  It is generally acknowledged that most complex diseases are affected in part
by interactions between genes and genes and/or between genes and environmental
factors. Taking into account environmental exposures and their interactions
with genetic factors in genome-wide association studies (GWAS) can help to
identify high-risk subgroups in the population and provide a better
understanding of the disease. For this reason, many methods have been developed
to detect gene-environment (G*E) interactions. Despite this, few loci that
interact with environmental exposures have been identified so far. Indeed, the
modest effect of G*E interactions as well as confounding factors entail low
statistical power to detect such interactions. In this work, we provide a
simulated dataset in order to study methods for detecting G*E interactions in
GWAS in presence of confounding factor and population structure. Our work
applies a recently introduced non-subjective method for H1 simulations called
waffect and exploits the publicly available HapMap project to build a datasets
with real genotypes and population structures. We use this dataset to study the
impact of confounding factors and compare the relative performance of popular
methods such as PLINK, random forests and linear mixed models to detect G*E
interactions. Presence of confounding factor is an obstacle to detect G*E
interactions in GWAS and the approaches considered in our power study all have
insufficient power to detect the strong simulated interaction. Our simulated
dataset could help to develop new methods which account for confounding factors
through latent exposures in order to improve power.
