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It is generally acknowledged that most complex diseases are affected in partby interactions between genes and genes and/or between genes and environmentalfactors. Taking into account environmental exposures and their interactionswith genetic factors in genome-wide association studies (GWAS) can help toidentify high-risk subgroups in the population and provide a betterunderstanding of the disease. For this reason, many methods have been developedto detect gene-environment (G*E) interactions. Despite this, few loci thatinteract with environmental exposures have been identified so far. Indeed, themodest effect of G*E interactions as well as confounding factors entail lowstatistical power to detect such interactions. In this work, we provide asimulated dataset in order to study methods for detecting G*E interactions inGWAS in presence of confounding factor and population structure. Our workapplies a recently introduced non-subjective method for H1 simulations calledwaffect and exploits the publicly available HapMap project to build a datasetswith real genotypes and population structures. We use this dataset to study theimpact of confounding factors and compare the relative performance of popularmethods such as PLINK, random forests and linear mixed models to detect G*Einteractions. Presence of confounding factor is an obstacle to detect G*Einteractions in GWAS and the approaches considered in our power study all haveinsufficient power to detect the strong simulated interaction. Our simulateddataset could help to develop new methods which account for confounding factorsthrough latent exposures in order to improve power.

Non-subjective power analysis to detect G*E interactions in Genome-Wide Association Studies in presence of confounding factor