diff --git a/.github/workflows/run_quick_tests.yaml b/.github/workflows/run_quick_tests.yaml index 849024ff..9e6a06c4 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.GITHUB_TOKEN }} SIF_FILE: ${{ env.SIF_FILE }} BRANCH_NAME: ${{ env.BRANCH_NAME }} 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 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$output,'.log',sep=''), append = T) -cat('Analysis finished at',as.character(end.time),'\n') -cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n') -sink() diff --git a/Scripts/external_score_processor/external_score_processor.R b/Scripts/external_score_processor/external_score_processor.R index dc34f3b1..32c1026f 100644 --- a/Scripts/external_score_processor/external_score_processor.R +++ b/Scripts/external_score_processor/external_score_processor.R @@ -6,6 +6,8 @@ library("optparse") option_list = list( make_option("--ref_plink_chr", action="store", default=NULL, type='character', help="Path to per chromosome reference PLINK files [required]"), + make_option("--ref_pcs", action="store", default=NULL, type='character', + help="Reference PCs for continuous ancestry correction [optional]"), make_option("--pop_data", action="store", default=NULL, type='character', help="File containing the population code and location of the keep file [required]"), make_option("--plink2", action="store", default='plink2', type='character', @@ -266,17 +268,18 @@ if(nrow(targ_matched) < 0.75*n_snp_orig){ # Calculate scores in the full reference ref_pgs <- plink_score(pfile = opt$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$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA') } } diff --git a/Scripts/magma/magma_set_conditional.R b/Scripts/magma/magma_set_conditional.R new file mode 100644 index 00000000..ab3caff8 --- /dev/null +++ b/Scripts/magma/magma_set_conditional.R @@ -0,0 +1,140 @@ +#!/usr/bin/Rscript +# This script was written by Oliver Pain whilst at King's College London University. +start.time <- Sys.time() +suppressMessages(library("optparse")) + +option_list = list( + make_option("--gwas", action="store", default=NA, type='character', + help="GWAS ID [required]"), + make_option("--config", action="store", default=NA, type='character', + help="config file [required]") +) + +opt = parse_args(OptionParser(option_list=option_list)) + +# Load dependencies +library(GenoUtils) +library(data.table) +source('../functions/misc.R') +source_all('../functions') + +# Check required inputs +if(is.null(opt$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/model_builder/model_builder.R b/Scripts/model_builder/model_builder.R new file mode 100644 index 00000000..f9e1cf7c --- /dev/null +++ b/Scripts/model_builder/model_builder.R @@ -0,0 +1,593 @@ +#!/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_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]"), +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("--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("--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='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]") +) + +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$outcome)){ + stop('--outcome must be specified.\n') +} +if(is.null(opt$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$out,'.log') +log_header(log_file = log_file, opt = opt, script = 'model_builder.R', start.time = start.time) + +########### +# Read in predictors +########### +# We will create a table indicating which predictors belong to each group as well. + +predictors_file <- fread(opt$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(group = predictors_file$group[predfile], predictor = names(predictors[[predfile]])[-1]) + })) + + predictors <- Reduce(function(x, y) merge(x, y, by = "IID"), predictors) + + log_add(log_file = log_file, message = paste0('After merging the ', nrow(predictors_file), ' predictors files, ', ncol(predictors)-1, ' predictors remain.')) + log_add(log_file = log_file, message = paste0('After merging the ', nrow(predictors_file), ' predictors files, ', nrow(predictors), ' individuals remain.')) +} else { + predictors <- read_predictor(x = predictors_file$predictor[1], pred_miss = opt$pred_miss) + group_list <- data.table(group = predictors_file$group[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$predictor %in% names(predictors),] + +########### +# Create list of groups for downstream comparison +########### + +if(opt$compare_predictors){ + group_list <- + rbind(group_list, + data.table( + group = paste0(group_list$group, '.', group_list$predictor), + predictor = group_list$predictor + )) + log_add(log_file = log_file, message = 'Each predictor has been assigned to its own group.') +} + + +# Add a group containing all predictors +if(nrow(group_list) > 1 & opt$all_model){ + group_list <- + rbind(group_list, + data.table( + group = 'all', + predictor = unique(group_list$predictor) + )) + log_add(log_file = log_file, message = 'Group containing all predictors created.') +} + +log_add(log_file = log_file, message = paste0(length(unique(group_list$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)) + } + } else { + group_list_non_identical <- rbind( + group_list_non_identical, + group_list[group_list$group == i,] + ) + } +} +group_list <- group_list_non_identical + +# Calculate the number of predictors in each group +for(i in unique(group_list$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 +########### + +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'),'.')) + +############ +# Test association between outcome and each predictor +############ + +if(opt$assoc){ + # Initialise progress log + log_message <- 'Performing association analysis with each predictor... ' + progress_file <- initialise_progress(log_message = log_message, log_file = log_file) + + outcome_predictors_y <- outcome_predictors$outcome_var + outcome_predictors_x <- outcome_predictors[, -1:-2] + + assoc_res <- NULL + assoc_res <- foreach(i = 1:ncol(outcome_predictors_x), .combine=rbind) %dopar% { + + 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) + } + + 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) + ) + + } + + 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') + assoc_res$Liab_R2 <- h2l_R2( + opt$outcome_pop_prev, + assoc_res$obs_r2, + sum(outcome_predictors_y == 'CASE') / length(outcome_predictors_y) + ) + } + + # Write out the results + write.table(assoc_res, paste0(opt$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$out,'.assoc.txt.')) +} + +############ +# Prediction modelling +############ +# We will use elastic net model when groups contain more than one predictor, and a glm when the group contains only 1 predictor. +# Elastic net models will be derived and evaluated using nested cross validation, but glm will be evaluated using standard cross validation. +# We will store the predicted and observed values from the outer loops for each model, and then evaluate the models at the end + +# Split the sample into opt$n_outer_fold folds +set.seed(opt$seed) +d <- sample(1:nrow(outcome_predictors)) +train_ind <- createFolds(d, k = opt$n_outer_fold, returnTrain=TRUE) + +# Set seeds for internal loop of nested CV for elastic net +seeds <- fold_seeds(opt$n_outer_fold) + +# Create objects to store outputs +indep_pred_list <- list() + +#### +# Generate predictions using single best predictor from each group, identifying the best predictor using training data, and then evaluating in the test data +#### + +if(opt$top1){ + # Only run for groups containing more than 1 predictor + 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$group == top1_groups[i]] + cv_dat$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(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!')) +} + +### +# Generate predictions using single predictor groups +### + +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) + } + + # Derive and export final model using all data + if(opt$export_models){ + train_mod <- glm(as.formula(paste0('outcome_var ~ ', pred_name)), family=family, data=outcome_predictors) + + export_final_model(model = train_mod, + group = single_groups[i], + outdir = paste0(opt$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(single_groups)*100),'%')) + + # Output results + setNames(list(indep_pred), single_groups[i]) + } + + indep_pred_list <- c(indep_pred_list, single_indep_pred) + update_log_file(log_file = log_file, message = paste0(log_message, 'Done!')) +} + +### +# Generate predictions using elastic net model containing all predictors within each group +### +# Only apply to groups with more than one predictor +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$output_dir, + '/final_models')) + } + + # Update progress log + progress <- update_progress_file(progress_file) + update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(group_enet)*100),'%')) + + # Output results + enet_indep_pred[[group_enet[i]]] <- indep_pred + } + + indep_pred_list <- c(indep_pred_list, enet_indep_pred) + update_log_file(log_file = log_file, message = paste0(log_message, 'Done!')) +} + +################### +# Evaluate all models +################### + +# Initialise progress log +log_message <- 'Evaluating all models... ' +progress_file <- initialise_progress(log_message = log_message, log_file = log_file) + +pred_eval_all <- foreach(i = 1:length(indep_pred_list), .combine=rbind) %dopar% { + # Update progress log + progress <- update_progress_file(progress_file) + update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(indep_pred_list)*100),'%')) + + data.table( + Group = names(indep_pred_list)[i], + eval_pred( + obs = indep_pred_list[[i]]$obs, + pred = indep_pred_list[[i]]$pred, + family = family + ) + ) +} + +update_log_file(log_file = log_file, message = paste0(log_message, 'Done!')) + +# Write out the results +write.table(pred_eval_all, paste0(opt$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'))) diff --git a/Scripts/model_builder/model_builder_top1.R b/Scripts/model_builder/model_builder_top1.R new file mode 100644 index 00000000..14c1378a --- /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$export_models){ + system(paste0('mkdir -p ', opt$out, '_final_models')) +} + +# Initiate log file +log_file <- paste0(opt$out,'.log') +log_header(log_file = log_file, opt = opt, script = 'model_builder.R', start.time = start.time) + +########### +# Read in the outcome data +########### + +outcome<-read_outcome(x = opt$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$outcome_var, labels = c('CONTROL', 'CASE')) +} + +log_add(log_file = log_file, message = paste0('Phenotype is ', ifelse(family == 'binomial', 'binary', 'quantitative'),'.')) + +########### +# Read in predictors +########### + +predictors_file <- fread(opt$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, keep = outcome$IID) + } + + group_list <- do.call(rbind, lapply(1:nrow(predictors_file), function(predfile) { + data.table(multi = predictors_file$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$out,'.group_list.txt.')) + +########### +# Merge the outcome and predictors +########### + +outcome_predictors <- merge(outcome, predictors, by='IID') + +rm(outcome, predictors) + +log_add(log_file = log_file, message = paste0(nrow(outcome_predictors),' individuals have both phenotypic and predictor data.')) + +# Report the size of the combined outcome and predictor data +log_add(log_file = log_file, message = paste0('Data to be carried foward is ',format(object.size(outcome_predictors), units='auto'),'.')) + +############ +# Prediction modelling +############ + +# We will derive top1 models, where the best predictor within each multi-top1 combo is evaluated using cross-validation +# We will then derive multi models, combining top1 predictors within each multi group, agian using cross-validation. + +# Split the sample into opt$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(as.numeric(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$x)){ + res_pred_i <- cor(as.numeric(outcome_predictors$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$out, + '_final_models')) + } + + # Update progress log + progress <- update_progress_file(progress_file) + update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(unique(group_list$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(as.numeric(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(as.numeric(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$out, + '_final_models')) + } + + # Update progress log + progress <- update_progress_file(progress_file) + update_log_file(log_file = log_file, message = paste0(log_message, floor(progress/length(unique(group_list$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'))) diff --git a/Scripts/pgs_methods/apply_leopard_weights.R b/Scripts/pgs_methods/apply_leopard_weights.R new file mode 100644 index 00000000..1ec6fc9a --- /dev/null +++ b/Scripts/pgs_methods/apply_leopard_weights.R @@ -0,0 +1,180 @@ +#!/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[[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/Scripts/pgs_methods/bridgeprs.R b/Scripts/pgs_methods/bridgeprs.R new file mode 100644 index 00000000..fefec7e8 --- /dev/null +++ b/Scripts/pgs_methods/bridgeprs.R @@ -0,0 +1,247 @@ +#!/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=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', + 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', + 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') + +# 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') +} +if(is.null(opt$bridgeprs_repo)){ + stop('--bridgeprs_repo must be specified.\n') +} + +# Create output directory +opt$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$populations +populations<-unlist(strsplit(opt$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 _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$pop_data) +pop_data$FID <- 0 +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)){ + + 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','A1','A2','BETA','P','N')) + + # Store average sample size + gwas_N <- c(gwas_N, round(mean(gwas$N), 0)) + gwas$N ',tmp_dir,'/bridge_log.txt 2>&1' +)) + +#### +# Read in the score file +#### + +ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F] + +score_list<-list() +for(i in populations){ + score_list[[i]] <- fread(paste0(tmp_dir,'/bridge_out/',i,'/fold2/snp_weights_weighted_model.dat')) + names(score_list[[i]]) <- c('SNP','A1','A2',paste0('SCORE_targ_', i)) + + # Replace NA values with 0 + score_list[[i]][is.na(score_list[[i]])] <- 0 + + # Flip effects to match reference alleles + score_list[[i]] <- map_score(ref = ref, score = score_list[[i]]) +} + +fwrite(score_new, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F) + +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/dbslmm.R b/Scripts/pgs_methods/dbslmm.R index 77ca2eaf..2df25cc0 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', @@ -32,7 +34,7 @@ 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 +123,7 @@ 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$hm3_snplist) @@ -149,6 +151,11 @@ opt$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$h2f*ldsc_h2)) log_add(log_file = log_file, message = paste0('SNP-h2 was set to ',ldsc_h2,' to avoid SNP-h2*h2f > 1.')) @@ -234,23 +241,6 @@ if(!is.na(opt$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) - -# 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 4c3b312e..99062bc7 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', @@ -156,6 +158,9 @@ 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'))){ @@ -192,23 +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$n_cores) - -# Calculate scale within each reference population -pop_data <- read_pop_data(opt$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$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA') -} - end.time <- Sys.time() time.taken <- end.time - start.time sink(file = log_file, append = T) diff --git a/Scripts/pgs_methods/ldpred2.R b/Scripts/pgs_methods/ldpred2.R index c5629796..e57ef353 100644 --- a/Scripts/pgs_methods/ldpred2.R +++ b/Scripts/pgs_methods/ldpred2.R @@ -6,10 +6,10 @@ library("optparse") option_list = list( make_option("--ref_plink_chr", action="store", default=NULL, type='character', help="Path to per chromosome reference PLINK files [required]"), + make_option("--ref_pcs", action="store", default=NULL, type='character', + help="Reference PCs for continuous ancestry correction [optional]"), make_option("--ldpred2_ref_dir", action="store", default=NULL, type='character', help="Path to directory containing LDpred2 reference data [required]"), - make_option("--ref_keep", action="store", default=NULL, type='character', - help="Keep file to subset individuals in reference for auto model [optional]"), make_option("--pop_data", action="store", default=NULL, type='character', help="File containing the population code and location of the keep file [required]"), make_option("--plink2", action="store", default='plink2', type='character', @@ -124,7 +124,8 @@ if(!is.na(opt$test)){ # Harmonise with the LDpred2 reference map<-readRDS(paste0(opt$ldpred2_ref_dir, '/map.rds')) -map<-map[, c('chr', 'pos', 'a0', 'a1', 'af_UKBB', 'ld')] +names(map)[names(map) == 'af_UKBB']<-'af' +map<-map[, c('chr', 'pos', 'a0', 'a1', 'af', 'ld')] info_snp <- snp_match(sumstats, map) ##### @@ -132,7 +133,7 @@ info_snp <- snp_match(sumstats, map) ##### # Remove SDss < 0.5 * SDval or SDss > 0.1 + SDval or SDss < 0.1 or SDval < 0.05 -sd_val <- with(info_snp, sqrt(2 * af_UKBB * (1 - af_UKBB))) +sd_val <- with(info_snp, sqrt(2 * af * (1 - af))) if(opt$binary == F){ sd_y_est = median(sd_val * info_snp$beta_se * sqrt(info_snp$n_eff)) @@ -176,6 +177,7 @@ 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.') @@ -245,13 +247,25 @@ 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 @@ -316,6 +330,9 @@ 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$output,'.score.gz'))){ @@ -329,23 +346,6 @@ if(!is.na(opt$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) - -# 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 #### @@ -353,13 +353,12 @@ 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))) @@ -387,7 +386,7 @@ 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)) })) diff --git a/Scripts/pgs_methods/leopard_quickprs.R b/Scripts/pgs_methods/leopard_quickprs.R new file mode 100644 index 00000000..1170693f --- /dev/null +++ b/Scripts/pgs_methods/leopard_quickprs.R @@ -0,0 +1,277 @@ +#!/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("--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$pop_data)){ + stop('--pop_data must be specified.\n') +} +if(is.null(opt$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 ', 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, ' ', + '--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 +#### + +# 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$pop_data) +scale_all <- NULL +for(i in 1:length(score_files)){ + # Calculate scores in the full reference + ref_pgs <- plink_score(pfile = opt$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$POP == populations[i], c('FID','IID'), with=F]) + + scale_all <- rbind( + scale_all, + data.table( + SD = ref_pgs_scale_i$SD, + Discovery = populations[i] + ) + ) +} + +# 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$SD) + + log_add(log_file = log_file, message = paste0("Adjusted LEOPARD weights - ", i, " target: ")) + for(j in populations){ + log_add(log_file = log_file, message = paste0(j, ' = ', mix_weights[[i]][which(populations == j)])) + } + log_add(log_file = log_file, message = '------------------------') +} + +saveRDS(mix_weights, paste0(opt$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/Scripts/pgs_methods/megaprs.R b/Scripts/pgs_methods/megaprs.R index d8c43214..2cb84590 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', @@ -96,7 +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 @@ -257,23 +274,6 @@ 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$n_cores) - -# Calculate scale within each reference population -pop_data <- read_pop_data(opt$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/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/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/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/pgs_methods/prscs.R b/Scripts/pgs_methods/prscs.R index 3079e2aa..c77bccdf 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', @@ -126,20 +128,36 @@ 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$phi[i]) + } + + # Run command + log_i <- system(command) + + # Check for an error + if(log_i != 0){ + write("", paste0(tmp_dir, '/checker.txt')) + } } } - #### # Combine score files #### @@ -178,23 +196,6 @@ if(!is.na(opt$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) - -# 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 new file mode 100644 index 00000000..cdb63cf6 --- /dev/null +++ b/Scripts/pgs_methods/prscsx.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=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', + 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$CHR == i, c('CHR','SNP','POS','BP','A1','A2'), with=F], paste0(tmp_dir,'/ref.chr',i,'.bim'), col.names=F, row.names=F, quote=F) +} + +rm(pvar) +gc() + +# Make a data.frame listing chromosome and phi combinations +jobs<-NULL +for(i in CHROMS){ + jobs<-rbind(jobs, data.frame(CHR=i, phi=phi_param)) +} + +# Run using PRS-CSx auto, and specifying a range of global shrinkage parameters +file.remove(paste0(tmp_dir, '/checker.txt')) +log <- foreach(i = 1:nrow(jobs), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% { + if(!file.exists(paste0(tmp_dir, '/checker.txt'))) { + # Base command + command <- paste0(opt$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$phi[i]) + } + + # Run command + log_i <- system(command) + + # Check for an error + if(log_i != 0){ + write("", paste0(tmp_dir, '/checker.txt')) + } + } +} + +#### +# Combine score files +#### + +# Read in ref to harmonise score files +ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F] + +score_all<-NULL +for(pop_i in c(unlist(strsplit(opt$populations, ',')), 'META')){ + score_pop<-NULL + for(phi_i in phi_param){ + score_phi<-NULL + for(i in CHROMS){ + score_phi_i<-fread(paste0(tmp_dir,'/output_',pop_i,'_pst_eff_a1_b0.5_phi',phi_i,'_chr',i,'.txt')) + score_phi<-rbind(score_phi, score_phi_i) + } + if(phi_i == phi_param[1]){ + score_phi<-score_phi[,c('V2', 'V4','V5', 'V6'), with=F] + names(score_phi)<-c('SNP', 'A1', 'A2', paste0('SCORE_',pop_i,'_phi_',phi_i)) + } else { + score_phi<-score_phi[,'V6', with=F] + names(score_phi)<-paste0('SCORE_',pop_i,'_phi_',phi_i) + } + score_pop<-cbind(score_pop, score_phi) + } + + # Sort and flip effects to match reference alleles + score_pop <- map_score(ref = ref, score = score_pop) + + if(pop_i == c(unlist(strsplit(opt$populations, ',')), 'META')[1]){ + score_all<-score_pop + } else { + score_all<-cbind(score_all, score_pop[, !(names(score_pop) %in% c('SNP','A1','A2')), with=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$output,'.score.gz')) +} + +system(paste0('gzip ',opt$output,'.score')) + +# Record end time of test +if(!is.na(opt$test)){ + test_finish(log_file = log_file, test_start.time = test_start.time) +} + +end.time <- Sys.time() +time.taken <- end.time - start.time +sink(file = log_file, append = T) +cat('Analysis finished at', as.character(end.time),'\n') +cat('Analysis duration was', as.character(round(time.taken,2)), attr(time.taken, 'units'), '\n') +sink() diff --git a/Scripts/pgs_methods/ptclump.R b/Scripts/pgs_methods/ptclump.R index f48c9bb8..5ac821c9 100644 --- a/Scripts/pgs_methods/ptclump.R +++ b/Scripts/pgs_methods/ptclump.R @@ -8,6 +8,8 @@ make_option("--ref_plink_chr", action="store", default=NULL, type='character', help="Path to per chromosome reference PLINK files [required]"), make_option("--ref_keep", action="store", default=NULL, type='character', help="Keep file to subset individuals in reference for clumping [optional]"), +make_option("--ref_pcs", action="store", default=NULL, type='character', + help="Reference PCs for continuous ancestry correction [optional]"), make_option("--pop_data", action="store", default=NULL, type='character', help="File containing the population code and location of the keep file [required]"), make_option("--plink2", action="store", default='plink2', type='character', @@ -170,23 +172,6 @@ for(i in 1:nrow(range_list)){ fwrite(range_list, paste0(opt$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$output,'.score.gz')) - -# Calculate scale within each reference population -pop_data <- read_pop_data(opt$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 8bf3f1e6..6913860a 100644 --- a/Scripts/pgs_methods/quickprs.R +++ b/Scripts/pgs_methods/quickprs.R @@ -6,10 +6,16 @@ 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("--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("--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', @@ -17,9 +23,9 @@ 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]"), - make_option("--quick_prs_ref", action="store", default=NA, type='character', - help="Path to folder containing ldak quick prs reference [required]"), + 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("--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', @@ -32,139 +38,100 @@ 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$quickprs_ldref)){ + stop('--quickprs_ldref must be specified.\n') +} + +# Create output directory +opt$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() +log_add(log_file = log_file, message = 'Reading in GWAS.') -GWAS<-fread(cmd=paste0('zcat ',opt$sumstats), nThread=opt$n_cores) -GWAS<-GWAS[complete.cases(GWAS),] +# Read in, check and format GWAS summary statistics +gwas <- read_sumstats(sumstats = opt$sumstats, chr = CHROMS, log_file = log_file, req_cols = c('CHR','BP','SNP','A1','A2','BETA','SE','N','FREQ','REF.FREQ')) -# Update BP to match the reference -ref_bim<-NULL -for(i in 1:22){ - ref_bim<-rbind(ref_bim, fread(paste0(opt$ref_plink_chr,i,'.bim'), header=F)) -} +# Check allele frequency difference +ref_psam<-fread(paste0(opt$ref_plink_chr, CHROMS[1],'.psam')) +names(ref_psam)<-gsub('\\#', '', names(ref_psam)) -GWAS$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),] - -# 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$CHR)) - } +if(!is.null(opt$ref_keep)){ + ref_keep <- fread(opt$ref_keep, header=F)$V1 + ref_psam <- ref_psam[ref_psam$IID %in% ref_keep,] } -# Update RSID to CHR:BP to match reference data -# During sumstat cleaning, these columns are replaced with those found in build hg19 which is appropriate -GWAS$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$output,'.log',sep=''), append = T) - cat('GWAS-reference overlap check is skipped as this is a test run.\n', sep='') - sink() -} +ref_n <- nrow(ref_psam) -# Calculate Z -if(('BETA' %in% names(GWAS))){ - GWAS$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$OR < 1] -} +gwas$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),] -GWAS$Predictor<-paste0(GWAS$CHR, ':', GWAS$BP) +# Format for LDAK +snplist <- gwas$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') -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$quickprs_ldref) +ldak_hm3_file <- ldak_hm3_file[grepl('.cors.bim', ldak_hm3_file)][1] +ldak_hm3 <- fread(paste0(opt$quickprs_ldref, '/', ldak_hm3_file)) +ldak_hm3 <- ldak_hm3[ldak_hm3$V1 %in% CHROMS,] +ref_overlap <- sum(gwas$Predictor %in% ldak_hm3$V2) / nrow(ldak_hm3) -sink(file = paste(opt$output,'.log',sep=''), append = T) -cat('GWAS contains',dim(GWAS)[1],'variants.\n') -sink() +log_add(log_file = log_file, message = paste0('GWAS-reference overlap is ', round(ref_overlap * 100, 2), '%.')) -fwrite(GWAS, paste0(opt$output_dir,'GWAS_sumstats_temp.txt'), sep=' ') +# Subset GWAS to LDAK reference data +gwas <- gwas[gwas$Predictor %in% ldak_hm3$V2, ] -rm(GWAS) -gc() +# Output formatted sumstats +fwrite(gwas, paste0(tmp_dir,'/GWAS_sumstats_temp.txt'), sep=' ') +# Record start time for test if(!is.na(opt$test)){ - sink(file = paste(opt$output,'.log',sep=''), append = T) - test_start.time <- Sys.time() - cat('Test started at',as.character(test_start.time),'\n') - sink() + test_start.time <- test_start(log_file = log_file) } ############ @@ -172,77 +139,62 @@ if(!is.na(opt$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 ',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$quickprs_ldref, '/', tagging_file, ' --summary ', tmp_dir, '/GWAS_sumstats_temp.txt --matrix ', opt$quickprs_ldref, '/', matrix_file, ' --max-threads ', opt$n_cores, ' --check-sums NO')) } else{ - system(paste0(opt$ldak,' --sum-hers ',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$quickprs_ldref, '/', tagging_file, ' --summary ', tmp_dir, '/GWAS_sumstats_temp.txt --matrix ', opt$quickprs_ldref, '/', matrix_file, ' --max-threads ', opt$n_cores, ' --check-sums NO')) } -ldak_res_her<-fread(paste0(opt$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$SD[nrow(ldak_res_her)],").")) ###### # Estimate effect sizes for training and full prediction models. ###### -cor_file_prefix<-gsub('.cors.bin','',ref_files[grepl('.cors.bin',ref_files)]) +cor_file_prefix<-gsub('.cors.bin','',ref_files[grepl('.cors.bin',ref_files) & !grepl('subset', ref_files)]) -sink(file = paste(opt$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$prs_model,' --cors ',opt$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 ',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$output,'.log',sep=''), append = T) - cat('Test run finished at',as.character(end.time),'\n') - cat('Test duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n') - sink() -} +log_add(log_file = log_file, message = paste0('Model ', gsub('Score_','',best_score$Model[1]),' is identified as the best with correlation of ', best_score$Correlation[1])) ###### # Format final score file ###### # Read in the scores -score<-fread(paste0(opt$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 +202,38 @@ 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() +log_add(log_file = log_file, message = 'Calculating polygenic scores in reference.') -scores<-calc_score( - bfile=opt$ref_plink_chr, - score=paste0(opt$output,'.score.gz') -) +# 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) -sink(file = paste(opt$output,'.log',sep=''), append = T) -cat('Done!\n') -sink() - -# Calculate the mean and sd of scores for each population specified in pop_scale -pop_keep_files<-read.table(opt$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) - names(keep)<-c('FID','IID') - ref_scale<-score_mean_sd(scores=scores, keep=keep) - fwrite(ref_scale, paste0(opt$output,'.',pop,'.scale'), sep=' ') +if(!is.null(opt$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) } -### -# Clean up temporary files -### +# Calculate scale within each reference population +pop_data <- read_pop_data(opt$pop_data) -system(paste0('rm ',opt$output_dir,'bld*')) -system(paste0('rm ',opt$output_dir,'GWAS_sums*')) -system(paste0('rm ',opt$output_dir,'mega*')) +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$output, '-', pop_i, '.scale'), row.names = F, quote=F, sep=' ', na='NA') +} end.time <- Sys.time() time.taken <- end.time - start.time -sink(file = paste(opt$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/sbayesr.R b/Scripts/pgs_methods/sbayesr.R index aaaa2618..a360775e 100644 --- a/Scripts/pgs_methods/sbayesr.R +++ b/Scripts/pgs_methods/sbayesr.R @@ -1,246 +1,231 @@ -#!/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'],").")) + +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 new file mode 100644 index 00000000..1768d0c7 --- /dev/null +++ b/Scripts/pgs_methods/sbayesrc.R @@ -0,0 +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_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', + 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.", 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'), + LDdir = opt$sbayesrc_ldref, + outPrefix = paste0(tmp_dir, '/sbrc'), + annot = opt$sbayesrc_annot, + bTune = FALSE, + log2file = FALSE + ) + } 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) + 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 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: ", conditionMessage(e)) + } + }, + warning = function(w) { + message("A warning occurred while running SBayesRC::sbayesrc:") + message(conditionMessage(e)) + } +) + +### +# 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) +} + +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 new file mode 100644 index 00000000..9c020264 --- /dev/null +++ b/Scripts/pgs_methods/tlprs.R @@ -0,0 +1,276 @@ +#!/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 when pseudo_only is TRUE [optional]"), + 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', + 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("--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', + 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]") +) + +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$config) && opt$pseudo_only){ + stop('--config must be specified when --pseudo_only is TRUE.\n') +} +if(is.null(opt$ref_plink_chr)){ + stop('--ref_plink_chr must be specified.\n') +} +if(is.null(opt$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$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F] + +# Run for each parameter in each score file +tl_betas_list<-list() +for(i in 1:length(populations)){ + + # Read in target sumstats + target_gwas <- read_sumstats(sumstats = sumstats[i], chr = CHROMS, log_file = log_file, req_cols = c('SNP', 'A1', 'BETA', 'P', 'N')) + names(target_gwas)[names(target_gwas) == 'BETA']<-'beta' + names(target_gwas)[names(target_gwas) == 'P']<-'p' + + # Identify LD block data to be used + if(populations[i] %in% c('EUR','AFR')){ + ld_block_dat <- paste0(populations[i],'.hg19') + } + if(populations[i] %in% 'EAS'){ + ld_block_dat <- 'ASN.hg19' + } + if(populations[i] %in% c('AMR','SAS')){ + ld_block_dat <- 'EUR.hg19' + log_add(log_file = log_file, message = 'Using LD block data for EUR.') + } + + # Read in PGS score file (set up for pairs of GWAS/populations only) + score_file <- fread(scores[-i]) + + if(opt$pseudo_only){ + method <- sub('/.*','',gsub('.*pgs_score_files/','', scores[-i])) + gwas <- sub('/.*','',gsub(paste0('.*/',method,'/'),'', scores[-i])) + param <- find_pseudo( + config = opt$config, + gwas = gwas, + pgs_method = method, + target_pop = populations[i] + ) + score_file <- score_file[, c('SNP', 'A1', 'A2', paste0('SCORE_', param)), with = F] + log_add(log_file = log_file, message = 'Using pseudovalidated PGS only.') + } + + names(score_file)<-gsub('^SCORE','Beta', names(score_file)) + + target_gwas_j=merge(score_file, target_gwas, by="SNP",sort=F) + + if (sum(target_gwas_j$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$plink1, + temp.file = paste0(tmp_dir, '/', populations[i], '_step1') + ) + ) + + # Flip effects to correspond to original A1 + beta.info<-beta_list[, 1:9] + beta_list<-beta_list[, -1:-9] + flip<-beta.info$V5 != beta.info$A1 + beta_list[flip,]<- -beta_list[flip,] + + for (k in 1:ncol(beta_list)){ + sdtemp=sd(beta_list[,k],na.rm=T) + if (sdtemp>1){ + beta_list[,k:ncol(beta_list)]=1 + } + } + + beta_list=beta_list/beta.info$sd + + 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)) + + if(opt$retain_nonoverlapping){ + # Insert original BETA if variant not present in target GWAS + miss_snps <- score_file[!(score_file$SNP %in% beta_list$SNP),] + beta_list <- merge(beta_list, miss_snps, by = c('SNP','A1','A2'), all=T) + for(j in gsub('Beta_', '', names(score_file)[-1:-3])){ + beta_list[!is.na(get(paste0('Beta_', j))), + (which(grepl('SCORE', names(beta_list)) & grepl(j, names(beta_list))))] <- + beta_list[[paste0('Beta_', j)]][!is.na(beta_list[[paste0('Beta_', j)]])] + } + beta_list<-beta_list[, !grepl('Beta_', names(beta_list)), with=F] + } + + # Flip effects to match reference alleles + beta_list <- map_score(ref = ref, score = beta_list) + + tl_betas_list[[i]]<-beta_list + +} + +tl_betas_all<-Reduce(function(dtf1, dtf2) merge(dtf1, dtf2, by = c('SNP','A1','A2'), all = TRUE, sort = F), tl_betas_list) + +# Reduce number of significant figures to save space +tl_betas_all[, (4:ncol(tl_betas_all)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(tl_betas_all)] + +fwrite(tl_betas_all, paste0(opt$output,'.score'), col.names=T, sep=' ', quote=F) + +if(file.exists(paste0(opt$output,'.score.gz'))){ + system(paste0('rm ',opt$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/xwing.R b/Scripts/pgs_methods/xwing.R new file mode 100644 index 00000000..4d1f4084 --- /dev/null +++ b/Scripts/pgs_methods/xwing.R @@ -0,0 +1,394 @@ +#!/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=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', + 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("--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', + 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$ref_freq_chr)){ + stop('--ref_freq_chr must be specified.\n') +} +if(is.null(opt$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$xwing_repo)){ + stop('--xwing_repo must be specified.\n') +} +if(is.null(opt$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$CHR == i, c('CHR','SNP','POS','BP','A1','A2'), with=F], paste0(tmp_dir,'/ref.chr',i,'.bim'), col.names=F, row.names=F, quote=F) +} + +rm(pvar) +gc() + +combinations <- expand.grid(targ_pop = populations, pst_pop = populations, chr = CHROMS) + +file.remove(paste0(tmp_dir, '/checker.txt')) +log <- foreach(i = 1:nrow(combinations), .combine = c, .options.multicore = list(preschedule = FALSE)) %dopar% { + if(!file.exists(paste0(tmp_dir, '/checker.txt'))) { + targ_pop <- combinations$targ_pop[i] + pst_pop <- combinations$pst_pop[i] + chr <- combinations$chr[i] + + # 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$populations ,' ', + '--target_pop ', targ_pop,' ', + '--pst_pop ', pst_pop, ' ', + '--out_name output ', + '--seed 1 ', + '--out_dir ', tmp_dir, '/PANTHER/post_targ_', targ_pop + ) + + # Run command + log_i <- system(command) + + # Check for an error + if(log_i != 0){ + write("", paste0(tmp_dir, '/checker.txt')) + } + } +} + +if(opt$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$populations ,' ', + '--target_pop ', targ_pop,' ', + '--pst_pop ', pst_pop, ' ', + '--out_name output_', index, ' ', + '--seed 1 ', + '--out_dir ', tmp_dir, '/LEOPARD/post_targ_', targ_pop + ) + + # Run command + log_i <- system(command) + + # Check for an error + if(log_i != 0){ + write("", paste0(tmp_dir, '/checker.txt')) + } + } + } + + for(targ_pop in populations){ + for(pst_pop in populations){ + for(i in 1:4){ + system(paste0('cat ', tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i,'_', pst_pop, '_pst_eff_phiauto_chr*.txt > ', tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i,'_', pst_pop, '_Post.txt')) + system(paste0("sed -i '1iCHR\tSNP\tBP\tA1\tA2\tBETA' ", tmp_dir, '/LEOPARD/post_targ_', targ_pop, '/output_', i, '_', pst_pop, '_Post.txt')) + } + } + } + + ## + # Run LEOPARD to to find optimal weights for GWAS from each population + ## + + # Estimating the linear combination weights + for(targ_pop in populations){ + dir.create(paste0(tmp_dir,'/LEOPARD/weights_', targ_pop), recursive = T) + + for(i in 1:4){ + targ_gwas_valid_n<-fread(paste0(tmp_dir,'/LEOPARD/sampled_sumstats/GWAS_', which(populations == targ_pop), '_rep', i, '_train_valid_N.txt'))$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) +} + +#### +# Combine score files +#### + +# Read in reference SNP data +ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F] + +# We should combine the raw PANTHER score files for each population, +# and then combine using mixing weights for each population if LEOPARD was run +score_all<-NULL +for(targ_pop in populations){ + # Read in the .freq file for target population + freq_data <- read_frq(freq_dir = opt$ref_freq_chr, population = targ_pop, chr = CHROMS) + + 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) + } + 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, ref = ref) + + if(opt$leopard){ + # Adjust SNP-weights according to mixing weights + score_i$BETA <- score_i$BETA * mix_weights[[targ_pop]][which(populations == pst_pop)] + } + names(score_i)<-c('SNP', 'A1', 'A2', paste0('SCORE_targ_', targ_pop, '_pst_', pst_pop)) + + if(is.null(score_pop)){ + score_pop<-score_i + } else { + score_pop<-merge(score_pop, score_i, by=c('SNP','A1','A2'), all=T) + } + } + + score_pop[is.na(score_pop)]<-0 + + if(opt$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 { + score_all<-merge(score_all, score_pop, by=c('SNP','A1','A2'), all=T) + } +} + +# Flip effects to match reference alleles +ref <- read_pvar(opt$ref_plink_chr, chr = CHROMS)[, c('SNP','A1','A2'), with=F] +score_new <- map_score(ref = ref, score = score_all) + +# Reduce number of significant figures to save space +score_new[, (4:ncol(score_new)) := lapply(.SD, signif, digits = 7), .SDcols = 4:ncol(score_new)] + +fwrite(score_new, paste0(opt$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) +} + +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/pipeline_reports/indiv_report_creator.Rmd b/Scripts/pipeline_reports/indiv_report_creator.Rmd index a781ee1c..921e640a 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} @@ -57,6 +56,9 @@ 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') @@ -186,19 +188,12 @@ 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") } ``` @@ -209,14 +204,12 @@ 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$value, na.rm = T) > ancestry_prob_thresh, 105, 100) png(paste0(tmp_folder,'/plot_ancestry.png'), height = 450, width = 800, res = 120) - ggplot(dat, aes(x=label, y=value*100, fill=label)) + + ggplot(dat, aes(x=variable, y=value*100, fill=variable)) + geom_chicklet(radius = grid::unit(1, 'mm')) + geom_hline(yintercept = ancestry_prob_thresh*100, linetype = 'dashed') + labs(x='Population', y="Probability (%)") + @@ -269,6 +262,7 @@ if(!pgs_incl){ ```{r, results='asis', eval = pgs_incl} cat0("- ", ifelse(is.null(gwas_list), 0, nrow(gwas_list)), " GWAS summary statistics were provided for polygenic scoring.\n") +cat0("- ", ifelse(is.null(gwas_groups), 0, nrow(gwas_groups)), " GWAS groups were specified.\n") cat0("- ", length(pgs_methods_list), " PGS methods were applied, including ", paste0(pgs_method_labels$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)){ @@ -291,6 +285,7 @@ cat0("## GWAS summary statistics \n\n") # Create a summary table for GWAS sumstats sumstat_qc <- NULL +column_interp <- list() for(gwas in gwas_list$name) { log <- readLines(paste0(outdir,'/reference/gwas_sumstat/', gwas, '/', gwas, '-cleaned.log')) @@ -303,6 +298,26 @@ for(gwas in gwas_list$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(' | $','',sub('.*NA ','', data)) + column_interp_i <- data.table( + original = original, + interp = interp) + names(column_interp_i)<-c('Header','Interpretation') + + column_interp[[gwas]] <- column_interp_i } names(sumstat_qc) <- c('Name', 'Label', 'Population', 'NSNP Original', 'NSNP Final') @@ -322,6 +337,36 @@ datatable(sumstat_qc, cat0("***\n\n") +cat0("
Show GWAS header interpretations\n\n") +for(i in 1:length(column_interp)){ + cat0("**",names(column_interp)[i], "** \n\n") + cat0("\n\n") + + # Print the datatable + print(htmltools::tagList( + datatable( + column_interp[[i]], + rownames = FALSE, + options = list( + dom = 't', + ordering = FALSE, + columnDefs = list( + list(className = "dt-center", targets = '_all') + ), + scrollX = TRUE + ), + width = '50%', + selection = 'none' + ))) + + cat0("***\n\n") + +} +cat0("**Note.** Columns that were dropped are not shown here.\n\n") +cat0("
\n\n") + +cat0("***\n\n") + ``` ```{r, results='asis', eval = all(pgs_incl, !is.null(score_list))} @@ -370,36 +415,95 @@ cat0("***\n\n") ```{r, results='asis', eval = pgs_incl} +cat0("## PGS correlation \n\n") +cat0("This section shows the correlation between PGS in the reference sample. This is intended as a sanity check that the PGS correlations are in the expected direction, thereby confirming the GWAS alleles are being interpreted correctly.\n\n") + +``` + +```{r, eval = pgs_incl, include = F} + +# Read in the reference pgs +ref_pgs <- read_reference_pgs(config = params$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 <- reshape2::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') +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("
Show reference PGS correlation matrix\n\n") +cat0(paste0("![](", plot_file,")\n\n")) +cat0("**Note.** Plot shows correlation between pseudovalidated PGS across all populations, adjusted for ancestry.\n\n") +cat0("
\n\n") +cat0("***\n\n") + +``` + +```{r, results='asis', eval = pgs_incl} + 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]] +# 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]] -# 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$config, - gwas = gwas_i, - pgs_method = pgs_method_i) - - tmp <- pgs[[gwas_i]][[pgs_method_i]] - names(tmp) <- gsub(paste0(gwas_i, '_'), '', names(tmp)) - tmp <- tmp[paste0(tmp$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) + } } } @@ -765,7 +869,7 @@ 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$name == gwas_i])){ diff --git a/Scripts/pipeline_reports/samp_report_creator.Rmd b/Scripts/pipeline_reports/samp_report_creator.Rmd index 78e29d5e..62450640 100644 --- a/Scripts/pipeline_reports/samp_report_creator.Rmd +++ b/Scripts/pipeline_reports/samp_report_creator.Rmd @@ -53,6 +53,9 @@ target_list <- read_param(config = params$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)){ @@ -197,6 +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$name) { log <- readLines(paste0(outdir,'/reference/gwas_sumstat/', gwas, '/', gwas, '-cleaned.log')) @@ -209,6 +214,26 @@ for(gwas in gwas_list$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(' | $','',sub('.*NA ','', data)) + column_interp_i <- data.table( + original = original, + interp = interp) + names(column_interp_i)<-c('Header','Interpretation') + + column_interp[[gwas]] <- column_interp_i } names(sumstat_qc) <- c('Name', 'Label', 'Population', 'NSNP Original', 'NSNP Final') @@ -228,6 +253,36 @@ datatable(sumstat_qc, cat0("***\n\n") +cat0("
Show GWAS header interpretations\n\n") +for(i in 1:length(column_interp)){ + cat0("**",names(column_interp)[i], "** \n\n") + cat0("\n\n") + + # Print the datatable + print(htmltools::tagList( + datatable( + column_interp[[i]], + rownames = FALSE, + options = list( + dom = 't', + ordering = FALSE, + columnDefs = list( + list(className = "dt-center", targets = '_all') + ), + scrollX = TRUE + ), + width = '50%', + selection = 'none' + ))) + + cat0("***\n\n") + +} +cat0("**Note.** Columns that were dropped are not shown here.\n\n") +cat0("
\n\n") + +cat0("***\n\n") + ``` ```{r, results='asis', eval = all(pgs_incl, !is.null(score_list))} @@ -274,44 +329,121 @@ cat0("***\n\n") ``` +```{r, results='asis', eval = pgs_incl} + +cat0("## PGS correlation \n\n") +cat0("This section shows the correlation between PGS in the reference sample. This is intended as a sanity check that the PGS correlations are in the expected direction, thereby confirming the GWAS alleles are being interpreted correctly.\n\n") + +``` + +```{r, eval = pgs_incl, include = F} + +# Read in the reference pgs +ref_pgs <- read_reference_pgs(config = params$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 <- reshape2::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') +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("
Show reference PGS correlation matrix\n\n") +cat0(paste0("![](", plot_file,")\n\n")) +cat0("**Note.** Plot shows correlation between pseudovalidated PGS across all populations, adjusted for ancestry.\n\n") +cat0("
\n\n") +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$config, gwas = gwas, pgs_method = pgs_method) for(pop in names(pgs)){ tmp <- pgs[[pop]][[gwas]][[pgs_method]] - names(tmp) <- gsub(paste0(gwas, '_'), '', names(tmp)) - - pgs_pseudo <- rbind(pgs_pseudo, data.table( - score = tmp[[pseudo_param[1]]], - pop = pop, - gwas = gwas, - pgs_method = pgs_method)) + names(tmp)<-c('FID','IID','score') + tmp$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$keep_files)){ + pgs$assigned_pop[paste0(pgs$FID, '.', pgs$IID) %in% paste0(ancestry_log$keep_files[[i]]$V1, '.', ancestry_log$keep_files[[i]]$V2)] <- i +} +pgs$assigned_pop[is.na(pgs$assigned_pop)]<-'Unassigned' # Plot the distribution of polygenic scores +plot_width <- 250 + length(unique(pgs$pop)) * 255 +if(plot_width < 1000) plot_width <- 1000 + pgs_dist_plots <- list() -for(pgs_method_i in unique(pgs_pseudo$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$pgs_method %in% pgs_method_i,] pgs_dist_plots[[pgs_method_i]] <- - ggplot(pgs_pseudo_i, aes(x=score, fill=1)) + + ggplot(pgs_i, aes(x=score, fill=assigned_pop)) + geom_density(alpha=0.5) + - labs(x='Polygenic Z-Score', y='Density') + + labs(x='Polygenic Z-Score', y='Density', fill = 'Population') + theme_half_open() + background_grid() + panel_border() + facet_grid(gwas ~ pop) + - theme(legend.position = "none") - - png(paste0(tmp_folder,'/plot_', pgs_method_i,'.png'), height = 150 + (300*length(unique(pgs_pseudo_i$gwas))), width = 2000, res = 170) + 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_i$gwas))), width = plot_width, res = 170) print(pgs_dist_plots[[pgs_method_i]]) dev.off() } @@ -321,8 +453,9 @@ for(pgs_method_i in unique(pgs_pseudo$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. 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") @@ -334,6 +467,7 @@ for(pgs_method_i in unique(pgs_pseudo$pgs_method)){ } cat0("## {-} \n\n") +cat0("**Note.** Plot shows distribution of pseudovalidated PGS.\n\n") ``` diff --git a/Scripts/ref_pca/ref_pca.R b/Scripts/ref_pca/ref_pca.R index 6a4dae91..ea6093c9 100644 --- a/Scripts/ref_pca/ref_pca.R +++ b/Scripts/ref_pca/ref_pca.R @@ -58,6 +58,11 @@ tmp_dir<-tempdir() log_file <- paste(opt$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/ref_scoring/ref_scoring.R b/Scripts/ref_scoring/ref_scoring.R new file mode 100644 index 00000000..2044ecb8 --- /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)$mtime + if (score_i_time > pgs_i_time | (!is.null(ref_pcs_file_time) && ref_pcs_file_time > pgs_i_time)) { + score_files_to_do <- rbind(score_files_to_do, score_files[i,]) + system(paste0('rm ', pgs_i)) + } + } + } + log_add(log_file = log_file, message = paste0('After checking timestamps, ', nrow(score_files_to_do), '/', nrow(score_files), ' score files will be used for reference scoring.')) + score_files <- score_files_to_do +} + +if(is.null(score_files) || nrow(score_files) == 0){ + log_add(log_file = log_file, message = paste0('No score files to be used for reference scoring.')) + end.time <- Sys.time() + time.taken <- end.time - start.time + sink(file = log_file, append = T) + cat('Analysis finished at',as.character(end.time),'\n') + cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n') + sink() + quit(save = "no", status = 0) +} + +# Set params for plink_score +refdir <- read_param(config = opt$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$POP == pop_i]),] + scores_i_pop<-score_scale(score=scores_i_pop, ref_scale=ref_pgs_scale_i) + fwrite(scores_i_pop, paste0(output_i, '-', pop_i, '.profiles'), sep=' ', na='NA', quote=F) + } +} + +end.time <- Sys.time() +time.taken <- end.time - start.time +sink(file = log_file, append = T) +cat('Analysis finished at',as.character(end.time),'\n') +cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n') +sink() diff --git a/Scripts/target_scoring/target_scoring.R b/Scripts/target_scoring/target_scoring.R index e145b240..11fe98fa 100644 --- a/Scripts/target_scoring/target_scoring.R +++ b/Scripts/target_scoring/target_scoring.R @@ -6,7 +6,7 @@ library("optparse") option_list = list( make_option("--target_plink_chr", action="store", default=NULL, type='character', help="Path to per chromosome target PLINK2 files [required]"), -make_option("--target_keep", action="store", default=NULL, type='character', +make_option("--target_keep", action="store", default=NA, type='character', help="Path to keep file for target [optional]"), make_option("--ref_score", action="store", default=NULL, type='character', help="Path to reference scoring files [required]"), @@ -67,6 +67,11 @@ tmp_dir<-tempdir() log_file <- paste0(opt$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_partitioned_pipeline.R b/Scripts/target_scoring/target_scoring_partitioned_pipeline.R new file mode 100644 index 00000000..00902f99 --- /dev/null +++ b/Scripts/target_scoring/target_scoring_partitioned_pipeline.R @@ -0,0 +1,273 @@ +#!/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 <- 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, '_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 +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 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/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$n_sig > 0],] + + score_files_to_do <- data.table() + for(i in 1:nrow(score_files)){ + pgs_i <- paste0(outdir, '/', opt$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$name[i], '.stratified.score.gz') + if(!file.exists(pgs_i)){ + score_files_to_do <- rbind(score_files_to_do, score_files[i,]) + } else { + score_i_time <- file.info(score_i)$mtime + pgs_i_time <- file.info(pgs_i)$mtime + if (score_i_time > pgs_i_time | ancestry_reporter_file_time > pgs_i_time) { + score_files_to_do <- rbind(score_files_to_do, score_files[i,]) + system(paste0('rm ', pgs_i)) + } + } + } + log_add(log_file = log_file, message = paste0('After checking timestamps, ', nrow(score_files_to_do), '/', nrow(score_files), ' score files will be used for target scoring.')) + score_files <- score_files_to_do +} + +if(is.null(score_files) || nrow(score_files) == 0){ + log_add(log_file = log_file, message = paste0('No score files to be used for target scoring.')) + end.time <- Sys.time() + time.taken <- end.time - start.time + sink(file = log_file, append = T) + cat('Analysis finished at',as.character(end.time),'\n') + cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n') + sink() + quit(save = "no", status = 0) +} + +# Set params for plink_score +opt$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[as.numeric(row_index),] + + # Create file containing SNP, A1, and A2 information for each chromosome + 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% { + system(paste0( + '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$name[i]), '.txt' + )) + } + + # Paste files together in batches + # Set number of batches according to the number of score files to combine + num_batches <- max(c(1, min(c(opt$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$name[i],'.stratified-TRANS.model.rds')) + names(models[[paste0('score_file_', i)]])<-gsub('SCORE_', paste0('score_file_', i, '.'), names(models[[paste0('score_file_', i)]])) + } + + models <- do.call(c, unname(models)) + + # Read in target projected PCs + target_pcs<-fread(paste0(outdir,'/',opt$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],'.stratified-', 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$population,'.partitioned.profiles'), sep=' ', na='NA', quote=F) +} + +log_add(log_file = log_file, message = paste0('Saved polygenic scores.')) + +end.time <- Sys.time() +time.taken <- end.time - start.time +sink(file = log_file, append = T) +cat('Analysis finished at',as.character(end.time),'\n') +cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n') +sink() diff --git a/Scripts/target_scoring/target_scoring_pipeline.R b/Scripts/target_scoring/target_scoring_pipeline.R index d6a272ce..fd22343d 100644 --- a/Scripts/target_scoring/target_scoring_pipeline.R +++ b/Scripts/target_scoring/target_scoring_pipeline.R @@ -48,14 +48,14 @@ if(is.null(opt$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,'.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$name[i], '-', opt$population, '.scale') + 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$output,'.log',sep=''), append = T) @@ -88,88 +111,147 @@ if(!is.null(opt$score)){ score_files <- score_files[score_files$name == opt$score,] } -##### -# Combine score files -##### +# Read in target_list +target_list <- read_param(config = opt$config, param = 'target_list', return_obj = T) -log_add(log_file = log_file, message = paste0('Processing ', nrow(score_files),' score files.')) +# Set params for plink_score +opt$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$population,'.chr') -# Extract SNP A1 and A2 information -system(paste0('zcat ', outdir, '/reference/pgs_score_files/', score_files$method[1],'/', score_files$name[1],'/ref-',score_files$name[1],".score.gz | cut -d' ' -f1-3 - > ", tmp_dir,'/map.txt')) +# Read in reference SNP data +ref <- read_pvar(paste0(refdir, '/ref.chr'), chr = CHROMS)[, c('CHR','SNP','A1','A2'), with=F] -# 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$name[i]),'.txt')) -} +# We will process score files and perform target scoring for one chromosome for efficiency +for(chr_i in CHROMS){ + log_add(log_file = log_file, message = '########################') + log_add(log_file = log_file, message = paste0('Processing chromosome ', chr_i,':')) -# Paste files together in batches -# Set number of batches according to the number of score files to combine -num_batches <- max(c(1, min(c(opt$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 = " "))) -} + ##### + # Combine score files + ##### + # Create row number index to subset score files by chromosome + row_index <- format(which(ref$CHR == chr_i) + 1, scientific = FALSE) + write.table(row_index, paste0(tmp_dir,'/row_index.txt'), row.names=F, quote=F, col.names = F) -# Paste batches together -log_add(log_file = log_file, message = paste0('Aggregating batched score files.')) -tmp_batch_files <- paste0(tmp_dir,'/tmp_batch_',1:length(batches)) -system(paste0("paste -d ' ' ", tmp_dir,'/map.txt ', paste(tmp_batch_files, collapse = " "), ' > ', tmp_dir, '/all_score.txt')) -system(paste0('rm ', paste(tmp_batch_files, collapse = " "))) + # Create file containing SNP, A1, and A2 information for each chromosome + fwrite(ref[ref$CHR == chr_i, c('SNP','A1','A2'), with=F], paste0(tmp_dir,'/map.txt'), row.names=F, quote=F, sep=' ') -# 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_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) -} + # 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$name[i]), '.txt' + )) + } -# Concatenate scale files -all_scale<-do.call(rbind, scale_files) + # Paste files together in batches + # Set number of batches according to the number of score files to combine + num_batches <- max(c(1, min(c(opt$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 = " "))) + } -##### -# Perform polygenic risk scoring -##### + # 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 = " "))) -# Read in target_list -target_list <- read_param(config = opt$config, param = 'target_list', return_obj = T) + # Perform polygenic risk scoring + scores_i <- + plink_score( + pfile = opt$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 + ) -# 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') + # 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() +} -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$n_cores - ) +# Combine score with IDs +scores<-data.table(scores_ids, + scores) ### # Scale the polygenic scores based on the reference ### -log_add(log_file = log_file, message = 'Scaling target polygenic scores to the reference.') -scores<-score_scale(score=scores, ref_scale=all_scale) +if(opt$population == 'TRANS'){ + log_add(log_file = log_file, message = paste0('Reading in ancestry adjustment models.')) + + models<-list() + for(i in 1:nrow(score_files)){ + 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$name[i],'-TRANS.model.rds')) + names(models[[paste0('score_file_', i)]])<-gsub('SCORE_', paste0('score_file_', i, '.'), names(models[[paste0('score_file_', i)]])) + } + + models <- do.call(c, unname(models)) + + # Read in target projected PCs + target_pcs<-fread(paste0(outdir,'/',opt$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)]]$Param) + } + + # Concatenate scale files + all_scale<-do.call(rbind, scale_files) + + # Scale scores + log_add(log_file = log_file, message = 'Scaling target polygenic scores to the reference.') + scores<-score_scale(score=scores, ref_scale=all_scale) +} ### # Write out the target sample scores ### for(i in 1:nrow(score_files)){ - scores_i <- scores[, c('FID','IID', names(scores)[grepl(paste0(score_files$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) } @@ -178,7 +260,7 @@ 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$output,'.log',sep=''), append = T) +sink(file = log_file, append = T) cat('Analysis finished at',as.character(end.time),'\n') cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n') sink() diff --git a/Scripts/ukb_relative_remover/ukb_relative_remover.R b/Scripts/ukb_relative_remover/ukb_relative_remover.R new file mode 100644 index 00000000..d3ee9d04 --- /dev/null +++ b/Scripts/ukb_relative_remover/ukb_relative_remover.R @@ -0,0 +1,87 @@ +#!/usr/bin/Rscript +# This script was written by Oliver Pain whilst at King's College London University. +start.time <- Sys.time() +suppressMessages(library("optparse")) + +option_list = list( + make_option("--keep", action="store", default=NA, type='character', + help="File listing individuals to keep - only first column used [optional]"), + make_option("--rel_file", action="store", default=NA, type='character', + help="UKB relatedness file [required]"), + make_option("--rel_thresh", action="store", default=0.044, type='numeric', + help="Kingship threshold [optional]"), + make_option("--seed", action="store", default=1234, type='numeric', + help="Seed number [optional]"), + make_option("--GreedyRelated", action="store", default=NA, type='character', + help="Path to GreedyRelated binary [required]"), + make_option("--output", action="store", default='./unrelated', type='character', + help="Output file name [optional]") +) + +opt = parse_args(OptionParser(option_list=option_list)) + +sink(file = paste(opt$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$output,'.log',sep=''), append = T) +cat('Analysis finished at',as.character(end.time),'\n') +cat('Analysis duration was',as.character(round(time.taken,2)),attr(time.taken, 'units'),'\n') +sink() diff --git a/docs/CrossPop.Rmd b/docs/CrossPop.Rmd new file mode 100644 index 00000000..6d4195ef --- /dev/null +++ b/docs/CrossPop.Rmd @@ -0,0 +1,12805 @@ +--- +title: Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations +output: + html_document: + theme: cosmo + toc: true + toc_float: true + toc_depth: 2 + css: styles/styles.css + includes: + in_header: header.html + after_body: footer.html + +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$set(eval = FALSE) +library(knitr) +library(kableExtra) +library(data.table) +``` + + + +*** + +# Preprint + +This document provides code and key figures and tables for the following preprint: + +
+ +**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) + +
+ +*** + +**Document overview**: + +- [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 + +*** + +## UKB + +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. + +*** + +### Ancestry inference + +We will perform this using the GenoPred pipeline. We will need to prepare the configuration files before running the pipeline. + +
Show code +
+ +

