Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity

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