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PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics.

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.

SUBMITTER: Zhao Z

PROVIDER: S-EPMC8419981 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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PUMAS: fine-tuning polygenic risk scores with GWAS summary statistics.

Zhao Zijie Z Yi Yanyao Y Song Jie J Wu Yuchang Y Zhong Xiaoyuan X Lin Yupei Y Hohman Timothy J TJ Fletcher Jason J Lu Qiongshi Q

Genome biology 20210906 1

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 ...[more]

PMID: 34488838

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