Create symlinks

+ +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} +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 +``` + +*** + +

Create list of unrelated individuals

+ +```{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) +``` + +*** + +

Create target_list

+ +```{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) +``` + +*** + +

Create configfile

+ +```{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) +``` + +*** + +

Run pipeline

+ +```{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 +``` +
+ +*** + +### Phenotype extraction + +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. + +
Show code +```{bash} +mkdir /users/k1806347/oliverpainfel/Data/ukb/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 +) + +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) + +# Extract outcomes from UKB (project ukb82087) +project_dir <- "/datasets/ukbiobank/ukb82087" + +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$field, '-'), collapse='|'))) %>% + bio_field_add("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.txt") + +bio_phen( + project_dir, + field = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.txt", + out = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset" +) + +system("ls -lh /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.rds") +df <- readRDS("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.rds") + +# Take the first observation of each outcome +library(tidyr) +df_long <- df %>% + pivot_longer(cols = names(df)[!grepl('eid', names(df))], names_to = "variable", values_to = "outcome") %>% + drop_na(outcome) +df_long$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$outcome + ) + + fwrite( + tmp, + paste0( + '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', + prscsx_dat$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 = '/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$POP +) + +# Merge ancestry info with phenotype data +df_short <- dcast(df_long, eid ~ variable, value.var = "outcome") +df_short<-merge(df_short, pop_dat, by='eid') + +# Remove related individuals +greedy_related <- "/scratch/prj/ukbiobank/recovered/KCL_Data/Software/tools/GreedyRelated-master-v1.2.1/GreedyRelated" +rel<-bio_gen_related_remove( + project_dir = project_dir, + greedy_related = greedy_related, + keep = df_short$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$POP == j])) + ) + n_table<-rbind(n_table, tmp) + } +} + +write.csv(n_table, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/n_table') + +# Define training subset for EUR +df_short_unrel_eur<-df_short_unrel[df_short_unrel$POP == 'EUR',] +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$field[i]]])) + ) + n_table_eur<-rbind(n_table_eur, tmp) +} + +write.csv(n_table_eur, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/n_table_eur') + +df_short_unrel$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$eid]<-'EUR_test' + +# Output phenotype data for each population +for(i in 1:nrow(prscsx_dat)){ + for(j in unique(df_short_unrel$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( + '/users/k1806347/oliverpainfel/Data/ukb/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( + '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', + prscsx_dat$label[i], + '.unrel.', j, '.row_number.txt' + ), + row.names = F, + quote = F, + na = 'NA', + sep = '\t' + ) + } +} + +# For the EUR training GWAS, normalise and regress covariates +# Use age, sex and PCs as covariates +# Read in PC data released by UKB +qc_dat<-bio_gen_sqc(project_dir) +qc_dat<-qc_dat[,c('eid',paste0('pc',1:20))] +df_short_unrel<-merge(df_short_unrel, qc_dat, by='eid') + +# Read in sex and age information +system('rm /users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt') +f <- bio_field(project_dir) +f %>% + select(field, name) %>% + filter(str_detect(field, "^21022-0.0|^31-0.0")) %>% + bio_field_add("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt") + +bio_phen( + project_dir, + field = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt", + out = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset" +) + +system("ls -lh /users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.rds") +df <- readRDS("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.rds") +names(df)<-gsub('-.*','',names(df)) +names(df)[names(df) == '31']<-'sex' +names(df)[names(df) == '21022']<-'age' +df_short_unrel<-merge(df_short_unrel, df, by='eid') + +# Within each population, normalise each outcome and regress out covariates +library(RNOmni) +covs<-c(paste0('pc',1:20), 'sex', 'age') +df_short_unrel_eur_train<-df_short_unrel[df_short_unrel$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$pheno_norm_resid_scale + ) + + fwrite( + tmp, + paste0( + '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', + prscsx_dat$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( + '/users/k1806347/oliverpainfel/Data/ukb/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( + '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', + prscsx_dat$label[i], + '.unrel.EUR_train.norm_resid_scale.row_number.txt' + ), + row.names = F, + quote = F, + na = 'NA', + sep = '\t' + ) +} + +``` +
+ +*** + +## 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 + +
Show code +```{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 $(seq 1 22); do + sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/plink2 \ + --pfile /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${chr} \ + --pheno /users/k1806347/oliverpainfel/Data/ukb/phenotypes/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}" + 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.${pheno}.chr1.outcome.glm.linear > /users/k1806347/oliverpainfel/Data/ukb/gwas/${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 + 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 'BEGIN{FS=OFS="\t"} NR==1 {$5="A2"} 1' > temp.txt && mv temp.txt /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt + + gzip /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt +done + +# Delete per chromosome files +rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*/*chr* + +``` +
+ +*** + +### Download BBJ sumstats + +
Show code + +```{r} +# Identify wget command for relevant phenotypes +library(data.table) + +# Read in BBJ GWAS info from BBJ website +bbj_gwas<-fread('~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas.csv') + +# Map BBJ trait names to those used for UKB +bbj_gwas$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$trait == 'G-glutamyl transpeptidase']<-'γ-glutamyl transpeptidase' + +# Merge the bbj trait info with the prscsx trait info +prscsx_dat<-fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv') +prscsx_dat <- merge(bbj_gwas, prscsx_dat, by='trait', all=T) + +write.csv(prscsx_dat, '~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_prscsx.csv', row.names = F) + +# Create column showing what label is used in the wget command +prscsx_dat$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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do + url=$(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 + 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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do + wget_label=$(awk -v var="$pheno" '$1 == var {print $3}' ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt) +if [ "$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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do +sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/pigz/pigz -dc ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${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.${pheno}.reformat.txt.gz" +done + +``` +
+ +*** + +### Download UGR sumstats + +
Show code + +```{r} +# Identify wget command for relevant phenotypes +library(data.table) + +# Read in UGR GWAS info from GWAS catalogue +ugr_gwas<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats.csv') + +# Map UGR trait names to those used for UKB +ugr_gwas$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$trait) + +# Merge the ugr trait info with the prscsx trait info +prscsx_dat<-fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv') +prscsx_dat <- merge(ugr_gwas, prscsx_dat, by='trait') + +write.csv(prscsx_dat, '~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv', row.names = F) + +# Create column indicating wget command +prscsx_dat$wget<-NA +for(i in 1:nrow(prscsx_dat)){ + if(!grepl('.txt', prscsx_dat$wget[i])){ + print(i) + Sys.sleep(2) + log<-system(paste0('curl --max-time 10 ', gsub('http:','ftp:', prscsx_dat$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) + } +} +# 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) + +``` + +```{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') + +}) + +``` + +
+ +*** + +# 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 + +Use GenoPred for this. + +
Show code + +
+ +

Prepare configuration

+ +```{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( + name=paste0(prscsx_dat$labels,'_UKB'), + path=paste0('/users/k1806347/oliverpainfel/Data/ukb/gwas/',prscsx_dat$labels,'/ukb.eur_train.',prscsx_dat$labels,'.GW.txt.gz'), + population='EUR', + n=NA, + sampling=NA, + prevalence=NA, + mean=0, + sd=1, + label=paste0('"', prscsx_dat$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, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt', col.names = T, 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: /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, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml', col.names = F, row.names = F, quote = F) + +``` + +*** + +

Run pipeline

+ +```{bash} +snakemake \ + --profile slurm \ + --use-conda \ + --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml \ + target_pgs -n +``` + +*** + +

Reformat for LDSC and POPCORN

+ +```{r} +library(data.table) +dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats', recursive = T) +gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/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$name[i], + ".sumstats.gz"))){ + next + } + print(i) + gwas_file <- + paste0( + "/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/", + gwas_list$name[i], + "/", + gwas_list$name[i], + "-cleaned.gz" + ) + + gwas_header <- fread(gwas_file, nrows = 1) + cols_index <- which(names(gwas_header) %in% c('SNP','A1','A2','BETA','SE','P','N')) + + system( + paste0( + "zcat ", + gwas_file, + " | cut -f ", + paste0(cols_index, collapse = ','), + " | sed -e '1s/BETA/beta/'", + " | /users/k1806347/oliverpainfel/Software/pigz/pigz -f", + " > /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/", + gwas_list$name[i], + ".sumstats.gz" + ) + ) +} +``` + +
+ +*** + +## LDSC + +
Show code +```{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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do + mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats + + sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \ + --sumstats /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/${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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do + mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/${pheno}/${pop} + + sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \ + --h2 /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats/${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 + +``` + +
+ +*** + +## POPCORN + +
Show code + +
+ +

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 $(seq 1 22); do + /users/k1806347/oliverpainfel/Software/plink2 \ + --pfile /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr${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 $(seq 1 22); do + sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="popcorn \ + compute \ + -v 1 \ + --bfile1 /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.EUR.chr${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 +rm /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_*_CSCORES/*chr*.txt +``` + +*** + +

Run POPCORN

+ +```{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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do + mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/${pheno}/EUR_${pop} + sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="popcorn \ + fit -v 3 \ + --cfile /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${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_${pop}/rG_gen_effect" + done +done + +``` + +
+ +*** + +## Plot LDSC and POPCORN results + +
Show code + +```{r} + +library(data.table) +library(ggplot2) +library(cowplot) + +# Read in phenotypes +pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1 + +# Plot the heritability estimates +h2_res <- NULL + +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) + +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)", fill = NULL) + + theme_half_open() + + 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 +for(pop in c('AFR','EAS')){ + 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',] + 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) + +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)", fill = NULL) + + theme_half_open() + + 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() + +``` +
+ +```{bash, eval=T, echo=F} +mkdir -p /scratch/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/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/ + +``` + +
Show LDSC SNP-heritability + +
+
+ +
+
+ +
+ +
Show POPCORN genetic correlation estimates + +
+
+ +
+
+ +
+ +*** + +## AVENGEME + +
Show code + +
+ +

Create predictor list

+ +```{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_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('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1 + +# Create files for EAS and AFR targets +pop <- c('EUR','EAS','AFR') +for(trait_i in pheno_intersect){ + # Make a group containing both GWAS for each single source method + # Make a group for each multisource method + scores_i <- scores[grepl(paste0('^', trait_i, '_'), scores$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/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/targ_', + pop_i, + '.disc_', + pop_i, + '/', + trait_i, + '/predictor_list.ptclump.txt' + ), + col.names = T, + row.names = F, + quote = F + ) + } +} + +``` + +*** + +

Run model_builder

+ +```{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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do + sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \ + --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${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" + done +done + +``` + +*** + +

Plot pT+clump association 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 phenotypes +pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1 + +# Read in results +pop = c('EUR','EAS','AFR') +res_all <- NULL +for(pheno_i in pheno_intersect){ + res_i<-NULL + for(pop_i in pop){ + assoc_i <- + fread( + paste0( + '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_', + pop_i, + '.disc_', + pop_i, + '/', + pheno_i, + '/res.ptclump.assoc.txt' + ) + ) + assoc_i$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)) + +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) + + 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), + 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() + +``` + +*** + +

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<-'/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('/users/k1806347/oliverpainfel/Data/ukb/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, + 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$pi0[3] + ) + ) + } +} + +dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme') +write.csv(mod_res_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv', row.names = F, quote = F) + +mod_res_all<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv') + +png('~/oliverpainfel/Analyses/crosspop/plots/avengeme_h2.png', res = 100, width = 900, height = 500, units = 'px') +ggplot(mod_res_all, aes(x = Phenotype, y = vg_est, fill = Population)) + + geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) + + geom_errorbar(aes(ymin=vg_lowCI, ymax=vg_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) + + labs(y="SNP-based Heritability (95%CI)", fill = NULL) + + theme_half_open() + + theme(axis.text.x = element_text(angle = 45, hjust = 1), + legend.position = "top", + legend.key.spacing.x = unit(1, "cm"), + legend.justification = "center") + + background_grid(major = 'y', minor = 'y') +dev.off() + +png('~/oliverpainfel/Analyses/crosspop/plots/avengeme_polygenicity.png', res = 100, width = 900, height = 500, units = 'px') +ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) + + geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) + + geom_errorbar(aes(ymin=1 - pi0_lowCI, ymax=1 - pi0_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) + + labs(y="Proporition non-zero\neffects (95%CI)", fill = NULL) + + theme_half_open() + + coord_cartesian(ylim = c(0, 0.15)) + + theme(axis.text.x = element_text(angle = 45, hjust = 1), + legend.position = "top", + legend.key.spacing.x = unit(1, "cm"), + legend.justification = "center") + + background_grid(major = 'y', minor = 'y') +dev.off() + +summary(mod_res_all$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']) + +``` + +
+ +```{bash, eval=T, echo=F} +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/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/ + +``` + +
Show AVENGEME results + +
+
+ +
+
+ +
+ +
+
+ +
+
+ +
+ +*** + +## 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. + +
Show code + +```{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$trait, '/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', col.names = F, row.names = F, quote = F) + +# Plot max R2 for selected traits +mod_res_all <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv') +mod_res_all_selected <- mod_res_all[mod_res_all$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$Population == 'AFR'])), 2) + +``` + +
+ +*** + +## GWAS descriptives + +Make a table showing GWAS information for the manuscript. + +
Show code + +```{r} +library(data.table) + +##### +# Trait names, labels, and URLs +##### + +### +# UKB +### +ukb <- fread('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_data.csv') +names(ukb) <- c('trait', 'labels','field') +trait_labels <- ukb[, c('trait','labels'), with=F] +ukb<-ukb[, c('trait','field'), with=F] +ukb$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])) + ) + ) +} + +``` + +
+ +
Show descriptives table + +```{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") + +``` + +
+ +*** + +# Main analysis + +*** + +## PGS calculation + +We will do this using GenoPred. + +
Show code + +
+ +

Prepare configuration

+ +```{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, '"') + +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 +###### + +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, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.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.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.yaml', col.names = F, row.names = F, quote = F) + +``` + +*** + +

Run pipeline

+ +```{bash} +snakemake \ + --profile slurm \ + --use-conda \ + --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml \ + target_pgs -n +``` + +
+ +**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) + +
+ +
+ +*** + +## 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 + +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. + +
Show code + +
+ +

Create predictor list

+ +```{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.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){ + 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)),] + + # 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/targ_', + targ_pop_i, + '.disc_EUR_', + disc_pop_j_2, + '/', + trait_i, + '/predictor_list.txt' + ), + col.names = T, + row.names = F, + quote = F + ) + } + } +} + +``` + +*** + +

Run model_builder

+ +```{bash} +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" + 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 $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do + if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt" ]; then + 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 \ + --n_core 5" + fi + done + done +done + +``` + +*** + +

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') + +# Read in list of outcomes +selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1 +info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv') + +# Calculate correlation between all phenotypes in each target population +cors <- list() +for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){ + if(pop_i == 'EUR'){ + pop_i_2 <- 'EUR_test' + } else { + pop_i_2 <- pop_i + } + pheno_pop_i <- list() + for(pheno_i in selected_traits){ + pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt')) + names(pheno_pop_i[[pheno_i]])[3] <- pheno_i + } + + pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i) + + cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p')) + cors[[pop_i]] <- cors_i +} + +# Read in results +targ_pop = c('EUR','EAS','AFR') +res_eval <- list() +for(pheno_i in selected_traits){ + res_eval_i<-NULL + for(targ_pop_i in targ_pop){ + if(targ_pop_i == 'EAS'){ + disc_pop <- 'EAS' + } + if(targ_pop_i == 'AFR'){ + disc_pop <- 'AFR' + } + if(targ_pop_i == 'EUR'){ + disc_pop <- c('EAS','AFR') + } + for(disc_pop_i in disc_pop){ + eval_i <- + fread( + paste0( + '/users/k1806347/oliverpainfel/Analyses/crosspop/', + 'targ_', + targ_pop_i, + '.disc_EUR_', + disc_pop_i, + '/', + pheno_i, + '/res.pred_eval.txt' + ) + ) + eval_i$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$Group) + res_eval_i$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) +} + +# 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$Target, ' Target') + + png(paste0('~/oliverpainfel/Analyses/crosspop/plots/', pheno_i,'.png'), res=300, width = 3400, height = 2000, units = 'px') + plot_tmp<-ggplot(tmp, aes(x=label, y=R , fill = Model)) + + geom_errorbar(aes(ymin = R - SE, ymax = R + SE), + width = 0, + position = position_dodge(width = 1)) + + geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) + + geom_vline(xintercept = seq(1.5, length(unique(tmp$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){ + 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$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')) + +write.csv(meta_res_eval, '~/oliverpainfel/Analyses/crosspop/r_eval.csv', row.names = F) + +# Plot average performance across phenotypes for AFR and EAS targets +tmp <- meta_res_eval +tmp <- tmp[tmp$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/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/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$Target, ' Target') + +png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_leopard.png'), res=300, width = 1500, height = 2000, units = 'px') +ggplot(tmp, aes(x=label, y=R , fill = Model)) + + geom_errorbar(aes(ymin = R - SE, ymax = R + SE), + width = 0, + position = position_dodge(width = 1)) + + geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) + + geom_vline(xintercept = seq(1.5, length(unique(tmp$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/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$label))), linetype="dotted") + + labs(y = "R (SE)", x='Method') + + facet_grid(Target ~ ., scales='free', space = 'free_x') + + theme_half_open() + + background_grid(major = 'y', minor = 'y') + + panel_border() + + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1)) +dev.off() + + +#### +# Create heatmap showing difference between all methods and models +#### + +# Create a function to mirror pred_comp results +mirror_comp<-function(x){ + x_sym <- x + x_sym$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$Model_1_R, + R_diff=NA, + R_diff_pval=NA + ) + x_comp<-rbind(x_mirrored, x_diag) + return(x_comp) +} + +# Read in results +targ_pop=c('EUR','EAS','AFR') +res_comp <- list() +for(pheno_i in selected_traits){ + res_comp_i<-NULL + for(targ_pop_i in targ_pop){ + if(targ_pop_i == 'EAS'){ + disc_pop <- 'EAS' + } + if(targ_pop_i == 'AFR'){ + disc_pop <- 'AFR' + } + if(targ_pop_i == 'EUR'){ + disc_pop <- c('EAS','AFR') + } + for(disc_pop_i in disc_pop){ + comp_i <- + fread( + paste0( + '/users/k1806347/oliverpainfel/Analyses/crosspop/', + 'targ_', + targ_pop_i, + '.disc_EUR_', + disc_pop_i, + '/', + pheno_i, + '/res.pred_comp.txt' + ) + ) + comp_i<-mirror_comp(comp_i) + comp_i$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$`R (Model 2)`, 3) + +write.csv(tmp, '~/oliverpainfel/Analyses/crosspop/r_diff.csv', row.names=F) + +########### + +library(MAd) + +# Average R across phenotypes +meta_res_comp <- NULL +for(targ_pop_i in targ_pop){ + if(targ_pop_i == 'EAS'){ + disc_pop <- 'EAS' + } + if(targ_pop_i == 'AFR'){ + disc_pop <- 'AFR' + } + if(targ_pop_i == 'EUR'){ + disc_pop <- c('EAS','AFR') + } + for(disc_pop_i in disc_pop){ + + # Subset res_comp for each scenario + res_comp_i <- res_comp_all[res_comp_all$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$`R (Model 2)`, 3) + +write.csv(tmp, '~/oliverpainfel/Analyses/crosspop/r_diff_average.csv', row.names=F) + +############ + +# Group differences +meta_res_comp$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$R_diff_catagory), + palette = "RdBu", + drop = F, + na.value = 'grey' + ) + + theme_half_open() + + background_grid() + + panel_border() + + theme(axis.text.x = element_text( + angle = 45, + vjust = 1, + hjust = 1 + )) + + png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_diff.Discovery_EUR_', disc_pop_i,'.Target_', targ_pop_i, '.png'), res=300, width = 4400, height = 3200, units = 'px') + print(plot_tmp) + dev.off() + } +} + +#### +# Plot relative improvement of methods +#### +# Use ptclump IndivTune using EUR GWAS as the reference, as provides an interpretable scale + +meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$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/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$Discovery1,' GWAS') + +meta_tmp_simple<-meta_tmp_simple[!duplicated(meta_tmp_simple[, c('label', 'Discovery_clean', 'Model1'), with=F]),] +meta_tmp_ref_simple<-meta_tmp_ref_simple[!duplicated(meta_tmp_ref_simple[, c('label', 'Discovery_clean', 'Model1'), with=F]),] +tmp_simple<-tmp_simple[!duplicated(tmp_simple[, c('label', 'Discovery_clean', 'Model1','pheno'), with=F]),] + +png('~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.perc_improv.png', width = 4000, height = 1500, res= 300, units = 'px') +ggplot(meta_tmp_simple[meta_tmp_simple$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$label))), linetype="dotted") + + scale_y_continuous(labels = percent_format()) + + labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) + + facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') + + theme_half_open() + + background_grid(major = 'y', minor = 'y') + + panel_border() + + theme( + axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1), # Increase x-axis labels + legend.position = "top", + legend.key.spacing.x = unit(2, "cm"), + legend.justification = "center" + ) +dev.off() + +######## +# Plot relative improvement of LEOPARD over IndivTune of SumStatTune scores +######## + +# meta res +meta_res_comp_ref <- meta_res_comp[meta_res_comp$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$R_diff_perc < -0.25, + ], + aes(label = pheno), # label as percent with 1 decimal + position = position_dodge(width = 0.7), + size = 3, + min.segment.length = 0, + segment.color = NA, + show.legend = FALSE + ) +dev.off() + +# It shows PRS-CSx --meta flag is actually does very well, except when the AFR GWAS is very small (~2700). + +``` +
+ +```{bash, eval=T, echo=F} + +cp ~/oliverpainfel/Analyses/crosspop/plots/average_r.perc_improv.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/ + +cp ~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.perc_improv.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/ + +cp ~/oliverpainfel/Analyses/crosspop/plots/average_r_leopard.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/ + +``` + +
Show average improvement in AFR + EAS + +
+
+ +
+
+ +
+ +
Show average improvement in EUR + +
+
+ +
+
+ +
+ +
Show LEOPARD comparison + +
+
+ +
+
+ +
+ +*** + +## LEOPARD+QuickPRS + +Here we will compare the LEOPARD estimated weights for population specific PGS, to the weights estimated using observed data in the UKB target sample. + +
Show code + +```{r} +setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/') + +library(data.table) +library(ggplot2) +library(cowplot) + +source('../functions/misc.R') +source_all('../functions') + +# Read in list of outcomes +selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$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 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('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$Method <- method_i + model<-model[,c('Target','Discovery','Weight','Method','Trait'), with=F] + obs_weights<-rbind(obs_weights, model) + } + } + } +} + +#### +## Estimate weights if using the inverse variance weighting (realised this doesn't make sense as PGS are standardised whereas SNP effects in PRS-CSx are not) +#### +# +## Read in GWAS descriptives +#gwas_desc<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv') +#gwas_desc <- gwas_desc[, c('Trait Label','Ancestry','GWAS N'), with=F] +#names(gwas_desc)<-c('trait','ancestry','n') +#gwas_desc<-gwas_desc[gwas_desc$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)) + +# 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$label) +default_colors["LEOPARD"] <- "black" + +# Plot the estimated and observed weights +png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights.png', units = 'px', res = 300, width = 2500, height = 1500) +ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) + + geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) + + scale_fill_manual(values = default_colors) + + facet_grid(Target ~ .) + + theme_half_open() + + labs(title = 'Weight of target ancestry-matched PGS', fill = NULL) + + background_grid(major = 'y', minor = 'y') + + panel_border() + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + + ylim(c(0,1)) +dev.off() + +# Plot EUR target +tmp <- both[both$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/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/plots/leopard_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300) +ggplot(tmp_wide[tmp_wide$Discovery != 'EUR',], aes(x = LEOPARD, y = QuickPRS)) + + geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") + # Perfect calibration + geom_smooth(method = "lm", se = TRUE, colour = "blue") + # Regression line + geom_point(alpha = 0.7) + + geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) + + labs( + x = "LEOPARD weight", + y = "Observed weight", + ) + + theme_half_open() + + panel_border() + + theme( + axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), + ) + + facet_grid(. ~ Discovery) + + coord_fixed() +dev.off() + +#### +## Check calibration of inverse_var compared to QuickPRS observed weights (again realised this doesn't make sense) +#### +# +#tmp <- both[both$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 +### + +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() + +``` + +
+ +```{bash, eval=T, echo=F} + +cp ~/oliverpainfel/Analyses/crosspop/plots/leopard_weights.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/ + +``` + +
Show observed and LEOPARD PGS weights + +
+
+ +
+
+ +
+ +*** + +## Computational resoures + +Here we will read in the benchmark data for PGS methods and create a table for the manuscript. + +
Show code + +```{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/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_|leopard_quickprs_', 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$rule)) + +bm_dat_all <- merge(bm_dat_all, pgs_method_labels, by = 'method', all.x=T) + +bm_dat_all$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)){ + 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]) + ) + ) +} + +# 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 + +# 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 + +# 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] <- + 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') + +# 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$Type[method_avg$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$Time_hour, 2) +names(method_avg)<-c('Method',"Time (hrs)") + +############# +# Memory +############# + +# Calculate average max_rss for each method +method_avg_mem <- NULL +for(i in unique(bm_dat_all$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]) + ) + ) +} + +# 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) +``` + +
+ +
Show computational resources table + +```{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) + +``` + +
+ +*** + +# 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. + +*** + +## 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. + +
Show code + +```{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 + +``` + +
+ +*** + +## 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). + +
Show code + +
+ +

Prepare configuration

+ +```{r} +library(data.table) + +dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs') + +###### +# 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_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(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml', col.names = F, row.names = F, quote = F) + +``` + +*** + +

Run pipeline

+ +```{bash} +snakemake \ + --profile slurm \ + --use-conda \ + --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml \ + target_pgs -n +``` + +
+ +*** + +## PGS evaluation + +
Show code + +
+ +

Create predictor list

+ +```{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 + ) + } + } +} + +``` + +*** + +

Run model_builder

+ +```{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 +``` + +*** + +

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') + +# 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')) + +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$', x) & !grepl('pseudo', x)] <- 'IndivTune' + mod[grepl('top1$', 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", "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$Model_2_R + +# Compare IndivTune SBayesRC-multi to TL-SBayesRC-multi +tmp_sbayesrc <- meta_res_comp[meta_res_comp$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$R_diff_p + +``` + +
+ + +```{bash, eval=T, echo=F} + +cp ~/oliverpainfel/Analyses/crosspop/tlprs/plots/average_r.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_tlprs.png + +``` + +
Show TLPRS results + +
+
+ +
+
+ +
+ +*** + +## Computational resoures + +
Show code + +```{r} +library(data.table) +library(ggplot2) +library(cowplot) + +setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/') +source('../functions/misc.R') +source_all('../functions') + +# Get some key variables from config +config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml' +pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F) +outdir <- read_param(config = config, param = 'outdir', return_obj = F) + +# Read in configuration specific benchmark files +bm_files_i <- list.files(paste0(outdir, '/reference/benchmarks/'), full.names = T) + +# Subset benchmarks for pgs_methods +bm_files_i <- bm_files_i[grepl('prep_pgs_tlprs', bm_files_i)] + +# Read in benchmark files +bm_dat_all <- do.call(rbind, lapply(bm_files_i, function(file) { + tmp <- fread(file) + tmp$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_tlprs.csv', row.names=F) +``` + +
+ +
Show computational resources table + +```{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) + +``` + +
+ +*** + +# Sensitivity analyses + +*** + +## 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. + +
Show code + +```{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 +``` + +
+ +*** + +### 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). + +
Show code + +
+ +

Prepare configuration

+ +```{r} +library(data.table) + +dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only') + +###### +# 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) + +``` + +*** + +

Run pipeline

+ +```{bash} +snakemake \ + --profile slurm \ + --use-conda \ + --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \ + target_pgs -n +``` + +
+ +*** + +### PGS evaluation + +
Show code + +
+ +

Create predictor list

+ +```{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/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 +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),] + 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' + } + + 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)),] + + # 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] + + 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 + ) + } + } +} + +``` + +*** + +

Run model_builder

+ +```{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 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 + +``` + +*** + +

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') + +# Read in list of outcomes +selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1 +info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv') + +# Calculate correlation between all phenotypes in each target population +cors <- list() +for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){ + if(pop_i == 'EUR'){ + pop_i_2 <- 'EUR_test' + } else { + pop_i_2 <- pop_i + } + pheno_pop_i <- list() + for(pheno_i in selected_traits){ + pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt')) + names(pheno_pop_i[[pheno_i]])[3] <- pheno_i + } + + pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i) + + cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p')) + cors[[pop_i]] <- cors_i +} + +# Read in results +targ_pop = c('EAS','AFR') +res_eval <- list() +for(pheno_i in selected_traits){ + res_eval_i<-NULL + for(targ_pop_i in targ_pop){ + if(targ_pop_i == 'EAS'){ + disc_pop <- 'EAS' + } + if(targ_pop_i == 'AFR'){ + disc_pop <- 'AFR' + } + if(targ_pop_i == 'EUR'){ + disc_pop <- c('EAS','AFR') + } + for(disc_pop_i in disc_pop){ + eval_i <- + fread( + paste0( + '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/', + 'targ_', + targ_pop_i, + '.disc_EUR_', + disc_pop_i, + '/', + pheno_i, + '/res.pred_eval.txt' + ) + ) + eval_i$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$Group)]<-'IndivTune' + res_eval_i$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$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS')) + +# Plot average performance across phenotypes for AFR and EAS targets +tmp <- meta_res_eval +tmp <- tmp[tmp$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_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 != 'SumStatTune'] <- 'IndivTune' +tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune' +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$Model != 'SumStatTune'] <- 'IndivTune' +tmp$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$Method,] +main_results<-main_results[main_results$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() + +``` +
+ +```{bash, eval=T, echo=F} + +cp ~/oliverpainfel/Analyses/crosspop/sensitivity_1kgrefplots/comparison_to_main_result.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/ + +``` + +
Show results + +
+
+ +
+
+ +
+ +*** + +## 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. + +*** + +### PGS calculation + +
Show code + +
+ +

Prepare configuration

+ +```{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_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 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", + "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) + +``` + +*** + +

Run pipeline

+ +```{bash} +snakemake \ + --profile slurm \ + --use-conda \ + --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml \ + target_pgs -n +``` + +
+ +*** + +### PGS evaluation + +
Show code + +
+ +

Create predictor list

+ +```{r} + +setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/') +source('../functions/misc.R') +source_all('../functions') +library(data.table) + +# Get some key variables from config +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)$V1 + +# Get a list of score files +scores_2_pop <- list_score_files(config_2_pop) +scores_3_pop <- list_score_files(config_3_pop) +scores <- rbind(scores_2_pop, scores_3_pop) +scores <- scores[!duplicated(scores),] + +# Remove xwing +scores <- scores[scores$method != 'xwing', ] + +# Create files for EAS and AFR targets +targ_pop <- c('EAS','AFR') +for(trait_i in selected_traits){ + scores_j <- scores[grepl(trait_i, scores$name),] + scores_j$multi <- scores_j$method + + for(targ_pop_i in targ_pop){ + disc_pop_j <- c('UGR','BBJ','UKB') + disc_pop_j_2 <- c('AFR','EAS','EUR') + + dir.create( + paste0( + '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_', + targ_pop_i, + '.disc_', paste(disc_pop_j_2, collapse = '_'), + '/', + trait_i + ), + recursive = T + ) + + # Insert path to score file + scores_i <- scores_j[!grepl(paste0('UKB_', disc_pop_j[!(disc_pop_j_2 %in% c('EUR', targ_pop_i))], '$'), scores_j$name),] + + scores_i$predictor <- paste0( + outdir, + '/ukb/pgs/TRANS/', + 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 = 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 + ) + + 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 = 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 + ) + + 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 which(scores_i_multi$method %in% c('prscsx'))){ + score_header<-fread(scores_i_multi$predictor[i], nrow = 1) + + 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'), 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$predictor[i], nrow = 1) + + pops <- gsub(paste0(trait_i, '_'),'', scores_i_multi$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$|_', pop, '_phi_auto'), names(score_header))) + } + if(scores_i_multi$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$predictor[i]) + + scores_i_multi_pop_pseudo <- rbind(scores_i_multi_pop_pseudo, tmp) + } + } + + # Create pseudo score for multi-source methods + scores_i_multi_pseudo<-NULL + for(i in 1:nrow(scores_i_multi)) { + param <- find_pseudo( + config = ifelse(scores_i_multi$name[i] %in% scores_2_pop$name, config_2_pop, config_3_pop), + gwas = scores_i_multi$name[i], + pgs_method = scores_i_multi$method[i], + target_pop = targ_pop_i + ) + + score_header <- + fread(scores_i_multi$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 + )) + + #### + # 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) + + #### + # 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_clean, + 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 + ) + } +} + +``` + +*** + +

Run model_builder

+ +```{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 $(echo EAS AFR); do + if [ "$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/${pheno}/res.pred_comp.txt" ]; then + sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \ + --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${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 1" + fi + done +done + +``` + +*** + +

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') + +# 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() + +``` +
+ +```{bash, eval=T, echo=F} + +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 + +``` + +
Show results + +
+
+ +
+
+ +
+ +*** + +## Using external GWAS sumstats + +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. + +*** + +### PGS calculation + +We will do this using GenoPred. + +
Show code + +
+ +

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: ['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", + "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

+ +```{bash} +snakemake \ + --profile slurm \ + --use-conda \ + --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_meta.yaml \ + target_pgs -n +``` + +
+ +*** + +### PGS evaluation + +
Show code + +
+ +

Create predictor list

+ +```{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) +} + +``` + +*** + +

Run model_builder

+ +```{bash} +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 $(echo bc ckd gout ibd pc ra stroke t1d t2d height bmi egfr hba1c urate hdl); do + if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/${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 + +``` + +*** + +

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$label, levels = model_order) + +png(paste0('~/oliverpainfel/Analyses/crosspop/plots_meta/average_r.png'), res=100, width = 500, height = 300, units = 'px') +ggplot(tmp, aes(x=label, y=R , fill = Model)) + + geom_errorbar(aes(ymin = R - SE, ymax = R + SE), + width = 0, + position = position_dodge(width = 1)) + + geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) + + geom_vline(xintercept = seq(1.5, length(unique(tmp$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() + +# Results and conclusions remain consistent. + +``` +
+ +```{bash, eval=T, echo=F} + +cp ~/oliverpainfel/Analyses/crosspop/plots_meta/average_r.png /scratch/prj/oliverpainfel/Software/MyGit/GenoPred/docs/Images/CrossPop_2025/average_r_meta.png + +``` + +
Show results + +
+
+ +
+
+ +
+ +*** + +## 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. + +*** + +### Downsample GWAS + +
Show code + +```{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 $(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 $(seq 1 22); do + if [ ! -s "/users/k1806347/oliverpainfel/Data/ukb/gwas/${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}.${n}.txt \ + --linear omit-ref cols=+a1freq,+ax \ + --maf 0.01 \ + --geno 0.05 \ + --out /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}" + fi + fi + done + done +done + +# Once complete, merge results across chromosomes +for pheno in $(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 + 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}.${n}.GW.txt + fi + done +done + +# Delete per chromosome files +rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*_subsampled/*chr* + +``` + +
+ +*** + +### Subset EUR test individuals in UKB data + +To make this quicker, focus on evaluating the PGS methods in the EUR test subset in UKB. This will avoid reprocessing the full UKB data. + +
Show code + +```{r} +library(data.table) + +selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$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 + +``` + +
+ +*** + +### PGS calculation + +
Show code + +
+ +

Prepare configuration

+ +```{r} +###### +# gwas_list +###### + +gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt') +pheno<-gsub('_.*','', gwas_list$name) +gwas_list<-gwas_list[gwas_list$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$pheno, + '.', + n, + '.GW.txt.gz' + ) + gwas_list_tmp$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) + +###### +# 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_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/eur_test_only/target_list.txt", + "pgs_methods: ['quickprs','ptclump','dbslmm','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) + +#### +# 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) + + +``` + +*** + +

Run pipeline

+ +```{bash} +snakemake \ + --profile slurm \ + --use-conda \ + --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml \ + target_pgs -n +``` + +
+ +*** + +### PGS evaluation + +
Show code + +
+ +

Create predictor list

+ +```{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)$V1 + +# Get a list of score files +scores <- list_score_files(config) + +# Get list of score files using full EUR GWAS +config_full<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml' +outdir_full <- read_param(config = config_full, param = 'outdir', return_obj = F) +scores_full <- list_score_files(config_full) +scores_full <- scores_full[grepl('UKB$', 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){ + + dir.create( + paste0( + '/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/', trait_i + ), + recursive = T + ) + + scores_i <- scores[grepl(paste0('^', trait_i,'_'), 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)) + + # 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$method,'.full') + + #### + # Make groups containing pseudo scores for single source methods + #### + + # Extract the pseudo score for each method and specify as a separate group + # This can be skipped as it was done before + for(i in 1:nrow(scores_i)) { + param <- find_pseudo( + config = config, + gwas = scores_i$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$predictor) + + # Now for full GWAS - skip subsetting pseudo as done before + scores_full_i_pseudo <- scores_full_i + scores_full_i_pseudo$top1<-paste0(scores_full_i_pseudo$top1,'.pseudo') + + scores_full_i_pseudo$predictor <- gsub('.profiles', + paste0('.EUR_pseudo.profiles'), + scores_full_i_pseudo$predictor) + + #### + # Combine the different predictor groups + #### + predictors_i<- do.call(rbind, list( + scores_i, + scores_full_i, + scores_i_pseudo, + scores_full_i_pseudo + )) + predictors_i$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 + ) +} + +``` + +*** + +

Run model_builder

+ +```{bash} +cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline +conda activate model_builder + +#rm /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/*/res* + +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,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 1" + fi +done + +``` + +*** + +

Create predictor list

+ +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 + ) + } +} + +``` + +

Run model_builder

+ +```{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 + +``` + +*** + +

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') + +selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1 + +# Calculate correlation between all phenotypes in each target population +pheno_pop_i <- list() +for(pheno_i in selected_traits){ + pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.EUR_test.row_number.txt')) + names(pheno_pop_i[[pheno_i]])[3] <- pheno_i +} + +pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i) + +cors <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p')) + +# Read in results +res_eval <- list() +for(pheno_i in selected_traits){ + eval_i <- + fread( + paste0( + '/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/', pheno_i,'/res.pred_eval.txt' + ) + ) + + eval_i$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$GWAS_N[eval_i$GWAS_N == 'top1K'] <- '297K' + + 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$Model %in% c('SumStatTune')),] + + res_eval[[pheno_i]]<-eval_i + +} + +# Create vector defining or of methods in plots +model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC") + +res_eval_simp <- NULL +for(pheno_i in selected_traits){ + tmp <- res_eval[[pheno_i]] + tmp$Trait <- pheno_i + + # Insert nice PGS method names + tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T) + tmp$label <- factor(tmp$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$GWAS_N)) + +png(paste0('~/oliverpainfel/Analyses/crosspop/plots_downsample/per_trait_r.png'), res=100, width = 1000, height = 6000, units = 'px') +ggplot(tmp, aes(x=label, y=R , fill = Model)) + + geom_errorbar(aes(ymin = R - SE, ymax = R + SE), + width = 0, + position = position_dodge(width = 1)) + + geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) + + geom_vline(xintercept = seq(1.5, length(unique(tmp$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() + +``` + +

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') + +# 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() + +``` + +
+ +```{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 + +``` + +
Show results + +
+
+ +
+
+ +
+ +*** + +## 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. + +
Show code + +```{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$method == method_i] + method_i <- gsub('_multi','', method_i) + + for(i in selected_traits){ + for(j in c('EUR')){ + if(j == 'EUR'){ + pops <- c('EAS') + } else { + pops <- j + } + + for(k in pops){ + for(n in c('5','15','45','135')){ + model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/subsampled/targ_', j, '.disc_EUR_', k, '/', i, '/res', n, '_final_models/', method_i, '.pseudo.multi.final_model.txt')) + model<-model[-1,] + + # Set weight to zero if negative, as this is what LEOPARD does + if(any(model$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$UKB_GWAS_N)) + +# Plot the estimated and observed weights +png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights_eur.png', units = 'px', res = 300, width = 3500, height = 1500) +ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) + + geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) + + scale_fill_manual(values = default_colors) + + facet_grid(. ~ UKB_GWAS_N) + + theme_half_open() + + labs(title = 'Weight of EUR PGS for EUR Target', fill = NULL) + + background_grid(major = 'y', minor = 'y') + + panel_border() + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + + ylim(c(0,1)) +dev.off() + +### +# Check calibration of LEOPARD compared to QuickPRS observed weights +### + +tmp <- both[both$Target == 'EUR',] +tmp <- tmp[both$Discovery == 'EUR',] +tmp$Target<-NULL +tmp_wide <- reshape(tmp, + 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] + +# Calculate metrics +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$LEOPARD) + )) +} + +# Create annotation data.frame +metrics_df <- data.frame( + UKB_GWAS_N = metrics$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, 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(. ~ UKB_GWAS_N) + + coord_fixed() +dev.off() + +### +# Check calibration of inverse_var compared to QuickPRS observed weights +### + +tmp <- both[both$Target == 'EUR',] +tmp <- tmp[both$Discovery == 'EUR',] +tmp$Target<-NULL +tmp_wide <- reshape(tmp, + idvar = c("Trait", "Discovery","UKB_GWAS_N"), + timevar = "label", + direction = "wide") + +names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide)) +tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F] + +# Calculate metrics +metrics <- NULL +for(n in c('5','15','45','135')){ + tmp_wide_n <- tmp_wide[tmp_wide$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( + UKB_GWAS_N = metrics$n, + x = 0.3, + y = 0.25, + label = paste0("RMSE = ", round(metrics$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$UKB_GWAS_N)) + +png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights_calibration_inverse_var.png', units = 'px', width = 2000, height = 2000, res = 300) +ggplot(tmp_wide, aes(x = inverse_var, y = QuickPRS)) + + geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") + # Perfect calibration + geom_smooth(method = "lm", se = TRUE, colour = "blue") + # Regression line + geom_point(alpha = 0.7) + + geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 0, size = 3.5) + + labs( + x = "inverse_var weight", + y = "Observed weight", + ) + + theme_half_open() + + panel_border() + + theme( + axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1), + ) + + facet_grid(. ~ UKB_GWAS_N) + + coord_fixed() +dev.off() + +``` + +
+ +*** + +## Using MVP sumstats + +### Download MVP sumstats + +
Show code + +```{r} +library(data.table) + +mvp <- fread('~/oliverpainfel/Data/GWAS_sumstats/MVP/MVP_sumstats.txt') +mvp_afr <- mvp[grepl('Afr', mvp$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' + ) +, ] + +# 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$efoTraits == 'platelet count'] <- 'PLT' +mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'systolic blood pressure'] <- 'SBP' +mvp_afr_subset$labels[mvp_afr_subset$efoTraits == 'total cholesterol measurement'] <- 'TC' + +mvp_afr_subset$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 +) + +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) + +``` + +```{bash} +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 +``` + +
+ +*** + +### PGS calculation + +Run leading single-source PGS methods using MVP GWAS sumstats. + +
Show code + +
+ +

Prepare configuration

+ +```{r} + +library(data.table) + +# 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$population == 'AFR',] +selected_traits <- gsub('_UGR', '', gwas_list$name) +gwas_list$name <- gsub('UGR', 'MVP_AFR', gwas_list$name) +gwas_list$label <- gsub('UGR', 'MVP_AFR', gwas_list$label) +gwas_list$path <- + paste0('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/', + selected_traits,'.txt.gz') + +gwas_list_eur<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt') +gwas_list_eur<-gwas_list_eur[gwas_list_eur$population == 'EUR', ] + +gwas_list <- 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 +###### + +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','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: 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) + +``` + +*** + +

Run pipeline

+ +```{bash} +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 + +``` + +
+ +*** + +### PGS evaluation + +Compare the single-source PGS within AFR ancestry target individuals. + +
Show code + +
+ +

Create predictor list

+ +```{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)$V1 +selected_traits<-selected_traits[selected_traits != 'NEU'] + +# Get a list of score files +scores <- list_score_files(config) + +# Create files for AFR targets +targ_pop <- c('AFR') +for(trait_i in selected_traits){ + scores_i <- scores[grepl(trait_i, scores$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 + ) + } + } +} + +``` + +*** + +

Run model_builder

+ +```{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 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 5" + fi + done + done +done + +``` + +*** + +

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') + +# Read in list of outcomes +selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$V1 +selected_traits <- selected_traits[selected_traits != 'NEU'] +info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv') + +# Calculate correlation between all phenotypes in each target population +cors <- list() +for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){ + if(pop_i == 'EUR'){ + pop_i_2 <- 'EUR_test' + } else { + pop_i_2 <- pop_i + } + pheno_pop_i <- list() + for(pheno_i in selected_traits){ + pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt')) + names(pheno_pop_i[[pheno_i]])[3] <- pheno_i + } + + pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i) + + cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p')) + cors[[pop_i]] <- cors_i +} + +# Read in results +targ_pop = c('AFR') +res_eval <- list() +for(pheno_i in selected_traits){ + res_eval_i<-NULL + for(targ_pop_i in targ_pop){ + if(targ_pop_i == 'EAS'){ + disc_pop <- 'EAS' + } + if(targ_pop_i == 'AFR'){ + disc_pop <- 'AFR' + } + if(targ_pop_i == 'EUR'){ + disc_pop <- c('EAS','AFR') + } + for(disc_pop_i in disc_pop){ + eval_i <- + fread( + paste0( + '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/', + 'targ_', + targ_pop_i, + '.disc_EUR_', + disc_pop_i, + '/', + pheno_i, + '/res.pred_eval.txt' + ) + ) + eval_i$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) +} + +# 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',] + 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$`Trait Label` == pheno_i]) + + facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') + + theme_half_open() + + background_grid(major = 'y', minor = 'y') + + panel_border() + + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), + legend.position = "top", + legend.key.spacing.x = unit(1, "cm"), + legend.justification = "center") + print(plot_tmp) + dev.off() + per_trait_plot[[pheno_i]]<-plot_tmp +} + +tmp <- res_eval_simp +tmp$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$label))), linetype="dotted") + + labs(y = "R (SE)", x=NULL, fill = NULL) + + facet_grid(Trait ~ Discovery_clean, scales='free', space = 'free_x') + + theme_half_open() + + background_grid(major = 'y', minor = 'y') + + panel_border() + + theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), + legend.position = "top", + legend.key.spacing.x = unit(1, "cm"), + legend.justification = "center") +dev.off() + + +#### +# Average results across phenotypes +#### + +library(MAd) + +# Average R across phenotypes +meta_res_eval <- NULL +for(targ_pop_i in targ_pop){ + if(targ_pop_i == 'EAS'){ + disc_pop <- 'EAS' + } + if(targ_pop_i == 'AFR'){ + disc_pop <- 'AFR' + } + if(targ_pop_i == 'EUR'){ + disc_pop <- c('EAS','AFR') + } + for(disc_pop_i in disc_pop){ + + # Subset res_eval for each scenario + res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) { + x <- res_eval[[i]] + x$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 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('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'] <- 'AFR GWAS' +meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Multi'] <- 'EUR + AFR GWAS' +meta_tmp$Discovery_clean <- factor(meta_tmp$Discovery_clean, + levels = c('AFR GWAS', + 'EUR GWAS', + 'EUR + AFR GWAS')) +meta_tmp$Target <- paste0(meta_tmp$Target, ' Target') +meta_tmp$Model1 <- factor(meta_tmp$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune')) + +meta_tmp_ref <- meta_res_comp_reference +meta_tmp_ref <- merge(meta_tmp_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T) +meta_tmp_ref$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'] <- '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$Discovery_clean, + levels = c('AFR GWAS', + 'EUR GWAS', + 'EUR + AFR GWAS')) +meta_tmp_ref$Target <- paste0(meta_tmp_ref$Target, ' Target') +meta_tmp_ref$Model1 <- factor(meta_tmp_ref$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune')) + +tmp <- res_comp_all_ptclump_top1 +tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T) +tmp$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'] <- 'AFR GWAS' +tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Multi'] <- 'EUR + AFR GWAS' +tmp$Discovery_clean <- factor(tmp$Discovery_clean, + levels = c('AFR GWAS', + 'EUR GWAS', + 'EUR + AFR GWAS')) +tmp$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$Group,] + +# Export plot for manuscript +png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r.perc_improv.png', width = 3200, height = 1500, res= 300, units = 'px') +ggplot(meta_tmp_simple[meta_tmp_simple$Target != 'EUR Target',], aes(x=label, y=R_diff_perc , fill = Model1)) + +# geom_boxplot( +# data = tmp_simple[tmp_simple$Target != 'EUR Target',], +# mapping = aes(x=label, y=R_diff_perc, colour=Model1), +# position = position_dodge(0.7), +# alpha = 0.3 +# ) + + geom_point( + data = tmp_simple[tmp_simple$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 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() + +``` +
+ +*** + +### Descriptive statistics + +Create a table showing descriptive statistics for the MVP sumstats. This should include LDSC SNP-heritability and AVENGEME results. + +*** + +#### LDSC + +
Show code +```{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) + +``` +
+ +*** + +#### AVENGEME + + +
Show code + +
+ +

Create predictor list

+ +```{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 + ) + } +} + +``` + +*** + +

Run model_builder

+ +```{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 + +``` + +*** + +

Plot pT+clump association results

+ +```{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']) + +``` + +
+ +*** + +#### Make table + +Make a table showing GWAS information for the manuscript. + +
Show code + +```{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$`GWAS Sample` == i])) + ) + ) +} + +``` + +
+ +
Show descriptives table + +```{r, eval = T, echo = F} +info_all<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/gwas_descriptives.csv') + +kable(info_all, "html") %>% + kable_styling(bootstrap_options = c("striped", "hover"), + full_width = F) %>% + scroll_box(width = "100%", height = "500px") + +``` + +
+ +*** + +### LEOPARD+QuickPRS + +Here we will compare the LEOPARD estimated weights for population specific PGS, to the weights estimated using observed data in the UKB target sample. + +
Show code + +```{r} +setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/') + +library(data.table) +library(ggplot2) +library(cowplot) + +source('../functions/misc.R') +source_all('../functions') + +# Read in list of outcomes +selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$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('MVP', model$x)]<-'AFR' + model$Target <- j + model$Weight <- model$BETA/sum(model$BETA) + model$Trait <- i + model$Method <- method_i + model<-model[,c('Target','Discovery','Weight','Method','Trait'), with=F] + obs_weights<-rbind(obs_weights, model) + } + } + } +} + +### +# Estimate weights if using the inverse variance weighting +### + +# Read in GWAS descriptives +gwas_desc<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv') +gwas_desc <- gwas_desc[, c('Trait Label','Ancestry','GWAS N'), with=F] +names(gwas_desc)<-c('trait','ancestry','n') +gwas_desc<-gwas_desc[gwas_desc$trait %in% selected_traits,] + +gwas_desc <- 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,] + +gwas_desc<-rbind(gwas_desc, gwas_desc_mvp) + +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"), + 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() + +### +# 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$label) +default_colors["LEOPARD"] <- "black" + +# Plot the estimated and observed weights +png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/leopard_weights.png', units = 'px', res = 300, width = 2500, height = 1500) +ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) + + geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) + + scale_fill_manual(values = default_colors) + + facet_grid(Target ~ .) + + theme_half_open() + + labs(title = 'Weight of target ancestry-matched PGS', fill = NULL) + + background_grid(major = 'y', minor = 'y') + + panel_border() + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + + ylim(c(0,1)) +dev.off() + +### +# Check calibration of LEOPARD compared to QuickPRS observed weights +### + +tmp <- both[both$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 + ) + ) + } + } +} + +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() + +# 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$label)){ + for(j in c('AFR')){ + metrics_unique_tmp <- metrics_unique[metrics_unique$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$rmse) + ) + ) + } +} + +png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/avg_observed_weight_rmse.png', units = 'px', width = 1500, height = 800, res = 300) +ggplot(mean_rmse, aes(x = Method, y = avg_rmse, fill = Method)) + + geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", size = 0.1) + + geom_text(aes(label = round(avg_rmse, 3)), # <-- Add this + vjust = 1.5, # <-- Move the text slightly above the bar + size = 3) + # <-- Adjust text size + scale_fill_manual(values = default_colors) + + theme_half_open() + + background_grid(major = 'y', minor = 'y') + + panel_border() + + labs(y = 'Average RMSE') + + theme(axis.text.x = element_text(angle = 45, hjust = 1), + legend.position="none") +dev.off() + +### +# Check calibration of estimated (LEOPARD and inverse_var) weights compared to observed QuickPRS weights +### + +tmp <- both[both$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$Discovery %in% c('EAS','AFR'),] + +metrics <- NULL +for(i in c('AFR')){ + for(j in unique(tmp$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() + + 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 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. + +
Show code + +```{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') + +# 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 +} + +# 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)) +} + +# 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<-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$name[i]]] <- ss_i[, c('SNP','A1','A2','FREQ'), with = F] +} + +gwas_pc_all <- NULL +for(i in names(gwas_freq)){ + # Merge on SNP ID + merged <- merge( + pc_score_file, + gwas_freq[[i]], + by = "SNP" + ) + + # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ) + merged[, FREQ := ifelse(A1.x == A1.y, FREQ, 1 - FREQ)] + + # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC + ref_PC <- merged[, lapply(.SD, function(w) sum(w * FREQ, na.rm = TRUE)), + .SDcols = patterns("^PC")] + + gwas_pc_all <- rbind(gwas_pc_all, data.frame(group = i, + ref_PC)) +} + +ref_pc_all$type <- 'Reference' +gwas_pc_all$type <- 'GWAS' + +pc_all <- rbind(ref_pc_all, gwas_pc_all) + +ggplot(pc_all, aes(x=PC1, y = PC2, colour = group, shape = type)) + + geom_point(size = 5) + +# There is a big shift in the PCs for GWAS and reference data, probably due to missing variants. Restrict to variant present in all GWAS. + +extract <- Reduce(intersect, list( + pc_score_file$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 <- pc_score_file[pc_score_file$SNP %in% extract,] + +# Calculate PCs for reference populations +ref_pc_all <- NULL +for(i in c('EUR','EAS','AFR','AMR','MID','CSA')){ + # Merge on SNP ID + merged <- merge( + pc_score_file_overlapping, + ref_pop_freq[[i]], + by.x = "SNP", + by.y = "ID" + ) + + # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ) + merged[, A1_FREQ := ifelse(ALT == A1, ALT_FREQS, 1 - ALT_FREQS)] + + # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC + ref_PC <- merged[, lapply(.SD, function(w) sum(w * A1_FREQ, na.rm = TRUE)), + .SDcols = patterns("^PC")] + + ref_pc_all <- rbind(ref_pc_all, data.frame(group = i, + ref_PC)) +} + +gwas_pc_all <- NULL +for(i in names(gwas_freq)){ + # Merge on SNP ID + merged <- merge( + pc_score_file_overlapping, + gwas_freq[[i]], + by = "SNP" + ) + + # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ) + merged[, FREQ := ifelse(A1.x == A1.y, FREQ, 1 - FREQ)] + + # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC + ref_PC <- merged[, lapply(.SD, function(w) sum(w * FREQ, na.rm = TRUE)), + .SDcols = patterns("^PC")] + + gwas_pc_all <- rbind(gwas_pc_all, data.frame(group = i, + ref_PC)) +} + +ref_pc_all$Population <- ref_pc_all$group +ref_pc_all$GWAS <- NA + +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$Population <- NA + +ref_pc_all$type <- 'Reference' +gwas_pc_all$type <- 'GWAS' + +pc_all <- rbind(ref_pc_all, gwas_pc_all) + +png('~/oliverpainfel/Analyses/crosspop/plots/genetic_distance.png', units = 'px', width = 2000, height = 1500, res = 300) + +ggplot(pc_all, aes(x=PC1, y = PC2)) + + geom_point(data=pc_all[pc_all$type == 'Reference',], aes(x=PC1, y = PC2, fill = Population), size = 5, shape = 21) + + geom_point(data=pc_all[pc_all$type == 'GWAS',], aes(x=PC1, y = PC2, colour = GWAS), size = 4, shape = 3, stroke = 1.5) + + theme_half_open() + + background_grid() + +dev.off() + +# Perfect. Shows UKB match EUR, BBJ match EAS, UGR matches AFR, but MVP is close to AFR. + +``` + +
+ +*** + +## Estimate ancestry proportions + +This involves using Florian Prive's bigsnpr packages and UKB reference data to calculate principal components and then estimate ancestral proportions. + +
Show code + +```{r} +library(bigsnpr) +library(dplyr) + +DIR <- "~/oliverpainfel/Data/bigsnpr" # you can replace by e.g. "data" or "tmp-data" + +# /!\ This downloads 850 Mb (each) +all_freq <- bigreadr::fread2( + runonce::download_file("https://figshare.com/ndownloader/files/31620968", + dir = DIR, fname = "ref_freqs.csv.gz")) +projection <- bigreadr::fread2( + runonce::download_file("https://figshare.com/ndownloader/files/31620953", + dir = DIR, fname = "projection.csv.gz")) + +# Read in allele frequencies for each reference population +ref_rds <- NULL +for(i in 1:22){ + ref_rds <- rbind(ref_rds, + readRDS(paste0('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr', i, '.rds'))) +} + +ref_pop_freq <- list() +for(i in c('EUR','EAS','AFR','AMR','MID','CSA')){ + ref_pop_freq[[i]] <- ref_rds[, c('CHR','SNP','BP_GRCh37','A1','A2',paste0('REF.FRQ.',i)), with=F] + names(ref_pop_freq[[i]]) <-c('chr','rsid','pos','a1','a0','freq') +} + +# Read in GWAS allele frequencies for BMI +gwas_list_main <- fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt') +gwas_list_main <- gwas_list_main[grepl('BMI', gwas_list_main$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$value >= 0.02,] + +library(scales) # for percent_format() + +png('~/oliverpainfel/Analyses/crosspop/plots/gwas_ancestry.png', units = 'px', width = 2000, height = 1500, res = 300) +ggplot(gwas_ancestry_table_melt, aes(x = Var2, y = value, fill = Var1)) + + geom_bar(stat = "identity", colour = "black", size = 0.2) + + geom_text(data = subset(gwas_ancestry_table_melt, value >= 0.02), + aes(label = scales::percent(value, accuracy = 1)), + position = position_stack(vjust = 0.5), + size = 3) + + labs(x = "GWAS", y = "Proportion", fill = "Population", + title = "Ancestry proportions in BMI GWAS") + + theme_half_open() + + theme(axis.text.x = element_text(angle = -45, hjust = 1)) +dev.off() + +write.csv(gwas_ancestry_table, '~/oliverpainfel/Analyses/crosspop/plots/gwas_ancestry.csv') + +ref_ancestry_table <- do.call(cbind, ref_ancestry) +ref_ancestry_table_melt <- melt(ref_ancestry_table) + +ref_ancestry_table_melt<-ref_ancestry_table_melt[ref_ancestry_table_melt$value >= 0.02,] + +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) + + 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 + +The below code is incomplete due to current compatability issues between BridgePRS and GenoPred. Work to incorporate BridgePRS into GenoPred is on going. + +
Show code + +```{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) + +``` + +*** + +

Run pipeline

+ +```{bash} +snakemake \ + --profile slurm \ + --use-conda \ + --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_bridgeprs.yaml \ + target_pgs -n +``` + +
diff --git a/docs/CrossPop.html b/docs/CrossPop.html new file mode 100644 index 00000000..12989093 --- /dev/null +++ b/docs/CrossPop.html @@ -0,0 +1,17541 @@ + + + + + + + + + + + + + +Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + + + + +
+
+

Preprint

+

This document provides code and key figures and tables for the +following preprint:

+
+

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

+
+
+

Document overview:

+ +
+
+
+

Data Preparation

+
+
+

UKB

+

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.

+
+
+

Ancestry inference

+

We will perform this using the GenoPred pipeline. We will need to +prepare the configuration files before running the pipeline.

+
+ +Show code + +


+

+Create symlinks +

+

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.

+
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
+
# 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)
+
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
+
+

+Create list of unrelated individuals +

+
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)
+
+

+Create target_list +

+
mkdir -p /users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/basic
+
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)
+
+

+Create configfile +

+
# 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)
+
+

+Run pipeline +

+
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
+
+
+
+
+

Phenotype extraction

+

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.

+
+ +Show code + +
mkdir /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx
+
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
+)
+
+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)
+
+# Extract outcomes from UKB (project ukb82087)
+project_dir <- "/datasets/ukbiobank/ukb82087"
+
+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$field, '-'), collapse='|'))) %>%
+    bio_field_add("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.txt")
+
+bio_phen(
+    project_dir,
+    field = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.txt",
+    out = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset"
+)
+
+system("ls -lh /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.rds")
+df <- readRDS("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_field_subset.rds")
+
+# Take the first observation of each outcome
+library(tidyr)
+df_long <- df %>%
+  pivot_longer(cols = names(df)[!grepl('eid', names(df))], names_to = "variable", values_to = "outcome") %>%
+  drop_na(outcome)
+df_long$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$outcome
+  )
+  
+  fwrite(
+    tmp,
+    paste0(
+      '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
+      prscsx_dat$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 = '/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$POP
+)
+
+# Merge ancestry info with phenotype data
+df_short <- dcast(df_long, eid ~ variable, value.var = "outcome")
+df_short<-merge(df_short, pop_dat, by='eid')
+
+# Remove related individuals
+greedy_related <- "/scratch/prj/ukbiobank/recovered/KCL_Data/Software/tools/GreedyRelated-master-v1.2.1/GreedyRelated"
+rel<-bio_gen_related_remove(
+      project_dir = project_dir,
+      greedy_related = greedy_related,
+      keep = df_short$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$POP == j]))
+    )
+    n_table<-rbind(n_table, tmp)
+  }
+}
+
+write.csv(n_table, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/n_table')
+
+# Define training subset for EUR
+df_short_unrel_eur<-df_short_unrel[df_short_unrel$POP == 'EUR',]
+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$field[i]]]))
+  )
+  n_table_eur<-rbind(n_table_eur, tmp)
+}
+
+write.csv(n_table_eur, '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/n_table_eur')
+
+df_short_unrel$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$eid]<-'EUR_test'
+
+# Output phenotype data for each population
+for(i in 1:nrow(prscsx_dat)){
+  for(j in unique(df_short_unrel$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(
+        '/users/k1806347/oliverpainfel/Data/ukb/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(
+        '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
+        prscsx_dat$label[i],
+        '.unrel.', j, '.row_number.txt'
+      ),
+      row.names = F,
+      quote = F,
+      na = 'NA',
+      sep = '\t'
+    )
+  }
+}
+
+# For the EUR training GWAS, normalise and regress covariates
+# Use age, sex and PCs as covariates
+# Read in PC data released by UKB
+qc_dat<-bio_gen_sqc(project_dir)
+qc_dat<-qc_dat[,c('eid',paste0('pc',1:20))]
+df_short_unrel<-merge(df_short_unrel, qc_dat, by='eid')
+
+# Read in sex and age information
+system('rm /users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt')
+f <- bio_field(project_dir)
+f %>%
+    select(field, name) %>%
+    filter(str_detect(field, "^21022-0.0|^31-0.0")) %>%
+    bio_field_add("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt")
+
+bio_phen(
+    project_dir,
+    field = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.txt",
+    out = "/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset"
+)
+
+system("ls -lh /users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.rds")
+df <- readRDS("/users/k1806347/oliverpainfel/Data/ukb/phenotypes/age_sex_field_subset.rds")
+names(df)<-gsub('-.*','',names(df))
+names(df)[names(df) == '31']<-'sex'
+names(df)[names(df) == '21022']<-'age'
+df_short_unrel<-merge(df_short_unrel, df, by='eid')
+
+# Within each population, normalise each outcome and regress out covariates
+library(RNOmni)
+covs<-c(paste0('pc',1:20), 'sex', 'age')
+df_short_unrel_eur_train<-df_short_unrel[df_short_unrel$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$pheno_norm_resid_scale
+  )
+  
+  fwrite(
+    tmp,
+    paste0(
+      '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
+      prscsx_dat$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(
+      '/users/k1806347/oliverpainfel/Data/ukb/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(
+      '/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/',
+      prscsx_dat$label[i],
+      '.unrel.EUR_train.norm_resid_scale.row_number.txt'
+    ),
+    row.names = F,
+    quote = F,
+    na = 'NA',
+    sep = '\t'
+  )
+}
+
+
+
+
+
+

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

+
+ +Show 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 $(seq 1 22); do
+      sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/plink2 \
+        --pfile /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/ukb/geno/ukb.ref.chr${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}"
+  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.${pheno}.chr1.outcome.glm.linear > /users/k1806347/oliverpainfel/Data/ukb/gwas/${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
+    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 'BEGIN{FS=OFS="\t"} NR==1 {$5="A2"} 1' > temp.txt && mv temp.txt /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+
+    gzip /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}/ukb.eur_train.${pheno}.GW.txt
+done
+
+# Delete per chromosome files
+rm /users/k1806347/oliverpainfel/Data/ukb/gwas/*/*chr*
+
+
+
+
+
+

Download BBJ sumstats

+
+ +Show code + +
# 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('/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)
+
+# 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)
+
# wget and unzip sumstats
+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
+    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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+  wget_label=$(awk -v var="$pheno" '$1 == var {print $3}' ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas_wget.txt)
+if [ "$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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+sbatch -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/pigz/pigz -dc ~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj.${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
+
+
+
+
+
+

Download UGR sumstats

+
+ +Show code + +
# 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('/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)
+
+# 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)
+    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)
+  }
+}
+# 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)
+
# 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 ${url}"
+done
+
+
library(future.batchtools)
+library(furrr)
+library(data.table)
+ugr_data<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats_prscsx.csv')
+
+plan(batchtools_slurm(resources = list(
+  time = "12:00:00",
+  ntasks = 2,
+  mem = "10g",
+  partition = "neurohack_cpu"
+)))
+
+furrr::future_map_dfr(1:nrow(ugr_data), function(i) {
+  print(i)
+  sumstats <- fread(paste0("~/oliverpainfel/Data/GWAS_sumstats/UGR/", ugr_data$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')
+  
+})
+
+
+
+
+
+
+

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

+

Use GenoPred for this.

+
+ +Show code + +
+ +
+

+Prepare configuration +

+
######
+# 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(
+  name=paste0(prscsx_dat$labels,'_UKB'),
+  path=paste0('/users/k1806347/oliverpainfel/Data/ukb/gwas/',prscsx_dat$labels,'/ukb.eur_train.',prscsx_dat$labels,'.GW.txt.gz'),
+  population='EUR',
+  n=NA,
+  sampling=NA,
+  prevalence=NA,
+  mean=0,
+  sd=1,
+  label=paste0('"', prscsx_dat$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, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list_all.txt', col.names = T, 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: /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, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml', col.names = F, row.names = F, quote = F)
+
+

+Run pipeline +

+
snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_all.yaml \
+  target_pgs -n
+
+

+Reformat for LDSC and POPCORN +

+
library(data.table)
+dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats', recursive = T)
+gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/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$name[i], 
+        ".sumstats.gz"))){
+    next    
+  }
+  print(i)
+  gwas_file <-
+    paste0(
+      "/users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/",
+      gwas_list$name[i],
+      "/",
+      gwas_list$name[i],
+      "-cleaned.gz"
+    )
+  
+  gwas_header <- fread(gwas_file, nrows = 1)
+  cols_index <- which(names(gwas_header) %in% c('SNP','A1','A2','BETA','SE','P','N'))
+  
+  system(
+    paste0(
+      "zcat ",
+      gwas_file,
+      " | cut -f ", 
+      paste0(cols_index, collapse = ','),
+      " | sed -e '1s/BETA/beta/'",
+      " | /users/k1806347/oliverpainfel/Software/pigz/pigz -f",
+      " > /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/",
+      gwas_list$name[i], 
+      ".sumstats.gz"
+      )
+    )
+}
+
+
+
+
+

LDSC

+
+ +Show code + +
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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats
+
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
+     --sumstats /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/sumstats/${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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/results/${pheno}/${pop}
+
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/ldsc.py \
+     --h2 /users/k1806347/oliverpainfel/Analyses/crosspop/ldsc/sumstats/${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
+
+
+
+
+
+

POPCORN

+
+ +Show code + +
+ +
+

+Calculate CSCOREs +

+

+# 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 $(seq 1 22); do
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="popcorn \
+      compute \
+      -v 1 \
+      --bfile1 /users/k1806347/oliverpainfel/Data/POPCORN/1KG/temp/ref.EUR.chr${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
+rm /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_*_CSCORES/*chr*.txt
+
+

+Run POPCORN +

+
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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/prscsx_labels.txt); do
+    mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/popcorn/results/${pheno}/EUR_${pop}
+    sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="popcorn \
+       fit -v 3 \
+       --cfile /users/k1806347/oliverpainfel/Data/POPCORN/1KG/EUR_${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
+
+
+
+
+
+

Plot LDSC and POPCORN results

+
+ +Show code + +
library(data.table)
+library(ggplot2)
+library(cowplot)
+
+# Read in phenotypes
+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
+
+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)
+
+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)", fill = NULL) +
+  theme_half_open() +
+  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
+for(pop in c('AFR','EAS')){
+  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$Test == 'pge',]
+    pop_res_i$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)
+
+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)", fill = NULL) +
+  theme_half_open() +
+  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()
+
+
+ +Show LDSC SNP-heritability + +
+
+

+
+
+
+
+ +Show POPCORN genetic correlation estimates + +
+
+

+
+
+
+
+
+
+

AVENGEME

+
+ +Show code + +
+ +
+

+Create predictor list +

+
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_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('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$V1
+
+# Create files for EAS and AFR targets
+pop <- c('EUR','EAS','AFR')
+for(trait_i in pheno_intersect){
+  # Make a group containing both GWAS for each single source method
+  # Make a group for each multisource method
+  scores_i <- scores[grepl(paste0('^', trait_i, '_'), scores$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/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/targ_',
+        pop_i,
+        '.disc_',
+        pop_i,
+        '/',
+        trait_i,
+        '/predictor_list.ptclump.txt'
+      ),
+      col.names = T,
+      row.names = F,
+      quote = F
+    )
+  }
+}
+
+

+Run model_builder +

+
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 $(cat /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/pheno_eur_eas_afr.txt); do
+    sbatch --mem 5G -n 5 -p neurohack_cpu --wrap="Rscript ../Scripts/model_builder/model_builder.R \
+      --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${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"
+  done
+done
+
+
+

+Plot pT+clump association results +

+
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/Data/ukb/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))
+
+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) +
+  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),
+        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()
+
+

+Run AVENGEME +

+
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_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('/users/k1806347/oliverpainfel/Data/ukb/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,
+        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$pi0[3]
+      )
+    )
+  }
+}
+
+dir.create('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme')
+write.csv(mod_res_all, '/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv', row.names = F, quote = F)
+
+mod_res_all<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv')
+
+png('~/oliverpainfel/Analyses/crosspop/plots/avengeme_h2.png', res = 100, width = 900, height = 500, units = 'px')
+ggplot(mod_res_all, aes(x = Phenotype, y = vg_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=vg_lowCI, ymax=vg_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="SNP-based Heritability (95%CI)", fill = NULL) +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = "top",
+        legend.key.spacing.x = unit(1, "cm"),
+        legend.justification = "center") +
+  background_grid(major = 'y', minor = 'y')
+dev.off()
+
+png('~/oliverpainfel/Analyses/crosspop/plots/avengeme_polygenicity.png', res = 100, width = 900, height = 500, units = 'px')
+ggplot(mod_res_all, aes(x = Phenotype, y = 1 - pi0_est, fill = Population)) +
+  geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.7) +
+  geom_errorbar(aes(ymin=1 - pi0_lowCI, ymax=1 - pi0_highCI), width=.2, position=position_dodge(width = 0.7, preserve = "single")) +
+  labs(y="Proporition non-zero\neffects (95%CI)", fill = NULL) +
+  theme_half_open() +
+  coord_cartesian(ylim = c(0, 0.15)) + 
+  theme(axis.text.x = element_text(angle = 45, hjust = 1),
+        legend.position = "top",
+        legend.key.spacing.x = unit(1, "cm"),
+        legend.justification = "center") +
+  background_grid(major = 'y', minor = 'y')
+dev.off()
+
+summary(mod_res_all$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'])
+
+
+ +Show AVENGEME results + +
+
+

+
+
+


+
+
+

+
+
+
+
+
+
+

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.

+
+ +Show code + +
#########
+# 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$trait, '/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', col.names = F, row.names = F, quote = F)
+
+# Plot max R2 for selected traits
+mod_res_all <- fread('/users/k1806347/oliverpainfel/Analyses/crosspop/avengeme/results.csv')
+mod_res_all_selected <- mod_res_all[mod_res_all$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)
+
+
+
+
+

GWAS descriptives

+

Make a table showing GWAS information for the manuscript.

+
+ +Show code + +
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]))
+    )
+  )
+}
+
+
+ +Show descriptives table + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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+EOS + +Eosinophil + +EUR + +UKB + +296570 + +74442 + +0.15 (95%CI = 0.15 - 0.16) + +0.93 (95%CI = 0.93 - 0.94) + +0.19 (SE = 0.02) + +1.17 (SE = 0.02) + +1.4962 + +30150 + +NA + +FALSE +
+GGT + +γ-glutamyl transpeptidase + +AFR + +UGR + +8995 + +6322 + +0.05 (95%CI = 0.03 - 0.08) + +1 (95%CI = 1 - 1) + +0.23 (SE = 0.07) + +0.99 (SE = 0.01) + +1.0075 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009050 + +FALSE +
+GGT + +γ-glutamyl transpeptidase + +EAS + +BBJ + +133471 + +1952 + +0.08 (95%CI = 0.06 - 0.09) + +0.97 (95%CI = 0.95 - 0.98) + +0.15 (SE = 0.05) + +1.07 (SE = 0.01) + +1.2005 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.GGT.v1.zip + +FALSE +
+GGT + +γ-glutamyl transpeptidase + +EUR + +UKB + +291885 + +73270 + +0.1 (95%CI = 0.1 - 0.1) + +0.95 (95%CI = 0.95 - 0.95) + +0.18 (SE = 0.01) + +1.16 (SE = 0.02) + +1.5696 + +30730 + +NA + +FALSE +
+HB + +Hemoglobin + +AFR + +UGR + +2741 + +6375 + +0.25 (95%CI = 0.16 - 0.36) + +0.97 (95%CI = 0 - 1) + +0.22 (SE = 0.25) + +1 (SE = 0.01) + +1.0105 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009034 + +TRUE +
+HB + +Hemoglobin + +EAS + +BBJ + +152447 + +1999 + +0.06 (95%CI = 0.05 - 0.08) + +0.95 (95%CI = 0.89 - 0.97) + +0.07 (SE = 0.01) + +1.09 (SE = 0.01) + +1.2005 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.Hb.v1.zip + +TRUE +
+HB + +Hemoglobin + +EUR + +UKB + +297083 + +74575 + +0.13 (95%CI = 0.13 - 0.13) + +0.92 (95%CI = 0.92 - 0.92) + +0.15 (SE = 0.01) + +1.16 (SE = 0.02) + +1.5217 + +30020 + +NA + +TRUE +
+HCT + +Hematocrit + +AFR + +UGR + +2744 + +6375 + +0.26 (95%CI = 0.16 - 0.36) + +0.88 (95%CI = 0 - 1) + +0.18 (SE = 0.26) + +1.01 (SE = 0.01) + +1.0105 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009033 + +FALSE +
+HCT + +Hematocrit + +EAS + +BBJ + +153015 + +1999 + +0.05 (95%CI = 0.03 - 0.06) + +0.97 (95%CI = 0.94 - 0.98) + +0.07 (SE = 0.01) + +1.09 (SE = 0.01) + +1.2005 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.Ht.v1.zip + +FALSE +
+HCT + +Hematocrit + +EUR + +UKB + +297084 + +74575 + +0.13 (95%CI = 0.13 - 0.13) + +0.92 (95%CI = 0.92 - 0.93) + +0.14 (SE = 0.01) + +1.16 (SE = 0.02) + +1.5035 + +30030 + +NA + +FALSE +
+HDL + +HDL-cholesterol + +AFR + +UGR + +13114 + +5863 + +0.06 (95%CI = 0.04 - 0.09) + +1 (95%CI = 1 - 1) + +0.02 (SE = 0.05) + +1.02 (SE = 0.01) + +1.0225 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009044 + +TRUE +
+HDL + +HDL-cholesterol + +EAS + +BBJ + +74970 + +1783 + +0.15 (95%CI = 0.13 - 0.17) + +0.98 (95%CI = 0.97 - 0.99) + +0.17 (SE = 0.03) + +1.09 (SE = 0.01) + +1.2005 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.HDLC.v1.zip + +TRUE +
+HDL + +HDL-cholesterol + +EUR + +UKB + +267973 + +67346 + +0.2 (95%CI = 0.2 - 0.2) + +0.92 (95%CI = 0.92 - 0.93) + +0.23 (SE = 0.02) + +1.15 (SE = 0.02) + +1.6524 + +30760 + +NA + +TRUE +
+HT + +Height + +AFR + +UGR + +14126 + +6658 + +0.23 (95%CI = 0.19 - 0.26) + +0.98 (95%CI = 0.96 - 0.99) + +0.12 (SE = 0.06) + +1.02 (SE = 0.01) + +1.0345 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009055 + +TRUE +
+HT + +Height + +EAS + +BBJ + +165056 + +2048 + +0.2 (95%CI = 0.19 - 0.22) + +0.93 (95%CI = 0.92 - 0.95) + +0.41 (SE = 0.02) + +1.33 (SE = 0.03) + +1.7648 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.Hei.v1.zip + +TRUE +
+HT + +Height + +EUR + +UKB + +304826 + +76470 + +0.24 (95%CI = 0.24 - 0.25) + +0.91 (95%CI = 0.91 - 0.91) + +0.45 (SE = 0.02) + +1.39 (SE = 0.03) + +2.3412 + +50 + +NA + +TRUE +
+HbA1c + +HbA1c + +AFR + +UGR + +6116 + +5405 + +0 (95%CI = 0 - 0.05) + +1 (95%CI = 0.9 - 1) + +0.42 (SE = 0.15) + +1.01 (SE = 0.01) + +1.0405 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009054 + +FALSE +
+HbA1c + +HbA1c + +EAS + +BBJ + +71221 + +1949 + +0.08 (95%CI = 0.06 - 0.1) + +0.98 (95%CI = 0.96 - 0.99) + +0.07 (SE = 0.01) + +1.05 (SE = 0.01) + +1.0957 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.HbA1c.v1.zip + +FALSE +
+HbA1c + +HbA1c + +EUR + +UKB + +292134 + +73182 + +0.14 (95%CI = 0.13 - 0.14) + +0.93 (95%CI = 0.93 - 0.94) + +0.2 (SE = 0.02) + +1.17 (SE = 0.02) + +1.5659 + +30750 + +NA + +FALSE +
+LDL + +LDL-cholesterol + +AFR + +UGR + +13086 + +6313 + +0.07 (95%CI = 0.07 - 0.08) + +1 (95%CI = 1 - 1) + +0.03 (SE = 0.05) + +1.03 (SE = 0.01) + +1.0285 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009043 + +FALSE +
+LDL + +LDL-cholesterol + +EAS + +BBJ + +72866 + +1953 + +0.05 (95%CI = 0.03 - 0.06) + +1 (95%CI = 0.99 - 1) + +0.07 (SE = 0.01) + +1.05 (SE = 0.01) + +1.0957 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.LDLC.v1.zip + +FALSE +
+LDL + +LDL-cholesterol + +EUR + +UKB + +291538 + +73172 + +0.12 (95%CI = 0.11 - 0.12) + +0.97 (95%CI = 0.97 - 0.97) + +0.1 (SE = 0.01) + +1.06 (SE = 0.01) + +1.2697 + +30780 + +NA + +FALSE +
+LYM + +Lymphocyte + +AFR + +UGR + +2681 + +6353 + +0 (95%CI = 0 - 0.06) + +1 (95%CI = 0 - 1) + +0.08 (SE = 0.28) + +1.01 (SE = 0.01) + +1.0135 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009037 + +FALSE +
+LYM + +Lymphocyte + +EAS + +BBJ + +95717 + +1997 + +0.16 (95%CI = 0.14 - 0.17) + +0.94 (95%CI = 0.91 - 0.96) + +0.08 (SE = 0.01) + +1.05 (SE = 0.01) + +1.1459 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.LYM.v1.zip + +FALSE +
+LYM + +Lymphocyte + +EUR + +UKB + +296570 + +74442 + +0.08 (95%CI = 0.08 - 0.08) + +0.94 (95%CI = 0.94 - 0.94) + +0.18 (SE = 0.01) + +1.18 (SE = 0.02) + +1.6070 + +30120 + +NA + +FALSE +
+MCH + +Mean corpuscular hemoglobin + +AFR + +UGR + +2742 + +6375 + +0.21 (95%CI = 0.18 - 0.27) + +1 (95%CI = 1 - 1) + +0.26 (SE = 0.22) + +1.01 (SE = 0.01) + +1.0225 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009063 + +FALSE +
+MCH + +Mean corpuscular hemoglobin + +EAS + +BBJ + +128028 + +1999 + +0.12 (95%CI = 0.1 - 0.13) + +0.98 (95%CI = 0.97 - 0.98) + +0.19 (SE = 0.03) + +1.11 (SE = 0.02) + +1.2005 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MCH.v1.zip + +FALSE +
+MCH + +Mean corpuscular hemoglobin + +EUR + +UKB + +297080 + +74575 + +0.19 (95%CI = 0.18 - 0.19) + +0.94 (95%CI = 0.94 - 0.95) + +0.25 (SE = 0.03) + +1.12 (SE = 0.02) + +1.4781 + +30050 + +NA + +FALSE +
+MCHC + +Mean corpuscular hemoglobin concentration + +AFR + +UGR + +2744 + +6375 + +0.05 (95%CI = 0.04 - 0.16) + +1 (95%CI = 1 - 1) + +0.01 (SE = 0.23) + +1.01 (SE = 0.01) + +1.0225 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009064 + +TRUE +
+MCHC + +Mean corpuscular hemoglobin concentration + +EAS + +BBJ + +135482 + +1999 + +0.04 (95%CI = 0.03 - 0.05) + +0.99 (95%CI = 0.98 - 1) + +0.07 (SE = 0.01) + +1.06 (SE = 0.01) + +1.1459 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MCHC.v1.zip + +TRUE +
+MCHC + +Mean corpuscular hemoglobin concentration + +EUR + +UKB + +297079 + +74573 + +0.05 (95%CI = 0.05 - 0.05) + +0.97 (95%CI = 0.97 - 0.97) + +0.06 (SE = 0.01) + +1.04 (SE = 0.01) + +1.1843 + +30060 + +NA + +TRUE +
+MCV + +Mean corpuscular volume + +AFR + +UGR + +2742 + +6375 + +0.23 (95%CI = 0.18 - 0.29) + +1 (95%CI = 1 - 1) + +0.41 (SE = 0.23) + +1.01 (SE = 0.01) + +1.0225 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009065 + +FALSE +
+MCV + +Mean corpuscular volume + +EAS + +BBJ + +129832 + +1999 + +0.12 (95%CI = 0.1 - 0.13) + +0.98 (95%CI = 0.97 - 0.98) + +0.22 (SE = 0.03) + +1.1 (SE = 0.02) + +1.2531 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MCV.v1.zip + +FALSE +
+MCV + +Mean corpuscular volume + +EUR + +UKB + +297083 + +74574 + +0.22 (95%CI = 0.21 - 0.22) + +0.94 (95%CI = 0.93 - 0.94) + +0.25 (SE = 0.03) + +1.13 (SE = 0.02) + +1.5144 + +30040 + +NA + +FALSE +
+MON + +Monocyte + +AFR + +UGR + +2681 + +6353 + +0.15 (95%CI = 0.06 - 0.24) + +1 (95%CI = 0 - 1) + +-0.25 (SE = 0.22) + +1.02 (SE = 0.01) + +1.0135 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009038 + +FALSE +
+MON + +Monocyte + +EAS + +BBJ + +95119 + +1997 + +0.1 (95%CI = 0.09 - 0.12) + +0.98 (95%CI = 0.97 - 0.99) + +0.08 (SE = 0.01) + +1.09 (SE = 0.01) + +1.1459 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.MON.v1.zip + +FALSE +
+MON + +Monocyte + +EUR + +UKB + +296570 + +74442 + +0.13 (95%CI = 0.13 - 0.14) + +0.95 (95%CI = 0.95 - 0.95) + +0.19 (SE = 0.02) + +1.2 (SE = 0.02) + +1.5144 + +30130 + +NA + +FALSE +
+NEU + +Neutrophil + +AFR + +UGR + +2671 + +6353 + +0.11 (95%CI = 0.1 - 0.15) + +1 (95%CI = 1 - 1) + +0.1 (SE = 0.23) + +1.01 (SE = 0.01) + +1.0105 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009040 + +TRUE +
+NEU + +Neutrophil + +EAS + +BBJ + +82810 + +1997 + +0.07 (95%CI = 0.05 - 0.09) + +0.99 (95%CI = 0.98 - 0.99) + +0.11 (SE = 0.01) + +1.05 (SE = 0.01) + +1.1459 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.NEU.v1.zip + +TRUE +
+NEU + +Neutrophil + +EUR + +UKB + +296570 + +74442 + +0.14 (95%CI = 0.14 - 0.15) + +0.93 (95%CI = 0.93 - 0.94) + +0.15 (SE = 0.01) + +1.15 (SE = 0.02) + +1.5401 + +30140 + +NA + +TRUE +
+PLT + +Platelet + +AFR + +UGR + +2723 + +6375 + +0.11 (95%CI = 0.02 - 0.21) + +0.99 (95%CI = 0 - 1) + +0.26 (SE = 0.25) + +1.01 (SE = 0.01) + +1.0225 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009036 + +TRUE +
+PLT + +Platelet + +EAS + +BBJ + +148623 + +1999 + +0.15 (95%CI = 0.14 - 0.17) + +0.95 (95%CI = 0.94 - 0.96) + +0.18 (SE = 0.02) + +1.13 (SE = 0.01) + +1.3101 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.PLT.v1.zip + +TRUE +
+PLT + +Platelet + +EUR + +UKB + +297082 + +74575 + +0.24 (95%CI = 0.24 - 0.24) + +0.94 (95%CI = 0.93 - 0.94) + +0.27 (SE = 0.02) + +1.2 (SE = 0.02) + +1.6108 + +30080 + +NA + +TRUE +
+RBC + +Red blood cell + +AFR + +UGR + +2744 + +6375 + +0.32 (95%CI = 0.24 - 0.42) + +0.99 (95%CI = 0.97 - 1) + +0.54 (SE = 0.24) + +1 (SE = 0.01) + +1.0165 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009062 + +FALSE +
+RBC + +Red blood cell + +EAS + +BBJ + +153512 + +1999 + +0.08 (95%CI = 0.07 - 0.1) + +0.97 (95%CI = 0.96 - 0.98) + +0.12 (SE = 0.01) + +1.12 (SE = 0.02) + +1.2531 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.RBC.v1.zip + +FALSE +
+RBC + +Red blood cell + +EUR + +UKB + +297084 + +74575 + +0.17 (95%CI = 0.17 - 0.18) + +0.93 (95%CI = 0.93 - 0.93) + +0.2 (SE = 0.02) + +1.19 (SE = 0.02) + +1.6108 + +30010 + +NA + +FALSE +
+SBP + +Sytolic blood pressure + +AFR + +UGR + +13613 + +6658 + +0.17 (95%CI = 0.13 - 0.22) + +0.94 (95%CI = 0 - 0.98) + +0.06 (SE = 0.06) + +1.02 (SE = 0.01) + +1.0285 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009053 + +TRUE +
+SBP + +Sytolic blood pressure + +EAS + +BBJ + +145505 + +1976 + +0.11 (95%CI = 0.09 - 0.12) + +0.94 (95%CI = 0.9 - 0.96) + +0.07 (SE = 0.01) + +1.08 (SE = 0.01) + +1.2005 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.SBP.v1.zip + +TRUE +
+SBP + +Sytolic blood pressure + +EUR + +UKB + +289696 + +72639 + +0.12 (95%CI = 0.12 - 0.12) + +0.89 (95%CI = 0.88 - 0.9) + +0.13 (SE = 0.01) + +1.1 (SE = 0.01) + +1.5995 + +4080 + +NA + +TRUE +
+TC + +Total cholesterol + +AFR + +UGR + +13116 + +6324 + +0.05 (95%CI = 0.05 - 0.06) + +1 (95%CI = 1 - 1) + +0.02 (SE = 0.05) + +1.03 (SE = 0.01) + +1.0285 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009042 + +TRUE +
+TC + +Total cholesterol + +EAS + +BBJ + +135808 + +1955 + +0.08 (95%CI = 0.07 - 0.09) + +0.99 (95%CI = 0.99 - 1) + +0.07 (SE = 0.01) + +1.07 (SE = 0.01) + +1.1459 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.TC.v1.zip + +TRUE +
+TC + +Total cholesterol + +EUR + +UKB + +292046 + +73305 + +0.12 (95%CI = 0.12 - 0.12) + +0.96 (95%CI = 0.96 - 0.97) + +0.11 (SE = 0.01) + +1.07 (SE = 0.02) + +1.3068 + +30690 + +NA + +TRUE +
+TG + +Triglycerides + +AFR + +UGR + +13115 + +6323 + +0.11 (95%CI = 0.08 - 0.15) + +1 (95%CI = 0.99 - 1) + +0.08 (SE = 0.05) + +1.02 (SE = 0.01) + +1.0315 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009045 + +FALSE +
+TG + +Triglycerides + +EAS + +BBJ + +111667 + +1954 + +0.07 (95%CI = 0.06 - 0.09) + +0.99 (95%CI = 0.99 - 1) + +0.13 (SE = 0.03) + +1.05 (SE = 0.01) + +1.1459 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.TG.v1.zip + +FALSE +
+TG + +Triglycerides + +EUR + +UKB + +291813 + +73241 + +0.16 (95%CI = 0.16 - 0.16) + +0.94 (95%CI = 0.93 - 0.94) + +0.18 (SE = 0.02) + +1.14 (SE = 0.02) + +1.5696 + +30870 + +NA + +FALSE +
+WBC + +White blood cell + +AFR + +UGR + +2741 + +6375 + +0.03 (95%CI = 0.03 - 0.05) + +1 (95%CI = 1 - 1) + +0.03 (SE = 0.26) + +1.01 (SE = 0.01) + +1.0105 + +NA + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST009001-GCST010000/GCST009061 + +FALSE +
+WBC + +White blood cell + +EAS + +BBJ + +154355 + +1999 + +0.12 (95%CI = 0.11 - 0.14) + +0.93 (95%CI = 0.91 - 0.95) + +0.11 (SE = 0.01) + +1.1 (SE = 0.01) + +1.2531 + +NA + +https://humandbs.dbcls.jp/files/hum0197/hum0197.v3.BBJ.WBC.v1.zip + +FALSE +
+WBC + +White blood cell + +EUR + +UKB + +297079 + +74575 + +0.14 (95%CI = 0.14 - 0.14) + +0.92 (95%CI = 0.92 - 0.92) + +0.17 (SE = 0.01) + +1.18 (SE = 0.02) + +1.6334 + +30000 + +NA + +FALSE +
+
+
+
+
+
+
+

