Controlling for polygenic genetic confounding in epidemiologic association studies.

ABSTRACT: Epidemiologic associations estimated from observational data are often confounded by genetics due to pervasive pleiotropy among complex traits. Many studies either neglect genetic confounding altogether or rely on adjusting for polygenic scores (PGS) in regression analysis. In this study, we unveil that the commonly employed PGS approach is inadequate for removing genetic confounding due to measurement error and model misspecification. To tackle this challenge, we introduce PENGUIN, a principled framework for polygenic genetic confounding control based on variance component estimation. In addition, we present extensions of this approach that can estimate genetically unconfounded associations using GWAS summary statistics alone as input and between multiple generations of study samples. Thr

SUBMITTER: Zhao Z 

PROVIDER: S-EPMC11536117 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature