Browse > Article
http://dx.doi.org/10.5808/GI.2014.12.4.181

Understanding Epistatic Interactions between Genes Targeted by Non-coding Regulatory Elements in Complex Diseases  

Sung, Min Kyung (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST))
Bang, Hyoeun (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST))
Choi, Jung Kyoon (Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST))
Abstract
Genome-wide association studies have proven the highly polygenic architecture of complex diseases or traits; therefore, single-locus-based methods are usually unable to detect all involved loci, especially when individual loci exert small effects. Moreover, the majority of associated single-nucleotide polymorphisms resides in non-coding regions, making it difficult to understand their phenotypic contribution. In this work, we studied epistatic interactions associated with three common diseases using Korea Association Resource (KARE) data: type 2 diabetes mellitus (DM), hypertension (HT), and coronary artery disease (CAD). We showed that epistatic single-nucleotide polymorphisms (SNPs) were enriched in enhancers, as well as in DNase I footprints (the Encyclopedia of DNA Elements [ENCODE] Project Consortium 2012), which suggested that the disruption of the regulatory regions where transcription factors bind may be involved in the disease mechanism. Accordingly, to identify the genes affected by the SNPs, we employed whole-genome multiple-cell-type enhancer data which discovered using DNase I profiles and Cap Analysis Gene Expression (CAGE). Assigned genes were significantly enriched in known disease associated gene sets, which were explored based on the literature, suggesting that this approach is useful for detecting relevant affected genes. In our knowledge-based epistatic network, the three diseases share many associated genes and are also closely related with each other through many epistatic interactions. These findings elucidate the genetic basis of the close relationship between DM, HT, and CAD.
Keywords
coronary artery disease; diabetes mellitus; epistasis; hypertension; regulatory region;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Chung HH, Won KC. Prevalence, awareness, and control of hypertension among diabetic Koreans. Diabetes Metab J 2011;35:337-339.   DOI
2 Phillips PC. Epistasis: the essential role of gene interactions in the structure and evolution of genetic systems. Nat Rev Genet 2008;9:855-867.   DOI
3 Lippert C, Listgarten J, Davidson RI, Baxter S, Poon H, Kadie CM, et al. An exhaustive epistatic SNP association analysis on expanded Wellcome Trust data. Sci Rep 2013;3:1099.   DOI
4 Ward LD, Kellis M. Interpreting noncoding genetic variation in complex traits and human disease. Nat Biotechnol 2012;30:1095-1106.   DOI
5 Yung LS, Yang C, Wan X, Yu W. GBOOST: a GPU-based tool for detecting gene-gene interactions in genome-wide case control studies. Bioinformatics 2011;27:1309-1310.   DOI
6 Wan X, Yang C, Yang Q, Xue H, Fan X, Tang NL, et al. BOOST: a fast approach to detecting gene-gene interactions in genome-wide case-control studies. Am J Hum Genet 2010;87:325-340.   DOI   ScienceOn
7 Neph S, Vierstra J, Stergachis AB, Reynolds AP, Haugen E, Vernot B, et al. An expansive human regulatory lexicon encoded in transcription factor footprints. Nature 2012;489:83-90.   DOI   ScienceOn
8 Maurano MT, Humbert R, Rynes E, Thurman RE, Haugen E, Wang H, et al. Systematic localization of common disease-associated variation in regulatory DNA. Science 2012;337:1190-1195.   DOI   ScienceOn
9 Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M, et al. An atlas of active enhancers across human cell types and tissues. Nature 2014;507:455-461.   DOI
10 Johnson AD, Handsaker RE, Pulit SL, Nizzari MM, O'Donnell CJ, de Bakker PI. SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap. Bioinformatics 2008;24:2938-2939.   DOI
11 Kotani K, Ogawa W, Hashiramoto M, Onishi T, Ohno S, Kasuga M. Inhibition of insulin-induced glucose uptake by atypical protein kinase C isotype-specific interacting protein in 3T3-L1 adipocytes. J Biol Chem 2000;275:26390-26395.   DOI
12 Echwald SM, Bjorbaek C, Hansen T, Clausen JO, Vestergaard H, Zierath JR, et al. Identification of four amino acid substitutions in hexokinase II and studies of relationships to NIDDM, glucose effectiveness, and insulin sensitivity. Diabetes 1995;44:347-353.   DOI
13 Sowers JR, Epstein M, Frohlich ED. Diabetes, hypertension, and cardiovascular disease: an update. Hypertension 2001;37:1053-1059.   DOI   ScienceOn
14 Yu W, Clyne M, Khoury MJ, Gwinn M. Phenopedia and Genopedia: disease-centered and gene-centered views of the evolving knowledge of human genetic associations. Bioinformatics 2010;26:145-146.   DOI