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This article is part of the supplement: The International Conference on Intelligent Biology and Medicine (ICIBM): Systems Biology

Open Access Research

Gene, pathway and network frameworks to identify epistatic interactions of single nucleotide polymorphisms derived from GWAS data

Yu Liu1, Sean Maxwell15, Tao Feng2, Xiaofeng Zhu2, Robert C Elston2, Mehmet Koyutürk13 and Mark R Chance145*

Author Affiliations

1 Center for Proteomics and Bioinformatics, Case Western Reserve University, Cleveland, OH, USA

2 Department of Epidemiology and Biostatistics, Case Western Reserve University, Cleveland, OH, USA

3 Department of Electrical Engineering & Computer Science, Case Western Reserve University, Cleveland, OH, USA

4 Department of Genetics & Genome Sciences, Case Western Reserve University, Cleveland, OH, USA

5 NeoProteomics, Inc., Cleveland, OH, USA

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BMC Systems Biology 2012, 6(Suppl 3):S15  doi:10.1186/1752-0509-6-S3-S15

Published: 17 December 2012

Abstract

Background

Interactions 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.

Results

We 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.

Conclusion

We 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.