A Unified Model for the Analysis of Gene-Environment Interaction. Issue 4 (10th January 2019) - Gauderman, W James; Kim, Andre; Conti, David V; Morrison, John; Thomas, Duncan C; Vora, Hita; Lewinger, Juan Pablo
Abstract: Gene-environment ( G × E ) interaction is important for many complex traits. In a case-control study of a disease trait, logistic regression is the standard approach used to model disease as a function of a gene ( G ), an environmental factor ( E ), G × E interaction, and adjustment covariates. We propose an alternative model with G as the outcome and show how it provides a unified framework for obtaining results from all of the common G × E tests. These include the 1–degree-of-freedom (df) test of G × E interaction, the 2-df joint test of G and G × E, the case-only and empirical Bayes tests, and several 2-step tests. In the context of this unified model, we propose a novel 3-df test and demonstrate that it provides robust power across a wide range of underlying G × E interaction models. We demonstrate the 3-df test in a genome-wide scan of G × sex interaction for childhood asthma using data from the Children's Health Study (Southern California, 1993–2001). This scan identified a strong G × sex interaction at the phosphodiesterase gene 4D locus ( PDE4D ), a known asthma-related locus, with a strong effect in males (per-allele odds ratio = 1.70; P = 3.8 × 10 −8 ) and virtually no effect in females. We describe a software program, G×EScan (University of Southern California, Los Angeles, California), which can be used to fit standard and unified models for genome-wide G × E studies.