Gene-Based Association Tests Using New Polygenic Risk Scores and Incorporating Gene Expression Data.

ABSTRACT: Recently, gene-based association studies have shown that integrating genome-wide association studies (GWAS) with expression quantitative trait locus (eQTL) data can boost statistical power and that the genetic liability of traits can be captured by polygenic risk scores (PRSs). In this paper, we propose a new gene-based statistical method that leverages gene-expression measurements and new PRSs to identify genes that are associated with phenotypes of interest. We used a generalized linear model to associate phenotypes with gene expression and PRSs and used a score-test statistic to test the association between phenotypes and genes. Our simulation studies show that the newly developed method has correct type I error rates and can boost statistical power compared with other methods that use

SUBMITTER: Yan S 

PROVIDER: S-EPMC9318573 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature