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.
CITATION STYLE
Zhao, Z., Yi, Y., Song, J., Wu, Y., Zhong, X., Lin, Y., … Lu, Q. (2021). PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics. Genome Biology, 22(1). https://doi.org/10.1186/s13059-021-02479-9
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