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


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.

SUBMITTER: Liu G 

PROVIDER: S-EPMC5860584 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

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

Liu Gang G   Mukherjee Bhramar B   Lee Seunggeun S   Lee Alice W AW   Wu Anna H AH   Bandera Elisa V EV   Jensen Allan A   Rossing Mary Anne MA   Moysich Kirsten B KB   Chang-Claude Jenny J   Doherty Jennifer A JA   Gentry-Maharaj Aleksandra A   Kiemeney Lambertus L   Gayther Simon A SA   Modugno Francesmary F   Massuger Leon L   Goode Ellen L EL   Fridley Brooke L BL   Terry Kathryn L KL   Cramer Daniel W DW   Ramus Susan J SJ   Anton-Culver Hoda H   Ziogas Argyrios A   Tyrer Jonathan P JP   Schildkraut Joellen M JM   Kjaer Susanne K SK   Webb Penelope M PM   Ness Roberta B RB   Menon Usha U   Berchuck Andrew A   Pharoah Paul D PD   Risch Harvey H   Pearce Celeste Leigh CL  

American journal of epidemiology 20180201 2


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  ...[more]

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