Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence.
There have been recent proposals advocating the use of additive gene-environment interaction instead of the widely used multiplicative scale, as a more relevant public health measure. Using gene-environment independence enhances statistical power for testing multiplicative interaction in case-control studies. However, under departure from this assumption, substantial bias in the estimates and inflated type I error in the corresponding tests can occur. In this paper, we extend the empirical Bayes (EB) approach previously developed for multiplicative interaction, which trades off between bias and efficiency in a data-adaptive way, to the additive scale. An EB estimator of the relative excess risk due to interaction is derived, and the corresponding Wald test is proposed with a general regression setting under a retrospective likelihood framework. We study the impact of gene-environment association on the resultant test with case-control data. Our simulation studies suggest that the EB approach uses the gene-environment independence assumption in a data-adaptive way and provides a gain in power compared with the standard logistic regression analysis and better control of type I error when compared with the analysis assuming gene-environment independence. We illustrate the methods with data from the Ovarian Cancer Association Consortium.
Liu, G; Mukherjee, B; Lee, S; Lee, AW; Wu, AH; Bandera, EV; Jensen, A; Rossing, MA; Moysich, KB; Chang-Claude, J; Doherty, JA; Gentry-Maharaj, A; Kiemeney, L; Gayther, SA; Modugno, F; Massuger, L; Goode, EL; Fridley, BL; Terry, KL; Cramer, DW; Ramus, SJ; Anton-Culver, H; Ziogas, A; Tyrer, JP; Schildkraut, JM; Kjaer, SK; Webb, PM; Ness, RB; Menon, U; Berchuck, A; Pharoah, PD; Risch, H; Pearce, CL; Ovarian Cancer Association Consortium,
Volume / Issue
Start / End Page
Pubmed Central ID
Electronic International Standard Serial Number (EISSN)
Digital Object Identifier (DOI)