Detecting gene-environment interactions in genome-wide association data.
Despite the importance of gene-environment (GxE) 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 GxE interactions in both case-control and family-based data using both cross-sectional and longitudinal study designs. Many of these contributions detected significant GxE 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 GxE interactions are discussed.
Engelman
Corinne D
CD
Department of Population Health Sciences, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin 53726-2397, USA. cengelman@wisc.edu
Baurley
James W
JW
Chiu
Yen-Feng
YF
Joubert
Bonnie R
BR
Lewinger
Juan P
JP
Maenner
Matthew J
MJ
Murcray
Cassandra E
CE
Shi
Gang
G
Gauderman
W James
WJ
D001172:Arthritis, Rheumatoid / Q000453:epidemiology / Q000235:genetics; D002318:Cardiovascular Diseases / Q000453:epidemiology / Q000235:genetics; D016022:Case-Control Studies; D003430:Cross-Sectional Studies; D004777:Environment; D055106:Genome-Wide Association Study / Q000379:methods* / Q000706:statistics & numerical data; D006801:Humans; D008137:Longitudinal Studies; D017720:Molecular Epidemiology; D010641:Phenotype; D020641:Polymorphism, Single Nucleotide