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Does Accounting for Gene-Environment Interactions Help Uncover Association between Rare Variants and Complex Diseases?

Kazma R. · Cardin N.J. · Witte J.S.

Author affiliations

Department of Epidemiology and Biostatistics and Institute for Human Genetics, University of California, San Francisco, Calif., USA

Corresponding Author

John S. Witte, PhD

MC 3110, UCSF, Helen Diller Family Cancer Research Building

1450 3rd Street, PO Box 589001

San Francisco, CA 94158-9001 (USA)

E-Mail WitteJ@humgen.ucsf.edu

Related Articles for ""

Hum Hered 2012;74:205-214

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Objective: To determine whether accounting for gene-environment (G×E) interactions improves the power to detect associations between rare variants and a disease, we have extended three statistical methods and compared their power under various simulated disease models. Methods: To test for association of a group of rare variants with a disease, Min-P uses the lowest p value within the group of variants, CAST (Cohort Allelic Sums Test) uses an indicator variable to quantify the rare alleles within the group of variants, and SKAT (Sequence Kernel Association Test) uses a logistic regression based on kernel machine. For each method, we incorporate a term for the G×E interaction and test for association and interaction jointly. Results: When testing for disease association with a set of rare variants, accounting for G×E interactions can improve power in specific situations (pure interaction or high proportion of causal variants interacting with the environment). However, the power of this approach can decrease, in particular in the presence of main genetic or environmental effects. Among the methods compared, the optimized and weighted SKAT performed best, whether to test for genetic association or to test it jointly with G×E interactions. Conclusion: This approach can be used in specific situations but is not appropriate for a primary analysis.

© 2013 S. Karger AG, Basel

Article / Publication Details

Published online: April 11, 2013
Issue release date: April 2013

Number of Print Pages: 10
Number of Figures: 2
Number of Tables: 2

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

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

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