TY - JOUR T1 - Fine-tuning Polygenic Risk Scores with GWAS Summary Statistics JF - bioRxiv DO - 10.1101/810713 SP - 810713 AU - Zijie Zhao AU - Yanyao Yi AU - Yuchang Wu AU - Xiaoyuan Zhong AU - Yupei Lin AU - Timothy J. Hohman AU - Jason Fletcher AU - Qiongshi Lu Y1 - 2019/01/01 UR - http://biorxiv.org/content/early/2019/10/18/810713.abstract N2 - 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 Alzheimer’s 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. ER -