Case-control studies of gene-environment interaction: Bayesian design and analysis

Bhramar Mukherjee, Jaeil Ahn, Stephen B. Gruber, Malay Ghosh, Nilanjan Chatterjee

Research output: Contribution to journalArticle

Abstract

With increasing frequency, epidemiologic studies are addressing hypotheses regarding gene-environment interaction. In many well-studied candidate genes and for standard dietary and behavioral epidemiologic exposures, there is often substantial prior information available that may be used to analyze current data as well as for designing a new study. In this article, first, we propose a proper full Bayesian approach for analyzing studies of gene-environment interaction. The Bayesian approach provides a natural way to incorporate uncertainties around the assumption of gene-environment independence, often used in such an analysis. We then consider Bayesian sample size determination criteria for both estimation and hypothesis testing regarding the multiplicative gene-environment interaction parameter. We illustrate our proposed methods using data from a large ongoing case-control study of colorectal cancer investigating the interaction of N-acetyl transferase type 2 (NAT2) with smoking and red meat consumption. We use the existing data to elicit a design prior and show how to use this information in allocating cases and controls in planning a future study that investigates the same interaction parameters. The Bayesian design and analysis strategies are compared with their corresponding frequentist counterparts.

Original languageEnglish (US)
Pages (from-to)934-948
Number of pages15
JournalBiometrics
Volume66
Issue number3
DOIs
StatePublished - Sep 2010

Keywords

  • Case-only design
  • Gene-environment independence
  • Highest posterior density interval
  • Molecular epidemiology of colorectal cancer
  • Multinomial-Dirichlet
  • Posterior odds

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

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