ArticleBioData mining2015
Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR).
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 11 papers.
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Who cites it
11 citing papers in PubMed, 22 citations in OpenAlex.
- Mediterranean Diet Adherence and Genetic Background Roles within a Web-Based Nutritional Intervention: The Food4Me Study.Nutrients · 2017Trial
- Evaluation of epistasis detection methods for quantitative phenotypes.Bioinformatics (Oxford, England) · 2026Article
- Examining SNP-SNP interactions and risk of clinical outcomes in colorectal cancer using multifactor dimensionality reduction based methods.Frontiers in genetics · 2022Article
- 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
- Genetic Variations of Ultraconserved Elements in the Human Genome.Omics : a journal of integrative biology · 2019Article
- The eMERGE genotype set of 83,717 subjects imputed to ~40 million variants genome wide and association with the herpes zoster medical record phenotype.Genetic epidemiology · 2019Article
- Review
- Collective feature selection to identify crucial epistatic variants.BioData mining · 2018Article
- Penetrance of Polygenic Obesity Susceptibility Loci across the Body Mass Index Distribution.American journal of human genetics · 2017Article
- Reverse Pathway Genetic Approach Identifies Epistasis in Autism Spectrum Disorders.PLoS genetics · 2017Article
- Harnessing publicly available genetic data to prioritize lipid modifying therapeutic targets for prevention of coronary heart disease based on dysglycemic risk.Human genetics · 2016Article
Corrections and comments
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Authors and funding
19 authors at 11 institutions in 3 countries.
Funding
Abstract
backgroundDespite heritability estimates of 40-70 % for obesity, less than 2 % of its variation is explained by Body Mass Index (BMI) associated loci that have been identified so far. Epistasis, or gene-gene interactions are a plausible source to explain portions of the missing heritability of BMI.
methodsUsing genotypic data from 18,686 individuals across five study cohorts - ARIC, CARDIA, FHS, CHS, MESA - we filtered SNPs (Single Nucleotide Polymorphisms) using two parallel approaches. SNPs were filtered either on the strength of their main effects of association with BMI, or on the number of knowledge sources supporting a specific SNP-SNP interaction in the context of BMI. Filtered SNPs were specifically analyzed for interactions that are highly associated with BMI using QMDR (Quantitative Multifactor Dimensionality Reduction). QMDR is a nonparametric, genetic model-free method that detects non-linear interactions associated with a quantitative trait.
resultsWe identified seven novel, epistatic models with a Bonferroni corrected p-value of association < 0.1. Prior experimental evidence helps explain the plausible biological interactions highlighted within our results and their relationship with obesity. We identified interactions between genes involved in mitochondrial dysfunction (POLG2), cholesterol metabolism (SOAT2), lipid metabolism (CYP11B2), cell adhesion (EZR), cell proliferation (MAP2K5), and insulin resistance (IGF1R). Moreover, we found an 8.8 % increase in the variance in BMI explained by these seven SNP-SNP interactions, beyond what is explained by the main effects of an index FTO SNP and the SNPs within these interactions. We also replicated one of these interactions and 58 proxy SNP-SNP models representing it in an independent dataset from the eMERGE study.
conclusionThis study highlights a novel approach for discovering gene-gene interactions by combining methods such as QMDR with traditional statistics.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.