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Table of Contents
Vol. 70, No. 4, 2010
Issue release date: February 2011
Section title: Original Paper
Free Access
Hum Hered 2010;70:292–300

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

Aschard H.a · Hancock D.B.b · London S.J.b · Kraft P.a
aProgram in Molecular and Genetic Epidemiology, Harvard School of Public Health, Boston, Mass., bEpidemiology Branch, National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, N.C., USA
email Corresponding Author

Hugues Aschard

Harvard School of Public Health, Department of Epidemiology

Building 2, Room 205, 665 Huntington Avenue

Boston, MA 02115 (USA)

Tel. +1 617 432 5900, Fax +1 617 432 1722, E-Mail haschard@hsph.harvard.edu

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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.

© 2011 S. Karger AG, Basel

Article / Publication Details

First-Page Preview
Abstract of Original Paper

Received: June 18, 2010
Accepted: December 02, 2010
Published online: February 03, 2011
Issue release date: February 2011

Number of Print Pages: 9
Number of Figures: 3
Number of Tables: 2

ISSN: 0001-5652 (Print)
eISSN: 1423-0062 (Online)

For additional information: http://www.karger.com/HHE

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