Identifying gene-gene interactions that are highly associated with Body Mass Index using Quantitative Multifactor Dimensionality Reduction (QMDR).

Rishika De,David S Carrell,Michael V Holmes,Shefali S Verma,Brendan J Keating,Molly A Hall,Emily R Holzinger,Carrie B Moore,Diane Gilbert-Diamond,Fotios Drenos,Jennifer A Pacheco,Laura J Rasmussen-Torvik,Marylyn D Ritchie,Jason H Moore,Folkert W Asselbergs,David R Crosslin,Eric Larson,Gail Jarvik,Hakon Hakonarson

doi:10.1186/s13040-015-0074-0

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.Electronic supplementary materialThe online version of this article (doi:10.1186/s13040-015-0074-0) contains supplementary material, which is available to authorized users.

Highlights

Despite 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
Main effect filter Using the set of Single nucleotide polymorphism (SNP) that emerged from the main effect filter, Quantitative Multifactor Dimensionality Reduction (QMDR) analysis identified seven novel SNP-SNP interaction models that were associated with BMI (Bonferroni corrected P-value
Biofilter Using the set of SNPs that emerged from the Biofilter procedure, QMDR analysis did not identify any significant SNP-SNP interaction models that were associated with BMI

Summary

Introduction

Despite 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. Technological advancements in genomics and highly characterized genome-wide reference maps in major populations allow researchers to query a million or more genetic variants by designing genome-wide association studies (GWAS), [9,10,11] and so far, researchers have identified BMI-related signals in 32 loci that are associated with the trait at a genome-wide level [1]. These primary associations have been able to explain only about 2 % of the variation observed in BMI [1]

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