PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics
2021
Zhao, Zijie | Yi, Yanyao | Song, Jie | Wu, Yuchang | Zhong, Xiaoyuan | Lin, Yupei | Hohman, Timothy J. | Fletcher, Jason | Lu, Qiongshi
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.
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