ArticleBioData mining2015
Characterizing gene-gene interactions in a statistical epistasis network of twelve candidate genes for obesity.
Article in BioData mining, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 27 citations in OpenAlex.
- Decoding the dark genome reveals its organisation into modular disease networks.Scientific reports · 2026Article
- Turning heterogeneity of statistical epistasis networks to an advantage.Briefings in bioinformatics · 2026Article
- Mapping of genotype-by-environment interaction loci for Metabolic Syndrome-like traits using the multi-parentbioRxiv : the preprint server for biology · 2025Article
- The Possible Roles of Glucosamine-6-Phosphate Deaminases in Ammonium Metabolism in Cancer.International journal of molecular sciences · 2024Review
- The Influence ofInternational journal of environmental research and public health · 2022Article
- Effect of interaction between obesity-promoting genetic variants and behavioral factors on the risk of obese phenotypes.Molecular genetics and genomics : MGG · 2021Article
- An epistatic interaction between pre-natal smoke exposure and socioeconomic status has a significant impact on bronchodilator drug response in African American youth with asthma.BioData mining · 2020Article
- Chromatin profiling of cortical neurons identifies individual epigenetic signatures in schizophrenia.Translational psychiatry · 2019Article
- Epi-GTBN: an approach of epistasis mining based on genetic Tabu algorithm and Bayesian network.BMC bioinformatics · 2019Article
- Evaluating relevance and redundancy to quantify how binary node metadata interplay with the network structure.Scientific reports · 2019Article
- An ancestry-based approach for detecting interactions.Genetic epidemiology · 2018Article
- Integrative information theoretic network analysis for genome-wide association study of aspirin exacerbated respiratory disease in Korean population.BMC medical genomics · 2017Article
- A Review of Pathway-Based Analysis Tools That Visualize Genetic Variants.Frontiers in genetics · 2017Review
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Authors and funding
4 authors at 3 institutions in 2 countries.
Funding
Abstract
backgroundRecent 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).
methodsWe 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).
resultsWe 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.
conclusionThis 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.
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