13 +-
 5 files changed, 375 insertions(+), 47 deletions(-)

diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 6431cfaa..99b68380 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -132,8 +132,8 @@ If you don't already have Miniforge installed, you can install it using the comm
 
 ```{bash}
 # Download and install Miniforge (for Linux)
-wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
-bash Miniforge3-Linux-x86_64.sh
+wget https://github.com/conda-forge/miniforge/releases/download/24.11.3-0/Miniforge3-24.11.3-0-Linux-x86_64.sh
+bash Miniforge3-24.11.3-0-Linux-x86_64.sh
 ```
 
 Accept the default installation options. Once installed, you may need to run source ~/.bashrc or restart your terminal. You should see (base) appear at the beginning of your terminal prompt.
@@ -1420,16 +1420,100 @@ An alternative multi-source PGS approach is to linearly combine population-speci
 
 ***
 
+We have provided some test data to run the multi-source functionality within GenoPred. To run the pipeline with the test_data, we will use the `example_input/config.multisource.yaml`. We have made some slight edits to the configuration used in the [previous test run](#run-using-test-data). Mainly we have provided a `gwas_groups` file, and we have specified the methods that we want to be linearly combined using LEOPARD+QuickPRS using the `leopard_methods` parameter.
+
+<details>
+
+<summary>See contents of multisource configfile</summary>
+
+```{bash}
+# Specify output directory
+outdir: test_data/output/test1
+
+# Location of this config file
+config_file: example_input/config.multisource.yaml
+
+# Specify location of gwas_list file
+gwas_list: example_input/gwas_list.multisource.txt
+
+# Specify location of gwas_groups file
+gwas_groups: example_input/gwas_groups.multisource.txt
+
+# Specify location of target_list file
+target_list: example_input/target_list.txt
+
+# Specify pgs_methods ('ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs')
+pgs_methods: ['lassosum']
+
+# Specify methods for which PGS should be combined using LEOPARD+QuickPRS
+leopard_methods: ['lassosum']
+
+# Specify if you want test mode. Set to NA if you don't want test mode
+testing: chr22
+```
+
+</details>
+
+<details>
+
+<summary>See contents of example gwas_list</summary>
+
+```{r, eval = T, echo = F, results = 'asis'}
+
+gwas_list <- fread('../pipeline/example_input/gwas_list.multisource.txt')
+
+kable(gwas_list, 'markdown')
+
+```
+
+</details>
+
+<details>
+
+<summary>See contents of example gwas_groups</summary>
+
+```{r, eval = T, echo = F, results = 'asis'}
+
+gwas_groups <- fread('../pipeline/example_input/gwas_groups.multisource.txt')
+
+kable(gwas_groups, 'markdown')
+
+```
+
+</details>
+
+***
+
+```{bash}
+# Remember to activate the genopred environment and go into to the GenoPred/pipeline directory before running the pipeline
+conda activate genopred
+cd ~/GenoPred/pipeline
+
+# Do a dry run to see what would happen
+snakemake -n --configfile=example_input/config.multisource.yaml output_all
+
+# Run the pipeline running one step at a time 
+snakemake -j1 --configfile=example_input/config.multisource.yaml --use-conda output_all
+```
+
+Once the pipeline is complete, you can check that there is nothing else to do by doing another dry run, and it should say 'Nothing to be done'.
+
+When using LEOPARD to combine single-source PGS, we append `_multi` to the end method name. For example, in this case we are combining `lassosum` PGS, so we would refer to this as `lassosum_multi`. This is important when navigating output files. For example, you will the PGS will be in the following path: `[outdir]/[target name]/pgs/[population]/lassosum_multi`. Similarly, this method is referred to as `lassosum_multi` when reading the PGS into R using the `read_pgs()` function ([here](#read_pgs)).
+
+When applying TL-PRS to a given method, we append `tlprs_` to the begining of the method name. For example, if were applying TL-PRS to `lassosum` PGS, so we would refer to this as `tlprs_lassosum`.
+
+***
+
 ## Select ancestry adjustment approach
 
 The GenoPred pipeline offers two approaches for adjusting PGS for ancestry: discrete or continuous.
 
-### Discrete Correction:
+### Discrete Correction
 * This approach assigns each target individual to an ancestry-matched reference population.
 * The target individual's PGS is then standardized based on the mean and standard deviation (SD) of the PGS within the corresponding reference population.
 * This is the approach used by GenoPred until v3.0.0.
 
-### Continuous Correction:
+### Continuous Correction
 * This method utilizes genetic principal components projected from reference populations to standardize target PGS.
 * It accounts for variation within reference populations and accommodates individuals whose ancestry does not align closely with a single reference population.
 * GenoPred uses the approach described by Linders et al. ([here](https://doi.org/10.1016/j.gim.2023.100006)), which adjust both the mean and variance of the target PGS.
@@ -1454,15 +1538,56 @@ pgs_scaling: ['discrete']
 
 ## Specifying alternative reference data for PGS methods
 
-PGS methods require linkage disequilibrium (LD) data. The default LD data used for each method is indicated in the technical documentation ([here]()). Several methods use the reference data used for the pipeline in general, so altering the `ref_dir` parameter (as [described above]()) will also alter the reference data used by these PGS methods. However, several PGS methods use pre-computed LD matrices, and the user can specify alternative LD matrices for these methods, using the configfile parameters below:
+PGS methods require linkage disequilibrium (LD) data. The default LD reference data used for each method is described in the [technical documentation](pipeline_technical.html). Several methods use the global `ref_dir` parameter (as described [here](#using-your-own-reference)), which affects the reference data used across the pipeline.
+
+However, many PGS methods rely on *precomputed LD matrices*, and the user can override the default reference data for these methods by specifying method-specific LD matrices using the `configfile` parameters described below.
+
+For `PRS-CS`, `PRS-CSx`, and `X-Wing`, use the `prs_cs_ldref` parameter to specify whether LD is estimated from the 1KG or UK Biobank samples. Options are:
+
+- `1kg` (default): 1000 Genomes-based LD  
+- `ukb`: UK Biobank-based LD
+
+<div class="note-box">
+
+**Note**: In OpenSNP target samples, `sumstats` performed significantly better using 1KG LD data (Yengo et al.), although this may vary by GWAS.
+
+</div>
+
+For other methods, the `configfile` must point to a directory containing precomputed LD matrices in the required format. This directory must contain one subdirectory per population (e.g., `AFR`, `EUR`), each containing the required reference files.
 
-- PRS-CS
-- PRS-CSx
-- SBayesR
-- SBayesRC
-- LDpred2
-- QuickPRS
-- LEOPARD+QuickPRS
+The relevant `configfile` parameters are:
+
+- `sbayesr_ldref`: for **SBayesR**
+- `sbayesrc_ldref`: for **SBayesRC**
+- `ldpred2_ldref`: for **LDpred2**
+- `quickprs_ref`: for **QuickPRS**
+- `quickprs_multi_ldref`: for **LEOPARD+QuickPRS**
+
+For example, if you want to supply LD data for `AFR` and `EUR` populations with QuickPRS, the `quickprs_ref` directory should be structured as follows:
+
+```{}
+[quickprs_ref]
+  ├── AFR
+  │   ├──AFR.bld.ldak.quickprs.matrix
+  │   ├──AFR.cors.bim
+  │   ├──AFR.cors.bin
+  │   ├──AFR.cors.noise
+  │   ├──AFR.cors.root
+  │   └──highld.snps
+  └── EUR
+      ├──EUR.bld.ldak.quickprs.matrix
+      ├──EUR.cors.bim
+      ├──EUR.cors.bin
+      ├──EUR.cors.noise
+      ├──EUR.cors.root
+      └──highld.snps
+```
+
+<div class="note-box">
+
+**Note**: If alternative LD reference data is specified, it must be provided for **all GWAS populations listed in the `gwas_list`**. File formats required vary by method — see the documentation or GitHub README for each tool for exact format specifications.
+
+</div>
 
 ***
 
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index bbfc6264..cf97fcdd 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -575,15 +575,15 @@ <h2>Step 2: Create software environment for pipeline</h2>
 <p>If you don’t already have Miniforge installed, you can install it
 using the commands below:</p>
 <pre class="bash"><code># Download and install Miniforge (for Linux)
-wget https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-Linux-x86_64.sh
-bash Miniforge3-Linux-x86_64.sh</code></pre>
+wget https://github.com/conda-forge/miniforge/releases/download/24.11.3-0/Miniforge3-24.11.3-0-Linux-x86_64.sh
+bash Miniforge3-24.11.3-0-Linux-x86_64.sh</code></pre>
 <p>Accept the default installation options. Once installed, you may need
 to run source ~/.bashrc or restart your terminal. You should see (base)
 appear at the beginning of your terminal prompt.</p>
 <p>Now, create the GenoPred environment using Mamba:</p>
-<pre class="bash"><code>conda env create -f GenoPred/pipeline/envs/pipeline.yaml</code></pre>
+<pre class="bash"><code>mamba env create -f GenoPred/pipeline/envs/pipeline.yaml</code></pre>
 <p>Activate the new <code>genopred</code> environment:</p>
-<pre class="bash"><code>conda activate genopred</code></pre>
+<pre class="bash"><code>mamba activate genopred</code></pre>
 <div class="note-box">
 <p><strong>Note:</strong> If you are working on an HPC, check whether
 mamba is an available module. Using the centrally installed version of
@@ -2259,13 +2259,154 @@ <h2>Multi-source PGS methods</h2>
 is only applied to the PGS from a given method selected using
 pseudovalidation.</p>
 <hr />
+<p>We have provided some test data to run the multi-source functionality
+within GenoPred. To run the pipeline with the test_data, we will use the
+<code>example_input/config.multisource.yaml</code>. We have made some
+slight edits to the configuration used in the <a
+href="#run-using-test-data">previous test run</a>. Mainly we have
+provided a <code>gwas_groups</code> file, and we have specified the
+methods that we want to be linearly combined using LEOPARD+QuickPRS
+using the <code>leopard_methods</code> parameter.</p>
+<details>
+<summary>
+See contents of multisource configfile
+</summary>
+<pre class="bash"><code># Specify output directory
+outdir: test_data/output/test1
+
+# Location of this config file
+config_file: example_input/config.multisource.yaml
+
+# Specify location of gwas_list file
+gwas_list: example_input/gwas_list.multisource.txt
+
+# Specify location of gwas_groups file
+gwas_groups: example_input/gwas_groups.multisource.txt
+
+# Specify location of target_list file
+target_list: example_input/target_list.txt
+
+# Specify pgs_methods (&#39;ptclump&#39;,&#39;dbslmm&#39;,&#39;prscs&#39;,&#39;sbayesr&#39;,&#39;lassosum&#39;,&#39;ldpred2&#39;,&#39;megaprs&#39;)
+pgs_methods: [&#39;lassosum&#39;]
+
+# Specify methods for which PGS should be combined using LEOPARD+QuickPRS
+leopard_methods: [&#39;lassosum&#39;]
+
+# Specify if you want test mode. Set to NA if you don&#39;t want test mode
+testing: chr22</code></pre>
+</details>
+<details>
+<summary>
+See contents of example gwas_list
+</summary>
+<table>
+<colgroup>
+<col width="9%" />
+<col width="44%" />
+<col width="9%" />
+<col width="2%" />
+<col width="7%" />
+<col width="9%" />
+<col width="4%" />
+<col width="2%" />
+<col width="9%" />
+</colgroup>
+<thead>
+<tr class="header">
+<th align="left">name</th>
+<th align="left">path</th>
+<th align="left">population</th>
+<th align="left">n</th>
+<th align="left">sampling</th>
+<th align="left">prevalence</th>
+<th align="right">mean</th>
+<th align="right">sd</th>
+<th align="left">label</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">height_eur</td>
+<td
+align="left">test_data/reference/gwas_sumstats/height_eur.txt.gz</td>
+<td align="left">EUR</td>
+<td align="left">NA</td>
+<td align="left">NA</td>
+<td align="left">NA</td>
+<td align="right">0</td>
+<td align="right">1</td>
+<td align="left">Height EUR</td>
+</tr>
+<tr class="even">
+<td align="left">height_eas</td>
+<td
+align="left">test_data/reference/gwas_sumstats/height_eas.txt.gz</td>
+<td align="left">EAS</td>
+<td align="left">NA</td>
+<td align="left">NA</td>
+<td align="left">NA</td>
+<td align="right">0</td>
+<td align="right">1</td>
+<td align="left">Height EAS</td>
+</tr>
+</tbody>
+</table>
+</details>
+<details>
+<summary>
+See contents of example gwas_groups
+</summary>
+<table>
+<thead>
+<tr class="header">
+<th align="left">name</th>
+<th align="left">gwas</th>
+<th align="left">label</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">height</td>
+<td align="left">height_eur,height_eas</td>
+<td align="left">Height EUR+EAS</td>
+</tr>
+</tbody>
+</table>
+</details>
+<hr />
+<pre class="bash"><code># Remember to activate the genopred environment and go into to the GenoPred/pipeline directory before running the pipeline
+conda activate genopred
+cd ~/GenoPred/pipeline
+
+# Do a dry run to see what would happen
+snakemake -n --configfile=example_input/config.multisource.yaml output_all
+
+# Run the pipeline running one step at a time 
+snakemake -j1 --configfile=example_input/config.multisource.yaml --use-conda output_all</code></pre>
+<p>Once the pipeline is complete, you can check that there is nothing
+else to do by doing another dry run, and it should say ‘Nothing to be
+done’.</p>
+<p>When using LEOPARD to combine single-source PGS, we append
+<code>_multi</code> to the end method name. For example, in this case we
+are combining <code>lassosum</code> PGS, so we would refer to this as
+<code>lassosum_multi</code>. This is important when navigating output
+files. For example, you will the PGS will be in the following path:
+<code>[outdir]/[target name]/pgs/[population]/lassosum_multi</code>.
+Similarly, this method is referred to as <code>lassosum_multi</code>
+when reading the PGS into R using the <code>read_pgs()</code> function
+(<a href="#read_pgs">here</a>).</p>
+<p>When applying TL-PRS to a given method, we append <code>tlprs_</code>
+to the begining of the method name. For example, if were applying TL-PRS
+to <code>lassosum</code> PGS, so we would refer to this as
+<code>tlprs_lassosum</code>.</p>
+<hr />
 </div>
 <div id="select-ancestry-adjustment-approach" class="section level2">
 <h2>Select ancestry adjustment approach</h2>
 <p>The GenoPred pipeline offers two approaches for adjusting PGS for
 ancestry: discrete or continuous.</p>
 <div id="discrete-correction" class="section level3">
-<h3>Discrete Correction:</h3>
+<h3>Discrete Correction</h3>
 <ul>
 <li>This approach assigns each target individual to an ancestry-matched
 reference population.</li>
@@ -2276,7 +2417,7 @@ <h3>Discrete Correction:</h3>
 </ul>
 </div>
 <div id="continuous-correction" class="section level3">
-<h3>Continuous Correction:</h3>
+<h3>Continuous Correction</h3>
 <ul>
 <li>This method utilizes genetic principal components projected from
 reference populations to standardize target PGS.</li>
@@ -2311,22 +2452,68 @@ <h3>Continuous Correction:</h3>
 class="section level2">
 <h2>Specifying alternative reference data for PGS methods</h2>
 <p>PGS methods require linkage disequilibrium (LD) data. The default LD
-data used for each method is indicated in the technical documentation
-(<a href="">here</a>). Several methods use the reference data used for
-the pipeline in general, so altering the <code>ref_dir</code> parameter
-(as <a href="">described above</a>) will also alter the reference data
-used by these PGS methods. However, several PGS methods use pre-computed
-LD matrices, and the user can specify alternative LD matrices for these
-methods, using the configfile parameters below:</p>
+reference data used for each method is described in the <a
+href="pipeline_technical.html">technical documentation</a>. Several
+methods use the global <code>ref_dir</code> parameter (as described <a
+href="#using-your-own-reference">here</a>), which affects the reference
+data used across the pipeline.</p>
+<p>However, many PGS methods rely on <em>precomputed LD matrices</em>,
+and the user can override the default reference data for these methods
+by specifying method-specific LD matrices using the
+<code>configfile</code> parameters described below.</p>
+<p>For <code>PRS-CS</code>, <code>PRS-CSx</code>, and
+<code>X-Wing</code>, use the <code>prs_cs_ldref</code> parameter to
+specify whether LD is estimated from the 1KG or UK Biobank samples.
+Options are:</p>
 <ul>
-<li>PRS-CS</li>
-<li>PRS-CSx</li>
-<li>SBayesR</li>
-<li>SBayesRC</li>
-<li>LDpred2</li>
-<li>QuickPRS</li>
-<li>LEOPARD+QuickPRS</li>
+<li><code>1kg</code> (default): 1000 Genomes-based LD<br />
+</li>
+<li><code>ukb</code>: UK Biobank-based LD</li>
+</ul>
+<div class="note-box">
+<p><strong>Note</strong>: In OpenSNP target samples,
+<code>sumstats</code> performed significantly better using 1KG LD data
+(Yengo et al.), although this may vary by GWAS.</p>
+</div>
+<p>For other methods, the <code>configfile</code> must point to a
+directory containing precomputed LD matrices in the required format.
+This directory must contain one subdirectory per population (e.g.,
+<code>AFR</code>, <code>EUR</code>), each containing the required
+reference files.</p>
+<p>The relevant <code>configfile</code> parameters are:</p>
+<ul>
+<li><code>sbayesr_ldref</code>: for <strong>SBayesR</strong></li>
+<li><code>sbayesrc_ldref</code>: for <strong>SBayesRC</strong></li>
+<li><code>ldpred2_ldref</code>: for <strong>LDpred2</strong></li>
+<li><code>quickprs_ref</code>: for <strong>QuickPRS</strong></li>
+<li><code>quickprs_multi_ldref</code>: for
+<strong>LEOPARD+QuickPRS</strong></li>
 </ul>
+<p>For example, if you want to supply LD data for <code>AFR</code> and
+<code>EUR</code> populations with QuickPRS, the
+<code>quickprs_ref</code> directory should be structured as follows:</p>
+<pre><code>[quickprs_ref]
+  ├── AFR
+  │   ├──AFR.bld.ldak.quickprs.matrix
+  │   ├──AFR.cors.bim
+  │   ├──AFR.cors.bin
+  │   ├──AFR.cors.noise
+  │   ├──AFR.cors.root
+  │   └──highld.snps
+  └── EUR
+      ├──EUR.bld.ldak.quickprs.matrix
+      ├──EUR.cors.bim
+      ├──EUR.cors.bin
+      ├──EUR.cors.noise
+      ├──EUR.cors.root
+      └──highld.snps</code></pre>
+<div class="note-box">
+<p><strong>Note</strong>: If alternative LD reference data is specified,
+it must be provided for <strong>all GWAS populations listed in the
+<code>gwas_list</code></strong>. File formats required vary by method —
+see the documentation or GitHub README for each tool for exact format
+specifications.</p>
+</div>
 <hr />
 </div>
 </div>
diff --git a/docs/pipeline_technical.Rmd b/docs/pipeline_technical.Rmd
index 29d07053..c28848b6 100644
--- a/docs/pipeline_technical.Rmd
+++ b/docs/pipeline_technical.Rmd
@@ -181,7 +181,7 @@ lassosum, an R package, applies the LASSO method for constructing PGS, is implem
 
 #### LDpred2
 
-LDpred2, part of the bigsnpr R package, operates in 'inf', 'grid', and 'auto' modes. The 'inf' mode is better suited for highly polygenic traits, whereas 'grid' and 'auto' adjust effect sizes using various hyperparameters, including SNP heritability. In GenoPred, LDpred2 is run using the script [pgs_methods/ldpred2](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/ldpred2.R).R. GenoPred uses bigsnpr v1.12.2, with the default LDpred2 grid search, and recommended GWAS quality control checks. GenoPred employs LDpred2's precomputed LD matrices based on the European individuals from the UK Biobank, and it is applied only to GWAS based on a EUR population.  The user can specify alternative LD reference data ([see here](pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods)). If the SNP-h2 estimated using LDSC is <0.05, the SNP-heritability used by LDpred2 is set to 0.05.
+LDpred2, part of the bigsnpr R package, operates in 'inf', 'grid', and 'auto' modes. The 'inf' mode is better suited for highly polygenic traits, whereas 'grid' and 'auto' adjust effect sizes using various hyperparameters, including SNP heritability. In GenoPred, LDpred2 is run using the script [pgs_methods/ldpred2](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/ldpred2.R).R. GenoPred uses bigsnpr v1.12.2, with the default LDpred2 grid search, and recommended GWAS quality control checks. GenoPred employs LDpred2's precomputed LD matrices based on the European individuals from the UK Biobank, and it is applied only to GWAS based on a EUR population. The user can specify alternative LD reference data to include additional populations ([see here](pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods)). If the SNP-h2 estimated using LDSC is <0.05, the SNP-heritability used by LDpred2 is set to 0.05.
 
 ***
 
@@ -205,19 +205,19 @@ In GenoPred, pT+clump is run using the script [pgs_methods/ptclump.R](https://gi
 
 #### QuickPRS
 
-In GenoPred, QuickPRS is run using the script [pgs_methods/quickprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/quickprs.R). QuickPRS is a faster implementation of MegaPRS, sped up by using precomputed LD and annotation data. It only inlcudes the model selected using pseudovalidation. GenoPred employs the 1KG+HGDP reference for LD estimation, matching the population of the GWAS sample.
+In GenoPred, QuickPRS is run using the script [pgs_methods/quickprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/quickprs.R). QuickPRS is a faster implementation of MegaPRS, sped up by using precomputed LD and annotation data. It only inlcudes the model selected using pseudovalidation. GenoPred employs the 1KG+HGDP reference for LD estimation, matching the population of the GWAS sample. The user can specify alternative LD reference data to include additional populations ([see here](pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods)).
 
 ***
 
 #### SBayesR
 
-In GenoPred, SBayesR is run using the script [pgs_methods/sbayesr.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/sbayesr.R). Implemented with GCTB software, SBayesR is a Bayesian method that uses GWAS summary statistics to estimate key parameters. GenoPred uses GCTB v2.03 with LD matrices from the GCTB authors, calculated using European individuals in the UK Biobank, restricting its application to GWAS based on EUR populations. GenoPred uses the robust parameterisation option in SBayesR.
+In GenoPred, SBayesR is run using the script [pgs_methods/sbayesr.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/sbayesr.R). Implemented with GCTB software, SBayesR is a Bayesian method that uses GWAS summary statistics to estimate key parameters. GenoPred uses GCTB v2.03 with LD matrices from the GCTB authors, calculated using European individuals in the UK Biobank, restricting its application to GWAS based on EUR populations. GenoPred uses the robust parameterisation option in SBayesR. The user can specify alternative LD reference data to include additional populations ([see here](pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods)).
 
 ***
 
 #### SBayesRC
 
-In GenoPred, SBayesRC is implemented via pgs_[pgs_methods/sbayesrc.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/sbayesrc.R). This method extends SBayesR to integrate functional annotations into the Bayesian framework for estimating SNP effects from GWAS summary statistics. By default SBayesRC will use the LD matrices released by the developers, restricted to GWAS from EUR, EAS, and AFR populations. The user can specify alternative LD reference data ([see here](pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods))
+In GenoPred, SBayesRC is implemented via pgs_[pgs_methods/sbayesrc.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/sbayesrc.R). This method extends SBayesR to integrate functional annotations into the Bayesian framework for estimating SNP effects from GWAS summary statistics. By default SBayesRC will use the LD matrices released by the developers, restricted to GWAS from EUR, EAS, and AFR populations. The user can specify alternative LD reference data to include additional populations ([see here](pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods)).
 
 ***
 
@@ -282,7 +282,7 @@ TL-PRS (Transfer Learning Polygenic Risk Score) is implemented using the script
 
 #### Independently optimised
 
-The independentdently optimised multi-source approach describes the process of applying single-source PGS methods to each GWAS separately, and then linearly combining the population-specific PGS for the target population of interest. This linear combination can be estimated using individual-level data (simply by fitting a linear model containing all population-specific PGS), or using summary-statistics alone (using an approach called LEOPARD, from the Xwing software). In the GenoPred pipeline, we have adapted LEOPARD to use the super fast PGS method QuickPRS, to quickly and accurately estimate the optimal weights for linearly combining the population-specific PGS. These weights can be used to linearly combine population-specific PGS from single-source PGS method. We decribe and evaluate this process in detail in [this paper](https://doi.org/10.1101/2025.03.27.25324773).
+The independentdently optimised multi-source approach describes the process of applying single-source PGS methods to each GWAS separately, and then linearly combining the population-specific PGS for the target population of interest. This linear combination can be estimated using individual-level data (simply by fitting a linear model containing all population-specific PGS), or using summary-statistics alone (using an approach called LEOPARD, from the Xwing software). In the GenoPred pipeline, we have adapted LEOPARD to use the super fast PGS method QuickPRS, to quickly and accurately estimate the optimal weights for linearly combining the population-specific PGS. These weights can be used to linearly combine population-specific PGS from single-source PGS method. We describe and evaluate this process in detail in [this paper](https://doi.org/10.1101/2025.03.27.25324773).
 
 The script used to estimate the weights for population-specific PGS is [pgs_methods/leopard_quickprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/leopard_quickprs.R), and the script used to linearly combine the PGS using these weights is [pgs_methods/apply_leopard_weights.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/apply_leopard_weights.R).
 
diff --git a/pipeline/example_input/config.multisource.yaml b/pipeline/example_input/config.multisource.yaml
index e31a175e..c78e070b 100644
--- a/pipeline/example_input/config.multisource.yaml
+++ b/pipeline/example_input/config.multisource.yaml
@@ -1,11 +1,32 @@
+# Specify output directory
 outdir: test_data/output/test1
+
+# Location of this config file
 config_file: example_input/config.multisource.yaml
+
+# Specify location of gwas_list file
 gwas_list: example_input/gwas_list.multisource.txt
+
+# Specify location of gwas_groups file
 gwas_groups: example_input/gwas_groups.multisource.txt
+
+# Specify location of target_list file
 target_list: example_input/target_list.txt
+
+# Specify pgs_methods ('ptclump','dbslmm','prscs','sbayesr','lassosum','ldpred2','megaprs')
 pgs_methods: ['lassosum']
+
+# Specify methods for which PGS should be combined using LEOPARD+QuickPRS
 leopard_methods: ['lassosum']
+
+# Specify if you want test mode. Set to NA if you don't want test mode
 testing: chr22
+
+# Specify number of cores for polygenic scoring methods (ptclump always uses 1)
 cores_prep_pgs: 5
+
+# Specify number of cores for target scoring
 cores_target_pgs: 5
-mem_target_pgs: 2000
+
+# Specify memory in megabytes for target scoring
+mem_target_pgs: 2000
\ No newline at end of file
diff --git a/pipeline/misc/dev/setup_test.Rmd b/pipeline/misc/dev/setup_test.Rmd
index 1d148146..d2cc0fb0 100644
--- a/pipeline/misc/dev/setup_test.Rmd
+++ b/pipeline/misc/dev/setup_test.Rmd
@@ -10,24 +10,19 @@ knitr::opts_chunk$set(echo = TRUE)
 This document is just for me to remember how I test the installation of the pipeline within a native environment.
 
 ```{bash}
+# Create working directory
 mkdir -p /users/k1806347/oliverpainfel/test/genopred_demo/tmp
 
+# Start container to be native environment but space to store files
 singularity shell \
   --cleanenv \
   --containall \
   --home /users/k1806347/oliverpainfel/test/genopred_demo:/home/genouser \
-  --bind /scratch/k1806347:/tmp \
+  --bind /users/k1806347/oliverpainfel/test/genopred_demo/tmp:/tmp \
   /users/k1806347/oliverpainfel/Software/singularity/my_ubuntu_latest.sif
 
+# Clean up the working directory
 mkdir /users/k1806347/oliverpainfel/test/empty_dir
 rsync -a --delete /users/k1806347/oliverpainfel/test/empty_dir/    /users/k1806347/oliverpainfel/test/genopred_demo/
-
-cd /users/k1806347/oliverpainfel/test/genopred_demo
-
-
-singularity exec \
-  genopred.sif \
-  bash -c "cd /data && ./run_genopred.sh"
-  
 ```
 

From 5a605b2320776943fc5479291f44818e09caa354 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Thu, 12 Jun 2025 13:44:35 +0100
Subject: [PATCH 150/153] Updated docs

---
 docs/CrossPop.Rmd                             |   78 +-
 docs/CrossPop.html                            | 5842 ++++++++++++++++-
 docs/CrossPop_summary.Rmd                     |   53 +
 docs/CrossPop_summary.html                    |  615 ++
 .../CrossPop_2025/average_r.perc_improv.png   |  Bin 740441 -> 746348 bytes
 .../CrossPop_2025/average_r_downsample.png    |  Bin 70150 -> 68628 bytes
 .../average_r_eur.perc_improv.png             |  Bin 548477 -> 544720 bytes
 .../CrossPop_2025/average_r_leopard.png       |  Bin 154616 -> 157207 bytes
 docs/Images/CrossPop_2025/leopard_weights.png |  Bin 142153 -> 137749 bytes
 docs/Images/research_summary/cross_pop_2.png  |  Bin 0 -> 386032 bytes
 docs/_site.yml                                |    2 +
 docs/index.html                               |    3 +
 docs/more_index.Rmd                           |    3 +
 docs/more_index.html                          |   44 +
 docs/opensnp_benchmark.html                   |   35 +
 docs/pipeline_overview.Rmd                    |    4 +-
 docs/pipeline_overview.html                   |   15 +-
 docs/pipeline_readme.Rmd                      |   60 +-
 docs/pipeline_readme.html                     |  126 +-
 docs/research_index.Rmd                       |   20 +-
 docs/research_index.html                      |   64 +-
 21 files changed, 6774 insertions(+), 190 deletions(-)
 create mode 100644 docs/CrossPop_summary.Rmd
 create mode 100644 docs/CrossPop_summary.html
 create mode 100644 docs/Images/research_summary/cross_pop_2.png

diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd
index 55cd2d9a..6d4195ef 100644
--- a/docs/CrossPop.Rmd
+++ b/docs/CrossPop.Rmd
@@ -37,7 +37,7 @@ ul li p, ol li p {
 
 # Preprint
 
-This document provides code and summarises the findings reported in the following preprint:
+This document provides code and key figures and tables for the following preprint:
 
 <div style="border-left: 4px solid #4A90E2; padding-left: 10px; margin: 10px 0;">
 
@@ -46,15 +46,6 @@ Pain, O. (2025). *Leveraging Global Genetics Resources to Enhance Polygenic Pred
 
 </div>
 
-***
-
-
-# Introduction
-
-Genome-wide association studies (GWAS) from multiple ancestral populations are increasingly available, offering opportunities to improve the accuracy and equity of polygenic scores (PGS). Several methods now aim to leverage multiple GWAS sources, but predictive performance and computational efficiency across contexts remain unclear, especially in the absence of individual-level tuning data. This study evaluates a comprehensive set of PGS methods across African (AFR), East Asian (EAS), and European (EUR) ancestry groups.
-
-This study evaluated the performance of PGS methods and modelling approaches across diverse populations using GWAS and target datasets of EUR, EAS, and AFR ancestry (Figure 2). EUR GWAS summary statistics were generated within a training subset of European ancestry individuals in the UK Biobank sample (UKB)(Bycroft et al., 2018). Publicly available EAS and AFR GWAS were obtained from Biobank Japan (BBJ) (Sakaue et al., 2021) and the Ugandan Genome Resource (UGR) (Gurdasani et al., 2019), respectively. An independent target sample of EUR, EAS and AFR ancestry individuals within UKB were used to evaluate the predictive performance of the PGS. To compare PGS methods, 10 traits available in UKB, BBJ, and UGR were selected to represent a range of genetic architectures.
-
 *** 
 
 **Document overview**:
@@ -1867,6 +1858,22 @@ snakemake \
   target_pgs  -n
 ```
 
+<div class="note-box">
+
+**Note**: The LD reference data for SBayesRC, LDpred2, QuickPRS, and QuickPRS+LEOPARD can be download using the links below:
+
+- LDpred2
+  - AFR: [downoad link](https://drive.google.com/file/d/1Fc3v_N1m-ocGqw6E4HpzEVq43jS2oWUQ/view?usp=sharing)
+  - EAS: [download link](https://drive.google.com/file/d/1NwGHs5zwk1p4Vui-1cRb84xkoYld-kyK/view?usp=sharing)
+  - EUR: [download link](https://drive.google.com/file/d/1Vo4QAw7HiI3Y5Wk1y5-9JLgW34U4KPTZ/view?usp=sharing)
+  
+- SBayesRC
+  - AFR: [download link](https://drive.google.com/file/d/1osaNy_EyFAoIQd2ZNOoRJbZsr9Vwr9NP/view?usp=sharing)
+  - EAS: [download link](https://drive.google.com/file/d/1eUtC9JEodJf2tnDFbU-xabO_UwL2JutF/view?usp=sharing)
+  - EUR: [download link](https://drive.google.com/file/d/1O05z8nQhPqATuhfQvhJq7LEuxS0bzlXm/view?usp=sharing)
+
+</div>
+
 </details>
 
 ***
@@ -9249,6 +9256,7 @@ cp ~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png /scratch/prj
 
 </details>
 
+***
 
 ## LEOPARD+QuickPRS
 
@@ -9538,28 +9546,14 @@ dev.off()
 
 </details>
 
-```{bash, eval=T, echo=F}
-
-cp ~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/
-
-```
-
-<details><summary>Show observed and LEOPARD PGS weights</summary>
-
-<div class="centered-container">
-<div class="rounded-image-container" style="width: 100%;">
-<img src="Images/CrossPop_2025/leopard_weights.png">
-</div>
-</div>
-
-</details>
-
 ***
 
 ## Using MVP sumstats
 
 ### Download MVP sumstats
 
+<details><summary>Show code</summary>
+
 ```{r}
 library(data.table)
 
@@ -9627,6 +9621,8 @@ for pheno in $(cat ~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/urls.txt | cut -d'
 done
 ```
 
+</details>
+
 ***
 
 ### PGS calculation
@@ -12352,6 +12348,18 @@ dev.off()
 
 # Check genetic distances
 
+We will use two approaches to compare the ancestry within the GWAS and LD reference samples:
+1. Project reference genetic principal components into the GWAS samples
+2. Estimate ancestral proportions within GWAS and LD reference samples using bigsnpr
+
+***
+
+## Compare genetic principal components 
+
+This involves using the allele frequencies within the GWAS summary statistics to estimate the mean of reference genetic principal components in the GWAS samples.
+
+<details><summary>Show code</summary>
+
 ```{r}
 # Read in reference PC SNP-weights
 pc_score_file <- fread('~/oliverpainfel/Data/ukb/GenoPred/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.eigenvec.var.gz')
@@ -12523,7 +12531,15 @@ dev.off()
 
 ```
 
-Use Florian's data to estimate population proportions in GWAS and reference datasets.
+</details>
+
+***
+
+## Estimate ancestry proportions
+
+This involves using Florian Prive's bigsnpr packages and UKB reference data to calculate principal components and then estimate ancestral proportions.
+
+<details><summary>Show code</summary>
 
 ```{r}
 library(bigsnpr)
@@ -12701,10 +12717,16 @@ dev.off()
 
 ```
 
+</details>
+
 ***
 
 # BridgePRS
 
+The below code is incomplete due to current compatability issues between BridgePRS and GenoPred. Work to incorporate BridgePRS into GenoPred is on going.
+
+<details><summary>Show code</summary>
+
 ```{r}
 ######
 # gwas_list
@@ -12780,4 +12802,4 @@ snakemake \
   target_pgs  -n
 ```
 
-</details>
\ No newline at end of file
+</details>
diff --git a/docs/CrossPop.html b/docs/CrossPop.html
index cea7bcc2..12989093 100644
--- a/docs/CrossPop.html
+++ b/docs/CrossPop.html
@@ -80,6 +80,41 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
+      dismiss: "Accept",
+      link: "Google's privacy info",
+      href: "https://policies.google.com/technologies/partner-sites"
+    },
+    onStatusChange: function(status) {
+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
+        var gtagScript = document.createElement('script');
+        gtagScript.setAttribute('async', '');
+        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}
@@ -445,8 +480,8 @@ <h1 class="title toc-ignore">Leveraging Global Genetics Resources to
 <hr />
 <div id="preprint" class="section level1">
 <h1>Preprint</h1>
-<p>This document provides code and summarises the findings reported in
-the following preprint:</p>
+<p>This document provides code and key figures and tables for the
+following preprint:</p>
 <div
 style="border-left: 4px solid #4A90E2; padding-left: 10px; margin: 10px 0;">
 <p><strong>Citation:</strong><br />
@@ -456,29 +491,6 @@ <h1>Preprint</h1>
 href="https://doi.org/10.1101/2025.03.27.25324773">https://doi.org/10.1101/2025.03.27.25324773</a></p>
 </div>
 <hr />
-</div>
-<div id="introduction" class="section level1">
-<h1>Introduction</h1>
-<p>Genome-wide association studies (GWAS) from multiple ancestral
-populations are increasingly available, offering opportunities to
-improve the accuracy and equity of polygenic scores (PGS). Several
-methods now aim to leverage multiple GWAS sources, but predictive
-performance and computational efficiency across contexts remain unclear,
-especially in the absence of individual-level tuning data. This study
-evaluates a comprehensive set of PGS methods across African (AFR), East
-Asian (EAS), and European (EUR) ancestry groups.</p>
-<p>This study evaluated the performance of PGS methods and modelling
-approaches across diverse populations using GWAS and target datasets of
-EUR, EAS, and AFR ancestry (Figure 2). EUR GWAS summary statistics were
-generated within a training subset of European ancestry individuals in
-the UK Biobank sample (UKB)(Bycroft et al., 2018). Publicly available
-EAS and AFR GWAS were obtained from Biobank Japan (BBJ) (Sakaue et al.,
-2021) and the Ugandan Genome Resource (UGR) (Gurdasani et al., 2019),
-respectively. An independent target sample of EUR, EAS and AFR ancestry
-individuals within UKB were used to evaluate the predictive performance
-of the PGS. To compare PGS methods, 10 traits available in UKB, BBJ, and
-UGR were selected to represent a range of genetic architectures.</p>
-<hr />
 <p><strong>Document overview</strong>:</p>
 <ul>
 <li><p><a href="#data-preparation">Data preparation</a></p>
@@ -6079,6 +6091,37 @@ <h4>
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml \
   target_pgs  -n</code></pre>
+<div class="note-box">
+<p><strong>Note</strong>: The LD reference data for SBayesRC, LDpred2,
+QuickPRS, and QuickPRS+LEOPARD can be download using the links
+below:</p>
+<ul>
+<li>LDpred2
+<ul>
+<li>AFR: <a
+href="https://drive.google.com/file/d/1Fc3v_N1m-ocGqw6E4HpzEVq43jS2oWUQ/view?usp=sharing">downoad
+link</a></li>
+<li>EAS: <a
+href="https://drive.google.com/file/d/1NwGHs5zwk1p4Vui-1cRb84xkoYld-kyK/view?usp=sharing">download
+link</a></li>
+<li>EUR: <a
+href="https://drive.google.com/file/d/1Vo4QAw7HiI3Y5Wk1y5-9JLgW34U4KPTZ/view?usp=sharing">download
+link</a></li>
+</ul></li>
+<li>SBayesRC
+<ul>
+<li>AFR: <a
+href="https://drive.google.com/file/d/1osaNy_EyFAoIQd2ZNOoRJbZsr9Vwr9NP/view?usp=sharing">download
+link</a></li>
+<li>EAS: <a
+href="https://drive.google.com/file/d/1eUtC9JEodJf2tnDFbU-xabO_UwL2JutF/view?usp=sharing">download
+link</a></li>
+<li>EUR: <a
+href="https://drive.google.com/file/d/1O05z8nQhPqATuhfQvhJq7LEuxS0bzlXm/view?usp=sharing">download
+link</a></li>
+</ul></li>
+</ul>
+</div>
 </details>
 <hr />
 </div>
@@ -7783,7 +7826,147 @@ <h4>
         legend.key.spacing.x = unit(2, &quot;cm&quot;),
         legend.justification = &quot;center&quot;
     )
-dev.off()</code></pre>
+dev.off()
+
+########
+# Plot relative improvement of LEOPARD over IndivTune of SumStatTune scores
+########
+
+# meta res
+meta_res_comp_ref &lt;- meta_res_comp[meta_res_comp$Model2 == &#39;Multi-SumStatTune&#39;,]
+meta_res_comp_ref &lt;- meta_res_comp_ref[meta_res_comp_ref$Method1 != &#39;all&#39; &amp; meta_res_comp_ref$Method2 != &#39;all&#39;,]
+meta_res_comp_ref &lt;- meta_res_comp_ref[meta_res_comp_ref$Model1 == &#39;SumStatTune&#39; &amp; meta_res_comp_ref$Source1 == &#39;Multi&#39;,]
+meta_res_comp_ref &lt;- meta_res_comp_ref[gsub(&#39;_multi&#39;,&#39;&#39;, meta_res_comp_ref$Method1) == gsub(&#39;_multi&#39;,&#39;&#39;, meta_res_comp_ref$Method2),]
+
+meta_res_comp_ref$R_diff_perc &lt;- meta_res_comp_ref$R_diff / meta_res_comp_ref$Model_2_R
+meta_res_comp_ref$R_diff_perc_SE &lt;- meta_res_comp_ref$R_diff_SE / meta_res_comp_ref$Model_2_R
+
+meta_res_comp_ref$Discovery_clean &lt;- paste0(meta_res_comp_ref$Discovery1,&#39; GWAS&#39;)
+meta_res_comp_ref$Discovery_clean[meta_res_comp_ref$Target != &#39;EUR&#39;] &lt;- &#39;EUR + Target-matched GWAS&#39;
+
+meta_res_comp_ref &lt;- merge(meta_res_comp_ref, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+meta_res_comp_ref$label[grepl(&#39;Multi&#39;, meta_res_comp_ref$Model1) &amp; !(meta_res_comp_ref$Method1 %in% pgs_group_methods)] &lt;- paste0(meta_res_comp_ref$label[grepl(&#39;Multi&#39;, meta_res_comp_ref$Model1) &amp; !(meta_res_comp_ref$Method1 %in% pgs_group_methods)], &#39;-multi&#39;)
+meta_res_comp_ref$label &lt;- factor(meta_res_comp_ref$label, levels = model_order)
+
+meta_res_comp_ref$Target_clean &lt;- paste0(meta_res_comp_ref$Target,&#39; Target&#39;)
+
+# trait-specific res
+res_comp_all_ref &lt;- res_comp_all[res_comp_all$Model2 == &#39;Multi-SumStatTune&#39;,]
+res_comp_all_ref &lt;- res_comp_all_ref[res_comp_all_ref$Method1 != &#39;all&#39; &amp; res_comp_all_ref$Method2 != &#39;all&#39;,]
+res_comp_all_ref &lt;- res_comp_all_ref[res_comp_all_ref$Model1 == &#39;SumStatTune&#39; &amp; res_comp_all_ref$Source1 == &#39;Multi&#39;,]
+res_comp_all_ref &lt;- res_comp_all_ref[gsub(&#39;_multi&#39;,&#39;&#39;, res_comp_all_ref$Method1) == gsub(&#39;_multi&#39;,&#39;&#39;, res_comp_all_ref$Method2),]
+
+res_comp_all_ref$R_diff_perc &lt;- res_comp_all_ref$R_diff / res_comp_all_ref$Model_2_R
+res_comp_all_ref$R_diff_perc_SE &lt;- res_comp_all_ref$R_diff_SE / res_comp_all_ref$Model_2_R
+
+res_comp_all_ref$Discovery_clean &lt;- paste0(res_comp_all_ref$Discovery1,&#39; GWAS&#39;)
+res_comp_all_ref$Discovery_clean[res_comp_all_ref$Target != &#39;EUR&#39;] &lt;- &#39;EUR + Target-matched GWAS&#39;
+
+res_comp_all_ref &lt;- merge(res_comp_all_ref, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+res_comp_all_ref$label[grepl(&#39;Multi&#39;, res_comp_all_ref$Model1) &amp; !(res_comp_all_ref$Method1 %in% pgs_group_methods)] &lt;- paste0(res_comp_all_ref$label[grepl(&#39;Multi&#39;, res_comp_all_ref$Model1) &amp; !(res_comp_all_ref$Method1 %in% pgs_group_methods)], &#39;-multi&#39;)
+res_comp_all_ref$label &lt;- factor(res_comp_all_ref$label, levels = model_order)
+
+res_comp_all_ref$Target_clean &lt;- paste0(res_comp_all_ref$Target,&#39; Target&#39;)
+
+tmp_meta&lt;-meta_res_comp_ref
+tmp_all&lt;-res_comp_all_ref
+
+tmp_meta&lt;-tmp_meta[!(tmp_meta$Method1 %in% c(&#39;prscsx&#39;,&#39;xwing&#39;)),]
+tmp_meta&lt;-tmp_meta[tmp_meta$Target != &#39;EUR&#39;,]
+
+tmp_all&lt;-tmp_all[!(tmp_all$Method1 %in% c(&#39;prscsx&#39;,&#39;xwing&#39;)),]
+tmp_all&lt;-tmp_all[tmp_all$Target != &#39;EUR&#39;,]
+
+library(ggrepel)
+
+# plot
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/leopard_perc_improv.png&#39;, width = 1800, height = 1100, res= 300, units = &#39;px&#39;)
+
+ggplot(tmp_meta, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp_meta$label))), linetype=&quot;dotted&quot;) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = &quot;Relative Difference (SE)&quot;, fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(. ~ Target_clean) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = &#39;none&#39;
+    )
+dev.off()
+
+# Now compare quickPRS-multi and prs-csx only with trait
+tmp_meta&lt;-meta_res_comp_ref
+tmp_all&lt;-res_comp_all_ref
+
+tmp_meta&lt;- tmp_meta[tmp_meta$Target != &#39;EUR&#39; &amp; tmp_meta$Method1 %in% c(&#39;quickprs_multi&#39;,&#39;prscsx&#39;),]
+tmp_all&lt;- tmp_all[tmp_all$Target != &#39;EUR&#39; &amp; tmp_all$Method1 %in% c(&#39;quickprs_multi&#39;,&#39;prscsx&#39;),]
+
+library(ggrepel)
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/leopard_perc_improv_restricted.png&#39;, width = 1500, height = 1500, res= 300, units = &#39;px&#39;)
+ggplot(tmp_meta, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp_all,
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = &quot;Relative Improvement (SE)&quot;, fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(. ~ Target_clean) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = &#39;none&#39;
+    ) +
+    geom_text_repel(
+      data = tmp_all[
+        tmp_all$R_diff_perc &lt; -0.25,
+      ],
+      aes(label = pheno),  # label as percent with 1 decimal
+      position = position_dodge(width = 0.7),
+      size = 3,
+      min.segment.length = 0,
+      segment.color = NA,
+      show.legend = FALSE
+    )
+dev.off()
+
+# It shows PRS-CSx --meta flag is actually does very well, except when the AFR GWAS is very small (~2700).</code></pre>
 </details>
 <details>
 <summary>
@@ -7913,11 +8096,57 @@ <h2>LEOPARD+QuickPRS</h2>
   }
 }
 
+####
+## Estimate weights if using the inverse variance weighting (realised this doesn&#39;t make sense as PGS are standardised whereas SNP effects in PRS-CSx are not)
+####
+#
+## Read in GWAS descriptives
+#gwas_desc&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv&#39;)
+#gwas_desc &lt;- gwas_desc[, c(&#39;Trait Label&#39;,&#39;Ancestry&#39;,&#39;GWAS N&#39;), with=F]
+#names(gwas_desc)&lt;-c(&#39;trait&#39;,&#39;ancestry&#39;,&#39;n&#39;)
+#gwas_desc&lt;-gwas_desc[gwas_desc$trait %in% selected_traits,]
+#
+#library(dplyr)
+#library(tidyr)
+#
+## Reshape GWAS table to wide format
+#wide_gwas &lt;- gwas_desc %&gt;%
+#  pivot_wider(names_from = ancestry, values_from = n, values_fill = 0)
+#
+## Function to create rows for each pair
+#make_weights_long &lt;- wide_gwas %&gt;%
+#  rowwise() %&gt;%
+#  do({
+#    trait &lt;- .$trait
+#    eur &lt;- .$EUR
+#    afr &lt;- .$AFR
+#    eas &lt;- .$EAS
+#    
+#    tibble(
+#      Trait = trait,
+#      Method = &quot;inverse_var&quot;,
+#      Target = c(&quot;AFR&quot;, &quot;AFR&quot;, &quot;EUR&quot;, &quot;EUR&quot;, &quot;EUR&quot;, &quot;EAS&quot;, &quot;EAS&quot;),
+#      Discovery = c(&quot;EUR&quot;, &quot;AFR&quot;, &quot;EUR&quot;, &quot;AFR&quot;, &quot;EAS&quot;, &quot;EUR&quot;, &quot;EAS&quot;),
+#      Weight = c(
+#        eur / (eur + afr), afr / (eur + afr),  # AFR target
+#        eur / (eur + afr), afr / (eur + afr),  # EUR target (vs AFR)
+#        eas / (eur + eas),                     # EUR target (vs EAS)
+#        eur / (eur + eas), eas / (eur + eas)   # EAS target (vs EUR)
+#      )
+#    )
+#  }) %&gt;%
+#  bind_rows()
+
 ###
 # Combine and compare
 ###
 
+#both &lt;- do.call(rbind, list(obs_weights, leopard_weights, make_weights_long))
 both &lt;- do.call(rbind, list(obs_weights, leopard_weights))
+
+# Remove ptclump as it doesn&#39;t have a sumstattune method
+both &lt;- both[both$Method != &#39;ptclump&#39;,]
+
 both&lt;-merge(both, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x=T, sort = F)
 both$label[is.na(both$label)] &lt;- both$Method[is.na(both$label)]
 both$label &lt;- factor(both$label, levels=unique(both$label))
@@ -7927,7 +8156,7 @@ <h2>LEOPARD+QuickPRS</h2>
 tmp &lt;- tmp[tmp$Discovery != &#39;EUR&#39;,]
 
 # Set LEOPARD to black fill
-default_colors &lt;- hue_pal()(9)
+default_colors &lt;- hue_pal()(10)
 names(default_colors) &lt;- levels(tmp$label)
 default_colors[&quot;LEOPARD&quot;] &lt;- &quot;black&quot;
 
@@ -7950,7 +8179,7 @@ <h2>LEOPARD+QuickPRS</h2>
 tmp &lt;- tmp[tmp$Discovery != &#39;EUR&#39;,]
 
 # Set LEOPARD to black fill
-default_colors &lt;- hue_pal()(9)
+default_colors &lt;- hue_pal()(10)
 names(default_colors) &lt;- levels(tmp$label)
 default_colors[&quot;LEOPARD&quot;] &lt;- &quot;black&quot;
 
@@ -7968,46 +8197,120 @@ <h2>LEOPARD+QuickPRS</h2>
   ylim(c(0,1))
 dev.off()
 
-# Calculate correlation between weights from each method
-# Start with non-EUR target
+###
+# Check calibration of LEOPARD compared to QuickPRS observed weights
+###
+
 tmp &lt;- both[both$Target != &#39;EUR&#39;,]
-tmp$Discovery&lt;-NULL
+tmp$Target&lt;-NULL
 tmp_wide &lt;- reshape(tmp, 
-                     idvar = c(&quot;Trait&quot;, &quot;Target&quot;), 
+                     idvar = c(&quot;Trait&quot;, &quot;Discovery&quot;), 
                      timevar = &quot;label&quot;, 
                      direction = &quot;wide&quot;)
 
 names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
 tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
 
-cor_matrix_EAS &lt;- cor(tmp_wide[tmp_wide$Target == &#39;EAS&#39;, -1:-2])
-cor_matrix_AFR &lt;- cor(tmp_wide[tmp_wide$Target == &#39;AFR&#39;, -1:-2])
+tmp_wide_eas &lt;- tmp_wide[tmp_wide$Discovery == &#39;EAS&#39;,]
+tmp_wide_afr &lt;- tmp_wide[tmp_wide$Discovery == &#39;AFR&#39;,]
 
-# Convert correlation matrix to long format for ggplot
-cor_df_EAS &lt;- melt(cor_matrix_EAS)
-cor_df_AFR &lt;- melt(cor_matrix_AFR)
-cor_df_EAS$Target &lt;- &#39;Target = EAS&#39;
-cor_df_AFR$Target &lt;- &#39;Target = AFR&#39;
-cor_df &lt;- rbind(cor_df_AFR, cor_df_EAS)
+# Calculate metrics
+rmse_afr &lt;- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)^2))
+me_afr &lt;- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)
 
-# Create ggplot correlation heatmap with text inside
-png(&#39;~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_corr.png&#39;, units = &#39;px&#39;, width = 3300, height = 1700, res = 300)
-ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
-  geom_tile(color = &quot;white&quot;) +  # Tile plot with white borders
-  geom_text(aes(label = round(value, 2)), color = &quot;black&quot;) +  # Add correlation values
-  scale_fill_gradient2(low = &quot;blue&quot;, mid = &quot;white&quot;, high = &quot;red&quot;, midpoint = 0) +  # Color scale
+rmse_eas &lt;- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)^2))
+me_eas &lt;- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$LEOPARD)
+
+# Create annotation data.frame
+metrics_df &lt;- data.frame(
+  Discovery = c(&quot;AFR&quot;, &quot;EAS&quot;),
+  x = c(0.5, 0.5),         # Adjust positions as needed
+  y = c(-0.05, -0.05),
+  label = c(
+    paste0(&quot;RMSE = &quot;, round(rmse_afr, 2), &quot;\nME = &quot;, round(me_afr, 2)),
+    paste0(&quot;RMSE = &quot;, round(rmse_eas, 2), &quot;\nME = &quot;, round(me_eas, 2))
+  )
+)
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_calibration.png&#39;, units = &#39;px&#39;, width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != &#39;EUR&#39;,], aes(x = LEOPARD, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = &quot;dashed&quot;, colour = &quot;grey40&quot;) +  # Perfect calibration
+  geom_smooth(method = &quot;lm&quot;, se = TRUE, colour = &quot;blue&quot;) +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
+  labs(
+    x = &quot;LEOPARD weight&quot;,
+    y = &quot;Observed weight&quot;,
+  ) +
   theme_half_open() +
   panel_border() + 
   theme(
-    axis.text.x = element_text(angle = 45, hjust = 1),
-    axis.title = element_blank()
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
   ) +
-  facet_grid(. ~ Target) +
-  labs(fill = &quot;Correlation&quot;)
+  facet_grid(. ~ Discovery) +
+  coord_fixed()    
 dev.off()
 
-# Start with non-EUR target
-tmp &lt;- both[both$Target == &#39;EUR&#39;,]
+####
+## Check calibration of inverse_var compared to QuickPRS observed weights (again realised this doesn&#39;t make sense)
+####
+#
+#tmp &lt;- both[both$Target != &#39;EUR&#39;,]
+#tmp$Target&lt;-NULL
+#tmp_wide &lt;- reshape(tmp, 
+#                     idvar = c(&quot;Trait&quot;, &quot;Discovery&quot;), 
+#                     timevar = &quot;label&quot;, 
+#                     direction = &quot;wide&quot;)
+#
+#names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+#tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
+#
+#tmp_wide_eas &lt;- tmp_wide[tmp_wide$Discovery == &#39;EAS&#39;,]
+#tmp_wide_afr &lt;- tmp_wide[tmp_wide$Discovery == &#39;AFR&#39;,]
+#
+## Calculate metrics
+#rmse_afr &lt;- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)^2))
+#me_afr &lt;- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)
+#
+#rmse_eas &lt;- sqrt(mean((tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)^2))
+#me_eas &lt;- mean(tmp_wide_eas$QuickPRS - tmp_wide_eas$inverse_var)
+#
+## Create annotation data.frame
+#metrics_df &lt;- data.frame(
+#  Discovery = c(&quot;AFR&quot;, &quot;EAS&quot;),
+#  x = c(0.5, 0.5),         # Adjust positions as needed
+#  y = c(-0.05, -0.05),
+#  label = c(
+#    paste0(&quot;RMSE = &quot;, round(rmse_afr, 2), &quot;\nME = &quot;, round(me_afr, 2)),
+#    paste0(&quot;RMSE = &quot;, round(rmse_eas, 2), &quot;\nME = &quot;, round(me_eas, 2))
+#  )
+#)
+#
+#png(&#39;~/oliverpainfel/Analyses/crosspop/plots/inverse_var_weights_calibration.png&#39;, units = &#39;px&#39;, width = 2000, height = 2000, res = 300)
+#ggplot(tmp_wide[tmp_wide$Discovery != &#39;EUR&#39;,], aes(x = inverse_var, y = QuickPRS)) +
+#  geom_abline(slope = 1, intercept = 0, linetype = &quot;dashed&quot;, colour = &quot;grey40&quot;) +  # Perfect calibration
+#  geom_smooth(method = &quot;lm&quot;, se = TRUE, colour = &quot;blue&quot;) +  # Regression line
+#  geom_point(alpha = 0.7) +
+#  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 1.5, size = 3.5) +
+#  labs(
+#    x = &quot;inverse_var weight&quot;,
+#    y = &quot;Observed weight&quot;,
+#  ) +
+#  theme_half_open() +
+#  panel_border() + 
+#  theme(
+#    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+#  ) +
+#  facet_grid(. ~ Discovery) +
+#  coord_fixed()    
+#dev.off()
+
+###
+# Check calibration of observed weights across all methods
+###
+
+tmp &lt;- both[both$Target != &#39;EUR&#39;,]
+tmp &lt;- tmp[!(tmp$label %in% c(&#39;LEOPARD&#39;,&#39;inverse_var&#39;)),]
 tmp$Target&lt;-NULL
 tmp_wide &lt;- reshape(tmp, 
                      idvar = c(&quot;Trait&quot;, &quot;Discovery&quot;), 
@@ -8017,31 +8320,145 @@ <h2>LEOPARD+QuickPRS</h2>
 names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
 tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
 
-cor_matrix_EAS &lt;- cor(tmp_wide[tmp_wide$Discovery == &#39;EAS&#39;, -1:-2])
-cor_matrix_AFR &lt;- cor(tmp_wide[tmp_wide$Discovery == &#39;AFR&#39;, -1:-2])
+tmp_wide &lt;- tmp_wide[tmp_wide$Discovery %in% c(&#39;EAS&#39;,&#39;AFR&#39;),]
 
-# Convert correlation matrix to long format for ggplot
-cor_df_EAS &lt;- melt(cor_matrix_EAS)
-cor_df_AFR &lt;- melt(cor_matrix_AFR)
-cor_df_EAS$Target &lt;- &#39;Discovery = EAS + EUR&#39;
-cor_df_AFR$Target &lt;- &#39;Discovery = AFR + EUR&#39;
-cor_df &lt;- rbind(cor_df_AFR, cor_df_EAS)
+metrics &lt;- NULL
+for(i in c(&#39;EAS&#39;,&#39;AFR&#39;)){
+  for(j in unique(tmp$label)){
+    for(k in unique(tmp$label)){
+      tmp_wide_i &lt;- tmp_wide[tmp_wide$Discovery == i,]
+      rmse &lt;- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me &lt;- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics &lt;- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
+      )
+    }
+  }
+}
 
-# Create ggplot correlation heatmap with text inside
-png(&#39;~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_corr_eur.png&#39;, units = &#39;px&#39;, width = 3300, height = 1700, res = 300)
-ggplot(cor_df, aes(x = Var1, y = Var2, fill = value)) +
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/observed_weights_calibration.png&#39;, units = &#39;px&#39;, width = 3000, height = 1650, res = 300)
+ggplot(metrics, aes(x = Method1, y = Method2, fill = rmse)) +
   geom_tile(color = &quot;white&quot;) +  # Tile plot with white borders
-  geom_text(aes(label = round(value, 2)), color = &quot;black&quot;) +  # Add correlation values
-  scale_fill_gradient2(low = &quot;blue&quot;, mid = &quot;white&quot;, high = &quot;red&quot;, midpoint = 0) +  # Color scale
+  geom_text(aes(label = round(rmse, 2)), color = &quot;black&quot;) +  # Add correlation values
+  scale_fill_gradient2(mid = &quot;white&quot;, high = &quot;red&quot;, midpoint = 0) +  # Color scale
   theme_half_open() +
   panel_border() + 
   theme(
     axis.text.x = element_text(angle = 45, hjust = 1),
     axis.title = element_blank()
   ) +
-  facet_grid(. ~ Target) +
-  labs(fill = &quot;Correlation&quot;)
-dev.off()</code></pre>
+  facet_grid(. ~ Population) +
+  labs(fill = &quot;RMSE&quot;)
+dev.off()
+
+# Calculate average RMSE for each method against all other methods
+metrics_unique &lt;- metrics[metrics$Method1 != metrics$Method2, ]
+metrics_unique$Comparison &lt;- NA
+for (i in 1:nrow(metrics_unique)) {
+  metrics_unique$Comparison[i] &lt;-
+    paste0(sort(c(
+      metrics_unique$Method1[i], metrics_unique$Method2[i]
+    )), collapse = &#39; vs. &#39;)
+}
+metrics_unique &lt;- metrics_unique[!duplicated(paste0(metrics_unique$Population, metrics_unique$Comparison)),]
+
+mean_rmse &lt;- NULL
+for(i in unique(tmp$label)){
+  for(j in c(&#39;AFR&#39;,&#39;EAS&#39;)){
+    metrics_unique_tmp &lt;- metrics_unique[metrics_unique$Method1 == i | metrics_unique$Method2 == i,]
+    metrics_unique_tmp &lt;- metrics_unique_tmp[metrics_unique_tmp$Population == j,]
+    mean_rmse &lt;- rbind(
+      mean_rmse, 
+      data.frame(
+        Method = i,
+        Population = j,
+        avg_rmse = mean(metrics_unique_tmp$rmse)
+      )
+    )
+  }
+}
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/avg_observed_weight_rmse.png&#39;, units = &#39;px&#39;, width = 1500, height = 1500, res = 300)
+ggplot(mean_rmse, aes(x = Method, y = avg_rmse, fill = Method)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat=&quot;identity&quot;, size = 0.1) +
+  geom_text(aes(label = round(avg_rmse, 3)),  # &lt;-- Add this
+          vjust = 1.5,                    # &lt;-- Move the text slightly above the bar
+          size = 3) +                      # &lt;-- Adjust text size
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Population ~ .) +
+  theme_half_open() +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+  panel_border() +
+  labs(y = &#39;Average RMSE&#39;) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position=&quot;none&quot;)
+dev.off()
+
+####
+## Check calibration of estimated (LEOPARD and inverse_var) weights compared to observed QuickPRS weights
+####
+#
+#tmp &lt;- both[both$Target != &#39;EUR&#39;,]
+#tmp &lt;- tmp[(tmp$label %in% c(&#39;LEOPARD&#39;,&#39;inverse_var&#39;,&#39;QuickPRS&#39;)),]
+#tmp$Target&lt;-NULL
+#tmp_wide &lt;- reshape(tmp, 
+#                     idvar = c(&quot;Trait&quot;, &quot;Discovery&quot;), 
+#                     timevar = &quot;label&quot;, 
+#                     direction = &quot;wide&quot;)
+#
+#names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+#tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
+#
+#tmp_wide &lt;- tmp_wide[tmp_wide$Discovery %in% c(&#39;EAS&#39;,&#39;AFR&#39;),]
+#
+#metrics &lt;- NULL
+#for(i in c(&#39;EAS&#39;,&#39;AFR&#39;)){
+#  for(j in unique(tmp$label)){
+#    for(k in unique(tmp$label)){
+#      tmp_wide_i &lt;- tmp_wide[tmp_wide$Discovery == i,]
+#      rmse &lt;- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+#      me &lt;- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+#      
+#      metrics &lt;- rbind(
+#        metrics,
+#        data.frame(
+#          Population = i,
+#          Method1 = j,
+#          Method2 = k,
+#          rmse = rmse,
+#          me = me
+#        )
+#      )
+#    }
+#  }
+#}
+#
+## Plot the rmse for LEOPARD and inverse_var predicting observed QuickPRS weight
+#metrics &lt;- metrics[metrics$Method1 == &#39;QuickPRS&#39;,]
+#metrics &lt;- metrics[metrics$Method2 != &#39;QuickPRS&#39;,]
+#
+#png(&#39;~/oliverpainfel/Analyses/crosspop/plots/inverse_var_comp_rmse.png&#39;, units = &#39;px&#39;, width = 800, height = 1500, res = 300)
+#ggplot(metrics, aes(x = Method2, y = rmse, fill = Method2)) +
+#  geom_bar(width= 0.7, position=position_dodge(0.7), stat=&quot;identity&quot;, size = 0.1) +
+#  geom_text(aes(label = round(rmse, 3)),  # &lt;-- Add this
+#          vjust = 1.5,                    # &lt;-- Move the text slightly above the bar
+#          size = 3) +                      # &lt;-- Adjust text size
+#  facet_grid(Population ~ .) +
+#  theme_half_open() +
+#  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+#  panel_border() +
+#  labs(y = &#39;RMSE relative to QuickPRS&#39;, x = &#39;Method&#39;) +
+#  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+#        legend.position=&quot;none&quot;)
+#dev.off()</code></pre>
 </details>
 <details>
 <summary>
@@ -8281,7 +8698,7 @@ <h2>Computational resoures</h2>
 0.23
 </td>
 <td style="text-align:right;">
-8.22
+8.03
 </td>
 </tr>
 <tr>
@@ -8289,10 +8706,10 @@ <h2>Computational resoures</h2>
 MegaPRS
 </td>
 <td style="text-align:right;">
-0.85
+0.72
 </td>
 <td style="text-align:right;">
-12.51
+11.92
 </td>
 </tr>
 <tr>
@@ -8336,7 +8753,7 @@ <h2>Computational resoures</h2>
 0.06
 </td>
 <td style="text-align:right;">
-4.87
+4.41
 </td>
 </tr>
 <tr>
@@ -8480,7 +8897,7 @@ <h4>
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml \
-  output_all -n</code></pre>
+  target_pgs -n</code></pre>
 </details>
 <hr />
 </div>
@@ -9457,7 +9874,7 @@ <h4>
   --profile slurm \
   --use-conda \
   --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \
-  output_all -n</code></pre>
+  target_pgs -n</code></pre>
 </details>
 <hr />
 </div>
@@ -11442,12 +11859,13 @@ <h3>Downsample GWAS</h3>
            quote=F)
   }
 } </code></pre>
-<pre class="bash"><code>for pheno in $(echo TC HT HB); do
+<pre class="bash"><code>for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
   for n in $(echo 5 15 45 135); do
     mkdir -p /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled
     for chr in $(seq 1 22); do
-      if [ ! -f &quot;/users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}.outcome.glm.linear&quot; ]; then
-        sbatch -p interruptible_cpu --wrap=&quot;/users/k1806347/oliverpainfel/Software/plink2 \
+      if [ ! -s &quot;/users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}.outcome.glm.linear&quot; ]; then
+      if [ ! -f &quot;/users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt.gz&quot; ]; then
+        sbatch -p interruptible_cpu,cpu,neurohack_cpu --wrap=&quot;/users/k1806347/oliverpainfel/Software/plink2 \
           --pfile /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
           --pheno /users/k1806347/oliverpainfel/Data/ukb/phenotypes/subsample/${pheno}.${n}.txt \
           --linear omit-ref cols=+a1freq,+ax \
@@ -11455,13 +11873,16 @@ <h3>Downsample GWAS</h3>
           --geno 0.05 \
           --out /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}&quot;
       fi
+      fi
     done
   done
 done
 
 # Once complete, merge results across chromosomes
-for pheno in $(echo TC HT HB); do
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
   for n in $(echo 5 15 45 135); do
+    if [ ! -f &quot;/users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt.gz&quot; ]; then
+
     head -n 1 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr1.outcome.glm.linear &gt; /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
       for chr in $(seq 1 22); do
         tail -n +2 /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}.outcome.glm.linear &gt;&gt; /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
@@ -11471,6 +11892,7 @@ <h3>Downsample GWAS</h3>
       cut -f 4,5 --complement /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt | awk &#39;BEGIN{FS=OFS=&quot;\t&quot;} NR==1 {$5=&quot;A2&quot;} 1&#39; &gt; temp.txt &amp;&amp; mv temp.txt /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
   
       gzip /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.GW.txt
+    fi
   done
 done
 
@@ -11480,6 +11902,58 @@ <h3>Downsample GWAS</h3>
 </details>
 <hr />
 </div>
+<div id="subset-eur-test-individuals-in-ukb-data"
+class="section level3">
+<h3>Subset EUR test individuals in UKB data</h3>
+<p>To make this quicker, focus on evaluating the PGS methods in the EUR
+test subset in UKB. This will avoid reprocessing the full UKB data.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+
+pheno_long &lt;- NULL
+for(i in selected_traits){
+  pheno_i &lt;- fread(paste0(
+    &#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;,
+    i ,
+    &#39;.unrel.EUR_test.row_number.txt&#39;
+  ))
+  
+  pheno_long &lt;- rbind(
+    pheno_long, 
+    pheno_i
+  )
+}
+
+test_subset &lt;- unique(pheno_long$FID)
+keep &lt;- data.frame(FID = test_subset,
+                   IID = test_subset)
+
+write.table(
+  keep,
+  &#39;~/oliverpainfel/Data/ukb/eur_test.keep&#39;,
+  row.names = F,
+  col.names = F,
+  quote = F
+)</code></pre>
+<pre class="bash"><code>mkdir ~/oliverpainfel/Data/ukb/eur_test_subset
+
+for chr in $(seq 1 22); do
+  sbatch -p interruptible_cpu,cpu,neurohack_cpu -n 1 --mem 5G \
+  --wrap=&quot;~/oliverpainfel/Software/plink2 \
+    --pfile ~/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \
+    --keep ~/oliverpainfel/Data/ukb/eur_test.keep \
+    --make-pgen \
+    --out ~/oliverpainfel/Data/ukb/eur_test_subset/ukb.chr${chr}&quot;
+done
+</code></pre>
+</details>
+<hr />
+</div>
 <div id="pgs-calculation-5" class="section level3">
 <h3>PGS calculation</h3>
 <details>
@@ -11496,9 +11970,8 @@ <h4>
 # gwas_list
 ######
 
-gwas_list&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt&#39;)
+gwas_list&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&#39;)
 pheno&lt;-gsub(&#39;_.*&#39;,&#39;&#39;, gwas_list$name)
-gwas_list&lt;-gwas_list[pheno %in% c(&#39;TC&#39;, &#39;HT&#39;, &#39;HB&#39;),]
 gwas_list&lt;-gwas_list[gwas_list$population == &#39;EUR&#39;,]
 gwas_list$pheno&lt;-gsub(&#39;_UKB&#39;,&#39;&#39;,gwas_list$name)
 
@@ -11534,17 +12007,74 @@ <h4>
 
 write.table(gwas_list_subsampled, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt&#39;, col.names = T, row.names = F, quote = F)
 
+######
+# target_list
+######
+target_list &lt;- data.frame(
+  name=&#39;ukb&#39;,
+  path=&#39;/users/k1806347/oliverpainfel/Data/ukb/eur_test_subset/ukb&#39;,
+  type=&#39;plink2&#39;,
+  indiv_report=F,
+  unrel=&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/unrelated.row_number.txt&#39;
+)
+
+dir.create(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eur_test_only&#39;)
+
+write.table(target_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eur_test_only/target_list.txt&#39;, col.names=T, row.names=F, quote=F)
+
 ######
 # config
 ######
 
 config&lt;-c(
-  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&quot;,
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_eur_test_only&quot;,
   &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml&quot;,
   &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt&quot;,
-  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&quot;,
-  &quot;pgs_methods: [&#39;quickprs&#39;,&#39;sbayesrc&#39;,&#39;ptclump&#39;,&#39;ldpred2&#39;]&quot;,
-#  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eur_test_only/target_list.txt&quot;,
+  &quot;pgs_methods: [&#39;quickprs&#39;,&#39;ptclump&#39;,&#39;dbslmm&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
+  &quot;cores_prep_pgs: 10&quot;,
+  &quot;cores_target_pgs: 50&quot;,
+  &quot;ldpred2_inference: F&quot;,
+  &quot;ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3&quot;,
+  &quot;quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3&quot;,
+  &quot;quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset&quot;,
+  &quot;sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml&#39;, col.names = F, row.names = F, quote = F)
+
+####
+# Make a second configuration using EAS GWAS and subsample EUR GWAS with QuickPRS and LEOPARD
+####
+
+gwas_groups&lt;-data.frame(
+  name=paste0(gwas_list_subsampled$name, &#39;_BBJ&#39;),
+  gwas=paste0(gwas_list_subsampled$name),
+  label=gsub(&quot;\\)&quot;, &quot; + BBJ)&quot;, gwas_list_subsampled$label)
+)
+
+gwas_groups$trait &lt;- gsub(&#39;_.*&#39;,&#39;&#39;,gwas_groups$name)
+gwas_groups$gwas&lt;-paste0(gwas_groups$gwas,&#39;,&#39;,gwas_groups$trait,&#39;_BBJ&#39;)
+
+gwas_groups$trait&lt;-NULL
+
+write.table(gwas_groups, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_subsampled.txt&#39;, col.names = T, row.names = F, quote = F)
+
+gwas_list&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&#39;)
+gwas_list_eas&lt;-gwas_list[gwas_list$population == &#39;EAS&#39;,]
+gwas_list_eas$label&lt;-paste0(&#39;&quot;&#39;, gwas_list_eas$label, &#39;&quot;&#39;)
+gwas_list_subsampled &lt;- rbind(gwas_list_subsampled, gwas_list_eas)
+
+write.table(gwas_list_subsampled, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt&#39;, col.names = T, row.names = F, quote = F)
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output_eur_test_only&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_subsampled.txt&quot;,
+  &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_subsampled.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eur_test_only/target_list.txt&quot;,
+  &quot;pgs_methods: [&#39;quickprs&#39;]&quot;,
+  &quot;leopard_methods: [&#39;quickprs&#39;]&quot;,
   &quot;cores_prep_pgs: 10&quot;,
   &quot;cores_target_pgs: 50&quot;,
   &quot;ldpred2_inference: F&quot;,
@@ -11590,11 +12120,18 @@ <h4>
 outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
 
 # Read in list of outcomes 
-selected_traits&lt;-c(&#39;TC&#39;, &#39;HT&#39;, &#39;HB&#39;)
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
 
 # Get a list of score files
 scores &lt;- list_score_files(config)
 
+# Get list of score files using full EUR GWAS
+config_full&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml&#39;
+outdir_full &lt;- read_param(config = config_full, param = &#39;outdir&#39;, return_obj = F)
+scores_full &lt;- list_score_files(config_full)
+scores_full &lt;- scores_full[grepl(&#39;UKB$&#39;, scores_full$name),]
+scores_full &lt;- scores_full[scores_full$method %in% pgs_methods,]
+
 # Create files for EAS and AFR targets
 for(trait_i in selected_traits){
   
@@ -11620,6 +12157,22 @@ <h4>
   
   scores_i$top1 &lt;- paste0(scores_i$method,&#39;.&#39;,gsub(&#39;.*_&#39;, &#39;&#39;, scores_i$name))
 
+  # Now for full GWAS
+  scores_full_i &lt;- scores_full[grepl(paste0(&#39;^&#39;, trait_i,&#39;_&#39;), scores_full$name),]
+
+  scores_full_i$predictor &lt;- paste0(
+    outdir_full,
+    &#39;/ukb/pgs/TRANS/&#39;,
+    scores_full_i$method,
+    &#39;/&#39;,
+    scores_full_i$name,
+    &#39;/ukb-&#39;,
+    scores_full_i$name,
+    &#39;-TRANS.profiles&#39;
+  )
+  
+  scores_full_i$top1 &lt;- paste0(scores_full_i$method,&#39;.full&#39;)
+
   ####
   # Make groups containing pseudo scores for single source methods
   ####
@@ -11660,13 +12213,22 @@ <h4>
                                 paste0(&#39;.EUR_pseudo.profiles&#39;),
                                 scores_i_pseudo$predictor)
 
+  # Now for full GWAS - skip subsetting pseudo as done before
+  scores_full_i_pseudo &lt;- scores_full_i
+  scores_full_i_pseudo$top1&lt;-paste0(scores_full_i_pseudo$top1,&#39;.pseudo&#39;)
   
+  scores_full_i_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                paste0(&#39;.EUR_pseudo.profiles&#39;),
+                                scores_full_i_pseudo$predictor)
+
   ####
   # Combine the different predictor groups
   ####
   predictors_i&lt;- do.call(rbind, list(
     scores_i, 
-    scores_i_pseudo
+    scores_full_i, 
+    scores_i_pseudo, 
+    scores_full_i_pseudo
   ))
   predictors_i$multi &lt;- &#39;All&#39;
   predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;multi&#39;,&#39;top1&#39;), with=F]
@@ -11690,9 +12252,9 @@ <h4>
 
 #rm /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/*/res*
 
-for pheno in $(echo TC HT HB); do
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
   if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/res.pred_comp.txt&quot; ]; then
-    sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
+    sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu,cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
       --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.EUR_test.row_number.txt \
       --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/predictor_list.txt \
       --out /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/${pheno}/res \
@@ -11702,7 +12264,214 @@ <h4>
 </code></pre>
 <hr />
 <h4>
-Plot results
+Create predictor list
+</h4>
+<p>When using EUR and EAS GWAS.</p>
+<pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+library(data.table)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml&#39;
+pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+# Create files for EAS and AFR targets
+targ_pop_i &lt;- &#39;EUR&#39;
+disc_pop_j &lt;-&#39;BBJ&#39;
+disc_pop_j_2 &lt;-&#39;EAS&#39;
+
+for(trait_i in selected_traits){
+  dir.create(paste0(
+        &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_&#39;,
+        targ_pop_i,
+        &#39;.disc_EUR_&#39;,
+        disc_pop_j_2,
+        &#39;/&#39;,
+        trait_i))
+        
+  for(n in c(5, 15, 45, 135)){
+    scores_i &lt;- scores[grepl(paste0(&#39;^&#39;, trait_i,&#39;_&#39;), scores$name),]
+    scores_i &lt;- scores_i[grepl(paste0(&#39;_&#39;, n, &#39;K_BBJ|_&#39;, n, &#39;K|&#39;,trait_i,&#39;_BBJ&#39;), scores_i$name),]
+  
+    scores_i$predictor &lt;- paste0(
+      outdir,
+      &#39;/ukb/pgs/TRANS/&#39;,
+      scores_i$method,
+      &#39;/&#39;,
+      scores_i$name,
+      &#39;/ukb-&#39;,
+      scores_i$name,
+      &#39;-TRANS.profiles&#39;
+    )
+    
+    scores_i$multi &lt;- scores_i$method
+
+    scores_i_j &lt;- scores_i
+
+    # Insert path to score file
+    scores_i_j$predictor &lt;- paste0(
+      outdir,
+      &#39;/ukb/pgs/TRANS/&#39;,
+      scores_i_j$method,
+      &#39;/&#39;,
+      scores_i_j$name,
+      &#39;/ukb-&#39;,
+      scores_i_j$name,
+      &#39;-TRANS.profiles&#39;
+    )
+    
+    ####
+    # Make groups single source methods
+    ####
+    
+    scores_i_j_single_top1 &lt;-
+      scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &amp;
+                   !grepl(&#39;_multi$&#39;, scores_i_j$method), ]
+
+    # Create top1 column indicating which predictors top1 models should be derived
+    scores_i_j_single_top1$top1[grepl(&#39;UKB&#39;, scores_i_j_single_top1$name, ignore.case = F)] &lt;- &#39;EUR&#39;
+    scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] &lt;- disc_pop_j_2
+    
+    ####
+    # Make groups containing pseudo scores for single source methods
+    ####
+
+    # Extract the pseudo score for each method and specify as a separate group
+    for(i in 1:nrow(scores_i_j_single_top1)) {
+      param &lt;- find_pseudo(
+        config = config,
+        gwas = scores_i_j_single_top1$name[i],
+        pgs_method = scores_i_j_single_top1$method[i],
+        target_pop = targ_pop_i
+      )
+      
+      score_header &lt;-
+        fread(scores_i_j_single_top1$predictor[i], nrows = 1)
+      score_cols &lt;-
+        which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_single_top1$name[i], &#39;_&#39;, param)))
+      
+      system(
+        paste0(
+          &quot;cut -d&#39; &#39; -f &quot;, 
+          paste0(score_cols, collapse=&#39;,&#39;),
+          &quot; &quot;, 
+          scores_i_j_single_top1$predictor[i], 
+          &quot; &gt; &quot;, 
+          gsub(&#39;.profiles&#39;,
+               paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+               scores_i_j_single_top1$predictor[i])
+        )
+      )
+    }
+    
+    scores_i_j_single_pseudo &lt;- scores_i_j_single_top1
+    scores_i_j_single_pseudo$multi &lt;- paste0(scores_i_j_single_pseudo$multi, &#39;.pseudo&#39;)
+
+    scores_i_j_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                  paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                                  scores_i_j_single_pseudo$predictor)
+
+    ####
+    # Make groups for multi-single-source pseudo scores
+    ####
+    
+    scores_i_j_multi_single_pseudo &lt;- scores_i_j[grepl(&#39;_multi$&#39;, scores_i_j$method),]
+
+    # Extract the pseudo score for each method and specify as a separate group
+    for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
+      param &lt;- find_pseudo(
+        config = config,
+        gwas = scores_i_j_multi_single_pseudo$name[i],
+        pgs_method = scores_i_j_multi_single_pseudo$method[i],
+        target_pop = targ_pop_i
+      )
+      
+      score_header &lt;-
+        fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
+      score_cols &lt;-
+        which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_multi_single_pseudo$name[i], &#39;_&#39;, param)))
+      
+      system(
+        paste0(
+          &quot;cut -d&#39; &#39; -f &quot;, 
+          paste0(score_cols, collapse=&#39;,&#39;),
+          &quot; &quot;, 
+          scores_i_j_multi_single_pseudo$predictor[i], 
+          &quot; &gt; &quot;, 
+          gsub(&#39;.profiles&#39;,
+               paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+               scores_i_j_multi_single_pseudo$predictor[i])
+        )
+      )
+    }
+    
+    scores_i_j_multi_single_pseudo$multi &lt;- paste0(scores_i_j_multi_single_pseudo$multi, &#39;.pseudo&#39;)
+
+    scores_i_j_multi_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                  paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                                  scores_i_j_multi_single_pseudo$predictor)
+    
+    scores_i_j_multi_single_pseudo$top1&lt;-paste0(&#39;EUR_&#39;, disc_pop_j_2)
+
+    ####
+    # Combine the different predictor groups
+    ####
+    predictors_i&lt;- do.call(rbind, list(
+      scores_i_j_single_top1, 
+      scores_i_j_single_pseudo, 
+      scores_i_j_multi_single_pseudo
+    ))
+    
+    predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;multi&#39;,&#39;top1&#39;), with=F]
+    
+    write.table(
+      predictors_i,
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_&#39;,
+        targ_pop_i,
+        &#39;.disc_EUR_&#39;,
+        disc_pop_j_2,
+        &#39;/&#39;,
+        trait_i,
+        &#39;/predictor_list_n&#39;, n, &#39;.txt&#39;
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}</code></pre>
+<h4>
+Run model_builder
+</h4>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR_EAS/*/res*
+
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+  for n in $(echo 5 15 45 135); do
+    if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR_EAS/${pheno}/res_n${n}.pred_comp.txt&quot; ]; then
+      sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu,cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
+        --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.EUR_test.row_number.txt \
+        --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR_EAS/${pheno}/predictor_list_n${n}.txt \
+        --out /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR_EAS/${pheno}/res_n${n} \
+        --n_core 1&quot;
+    fi
+  done
+done
+</code></pre>
+<hr />
+<h4>
+Plot results
 </h4>
 <pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
 
@@ -11713,11 +12482,11 @@ <h4>
 source(&#39;../functions/misc.R&#39;)
 source_all(&#39;../functions&#39;)
 
-phenos&lt;-c(&#39;TC&#39;, &#39;HT&#39;, &#39;HB&#39;)
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
 
 # Calculate correlation between all phenotypes in each target population
 pheno_pop_i &lt;- list()
-for(pheno_i in phenos){
+for(pheno_i in selected_traits){
   pheno_pop_i[[pheno_i]] &lt;- fread(paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;, pheno_i, &#39;.unrel.EUR_test.row_number.txt&#39;))
   names(pheno_pop_i[[pheno_i]])[3] &lt;- pheno_i
 }
@@ -11728,7 +12497,7 @@ <h4>
 
 # Read in results
 res_eval &lt;- list()
-for(pheno_i in phenos){
+for(pheno_i in selected_traits){
   eval_i &lt;-
     fread(
       paste0(
@@ -11749,6 +12518,7 @@ <h4>
   eval_i$GWAS_N &lt;- gsub(&#39;K\\..*&#39;,&#39;&#39;,eval_i$Group)
   eval_i$GWAS_N &lt;- gsub(&#39;.*\\.&#39;,&#39;&#39;,eval_i$GWAS_N)
   eval_i$GWAS_N &lt;- paste0(eval_i$GWAS_N,&#39;K&#39;)
+  eval_i$GWAS_N[eval_i$GWAS_N == &#39;top1K&#39;] &lt;- &#39;297K&#39;
   
   eval_i$Model[grepl(&#39;top1$&#39;, eval_i$Group) &amp;
                    !grepl(&#39;pseudo&#39;, eval_i$Group)]&lt;-&#39;IndivTune&#39;
@@ -11776,7 +12546,7 @@ <h4>
 model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;) 
 
 res_eval_simp &lt;- NULL
-for(pheno_i in phenos){
+for(pheno_i in selected_traits){
   tmp &lt;- res_eval[[pheno_i]]
   tmp$Trait &lt;- pheno_i
   
@@ -11794,7 +12564,7 @@ <h4>
 tmp$GWAS_N &lt;- paste0(&#39;GWAS N = &#39;, tmp$GWAS_N)
 tmp$GWAS_N &lt;-factor(tmp$GWAS_N, levels = unique(tmp$GWAS_N))
 
-png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png&#39;), res=100, width = 1000, height = 600, units = &#39;px&#39;)
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots_downsample/per_trait_r.png&#39;), res=100, width = 1000, height = 6000, units = &#39;px&#39;)
 ggplot(tmp, aes(x=label, y=R , fill = Model)) +
     geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
                   width = 0,
@@ -11810,21 +12580,4877 @@ <h4>
           legend.position = &quot;top&quot;,
           legend.key.spacing.x = unit(1, &quot;cm&quot;),
           legend.justification = &quot;center&quot;)
+dev.off()
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval &lt;- NULL
+# Subset res_eval for each scenario
+res_eval_i &lt;- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+  x &lt;- res_eval[[i]]
+  x$pheno &lt;- names(res_eval)[i]
+  return(x)
+}))
+
+# Average res_evalults for each test across phenotypes
+# Use MAd to account for correlation between them
+res_eval_i$Sample&lt;-&#39;A&#39;
+
+for(group_i in unique(res_eval_i$Group)){
+  res_eval_group_i &lt;- res_eval_i[res_eval_i$Group == group_i,]
+  missing_pheno &lt;-
+    colnames(cors)[!(colnames(cors) %in% unique(res_eval_group_i$pheno))]
+  
+  cors_i &lt;- cors[unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+  
+  meta_res_eval_i &lt;-
+    agg(
+      id = Sample,
+      es = R,
+      var = SE ^ 2,
+      cor = cors_i,
+      method = &quot;BHHR&quot;,
+      mod = NULL,
+      data = res_eval_group_i
+    )
+  
+  tmp &lt;- data.table(Group = group_i,
+                    Method = res_eval_group_i$Method[1],
+                    Model = res_eval_group_i$Model[1],
+                    Source = res_eval_group_i$Source[1],
+                    Discovery = &#39;EUR&#39;,
+                    Target = &#39;EUR&#39;,
+                    R = meta_res_eval_i$es,
+                    SE = sqrt(meta_res_eval_i$var),
+                    GWAS_N = res_eval_group_i$GWAS_N[1])
+  
+  meta_res_eval &lt;- rbind(meta_res_eval, tmp)
+}
+
+tmp &lt;- meta_res_eval
+# Insert nice PGS method names
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$GWAS_N &lt;- paste0(&#39;GWAS N = &#39;, tmp$GWAS_N)
+tmp$GWAS_N &lt;-factor(tmp$GWAS_N, levels = unique(tmp$GWAS_N))
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/plots_downsample/average_r.png&#39;), res=100, width = 1000, height = 600, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=NULL, fill = NULL) +
+    facet_grid(~ GWAS_N, scales = &#39;free_y&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
 dev.off()</code></pre>
-</details>
-<details>
-<summary>
-Show results
-</summary>
-<div class="centered-container">
-<div class="rounded-image-container" style="width: 100%;">
-<p><img src="Images/CrossPop_2025/average_r_downsample.png"></p>
-</div>
-</div>
-</details>
-<hr />
-</div>
-</div>
+<h4>
+Plot results
+</h4>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+info_all &lt;- fread(&#39;~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv&#39;)
+
+# Calculate correlation between all phenotypes in each target population
+cors &lt;- list()
+for(pop_i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;CSA&#39;,&#39;AMR&#39;)){
+  if(pop_i == &#39;EUR&#39;){
+    pop_i_2 &lt;- &#39;EUR_test&#39;
+  } else {
+    pop_i_2 &lt;- pop_i
+  }
+  pheno_pop_i &lt;- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] &lt;- fread(paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;, pheno_i, &#39;.unrel.&#39;, pop_i_2, &#39;.row_number.txt&#39;))
+    names(pheno_pop_i[[pheno_i]])[3] &lt;- pheno_i
+  }
+  
+  pheno_pop_i_merged &lt;- merged_df &lt;- Reduce(function(x, y) merge(x, y, all = TRUE, by = c(&#39;FID&#39;,&#39;IID&#39;)), pheno_pop_i)
+
+  cors_i &lt;- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use=&#39;p&#39;))
+  cors[[pop_i]] &lt;- cors_i
+}
+
+# Read in results
+targ_pop_i = &#39;EUR&#39;
+disc_pop_i = &#39;EAS&#39;
+res_eval &lt;- list()
+for(pheno_i in selected_traits){
+  res_eval_i &lt;- NULL
+  for (n_i in c(5, 15, 45, 135)) {
+    eval_i &lt;-
+      fread(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/&#39;,
+          &#39;targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_i,
+          &#39;/&#39;,
+          pheno_i,
+          &#39;/res_n&#39;, n_i, &#39;.pred_eval.txt&#39;
+        )
+      )
+    eval_i$Target&lt;-targ_pop_i
+    eval_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
+    eval_i$UKB_GWAS_N &lt;- paste0(n_i,&#39;k&#39;)
+    res_eval_i&lt;-rbind(res_eval_i, eval_i)
+  }
+  
+  res_eval_i$Method&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_eval_i$Group)
+  res_eval_i$Method&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_eval_i$Method)
+  
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTune&#39;
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTune&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTune&#39;
+  
+  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;prscsx.pseudo.multi&#39;]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;xwing.pseudo.multi&#39;]&lt;-&#39;SumStatTune&#39;
+  
+  res_eval_i$Source&lt;-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_eval_i$Method) | 
+    !grepl(&#39;EUR|EAS|AFR&#39;, res_eval_i$Group), &#39;Multi&#39;, &#39;Single&#39;)
+  
+  res_eval_i$Discovery[grepl(&#39;EUR&#39;, res_eval_i$Group)] &lt;- &#39;EUR&#39;
+  res_eval_i$Discovery[grepl(&#39;EAS&#39;, res_eval_i$Group)] &lt;- &#39;EAS&#39;
+  res_eval_i$Discovery[grepl(&#39;AFR&#39;, res_eval_i$Group)] &lt;- &#39;AFR&#39;
+  res_eval_i$Discovery[res_eval_i$Source == &#39;Multi&#39;] &lt;- res_eval_i$gwas_group[res_eval_i$Source == &#39;Multi&#39;]
+  
+  res_eval_i$Method&lt;-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+  res_eval_i$Discovery&lt;-factor(res_eval_i$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
+  
+  # Remove pseudo model for methods that don&#39;t really have one 
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
+  res_eval_i &lt;- res_eval_i[
+    !((res_eval_i$Method %in%  c(&#39;prscsx&#39;, &#39;xwing&#39;) | grepl(&#39;_multi$&#39;, res_eval_i$Method)) &amp; 
+      grepl(&#39;top1&#39;, res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i &lt;- res_eval_i[!duplicated(res_eval_i[, c(
+    &quot;Target&quot;, &quot;Method&quot;, &quot;Model&quot;, &quot;Source&quot;, &quot;Discovery&quot;,&quot;gwas_group&quot;, &quot;UKB_GWAS_N&quot;
+  )]),]
+  
+  res_eval[[pheno_i]]&lt;-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;, &quot;DBSLMM-multi&quot;, &quot;lassosum-multi&quot;, &quot;LDpred2-multi&quot;, &quot;MegaPRS-multi&quot;, &quot;PRS-CS-multi&quot;, &quot;pT+clump-multi&quot;, &quot;QuickPRS-multi&quot;, &quot;SBayesRC-multi&quot;, &quot;PRS-CSx&quot;, &quot;X-Wing&quot;, &quot;All&quot;) 
+
+# Plot results for each phenotype separately
+dir.create(&#39;~/oliverpainfel/Analyses/crosspop/subsampled/plots&#39;)
+
+res_eval_simp &lt;- NULL
+for(pheno_i in selected_traits){
+  tmp &lt;- res_eval[[pheno_i]]
+  tmp$Trait &lt;- pheno_i
+  
+  # Insert nice PGS method names
+  tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+  tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+  tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+  tmp$label &lt;- factor(tmp$label, levels = model_order)
+  
+  # Simplify result to either SumStatTune or IndivTune
+  tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+  tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+  tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery&#39;,&#39;Model&#39;), with=F]),]
+  
+  res_eval_simp &lt;- rbind(res_eval_simp, tmp)
+}
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval &lt;- NULL
+for (n_i in c(5, 15, 45, 135)) {
+  # Subset res_eval for each scenario
+  res_eval_i &lt;- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+    x &lt;- res_eval[[i]]
+    x$pheno &lt;- names(res_eval)[i]
+    x &lt;- x[x$Target == targ_pop_i]
+    x &lt;- x[x$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+    x &lt;- x[x$UKB_GWAS_N == paste0(n_i,&#39;k&#39;)]
+  }))
+  
+  # Average res_evalults for each test across phenotypes
+  # Use MAd to account for correlation between them
+  res_eval_i$Sample&lt;-&#39;A&#39;
+
+  for(group_i in unique(res_eval_i$Group)){
+    res_eval_group_i &lt;- res_eval_i[res_eval_i$Group == group_i,]
+    missing_pheno &lt;-
+      colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+    
+    if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+      print(paste0(
+        &#39;res_evalults missing for &#39;,
+        targ_pop_i,
+        &#39; &#39;,
+        group_i,
+        &#39; &#39;,
+        paste0(missing_pheno, collapse = &#39; &#39;)
+      ))
+    }
+    
+    cors_i &lt;- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+    
+    meta_res_eval_i &lt;-
+      agg(
+        id = Sample,
+        es = R,
+        var = SE ^ 2,
+        cor = cors_i,
+        method = &quot;BHHR&quot;,
+        mod = NULL,
+        data = res_eval_group_i
+      )
+    
+    tmp &lt;- data.table(Group = group_i,
+                      Method = res_eval_group_i$Method[1],
+                      Model = res_eval_group_i$Model[1],
+                      Source = res_eval_group_i$Source[1],
+                      Discovery = res_eval_group_i$Discovery[1],
+                      gwas_group = res_eval_group_i$gwas_group[1],
+                      UKB_GWAS_N = paste0(n_i,&#39;k&#39;),
+                      Target = targ_pop_i,
+                      R = meta_res_eval_i$es,
+                      SE = sqrt(meta_res_eval_i$var))
+    
+    meta_res_eval &lt;- rbind(meta_res_eval, tmp)
+  }
+}
+
+meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+meta_res_eval$Discovery&lt;-factor(meta_res_eval$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+meta_res_eval$UKB_GWAS_N&lt;-factor(meta_res_eval$UKB_GWAS_N, levels=unique(meta_res_eval$UKB_GWAS_N))
+
+write.csv(meta_res_eval, &#39;~/oliverpainfel/Analyses/crosspop/subsampled/r_eval.csv&#39;, row.names = F)
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp &lt;- meta_res_eval
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;EAS GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;EUR GWAS&#39;,
+                                         &#39;EAS GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;,&#39;UKB_GWAS_N&#39;), with=F]),]
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/subsampled/plots/average_r.png&#39;), res=300, width = 3500, height = 1200, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(. ~ UKB_GWAS_N + Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+# Plot performance of -multi models trained using LEOPARD vs using indiv-level data
+tmp &lt;- meta_res_eval
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;)
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods)] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods)], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = unique(tmp$label[order(!(grepl(&#39;Multi&#39;, tmp$label)), tmp$label)]))
+tmp&lt;-tmp[grepl(&#39;multi&#39;, tmp$label),]
+tmp &lt;- tmp[tmp$Model != &#39;Multi-IndivTune&#39;,]
+tmp$Model&lt;-as.character(tmp$Model)
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;]&lt;-&#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;]&lt;-&#39;LEOPARD&#39;
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/subsampled/plots/average_r_leopard.png&#39;), res=300, width = 1500, height = 1200, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid( ~ UKB_GWAS_N, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Create a function to mirror pred_comp results
+mirror_comp&lt;-function(x){
+  x_sym &lt;- x
+  x_sym$Model_1 &lt;- x$Model_2
+  x_sym$Model_2 &lt;- x$Model_1
+  x_sym$Model_1_R &lt;- x$Model_2_R
+  x_sym$Model_2_R &lt;- x$Model_1_R
+  x_sym$R_diff &lt;- -x_sym$R_diff
+  x_mirrored &lt;- rbind(x, x_sym)
+  x_diag&lt;-data.frame(
+      Model_1=unique(x_mirrored$Model_1),
+      Model_2=unique(x_mirrored$Model_1),
+      Model_1_R=x_mirrored$Model_1_R,
+      Model_2_R=x_mirrored$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp&lt;-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
+# Read in results
+res_comp &lt;- list()
+for(pheno_i in selected_traits){
+  res_comp_i&lt;-NULL
+  for (n_i in c(5, 15, 45, 135)) {
+    comp_i &lt;-
+      fread(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/&#39;,
+          &#39;targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_i,
+          &#39;/&#39;,
+          pheno_i,
+          &#39;/res_n&#39;, n_i, &#39;.pred_comp.txt&#39;
+        )
+      )
+    comp_i&lt;-mirror_comp(comp_i)
+    comp_i$Target&lt;-targ_pop_i
+    comp_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
+    comp_i$UKB_GWAS_N&lt;-paste0(n_i,&#39;k&#39;)
+    res_comp_i&lt;-rbind(res_comp_i, comp_i)
+  }
+  
+  res_comp[[pheno_i]]&lt;-res_comp_i
+}
+
+res_comp_all &lt;- do.call(rbind, lapply(names(res_comp), function(name) {
+  x &lt;- res_comp[[name]]
+  x$pheno &lt;- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Annotate tests to get order correct
+res_comp_all$Method1&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_1)
+res_comp_all$Method1&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method1)
+res_comp_all$Method2&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_2)
+res_comp_all$Method2&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method2)
+
+find_model&lt;-function(x){
+  mod &lt;- x
+  mod[grepl(&#39;top1$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;IndivTune&#39;
+  mod[grepl(&#39;top1$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;SumStatTune&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-IndivTune&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-SumStatTune&#39;
+  mod[grepl(&#39;_multi&#39;, x)] &lt;- &#39;SumStatTune&#39;
