Novel Methods for Epistasis Detection in Genome-Wide Association Studies
More and more genome-wide association studies are being designed to uncover the full genetic basis of common diseases. Nonetheless, the resulting loci are often insufficient to fully recover the observed heritability. Epistasis, or gene-gene interaction, is one of many hypotheses put forward to explain this missing heritability. In the present work, we propose epiGWAS, a new approach for epistasis detection that identifies interactions between a target SNP and the rest of the genome. This contrasts with the classical strategy of epistasis detection through exhaustive pairwise SNP testing. We draw inspiration from causal inference in randomized clinical trials, which allows us to take into account linkage disequilibrium. EpiGWAS encompasses several methods, which we compare to state-of-the-art techniques for epistasis detection on simulated and real data. The promising results demonstrate empirically the benefits of EpiGWAS to identify pairwise interactions. Author summaryGenome-wide association studies are now a major tool for the discovery of biomarkers for complex diseases. However, the complexity of genetic architecture, in particular linkage disequilibrium, complicates that mission. Moreover, intergenic interactions, or epistasis, are often not correctly captured by the classical statistical methodologies. In our work, we propose a new framework to model linkage disequilibrium, which is based on propensity scores. Our goal is to detect epistatic interactions between a predetermined target locus and the rest of the genotype. The target may be identified from the literature, experiments, or top hits in previous genome-wide association studies. Recovering interactions with validated causal loci helps improve both interpretability and statistical power. Multi-targeting drug discovery can also benefit from our work through the combination of existing drugs with new ones for greater drug response.