4.0 Article

Genome-Wide Meta-Analysis of Joint Tests for Genetic and Gene-Environment Interaction Effects

期刊

HUMAN HEREDITY
卷 70, 期 4, 页码 292-300

出版社

KARGER
DOI: 10.1159/000323318

关键词

Gene-environment interaction; Genome-wide scan; Meta-analysis; Case-control association analysis; Complex disease

资金

  1. Foundation Bettencourt-Schueller
  2. National Institute of Environmental Health Sciences
  3. [P01CA087969]
  4. [5U01HG004399-02]
  5. [HL35464]
  6. [R21DK084529]
  7. NATIONAL CANCER INSTITUTE [U19CA148065, P01CA087969] Funding Source: NIH RePORTER
  8. NATIONAL HEART, LUNG, AND BLOOD INSTITUTE [R01HL035464] Funding Source: NIH RePORTER
  9. NATIONAL HUMAN GENOME RESEARCH INSTITUTE [U01HG004399] Funding Source: NIH RePORTER
  10. NATIONAL INSTITUTE OF DIABETES AND DIGESTIVE AND KIDNEY DISEASES [R21DK084529] Funding Source: NIH RePORTER

向作者/读者索取更多资源

Background: There is growing interest in the study of gene-environment interactions in the context of genome-wide association studies (GWASs). These studies will likely require meta-analytic approaches to have sufficient power. Methods: We describe an approach for meta-analysis of a joint test for genetic main effects and gene-environment interaction effects. Using simulation studies based on a meta-analysis of five studies (total n = 10,161), we compare the power of this test to the meta-analysis of marginal test of genetic association and the meta-analysis of standard 1 d.f. interaction tests across a broad range of genetic main effects and gene-environment interaction effects. Results: We show that the joint meta-analysis is valid and can be more powerful than classical meta-analytic approaches, with a potential gain of power over 50% compared to the marginal test. The standard interaction test had less than 1% power in almost all the situations we considered. We also show that regardless of the test used, sample sizes far exceeding those of a typical individual GWAS will be needed to reliably detect genes with subtle gene-environment interaction patterns. Conclusion: The joint meta-analysis is an attractive approach to discover markers which may have been missed by initial GWASs focusing on marginal marker-trait associations. Copyright (C) 2011 S. Karger AG, Basel

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