Main analysis

+
+
+

PGS calculation

+

We will do this using GenoPred.

+
+ +Show code + +
+ +
+

+Prepare configuration +

+
######
+# 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, '"')
+
+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
+######
+
+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, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_groups.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.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.yaml', col.names = F, row.names = F, quote = F)
+
+

+Run pipeline +

+
snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml \
+  target_pgs  -n
+
+

Note: The LD reference data for SBayesRC, LDpred2, +QuickPRS, and QuickPRS+LEOPARD can be download using the links +below:

+ +
+
+
+
+
+

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

+

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.

+
+ +Show code + +
+ +
+

+Create predictor list +

+
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)$V1
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Create files for EAS and AFR targets
+targ_pop <- c('EUR','EAS','AFR')
+for(trait_i in selected_traits){
+  scores_i <- scores[grepl(trait_i, scores$name),]
+  scores_i$multi <- scores_i$method
+  
+  for(targ_pop_i in targ_pop){
+    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)),]
+
+      # 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/targ_',
+          targ_pop_i,
+          '.disc_EUR_',
+          disc_pop_j_2,
+          '/',
+          trait_i,
+          '/predictor_list.txt'
+        ),
+        col.names = T,
+        row.names = F,
+        quote = F
+      )
+    }
+  }
+}
+
+

+Run model_builder +

+
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"
+  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 $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt); do
+      if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/targ_${targ_pop}.disc_EUR_${disc_pop_i}/${pheno}/res.pred_comp.txt" ]; then
+        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 \
+          --n_core 5"
+      fi
+    done
+  done
+done
+
+
+

+Plot results +

+
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)$V1
+info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+
+# Calculate correlation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop = c('EUR','EAS','AFR')
+res_eval <- list()
+for(pheno_i in selected_traits){
+  res_eval_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.pred_eval.txt'
+          )
+        )
+      eval_i$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$Group)
+  res_eval_i$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)
+}
+
+# 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$Target, ' Target')
+
+  png(paste0('~/oliverpainfel/Analyses/crosspop/plots/', pheno_i,'.png'), res=300, width = 3400, height = 2000, units = 'px')
+  plot_tmp<-ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$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){
+  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$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+write.csv(meta_res_eval, '~/oliverpainfel/Analyses/crosspop/r_eval.csv', row.names = F)
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp <- meta_res_eval
+tmp <- tmp[tmp$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/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/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$Target, ' Target')
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_leopard.png'), res=300, width = 1500, height = 2000, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$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/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$label))), linetype="dotted") +
+    labs(y = "R (SE)", x='Method') +
+    facet_grid(Target ~ ., scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1))
+dev.off()
+
+
+####
+# Create heatmap showing difference between all methods and models
+####
+
+# Create a function to mirror pred_comp results
+mirror_comp<-function(x){
+  x_sym <- x
+  x_sym$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$Model_1_R,
+      R_diff=NA,
+      R_diff_pval=NA
+    )
+  x_comp<-rbind(x_mirrored, x_diag)
+  return(x_comp)
+}
+  
+# Read in results
+targ_pop=c('EUR','EAS','AFR')
+res_comp <- list()
+for(pheno_i in selected_traits){
+  res_comp_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      comp_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.pred_comp.txt'
+          )
+        )
+      comp_i<-mirror_comp(comp_i)
+      comp_i$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$`R (Model 2)`, 3)
+
+write.csv(tmp, '~/oliverpainfel/Analyses/crosspop/r_diff.csv', row.names=F)
+
+###########
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_comp <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_comp for each scenario
+    res_comp_i <- res_comp_all[res_comp_all$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$`R (Model 2)`, 3)
+
+write.csv(tmp, '~/oliverpainfel/Analyses/crosspop/r_diff_average.csv', row.names=F)
+
+############
+
+# Group differences
+meta_res_comp$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$R_diff_catagory),
+        palette = "RdBu",
+        drop = F,
+        na.value = 'grey'
+      ) +
+      theme_half_open() +
+      background_grid() +
+      panel_border() +
+      theme(axis.text.x = element_text(
+        angle = 45,
+        vjust = 1,
+        hjust = 1
+      ))
+    
+    png(paste0('~/oliverpainfel/Analyses/crosspop/plots/average_r_diff.Discovery_EUR_', disc_pop_i,'.Target_', targ_pop_i, '.png'), res=300, width = 4400, height = 3200, units = 'px')
+      print(plot_tmp)
+    dev.off()
+  }
+}
+
+####
+# Plot relative improvement of methods
+####
+# Use ptclump IndivTune using EUR GWAS as the reference, as provides an interpretable scale
+
+meta_res_comp_ptclump_top1<-meta_res_comp[meta_res_comp$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/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$Discovery1,' GWAS')
+
+meta_tmp_simple<-meta_tmp_simple[!duplicated(meta_tmp_simple[, c('label', 'Discovery_clean', 'Model1'), with=F]),]
+meta_tmp_ref_simple<-meta_tmp_ref_simple[!duplicated(meta_tmp_ref_simple[, c('label', 'Discovery_clean', 'Model1'), with=F]),]
+tmp_simple<-tmp_simple[!duplicated(tmp_simple[, c('label', 'Discovery_clean', 'Model1','pheno'), with=F]),]
+
+png('~/oliverpainfel/Analyses/crosspop/plots/average_r_eur.perc_improv.png', width = 4000, height = 1500, res= 300, units = 'px')
+ggplot(meta_tmp_simple[meta_tmp_simple$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$label))), linetype="dotted") +
+    scale_y_continuous(labels = percent_format()) +
+    labs(y = "Relative Improvement (SE)", fill = NULL, colour = NULL, x = NULL) +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(
+        axis.text.x = element_text(angle = 45, vjust = 1, hjust = 1),  # Increase x-axis labels
+        legend.position = "top",
+        legend.key.spacing.x = unit(2, "cm"),
+        legend.justification = "center"
+    )
+dev.off()
+
+########
+# Plot relative improvement of LEOPARD over IndivTune of SumStatTune scores
+########
+
+# meta res
+meta_res_comp_ref <- meta_res_comp[meta_res_comp$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).
+
+
+ +Show average improvement in AFR + EAS + +
+
+

+
+
+
+
+ +Show average improvement in EUR + +
+
+

+
+
+
+
+ +Show LEOPARD comparison + +
+
+

+
+
+
+
+
+
+

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.

+
+ +Show code + +
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)$V1
+
+# Get some key variables from config
+config<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
+outdir <- read_param(config = config, param = 'outdir', return_obj = F)
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+###
+# Read in weights estimated by LEOPARD (QuickPRS)
+###
+
+leopard_weights<-NULL
+scores_quickprs <- scores$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$method == method_i]
+  method_i <- gsub('_multi','', method_i)
+
+  for(i in selected_traits){
+    for(j in c('EAS','AFR','EUR')){
+      if(j == 'EUR'){
+        pops <- c('EAS','AFR')
+      } else {
+        pops <- j
+      }
+      
+      for(k in pops){
+        model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/targ_', j, '.disc_EUR_', k, '/', i, '/final_models/', method_i, '.pseudo.multi.final_model.txt'))
+        model<-model[-1,]
+        
+        # Set weight to zero if negative, as this is what LEOPARD does
+        if(any(model$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)
+      }
+    }
+  }
+}
+
+####
+## 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 <- .$EAS
+#    
+#    tibble(
+#      Trait = trait,
+#      Method = "inverse_var",
+#      Target = c("AFR", "AFR", "EUR", "EUR", "EUR", "EAS", "EAS"),
+#      Discovery = c("EUR", "AFR", "EUR", "AFR", "EAS", "EUR", "EAS"),
+#      Weight = c(
+#        eur / (eur + afr), afr / (eur + afr),  # AFR target
+#        eur / (eur + afr), afr / (eur + afr),  # EUR target (vs AFR)
+#        eas / (eur + eas),                     # EUR target (vs EAS)
+#        eur / (eur + eas), eas / (eur + eas)   # EAS target (vs EUR)
+#      )
+#    )
+#  }) %>%
+#  bind_rows()
+
+###
+# Combine and compare
+###
+
+#both <- do.call(rbind, list(obs_weights, leopard_weights, make_weights_long))
+both <- do.call(rbind, list(obs_weights, leopard_weights))
+
+# Remove ptclump as it doesn't have a sumstattune method
+both <- both[both$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/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$label)
+default_colors["LEOPARD"] <- "black"
+
+# Plot the estimated and observed weights
+png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_eur.png', units = 'px', res = 300, width = 2500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(Discovery ~ .) +
+  theme_half_open() +
+  labs(title = 'Weight of non-EUR PGS for EUR Target', fill = NULL) +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
+
+###
+# Check calibration of LEOPARD compared to QuickPRS observed weights
+###
+
+tmp <- both[both$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$LEOPARD)
+
+# Create annotation data.frame
+metrics_df <- data.frame(
+  Discovery = c("AFR", "EAS"),
+  x = c(0.5, 0.5),         # Adjust positions as needed
+  y = c(-0.05, -0.05),
+  label = c(
+    paste0("RMSE = ", round(rmse_afr, 2), "\nME = ", round(me_afr, 2)),
+    paste0("RMSE = ", round(rmse_eas, 2), "\nME = ", round(me_eas, 2))
+  )
+)
+
+png('~/oliverpainfel/Analyses/crosspop/plots/leopard_weights_calibration.png', units = 'px', width = 2000, height = 2000, res = 300)
+ggplot(tmp_wide[tmp_wide$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 (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$Discovery != 'EUR',], aes(x = inverse_var, y = QuickPRS)) +
+#  geom_abline(slope = 1, intercept = 0, linetype = "dashed", colour = "grey40") +  # Perfect calibration
+#  geom_smooth(method = "lm", se = TRUE, colour = "blue") +  # Regression line
+#  geom_point(alpha = 0.7) +
+#  geom_text(data = metrics_df, aes(x = x, y = y, label = label), inherit.aes = FALSE, hjust = 1.5, size = 3.5) +
+#  labs(
+#    x = "inverse_var weight",
+#    y = "Observed weight",
+#  ) +
+#  theme_half_open() +
+#  panel_border() + 
+#  theme(
+#    axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
+#  ) +
+#  facet_grid(. ~ Discovery) +
+#  coord_fixed()    
+#dev.off()
+
+###
+# Check calibration of observed weights across all methods
+###
+
+tmp <- both[both$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()
+
+
+ +Show observed and LEOPARD PGS weights + +
+
+

+
+
+
+
+
+
+

Computational resoures

+

Here we will read in the benchmark data for PGS methods and create a +table for the manuscript.

+
+ +Show code + +
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/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_|leopard_quickprs_', 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))
+
+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$label)){
+  method_avg <- rbind(
+    method_avg,
+    data.frame(
+      method = bm_dat_all$method[bm_dat_all$label == i][1],
+      Method = i,
+      Time = mean(bm_dat_all$s[bm_dat_all$label == i])
+    )
+  )
+}
+
+# Times X-Wing time by two since it used 20 cores, but other methods used 10
+method_avg$Time[method_avg$method == 'xwing'] <- method_avg$Time[method_avg$method == 'xwing'] * 2
+
+# Divide the multi-source methods (PRS-CSx and X-Wing by 2 so it is time per GWAS)
+method_avg$Time[method_avg$method %in% c('prscsx','xwing','leopard_quickprs')] <- method_avg$Time[ method_avg$method %in% c('prscsx','xwing','leopard_quickprs')] / 2
+
+# Approximate times for either tuning or grid only
+method_avg$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$Time < 60] <-
+  paste0(round(method_avg$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$Time > 3600] / 60 / 60, 1), ' hr')
+
+# Convert time in seconds to hours
+method_avg$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[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)")
+
+#############
+# Memory
+#############
+
+# 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$label == i])
+    )
+  )
+}
+
+# Divide X-Wing memory by two, since it used 20 cores, but other methods used 10
+method_avg_mem$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)
+
+
+ +Show computational resources table + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Method + +Time (hrs) + +Memory (Gb) +
+DBSLMM + +0.15 + +1.15 +
+lassosum + +0.08 + +7.30 +
+LDpred2 + +0.38 + +20.54 +
+LEOPARD (QuickPRS) + +0.23 + +8.03 +
+MegaPRS + +0.72 + +11.92 +
+PRS-CS + +4.40 + +10.57 +
+PRS-CSx + +6.84 + +15.18 +
+pT+clump + +0.02 + +0.83 +
+QuickPRS + +0.06 + +4.41 +
+SBayesRC + +0.38 + +4.40 +
+X-Wing + +34.12 + +48.70 +
+
+
+
+
+
+

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.

+
+
+

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.

+
+ +Show code + +
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
+)
+
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
+
+
+
+
+
+

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).

+
+ +Show code + +
+ +
+

+Prepare configuration +

+
library(data.table)
+
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs')
+
+######
+# 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_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(config, '/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml', col.names = F, row.names = F, quote = F)
+
+

+Run pipeline +

+
snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/tlprs/config.yaml \
+  target_pgs -n
+
+
+
+
+

PGS evaluation

+
+ +Show code + +
+ +
+

+Create predictor list +

+
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
+      )
+    }
+  }
+}
+
+

+Run model_builder +

+
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
+
+

+Plot results +

+
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'))
+
+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$', x) & !grepl('pseudo', x)] <- 'IndivTune'
+  mod[grepl('top1$', 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", "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$Model_2_R
+  
+# Compare IndivTune SBayesRC-multi to TL-SBayesRC-multi
+tmp_sbayesrc <- meta_res_comp[meta_res_comp$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
+
+
+ +Show TLPRS results + +
+
+

+
+
+
+
+
+
+

Computational resoures

+
+ +Show code + +
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_tlprs.csv', row.names=F)
+
+
+ +Show computational resources table + + + + + + + + + + + + + + + + + + +
+Method + +Time + +Time_hour + +Memory (Gb) +
+tlprs + +2614.237 + +0.73 + +31.38 +
+
+
+
+
+
+

Sensitivity analyses

+
+
+

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.

+
+ +Show 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*
+
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)
+
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
+
+
+
+
+

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).

+
+ +Show code + +
+ +
+

+Prepare configuration +

+
library(data.table)
+
+dir.create('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only')
+
+######
+# 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)
+
+

+Run pipeline +

+
snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/eas_afr_only/config.yaml \
+  target_pgs -n
+
+
+
+
+

PGS evaluation

+
+ +Show code + +
+ +
+

+Create predictor list +

+
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/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 
+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),]
+  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'
+      }
+
+      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)),]
+
+      # 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]
+      
+      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
+      )
+    }
+  }
+}
+
+

+Run model_builder +

+
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 $(echo EAS AFR); do
+  if [ "$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
+
+
+

+Plot results +

+
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)$V1
+info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+
+# Calculate correlation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop = c('EAS','AFR')
+res_eval <- list()
+for(pheno_i in selected_traits){
+  res_eval_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_1kgref/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.pred_eval.txt'
+          )
+        )
+      eval_i$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$Group)]<-'IndivTune'
+  res_eval_i$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$Discovery, levels=c('AFR','EAS','EUR','EUR+AFR','EUR+EAS'))
+
+# Plot average performance across phenotypes for AFR and EAS targets
+tmp <- meta_res_eval
+tmp <- tmp[tmp$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_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 != 'SumStatTune'] <- 'IndivTune'
+tmp$Model[tmp$Model == 'SumStatTune'] <- 'SumStatTune'
+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$Model != 'SumStatTune'] <- 'IndivTune'
+tmp$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$Method,]
+main_results<-main_results[main_results$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()
+
+
+ +Show results + +
+
+

+
+
+
+
+
+
+
+

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.

+
+
+

PGS calculation

+
+ +Show code + +
+ +
+

+Prepare configuration +

+
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_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 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",
+  "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)
+
+

+Run pipeline +

+
snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_three_pop.yaml \
+  target_pgs  -n
+
+
+
+
+

PGS evaluation

+
+ +Show code + +
+ +
+

+Create predictor list +

+
setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+source('../functions/misc.R')
+source_all('../functions')
+library(data.table)
+
+# Get some key variables from config
+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)$V1
+
+# Get a list of score files
+scores_2_pop <- list_score_files(config_2_pop)
+scores_3_pop <- list_score_files(config_3_pop)
+scores <- rbind(scores_2_pop, scores_3_pop)
+scores <- scores[!duplicated(scores),]
+
+# Remove xwing 
+scores <- scores[scores$method != 'xwing', ]
+
+# Create files for EAS and AFR targets
+targ_pop <- c('EAS','AFR')
+for(trait_i in selected_traits){
+  scores_j <- scores[grepl(trait_i, scores$name),]
+  scores_j$multi <- scores_j$method
+  
+  for(targ_pop_i in targ_pop){
+    disc_pop_j <- c('UGR','BBJ','UKB')
+    disc_pop_j_2 <- c('AFR','EAS','EUR')
+
+    dir.create(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/targ_',
+        targ_pop_i,
+        '.disc_', paste(disc_pop_j_2, collapse = '_'),
+        '/',
+        trait_i
+      ),
+      recursive = T
+    )
+    
+    # Insert path to score file
+    scores_i <- scores_j[!grepl(paste0('UKB_', disc_pop_j[!(disc_pop_j_2 %in% c('EUR', targ_pop_i))], '$'), scores_j$name),]
+    
+    scores_i$predictor <- paste0(
+      outdir,
+      '/ukb/pgs/TRANS/',
+      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 = 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
+      )
+      
+      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 = 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
+      )
+      
+      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 which(scores_i_multi$method %in% c('prscsx'))){
+      score_header<-fread(scores_i_multi$predictor[i], nrow = 1)
+      
+      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'), 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$predictor[i], nrow = 1)
+      
+      pops <- gsub(paste0(trait_i, '_'),'', scores_i_multi$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$|_', pop, '_phi_auto'), names(score_header)))
+        }
+        if(scores_i_multi$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$predictor[i])
+        
+        scores_i_multi_pop_pseudo <- rbind(scores_i_multi_pop_pseudo, tmp)
+      }
+    }
+    
+    # Create pseudo score for multi-source methods
+    scores_i_multi_pseudo<-NULL
+    for(i in 1:nrow(scores_i_multi)) {
+      param <- find_pseudo(
+        config = ifelse(scores_i_multi$name[i] %in% scores_2_pop$name, config_2_pop, config_3_pop),
+        gwas = scores_i_multi$name[i],
+        pgs_method = scores_i_multi$method[i],
+        target_pop = targ_pop_i
+      )
+      
+      score_header <-
+        fread(scores_i_multi$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
+    ))
+    
+    ####
+    # 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)
+    
+    ####
+    # 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_clean,
+      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
+    )
+  }
+}
+
+

+Run model_builder +

+
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 $(echo EAS AFR); do
+  if [ "$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/${pheno}/res.pred_comp.txt" ]; then
+      sbatch --mem 10G -n 1 -p neurohack_cpu,interruptible_cpu -t 1:00:00 --wrap="Rscript ../Scripts/model_builder/model_builder_top1.R \
+        --outcome /users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/${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 1"
+    fi
+  done
+done
+
+
+

+Plot results +

+
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()
+
+
+ +Show results + +
+
+

+
+
+
+
+
+
+
+

Using external GWAS sumstats

+

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.

+
+
+

PGS calculation

+

We will do this using GenoPred.

+
+ +Show code + +
+ +
+

+Prepare configuration +

+

We can use the gwas_list from the GenoPred pipeline paper. Just make +the new configuration file.

+
######
+# 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: ['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",
+  "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 +

+
snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_meta.yaml \
+  target_pgs -n
+
+
+
+
+

PGS evaluation

+
+ +Show code + +
+ +
+

+Create predictor list +

+
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)
+}
+
+

+Run model_builder +

+
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 $(echo bc ckd gout ibd pc ra stroke t1d t2d height bmi egfr hba1c urate hdl); do
+  if [ ! -f "/users/k1806347/oliverpainfel/Analyses/crosspop/meta/targ_EUR.disc_EUR/${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
+
+
+

+Plot results +

+
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$label, levels = model_order)
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots_meta/average_r.png'), res=100, width = 500, height = 300, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$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()
+
+# Results and conclusions remain consistent.
+
+
+ +Show results + +
+
+

+
+
+
+
+
+
+
+

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.

+
+
+

Downsample GWAS

+
+ +Show code + +
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)
+  }
+}
+
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 $(seq 1 22); do
+      if [ ! -s "/users/k1806347/oliverpainfel/Data/ukb/gwas/${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}.${n}.txt \
+          --linear omit-ref cols=+a1freq,+ax \
+          --maf 0.01 \
+          --geno 0.05 \
+          --out /users/k1806347/oliverpainfel/Data/ukb/gwas/${pheno}_subsampled/ukb.eur_train.${pheno}.${n}.chr${chr}"
+      fi
+      fi
+    done
+  done
+done
+
+# Once complete, merge results across chromosomes
+for pheno in $(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
+      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
+    fi
+  done
+done
+
+# Delete per chromosome files
+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.

+
+ +Show code + +
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
+)
+
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
+
+
+
+
+
+

PGS calculation

+
+ +Show code + +
+ +
+

+Prepare configuration +

+
######
+# gwas_list
+######
+
+gwas_list<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt')
+pheno<-gsub('_.*','', gwas_list$name)
+gwas_list<-gwas_list[gwas_list$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$pheno,
+      '.',
+      n,
+      '.GW.txt.gz'
+    )
+  gwas_list_tmp$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)
+
+######
+# 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_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/eur_test_only/target_list.txt",
+  "pgs_methods: ['quickprs','ptclump','dbslmm','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)
+
+####
+# 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)
+
+

+Run pipeline +

+
snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_subsampled.yaml \
+  target_pgs -n
+
+
+
+
+

PGS evaluation

+
+ +Show code + +
+ +
+

+Create predictor list +

+
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)$V1
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Get list of score files using full EUR GWAS
+config_full<-'/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config.yaml'
+outdir_full <- read_param(config = config_full, param = 'outdir', return_obj = F)
+scores_full <- list_score_files(config_full)
+scores_full <- scores_full[grepl('UKB$', 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){
+  
+  dir.create(
+    paste0(
+      '/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/', trait_i
+    ),
+    recursive = T
+  )
+  
+  scores_i <- scores[grepl(paste0('^', trait_i,'_'), 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))
+
+  # 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$method,'.full')
+
+  ####
+  # Make groups containing pseudo scores for single source methods
+  ####
+
+  # Extract the pseudo score for each method and specify as a separate group
+  # This can be skipped as it was done before
+  for(i in 1:nrow(scores_i)) {
+    param <- find_pseudo(
+      config = config,
+      gwas = scores_i$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$predictor)
+
+  # Now for full GWAS - skip subsetting pseudo as done before
+  scores_full_i_pseudo <- scores_full_i
+  scores_full_i_pseudo$top1<-paste0(scores_full_i_pseudo$top1,'.pseudo')
+  
+  scores_full_i_pseudo$predictor <- gsub('.profiles', 
+                                paste0('.EUR_pseudo.profiles'),
+                                scores_full_i_pseudo$predictor)
+
+  ####
+  # Combine the different predictor groups
+  ####
+  predictors_i<- do.call(rbind, list(
+    scores_i, 
+    scores_full_i, 
+    scores_i_pseudo, 
+    scores_full_i_pseudo
+  ))
+  predictors_i$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
+  )
+}
+
+

+Run model_builder +

+
cd /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline
+conda activate model_builder
+
+#rm /users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/*/res*
+
+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,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 1"
+  fi
+done
+
+
+

+Create predictor list +

+

When using EUR and EAS GWAS.

+
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
+    )
+  }
+}
+

+Run model_builder +

+
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
+
+
+

+Plot results +

+
setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/')
+
+library(data.table)
+library(ggplot2)
+library(cowplot)
+
+source('../functions/misc.R')
+source_all('../functions')
+
+selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$V1
+
+# Calculate correlation between all phenotypes in each target population
+pheno_pop_i <- list()
+for(pheno_i in selected_traits){
+  pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.EUR_test.row_number.txt'))
+  names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+}
+
+pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+cors <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+
+# Read in results
+res_eval <- list()
+for(pheno_i in selected_traits){
+  eval_i <-
+    fread(
+      paste0(
+        '/users/k1806347/oliverpainfel/Analyses/crosspop/subsampled/targ_EUR.disc_EUR/', pheno_i,'/res.pred_eval.txt'
+      )
+    )
+
+  eval_i$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$GWAS_N[eval_i$GWAS_N == 'top1K'] <- '297K'
+  
+  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$Model %in% c('SumStatTune')),]
+  
+  res_eval[[pheno_i]]<-eval_i
+  
+}
+
+# Create vector defining or of methods in plots
+model_order <- c("DBSLMM", "lassosum", "LDpred2", "MegaPRS", "PRS-CS", "pT+clump", "QuickPRS", "SBayesRC") 
+
+res_eval_simp <- NULL
+for(pheno_i in selected_traits){
+  tmp <- res_eval[[pheno_i]]
+  tmp$Trait <- pheno_i
+  
+  # Insert nice PGS method names
+  tmp <- merge(tmp, pgs_method_labels, by.x = 'Method', by.y = 'method', all.x = T)
+  tmp$label <- factor(tmp$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$GWAS_N))
+
+png(paste0('~/oliverpainfel/Analyses/crosspop/plots_downsample/per_trait_r.png'), res=100, width = 1000, height = 6000, units = 'px')
+ggplot(tmp, aes(x=label, y=R , fill = Model)) +
+    geom_errorbar(aes(ymin = R - SE, ymax = R + SE),
+                  width = 0,
+                  position = position_dodge(width = 1)) +
+    geom_point(stat="identity", position=position_dodge(1), size=3, shape=23) +
+    geom_vline(xintercept = seq(1.5, length(unique(tmp$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()
+

+Plot results +

+
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()
+
+
+ +Show results + +
+
+

+
+
+
+
+
+
+
+

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.

+
+ +Show code + +
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$method == method_i]
+  method_i <- gsub('_multi','', method_i)
+
+  for(i in selected_traits){
+    for(j in c('EUR')){
+      if(j == 'EUR'){
+        pops <- c('EAS')
+      } else {
+        pops <- j
+      }
+      
+      for(k in pops){
+          for(n in c('5','15','45','135')){
+          model <- fread(paste0('~/oliverpainfel/Analyses/crosspop/subsampled/targ_', j, '.disc_EUR_', k, '/', i, '/res', n, '_final_models/', method_i, '.pseudo.multi.final_model.txt'))
+          model<-model[-1,]
+          
+          # Set weight to zero if negative, as this is what LEOPARD does
+          if(any(model$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$UKB_GWAS_N))
+
+# Plot the estimated and observed weights
+png('~/oliverpainfel/Analyses/crosspop/subsampled/plots/leopard_weights_eur.png', units = 'px', res = 300, width = 3500, height = 1500)
+ggplot(tmp, aes(x = Trait, y = Weight, fill = label)) +
+  geom_bar(width= 0.7, position=position_dodge(0.7), stat="identity", colour = 'black', size = 0.1) +
+  scale_fill_manual(values = default_colors) +
+  facet_grid(. ~ UKB_GWAS_N) +
+  theme_half_open() +
+  labs(title = 'Weight of EUR PGS for EUR Target', fill = NULL) +
+  background_grid(major = 'y', minor = 'y') + 
+  panel_border() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
+  ylim(c(0,1))
+dev.off()
+
+###
+# Check calibration of LEOPARD compared to QuickPRS observed weights
+###
+
+tmp <- both[both$Target == 'EUR',]
+tmp <- tmp[both$Discovery == 'EUR',]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     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]
+
+# Calculate metrics
+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$LEOPARD)
+                 ))
+}
+
+# Create annotation data.frame
+metrics_df <- data.frame(
+  UKB_GWAS_N = metrics$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, 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(. ~ UKB_GWAS_N) +
+  coord_fixed()    
+dev.off()
+
+###
+# Check calibration of inverse_var compared to QuickPRS observed weights
+###
+
+tmp <- both[both$Target == 'EUR',]
+tmp <- tmp[both$Discovery == 'EUR',]
+tmp$Target<-NULL
+tmp_wide <- reshape(tmp, 
+                     idvar = c("Trait", "Discovery","UKB_GWAS_N"), 
+                     timevar = "label", 
+                     direction = "wide")
+
+names(tmp_wide) <- gsub('Weight.', '', names(tmp_wide))
+tmp_wide<-tmp_wide[, !(grepl('Method', names(tmp_wide))), with = F]
+
+# Calculate metrics
+metrics <- NULL
+for(n in c('5','15','45','135')){
+  tmp_wide_n <- tmp_wide[tmp_wide$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(
+  UKB_GWAS_N = metrics$n,
+  x = 0.3,
+  y = 0.25,
+  label = paste0("RMSE = ", round(metrics$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_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 = 0, 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(. ~ UKB_GWAS_N) +
+  coord_fixed()    
+dev.off()
+
+
+
+
+

Using MVP sumstats

+
+

Download MVP sumstats

+
+ +Show code + +
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$accessionId %in% 
+    c(
+      'GCST90475361', 'GCST90475375', 'GCST90476298', 'GCST90475155', 'GCST90476462', 'GCST90475457', 'GCST90476423', 'GCST90475528', 'GCST90475351', 'GCST90476402'
+    )
+, ]
+
+# Insert labels
+mvp_afr_subset$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 == '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'
+
+mvp_afr_subset$url <-paste0(mvp_afr_subset$summaryStatistics, '/', mvp_afr_subset$accessionId, '.tsv.gz')
+
+dir.create('~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR')
+
+write.table(
+  mvp_afr_subset[, c('url', 'labels'), with = F],
+  '~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/urls.txt',
+  row.names = F,
+  quote = F,
+  col.names = F
+)
+
+write.table(
+  mvp_afr_subset,
+  '~/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/info.txt',
+  row.names = F,
+  quote = T,
+  col.names = T
+)
+
+write.table(mvp_afr_subset$labels, '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', col.names = F, row.names = F, quote = F)
+
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
+
+
+
+
+

PGS calculation

+

Run leading single-source PGS methods using MVP GWAS sumstats.

+
+ +Show code + +
+ +
+

+Prepare configuration +

+
library(data.table)
+
+# 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$population == 'AFR',]
+selected_traits <- gsub('_UGR', '', gwas_list$name)
+gwas_list$name <- gsub('UGR', 'MVP_AFR', gwas_list$name)
+gwas_list$label <- gsub('UGR', 'MVP_AFR', gwas_list$label)
+gwas_list$path <-
+  paste0('/users/k1806347/oliverpainfel/Data/GWAS_sumstats/MVP/AFR/',
+         selected_traits,'.txt.gz')
+
+gwas_list_eur<-fread('/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/gwas_list.txt')
+gwas_list_eur<-gwas_list_eur[gwas_list_eur$population == 'EUR', ]
+
+gwas_list <- 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
+######
+
+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','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: 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)
+
+

+Run pipeline +

+
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
+
+
+
+
+
+

PGS evaluation

+

Compare the single-source PGS within AFR ancestry target +individuals.

+
+ +Show code + +
+ +
+

+Create predictor list +

+
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)$V1
+selected_traits<-selected_traits[selected_traits != 'NEU']
+
+# Get a list of score files
+scores <- list_score_files(config)
+
+# Create files for AFR targets
+targ_pop <- c('AFR')
+for(trait_i in selected_traits){
+  scores_i <- scores[grepl(trait_i, scores$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
+      )
+    }
+  }
+}
+
+

+Run model_builder +

+
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 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 5"
+      fi
+    done
+  done
+done
+
+
+

+Plot results +

+
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)$V1
+selected_traits <- selected_traits[selected_traits != 'NEU']
+info_all <- fread('~/oliverpainfel/Analyses/crosspop/gwas_descriptives.csv')
+
+# Calculate correlation between all phenotypes in each target population
+cors <- list()
+for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){
+  if(pop_i == 'EUR'){
+    pop_i_2 <- 'EUR_test'
+  } else {
+    pop_i_2 <- pop_i
+  }
+  pheno_pop_i <- list()
+  for(pheno_i in selected_traits){
+    pheno_pop_i[[pheno_i]] <- fread(paste0('/users/k1806347/oliverpainfel/Data/ukb/phenotypes/prscsx/', pheno_i, '.unrel.', pop_i_2, '.row_number.txt'))
+    names(pheno_pop_i[[pheno_i]])[3] <- pheno_i
+  }
+  
+  pheno_pop_i_merged <- merged_df <- Reduce(function(x, y) merge(x, y, all = TRUE, by = c('FID','IID')), pheno_pop_i)
+
+  cors_i <- abs(cor(as.matrix(pheno_pop_i_merged[,-1:-2, with=F]), use='p'))
+  cors[[pop_i]] <- cors_i
+}
+
+# Read in results
+targ_pop = c('AFR')
+res_eval <- list()
+for(pheno_i in selected_traits){
+  res_eval_i<-NULL
+  for(targ_pop_i in targ_pop){
+    if(targ_pop_i == 'EAS'){
+      disc_pop <- 'EAS'
+    }
+    if(targ_pop_i == 'AFR'){
+      disc_pop <- 'AFR'
+    }
+    if(targ_pop_i == 'EUR'){
+      disc_pop <- c('EAS','AFR')
+    }
+    for(disc_pop_i in disc_pop){
+      eval_i <-
+        fread(
+          paste0(
+            '/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/',
+            'targ_',
+            targ_pop_i,
+            '.disc_EUR_',
+            disc_pop_i,
+            '/',
+            pheno_i,
+            '/res.pred_eval.txt'
+          )
+        )
+      eval_i$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)
+}
+
+# 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',]
+  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$`Trait Label` == pheno_i]) +
+    facet_grid(Target ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+  print(plot_tmp)
+  dev.off()
+  per_trait_plot[[pheno_i]]<-plot_tmp
+}
+
+tmp <- res_eval_simp
+tmp$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$label))), linetype="dotted") +
+    labs(y = "R (SE)", x=NULL, fill = NULL) +
+    facet_grid(Trait ~ Discovery_clean, scales='free', space = 'free_x') +
+    theme_half_open() +
+    background_grid(major = 'y', minor = 'y') + 
+    panel_border() + 
+    theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1),
+          legend.position = "top",
+          legend.key.spacing.x = unit(1, "cm"),
+          legend.justification = "center")
+dev.off()
+
+
+####
+# Average results across phenotypes
+####
+
+library(MAd)
+
+# Average R across phenotypes
+meta_res_eval <- NULL
+for(targ_pop_i in targ_pop){
+  if(targ_pop_i == 'EAS'){
+    disc_pop <- 'EAS'
+  }
+  if(targ_pop_i == 'AFR'){
+    disc_pop <- 'AFR'
+  }
+  if(targ_pop_i == 'EUR'){
+    disc_pop <- c('EAS','AFR')
+  }
+  for(disc_pop_i in disc_pop){
+  
+    # Subset res_eval for each scenario
+    res_eval_i <- do.call(rbind, lapply(seq_along(res_eval), function(i) {
+      x <- res_eval[[i]]
+      x$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 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('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'] <- 'AFR GWAS'
+meta_tmp$Discovery_clean[meta_tmp$Discovery1 != 'EUR' & meta_tmp$Source1 == 'Multi'] <- 'EUR + AFR GWAS'
+meta_tmp$Discovery_clean <- factor(meta_tmp$Discovery_clean, 
+                              levels = c('AFR GWAS',
+                                         'EUR GWAS',
+                                         'EUR + AFR GWAS'))
+meta_tmp$Target <- paste0(meta_tmp$Target, ' Target')
+meta_tmp$Model1 <- factor(meta_tmp$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+
+meta_tmp_ref <- meta_res_comp_reference
+meta_tmp_ref <- merge(meta_tmp_ref, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+meta_tmp_ref$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'] <- '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$Discovery_clean, 
+                              levels = c('AFR GWAS',
+                                         'EUR GWAS',
+                                         'EUR + AFR GWAS'))
+meta_tmp_ref$Target <- paste0(meta_tmp_ref$Target, ' Target')
+meta_tmp_ref$Model1 <- factor(meta_tmp_ref$Model1, levels = c('IndivTune','SumStatTune','Multi-IndivTune','Multi-SumStatTune'))
+
+tmp <- res_comp_all_ptclump_top1
+tmp <- merge(tmp, pgs_method_labels, by.x = 'Method1', by.y = 'method', all.x = T)
+tmp$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'] <- 'AFR GWAS'
+tmp$Discovery_clean[tmp$Discovery1 != 'EUR' & tmp$Source1 == 'Multi'] <- 'EUR + AFR GWAS'
+tmp$Discovery_clean <- factor(tmp$Discovery_clean, 
+                              levels = c('AFR GWAS',
+                                         'EUR GWAS',
+                                         'EUR + AFR GWAS'))
+tmp$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$Group,]
+
+# Export plot for manuscript
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/average_r.perc_improv.png', width = 3200, height = 1500, res= 300, units = 'px')
+ggplot(meta_tmp_simple[meta_tmp_simple$Target != 'EUR Target',], aes(x=label, y=R_diff_perc , fill = Model1)) +
+#    geom_boxplot(
+#      data = tmp_simple[tmp_simple$Target != 'EUR Target',],
+#        mapping = aes(x=label, y=R_diff_perc, colour=Model1),
+#        position = position_dodge(0.7),
+#        alpha = 0.3
+#      ) +
+    geom_point(
+        data = tmp_simple[tmp_simple$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 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()
+
+
+
+
+

Descriptive statistics

+

Create a table showing descriptive statistics for the MVP sumstats. +This should include LDSC SNP-heritability and AVENGEME results.

+
+
+

LDSC

+
+ +Show code + +
conda activate ldsc
+
+for pheno in $(cat /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt); do
+  mkdir -p /users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/ldsc/sumstats
+
+  sbatch --mem 10G -n 1 -p neurohack_cpu --wrap="/users/k1806347/oliverpainfel/Software/ldsc/munge_sumstats.py \
+   --sumstats /users/k1806347/oliverpainfel/Data/ukb/GenoPred/output/reference/gwas_sumstat/${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/${pheno}/MVP_AFR/res"
+done
+
+
library(data.table)
+library(ggplot2)
+library(cowplot)
+
+# Read in phenotypes
+pheno_intersect <- read.table('/users/k1806347/oliverpainfel/Analyses/crosspop/sensitivity_mvp/trait_subset.txt', header=F)$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)
+
+
+
+
+

AVENGEME

+
+ +Show code + +
+ +
+

+Create predictor list +

+
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
+    )
+  }
+}
+
+

+Run model_builder +

+
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
+
+
+

+Plot pT+clump association results +

+
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'])
+
+
+
+
+

Make table

+

Make a table showing GWAS information for the manuscript.

+
+ +Show code + +
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]))
+    )
+  )
+}
+
+
+ +Show descriptives table + +
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+Trait Label + +Trait Description + +Ancestry + +GWAS Sample + +GWAS N + +Target N + +SNP-h2 (AVENGEME) + +pi0 (AVENGEME) + +SNP-h2 (LDSC) + +Intercept (LDSC) + +Lambda + +URL +
+BMI + +body mass index + +AFR + +MVP + +118993 + +6646 + +0.12 (95%CI = 0.11 - 0.14) + +0.96 (95%CI = 0.95 - 0.97) + +0.37 (SE = 0.03) + +1.32 (SE = 0.02) + +1.6715 + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475155/GCST90475155.tsv.gz +
+BWT + +body weight + +AFR + +MVP + +119279 + +6659 + +0.14 (95%CI = 0.13 - 0.16) + +0.96 (95%CI = 0.95 - 0.97) + +0.4 (SE = 0.03) + +1.36 (SE = 0.03) + +1.7334 + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476462/GCST90476462.tsv.gz +
+HB + +hemoglobin measurement + +AFR + +MVP + +114985 + +6375 + +0.06 (95%CI = 0.05 - 0.07) + +0.99 (95%CI = 0.99 - 0.99) + +0.19 (SE = 0.02) + +1.2 (SE = 0.02) + +1.3685 + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475375/GCST90475375.tsv.gz +
+HDL + +high density lipoprotein cholesterol measurement + +AFR + +MVP + +113085 + +5863 + +0.11 (95%CI = 0.1 - 0.12) + +0.99 (95%CI = 0.99 - 0.99) + +0.32 (SE = 0.04) + +1.3 (SE = 0.02) + +1.5071 + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475351/GCST90475351.tsv.gz +
+HT + +body height + +AFR + +MVP + +119012 + +6658 + +0.14 (95%CI = 0.12 - 0.15) + +0.96 (95%CI = 0.95 - 0.97) + +0.93 (SE = 0.08) + +1.69 (SE = 0.06) + +2.2871 + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475361/GCST90475361.tsv.gz +
+MCHC + +mean corpuscular hemoglobin concentration + +AFR + +MVP + +114851 + +6375 + +0.09 (95%CI = 0.08 - 0.1) + +0.99 (95%CI = 0.99 - 0.99) + +0.27 (SE = 0.1) + +1.32 (SE = 0.03) + +1.4034 + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475457/GCST90475457.tsv.gz +
+NEU + +neutrophil count + +AFR + +MVP + +73258 + +6353 + +0.36 (95%CI = 0.34 - 0.38) + +0.97 (95%CI = 0.96 - 0.97) + +2.32 (SE = 1.19) + +1.43 (SE = 0.07) + +1.4459 + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90475001-GCST90476000/GCST90475528/GCST90475528.tsv.gz +
+PLT + +platelet count + +AFR + +MVP + +114731 + +6375 + +0.1 (95%CI = 0.09 - 0.11) + +0.99 (95%CI = 0.99 - 0.99) + +0.38 (SE = 0.03) + +1.38 (SE = 0.02) + +1.6639 + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476298/GCST90476298.tsv.gz +
+SBP + +systolic blood pressure + +AFR + +MVP + +119331 + +6658 + +0.06 (95%CI = 0.05 - 0.07) + +0.99 (95%CI = 0.98 - 0.99) + +0.19 (SE = 0.02) + +1.2 (SE = 0.01) + +1.3964 + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476402/GCST90476402.tsv.gz +
+TC + +total cholesterol measurement + +AFR + +MVP + +112265 + +6324 + +0.1 (95%CI = 0.09 - 0.12) + +0.99 (95%CI = 0.99 - 0.99) + +0.2 (SE = 0.04) + +1.25 (SE = 0.02) + +1.3615 + +http://ftp.ebi.ac.uk/pub/databases/gwas/summary_statistics/GCST90476001-GCST90477000/GCST90476423/GCST90476423.tsv.gz +
+
+
+
+
+
+
+

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.

+
+ +Show code + +
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$Discovery[grepl('MVP', model$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)
+      }
+    }
+  }
+}
+
+###
+# 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$trait %in% selected_traits,]
+
+gwas_desc <- gwas_desc[gwas_desc$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$trait %in% selected_traits,]
+
+gwas_desc<-rbind(gwas_desc, gwas_desc_mvp)
+
+library(dplyr)
+library(tidyr)
+
+# Reshape GWAS table to wide format
+wide_gwas <- gwas_desc %>%
+  pivot_wider(names_from = ancestry, values_from = n, values_fill = 0)
+
+# Function to create rows for each pair
+make_weights_long <- wide_gwas %>%
+  rowwise() %>%
+  do({
+    trait <- .$trait
+    eur <- .$EUR
+    afr <- .$AFR
+    eas <- .$EAS
+    
+    tibble(
+      Trait = trait,
+      Method = "inverse_var",
+      Target = c("AFR", "AFR", "EUR", "EUR"),
+      Discovery = c("EUR", "AFR", "EUR", "AFR"),
+      Weight = c(
+        eur / (eur + afr), afr / (eur + afr),  # AFR target
+        eur / (eur + afr), afr / (eur + afr)  # EUR target (vs AFR)
+      )
+    )
+  }) %>%
+  bind_rows()
+
+###
+# Combine and compare
+###
+
+both <- do.call(rbind, list(obs_weights, leopard_weights, make_weights_long))
+
+# Remove ptclump as it doesn't have a sumstattune method
+both <- both[both$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()
+
+###
+# 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$Discovery == i,]
+      rmse <- sqrt(mean((tmp_wide_i[[j]] - tmp_wide_i[[k]])^2))
+      me <- mean(tmp_wide_i[[j]] - tmp_wide_i[[k]])
+      
+      metrics <- rbind(
+        metrics,
+        data.frame(
+          Population = i,
+          Method1 = j,
+          Method2 = k,
+          rmse = rmse,
+          me = me
+        )
+      )
+    }
+  }
+}
+
+png('~/oliverpainfel/Analyses/crosspop/sensitivity_mvp/plots/observed_weights_calibration.png', units = 'px', width = 2000, height = 1650, res = 300)
+ggplot(metrics, aes(x = Method1, y = Method2, fill = rmse)) +
+  geom_tile(color = "white") +  # Tile plot with white borders
+  geom_text(aes(label = round(rmse, 2)), color = "black") +  # Add correlation values
+  scale_fill_gradient2(mid = "white", high = "red", midpoint = 0) +  # Color scale
+  theme_half_open() +
+  panel_border() + 
+  theme(
+    axis.text.x = element_text(angle = 45, hjust = 1),
+    axis.title = element_blank()
+  ) +
+  labs(fill = "RMSE")
+dev.off()
+
+# Calculate average RMSE for each method against all other methods
+metrics_unique <- metrics[metrics$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')){
+    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="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('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() +
+  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 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.

+
+ +Show code + +
# 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
+}
+
+# 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))
+}
+
+# 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<-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$name[i]]] <- ss_i[, c('SNP','A1','A2','FREQ'), with = F]
+}
+
+gwas_pc_all <- NULL
+for(i in names(gwas_freq)){
+  # Merge on SNP ID
+  merged <- merge(
+    pc_score_file, 
+    gwas_freq[[i]], 
+    by = "SNP"
+  )
+  
+  # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ)
+  merged[, FREQ := ifelse(A1.x == A1.y, FREQ, 1 - FREQ)]
+  
+  # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC
+  ref_PC <- merged[, lapply(.SD, function(w) sum(w * FREQ, na.rm = TRUE)), 
+                   .SDcols = patterns("^PC")]
+  
+  gwas_pc_all <- rbind(gwas_pc_all, data.frame(group = i,
+                                             ref_PC))
+}
+
+ref_pc_all$type <- 'Reference'
+gwas_pc_all$type <- 'GWAS'
+
+pc_all <- rbind(ref_pc_all, gwas_pc_all)
+
+ggplot(pc_all, aes(x=PC1, y = PC2, colour = group, shape = type)) +
+  geom_point(size = 5)
+
+# There is a big shift in the PCs for GWAS and reference data, probably due to missing variants. Restrict to variant present in all GWAS.
+
+extract <- Reduce(intersect, list(
+  pc_score_file$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 <- pc_score_file[pc_score_file$SNP %in% extract,]
+
+# Calculate PCs for reference populations
+ref_pc_all <- NULL
+for(i in c('EUR','EAS','AFR','AMR','MID','CSA')){
+  # Merge on SNP ID
+  merged <- merge(
+    pc_score_file_overlapping, 
+    ref_pop_freq[[i]], 
+    by.x = "SNP", 
+    by.y = "ID"
+  )
+  
+  # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ)
+  merged[, A1_FREQ := ifelse(ALT == A1, ALT_FREQS, 1 - ALT_FREQS)]
+  
+  # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC
+  ref_PC <- merged[, lapply(.SD, function(w) sum(w * A1_FREQ, na.rm = TRUE)), 
+                   .SDcols = patterns("^PC")]
+  
+  ref_pc_all <- rbind(ref_pc_all, data.frame(group = i,
+                                             ref_PC))
+}
+
+gwas_pc_all <- NULL
+for(i in names(gwas_freq)){
+  # Merge on SNP ID
+  merged <- merge(
+    pc_score_file_overlapping, 
+    gwas_freq[[i]], 
+    by = "SNP"
+  )
+  
+  # Flip frequencies where needed (if ALT != A1, assume ALT_FREQ = 1 - A1_FREQ)
+  merged[, FREQ := ifelse(A1.x == A1.y, FREQ, 1 - FREQ)]
+  
+  # Calculate PCs using dot product: sum(allele_freq * SNP_weight) for each PC
+  ref_PC <- merged[, lapply(.SD, function(w) sum(w * FREQ, na.rm = TRUE)), 
+                   .SDcols = patterns("^PC")]
+  
+  gwas_pc_all <- rbind(gwas_pc_all, data.frame(group = i,
+                                             ref_PC))
+}
+
+ref_pc_all$Population <- ref_pc_all$group
+ref_pc_all$GWAS <- NA
+
+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$Population <- NA
+
+ref_pc_all$type <- 'Reference'
+gwas_pc_all$type <- 'GWAS'
+
+pc_all <- rbind(ref_pc_all, gwas_pc_all)
+
+png('~/oliverpainfel/Analyses/crosspop/plots/genetic_distance.png', units = 'px', width = 2000, height = 1500, res = 300)
+
+ggplot(pc_all, aes(x=PC1, y = PC2)) +
+  geom_point(data=pc_all[pc_all$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()
+
+dev.off()
+
+# Perfect. Shows UKB match EUR, BBJ match EAS, UGR matches AFR, but MVP is close to AFR.
+
+
+
+
+

Estimate ancestry proportions

+

This involves using Florian Prive’s bigsnpr packages and UKB +reference data to calculate principal components and then estimate +ancestral proportions.

+
+ +Show code + +
library(bigsnpr)
+library(dplyr)
+
+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 >= 0.02,]
+
+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) +
+  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))
+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$value >= 0.02,]
+
+png('~/oliverpainfel/Analyses/crosspop/plots/ref_ancestry.png', units = 'px', width = 2000, height = 1500, res = 300)
+ggplot(ref_ancestry_table_melt, aes(x = Var2, y = value, fill = Var1)) +
+  geom_bar(stat = "identity", colour = 'black', size = 0.2) +
+  geom_text(data = subset(ref_ancestry_table_melt, value >= 0.02),
+            aes(label = scales::percent(value, accuracy = 1)),
+            position = position_stack(vjust = 0.5),
+            size = 3) +
+  labs(x = "Reference Label", y = "Proportion", fill = 'Population', title = 'Ancestry proportions in 1KG+HGDP reference') +
+  theme_half_open() +
+  theme(axis.text.x = element_text(angle = 45, hjust = 1))
+dev.off()
+
+write.csv(ref_ancestry_table, '~/oliverpainfel/Analyses/crosspop/plots/ref_ancestry.csv')
+
+gwas_ancestry_table_melt$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

+

The below code is incomplete due to current compatability issues +between BridgePRS and GenoPred. Work to incorporate BridgePRS into +GenoPred is on going.

+
+ +Show code + +
######
+# 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)
+
+

+Run pipeline +

+
snakemake \
+  --profile slurm \
+  --use-conda \
+  --configfile=/users/k1806347/oliverpainfel/Data/ukb/GenoPred/configs/crosspop/config_bridgeprs.yaml \
+  target_pgs  -n
+
+
+ + +
+ +
+
+ +
+
+ + +
+
+ +
+ + + + + + + + + + + + + + + + diff --git a/docs/CrossPop_dnanexus.Rmd b/docs/CrossPop_dnanexus.Rmd new file mode 100644 index 00000000..538e936f --- /dev/null +++ b/docs/CrossPop_dnanexus.Rmd @@ -0,0 +1,3330 @@ +--- +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 +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 +~/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 ~/ukb/ukb_symlinks + +# pgen and pvar files +for chr in $(seq 1 22);do + for file in $(echo bgen bgen.bgi);do + 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 ~/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 + +``` + +Create the require configuration files. + +```{bash} +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', + '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} +conda activate genopred +cd ~/GenoPred/pipeline +git describe --tags +# v2.2.2-179-gfa4ccec + +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.'. + +*** + +# 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} +# 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 + +# 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 + +# 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. +``` + + +*** + + + + +```{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 + +
Show code +```{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 + +``` +
+ +*** + +## Collect phenotype data + +We will use the same 33 quantitative traits that were used in the PRS-CSx paper (Supp Table 10). + +
Show code +```{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' + ) +} + +``` +
+ +*** + +## Run GWAS + +
Show code +```{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.${pheno}.GW.txt +done + +``` +
+ +*** + +# Download relevant BBJ sumstats + +
Show code + +```{r} +# Identify wget command for relevant phenotypes +library(data.table) + +# Read in BBJ GWAS info from BBJ website +bbj_gwas<-fread('~/oliverpainfel/Data/GWAS_sumstats/BBJ/prscsx/bbj_gwas.csv') + +# Map BBJ trait names to those used for UKB +bbj_gwas$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.${pheno}.reformat.txt.gz" +done + +``` +
+ +*** + +# Download relevant UGR sumstats + +
Show code + +```{r} +# Identify wget command for relevant phenotypes +library(data.table) + +# Read in UGR GWAS info from GWAS catalogue +ugr_gwas<-fread('~/oliverpainfel/Data/GWAS_sumstats/UGR/ugr_sumstats.csv') + +# Map UGR trait names to those used for UKB +ugr_gwas$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$label[i], ".reformat.txt.gz"), sep=' ', quote=F, na='NA') + +}) + +``` + +
+ +*** + +# Estimate SNP-h2, polygenicity and rG across populations + +We will estimate SNP-h2 using LD-score regression, and the rG using POPCORN. +POPCORN can estimate the SNP-h2, but it will vary according to the other GWAS included due to SNP overlap. +Use the sumstats QC'd by GenoPred. +To estimate polygenicity, lets use AVENGEME based on ptclump score association results. Lets generate those using GenoPred. + +*** + +## Prepare configuration for GenoPred + +
Show code + +```{r} +###### +# gwas_list +###### + +prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv') + +gwas_list_eur<-data.frame( + name=paste0(prscsx_dat$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) + +``` +
+ +*** + +## 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 + +
Show code +```{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" + ) + ) +} +``` + +
+ +*** + +## Run LDSC + +
Show code +```{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 + +``` + +
+ +*** + +## 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 + +
Show code +```{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_${pop}/rG_gen_effect" + done +done + +``` + +
+ +*** + +## Plot the LDSC and POPCORN results + +
Show code + +```{r} + +library(data.table) +library(ggplot2) +library(cowplot) + +# Read in phenotypes +pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$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') + +``` +
+ +## AVENGEME + +### Create predictor lists + +
Show code + +```{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 + ) + } +} + +``` +
+ +*** + +### Run model_builder + +
Show code + +```{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}/${pheno}/res.ptclump \ + --n_core 5 \ + --all_model F \ + --assoc T" + done +done + +``` +
+ +*** + +### Plot assoc results + +
Show code + +```{r} +setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/') + +library(data.table) +library(ggplot2) +library(cowplot) + +source('../functions/misc.R') +source_all('../functions') + +# Read in phenotypes +pheno_intersect <- read.table('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/pheno_eur_eas_afr.txt', header=F)$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 ~.) + +``` +
+ +### 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$trait, '/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', col.names = F, row.names = F, quote = F) + +``` + +*** + +# Run GenoPred + +## Prepare configuration for GenoPred + +
Show code + +```{r} +###### +# gwas_list +###### +library(data.table) +# Subset original gwas_list to include selected traits +gwas_list<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/gwas_list_all.txt') +pheno<-gsub('_.*','', gwas_list$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) + +``` +
+ +*** + +## 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 + +
Show code + +```{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 + ) + } + } +} + +``` +
+ +*** + +### Run model_builder + +
Show code + +```{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}/${pheno}/res \ + --n_core 10 \ + --top1 T \ + --all_model T \ + --assoc T" + done + done +done + +``` +
+ +*** + +### Plot results + +
Show code + +```{r} +setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/') + +library(data.table) +library(ggplot2) +library(cowplot) + +source('../functions/misc.R') +source_all('../functions') + +# Read in list of outcomes +selected_traits<-fread('/users/k1806347/oliverpainfel/Analyses/crosspop/trait_subset.txt', header=F)$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)) + +``` +
+ +*** + +## Evaluate TLPRS +### Create predictor lists + +
Show code + +```{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 + ) + } + } +} + +``` + +
+ +*** + +### Run model_builder + +
Show code + +```{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}/${pheno}/res.tlprs \ + --n_core 10 \ + --top1 T \ + --all_model F \ + --assoc T" + done + done +done + +``` +
+ +*** + +### Plot results + +
Show code + +```{r} + +setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/') + +library(data.table) +library(ggplot2) +library(cowplot) + +source('../functions/misc.R') +source_all('../functions') + +# Read in list of outcomes +prscsx_dat<-fread('/scratch/prj/ukbiobank/usr/ollie_pain/phenotypes/prscsx/prscsx_data.csv') +prscsx_dat<-prscsx_dat[1:5,] + +config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml' + +# Define pgs_methods used +pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F) + +# Define gwas_list used +gwas_list<-read_param(config = config, param = 'gwas_list') + +# Define gwas_groups used +gwas_groups<-read_param(config = config, param = 'gwas_groups') + +# Calculate corelation between all phenotypes in each target population +cors <- list() +for(pop_i in c('EUR','EAS','AFR','CSA','AMR')){ + if(pop_i == 'EUR'){ + pop_i_2 <- 'EUR_test' + } else { + pop_i_2 <- pop_i + } + pheno_pop_i <- list() + for(pheno_i in prscsx_dat$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$TLPRS_target_1 == 'Both',], + mapping = aes(x=Method_1, y=R_diff, colour=Test_1), + position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.7), + alpha = 0.3 + ) + + geom_errorbar( + aes( + ymin = R_diff - R_diff_SE, + ymax = R_diff + R_diff_SE + ), + width = 0, + position = position_dodge(width = 0.7) + ) + + geom_point( + stat = "identity", + position = position_dodge(0.7), + size = 2, + shape = 23, + colour = 'black' + ) + + labs(y = "Difference in\nCorrelations (SE)", x = '', fill = 'Model', colour = 'Model') + + theme_half_open() + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + + geom_vline(xintercept = 1.5, linetype = "dotted") + + geom_vline(xintercept = 2.5, linetype = "dotted") + + geom_vline(xintercept = 3.5, linetype = "dotted") + + background_grid(major = 'y', minor = 'y') + + facet_grid(Target ~ ., scales='free', space = 'free_x') + + panel_border() + + +``` +
+ +*** + +# Computational resoures + +```{r} +library(data.table) +library(ggplot2) +library(cowplot) + +setwd('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/') +source('../functions/misc.R') +source_all('../functions') + +# Get some key variables from config +config<-'/scratch/prj/ukbiobank/usr/ollie_pain/GenoPredPipe/usr/k1806347/configs/crosspop/config.yaml' +pgs_methods <- read_param(config = config, param = 'pgs_methods', return_obj = F) +outdir <- read_param(config = config, param = 'outdir', return_obj = F) + +# Read in configuration specific benchmark files +bm_files_i <- list.files(paste0(outdir, '/reference/benchmarks/'), full.names = T) + +# Subset benchmarks for pgs_methods +bm_files_i <- bm_files_i[grepl('prep_pgs_', bm_files_i)] + +# Subset to benchmarks for gwas/gwas_groups in config +scores <- list_score_files(config) +bm_files_i <- bm_files_i[grepl(paste0('-', unique(scores$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") + +``` + diff --git a/docs/CrossPop_summary.Rmd b/docs/CrossPop_summary.Rmd new file mode 100644 index 00000000..bf619363 --- /dev/null +++ b/docs/CrossPop_summary.Rmd @@ -0,0 +1,54 @@ +--- +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. + +
+ +# 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. + +
+ +# 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 + +
+ +# 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) + +
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diff --git a/docs/CrossPop_summary.html b/docs/CrossPop_summary.html new file mode 100644 index 00000000..ce34a462 --- /dev/null +++ b/docs/CrossPop_summary.html @@ -0,0 +1,618 @@ + + + + + + + + + + + + + +Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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Summary

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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.

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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.

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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.

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Key Findings

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  • Multi-source methods consistently outperform single-source models +across ancestries, especially for AFR and EAS populations.

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  • Independently optimised scores, when combined using LEOPARD, +outperform jointly optimised methods (e.g. PRS-CSx, X-Wing) with far +lower computational cost.

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  • SBayesRC and LDpred2 were the top-performing single-source +methods, with SBayesRC performing best when no individual-level tuning +data were available.

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  • LEOPARD + QuickPRS enables fast, summary-statistic-only tuning of +multi-source models (independently or jontly optimised), nearly matching +individual-level tuning performance.

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  • 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.

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Citation

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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

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Additional resources

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  • Code used to conduct the study: here
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  • Example applying LEOPARD + QuickPRS to height GWAS from 5 +populations and then evaluated in OpenSNP target sample: here
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+ + + + + + + + + + + + + + + + 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/docs/GenoClust.Rmd b/docs/GenoClust.Rmd new file mode 100644 index 00000000..d3d72eda --- /dev/null +++ b/docs/GenoClust.Rmd @@ -0,0 +1,2523 @@ +--- +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 + +*** + +### Simulate genetic data + +Simulate chr22 genotype data for 100k EUR individuals, using HAPNEST. + +
Show code + +```{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 + +``` + +
+ +*** + +### 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$sample.ID, outcome = traits[["trait_2"]]), + '~/oliverpainfel/Data/HAPNEST/synth_2/trait_2.txt', row.names = F, quote=F) + +``` + + +*** + +### Define causal variants + +Download GO data to identify genes within these sets. + +```{r} +# Read in .gmt file +gmt <- readLines('/scratch/prj/oliverpainfel/recovered/Data/MSigDB/c5.go.bp.v7.5.1.entrez.gmt') +gene_sets <- strsplit(gmt, "\t") +gene_list <- setNames(lapply(gene_sets, `[`, -c(1,2)), sapply(gene_sets, `[`, 1)) + +# Subset sets of interest +gene_list <- gene_list[ c('GOBP_DNA_REPAIR', 'GOBP_REGULATION_OF_SYNAPTIC_PLASTICITY')] + +# Identify variants within genes within each set +gene_loc <- fread('/scratch/prj/oliverpainfel/recovered/Data/Gene_Locations/MAGMA_NCBI37.3/NCBI37.3.gene.loc') +names(gene_loc)<-c('ID','CHR','START','STOP','STRAND','NAME') +gene_loc <- gene_loc[gene_loc$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 + +
Show code + +```{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 + +# Can't figure out how run with causal variants specified. Lets do this using R + +``` + +```{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) + +# Access the genotype matrix +G <- bigSNP$genotypes + +# Get SNP names +snp_names <- bigSNP$map$marker.ID + +# Read in causal variants +library(data.table) +effect_sizes<-fread('~/oliverpainfel/Data/HAPNEST/synth_2/causal.snplist') +names(effect_sizes)<-c('rsid','trait_1','trait_2') + +rsids<-fread('~/oliverpainfel/Data/HAPNEST/data/inputs/processed/1KG+HGDP/rsid_map_list_chr22.txt') +effect_sizes<-merge(effect_sizes, rsids, by='rsid') + +h2 <- 0.05 # Desired heritability +set.seed(1) +traits<-list() +for(i in 1:2){ + effect_sizes_i<-effect_sizes[effect_sizes[[paste0('trait_',i)]] == 1,] + causal_indices <- match(effect_sizes_i$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) + +``` + +
+ +*** + +### 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 + +
Show code + +```{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$target_plink_chr<-'~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/synth_2_chr-' +opt$output<-'~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/pca/' +opt$test<-'chr22' + +library(GenoUtils) +library(data.table) +source('../functions/misc.R') +source_all('../functions') + +# Create temp directory +tmp_dir<-tempdir() + +if(!is.na(opt$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) + +########### +# 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) + +# 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) + +# 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),] + +# 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') + +# 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$n_cores) + +dir.create('~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/pca') +fwrite(target_pcs, paste0(opt$output,'pcs.txt'), quote=F, sep=' ', na='NA') + +``` + +
+ +*** + +#### Perform GWAS + +
Show code + +```{bash} + +module add plink2 +for pheno in $(seq 1 2); do + mkdir -p ~/oliverpainfel/Analysis/HAPNEST/synth_2/gwas/pheno${pheno} + for chr in $(seq 22 22); do + sbatch -p neurohack_cpu --mem 20G -n 4 --wrap="plink2 \ + --bfile ~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/synth_2_chr-${chr} \ + --pheno ~/oliverpainfel/Data/HAPNEST/synth_2/trait_${pheno}.txt \ + --1 \ + --covar ~/oliverpainfel/Data/HAPNEST/data/outputs/synth_2/pca/pcs.txt \ + --covar-variance-standardize \ + --linear omit-ref cols=+a1freq,+ax hide-covar \ + --maf 0.01 \ + --geno 0.05 \ + --out ~/oliverpainfel/Analysis/HAPNEST/synth_2/gwas/pheno${pheno}/pheno${pheno}.chr${chr}" + 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') +} +``` + + +
+ +*** + +#### Run MAGMA + +
Show code + +```{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 +``` + +
+ +*** + +## 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','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/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, + sampling=NA, + prevalence=NA, + mean=NA, + sd=NA, + 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) + +###### +# 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", + "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) + +``` + +```{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 target_pgs_partitioned -n +``` + +*** + +### Genome-wide PGS + +#### 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$TRANS, function(x) x[[1]])) + +# Read in outcome data +outcome_names<-c('CAD','RA','MIX') +outcomes <- list() +for(i in outcome_names){ + outcomes[[i]]<-fread(paste0('~/oliverpainfel/Data/ukb/phenotypes/ra_cad_mix.subset.', i, '_only.txt')) + names(outcomes[[i]])[names(outcomes[[i]]) == 'outcome']<-paste0(i,'_outcome') +} +outcomes <- Reduce(function(x, y) merge(x, y, by = c("FID", "IID"), all = TRUE), outcomes) + +# Test correlation +both <- merge(pgs, outcomes, by = c('FID', 'IID')) + +round(cor(both[,-1:-2]), 2) + +# CAD_SBayesRC RA_SBayesRC MIX_SBayesRC CAD_outcome RA_outcome MIX_outcome +# CAD_SBayesRC 1.00 -0.01 0.24 0.20 -0.02 0.20 +# RA_SBayesRC -0.01 1.00 0.87 0.00 0.12 0.03 +# MIX_SBayesRC 0.24 0.87 1.00 0.06 0.11 0.09 +# CAD_outcome 0.20 0.00 0.06 1.00 -0.10 0.97 +# RA_outcome -0.02 0.12 0.11 -0.10 1.00 0.15 +# MIX_outcome 0.20 0.03 0.09 0.97 0.15 1.00 + +# This looks good so far: +# - The MIX PGS is more correlated with the RA PGS than CAD PGS, due to larger genetic effects on RA +# - The MIX outcome is more correlated with the CAD outcome, due to large number of CAD cases +# - The MIX PGS predicts CAD and RA outcome worse that CAD and RA PGS respectively + +# The reason for PGS stratification is becoming clearer - If we were to extract genetic effects relevant to CAD, then the CAD PGS would predict CAD cases better. Likewise for RA. + +# It would be interesting to see whether we can cluster individuals based on these scores alone. This would be similar to the T2D paper, which stratifies PGS by relationship with other traits. Our pathway specific strategy will take a different approach. + +library(pROC) + +auc( + both$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 + +```{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)) + +# + +``` + +*** + +### 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. + +# 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$CAD_vs_RA_outcome) + +tmp <- data.frame( + outcome = 'CAD vs RA', + model = c('gw-pgs + p-pgs', 'gw-pgs'), + cor = c(cor1, cor2) +) + +ggplot(tmp, aes(x=outcome, y = cor, fill = model)) + + geom_bar(stat="identity", position = position_dodge()) + +``` + +*** + +#### Clustering + +```{r} + +library(NbClust) + +both$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: + - 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. + diff --git a/docs/Images/CrossPop_2025/avengeme_h2.png b/docs/Images/CrossPop_2025/avengeme_h2.png new file mode 100644 index 00000000..9b5940d2 Binary files /dev/null and b/docs/Images/CrossPop_2025/avengeme_h2.png differ diff --git a/docs/Images/CrossPop_2025/avengeme_polygenicity.png b/docs/Images/CrossPop_2025/avengeme_polygenicity.png new file mode 100644 index 00000000..1ba24a15 Binary files /dev/null and b/docs/Images/CrossPop_2025/avengeme_polygenicity.png differ diff --git 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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 +prscsx,36206.2063,10:03:26,15745.98,34474.07,12699.46,12977.34,0,1241.94,967.52,351757.38,prep_pgs_prscsx_i-height.txt,prep_pgs_prscsx_i,PRS-CSx diff --git a/docs/Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.png b/docs/Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.png new file mode 100644 index 00000000..270587e2 Binary files /dev/null and b/docs/Images/OpenSNP/time_cpu_bench_pgs_methods_prscsx.png differ diff --git a/docs/Images/hapnest/pgs_eval.png b/docs/Images/hapnest/pgs_eval.png new file mode 100644 index 00000000..b2eff450 Binary files /dev/null and b/docs/Images/hapnest/pgs_eval.png differ diff --git a/docs/Images/pipeline_readme/input_schematic_wide.png b/docs/Images/pipeline_readme/input_schematic_wide.png index f7475ee4..45938395 100644 Binary files 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b/docs/cookie-consent.html @@ -0,0 +1,35 @@ + + + + diff --git a/docs/hapnest_benchmark.Rmd b/docs/hapnest_benchmark.Rmd new file mode 100644 index 00000000..f95f7564 --- /dev/null +++ b/docs/hapnest_benchmark.Rmd @@ -0,0 +1,777 @@ +--- +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 +
Show code + +```{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. + +
Show code + +```{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 + +
Show code + +```{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. + +
Show code + +```{bash} +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 + +``` + +
+ +*** + +# Identify unrelated individuals + +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. + +
Show code + +```{r} +# Subset simulated data by population +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 + +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$n_cores, keep = paste0('~/oliverpainfel/Analysis/HAPNEST/synth_1/', i,'.keep')) + +} + +``` + +
+ +*** + +# Subsample and GWAS + +## Subsample + +
Show code + +```{r} +library(data.table) + +fam <- NULL +for(i in c('EUR','EAS','AFR')){ + tmp <- fread(paste0('~/oliverpainfel/Analysis/HAPNEST/synth_1/relatedness/synth_1.',i,'.unrelated.keep'), header = F) + tmp$pop <- i + fam <- rbind(fam, tmp) +} + +set.seed(1) +for(pop_i in 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) + + 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 + +
Show code + +```{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') +} + +``` + +
+ +*** + +### Perform GWAS + +
Show code + +```{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 + +``` + +
+ +*** + +# Run GenoPred + +## Subset HAPNEST testing data + +
Show code + +```{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 ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing.txt \ + --make-bed \ + --out ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing_subset/synth_1.chr${chr} +done +``` + +
+ +## Prepare config + +
Show code + +```{r} + +dir.create('/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config', recursive = T) + +# gwas_list +sample_file<-fread('/users/k1806347/oliverpainfel/HAPNEST/data/outputs/synth_1/synth_1_chr-22.sample', header=F)$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 + +
Show code + +```{bash} +snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml output_all outlier_detection -n +``` + +
+ +*** + +# Evaluate PGS + +Evaluate PGS R2 within each population. + +
Show code + +```{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) + +# Read in PGS for each population +pop <- c('EUR','EAS','AFR') +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$pheno + tmp_pgs <- tmp[, !(names(tmp) %in% c('FID','IID','pheno')), with = F] + + for(k in names(tmp_pgs)){ + mod <- glm(tmp_pheno ~ scale(tmp_pgs[[k]]), family='binomial') + obs_r2 <- cor(predict(mod), as.numeric(tmp_pheno))^2 + sum_mod <- summary(mod) + + assoc <- rbind( + assoc, + data.table( + Predictor = k, + BETA = coef(sum_mod)[2, 1], + SE = coef(sum_mod)[2, 2], + P = coef(sum_mod)[2, 4], + Obs_R2 = obs_r2, + N = length(tmp_pheno), + gwas = scores$name[j], + method = scores$method[j], + pop = i, + trans = F + ) + ) + } + } +} + +# Evaluate PGS (without adjustment for reference PCs) +assoc_trans<-NULL +for(i in pop){ + for(j in 1:nrow(scores)){ + tmp <- pgs$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 + ) + ) + } + } +} + +# 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 = "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. + +# Plot the results +assoc_both <- rbind(assoc, assoc_trans) +library(ggplot2) +library(cowplot) + +# 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$pT)) + +# Improve labelling for plot +assoc_both$pop <- paste0('Target = ', assoc_both$pop) +assoc_both$pop <- factor(assoc_both$pop, levels = unique(assoc_both$pop)) + +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$gwas)) + +assoc_both$trans <- ifelse(assoc_both$trans, 'Unadjusted', 'Adjusted') + +setwd('/scratch_tmp/prj/oliverpainfel/GenoPred') +dir.create('docs/Images/hapnest') +png('docs/Images/hapnest/pgs_eval.png', res = 100, height = 700, width = 1000, units = 'px') + +ggplot(assoc_both, aes(x = trans, y = BETA, fill = pT)) + + geom_hline(yintercept = 0, colour = 'darkgrey') + + geom_bar(stat="identity", position=position_dodge(preserve = "single"), width = 0.8) + + geom_errorbar(aes(ymin=BETA-SE, ymax=BETA+SE), width=0, position=position_dodge(width = 0.8, preserve = "single")) + + labs(y="BETA (SE)", x=NULL) + + theme_half_open() + + background_grid() + + panel_border() + + theme(axis.text.x = element_text(angle = 45, hjust = 1)) + + background_grid(major = 'y', minor = 'y') + + scale_fill_manual(values = colorRampPalette(c("lightblue", "darkblue"))(length(unique(assoc_both$pT)))) + + facet_grid(pop ~ gwas) + +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. + +``` + +
+ +
+ +
+
+![Performance of PGS when adjusted for ancestry](Images/hapnest/pgs_eval.png) +
+
+ +
+ +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 @@ + + + + + + + + + + + + + +HapNest Benchmark + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + + + +
+

Here will use simulated genetic data to benchmark GenoPred. We will +simulate the genetic data using HAPNEST.

+
+
+

Set up HAPNEST

+
+ +Show 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
+
+
+
+
+
+

Test run using default config

+

Generates data for chromosome 1, for 6 populations, HapMap3 SNPs, and +1 phenotype.

+
+ +Show 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
+
+

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

+
+ +Show 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 '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.

+
+ +Show 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
+
+
+
+
+
+

Identify unrelated individuals

+

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.

+
+ +Show code + +
# Subset simulated data by population
+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
+
+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
+  )
+}
+
# 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'))
+  
+}
+
+
+
+
+

Subsample and GWAS

+
+

Subsample

+
+ +Show code + +
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$pop <- i
+  fam <- rbind(fam, tmp)
+}
+
+set.seed(1)
+for(pop_i in c('EUR','EAS','AFR')){
+  fam_pop_i <- fam[fam$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)
+  
+  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

+
+ +Show code + +
# 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')
+}
+
+
+
+
+

Perform GWAS

+
+ +Show code + +

+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
+
+
+
+
+
+
+
+

Run GenoPred

+
+

Subset HAPNEST testing data

+
+ +Show code + +
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 ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing.txt \
+      --make-bed \
+      --out ~/oliverpainfel/Analysis/HAPNEST/synth_1/testing_subset/synth_1.chr${chr}
+done
+
+
+
+

Prepare config

+
+ +Show code + +
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

+
+ +Show code + +
snakemake --profile slurm --use-conda --configfile=/users/k1806347/oliverpainfel/Analysis/HAPNEST/genopred/config/config.yaml output_all -n
+
+
+
+
+
+

Evaluate PGS

+

Evaluate PGS R2 within each population.

+
+ +Show code + +
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)
+
+# Read in PGS for each population
+pop <- c('EUR','EAS','AFR')
+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]
+    
+    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
+        )
+      )
+      }
+  }
+}
+
+# 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$method[j],
+            pop = i,
+            trans = T
+        )
+      )
+      }
+  }
+}
+
+# Check the correlation between pheno and PGS, with and without adjustments for reference PCs
+cor(assoc_trans$BETA, assoc$BETA) # 0.9996044
+
+plot(assoc_trans$BETA, assoc$BETA)
+abline(a = 0, b = 1, col = "red", lwd = 2, lty = 2)
+# The results are almost identical. This will vary depending on whether the phenotype varies according to ancestry. This simulation confirms that the PGS R2 is not impacted when there is no correlation between ancestry and the phenotype.
+
+# Plot the results
+assoc_both <- rbind(assoc, assoc_trans)
+library(ggplot2)
+library(cowplot)
+
+# Extract pT variable from Predictor
+assoc_both$pT <- gsub('e.','e-', gsub('.*0_', '', assoc_both$Predictor))
+assoc_both$pT <- factor(assoc_both$pT, levels = unique(assoc_both$pT))
+
+# Improve labelling for plot
+assoc_both$pop <- paste0('Target = ', assoc_both$pop)
+assoc_both$pop <- factor(assoc_both$pop, levels = unique(assoc_both$pop))
+
+assoc_both$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))
+
+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)", 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(assoc_both$pT)))) +
+  facet_grid(pop ~ gwas)
+
+dev.off()
+
+


+
+
+
+ +

Performance of PGS when adjusted for ancestry

+
+
+
+


+

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/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'); + + + + 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 new file mode 100644 index 00000000..50fe6ad9 --- /dev/null +++ b/docs/incomplete/ref_stand_rethink.html @@ -0,0 +1,880 @@ + + + + + + + + + + + + + +Rethinking the reference standardisation process + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + + + +
+
+

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

+
# 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()
+
# 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/index.html b/docs/index.html index 6b0820a3..1a9bc311 100644 --- a/docs/index.html +++ b/docs/index.html @@ -13,7 +13,7 @@ GenoPred - Homepage - + @@ -75,6 +75,41 @@ gtag('config', 'G-YR18ZB3PR3'); + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + + + +
+

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. +
  2. Multi Model: Constructs a generalized linear model +(GLM) incorporating all best predictors from different top1 +groups within the same multi group.
    3. +
  3. Evaluation: Uses cross-validation to train and +evaluate both top1 and multi models.
    4. +
  4. Comparison: Applies Williams’ test to compare the +predictive utility of the models (using psych::paired.r +function).
    5. +
+
+
+

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. +
    2. IID: Individual ID
      3. +
    3. Outcome Variable (any column name is +acceptable)
      4. +
    • +
+
+
+

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. +
    2. IID: Individual ID
      3. +
    3. Additional columns: Predictor variables (any column names are +acceptable)
      4. +
    • +
+
+
+
+
+

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.

+
# 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.

+
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.

+
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/more_index.Rmd b/docs/more_index.Rmd index 572a1aba..c47a1fe9 100644 --- a/docs/more_index.Rmd +++ b/docs/more_index.Rmd @@ -18,6 +18,7 @@ output: - Instructions - Link - Technical documentation - Link - Running in an offline environment - Link +- Running on DNAnexus/UKB-RAP - Link - Demonstration using 23anMe data - Link - Computational time/memory benchmark - Link - Benchmark in OpenSNP dataset - Link @@ -38,4 +39,8 @@ output: - Shiny app - Link - Code - Link - Conference poster and flash talk - Link +- Cross-Ancestry Polygenic Prediction + - Summary - Link + - Code - Link + - Application of multi-source methods to height GWAS and tested in OpenSNP target sample - Link diff --git a/docs/more_index.html b/docs/more_index.html index 6e873868..0be2de73 100644 --- a/docs/more_index.html +++ b/docs/more_index.html @@ -75,6 +75,41 @@ gtag('config', 'G-YR18ZB3PR3'); + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + + + +
+

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, 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.

+
+ +Show 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
+
+
+
+
+

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.

+
+ +Show code + +
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)
+
+

Note: The original configfile for OpenSNP, and the +target_list that it refers to, were created in the OpenSNP benchmark +document (here).

+
+
+
+
+
+

Run pipeline

+
snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_cross_pop.yaml output_all
+
+
+
+

Evaluate PGS

+
+ +Show code + +
# 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()
+
+
+
+
+

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.
    • +
+
+
+
+ +

Correlation between PGS and height

+
+
+
+
+ + +
+ +
+
+ +
+
+ + +
+
+ +
+ + + + + + + + + + + + + + + + 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$set(eval = FALSE) +``` + +*** + +# Overview + +We have previously used OpenSNP as a dataset for testing GenoPred. An important constraint within the default GenoPred implementation is its restriction to HapMap3 variants. This is particularly problematic when using externally derived score files, such as from the PGS catalogue, which may include variant variants that are not in the default reference. To remedy this we can use a reference dataset that includes more variant, and that is what we will test here. + +We will run GenoPred on the OpenSNP data using the default reference data (restricted to HapMap3) and a denser reference (all variants in the 1KG+HGDP dataset). We will calculate PGS based on height GWAS summary statistics, but also a range of height PGS score files on the PGS catalogue, and see the difference in performance. + +# Prepare input + +*** + +## Download genotypes + +
Show code + +```{bash} +mkdir -p /users/k1806347/oliverpainfel/Data/OpenSNP/raw +cd /users/k1806347/oliverpainfel/Data/OpenSNP/raw +wget https://zenodo.org/records/1442755/files/CrowdAI_v3.tar.gz?download=1 +mv 'CrowdAI_v3.tar.gz?download=1' CrowdAI_v3.tar.gz +tar -xvzf CrowdAI_v3.tar.gz +rm CrowdAI_v3.tar.gz + +# Use both training and testing data to maximise sample size for testing. +``` + +
+ +*** + +## Prepare phenotype + +
Show code + +```{r} +library(data.table) + +# Training +train_dat <- fread('/users/k1806347/oliverpainfel/Data/OpenSNP/raw/CrowdAI_v3/training_set_details.txt') +train_dat$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 +) +``` + +
+ +*** + +## Split VCF by chromosome + +
Show code + +```{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 +``` + +
+ +*** + +## Download height GWAS + +
Show code + +```{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 +``` + +
+ +*** + +## Create gwas_list, target_list and config + +
Show code + +```{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=' ') + +``` + +
+ +*** + +# Run GenoPred + +
Show code + +```{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 +``` + +
+ +*** + +## Evaluate PGS + +
Show code + +```{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() + +``` + +
+ +*** + +
Show results in OpenSNP + +
+
+![Yengo - EUR PGS correlation with height in EUR OpenSNP ](Images/OpenSNP/genopred-yengo_eur.png) +
+
+ +*** + +
+
+![Yengo - EAS PGS correlation with height in EUR OpenSNP ](Images/OpenSNP/genopred-yengo_eas.png) +
+
+ +
+ +
Show results in OpenSNP including score from PGS catalogue + +
+
+![Yengo - EUR PGS correlation with height in EUR OpenSNP - Includes externally derived PGS ](Images/OpenSNP/genopred-yengo_eur-external.png) +
+
+ +
+ +Note. 'external' is based on a larger GWAS (incl. 23andMe) + +
+ +
Show results in OpenSNP when using PRS-CSx + +
+
+![Yengo - EUR/EAS PGS correlation with height in OpenSNP EUR and EAS populations ](Images/OpenSNP/genopred-prscsx.png) +
+
+ +
+ +Note. 'external' is based on a larger GWAS (incl. 23andMe) + +
+ +*** diff --git a/docs/pgs_shift.Rmd b/docs/pgs_shift.Rmd new file mode 100644 index 00000000..c8bb4f20 --- /dev/null +++ b/docs/pgs_shift.Rmd @@ -0,0 +1,364 @@ +--- +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 +``` + +```{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 ccdbf7e6..c663ffe1 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$POP)){ } } +# Create frequency files across all reference individuals (TRANS) +dir.create(paste0('/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS'), recursive = T) +for(i in 1:22){ + system(paste0('plink2 --pfile /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/hgdp_1kg_hm3.chr',i,' --chr ',i,' --freq --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/freq_files/TRANS/hgdp_1kg_hm3.chr', i)) +} + #### # Create .rds files for SNP stats #### @@ -225,7 +231,7 @@ write.table(keep_list, '/users/k1806347/oliverpainfel/Data/hgdp_1kg/genopred/ref *** -# Package for zenodo +## Package for zenodo ├── ref.chr<1-22>. @@ -295,3 +301,253 @@ cd .. tar -czvf ld_scores.tar.gz ld_scores ``` + +*** + +# Make dense version of the reference data + +We will create a reference dataset that is not restricted to HapMap3 variants, to allow for denser coverage, and broader applicability to the PGS catalogue. + +```{r} + +setwd('/users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline') +source('../functions/misc.R') +source_all('../functions') +library(data.table) +library(GenoUtils) + +# Read in variant data for 1KG+HGDP +pvar <- fread('/users/k1806347/oliverpainfel/Data/hgdp_1kg/GRCh37_HGDP+1kGP_ALL.pvar', nThread=5) + +# Extract RSID information +pvar$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.chr<1-22>. +├── ref.chr<1-22>.rds +├── ref.pop.txt (#IID, POP - with header) +├── ref.keep.list (POP and PATH - without header) +├── keep_files +│ └──.keep (#IID - with header) +└── freq_files + └── + └──ref..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/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 @@ GenoPred Pipeline - Reference preparation - + @@ -594,6 +594,12 @@

Format and output required reference files

} } +# 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/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." - Link +Check out our publication in *Bioinformatics* describing the pipeline: "The GenoPred Pipeline: A Comprehensive and Scalable Pipeline for Polygenic Scoring." - Link *** diff --git a/docs/pipeline_overview.html b/docs/pipeline_overview.html index de94f042..abbe098b 100644 --- a/docs/pipeline_overview.html +++ b/docs/pipeline_overview.html @@ -75,6 +75,41 @@ gtag('config', 'G-YR18ZB3PR3'); + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + + + + + + + +
+

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).

+
# 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.${pop}.GW
+done
+
library(bigsnpr)
+library(bigreadr)
+
+# Create object for genetic data in ldpred2 format, and impute missing values
+for(pop in c('EUR', 'EAS', 'AFR')){
+  # Read in reference data
+  snp_readBed(
+    paste0(
+      '/users/k1806347/oliverpainfel/Data/1KG/ldpred2/',
+      pop,
+      '/ref.',
+      pop,
+      '.GW.bed'
+    )
+  )
+  
+  # Attach the ref object in R session
+  ref <-
+    snp_attach(
+      paste0(
+        '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+        pop,
+        '/ref.',
+        pop,
+        '.GW.rds'
+      )
+    )
+  G <- ref$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$genotypes<-G_imp
+  saveRDS(ref, paste0(
+        '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+        pop,
+        '/ref.',
+        pop,
+        '.GW.rds'
+      ))
+}
+
+################
+# Now calculate LD matrices following code from https://doi.org/10.1016/j.xhgg.2022.100136
+
+library(dplyr)
+library(bigreadr)
+library(bigsnpr)
+
+for(pop in c('EUR', 'EAS', 'AFR')){
+  obj.1000G <- snp_attach(paste0(
+          '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+          pop,
+          '/ref.',
+          pop,
+          '.GW.rds'
+        ))
+  G <- obj.1000G$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$ld <- do.call('c', lapply(1:22, function(chr) {
+    cat(chr, ".. ", sep = "")
+    corr_chr <- readRDS(
+      paste0(
+        '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+        pop,
+        '/LD_chr',
+        chr,
+        ".rds"
+      )
+    )
+    Matrix::colSums(corr_chr ^ 2)
+  }))
+  
+  map <- map[, c('chromosome','physical.pos','allele2','allele1','marker.ID','af','ld')]
+  names(map) <- c('chr','pos','a0','a1','rsid','af','ld')
+  
+  saveRDS(
+    map,
+    paste0(
+      '/scratch/prj/oliverpainfel/Data/1KG/ldpred2/',
+      pop,
+      '/map.rds'
+    )
+  )
+}
+ + +
+ +
+
+ +
+
+ + + +
+ + + + + + + + + + + + + + + diff --git a/docs/prep_quickprs_ref.Rmd b/docs/prep_quickprs_ref.Rmd new file mode 100644 index 00000000..e83acd95 --- /dev/null +++ b/docs/prep_quickprs_ref.Rmd @@ -0,0 +1,248 @@ +--- +title: Preparing LDpred2 LD matrices +output: + html_document: + theme: cosmo + css: styles/styles.css + includes: + in_header: header.html + after_body: footer.html + +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$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. + +*** + +## Full reference + +```{r} +# conda activate .snakemake/conda/329e0288cb99508f5e6c50a0996b234c_ +# 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','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)) + + 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, '/*')) +} + +``` + +*** + +## 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} +# conda activate .snakemake/conda/2af04663624aee04f7a150a9d54c1cdf_ +# 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','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)) + + 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$output, '.subset_', subset_i)) + } + + for(subset_i in 2:2){ + # Calculate predictor-predictor correlations + system(paste0(opt$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$n_cores)) + + # Export the files required for QuickPRS + for (i in c('bim', 'bin', 'noise', 'root')) { + system(paste0('cp ', tmp_dir, '/tmp.subset_', subset_i, '.cors.', i, ' ', opt$output, '.subset_', subset_i, '.cors.', i)) + } + } + + # 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, '/*')) +} + +``` + +*** + +# 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 $(echo EUR EAS AFR AMR CSA MID); do + tar -czvf ldak_quickprs_hm3_${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 $(echo EUR EAS AFR AMR CSA MID); do + tar -czvf ldak_quickprs_hm3_subset_${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/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' + } +} + +``` + diff --git a/docs/prep_sbayesrc_ref.Rmd b/docs/prep_sbayesrc_ref.Rmd new file mode 100644 index 00000000..696255d9 --- /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 # /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/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 $(seq 1 22); do + echo /users/k1806347/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.chr${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/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files/${pop}.keep \ + --maf 0.01 \ + --out /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${pop}/ref.${pop}.GW + +# Create ma_file.txt +echo -e "SNP\tA1\tA2" > /users/k1806347/oliverpainfel/Data/hgdp_1kg/sbayesrc/${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 < -
+# 6. Cross-Ancestry Polygenic Prediction + +
+

+ 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. +

Overview and code: + Click here +
Preprint: + + Pain. Leveraging Global Genetics Resources to Enhance Polygenic Prediction Across Ancestrally Diverse Populations. medRxiv (2025). + +

+ +
+
+
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 @@ GenoPred Research - + @@ -78,6 +78,41 @@ gtag('config', 'G-YR18ZB3PR3'); + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
+ + + +
+
+
+
+
+ +
+ + + + + + + + + +
+
+

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.

+
# 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:

+
# 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 ~
+
+

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.

+
+

After installing Singularity, download the GenoPred container using +the following command:

+
# Pull the GenoPred container
+singularity pull library://opain/genopred/genopred_pipeline:latest
+
+

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.

+
+
+
+
+

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.

+
# 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.

+
# 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 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:

+
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:

+
tmux attach
+

Further instructions on using tmux can be found here.

+

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:

+
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:

+
# 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
+

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:

+
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.

+
# Compress the GenoPred working directory
+cd /home/rstudio-server
+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
+

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.

+
# Download the Outputs from the Previous Run:
+dx download test_run_genopred.tar
+tar -xvf test_run_genopred.tar
+
+# 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:

+
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.

+
# 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:

+
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.

+
+
+
+

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. We will use the shell script we created here:

+
# 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

+

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.

+
# 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.

+
# 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:

+
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:

+
tmux attach
+

Further instructions on using tmux can be found here.

+

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.

+
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.

+
# 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
+
+

Note: Since the instance has 8 cores available, I +use the -j8 parameter when running snakemake +to ensure it utilizes all 8 cores efficiently.

+
+

After the analysis is complete, you can leave the container by +typing:

+
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.

+
# 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.

+
# 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.

+
+
+
+

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:

+
# 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.

+
# 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, +a script I have written previously ukb_relative_remover.R.

+
# 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
+
# 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.

+
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

+
# 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.

+
# 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. +

  2. Copy these score files to DNAnexus and use them to perform target +sample scoring in the UKB dataset on DNAnexus.

    3. +
+

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).

+
# 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)
    • +
+
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)
    • +
+
# 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.

+
# 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
    • +
+
# 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.

+
# 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.

+
export LC_ALL=C
+export LANG=C
+
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")
+
dx upload ukb_pgs_COAD01.rds
+
+
+
+
+

Troubleshooting

+

Please post questions as an issue on the GenoPred GitHub repo here.

+
+ + +
+ +
+
+ +
+
+ + +
+
+ +
+ + + + + + + + + + + + + + + + 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 a132b7f0..9ba0b16a 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 */ } @@ -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 { diff --git a/functions/constants.R b/functions/constants.R index 86568932..d04161bb 100644 --- a/functions/constants.R +++ b/functions/constants.R @@ -37,7 +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'), - label = c('pT+clump','DBSLMM','LDpred2','SBayesR','lassosum','PRS-CS','MegaPRS','External') + 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[order(pgs_method_labels$method),] +pgs_method_labels<-pgs_method_labels[order(pgs_method_labels$method),] + +# 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') + diff --git a/functions/model_builder.R b/functions/model_builder.R new file mode 100644 index 00000000..f78b883e --- /dev/null +++ b/functions/model_builder.R @@ -0,0 +1,260 @@ +#!/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) +} + +# Functions for reading in predictor file +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$pred_miss) + + # Remove individuals with any missing data + tmp <- tmp[complete.cases(tmp), ] + + # Update column names to avoid duplicate column names between predictor files + names(tmp) <- gsub("[[:punct:]]", ".", names(tmp)) + names(tmp)[-1] <- paste0('PredFile', file_index, '.', names(tmp)[-1]) + + log_add(log_file = log_file, message = paste0('Predictors file ', file_index, ' contains ', ncol(tmp) - 1, ' predictors with sufficient data.')) + log_add(log_file = log_file, message = paste0('Predictors file ', file_index, ' contains ', nrow(tmp) ,' individuals with complete data for remaining predictors.')) + + return(tmp) +} + +# Function for merging by IID +predictor_merger <- function(x, y) { + return(merge(x, y, by = 'IID')) +} + +# Function to combine FID and IID, into a variable called IID +combine_fid_iid <- function(data, fid_col = "FID", iid_col = "IID") { + # Check if the specified columns exist in the data + if (!all(c(fid_col, iid_col) %in% names(data))) { + stop("Both FID and IID columns must be present in the data.") + } + + # Combine FID and IID into a new IID column + data[[iid_col]] <- paste0(data[[fid_col]], ':', data[[iid_col]]) + + # Remove the FID column + data[[fid_col]] <- NULL + + return(data) +} + +# Remove variables with > opt$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) +} + +# 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 +) + +# 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$outcome_pop_prev, + coef(mod_sum)[2, 1] ^ 2, + sum(obs == 'CASE') / length(obs) + ), + N = length(obs), + Ncase = sum(obs == 'CASE'), + Ncont = sum(obs == 'CONTROL') + ) + } else { + mod_sum <- data.table( + mod_sum, + R2o = coef(mod)[2, 1] ^ 2, + N = length(obs) + ) + } + return(mod_sum) +} + +######### +# Function for progress bar +######### + +initialise_progress <- function(log_message, log_file){ + progress_file <- tempfile() + saveRDS(0, progress_file) + log_add(log_file = log_file, message = log_message) + return(progress_file) +} + +update_progress_file <- function(progress_file) { + # Lock the file for writing (this ensures only one worker can modify it at a time) + lockfile <- paste0(progress_file, ".lock") + while (file.exists(lockfile)) { + Sys.sleep(0.01) # Wait if another worker is updating the file + } + + # Create the lockfile + file.create(lockfile) + + # Read current progress + progress <- readRDS(progress_file) + + # Update progress_file + progress <- progress + 1 + saveRDS(progress, progress_file) + + # Remove the lockfile + file.remove(lockfile) + + return(progress) # Return progress +} + +update_log_file <- function(log_file, message) { + # Read the current content of the log file + log_content <- readLines(log_file) + + # Update the last line with the new message + if (length(log_content) > 0) { + log_content[length(log_content)] <- message + } else { + log_content <- message + } + + # Write the updated log content back to the file + writeLines(log_content, con = log_file) +} + +export_final_model <- function(model, group, outdir){ + if ("glm" %in% class(model)) { + model_coefficients <- as.matrix(coef(model)) + rownames(model_coefficients)[1]<-'intercept' + } + if ("glmnet" %in% class(model)) { + model_coefficients <- coef(model, s = model$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 + ) +} diff --git a/functions/pgs.R b/functions/pgs.R index 8c42e12f..ac45a2dd 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, @@ -42,11 +44,91 @@ list_score_files <- function(config){ method = 'external')) } + # 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$gwas, function(x) sum(strsplit(x, ",")[[1]] != "") == 2), ] + + # Identify PGS methods to be included + pgs_methods_list <- read_param(config = config, param = 'pgs_methods', return_obj = F) + + # Retain methods that are applied to groups with only 2 gwas + pgs_methods_list <- pgs_methods_list[(pgs_methods_list %in% c('prscsx', 'xwing'))] + + # Provide combos for methods applied to groups of gwas + combos <- rbind(combos, expand.grid(name = gwas_groups_2$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_2$name, method = paste0('tlprs_', tlprs_methods))) + } + + # For LEOPARD, list combos for leopard_methods + leopard_methods<-read_param(config = config, param = 'leopard_methods', return_obj = F) + if(length(leopard_methods) > 1 || !is.na(leopard_methods)){ + combos <- rbind(combos, expand.grid(name = gwas_groups_2$name, method = paste0(leopard_methods,'_multi'))) + } + } + + # 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$name, method = paste0(leopard_methods,'_multi'))) + } + } + + combos <- data.table(combos) + combos <- combos[, lapply(.SD, as.character)] + return(combos) } # Flip effects in score file to match A1 reference map_score<-function(ref, score){ + # Check if required columns exist + required_cols <- c('SNP', 'A1', 'A2') + if (!all(required_cols %in% names(ref)) | + !all(required_cols %in% names(score))) { + stop('ref and score must contain SNP, A1, and A2 columns.') + } + + # Valid alleles + valid_alleles <- c('A', 'T', 'C', 'G') + + # Check for NA or invalid alleles in A1 and A2 columns + for (col in c('A1', 'A2')) { + if (any(is.na(ref[[col]])) | any(!ref[[col]] %in% valid_alleles)) { + stop(paste('Invalid allele values detected in ref column:', col)) + } + if (any(is.na(score[[col]])) | any(!score[[col]] %in% valid_alleles)) { + stop(paste('Invalid allele values detected in score column:', col)) + } + } + + # Check for NA values in SNP column + if (any(is.na(ref$SNP)) | any(is.na(score$SNP))) { + stop('NA values detected in SNP column of ref or score.') + } + + ref <- ref[, c('SNP','A1','A2'), with = F] tmp <- merge(ref, score, by = 'SNP', all.x=T, sort = F) flip <- which(tmp$A1.x != tmp$A1.y) tmp <- as.matrix(tmp[, -1:-5, drop = FALSE]) @@ -60,7 +142,7 @@ map_score<-function(ref, score){ # Calculate mean and sd of scores in file with plink .sscore format score_mean_sd<-function(scores, keep=NULL){ if(!is.null(keep)){ - scores<-scores[paste0(scores$FID, '_', scores$FID) %in% paste0(keep$FID, '_', keep$FID),] + scores<-scores[paste0(scores$FID, '_', scores$IID) %in% paste0(keep$FID, '_', keep$IID),] } scale<-data.table( Param=names(scores)[-1:-2], @@ -158,6 +240,22 @@ read_pvar<-function(dat, chr = 1:22){ return(pvar) } +# Read in the .freq file for target population +read_frq<-function(freq_dir, population, chr){ + freq_data<-NULL + for(i in chr){ + tmp<-fread(paste0(freq_dir,'/',population,'/ref.',population,'.chr',i,'.afreq')) + tmp<-data.table( + SNP = tmp$ID, + A1 = tmp$ALT, + A2 = tmp$REF, + FREQ = tmp$ALT_FREQS + ) + freq_data<-rbind(freq_data, tmp) + } + return(freq_data) +} + # Remove variants within genomic regions (REF: PMC2443852) remove_regions<-function(dat, regions){ exclude<-NULL @@ -188,7 +286,7 @@ read_sumstats<-function(sumstats, chr = 1:22, log_file = NULL, extract = NULL, r # If FREQ is missing, use REF.FREQ if('FREQ' %in% req_cols){ if(all(names(gwas) != 'FREQ')){ - names(gwas)[names(gwas) == 'REF.FREQ']<-'FREQ' + gwas$FREQ <- gwas$REF.FREQ log_add(log_file = log_file, message = 'REF.FREQ being used as FREQ.') } } @@ -381,9 +479,315 @@ read_score <- function(score, chr = 1:22, log_file = NULL){ } if(any(names(score) == 'CHR')){ + # Remove for 'chr' string in CHR column + score$CHR <- gsub('chr', '', score$CHR) score <- score[score$CHR %in% chr,] } return(score) } + +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. + quickprs_ldref_files<-list.files(quickprs_ldref) + + 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 ', quickprs_ldref, '/', tagging_file, ' --summary ', sumstats, ' --matrix ', quickprs_ldref, '/', matrix_file, ' --max-threads ', n_cores, ' --check-sums NO')) + } else{ + system(paste0(opt$ldak,' --sum-hers ', tmp_dir, '/bld.ldak --genomic-control YES --tagfile ', quickprs_ldref, '/', tagging_file, ' --summary ', sumstats, ' --matrix ', quickprs_ldref, '/', matrix_file, ' --max-threads ', n_cores, ' --check-sums NO')) + } + + ldak_res_her<-fread(paste0(tmp_dir,'/bld.ldak.hers')) + + ###### + # Estimate effect sizes for training and full prediction models. + ###### + + if(!is.null(ref_subset)){ + quickprs_multi_ldref_files<-list.files(quickprs_multi_ldref) + ref_dir <- quickprs_multi_ldref + cor_file_prefix<-gsub('.cors.bin','',quickprs_multi_ldref_files[grepl(paste0('subset_', ref_subset, '.cors.bin'),quickprs_multi_ldref_files)]) + } else { + cor_file_prefix<-gsub('.cors.bin','',quickprs_ldref_files[grepl('.cors.bin',quickprs_ldref_files) & !grepl('subset', quickprs_ldref_files)]) + ref_dir <- quickprs_ldref + } + + system(paste0(opt$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 ', 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) + best_score <- ldak_res_cors[which.max(ldak_res_cors$Correlation),] + + ###### + # Format final score file + ###### + + # Read in the scores + score <- fread(paste0(tmp_dir,'/mega_full.effects'), nThread = n_cores) + score <- score[, c(1, 2, 3, 5), with = F] + names(score) <- c('SNP','A1','A2','BETA') + + return(score) +} + +# Derive trans-ancestry PGS models and estimate PGS residual scale +model_trans_pgs<-function(scores=NULL, pcs=NULL, output=NULL){ + if(any(is.null(c(scores, pcs, output)))){ + stop('Error: All parameters must be specified.') + } + + if(is.character(pcs)){ + # Read in the reference PCs, extract PC columns, and update headers + pcs_dat<-fread(pcs) + names(pcs_dat)[1]<-'FID' + pcs_dat<-pcs_dat[,grepl('FID|IID|^PC', names(pcs_dat)), with=F] + } else { + pcs_dat<-pcs + } + + # Merge PGS and PCs + scores_pcs<-merge(scores, pcs_dat, by=c('FID','IID')) + + # Calculate PGS residuals + pcs_noid<-scores_pcs[,grepl('^PC', names(scores_pcs)), with=F] + + mod_list<-NULL + scores_pcs_resid<-scores_pcs + for(i in names(scores)[-1:-2]){ + mod_list[[i]]<-list() + + tmp<-data.table(y=scores_pcs[[i]], pcs_noid) + + # Model differences in mean + pgs_pc_mean_mod<-lm(y ~ ., data=tmp) + + # Model differences in variance of residuals + # Use gamma distribution to constrain predicted variance to be non-negative + predicted_pgs <- predict(pgs_pc_mean_mod, newdata = tmp) + residual_pgs <- tmp$y - predicted_pgs + squared_residuals <- residual_pgs^2 + squared_residuals <- pmax(squared_residuals, 1e-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)) + + scores_pcs_resid[[i]]<-residual_pgs/sqrt(predicted_pgs_var) + + 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] + + # 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')) + + # Save TRANS PGS in reference + 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$data = c() + cm +} + +# Adjust PGS for ancestry using reference PC models with parallel processing +score_adjust <- function(score, pcs, ref_model, chunk_size = 10) { + original_order <- names(score) + + # Ensure 'pcs' keyed for fast lookup + setkey(pcs, FID, IID) + + # List of score columns + score_cols <- setdiff(names(score), c("FID", "IID")) + + # Split into chunks for memory efficiency + score_chunks <- split(score_cols, ceiling(seq_along(score_cols) / chunk_size)) + + # Match PC rows by FID/IID directly (minimal memory usage) + matched_idx <- pcs[score[, .(FID, IID)], which = TRUE, nomatch = 0] + + # Process each chunk sequentially to manage RAM + for (chunk in score_chunks) { + + # Run in parallel across columns within chunk + chunk_results <- mclapply(chunk, function(col_name) { + cat("Processing:", col_name, "\n") + + # Fetch models for current score column + mean_model <- ref_model[[col_name]]$mean_model + var_model <- ref_model[[col_name]]$var_model + + # Pre-allocate adjusted score vector + adjusted_score <- rep(NA_real_, nrow(score)) + + # Predict mean and variance using matched PCs + if (length(matched_idx) > 0) { + adjusted_score[matched_idx] <- round( + (score[[col_name]][matched_idx] - predict(mean_model, newdata = pcs[matched_idx])) / + sqrt(exp(predict(var_model, newdata = pcs[matched_idx]))), + 3 + ) + } + + adjusted_score + }, mc.cores = min(getDoParWorkers(), 10)) + + # Write results directly back to 'score' object by reference + for (idx in seq_along(chunk)) { + set(score, j = chunk[idx], value = chunk_results[[idx]]) + } + + # Explicitly clean memory after each chunk + rm(chunk_results) + gc() + + } + + setcolorder(score, original_order) + + return(score) +} + +# Helper function to calculate relative weights for a single file from LEOPARD +cal_avg_rel_weights <- function(path){ + weights_file <- fread(path) + weights_non_0 <- ifelse(weights_file$Weights < 0, 0, weights_file$Weights) + rel_weights <- weights_non_0/(sum(weights_non_0)) + return(rel_weights) +} + +# Function to calculate average weights across replications from LEOPARD +calculate_avg_weights <- function(populations, leopard_dir, log_file = NULL) { + avg_weights <- list() + + # Iterate over populations + for (targ_pop in populations) { + # Define the file prefix for the population + weights_prefix <- paste0(leopard_dir, '/weights_', targ_pop, '/output_LEOPARD_weights_rep') + + # Calculate relative weights for each replication + rel_weights_list <- lapply(1:4, function(i) { + cal_avg_rel_weights(paste0(weights_prefix, i, ".txt")) + }) + + # Calculate the average weights across replications + avg_weights[[targ_pop]] <- as.numeric(colMeans(do.call(rbind, rel_weights_list), na.rm = TRUE)) + } + + log_add(log_file = log_file, message = '------------------------') + for(i in names(avg_weights)){ + log_add(log_file = log_file, message = paste0("LEOPARD weights - ", i, " target: ")) + for(j in populations){ + log_add(log_file = log_file, message = paste0(j, ' = ', avg_weights[[i]][which(populations == j)])) + } + log_add(log_file = log_file, message = '------------------------') + } + + return(avg_weights) +} + +# Centre SNP-weights +centre_weights <- function(score, freq, ref){ + # Sort and flip according to reference data + score <- map_score(ref = ref, score = score) + + # Sort and flip freq according to reference just in case they are different + freq <- map_score(ref = ref, score = freq) + + # Calculate mean genotype dosage and denominator + freq$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) +} + +# 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) +} + +# 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) +} + +# 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/functions/pipeline.R b/functions/pipeline.R index abb92c8a..06af977e 100644 --- a/functions/pipeline.R +++ b/functions/pipeline.R @@ -1,332 +1,488 @@ -#!/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 '/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, pseudo_only = 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$name %in% name_i,] + } + + # Identify score files + score_file_list <- list_score_files(config) + + # Subset requested gwas + if(!is.null(gwas)){ + if(any(!(gwas %in% score_file_list$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$method %in% pgs_methods,] + } + + # Identify outdir parameter + outdir <- read_param(config = config, param = 'outdir', return_obj = F) + + # Identify pgs_scaling parameter + pgs_scaling <- read_param(config = config, param = 'pgs_scaling', return_obj = F) + + pgs <- list() + for (name_i in target_list$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$method[score_i] + if (is.null(pgs[[name_i]][[pop_i]][[gwas_i]])) { + pgs[[name_i]][[pop_i]][[gwas_i]] <- list() + } + file_i<-paste0(outdir, '/', name_i, '/pgs/', pop_i, '/', pgs_method_i, '/', gwas_i, '/', name_i, '-', gwas_i, '-', pop_i, '.profiles') + if(pseudo_only){ + pseudo_param <- find_pseudo(config = config, gwas = gwas_i, target_pop = pop_i, pgs_method = pgs_method_i) + + score_header <- + fread(file_i, nrows = 1) + score_cols <- + which(names(score_header) %in% c('FID', 'IID', paste0(gwas_i, '_',pseudo_param))) + + pgs[[name_i]][[pop_i]][[gwas_i]][[pgs_method_i]] <- + fread(cmd = paste0("cut -d' ' -f ", paste0(score_cols, collapse=','), " ", file_i)) + } else { + pgs[[name_i]][[pop_i]][[gwas_i]][[pgs_method_i]] <- fread(file_i) + } + } + } + } + + return(pgs) +} + +# Read in PGS +read_pgs_2 <- function(config, name = NULL, pgs_methods = NULL, gwas = NULL, pop = NULL, pseudo_only = F, partitioned = F){ + # Identify outdir parameter + outdir <- read_param(config = config, param = 'outdir', return_obj = F) + + # Read in target_list + target_list <- read_param(config = config, param = 'target_list') + if(!is.null(name)){ + if(any(!(name %in% target_list$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) + + # 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.\n') + return(NULL) + } else { + cat(param, 'parameter is not present in user specified config file, so will use value in default config file.\n') + } + } + + # 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 '/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$POP == pop], header = F) + } + + # Read in model predictions + model_pred <- fread(paste0(outdir,'/',name,'/ancestry/',name,'.Ancestry.model_pred')) + + # Read in ancestry log file + log <- readLines(paste0(outdir,'/',name,'/ancestry/',name,'.Ancestry.log')) + + output <- list( + keep_list = keep_list, + keep_files = keep_files, + model_pred = model_pred, + log = log + ) + + return(output) +} + +# Return score corresponding to pseudovalidation +find_pseudo <- function(config, gwas, pgs_method, target_pop = NULL){ + + if(length(pgs_method) > 1){ + stop('Only one pgs_method can be specified at a time') + } + if(length(gwas) > 1){ + stop('Only one gwas can be specified at a time') + } + if(length(target_pop) > 1){ + stop('Only one target_pop can be specified at a time') + } + if(pgs_method %in% pgs_group_methods & is.null(target_pop)){ + stop('target_pop must be specified when using multi-ancestry PGS method') + } + + # Read in gwas_list + gwas_list <- read_param(config = config, param = 'gwas_list') + + # Read in gwas_groups + gwas_groups <- read_param(config = config, param = 'gwas_groups') + + # If pgs_method is multi-source, subset gwas_list to gwas in relevant group + if(grepl(paste0('^', pgs_group_methods, '$', collapse = '|'), pgs_method) | grepl('_multi$', pgs_method)){ + gwas_list <- gwas_list[gwas_list$name %in% unlist(strsplit(gwas_groups$gwas[gwas_groups$name == gwas], ','))] + } + + # 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) + + # 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$population != target_pop] + } + } + + # Use most stringent p-value threshold of 0.05 as pseudo + if(pgs_method == 'ptclump'){ + pseudo_val <- '0_1' + } + + # Pseudoval only methods + if(pgs_method == 'sbayesr'){ + pseudo_val <- 'SBayesR' + } + if(pgs_method == 'sbayesrc'){ + pseudo_val <- 'SBayesRC' + } + if(pgs_method == 'quickprs'){ + pseudo_val <- 'quickprs' + } + + # Retrieve pseudoval param + if(pgs_method == 'dbslmm'){ + pseudo_val <- 'DBSLMM_1' + } + if(pgs_method == 'ldpred2'){ + pseudo_val <- 'beta_auto' + } + if(pgs_method == 'prscs'){ + pseudo_val <- 'phi_auto' + } + + if(pgs_method == 'megaprs'){ + # Read in megaprs log file + log <- readLines(paste0(outdir,'/reference/pgs_score_files/',pgs_method,'/',gwas,'/ref-',gwas,'.log')) + log <- log[grepl('identified as the best with correlation', log)] + pseudoval <- gsub(' .*','', gsub('Model ', '', log)) + pseudo_val <- paste0('ldak_Model', pseudoval) + } + if(pgs_method == 'lassosum'){ + # Read in megaprs log file + log <- readLines(paste0(outdir,'/reference/pgs_score_files/',pgs_method,'/',gwas,'/ref-',gwas,'.log')) + s_val <- gsub('.* ', '', log[grepl('^s = ', log)]) + lambda_val <- gsub('.* ', '', log[grepl('^lambda = ', log)]) + pseudo_val <- paste0('s', s_val, '_lambda', lambda_val) + } + + # If pgs_method is external, return the only score + if(pgs_method == 'external'){ + pseudo_val <- 'external' + } + + # Multi-population methods + if(pgs_method == 'prscsx'){ + pseudo_val <- 'META_phi_auto' + } + + if(pgs_method == 'xwing'){ + if(!is.null(target_pop) && target_pop == 'TRANS'){ + cat('No pseudovalidation for TRANS target population available for xwing.\n') + cat(paste0('Returning result for ', gwas_list$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(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')) + 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'){ + 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$population)){ + cat(paste0('target_pop ', target_pop,' is not present in gwas_group ', gwas, '.\n')) + cat(paste0('Returning result for ', gwas_list$population[1],' target population.\n')) + target_pop <- gwas_list$population[1] + } + pseudo_val <- paste0('targ_', target_pop, '_', pseudo_val, '_TLPRS_61') + } + return(pseudo_val) +} + +# Read in lassosum pseudoval results +read_pseudo_r <- function(config, gwas){ + + if(length(gwas) > 1){ + stop('Only one gwas can be specified at a time') + } + + # Find outdir param + outdir <- read_param(config = config, param = 'outdir', return_obj = F) + + # Read in lassosum log file + log <- readLines(paste0(outdir,'/reference/pgs_score_files/lassosum/',gwas,'/ref-',gwas,'.log')) + r <- as.numeric(gsub('value = ','',log[grepl('value = ', log)])) + + return(r) +} + +# Read in reference PGS +# Read in TRANS scores (adjusted for ancestry), and restrict to pseudovalidated models +read_reference_pgs <- function(config){ + + # Identify score files + score_file_list <- list_score_files(config) + + # Identify outdir parameter + outdir <- read_param(config = config, param = 'outdir', return_obj = F) + + pgs <- list() + for(score_i in 1:nrow(score_file_list)){ + gwas_i <- score_file_list$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, target_pop = 'TRANS') + pgs[[gwas_i]][[pgs_method_i]]<-pgs[[gwas_i]][[pgs_method_i]][,c('FID','IID',paste0('SCORE_',pseudo_param)), with=F] + } + + return(pgs) +} + diff --git a/functions/plink.R b/functions/plink.R index 87057064..3e32c325 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) } @@ -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.") } @@ -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 @@ -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 == 2) { + 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 575a214f..6730d8ee 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/config.yaml b/pipeline/config.yaml index 15259cab..4e115c6e 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','quickprs','sbayesrc','prscsx','xwing') pgs_methods: NA # Specify p-value thresholds for ptclump @@ -28,12 +31,27 @@ 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 reference for SBayesR +sbayesr_ldref: NA + +# Specify reference for SBayesRC +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 + # Specify models for LDpred2 ldpred2_model: ['grid', 'auto', 'inf'] @@ -60,3 +78,12 @@ 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 + +# 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/envs/bridgeprs.yaml b/pipeline/envs/bridgeprs.yaml new file mode 100644 index 00000000..3e65a394 --- /dev/null +++ b/pipeline/envs/bridgeprs.yaml @@ -0,0 +1,21 @@ +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=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/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") diff --git a/pipeline/envs/pipeline.yaml b/pipeline/envs/pipeline.yaml index 8aa9c9c2..6cd3f68a 100644 --- a/pipeline/envs/pipeline.yaml +++ b/pipeline/envs/pipeline.yaml @@ -5,7 +5,9 @@ channels: - defaults dependencies: - python=3.8 - - mamba=1.5.6 - snakemake-minimal=7.32.3 - pandas=2.0.3 - ghostscript=10.02.1 + - pip + - pip: + - gdown 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/envs/xwing.yaml b/pipeline/envs/xwing.yaml new file mode 100644 index 00000000..f0061961 --- /dev/null +++ b/pipeline/envs/xwing.yaml @@ -0,0 +1,22 @@ +name: xwing +channels: + - dranew + - bioconda + - 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/example_input/config.multisource.yaml b/pipeline/example_input/config.multisource.yaml new file mode 100644 index 00000000..c78e070b --- /dev/null +++ b/pipeline/example_input/config.multisource.yaml @@ -0,0 +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 + +# Specify memory in megabytes for target scoring +mem_target_pgs: 2000 \ No newline at end of file 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 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_list.multisource.txt b/pipeline/example_input/gwas_list.multisource.txt new file mode 100644 index 00000000..92087fbb --- /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 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/cross_pop_test/config_cross_pop.yaml b/pipeline/misc/cross_pop_test/config_cross_pop.yaml new file mode 100644 index 00000000..b19ea02e --- /dev/null +++ b/pipeline/misc/cross_pop_test/config_cross_pop.yaml @@ -0,0 +1,10 @@ +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', 'xwing'] +testing: chr22 +cores_target_pgs: 1 +mem_target_pgs: 2000 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..88baab39 --- /dev/null +++ b/pipeline/misc/cross_pop_test/cross_pop_test.Rmd @@ -0,0 +1,171 @@ +--- +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. + +
Show code + +```{r} +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<-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','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 -n +snakemake --profile slurm --use-conda --configfile=misc/opensnp/config_cross_pop_gw.yaml output_all -n +``` + +*** + +## Evaluate PGS + +
Show code + +```{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')$opensnp + +# Read in ancestry +ancestry <- read_ancestry(config = 'misc/opensnp/config_cross_pop_gw.yaml', name = 'opensnp') + +# Estimate correlation between pheno and pgs +cor <- NULL +for(pop_i in names(pgs)){ + for(gwas_i in names(pgs[[pop_i]])){ + for(pgs_method_i in names(pgs[[pop_i]][[gwas_i]])){ + pgs_i <- pgs[[pop_i]][[gwas_i]][[pgs_method_i]] + pheno_pgs<-merge(pheno, pgs_i, by = c('FID','IID')) + + # Restrict to EUR + pheno_pgs <- pheno_pgs[pheno_pgs$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]]) + + 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) + + } + } + } +} + +# Restrict to best and and pseudoval only +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_i <- cor[ + cor$pop == pop_i & + cor$gwas == gwas_i & + cor$pgs_method == pgs_method_i,] + + # Top R + if(pgs_method_i %in% c('ptclump','ldpred2','megaprs','prscs','prscsx','lassosum','dbslmm') | any(grepl('tlprs', pgs_method_i))){ + top_i <- cor_i[which(cor_i$r == max(cor_i$r, na.rm = T))[1],] + top_i$model <- 'Top' + cor_subset <- rbind(cor_subset, top_i) + } + + # PseudoVal + 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_subset <- rbind(cor_subset, pseudo_i) + } + } + } +} + +# Plot the results +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 <- + 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), + width = .2, + position = position_dodge(width = 0.7) + ) + + labs( + y = "Correlation (SE)", + x = 'PGS Method', + fill = 'Model', + title = paste0("OpenSNP - EUR\n(N = ", cor_subset$n[1], ")") + ) + + theme_half_open() + + background_grid() + + theme(axis.text.x = element_text(angle = 45, hjust = 1), + plot.title = element_text(hjust = 0.5, size=12)) + + facet_grid(. ~ gwas, scales = 'free_x', space = 'free_x') + +``` diff --git a/pipeline/misc/cross_pop_test/gwas_groups.txt b/pipeline/misc/cross_pop_test/gwas_groups.txt new file mode 100644 index 00000000..524768a0 --- /dev/null +++ b/pipeline/misc/cross_pop_test/gwas_groups.txt @@ -0,0 +1,3 @@ +name gwas label +height yengo_eur,yengo_eas "Height (EUR+EAS)" +HT_UKB_UGR HT_UKB,HT_UGR "HT (UKB+UGR)" \ No newline at end of file diff --git a/pipeline/misc/cross_pop_test/gwas_list.txt b/pipeline/misc/cross_pop_test/gwas_list.txt new file mode 100644 index 00000000..28848fc5 --- /dev/null +++ b/pipeline/misc/cross_pop_test/gwas_list.txt @@ -0,0 +1,3 @@ +name path population n sampling prevalence mean sd label +yengo_eur misc/dev/test_data/gwas/yengo_2022_height_eur.txt.gz EUR NA NA NA NA NA "Yengo 2022 Height EUR" +yengo_eas misc/dev/test_data/gwas/yengo_2022_height_eas.txt.gz EAS NA NA NA NA NA "Yengo 2022 Height EAS" diff --git a/pipeline/misc/dev/sequence_test.Rmd b/pipeline/misc/dev/sequence_test.Rmd new file mode 100644 index 00000000..8b6e9058 --- /dev/null +++ b/pipeline/misc/dev/sequence_test.Rmd @@ -0,0 +1,50 @@ +--- +title: "Sequence test" +output: html_document +--- + +```{r setup, include=FALSE} +knitr::opts_chunk$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/misc/dev/setup_test.Rmd b/pipeline/misc/dev/setup_test.Rmd new file mode 100644 index 00000000..d2cc0fb0 --- /dev/null +++ b/pipeline/misc/dev/setup_test.Rmd @@ -0,0 +1,28 @@ +--- +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} +# 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 /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/ +``` + diff --git a/pipeline/misc/dev/test_data/config/config.yaml b/pipeline/misc/dev/test_data/config/config.yaml index e6ecfa53..e693aa64 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/gwas/yengo_2022_height_eas.txt.gz b/pipeline/misc/dev/test_data/gwas/yengo_2022_height_eas.txt.gz new file mode 100644 index 00000000..24b71cc7 Binary files /dev/null and b/pipeline/misc/dev/test_data/gwas/yengo_2022_height_eas.txt.gz differ 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 new file mode 100644 index 00000000..9c9bb0cf Binary files /dev/null and b/pipeline/misc/dev/test_data/gwas/yengo_2022_height_eur.txt.gz differ 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 @@

Ancestry

reference population with a probability >50%.
  • The number of individuals assigned to each population were:
    • -
    - +
    +


    @@ -3997,54 +3997,91 @@

    Ancestry

    Polygenic Scores

    • 1 GWAS summary statistics were provided for polygenic scoring.
      • +
    • 0 GWAS groups were specified.
    • 2 PGS methods were applied, including lassosum, ptclump.
    • 1 external score files were provided for polygenic scoring.

    GWAS summary statistics

    -
    - +
    + +
    +
    + +Show GWAS header interpretations + +

    BODY04

    +
    + +
    + +
    +

    Note. Columns that were dropped are not shown +here.

    +

    Score files

    -
    - +
    +

    Note. The Pass column indicates whether a sufficient number of variants within the score file were present in the reference data.

    +
    +

    PGS correlation

    +

    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.

    +
    + +Show reference PGS correlation matrix + +

    +

    Note. Plot shows correlation between pseudovalidated +PGS across all populations, adjusted for ancestry.

    +
    +
    +

    PGS Distribution

    +

    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.

    lassosum

    -Density plot of PGS from lassosum +Density plot of PGS from lassosum
    Density plot of PGS from lassosum

    ptclump

    -Density plot of PGS from ptclump +Density plot of PGS from ptclump
    Density plot of PGS from ptclump

    external

    -Density plot of PGS from external +Density plot of PGS from external
    Density plot of PGS from external

    +

    Note. Plot shows distribution of pseudovalidated +PGS.

    -

    This report was created using GenoPred (v2.2.2-110-gb4e52b5).

    +

    This report was created using GenoPred (v2.2.2-258-gd2f2a91).

    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 @@

    Ancestry

    Polygenic Scores

    • 1 GWAS summary statistics were provided for polygenic scoring.
      • -
    • 2 PGS methods were applied, including pT+clump, lassosum.
      • +
    • 0 GWAS groups were specified.
      • +
    • 2 PGS methods were applied, including lassosum, pT+clump.
    • 1 external score files were provided for polygenic scoring.

    GWAS summary statistics

    -
    - +
    + +
    +
    + +Show GWAS header interpretations + +

    BODY04

    +
    + +
    + +
    +

    Note. Columns that were dropped are not shown +here.

    +

    Score files

    -
    - +
    +

    Note. The Pass column indicates whether a sufficient number of variants within the score file were present in the reference data.

    +
    +

    PGS correlation

    +

    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.

    +
    + +Show reference PGS correlation matrix + +

    +

    Note. Plot shows correlation between pseudovalidated +PGS across all populations, adjusted for ancestry.

    +
    +
    +

    Target Polygenic Profile

    @@ -4092,7 +4123,7 @@

    Method: lassosum

    1 -0.994 +0.058 NA @@ -4114,11 +4145,11 @@

    Method: lassosum

    GWAS: Body Mass Index

    -

    +

      -

    • Your PGS Z-score for Body Mass IndexBody Mass Index is 0.994 -which is higher than 84% of other EAS individuals.

      • +

    • Your PGS Z-score for Body Mass IndexBody Mass Index is 0.058 +which is higher than 52.3% of other EAS individuals.

    • 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 @@

      Method: pT+clump

      1 -1.486 +1.27 NA @@ -4229,11 +4260,11 @@

      Method: pT+clump

      GWAS: Body Mass Index

      -

      +

        -

      • Your PGS Z-score for Body Mass IndexBody Mass Index is 1.486 -which is higher than 93.1% of other EAS individuals.

        • +

      • Your PGS Z-score for Body Mass IndexBody Mass Index is 1.27 which +is higher than 89.8% of other EAS individuals.

      • 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 @@

        GWAS: Body Mass Index

    -
    -

    Method: External

    - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
    - -
    -PGS Descriptives -
    -    		 -
    -Distribution in General
    Population -
    -    		 -
    -Distribution in People
    Like You -
    -
    -Outcome - -PGS R-squared - -PGS AUC - -Prevelance - -Mean - -SD - -PGS Z-score - -Prevalence - -Mean - -SD -
    -Height Yengo EUR - -NA - -NA - -NA - -NA - -NA - --0.711 - -NA - -NA - -NA -
    -

    Note. PGS R-squared/AUC cannot be estimated when -using externally derived score files. To specify parameters, check out -our interactive -tool for converting polygenic scores to the absolute scale.

    -
    -
    -

    GWAS: Height Yengo EUR

    -
    -

    -
    -
      -
    • Your PGS Z-score for Height Yengo EUR is -0.711 which is higher than -23.9% of other EAS individuals.
      • -
    -
    -

    -

    This report was created using GenoPred (v2.2.2-110-gb4e52b5).

    +

    This report was created using GenoPred (v2.2.2-258-gd2f2a91).

    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 - 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 + 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 -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_plink2-2_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 - | | | 0% | |. | 3% | |... | 6% [setup] | |.... | 9% | |...... | 11% [unnamed-chunk-1] | |....... | 14% | |......... | 17% [unnamed-chunk-2] | |.......... | 20% | |............ | 23% [unnamed-chunk-3] | |............. | 26% | |............... | 29% [unnamed-chunk-4] | |................ | 31% | |................. | 34% [unnamed-chunk-5] | |................... | 37% | |.................... | 40% [unnamed-chunk-6] | |...................... | 43% | |....................... | 46% [unnamed-chunk-7] | |......................... | 49% | |.......................... | 51% [unnamed-chunk-8] | |............................ | 54% | |............................. | 57% [unnamed-chunk-9] | |............................... | 60% | |................................ | 63% [unnamed-chunk-10] | |.................................. | 66% | |................................... | 69% [unnamed-chunk-11] | |.................................... | 71% | |...................................... | 74% [unnamed-chunk-12] | |....................................... | 77% | |......................................... | 80% [unnamed-chunk-13] | |.......................................... | 83% | |............................................ | 86% [unnamed-chunk-14] | |............................................. | 89% | |............................................... | 91% [unnamed-chunk-15] | |................................................ | 94% | |.................................................. | 97% [unnamed-chunk-16] | |...................................................| 100% +1/41 +2/41 [setup] +3/41 +4/41 [unnamed-chunk-1] +5/41 +6/41 [unnamed-chunk-2] +7/41 +8/41 [unnamed-chunk-3] +9/41 +10/41 [unnamed-chunk-4] +11/41 +12/41 [unnamed-chunk-5] +13/41 +14/41 [unnamed-chunk-6] +15/41 +16/41 [unnamed-chunk-7] +17/41 +18/41 [unnamed-chunk-8] +19/41 +20/41 [unnamed-chunk-9] +21/41 +22/41 [unnamed-chunk-10] +23/41 +24/41 [unnamed-chunk-11] +25/41 +26/41 [unnamed-chunk-12] +27/41 +28/41 [unnamed-chunk-13] +29/41 +30/41 [unnamed-chunk-14] +31/41 +32/41 [unnamed-chunk-15] +33/41 +34/41 [unnamed-chunk-16] +35/41 +36/41 [unnamed-chunk-17] +37/41 +38/41 [unnamed-chunk-18] +39/41 +40/41 [unnamed-chunk-19] +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] | |................ | 30% | |.................. | 35% [unnamed-chunk-3] | |.................... | 39% | |...................... | 43% [unnamed-chunk-4] | |........................ | 48% | |........................... | 52% [unnamed-chunk-5] | |............................. | 57% | |............................... | 61% [unnamed-chunk-6] | |................................. | 65% | |................................... | 70% [unnamed-chunk-7] | |...................................... | 74% | |........................................ | 78% [unnamed-chunk-8] | |.......................................... | 83% | |............................................ | 87% [unnamed-chunk-9] | |............................................... | 91% | |................................................. | 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 +8/29 [unnamed-chunk-3] +9/29 +10/29 [unnamed-chunk-4] +11/29 +12/29 [unnamed-chunk-5] +13/29 +14/29 [unnamed-chunk-6] +15/29 +16/29 [unnamed-chunk-7] +17/29 +18/29 [unnamed-chunk-8] +19/29 +20/29 [unnamed-chunk-9] +21/29 +22/29 [unnamed-chunk-10] +23/29 +24/29 [unnamed-chunk-11] +25/29 +26/29 [unnamed-chunk-12] +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 00000000..d0b64990 Binary files /dev/null and b/pipeline/misc/dev/test_data/output/reference/pc_score_files/TRANS/ref-TRANS-pcs.eigenvec.var.gz differ 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 +HG00242 HG00242 -0.351 1.698 0.725 0.033 0.482 -2.042 +HG00243 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-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 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1.92880880422681 +NA21133 NA21133 -1.35327826483456 +NA21135 NA21135 0.68037666482625 +NA21137 NA21137 0.977427018721194 +NA21141 NA21141 0.544673218862827 +NA21142 NA21142 -1.33841700972981 +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 5050e952..cbea74dd 100644 Binary files a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804.score.gz and b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/external/PGS002804/ref-PGS002804.score.gz differ 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 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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 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NA NA 0.457 0.38 0.394 0.592 1.039 1.455 1.686 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.199 0.195 0.167 0.22 0.521 1.373 1.665 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.131 -0.174 -0.237 -0.174 0.169 1.322 1.659 NA NA NA NA NA NA NA NA NA NA NA NA NA +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 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NA NA NA NA NA NA NA NA NA NA NA NA 1.177 1.129 1.247 1.279 0.959 0.367 -0.48 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.113 1.183 1.31 1.291 1.095 0.513 -0.439 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.923 0.981 1.088 1.198 1.133 0.549 -0.429 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21119 NA21119 -0.325 -0.425 -0.742 -1.145 -0.987 -0.308 -0.027 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.814 -0.771 -0.96 -1.218 -1.095 -0.489 -0.049 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.988 -0.952 -0.962 -1.084 -1.057 -0.707 -0.079 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.884 -0.904 -0.948 -0.99 -0.959 -0.759 -0.087 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21120 NA21120 -0.227 -0.241 -0.296 -0.454 -0.796 -1.217 -1.189 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.057 -0.048 -0.201 -0.393 -0.748 -1.307 -1.111 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.459 0.39 0.256 0.031 -0.544 -1.414 -1.004 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.736 0.656 0.535 0.334 -0.176 -1.438 -0.976 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21122 NA21122 -0.363 -0.598 -0.725 -0.833 -0.73 -0.309 -0.027 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.456 -0.69 -0.842 -0.93 -0.745 -0.361 -0.049 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.414 -0.513 -0.588 -0.535 -0.591 -0.416 -0.079 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.338 -0.453 -0.529 -0.492 -0.469 -0.41 -0.087 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21123 NA21123 0.122 0.218 0.31 0.273 -0.005 -0.012 -0.027 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.052 0.154 0.271 0.395 0.183 0.124 -0.049 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.384 0.365 0.405 0.381 0.457 0.376 -0.079 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.548 0.53 0.512 0.453 0.575 0.464 -0.087 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21124 NA21124 -0.724 -0.563 -0.497 -0.313 -0.082 -0.208 -0.027 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.791 -0.676 -0.445 -0.24 -0.15 -0.18 -0.049 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.612 -0.497 -0.388 -0.25 -0.272 -0.126 -0.079 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.452 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NA NA NA NA NA NA NA NA NA NA NA NA 0.628 0.688 0.704 0.66 0.671 -0.275 -0.634 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21128 NA21128 0.572 0.542 0.576 0.605 0.315 0.028 -0.027 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.665 0.678 0.704 0.622 0.395 0.131 -0.049 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.67 0.613 0.573 0.435 0.289 0.22 -0.079 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.515 0.411 0.247 0.083 0.052 0.237 -0.087 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21129 NA21129 1.028 1.089 1.355 1.424 1.327 1.707 1.685 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.73 0.849 1.066 1.177 1.219 1.611 1.736 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.484 0.596 0.693 0.767 0.824 1.423 1.803 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.284 0.338 0.388 0.449 0.589 1.323 1.82 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21130 NA21130 1.119 0.925 0.907 1.077 1.307 1.257 1.972 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.118 1.04 1.164 1.299 1.425 1.25 1.967 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.226 1.317 1.468 1.544 1.459 1.169 1.958 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.126 1.205 1.323 1.437 1.396 1.143 1.955 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21133 NA21133 -0.241 -0.186 -0.198 -0.305 -0.386 -0.338 -0.51 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.783 -0.723 -0.615 -0.611 -0.544 -0.407 -0.48 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.263 -1.287 -1.28 -1.284 -1.184 -0.523 -0.439 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.613 -1.634 -1.614 -1.607 -1.537 -0.607 -0.429 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21135 NA21135 0.356 0.307 0.118 -0.255 -0.508 -0.207 -0.027 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.266 0.17 -0.032 -0.385 -0.624 -0.322 -0.049 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.154 0.012 -0.132 -0.255 -0.411 -0.603 -0.079 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.413 0.237 0.066 -0.056 -0.123 -0.613 -0.087 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21137 NA21137 -0.162 0.096 0.431 0.587 0.553 0.233 -0.027 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.066 0.098 0.336 0.346 0.268 0.171 -0.049 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.038 0.051 0.097 0.22 0.152 0 -0.079 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.313 0.245 0.166 0.247 0.18 -0.018 -0.087 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21141 NA21141 0.235 0.005 0.111 0.682 0.934 1.05 0.169 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.091 -0.133 -0.048 0.368 0.682 1.142 0.151 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.5 -0.514 -0.46 -0.314 0.197 1.055 0.125 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.875 -0.876 -0.787 -0.586 -0.294 1.068 0.119 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21142 NA21142 0.233 0.398 0.57 0.697 0.918 0.462 -0.027 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.27 0.342 0.36 0.36 0.533 0.4 -0.049 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.089 -0.043 0.041 0.072 0.203 0.242 -0.079 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.324 -0.271 -0.198 -0.028 0.173 0.185 -0.087 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21143 NA21143 0.204 0.159 0.078 -0.154 -0.403 -0.366 -0.027 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.308 0.309 0.196 -0.103 -0.358 -0.252 -0.049 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.578 0.532 0.488 0.334 -0.18 -0.246 -0.079 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.907 0.783 0.643 0.48 -0.02 -0.217 -0.087 NA NA NA NA NA NA NA NA NA NA NA NA NA +NA21144 NA21144 0.518 0.659 0.805 0.791 0.481 0.406 -0.027 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.343 0.472 0.568 0.522 0.373 0.51 -0.049 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.09 0.149 0.205 0.233 0.346 0.523 -0.079 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.135 -0.065 0.028 0.221 0.33 0.528 -0.087 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-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 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NA NA NA NA NA NA NA NA NA NA NA NA 1.828 1.788 1.733 1.662 1.707 0.993 -0.386 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.729 1.658 1.631 1.639 1.743 1.066 -0.388 NA NA NA NA NA NA NA NA NA NA NA NA NA +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 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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 @@ +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 +HGDP00557 HGDP00557 0.861 0.725 0.573 0.264 -0.392 -0.864 -0.816 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.738 0.624 0.503 0.136 -0.465 -0.976 -0.804 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.503 0.421 0.257 0.06 -0.477 -1.091 -0.787 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.42 0.329 0.198 0.07 -0.43 -1.1 -0.782 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP00558 HGDP00558 -1.575 -1.682 -1.795 -1.392 -0.623 -0.153 -0.302 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.359 -1.42 -1.488 -1.126 -0.554 -0.065 -0.319 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.175 -1.173 -1.16 -0.952 -0.432 0.057 -0.342 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.036 -1.065 -1.047 -0.888 -0.403 0.109 -0.348 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP00559 HGDP00559 -0.015 -0.021 -0.333 -0.633 -0.534 -0.066 0.757 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.088 0.038 -0.193 -0.591 -0.491 -0.018 0.741 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.032 -0.009 -0.133 -0.257 -0.351 -0.12 0.719 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.135 0.077 -0.016 -0.098 -0.22 -0.123 0.713 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP00560 HGDP00560 1.314 1.51 1.604 1.53 1.819 1.817 1.648 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.989 1.194 1.384 1.352 1.687 1.858 1.666 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.525 0.624 0.769 0.935 1.42 1.758 1.689 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.237 0.274 0.404 0.659 1.146 1.671 1.695 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP00561 HGDP00561 1.347 1.637 1.837 2.238 2.385 1.586 0.873 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.522 1.677 1.867 2.162 2.362 1.591 0.858 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.788 1.855 1.963 2.176 2.31 1.491 0.837 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.723 1.738 1.778 1.964 2.206 1.458 0.832 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP00562 HGDP00562 0.18 -0.131 -0.259 -0.391 -0.127 -0.321 -0.701 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.193 -0.019 -0.15 -0.227 -0.058 -0.281 -0.687 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.572 0.434 0.261 0.087 0.114 -0.084 -0.668 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.666 0.603 0.448 0.212 0.183 0.009 -0.664 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP00563 HGDP00563 -1.633 -1.45 -1.263 -0.971 -0.168 1.185 1.932 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.89 -1.767 -1.68 -1.505 -0.755 0.83 1.917 NA NA 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NA NA NA NA NA NA NA NA NA NA 1.998 2.034 2.059 1.944 2.019 1.319 -0.348 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP00746 HGDP00746 0.101 0 -0.138 -0.325 -0.164 0.208 -0.187 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.054 -0.083 -0.157 -0.322 -0.131 0.27 -0.202 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.305 0.213 0.038 -0.178 -0.197 0.425 -0.223 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.295 0.171 0.012 -0.154 -0.2 0.485 -0.229 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01254 HGDP01254 -0.062 -0.287 -0.489 -0.684 -0.59 -0.908 -1.299 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.085 -0.038 -0.214 -0.313 -0.369 -0.786 -1.331 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.002 -0.035 -0.073 0.047 0.074 -0.543 -1.373 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.059 0.052 0 0.085 0.157 -0.422 -1.383 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01255 HGDP01255 1.105 1.191 1.133 0.985 1.147 0.367 -0.417 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.916 1.033 1.044 1.024 1.278 0.498 -0.435 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.513 0.557 0.534 0.625 1.174 0.888 -0.46 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.143 0.239 0.287 0.353 0.78 1.003 -0.466 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01257 HGDP01257 0.195 0.005 -0.23 -0.66 -1.024 -1.477 -1.698 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.087 -0.252 -0.408 -0.734 -0.946 -1.338 -1.7 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.336 -0.434 -0.586 -0.74 -0.94 -1.093 -1.7 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.389 -0.452 -0.544 -0.651 -0.892 -1.04 -1.699 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01258 HGDP01258 -0.707 -0.91 -0.965 -0.937 -0.793 -0.576 -0.417 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.706 -0.845 -0.853 -0.78 -0.758 -0.478 -0.435 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.743 -0.773 -0.706 -0.579 -0.528 -0.51 -0.46 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.584 -0.593 -0.583 -0.485 -0.381 -0.501 -0.466 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01259 HGDP01259 0.185 0.093 0.022 -0.002 -0.306 -0.575 0.358 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.07 -0.095 -0.153 -0.146 -0.251 -0.574 0.373 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.097 -0.24 -0.33 -0.393 -0.434 -0.495 0.392 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.085 -0.211 -0.386 -0.539 -0.557 -0.516 0.397 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01260 HGDP01260 -0.758 -0.693 -0.612 -0.621 -0.662 -1.537 -2.181 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.147 -0.156 -0.185 -0.347 -0.583 -1.461 -2.227 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.011 -0.055 -0.088 -0.191 -0.306 -1.441 -2.286 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.024 -0.057 -0.125 -0.096 -0.195 -1.356 -2.3 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01261 HGDP01261 -1.063 -0.733 -0.496 -0.179 0.246 0.36 0.474 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.58 -0.468 -0.384 -0.212 0.142 0.334 0.489 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.419 -0.398 -0.383 -0.359 -0.076 0.185 0.51 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.357 -0.344 -0.366 -0.413 -0.158 0.114 0.516 NA NA NA NA NA NA NA NA NA NA NA NA NA 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0.882 0.878 1.094 -0.014 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01265 HGDP01265 -1.288 -1.419 -1.332 -1.016 -0.791 -1.052 -1.215 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.917 -1.043 -1.063 -1.009 -0.939 -1.086 -1.172 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.882 -0.981 -1.087 -1.246 -1.333 -1.258 -1.113 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.977 -1.004 -1.094 -1.227 -1.503 -1.366 -1.099 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01266 HGDP01266 -0.132 -0.11 -0.219 -0.453 -0.513 -0.764 -0.816 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.071 -0.038 -0.115 -0.319 -0.386 -0.644 -0.804 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.014 0.036 -0.031 -0.167 -0.186 -0.374 -0.787 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.159 0.128 0.059 -0.088 -0.167 -0.307 -0.782 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01267 HGDP01267 -1.626 -1.877 -2.117 -2.161 -2.333 -2.467 -1.698 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.726 -1.83 -2.002 -2.134 -2.245 -2.516 -1.7 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.298 -1.35 -1.412 -1.555 -1.899 -2.481 -1.7 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.917 -0.936 -0.945 -1.041 -1.474 -2.438 -1.699 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01268 HGDP01268 -0.392 -0.38 -0.506 -0.607 -0.776 -0.859 -0.523 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.33 -0.343 -0.486 -0.624 -0.864 -0.855 -0.523 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.287 -0.327 -0.359 -0.618 -0.914 -1.051 -0.521 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.049 -0.108 -0.232 -0.456 -0.793 -1.114 -0.52 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01269 HGDP01269 0.915 0.943 1.037 1.34 1.538 1.302 1.272 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.019 1.115 1.143 1.349 1.497 1.22 1.285 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.089 1.062 0.995 1.004 1.25 1.317 1.304 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.138 1.136 1.062 0.959 1.076 1.309 1.309 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01272 HGDP01272 0.581 0.862 1.148 1.562 1.92 1.861 1.493 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.922 1.169 1.311 1.559 1.87 1.828 1.49 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.259 1.381 1.5 1.556 1.845 1.858 1.484 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.388 1.42 1.485 1.582 1.818 1.878 1.482 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01275 HGDP01275 -1.376 -1.17 -1.057 -0.773 -0.193 -0.012 -0.586 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.86 -0.818 -0.8 -0.664 -0.362 -0.11 -0.57 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.467 -0.454 -0.455 -0.372 -0.308 -0.293 -0.55 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.206 -0.181 -0.14 -0.043 -0.042 -0.342 -0.545 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01276 HGDP01276 0.249 -0.033 -0.085 -0.087 -0.066 -0.33 0.358 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.195 -0.229 -0.184 -0.267 -0.295 -0.36 0.373 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.812 -0.72 -0.634 -0.61 -0.685 -0.518 0.392 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.253 -1.143 -1.009 -0.915 -0.887 -0.58 0.397 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01277 HGDP01277 1.423 1.229 1.055 1.105 0.836 0.584 0.474 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.265 1.112 1.045 1.048 0.882 0.598 0.489 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.038 0.98 1.016 1.02 0.945 0.741 0.51 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.025 1.002 0.998 0.992 0.962 0.788 0.516 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01279 HGDP01279 0.663 0.631 0.397 -0.113 -0.551 -1.115 -1.299 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.164 1.102 0.774 0.187 -0.342 -1.039 -1.331 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.271 1.197 1.043 0.719 0.14 -0.736 -1.373 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.322 1.211 1.043 0.798 0.348 -0.605 -1.383 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01280 HGDP01280 -1.206 -1.123 -0.974 -1.202 -1.742 -1.976 -1.215 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.207 -1.043 -1.037 -1.35 -1.668 -2.02 -1.172 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.663 -1.617 -1.641 -1.785 -1.781 -2.021 -1.113 NA NA NA NA NA NA NA NA NA NA NA NA NA -2.174 -2.106 -2.113 -2.183 -2.019 -2.07 -1.099 NA NA NA NA NA NA NA NA NA NA NA NA NA +HGDP01282 HGDP01282 -0.412 -0.607 -0.933 -1.501 -2.188 -2.667 -1.804 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.469 -0.691 -1.093 -1.691 -2.211 -2.685 -1.788 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.574 -0.727 -1.023 -1.479 -2.078 -2.706 -1.76 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.64 -0.767 -0.961 -1.275 -1.842 -2.716 -1.753 NA NA NA NA NA NA NA NA NA NA NA NA NA +LP6005441-DNA_E02 LP6005441-DNA_E02 -0.319 -0.564 -0.76 -0.909 -0.886 -0.05 1.365 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.684 -0.806 -1.04 -1.192 -1.123 -0.223 1.414 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.193 -1.273 -1.401 -1.514 -1.548 -0.55 1.481 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.455 -1.59 -1.704 -1.827 -1.681 -0.739 1.498 NA NA NA NA NA NA NA NA NA NA NA NA NA +LP6005441-DNA_F02 LP6005441-DNA_F02 -0.648 -0.803 -0.757 -0.478 -0.085 0.341 0.474 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.844 -0.828 -0.761 -0.452 -0.196 0.279 0.489 NA NA NA NA NA NA NA NA NA NA NA NA NA -1.06 -1.001 -0.856 -0.668 -0.478 0.082 0.51 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.917 -0.923 -0.888 -0.845 -0.603 -0.026 0.516 NA NA NA NA NA NA NA NA NA NA NA NA NA +LP6005441-DNA_G04 LP6005441-DNA_G04 2.295 2.319 2.14 1.92 1.049 -0.169 -0.701 NA NA NA NA NA NA NA NA NA NA NA NA NA 2.201 2.197 2.047 1.845 1.085 -0.245 -0.687 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.951 1.888 1.777 1.622 1.157 -0.206 -0.668 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.719 1.664 1.622 1.502 1.179 -0.194 -0.664 NA NA NA NA NA NA NA NA NA NA NA NA NA +LP6005441-DNA_G08 LP6005441-DNA_G08 -0.003 -0.001 -0.162 -0.382 -0.34 -0.886 -0.922 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.023 -0.114 -0.304 -0.467 -0.339 -0.876 -0.892 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.273 0.16 0.007 -0.133 -0.1 -0.806 -0.848 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.462 0.399 0.338 0.242 0.109 -0.769 -0.836 NA NA NA NA NA NA NA NA NA NA NA NA NA +LP6005441-DNA_H08 LP6005441-DNA_H08 1.18 1.214 1.387 1.575 1.591 1.133 0.979 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.246 1.357 1.448 1.584 1.523 1.082 1.005 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.306 1.334 1.333 1.305 1.349 1.086 1.038 NA NA NA NA NA NA NA NA NA NA NA NA NA 1.243 1.261 1.21 1.181 1.201 1.061 1.047 NA NA NA NA NA NA NA NA NA NA NA NA NA +LP6005441-DNA_H09 LP6005441-DNA_H09 -0.714 -0.643 -0.285 0.236 0.142 -0.004 -0.302 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.555 -0.443 -0.16 0.253 0.16 0.072 -0.319 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.009 0.132 0.315 0.42 0.317 0.214 -0.342 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.188 0.291 0.418 0.542 0.476 0.292 -0.348 NA NA NA NA NA NA NA NA NA NA NA NA NA +LP6005443-DNA_D01 LP6005443-DNA_D01 0.049 0.189 0.423 0.459 0.322 -0.019 -0.302 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.006 0.108 0.306 0.401 0.347 0.094 -0.319 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.299 0.336 0.399 0.434 0.373 0.189 -0.342 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.44 0.383 0.358 0.301 0.315 0.236 -0.348 NA NA NA NA NA NA NA NA NA NA NA NA NA +LP6005592-DNA_B03 LP6005592-DNA_B03 -0.375 -0.367 -0.304 -0.072 0.513 1.288 1.648 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.491 -0.386 -0.302 0.028 0.479 1.205 1.666 NA NA NA NA NA NA NA NA NA NA NA NA NA -0.12 -0.076 -0.057 0.065 0.342 1.009 1.689 NA NA NA NA NA NA NA NA NA NA NA NA NA 0.442 0.412 0.329 0.259 0.435 0.876 1.695 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-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 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0.200406036477159 0.290988102523288 0.0589654978909726 0.00594659317404047 0 0 0 0 0 0 0 0 0 0 0 0 0 -0.12463997440729 -0.0379461649266651 0.166574031782301 0.36899888066229 0.353521436397747 0.0525851425309673 0.00610463688645454 0 0 0 0 0 0 0 0 0 0 0 0 0 1.12975548694737 1.11105688771575 1.06842100848268 1.01614039274532 0.550651478776266 0.0684843378408 0.00616640094522739 0 0 0 0 0 0 0 0 0 0 0 0 0 1.87377019095365 1.85506553022039 1.79308060725267 1.57383986407113 0.888905207258968 0.0804448900735853 0.00615414463382513 0 0 0 0 0 0 0 0 0 0 0 0 0 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 +SCORE_s0.2_lambda0.001 -1.72193342787528e-05 0.867740128428572 +SCORE_s0.2_lambda0.00127427498570313 -1.29607669230678e-05 0.800229828162781 +SCORE_s0.2_lambda0.00162377673918872 -2.97669238860855e-06 0.691120914801276 +SCORE_s0.2_lambda0.00206913808111479 -1.04591989140578e-07 0.522681013950191 +SCORE_s0.2_lambda0.00263665089873036 -9.55616325260638e-10 0.33658831172352 +SCORE_s0.2_lambda0.00335981828628378 3.09961053300983e-17 0.185542796787219 +SCORE_s0.2_lambda0.00428133239871939 -8.42895320420045e-19 0.0754962797864694 +SCORE_s0.2_lambda0.00545559478116852 0 0 +SCORE_s0.2_lambda0.00695192796177561 0 0 +SCORE_s0.2_lambda0.00885866790410083 0 0 +SCORE_s0.2_lambda0.0112883789168469 0 0 +SCORE_s0.2_lambda0.0143844988828766 0 0 +SCORE_s0.2_lambda0.0183298071083244 0 0 +SCORE_s0.2_lambda0.0233572146909012 0 0 +SCORE_s0.2_lambda0.0297635144163132 0 0 +SCORE_s0.2_lambda0.0379269019073225 0 0 +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 -1.64538333894864e-05 0.894574673677941 +SCORE_s0.5_lambda0.00127427498570313 -1.31222753446071e-05 0.843274292283521 +SCORE_s0.5_lambda0.00162377673918872 -5.47684806584081e-06 0.747186482936761 +SCORE_s0.5_lambda0.00206913808111479 -5.22987425872701e-08 0.5860818424202 +SCORE_s0.5_lambda0.00263665089873036 1.80095666982584e-08 0.374351536819204 +SCORE_s0.5_lambda0.00335981828628378 7.62030826054753e-18 0.187811828879282 +SCORE_s0.5_lambda0.00428133239871939 4.02606801861908e-19 0.0744249017725375 +SCORE_s0.5_lambda0.00545559478116852 0 0 +SCORE_s0.5_lambda0.00695192796177561 0 0 +SCORE_s0.5_lambda0.00885866790410083 0 0 +SCORE_s0.5_lambda0.0112883789168469 0 0 +SCORE_s0.5_lambda0.0143844988828766 0 0 +SCORE_s0.5_lambda0.0183298071083244 0 0 +SCORE_s0.5_lambda0.0233572146909012 0 0 +SCORE_s0.5_lambda0.0297635144163132 0 0 +SCORE_s0.5_lambda0.0379269019073225 0 0 +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 6.18104873760974e-05 0.980657864400319 +SCORE_s0.9_lambda0.00127427498570313 3.90352691015627e-05 0.967465076762771 +SCORE_s0.9_lambda0.00162377673918872 4.45071705941631e-06 0.933317370530192 +SCORE_s0.9_lambda0.00206913808111479 -9.96215490265789e-06 0.825184736770895 +SCORE_s0.9_lambda0.00263665089873036 9.11722564044899e-07 0.531963422392032 +SCORE_s0.9_lambda0.00335981828628378 1.61513561636905e-18 0.209943977751392 +SCORE_s0.9_lambda0.00428133239871939 -1.19754044761917e-18 0.0732154695289679 +SCORE_s0.9_lambda0.00545559478116852 0 0 +SCORE_s0.9_lambda0.00695192796177561 0 0 +SCORE_s0.9_lambda0.00885866790410083 0 0 +SCORE_s0.9_lambda0.0112883789168469 0 0 +SCORE_s0.9_lambda0.0143844988828766 0 0 +SCORE_s0.9_lambda0.0183298071083244 0 0 +SCORE_s0.9_lambda0.0233572146909012 0 0 +SCORE_s0.9_lambda0.0297635144163132 0 0 +SCORE_s0.9_lambda0.0379269019073225 0 0 +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 0.000145336403524435 0.996155081402662 +SCORE_s1_lambda0.00127427498570313 0.000116358150829023 0.992731785979237 +SCORE_s1_lambda0.00162377673918872 6.9581542087965e-05 0.982570705892848 +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 index 75f7cf1a..73732c8e 100644 Binary files a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04.score.gz and b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/lassosum/BODY04/ref-BODY04.score.gz differ 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 +HG01885 HG01885 1.347 0.04 0.962 0.887 1.104 0.903 +HG01886 HG01886 0.137 -0.428 -0.261 -1.091 -0.935 -0.563 +HG01889 HG01889 -0.495 -0.765 -0.057 -0.672 -1.372 -1.534 +HG01890 HG01890 1.22 -0.271 0.112 -1.066 -0.866 -1.056 +HG01891 HG01891 1.379 0.249 0.818 -0.309 -0.673 -1.048 +HG01894 HG01894 1.741 0.987 0.706 -0.242 0.079 -0.039 +HG01896 HG01896 0.24 0.38 0.604 0.361 -0.09 0.146 +HG01912 HG01912 0.982 1.374 2.119 1.737 1.632 1.198 +HG01914 HG01914 1.116 0.45 0.83 0.955 1.138 0.903 +HG01915 HG01915 -0.436 -0.695 -0.904 -0.676 -0.413 -0.334 +HG01956 HG01956 -0.923 -1.596 -1.759 -1.615 -0.923 -0.782 +HG01958 HG01958 0.557 0.333 0.505 0.28 0.455 0.93 +HG01985 HG01985 -0.146 -1.07 -1.48 -1.348 -1.38 -1.312 +HG01986 HG01986 0.533 0.99 0.482 0.932 1.201 1.137 +HG01988 HG01988 1.375 0.836 0.502 0.493 0.453 0.638 +HG01989 HG01989 -1.133 -0.826 -1.513 -2.121 -2.203 -2.137 +HG01990 HG01990 0.844 0.99 1.091 0.872 0.943 0.923 +HG02009 HG02009 1.168 0.203 0.492 0.749 0.811 0.641 +HG02010 HG02010 0.254 0.136 -0.02 -0.452 -0.707 -0.942 +HG02012 HG02012 0.789 0.426 0.482 0.522 0.487 0.757 +HG02013 HG02013 -0.315 -0.079 -0.176 0.289 -0.019 0.135 +HG02014 HG02014 -1.423 -1.5 -2.027 -1.779 -1.197 -1.01 +HG02051 HG02051 1.327 0.328 0.591 0.489 -0.304 -0.449 +HG02052 HG02052 -0.15 -0.411 -0.457 0.298 0.561 0.57 +HG02053 HG02053 -0.467 -0.658 -0.705 -0.278 -0.16 -0.033 +HG02054 HG02054 0.326 0.319 0.974 1.636 1.995 1.557 +HG02095 HG02095 0.116 0.392 0.077 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-0.688 -0.612 -0.531 -0.231 -0.244 -0.376 +HG04025 HG04025 1.677 1.084 1.095 1.08 1.022 1.184 +HG04026 HG04026 0.43 0.59 0.653 0.816 0.382 0.737 +HG04029 HG04029 1.407 1.466 1.223 1.119 1.045 0.827 +HG04033 HG04033 -0.845 -0.129 -0.563 -0.428 -0.013 0.201 +HG04035 HG04035 0.166 -1.383 -1.596 -1.407 -0.912 -1.049 +HG04036 HG04036 0.295 0.148 0.004 -0.215 -0.44 -0.231 +HG04038 HG04038 -0.075 -0.53 0.304 0.427 0.367 0.105 +HG04039 HG04039 0.672 0.148 -0.499 0.23 0.336 0.479 +HG04042 HG04042 -0.463 -0.234 -1.151 -0.632 -0.445 -0.236 +HG04047 HG04047 0.014 0.463 0.711 0.365 0.379 0.538 +HG04054 HG04054 1.34 1.099 0.49 0.238 0.218 0.515 +HG04056 HG04056 -1.85 -0.84 -0.994 -0.275 -0.593 0.447 +HG04059 HG04059 1.205 1.309 0.83 0.676 0.766 0.307 +HG04060 HG04060 0.087 -0.979 -0.866 -0.718 -0.869 -0.996 +HG04061 HG04061 -0.53 -0.788 -0.808 -1.088 -1.241 -0.829 +HG04062 HG04062 0.981 0.036 -0.38 0.065 0.623 0.536 +HG04063 HG04063 1.481 1.062 1.485 1.748 1.487 1.963 +HG04070 HG04070 -0.036 0.118 0.845 0.971 0.874 1.308 +HG04075 HG04075 0.711 0.904 0.516 0.575 0.64 0.595 +HG04076 HG04076 -0.564 0.257 0.304 0.202 0.188 0.314 +HG04080 HG04080 -1.491 -1.117 -0.921 -0.733 -0.344 -0.717 +HG04090 HG04090 -1.603 -0.211 0.132 0.526 0.409 0.261 +HG04093 HG04093 1.357 -0.189 -0.29 0.274 -0.091 0.357 +HG04094 HG04094 -0.969 -0.87 -1.026 -0.008 -0.327 0.021 +HG04096 HG04096 0.458 0.279 -0.883 -0.332 -0.337 0.314 +HG04098 HG04098 1.233 1.795 2.317 2.02 2.147 1.747 +HG04099 HG04099 -1.879 -1.151 0.12 0.689 0.402 0.529 +HG04100 HG04100 -0.345 -0.844 -0.008 0.049 0.161 0.247 +HG04106 HG04106 -0.62 -0.934 -0.976 -0.671 -1.047 -1.296 +HG04107 HG04107 0.52 1.447 1.246 0.311 0.412 0.401 +HG04118 HG04118 1.745 1.005 1.441 1.66 1.657 1.88 +HG04127 HG04127 0.149 0.785 1.255 1.023 0.618 1.294 +HG04131 HG04131 1.003 -0.017 -0.229 0.536 0.495 0.758 +HG04132 HG04132 -0.693 -0.462 -0.101 0.256 -0.025 0.11 +HG04134 HG04134 -0.997 -1.9 -2.004 -2.885 -2.713 -2.618 +HG04135 HG04135 2.003 1.144 1.104 0.51 0.52 0.962 +HG04140 HG04140 -1.536 -0.35 -0.787 -1.619 -1.6 -1.241 +HG04141 HG04141 -0.553 -1.117 -1.675 -1.653 -1.356 -1.438 +HG04144 HG04144 0.851 -0.335 0.152 -0.637 -0.949 -0.822 +HG04146 HG04146 -0.064 -0.095 -0.165 0.018 -0.126 1.042 +HG04147 HG04147 -0.39 -0.96 -0.979 -1.469 -1.653 -1.867 +HG04150 HG04150 -0.047 -0.53 -0.68 -0.92 -0.939 -1.416 +HG04151 HG04151 0.666 0.796 0.551 0.184 0.326 0.165 +HG04152 HG04152 -0.002 -0.559 -0.342 -0.205 0.306 0.268 +HG04153 HG04153 -0.457 -1.305 -1.343 -0.847 -0.985 -1.058 +HG04155 HG04155 1.632 1.155 1.191 1.015 0.944 0.547 +HG04156 HG04156 -0.401 -0.256 -0.962 -1.469 -1.482 -1.592 +HG04158 HG04158 0.52 1.062 1.273 0.886 0.851 0.431 +HG04159 HG04159 3.682 2.424 2.562 2.271 2.439 1.798 +HG04161 HG04161 1.745 1.08 0.729 1.432 1.344 1.466 +HG04164 HG04164 0.458 0.489 0.574 0.593 0.987 1.134 +HG04171 HG04171 -0.384 -0.219 -0.374 -0.586 -0.55 -0.316 +HG04173 HG04173 0.481 0.815 1.209 1.295 1.334 1.697 +HG04174 HG04174 0.166 0.294 0.388 0.401 0.05 1.312 +HG04176 HG04176 -1.008 0.373 0.374 0.347 0.163 0.343 +HG04177 HG04177 1.16 0.691 0.717 1.137 0.703 0.692 +HG04180 HG04180 0.902 0.193 0.231 0.194 0.291 0.595 +HG04182 HG04182 0.413 -0.354 -0.293 -0.101 -0.06 0.584 +HG04183 HG04183 0.93 0.096 0.132 -0.034 -0.008 0.208 +HG04185 HG04185 0.385 0.088 -1.375 -1.342 -0.965 -0.967 +HG04186 HG04186 0.767 1.339 0.769 0.339 0.035 -0.154 +HG04188 HG04188 1.323 1.496 1.491 1.595 1.379 1.463 +HG04189 HG04189 -0.014 -1.027 -0.651 -0.756 -0.859 -0.671 +HG04191 HG04191 -1.519 -1.597 -2.094 -2.608 -2.366 -1.718 +HG04192 HG04192 0.318 -0.32 -0.534 -0.415 -0.131 0.124 +HG04194 HG04194 -0.879 -0.571 -0.351 -0.293 -0.261 -0.167 +HG04195 HG04195 1.396 1.788 2.25 1.644 1.565 1.418 +HG04198 HG04198 -0.199 -1.319 -1.395 -0.953 -1.173 -0.987 +HG04200 HG04200 0.082 -0.844 -0.057 0.484 0.743 0.946 +HG04204 HG04204 1.16 1.234 1.124 1.069 1.112 1.021 +HG04206 HG04206 1.829 1.017 0.024 0.238 0.444 0.822 +HG04209 HG04209 0.762 0.788 -0.04 0.277 0.454 0.238 +HG04210 HG04210 0.312 0.04 -0.581 -0.798 -0.56 -0.376 +HG04211 HG04211 0.919 0.815 0.257 0.251 0.118 -0.023 +HG04212 HG04212 0.593 0.957 0.435 1.08 0.63 1.381 +HG04214 HG04214 0.29 0.283 0.027 0.531 0.736 0.522 +HG04216 HG04216 0.458 0.889 0.583 0.305 0.254 0.644 +HG04217 HG04217 -0.036 -1.147 -0.549 0.523 0.502 0.708 +HG04219 HG04219 0.762 0.287 0.481 0.329 0.055 -0.243 +HG04222 HG04222 0.997 1.256 1.136 0.764 1.118 0.602 +HG04225 HG04225 0.672 0.463 -0.38 -0.451 -0.244 -0.502 +HG04227 HG04227 1.211 0.916 0.33 -0.205 -0.646 -0.241 +HG04229 HG04229 -1.042 -0.691 -0.828 -0.821 -1.311 -0.825 +HG04235 HG04235 -0.794 -0.398 -0.109 0.096 -0.166 -0.005 +HG04238 HG04238 0.492 -0.106 -0.005 -0.034 -0.176 0.073 +HG04239 HG04239 -0.766 -1.293 -2.001 -1.368 -1.417 -1.008 +HGDP00003 HGDP00003 -0.733 0.698 0.868 0.909 0.834 0.742 +HGDP00007 HGDP00007 -1.62 -1.072 -1.253 -1.127 -1.17 -1.186 +HGDP00011 HGDP00011 0.059 -0.488 -0.607 -0.834 -0.681 -0.561 +HGDP00015 HGDP00015 -0.514 0.234 0.286 -0.529 0.444 0.483 +HGDP00017 HGDP00017 1.351 1.316 1.025 1.103 1.836 2.052 +HGDP00021 HGDP00021 -0.182 -2.196 -2.498 -2.318 -2.486 -2.691 +HGDP00023 HGDP00023 0.784 1.844 1.692 1.051 0.778 0.176 +HGDP00025 HGDP00025 0.806 0.245 0.173 -0.166 -0.342 -0.387 +HGDP00031 HGDP00031 -1.053 -0.646 -0.915 -1.407 -1.251 -1.106 +HGDP00033 HGDP00033 -1.94 0.71 0.842 0.375 0.1 -0.566 +HGDP00035 HGDP00035 -1.306 -1.012 -1.66 -1.326 -1.098 -1.791 +HGDP00037 HGDP00037 0.217 1.279 1.025 0.45 0.06 -0.325 +HGDP00039 HGDP00039 1.469 1.219 1.165 0.445 0.286 0.018 +HGDP00041 HGDP00041 0.284 0.047 0.746 0.381 0.52 0.815 +HGDP00043 HGDP00043 -1.575 -1.465 -1.419 -1.526 -1.344 -1.379 +HGDP00045 HGDP00045 -1.143 -0.548 -1.296 -1.438 -1.492 -0.747 +HGDP00047 HGDP00047 -0.104 1.837 2.317 2.377 2.624 1.386 +HGDP00049 HGDP00049 0.273 -0.024 -0.415 -0.228 -0.141 -0.534 +HGDP00052 HGDP00052 -0.21 -1.102 -1.273 -1.111 -1.221 -0.838 +HGDP00054 HGDP00054 0.245 0.979 1.276 1.331 1.311 0.698 +HGDP00060 HGDP00060 0.604 0.418 1.348 1.189 0.696 0.978 +HGDP00064 HGDP00064 -0.688 -0.765 -0.837 -0.513 -0.648 -0.987 +HGDP00066 HGDP00066 1.166 0.287 0.807 0.557 0.635 0.513 +HGDP00068 HGDP00068 0.059 0.478 -0.063 -0.51 -0.57 -0.305 +HGDP00070 HGDP00070 -1.53 -0.807 -0.915 -0.518 -0.668 -0.465 +HGDP00072 HGDP00072 1.346 1.346 0.897 1.225 0.761 0.474 +HGDP00074 HGDP00074 0.801 0.654 0.589 -0.257 -0.774 -1.01 +HGDP00076 HGDP00076 -2.272 -0.2 -0.333 -0.163 -0.161 -0.064 +HGDP00078 HGDP00078 -0.783 0.594 0.475 0.981 1.143 1.102 +HGDP00080 HGDP00080 -1.272 -0.249 -0.299 -0.404 -0.266 -0.037 +HGDP00082 HGDP00082 -1.025 -1.293 -1.186 -1.35 -0.414 -0.969 +HGDP00086 HGDP00086 0.863 -0.859 -0.982 -1.161 -1.078 -1.26 +HGDP00088 HGDP00088 1.121 0.058 0.147 0.155 0.467 0.369 +HGDP00092 HGDP00092 1.525 0.04 0.176 -0.21 0.588 0.538 +HGDP00094 HGDP00094 -0.126 -1.114 -0.575 -0.031 0.259 0.479 +HGDP00096 HGDP00096 -1.424 -1.454 -1.122 -0.632 -0.256 -0.005 +HGDP00098 HGDP00098 0.458 1.533 1.325 0.728 0.414 0.641 +HGDP00099 HGDP00099 -0.182 -0.002 -0.363 -0.637 -0.791 -1.054 +HGDP00100 HGDP00100 1.739 1.649 1.436 1.559 1.404 1.214 +HGDP00102 HGDP00102 -1.845 -2.431 -1.966 -1.324 -1.434 -1.759 +HGDP00103 HGDP00103 -1.103 -1.016 -1.381 -0.948 -0.967 -0.978 +HGDP00105 HGDP00105 -1.615 -2.192 -1.899 -1.663 -1.738 -2.474 +HGDP00106 HGDP00106 -0.536 -0.941 -1.541 -0.93 -0.944 -1.118 +HGDP00109 HGDP00109 -0.373 -1.009 -0.843 -0.876 -0.819 -0.825 +HGDP00110 HGDP00110 -1.716 -1.252 -1.765 -2.108 -1.974 -1.583 +HGDP00115 HGDP00115 -0.227 -1.046 -0.636 -1.482 -1.437 -1.338 +HGDP00118 HGDP00118 0.615 0.365 0.612 0.665 0.756 0.346 +HGDP00119 HGDP00119 -1.131 -0.421 -0.363 -0.5 -0.869 -0.706 +HGDP00120 HGDP00120 -0.019 -1.578 -1.765 -1.943 -1.926 -2.281 +HGDP00122 HGDP00122 -1.468 -1.769 -2.335 -2.761 -2.71 -2.753 +HGDP00127 HGDP00127 -1.176 -1.252 -1.462 -2.054 -1.715 -1.596 +HGDP00129 HGDP00129 -2.468 -1.368 -0.942 -0.896 -0.826 -0.827 +HGDP00131 HGDP00131 -0.609 0.444 0.307 0.153 0.188 0.236 +HGDP00133 HGDP00133 -0.261 -0.911 0.141 0.163 0.681 0.048 +HGDP00134 HGDP00134 0.509 -0.41 -0.319 -1.326 -1.321 -1.931 +HGDP00135 HGDP00135 -1.637 -1.218 -1.762 -1.072 -0.922 -0.66 +HGDP00136 HGDP00136 1.98 1.702 1.031 0.704 0.894 1.337 +HGDP00137 HGDP00137 0.245 0.178 -0.368 0.153 0.188 0.165 +HGDP00139 HGDP00139 0.149 0.964 0.987 0.849 0.241 0.25 +HGDP00140 HGDP00140 -0.098 0.455 0.118 0.005 0.133 0.346 +HGDP00141 HGDP00141 1.233 0.579 0.464 0.544 0.105 -0.323 +HGDP00143 HGDP00143 -0.238 0.421 0.184 -0.096 0.284 -0.119 +HGDP00144 HGDP00144 -0.615 -0.155 -0.546 -0.474 -0.716 -1.035 +HGDP00145 HGDP00145 -2.3 -1.597 -1.643 -1.658 -1.253 -1.228 +HGDP00146 HGDP00146 -0.199 -0.672 -0.418 -0.699 -0.52 -1.031 +HGDP00148 HGDP00148 -1.255 -1.245 -1.302 -1.515 -0.497 -0.614 +HGDP00149 HGDP00149 -0.828 -1.136 -0.301 -0.627 -0.568 -1.09 +HGDP00151 HGDP00151 -0.721 -0.002 0.28 0.958 1.331 1.212 +HGDP00153 HGDP00153 -2.423 -1.346 -1.506 -1.215 -1.052 -0.738 +HGDP00154 HGDP00154 -0.856 -0.919 -0.56 -0.72 -1.236 -1.182 +HGDP00155 HGDP00155 0.486 0.919 0.472 0.764 1.203 0.625 +HGDP00158 HGDP00158 -0.339 -0.773 -0.56 -0.92 -0.015 -0.3 +HGDP00161 HGDP00161 -0.345 0.073 0.848 0.891 0.55 0.188 +HGDP00167 HGDP00167 0.34 0.062 -0.194 -0.179 0 -0.273 +HGDP00169 HGDP00169 0.514 0.324 0.138 -0.07 0.065 -0.254 +HGDP00171 HGDP00171 0.082 1.077 1.293 0.904 0.542 0.165 +HGDP00175 HGDP00175 -2.715 -1.162 -1.593 -1.357 -1.71 -1.951 +HGDP00177 HGDP00177 0.402 0.646 0.08 -0.005 0.156 0.387 +HGDP00179 HGDP00179 -0.317 0.646 0.9 0.94 1.022 1.195 +HGDP00181 HGDP00181 -0.851 -0.02 -0.345 -0.396 -0.151 -0.483 +HGDP00183 HGDP00183 1.857 0.755 0.609 0.709 0.909 0.65 +HGDP00187 HGDP00187 -1.474 -1.185 -1.497 -1.412 -1.183 -1.535 +HGDP00189 HGDP00189 -1.036 -0.136 -0.517 -0.855 -0.432 -0.312 +HGDP00191 HGDP00191 0.43 0.111 0.216 -0.023 0.158 0.183 +HGDP00192 HGDP00192 0.059 0.129 0.484 0.316 -0.035 -0.543 +HGDP00197 HGDP00197 0.705 -0.106 -1.026 -1.031 -0.952 -0.213 +HGDP00199 HGDP00199 0.885 0.084 0.216 0.062 -0.005 0.469 +HGDP00201 HGDP00201 -0.384 0.354 -0.098 -0.047 -0.153 -0.273 +HGDP00205 HGDP00205 0.031 0.189 -0.325 -0.588 -0.801 -0.108 +HGDP00206 HGDP00206 -0.311 0.025 -0.072 0.761 0.778 0.591 +HGDP00210 HGDP00210 2.261 1.02 0.365 0.505 0.552 0.786 +HGDP00214 HGDP00214 0.278 0.354 0.568 -0.176 -0.234 0.069 +HGDP00222 HGDP00222 0.104 0.114 -0.031 -0.29 -0.078 -0.266 +HGDP00224 HGDP00224 -1.249 -0.949 -0.811 -1.062 -0.744 -0.117 +HGDP00228 HGDP00228 -0.266 -1.114 -1.75 -1.896 -1.565 -1.681 +HGDP00230 HGDP00230 0.357 1.099 1.409 1.546 1.18 0.744 +HGDP00234 HGDP00234 0.278 0.672 0.621 0.774 0.407 0.627 +HGDP00237 HGDP00237 -0.536 -0.395 -0.537 -1.513 -1.113 -0.689 +HGDP00239 HGDP00239 -0.626 0.698 -0.342 -0.63 -0.641 -0.912 +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 +HGDP00251 HGDP00251 -0.957 -1.507 -1.136 -1.021 -0.781 -0.351 +HGDP00254 HGDP00254 -0.092 -1.065 -0.744 -1.041 -0.713 -0.405 +HGDP00258 HGDP00258 -1.558 -1.076 -0.752 -0.244 -0.204 -0.357 +HGDP00259 HGDP00259 0.172 0.365 -0.246 -0.536 -0.565 -0.412 +HGDP00262 HGDP00262 0.436 0.773 1.06 1.062 1.095 0.646 +HGDP00264 HGDP00264 -0.272 -0.342 0.47 0.228 0.497 -0.142 +HGDP00277 HGDP00277 0.863 0.609 0.72 1.37 1.391 1.021 +HGDP00281 HGDP00281 -0.205 -1.353 -0.944 -1.08 -0.811 -0.545 +HGDP00285 HGDP00285 -1.165 -2.364 -2.484 -2.443 -2.479 -2.38 +HGDP00290 HGDP00290 -0.48 -0.032 0.051 0.357 0.188 0.13 +HGDP00298 HGDP00298 0.784 0.83 1.491 1.455 1.527 1.289 +HGDP00302 HGDP00302 0.638 -0.039 0.216 -0.28 -0.028 -0.3 +HGDP00304 HGDP00304 -0.227 0.058 -0.456 -0.435 -0.377 -0.472 +HGDP00307 HGDP00307 -2.3 -0.616 -1.035 -1.041 -1.281 -1.722 +HGDP00309 HGDP00309 -0.598 -0.634 -0.703 0.062 0.173 -0.344 +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 +HGDP00315 HGDP00315 0.503 1.312 0.688 0.761 0.392 0.298 +HGDP00321 HGDP00321 -0.255 -0.174 -1.291 -1.725 -1.931 -1.871 +HGDP00323 HGDP00323 -0.109 0.099 -0.022 0.937 1.339 1.205 +HGDP00330 HGDP00330 0.571 1.047 0.406 -0.233 -0.226 -0.367 +HGDP00333 HGDP00333 -0.603 -0.515 -0.616 0.147 0.346 0.314 +HGDP00346 HGDP00346 1.419 1.189 0.752 0.774 0.665 0.799 +HGDP00351 HGDP00351 0.38 0.762 1.712 1.678 1.464 2.093 +HGDP00356 HGDP00356 -0.115 0.904 1.13 1.025 0.984 1.347 +HGDP00364 HGDP00364 -0.081 -0.631 -2.164 -1.544 -1.844 -2.041 +HGDP00371 HGDP00371 1.542 0.515 0.644 0.738 0.663 0.101 +HGDP00372 HGDP00372 -0.929 -0.616 -0.482 -0.49 -0.5 -0.176 +HGDP00376 HGDP00376 2.138 1.264 1.072 0.658 0.839 0.547 +HGDP00382 HGDP00382 -0.115 0.245 -0.141 0.391 0.362 0.403 +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 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1.116 0.94 0.972 +HG02396 HG02396 0.296 0.037 -1.297 -0.908 -0.875 -0.961 +HG02397 HG02397 -0.744 -1.412 -0.413 -0.71 -1.039 -0.427 +HG02398 HG02398 0.399 -0.287 -0.046 -0.602 -0.818 -1.003 +HG02399 HG02399 -1.487 -0.645 -0.393 -0.307 -0.542 -0.579 +HG02401 HG02401 2.408 1.64 1.119 1.246 1.044 0.507 +HG02402 HG02402 -0.821 0.071 0.545 0.387 0.635 0.477 +HG02406 HG02406 -0.414 0.106 0.839 1.69 1.763 1.398 +HG02407 HG02407 -1.474 1.619 0.952 1.084 1.066 0.614 +HG02408 HG02408 -0.931 0.651 -0.249 -0.199 -0.304 -0.175 +HG02409 HG02409 -0.485 -0.316 -0.681 -0.194 -0.2 0.025 +HG02410 HG02410 -0.485 0.178 -0.814 -1.359 -1.291 -1.335 +HG02512 HG02512 0.387 0.042 -0.594 -0.57 -0.425 -0.047 +HG02513 HG02513 -1.687 -2.559 -1.67 -2.059 -1.76 -1.754 +HG02521 HG02521 0.044 -0.726 -0.173 0.12 -0.247 -0.635 +HG02522 HG02522 1.53 1.712 1.288 1.654 0.994 0.877 +HG02524 HG02524 -0.944 -1.097 -1.747 -2.203 -2.207 -1.95 +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 +HGDP00945 HGDP00945 0.496 -0.137 -0.54 -0.049 -0.119 -0.451 +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 +HGDP00959 HGDP00959 -2.275 -1.808 -1.176 -1.641 -1.527 -0.883 +HGDP00960 HGDP00960 -1.396 -0.056 -0.961 -0.37 -0.1 -0.16 +HGDP00961 HGDP00961 -0.033 -0.721 -0.826 -0.525 -0.623 -0.548 +HGDP00962 HGDP00962 -0.569 -1.459 -0.026 -0.303 0.181 -0.099 +HGDP00963 HGDP00963 0.845 -0.056 -0.272 -0.498 -0.442 -0.097 +HGDP00964 HGDP00964 -0.072 -0.858 -1.094 -0.534 -0.444 -0.581 +HGDP00965 HGDP00965 -0.382 -1.617 -0.775 -0.674 -0.121 -0.236 +HGDP00966 HGDP00966 -0.847 -1.877 -1.634 -1.562 -1.417 -1.026 +HGDP00967 HGDP00967 -1.403 -0.802 -0.258 0.549 0.716 1.2 +HGDP00968 HGDP00968 0.503 -1.229 -0.396 -0.819 -0.311 -0.301 +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 +HGDP00972 HGDP00972 0.199 0.289 0.313 0.45 0.309 0.04 +HGDP00973 HGDP00973 -0.298 -1.058 -1.391 -0.168 -0.192 -0.446 +HGDP00974 HGDP00974 -0.705 -0.201 -1.199 -1.965 -1.845 -1.385 +HGDP00975 HGDP00975 -0.647 1.094 0.178 0.288 0.269 0.012 +HGDP00976 HGDP00976 -0.602 -1.101 -1.959 -1.955 -1.879 -1.515 +HGDP00977 HGDP00977 0.509 1.141 2.382 1.825 1.87 1.342 +HGDP01021 HGDP01021 -0.912 -0.93 -1.693 -2.259 -2.257 -1.978 +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 +HGDP01186 HGDP01186 -0.007 0.314 -0.017 0.544 0.44 0.662 +HGDP01187 HGDP01187 -0.925 -0.248 -0.902 -1.026 -0.485 -0.805 +HGDP01189 HGDP01189 0.064 -0.095 0.042 -0.049 0.364 0.397 +HGDP01190 HGDP01190 -0.356 -0.231 -0.498 -0.493 -0.732 -0.59 +HGDP01192 HGDP01192 -2.087 0.267 1.025 0.308 0.157 -0.194 +HGDP01193 HGDP01193 -1.383 -0.645 0.18 -0.006 0.217 -0.147 +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 +HGDP01196 HGDP01196 2.072 0.907 0.853 0.462 0.362 0.264 +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 +HGDP01222 HGDP01222 0.328 0.178 -0.17 -0.189 -0.131 -0.088 +HGDP01224 HGDP01224 -1.383 -1.744 -0.978 -1.6 -1.607 -1.789 +HGDP01225 HGDP01225 0.619 1.09 1.472 1.606 1.732 1.398 +HGDP01226 HGDP01226 -0.414 -0.585 -0.162 0.37 0.562 0.625 +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 +HGDP01230 HGDP01230 1.116 2.049 2.441 2.274 2.015 2.109 +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 +HGDP01247 HGDP01247 -1.771 -1.037 -0.145 -1.193 -1.117 -1.005 +HGDP01248 HGDP01248 -0.957 -0.393 0.296 -0.257 -0.219 -0.018 +HGDP01249 HGDP01249 -0.305 0.928 1.542 1.866 2.181 1.967 +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 +HGDP01288 HGDP01288 -1.474 0.843 0.884 0.455 0.193 -0.062 +HGDP01289 HGDP01289 1.982 2.241 1.554 1.844 2.084 1.939 +HGDP01290 HGDP01290 -1.487 -1.408 -0.588 -0.529 -0.804 -1.05 +HGDP01291 HGDP01291 0.399 -0.483 -0.142 -0.24 -0.009 -0.605 +HGDP01292 HGDP01292 0.477 0.071 0.579 0.329 0.198 0.038 +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 +HGDP01301 HGDP01301 -1.525 0.157 -0.297 -0.363 -0.406 -0.483 +HGDP01302 HGDP01302 1.032 -0.022 -0.495 0.182 0.057 -0.147 +HGDP01304 HGDP01304 0.574 0.127 0.511 1.179 0.778 0.529 +HGDP01305 HGDP01305 1.078 0.749 0.98 1.2 0.742 0.812 +HGDP01309 HGDP01309 -0.13 -1.207 -1.241 -1.4 -1.208 -0.611 +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 +HGDP01321 HGDP01321 0.587 2.079 1.203 1.311 1.701 1.263 +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 +HGDP01328 HGDP01328 1.465 0.689 0.24 1.215 1.256 1.087 +HGDP01329 HGDP01329 -0.524 -0.969 -1.317 -1.214 -0.858 -1.394 +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 +HGDP01334 HGDP01334 -0.602 -1.045 -0.958 -0.286 0.043 0.012 +HGDP01336 HGDP01336 0.154 0.425 0.257 0.534 0.062 0.405 +HGDP01337 HGDP01337 -0.602 -0.939 -1.269 -1.593 -1.738 -1.578 +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 +HGDP01341 HGDP01341 -0.143 0.574 -0.464 -0.414 -0.906 -0.716 +HGDP01346 HGDP01346 -0.485 -0.845 -0.69 -0.332 -0.399 -0.701 +HGDP01347 HGDP01347 -1.383 -1.361 -2.19 -1.682 -1.439 -1.12 +HGDP01348 HGDP01348 2.77 1.252 0.712 1.174 1.246 0.768 +HGDP01349 HGDP01349 1.091 0.11 -1.255 -1.284 -1.16 -1.061 +HGDP01351 HGDP01351 -1.474 -0.785 0.234 -0.028 -0.021 -0.314 +HGDP01352 HGDP01352 -0.027 -0.636 -0.3 -0.58 -0.532 -0.859 +HGDP01353 HGDP01353 -0.492 -0.67 -1.535 -1.856 -1.565 -1.246 +HGDP01354 HGDP01354 0.399 0.272 0.26 -0.353 -0.418 -0.357 +HGDP01356 HGDP01356 -0.634 -0.675 -1.091 -1.328 -1.548 -1.298 +LP6005441-DNA_A09 LP6005441-DNA_A09 -0.957 -0.807 -0.21 -0.558 -0.561 -0.551 +LP6005441-DNA_B07 LP6005441-DNA_B07 -0.569 -0.666 0 0.496 0.476 0.666 +LP6005441-DNA_B09 LP6005441-DNA_B09 -0.841 -0.415 -0.436 -0.508 -0.697 -0.751 +LP6005441-DNA_C05 LP6005441-DNA_C05 -0.085 0.029 -0.195 0.064 -0.014 -0.22 +LP6005441-DNA_C06 LP6005441-DNA_C06 0.535 1.21 1.752 1.147 0.906 0.87 +LP6005441-DNA_C08 LP6005441-DNA_C08 -0.246 0.447 0.582 0.366 0.378 -0.207 +LP6005441-DNA_D04 LP6005441-DNA_D04 0.251 0.894 1.277 1.029 0.966 1.029 +LP6005441-DNA_D05 LP6005441-DNA_D05 -0.059 0.442 1.104 1.359 1.265 1.329 +LP6005441-DNA_D06 LP6005441-DNA_D06 0.535 1.798 1.774 1.724 1.551 1.776 +LP6005441-DNA_D08 LP6005441-DNA_D08 -0.569 -0.534 -0.207 -0.322 -0.264 -0.296 +LP6005441-DNA_D12 LP6005441-DNA_D12 1.052 1.21 0.562 0.337 0.122 -0.183 +LP6005441-DNA_E08 LP6005441-DNA_E08 0.632 0.169 -0.308 0.136 0.369 0.373 +LP6005441-DNA_E09 LP6005441-DNA_E09 -0.033 -0.223 -0.625 -0.218 -0.238 0.242 +LP6005441-DNA_F04 LP6005441-DNA_F04 0.07 -1.399 -1.518 -1.171 -1.5 -1.722 +LP6005441-DNA_F08 LP6005441-DNA_F08 -0.363 0.285 -0.376 0.689 1.056 0.909 +LP6005441-DNA_F09 LP6005441-DNA_F09 -0.485 -0.883 -0.713 -0.037 -0.249 -0.118 +LP6005441-DNA_F12 LP6005441-DNA_F12 -1.067 -0.112 -0.602 -1.321 -1.469 -1.011 +LP6005441-DNA_G03 LP6005441-DNA_G03 -0.511 -0.871 -0.184 -1.35 -1.282 -1.372 +LP6005441-DNA_G05 LP6005441-DNA_G05 -0.628 -1.561 -0.475 -0.558 -0.592 -0.609 +LP6005441-DNA_H03 LP6005441-DNA_H03 -1.952 -0.346 -0.277 -0.201 -0.145 0.032 +LP6005441-DNA_H05 LP6005441-DNA_H05 0.051 0.007 0.421 0.329 0.718 0.427 +LP6005442-DNA_D01 LP6005442-DNA_D01 -1.189 0.783 1.774 1.883 2.003 1.685 +LP6005442-DNA_F01 LP6005442-DNA_F01 -1.351 -2.375 -1.45 -1.473 -1.417 -1.268 +LP6005442-DNA_G01 LP6005442-DNA_G01 0.044 -0.453 -0.523 -0.534 -0.459 0.014 +LP6005442-DNA_H01 LP6005442-DNA_H01 0.399 1.529 2.308 2.484 2.367 2.678 +LP6005443-DNA_A02 LP6005443-DNA_A02 -0.389 0.059 0.195 -0.319 -0.494 -0.377 +LP6005443-DNA_B01 LP6005443-DNA_B01 0.535 -0.751 -0.795 -0.158 0.176 0.088 +LP6005443-DNA_C02 LP6005443-DNA_C02 -0.634 -0.879 -0.331 -0.462 -0.373 -0.197 +LP6005443-DNA_D02 LP6005443-DNA_D02 0.038 -0.299 0.291 -0.438 -0.283 -0.553 +LP6005443-DNA_E09 LP6005443-DNA_E09 -1.732 -0.807 -0.642 -0.143 -0.333 0.168 +LP6005443-DNA_F01 LP6005443-DNA_F01 -0.201 -0.091 -0.311 0.257 0.317 0.451 +LP6005443-DNA_G01 LP6005443-DNA_G01 -0.498 -0.836 -0.763 -0.689 -0.701 -0.39 +LP6005443-DNA_H01 LP6005443-DNA_H01 -1.202 -0.261 0.025 -0.225 -0.207 -0.566 +LP6005592-DNA_D03 LP6005592-DNA_D03 0.135 -0.116 0.658 0.727 0.695 0.696 +NA18525 NA18525 0.606 0.174 0.192 0.226 0.245 0.064 +NA18526 NA18526 0.535 1.248 0.474 0.004 -0.478 -0.386 +NA18528 NA18528 -0.382 -1.143 -1.803 -1.504 -2.157 -2.441 +NA18530 NA18530 -1.383 -0.96 -1.843 -1.574 -1.317 -1.741 +NA18531 NA18531 -0.524 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-0.767 -0.579 -0.243 0.074 +HG02221 HG02221 0.9 1.577 1.356 0.803 0.786 1.161 +HG02223 HG02223 0.396 1.012 0.577 0.585 0.517 0.519 +HG02224 HG02224 -0.246 -0.47 -0.376 -0.476 -0.501 -0.482 +HG02230 HG02230 -0.274 -0.233 0.047 -0.193 -0.825 -0.996 +HG02231 HG02231 1.027 0.895 1.408 1.137 0.895 0.782 +HG02232 HG02232 -1.189 -0.662 -0.613 -0.403 -0.448 -0.82 +HG02233 HG02233 -0.373 -1.18 -0.996 -0.78 -0.917 -1.26 +HG02234 HG02234 1.758 0.579 0.606 1.134 1.242 0.862 +HG02235 HG02235 0.532 1.409 1.298 0.981 1.224 0.758 +HG02236 HG02236 -0.972 0.182 -0.744 -1.09 -1.251 -1.349 +HG02238 HG02238 -0.147 -1.019 -1.025 -1.279 -1.076 -1.311 +HG02239 HG02239 0.49 -0.309 -0.677 -0.705 -0.483 -0.458 +HGDP00511 HGDP00511 1.136 0.912 0.976 0.326 0.482 1.229 +HGDP00512 HGDP00512 -0.679 -0.079 0.299 0.838 0.692 0.504 +HGDP00513 HGDP00513 -1.042 -1.437 -1.181 -1.726 -1.783 -2.137 +HGDP00514 HGDP00514 0.579 -1.543 -2.895 -2.493 -2.227 -1.762 +HGDP00515 HGDP00515 -0.26 -0.271 -0.509 -0.533 -0.995 -0.97 +HGDP00516 HGDP00516 0.787 1.135 0.918 1.094 1.001 0.966 +HGDP00517 HGDP00517 -1.349 -1.68 -1.514 -1.273 -1.608 -1.318 +HGDP00518 HGDP00518 0.065 0.309 0.788 0.092 0.647 0.71 +HGDP00519 HGDP00519 -0.793 -0.947 -0.822 -0.579 -0.615 -0.789 +HGDP00520 HGDP00520 -1.999 -1.941 -1.578 -1.984 -1.682 -1.9 +HGDP00522 HGDP00522 -1.226 -0.422 0.632 1.341 1.267 1.277 +HGDP00523 HGDP00523 -1.292 -0.988 -0.747 -0.158 -0.671 -0.818 +HGDP00524 HGDP00524 0.315 1.478 1.254 1.333 1.171 1.062 +HGDP00525 HGDP00525 1.136 0.974 0.414 -0.269 0.013 -0.223 +HGDP00527 HGDP00527 -1.547 -0.662 -1.083 -0.697 -0.729 -0.822 +HGDP00528 HGDP00528 0.773 1.303 1.156 1.39 1.262 1.616 +HGDP00529 HGDP00529 1.315 1.965 1.596 1.635 1.602 1.505 +HGDP00531 HGDP00531 -0.708 -0.971 -0.689 -0.826 -0.529 -0.356 +HGDP00534 HGDP00534 -0.91 -0.312 -0.194 -0.199 -0.899 -1.255 +HGDP00535 HGDP00535 0.004 0.573 0.565 0.105 0.548 1.186 +HGDP00536 HGDP00536 0.702 1.176 0.669 0.841 0.581 0.328 +HGDP00537 HGDP00537 -1.787 -0.525 -0.593 -0.479 -0.488 -0.424 +HGDP00538 HGDP00538 -0.09 -0.494 -0.324 -0.188 -0.081 -0.066 +HGDP00539 HGDP00539 -0.17 1.406 1.804 2.349 2.164 2.082 +HGDP00666 HGDP00666 1.046 0.501 0.423 0.526 1.131 1.2 +HGDP00667 HGDP00667 -2.235 -0.652 -0.854 -0.619 -0.841 -0.917 +HGDP00668 HGDP00668 0.782 1.519 0.886 0.892 0.819 0.809 +HGDP00669 HGDP00669 0.216 0.645 -0.014 0.17 0.155 -0.153 +HGDP00670 HGDP00670 -0.302 0.243 0.061 -0.29 -0.23 -0.296 +HGDP00671 HGDP00671 -0.623 -0.37 -0.784 -0.032 0.12 0.122 +HGDP00672 HGDP00672 -1.047 -0.538 -0.718 -0.412 -0.352 -0.412 +HGDP00673 HGDP00673 -0.288 -0.196 0.548 0.682 0.677 0.879 +HGDP00674 HGDP00674 0.975 0.243 0.139 -0.024 -0.124 -0.218 +HGDP00794 HGDP00794 -1.179 -0.844 -0.836 -1.257 -1.135 -1.105 +HGDP00797 HGDP00797 1.197 0.967 0.681 0.17 0.251 0.243 +HGDP00799 HGDP00799 1.857 2.356 1.816 1.896 1.926 1.763 +HGDP00800 HGDP00800 1.466 0.682 1.515 1.317 1.837 1.686 +HGDP00802 HGDP00802 0.259 -0.442 0.142 0.466 0.495 0.478 +HGDP00803 HGDP00803 -0.552 -1.866 -2.189 -1.941 -1.862 -1.886 +HGDP00804 HGDP00804 -2.004 -1.715 -1.416 -0.465 -0.228 -0.201 +HGDP00805 HGDP00805 0.358 1.001 0.591 0.515 1.006 0.171 +HGDP00807 HGDP00807 -0.934 -0.895 -0.388 -0.517 -0.81 -0.854 +HGDP00808 HGDP00808 0.396 0.384 0.275 0.224 0.049 -0.308 +HGDP00810 HGDP00810 -0.59 -0.408 -0.228 0.011 0.089 0.202 +HGDP00879 HGDP00879 -0.613 0.192 -0.382 -0.371 -0.02 0.444 +HGDP00880 HGDP00880 -0.458 -1.625 -1.618 -1.082 -0.932 -1.013 +HGDP00881 HGDP00881 -0.345 0.055 0.629 0.496 0.5 0.867 +HGDP00882 HGDP00882 1.098 1.231 1.327 0.967 0.821 1 +HGDP00883 HGDP00883 0.127 -0.624 -0.049 0.243 0.426 0.813 +HGDP00884 HGDP00884 0.839 -0.079 0.684 1.113 0.799 0.76 +HGDP00885 HGDP00885 -3.041 -1.759 -1.169 -1.074 -0.808 -0.728 +HGDP00886 HGDP00886 -1.608 -1.255 -1.395 -1.618 -1.938 -2.171 +HGDP00888 HGDP00888 1.051 1.937 1.634 1.718 1.784 1.99 +HGDP00889 HGDP00889 1.136 1.162 0.498 0.752 0.697 1.101 +HGDP00890 HGDP00890 -0.628 0.514 0.979 0.386 1.042 1.077 +HGDP00891 HGDP00891 0.047 0.237 -0.176 0.194 0.452 0.514 +HGDP00892 HGDP00892 1.296 0.593 0.658 0.671 0.629 0.765 +HGDP00893 HGDP00893 -1.669 -0.226 -0.24 -0.261 -0.172 -0.032 +HGDP00894 HGDP00894 -0.184 -0.466 -0.657 -0.514 -0.007 -0.153 +HGDP00895 HGDP00895 0.683 0.799 0.962 1.263 1.447 1.425 +HGDP00896 HGDP00896 0.306 0.641 0.553 0.628 0.378 0.275 +HGDP00897 HGDP00897 -1.203 -0.463 0.192 0.453 -0.494 -0.511 +HGDP00898 HGDP00898 -1.424 -0.888 -0.194 -0.045 -0.562 -0.542 +HGDP00899 HGDP00899 1.503 1.135 0.968 0.703 0.733 1.002 +HGDP00900 HGDP00900 -1.674 -1.499 -0.275 0.095 -0.554 -0.697 +HGDP00901 HGDP00901 -1.735 -1.653 -1.508 -1.009 -0.843 -1.071 +HGDP00902 HGDP00902 -0.552 -0.333 0.568 0.749 0.766 0.971 +HGDP01062 HGDP01062 -0.203 -0.072 0.203 -0.18 -0.146 -0.378 +HGDP01063 HGDP01063 0.155 0.189 0.93 0.806 0.581 0.707 +HGDP01064 HGDP01064 -0.915 -0.288 0.003 0.738 0.555 0.069 +HGDP01065 HGDP01065 1.72 1.639 2.048 2.165 2.587 2.193 +HGDP01066 HGDP01066 1.362 0.744 0.304 0.499 0.631 0.437 +HGDP01067 HGDP01067 0.207 0.144 0.313 0.644 0.642 0.263 +HGDP01068 HGDP01068 0.905 0.795 0.322 -0.091 -0.628 -0.535 +HGDP01069 HGDP01069 0.273 -0.425 -0.17 -0.048 -0.063 -0.209 +HGDP01070 HGDP01070 -0.199 -0.072 0.171 0.135 0.358 0.649 +HGDP01071 HGDP01071 0.235 0.631 0.183 0.698 0.36 0.548 +HGDP01072 HGDP01072 -0.887 -1.824 -1.424 -1.166 -0.863 -0.941 +HGDP01073 HGDP01073 0.15 -1.156 -0.961 -0.98 -0.572 -0.528 +HGDP01074 HGDP01074 0.155 0.079 -0.17 -0.277 -0.836 -1.282 +HGDP01075 HGDP01075 0.773 0.795 1.156 1.285 0.895 0.794 +HGDP01077 HGDP01077 0.33 1.08 0.53 0.394 0.102 0.202 +HGDP01149 HGDP01149 1.023 0.696 1.532 1.514 1.199 1.418 +HGDP01151 HGDP01151 2.338 1.344 1.014 1.207 1.417 1.227 +HGDP01152 HGDP01152 -0.425 -0.161 0.249 0.114 -0.017 -0.395 +HGDP01155 HGDP01155 -0.75 -0.525 0.003 -0.414 -0.215 -0.619 +HGDP01156 HGDP01156 1.376 0.97 0.342 0.466 0.345 0.395 +HGDP01161 HGDP01161 -2.042 -2.092 -1.821 -1.707 -1.788 -1.475 +HGDP01162 HGDP01162 0.848 1.135 1.422 1.048 0.991 1.398 +HGDP01164 HGDP01164 1.06 1.183 -0.22 0.313 0.18 0.028 +HGDP01166 HGDP01166 -0.114 -0.124 -0.981 -0.912 -1.743 -1.509 +HGDP01167 HGDP01167 0.301 0.559 0.496 -0.01 0.016 0.202 +HGDP01169 HGDP01169 0.711 0.394 0.287 -0.088 -0.157 -0.429 +HGDP01171 HGDP01171 -0.566 -1.053 -0.596 -0.597 -0.511 -0.632 +HGDP01173 HGDP01173 -0.316 -0.076 0.024 -0.374 0.163 0.652 +HGDP01174 HGDP01174 0.9 0.545 1.141 1.169 1.351 1.563 +HGDP01177 HGDP01177 1.126 0.761 0.084 0.06 -0.301 0.42 +HGDP01357 HGDP01357 0.513 0.25 -0.205 0.167 0.218 -0.011 +HGDP01358 HGDP01358 -0.17 0.357 0.174 -0.5 -0.656 -0.922 +HGDP01359 HGDP01359 -0.991 -0.271 0.27 0.087 -0.017 0.313 +HGDP01360 HGDP01360 0.117 0.754 0.669 0.978 0.87 0.666 +HGDP01361 HGDP01361 -0.236 -0.792 -1.167 -0.727 -0.772 -0.796 +HGDP01362 HGDP01362 -1.377 -2.154 -2.305 -2.13 -2.095 -1.675 +HGDP01363 HGDP01363 0.891 0.449 0.241 -0.266 -0.197 -0.554 +HGDP01366 HGDP01366 -0.519 -0.689 -0.906 -0.762 -0.559 -0.914 +HGDP01367 HGDP01367 1.668 0.909 0.299 0.359 -0.045 0.316 +HGDP01369 HGDP01369 2.168 1.529 1.506 1.856 1.609 1.746 +HGDP01370 HGDP01370 -0.321 -0.847 -1.042 -0.49 -0.504 -0.54 +HGDP01372 HGDP01372 0.914 -0.244 -0.732 -0.964 -1.089 -1.004 +HGDP01373 HGDP01373 1.093 1.656 0.762 0.348 -0.225 0.192 +HGDP01374 HGDP01374 2.031 1.3 0.915 1.258 0.93 1.19 +HGDP01375 HGDP01375 1.145 1.149 1.243 0.822 0.981 1.154 +HGDP01376 HGDP01376 1.192 0.703 0.69 0.897 0.677 0.591 +HGDP01377 HGDP01377 -2.353 -1.591 -1.389 -0.845 -1.107 -0.803 +HGDP01378 HGDP01378 -0.382 -1.413 -0.949 -0.04 0.15 0.519 +HGDP01379 HGDP01379 -1.189 -0.83 -0.929 -0.912 -0.825 -0.738 +HGDP01380 HGDP01380 -0.387 0.315 0.015 0.105 -0.012 -0.09 +HGDP01381 HGDP01381 0.32 1.444 1.202 1.823 1.807 1.57 +HGDP01382 HGDP01382 -0.755 0.288 0.177 -0.193 -0.486 -0.663 +HGDP01384 HGDP01384 -0.929 -0.271 0.458 0.138 -0.015 -0.006 +HGDP01385 HGDP01385 -1.415 -1.941 -1.876 -1.804 -1.102 -1.154 +HGDP01386 HGDP01386 0.103 0.237 -0.463 -0.051 -0.684 -0.559 +HGDP01387 HGDP01387 -1.156 -1.111 -1.028 -1.54 -1.51 -1.888 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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 6df3c145..0a5dc2f3 100644 Binary files a/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04.score.gz and b/pipeline/misc/dev/test_data/output/reference/pgs_score_files/ptclump/BODY04/ref-BODY04.score.gz differ 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 a74ee002..00000000 Binary files a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/AFR/ref-AFR-pcs.eigenvec.var.gz and /dev/null differ 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 deleted file mode 100644 index 7e58c106..00000000 Binary files a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/CSA/ref-CSA-pcs.eigenvec.var.gz and /dev/null differ 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 deleted file mode 100644 index 333321db..00000000 Binary files a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EAS/ref-EAS-pcs.eigenvec.var.gz and /dev/null differ 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 11c7c802..00000000 Binary files a/pipeline/misc/dev/test_data/output/resources/data/ref/pc_score_files/EUR/ref-EUR-pcs.eigenvec.var.gz and /dev/null differ 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_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 +22 rs1139056 A G 0.323423 6626 +22 rs7285896 C T 0.1718 6624 +22 rs3764847 T C 0.191332 6622 +22 rs1079554 T C 0.880169 6626 +22 rs2231496 C T 0.692935 6624 +22 rs2231495 T C 0.357078 6626 +22 rs5992636 G T 0.0519324 6624 +22 rs1544504 C T 0.617567 6626 +22 rs4819973 C T 0.197917 6624 +22 rs5992637 C A 0.544686 6624 +22 rs11912507 A G 0.147479 6584 +22 rs5747018 C T 0.697615 6624 +22 rs1076103 A G 0.0642922 6626 +22 rs17807317 A C 0.316123 6624 +22 rs1076105 G A 0.0720339 6608 +22 rs1076106 C A 0.715515 6626 +22 rs2231489 C T 0.0854211 6626 +22 rs2240617 G A 0.138889 6624 +22 rs362129 G A 0.31567 6624 +22 rs5748952 A C 0.637036 6626 +22 rs5748954 T C 0.521588 6624 +22 rs5748955 A G 0.521588 6624 +22 rs8136533 C T 0.692424 6626 +22 rs9606655 A G 0.753848 6626 +22 rs737967 C T 0.651419 6624 +22 rs5748965 T C 0.519771 6626 +22 rs5748966 C T 0.662843 6626 +22 rs737963 T C 0.771204 6626 +22 rs5994213 G A 0.13613 6626 +22 rs737962 T C 0.766828 6626 +22 rs9606661 C T 0.108814 6626 +22 rs4819564 G A 0.575483 6624 +22 rs5747033 A G 0.076087 6624 +22 rs5748972 G A 0.0921729 6618 +22 rs4819979 G T 0.450634 6624 +22 rs5747035 T C 0.220042 6626 +22 rs4266110 T C 0.139946 6624 +22 rs2058119 G A 0.520682 6624 +22 rs1006015 G A 0.688708 6624 +22 rs5748979 A G 0.161787 6626 +22 rs5747037 G A 0.336251 6626 +22 rs12483926 A G 0.675219 6626 +22 rs8141904 T G 0.19348 6626 +22 rs9605246 T C 0.153034 6626 +22 rs9606669 T C 0.0748792 6624 +22 rs13056269 T C 0.0170058 6586 +22 rs17807605 C T 0.111228 6626 +22 rs5994231 T C 0.314519 6626 +22 rs16982255 T C 0.0911836 6624 +22 rs5749001 T C 0.0606884 6624 +22 rs5749002 G A 0.12451 6626 +22 rs5749003 G A 0.0673309 6624 +22 rs5994238 G A 0.0893559 6614 +22 rs5749006 G A 0.128735 6626 +22 rs4819993 C T 0.337813 6622 +22 rs5749011 T C 0.268569 6624 +22 rs11089423 G A 0.118775 6626 +22 rs11704699 T G 0.121189 6626 +22 rs8140080 G A 0.283967 6624 +22 rs4819994 T C 0.325785 6624 +22 rs4541325 C T 0.423883 6582 +22 rs5747058 G A 0.450015 6622 +22 rs5749016 C T 0.422125 6626 +22 rs12160031 G A 0.145575 6622 +22 rs4239866 T C 0.701781 6626 +22 rs5749028 A G 0.241322 6626 +22 rs9605252 C T 0.152627 6624 +22 rs4819996 A G 0.709931 6626 +22 rs9606682 T C 0.657958 6622 +22 rs9617982 G A 0.140399 6624 +22 rs5994253 G A 0.217069 6620 +22 rs5994254 G A 0.0715364 6626 +22 rs9605254 A G 0.252037 6626 +22 rs5994255 C A 0.186991 6626 +22 rs5994256 T C 0.316425 6624 +22 rs9605258 T G 0.422276 6626 +22 rs9605259 T C 0.308726 6624 +22 rs9306256 C T 0.240036 6624 +22 rs9619055 C T 0.323423 6626 +22 rs9306258 G A 0.210057 6622 +22 rs5994258 A G 0.369949 5544 +22 rs5994260 A G 0.20329 6626 +22 rs9617984 C T 0.343854 6622 +22 rs17808489 G A 0.0971929 6626 +22 rs5992665 A G 0.352852 6626 +22 rs5994271 G A 0.651918 6622 +22 rs4819575 C T 0.0610386 6586 +22 rs361626 G T 0.194444 6624 +22 rs4820001 A G 0.60323 6626 +22 rs2401081 T C 0.228714 6624 +22 rs2040692 C T 0.559499 6622 +22 rs7288834 G T 0.500151 6624 +22 rs5749050 G A 0.0863527 6624 +22 rs9606695 G A 0.0389493 6624 +22 rs5747072 A C 0.433082 6620 +22 rs4607032 G T 0.305463 6626 +22 rs737915 G A 0.664199 6620 +22 rs5747075 T C 0.637613 6620 +22 rs5749060 T C 0.607273 6600 +22 rs6518680 G A 0.299125 6626 +22 rs5992674 T C 0.1968 6626 +22 rs5992034 G T 0.174313 6626 +22 rs9618000 A G 0.283062 6624 +22 rs5747079 T C 0.274676 6626 +22 rs5747080 C T 0.0532911 6624 +22 rs5746359 C T 0.145337 6626 +22 rs9604737 A G 0.268639 6626 +22 rs5992041 C T 0.336303 6622 +22 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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 +22 rs2793064 G A 0.425445 6626 +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_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 55100d1e..96425425 100644 --- a/pipeline/misc/dev/test_setup.Rmd +++ b/pipeline/misc/dev/test_setup.Rmd @@ -7,16 +7,129 @@ 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 -- 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 +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 = 0, + sd = 1, + 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 = 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 = ' ') + +# 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 +tar -czvf test_data.tar.gz test_data +``` *** -# Create test data +# Create smaller test data + +This is for rapid testing of the pipeline. + +*** ## Reference data @@ -33,7 +146,6 @@ dir.create('misc/dev/test_data/ref') saveRDS(ref_rds[ref_rds$SNP %in% test_snps,], 'misc/dev/test_data/ref/ref.chr22.rds') system('cp ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.pop.txt misc/dev/test_data/ref/') -system('cp ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/ref.keep.list misc/dev/test_data/ref/') system('cp -r ~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/keep_files misc/dev/test_data/ref/') pops<-list.files('~/oliverpainfel/Software/MyGit/GenoPred/pipeline/resources/data/ref/freq_files') @@ -44,6 +156,7 @@ for(i in pops){ fwrite(freq_i, paste0('misc/dev/test_data/ref/freq_files/',i,'/ref.',i,'.chr22.afreq'), quote=F, sep=' ', na='NA') } ``` + ```{bash} cd ~/oliverpainfel/Software/MyGit/GenoPred/pipeline @@ -79,6 +192,7 @@ ss<-ss[ss$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 @@ -149,7 +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) @@ -164,7 +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 @@ -175,33 +290,23 @@ 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 \ -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 +/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/ -# ``` *** This output can now be used as a reference to check whether commits to the repo effects the output. - - - - - - - - - - +*** 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 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/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) diff --git a/pipeline/misc/hapnest/config.synth_1.yaml b/pipeline/misc/hapnest/config.synth_1.yaml new file mode 100644 index 00000000..13c0214d --- /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: 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 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 diff --git a/pipeline/misc/opensnp/config.yaml b/pipeline/misc/opensnp/config.yaml index 4ed349aa..27c8f361 100644 --- a/pipeline/misc/opensnp/config.yaml +++ b/pipeline/misc/opensnp/config.yaml @@ -1,7 +1,7 @@ -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'] +pgs_methods: ['ptclump','dbslmm'] testing: 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..160d241c --- /dev/null +++ b/pipeline/misc/opensnp/config_cross_pop.yaml @@ -0,0 +1,9 @@ +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_cross_pop.txt +target_list: misc/opensnp/target_list.txt +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/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/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..312d71d2 --- /dev/null +++ b/pipeline/misc/opensnp/gwas_groups.txt @@ -0,0 +1,5 @@ +name gwas label +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/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 b3b494e7..d3571dff 100644 --- a/pipeline/rules/dependencies.smk +++ b/pipeline/rules/dependencies.smk @@ -61,6 +61,31 @@ 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 ###### @@ -82,12 +107,153 @@ 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 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+') +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') + +# 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'] + +### +# 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)}") + +# 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 +### + +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' @@ -101,19 +267,227 @@ if config['resdir'] == 'NA': else: resdir=config['resdir'] -# Set refdir parameter -# If refdir is NA, set refdir to '${resdir}/data/ref' +# Set ldpred2 reference path +if config['ldpred2_ldref'] == 'NA': + ldpred2_ldref=f"{resdir}/data/ldpred2_ref" +else: + ldpred2_ldref=config['ldpred2_ldref'] + +# Check the ldpred2 ldref data is present for the required populations in the pgwas_list +if 'ldpred2' in config['pgs_methods']: + for pop in gwas_list_df['population'].unique(): + path = f"{ldpred2_ldref}/{pop}" + # Check if map.rds file exists + map_file = os.path.join(path, "map.rds") + if not os.path.exists(map_file): + print(f"File not found: {map_file}") + raise FileNotFoundError(f"Required file not found: {map_file}. LDpred2 reference data must include map.rds for all populations.") + + # Check if LD_with_blocks_chr${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 sbayesr reference path +if config['sbayesr_ldref'] == 'NA': + sbayesr_ldref=f"{resdir}/data/gctb_ref/ukbEURu_hm3_shrunk_sparse/ukbEURu_hm3_v3_50k_chr" +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 quickprs reference path +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" + + # Check if gwas_list contains invalid populations + 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, 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'] + + # 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 + 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 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_leopard" + + # Check if gwas_list contains invalid populations + 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, 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'] + + # 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 "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 +#### if config['refdir'] == 'NA': - refdir=f"{resdir}/data/ref" - ref_input=f"{refdir}/ref.pop.txt" + 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']] + \ - [os.path.join(refdir, file_name) for file_name in ['ref.pop.txt', 'ref.keep.list']] + 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 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) + + # 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): + # 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 - 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.") + valid_pgs_methods = { + "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] + + 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", "sbayesrc", "lassosum", "ldpred2", "megaprs", "quickprs" + } + + # 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 config["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 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 @@ -314,7 +688,7 @@ 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 +698,17 @@ 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 +721,7 @@ 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 +731,17 @@ 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 +759,60 @@ 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: @@ -425,10 +853,119 @@ 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 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: + "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: - directory(f"{resdir}/data/ldpred2_ref") + f"{resdir}/data/ldpred2_ref/EUR/map.rds" benchmark: f"{resdir}/data/benchmarks/download_ldpred2_ref.txt" log: @@ -436,16 +973,17 @@ 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 rule download_ldak: output: @@ -457,7 +995,7 @@ 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/; \ @@ -465,6 +1003,26 @@ 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: @@ -483,6 +1041,7 @@ 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: @@ -501,6 +1060,7 @@ 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: @@ -518,6 +1078,93 @@ rule download_ldak_highld: }} > {log} 2>&1 """ +# Download LDAK V5.2 for QuickPRS +# 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: + """ + {{ + 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 QuickPRS reference data +quickprs_ref_gdrive = { + 'EUR': '10fuqn6X23dA9WKjQd9xs7xUDiFjTwfz9', + 'EAS': '1m1OI9HpHbVcX88YvtIt80-1zonSWrZP_', + 'AFR': '11NoeBLOC-YsxrnRPa0TOPsxywZXCWbP3', + 'CSA': '10ENLyjnMNndBM8NtXy-BVmroP5m4qgtg', + 'AMR': '1bmnbWPw8MzVwFYw9F4TcGOVxxay20uOS', + 'MID': '16MuxjIi1Eb_kyf9HZgpBwRA1cbP_yE5U' +} + +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: + id=lambda w: quickprs_ref_gdrive.get(w.population) + shell: + """ + {{ + mkdir -p {resdir}/data/quickprs; \ + rm -r -f {resdir}/data/quickprs/{wildcards.population}; \ + 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', 'EAS', 'AFR']) + +# Download QuickPRS reference data that has been subset for LEOPARD +quickprs_leopard_ref_gdrive = { + 'EUR': '1basMTYv6VEIRDZ3qRdJ04hlVwNmzVpmU', + 'EAS': '1OGELjphyPbe9Qu9ZjRavhhnM9REuUhoc', + 'AFR': '1fWQ77dYKaYIcHJFLii-0Aic_9F96VnQK', + 'CSA': '1e-zCjT7gCdRobSwUvCepDkl-K8Y10ECj', + 'AMR': '12a6sxe338eX3hD6Q7FvIobIni_TxlDTY', + 'MID': '1nTqb8FTPlMFBN66dGZyteN4n1SIj1YRC' +} + +rule download_quickprs_leopard_ref: + output: + f"{resdir}/data/quickprs_leopard/{{population}}/{{population}}.subset_1.bed" + 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}}.subset_1.bed", population=['EUR', 'EAS', 'AFR']) + # Download preprocessed reference data (1KG+HGDP HapMap3) rule download_default_ref: output: @@ -531,7 +1178,7 @@ 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; \ @@ -557,6 +1204,7 @@ rule install_ggchicklet: Rscript -e 'remotes::install_github(\"hrbrmstr/ggchicklet@64c468dd0900153be1690dbfc5cfb35710da8183\")' }} > {log} 2>&1 """ + # install lassosum rule install_lassosum: input: @@ -575,6 +1223,7 @@ rule install_lassosum: Rscript -e 'remotes::install_github(\"tshmak/lassosum@v0.4.5\")' }} > {log} 2>&1 """ + # Install GenoUtils rule install_genoutils: input: @@ -590,17 +1239,10 @@ rule install_genoutils: shell: """ {{ - Rscript -e 'devtools::install_github(\"opain/GenoUtils@cd4495f554be835872e77db72b6a8500c753273e\")' + Rscript -e 'devtools::install_github(\"opain/GenoUtils@6334159ab5d95ce936896e6938a1031c38ed4f30\")' }} > {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: @@ -618,6 +1260,224 @@ rule download_pgscatalog_utils: touch {output} """ +# 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@6334159ab5d95ce936896e6938a1031c38ed4f30\")' + }} > {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/opain/X-Wing resources/software/xwing; \ + cd resources/software/xwing; \ + git reset --hard e9fcc264266e0e884323311816bfe20053fd3f7a + }} > {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 + """ + +############ + +# Install TL-PRS +rule install_tlprs: + output: + touch("resources/software/install_tlprs.done") + conda: + "../envs/analysis.yaml" + benchmark: + f"{resdir}/data/benchmarks/install_tlprs.txt" + log: + f"{resdir}/data/logs/install_tlprs.log" + shell: + """ + {{ + Rscript -e 'devtools::install_github(\"opain/TLPRS@5a5528a3f709ca7d627381a3f09ccdcb923b50f4\")' + }} > {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: + f"{resdir}/data/benchmarks/install_genoutils_bridgeprs.txt" + log: + f"{resdir}/data/logs/install_genoutils_bridgeprs.log" + shell: + """ + {{ + Rscript -e 'devtools::install_github(\"opain/GenoUtils@6334159ab5d95ce936896e6938a1031c38ed4f30\")' + }} > {log} 2>&1 + """ + +# Download BridgePRS +rule download_bridgeprs_software: + input: + rules.install_genoutils_bridgeprs.output + output: + f"{resdir}/software/bridgeprs/bridgePRS" + benchmark: + f"{resdir}/data/benchmarks/download_bridgeprs_software.txt" + log: + f"{resdir}/data/logs/download_bridgeprs_software.log" + shell: + """ + {{ + rm -r -f {resdir}/software/bridgeprs; \ + git clone https://github.com/opain/BridgePRS.git {resdir}/software/bridgeprs; \ + cd {resdir}/software/bridgeprs; \ + git reset --hard aeea807c9640e28f45dac24a9b5d524a3f11f7f2 + }} > {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 ############ @@ -643,28 +1503,6 @@ 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 - output: - touch(f"{resdir}/software/get_all_resources.done") - rule get_key_resources: input: rules.download_plink.output, @@ -680,7 +1518,7 @@ 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, @@ -690,6 +1528,17 @@ 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, @@ -704,3 +1553,45 @@ 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") + diff --git a/pipeline/rules/magma.smk b/pipeline/rules/magma.smk new file mode 100644 index 00000000..cc26b9c2 --- /dev/null +++ b/pipeline/rules/magma.smk @@ -0,0 +1,261 @@ +# 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.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'], + 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 [], + f"{outdir}/reference/pgs_score_files/pgs_stratifier.done" + 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']) diff --git a/pipeline/rules/pgs_methods.smk b/pipeline/rules/pgs_methods.smk index ab7aef0a..7ec1fa9a 100644 --- a/pipeline/rules/pgs_methods.smk +++ b/pipeline/rules/pgs_methods.smk @@ -3,49 +3,36 @@ 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" + f"{outdir}/reference/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" + 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 {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 \ + --output {outdir}/reference/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) + input: expand(f"{outdir}/reference/pc_score_files/{{population}}/ref-{{population}}-pcs.EUR.scale", population=populations) ## # 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"]) - -# Remove commas in the 'n' column and convert to numeric -gwas_list_df['n'] = gwas_list_df['n'].replace({',': ''}, regex=True) - -# 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: @@ -64,7 +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], @@ -90,7 +76,8 @@ 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"{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: @@ -107,6 +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 {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 \ @@ -136,7 +124,8 @@ 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"{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: @@ -156,6 +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 {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 \ @@ -184,12 +174,13 @@ rule prep_pgs_dbslmm: rule prep_pgs_prscs_i: resources: mem_mb=2000*config['cores_prep_pgs'], - time_min=800 + time_min=2800 threads: config['cores_prep_pgs'] 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", + 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: @@ -210,11 +201,12 @@ rule prep_pgs_prscs_i: export OPENBLAS_NUM_THREADS=1; \ Rscript ../Scripts/pgs_methods/prscs.R \ --ref_plink_chr {refdir}/ref.chr \ + --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 \ --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 @@ -233,8 +225,10 @@ 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 + rules.download_gctb_software.output, + 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: @@ -248,9 +242,10 @@ rule prep_pgs_sbayesr_i: shell: "Rscript ../Scripts/pgs_methods/sbayesr.R \ --ref_plink_chr {refdir}/ref.chr \ + --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 {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} \ @@ -270,7 +265,8 @@ 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"{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: @@ -287,9 +283,10 @@ 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 {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} \ + --n_cores {threads} \ --pop_data {refdir}/ref.pop.txt \ --test {params.testing} > {log} 2>&1" @@ -303,11 +300,12 @@ rule prep_pgs_lassosum: rule prep_pgs_ldpred2_i: resources: mem_mb=30000, - time_min=800 + time_min=2800 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", + 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: @@ -318,6 +316,7 @@ 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', @@ -326,8 +325,8 @@ 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} \ + --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} \ @@ -339,7 +338,7 @@ 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 @@ -348,14 +347,15 @@ rule prep_pgs_ldpred2: rule prep_pgs_megaprs_i: resources: mem_mb=20000, - time_min=800 + 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.output, rules.download_ldak_map.output, - rules.download_ldak_bld.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/megaprs/{{gwas}}/ref-{{gwas}}.score.gz" benchmark: @@ -371,6 +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 {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 \ @@ -384,26 +385,148 @@ 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']) + ## -# Process externally created score files +# LDAK QuickPRS ## -# 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') +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] - # 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'] + # Return the full path string + return f"{quickprs_ldref}/{population}/{population}.cors.bin" + +rule prep_pgs_quickprs_i: + resources: + mem_mb=20000, + time_min=2800 + threads: config['cores_prep_pgs'] + input: + f"{outdir}/reference/gwas_sumstat/{{gwas}}/{{gwas}}-cleaned.gz", + 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, + rules.download_ldak_bld.output, + 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: + 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], + testing=config["testing"] + 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 \ + --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 \ + --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']) + +## +# SBayesRC +## + +rule prep_pgs_sbayesrc_i: + resources: + mem_mb=20000, + time_min=2800 + 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]}/ldm.info", + rules.download_gctb252_software.output, + rules.download_sbayesrc_annot.output, + rules.install_genoutils_sbayesrc.output, + rules.install_sbayesrc.output, + 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: + 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]}", + testing=config["testing"] + shell: + "export OMP_NUM_THREADS={threads}; \ + Rscript ../Scripts/pgs_methods/sbayesrc.R \ + --ref_plink_chr {refdir}/ref.chr \ + --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} \ + --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" -# Check whether gwas_list paths exist -check_list_paths(score_list_df) +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 +## # Download PGS score files for PGSC if path is NA rule download_pgs_external: @@ -439,7 +562,8 @@ rule prep_pgs_external_i: input: lambda w: score_path(w), ref_input, - rules.install_genoutils.output + rules.install_genoutils.output, + 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: @@ -458,6 +582,7 @@ 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 {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" @@ -482,6 +607,342 @@ checkpoint score_reporter: shell: "Rscript ../Scripts/pipeline_misc/score_reporter.R {params.config_file} > {log} 2>&1" +########### +# 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 \ + --ref_plink_chr {refdir}/ref.chr \ + --pop_data {refdir}/ref.pop.txt \ + --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 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)) + +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"{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: + 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/apply_leopard_weights.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) + +#### +# 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 +######### + +#### +# 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=2800 + 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", + 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: + "../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 \ + --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} \ + --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']) + +#### +# X-WING +#### + +rule prep_pgs_xwing_i: + resources: + mem_mb=2000*config['cores_prep_pgs'], + time_min=2800 + 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)), + 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: + "../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 \ + --ref_freq_chr {refdir}/freq_files \ + --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 \ + --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_two['name']) + +#### +# TL-PRS +#### + +rule prep_pgs_tlprs_i: + resources: + mem_mb=10000, + time_min=2800 + 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), + 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: + "../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"], + config_file = config["config_file"] + shell: + """ + Rscript ../Scripts/pgs_methods/tlprs.R \ + --config {params.config_file} \ + --ref_plink_chr {refdir}/ref.chr \ + --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \ + --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_two['name'], method=config["tlprs_methods"]) + +#### +# BridgePRS +#### + +rule prep_pgs_bridgeprs_i: + resources: + mem_mb=2000*config['cores_prep_pgs'], + time_min=2800 + 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)), + 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: + "../envs/bridgeprs.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 \ + --ref_pcs {outdir}/reference/pc_score_files/TRANS/ref-TRANS-pcs.profiles \ + --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_two['name']) + +############################################### + ## # Use a rule to check requested PGS methods have been run for all GWAS ## @@ -502,9 +963,54 @@ 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 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 'bridgeprs' in pgs_methods_all: + pgs_methods_input.append(rules.prep_pgs_bridgeprs.input) rule prep_pgs: input: pgs_methods_input + +########################## + +# 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_qc.smk b/pipeline/rules/target_qc.smk index eb4eec47..390199d5 100644 --- a/pipeline/rules/target_qc.smk +++ b/pipeline/rules/target_qc.smk @@ -2,27 +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"]) - #### # Format target data #### -# Check specific target paths exist -check_target_paths(df = target_list_df, chr = str(get_chr_range(config['testing'])[0])) - ## # 23andMe ## @@ -48,7 +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: @@ -127,7 +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) diff --git a/pipeline/rules/target_scoring.smk b/pipeline/rules/target_scoring.smk index 67841a4f..a27281ff 100644 --- a/pipeline/rules/target_scoring.smk +++ b/pipeline/rules/target_scoring.smk @@ -1,92 +1,116 @@ -# 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']) - +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() + + # 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'] + +#### +# Projected PCs +#### + +rule pc_projection_i: + input: + f"{outdir}/reference/target_checks/{{name}}/ancestry_reporter.done", + 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: + 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"], + 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 {params.target_keep} \ + --ref_freq_chr {refdir}/freq_files/{wildcards.population}/ref.{wildcards.population}.chr \ + --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" + +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), scaling = config["pgs_scaling"])) + 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=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.ref_pgs.output + 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: + "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, scaling = config["pgs_scaling"])) + 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']) + diff --git a/pipeline/tests/testthat/test-pipeline.R b/pipeline/tests/testthat/test-pipeline.R index bed27722..2740e67d 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) } @@ -171,10 +171,10 @@ 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 \" + "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 && @@ -219,6 +215,7 @@ 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.") } @@ -290,10 +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) }) } @@ -312,7 +309,7 @@ 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 +318,10 @@ 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 +338,23 @@ 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(paste0('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) + }) + } } #######