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

Abstract

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.

Omics Biomarkers in Ophthalmology

“…Several epistatic effects have been identified in AMD datasets, mainly involving genegene interactions with complement factor H (CFH). [44][45][46][47][48] Furthermore, the effect of several variants has been found to be modified by smoking. 49,50 Combining genomic information with environmental factors or other types of omics data might improve the predictive accuracy to AMD susceptibility.…”

Time-series microarray data simulation modeled with a case-control label

“…In reference to SNP studies, risk models based on gene expression data have been conducted using relevant statistics (Tang et al, 2009), which will be explained in more detail in the following section. According to these models, samples are marked with a case-control label.…”

“…In regards to risk model based on SNPs (Tang et al, 2009), the following risk models are used to model the phenotypic probability of genes as a cause of a disease. Different from SNP data, gene expression data are continuous rather than discrete.…”

GxG-Viztool: A program for visualizing gene-gene interactions in genetic association analysis

“…We observed that the marginal effect of CFH is predominant in both two-842 interactions with SGCD and MED27. While SGCD has been reported to be associated with AMD [14], the relationship between MED27 and AMD has been uncovered.…”