Abstract
BackgroundGenome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, also known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated gene-gene interactions. However, these methods typically fail to identify interacting markers explaining more of the disease heritability over single locus GWAS, since many of the interactions significant for disease are obscured by uninformative marker interactions e.g., linkage disequilibrium (LD).ResultsIn this study, we present a novel SNP interaction prioritization algorithm, named iLOCi (Interacting Loci). This algorithm accounts for marker dependencies separately in case and control groups. Disease-associated interactions are then prioritized according to a novel ranking score calculated from the difference in marker dependencies for every possible pair between case and control groups. The analysis of a typical GWAS dataset can be completed in less than a day on a standard workstation with parallel processing capability. The proposed framework was validated using simulated data and applied to real GWAS datasets using the Wellcome Trust Case Control Consortium (WTCCC) data. The results from simulated data showed the ability of iLOCi to identify various types of gene-gene interactions, especially for high-order interaction. From the WTCCC data, we found that among the top ranked interacting SNP pairs, several mapped to genes previously known to be associated with disease, and interestingly, other previously unreported genes with biologically related roles.ConclusioniLOCi is a powerful tool for uncovering true disease interacting markers and thus can provide a more complete understanding of the genetic basis underlying complex disease. The program is available for download at http://www4a.biotec.or.th/GI/tools/iloci.
Highlights
Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, known as epistasis are not considered in single locus GWAS
Note that genetic factors primarily function through a complex mechanism; epistatic interactions are not limited to independent gene pairs
The tests with Wellcome Trust Case Control Consortium (WTCCC) datasets show that the top ranked pairs by our algorithm reveal novel disease genes, several of which are consistent with biological networks underpining disease etiology
Summary
Genome-wide association studies (GWAS) do not provide a full account of the heritability of genetic diseases since gene-gene interactions, known as epistasis are not considered in single locus GWAS. To address this problem, a considerable number of methods have been developed for identifying disease-associated genegene interactions. In. The existence of interactions among genes (epistasis) has been proposed to constitute a major proportion of disease heritability, which is not captured by single-locus GWAS [5]. Multiple genes interacting through a biological network (i.e. indirect interactions) exist which can modify disease penetrance and expressivity
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