title: Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity.
abstract: BACKGROUND
  Recent findings have reemphasized the importance of epistasis, 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, and TMEM18).
  
  
  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/m(2)) 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; and TMEM18, P-value = 0.001). We also identified a lncRNA SNP (rs4358154) as a key node within the SEN using multiple network measures.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, NH USA.
authors.forename: Rishika
authors.initials: R
authors.lastname: De
authors.affiliation: Department of Computer Science, Memorial University, St. John's, NL Canada.
authors.forename: Ting
authors.initials: T
authors.lastname: Hu
authors.affiliation: Institute for Biomedical Informatics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
authors.forename: Jason H
authors.initials: JH
authors.lastname: Moore
authors.affiliation: Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH USA.
authors.forename: Diane
authors.initials: D
authors.lastname: Gilbert-Diamond
keywords: Dyadicity; Epistasis; Gene-gene interaction; Heterophilicity; Statistical epistasis networks