+  mod[x == &#39;prscsx.pseudo.multi&#39;] &lt;- &#39;SumStatTune&#39;
+  mod[x == &#39;xwing.pseudo.multi&#39;] &lt;- &#39;SumStatTune&#39;
+  
+  return(mod)
+}
+
+res_comp_all$Model1&lt;-find_model(res_comp_all$Model_1)
+res_comp_all$Model2&lt;-find_model(res_comp_all$Model_2)
+
+res_comp_all$Source1&lt;-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method1) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_1), &#39;Multi&#39;, &#39;Single&#39;)
+res_comp_all$Source2&lt;-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method2) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_2), &#39;Multi&#39;, &#39;Single&#39;)
+  
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)){
+  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] &lt;- i
+  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] &lt;- i
+}
+res_comp_all$Discovery1[res_comp_all$Source1 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source1 == &#39;Multi&#39;]
+res_comp_all$Discovery2[res_comp_all$Source2 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source2 == &#39;Multi&#39;]
+
+res_comp_all$Method1&lt;-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
+res_comp_all$Method2&lt;-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
+res_comp_all$Model1&lt;-factor(res_comp_all$Model1, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+res_comp_all$Model2&lt;-factor(res_comp_all$Model2, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+res_comp_all$Discovery1&lt;-factor(res_comp_all$Discovery1, levels=rev(c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;)))
+res_comp_all$Discovery2&lt;-factor(res_comp_all$Discovery2, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+# Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method1 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+  res_comp_all$Model1 %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method2 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+  res_comp_all$Model2 %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
+
+# Remove pseudo model for methods that don&#39;t really have one 
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method1 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+  res_comp_all$Model1 %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method2 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+  res_comp_all$Model2 %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
+
+# Remove top1 models for PRS-CSx
+res_comp_all &lt;- res_comp_all[
+!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method1) &amp; 
+  grepl(&#39;top1&#39;, res_comp_all$Model_1)),]
+res_comp_all &lt;- res_comp_all[
+!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method2) &amp; 
+  grepl(&#39;top1&#39;, res_comp_all$Model_2)),]
+
+# Remove any comparisons
+res_comp_all &lt;- res_comp_all[!duplicated(res_comp_all[, c(&quot;Target&quot;, &quot;gwas_group&quot;, &quot;Method1&quot;, &quot;Model1&quot;, &quot;Source1&quot;, &quot;Discovery1&quot;, &quot;Method2&quot;, &quot;Model2&quot;, &quot;Source2&quot;, &quot;Discovery2&quot;,&#39;pheno&#39;,&#39;UKB_GWAS_N&#39;)]),]
+
+res_comp_all$r_diff_rel &lt;- res_comp_all$R_diff / res_comp_all$Model_2_R
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTune or SumStatTune result
+tmp &lt;- res_comp_all
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label1&#39;
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method2&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label2&#39;
+
+tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;] &lt;- paste0(tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;] &lt;- paste0(tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;], &#39;-multi&#39;)
+
+tmp$Model1[tmp$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model1[tmp$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp$Model2[tmp$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model2[tmp$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+
+tmp&lt;-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp&lt;-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` &lt;- paste0(tmp$label1, &#39; - &#39;, tmp$Model1, &#39; - &#39;, tmp$Discovery1)
+tmp$`Model 2` &lt;- paste0(tmp$label2, &#39; - &#39;, tmp$Model2, &#39; - &#39;, tmp$Discovery2)
+
+tmp &lt;- tmp[, c(&#39;Target&#39;, &#39;pheno&#39;, &#39;Model 1&#39;, &#39;Model 2&#39;, &#39;Model_1_R&#39;, &#39;Model_2_R&#39;, &#39;R_diff&#39;, &#39;R_diff_pval&#39;), with=F]
+names(tmp) &lt;- c(&#39;Target&#39;, &#39;Trait&#39;,&#39;Model 1&#39;, &#39;Model 2&#39;, &quot;R (Model 1)&quot;, &quot;R (Model 2)&quot;, &quot;R difference (Model 1 R - Model 2 R)&quot;, &quot;R difference p-value&quot;)
+
+tmp&lt;-tmp[order(tmp$Target, tmp$Trait, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` &lt;- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` &lt;- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` &lt;- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, &#39;~/oliverpainfel/Analyses/crosspop/subsampled/r_diff.csv&#39;, row.names=F)
+
+###########
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_comp &lt;- NULL
+for (n_i in c(5, 15, 45, 135)) {
+  # Subset res_comp for each scenario
+  res_comp_i &lt;- res_comp_all[res_comp_all$Target == targ_pop_i &amp; res_comp_all$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+  res_comp_i &lt;- res_comp_i[res_comp_i$UKB_GWAS_N == paste0(n_i,&#39;k&#39;), ]
+  
+  # Calculate diff SE based on p-value
+  res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] &lt;- 1-0.001
+  res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]&lt;-1-0.001
+  res_comp_i$R_diff_z&lt;-qnorm(res_comp_i$R_diff_pval/2)
+  res_comp_i$R_diff_SE&lt;-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
+      
+  # Average results for each test across phenotypes
+  # Use MAd to account for correlation between them
+  res_comp_i$Sample&lt;-&#39;A&#39;
+  res_comp_i$Group &lt;- paste0(res_comp_i$Model_1, &#39;_vs_&#39;, res_comp_i$Model_2)
+
+  for(group_i in unique(res_comp_i$Group)){
+    res_comp_group_i &lt;- res_comp_i[res_comp_i$Group == group_i,]
+    cors_i &lt;- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
+    
+    if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
+      
+      meta_res_comp_i &lt;-
+        agg(
+          id = Sample,
+          es = R_diff,
+          var = R_diff_SE ^ 2,
+          cor = cors_i,
+          method = &quot;BHHR&quot;,
+          mod = NULL,
+          data = res_comp_group_i
+        )
+      
+      tmp &lt;- res_comp_group_i[1,]
+      tmp$pheno &lt;- NULL
+      tmp$Model_1_R &lt;-
+        meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &amp;
+                          meta_res_eval$Target == targ_pop_i &amp;
+                          meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i) &amp; 
+                          meta_res_eval$UKB_GWAS_N == paste0(n_i,&#39;k&#39;)]
+      tmp$Model_2_R &lt;-
+        meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &amp;
+                          meta_res_eval$Target == targ_pop_i &amp;
+                          meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i) &amp; 
+                          meta_res_eval$UKB_GWAS_N == paste0(n_i,&#39;k&#39;)]
+      tmp$R_diff &lt;- meta_res_comp_i$es
+      tmp$R_diff_SE &lt;- sqrt(meta_res_comp_i$var)
+      tmp$R_diff_z &lt;- tmp$R_diff / tmp$R_diff_SE
+      tmp$R_diff_p &lt;- 2*pnorm(-abs(tmp$R_diff_z))
+    } else {
+      tmp &lt;- res_comp_group_i[1,]
+      tmp$pheno &lt;- NULL
+      tmp$R_diff &lt;- NA
+      tmp$R_diff_SE &lt;- NA
+      tmp$R_diff_z &lt;- NA
+      tmp$R_diff_p &lt;- NA
+    }
+    meta_res_comp &lt;- rbind(meta_res_comp, tmp)
+  }
+}
+
+meta_res_comp$R_diff_perc &lt;- meta_res_comp$R_diff / meta_res_comp$Model_2_R
+  
+# Compare QuickPRS-Multi vs QuickPRS to evaluate LEOPARD performance
+tmp_quickprs &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;quickprs_multi.pseudo.multi&#39; &amp; 
+                                meta_res_comp$Model_2 == &#39;quickprs.pseudo.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;EUR&#39;,]
+
+tmp_quickprs[,c(&#39;UKB_GWAS_N&#39;, &#39;R_diff_perc&#39;, &#39;R_diff_p&#39;), with = F]
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTune or SumStatTune result
+tmp &lt;- meta_res_comp
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label1&#39;
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method2&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label2&#39;
+
+tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;] &lt;- paste0(tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;] &lt;- paste0(tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;], &#39;-multi&#39;)
+
+tmp$Model1[tmp$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model1[tmp$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp$Model2[tmp$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model2[tmp$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+
+tmp&lt;-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp&lt;-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` &lt;- paste0(tmp$label1, &#39; - &#39;, tmp$Model1, &#39; - &#39;, tmp$Discovery1)
+tmp$`Model 2` &lt;- paste0(tmp$label2, &#39; - &#39;, tmp$Model2, &#39; - &#39;, tmp$Discovery2)
+
+tmp$`Percentage change (R difference / Model 2 R)` &lt;- paste0(round(tmp$R_diff_perc * 100, 1), &#39;%&#39;)
+
+tmp &lt;- tmp[, c(&#39;UKB_GWAS_N&#39;, &#39;Target&#39;, &#39;Model 1&#39;, &#39;Model 2&#39;, &#39;Model_1_R&#39;, &#39;Model_2_R&#39;, &#39;R_diff&#39;,&quot;Percentage change (R difference / Model 2 R)&quot;, &#39;R_diff_p&#39;), with=F]
+names(tmp) &lt;- c(&#39;UKB GWAS N&#39;, &#39;Target&#39;,&#39;Model 1&#39;, &#39;Model 2&#39;, &quot;R (Model 1)&quot;, &quot;R (Model 2)&quot;, &quot;R difference (Model 1 R - Model 2 R)&quot;, &quot;Percentage change (R difference / Model 2 R)&quot;, &quot;R difference p-value&quot;)
+
+tmp&lt;-tmp[order(tmp$Target, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` &lt;- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` &lt;- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` &lt;- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, &#39;~/oliverpainfel/Analyses/crosspop/subsampled/r_diff_average.csv&#39;, row.names=F)
+
+####
+# Plot relative improvement of methods
+####
+# Use the QuickPRS-Multi (IndivTune) as the reference for each UKB_GWAS_N
+
+meta_res_comp_ptclump_top1&lt;-meta_res_comp[meta_res_comp$Method2 == &#39;quickprs&#39; &amp; meta_res_comp$Source2 == &#39;Multi&#39; &amp; meta_res_comp$Model2 == &#39;Multi-SumStatTune&#39;,]
+meta_res_comp_ptclump_top1$reference_point&lt;-F
+meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Method1 == &#39;quickprs&#39; &amp; meta_res_comp_ptclump_top1$Source1 == &#39;Multi&#39; &amp; meta_res_comp_ptclump_top1$Model1 == &#39;Multi-SumStatTune&#39;]&lt;-T
+meta_res_comp_ptclump_top1$R_diff[is.na(meta_res_comp_ptclump_top1$R_diff)]&lt;-0
+meta_res_comp_ptclump_top1$Discovery1 &lt;- factor(meta_res_comp_ptclump_top1$Discovery1, levels=rev(levels(meta_res_comp_ptclump_top1$Discovery1)))
+
+res_comp_all_ptclump_top1&lt;-res_comp_all[res_comp_all$Method2 == &#39;quickprs&#39; &amp; res_comp_all$Source2 == &#39;Multi&#39; &amp; res_comp_all$Model2 == &#39;Multi-SumStatTune&#39;,]
+res_comp_all_ptclump_top1$Discovery1 &lt;-  factor(res_comp_all_ptclump_top1$Discovery1, levels=levels(meta_res_comp_ptclump_top1$Discovery1))
+
+# Create data to plot reference points
+meta_res_comp_reference &lt;- meta_res_comp_ptclump_top1
+meta_res_comp_reference$R_diff[meta_res_comp_ptclump_top1$reference_point == F] &lt;- NA
+meta_res_comp_reference$R_diff_SE [meta_res_comp_ptclump_top1$reference_point == F] &lt;- NA
+res_comp_all_ptclump_top1$reference_point&lt;-F
+
+meta_tmp &lt;- meta_res_comp_ptclump_top1
+meta_tmp &lt;- merge(meta_tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+meta_tmp$label[is.na(meta_tmp$label)] &lt;- &#39;All&#39;
+meta_tmp$label[grepl(&#39;Multi&#39;, meta_tmp$Model1) &amp; !(meta_tmp$Method1 %in% pgs_group_methods) &amp; meta_tmp$label != &#39;All&#39;] &lt;- paste0(meta_tmp$label[grepl(&#39;Multi&#39;, meta_tmp$Model1) &amp; !(meta_tmp$Method1 %in% pgs_group_methods) &amp; meta_tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+meta_tmp$label &lt;- factor(meta_tmp$label, levels = model_order)
+meta_tmp$Discovery_clean &lt;- as.character(meta_tmp$Discovery1)
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != &#39;EUR&#39; &amp; meta_tmp$Source1 == &#39;Single&#39;] &lt;- &#39;EAS GWAS&#39;
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != &#39;EUR&#39; &amp; meta_tmp$Source1 == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+meta_tmp$Discovery_clean &lt;- factor(meta_tmp$Discovery_clean, 
+                              levels = c(&#39;EUR GWAS&#39;,
+                                         &#39;EAS GWAS&#39;,
+                                         &#39;Both&#39;))
+meta_tmp$Target &lt;- paste0(meta_tmp$Target, &#39; Target&#39;)
+meta_tmp$Model1 &lt;- factor(meta_tmp$Model1, levels = c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+meta_tmp$UKB_GWAS_N &lt;- factor(meta_tmp$UKB_GWAS_N, levels = unique(meta_tmp$UKB_GWAS_N))
+
+meta_tmp_ref &lt;- meta_res_comp_reference
+meta_tmp_ref &lt;- merge(meta_tmp_ref, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+meta_tmp_ref$label[is.na(meta_tmp_ref$label)] &lt;- &#39;All&#39;
+meta_tmp_ref$label[grepl(&#39;Multi&#39;, meta_tmp_ref$Model1) &amp; !(meta_tmp_ref$Method1 %in% pgs_group_methods) &amp; meta_tmp_ref$label != &#39;All&#39;] &lt;- paste0(meta_tmp_ref$label[grepl(&#39;Multi&#39;, meta_tmp_ref$Model1) &amp; !(meta_tmp_ref$Method1 %in% pgs_group_methods) &amp; meta_tmp_ref$label != &#39;All&#39;], &#39;-multi&#39;)
+meta_tmp_ref$label &lt;- factor(meta_tmp_ref$label, levels = model_order)
+meta_tmp_ref$Discovery_clean &lt;- as.character(meta_tmp_ref$Discovery1)
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != &#39;EUR&#39; &amp; meta_tmp_ref$Source1 == &#39;Single&#39;] &lt;- &#39;EAS GWAS&#39;
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != &#39;EUR&#39; &amp; meta_tmp_ref$Source1 == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+meta_tmp_ref$Discovery_clean &lt;- factor(meta_tmp_ref$Discovery_clean, 
+                              levels = c(&#39;EUR GWAS&#39;,
+                                         &#39;EAS GWAS&#39;,
+                                         &#39;Both&#39;))
+meta_tmp_ref$Target &lt;- paste0(meta_tmp_ref$Target, &#39; Target&#39;)
+meta_tmp_ref$Model1 &lt;- factor(meta_tmp_ref$Model1, levels = c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+meta_tmp_ref$UKB_GWAS_N &lt;- factor(meta_tmp_ref$UKB_GWAS_N, levels = unique(meta_tmp_ref$UKB_GWAS_N))
+
+tmp &lt;- res_comp_all_ptclump_top1
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery1)
+tmp$Discovery_clean[tmp$Discovery1 == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery1 != &#39;EUR&#39; &amp; tmp$Source1 == &#39;Single&#39;] &lt;- &#39;EAS GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery1 != &#39;EUR&#39; &amp; tmp$Source1 == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;EUR GWAS&#39;,
+                                         &#39;EAS GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model1 &lt;- factor(tmp$Model1, levels = c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+tmp$UKB_GWAS_N &lt;- factor(tmp$UKB_GWAS_N, levels = unique(tmp$UKB_GWAS_N))
+
+ggplot(meta_tmp, aes(x=label, y=R_diff , fill = Model1)) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff - R_diff_SE,
+          ymax = R_diff + R_diff_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff, fill = Model1),
+        stat = &quot;identity&quot;,
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R_diff (SE)&quot;) +
+    facet_grid(UKB_GWAS_N ~ Discovery_clean, scales = &#39;free_x&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+
+# Plot as % change
+meta_tmp$R_diff_perc &lt;- meta_tmp$R_diff / meta_tmp$Model_2_R
+meta_tmp_ref$R_diff_perc &lt;- meta_tmp_ref$R_diff / meta_tmp_ref$Model_2_R
+tmp$R_diff_perc &lt;- tmp$R_diff / tmp$Model_2_R
+
+meta_tmp$R_diff_perc_SE &lt;- meta_tmp$R_diff_SE / meta_tmp$Model_2_R
+
+library(scales)
+ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = &quot;identity&quot;,
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R diff. (SE)&quot;) +
+    facet_grid(UKB_GWAS_N ~ Discovery_clean, scales = &#39;free_x&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+# Simplify results showing results only with or without training data
+meta_tmp_simple &lt;- meta_tmp
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+meta_tmp_simple&lt;-meta_tmp_simple[meta_tmp_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_simple&lt;-meta_tmp_simple[meta_tmp_simple$Model_2 %in% res_eval_simp$Group,]
+
+meta_tmp_ref_simple &lt;- meta_tmp_ref
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+meta_tmp_ref_simple&lt;-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_ref_simple&lt;-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_2 %in% res_eval_simp$Group,]
+
+tmp_simple &lt;- tmp
+tmp_simple$Model1[tmp_simple$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp_simple$Model1[tmp_simple$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp_simple$Model2[tmp_simple$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp_simple$Model2[tmp_simple$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp_simple&lt;-tmp_simple[tmp_simple$Model_1 %in% res_eval_simp$Group,]
+tmp_simple&lt;-tmp_simple[tmp_simple$Model_2 %in% res_eval_simp$Group,]
+
+# Export plot for manuscript
+png(&#39;~/oliverpainfel/Analyses/crosspop/subsampled/plots/average_r.perc_improv.png&#39;, width = 3200, height = 2000, res= 300, units = &#39;px&#39;)
+ggplot(meta_tmp_simple[meta_tmp_simple$Target == &#39;EUR Target&#39;,], aes(x=label, y=R_diff_perc , fill = Model1)) +
+#    geom_boxplot(
+#      data = tmp_simple[tmp_simple$Target != &#39;EUR Target&#39;,],
+#        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+#        position = position_dodge(0.7),
+#        alpha = 0.3
+#      ) +
+    geom_point(
+        data = tmp_simple[tmp_simple$Target != &#39;EUR Target&#39;,],
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target != &#39;EUR Target&#39;,],
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = &quot;identity&quot;,
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 4,
+        shape = 22,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype=&quot;dotted&quot;) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = &quot;Relative Improvement (SE)&quot;, fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(UKB_GWAS_N ~ Discovery_clean, scales = &#39;free_x&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(2, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;
+    )
+dev.off()
+
+# Export plot comparing sumstat vs indivtune for QuickPRS
+# Export plot for manuscript
+png(&#39;~/oliverpainfel/Analyses/crosspop/subsampled/plots/average_r.perc_improv.png&#39;, width = 1500, height = 1200, res= 300, units = &#39;px&#39;)
+ggplot(meta_tmp_simple[meta_tmp_simple$Target == &#39;EUR Target&#39; &amp; meta_tmp_simple$Discovery_clean == &#39;Both&#39;,], aes(x=label, y=R_diff_perc , fill = Model1)) +
+#    geom_boxplot(
+#      data = tmp_simple[tmp_simple$Target != &#39;EUR Target&#39;,],
+#        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+#        position = position_dodge(0.7),
+#        alpha = 0.3
+#      ) +
+    geom_point(
+        data = tmp_simple[tmp_simple$Target != &#39;EUR Target&#39;,],
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target != &#39;EUR Target&#39;,],
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = &quot;identity&quot;,
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 4,
+        shape = 22,
+        show.legend=F
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = &quot;Relative Improvement (SE)&quot;, fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(. ~ UKB_GWAS_N, scales = &#39;free_x&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(2, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;
+    )
+dev.off()</code></pre>
+</details>
+<details>
+<summary>
+Show results
+</summary>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 100%;">
+<p><img src="Images/CrossPop_2025/average_r_downsample.png"></p>
+</div>
+</div>
+</details>
+<hr />
+</div>
+</div>
+<div id="leopardquickprs-1" class="section level2">
+<h2>LEOPARD+QuickPRS</h2>
+<p>Here we will compare the LEOPARD estimated weights for population
+specific PGS, to the weights estimated using observed data in the UKB
+target sample.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml&#39;
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+###
+# Read in weights estimated by LEOPARD (QuickPRS)
+###
+
+leopard_weights&lt;-NULL
+scores_quickprs &lt;- scores$name[scores$method == &#39;quickprs_multi&#39;]
+for(i in selected_traits){
+  for(n in c(&#39;5&#39;,&#39;15&#39;,&#39;45&#39;,&#39;135&#39;)){
+    scores_i &lt;- scores_quickprs[grepl(paste0(&#39;^&#39;, i,&#39;_&#39;), scores_quickprs) &amp; grepl(paste0(&#39;_&#39;, n,&#39;K_&#39;), scores_quickprs)]
+    for(j in scores_i){
+        weights_file &lt;- readRDS(paste0(outdir, &#39;/reference/pgs_score_files/leopard/&#39;, j, &#39;/ref-&#39;, j, &#39;.weights.rds&#39;))
+        weights_file &lt;- data.frame(weights_file)
+        
+        weights &lt;-
+          data.table(
+            Target = do.call(c, lapply(names(weights_file), function(x) rep(x, 2))),
+            Discovery = names(weights_file),
+            Weight = do.call(c, lapply(weights_file, function(x) x)),
+            UKB_GWAS_N = paste0(n, &#39;k&#39;),
+            Trait = i,
+            Method = &#39;LEOPARD&#39;
+          )
+        
+        leopard_weights &lt;- rbind(leopard_weights, weights)
+    }
+  }
+}
+
+leopard_weights&lt;-leopard_weights[leopard_weights$Target == &#39;EUR&#39; &amp; leopard_weights$Discovery == &#39;EUR&#39;,]
+
+#####
+# Read in the PGS weights estimated using UKB data
+#####
+# Read in the final model coefficients for multi-source methods
+
+obs_weights&lt;-NULL
+for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_methods)]){
+  scores_method&lt;-scores$name[scores$method == method_i]
+  method_i &lt;- gsub(&#39;_multi&#39;,&#39;&#39;, method_i)
+
+  for(i in selected_traits){
+    for(j in c(&#39;EUR&#39;)){
+      if(j == &#39;EUR&#39;){
+        pops &lt;- c(&#39;EAS&#39;)
+      } else {
+        pops &lt;- j
+      }
+      
+      for(k in pops){
+          for(n in c(&#39;5&#39;,&#39;15&#39;,&#39;45&#39;,&#39;135&#39;)){
+          model &lt;- fread(paste0(&#39;~/oliverpainfel/Analyses/crosspop/subsampled/targ_&#39;, j, &#39;.disc_EUR_&#39;, k, &#39;/&#39;, i, &#39;/res&#39;, n, &#39;_final_models/&#39;, method_i, &#39;.pseudo.multi.final_model.txt&#39;))
+          model&lt;-model[-1,]
+          
+          # Set weight to zero if negative, as this is what LEOPARD does
+          if(any(model$V2 &lt; 0)){
+            model$V2[model$V2 &lt; 0] &lt;- 0
+            model$V2[model$V2 &gt; 0] &lt;- 1
+          }
+          
+          names(model) &lt;- c(&#39;x&#39;, &#39;BETA&#39;)
+          model$Discovery[grepl(&#39;UKB&#39;, model$x)]&lt;-&#39;EUR&#39;
+          model$Discovery[grepl(&#39;BBJ&#39;, model$x)]&lt;-&#39;EAS&#39;
+          model$Discovery[grepl(&#39;UGR&#39;, model$x)]&lt;-&#39;AFR&#39;
+          model$UKB_GWAS_N&lt;-paste0(n,&#39;k&#39;)
+          model$Target &lt;- j
+          model$Weight &lt;- model$BETA/sum(model$BETA)
+          model$Trait &lt;- i
+          model$Method &lt;- method_i
+          model&lt;-model[,c(&#39;Target&#39;,&#39;Discovery&#39;,&#39;Weight&#39;,&#39;Method&#39;,&#39;UKB_GWAS_N&#39;,&#39;Trait&#39;), with=F]
+          obs_weights&lt;-rbind(obs_weights, model)
+        }
+      }
+    }
+  }
+}
+
+obs_weights&lt;-obs_weights[obs_weights$Target == &#39;EUR&#39; &amp; obs_weights$Discovery == &#39;EUR&#39;,]
+
+###
+# Estimate weights if using the inverse variance weighting
+###
+
+# Read in GWAS descriptives
+gwas_desc&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv&#39;)
+gwas_desc &lt;- gwas_desc[, c(&#39;Trait Label&#39;,&#39;Ancestry&#39;,&#39;GWAS N&#39;), with=F]
+names(gwas_desc)&lt;-c(&#39;trait&#39;,&#39;ancestry&#39;,&#39;n&#39;)
+gwas_desc&lt;-gwas_desc[gwas_desc$trait %in% selected_traits,]
+
+gwas_desc_eas &lt;- gwas_desc[gwas_desc$ancestry == &#39;EAS&#39;,]
+gwas_desc_eas$name&lt;-&#39;BBJ&#39;
+
+gwas_desc_eur &lt;- data.frame(
+  trait = gwas_desc_eas$trait,
+  ancestry = &#39;EUR&#39;,
+  n = c(rep(5000, 10), rep(15000, 10), rep(45000, 10), rep(135000, 10))
+)
+
+gwas_desc&lt;-merge(gwas_desc_eas, gwas_desc_eur, by=&#39;trait&#39;)
+gwas_desc$inverse_var &lt;- gwas_desc$n.y / (gwas_desc$n.y + gwas_desc$n.x)
+
+gwas_desc$Target &lt;- &#39;EUR&#39;
+gwas_desc$Discovery &lt;- &#39;EUR&#39;
+gwas_desc$Weight &lt;- gwas_desc$inverse_var
+gwas_desc$Method &lt;- &#39;inverse_var&#39;
+gwas_desc$UKB_GWAS_N &lt;- paste0(gwas_desc$n.y/1000,&#39;k&#39;)
+gwas_desc$Trait &lt;- gwas_desc$trait
+
+gwas_desc &lt;- gwas_desc[, names(obs_weights), with=F]
+
+###
+# Combine and compare
+###
+
+both &lt;- do.call(rbind, list(obs_weights, leopard_weights, gwas_desc))
+
+both&lt;-merge(both, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x=T, sort = F)
+both$label[is.na(both$label)] &lt;- both$Method[is.na(both$label)]
+both$label &lt;- factor(both$label, levels=unique(both$label))
+
+# Plot EUR target
+tmp &lt;- both[both$Target == &#39;EUR&#39;,]
+tmp &lt;- tmp[tmp$Discovery == &#39;EUR&#39;,]
+
+# Set LEOPARD to black fill
+default_colors &lt;- hue_pal()(10)
+names(default_colors) &lt;- levels(tmp$label)
+default_colors[&quot;LEOPARD&quot;] &lt;- &quot;black&quot;
+
+tmp$UKB_GWAS_N &lt;- factor(tmp$UKB_GWAS_N, levels = unique(tmp$UKB_GWAS_N))
+
+# Plot the estimated and observed weights
+png(&#39;~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights_eur.png&#39;, units = &#39;px&#39;, res = 300, width = 3500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat=&quot;identity&quot;, colour = &#39;black&#39;, size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(. ~ UKB_GWAS_N) +
+  theme_half_open() +
+  labs(title = &#39;Weight of EUR PGS for EUR Target&#39;, fill = NULL) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
+
+###
+# Check calibration of LEOPARD compared to QuickPRS observed weights
+###
+
+tmp &lt;- both[both$Target == &#39;EUR&#39;,]
+tmp &lt;- tmp[both$Discovery == &#39;EUR&#39;,]
+tmp$Target&lt;-NULL
+tmp_wide &lt;- reshape(tmp, 
+                     idvar = c(&quot;Trait&quot;, &quot;Discovery&quot;,&quot;UKB_GWAS_N&quot;), 
+                     timevar = &quot;label&quot;, 
+                     direction = &quot;wide&quot;)
+
+names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
+
+# Calculate metrics
+metrics &lt;- NULL
+for(n in c(&#39;5&#39;,&#39;15&#39;,&#39;45&#39;,&#39;135&#39;)){
+  tmp_wide_n &lt;- tmp_wide[tmp_wide$UKB_GWAS_N == paste0(n,&#39;k&#39;),]
+  metrics&lt;-rbind(metrics, 
+                 data.frame(
+                   n = paste0(n, &#39;k&#39;),
+                   rmse = sqrt(mean((tmp_wide_n$QuickPRS - tmp_wide_n$LEOPARD)^2)),
+                   me = mean(tmp_wide_n$QuickPRS - tmp_wide_n$LEOPARD)
+                 ))
+}
+
+# Create annotation data.frame
+metrics_df &lt;- data.frame(
+  UKB_GWAS_N = metrics$n,
+  x = 0.1,
+  y = 0.15,
+  label = paste0(&quot;RMSE = &quot;, round(metrics$rmse, 2), &quot;\nME = &quot;, round(metrics$me, 2))
+)
+
+tmp_wide$UKB_GWAS_N &lt;- factor(tmp_wide$UKB_GWAS_N, levels = unique(tmp_wide$UKB_GWAS_N))
+metrics_df$UKB_GWAS_N &lt;- factor(metrics_df$UKB_GWAS_N, levels = unique(tmp_wide$UKB_GWAS_N))
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights_calibration.png&#39;, units = &#39;px&#39;, width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide, aes(x = LEOPARD, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = &quot;dashed&quot;, colour = &quot;grey40&quot;) +  # Perfect calibration
+  geom_smooth(method = &quot;lm&quot;, se = TRUE, colour = &quot;blue&quot;) +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
+  labs(
+    x = &quot;LEOPARD weight&quot;,
+    y = &quot;Observed weight&quot;,
+  ) +
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+  ) +
+  facet_grid(. ~ UKB_GWAS_N) +
+  coord_fixed()    
+dev.off()
+
+###
+# Check calibration of inverse_var compared to QuickPRS observed weights
+###
+
+tmp &lt;- both[both$Target == &#39;EUR&#39;,]
+tmp &lt;- tmp[both$Discovery == &#39;EUR&#39;,]
+tmp$Target&lt;-NULL
+tmp_wide &lt;- reshape(tmp, 
+                     idvar = c(&quot;Trait&quot;, &quot;Discovery&quot;,&quot;UKB_GWAS_N&quot;), 
+                     timevar = &quot;label&quot;, 
+                     direction = &quot;wide&quot;)
+
+names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
+
+# Calculate metrics
+metrics &lt;- NULL
+for(n in c(&#39;5&#39;,&#39;15&#39;,&#39;45&#39;,&#39;135&#39;)){
+  tmp_wide_n &lt;- tmp_wide[tmp_wide$UKB_GWAS_N == paste0(n,&#39;k&#39;),]
+  metrics&lt;-rbind(metrics, 
+                 data.frame(
+                   n = paste0(n, &#39;k&#39;),
+                   rmse = sqrt(mean((tmp_wide_n$QuickPRS - tmp_wide_n$inverse_var)^2)),
+                   me = mean(tmp_wide_n$QuickPRS - tmp_wide_n$inverse_var)
+                 ))
+}
+
+# Create annotation data.frame
+metrics_df &lt;- data.frame(
+  UKB_GWAS_N = metrics$n,
+  x = 0.3,
+  y = 0.25,
+  label = paste0(&quot;RMSE = &quot;, round(metrics$rmse, 2), &quot;\nME = &quot;, round(metrics$me, 2))
+)
+
+tmp_wide$UKB_GWAS_N &lt;- factor(tmp_wide$UKB_GWAS_N, levels = unique(tmp_wide$UKB_GWAS_N))
+metrics_df$UKB_GWAS_N &lt;- factor(metrics_df$UKB_GWAS_N, levels = unique(tmp_wide$UKB_GWAS_N))
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights_calibration_inverse_var.png&#39;, units = &#39;px&#39;, width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide, aes(x = inverse_var, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = &quot;dashed&quot;, colour = &quot;grey40&quot;) +  # Perfect calibration
+  geom_smooth(method = &quot;lm&quot;, se = TRUE, colour = &quot;blue&quot;) +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
+  labs(
+    x = &quot;inverse_var weight&quot;,
+    y = &quot;Observed weight&quot;,
+  ) +
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+  ) +
+  facet_grid(. ~ UKB_GWAS_N) +
+  coord_fixed()    
+dev.off()</code></pre>
+</details>
+<hr />
+</div>
+<div id="using-mvp-sumstats" class="section level2">
+<h2>Using MVP sumstats</h2>
+<div id="download-mvp-sumstats" class="section level3">
+<h3>Download MVP sumstats</h3>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+
+mvp &lt;- fread(&#39;~/oliverpainfel/Data/GWAS_sumstats/MVP/MVP_sumstats.txt&#39;)
+mvp_afr &lt;- mvp[grepl(&#39;Afr&#39;, mvp$discoverySampleAncestry),]
+mvp_afr &lt;- mvp_afr[!grepl(&#39;Eur|Asi|His&#39;, mvp_afr$discoverySampleAncestry),]
+
+# Identify traits in common with UKB, UGR, EAS
+prscsx_dat&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv&#39;)
+selected_traits &lt;-
+  fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;,
+        header = F)$V1
+
+prscsx_dat &lt;- prscsx_dat[prscsx_dat$labels %in% selected_traits, ]
+
+# Subset MVP to selected traits
+mvp_afr_subset &lt;- mvp_afr[
+  mvp_afr$accessionId %in% 
+    c(
+      &#39;GCST90475361&#39;, &#39;GCST90475375&#39;, &#39;GCST90476298&#39;, &#39;GCST90475155&#39;, &#39;GCST90476462&#39;, &#39;GCST90475457&#39;, &#39;GCST90476423&#39;, &#39;GCST90475528&#39;, &#39;GCST90475351&#39;, &#39;GCST90476402&#39;
+    )
+, ]
+
+# Insert labels
+mvp_afr_subset$labels &lt;- NA
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == &#39;body mass index&#39;] &lt;- &#39;BMI&#39;
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == &#39;body weight&#39;] &lt;- &#39;BWT&#39;
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == &#39;high density lipoprotein cholesterol measurement&#39;] &lt;- &#39;HDL&#39;
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == &#39;body height&#39;] &lt;- &#39;HT&#39;
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == &#39;hemoglobin measurement&#39;] &lt;- &#39;HB&#39;
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == &#39;mean corpuscular hemoglobin concentration&#39;] &lt;- &#39;MCHC&#39;
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == &#39;neutrophil count&#39;] &lt;- &#39;NEU&#39;
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == &#39;platelet count&#39;] &lt;- &#39;PLT&#39;
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == &#39;systolic blood pressure&#39;] &lt;- &#39;SBP&#39;
+mvp_afr_subset$labels[mvp_afr_subset$efoTraits == &#39;total cholesterol measurement&#39;] &lt;- &#39;TC&#39;
+
+mvp_afr_subset$url &lt;-paste0(mvp_afr_subset$summaryStatistics, &#39;/&#39;, mvp_afr_subset$accessionId, &#39;.tsv.gz&#39;)
+
+dir.create(&#39;~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR&#39;)
+
+write.table(
+  mvp_afr_subset[, c(&#39;url&#39;, &#39;labels&#39;), with = F],
+  &#39;~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/urls.txt&#39;,
+  row.names = F,
+  quote = F,
+  col.names = F
+)
+
+write.table(
+  mvp_afr_subset,
+  &#39;~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/info.txt&#39;,
+  row.names = F,
+  quote = T,
+  col.names = T
+)
+
+write.table(mvp_afr_subset$labels, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<pre class="bash"><code>for pheno in $(cat ~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/urls.txt | cut -d&#39; &#39; -f 2); do
+  url=$(awk -v var=&quot;$pheno&quot; &#39;$2 == var {print $1}&#39; ~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/urls.txt)
+  sbatch -p interruptible_cpu,neurohack_cpu -t 1:00:00 --wrap=&quot;wget -O ~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/${pheno}.txt.gz ${url}&quot;
+done</code></pre>
+</details>
+<hr />
+</div>
+<div id="pgs-calculation-6" class="section level3">
+<h3>PGS calculation</h3>
+<p>Run leading single-source PGS methods using MVP GWAS sumstats.</p>
+<details>
+<summary>
+Show code
+</summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Prepare configuration
+</h4>
+<pre class="r"><code>library(data.table)
+
+# Subset original gwas_list to include AFR traits
+gwas_list&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&#39;)
+gwas_list &lt;- gwas_list[gwas_list$population == &#39;AFR&#39;,]
+selected_traits &lt;- gsub(&#39;_UGR&#39;, &#39;&#39;, gwas_list$name)
+gwas_list$name &lt;- gsub(&#39;UGR&#39;, &#39;MVP_AFR&#39;, gwas_list$name)
+gwas_list$label &lt;- gsub(&#39;UGR&#39;, &#39;MVP_AFR&#39;, gwas_list$label)
+gwas_list$path &lt;-
+  paste0(&#39;/users/k1806347/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/&#39;,
+         selected_traits,&#39;.txt.gz&#39;)
+
+gwas_list_eur&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&#39;)
+gwas_list_eur&lt;-gwas_list_eur[gwas_list_eur$population == &#39;EUR&#39;, ]
+
+gwas_list &lt;- rbind(gwas_list, gwas_list_eur)
+
+gwas_list$label &lt;- paste0(&#39;&quot;&#39;, gwas_list$label, &#39;&quot;&#39;)
+
+write.table(gwas_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt&#39;, col.names = T, row.names = F, quote = F)
+
+######
+# gwas_groups
+######
+
+gwas_groups&lt;-data.frame(
+  name=paste0(selected_traits, &#39;_UKB_MVP_AFR&#39;),
+  gwas=sapply(selected_traits, function(x) paste0(x,&#39;_UKB,&#39;,x,&#39;_MVP_AFR&#39;)),
+  label=paste0(&#39;&quot;&#39;, selected_traits, &quot; (UKB+MVP_AFR)&quot;, &#39;&quot;&#39;)
+)
+
+write.table(gwas_groups, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_mvp.txt&#39;, col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt&quot;,
+  &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_mvp.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&quot;,
+  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;,&#39;prscsx&#39;,&#39;xwing&#39;]&quot;,
+  &quot;leopard_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
+  &quot;cores_prep_pgs: 10&quot;, # xwing run with 20 cores
+  &quot;cores_target_pgs: 50&quot;,
+  &quot;ldpred2_inference: F&quot;,
+  &quot;ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3&quot;,
+  &quot;quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3&quot;,
+  &quot;quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset&quot;,
+  &quot;sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml&#39;, col.names = F, row.names = F, quote = F)
+
+# Make a version of the gwas_list without NEU
+gwas_list &lt;- gwas_list[!grepl(&#39;NEU&#39;, gwas_list$name),]
+write.table(gwas_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp_noneu.txt&#39;, col.names = T, row.names = F, quote = F)
+
+gwas_groups &lt;- gwas_groups[!grepl(&#39;NEU&#39;, gwas_groups$name),]
+write.table(gwas_groups, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_mvp_noneu.txt&#39;, col.names = T, row.names = F, quote = F)
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp_noneu.txt&quot;,
+  &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_mvp_noneu.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&quot;,
+  &quot;pgs_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;,&#39;prscsx&#39;,&#39;xwing&#39;]&quot;,
+  &quot;leopard_methods: [&#39;ptclump&#39;,&#39;quickprs&#39;,&#39;dbslmm&#39;,&#39;lassosum&#39;,&#39;megaprs&#39;,&#39;prscs&#39;,&#39;ldpred2&#39;,&#39;sbayesrc&#39;]&quot;,
+  &quot;cores_prep_pgs: 10&quot;, # xwing run with 20 cores
+  &quot;cores_target_pgs: 50&quot;,
+  &quot;ldpred2_inference: F&quot;,
+  &quot;ldpred2_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/ldpred2/hm3&quot;,
+  &quot;quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3&quot;,
+  &quot;quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset&quot;,
+  &quot;sbayesrc_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/hm3&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<hr />
+<h4>
+Run pipeline
+</h4>
+<pre class="bash"><code>snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml \
+  target_pgs -n
+  
+# Note. xwing fails for PLT. Remove PLT from gwas_list to get scores for other traits.
+# Save PLT results for other methods to check the pattern is similar.
+snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml \
+  target_pgs -n
+  </code></pre>
+</details>
+<hr />
+</div>
+<div id="pgs-evaluation-6" class="section level3">
+<h3>PGS evaluation</h3>
+<p>Compare the single-source PGS within AFR ancestry target
+individuals.</p>
+<details>
+<summary>
+Show code
+</summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Create predictor list
+</h4>
+<pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+library(data.table)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml&#39;
+pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt&#39;, header=F)$V1
+selected_traits&lt;-selected_traits[selected_traits != &#39;NEU&#39;]
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+# Create files for AFR targets
+targ_pop &lt;- c(&#39;AFR&#39;)
+for(trait_i in selected_traits){
+  scores_i &lt;- scores[grepl(trait_i, scores$name),]
+  scores_i$multi &lt;- scores_i$method
+  
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;BBJ&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;MVP_AFR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;BBJ&#39;,&#39;UGR&#39;)
+    }
+    
+    for(disc_pop_j in disc_pop){
+      if(disc_pop_j == &#39;BBJ&#39;){
+        disc_pop_j_2 &lt;- &#39;EAS&#39;
+      }
+      if(disc_pop_j == &#39;MVP_AFR&#39;){
+        disc_pop_j_2 &lt;- &#39;AFR&#39;
+      }
+
+      dir.create(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_j_2,
+          &#39;/&#39;,
+          trait_i
+        ),
+        recursive = T
+      )
+      
+      scores_i_j &lt;- scores_i[
+        (grepl(&#39;UKB$&#39;, scores_i$name, ignore.case = F) | 
+         grepl(paste0(disc_pop_j, &#39;$&#39;), scores_i$name, ignore.case = T)),]
+
+      # Insert path to score file
+      scores_i_j$predictor &lt;- paste0(
+        outdir,
+        &#39;/ukb/pgs/TRANS/&#39;,
+        scores_i_j$method,
+        &#39;/&#39;,
+        scores_i_j$name,
+        &#39;/ukb-&#39;,
+        scores_i_j$name,
+        &#39;-TRANS.profiles&#39;
+      )
+      
+      ####
+      # Make groups single source methods
+      ####
+      
+      scores_i_j_single_top1 &lt;-
+        scores_i_j[!(scores_i_j$method %in% pgs_group_methods) &amp;
+                     !grepl(&#39;_multi$&#39;, scores_i_j$method), ]
+
+      # Create top1 column indicating which predictors top1 models should be derived
+      scores_i_j_single_top1$top1[grepl(&#39;UKB&#39;, scores_i_j_single_top1$name, ignore.case = F)] &lt;- &#39;EUR&#39;
+      scores_i_j_single_top1$top1[grepl(disc_pop_j, scores_i_j_single_top1$name, ignore.case = F)] &lt;- disc_pop_j_2
+      
+      ####
+      # Make groups containing pseudo scores for single source methods
+      ####
+
+      # Extract the pseudo score for each method and specify as a separate group
+      for(i in 1:nrow(scores_i_j_single_top1)) {
+        param &lt;- find_pseudo(
+          config = config,
+          gwas = scores_i_j_single_top1$name[i],
+          pgs_method = scores_i_j_single_top1$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header &lt;-
+          fread(scores_i_j_single_top1$predictor[i], nrows = 1)
+        score_cols &lt;-
+          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_single_top1$name[i], &#39;_&#39;, param)))
+        
+        system(
+          paste0(
+            &quot;cut -d&#39; &#39; -f &quot;, 
+            paste0(score_cols, collapse=&#39;,&#39;),
+            &quot; &quot;, 
+            scores_i_j_single_top1$predictor[i], 
+            &quot; &gt; &quot;, 
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                 scores_i_j_single_top1$predictor[i])
+          )
+        )
+      }
+      
+      scores_i_j_single_pseudo &lt;- scores_i_j_single_top1
+      scores_i_j_single_pseudo$multi &lt;- paste0(scores_i_j_single_pseudo$multi, &#39;.pseudo&#39;)
+
+      scores_i_j_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                    paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                                    scores_i_j_single_pseudo$predictor)
+
+      ####
+      # Make groups for multi-single-source pseudo scores
+      ####
+      
+      scores_i_j_multi_single_pseudo &lt;- scores_i_j[grepl(&#39;_multi$&#39;, scores_i_j$method),]
+
+      # Extract the pseudo score for each method and specify as a separate group
+      for(i in 1:nrow(scores_i_j_multi_single_pseudo)) {
+        param &lt;- find_pseudo(
+          config = config,
+          gwas = scores_i_j_multi_single_pseudo$name[i],
+          pgs_method = scores_i_j_multi_single_pseudo$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header &lt;-
+          fread(scores_i_j_multi_single_pseudo$predictor[i], nrows = 1)
+        score_cols &lt;-
+          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_multi_single_pseudo$name[i], &#39;_&#39;, param)))
+        
+        system(
+          paste0(
+            &quot;cut -d&#39; &#39; -f &quot;, 
+            paste0(score_cols, collapse=&#39;,&#39;),
+            &quot; &quot;, 
+            scores_i_j_multi_single_pseudo$predictor[i], 
+            &quot; &gt; &quot;, 
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                 scores_i_j_multi_single_pseudo$predictor[i])
+          )
+        )
+      }
+      
+      scores_i_j_multi_single_pseudo$multi &lt;- paste0(scores_i_j_multi_single_pseudo$multi, &#39;.pseudo&#39;)
+
+      scores_i_j_multi_single_pseudo$predictor &lt;- gsub(&#39;.profiles&#39;, 
+                                    paste0(&#39;.&#39;, targ_pop_i, &#39;_pseudo.profiles&#39;),
+                                    scores_i_j_multi_single_pseudo$predictor)
+      
+      scores_i_j_multi_single_pseudo$top1&lt;-paste0(&#39;EUR_&#39;, disc_pop_j_2)
+
+      ####
+      # Make groups for the Multi-Source methods
+      ####
+      
+      scores_i_j_multi &lt;- scores_i_j[(scores_i_j$method %in% pgs_group_methods),]
+
+      # Split top1 scores by target population
+      # This doesn&#39;t apply to xwing because it only has pop-specific pseudo scores
+      scores_i_j_multi_top1&lt;-NULL
+      for(i in 1:which(scores_i_j_multi$method %in% c(&#39;prscsx&#39;))){
+        score_header&lt;-fread(scores_i_j_multi$predictor[i], nrow = 1)
+        
+        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
+          
+          if(scores_i_j_multi$method[i] == &#39;prscsx&#39;){
+            score_cols &lt;-
+              which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi&#39;), names(score_header)))
+          }
+          if(scores_i_j_multi$method[i] == &#39;xwing&#39;){
+            score_cols &lt;-
+              which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst&#39;), names(score_header)))
+          }
+          
+          system(
+            paste0(
+              &quot;cut -d&#39; &#39; -f &quot;, 
+              paste0(score_cols, collapse=&#39;,&#39;),
+              &quot; &quot;, 
+              scores_i_j_multi$predictor[i], 
+              &quot; &gt; &quot;, 
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
+                   scores_i_j_multi$predictor[i])
+            )
+          )
+          
+          tmp &lt;- scores_i_j_multi[i,]
+          tmp$multi &lt;- paste0(tmp$multi, &#39;.grid&#39;)
+          tmp$top1 &lt;- pop
+          tmp$predictor &lt;-
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.&#39;, pop, &#39;_grid.profiles&#39;),
+                   scores_i_j_multi$predictor[i])
+          
+          scores_i_j_multi_top1 &lt;- rbind(scores_i_j_multi_top1, tmp)
+        }
+      }
+
+      # Split pop-specific pseudo scores by target population
+      scores_i_j_multi_pop_pseudo&lt;-NULL
+      for(i in 1:nrow(scores_i_j_multi)){
+        score_header&lt;-fread(scores_i_j_multi$predictor[i], nrow = 1)
+        
+        for(pop in c(&#39;EUR&#39;, disc_pop_j_2)){
+          if(scores_i_j_multi$method[i] == &#39;prscsx&#39;){
+            score_cols &lt;-
+              which(grepl(paste0(&#39;^FID$|^IID$|_&#39;, pop, &#39;_phi_auto&#39;), names(score_header)))
+          }
+          if(scores_i_j_multi$method[i] == &#39;xwing&#39;){
+            score_cols &lt;-
+              which(grepl(paste0(&#39;^FID$|^IID$|_targ_&#39;, pop, &#39;_pst_&#39;, pop), names(score_header)))
+          }
+          
+          system(
+            paste0(
+              &quot;cut -d&#39; &#39; -f &quot;, 
+              paste0(score_cols, collapse=&#39;,&#39;),
+              &quot; &quot;, 
+              scores_i_j_multi$predictor[i], 
+              &quot; &gt; &quot;, 
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
+                   scores_i_j_multi$predictor[i])
+            )
+          )
+          
+          tmp &lt;- scores_i_j_multi[i,]
+          tmp$multi &lt;- paste0(tmp$multi, &#39;.pop_pseudo&#39;)
+          tmp$top1 &lt;- pop
+          tmp$predictor &lt;-
+              gsub(&#39;.profiles&#39;,
+                   paste0(&#39;.&#39;, pop, &#39;_pseudo.profiles&#39;),
+                   scores_i_j_multi$predictor[i])
+          
+          scores_i_j_multi_pop_pseudo &lt;- rbind(scores_i_j_multi_pop_pseudo, tmp)
+        }
+      }
+      
+      # Create pseudo score for multi-source methods
+      scores_i_j_multi_pseudo&lt;-NULL
+      for(i in 1:nrow(scores_i_j_multi)) {
+        param &lt;- find_pseudo(
+          config = config,
+          gwas = scores_i_j_multi$name[i],
+          pgs_method = scores_i_j_multi$method[i],
+          target_pop = targ_pop_i
+        )
+        
+        score_header &lt;-
+          fread(scores_i_j_multi$predictor[i], nrows = 1)
+        score_cols &lt;-
+          which(names(score_header) %in% c(&#39;FID&#39;, &#39;IID&#39;, paste0(scores_i_j_multi$name[i], &#39;_&#39;, param)))
+
+        system(
+          paste0(
+            &quot;cut -d&#39; &#39; -f &quot;, 
+            paste0(score_cols, collapse=&#39;,&#39;),
+            &quot; &quot;, 
+            scores_i_j_multi$predictor[i], 
+            &quot; &gt; &quot;, 
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
+                 scores_i_j_multi$predictor[i])
+          )
+        )
+        
+        tmp &lt;- scores_i_j_multi[i,]
+        tmp$multi &lt;- paste0(tmp$multi, &#39;.pseudo&#39;)
+        tmp$top1 &lt;- paste0(&#39;EUR_&#39;, disc_pop_j_2)
+        tmp$predictor &lt;-
+            gsub(&#39;.profiles&#39;,
+                 paste0(&#39;.pseudo.targ_&#39;, targ_pop_i,&#39;.profiles&#39;),
+                 scores_i_j_multi$predictor[i])
+        
+        scores_i_j_multi_pseudo &lt;- rbind(scores_i_j_multi_pseudo, tmp)
+      }
+      
+      ####
+      # Combine the different predictor groups
+      ####
+      predictors_i&lt;- do.call(rbind, list(
+        scores_i_j_single_top1, 
+        scores_i_j_single_pseudo,
+        scores_i_j_multi_single_pseudo,
+        scores_i_j_multi_top1,
+        scores_i_j_multi_pop_pseudo,
+        scores_i_j_multi_pseudo
+      ))
+      
+      predictors_i &lt;- predictors_i[, c(&#39;predictor&#39;, &#39;multi&#39;,&#39;top1&#39;), with=F]
+      
+      ####
+      # Make a group that will combined all population specific PGS
+      ####
+      
+      predictors_i_all &lt;- predictors_i[predictors_i$top1 %in% c(&#39;EUR&#39;,&#39;AFR&#39;,&#39;EAS&#39;),]
+      predictors_i_all$multi &lt;- &#39;all&#39;
+      predictors_i&lt;-rbind(predictors_i, predictors_i_all)
+      
+      write.table(
+        predictors_i,
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_&#39;,
+          targ_pop_i,
+          &#39;.disc_EUR_&#39;,
+          disc_pop_j_2,
+          &#39;/&#39;,
+          trait_i,
+          &#39;/predictor_list.txt&#39;
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}</code></pre>
+<hr />
+<h4>
+Run model_builder
+</h4>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_*.disc_EUR_*/*/res*
+
+for targ_pop in $(echo AFR); do
+  if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
+      targ_pop2=&quot;EUR_test&quot;
+  else
+      targ_pop2=$targ_pop
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;EUR&quot; ]; then
+    disc_pop=$(echo EAS AFR)
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;EAS&quot; ]; then
+    disc_pop=&quot;EAS&quot;
+  fi
+  
+  if [ &quot;$targ_pop&quot; == &quot;AFR&quot; ]; then
+    disc_pop=&quot;AFR&quot;
+  fi
+  
+  for disc_pop_i in ${disc_pop}; do
+    for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+      if [ ! -f &quot;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt&quot; ]; then
+        sbatch --mem 10G -n 5 --exclude=erc-hpc-comp058 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap=&quot;Rscript ../Scripts/model_builder/model_builder_top1.R \
+          --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${targ_pop2}.row_number.txt \
+          --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/predictor_list.txt \
+          --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res \
+          --n_core 5&quot;
+      fi
+    done
+  done
+done
+</code></pre>
+<hr />
+<h4>
+Plot results
+</h4>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt&#39;, header=F)$V1
+selected_traits &lt;- selected_traits[selected_traits != &#39;NEU&#39;]
+info_all &lt;- fread(&#39;~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv&#39;)
+
+# Calculate correlation between all phenotypes in each target population
+cors &lt;- list()
+for(pop_i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;CSA&#39;,&#39;AMR&#39;)){
+  if(pop_i == &#39;EUR&#39;){
+    pop_i_2 &lt;- &#39;EUR_test&#39;
+  } else {
+    pop_i_2 &lt;- pop_i
+  }
+  pheno_pop_i &lt;- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] &lt;- fread(paste0(&#39;/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/&#39;, pheno_i, &#39;.unrel.&#39;, pop_i_2, &#39;.row_number.txt&#39;))
+    names(pheno_pop_i[[pheno_i]])[3] &lt;- pheno_i
+  }
+  
+  pheno_pop_i_merged &lt;- merged_df &lt;- Reduce(function(x, y) merge(x, y, all = TRUE, by = c(&#39;FID&#39;,&#39;IID&#39;)), pheno_pop_i)
+
+  cors_i &lt;- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use=&#39;p&#39;))
+  cors[[pop_i]] &lt;- cors_i
+}
+
+# Read in results
+targ_pop = c(&#39;AFR&#39;)
+res_eval &lt;- list()
+for(pheno_i in selected_traits){
+  res_eval_i&lt;-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;EAS&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;AFR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i &lt;-
+        fread(
+          paste0(
+            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/&#39;,
+            &#39;targ_&#39;,
+            targ_pop_i,
+            &#39;.disc_EUR_&#39;,
+            disc_pop_i,
+            &#39;/&#39;,
+            pheno_i,
+            &#39;/res.pred_eval.txt&#39;
+          )
+        )
+      eval_i$Target&lt;-targ_pop_i
+      eval_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
+      res_eval_i&lt;-rbind(res_eval_i, eval_i)
+    }
+  }
+  
+  res_eval_i$Method&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_eval_i$Group)
+  res_eval_i$Method&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_eval_i$Method)
+  
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;IndivTune&#39;
+  res_eval_i$Model[grepl(&#39;top1$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   !grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-IndivTune&#39;
+  res_eval_i$Model[grepl(&#39;multi$&#39;, res_eval_i$Group) &amp;
+                   grepl(&#39;pseudo&#39;, res_eval_i$Group)]&lt;-&#39;Multi-SumStatTune&#39;
+  
+  res_eval_i$Model[grepl(&#39;_multi&#39;, res_eval_i$Group)]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;prscsx.pseudo.multi&#39;]&lt;-&#39;SumStatTune&#39;
+  res_eval_i$Model[res_eval_i$Group == &#39;xwing.pseudo.multi&#39;]&lt;-&#39;SumStatTune&#39;
+  
+  res_eval_i$Source&lt;-ifelse(
+    res_eval_i$Method %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_eval_i$Method) | 
+    !grepl(&#39;EUR|EAS|AFR&#39;, res_eval_i$Group), &#39;Multi&#39;, &#39;Single&#39;)
+  
+  res_eval_i$Discovery[grepl(&#39;EUR&#39;, res_eval_i$Group)] &lt;- &#39;EUR&#39;
+  res_eval_i$Discovery[grepl(&#39;EAS&#39;, res_eval_i$Group)] &lt;- &#39;EAS&#39;
+  res_eval_i$Discovery[grepl(&#39;AFR&#39;, res_eval_i$Group)] &lt;- &#39;AFR&#39;
+  res_eval_i$Discovery[res_eval_i$Source == &#39;Multi&#39;] &lt;- res_eval_i$gwas_group[res_eval_i$Source == &#39;Multi&#39;]
+  
+  res_eval_i$Method&lt;-factor(res_eval_i$Method, levels=unique(res_eval_i$Method))
+  res_eval_i$Model&lt;-factor(res_eval_i$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+  res_eval_i$Discovery&lt;-factor(res_eval_i$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+  # Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
+  
+  # Remove pseudo model for methods that don&#39;t really have one 
+  res_eval_i &lt;- res_eval_i[
+    !(res_eval_i$Method %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+      res_eval_i$Model %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
+
+  # Remove top1 models for *-Multi, PRS-CSx, X-wing
+  res_eval_i &lt;- res_eval_i[
+    !((res_eval_i$Method %in%  c(&#39;prscsx&#39;, &#39;xwing&#39;) | grepl(&#39;_multi$&#39;, res_eval_i$Method)) &amp; 
+      grepl(&#39;top1&#39;, res_eval_i$Group)),]
+  
+  # Remove any duplicate models
+  res_eval_i &lt;- res_eval_i[!duplicated(res_eval_i[, c(
+    &quot;Target&quot;, &quot;Method&quot;, &quot;Model&quot;, &quot;Source&quot;, &quot;Discovery&quot;,&quot;gwas_group&quot;
+  )]),]
+  
+  res_eval[[pheno_i]]&lt;-res_eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order &lt;- c(&quot;DBSLMM&quot;, &quot;lassosum&quot;, &quot;LDpred2&quot;, &quot;MegaPRS&quot;, &quot;PRS-CS&quot;, &quot;pT+clump&quot;, &quot;QuickPRS&quot;, &quot;SBayesRC&quot;, &quot;DBSLMM-multi&quot;, &quot;lassosum-multi&quot;, &quot;LDpred2-multi&quot;, &quot;MegaPRS-multi&quot;, &quot;PRS-CS-multi&quot;, &quot;pT+clump-multi&quot;, &quot;QuickPRS-multi&quot;, &quot;SBayesRC-multi&quot;, &quot;PRS-CSx&quot;, &quot;X-Wing&quot;, &quot;All&quot;) 
+
+res_eval_simp &lt;- NULL
+for(pheno_i in selected_traits){
+  tmp &lt;- res_eval[[pheno_i]]
+  tmp$Trait &lt;- pheno_i
+  
+  # Insert nice PGS method names
+  tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+  tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+  tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+  tmp$label &lt;- factor(tmp$label, levels = model_order)
+  
+  # Simplify result to either SumStatTune or IndivTune
+  tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+  tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+  tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery&#39;,&#39;Model&#39;), with=F]),]
+  
+  res_eval_simp &lt;- rbind(res_eval_simp, tmp)
+}
+
+# Plot results for each phenotype separately
+dir.create(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots&#39;)
+
+per_trait_plot &lt;- list()
+for(pheno_i in selected_traits){
+  tmp &lt;- res_eval_simp[res_eval_simp$Trait == pheno_i,]
+  #tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+  tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+  tmp$Discovery_clean &lt;- paste0(tmp$Discovery_clean, &#39; GWAS&#39;)
+  tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+
+  png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/&#39;, pheno_i,&#39;.png&#39;), res=300, width = 3400, height = 2000, units = &#39;px&#39;)
+  plot_tmp&lt;-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=NULL, fill = NULL, title = info_all$`Trait Description`[info_all$`Trait Label` == pheno_i]) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+  print(plot_tmp)
+  dev.off()
+  per_trait_plot[[pheno_i]]&lt;-plot_tmp
+}
+
+tmp &lt;- res_eval_simp
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean &lt;- paste0(tmp$Discovery_clean, &#39; GWAS&#39;)
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/per_trait_part1.png&#39;), res=300, width = 3000, height = 3800, units = &#39;px&#39;)
+ggplot(tmp[tmp$Trait %in% selected_traits[order(selected_traits)][1:5],], aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=NULL, fill = NULL) +
+    facet_grid(Trait ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/per_trait_part2.png&#39;), res=300, width = 3000, height = 3800, units = &#39;px&#39;)
+ggplot(tmp[tmp$Trait %in% selected_traits[order(selected_traits)][6:10],], aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=NULL, fill = NULL) +
+    facet_grid(Trait ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval &lt;- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == &#39;EAS&#39;){
+    disc_pop &lt;- &#39;EAS&#39;
+  }
+  if(targ_pop_i == &#39;AFR&#39;){
+    disc_pop &lt;- &#39;AFR&#39;
+  }
+  if(targ_pop_i == &#39;EUR&#39;){
+    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_eval for each scenario
+    res_eval_i &lt;- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x &lt;- res_eval[[i]]
+      x$pheno &lt;- names(res_eval)[i]
+      x &lt;- x[x$Target == targ_pop_i]
+      x &lt;- x[x$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+    }))
+    
