Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity. Issue 1 (June 2015)

• Record Type: Journal Article
• Authors: De, Rishika; Hu, Ting; Moore, Jason; Gilbert-Diamond, Diane
• Is Part Of: Biodata mining. Volume 8:Issue 1(2015)
• Page Start: 1
• Page End: 16
• Publication Date: 2015-06
• Subjects: Dyadicity -- Heterophilicity -- Statistical epistasis networks -- Epistasis -- Gene-gene interaction; Bioinformatics -- Periodicals; Computational biology -- Periodicals; Data mining -- Periodicals; 570.285
• DOI: 10.1186/s13040-015-0077-x
• Languages: English
• ISSNs: 1756-0381

Abstract

Abstract Background Recent findings have reemphasized the importance ofepistasis, or gene-gene interactions, as a contributing factor to the unexplained heritability of obesity. Network-based methods such as statistical epistasis networks (SEN), present an intuitive framework to address the computational challenge of studying pairwise interactions between thousands of genetic variants. In this study, we aimed to analyze pairwise interactions that are associated with Body Mass Index (BMI) between SNPs from twelve genes robustly associated with obesity (BDNF, ETV5, FAIM2, FTO, GNPDA2, KCTD15, MC4R, MTCH2, NEGR1, SEC16B, SH2B1, andTMEM18 ). Methods We used information gain measures to identify all SNP-SNP interactions among and between these genes that were related to obesity (BMI > 30 kg/m2 ) within the Framingham Heart Study Cohort; interactions exceeding a certain threshold were used to build an SEN. We also quantified whether interactions tend to occur more between SNPs from the same gene (dyadicity ) or between SNPs from different genes (heterophilicity ). Results We identified a highly connected SEN of 709 SNPs and 1241 SNP-SNP interactions. Combining the SEN framework with dyadicity and heterophilicity analyses, we found 1 dyadic gene (TMEM18, P- value = 0.047) and 3 heterophilic genes (KCTD15, P- value = 0.045;SH2B1, P- value = 0.003; andTMEM18, P- value = 0.001). We also identified a lncRNA SNP (rs4358154) as a key node within the SEN using multiple network measures. Conclusion This study presents an analytical framework to characterize the global landscape of genetic interactions from genome-wide arrays and also to discover nodes of potential biological significance within the identified network.