Leveraging effect size distributions to improve polygenic risk scores derived from summary statistics of genome-wide association studies.

ABSTRACT: Genetic risk prediction is an important problem in human genetics, and accurate prediction can facilitate disease prevention and treatment. Calculating polygenic risk score (PRS) has become widely used due to its simplicity and effectiveness, where only summary statistics from genome-wide association studies are needed in the standard method. Recently, several methods have been proposed to improve standard PRS by utilizing external information, such as linkage disequilibrium and functional annotations. In this paper, we introduce EB-PRS, a novel method that leverages information for effect sizes across all the markers to improve prediction accuracy. Compared to most existing genetic risk prediction methods, our method does not need to tune parameters nor external information. Real data app

SUBMITTER: Song S 

PROVIDER: S-EPMC7039528 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature