Many complex diseases are likely to be a result of the interplay of genes and environmental exposures. The standard analysis in a genome-wide association study (GWAS) scans for main effects and ignores the potentially useful information in the available exposure data. Two recently proposed methods t
Detecting gene-environment interactions in genome-wide association data
✍ Scribed by Corinne D. Engelman; James W. Baurley; Yen-Feng Chiu; Bonnie R. Joubert; Juan P. Lewinger; Matthew J. Maenner; Cassandra E. Murcray; Gang Shi; W. James Gauderman
- Publisher
- John Wiley and Sons
- Year
- 2009
- Tongue
- English
- Weight
- 93 KB
- Volume
- 33
- Category
- Article
- ISSN
- 0741-0395
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✦ Synopsis
Abstract
Despite the importance of gene‐environment (G×E) interactions in the etiology of common diseases, little work has been done to develop methods for detecting these types of interactions in genome‐wide association study data. This was the focus of Genetic Analysis Workshop 16 Group 10 contributions, which introduced a variety of new methods for the detection of G×E interactions in both case‐control and family‐based data using both cross‐sectional and longitudinal study designs. Many of these contributions detected significant G×E interactions. Although these interactions have not yet been confirmed, the results suggest the importance of testing for interactions. Issues of sample size, quantifying the environmental exposure, longitudinal data analysis, family‐based analysis, selection of the most powerful analysis method, population stratification, and computational expense with respect to testing G×E interactions are discussed. Genet. Epidemiol. 33 (Suppl. 1):S68–S73, 2009. © 2009 Wiley‐Liss, Inc.
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