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A general approach to detect gene (G)‐environment (E) additive interaction leveraging G‐E independence in case‐control studies
Author(s) -
Tchetgen Tchetgen Eric J.,
Shi Xu,
Wong Benedict H.W.,
Sofer Tamar
Publication year - 2019
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.8337
Subject(s) - covariate , statistics , econometrics , null hypothesis , categorical variable , type i and type ii errors , nonparametric statistics , independence (probability theory) , mathematics , logistic regression , interaction , confounding , absolute risk reduction , computer science , confidence interval
It is increasingly of interest in statistical genetics to test for the presence of an additive interaction between genetic (G) and environmental (E) risk factors. In case‐control studies involving a rare disease, a statistical test of no additive G×E interaction typically entails a test of no relative excess risk due to interaction (RERI). It has been shown that a likelihood ratio test of a null RERI incorporating the G‐E independence assumption (RERI‐LRT) outperforms the standard approach. The RERI‐LRT relies on correct specification of a logistic model for the binary outcome, as a function of G, E, and auxiliary covariates. However, when at least one exposure is not categorical or auxiliary covariates are present, nonparametric estimation may not be feasible, while parametric logistic regression will a priori rule out the null hypothesis of no additive interaction in most practical situations, inflating type I error rate. In this paper, we present a general approach to test for G × E additive interaction exploiting G‐E independence. Unlike the RERI‐LRT, it allows the regression model for the binary outcome to remain unrestricted, and nonetheless still allows for covariate adjustment in order to ensure the G‐E independence assumption or to rule out residual confounding. The methods are illustrated through extensive simulation studies and an ovarian cancer study.