+    # Average res_evalults for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_eval_i$Sample&lt;-&#39;A&#39;
+  
+    for(group_i in unique(res_eval_i$Group)){
+      res_eval_group_i &lt;- res_eval_i[res_eval_i$Group == group_i,]
+      missing_pheno &lt;-
+        colnames(cors[[targ_pop_i]])[!(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))]
+      
+      if (!all(colnames(cors[[targ_pop_i]]) %in% unique(res_eval_group_i$pheno))) {
+        print(paste0(
+          &#39;res_evalults missing for &#39;,
+          targ_pop_i,
+          &#39; &#39;,
+          group_i,
+          &#39; &#39;,
+          paste0(missing_pheno, collapse = &#39; &#39;)
+        ))
+      }
+      
+      cors_i &lt;- cors[[targ_pop_i]][unique(res_eval_group_i$pheno), unique(res_eval_group_i$pheno)]
+      
+      meta_res_eval_i &lt;-
+        agg(
+          id = Sample,
+          es = R,
+          var = SE ^ 2,
+          cor = cors_i,
+          method = &quot;BHHR&quot;,
+          mod = NULL,
+          data = res_eval_group_i
+        )
+      
+      tmp &lt;- data.table(Group = group_i,
+                        Method = res_eval_group_i$Method[1],
+                        Model = res_eval_group_i$Model[1],
+                        Source = res_eval_group_i$Source[1],
+                        Discovery = res_eval_group_i$Discovery[1],
+                        gwas_group = res_eval_group_i$gwas_group[1],
+                        Target = targ_pop_i,
+                        R = meta_res_eval_i$es,
+                        SE = sqrt(meta_res_eval_i$var))
+      
+      meta_res_eval &lt;- rbind(meta_res_eval, tmp)
+    }
+  }
+}
+
+meta_res_eval$Model&lt;-factor(meta_res_eval$Model, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+meta_res_eval$Discovery&lt;-factor(meta_res_eval$Discovery, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+write.csv(meta_res_eval, &#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/r_eval.csv&#39;, row.names = F)
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r.png&#39;), res=300, width = 3200, height = 2000, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+# Plot performance of -multi models trained using LEOPARD vs using indiv-level data
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;)
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods)] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods)], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = unique(tmp$label[order(!(grepl(&#39;Multi&#39;, tmp$label)), tmp$label)]))
+tmp&lt;-tmp[grepl(&#39;multi&#39;, tmp$label),]
+tmp &lt;- tmp[tmp$Model != &#39;Multi-IndivTune&#39;,]
+tmp$Model&lt;-as.character(tmp$Model)
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;]&lt;-&#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;]&lt;-&#39;LEOPARD&#39;
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_leopard.png&#39;), res=300, width = 1500, height = 2000, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = &quot;top&quot;,
+          legend.key.spacing.x = unit(1, &quot;cm&quot;),
+          legend.justification = &quot;center&quot;)
+dev.off()
+
+# Make simplified plot
+# Just show performance when using IndivTrain (or SumStat), and Remove &#39;All&#39; model, with both GWAS.
+tmp &lt;- meta_res_eval
+tmp &lt;- tmp[tmp$Target != &#39;EUR&#39;,]
+tmp &lt;- tmp[tmp$Method != &#39;all&#39;,]
+tmp &lt;- tmp[tmp$Source  == &#39;Multi&#39;,]
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model) &amp; !(tmp$Method %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery)
+tmp$Discovery_clean[tmp$Discovery == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Single&#39;] &lt;- &#39;Target-matched GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery != &#39;EUR&#39; &amp; tmp$Source == &#39;Multi&#39;] &lt;- &#39;Both&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;Target-matched GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;Both&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model[tmp$Model != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model[tmp$Model == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp &lt;- tmp[!duplicated(tmp[, c(&#39;label&#39;,&#39;Target&#39;,&#39;Discovery_clean&#39;,&#39;Model&#39;), with=F]),]
+tmp&lt;-tmp[tmp$Model == &#39;IndivTune&#39;,]
+
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_simple.png&#39;), res=300, width = 3200, height = 2000, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23, fill = &#39;black&#39;) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+tmp&lt;-tmp[tmp$Method %in% c(&#39;ldpred2&#39;,&#39;prscsx&#39;,&#39;xwing&#39;),]
+png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_simple_ldpred2.png&#39;), res=300, width = 500, height = 500, units = &#39;px&#39;)
+ggplot(tmp, aes(x=label, y=R)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+  #  geom_point(stat=&quot;identity&quot;, position=position_dodge(1), fill = &#39;#3399FF&#39;) +
+    geom_point(stat=&quot;identity&quot;, position=position_dodge(1), size=3, shape=23, fill = &#39;#3399FF&#39;) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R (SE)&quot;, x=&#39;Method&#39;) +
+    facet_grid(Target ~ ., scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Create a function to mirror pred_comp results
+mirror_comp&lt;-function(x){
+  x_sym &lt;- x
+  x_sym$Model_1 &lt;- x$Model_2
+  x_sym$Model_2 &lt;- x$Model_1
+  x_sym$Model_1_R &lt;- x$Model_2_R
+  x_sym$Model_2_R &lt;- x$Model_1_R
+  x_sym$R_diff &lt;- -x_sym$R_diff
+  x_mirrored &lt;- rbind(x, x_sym)
+  x_diag&lt;-data.frame(
+      Model_1=unique(x_mirrored$Model_1),
+      Model_2=unique(x_mirrored$Model_1),
+      Model_1_R=x_mirrored$Model_1_R,
+      Model_2_R=x_mirrored$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp&lt;-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
+# Read in results
+targ_pop=c(&#39;AFR&#39;)
+res_comp &lt;- list()
+for(pheno_i in selected_traits){
+  res_comp_i&lt;-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == &#39;EAS&#39;){
+      disc_pop &lt;- &#39;EAS&#39;
+    }
+    if(targ_pop_i == &#39;AFR&#39;){
+      disc_pop &lt;- &#39;AFR&#39;
+    }
+    if(targ_pop_i == &#39;EUR&#39;){
+      disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+    }
+    for(disc_pop_i in disc_pop){
+      comp_i &lt;-
+        fread(
+          paste0(
+            &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/&#39;,
+            &#39;targ_&#39;,
+            targ_pop_i,
+            &#39;.disc_EUR_&#39;,
+            disc_pop_i,
+            &#39;/&#39;,
+            pheno_i,
+            &#39;/res.pred_comp.txt&#39;
+          )
+        )
+      comp_i&lt;-mirror_comp(comp_i)
+      comp_i$Target&lt;-targ_pop_i
+      comp_i$gwas_group&lt;-paste0(&#39;EUR+&#39;, disc_pop_i)
+      res_comp_i&lt;-rbind(res_comp_i, comp_i)
+    }
+  }
+  
+  res_comp[[pheno_i]]&lt;-res_comp_i
+}
+
+res_comp_all &lt;- do.call(rbind, lapply(names(res_comp), function(name) {
+  x &lt;- res_comp[[name]]
+  x$pheno &lt;- name  # Add a new column with the name of the element
+  x  # Return the updated dataframe
+}))
+
+# Annotate tests to get order correct
+res_comp_all$Method1&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_1)
+res_comp_all$Method1&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method1)
+res_comp_all$Method2&lt;-sub(&#39;\\..*&#39;,&#39;&#39;,res_comp_all$Model_2)
+res_comp_all$Method2&lt;-gsub(&#39;-.*&#39;,&#39;&#39;, res_comp_all$Method2)
+
+find_model&lt;-function(x){
+  mod &lt;- x
+  mod[grepl(&#39;top1$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;IndivTune&#39;
+  mod[grepl(&#39;top1$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;SumStatTune&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; !grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-IndivTune&#39;
+  mod[grepl(&#39;multi$&#39;, x) &amp; grepl(&#39;pseudo&#39;, x)] &lt;- &#39;Multi-SumStatTune&#39;
+  mod[grepl(&#39;_multi&#39;, x)] &lt;- &#39;SumStatTune&#39;
+  mod[x == &#39;prscsx.pseudo.multi&#39;] &lt;- &#39;SumStatTune&#39;
+  mod[x == &#39;xwing.pseudo.multi&#39;] &lt;- &#39;SumStatTune&#39;
+  
+  return(mod)
+}
+
+res_comp_all$Model1&lt;-find_model(res_comp_all$Model_1)
+res_comp_all$Model2&lt;-find_model(res_comp_all$Model_2)
+
+res_comp_all$Source1&lt;-ifelse(res_comp_all$Method1 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method1) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_1), &#39;Multi&#39;, &#39;Single&#39;)
+res_comp_all$Source2&lt;-ifelse(res_comp_all$Method2 %in% pgs_group_methods | grepl(&#39;_multi$&#39;, res_comp_all$Method2) | !grepl(&#39;AFR|EAS|EUR&#39;, res_comp_all$Model_2), &#39;Multi&#39;, &#39;Single&#39;)
+  
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;)){
+  res_comp_all$Discovery1[grepl(i, res_comp_all$Model_1)] &lt;- i
+  res_comp_all$Discovery2[grepl(i, res_comp_all$Model_2)] &lt;- i
+}
+res_comp_all$Discovery1[res_comp_all$Source1 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source1 == &#39;Multi&#39;]
+res_comp_all$Discovery2[res_comp_all$Source2 == &#39;Multi&#39;] &lt;- res_comp_all$gwas_group[res_comp_all$Source2 == &#39;Multi&#39;]
+
+res_comp_all$Method1&lt;-factor(res_comp_all$Method1, levels=unique(res_comp_all$Method1))
+res_comp_all$Method2&lt;-factor(res_comp_all$Method2, levels=unique(res_comp_all$Method2))
+res_comp_all$Model1&lt;-factor(res_comp_all$Model1, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+res_comp_all$Model2&lt;-factor(res_comp_all$Model2, levels=c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+res_comp_all$Discovery1&lt;-factor(res_comp_all$Discovery1, levels=rev(c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;)))
+res_comp_all$Discovery2&lt;-factor(res_comp_all$Discovery2, levels=c(&#39;AFR&#39;,&#39;EAS&#39;,&#39;EUR&#39;,&#39;EUR+AFR&#39;,&#39;EUR+EAS&#39;))
+
+# Remove IndivTune and Multi-IndivTune model for groups that contain one score (aka QuickPRS and SBayesRC)
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method1 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+  res_comp_all$Model1 %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method2 %in%  c(&#39;quickprs&#39;,&#39;sbayesrc&#39;) &amp; 
+  res_comp_all$Model2 %in% c(&#39;IndivTune&#39;,&#39;Multi-IndivTune&#39;)),]
+
+# Remove pseudo model for methods that don&#39;t really have one 
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method1 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+  res_comp_all$Model1 %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
+res_comp_all &lt;- res_comp_all[
+!(res_comp_all$Method2 %in%  c(&#39;ptclump&#39;,&#39;ptclump_multi&#39;) &amp; 
+  res_comp_all$Model2 %in% c(&#39;SumStatTune&#39;,&#39;Multi-SumStatTune&#39;)),]
+
+# Remove top1 models for PRS-CSx
+res_comp_all &lt;- res_comp_all[
+!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method1) &amp; 
+  grepl(&#39;top1&#39;, res_comp_all$Model_1)),]
+res_comp_all &lt;- res_comp_all[
+!(grepl(&#39;prscsx|xwing|_multi&#39;, res_comp_all$Method2) &amp; 
+  grepl(&#39;top1&#39;, res_comp_all$Model_2)),]
+
+# Remove any comparisons
+res_comp_all &lt;- res_comp_all[!duplicated(res_comp_all[, c(&quot;Target&quot;, &quot;gwas_group&quot;, &quot;Method1&quot;, &quot;Model1&quot;, &quot;Source1&quot;, &quot;Discovery1&quot;, &quot;Method2&quot;, &quot;Model2&quot;, &quot;Source2&quot;, &quot;Discovery2&quot;,&#39;pheno&#39;)]),]
+
+res_comp_all$r_diff_rel &lt;- res_comp_all$R_diff / res_comp_all$Model_2_R
+
+# Calculate relative improvement for ldpred2-multi vs ldpred2 as example
+tmp_ldpred2 &lt;- res_comp_all[res_comp_all$Model_1 == &#39;ldpred2.multi&#39; &amp; 
+                    grepl(&#39;ldpred2-&#39;, res_comp_all$Model_2) &amp;
+                    res_comp_all$Target == &#39;AFR&#39;,]
+tmp_ldpred2 &lt;- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+tmp_ldpred2 &lt;- tmp_ldpred2[!duplicated(tmp_ldpred2$pheno),]
+round(min(tmp_ldpred2$r_diff_rel)*100, 1)
+round(max(tmp_ldpred2$r_diff_rel)*100, 1)
+
+tmp_ldpred2 &lt;- res_comp_all[res_comp_all$Model_1 == &#39;ldpred2.multi&#39; &amp; 
+                    grepl(&#39;ldpred2-&#39;, res_comp_all$Model_2) &amp;
+                    res_comp_all$Target == &#39;EAS&#39;,]
+tmp_ldpred2 &lt;- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+tmp_ldpred2 &lt;- tmp_ldpred2[!duplicated(tmp_ldpred2$pheno),]
+round(min(tmp_ldpred2$r_diff_rel)*100, 1)
+round(max(tmp_ldpred2$r_diff_rel)*100, 1)
+
+# Calculate relative improvement for sbayesrc-multi vs sbayesrc in EUR target as example
+tmp_sbayesrc &lt;- res_comp_all[res_comp_all$Model_1 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                    grepl(&#39;sbayesrc.pseudo-&#39;, res_comp_all$Model_2) &amp;
+                    res_comp_all$Target == &#39;EUR&#39; &amp;
+                    res_comp_all$Discovery1 == &#39;EUR+EAS&#39; &amp;
+                    res_comp_all$Discovery2 == &#39;EUR&#39;,]
+tmp_sbayesrc &lt;- tmp_sbayesrc[order(-tmp_sbayesrc$Model_2_R),]
+round(min(tmp_sbayesrc$r_diff_rel)*100, 1)
+round(max(tmp_sbayesrc$r_diff_rel)*100, 1)
+
+tmp_sbayesrc &lt;- res_comp_all[res_comp_all$Model_1 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                    grepl(&#39;sbayesrc.pseudo-&#39;, res_comp_all$Model_2) &amp;
+                    res_comp_all$Target == &#39;EUR&#39; &amp;
+                    res_comp_all$Discovery1 == &#39;EUR+AFR&#39; &amp;
+                    res_comp_all$Discovery2 == &#39;EUR&#39;,]
+tmp_sbayesrc &lt;- tmp_sbayesrc[order(-tmp_sbayesrc$Model_2_R),]
+round(min(tmp_sbayesrc$r_diff_rel)*100, 1)
+round(max(tmp_sbayesrc$r_diff_rel)*100, 1)
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTune or SumStatTune result
+tmp &lt;- res_comp_all
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label1&#39;
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method2&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label2&#39;
+
+tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;] &lt;- paste0(tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;] &lt;- paste0(tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;], &#39;-multi&#39;)
+
+tmp$Model1[tmp$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model1[tmp$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp$Model2[tmp$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model2[tmp$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+
+tmp&lt;-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp&lt;-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` &lt;- paste0(tmp$label1, &#39; - &#39;, tmp$Model1, &#39; - &#39;, tmp$Discovery1)
+tmp$`Model 2` &lt;- paste0(tmp$label2, &#39; - &#39;, tmp$Model2, &#39; - &#39;, tmp$Discovery2)
+
+tmp &lt;- tmp[, c(&#39;Target&#39;, &#39;pheno&#39;, &#39;Model 1&#39;, &#39;Model 2&#39;, &#39;Model_1_R&#39;, &#39;Model_2_R&#39;, &#39;R_diff&#39;, &#39;R_diff_pval&#39;), with=F]
+names(tmp) &lt;- c(&#39;Target&#39;, &#39;Trait&#39;,&#39;Model 1&#39;, &#39;Model 2&#39;, &quot;R (Model 1)&quot;, &quot;R (Model 2)&quot;, &quot;R difference (Model 1 R - Model 2 R)&quot;, &quot;R difference p-value&quot;)
+
+tmp&lt;-tmp[order(tmp$Target, tmp$Trait, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` &lt;- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` &lt;- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` &lt;- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, &#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/r_diff.csv&#39;, row.names=F)
+
+###########
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_comp &lt;- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == &#39;EAS&#39;){
+    disc_pop &lt;- &#39;EAS&#39;
+  }
+  if(targ_pop_i == &#39;AFR&#39;){
+    disc_pop &lt;- &#39;AFR&#39;
+  }
+  if(targ_pop_i == &#39;EUR&#39;){
+    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_comp for each scenario
+    res_comp_i &lt;- res_comp_all[res_comp_all$Target == targ_pop_i &amp; res_comp_all$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+  
+    # Calculate diff SE based on p-value
+    res_comp_i$R_diff_pval[res_comp_i$R_diff == 0] &lt;- 1-0.001
+    res_comp_i$R_diff_pval[res_comp_i$R_diff_pval == 1]&lt;-1-0.001
+    res_comp_i$R_diff_z&lt;-qnorm(res_comp_i$R_diff_pval/2)
+    res_comp_i$R_diff_SE&lt;-abs(res_comp_i$R_diff/res_comp_i$R_diff_z)
+        
+    # Average results for each test across phenotypes
+    # Use MAd to account for correlation between them
+    res_comp_i$Sample&lt;-&#39;A&#39;
+    res_comp_i$Group &lt;- paste0(res_comp_i$Model_1, &#39;_vs_&#39;, res_comp_i$Model_2)
+  
+    for(group_i in unique(res_comp_i$Group)){
+      res_comp_group_i &lt;- res_comp_i[res_comp_i$Group == group_i,]
+      cors_i &lt;- cors[[targ_pop_i]][unique(res_comp_group_i$pheno), unique(res_comp_group_i$pheno)]
+      
+      if(res_comp_group_i$Model_1[1] != res_comp_group_i$Model_2[1]){
+        
+        meta_res_comp_i &lt;-
+          agg(
+            id = Sample,
+            es = R_diff,
+            var = R_diff_SE ^ 2,
+            cor = cors_i,
+            method = &quot;BHHR&quot;,
+            mod = NULL,
+            data = res_comp_group_i
+          )
+        
+        tmp &lt;- res_comp_group_i[1,]
+        tmp$pheno &lt;- NULL
+        tmp$Model_1_R &lt;-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_1 &amp;
+                            meta_res_eval$Target == targ_pop_i &amp;
+                            meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+        tmp$Model_2_R &lt;-
+          meta_res_eval$R[meta_res_eval$Group == tmp$Model_2 &amp;
+                            meta_res_eval$Target == targ_pop_i &amp;
+                            meta_res_eval$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i)]
+        tmp$R_diff &lt;- meta_res_comp_i$es
+        tmp$R_diff_SE &lt;- sqrt(meta_res_comp_i$var)
+        tmp$R_diff_z &lt;- tmp$R_diff / tmp$R_diff_SE
+        tmp$R_diff_p &lt;- 2*pnorm(-abs(tmp$R_diff_z))
+      } else {
+        tmp &lt;- res_comp_group_i[1,]
+        tmp$pheno &lt;- NULL
+        tmp$R_diff &lt;- NA
+        tmp$R_diff_SE &lt;- NA
+        tmp$R_diff_z &lt;- NA
+        tmp$R_diff_p &lt;- NA
+      }
+      meta_res_comp &lt;- rbind(meta_res_comp, tmp)
+    }
+  }
+}
+
+meta_res_comp$R_diff_perc &lt;- meta_res_comp$R_diff / meta_res_comp$Model_2_R
+  
+# Extract average improvement for ldpred2-multi vs ldpred2 as example
+tmp_ldpred2 &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;ldpred2.multi&#39; &amp; 
+                    grepl(&#39;ldpred2-&#39;, meta_res_comp$Model_2) &amp;
+                    meta_res_comp$Target == &#39;AFR&#39;,]
+tmp_ldpred2 &lt;- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+round(min(tmp_ldpred2$R_diff_perc)*100, 1)
+
+tmp_ldpred2 &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;ldpred2.multi&#39; &amp; 
+                    grepl(&#39;ldpred2-&#39;, meta_res_comp$Model_2) &amp;
+                    meta_res_comp$Target == &#39;EAS&#39;,]
+tmp_ldpred2 &lt;- tmp_ldpred2[order(-tmp_ldpred2$Model_2_R),]
+round(min(tmp_ldpred2$R_diff_perc)*100, 1)
+
+# Extract average improvement for sbayesrc-multi vs sbayesrc in EUR as example
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                    grepl(&#39;sbayesrc.pseudo&#39;, meta_res_comp$Model_2) &amp;
+                    meta_res_comp$Target == &#39;EUR&#39; &amp;
+                    meta_res_comp$Discovery1 == &#39;EUR+AFR&#39; &amp;
+                    meta_res_comp$Discovery2 == &#39;EUR&#39;,]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;sbayesrc.pseudo.multi&#39; &amp; 
+                    grepl(&#39;sbayesrc.pseudo&#39;, meta_res_comp$Model_2) &amp;
+                    meta_res_comp$Target == &#39;EUR&#39; &amp;
+                    meta_res_comp$Discovery1 == &#39;EUR+EAS&#39; &amp;
+                    meta_res_comp$Discovery2 == &#39;EUR&#39;,]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+
+# Extract average improvement for sbayesrc in EUR compared to all model
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_2 == &#39;sbayesrc.pseudo-EUR.top1&#39; &amp;
+                    meta_res_comp$Model_1 == &#39;all-EUR.top1&#39; &amp;
+                    meta_res_comp$Target == &#39;AFR&#39;,]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+tmp_sbayesrc$R_diff_p
+
+tmp_sbayesrc &lt;- meta_res_comp[meta_res_comp$Model_2 == &#39;sbayesrc.pseudo-EUR.top1&#39; &amp;
+                    meta_res_comp$Model_1 == &#39;all-EUR.top1&#39; &amp;
+                    meta_res_comp$Target == &#39;EAS&#39;,]
+round(tmp_sbayesrc$R_diff_perc*100, 1)
+tmp_sbayesrc$R_diff_p
+
+
+# Compare QuickPRS-Multi vs QuickPRS to evaluate LEOPARD performance
+tmp_quickprs &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;quickprs_multi.pseudo.multi&#39; &amp; 
+                                meta_res_comp$Model_2 == &#39;quickprs.pseudo.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;AFR&#39;,]
+round(min(tmp_quickprs$R_diff_perc)*100, 1)
+
+tmp_quickprs &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;quickprs_multi.pseudo.multi&#39; &amp; 
+                                meta_res_comp$Model_2 == &#39;quickprs.pseudo.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;EAS&#39;,]
+round(min(tmp_quickprs$R_diff_perc)*100, 1)
+
+# Compare all.multi method to next best method
+tmp_all &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;all.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;AFR&#39; &amp;
+                    meta_res_comp$Source2 == &#39;Multi&#39;,]
+tmp_all &lt;- tmp_all[order(tmp_all$R_diff),]
+tmp_all &lt;- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+tmp_all &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;all.multi&#39; &amp;
+                    meta_res_comp$Target == &#39;EAS&#39; &amp;
+                    meta_res_comp$Source2 == &#39;Multi&#39;,]
+tmp_all &lt;- tmp_all[order(tmp_all$R_diff),]
+tmp_all &lt;- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+# Compare all.multi method to next best method
+tmp_all &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;all-AFR.top1&#39; &amp;
+                    meta_res_comp$Target == &#39;AFR&#39; &amp;
+                    meta_res_comp$Discovery1 == &#39;AFR&#39; &amp;
+                    meta_res_comp$Discovery2 == &#39;AFR&#39;,]
+tmp_all &lt;- tmp_all[order(tmp_all$R_diff),]
+tmp_all &lt;- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+tmp_all &lt;- meta_res_comp[meta_res_comp$Model_1 == &#39;all-EAS.top1&#39; &amp;
+                    meta_res_comp$Target == &#39;EAS&#39; &amp;
+                    meta_res_comp$Discovery1 == &#39;EAS&#39; &amp;
+                    meta_res_comp$Discovery2 == &#39;EAS&#39;,]
+tmp_all &lt;- tmp_all[order(tmp_all$R_diff),]
+tmp_all &lt;- tmp_all[1,]
+round(tmp_all$R_diff_perc*100, 1)
+tmp_all$R_diff_p
+
+#####
+# Export a csv containing difference results for all traits
+#####
+# Simplify to contain only IndivTune or SumStatTune result
+tmp &lt;- meta_res_comp
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label1&#39;
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method2&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label2&#39;
+
+tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;] &lt;- paste0(tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;] &lt;- paste0(tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;], &#39;-multi&#39;)
+
+tmp$Model1[tmp$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model1[tmp$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp$Model2[tmp$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp$Model2[tmp$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+
+tmp&lt;-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+tmp&lt;-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+tmp$`Model 1` &lt;- paste0(tmp$label1, &#39; - &#39;, tmp$Model1, &#39; - &#39;, tmp$Discovery1)
+tmp$`Model 2` &lt;- paste0(tmp$label2, &#39; - &#39;, tmp$Model2, &#39; - &#39;, tmp$Discovery2)
+
+tmp$`Percentage change (R difference / Model 2 R)` &lt;- paste0(round(tmp$R_diff_perc * 100, 1), &#39;%&#39;)
+
+tmp &lt;- tmp[, c(&#39;Target&#39;, &#39;Model 1&#39;, &#39;Model 2&#39;, &#39;Model_1_R&#39;, &#39;Model_2_R&#39;, &#39;R_diff&#39;,&quot;Percentage change (R difference / Model 2 R)&quot;, &#39;R_diff_p&#39;), with=F]
+names(tmp) &lt;- c(&#39;Target&#39;,&#39;Model 1&#39;, &#39;Model 2&#39;, &quot;R (Model 1)&quot;, &quot;R (Model 2)&quot;, &quot;R difference (Model 1 R - Model 2 R)&quot;, &quot;Percentage change (R difference / Model 2 R)&quot;, &quot;R difference p-value&quot;)
+
+tmp&lt;-tmp[order(tmp$Target, tmp$`Model 1`, tmp$`Model 2`),]
+tmp$`R difference (Model 1 R - Model 2 R)` &lt;- round(tmp$`R difference (Model 1 R - Model 2 R)`, 3)
+tmp$`R (Model 1)` &lt;- round(tmp$`R (Model 1)`, 3)
+tmp$`R (Model 2)` &lt;- round(tmp$`R (Model 2)`, 3)
+
+write.csv(tmp, &#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/r_diff_average.csv&#39;, row.names=F)
+
+############
+
+# Group differences
+meta_res_comp$R_diff_catagory &lt;- cut(
+    meta_res_comp$R_diff,
+    breaks = c(-Inf, -0.08, -0.025, -0.002, 0.002, 0.025, 0.08, Inf),
+    labels = c(&#39;&lt; -0.08&#39;, &#39;-0.08 - -0.025&#39;, &#39;-0.025 - -0.002&#39;, &#39;-0.002 - 0.002&#39;, &#39;0.002 - 0.025&#39;, &#39;0.025 - 0.08&#39;, &#39;&gt; 0.08&#39;),
+    right = FALSE
+)
+meta_res_comp$R_diff_catagory &lt;- factor(meta_res_comp$R_diff_catagory, levels = rev(levels(meta_res_comp$R_diff_catagory)))
+
+# Assign significance stars
+meta_res_comp$indep_star&lt;-&#39; &#39;
+meta_res_comp$indep_star[meta_res_comp$R_diff_p &lt; 0.05]&lt;-&#39;*&#39;
+meta_res_comp$indep_star[meta_res_comp$R_diff_p &lt; 1e-3]&lt;-&#39;**&#39;
+# meta_res_comp$indep_star[meta_res_comp$R_diff_p &lt; 1e-6]&lt;-&#39;***&#39;
+
+meta_res_comp&lt;-meta_res_comp[order(meta_res_comp$Discovery1, meta_res_comp$Discovery2, meta_res_comp$Method1),]
+
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == &#39;EAS&#39;){
+    disc_pop &lt;- &#39;EAS&#39;
+  }
+  if(targ_pop_i == &#39;AFR&#39;){
+    disc_pop &lt;- &#39;AFR&#39;
+  }
+  if(targ_pop_i == &#39;EUR&#39;){
+    disc_pop &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+  }
+  for(disc_pop_i in disc_pop){
+
+    tmp &lt;- meta_res_comp[meta_res_comp$Target == targ_pop_i, ]
+
+    tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+    tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+    names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label1&#39;
+    tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method2&#39;, by.y = &#39;method&#39;, all.x = T)
+    tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+    names(tmp)[names(tmp) == &#39;label&#39;] &lt;- &#39;label2&#39;
+    
+    tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;] &lt;- paste0(tmp$label1[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label1 != &#39;All&#39;], &#39;-multi&#39;)
+    tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;] &lt;- paste0(tmp$label2[grepl(&#39;Multi&#39;, tmp$Model2) &amp; !(tmp$Method2 %in% pgs_group_methods) &amp; tmp$label2 != &#39;All&#39;], &#39;-multi&#39;)
+    
+    tmp$Model1[tmp$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+    tmp$Model1[tmp$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+    tmp$Model2[tmp$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+    tmp$Model2[tmp$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+    
+    tmp&lt;-tmp[tmp$Model_1 %in% res_eval_simp$Group,]
+    tmp&lt;-tmp[tmp$Model_2 %in% res_eval_simp$Group,]
+
+    tmp$label1 &lt;- factor(tmp$label1, levels = model_order)
+    tmp$label2 &lt;- factor(tmp$label2, levels = model_order)
+
+    tmp&lt;-tmp[order(tmp$label1, tmp$label2),]
+    
+    tmp$label1 &lt;- paste0(tmp$label1,&quot; (&quot;, ifelse(tmp$Model1 == &#39;SumStatTune&#39;, &#39;ST&#39;, &#39;IT&#39;), &quot;)&quot;)
+    tmp$label2 &lt;- paste0(tmp$label2,&quot; (&quot;, ifelse(tmp$Model2 == &#39;SumStatTune&#39;, &#39;ST&#39;, &#39;IT&#39;), &quot;)&quot;)
+
+    tmp$label1 &lt;- factor(tmp$label1, levels = unique(tmp$label1))
+    tmp$label2 &lt;- factor(tmp$label2, levels = unique(tmp$label2))
+    
+    tmp &lt;- tmp[tmp$gwas_group == paste0(&#39;EUR+&#39;, disc_pop_i), ]
+    
+    plot_tmp &lt;- ggplot(data = tmp, aes(label2, label1, fill = R_diff_catagory)) +
+      geom_tile(color = &quot;white&quot;, show.legend = TRUE) +
+      labs(y = &#39;Test&#39;, x = &#39;Comparison&#39;, fill = &#39;R difference&#39;, title = paste0(&#39;Target: &#39;, targ_pop_i)) +
+      facet_grid(Discovery1 ~ Discovery2, scales = &#39;free&#39;, space = &#39;free&#39;, switch=&quot;both&quot;) +
+      geom_text(
+        data = tmp,
+        aes(label2, label1, label = indep_star),
+        color = &quot;black&quot;,
+        size = 4,
+        angle = 0,
+        vjust = 0.8
+      ) +
+      scale_fill_brewer(
+        breaks = levels(tmp$R_diff_catagory),
+        palette = &quot;RdBu&quot;,
+        drop = F,
+        na.value = &#39;grey&#39;
+      ) +
+      theme_half_open() +
+      background_grid() +
+      panel_border() +
+      theme(axis.text.x = element_text(
+        angle = 45,
+        vjust = 1,
+        hjust = 1
+      ))
+    
+    png(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r_diff.Discovery_EUR_&#39;, disc_pop_i,&#39;.Target_&#39;, targ_pop_i, &#39;.png&#39;), res=300, width = 4400, height = 3200, units = &#39;px&#39;)
+      print(plot_tmp)
+    dev.off()
+  }
+}
+
+####
+# Plot relative improvement of methods
+####
+# Use ptclump IndivTune using EUR GWAS as the reference, as provides an interpretable scale
+
+meta_res_comp_ptclump_top1&lt;-meta_res_comp[meta_res_comp$Method2 == &#39;all&#39; &amp; meta_res_comp$Source2 == &#39;Multi&#39;,]
+meta_res_comp_ptclump_top1$reference_point&lt;-F
+meta_res_comp_ptclump_top1$reference_point[meta_res_comp_ptclump_top1$Method1 == &#39;all&#39; &amp; meta_res_comp_ptclump_top1$Source1 == &#39;Multi&#39;]&lt;-T
+meta_res_comp_ptclump_top1$R_diff[is.na(meta_res_comp_ptclump_top1$R_diff)]&lt;-0
+meta_res_comp_ptclump_top1$Discovery1 &lt;- factor(meta_res_comp_ptclump_top1$Discovery1, levels=rev(levels(meta_res_comp_ptclump_top1$Discovery1)))
+
+res_comp_all_ptclump_top1&lt;-res_comp_all[res_comp_all$Method2 == &#39;all&#39; &amp; res_comp_all$Source2 == &#39;Multi&#39;,]
+res_comp_all_ptclump_top1$Discovery1 &lt;-  factor(res_comp_all_ptclump_top1$Discovery1, levels=levels(meta_res_comp_ptclump_top1$Discovery1))
+
+# Create data to plot reference points
+meta_res_comp_reference &lt;- meta_res_comp_ptclump_top1
+meta_res_comp_reference$R_diff[meta_res_comp_ptclump_top1$reference_point == F] &lt;- NA
+meta_res_comp_reference$R_diff_SE [meta_res_comp_ptclump_top1$reference_point == F] &lt;- NA
+res_comp_all_ptclump_top1$reference_point&lt;-F
+
+meta_tmp &lt;- meta_res_comp_ptclump_top1
+meta_tmp &lt;- merge(meta_tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+meta_tmp$label[is.na(meta_tmp$label)] &lt;- &#39;All&#39;
+meta_tmp$label[grepl(&#39;Multi&#39;, meta_tmp$Model1) &amp; !(meta_tmp$Method1 %in% pgs_group_methods) &amp; meta_tmp$label != &#39;All&#39;] &lt;- paste0(meta_tmp$label[grepl(&#39;Multi&#39;, meta_tmp$Model1) &amp; !(meta_tmp$Method1 %in% pgs_group_methods) &amp; meta_tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+meta_tmp$label &lt;- factor(meta_tmp$label, levels = model_order)
+meta_tmp$Discovery_clean &lt;- as.character(meta_tmp$Discovery1)
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != &#39;EUR&#39; &amp; meta_tmp$Source1 == &#39;Single&#39;] &lt;- &#39;AFR GWAS&#39;
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != &#39;EUR&#39; &amp; meta_tmp$Source1 == &#39;Multi&#39;] &lt;- &#39;EUR + AFR GWAS&#39;
+meta_tmp$Discovery_clean &lt;- factor(meta_tmp$Discovery_clean, 
+                              levels = c(&#39;AFR GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;EUR + AFR GWAS&#39;))
+meta_tmp$Target &lt;- paste0(meta_tmp$Target, &#39; Target&#39;)
+meta_tmp$Model1 &lt;- factor(meta_tmp$Model1, levels = c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+
+meta_tmp_ref &lt;- meta_res_comp_reference
+meta_tmp_ref &lt;- merge(meta_tmp_ref, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+meta_tmp_ref$label[is.na(meta_tmp_ref$label)] &lt;- &#39;All&#39;
+meta_tmp_ref$label[grepl(&#39;Multi&#39;, meta_tmp_ref$Model1) &amp; !(meta_tmp_ref$Method1 %in% pgs_group_methods) &amp; meta_tmp_ref$label != &#39;All&#39;] &lt;- paste0(meta_tmp_ref$label[grepl(&#39;Multi&#39;, meta_tmp_ref$Model1) &amp; !(meta_tmp_ref$Method1 %in% pgs_group_methods) &amp; meta_tmp_ref$label != &#39;All&#39;], &#39;-multi&#39;)
+meta_tmp_ref$label &lt;- factor(meta_tmp_ref$label, levels = model_order)
+meta_tmp_ref$Discovery_clean &lt;- as.character(meta_tmp_ref$Discovery1)
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != &#39;EUR&#39; &amp; meta_tmp_ref$Source1 == &#39;Single&#39;] &lt;- &#39;AFR GWAS&#39;
+meta_tmp_ref$Discovery_clean[meta_tmp_ref$Discovery1 != &#39;EUR&#39; &amp; meta_tmp_ref$Source1 == &#39;Multi&#39;] &lt;- &#39;EUR + AFR GWAS&#39;
+meta_tmp_ref$Discovery_clean &lt;- factor(meta_tmp_ref$Discovery_clean, 
+                              levels = c(&#39;AFR GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;EUR + AFR GWAS&#39;))
+meta_tmp_ref$Target &lt;- paste0(meta_tmp_ref$Target, &#39; Target&#39;)
+meta_tmp_ref$Model1 &lt;- factor(meta_tmp_ref$Model1, levels = c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+
+tmp &lt;- res_comp_all_ptclump_top1
+tmp &lt;- merge(tmp, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+tmp$label[is.na(tmp$label)] &lt;- &#39;All&#39;
+tmp$label[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;] &lt;- paste0(tmp$label[grepl(&#39;Multi&#39;, tmp$Model1) &amp; !(tmp$Method1 %in% pgs_group_methods) &amp; tmp$label != &#39;All&#39;], &#39;-multi&#39;)
+tmp$label &lt;- factor(tmp$label, levels = model_order)
+tmp$Discovery_clean &lt;- as.character(tmp$Discovery1)
+tmp$Discovery_clean[tmp$Discovery1 == &#39;EUR&#39;] &lt;- &#39;EUR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery1 != &#39;EUR&#39; &amp; tmp$Source1 == &#39;Single&#39;] &lt;- &#39;AFR GWAS&#39;
+tmp$Discovery_clean[tmp$Discovery1 != &#39;EUR&#39; &amp; tmp$Source1 == &#39;Multi&#39;] &lt;- &#39;EUR + AFR GWAS&#39;
+tmp$Discovery_clean &lt;- factor(tmp$Discovery_clean, 
+                              levels = c(&#39;AFR GWAS&#39;,
+                                         &#39;EUR GWAS&#39;,
+                                         &#39;EUR + AFR GWAS&#39;))
+tmp$Target &lt;- paste0(tmp$Target, &#39; Target&#39;)
+tmp$Model1 &lt;- factor(tmp$Model1, levels = c(&#39;IndivTune&#39;,&#39;SumStatTune&#39;,&#39;Multi-IndivTune&#39;,&#39;Multi-SumStatTune&#39;))
+
+ggplot(meta_tmp, aes(x=label, y=R_diff , fill = Model1)) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff - R_diff_SE,
+          ymax = R_diff + R_diff_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff, fill = Model1),
+        stat = &quot;identity&quot;,
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R_diff (SE)&quot;) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+
+# Plot as % change
+meta_tmp$R_diff_perc &lt;- meta_tmp$R_diff / meta_tmp$Model_2_R
+meta_tmp_ref$R_diff_perc &lt;- meta_tmp_ref$R_diff / meta_tmp_ref$Model_2_R
+tmp$R_diff_perc &lt;- tmp$R_diff / tmp$Model_2_R
+
+meta_tmp$R_diff_perc_SE &lt;- meta_tmp$R_diff_SE / meta_tmp$Model_2_R
+
+library(scales)
+ggplot(meta_tmp, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp,
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref,
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = &quot;identity&quot;,
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 3,    # Increase size for emphasis
+        shape = 22,
+        stroke = 1.5,
+        show.legend=F
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp$label))), linetype=&quot;dotted&quot;) +
+    labs(y = &quot;R diff. (SE)&quot;) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid() + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+
+# Simplify results showing results only with or without training data
+meta_tmp_simple &lt;- meta_tmp
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_simple$Model1[meta_tmp_simple$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_simple$Model2[meta_tmp_simple$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+meta_tmp_simple&lt;-meta_tmp_simple[meta_tmp_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_simple&lt;-meta_tmp_simple[meta_tmp_simple$Model_2 %in% res_eval_simp$Group,]
+
+meta_tmp_ref_simple &lt;- meta_tmp_ref
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_ref_simple$Model1[meta_tmp_ref_simple$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+meta_tmp_ref_simple$Model2[meta_tmp_ref_simple$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+meta_tmp_ref_simple&lt;-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_1 %in% res_eval_simp$Group,]
+meta_tmp_ref_simple&lt;-meta_tmp_ref_simple[meta_tmp_ref_simple$Model_2 %in% res_eval_simp$Group,]
+
+tmp_simple &lt;- tmp
+tmp_simple$Model1[tmp_simple$Model1 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp_simple$Model1[tmp_simple$Model1 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp_simple$Model2[tmp_simple$Model2 != &#39;SumStatTune&#39;] &lt;- &#39;IndivTune&#39;
+tmp_simple$Model2[tmp_simple$Model2 == &#39;SumStatTune&#39;] &lt;- &#39;SumStatTune&#39;
+tmp_simple&lt;-tmp_simple[tmp_simple$Model_1 %in% res_eval_simp$Group,]
+tmp_simple&lt;-tmp_simple[tmp_simple$Model_2 %in% res_eval_simp$Group,]
+
+# Export plot for manuscript
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r.perc_improv.png&#39;, width = 3200, height = 1500, res= 300, units = &#39;px&#39;)
+ggplot(meta_tmp_simple[meta_tmp_simple$Target != &#39;EUR Target&#39;,], aes(x=label, y=R_diff_perc , fill = Model1)) +
+#    geom_boxplot(
+#      data = tmp_simple[tmp_simple$Target != &#39;EUR Target&#39;,],
+#        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+#        position = position_dodge(0.7),
+#        alpha = 0.3
+#      ) +
+    geom_point(
+        data = tmp_simple[tmp_simple$Target != &#39;EUR Target&#39;,],
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_point(
+        data = meta_tmp_ref_simple[meta_tmp_ref_simple$Target != &#39;EUR Target&#39;,],
+        aes(x = label, y = R_diff_perc, fill = Model1),
+        stat = &quot;identity&quot;,
+        position = position_dodge(0.7), # Ensure same dodge as other points
+        size = 4,
+        shape = 22,
+        show.legend=F
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(meta_tmp_simple$label))), linetype=&quot;dotted&quot;) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = &quot;Relative Improvement (SE)&quot;, fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(Target ~ Discovery_clean, scales=&#39;free&#39;, space = &#39;free_x&#39;) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(2, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;
+    )
+dev.off()
+
+########
+# Plot relative improvement of LEOPARD over IndivTune of SumStatTune scores
+########
+
+# meta res
+meta_res_comp_ref &lt;- meta_res_comp[meta_res_comp$Model2 == &#39;Multi-SumStatTune&#39;,]
+meta_res_comp_ref &lt;- meta_res_comp_ref[meta_res_comp_ref$Method1 != &#39;all&#39; &amp; meta_res_comp_ref$Method2 != &#39;all&#39;,]
+meta_res_comp_ref &lt;- meta_res_comp_ref[meta_res_comp_ref$Model1 == &#39;SumStatTune&#39; &amp; meta_res_comp_ref$Source1 == &#39;Multi&#39;,]
+meta_res_comp_ref &lt;- meta_res_comp_ref[gsub(&#39;_multi&#39;,&#39;&#39;, meta_res_comp_ref$Method1) == gsub(&#39;_multi&#39;,&#39;&#39;, meta_res_comp_ref$Method2),]
+
+meta_res_comp_ref$R_diff_perc &lt;- meta_res_comp_ref$R_diff / meta_res_comp_ref$Model_2_R
+meta_res_comp_ref$R_diff_perc_SE &lt;- meta_res_comp_ref$R_diff_SE / meta_res_comp_ref$Model_2_R
+
+meta_res_comp_ref$Discovery_clean &lt;- paste0(meta_res_comp_ref$Discovery1,&#39; GWAS&#39;)
+meta_res_comp_ref$Discovery_clean[meta_res_comp_ref$Target != &#39;EUR&#39;] &lt;- &#39;EUR + Target-matched GWAS&#39;
+
+meta_res_comp_ref &lt;- merge(meta_res_comp_ref, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+meta_res_comp_ref$label[grepl(&#39;Multi&#39;, meta_res_comp_ref$Model1) &amp; !(meta_res_comp_ref$Method1 %in% pgs_group_methods)] &lt;- paste0(meta_res_comp_ref$label[grepl(&#39;Multi&#39;, meta_res_comp_ref$Model1) &amp; !(meta_res_comp_ref$Method1 %in% pgs_group_methods)], &#39;-multi&#39;)
+meta_res_comp_ref$label &lt;- factor(meta_res_comp_ref$label, levels = model_order)
+
+meta_res_comp_ref$Target_clean &lt;- paste0(meta_res_comp_ref$Target,&#39; Target&#39;)
+
+# trait-specific res
+res_comp_all_ref &lt;- res_comp_all[res_comp_all$Model2 == &#39;Multi-SumStatTune&#39;,]
+res_comp_all_ref &lt;- res_comp_all_ref[res_comp_all_ref$Method1 != &#39;all&#39; &amp; res_comp_all_ref$Method2 != &#39;all&#39;,]
+res_comp_all_ref &lt;- res_comp_all_ref[res_comp_all_ref$Model1 == &#39;SumStatTune&#39; &amp; res_comp_all_ref$Source1 == &#39;Multi&#39;,]
+res_comp_all_ref &lt;- res_comp_all_ref[gsub(&#39;_multi&#39;,&#39;&#39;, res_comp_all_ref$Method1) == gsub(&#39;_multi&#39;,&#39;&#39;, res_comp_all_ref$Method2),]
+
+res_comp_all_ref$R_diff_perc &lt;- res_comp_all_ref$R_diff / res_comp_all_ref$Model_2_R
+res_comp_all_ref$R_diff_perc_SE &lt;- res_comp_all_ref$R_diff_SE / res_comp_all_ref$Model_2_R
+
+res_comp_all_ref$Discovery_clean &lt;- paste0(res_comp_all_ref$Discovery1,&#39; GWAS&#39;)
+res_comp_all_ref$Discovery_clean[res_comp_all_ref$Target != &#39;EUR&#39;] &lt;- &#39;EUR + Target-matched GWAS&#39;
+
+res_comp_all_ref &lt;- merge(res_comp_all_ref, pgs_method_labels, by.x = &#39;Method1&#39;, by.y = &#39;method&#39;, all.x = T)
+res_comp_all_ref$label[grepl(&#39;Multi&#39;, res_comp_all_ref$Model1) &amp; !(res_comp_all_ref$Method1 %in% pgs_group_methods)] &lt;- paste0(res_comp_all_ref$label[grepl(&#39;Multi&#39;, res_comp_all_ref$Model1) &amp; !(res_comp_all_ref$Method1 %in% pgs_group_methods)], &#39;-multi&#39;)
+res_comp_all_ref$label &lt;- factor(res_comp_all_ref$label, levels = model_order)
+
+res_comp_all_ref$Target_clean &lt;- paste0(res_comp_all_ref$Target,&#39; Target&#39;)
+
+tmp_meta&lt;-meta_res_comp_ref
+tmp_all&lt;-res_comp_all_ref
+
+tmp_meta&lt;-tmp_meta[!(tmp_meta$Method1 %in% c(&#39;prscsx&#39;,&#39;xwing&#39;)),]
+tmp_meta&lt;-tmp_meta[tmp_meta$Target != &#39;EUR&#39;,]
+
+tmp_all&lt;-tmp_all[!(tmp_all$Method1 %in% c(&#39;prscsx&#39;,&#39;xwing&#39;)),]
+tmp_all&lt;-tmp_all[tmp_all$Target != &#39;EUR&#39;,]
+
+library(ggrepel)
+
+# plot
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_perc_improv.png&#39;, width = 1800, height = 1100, res= 300, units = &#39;px&#39;)
+
+ggplot(tmp_meta, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp_meta$label))), linetype=&quot;dotted&quot;) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = &quot;Relative Difference (SE)&quot;, fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(. ~ Target_clean) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = &#39;none&#39;
+    )
+dev.off()
+
+# Now compare quickPRS-multi and prs-csx only with trait
+tmp_meta&lt;-meta_res_comp_ref
+tmp_all&lt;-res_comp_all_ref
+
+tmp_meta&lt;- tmp_meta[tmp_meta$Target != &#39;EUR&#39; &amp; tmp_meta$Method1 %in% c(&#39;quickprs_multi&#39;,&#39;prscsx&#39;),]
+tmp_all&lt;- tmp_all[tmp_all$Target != &#39;EUR&#39; &amp; tmp_all$Method1 %in% c(&#39;quickprs_multi&#39;,&#39;prscsx&#39;),]
+
+library(ggrepel)
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_perc_improv_restricted.png&#39;, width = 1500, height = 1500, res= 300, units = &#39;px&#39;)
+ggplot(tmp_meta, aes(x=label, y=R_diff_perc , fill = Model1)) +
+    geom_point(
+        data = tmp_all,
+        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+        position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7),
+        alpha = 0.3
+      ) +
+    geom_hline(yintercept = 0) +
+      geom_errorbar(
+        aes(
+          ymin = R_diff_perc - R_diff_perc_SE,
+          ymax = R_diff_perc + R_diff_perc_SE
+        ),
+        width = 0,
+        position = position_dodge(width = 0.7)
+      ) +    
+    geom_point(
+      stat = &quot;identity&quot;,
+      position = position_dodge(0.7),
+      size = 3,
+      shape = 23
+    ) +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = &quot;Relative Difference (SE)&quot;, fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(. ~ Target_clean) +
+    theme_half_open() +
+    background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = &#39;none&#39;
+    ) +
+    geom_text_repel(
+      data = tmp_all[
+        tmp_all$R_diff_perc &lt; -0.05,
+      ],
+      aes(label = pheno),  # label as percent with 1 decimal
+      position = position_dodge(width = 0.7),
+      size = 3,
+      min.segment.length = 0,
+      segment.color = NA,
+      show.legend = FALSE
+    )
+dev.off()</code></pre>
+</details>
+<hr />
+</div>
+<div id="descriptive-statistics" class="section level3">
+<h3>Descriptive statistics</h3>
+<p>Create a table showing descriptive statistics for the MVP sumstats.
+This should include LDSC SNP-heritability and AVENGEME results.</p>
+<hr />
+<div id="ldsc-1" class="section level4">
+<h4>LDSC</h4>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code>conda activate ldsc
+
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+  mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/sumstats
+
+  sbatch --mem 10G -n 1 -p neurohack_cpu --wrap=&quot;/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
+   --sumstats /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR-cleaned.gz \
+   --out /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR&quot;
+done
+
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+  mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/${pheno}/MVP_AFR
+
+  sbatch --mem 10G -n 1 -p neurohack_cpu --wrap=&quot;/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \
+   --h2 /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${pheno}_MVP_AFR/${pheno}_MVP_AFR.sumstats.gz \
+   --ref-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.AFR.rsid \
+   --w-ld /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ld_scores/UKBB.AFR.rsid \
+   --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/${pheno}/MVP_AFR/res&quot;
+done
+</code></pre>
+<pre class="r"><code>library(data.table)
+library(ggplot2)
+library(cowplot)
+
+# Read in phenotypes
+pheno_intersect &lt;- read.table(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt&#39;, header=F)$V1
+
+# Plot the heritability estimates
+h2_res &lt;- NULL
+
+for(pheno in pheno_intersect){
+  log &lt;-
+    readLines(
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results/&#39;,
+        pheno,
+        &#39;/&#39;,
+        &#39;MVP_AFR&#39;,
+        &#39;/res.log&#39;
+      )
+    )
+  
+  h2 &lt;- log[grepl(&#39;Total Observed scale h2:&#39;, log)]
+  h2_est &lt;- as.numeric(gsub(&#39; .*&#39;,&#39;&#39;, gsub(&#39;Total Observed scale h2: &#39;, &#39;&#39;, h2)))
+  h2_se &lt;- as.numeric(gsub(&quot;\\)&quot;,&#39;&#39;, gsub(&quot;.* \\(&quot;, &#39;&#39;, h2)))
+  int &lt;- log[grepl(&#39;Intercept:&#39;, log)]
+  int_est &lt;- as.numeric(gsub(&#39; .*&#39;,&#39;&#39;, gsub(&#39;Intercept: &#39;, &#39;&#39;, int)))
+  int_se &lt;- as.numeric(gsub(&quot;\\)&quot;,&#39;&#39;, gsub(&quot;.* \\(&quot;, &#39;&#39;, int)))
+  lambda &lt;- log[grepl(&#39;Lambda GC:&#39;, log)]
+  lambda &lt;- as.numeric(gsub(&#39;.* &#39;,&#39;&#39;, lambda))
+  
+  h2_res &lt;- rbind(
+    h2_res,
+    data.table(
+      Population = &#39;AFR&#39;,
+      Phenotype = pheno,
+      h2_est = h2_est,
+      h2_se = h2_se,
+      int_est = int_est,
+      int_se = int_se,
+      lambda = lambda
+    )
+  )
+}
+
+
+write.csv(h2_res, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results.csv&#39;, row.names = F, quote = F)</code></pre>
+</details>
+<hr />
+</div>
+<div id="avengeme-1" class="section level4">
+<h4>AVENGEME</h4>
+<details>
+<summary>
+Show code
+</summary>
+<div class="shallow-break">
+
+</div>
+<h4>
+Create predictor list
+</h4>
+<pre class="r"><code>setwd(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+library(data.table)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml&#39;
+pgs_methods &lt;- read_param(config = config, param = &#39;pgs_methods&#39;, return_obj = F)
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+# Read in phenotypes
+pheno_intersect &lt;- fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt&#39;, header=F)$V1
+
+# Create files for EAS and AFR targets
+pop &lt;- c(&#39;AFR&#39;)
+for(trait_i in pheno_intersect){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
+  scores_i &lt;- scores[grepl(paste0(&#39;^&#39;, trait_i, &#39;_&#39;), scores$name),]
+  scores_i$group &lt;- scores_i$method
+  
+  for(pop_i in pop){
+    # Subset GWAS based on EUR and/or targ_pop_i
+    samp_i &lt;- &#39;MVP_AFR&#39;
+
+    dir.create(
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_&#39;,
+        pop_i,
+        &#39;.disc_&#39;,
+        pop_i,
+        &#39;/&#39;,
+        trait_i
+      ),
+      recursive = T
+    )
+    
+    scores_i_j &lt;- scores_i[grepl(samp_i, scores_i$name, ignore.case = T),]
+    scores_i_j &lt;- scores_i_j[!grepl(&#39;UKB&#39;, scores_i_j$name),]
+    scores_i_j &lt;- scores_i_j[scores_i_j$method == &#39;ptclump&#39;,]
+    scores_i_j$predictor &lt;- paste0(
+      outdir,
+      &#39;/ukb/pgs/TRANS/&#39;,
+      scores_i_j$method,
+      &#39;/&#39;,
+      scores_i_j$name,
+      &#39;/ukb-&#39;,
+      scores_i_j$name,
+      &#39;-TRANS.profiles&#39;
+    )
+    
+    predictors_i &lt;- scores_i_j[, c(&#39;predictor&#39;, &#39;group&#39;), with=F]
+    
+    write.table(
+      predictors_i,
+      paste0(
+        &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_&#39;,
+        pop_i,
+        &#39;.disc_&#39;,
+        pop_i,
+        &#39;/&#39;,
+        trait_i,
+        &#39;/predictor_list.ptclump.txt&#39;
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}</code></pre>
+<hr />
+<h4>
+Run model_builder
+</h4>
+<pre class="bash"><code>cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+for pop in $(echo AFR); do
+  if [ &quot;$pop&quot; == &quot;EUR&quot; ]; then
+      pop2=&quot;EUR_test&quot;
+  else
+      pop2=$pop
+  fi
+  
+  for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap=&quot;Rscript ../Scripts/model_builder/model_builder.R \
+      --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${pheno}.unrel.${pop2}.row_number.txt \
+      --predictors /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${pop}.disc_${pop}/${pheno}/predictor_list.ptclump.txt \
+      --out /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_${pop}.disc_${pop}/${pheno}/res.ptclump \
+      --n_core 5 \
+      --all_model F \
+      --assoc T&quot;
+  done
+done
+</code></pre>
+<hr />
+<h4>
+Plot pT+clump association results
+</h4>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+library(avengeme)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml&#39;
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+gwas_list &lt;- read_param(config = config, param = &#39;gwas_list&#39;, return_obj = T)
+
+# Read in phenotypes
+pheno_intersect &lt;- read.table(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt&#39;, header=F)$V1
+
+pop = c(&#39;AFR&#39;)
+
+mod_res_all &lt;- NULL
+for(pop_i in pop){
+  for(pheno_i in pheno_intersect){
+    gwas_i&lt;-gwas_list$name[gwas_list$population == pop_i &amp; grepl(paste0(&#39;^&#39;, pheno_i, &#39;_&#39;),  gwas_list$name)]
+      
+    res_i &lt;-
+      fread(
+        paste0(
+          &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_&#39;,
+          pop_i,
+          &#39;.disc_&#39;,
+          pop_i,
+          &#39;/&#39;,
+          pheno_i,
+          &#39;/res.ptclump.assoc.txt&#39;
+        )
+      )
+    
+    res_i$Z &lt;- res_i$BETA / res_i$SE
+    
+    res_i$pT &lt;- as.numeric(gsub(&#39;e.&#39;,&#39;e-&#39;, gsub(&#39;.*UKB\\.0\\.|.*BBJ\\.0\\.|.*UGR\\.0\\.|.*MVP.AFR\\.0\\.&#39;, &#39;&#39;, res_i$Predictor)))
+
+    nsnp_log &lt;-
+      read.table(
+        paste0(
+          outdir,
+          &#39;/reference/pgs_score_files/ptclump/&#39;,
+          gwas_i,
+          &#39;/ref-&#39;,
+          gwas_i,
+          &#39;.NSNP_per_pT&#39;
+        ),
+        header = T
+      )
+    
+    nsnp&lt;-nsnp_log$NSNP[nrow(nsnp_log)]
+    
+    disc_N &lt;-
+      median(
+        fread(
+          paste0(
+            outdir,
+            &#39;/reference/gwas_sumstat/&#39;,
+            gwas_i,
+            &#39;/&#39;,
+            gwas_i,
+            &#39;-cleaned.gz&#39;
+          ), nrows = 10000
+        )$N
+      )
+    
+    targ_N &lt;- res_i$N[1]
+    
+    mod_res &lt;- estimatePolygenicModel(
+      p = res_i$Z,
+      nsnp = nsnp,
+      n = c(disc_N, targ_N),
+      pupper = c(0, res_i$pT),
+      fixvg2pi02 = T,
+      alpha = 0.05
+    )
+    
+    mod_res_all &lt;- rbind(
+      mod_res_all,
+      data.frame(
+        Phenotype = pheno_i,
+        Population = pop_i,
+        GWAS = gwas_i,
+        nsnp = nsnp,
+        max_r2 = max(res_i$Obs_R2),
+        n_disc = disc_N,
+        n_targ = targ_N,
+        vg_est = mod_res$vg[1],
+        vg_lowCI = mod_res$vg[2],
+        vg_highCI = mod_res$vg[3],
+        pi0_est = mod_res$pi0[1],
+        pi0_lowCI = mod_res$pi0[2],
+        pi0_highCI = mod_res$pi0[3]
+      )
+    )
+  }
+}
+
+dir.create(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme&#39;)
+write.csv(mod_res_all, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv&#39;, row.names = F, quote = F)
+
+mod_res_all&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv&#39;)
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avengeme_h2.png&#39;, res = 100, width = 900, height = 500, units = &#39;px&#39;)
+ggplot(mod_res_all, aes(x = Phenotype, y = vg_est, fill = Population)) +
+  geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.7) +
+  geom_errorbar(aes(ymin=vg_lowCI, ymax=vg_highCI), width=.2, position=position_dodge(width = 0.7, preserve = &quot;single&quot;)) +
+  labs(y=&quot;SNP-based Heritability (95%CI)&quot;, fill = NULL) +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(1, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
+dev.off()
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avengeme_polygenicity.png&#39;, res = 100, width = 900, height = 500, units = &#39;px&#39;)
+ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) +
+  geom_bar(stat=&quot;identity&quot;, position=position_dodge(preserve = &quot;single&quot;), width = 0.7) +
+  geom_errorbar(aes(ymin=1 - pi0_lowCI, ymax=1 - pi0_highCI), width=.2, position=position_dodge(width = 0.7, preserve = &quot;single&quot;)) +
+  labs(y=&quot;Proporition non-zero\neffects (95%CI)&quot;, fill = NULL) +
+  theme_half_open() +
+  coord_cartesian(ylim = c(0, 0.15)) + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = &quot;top&quot;,
+        legend.key.spacing.x = unit(1, &quot;cm&quot;),
+        legend.justification = &quot;center&quot;) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;)
+dev.off()
+
+summary(mod_res_all$max_r2)
+summary(mod_res_all$max_r2[mod_res_all$Population == &#39;EUR&#39;])
+summary(mod_res_all$max_r2[mod_res_all$Population == &#39;EAS&#39;])
+summary(mod_res_all$max_r2[mod_res_all$Population == &#39;AFR&#39;])</code></pre>
+</details>
+<hr />
+</div>
+<div id="make-table" class="section level4">
+<h4>Make table</h4>
+<p>Make a table showing GWAS information for the manuscript.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(data.table)
+
+#####
+# Trait names, labels, and URLs
+#####
+
+mvp &lt;- fread(&#39;~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/info.txt&#39;)
+mvp &lt;- mvp[, c(&#39;efoTraits&#39;,&#39;labels&#39;,&#39;url&#39;), with=F]
+names(mvp) &lt;- c(&#39;trait&#39;, &#39;labels&#39;,&#39;url&#39;)
+mvp$sample &lt;- &#39;MVP&#39;
+mvp$population &lt;- &#39;AFR&#39;
+
+#####
+# Sample size, SNP-h2 and polygenicity
+#####
+
+# Read in the AVENGEME and LDSC results
+avengeme &lt;- fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/avengeme/results.csv&#39;)
+ldsc &lt;- fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/results.csv&#39;)
+both &lt;- merge(avengeme, ldsc, by = c(&#39;Population&#39;,&#39;Phenotype&#39;))
+
+# Format for descriptives table
+both$h2_avengeme&lt;- paste0(
+  round(both$vg_est,2), 
+  &quot; (95%CI = &quot;, 
+  round(both$vg_lowCI, 2), 
+  &quot; - &quot; , 
+  round(both$vg_highCI, 2), &quot;)&quot;)
+
+both$pi0_avengeme &lt;- paste0(
+  round(both$pi0_est,2), 
+  &quot; (95%CI = &quot;, 
+  round(both$pi0_lowCI, 2), 
+  &quot; - &quot; , 
+  round(both$pi0_highCI, 2), &quot;)&quot;)
+
+both$h2_ldsc &lt;- paste0(
+  round(both$h2_est,2), 
+  &quot; (SE = &quot;, 
+  round(both$h2_se, 2), 
+  &quot;)&quot;)
+
+both$int_ldsc &lt;- paste0(
+  round(both$int_est,2), 
+  &quot; (SE = &quot;, 
+  round(both$int_se, 2), 
+  &quot;)&quot;)
+
+both&lt;-both[, c(&#39;Population&#39;,&#39;Phenotype&#39;,&#39;n_disc&#39;,&#39;n_targ&#39;,&#39;h2_avengeme&#39;,&#39;pi0_avengeme&#39;,&#39;h2_ldsc&#39;,&#39;int_ldsc&#39;,&#39;lambda&#39;), with = F]
+names(both)[1:2]&lt;-c(&#39;population&#39;,&#39;labels&#39;)
+
+info_all &lt;- merge(mvp, both, by = c(&#39;labels&#39;,&#39;population&#39;))
+info_all$n_disc&lt;-round(info_all$n_disc, 0)
+info_all$n_targ&lt;-round(info_all$n_targ, 0)
+
+info_all&lt;-info_all[, c(&#39;labels&#39;,&#39;trait&#39;,&#39;population&#39;,&#39;sample&#39;,&#39;n_disc&#39;,&#39;n_targ&#39;,&#39;h2_avengeme&#39;,&#39;pi0_avengeme&#39;,&#39;h2_ldsc&#39;,&#39;int_ldsc&#39;,&#39;lambda&#39;,&#39;url&#39;), with=F]
+names(info_all) &lt;- c(&#39;Trait Label&#39;, &#39;Trait Description&#39;, &#39;Ancestry&#39;, &#39;GWAS Sample&#39;, &#39;GWAS N&#39;, &#39;Target N&#39;,&quot;SNP-h2 (AVENGEME)&quot;,&quot;pi0 (AVENGEME)&quot;,&quot;SNP-h2 (LDSC)&quot;,&quot;Intercept (LDSC)&quot;,&#39;Lambda&#39;, &#39;URL&#39;)
+
+write.csv(info_all, &#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/gwas_descriptives.csv&#39;, row.names=F)
+
+# Estimate the mean and SD of sample size within each population for selected traits
+info_all_selected&lt;-info_all
+n_dat &lt;- NULL
+for(i in unique(info_all_selected$`GWAS Sample`)){
+  n_dat &lt;-rbind(
+    n_dat,
+    data.table(
+      sample = i,
+      gwas_n_median = round(median(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      gwas_n_mean = round(mean(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      gwas_n_sd = round(sd(info_all_selected$`GWAS N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_median = round(median(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_mean = round(mean(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i])),
+      target_n_sd = round(sd(info_all_selected$`Target N`[info_all_selected$`GWAS Sample` == i]))
+    )
+  )
+}</code></pre>
+</details>
+<details>
+<summary>
+Show descriptives table
+</summary>
+<div
+style="border: 1px solid #ddd; padding: 0px; overflow-y: scroll; height:500px; overflow-x: scroll; width:100%; ">
+<table class="table table-striped table-hover" style="color: black; width: auto !important; margin-left: auto; margin-right: auto;">
+<thead>
+<tr>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Trait Label
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Trait Description
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Ancestry
