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Open Access Publications from the University of California

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

  • Author(s): Liu, Gang
  • Mukherjee, Bhramar
  • Lee, Seunggeun
  • Lee, Alice W
  • Wu, Anna H
  • Bandera, Elisa V
  • Jensen, Allan
  • Rossing, Mary Anne
  • Moysich, Kirsten B
  • Chang-Claude, Jenny
  • Doherty, Jennifer A
  • Gentry-Maharaj, Aleksandra
  • Kiemeney, Lambertus
  • Gayther, Simon A
  • Modugno, Francesmary
  • Massuger, Leon
  • Goode, Ellen L
  • Fridley, Brooke L
  • Terry, Kathryn L
  • Cramer, Daniel W
  • Ramus, Susan J
  • Anton-Culver, Hoda
  • Ziogas, Argyrios
  • Tyrer, Jonathan P
  • Schildkraut, Joellen M
  • Kjaer, Susanne K
  • Webb, Penelope M
  • Ness, Roberta B
  • Menon, Usha
  • Berchuck, Andrew
  • Pharoah, Paul D
  • Risch, Harvey
  • Pearce, Celeste Leigh
  • Ovarian Cancer Association Consortium
  • et al.

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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