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It is increasingly of interest in statistical genetics to test for thepresence of a mechanistic interaction between genetic (G) and environmental (E)risk factors by testing for the presence of an additive GxE interaction. Incase-control studies involving a rare disease, a statistical test of noadditive interaction typically entails a test of no relative excess risk due tointeraction (RERI). It is also well known that a test of multiplicativeinteraction exploiting G-E independence can be dramatically more powerful thanstandard logistic regression for case-control data. Likewise, it has recentlybeen shown that a likelihood ratio test of a null RERI incorporating the G-Eindependence assumption (RERI-LRT) outperforms the standard RERI approach. Inthis paper, the authors describe a general, yet relatively straightforwardapproach to test for GxE additive interaction exploiting G-E independence. Theapproach which relies on regression models for G and E is particularlyattractive because, unlike the RERI-LRT, it allows the regression model for thebinary outcome to remain unrestricted. Therefore, the new methodology iscompletely robust to possible mis-specification in the outcome regression. Thisis particularly important for settings not easily handled by RERI-LRT, such aswhen E is a count or a continuous exposure with multiple components, or whenthere are several auxiliary covariates in the regression model. While theproposed approach avoids fitting an outcome regression, it nonetheless stillallows for straightforward covariate adjustment. The methods are illustratedthrough an extensive simulation study and an ovarian cancer empiricalapplication.

A general approach to detect gene (G)-environment (E) additive interaction leveraging G-E independence in case-control studies