Complex diseases result from an interplay between genetic and environmental risk factors, and it is of great interest to study the gene-environment interaction (GxE) to understand the etiology of complex diseases. Recent developments in genetics field allows one to study GxE systematically. However, one difficulty with GxE arises from the fact that environmental exposures are often measured with error. In this paper, we focus on testing GxE when the environmental exposure E is subject to measurement error. Surprisingly, contrast to the well-established results that the naive test ignoring measurement error is valid in testing the main effects, we find that the naive test for GxE leads to inflated type I error under the null hypothesis of no interaction. The naive test also leads to biased estimates of the GxE effect. The analytic form of the bias term for general linear models is obtained, which is shown to be closely related to regression calibration. We then propose a regression calibration based approach to correct measurement error for testing GxE when either validation data or replicates are available. Extensive simulation studies are conducted to illustrate the performance of various tests with moderate sample sizes. Based on both theoretical and empirical results, we recommend the proposed test, as its type I error is properly controlled and it has at least comparable power to the naive test even when naive test is valid. The proposed methods are applied to study the gene–blood pressure interaction for cardiovascular diseases in an ancillary study of the Women’s Health Initiative.


Biostatistics | Genetics