PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics
Zijie Zhao, Yanyao Yi, Jie Song, Yuchang Wu, Xiaoyuan Zhong, Yupei Lin, Timothy J. Hohman, Jason M. Fletcher, Qiongshi Lu
Abstract
Polygenic risk scores (PRSs) have wide applications in human genetics research, but often include tuning parameters which are difficult to optimize in practice due to limited access to individual-level data. 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 traits, we demonstrate that PUMAS can perform various model-tuning procedures using GWAS summary statistics and effectively benchmark and optimize PRS models under diverse genetic architecture. Furthermore, we show that fine-tuned PRSs will significantly improve statistical power in downstream association analysis.