LAUSR

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 formed to borrow strength across studies and populations. In this work, motivated by existing single‐study G × E tests with filtering and the needs for meta‐analysis G × E approaches based on consortia data, we propose a meta‐analysis framework for detecting gene‐based G × E effects, and introduce meta‐analysis‐based filtering statistics in the gene‐level G × E tests. Simulations demonstrate the advantages of the proposed method—the ofGEM test. We apply the proposed tests to existing data from two breast cancer consortia to identify the genes harboring genetic variants with age‐dependent penetrance (i.e., gene–age interaction effects). We develop an R software package ofGEM for the proposed meta‐analysis tests.