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+GWAS Sample
+</th>
+<th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;">
+GWAS N
+</th>
+<th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;">
+Target N
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+SNP-h2 (AVENGEME)
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+pi0 (AVENGEME)
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+SNP-h2 (LDSC)
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+Intercept (LDSC)
+</th>
+<th style="text-align:right;position: sticky; top:0; background-color: #FFFFFF;">
+Lambda
+</th>
+<th style="text-align:left;position: sticky; top:0; background-color: #FFFFFF;">
+URL
+</th>
+</tr>
+</thead>
+<tbody>
+<tr>
+<td style="text-align:left;">
+BMI
+</td>
+<td style="text-align:left;">
+body mass index
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+MVP
+</td>
+<td style="text-align:right;">
+118993
+</td>
+<td style="text-align:right;">
+6646
+</td>
+<td style="text-align:left;">
+0.12 (95%CI = 0.11 - 0.14)
+</td>
+<td style="text-align:left;">
+0.96 (95%CI = 0.95 - 0.97)
+</td>
+<td style="text-align:left;">
+0.37 (SE = 0.03)
+</td>
+<td style="text-align:left;">
+1.32 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.6715
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475155/GCST90475155.tsv.gz"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475155/GCST90475155.tsv.gz</a>
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+BWT
+</td>
+<td style="text-align:left;">
+body weight
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+MVP
+</td>
+<td style="text-align:right;">
+119279
+</td>
+<td style="text-align:right;">
+6659
+</td>
+<td style="text-align:left;">
+0.14 (95%CI = 0.13 - 0.16)
+</td>
+<td style="text-align:left;">
+0.96 (95%CI = 0.95 - 0.97)
+</td>
+<td style="text-align:left;">
+0.4 (SE = 0.03)
+</td>
+<td style="text-align:left;">
+1.36 (SE = 0.03)
+</td>
+<td style="text-align:right;">
+1.7334
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476462/GCST90476462.tsv.gz"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476462/GCST90476462.tsv.gz</a>
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HB
+</td>
+<td style="text-align:left;">
+hemoglobin measurement
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+MVP
+</td>
+<td style="text-align:right;">
+114985
+</td>
+<td style="text-align:right;">
+6375
+</td>
+<td style="text-align:left;">
+0.06 (95%CI = 0.05 - 0.07)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.99 - 0.99)
+</td>
+<td style="text-align:left;">
+0.19 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.2 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.3685
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475375/GCST90475375.tsv.gz"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475375/GCST90475375.tsv.gz</a>
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HDL
+</td>
+<td style="text-align:left;">
+high density lipoprotein cholesterol measurement
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+MVP
+</td>
+<td style="text-align:right;">
+113085
+</td>
+<td style="text-align:right;">
+5863
+</td>
+<td style="text-align:left;">
+0.11 (95%CI = 0.1 - 0.12)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.99 - 0.99)
+</td>
+<td style="text-align:left;">
+0.32 (SE = 0.04)
+</td>
+<td style="text-align:left;">
+1.3 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.5071
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475351/GCST90475351.tsv.gz"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475351/GCST90475351.tsv.gz</a>
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+HT
+</td>
+<td style="text-align:left;">
+body height
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+MVP
+</td>
+<td style="text-align:right;">
+119012
+</td>
+<td style="text-align:right;">
+6658
+</td>
+<td style="text-align:left;">
+0.14 (95%CI = 0.12 - 0.15)
+</td>
+<td style="text-align:left;">
+0.96 (95%CI = 0.95 - 0.97)
+</td>
+<td style="text-align:left;">
+0.93 (SE = 0.08)
+</td>
+<td style="text-align:left;">
+1.69 (SE = 0.06)
+</td>
+<td style="text-align:right;">
+2.2871
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475361/GCST90475361.tsv.gz"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475361/GCST90475361.tsv.gz</a>
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+MCHC
+</td>
+<td style="text-align:left;">
+mean corpuscular hemoglobin concentration
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+MVP
+</td>
+<td style="text-align:right;">
+114851
+</td>
+<td style="text-align:right;">
+6375
+</td>
+<td style="text-align:left;">
+0.09 (95%CI = 0.08 - 0.1)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.99 - 0.99)
+</td>
+<td style="text-align:left;">
+0.27 (SE = 0.1)
+</td>
+<td style="text-align:left;">
+1.32 (SE = 0.03)
+</td>
+<td style="text-align:right;">
+1.4034
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475457/GCST90475457.tsv.gz"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475457/GCST90475457.tsv.gz</a>
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+NEU
+</td>
+<td style="text-align:left;">
+neutrophil count
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+MVP
+</td>
+<td style="text-align:right;">
+73258
+</td>
+<td style="text-align:right;">
+6353
+</td>
+<td style="text-align:left;">
+0.36 (95%CI = 0.34 - 0.38)
+</td>
+<td style="text-align:left;">
+0.97 (95%CI = 0.96 - 0.97)
+</td>
+<td style="text-align:left;">
+2.32 (SE = 1.19)
+</td>
+<td style="text-align:left;">
+1.43 (SE = 0.07)
+</td>
+<td style="text-align:right;">
+1.4459
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475528/GCST90475528.tsv.gz"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475528/GCST90475528.tsv.gz</a>
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+PLT
+</td>
+<td style="text-align:left;">
+platelet count
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+MVP
+</td>
+<td style="text-align:right;">
+114731
+</td>
+<td style="text-align:right;">
+6375
+</td>
+<td style="text-align:left;">
+0.1 (95%CI = 0.09 - 0.11)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.99 - 0.99)
+</td>
+<td style="text-align:left;">
+0.38 (SE = 0.03)
+</td>
+<td style="text-align:left;">
+1.38 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.6639
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476298/GCST90476298.tsv.gz"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476298/GCST90476298.tsv.gz</a>
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+SBP
+</td>
+<td style="text-align:left;">
+systolic blood pressure
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+MVP
+</td>
+<td style="text-align:right;">
+119331
+</td>
+<td style="text-align:right;">
+6658
+</td>
+<td style="text-align:left;">
+0.06 (95%CI = 0.05 - 0.07)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.98 - 0.99)
+</td>
+<td style="text-align:left;">
+0.19 (SE = 0.02)
+</td>
+<td style="text-align:left;">
+1.2 (SE = 0.01)
+</td>
+<td style="text-align:right;">
+1.3964
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476402/GCST90476402.tsv.gz"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476402/GCST90476402.tsv.gz</a>
+</td>
+</tr>
+<tr>
+<td style="text-align:left;">
+TC
+</td>
+<td style="text-align:left;">
+total cholesterol measurement
+</td>
+<td style="text-align:left;">
+AFR
+</td>
+<td style="text-align:left;">
+MVP
+</td>
+<td style="text-align:right;">
+112265
+</td>
+<td style="text-align:right;">
+6324
+</td>
+<td style="text-align:left;">
+0.1 (95%CI = 0.09 - 0.12)
+</td>
+<td style="text-align:left;">
+0.99 (95%CI = 0.99 - 0.99)
+</td>
+<td style="text-align:left;">
+0.2 (SE = 0.04)
+</td>
+<td style="text-align:left;">
+1.25 (SE = 0.02)
+</td>
+<td style="text-align:right;">
+1.3615
+</td>
+<td style="text-align:left;">
+<a
+href="http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476423/GCST90476423.tsv.gz"
+class="uri">http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476423/GCST90476423.tsv.gz</a>
+</td>
+</tr>
+</tbody>
+</table>
+</div>
+</details>
+<hr />
+</div>
+</div>
+<div id="leopardquickprs-2" class="section level3">
+<h3>LEOPARD+QuickPRS</h3>
+<p>Here we will compare the LEOPARD estimated weights for population
+specific PGS, to the weights estimated using observed data in the UKB
+target sample.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in list of outcomes 
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+
+# Get some key variables from config
+config&lt;-&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_mvp.yaml&#39;
+outdir &lt;- read_param(config = config, param = &#39;outdir&#39;, return_obj = F)
+
+# Get a list of score files
+scores &lt;- list_score_files(config)
+
+###
+# Read in weights estimated by LEOPARD (QuickPRS)
+###
+
+leopard_weights&lt;-NULL
+scores_quickprs &lt;- scores$name[scores$method == &#39;quickprs_multi&#39;]
+for(i in selected_traits){
+  scores_i &lt;- scores_quickprs[grepl(paste0(&#39;^&#39;, i,&#39;_&#39;), scores_quickprs)]
+  for(j in scores_i){
+      weights_file &lt;- readRDS(paste0(outdir, &#39;/reference/pgs_score_files/leopard/&#39;, j, &#39;/ref-&#39;, j, &#39;.weights.rds&#39;))
+      weights_file &lt;- data.frame(weights_file)
+      
+      weights &lt;-
+        data.table(
+          Target = do.call(c, lapply(names(weights_file), function(x) rep(x, 2))),
+          Discovery = names(weights_file),
+          Weight = do.call(c, lapply(weights_file, function(x) x)),
+          Trait = i,
+          Method = &#39;LEOPARD&#39;
+        )
+      
+      leopard_weights &lt;- rbind(leopard_weights, weights)
+  }
+}
+
+#####
+# Read in the PGS weights estimated using UKB data
+#####
+# Read in the final model coefficients for multi-source methods
+
+obs_weights&lt;-NULL
+for(method_i in unique(scores$method)[!(unique(scores$method) %in% pgs_group_methods)]){
+  scores_method&lt;-scores$name[scores$method == method_i]
+  method_i &lt;- gsub(&#39;_multi&#39;,&#39;&#39;, method_i)
+
+  for(i in selected_traits){
+    for(j in c(&#39;AFR&#39;)){
+      if(j == &#39;EUR&#39;){
+        pops &lt;- c(&#39;EAS&#39;,&#39;AFR&#39;)
+      } else {
+        pops &lt;- j
+      }
+      
+      for(k in pops){
+        model &lt;- fread(paste0(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/targ_&#39;, j, &#39;.disc_EUR_&#39;, k, &#39;/&#39;, i, &#39;/final_models/&#39;, method_i, &#39;.pseudo.multi.final_model.txt&#39;))
+        model&lt;-model[-1,]
+        
+        # Set weight to zero if negative, as this is what LEOPARD does
+        if(any(model$V2 &lt; 0)){
+          model$V2[model$V2 &lt; 0] &lt;- 0
+          model$V2[model$V2 &gt; 0] &lt;- 1
+        }
+        
+        names(model) &lt;- c(&#39;x&#39;, &#39;BETA&#39;)
+        model$Discovery[grepl(&#39;UKB&#39;, model$x)]&lt;-&#39;EUR&#39;
+        model$Discovery[grepl(&#39;MVP&#39;, model$x)]&lt;-&#39;AFR&#39;
+        model$Target &lt;- j
+        model$Weight &lt;- model$BETA/sum(model$BETA)
+        model$Trait &lt;- i
+        model$Method &lt;- method_i
+        model&lt;-model[,c(&#39;Target&#39;,&#39;Discovery&#39;,&#39;Weight&#39;,&#39;Method&#39;,&#39;Trait&#39;), with=F]
+        obs_weights&lt;-rbind(obs_weights, model)
+      }
+    }
+  }
+}
+
+###
+# Estimate weights if using the inverse variance weighting
+###
+
+# Read in GWAS descriptives
+gwas_desc&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv&#39;)
+gwas_desc &lt;- gwas_desc[, c(&#39;Trait Label&#39;,&#39;Ancestry&#39;,&#39;GWAS N&#39;), with=F]
+names(gwas_desc)&lt;-c(&#39;trait&#39;,&#39;ancestry&#39;,&#39;n&#39;)
+gwas_desc&lt;-gwas_desc[gwas_desc$trait %in% selected_traits,]
+
+gwas_desc &lt;- gwas_desc[gwas_desc$ancestry == &#39;EUR&#39;,]
+
+gwas_desc_mvp &lt;- fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/gwas_descriptives.csv&#39;)
+gwas_desc_mvp &lt;- gwas_desc_mvp[, c(&#39;Trait Label&#39;,&#39;Ancestry&#39;,&#39;GWAS N&#39;), with=F]
+names(gwas_desc_mvp)&lt;-c(&#39;trait&#39;,&#39;ancestry&#39;,&#39;n&#39;)
+gwas_desc_mvp&lt;-gwas_desc_mvp[gwas_desc_mvp$trait %in% selected_traits,]
+
+gwas_desc&lt;-rbind(gwas_desc, gwas_desc_mvp)
+
+library(dplyr)
+library(tidyr)
+
+# Reshape GWAS table to wide format
+wide_gwas &lt;- gwas_desc %&gt;%
+  pivot_wider(names_from = ancestry, values_from = n, values_fill = 0)
+
+# Function to create rows for each pair
+make_weights_long &lt;- wide_gwas %&gt;%
+  rowwise() %&gt;%
+  do({
+    trait &lt;- .$trait
+    eur &lt;- .$EUR
+    afr &lt;- .$AFR
+    eas &lt;- .$EAS
+    
+    tibble(
+      Trait = trait,
+      Method = &quot;inverse_var&quot;,
+      Target = c(&quot;AFR&quot;, &quot;AFR&quot;, &quot;EUR&quot;, &quot;EUR&quot;),
+      Discovery = c(&quot;EUR&quot;, &quot;AFR&quot;, &quot;EUR&quot;, &quot;AFR&quot;),
+      Weight = c(
+        eur / (eur + afr), afr / (eur + afr),  # AFR target
+        eur / (eur + afr), afr / (eur + afr)  # EUR target (vs AFR)
+      )
+    )
+  }) %&gt;%
+  bind_rows()
+
+###
+# Combine and compare
+###
+
+both &lt;- do.call(rbind, list(obs_weights, leopard_weights, make_weights_long))
+
+# Remove ptclump as it doesn&#39;t have a sumstattune method
+both &lt;- both[both$Method != &#39;ptclump&#39;,]
+
+both&lt;-merge(both, pgs_method_labels, by.x = &#39;Method&#39;, by.y = &#39;method&#39;, all.x=T, sort = F)
+both$label[is.na(both$label)] &lt;- both$Method[is.na(both$label)]
+both$label &lt;- factor(both$label, levels=unique(both$label))
+
+# Plot non-EUR target first
+tmp &lt;- both[both$Target != &#39;EUR&#39;,]
+tmp &lt;- tmp[tmp$Discovery != &#39;EUR&#39;,]
+
+# Set LEOPARD to black fill
+default_colors &lt;- hue_pal()(10)
+names(default_colors) &lt;- levels(tmp$label)
+default_colors[&quot;LEOPARD&quot;] &lt;- &quot;black&quot;
+
+# Plot the estimated and observed weights
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_weights.png&#39;, units = &#39;px&#39;, res = 300, width = 2500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat=&quot;identity&quot;, colour = &#39;black&#39;, size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Target ~ .) +
+  theme_half_open() +
+  labs(title = &#39;Weight of target ancestry-matched PGS&#39;, fill = NULL) +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
+
+###
+# Check calibration of LEOPARD compared to QuickPRS observed weights
+###
+
+tmp &lt;- both[both$Target != &#39;EUR&#39;,]
+tmp$Target&lt;-NULL
+tmp_wide &lt;- reshape(tmp, 
+                     idvar = c(&quot;Trait&quot;, &quot;Discovery&quot;), 
+                     timevar = &quot;label&quot;, 
+                     direction = &quot;wide&quot;)
+
+names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
+
+tmp_wide_afr &lt;- tmp_wide[tmp_wide$Discovery == &#39;AFR&#39;,]
+
+# Calculate metrics
+rmse_afr &lt;- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)^2))
+me_afr &lt;- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$LEOPARD)
+
+# Create annotation data.frame
+metrics_df &lt;- data.frame(
+  Discovery = c(&quot;AFR&quot;),
+  x = c(0.5),         # Adjust positions as needed
+  y = c(-0.05),
+  label = c(
+    paste0(&quot;RMSE = &quot;, round(rmse_afr, 2), &quot;\nME = &quot;, round(me_afr, 2))
+  )
+)
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_weights_calibration.png&#39;, units = &#39;px&#39;, width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != &#39;EUR&#39;,], aes(x = LEOPARD, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = &quot;dashed&quot;, colour = &quot;grey40&quot;) +  # Perfect calibration
+  geom_smooth(method = &quot;lm&quot;, se = TRUE, colour = &quot;blue&quot;) +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) +
+  labs(
+    x = &quot;LEOPARD weight&quot;,
+    y = &quot;Observed weight&quot;,
+  ) +
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+  ) +
+  coord_fixed()    
+dev.off()
+
+###
+# Check calibration of inverse_var compared to QuickPRS observed weights
+###
+
+tmp &lt;- both[both$Target != &#39;EUR&#39;,]
+tmp$Target&lt;-NULL
+tmp_wide &lt;- reshape(tmp, 
+                     idvar = c(&quot;Trait&quot;, &quot;Discovery&quot;), 
+                     timevar = &quot;label&quot;, 
+                     direction = &quot;wide&quot;)
+
+names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
+
+tmp_wide_afr &lt;- tmp_wide[tmp_wide$Discovery == &#39;AFR&#39;,]
+
+# Calculate metrics
+rmse_afr &lt;- sqrt(mean((tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)^2))
+me_afr &lt;- mean(tmp_wide_afr$QuickPRS - tmp_wide_afr$inverse_var)
+
+
+# Create annotation data.frame
+metrics_df &lt;- data.frame(
+  Discovery = c(&quot;AFR&quot;),
+  x = c(0.5),         # Adjust positions as needed
+  y = c(-0.05),
+  label = c(
+    paste0(&quot;RMSE = &quot;, round(rmse_afr, 2), &quot;\nME = &quot;, round(me_afr, 2))
+  )
+)
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/inverse_var_weights_calibration.png&#39;, units = &#39;px&#39;, width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$Discovery != &#39;EUR&#39;,], aes(x = inverse_var, y = QuickPRS)) +
+  geom_abline(slope = 1, intercept = 0, linetype = &quot;dashed&quot;, colour = &quot;grey40&quot;) +  # Perfect calibration
+  geom_smooth(method = &quot;lm&quot;, se = TRUE, colour = &quot;blue&quot;) +  # Regression line
+  geom_point(alpha = 0.7) +
+  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 1.5, size = 3.5) +
+  labs(
+    x = &quot;inverse_var weight&quot;,
+    y = &quot;Observed weight&quot;,
+  ) +
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+  ) +
+  coord_fixed()    
+dev.off()
+
+###
+# Check calibration of observed weights across all methods
+###
+
+tmp &lt;- both[both$Target != &#39;EUR&#39;,]
+tmp &lt;- tmp[!(tmp$label %in% c(&#39;LEOPARD&#39;,&#39;inverse_var&#39;)),]
+tmp$Target&lt;-NULL
+tmp_wide &lt;- reshape(tmp, 
+                     idvar = c(&quot;Trait&quot;, &quot;Discovery&quot;), 
+                     timevar = &quot;label&quot;, 
+                     direction = &quot;wide&quot;)
+
+names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
+
+tmp_wide &lt;- tmp_wide[tmp_wide$Discovery %in% c(&#39;AFR&#39;),]
+
+metrics &lt;- NULL
+for(i in c(&#39;AFR&#39;)){
+  for(j in unique(tmp$label)){
+    for(k in unique(tmp$label)){
+      tmp_wide_i &lt;- tmp_wide[tmp_wide$Discovery == i,]
+      rmse &lt;- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me &lt;- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics &lt;- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
+      )
+    }
+  }
+}
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/observed_weights_calibration.png&#39;, units = &#39;px&#39;, width = 2000, height = 1650, res = 300)
+ggplot(metrics, aes(x = Method1, y = Method2, fill = rmse)) +
+  geom_tile(color = &quot;white&quot;) +  # Tile plot with white borders
+  geom_text(aes(label = round(rmse, 2)), color = &quot;black&quot;) +  # Add correlation values
+  scale_fill_gradient2(mid = &quot;white&quot;, high = &quot;red&quot;, midpoint = 0) +  # Color scale
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1),
+    axis.title = element_blank()
+  ) +
+  labs(fill = &quot;RMSE&quot;)
+dev.off()
+
+# Calculate average RMSE for each method against all other methods
+metrics_unique &lt;- metrics[metrics$Method1 != metrics$Method2, ]
+metrics_unique$Comparison &lt;- NA
+for (i in 1:nrow(metrics_unique)) {
+  metrics_unique$Comparison[i] &lt;-
+    paste0(sort(c(
+      metrics_unique$Method1[i], metrics_unique$Method2[i]
+    )), collapse = &#39; vs. &#39;)
+}
+metrics_unique &lt;- metrics_unique[!duplicated(paste0(metrics_unique$Population, metrics_unique$Comparison)),]
+
+mean_rmse &lt;- NULL
+for(i in unique(tmp$label)){
+  for(j in c(&#39;AFR&#39;)){
+    metrics_unique_tmp &lt;- metrics_unique[metrics_unique$Method1 == i | metrics_unique$Method2 == i,]
+    metrics_unique_tmp &lt;- metrics_unique_tmp[metrics_unique_tmp$Population == j,]
+    mean_rmse &lt;- rbind(
+      mean_rmse, 
+      data.frame(
+        Method = i,
+        Population = j,
+        avg_rmse = mean(metrics_unique_tmp$rmse)
+      )
+    )
+  }
+}
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avg_observed_weight_rmse.png&#39;, units = &#39;px&#39;, width = 1500, height = 800, res = 300)
+ggplot(mean_rmse, aes(x = Method, y = avg_rmse, fill = Method)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat=&quot;identity&quot;, size = 0.1) +
+  geom_text(aes(label = round(avg_rmse, 3)),  # &lt;-- Add this
+          vjust = 1.5,                    # &lt;-- Move the text slightly above the bar
+          size = 3) +                      # &lt;-- Adjust text size
+  scale_fill_manual(values = default_colors) +
+  theme_half_open() +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+  panel_border() +
+  labs(y = &#39;Average RMSE&#39;) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position=&quot;none&quot;)
+dev.off()
+
+###
+# Check calibration of estimated (LEOPARD and inverse_var) weights compared to observed QuickPRS weights
+###
+
+tmp &lt;- both[both$Target != &#39;EUR&#39;,]
+tmp &lt;- tmp[(tmp$label %in% c(&#39;LEOPARD&#39;,&#39;inverse_var&#39;,&#39;QuickPRS&#39;)),]
+tmp$Target&lt;-NULL
+tmp_wide &lt;- reshape(tmp, 
+                     idvar = c(&quot;Trait&quot;, &quot;Discovery&quot;), 
+                     timevar = &quot;label&quot;, 
+                     direction = &quot;wide&quot;)
+
+names(tmp_wide) &lt;- gsub(&#39;Weight.&#39;, &#39;&#39;, names(tmp_wide))
+tmp_wide&lt;-tmp_wide[, !(grepl(&#39;Method&#39;, names(tmp_wide))), with = F]
+
+tmp_wide &lt;- tmp_wide[tmp_wide$Discovery %in% c(&#39;EAS&#39;,&#39;AFR&#39;),]
+
+metrics &lt;- NULL
+for(i in c(&#39;AFR&#39;)){
+  for(j in unique(tmp$label)){
+    for(k in unique(tmp$label)){
+      tmp_wide_i &lt;- tmp_wide[tmp_wide$Discovery == i,]
+      rmse &lt;- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me &lt;- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics &lt;- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
+      )
+    }
+  }
+}
+
+# Plot the rmse for LEOPARD and inverse_var predicting observed QuickPRS weight
+metrics &lt;- metrics[metrics$Method1 == &#39;QuickPRS&#39;,]
+metrics &lt;- metrics[metrics$Method2 != &#39;QuickPRS&#39;,]
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/inverse_var_comp_rmse.png&#39;, units = &#39;px&#39;, width = 800, height = 1500, res = 300)
+ggplot(metrics, aes(x = Method2, y = rmse, fill = Method2)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat=&quot;identity&quot;, size = 0.1) +
+  geom_text(aes(label = round(rmse, 3)),  # &lt;-- Add this
+          vjust = 1.5,                    # &lt;-- Move the text slightly above the bar
+          size = 3) +                      # &lt;-- Adjust text size
+  theme_half_open() +
+  background_grid(major = &#39;y&#39;, minor = &#39;y&#39;) + 
+  panel_border() +
+  labs(y = &#39;RMSE relative to QuickPRS&#39;, x = &#39;Method&#39;) +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position=&quot;none&quot;)
+dev.off()</code></pre>
+</details>
+<hr />
+</div>
+</div>
+</div>
+<div id="check-genetic-distances" class="section level1">
+<h1>Check genetic distances</h1>
+<p>We will use two approaches to compare the ancestry within the GWAS
+and LD reference samples: 1. Project reference genetic principal
+components into the GWAS samples 2. Estimate ancestral proportions
+within GWAS and LD reference samples using bigsnpr</p>
+<hr />
+<div id="compare-genetic-principal-components" class="section level2">
+<h2>Compare genetic principal components</h2>
+<p>This involves using the allele frequencies within the GWAS summary
+statistics to estimate the mean of reference genetic principal
+components in the GWAS samples.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code># Read in reference PC SNP-weights
+pc_score_file &lt;- fread(&#39;~/oliverpainfel/Data/ukb/GenoPred/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.eigenvec.var.gz&#39;)
+
+# Read in allele frequencies for each reference population
+ref_pop_freq &lt;- list()
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;AMR&#39;,&#39;MID&#39;,&#39;CSA&#39;)){
+  freq_i &lt;- NULL
+  for(j in 1:22){
+    freq_i &lt;- rbind(freq_i, fread(paste0(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/freq_files/&#39;, i,&#39;/ref.&#39;, i, &#39;.chr&#39;, j, &#39;.afreq&#39;)))
+  }
+  ref_pop_freq[[i]] &lt;- freq_i
+}
+
+# Calculate PCs for reference populations
+ref_pc_all &lt;- NULL
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;AMR&#39;,&#39;MID&#39;,&#39;CSA&#39;)){
+  # Merge on SNP ID
+  merged &lt;- merge(
+    pc_score_file, 
+    ref_pop_freq[[i]], 
+    by.x = &quot;SNP&quot;, 
+    by.y = &quot;ID&quot;
+  )
+  
+  # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ)
+  merged[, A1_FREQ := ifelse(ALT == A1, ALT_FREQS, 1 - ALT_FREQS)]
+  
+  # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC
+  ref_PC &lt;- merged[, lapply(.SD, function(w) sum(w * A1_FREQ, na.rm = TRUE)), 
+                   .SDcols = patterns(&quot;^PC&quot;)]
+  
+  ref_pc_all &lt;- rbind(ref_pc_all, data.frame(group = i,
+                                             ref_PC))
+}
+
+# Read in GWAS allele frequencies for BMI
+gwas_list_main &lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&#39;)
+gwas_list_main &lt;- gwas_list_main[grepl(&#39;BMI&#39;, gwas_list_main$name),]
+
+gwas_list_mvp &lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt&#39;)
+gwas_list_mvp &lt;- gwas_list_mvp[grepl(&#39;BMI&#39;, gwas_list_mvp$name),]
+
+gwas_list&lt;-rbind(gwas_list_main, gwas_list_mvp)
+
+outdir &lt;- &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&#39;
+gwas_freq &lt;- list()
+for(i in 1:nrow(gwas_list)){
+  ss_i &lt;- fread(paste0(
+            outdir,
+            &#39;/reference/gwas_sumstat/&#39;,
+            gwas_list$name[i],
+            &#39;/&#39;,
+            gwas_list$name[i],
+            &#39;-cleaned.gz&#39;
+          ))
+  
+  gwas_freq[[gwas_list$name[i]]] &lt;- ss_i[, c(&#39;SNP&#39;,&#39;A1&#39;,&#39;A2&#39;,&#39;FREQ&#39;), with = F]
+}
+
+gwas_pc_all &lt;- NULL
+for(i in names(gwas_freq)){
+  # Merge on SNP ID
+  merged &lt;- merge(
+    pc_score_file, 
+    gwas_freq[[i]], 
+    by = &quot;SNP&quot;
+  )
+  
+  # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ)
+  merged[, FREQ := ifelse(A1.x == A1.y, FREQ, 1 - FREQ)]
+  
+  # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC
+  ref_PC &lt;- merged[, lapply(.SD, function(w) sum(w * FREQ, na.rm = TRUE)), 
+                   .SDcols = patterns(&quot;^PC&quot;)]
+  
+  gwas_pc_all &lt;- rbind(gwas_pc_all, data.frame(group = i,
+                                             ref_PC))
+}
+
+ref_pc_all$type &lt;- &#39;Reference&#39;
+gwas_pc_all$type &lt;- &#39;GWAS&#39;
+
+pc_all &lt;- rbind(ref_pc_all, gwas_pc_all)
+
+ggplot(pc_all, aes(x=PC1, y = PC2, colour = group, shape = type)) +
+  geom_point(size = 5)
+
+# There is a big shift in the PCs for GWAS and reference data, probably due to missing variants. Restrict to variant present in all GWAS.
+
+extract &lt;- Reduce(intersect, list(
+  pc_score_file$SNP,
+  gwas_freq$BMI_UKB$SNP,
+  gwas_freq$BMI_BBJ$SNP,
+  gwas_freq$BMI_UGR$SNP,
+  gwas_freq$BMI_MVP_AFR$SNP
+))
+
+pc_score_file_overlapping &lt;- pc_score_file[pc_score_file$SNP %in% extract,]
+
+# Calculate PCs for reference populations
+ref_pc_all &lt;- NULL
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;AMR&#39;,&#39;MID&#39;,&#39;CSA&#39;)){
+  # Merge on SNP ID
+  merged &lt;- merge(
+    pc_score_file_overlapping, 
+    ref_pop_freq[[i]], 
+    by.x = &quot;SNP&quot;, 
+    by.y = &quot;ID&quot;
+  )
+  
+  # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ)
+  merged[, A1_FREQ := ifelse(ALT == A1, ALT_FREQS, 1 - ALT_FREQS)]
+  
+  # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC
+  ref_PC &lt;- merged[, lapply(.SD, function(w) sum(w * A1_FREQ, na.rm = TRUE)), 
+                   .SDcols = patterns(&quot;^PC&quot;)]
+  
+  ref_pc_all &lt;- rbind(ref_pc_all, data.frame(group = i,
+                                             ref_PC))
+}
+
+gwas_pc_all &lt;- NULL
+for(i in names(gwas_freq)){
+  # Merge on SNP ID
+  merged &lt;- merge(
+    pc_score_file_overlapping, 
+    gwas_freq[[i]], 
+    by = &quot;SNP&quot;
+  )
+  
+  # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ)
+  merged[, FREQ := ifelse(A1.x == A1.y, FREQ, 1 - FREQ)]
+  
+  # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC
+  ref_PC &lt;- merged[, lapply(.SD, function(w) sum(w * FREQ, na.rm = TRUE)), 
+                   .SDcols = patterns(&quot;^PC&quot;)]
+  
+  gwas_pc_all &lt;- rbind(gwas_pc_all, data.frame(group = i,
+                                             ref_PC))
+}
+
+ref_pc_all$Population &lt;- ref_pc_all$group
+ref_pc_all$GWAS &lt;- NA
+
+gwas_pc_all$GWAS &lt;- gwas_pc_all$group
+gwas_pc_all$GWAS[gwas_pc_all$GWAS == &#39;BMI_UKB&#39;] &lt;- &quot;UKB (EUR)&quot;
+gwas_pc_all$GWAS[gwas_pc_all$GWAS == &#39;BMI_BBJ&#39;] &lt;- &quot;BBJ (EAS)&quot;
+gwas_pc_all$GWAS[gwas_pc_all$GWAS == &#39;BMI_UGR&#39;] &lt;- &quot;UGR (AFR)&quot;
+gwas_pc_all$GWAS[gwas_pc_all$GWAS == &#39;BMI_MVP_AFR&#39;] &lt;- &quot;MVP (AFR)&quot;
+gwas_pc_all$Population &lt;- NA
+
+ref_pc_all$type &lt;- &#39;Reference&#39;
+gwas_pc_all$type &lt;- &#39;GWAS&#39;
+
+pc_all &lt;- rbind(ref_pc_all, gwas_pc_all)
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/genetic_distance.png&#39;, units = &#39;px&#39;, width = 2000, height = 1500, res = 300)
+
+ggplot(pc_all, aes(x=PC1, y = PC2)) +
+  geom_point(data=pc_all[pc_all$type == &#39;Reference&#39;,], aes(x=PC1, y = PC2, fill = Population), size = 5, shape = 21) +
+  geom_point(data=pc_all[pc_all$type == &#39;GWAS&#39;,], aes(x=PC1, y = PC2, colour = GWAS), size = 4, shape = 3, stroke = 1.5) +
+  theme_half_open() +
+  background_grid()
+
+dev.off()
+
+# Perfect. Shows UKB match EUR, BBJ match EAS, UGR matches AFR, but MVP is close to AFR.</code></pre>
+</details>
+<hr />
+</div>
+<div id="estimate-ancestry-proportions" class="section level2">
+<h2>Estimate ancestry proportions</h2>
+<p>This involves using Florian Prive’s bigsnpr packages and UKB
+reference data to calculate principal components and then estimate
+ancestral proportions.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>library(bigsnpr)
+library(dplyr)
+
+DIR &lt;- &quot;~/oliverpainfel/Data/bigsnpr&quot;  # you can replace by e.g. &quot;data&quot; or &quot;tmp-data&quot;
+
+# /!\ This downloads 850 Mb (each)
+all_freq &lt;- bigreadr::fread2(
+  runonce::download_file(&quot;https://figshare.com/ndownloader/files/31620968&quot;,
+                         dir = DIR, fname = &quot;ref_freqs.csv.gz&quot;))
+projection &lt;- bigreadr::fread2(
+  runonce::download_file(&quot;https://figshare.com/ndownloader/files/31620953&quot;,
+                         dir = DIR, fname = &quot;projection.csv.gz&quot;))
+
+# Read in allele frequencies for each reference population
+ref_rds &lt;- NULL
+for(i in 1:22){
+  ref_rds &lt;- rbind(ref_rds, 
+                   readRDS(paste0(&#39;~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr&#39;, i, &#39;.rds&#39;)))
+}
+
+ref_pop_freq &lt;- list()
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;AMR&#39;,&#39;MID&#39;,&#39;CSA&#39;)){
+  ref_pop_freq[[i]] &lt;- ref_rds[, c(&#39;CHR&#39;,&#39;SNP&#39;,&#39;BP_GRCh37&#39;,&#39;A1&#39;,&#39;A2&#39;,paste0(&#39;REF.FRQ.&#39;,i)), with=F]
+  names(ref_pop_freq[[i]]) &lt;-c(&#39;chr&#39;,&#39;rsid&#39;,&#39;pos&#39;,&#39;a1&#39;,&#39;a0&#39;,&#39;freq&#39;)
+}
+
+# Read in GWAS allele frequencies for BMI
+gwas_list_main &lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt&#39;)
+gwas_list_main &lt;- gwas_list_main[grepl(&#39;BMI&#39;, gwas_list_main$name),]
+
+gwas_list_mvp &lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_mvp.txt&#39;)
+gwas_list_mvp &lt;- gwas_list_mvp[grepl(&#39;BMI&#39;, gwas_list_mvp$name),]
+gwas_list_mvp &lt;- gwas_list_mvp[gwas_list_mvp$population == &#39;AFR&#39;,]
+
+gwas_list&lt;-rbind(gwas_list_main, gwas_list_mvp)
+
+outdir &lt;- &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&#39;
+gwas_freq &lt;- list()
+for(i in 1:nrow(gwas_list)){
+  ss_i &lt;- fread(paste0(
+            outdir,
+            &#39;/reference/gwas_sumstat/&#39;,
+            gwas_list$name[i],
+            &#39;/&#39;,
+            gwas_list$name[i],
+            &#39;-cleaned.gz&#39;
+          ))
+  
+  gwas_freq[[gwas_list$name[i]]] &lt;- ss_i[, c(&#39;CHR&#39;,&#39;BP&#39;,&#39;SNP&#39;,&#39;A2&#39;,&#39;A1&#39;,&#39;FREQ&#39;), with = F]
+  names(gwas_freq[[gwas_list$name[i]]]) &lt;- c(&quot;chr&quot;, &quot;pos&quot;, &#39;rsid&#39;, &quot;a0&quot;, &quot;a1&quot;, &quot;freq&quot;)
+}
+
+# Harmonise the data
+gwas_freq_matched &lt;- list()
+for(i in 1:nrow(gwas_list)){
+  gwas_freq_matched[[gwas_list$name[i]]] &lt;- snp_match(
+    mutate(gwas_freq[[gwas_list$name[i]]], chr = as.integer(chr), beta = 1),
+    all_freq[1:5]
+  ) %&gt;%
+    mutate(freq = ifelse(beta &lt; 0, 1 - freq, freq))
+}
+
+ref_pop_freq_matched &lt;- list()
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;AMR&#39;,&#39;MID&#39;,&#39;CSA&#39;)){
+  ref_pop_freq_matched[[i]] &lt;- snp_match(
+    mutate(ref_pop_freq[[i]], chr = as.integer(chr), beta = 1),
+    all_freq[1:5]
+  ) %&gt;%
+    mutate(freq = ifelse(beta &lt; 0, 1 - freq, freq))
+}
+
+# Infer ancestry with shrinkage (https://privefl.github.io/bigsnpr/articles/ancestry.html)
+correction &lt;- c(1, 1, 1, 1.008, 1.021, 1.034, 1.052, 1.074, 1.099,
+                1.123, 1.15, 1.195, 1.256, 1.321, 1.382, 1.443)
+
+gwas_ancestry &lt;- list()
+for(i in 1:nrow(gwas_list)){
+  res &lt;- snp_ancestry_summary(
+    freq = gwas_freq_matched[[gwas_list$name[i]]]$freq,
+    info_freq_ref = all_freq[gwas_freq_matched[[gwas_list$name[i]]]$`_NUM_ID_`, -(1:5)],
+    projection = projection[gwas_freq_matched[[gwas_list$name[i]]]$`_NUM_ID_`, -(1:5)],
+    correction = correction
+  )
+  
+  # Group similar popualtions
+  group &lt;- colnames(all_freq)[-(1:5)]
+  group[group %in% c(&quot;Scandinavia&quot;, &quot;United Kingdom&quot;, &quot;Ireland&quot;)]   &lt;- &quot;Europe (North West)&quot;
+  group[group %in% c(&quot;Europe (South East)&quot;, &quot;Europe (North East)&quot;)] &lt;- &quot;Europe (East)&quot;
+  grp_fct &lt;- factor(group, unique(group))
+  
+  gwas_ancestry[[gwas_list$name[i]]] &lt;- tapply(res, grp_fct, sum)
+}
+
+ref_ancestry &lt;- list()
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;AFR&#39;,&#39;AMR&#39;,&#39;MID&#39;,&#39;CSA&#39;)){
+  res &lt;- snp_ancestry_summary(
+    freq = ref_pop_freq_matched[[i]]$freq,
+    info_freq_ref = all_freq[ref_pop_freq_matched[[i]]$`_NUM_ID_`, -(1:5)],
+    projection = projection[ref_pop_freq_matched[[i]]$`_NUM_ID_`, -(1:5)],
+    correction = correction
+  )
+  
+  # Group similar popualtions
+  group &lt;- colnames(all_freq)[-(1:5)]
+  group[group %in% c(&quot;Scandinavia&quot;, &quot;United Kingdom&quot;, &quot;Ireland&quot;)]   &lt;- &quot;Europe (North West)&quot;
+  group[group %in% c(&quot;Europe (South East)&quot;, &quot;Europe (North East)&quot;)] &lt;- &quot;Europe (East)&quot;
+  grp_fct &lt;- factor(group, unique(group))
+  
+  ref_ancestry[[i]] &lt;- tapply(res, grp_fct, sum)
+}
+
+# Plot the results
+gwas_ancestry_table &lt;- do.call(cbind, gwas_ancestry)
+colnames(gwas_ancestry_table) &lt;- c(&quot;UKB (EUR)&quot;, &quot;BBJ (EAS)&quot;, &quot;UGR (AFR)&quot;, &quot;MVP (AFR)&quot;)
+gwas_ancestry_table_melt &lt;- melt(gwas_ancestry_table)
+
+gwas_ancestry_table_melt&lt;-gwas_ancestry_table_melt[gwas_ancestry_table_melt$value &gt;= 0.02,]
+
+library(scales)  # for percent_format()
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/gwas_ancestry.png&#39;, units = &#39;px&#39;, width = 2000, height = 1500, res = 300)
+ggplot(gwas_ancestry_table_melt, aes(x = Var2, y = value, fill = Var1)) +
+  geom_bar(stat = &quot;identity&quot;, colour = &quot;black&quot;, size = 0.2) +
+  geom_text(data = subset(gwas_ancestry_table_melt, value &gt;= 0.02),
+            aes(label = scales::percent(value, accuracy = 1)),
+            position = position_stack(vjust = 0.5),
+            size = 3) +
+  labs(x = &quot;GWAS&quot;, y = &quot;Proportion&quot;, fill = &quot;Population&quot;,
+       title = &quot;Ancestry proportions in BMI GWAS&quot;) +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = -45, hjust = 1))
+dev.off()
+
+write.csv(gwas_ancestry_table, &#39;~/oliverpainfel/Analyses/crosspop/plots/gwas_ancestry.csv&#39;)
+
+ref_ancestry_table &lt;- do.call(cbind, ref_ancestry)
+ref_ancestry_table_melt &lt;- melt(ref_ancestry_table)
+
+ref_ancestry_table_melt&lt;-ref_ancestry_table_melt[ref_ancestry_table_melt$value &gt;= 0.02,]
+
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/ref_ancestry.png&#39;, units = &#39;px&#39;, width = 2000, height = 1500, res = 300)
+ggplot(ref_ancestry_table_melt, aes(x = Var2, y = value, fill = Var1)) +
+  geom_bar(stat = &quot;identity&quot;, colour = &#39;black&#39;, size = 0.2) +
+  geom_text(data = subset(ref_ancestry_table_melt, value &gt;= 0.02),
+            aes(label = scales::percent(value, accuracy = 1)),
+            position = position_stack(vjust = 0.5),
+            size = 3) +
+  labs(x = &quot;Reference Label&quot;, y = &quot;Proportion&quot;, fill = &#39;Population&#39;, title = &#39;Ancestry proportions in 1KG+HGDP reference&#39;) +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+dev.off()
+
+write.csv(ref_ancestry_table, &#39;~/oliverpainfel/Analyses/crosspop/plots/ref_ancestry.csv&#39;)
+
+gwas_ancestry_table_melt$Type &lt;- &#39;GWAS&#39;
+ref_ancestry_table_melt$Type &lt;- &#39;Reference&#39;
+
+both&lt;-rbind(gwas_ancestry_table_melt, ref_ancestry_table_melt)
+png(&#39;~/oliverpainfel/Analyses/crosspop/plots/ancestry_composition.png&#39;, units = &#39;px&#39;, width = 3000, height = 1500, res = 300)
+
+ggplot(both, aes(x = Var2, y = value, fill = Var1)) +
+  geom_bar(stat = &quot;identity&quot;, colour = &#39;black&#39;, size = 0.2) +
+  geom_text(data = subset(both, value &gt;= 0.02),
+            aes(label = scales::percent(value, accuracy = 1)),
+            position = position_stack(vjust = 0.5),
+            size = 3) +
+  labs(x = NULL, y = &quot;Proportion&quot;, fill = &#39;Population&#39;) +
+  facet_grid(. ~ Type, scales = &#39;free_x&#39;) +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  panel_border()
+dev.off()</code></pre>
+</details>
+<hr />
+</div>
+</div>
+<div id="bridgeprs" class="section level1">
+<h1>BridgePRS</h1>
+<p>The below code is incomplete due to current compatability issues
+between BridgePRS and GenoPred. Work to incorporate BridgePRS into
+GenoPred is on going.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>######
+# gwas_list
+######
+
+library(data.table)
+
+# Subset original gwas_list to include selected traits
+gwas_list&lt;-fread(&#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt&#39;)
+pheno&lt;-gsub(&#39;_.*&#39;,&#39;&#39;, gwas_list$name)
+selected_traits&lt;-fread(&#39;/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt&#39;, header=F)$V1
+gwas_list&lt;-gwas_list[pheno %in% selected_traits,]
+gwas_list$label&lt;-paste0(&#39;&quot;&#39;, gwas_list$label, &#39;&quot;&#39;)
+
+gwas_list&lt;-gwas_list[grepl(&#39;BMI&#39;, gwas_list$name),]
+
+write.table(
+  gwas_list, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_bridge.txt&#39;, 
+  col.names = T, 
+  row.names = F, 
+  quote = F)
+
+######
+# gwas_groups
+######
+
+gwas_groups_eas&lt;-data.frame(
+  name=paste0(selected_traits, &#39;_UKB_BBJ&#39;),
+  gwas=sapply(selected_traits, function(x) paste0(x,&#39;_UKB,&#39;,x,&#39;_BBJ&#39;)),
+  label=paste0(&#39;&quot;&#39;, selected_traits, &quot; (UKB+BBJ)&quot;, &#39;&quot;&#39;)
+)
+
+gwas_groups_afr&lt;-data.frame(
+  name=paste0(selected_traits, &#39;_UKB_UGR&#39;),
+  gwas=sapply(selected_traits, function(x) paste0(x,&#39;_UKB,&#39;,x,&#39;_UGR&#39;)),
+  label=paste0(&#39;&quot;&#39;, selected_traits, &quot; (UKB+UGR)&quot;, &#39;&quot;&#39;)
+)
+
+gwas_groups&lt;-rbind(gwas_groups_eas, gwas_groups_afr)
+
+gwas_groups&lt;-gwas_groups[grepl(&#39;BMI&#39;, gwas_groups$name),]
+
+write.table(gwas_groups, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_bridge.txt&#39;, col.names = T, row.names = F, quote = F)
+
+######
+# config
+######
+
+config&lt;-c(
+  &quot;outdir: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output&quot;,
+  &quot;config_file: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_bridgeprs.yaml&quot;,
+  &quot;gwas_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_bridge.txt&quot;,
+  &quot;target_list: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic/target_list.txt&quot;,
+  &quot;gwas_groups: /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups_bridge.txt&quot;,
+  &quot;pgs_methods: [&#39;bridgeprs&#39;]&quot;,
+  &quot;cores_prep_pgs: 10&quot;, 
+  &quot;cores_target_pgs: 50&quot;
+)
+
+write.table(config, &#39;/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_bridgeprs.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<hr />
+<h4>
+Run pipeline
+</h4>
+<pre class="bash"><code>snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_bridgeprs.yaml \
+  target_pgs  -n</code></pre>
+</details>
 </div>
 
 <!-- footer.html -->
diff --git a/docs/CrossPop_summary.Rmd b/docs/CrossPop_summary.Rmd
new file mode 100644
index 00000000..60a790f3
--- /dev/null
+++ b/docs/CrossPop_summary.Rmd
@@ -0,0 +1,53 @@
+---
+title: Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+***
+
+# Summary
+
+Polygenic scores (PGS) are increasingly used to estimate genetic predisposition to complex traits, but their performance remains inequitable across global populations. This study benchmarks leading polygenic scoring methods for ancestrally diverse populations, evaluating their predictive accuracy and computational efficiency across multiple GWAS datasets.
+
+We evaluated ten complex traits in African (AFR), East Asian (EAS), and European (EUR) populations using publicly available GWAS from the Ugandan Genome Resource, Biobank Japan, UK Biobank, and the Million Veteran Program. We tested both single-source and multi-source PGS methods, including recent advances like SBayesRC, LDpred2, PRS-CSx, TL-PRS, and X-Wing. Multi-source methods leverage GWAS from multiple ancestries to improve prediction.
+
+A key contribution is the application of the LEOPARD method for combining population-specific scores from single-source PGS methods using only summary statistics—avoiding the need for individual-level data. This approach enables practical implementation of multi-source PGS methods even in data-limited settings.
+
+<br/>
+
+# Key Findings
+
+- Multi-source methods consistently outperform single-source models across ancestries, especially for AFR and EAS populations.
+
+- Independently optimised scores, when combined using LEOPARD, outperform jointly optimised methods (e.g. PRS-CSx, X-Wing) with far lower computational cost.
+
+- SBayesRC and LDpred2 were the top-performing single-source methods, with SBayesRC performing best when no individual-level tuning data were available.
+
+- LEOPARD + QuickPRS enables fast, summary-statistic-only tuning of multi-source models (independently or jontly optimised), nearly matching individual-level tuning performance.
+
+- Computational benchmarks show that independently optimised approaches can be completed in under 2 hours per trait with 10 cores—compared to over 30 hours for X-Wing.
+
+<br/>
+
+# Citation
+
+Pain, Oliver. "Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations." MedRxiv (2025). https://doi.org/10.1101/2025.03.27.25324773
+
+<br/>
+
+# Additional resources
+
+- Code used to conduct the study: [here](CrossPop.html)
+
+<br/>
+
+<br/>
diff --git a/docs/CrossPop_summary.html b/docs/CrossPop_summary.html
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+</div>
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+    </div>
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+      <ul class="nav navbar-nav">
+        
+      </ul>
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+        <li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Pipeline
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li>
+      <a href="pipeline_overview.html">Overview</a>
+    </li>
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+      <a href="pipeline_readme.html">Instructions</a>
+    </li>
+    <li>
+      <a href="pipeline_technical.html">Technical documentation</a>
+    </li>
+  </ul>
+</li>
+<li class="dropdown">
+  <a href="#" class="dropdown-toggle" data-toggle="dropdown" role="button" data-bs-toggle="dropdown" aria-expanded="false">
+    Research
+     
+    <span class="caret"></span>
+  </a>
+  <ul class="dropdown-menu" role="menu">
+    <li>
+      <a href="research_index.html">Overview</a>
+    </li>
+    <li>
+      <a href="comparison_of_methods_summary.html">Polygenic Scoring Methods Comparison</a>
+    </li>
+    <li>
+      <a href="Functionally_informed_prediction.html">Quantifying Polygenic Signal Mediated by Altered Gene Expression</a>
+    </li>
+    <li>
+      <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
+    </li>
+    <li>
+      <a href="CrossPop_summary.html">Cross-Ancestry Polygenic Prediction</a>
+    </li>
+  </ul>
+</li>
+<li>
+  <a href="more_index.html">More</a>
+</li>
+<li>
+  <a href="https://github.com/opain/GenoPred">
+    <span class="fa fa-github fa-lg"></span>
+     
+  </a>
+</li>
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+    </div><!--/.nav-collapse -->
+  </div><!--/.container -->
+</div><!--/.navbar -->
+
+<div id="header">
+
+
+
+<h1 class="title toc-ignore">Leveraging Global Genetics Resources to
+Enhance Polygenic Prediction Across Ancestrally Diverse Populations</h1>
+
+</div>
+
+
+<hr />
+<div id="summary" class="section level1">
+<h1>Summary</h1>
+<p>Polygenic scores (PGS) are increasingly used to estimate genetic
+predisposition to complex traits, but their performance remains
+inequitable across global populations. This study benchmarks leading
+polygenic scoring methods for ancestrally diverse populations,
+evaluating their predictive accuracy and computational efficiency across
+multiple GWAS datasets.</p>
+<p>We evaluated ten complex traits in African (AFR), East Asian (EAS),
+and European (EUR) populations using publicly available GWAS from the
+Ugandan Genome Resource, Biobank Japan, UK Biobank, and the Million
+Veteran Program. We tested both single-source and multi-source PGS
+methods, including recent advances like SBayesRC, LDpred2, PRS-CSx,
+TL-PRS, and X-Wing. Multi-source methods leverage GWAS from multiple
+ancestries to improve prediction.</p>
+<p>A key contribution is the application of the LEOPARD method for
+combining population-specific scores from single-source PGS methods
+using only summary statistics—avoiding the need for individual-level
+data. This approach enables practical implementation of multi-source PGS
+methods even in data-limited settings.</p>
+<p><br/></p>
+</div>
+<div id="key-findings" class="section level1">
+<h1>Key Findings</h1>
+<ul>
+<li><p>Multi-source methods consistently outperform single-source models
+across ancestries, especially for AFR and EAS populations.</p></li>
+<li><p>Independently optimised scores, when combined using LEOPARD,
+outperform jointly optimised methods (e.g. PRS-CSx, X-Wing) with far
+lower computational cost.</p></li>
+<li><p>SBayesRC and LDpred2 were the top-performing single-source
+methods, with SBayesRC performing best when no individual-level tuning
+data were available.</p></li>
+<li><p>LEOPARD + QuickPRS enables fast, summary-statistic-only tuning of
+multi-source models (independently or jontly optimised), nearly matching
+individual-level tuning performance.</p></li>
+<li><p>Computational benchmarks show that independently optimised
+approaches can be completed in under 2 hours per trait with 10
+cores—compared to over 30 hours for X-Wing.</p></li>
+</ul>
+<p><br/></p>
+</div>
+<div id="citation" class="section level1">
+<h1>Citation</h1>
+<p>Pain, Oliver. “Leveraging Global Genetics Resources to Enhance
+Polygenic Prediction Across Ancestrally Diverse Populations.” MedRxiv
+(2025). <a href="https://doi.org/10.1101/2025.03.27.25324773"
+class="uri">https://doi.org/10.1101/2025.03.27.25324773</a></p>
+<p><br/></p>
+</div>
+<div id="additional-resources" class="section level1">
+<h1>Additional resources</h1>
+<ul>
+<li>Code used to conduct the study: <a
+href="CrossPop.html">here</a></li>
+</ul>
+<p><br/></p>
+<p><br/></p>
+</div>
+
+<!-- footer.html -->
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+
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diff --git a/docs/_site.yml b/docs/_site.yml
index 8e772d04..0f69d035 100644
--- a/docs/_site.yml
+++ b/docs/_site.yml
@@ -21,6 +21,8 @@ navbar:
         href: Functionally_informed_prediction.html
       - text: Translating Polygenic Scores onto the Absolute Scale
         href: Absolute_Conversion.html
+      - text: Cross-Ancestry Polygenic Prediction
+        href: CrossPop_summary.html
     - text: "More"
       href: more_index.html
     - icon: fa-github fa-lg
diff --git a/docs/index.html b/docs/index.html
index c336a36b..1a9bc311 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -361,6 +361,9 @@
     <li>
       <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
     </li>
+    <li>
+      <a href="CrossPop_summary.html">Cross-Ancestry Polygenic Prediction</a>
+    </li>
   </ul>
 </li>
 <li>
diff --git a/docs/more_index.Rmd b/docs/more_index.Rmd
index 65fe974b..5748bfce 100644
--- a/docs/more_index.Rmd
+++ b/docs/more_index.Rmd
@@ -39,4 +39,7 @@ output:
   - Shiny app - <a href="PRS_to_Abs_tool.html" class="inline_button">Link</a>
   - Code - <a href="Absolute_Conversion.html" class="inline_button">Link</a>
   - Conference poster and flash talk - <a href="personal_genome_2021.html" class="inline_button">Link</a>
+- Cross-Ancestry Polygenic Prediction
+  - Summary - <a href="CrossPop_summary.html" class="inline_button">Link</a>
+  - Code - <a href="CrossPop.html" class="inline_button">Link</a>
 
diff --git a/docs/more_index.html b/docs/more_index.html
index 1d2d6b8c..275135f7 100644
--- a/docs/more_index.html
+++ b/docs/more_index.html
@@ -75,6 +75,41 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
+      dismiss: "Accept",
+      link: "Google's privacy info",
+      href: "https://policies.google.com/technologies/partner-sites"
+    },
+    onStatusChange: function(status) {
+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
+        var gtagScript = document.createElement('script');
+        gtagScript.setAttribute('async', '');
+        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}
@@ -326,6 +361,9 @@
     <li>
       <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
     </li>
+    <li>
+      <a href="CrossPop_summary.html">Cross-Ancestry Polygenic Prediction</a>
+    </li>
   </ul>
 </li>
 <li>
@@ -402,6 +440,12 @@ <h1>Research</h1>
 <li>Conference poster and flash talk -
 <a href="personal_genome_2021.html" class="inline_button">Link</a></li>
 </ul></li>
+<li>Cross-Ancestry Polygenic Prediction
+<ul>
+<li>Summary -
+<a href="CrossPop_summary.html" class="inline_button">Link</a></li>
+<li>Code - <a href="CrossPop.html" class="inline_button">Link</a></li>
+</ul></li>
 </ul>
 </div>
 
diff --git a/docs/opensnp_benchmark.html b/docs/opensnp_benchmark.html
index b472e768..b2a1fd77 100644
--- a/docs/opensnp_benchmark.html
+++ b/docs/opensnp_benchmark.html
@@ -78,6 +78,41 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
+      dismiss: "Accept",
+      link: "Google's privacy info",
+      href: "https://policies.google.com/technologies/partner-sites"
+    },
+    onStatusChange: function(status) {
+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
+        var gtagScript = document.createElement('script');
+        gtagScript.setAttribute('async', '');
+        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}
diff --git a/docs/pipeline_overview.Rmd b/docs/pipeline_overview.Rmd
index 1ae0a07e..9632d782 100644
--- a/docs/pipeline_overview.Rmd
+++ b/docs/pipeline_overview.Rmd
@@ -51,9 +51,9 @@ The pipeline uses the Snakemake workflow manager and conda environments providin
 
 ***
 
-# Preprint
+# Publication
 
-Check out our preprint describing the pipeline: "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." - <a href="https://doi.org/10.1101/2024.06.12.24308843" class="inline_button">Link</a>
+Check out our publication in *Bioinformatics* describing the pipeline: "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." - <a href="https://doi.org/10.1093/bioinformatics/btae551" class="inline_button">Link</a>
 
 ***
 
diff --git a/docs/pipeline_overview.html b/docs/pipeline_overview.html
index b358cffb..abbe098b 100644
--- a/docs/pipeline_overview.html
+++ b/docs/pipeline_overview.html
@@ -361,6 +361,9 @@
     <li>
       <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
     </li>
+    <li>
+      <a href="CrossPop_summary.html">Cross-Ancestry Polygenic Prediction</a>
+    </li>
   </ul>
 </li>
 <li>
@@ -425,12 +428,12 @@ <h1>Promo Video</h1>
 </iframe>
 <hr />
 </div>
-<div id="preprint" class="section level1">
-<h1>Preprint</h1>
-<p>Check out our preprint describing the pipeline: “The GenoPred
-Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring.”
--
-<a href="https://doi.org/10.1101/2024.06.12.24308843" class="inline_button">Link</a></p>
+<div id="publication" class="section level1">
+<h1>Publication</h1>
+<p>Check out our publication in <em>Bioinformatics</em> describing the
+pipeline: “The GenoPred Pipeline: A Comprehensive and Scalable Pipeline
+for Polygenic Scoring.” -
+<a href="https://doi.org/10.1093/bioinformatics/btae551" class="inline_button">Link</a></p>
 <hr />
 </div>
 <div id="any-questions" class="section level1">
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 99b68380..26fa141d 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -1364,6 +1364,48 @@ By default, the pipeline allocates 5 cores when running the outlier_detection ru
 
 ## Multi-source PGS methods
 
+The GenoPred pipeline also implements a range of multi-source polygenic scoring methods that can combine GWAS summary statistics from multiple populations. To use these methods, an additional `gwas_groups` file must be provided, indicating which GWAS are to be jointly analyzed. Specify the location of the `gwas_groups` file in the `configfile` using the `gwas_groups` parameter. An example of a gwas_groups file can be found [here](../pipeline/example_input/gwas_groups.txt).
+
+<details>
+<summary>View gwas_groups format</summary>
+
+```{r, eval=TRUE, echo=FALSE, results='asis'}
+gwas_groups <- list(
+  Column = list(
+    name = list(
+      example = '`height`',
+      description = "ID for the group of GWAS. Cannot contain spaces (' ') or hyphens ('-')."
+    ),
+    gwas = list(
+      example = '`yengo_eur,yengo_eas`',
+      description = "Comma-separated list of GWAS names corresponding to those in the `gwas_list`."
+    ),
+    label = list(
+      example = '`"Height (EUR+EAS)"`',
+      description = "A human-readable name for the group of GWAS (wrap in double quotes if multiple words)."
+    )
+  )
+)
+
+gwas_groups_df <- NULL
+for (column in names(gwas_groups$Column)) {
+  description <- gwas_groups$Column[[column]]$description
+  example <- gwas_groups$Column[[column]]$example
+  
+  gwas_groups_df <- rbind(
+    gwas_groups_df,
+    data.frame(
+      Column = column,
+      Example = example,
+      Description = description,
+      stringsAsFactors = FALSE
+    )
+  )
+}
+
+kable(gwas_groups_df, 'markdown')
+```
+
 The pipeline also implements a range of multi-source polygenic scoring methods, that can combine GWAS summary statistics from multiple populations. To use these methods an additional `gwas_groups` file must be provided, indicating which GWAS are to be jointly analysed. The `gwas_groups` file must be specified in the `configfile` using the `gwas_groups` parameter. An example of a gwas_groups file can be found [here](../pipeline/example_input/gwas_groups.txt). 
 
 <details>
@@ -1414,13 +1456,13 @@ kable(gwas_groups_df, 'markdown')
 
 <div class="shallow-break"></div>
 
-Another multi-source PGS approach is TL-PRS, which tunes an existing PGS model based on GWAS summary statistics from another population. GenoPred can automate this process. The user must specify which PGS methods they want TL-PRS to be applied to, using the `tlprs_methods` parameter in the `configfile`. For example: `tlprs_methods: ['lassosum','dbslmm']`. TL-PRS is only applied to the PGS from a given method selected using pseudovalidation.
+Another multi-source PGS approach is TL-PRS, which tunes an existing PGS model based on GWAS summary statistics from another population. GenoPred can automate this process. The user must specify which PGS methods TL-PRS should be applied to using the `tlprs_methods` parameter in the `configfile` (e.g., `tlprs_methods: ['lassosum','dbslmm']`). TL-PRS is only applied to the PGS from a given method selected using pseudovalidation.
 
-An alternative multi-source PGS approach is to linearly combine population-specific PGS derived using single-source methods. GenoPred implements an approach called `LEOPARD+QuickPRS`, which estimates the optimal linear combination weights for population-specific PGS using summary statistics alone. Using the `leopard_methods` parameter in the `configfile`, the user can specify which single-source PGS should be linearly combined using the LEOPARD+QuickPRS estimated weights. For example: `leopard_methods: ['lassosum', 'dbslmm']`. LEOPARD weighting is only applied to the PGS from a given method selected using pseudovalidation.
+An alternative approach is to linearly combine population-specific PGS derived using single-source methods. GenoPred implements an approach called **LEOPARD+QuickPRS**, which estimates the optimal linear combination weights for population-specific PGS using summary statistics alone. Using the `leopard_methods` parameter in the `configfile`, the user can specify which single-source PGS should be linearly combined by the LEOPARD+QuickPRS method (for example, `leopard_methods: ['lassosum', 'dbslmm']`). LEOPARD weighting is only applied to the PGS from a given method that has been selected using pseudovalidation.
 
 ***
 
-We have provided some test data to run the multi-source functionality within GenoPred. To run the pipeline with the test_data, we will use the `example_input/config.multisource.yaml`. We have made some slight edits to the configuration used in the [previous test run](#run-using-test-data). Mainly we have provided a `gwas_groups` file, and we have specified the methods that we want to be linearly combined using LEOPARD+QuickPRS using the `leopard_methods` parameter.
+We have provided some test data to run the multi-source functionality within GenoPred. To run the pipeline with the test data, use the `example_input/config.multisource.yaml` file. In this configuration, we have added a `gwas_groups` file and specified the methods to be linearly combined via LEOPARD+QuickPRS using the LEOPARD+QuickPRS using the `leopard_methods` parameter.
 
 <details>
 
@@ -1496,11 +1538,9 @@ snakemake -n --configfile=example_input/config.multisource.yaml output_all
 snakemake -j1 --configfile=example_input/config.multisource.yaml --use-conda output_all
 ```
 
-Once the pipeline is complete, you can check that there is nothing else to do by doing another dry run, and it should say 'Nothing to be done'.
-
-When using LEOPARD to combine single-source PGS, we append `_multi` to the end method name. For example, in this case we are combining `lassosum` PGS, so we would refer to this as `lassosum_multi`. This is important when navigating output files. For example, you will the PGS will be in the following path: `[outdir]/[target name]/pgs/[population]/lassosum_multi`. Similarly, this method is referred to as `lassosum_multi` when reading the PGS into R using the `read_pgs()` function ([here](#read_pgs)).
+When using LEOPARD to combine single-source PGS, `_multi` is appended to the end method name. For example, when combining `lassosum` PGS, you will see the output in the following path: `[outdir]/[target name]/pgs/[population]/lassosum_multi`. Similarly, when reading the PGS into R using the `read_pgs()` function ([see here](#read_pgs)), this method is referred to as `lassosum_multi`.
 
-When applying TL-PRS to a given method, we append `tlprs_` to the begining of the method name. For example, if were applying TL-PRS to `lassosum` PGS, so we would refer to this as `tlprs_lassosum`.
+When applying TL-PRS to a given method, `tlprs_` is prefixed to the method name. For example, if TL-PRS is applied to `lassosum` PGS, it is referred to as `tlprs_lassosum`.
 
 ***
 
@@ -1589,6 +1629,12 @@ For example, if you want to supply LD data for `AFR` and `EUR` populations with
 
 </div>
 
+LD reference data in LDpred2 format for AFR, EAS and EUR populations can be downloaded here:
+
+- AFR: [download link](https://drive.google.com/file/d/1Fc3v_N1m-ocGqw6E4HpzEVq43jS2oWUQ/view?usp=sharing)
+- EAS: [download link](https://drive.google.com/file/d/1NwGHs5zwk1p4Vui-1cRb84xkoYld-kyK/view?usp=sharing)
+- EUR: [download link](https://drive.google.com/file/d/1Vo4QAw7HiI3Y5Wk1y5-9JLgW34U4KPTZ/view?usp=sharing)
+
 ***
 
 # Running in offline environment
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index cf97fcdd..cf9cd719 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -2189,6 +2189,53 @@ <h2>Control computational resources</h2>
 </div>
 <div id="multi-source-pgs-methods" class="section level2">
 <h2>Multi-source PGS methods</h2>
+<p>The GenoPred pipeline also implements a range of multi-source
+polygenic scoring methods that can combine GWAS summary statistics from
+multiple populations. To use these methods, an additional
+<code>gwas_groups</code> file must be provided, indicating which GWAS
+are to be jointly analyzed. Specify the location of the
+<code>gwas_groups</code> file in the <code>configfile</code> using the
+<code>gwas_groups</code> parameter. An example of a gwas_groups file can
+be found <a
+href="../pipeline/example_input/gwas_groups.txt">here</a>.</p>
+<details>
+<summary>
+View gwas_groups format
+</summary>
+<table>
+<colgroup>
+<col width="6%" />
+<col width="18%" />
+<col width="75%" />
+</colgroup>
+<thead>
+<tr class="header">
+<th align="left">Column</th>
+<th align="left">Example</th>
+<th align="left">Description</th>
+</tr>
+</thead>
+<tbody>
+<tr class="odd">
+<td align="left">name</td>
+<td align="left"><code>height</code></td>
+<td align="left">ID for the group of GWAS. Cannot contain spaces (’ ‘)
+or hyphens (’-’).</td>
+</tr>
+<tr class="even">
+<td align="left">gwas</td>
+<td align="left"><code>yengo_eur,yengo_eas</code></td>
+<td align="left">Comma-separated list of GWAS names corresponding to
+those in the <code>gwas_list</code>.</td>
+</tr>
+<tr class="odd">
+<td align="left">label</td>
+<td align="left"><code>"Height (EUR+EAS)"</code></td>
+<td align="left">A human-readable name for the group of GWAS (wrap in
+double quotes if multiple words).</td>
+</tr>
+</tbody>
+</table>
 <p>The pipeline also implements a range of multi-source polygenic
 scoring methods, that can combine GWAS summary statistics from multiple
 populations. To use these methods an additional <code>gwas_groups</code>
@@ -2242,31 +2289,29 @@ <h2>Multi-source PGS methods</h2>
 <p>Another multi-source PGS approach is TL-PRS, which tunes an existing
 PGS model based on GWAS summary statistics from another population.
 GenoPred can automate this process. The user must specify which PGS
-methods they want TL-PRS to be applied to, using the
-<code>tlprs_methods</code> parameter in the <code>configfile</code>. For
-example: <code>tlprs_methods: ['lassosum','dbslmm']</code>. TL-PRS is
-only applied to the PGS from a given method selected using
-pseudovalidation.</p>
-<p>An alternative multi-source PGS approach is to linearly combine
-population-specific PGS derived using single-source methods. GenoPred
-implements an approach called <code>LEOPARD+QuickPRS</code>, which
-estimates the optimal linear combination weights for population-specific
-PGS using summary statistics alone. Using the
-<code>leopard_methods</code> parameter in the <code>configfile</code>,
-the user can specify which single-source PGS should be linearly combined
-using the LEOPARD+QuickPRS estimated weights. For example:
-<code>leopard_methods: ['lassosum', 'dbslmm']</code>. LEOPARD weighting
-is only applied to the PGS from a given method selected using
+methods TL-PRS should be applied to using the <code>tlprs_methods</code>
+parameter in the <code>configfile</code> (e.g.,
+<code>tlprs_methods: ['lassosum','dbslmm']</code>). TL-PRS is only
+applied to the PGS from a given method selected using
 pseudovalidation.</p>
+<p>An alternative approach is to linearly combine population-specific
+PGS derived using single-source methods. GenoPred implements an approach
+called <strong>LEOPARD+QuickPRS</strong>, which estimates the optimal
+linear combination weights for population-specific PGS using summary
+statistics alone. Using the <code>leopard_methods</code> parameter in
+the <code>configfile</code>, the user can specify which single-source
+PGS should be linearly combined by the LEOPARD+QuickPRS method (for
+example, <code>leopard_methods: ['lassosum', 'dbslmm']</code>). LEOPARD
+weighting is only applied to the PGS from a given method that has been
+selected using pseudovalidation.</p>
 <hr />
 <p>We have provided some test data to run the multi-source functionality
-within GenoPred. To run the pipeline with the test_data, we will use the
-<code>example_input/config.multisource.yaml</code>. We have made some
-slight edits to the configuration used in the <a
-href="#run-using-test-data">previous test run</a>. Mainly we have
-provided a <code>gwas_groups</code> file, and we have specified the
-methods that we want to be linearly combined using LEOPARD+QuickPRS
-using the <code>leopard_methods</code> parameter.</p>
+within GenoPred. To run the pipeline with the test data, use the
+<code>example_input/config.multisource.yaml</code> file. In this
+configuration, we have added a <code>gwas_groups</code> file and
+specified the methods to be linearly combined via LEOPARD+QuickPRS using
+the LEOPARD+QuickPRS using the <code>leopard_methods</code>
+parameter.</p>
 <details>
 <summary>
 See contents of multisource configfile
@@ -2383,21 +2428,17 @@ <h2>Multi-source PGS methods</h2>
 
 # Run the pipeline running one step at a time 
 snakemake -j1 --configfile=example_input/config.multisource.yaml --use-conda output_all</code></pre>
-<p>Once the pipeline is complete, you can check that there is nothing
-else to do by doing another dry run, and it should say ‘Nothing to be
-done’.</p>
-<p>When using LEOPARD to combine single-source PGS, we append
-<code>_multi</code> to the end method name. For example, in this case we
-are combining <code>lassosum</code> PGS, so we would refer to this as
-<code>lassosum_multi</code>. This is important when navigating output
-files. For example, you will the PGS will be in the following path:
+<p>When using LEOPARD to combine single-source PGS, <code>_multi</code>
+is appended to the end method name. For example, when combining
+<code>lassosum</code> PGS, you will see the output in the following
+path:
 <code>[outdir]/[target name]/pgs/[population]/lassosum_multi</code>.
-Similarly, this method is referred to as <code>lassosum_multi</code>
-when reading the PGS into R using the <code>read_pgs()</code> function
-(<a href="#read_pgs">here</a>).</p>
-<p>When applying TL-PRS to a given method, we append <code>tlprs_</code>
-to the begining of the method name. For example, if were applying TL-PRS
-to <code>lassosum</code> PGS, so we would refer to this as
+Similarly, when reading the PGS into R using the <code>read_pgs()</code>
+function (<a href="#read_pgs">see here</a>), this method is referred to
+as <code>lassosum_multi</code>.</p>
+<p>When applying TL-PRS to a given method, <code>tlprs_</code> is
+prefixed to the method name. For example, if TL-PRS is applied to
+<code>lassosum</code> PGS, it is referred to as
 <code>tlprs_lassosum</code>.</p>
 <hr />
 </div>
@@ -2514,6 +2555,19 @@ <h2>Specifying alternative reference data for PGS methods</h2>
 see the documentation or GitHub README for each tool for exact format
 specifications.</p>
 </div>
+<p>LD reference data in LDpred2 format for AFR, EAS and EUR populations
+can be downloaded here:</p>
+<ul>
+<li>AFR: <a
+href="https://drive.google.com/file/d/1Fc3v_N1m-ocGqw6E4HpzEVq43jS2oWUQ/view?usp=sharing">download
+link</a></li>
+<li>EAS: <a
+href="https://drive.google.com/file/d/1NwGHs5zwk1p4Vui-1cRb84xkoYld-kyK/view?usp=sharing">download
+link</a></li>
+<li>EUR: <a
+href="https://drive.google.com/file/d/1Vo4QAw7HiI3Y5Wk1y5-9JLgW34U4KPTZ/view?usp=sharing">download
+link</a></li>
+</ul>
 <hr />
 </div>
 </div>
diff --git a/docs/research_index.Rmd b/docs/research_index.Rmd
index 2550a15f..cf57703c 100644
--- a/docs/research_index.Rmd
+++ b/docs/research_index.Rmd
@@ -65,6 +65,24 @@ output:
 
 <br/>
 
-<br/>
+# 6. Cross-Ancestry Polygenic Prediction
+
+<div style="display: flex; flex-wrap: wrap; align-items: center; justify-content: center;">
+  <p style="padding-right: 20px; flex: 1;">
+    As GWAS become available across diverse populations, polygenic scoring methods that combine multiple sources are essential for equitable prediction. 
+    This study benchmarked single- and multi-source PGS methods across African, East Asian, and European ancestries using public GWAS and UK Biobank target data. 
+    A novel application of the LEOPARD method enables accurate linear combinations of ancestry-specific PGS using only summary statistics, 
+    offering a scalable solution to optimise prediction in underrepresented groups. 
+    <br/><br/><strong>Overview and code:</strong> 
+    <a href="CrossPop.html" target="_blank">Click here</a> 
+    <br/><strong>Preprint:</strong> 
+    <a href="https://doi.org/10.1101/2025.03.27.25324773" target="_blank">
+      Pain. Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations. medRxiv (2025).
+    </a>
+  </p>
+  <img src="Images/research_summary/cross_pop_2.png" style="width: 300px; flex: 0 0 auto;"/>
+</div>
 
+<br/>
+<br/>
 
diff --git a/docs/research_index.html b/docs/research_index.html
index 953fda46..f0d24581 100644
--- a/docs/research_index.html
+++ b/docs/research_index.html
@@ -13,7 +13,7 @@
 
 <title>GenoPred Research</title>
 
-<script src="site_libs/header-attrs-2.25/header-attrs.js"></script>
+<script src="site_libs/header-attrs-2.26/header-attrs.js"></script>
 <script src="site_libs/jquery-3.6.0/jquery-3.6.0.min.js"></script>
 <meta name="viewport" content="width=device-width, initial-scale=1" />
 <link href="site_libs/bootstrap-3.3.5/css/cosmo.min.css" rel="stylesheet" />
@@ -78,6 +78,41 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
+      dismiss: "Accept",
+      link: "Google's privacy info",
+      href: "https://policies.google.com/technologies/partner-sites"
+    },
+    onStatusChange: function(status) {
+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
+        var gtagScript = document.createElement('script');
+        gtagScript.setAttribute('async', '');
+        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}
@@ -402,6 +437,9 @@
     <li>
       <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
     </li>
+    <li>
+      <a href="CrossPop_summary.html">Cross-Ancestry Polygenic Prediction</a>
+    </li>
   </ul>
 </li>
 <li>
@@ -533,7 +571,29 @@ <h1>5. Polygenic Prediction of Molecular Traits</h1>
 <p><img src="Images/research_summary/pred_mol.png" style="width: 300px; flex: 0 0 auto;"/></p>
 </div>
 <p><br/></p>
-<p><br/></p>
+</div>
+<div id="cross-ancestry-polygenic-prediction" class="section level1">
+<h1>6. Cross-Ancestry Polygenic Prediction</h1>
+<div
+style="display: flex; flex-wrap: wrap; align-items: center; justify-content: center;">
+<p style="padding-right: 20px; flex: 1;">
+As GWAS become available across diverse populations, polygenic scoring
+methods that combine multiple sources are essential for equitable
+prediction. This study benchmarked single- and multi-source PGS methods
+across African, East Asian, and European ancestries using public GWAS
+and UK Biobank target data. A novel application of the LEOPARD method
+enables accurate linear combinations of ancestry-specific PGS using only
+summary statistics, offering a scalable solution to optimise prediction
+in underrepresented groups. <br/><br/><strong>Overview and
+code:</strong> <a href="CrossPop.html" target="_blank">Click here</a>
+<br/><strong>Preprint:</strong>
+<a href="https://doi.org/10.1101/2025.03.27.25324773" target="_blank">
+Pain. Leveraging Global Genetics Resources to Enhance Polygenic
+Prediction Across Ancestrally Diverse Populations. medRxiv (2025). </a>
+</p>
+<p><img src="Images/research_summary/cross_pop_2.png" style="width: 300px; flex: 0 0 auto;"/></p>
+</div>
+<p><br/> <br/></p>
 </div>
 
 <!-- footer.html -->

From d64954157a7178f9df330df11e57b88f4fba19bf Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 13 Jun 2025 20:42:25 +0100
Subject: [PATCH 151/153] Updated documentation

---
 docs/CrossPop_summary.Rmd                     |   1 +
 docs/CrossPop_summary.html                    |   3 +
 docs/Images/OpenSNP/cross_pop_eur.png         | Bin 0 -> 77000 bytes
 docs/more_index.Rmd                           |   1 +
 docs/more_index.html                          |   3 +
 docs/opensnp_benchmark_crosspop.Rmd           | 294 +++++++
 docs/opensnp_benchmark_crosspop.html          | 823 ++++++++++++++++++
 docs/pipeline_readme.Rmd                      |   8 +-
 docs/pipeline_readme.html                     |  11 +-
 docs/pipeline_technical.Rmd                   |  12 +-
 docs/pipeline_technical.html                  |  49 +-
 docs/prep_quickprs_ref.Rmd                    |  17 +-
 functions/pipeline.R                          |   4 +-
 .../misc/cross_pop_test/cross_pop_test.Rmd    |   4 +-
 pipeline/misc/opensnp/config_cross_pop.yaml   |  15 +-
 pipeline/misc/opensnp/gwas_groups.txt         |   5 +-
 pipeline/misc/opensnp/gwas_list_cross_pop.txt |   7 +
 pipeline/rules/dependencies.smk               |  65 +-
 18 files changed, 1256 insertions(+), 66 deletions(-)
 create mode 100644 docs/Images/OpenSNP/cross_pop_eur.png
 create mode 100644 docs/opensnp_benchmark_crosspop.Rmd
 create mode 100644 docs/opensnp_benchmark_crosspop.html
 create mode 100644 pipeline/misc/opensnp/gwas_list_cross_pop.txt

diff --git a/docs/CrossPop_summary.Rmd b/docs/CrossPop_summary.Rmd
index 60a790f3..bf619363 100644
--- a/docs/CrossPop_summary.Rmd
+++ b/docs/CrossPop_summary.Rmd
@@ -47,6 +47,7 @@ Pain, Oliver. "Leveraging Global Genetics Resources to Enhance Polygenic Predict
 # Additional resources
 
 - Code used to conduct the study: [here](CrossPop.html)
+- Example applying LEOPARD + QuickPRS to height GWAS from 5 populations and then evaluated in OpenSNP target sample: [here](opensnp_benchmark_crosspop.html)
 
 <br/>
 
diff --git a/docs/CrossPop_summary.html b/docs/CrossPop_summary.html
index c47710a1..ce34a462 100644
--- a/docs/CrossPop_summary.html
+++ b/docs/CrossPop_summary.html
@@ -522,6 +522,9 @@ <h1>Additional resources</h1>
 <ul>
 <li>Code used to conduct the study: <a
 href="CrossPop.html">here</a></li>
+<li>Example applying LEOPARD + QuickPRS to height GWAS from 5
+populations and then evaluated in OpenSNP target sample: <a
+href="opensnp_benchmark_crosspop.html">here</a></li>
 </ul>
 <p><br/></p>
 <p><br/></p>
diff --git a/docs/Images/OpenSNP/cross_pop_eur.png b/docs/Images/OpenSNP/cross_pop_eur.png
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diff --git a/docs/more_index.Rmd b/docs/more_index.Rmd
index 5748bfce..c47a1fe9 100644
--- a/docs/more_index.Rmd
+++ b/docs/more_index.Rmd
@@ -42,4 +42,5 @@ output:
 - Cross-Ancestry Polygenic Prediction
   - Summary - <a href="CrossPop_summary.html" class="inline_button">Link</a>
   - Code - <a href="CrossPop.html" class="inline_button">Link</a>
+  - Application of multi-source methods to height GWAS and tested in OpenSNP target sample - <a href="opensnp_benchmark_crosspop.html" class="inline_button">Link</a>
 
diff --git a/docs/more_index.html b/docs/more_index.html
index 275135f7..0be2de73 100644
--- a/docs/more_index.html
+++ b/docs/more_index.html
@@ -445,6 +445,9 @@ <h1>Research</h1>
 <li>Summary -
 <a href="CrossPop_summary.html" class="inline_button">Link</a></li>
 <li>Code - <a href="CrossPop.html" class="inline_button">Link</a></li>
+<li>Application of multi-source methods to height GWAS and tested in
+OpenSNP target sample -
+<a href="opensnp_benchmark_crosspop.html" class="inline_button">Link</a></li>
 </ul></li>
 </ul>
 </div>
diff --git a/docs/opensnp_benchmark_crosspop.Rmd b/docs/opensnp_benchmark_crosspop.Rmd
new file mode 100644
index 00000000..b9b1810a
--- /dev/null
+++ b/docs/opensnp_benchmark_crosspop.Rmd
@@ -0,0 +1,294 @@
+---
+title: OpenSNP Benchmark
+output: 
+  html_document:
+    theme: cosmo
+    toc: true
+    toc_float: true
+    toc_depth: 2
+    css: styles/styles.css
+    includes:
+      in_header: header.html
+      after_body: footer.html
+
+---
+
+```{r setup, include=FALSE}
+knitr::opts_chunk$set(eval = FALSE)
+```
+
+***
+
+This document will test multi-source PGS methods using height GWAS with OpenSNP as the target sample. A previous demonstration of GenoPred using the OpenSNP data is shown [here](opensnp_benchmark.html), which shows some additional information regarding where the OpenSNP data was downloaded.
+
+***
+
+# Download the sumstats
+
+We will use height GWAS sumstats from the Yengo paper, including UKB and for all populations.
+
+<details><summary>Show code</summary>
+
+```{bash}
+# These are from the Yengo 2022 paper
+mkdir -p /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eur.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245992/GCST90245992_buildGRCh37.tsv
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eas.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245991/GCST90245991_buildGRCh37.tsv
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_afr.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245989/GCST90245989_buildGRCh37.tsv
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_amr.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245993/GCST90245993_buildGRCh37.tsv
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_sas.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245994/GCST90245994_buildGRCh37.tsv
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_all.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245990/GCST90245990_buildGRCh37.tsv
+```
+
+</details>
+
+
+***
+
+# Create configuration
+
+We want to compare PGS derived using each GWAS seperately, PGS derived using GWAS from multiple populations.
+
+We will only use QuickPRS, with LEOPARD to linearly combine across GWAS populations. Neither of these approaches require individual-level data to tune the PGS.
+
+<details><summary>Show code</summary>
+
+```{r}
+
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline')
+
+# Create gwas_list
+gwas_list <- NULL
+
+for(i in c('EUR','EAS','CSA','AMR','AFR')){
+  gwas_list <- rbind(gwas_list, data.table(
+    name = paste0('yengo_', tolower(i)),
+    path = paste0(
+      '/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_',
+      ifelse(i == 'CSA', 'sas', tolower(i)),
+      '.txt'
+    ),
+    population = i,
+    n = NA,
+    sampling = NA,
+    prevalence = NA,
+    mean = NA,
+    sd = NA,
+    label = paste0('"Yengo 2022 Height ', i,'\"')))
+}
+
+# Insert GWAS from all, and assign EUR as the population
+gwas_list <- rbind(gwas_list, data.table(
+  name = 'yengo_all',
+  path = '/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_all.txt',
+  population = 'EUR',
+  n = NA,
+  sampling = NA,
+  prevalence = NA,
+  mean = NA,
+  sd = NA,
+  label = "\"Yengo 2022 Height All\""))
+
+write.table(gwas_list, 'misc/opensnp/gwas_list_cross_pop.txt', col.names = T, row.names = F, quote = F, sep = ' ')
+
+# Create gwas_groups file
+gwas_groups <- NULL
+gwas_groups <- rbind(gwas_groups, data.frame(
+  name = 'height_eur_eas',
+  gwas = 'yengo_eur,yengo_eas',
+  label = "\"Yengo 2022 Height EUR+EAS\""
+))
+
+gwas_groups <- rbind(gwas_groups, data.frame(
+  name = 'height_eur_eas_afr',
+  gwas = 'yengo_eur,yengo_eas,yengo_afr',
+  label = "\"Yengo 2022 Height EUR+EAS+AFR\""
+))
+
+gwas_groups <- rbind(gwas_groups, data.frame(
+  name = 'height_eur_eas_afr_csa',
+  gwas = 'yengo_eur,yengo_eas,yengo_afr,yengo_csa',
+  label = "\"Yengo 2022 Height EUR+EAS+AFR+CSA\""
+))
+
+gwas_groups <- rbind(gwas_groups, data.frame(
+  name = 'height_eur_eas_afr_csa_amr',
+  gwas = 'yengo_eur,yengo_eas,yengo_afr,yengo_csa,yengo_amr',
+  label = "\"Yengo 2022 Height EUR+EAS+AFR+CSA+AMR\""
+))
+
+write.table(gwas_groups, 'misc/opensnp/gwas_groups.txt', col.names = T, row.names = F, quote = F, sep = ' ')
+
+# Create config file
+config <- readLines('misc/opensnp/config.yaml')
+
+config[grepl('^config_file:', config)]<- 'config_file: misc/opensnp/config_cross_pop.yaml'
+config <- config[!grepl('^score_list:', config)]
+config[grepl('^outdir:', config)]<- 'outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_2'
+config[grepl('^pgs_methods:', config)]<- "pgs_methods: ['quickprs']"
+config[grepl('^gwas_list:', config)]<- "gwas_list: misc/opensnp/gwas_list_cross_pop.txt"
+config<-c(config, 'gwas_groups: misc/opensnp/gwas_groups.txt')
+config<-c(config, "leopard_methods: ['quickprs']")
+config<-c(config, "pgs_scaling: ['continuous', 'discrete']")
+
+write.table(config, 'misc/opensnp/config_cross_pop.yaml', col.names = F, row.names = F, quote = F)
+
+```
+
+<div class="note-box">
+
+**Note:** The original configfile for OpenSNP, and the target_list that it refers to, were created in the OpenSNP benchmark document ([here](opensnp_benchmark.html)).
+
+</div>
+
+</details>
+
+***
+
+# Run pipeline
+
+```{bash}
+snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_cross_pop.yaml output_all
+```
+
+***
+
+# Evaluate PGS
+
+<details><summary>Show code</summary>
+
+```{r}
+# Test correlation between PGS and observed height
+
+setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+# Read in pheno data
+pheno <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno/height.txt')
+
+# Read in PGS
+pgs <- read_pgs(config = 'misc/opensnp/config_cross_pop.yaml', name = 'opensnp')$opensnp
+
+# Read in ancestry
+ancestry <- read_ancestry(config = 'misc/opensnp/config_cross_pop_gw.yaml', name = 'opensnp')
+
+# Estimate correlation between pheno and pgs
+cor <- NULL
+for(pop_i in names(pgs)){
+  for(gwas_i in names(pgs[[pop_i]])){
+    for(pgs_method_i in names(pgs[[pop_i]][[gwas_i]])){
+      pgs_i <- pgs[[pop_i]][[gwas_i]][[pgs_method_i]]
+      pheno_pgs<-merge(pheno, pgs_i, by = c('FID','IID'))
+      
+      for(model_i in names(pgs_i)[-1:-2]){
+        y <- scale(pheno_pgs$height)
+        x <- scale(pheno_pgs[[model_i]])
+        
+        if(all(is.na(x))){
+          next
+        }
+        
+        coef_i <- coef(summary(mod <- lm(y ~ x)))
+        
+        tmp <- data.table(
+          pop = pop_i,
+          gwas = gwas_i,
+          pgs_method = pgs_method_i,
+          name = model_i,
+          r = coef_i[2,1],
+          se = coef_i[2,2],
+          p = coef_i[2,4],
+          n = nobs(mod))
+      cor <- rbind(cor, tmp)
+      
+      }
+    }
+  }
+}
+
+library(ggplot2)
+library(cowplot)
+ggplot(cor, aes(x = name, y = r, group = gwas)) +
+  geom_bar(stat = "identity", position = position_dodge2(preserve = "single"), width = 0.7) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  facet_wrap(. ~ pop, scales = 'free') +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12))
+
+ggplot(cor[cor$pop %in% c('EUR'),], aes(x = name, y = r, group = gwas)) +
+  geom_bar(stat = "identity", position = position_dodge2(preserve = "single"), width = 0.7) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  facet_wrap(. ~ pop) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12))
+
+tmp <- cor[cor$pop %in% c('EUR') & grepl('yengo|EUR_weighted', cor$name) ,]
+tmp$name <- gsub('yengo_|_quickprs|height_|_targ.*', '', tmp$name)
+tmp$name <- gsub('_','+',tmp$name)
+tmp$name <- toupper(tmp$name)
+tmp$name[tmp$name == 'ALL'] <- "ALL (META)"
+tmp$name <- factor(tmp$name, levels = tmp$name[order(tmp$r)])
+tmp$pop <- 'Target = EUR'
+
+png('~/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP/cross_pop_eur.png', res = 200, width = 1500, height = 1500, units = 'px')
+ggplot(tmp, aes(x = name, y = r, fill = gwas)) +
+  geom_bar(stat = "identity", position = position_dodge2(preserve = "single"), width = 0.7) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  geom_text(
+    aes(y = r + se + 0.05,label = round(r, 3)),
+    position = position_dodge2(width = 0.7, preserve = "single"),
+  ) +
+  labs(x = 'GWAS populations', y = "R (SE)") +
+  ylim(c(0,0.6)) +
+  facet_wrap(. ~ pop) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12),
+        legend.position = "none")
+dev.off()
+
+```
+
+</details>
+
+***
+
+# Result
+
+EUR is the only population of sufficient sample size to accurately estimate the performance of the PGS.
+
+Observations:
+
+- The PGS based on EUR is the best single-source PGS
+- The PGS based on meta-analysis of each GWAS, with a EUR LD reference performs worse than the PGS based on EUR GWAS alone.
+- The PGS based on GWAS from multiple populations (multi-source) improved prediction slightly over the PGS based on EUR GWAS alone (single-source), with the prediction improving as more populations were considered.
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 75%;">
+![Correlation between PGS and height](Images/OpenSNP/cross_pop_eur.png)
+</div>
+</div>
+
+
diff --git a/docs/opensnp_benchmark_crosspop.html b/docs/opensnp_benchmark_crosspop.html
new file mode 100644
index 00000000..cc9ad2a7
--- /dev/null
+++ b/docs/opensnp_benchmark_crosspop.html
@@ -0,0 +1,823 @@
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+
+<h1 class="title toc-ignore">OpenSNP Benchmark</h1>
+
+</div>
+
+
+<hr />
+<p>This document will test multi-source PGS methods using height GWAS
+with OpenSNP as the target sample. A previous demonstration of GenoPred
+using the OpenSNP data is shown <a
+href="opensnp_benchmark.html">here</a>, which shows some additional
+information regarding where the OpenSNP data was downloaded.</p>
+<hr />
+<div id="download-the-sumstats" class="section level1">
+<h1>Download the sumstats</h1>
+<p>We will use height GWAS sumstats from the Yengo paper, including UKB
+and for all populations.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="bash"><code># These are from the Yengo 2022 paper
+mkdir -p /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eur.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245992/GCST90245992_buildGRCh37.tsv
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eas.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245991/GCST90245991_buildGRCh37.tsv
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_afr.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245989/GCST90245989_buildGRCh37.tsv
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_amr.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245993/GCST90245993_buildGRCh37.tsv
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_sas.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245994/GCST90245994_buildGRCh37.tsv
+wget --no-check-certificate -O /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_all.txt https://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90245001-GCST90246000/GCST90245990/GCST90245990_buildGRCh37.tsv</code></pre>
+</details>
+<hr />
+</div>
+<div id="create-configuration" class="section level1">
+<h1>Create configuration</h1>
+<p>We want to compare PGS derived using each GWAS seperately, PGS
+derived using GWAS from multiple populations.</p>
+<p>We will only use QuickPRS, with LEOPARD to linearly combine across
+GWAS populations. Neither of these approaches require individual-level
+data to tune the PGS.</p>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code>setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline&#39;)
+
+# Create gwas_list
+gwas_list &lt;- NULL
+
+for(i in c(&#39;EUR&#39;,&#39;EAS&#39;,&#39;CSA&#39;,&#39;AMR&#39;,&#39;AFR&#39;)){
+  gwas_list &lt;- rbind(gwas_list, data.table(
+    name = paste0(&#39;yengo_&#39;, tolower(i)),
+    path = paste0(
+      &#39;/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_&#39;,
+      ifelse(i == &#39;CSA&#39;, &#39;sas&#39;, tolower(i)),
+      &#39;.txt&#39;
+    ),
+    population = i,
+    n = NA,
+    sampling = NA,
+    prevalence = NA,
+    mean = NA,
+    sd = NA,
+    label = paste0(&#39;&quot;Yengo 2022 Height &#39;, i,&#39;\&quot;&#39;)))
+}
+
+# Insert GWAS from all, and assign EUR as the population
+gwas_list &lt;- rbind(gwas_list, data.table(
+  name = &#39;yengo_all&#39;,
+  path = &#39;/users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_all.txt&#39;,
+  population = &#39;EUR&#39;,
+  n = NA,
+  sampling = NA,
+  prevalence = NA,
+  mean = NA,
+  sd = NA,
+  label = &quot;\&quot;Yengo 2022 Height All\&quot;&quot;))
+
+write.table(gwas_list, &#39;misc/opensnp/gwas_list_cross_pop.txt&#39;, col.names = T, row.names = F, quote = F, sep = &#39; &#39;)
+
+# Create gwas_groups file
+gwas_groups &lt;- NULL
+gwas_groups &lt;- rbind(gwas_groups, data.frame(
+  name = &#39;height_eur_eas&#39;,
+  gwas = &#39;yengo_eur,yengo_eas&#39;,
+  label = &quot;\&quot;Yengo 2022 Height EUR+EAS\&quot;&quot;
+))
+
+gwas_groups &lt;- rbind(gwas_groups, data.frame(
+  name = &#39;height_eur_eas_afr&#39;,
+  gwas = &#39;yengo_eur,yengo_eas,yengo_afr&#39;,
+  label = &quot;\&quot;Yengo 2022 Height EUR+EAS+AFR\&quot;&quot;
+))
+
+gwas_groups &lt;- rbind(gwas_groups, data.frame(
+  name = &#39;height_eur_eas_afr_csa&#39;,
+  gwas = &#39;yengo_eur,yengo_eas,yengo_afr,yengo_csa&#39;,
+  label = &quot;\&quot;Yengo 2022 Height EUR+EAS+AFR+CSA\&quot;&quot;
+))
+
+gwas_groups &lt;- rbind(gwas_groups, data.frame(
+  name = &#39;height_eur_eas_afr_csa_amr&#39;,
+  gwas = &#39;yengo_eur,yengo_eas,yengo_afr,yengo_csa,yengo_amr&#39;,
+  label = &quot;\&quot;Yengo 2022 Height EUR+EAS+AFR+CSA+AMR\&quot;&quot;
+))
+
+write.table(gwas_groups, &#39;misc/opensnp/gwas_groups.txt&#39;, col.names = T, row.names = F, quote = F, sep = &#39; &#39;)
+
+# Create config file
+config &lt;- readLines(&#39;misc/opensnp/config.yaml&#39;)
+
+config[grepl(&#39;^config_file:&#39;, config)]&lt;- &#39;config_file: misc/opensnp/config_cross_pop.yaml&#39;
+config &lt;- config[!grepl(&#39;^score_list:&#39;, config)]
+config[grepl(&#39;^outdir:&#39;, config)]&lt;- &#39;outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_2&#39;
+config[grepl(&#39;^pgs_methods:&#39;, config)]&lt;- &quot;pgs_methods: [&#39;quickprs&#39;]&quot;
+config[grepl(&#39;^gwas_list:&#39;, config)]&lt;- &quot;gwas_list: misc/opensnp/gwas_list_cross_pop.txt&quot;
+config&lt;-c(config, &#39;gwas_groups: misc/opensnp/gwas_groups.txt&#39;)
+config&lt;-c(config, &quot;leopard_methods: [&#39;quickprs&#39;]&quot;)
+config&lt;-c(config, &quot;pgs_scaling: [&#39;continuous&#39;, &#39;discrete&#39;]&quot;)
+
+write.table(config, &#39;misc/opensnp/config_cross_pop.yaml&#39;, col.names = F, row.names = F, quote = F)</code></pre>
+<div class="note-box">
+<p><strong>Note:</strong> The original configfile for OpenSNP, and the
+target_list that it refers to, were created in the OpenSNP benchmark
+document (<a href="opensnp_benchmark.html">here</a>).</p>
+</div>
+</details>
+<hr />
+</div>
+<div id="run-pipeline" class="section level1">
+<h1>Run pipeline</h1>
+<pre class="bash"><code>snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_cross_pop.yaml output_all</code></pre>
+<hr />
+</div>
+<div id="evaluate-pgs" class="section level1">
+<h1>Evaluate PGS</h1>
+<details>
+<summary>
+Show code
+</summary>
+<pre class="r"><code># Test correlation between PGS and observed height
+
+setwd(&#39;/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/&#39;)
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source(&#39;../functions/misc.R&#39;)
+source_all(&#39;../functions&#39;)
+
+# Read in pheno data
+pheno &lt;- fread(&#39;/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno/height.txt&#39;)
+
+# Read in PGS
+pgs &lt;- read_pgs(config = &#39;misc/opensnp/config_cross_pop.yaml&#39;, name = &#39;opensnp&#39;)$opensnp
+
+# Read in ancestry
+ancestry &lt;- read_ancestry(config = &#39;misc/opensnp/config_cross_pop_gw.yaml&#39;, name = &#39;opensnp&#39;)
+
+# Estimate correlation between pheno and pgs
+cor &lt;- NULL
+for(pop_i in names(pgs)){
+  for(gwas_i in names(pgs[[pop_i]])){
+    for(pgs_method_i in names(pgs[[pop_i]][[gwas_i]])){
+      pgs_i &lt;- pgs[[pop_i]][[gwas_i]][[pgs_method_i]]
+      pheno_pgs&lt;-merge(pheno, pgs_i, by = c(&#39;FID&#39;,&#39;IID&#39;))
+      
+      for(model_i in names(pgs_i)[-1:-2]){
+        y &lt;- scale(pheno_pgs$height)
+        x &lt;- scale(pheno_pgs[[model_i]])
+        
+        if(all(is.na(x))){
+          next
+        }
+        
+        coef_i &lt;- coef(summary(mod &lt;- lm(y ~ x)))
+        
+        tmp &lt;- data.table(
+          pop = pop_i,
+          gwas = gwas_i,
+          pgs_method = pgs_method_i,
+          name = model_i,
+          r = coef_i[2,1],
+          se = coef_i[2,2],
+          p = coef_i[2,4],
+          n = nobs(mod))
+      cor &lt;- rbind(cor, tmp)
+      
+      }
+    }
+  }
+}
+
+library(ggplot2)
+library(cowplot)
+ggplot(cor, aes(x = name, y = r, group = gwas)) +
+  geom_bar(stat = &quot;identity&quot;, position = position_dodge2(preserve = &quot;single&quot;), width = 0.7) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  facet_wrap(. ~ pop, scales = &#39;free&#39;) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12))
+
+ggplot(cor[cor$pop %in% c(&#39;EUR&#39;),], aes(x = name, y = r, group = gwas)) +
+  geom_bar(stat = &quot;identity&quot;, position = position_dodge2(preserve = &quot;single&quot;), width = 0.7) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  facet_wrap(. ~ pop) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12))
+
+tmp &lt;- cor[cor$pop %in% c(&#39;EUR&#39;) &amp; grepl(&#39;yengo|EUR_weighted&#39;, cor$name) ,]
+tmp$name &lt;- gsub(&#39;yengo_|_quickprs|height_|_targ.*&#39;, &#39;&#39;, tmp$name)
+tmp$name &lt;- gsub(&#39;_&#39;,&#39;+&#39;,tmp$name)
+tmp$name &lt;- toupper(tmp$name)
+tmp$name[tmp$name == &#39;ALL&#39;] &lt;- &quot;ALL (META)&quot;
+tmp$name &lt;- factor(tmp$name, levels = tmp$name[order(tmp$r)])
+tmp$pop &lt;- &#39;Target = EUR&#39;
+
+png(&#39;~/oliverpainfel/Software/MyGit/GenoPred/docs/Images/OpenSNP/cross_pop_eur.png&#39;, res = 200, width = 1500, height = 1500, units = &#39;px&#39;)
+ggplot(tmp, aes(x = name, y = r, fill = gwas)) +
+  geom_bar(stat = &quot;identity&quot;, position = position_dodge2(preserve = &quot;single&quot;), width = 0.7) +
+  geom_errorbar(
+    aes(ymin = r - se, ymax = r + se),
+    width = .2,
+    position = position_dodge(width = 0.7)
+  ) +
+  geom_text(
+    aes(y = r + se + 0.05,label = round(r, 3)),
+    position = position_dodge2(width = 0.7, preserve = &quot;single&quot;),
+  ) +
+  labs(x = &#39;GWAS populations&#39;, y = &quot;R (SE)&quot;) +
+  ylim(c(0,0.6)) +
+  facet_wrap(. ~ pop) +
+  theme_half_open() +
+  background_grid() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        plot.title = element_text(hjust = 0.5, size=12),
+        legend.position = &quot;none&quot;)
+dev.off()</code></pre>
+</details>
+<hr />
+</div>
+<div id="result" class="section level1">
+<h1>Result</h1>
+<p>EUR is the only population of sufficient sample size to accurately
+estimate the performance of the PGS.</p>
+<p>Observations:</p>
+<ul>
+<li>The PGS based on EUR is the best single-source PGS</li>
+<li>The PGS based on meta-analysis of each GWAS, with a EUR LD reference
+performs worse than the PGS based on EUR GWAS alone.</li>
+<li>The PGS based on GWAS from multiple populations (multi-source)
+improved prediction slightly over the PGS based on EUR GWAS alone
+(single-source), with the prediction improving as more populations were
+considered.</li>
+</ul>
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 75%;">
+<div class="figure">
+<img src="Images/OpenSNP/cross_pop_eur.png" alt="" />
+<p class="caption">Correlation between PGS and height</p>
+</div>
+</div>
+</div>
+</div>
+
+<!-- footer.html -->
+<hr/>
+
+<div class="centered-container">
+<div class="rounded-image-container" style="width: 500px;">
+<img src="Images/logo/sponsors.png">
+</div>
+</div>
+
+
+</div>
+</div>
+
+</div>
+
+<script>
+
+// add bootstrap table styles to pandoc tables
+function bootstrapStylePandocTables() {
+  $('tr.odd').parent('tbody').parent('table').addClass('table table-condensed');
+}
+$(document).ready(function () {
+  bootstrapStylePandocTables();
+});
+
+
+</script>
+
+<!-- tabsets -->
+
+<script>
+$(document).ready(function () {
+  window.buildTabsets("TOC");
+});
+
+$(document).ready(function () {
+  $('.tabset-dropdown > .nav-tabs > li').click(function () {
+    $(this).parent().toggleClass('nav-tabs-open');
+  });
+});
+</script>
+
+<!-- code folding -->
+
+<script>
+$(document).ready(function ()  {
+
+    // temporarily add toc-ignore selector to headers for the consistency with Pandoc
+    $('.unlisted.unnumbered').addClass('toc-ignore')
+
+    // move toc-ignore selectors from section div to header
+    $('div.section.toc-ignore')
+        .removeClass('toc-ignore')
+        .children('h1,h2,h3,h4,h5').addClass('toc-ignore');
+
+    // establish options
+    var options = {
+      selectors: "h1,h2",
+      theme: "bootstrap3",
+      context: '.toc-content',
+      hashGenerator: function (text) {
+        return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_');
+      },
+      ignoreSelector: ".toc-ignore",
+      scrollTo: 0
+    };
+    options.showAndHide = true;
+    options.smoothScroll = true;
+
+    // tocify
+    var toc = $("#TOC").tocify(options).data("toc-tocify");
+});
+</script>
+
+<!-- dynamically load mathjax for compatibility with self-contained -->
+<script>
+  (function () {
+    var script = document.createElement("script");
+    script.type = "text/javascript";
+    script.src  = "https://mathjax.rstudio.com/latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML";
+    document.getElementsByTagName("head")[0].appendChild(script);
+  })();
+</script>
+
+</body>
+</html>
diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 26fa141d..9d299b0c 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -1460,6 +1460,12 @@ Another multi-source PGS approach is TL-PRS, which tunes an existing PGS model b
 
 An alternative approach is to linearly combine population-specific PGS derived using single-source methods. GenoPred implements an approach called **LEOPARD+QuickPRS**, which estimates the optimal linear combination weights for population-specific PGS using summary statistics alone. Using the `leopard_methods` parameter in the `configfile`, the user can specify which single-source PGS should be linearly combined by the LEOPARD+QuickPRS method (for example, `leopard_methods: ['lassosum', 'dbslmm']`). LEOPARD weighting is only applied to the PGS from a given method that has been selected using pseudovalidation.
 
+<div class="note-box">
+
+**Note:** X-Wing and TL-PRS implementations in GenoPred are restricted to pairs of GWAS (in the gwas_groups config file), so groups of GWAS with >2 GWAS will not be analysed using these methods.
+
+</div>
+
 ***
 
 We have provided some test data to run the multi-source functionality within GenoPred. To run the pipeline with the test data, use the `example_input/config.multisource.yaml` file. In this configuration, we have added a `gwas_groups` file and specified the methods to be linearly combined via LEOPARD+QuickPRS using the LEOPARD+QuickPRS using the `leopard_methods` parameter.
@@ -1565,7 +1571,7 @@ By default, the GenoPred pipeline applies only the continuous correction for anc
 For example, to produce target PGS using both continuous and discrete correction, specify:
 
 ```{yaml}
-pgs_scaling: ['continuous', 'discrete]
+pgs_scaling: ['continuous', 'discrete']
 ```
 
 Or, to produce target PGS only the discrete correction, specify:
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index cf9cd719..e69bf4a9 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -437,6 +437,9 @@
     <li>
       <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
     </li>
+    <li>
+      <a href="CrossPop_summary.html">Cross-Ancestry Polygenic Prediction</a>
+    </li>
   </ul>
 </li>
 <li>
@@ -2304,6 +2307,12 @@ <h2>Multi-source PGS methods</h2>
 example, <code>leopard_methods: ['lassosum', 'dbslmm']</code>). LEOPARD
 weighting is only applied to the PGS from a given method that has been
 selected using pseudovalidation.</p>
+<div class="note-box">
+<p><strong>Note:</strong> X-Wing and TL-PRS implementations in GenoPred
+are restricted to pairs of GWAS (in the gwas_groups config file), so
+groups of GWAS with &gt;2 GWAS will not be analysed using these
+methods.</p>
+</div>
 <hr />
 <p>We have provided some test data to run the multi-source functionality
 within GenoPred. To run the pipeline with the test data, use the
@@ -2483,7 +2492,7 @@ <h3>Continuous Correction</h3>
 <code>continuous</code> and <code>discrete</code>.</p>
 <p>For example, to produce target PGS using both continuous and discrete
 correction, specify:</p>
-<pre class="yaml"><code>pgs_scaling: [&#39;continuous&#39;, &#39;discrete]</code></pre>
+<pre class="yaml"><code>pgs_scaling: [&#39;continuous&#39;, &#39;discrete&#39;]</code></pre>
 <p>Or, to produce target PGS only the discrete correction, specify:</p>
 <pre class="yaml"><code>pgs_scaling: [&#39;discrete&#39;]</code></pre>
 <hr />
diff --git a/docs/pipeline_technical.Rmd b/docs/pipeline_technical.Rmd
index c28848b6..b23d6811 100644
--- a/docs/pipeline_technical.Rmd
+++ b/docs/pipeline_technical.Rmd
@@ -256,9 +256,13 @@ kable(methods_table, "html", caption = "Table 2 provides a summary of each appro
 
 ```
 
-\* = Time per GWAS/population, using genome-wide data with 10 cores. See [here](crosspop.html#computational-resources) for full benchmark information.
+\* = Time per GWAS/population, using genome-wide data with 10 cores (default in GenoPred). See [here](crosspop.html#computational-resources) for full benchmark information.
 
-Note.This is the default behaviour of the GenoPred pipeline. Certain aspects can be modified using the configfile ([see here](pipeline_readme.html#additional-parameters)).
+<div class="note-box">
+
+**Note:** X-Wing and TL-PRS implementations in GenoPred are restricted to pairs of GWAS (in the gwas_groups config file), so groups of GWAS with >2 GWAS will not be analysed using these methods.
+
+</div>
 
 ***
 
@@ -270,13 +274,13 @@ PRS-CSx, an extension of PRS-CS, integrates GWAS summary statistics from multipl
 
 ##### X-Wing
 
-X-Wing is a polygenic scoring method designed to enhance genetic prediction accuracy across ancestrally diverse populations by leveraging a cross-population weighting framework. The method operates through a combination of several software tools: LOGODetect, PANTHER, and LEOPARD. GenoPred integrates this method using the script [pgs_methods/xwing.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/xwing.R). GenoPred downloads X-WING software from GitHub (e9fcc264266e0e884323311816bfe20053fd3f7a). The LD information derived from the 1KG dataset is provided by the developers of X-Wing. 
+X-Wing is a polygenic scoring method designed to enhance genetic prediction accuracy across ancestrally diverse populations by leveraging a cross-population weighting framework. The method operates through a combination of several software tools: LOGODetect, PANTHER, and LEOPARD. GenoPred integrates this method using the script [pgs_methods/xwing.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/xwing.R). GenoPred downloads X-WING software from GitHub (e9fcc264266e0e884323311816bfe20053fd3f7a). The LD information derived from the 1KG dataset is provided by the developers of X-Wing. The X-Wing implementation in GenoPred is restricted to pairs of GWAS (in the gwas_groups config file), so groups of GWAS with >2 GWAS will not be analysed.
 
 ***
 
 ##### TL-PRS
 
-TL-PRS (Transfer Learning Polygenic Risk Score) is implemented using the script [pgs_methods/tlprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/tlprs.R). The method combines summary statistics from target GWAS and a reference dataset via transfer learning, iteratively updating SNP effect sizes. GenoPred uses a modified version of TL-PRS to improve efficiency when being applied to many score files (https://github.com/opain/TLPRS). No modifications to the parameters used has been altered from the original version of TL-PRS (https://github.com/ZhangchenZhao/TLPRS/commit/6c8d8905f91ce902f0cca8d5465f9952b0b3e274). Multiple learning rates (factors of 1, 10, 100, 1000) are tested in an iterative approach with 15 steps per factor to update the SNP effect sizes. GenoPred uses the 1KG+HGDP reference for LD estimation, matching the population of the GWAS sample.
+TL-PRS (Transfer Learning Polygenic Risk Score) is implemented using the script [pgs_methods/tlprs.R](https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/tlprs.R). The method combines summary statistics from target GWAS and a reference dataset via transfer learning, iteratively updating SNP effect sizes. GenoPred uses a modified version of TL-PRS to improve efficiency when being applied to many score files (https://github.com/opain/TLPRS). No modifications to the parameters used has been altered from the original version of TL-PRS (https://github.com/ZhangchenZhao/TLPRS/commit/6c8d8905f91ce902f0cca8d5465f9952b0b3e274). Multiple learning rates (factors of 1, 10, 100, 1000) are tested in an iterative approach with 15 steps per factor to update the SNP effect sizes. GenoPred uses the 1KG+HGDP reference for LD estimation, matching the population of the GWAS sample. The TL-PRS implementation in GenoPred is restricted to pairs of GWAS (in the gwas_groups config file), so groups of GWAS with >2 GWAS will not be analysed.
 
 ***
 
diff --git a/docs/pipeline_technical.html b/docs/pipeline_technical.html
index 2bed8b6d..aa25f3d4 100644
--- a/docs/pipeline_technical.html
+++ b/docs/pipeline_technical.html
@@ -439,6 +439,9 @@
     <li>
       <a href="Absolute_Conversion.html">Translating Polygenic Scores onto the Absolute Scale</a>
     </li>
+    <li>
+      <a href="CrossPop_summary.html">Cross-Ancestry Polygenic Prediction</a>
+    </li>
   </ul>
 </li>
 <li>
@@ -1016,7 +1019,8 @@ <h4>LDpred2</h4>
 recommended GWAS quality control checks. GenoPred employs LDpred2’s
 precomputed LD matrices based on the European individuals from the UK
 Biobank, and it is applied only to GWAS based on a EUR population. The
-user can specify alternative LD reference data (<a
+user can specify alternative LD reference data to include additional
+populations (<a
 href="pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods">see
 here</a>). If the SNP-h2 estimated using LDSC is &lt;0.05, the
 SNP-heritability used by LDpred2 is set to 0.05.</p>
@@ -1079,7 +1083,11 @@ <h4>QuickPRS</h4>
 QuickPRS is a faster implementation of MegaPRS, sped up by using
 precomputed LD and annotation data. It only inlcudes the model selected
 using pseudovalidation. GenoPred employs the 1KG+HGDP reference for LD
-estimation, matching the population of the GWAS sample.</p>
+estimation, matching the population of the GWAS sample. The user can
+specify alternative LD reference data to include additional populations
+(<a
+href="pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods">see
+here</a>).</p>
 <hr />
 </div>
 <div id="sbayesr" class="section level4">
@@ -1091,7 +1099,10 @@ <h4>SBayesR</h4>
 v2.03 with LD matrices from the GCTB authors, calculated using European
 individuals in the UK Biobank, restricting its application to GWAS based
 on EUR populations. GenoPred uses the robust parameterisation option in
-SBayesR.</p>
+SBayesR. The user can specify alternative LD reference data to include
+additional populations (<a
+href="pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods">see
+here</a>).</p>
 <hr />
 </div>
 <div id="sbayesrc" class="section level4">
@@ -1102,9 +1113,10 @@ <h4>SBayesRC</h4>
 Bayesian framework for estimating SNP effects from GWAS summary
 statistics. By default SBayesRC will use the LD matrices released by the
 developers, restricted to GWAS from EUR, EAS, and AFR populations. The
-user can specify alternative LD reference data (<a
+user can specify alternative LD reference data to include additional
+populations (<a
 href="pipeline_readme.html#specifying-alternative-reference-data-for-pgs-methods">see
-here</a>)</p>
+here</a>).</p>
 <hr />
 </div>
 </div>
@@ -1249,12 +1261,16 @@ <h4>Jointly optimised</h4>
 </tbody>
 </table>
 </div>
-<p>* = Time per GWAS/population, using genome-wide data with 10 cores.
-See <a href="crosspop.html#computational-resources">here</a> for full
-benchmark information.</p>
-<p>Note.This is the default behaviour of the GenoPred pipeline. Certain
-aspects can be modified using the configfile (<a
-href="pipeline_readme.html#additional-parameters">see here</a>).</p>
+<p>* = Time per GWAS/population, using genome-wide data with 10 cores
+(default in GenoPred). See <a
+href="crosspop.html#computational-resources">here</a> for full benchmark
+information.</p>
+<div class="note-box">
+<p><strong>Note:</strong> X-Wing and TL-PRS implementations in GenoPred
+are restricted to pairs of GWAS (in the gwas_groups config file), so
+groups of GWAS with &gt;2 GWAS will not be analysed using these
+methods.</p>
+</div>
 <hr />
 <div id="prs-csx" class="section level5">
 <h5>PRS-CSx</h5>
@@ -1290,7 +1306,10 @@ <h5>X-Wing</h5>
 href="https://github.com/opain/GenoPred/blob/master/Scripts/pgs_methods/xwing.R">pgs_methods/xwing.R</a>.
 GenoPred downloads X-WING software from GitHub
 (e9fcc264266e0e884323311816bfe20053fd3f7a). The LD information derived
-from the 1KG dataset is provided by the developers of X-Wing.</p>
+from the 1KG dataset is provided by the developers of X-Wing. The X-Wing
+implementation in GenoPred is restricted to pairs of GWAS (in the
+gwas_groups config file), so groups of GWAS with &gt;2 GWAS will not be
+analysed.</p>
 <hr />
 </div>
 <div id="tl-prs" class="section level5">
@@ -1310,7 +1329,9 @@ <h5>TL-PRS</h5>
 Multiple learning rates (factors of 1, 10, 100, 1000) are tested in an
 iterative approach with 15 steps per factor to update the SNP effect
 sizes. GenoPred uses the 1KG+HGDP reference for LD estimation, matching
-the population of the GWAS sample.</p>
+the population of the GWAS sample. The TL-PRS implementation in GenoPred
+is restricted to pairs of GWAS (in the gwas_groups config file), so
+groups of GWAS with &gt;2 GWAS will not be analysed.</p>
 <hr />
 </div>
 </div>
@@ -1327,7 +1348,7 @@ <h4>Independently optimised</h4>
 QuickPRS, to quickly and accurately estimate the optimal weights for
 linearly combining the population-specific PGS. These weights can be
 used to linearly combine population-specific PGS from single-source PGS
-method. We decribe and evaluate this process in detail in <a
+method. We describe and evaluate this process in detail in <a
 href="https://doi.org/10.1101/2025.03.27.25324773">this paper</a>.</p>
 <p>The script used to estimate the weights for population-specific PGS
 is <a
diff --git a/docs/prep_quickprs_ref.Rmd b/docs/prep_quickprs_ref.Rmd
index 396ed363..e83acd95 100644
--- a/docs/prep_quickprs_ref.Rmd
+++ b/docs/prep_quickprs_ref.Rmd
@@ -56,7 +56,7 @@ opt$ldak_highld<-'resources/data/ldak_highld/highld.txt'
 opt$ldak<-'/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/software/ldak5.2/ldak5.2.linux'
 opt$n_cores<-20
 
-for(pop in c('EUR','EAS','AFR')){
+for(pop in c('EUR','EAS','AFR','AMR','CSA','MID')){
   opt$output <- paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3/', pop, '/', pop)
   opt$output_dir <- paste0(dirname(opt$output),'/')
   system(paste0('mkdir -p ',opt$output_dir))
@@ -127,7 +127,7 @@ Only the correlations part needs to be derived using subset data
 Follow X-wing strategy first - Split into three parts
 
 ```{r}
-# conda activate .snakemake/conda/329e0288cb99508f5e6c50a0996b234c_
+# conda activate .snakemake/conda/2af04663624aee04f7a150a9d54c1cdf_
 # Load dependencies
 library(GenoUtils)
 library(data.table)
@@ -147,7 +147,7 @@ opt$ldak_highld<-'resources/data/ldak_highld/highld.txt'
 opt$ldak<-'/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/software/ldak5.2/ldak5.2.linux'
 opt$n_cores<-20
 
-for(pop in c('EUR','EAS','AFR')){
+for(pop in c('EUR','EAS','AFR','AMR','CSA','MID')){
   opt$output <- paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset/', pop, '/', pop)
   opt$output_dir <- paste0(dirname(opt$output),'/')
   system(paste0('mkdir -p ',opt$output_dir))
@@ -229,13 +229,20 @@ We should provide the quickprs reference using full sample, and split for LEOPAR
 
 ```{bash}
 cd /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3
-for pop in $(echo EUR EAS AFR); do
+for pop in $(echo EUR EAS AFR AMR CSA MID); do
   tar -czvf ldak_quickprs_hm3_${pop}.tar.gz ${pop}
 done
 
+for pop in $(echo EUR EAS AFR AMR CSA MID); do
+  ~/oliverpainfel/Software/gdrive files upload ldak_quickprs_hm3_${pop}.tar.gz
+done
+
 cd /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset
-for pop in $(echo EUR EAS AFR); do
+for pop in $(echo EUR EAS AFR AMR CSA MID); do
   tar -czvf ldak_quickprs_hm3_subset_${pop}.tar.gz ${pop}
 done
 
+for pop in $(echo EUR EAS AFR AMR CSA MID); do
+  ~/oliverpainfel/Software/gdrive files upload ldak_quickprs_hm3_subset_${pop}.tar.gz
+done
 ```
\ No newline at end of file
diff --git a/functions/pipeline.R b/functions/pipeline.R
index ce9d85be..06af977e 100644
--- a/functions/pipeline.R
+++ b/functions/pipeline.R
@@ -299,7 +299,7 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
   gwas_groups <- read_param(config = config, param = 'gwas_groups')
 
   # If pgs_method is multi-source, subset gwas_list to gwas in relevant group
-  if(grepl(paste0('^', pgs_group_methods, collapse = '|'), pgs_method)){
+  if(grepl(paste0('^', pgs_group_methods, '$', collapse = '|'), pgs_method) | grepl('_multi$', pgs_method)){
     gwas_list <- gwas_list[gwas_list$name %in% unlist(strsplit(gwas_groups$gwas[gwas_groups$name == gwas], ','))]
   }
 
@@ -397,6 +397,7 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
   if(pgs_method == 'prscsx'){
     pseudo_val <- 'META_phi_auto'
   }
+  
   if(pgs_method == 'xwing'){
     if(!is.null(target_pop) && target_pop == 'TRANS'){
       cat('No pseudovalidation for TRANS target population available for xwing.\n')
@@ -409,6 +410,7 @@ find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){
     }
     pseudo_val <- paste0('targ_', target_pop, '_weighted')
   }
+  
   if(grepl('_multi$', pgs_method)){
     if(!is.null(target_pop) && target_pop == 'TRANS'){
       cat('No pseudovalidation for TRANS target population available for xwing.\n')
diff --git a/pipeline/misc/cross_pop_test/cross_pop_test.Rmd b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
index d887a603..88baab39 100644
--- a/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
+++ b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd
@@ -71,9 +71,9 @@ source_all('../functions')
 pheno <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/processed/pheno/height.txt')
 
 # Read in PGS
-pgs <- read_pgs(config = 'misc/opensnp/config_cross_pop_gw.yaml', name = 'opensnp')$opensnp
+pgs <- read_pgs(config = 'misc/opensnp/config_cross_pop.yaml', name = 'opensnp')$opensnp
 
-# Read in PGS
+# Read in ancestry
 ancestry <- read_ancestry(config = 'misc/opensnp/config_cross_pop_gw.yaml', name = 'opensnp')
 
 # Estimate correlation between pheno and pgs
diff --git a/pipeline/misc/opensnp/config_cross_pop.yaml b/pipeline/misc/opensnp/config_cross_pop.yaml
index dddf6ba1..160d241c 100644
--- a/pipeline/misc/opensnp/config_cross_pop.yaml
+++ b/pipeline/misc/opensnp/config_cross_pop.yaml
@@ -1,10 +1,9 @@
-outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_1
+outdir: /users/k1806347/oliverpainfel/Data/OpenSNP/GenoPred/test_cross_pop_2
 config_file: misc/opensnp/config_cross_pop.yaml
-gwas_list: misc/opensnp/gwas_list.txt
+gwas_list: misc/opensnp/gwas_list_cross_pop.txt
 target_list: misc/opensnp/target_list.txt
-pgs_methods: ['ptclump', 'megaprs','xwing','quickprs','quickprs_multi']
-testing: chr22
-gwas_groups: misc/cross_pop_test/gwas_groups.txt
-cores_prep_pgs: 10 # Set to 20 when running xwing
-quickprs_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3
-quickprs_multi_ldref: /users/k1806347/oliverpainfel/Data/hgdp_1kg/quickprs/hm3_subset
\ No newline at end of file
+pgs_methods: ['quickprs']
+testing: NA
+gwas_groups: misc/opensnp/gwas_groups.txt
+leopard_methods: ['quickprs']
+pgs_scaling: ['continuous', 'discrete']
diff --git a/pipeline/misc/opensnp/gwas_groups.txt b/pipeline/misc/opensnp/gwas_groups.txt
index 2cac132c..312d71d2 100644
--- a/pipeline/misc/opensnp/gwas_groups.txt
+++ b/pipeline/misc/opensnp/gwas_groups.txt
@@ -1,2 +1,5 @@
 name gwas label
-height yengo_eur,yengo_eas "Yengo 2022 Height EUR+EAS"
+height_eur_eas yengo_eur,yengo_eas "Yengo 2022 Height EUR+EAS"
+height_eur_eas_afr yengo_eur,yengo_eas,yengo_afr "Yengo 2022 Height EUR+EAS+AFR"
+height_eur_eas_afr_csa yengo_eur,yengo_eas,yengo_afr,yengo_csa "Yengo 2022 Height EUR+EAS+AFR+CSA"
+height_eur_eas_afr_csa_amr yengo_eur,yengo_eas,yengo_afr,yengo_csa,yengo_amr "Yengo 2022 Height EUR+EAS+AFR+CSA+AMR"
diff --git a/pipeline/misc/opensnp/gwas_list_cross_pop.txt b/pipeline/misc/opensnp/gwas_list_cross_pop.txt
new file mode 100644
index 00000000..ee2fa3ab
--- /dev/null
+++ b/pipeline/misc/opensnp/gwas_list_cross_pop.txt
@@ -0,0 +1,7 @@
+name path population n sampling prevalence mean sd label
+yengo_eur /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eur.txt EUR NA NA NA NA NA "Yengo 2022 Height EUR"
+yengo_eas /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_eas.txt EAS NA NA NA NA NA "Yengo 2022 Height EAS"
+yengo_csa /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_sas.txt CSA NA NA NA NA NA "Yengo 2022 Height CSA"
+yengo_amr /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_amr.txt AMR NA NA NA NA NA "Yengo 2022 Height AMR"
+yengo_afr /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_afr.txt AFR NA NA NA NA NA "Yengo 2022 Height AFR"
+yengo_all /users/k1806347/oliverpainfel/Data/GWAS_sumstats/opensnp_test/yengo_2022_height_all.txt EUR NA NA NA NA NA "Yengo 2022 Height All"
diff --git a/pipeline/rules/dependencies.smk b/pipeline/rules/dependencies.smk
index 3c2c09f0..d3571dff 100644
--- a/pipeline/rules/dependencies.smk
+++ b/pipeline/rules/dependencies.smk
@@ -319,12 +319,12 @@ if (config["leopard_methods"] and config["leopard_methods"] != "NA") or "quickpr
     quickprs_ldref=f"{resdir}/data/quickprs"
     
     # Check if gwas_list contains invalid populations
-    valid_pops = {'EUR', 'EAS', 'AFR'}
+    valid_pops = {'EUR', 'EAS', 'AFR', 'CSA', 'AMR', 'MID'}
     invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
   
     if invalid_pops:
       raise ValueError(
-        f"Default quickprs reference data is only available for EUR, EAS, and AFR populations. For other populations, please provide your own quickprs reference data using the quickprs_ldref parameter."
+        f"Default quickprs reference data is only available for EUR, EAS, AFR, CSA, AMR, and MID populations. For other populations, please provide your own quickprs reference data using the quickprs_ldref parameter."
       )
   else:
     quickprs_ldref=config['quickprs_ldref']
@@ -344,12 +344,12 @@ if (config["leopard_methods"] and config["leopard_methods"] != "NA"):
     quickprs_multi_ldref=f"{resdir}/data/quickprs_leopard"
     
     # Check if gwas_list contains invalid populations
-    valid_pops = {'EUR', 'EAS', 'AFR'}
+    valid_pops = {'EUR', 'EAS', 'AFR', 'CSA', 'AMR', 'MID'}
     invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
   
     if invalid_pops:
       raise ValueError(
-        f"Default LEOPARD+QuickPRS reference data is only available for EUR, EAS, and AFR populations. For other populations, please provide your own LEOPARD+QuickPRS reference data using the quickprs_multi_ldref parameter."
+        f"Default LEOPARD+QuickPRS reference data is only available for EUR, EAS, AFR, CSA, AMR, and MID populations. For other populations, please provide your own LEOPARD+QuickPRS reference data using the quickprs_multi_ldref parameter."
       )
   else:
     quickprs_multi_ldref=config['quickprs_multi_ldref']
@@ -369,29 +369,30 @@ if (config["leopard_methods"] and config["leopard_methods"] != "NA"):
         raise FileNotFoundError(f"The following quickprs_multi reference data are missing: {', '.join(missing_files)}")
 
 # Set sbayesrc reference path
-if config['sbayesrc_ldref'] == 'NA':
-  sbayesrc_ldref=f"{resdir}/data/sbayesrc_ref"
-
-  # Check if gwas_list contains invalid populations
-  valid_pops = {'EUR', 'EAS', 'AFR'}
-  invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
-
-  if invalid_pops:
-    raise ValueError(
-      f"Default sbayesrc reference data is only available for EUR, EAS, and AFR populations. For other populations, please provide your own sbayesrc reference data using the sbayesrc_ldref parameter."
-    )
-else:
-  sbayesrc_ldref=config['sbayesrc_ldref']
-
-  # Check the sbayesrc ldref data is present for the required populations in the gwas_list
-  if 'sbayesrc' in config['pgs_methods']:
-    for pop in gwas_list_df['population'].unique():
-      path = f"{sbayesrc_ldref}/{pop}"
-      # Check if required files exists
-      cors_file = os.path.join(path, f"ldm.info")
-      if not os.path.exists(cors_file):
-        print(f"File not found: {cors_file}")
-        raise FileNotFoundError(f"Required file not found: {cors_file}. sbayesrc reference data must include ldm.info for all populations.")
+if "sbayesrc" in config["pgs_methods"]:
+  if config['sbayesrc_ldref'] == 'NA':
+    sbayesrc_ldref=f"{resdir}/data/sbayesrc_ref"
+  
+    # Check if gwas_list contains invalid populations
+    valid_pops = {'EUR', 'EAS', 'AFR'}
+    invalid_pops = set(gwas_list_df['population'].unique()) - valid_pops
+  
+    if invalid_pops:
+      raise ValueError(
+        f"Default sbayesrc reference data is only available for EUR, EAS, and AFR populations. For other populations, please provide your own sbayesrc reference data using the sbayesrc_ldref parameter."
+      )
+  else:
+    sbayesrc_ldref=config['sbayesrc_ldref']
+  
+    # Check the sbayesrc ldref data is present for the required populations in the gwas_list
+    if 'sbayesrc' in config['pgs_methods']:
+      for pop in gwas_list_df['population'].unique():
+        path = f"{sbayesrc_ldref}/{pop}"
+        # Check if required files exists
+        cors_file = os.path.join(path, f"ldm.info")
+        if not os.path.exists(cors_file):
+          print(f"File not found: {cors_file}")
+          raise FileNotFoundError(f"Required file not found: {cors_file}. sbayesrc reference data must include ldm.info for all populations.")
 
 ####
 # Check reference data
@@ -1100,7 +1101,10 @@ rule download_ldak5_2:
 quickprs_ref_gdrive = {
     'EUR': '10fuqn6X23dA9WKjQd9xs7xUDiFjTwfz9',
     'EAS': '1m1OI9HpHbVcX88YvtIt80-1zonSWrZP_',
-    'AFR': '11NoeBLOC-YsxrnRPa0TOPsxywZXCWbP3'
+    'AFR': '11NoeBLOC-YsxrnRPa0TOPsxywZXCWbP3',
+    'CSA': '10ENLyjnMNndBM8NtXy-BVmroP5m4qgtg',
+    'AMR': '1bmnbWPw8MzVwFYw9F4TcGOVxxay20uOS',
+    'MID': '16MuxjIi1Eb_kyf9HZgpBwRA1cbP_yE5U'
 }
 
 rule download_quickprs_ref:
@@ -1131,7 +1135,10 @@ rule download_quickprs_ref_all:
 quickprs_leopard_ref_gdrive = {
     'EUR': '1basMTYv6VEIRDZ3qRdJ04hlVwNmzVpmU',
     'EAS': '1OGELjphyPbe9Qu9ZjRavhhnM9REuUhoc',
-    'AFR': '1fWQ77dYKaYIcHJFLii-0Aic_9F96VnQK'
+    'AFR': '1fWQ77dYKaYIcHJFLii-0Aic_9F96VnQK',
+    'CSA': '1e-zCjT7gCdRobSwUvCepDkl-K8Y10ECj',
+    'AMR': '12a6sxe338eX3hD6Q7FvIobIni_TxlDTY',
+    'MID': '1nTqb8FTPlMFBN66dGZyteN4n1SIj1YRC'
 }
 
 rule download_quickprs_leopard_ref:

From 12f729db0b4d96e4f1cce3f6e963f0e2c501c790 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 13 Jun 2025 21:07:14 +0100
Subject: [PATCH 152/153] Updated docs

---
 docs/pipeline_readme.Rmd  | 12 +++--
 docs/pipeline_readme.html | 93 ++++++++++++++++++++++++++++++++++++---
 2 files changed, 94 insertions(+), 11 deletions(-)

diff --git a/docs/pipeline_readme.Rmd b/docs/pipeline_readme.Rmd
index 72903175..45885cf5 100644
--- a/docs/pipeline_readme.Rmd
+++ b/docs/pipeline_readme.Rmd
@@ -39,15 +39,19 @@ For a technical details of the GenoPred pipeline, click [here](pipeline_technica
 
 **Citations**
 
-Please cite our preprint when using the pipeline:
+Please cite our publication when using the pipeline:
 
-- "Pain, O. et al. "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." MedRxiv 2024. https://doi.org/10.1101/2024.06.12.24308843
+- "Pain, O. et al. "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." Bioinformatics (2024). https://doi.org/10.1093/bioinformatics/btae551
+
+If using multi-source PGS methods, please also cite our study describing their evaluation and implementation within GenoPred:
+
+- "Pain, O. "Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations." MedRxiv (2026). https://doi.org/10.1101/2025.03.27.25324773
 
 If relevant, please also cite our paper comparing polygenic scoring methods and describing the reference-standardised approach:
 
-- Pain, O. et al. "Evaluation of polygenic prediction methodology within a reference-standardized framework." PLoS genetics. https://doi.org/10.1371/journal.pgen.1009021
+- Pain, O. et al. "Evaluation of polygenic prediction methodology within a reference-standardized framework." PLoS genetics (2024). https://doi.org/10.1371/journal.pgen.1009021
 
-Please also cite the relevant studies for the tools and data used by the GenoPred pipeline.
+Please also remember to cite the relevant studies for the tools and data used by the GenoPred pipeline.
 
 ***
 
diff --git a/docs/pipeline_readme.html b/docs/pipeline_readme.html
index fb8aa65e..13202138 100644
--- a/docs/pipeline_readme.html
+++ b/docs/pipeline_readme.html
@@ -78,6 +78,41 @@
   gtag('config', 'G-YR18ZB3PR3');
 </script>
 
+<!-- Osano Cookie Consent -->
+<link rel="stylesheet" href="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.css" />
+<script src="https://cdn.jsdelivr.net/npm/cookieconsent@3/build/cookieconsent.min.js"></script>
+<script>
+window.addEventListener("load", function(){
+  window.cookieconsent.initialise({
+    palette: {
+      popup: { background: "#000" },
+      button: { background: "#f1d600" }
+    },
+    theme: "classic",
+    position: "bottom-right",
+    content: {
+      message: "We use a cookie for Google Analytics to understand how people use this site. This helps us improve GenoPred and demonstrate its impact. Please click 'Accept' to help us with this.",
+      dismiss: "Accept",
+      link: "Google's privacy info",
+      href: "https://policies.google.com/technologies/partner-sites"
+    },
+    onStatusChange: function(status) {
+      if (status === 'allow') {
+        // Now load Google Analytics only if user consents
+        var gtagScript = document.createElement('script');
+        gtagScript.setAttribute('async', '');
+        gtagScript.src = 'https://www.googletagmanager.com/gtag/js?id=G-YR18ZB3PR3';
+        document.head.appendChild(gtagScript);
+
+        window.dataLayer = window.dataLayer || [];
+        function gtag(){ dataLayer.push(arguments); }
+        gtag('js', new Date());
+        gtag('config', 'G-YR18ZB3PR3');
+      }
+    }
+  });
+});
+</script>
 
 <style type="text/css">
   code{white-space: pre-wrap;}
@@ -452,23 +487,31 @@ <h1 class="title toc-ignore">GenoPred Pipeline - Instructions</h1>
 GenoPred pipeline, click <a href="pipeline_technical.html">here</a>.</p>
 <hr />
 <p><strong>Citations</strong></p>
-<p>Please cite our preprint when using the pipeline:</p>
+<p>Please cite our publication when using the pipeline:</p>
 <ul>
 <li>“Pain, O. et al. ”The GenoPred Pipeline: A Comprehensive and
-Scalable Pipeline for Polygenic Scoring.” MedRxiv 2024. <a
-href="https://doi.org/10.1101/2024.06.12.24308843"
-class="uri">https://doi.org/10.1101/2024.06.12.24308843</a></li>
+Scalable Pipeline for Polygenic Scoring.” Bioinformatics (2024). <a
+href="https://doi.org/10.1093/bioinformatics/btae551"
+class="uri">https://doi.org/10.1093/bioinformatics/btae551</a></li>
+</ul>
+<p>If using multi-source PGS methods, please also cite our study
+describing their evaluation and implementation within GenoPred:</p>
+<ul>
+<li>“Pain, O.”Leveraging Global Genetics Resources to Enhance Polygenic
+Prediction Across Ancestrally Diverse Populations.” MedRxiv (2026). <a
+href="https://doi.org/10.1101/2025.03.27.25324773"
+class="uri">https://doi.org/10.1101/2025.03.27.25324773</a></li>
 </ul>
 <p>If relevant, please also cite our paper comparing polygenic scoring
 methods and describing the reference-standardised approach:</p>
 <ul>
 <li>Pain, O. et al. “Evaluation of polygenic prediction methodology
-within a reference-standardized framework.” PLoS genetics. <a
+within a reference-standardized framework.” PLoS genetics (2024). <a
 href="https://doi.org/10.1371/journal.pgen.1009021"
 class="uri">https://doi.org/10.1371/journal.pgen.1009021</a></li>
 </ul>
-<p>Please also cite the relevant studies for the tools and data used by
-the GenoPred pipeline.</p>
+<p>Please also remember to cite the relevant studies for the tools and
+data used by the GenoPred pipeline.</p>
 <hr />
 <div id="video-tutorials" class="section level1">
 <h1>Video tutorials</h1>
@@ -527,6 +570,7 @@ <h2>Step 1: Download GenoPred repository</h2>
 </div>
 <div id="step-2-create-conda-environment-for-pipeline"
 class="section level2">
+<<<<<<< HEAD
 <h2>Step 2: Create software environment for pipeline</h2>
 <p>Conda is a software environment management system that simplifies
 installing and managing dependencies. We recommend using Miniforge — a
@@ -549,6 +593,41 @@ <h2>Step 2: Create software environment for pipeline</h2>
 mamba is an available module. Using the centrally installed version of
 mamba may avoid issues down the road.</p>
 </div>
+=======
+<h2>Step 2: Create conda environment for pipeline</h2>
+<p>Conda is a software environment management system which is great way
+for easily downloading and storing software. We will use conda to create
+an environment that the GenoPred pipeline will run in.</p>
+<p>If you don’t already have conda installed, we will install it using
+miniconda.</p>
+<<<<<<< Updated upstream
+<pre class="bash"><code>wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
+sh Miniconda3-latest-Linux-x86_64.sh</code></pre>
+=======
+<pre class="bash"><code># Download and install Miniforge (for Linux)
+wget https://github.com/conda-forge/miniforge/releases/download/24.11.3-0/Miniforge3-24.11.3-0-Linux-x86_64.sh
+bash Miniforge3-24.11.3-0-Linux-x86_64.sh</code></pre>
+>>>>>>> Stashed changes
+<p>I would say <code>yes</code> to the default options. You may then
+need to refresh your workspace to initiate conda, by running
+<code>source ~/.bashrc</code>. You should see <code>(base)</code>
+written in the bottom left of the terminal. Once miniconda installation
+is complete, you can then delete the
+<code>Miniconda3-latest-Linux-x86_64.sh</code> file.</p>
+<p>Now create the conda environment based on the
+<code>GenoPred/pipeline/envs/pipeline.yaml</code> file. This will create
+an environment called <code>genopred</code> with some essential packages
+installed.</p>
+<<<<<<< Updated upstream
+<pre class="bash"><code>conda env create -f GenoPred/pipeline/envs/pipeline.yaml</code></pre>
+<p>Now activate the new <code>genopred</code> environment.</p>
+<pre class="bash"><code>conda activate genopred</code></pre>
+=======
+<pre class="bash"><code>mamba env create -f GenoPred/pipeline/envs/pipeline.yaml</code></pre>
+<p>Now activate the new <code>genopred</code> environment.</p>
+<pre class="bash"><code>mamba activate genopred</code></pre>
+>>>>>>> Stashed changes
+>>>>>>> 999a324 (Updated docs)
 <hr />
 </div>
 <div id="step-3-download-dependencies" class="section level2">

From 58af52daa0d9c9758d3eb02b0b002dbd10271657 Mon Sep 17 00:00:00 2001
From: Ollie <olliepain@yahoo.com>
Date: Fri, 13 Jun 2025 21:28:18 +0100
Subject: [PATCH 153/153] Updated dockerfile

---
 pipeline/misc/docker/Dockerfile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pipeline/misc/docker/Dockerfile b/pipeline/misc/docker/Dockerfile
index 5561ca87..124d9685 100644
--- a/pipeline/misc/docker/Dockerfile
+++ b/pipeline/misc/docker/Dockerfile
@@ -34,7 +34,7 @@ RUN /opt/mambaforge/bin/conda init bash && \
     . /opt/mambaforge/etc/profile.d/conda.sh && \
     conda activate genopred && \
     cd /tools/GenoPred/pipeline && \
-    snakemake --restart-times 3 -j1 --use-conda --conda-frontend mamba install_r_packages resources/software/pgscatalog_utils/download_pgscatalog_utils.done download_xwing_software && \
+    snakemake --restart-times 3 -j1 --use-conda --conda-frontend mamba install_r_packages resources/software/pgscatalog_utils/download_pgscatalog_utils.done resources/software/xwing/block_partition.txt && \
     mamba clean -a -y
 
 ENTRYPOINT ["/bin/bash"]