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TransCisPredict: A Comprehensive Framework for Protein Level Prediction

This repository contains the complete analysis pipeline for the comprehensive framework to predict protein levels that incorporates both cis- and trans- variants to facilitate conducting proteome-wide association studies (PWAS) on a biobank scale. The weights that have been generated from UK Biobank are also available through Synapse ID: 69052240.

Repository Structure

TransCisPredict/
├── README.md                    # This file
├── readme_scripts.md            # Detailed analysis pipeline documentation
├── step1_data_processing/       # Raw data processing and QC
├── step2_covariate_regression/  # Covariate adjustment
├── step3_ld_block_selection/    # Genomic region selection
├── step4_cross_validation/      # CV using BayesR, SuSiE, LASSO, and Elastic Net
├── step5_cv_evaluation/         # CV performance evaluation
├── step6_whole_sample_analysis/ # Weight Estimation using the "optimal" method
├── step7_population_prediction/ # Protein expression level prediction in the target sample
├── step8_pwas_analysis/         # Proteome-wide association analyses (PWAS)
└── utilities/                   # Commonly used functions

Quick Start

a. Setup Environment: Install required R packages (see readme_scripts.md)

b. Configure Paths: Modify placeholder paths in each script's configuration section

c. Obtain Prediction Weights: Either download pre-computed weights from Synapse ID: 69052240, or generate custom weights by running steps 1-6 of the analysis pipeline

d. Predict Protein Levels: Use the obtained weights to predict protein expression levels in your target sample (see step7 scripts)

e. Perform PWAS: Conduct proteome-wide association studies using the predicted protein levels (see step8 script)

Analysis Pipeline

The complete analysis pipeline with comprehensive documentation and usage examples is available in readme_scripts.md. Each step is self-contained with clear input/output specifications.

Applications

This framework enables:

  • Weight Estimation: Generate weight estimation between protein expression level and genetic vriants from the reference sample
  • Protein Expression Prediction: Predict heritable component of protein levels using only genotype data from the target sample
  • PWAS in Target Cohort: Conduct PWAS to identify associations with complex traits

Citation

Please cite the associated manuscript when using these analysis scripts and prediction weights for research purposes:

Dong R, Lamb D, Wang GT, DeWan AT, Leal SM. Leveraging cis- and trans-variants to improve plasma protein level prediction for proteome-wide association studies (2025). Submitted.

License

This repository is intended for academic research and manuscript reproducibility. Please follow Guidance on Researcher Responsibilities for UK Biobank resource.