Detecting Gene-Environment Interactions in Genome-Wide Association Data

作者：Engelman Corinne D^*; Baurley James W; Chiu Yen Feng; Joubert Bonnie R; Lewinger Juan P; Maenner Matthew J; Murcray Cassandra E; Shi Gang; Gauderman W James

来源：Genetic Epidemiology, 2009, 33(S1): S68-S73.

DOI：10.1002/gepi.20475

• GAW
• case-control
• family-based
• cross-sectional
• longitudinal
• rheumatoid arthritis
• Framingham Heart Study
• GWAS

Despite the importance of gene-environment (G x 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 x 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 x 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 x E interactions are discussed. Genet. Epiderniol. 33 (Suppl. 1):S68-S73, 2009.

• 出版日期2009