Polygenic risk scores: effect estimation and model optimization

被引：1

作者：

Zhao, Zijie ^{[1
]}

Song, Jie ^{[2
]}

Wang, Tuo ^{[1
]}

Lu, Qiongshi ^{[1
,2
,3
]}

机构：

[1] Univ Wisconsin, Dept Biostat & Med Informat, Madison, WI 53726 USA

[2] Univ Wisconsin, Dept Stat, Madison, WI 53726 USA

[3] Univ Wisconsin, Ctr Demog Hlth & Aging, Madison, WI 53726 USA

来源：

QUANTITATIVE BIOLOGY | 2021年 / 9卷 / 02期

关键词：

GWAS; polygenic risk score; summary statistics; model selection; INCREASES ACCURACY; COMPLEX TRAITS; GENETIC RISK; PREDICTION; ASSOCIATION; DISEASE;

D O I：

10.15302/J-QB-021-0238

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

Background: Polygenic risk score (PRS) derived from summary statistics of genome-wide association studies (GWAS) is a useful tool to infer an individual's genetic risk for health outcomes and has gained increasing popularity in human genetics research. PRS in its simplest form enjoys both computational efficiency and easy accessibility, yet the predictive performance of PRS remains moderate for diseases and traits. Results: We provide an overview of recent advances in statistical methods to improve PRS's performance by incorporating information from linkage disequilibrium, functional annotation, and pleiotropy. We also introduce model validation methods that fine-tune PRS using GWAS summary statistics. Conclusion: In this review, we showcase methodological advances and current limitations of PRS, and discuss several emerging issues in risk prediction research.

引用

页码：133 / 140

页数：8

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