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

Genome Biology

Yuchang Wu¹  Zijie Zhao¹  Qiongshi Lu²  Jie Song³  Yanyao Yi³  Jason Fletcher⁴  Yupei Lin⁵  Xiaoyuan Zhong⁵  Timothy J. Hohman⁶ 

[1] Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 53703, Madison, WI, USA;Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, 53703, Madison, WI, USA;Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA;Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI, USA;Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA;La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA;Department of Sociology, University of Wisconsin-Madison, Madison, WI, USA;Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI, USA;University of Wisconsin-Madison, Madison, WI, USA;Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, TN, USA;Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA;

关键词: GWAS;    Polygenic risk score;    Model tuning;    Summary statistics;   

DOI  :  10.1186/s13059-021-02479-9

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY