Tests for gene-environment interactions and joint effects with exposure misclassification

Philip S. Boonstra, Bhramar Mukherjee, Stephen B. Gruber, Jaeil Ahn, Stephanie L. Schmit, Nilanjan Chatterjee

Research output: Contribution to journalArticlepeer-review

Abstract

The number of methods for genome-wide testing of gene-environment (G-E) interactions continues to increase, with the aim of discovering new genetic risk factors and obtaining insight into the disease-gene-environment relationship. The relative performance of these methods, assessed on the basis of family-wise type I error rate and power, depends on underlying disease-gene-environment associations, estimates of which may be biased in the presence of exposure misclassification. This simulation study expands on a previously published simulation study of methods for detecting G-E interactions by evaluating the impact of exposure misclassification. We consider 7 single-step and modular screening methods for identifying G-E interaction at a genome-wide level and 7 joint tests for genetic association and G-E interaction, for which the goal is to discover new genetic susceptibility loci by leveraging G-E interaction when present. In terms of statistical power, modular methods that screen on the basis of the marginal disease-gene relationship are more robust to exposure misclassification. Joint tests that include main/marginal effects of a gene display a similar robustness, which confirms results from earlier studies. Our results offer an increased understanding of the strengths and limitations of methods for genome-wide searches for G-E interaction and joint tests in the presence of exposure misclassification.

Original languageEnglish (US)
Pages (from-to)237-247
Number of pages11
JournalAmerican journal of epidemiology
Volume183
Issue number3
DOIs
StatePublished - Feb 1 2016

Keywords

  • case-control
  • gene discovery
  • gene-environment independence
  • genome-wide association
  • modular methods
  • multiple testing
  • screening test
  • weighted hypothesis test

ASJC Scopus subject areas

  • Epidemiology

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