Testing gene–environment interactions in the presence of confounders and mismeasured environmental exposures. Issue 10 (16th July 2021)

• Record Type: Journal Article
• Authors: Cheng, Chao; Spiegelman, Donna; Wang, Zuoheng; Wang, Molin
• Editors: Matise, T
• Is Part Of: G3. Volume 11:Issue 10(2021)
• Publication Date: 2021-07-16
• Subjects: computational efficiency -- confounders -- gene–environment interaction -- measurement error -- relative efficiency -- reverse test; Genetics -- Research -- Periodicals; Genomics -- Periodicals; Genetics; Genomics; Genes; Genetics -- Research; Genomics; Electronic journals; Periodical; Periodicals; Fulltext; Internet Resources; Periodicals; 572.8
• DOI: 10.1093/g3journal/jkab236
• Languages: English
• ISSNs: 2160-1836

Abstract

Abstract: Interest in investigating gene–environment (GxE) interactions has rapidly increased over the last decade. Although GxE interactions have been extremely investigated in large studies, few such effects have been identified and replicated, highlighting the need to develop statistical GxE tests with greater statistical power. The reverse test has been proposed for testing the interaction effect between continuous exposure and genetic variants in relation to a binary disease outcome, which leverages the idea of linear discriminant analysis, significantly increasing statistical power comparing to the standard logistic regression approach. However, this reverse approach did not take into consideration adjustment for confounders. Since GxE interaction studies are inherently nonexperimental, adjusting for potential confounding effects is critical for valid evaluation of GxE interactions. In this study, we extend the reverse test to allow for confounders. The proposed reverse test also allows for exposure measurement errors as typically occurs. Extensive simulation experiments demonstrated that the proposed method not only provides greater statistical power under most simulation scenarios but also provides substantive computational efficiency, which achieves a computation time that is more than sevenfold less than that of the standard logistic regression test. In an illustrative example, we applied the proposed approach to the Veterans Aging Cohort Study (VACS) to search for genetic susceptibility loci modifying the smoking-HIV status association.