Epistatic Module Detection for Case-Control Studies: A Bayesian Model with a Gibbs Sampling Strategy

The detection of epistatic interactive effects of multiple genetic variants on the susceptibility of human complex diseases is a great challenge in genome-wide association studies (GWAS). Although methods have been proposed to identify such interactions, the lack of an explicit definition of epistatic effects, together with computational difficulties, makes the development of new methods indispensable. In this paper, we introduce epistatic modules to describe epistatic interactive effects of multiple loci on diseases. On the basis of this notion, we put forward a Bayesian marker partition model to explain observed case-control data, and we develop a Gibbs sampling strategy to facilitate the detection of epistatic modules. Comparisons of the proposed approach with three existing methods on seven simulated disease models demonstrate the superior performance of our approach. When applied to a genome-wide case-control data set for Age-related Macular Degeneration (AMD), the proposed approach successfully identifies two known susceptible loci and suggests that a combination of two other loci—one in the gene SGCD and the other in SCAPER—is associated with the disease. Further functional analysis supports the speculation that the interaction of these two genetic variants may be responsible for the susceptibility of AMD. When applied to a genome-wide case-control data set for Parkinson's disease, the proposed method identifies seven suspicious loci that may contribute independently to the disease.Author Summary: Although genome-wide association studies (GWAS) have been quite popular due to recent advances in low-cost genotyping techniques, most of the reported studies only analyze single-locus effects because traditional multi-locus methods are not computationally practical in the detection of epistatic interactive effects of multiple loci. Here, on the basis of a rigorous definition of epistatic modules that describe interactive effects of multiple loci, we take advantage of a Bayesian model with a properly designed Gibbs sampling strategy to facilitate the detection of such modules. We confirm via extensive simulation studies that the proposed method, named epiMODE, is not only feasible in detecting multi-locus effects but also more powerful than three representative methods on seven disease models. We apply the proposed method to an Age-related Macular Degeneration (AMD) data and discover that a combination of two loci—one in the gene SGCD and the other in SCAPER—might be associated with AMD. Considering its advantages, we suggest that the proposed method be applied to more GWAS data for the detection of multi-locus interactive effects.