Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

Gang Liu, Liu G, Mukherjee B, Bhramar Mukherjee, Lee S, Seunggeun Lee, Alice W. Lee, Lee AW, Anna H. Wu, Elisa V. Bandera, Bandera EV, Allan Jensen, Rossing MA, Mary Anne Rossing, Kirsten B. Moysich, Moysich KB, Chang-Claude J, Jenny Chang-Claude, Doherty JA, Jennifer A. Doherty, Aleksandra Gentry-Maharaj, Gentry-Maharaj A, Lambertus Kiemeney, Lambertus A. Kiemeney, Simon A. Gayther, Gayther SA, Francesmary Modugno, Modugno F, Leon Massuger, Massuger L, Goode EL, Ellen L. Goode, Brooke L. Fridley, Fridley BL, Kathryn L. Terry, Cramer DW, Daniel W. Cramer, Susan J. Ramus, Anton-Culver H, Hoda Anton-Culver, Argyrios Ziogas, Ziogas A, Tyrer JP, Jonathan P. Tyrer, Joellen M. Schildkraut, Schildkraut JM, Kjaer SK, Susanne K Kjaer, Penelope M. Webb, Ness RB, Roberta B. Ness, Usha Menon, Berchuck A, Andrew Berchuck, Paul D Pharoah, Pharoah PD, Risch H, Harvey Risch, Celeste Leigh Pearce, Pearce CL, Ovarian Cancer Association Consortium
American Journal of Epidemiology · 2018 · vol. 187(2) , pp. 366–377 · doi:10.1093/aje/kwx243 · PMID:28633381 · PMC5860584
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This paper extends the empirical Bayes approach for testing gene-environment interaction to the additive scale, improving power and type I error control compared to standard logistic regression and analyses assuming gene-environment independence.

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Abstract

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.

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MeSH descriptors

Case-Control Studies Epidemiologic Research Design Gene-Environment Interaction Bayes Theorem Bias Computer Simulation Humans Regression Analysis Retrospective Studies

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