Skip to main content
Open Access Publications from the University of California

UC Irvine

UC Irvine Previously Published Works bannerUC Irvine

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.

Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.

Main Content
For improved accessibility of PDF content, download the file to your device.
Current View