Abstract
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.
Highlights
Decrease in sequencing cost has widened the scope of genome-wide association studies (GWAS)
Inspired by the outcome weighted learning (OWL) model of Zhao et al [15], developed in the context of randomized clinical trials, we propose an alternative to the modified outcome approach to estimate δ(X) and its support using a weighted binary classification formulation
We instead propose to study the synergies with a particular target: rs41475248 on chromosome 8. We focus on this target single nucleotide polymorphism (SNP) because (i) GBOOST finds that it is involved in 3 epistatic interactions, when controlling for a false discovery rate of 0.05, and (ii) it is a common variant, with a minor allele frequencies (MAF) of 0.45
Summary
Decrease in sequencing cost has widened the scope of genome-wide association studies (GWAS). The disease risk depends on a large number of genes connected through complex interaction networks. The classical approach and still widespread methodology in GWAS is to implement univariate association tests between each single nucleotide polymorphism (SNP) and the phenotype of interest. Such an approach is limited for common diseases, where the interactions between distant genes, or epistasis, need to be taken into account. Several epistatic mechanisms have been highlighted in the onset of Alzheimer’s disease [1]. At least two epistatic interactions were reported for multiple sclerosis [2, 3]
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