A meta-analysis approach with filtering for identifying gene-level gene-environment interactions.

ABSTRACT: There is a growing recognition that gene-environment interaction (G × E) plays a pivotal role in the development and progression of complex diseases. Despite a wealth of genetic data on various complex diseases/traits generated from association and sequencing studies, detecting G × E via genome-wide analysis remains challenging due to power issues. In genome-wide G × E studies, a common strategy to improve power is to first conduct a filtering test and retain only the genetic variants that pass the filtering step for subsequent G × E analyses. Two-stage, multistage, and unified tests have been proposed to jointly consider the filtering statistics in G × E tests. However, such G × E tests based on data from a single study may still be underpowered. Meanwhile, large-scale consortia have been

SUBMITTER: Wang J 

PROVIDER: S-EPMC6013347 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature