Fine-tuning Polygenic Risk Scores with GWAS Summary Statistics

Polygenic risk scores (PRSs) have wide applications in human genetics research. Notably, most PRS models include tuning parameters which improve predictive performance when properly selected. However, existing model-tuning methods require validation data that is independent with both training and testing samples. These data rarely exist in practice, creating a significant gap between PRS methodology and applications. Here, we introduce PUMAS, a novel method to fine-tune PRS models using summary statistics from genome-wide association studies (GWASs). Through extensive simulations, external validations, and analysis of 65 GWAS traits, we demonstrate that PUMAS can perform a variety of model-tuning procedures (e.g. cross-validation) using GWAS summary statistics and can effectively benchmark and optimize PRS models under diverse genetic architecture. Applied to 211 neuroimaging traits and Alzheimer9s disease, we show that fine-tuned PRSs will improve statistical power in association analysis. We believe our method resolves a fundamental problem without a current solution and will greatly benefit genetic prediction applications.