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

TY - JOUR

T1 - Robust Tests for Additive Gene-Environment Interaction in Case-Control Studies Using Gene-Environment Independence

AU - Liu, Gang

AU - Mukherjee, Bhramar

AU - Lee, Seunggeun

AU - Lee, Alice W

AU - Wu, Anna H

AU - Bandera, Elisa V

AU - Jensen, Allan

AU - Rossing, Mary Anne

AU - Moysich, Kirsten B

AU - Chang-Claude, Jenny

AU - Doherty, Jennifer A

AU - Gentry-Maharaj, Aleksandra

AU - Kiemeney, Lambertus

AU - Gayther, Simon A

AU - Modugno, Francesmary

AU - Massuger, Leon

AU - Goode, Ellen L

AU - Fridley, Brooke L

AU - Terry, Kathryn L

AU - Cramer, Daniel W

AU - Ramus, Susan J

AU - Anton-Culver, Hoda

AU - Ziogas, Argyrios

AU - Tyrer, Jonathan P

AU - Schildkraut, Joellen M

AU - Kjaer, Susanne K

AU - Webb, Penelope M

AU - Ness, Roberta B

AU - Menon, Usha

AU - Berchuck, Andrew

AU - Pharoah, Paul D

AU - Risch, Harvey

AU - Pearce, Celeste Leigh

AU - Ovarian Cancer Association Consortium

N1 - © The Author(s) 2017. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. For permissions, please e-mail: [email protected].

PY - 2018/2/1

Y1 - 2018/2/1

N2 - 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.

AB - 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.

U2 - 10.1093/aje/kwx243

DO - 10.1093/aje/kwx243

M3 - Journal article

C2 - 28633381

SN - 0002-9262

VL - 187

SP - 366

EP - 377

JO - American Journal of Epidemiology

JF - American Journal of Epidemiology

IS - 2

ER -