  A large amount of research has been devoted to the detection and
investigation of epistatic interactions in genome-wide association studies
(GWASs). Most of the literature focuses on low-order interactions between
single-nucleotide polymorphisms (SNPs) with significant main effects.In this
paper we propose an original approach for detecting epistasis at the gene
level, without systematically filtering on significant genes. We first compute
interaction variables for each gene pair by finding its Eigen-Epistasis
component, defined as the linear combination of Gene SNPs having the highest
correlation with the phenotype. The selection of significant effects is done
using a penalized regression method based on Group Lasso controlling the False
Discovery Rate.The method is tested against two recent alternative proposals
from the literature using synthetic data, and shows good performances in
different settings. We demonstrate the power of our approach by detecting new
gene-gene interactions on three genome-wide association studies.
