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 Fletcher; Qiongshi Lu

doi:10.1186/s13059-021-02479-9

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

Genome Biol. 2021 Sep 6;22(1):257. doi: 10.1186/s13059-021-02479-9.

Authors

Zijie Zhao^#¹, Yanyao Yi^#², Jie Song², Yuchang Wu¹, Xiaoyuan Zhong³, Yupei Lin³, Timothy J Hohman^{4

5}, Jason Fletcher^{6

7

8}, Qiongshi Lu^{9

10

11}

Affiliations

¹ Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53703, USA.
² Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA.
³ University of Wisconsin-Madison, Madison, WI, USA.
⁴ Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Vanderbilt University School of Medicine, Nashville, TN, USA.
⁵ Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA.
⁶ La Follette School of Public Affairs, University of Wisconsin-Madison, Madison, WI, USA.
⁷ Department of Sociology, University of Wisconsin-Madison, Madison, WI, USA.
⁸ Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI, USA.
⁹ Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53703, USA. qlu@biostat.wisc.edu.
¹⁰ Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA. qlu@biostat.wisc.edu.
¹¹ Center for Demography of Health and Aging, University of Wisconsin-Madison, Madison, WI, USA. qlu@biostat.wisc.edu.

^# Contributed equally.

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.

Keywords: GWAS; Model tuning; Polygenic risk score; Summary statistics.

MeSH terms

Alzheimer Disease / diagnostic imaging
Alzheimer Disease / genetics
Computer Simulation
Genetic Predisposition to Disease*
Genome-Wide Association Study*
Humans
Linkage Disequilibrium / genetics
Models, Genetic
Multifactorial Inheritance / genetics*
Neuroimaging
Quantitative Trait, Heritable
Risk Factors
Sample Size
Software*
Statistics as Topic*

Abstract

MeSH terms

Grants and funding