The impact of exposure-biased sampling designs on detection of gene–environment interactions in case–control studies with potential exposure misclassification

With limited funding and biological specimen availability, choosing an optimal sampling design to maximize power for detecting gene-by-environment (G–E) interactions is critical. Exposure-enriched sampling is often used to select subjects with rare exposures for genotyping to enhance power for tests of G–E effects. However, exposure misclassification (MC) combined with biased sampling can affect characteristics of tests for G–E interaction and joint tests for marginal association and G–E interaction. Here, we characterize the impact of exposure-biased sampling under conditions of perfect exposure information and exposure MC on properties of several methods for conducting inference. We assess the Type I error, power, bias, and mean squared error properties of case-only, case–control, and empirical Bayes methods for testing/estimating G–E interaction and a joint test for marginal G (or E) effect and G–E interaction across three biased sampling schemes. Properties are evaluated via empirical simulation studies. With perfect exposure information, exposure-enriched sampling schemes enhance power as compared to random selection of subjects irrespective of exposure prevalence but yield bias in estimation of the G–E interaction and marginal E parameters. Exposure MC modifies the relative performance of sampling designs when compared to the case of perfect exposure information. Those conducting G–E interaction studies should be aware of exposure MC properties and the prevalence of exposure when choosing an ideal sampling scheme and method for characterizing G–E interactions and joint effects.