Abstract
BackgroundInteractions among genomic loci (also known as epistasis) have been suggested as one of the potential sources of missing heritability in single locus analysis of genome-wide association studies (GWAS). The computational burden of searching for interactions is compounded by the extremely low threshold for identifying significant p-values due to multiple hypothesis testing corrections. Utilizing prior biological knowledge to restrict the set of candidate SNP pairs to be tested can alleviate this problem, but systematic studies that investigate the relative merits of integrating different biological frameworks and GWAS data have not been conducted.ResultsWe developed four biologically based frameworks to identify pairwise interactions among candidate SNP pairs as follows: (1) for each human protein-coding gene, a set of SNPs associated with that gene was constructed providing a gene-based interaction model, (2) for each known biological pathway, a set of SNPs associated with the genes in the pathway was constructed providing a pathway-based interaction model, (3) a set of SNPs associated with genes in a disease-related subnetwork provides a network-based interaction model, and (4) a framework is based on the function of SNPs. The last approach uses expression SNPs (eSNPs or eQTLs), which are SNPs or loci that have defined effects on the abundance of transcripts of other genes. We constructed pairs of eSNPs and SNPs located in the target genes whose expression is regulated by eSNPs. For all four frameworks the SNP sets were exhaustively tested for pairwise interactions within the sets using a traditional logistic regression model after excluding genes that were previously identified to associate with the trait. Using previously published GWAS data for type 2 diabetes (T2D) and the biologically based pair-wise interaction modeling, we identify twelve genes not seen in the previous single locus analysis.ConclusionWe present four approaches to detect interactions associated with complex diseases. The results show our approaches outperform the traditional single locus approaches in detecting genes that previously did not reach significance; the results also provide novel drug targets and biomarkers relevant to the underlying mechanisms of disease.
Highlights
Interactions among genomic loci have been suggested as one of the potential sources of missing heritability in single locus analysis of genome-wide association studies (GWAS)
ZFAT, NDST3, C9orf3 and PPM1A, with one to three SNP pairs identified for each gene, were found to significantly associate with type 2 diabetes (T2D) in the Wellcome Trust Case Control Consortium (WTCCC) data set on the basis of an interaction term using the logistic regression model (Table 1)
In conclusion, this study presented several approaches to search for disease-associated gene-gene interactions from GWAS data based on prior biological knowledge and discrete biological frameworks
Summary
Interactions among genomic loci ( known as epistasis) have been suggested as one of the potential sources of missing heritability in single locus analysis of genome-wide association studies (GWAS). In this paper we come to the problem of GWAS analysis using an alternative assumption, not one of the independent action of SNPs or genes but rather one that assumes that they may interact in causing complex disease. This is a well-known idea and when genes function primarily through a complex mechanism that involves multiple genes, the joint effect (behavior) of those genes’ variants is referred to as a gene-gene interaction (or epistasis) [8,9,10], though biological interaction and statistical interaction are often confused [11]. The contributions of gene-gene interaction to the risk of diseases have been well documented, e.g., in the case of breast cancer and coronary heart disease [8,12]
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