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1.  Detecting Gene-Environment Interactions in Genome-Wide Association Data 
Genetic epidemiology  2009;33(Suppl 1):S68-S73.
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.
PMCID: PMC2924567  PMID: 19924704
GAW; case-control; family-based; cross-sectional; longitudinal; rheumatoid arthritis; Framingham Heart Study
2.  The Challenge of Detecting Epistasis (G×G Interactions): Genetic Analysis Workshop 16 
Genetic epidemiology  2009;33(0 1):S58-S67.
Interest is increasing in epistasis as a possible source of the unexplained variance missed by genome-wide association studies. The Genetic Analysis Workshop 16 Group 9 participants evaluated a wide variety of classical and novel analytical methods for detecting epistasis, in both the statistical and machine learning paradigms, applied to both real and simulated data. Because the magnitude of epistasis is clearly relative to scale of penetrance, and therefore to some extent, to the choice of model framework, it is not surprising that strong interactions under one model might be minimized or even disappear entirely under a different modeling framework.
PMCID: PMC3692280  PMID: 19924703
generalized linear model; machine learning methods

Results 1-2 (2)