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

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.

[1]  S. Kahn,et al.  Mechanisms linking obesity to insulin resistance and type 2 diabetes , 2006, Nature.

[2]  K. Reynolds,et al.  Global burden of obesity in 2005 and projections to 2030 , 2008, International Journal of Obesity.

[3]  R. Evans,et al.  Regulation of Muscle Fiber Type and Running Endurance by PPARδ , 2004, PLoS biology.

[4]  Tanya M. Teslovich,et al.  Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index , 2010 .

[5]  Peter Kraft,et al.  Heritability in the genome-wide association era , 2012, Human Genetics.

[6]  Rodney P. Carlisle,et al.  The European Americans , 2011 .

[7]  R. Kronmal,et al.  Multi-Ethnic Study of Atherosclerosis: objectives and design. , 2002, American journal of epidemiology.

[8]  H. Gylling,et al.  Introducing a new component of the metabolic syndrome: low cholesterol absorption. , 2000, The American journal of clinical nutrition.

[9]  Scott M. Williams,et al.  New strategies for identifying gene-gene interactions in hypertension , 2002, Annals of medicine.

[10]  Marylyn D. Ritchie,et al.  Genomic analyses with biofilter 2.0: knowledge driven filtering, annotation, and model development , 2013, BioData Mining.

[11]  E. Davie,et al.  Characterization of the gene for the a subunit of human factor XIII (plasma transglutaminase), a blood coagulation factor. , 1988, Proceedings of the National Academy of Sciences of the United States of America.

[12]  Jason H. Moore,et al.  STUDENTJAMA. The challenges of whole-genome approaches to common diseases. , 2004, JAMA.

[13]  Marylyn D. Ritchie,et al.  Pacific Symposium on Biocomputing 14:368-379 (2009) BIOFILTER: A KNOWLEDGE-INTEGRATION SYSTEM FOR THE MULTI-LOCUS ANALYSIS OF GENOME-WIDE ASSOCIATION STUDIES * , 2022 .

[14]  D. Lau,et al.  Adipokines: molecular links between obesity and atheroslcerosis. , 2005, American journal of physiology. Heart and circulatory physiology.

[15]  Jason H. Moore,et al.  BIOINFORMATICS REVIEW , 2005 .

[16]  Marylyn D. Ritchie,et al.  Knowledge-Driven Multi-Locus Analysis Reveals Gene-Gene Interactions Influencing HDL Cholesterol Level in Two Independent EMR-Linked Biobanks , 2011, PloS one.

[17]  Mark I. McCarthy,et al.  Concept, Design and Implementation of a Cardiovascular Gene-Centric 50 K SNP Array for Large-Scale Genomic Association Studies , 2008, PloS one.

[18]  Wendy A. Wolf,et al.  The eMERGE Network: A consortium of biorepositories linked to electronic medical records data for conducting genomic studies , 2011, BMC Medical Genomics.

[19]  Maria-Christina Zennaro,et al.  Pivotal role of the mineralocorticoid receptor in corticosteroid‐induced adipogenesis , 2007, FASEB journal : official publication of the Federation of American Societies for Experimental Biology.

[20]  Andrew D. Johnson,et al.  SNAP: a web-based tool for identification and annotation of proxy SNPs using HapMap , 2008, Bioinform..

[21]  Yiran Guo,et al.  Gene-centric meta-analyses of 108 912 individuals confirm known body mass index loci and reveal three novel signals. , 2013, Human molecular genetics.

[22]  Lester L. Peters,et al.  Genome-wide association study identifies novel breast cancer susceptibility loci , 2007, Nature.

[23]  Marc Liesa,et al.  Role of mitochondrial dynamics proteins in the pathophysiology of obesity and type 2 diabetes. , 2009, The international journal of biochemistry & cell biology.

[24]  H. Gylling,et al.  Cholesterol absorption efficiency and sterol metabolism in obesity. , 2000, Atherosclerosis.

[25]  Marylyn D. Ritchie,et al.  Analysis pipeline for the epistasis search – statistical versus biological filtering , 2014, Front. Genet..

[26]  Jason H. Moore,et al.  Pacific Symposium on Biocomputing 15:327-336(2010) ENABLING PERSONAL GENOMICS WITH AN EXPLICIT TEST OF EPISTASIS , 2022 .

[27]  Reiko Kurotani,et al.  Caveolin gene transfer improves glucose metabolism in diabetic mice. , 2010, American journal of physiology. Cell physiology.

[28]  J. H. Moore,et al.  Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. , 2001, American journal of human genetics.

[29]  Joshua Lederberg,et al.  Children's Hospital of Philadelphia. , 1975, The Australasian nurses journal.

[30]  H. Hauner,et al.  Obesity and impaired fibrinolysis: role of adipose production of plasminogen activator inhibitor-1 , 2004, International Journal of Obesity.

[31]  Henning Hermjakob,et al.  The Reactome pathway Knowledgebase , 2015, Nucleic acids research.

[32]  Scott M. Williams,et al.  A Simple and Computationally Efficient Approach to Multifactor Dimensionality Reduction Analysis of Gene-Gene Interactions for Quantitative Traits , 2013, PloS one.

[33]  Michael Schuler,et al.  PGC1α expression is controlled in skeletal muscles by PPARβ, whose ablation results in fiber-type switching, obesity, and type 2 diabetes , 2006 .

[34]  Robert L. Hamilton,et al.  Resistance to diet-induced hypercholesterolemia and gallstone formation in ACAT2-deficient mice , 2000, Nature Medicine.

[35]  J. Hirschhorn,et al.  A comprehensive review of genetic association studies , 2002, Genetics in Medicine.

[36]  A. Stunkard,et al.  A twin study of human obesity. , 1986, JAMA.

[37]  E. Calle,et al.  Overweight, obesity and cancer: epidemiological evidence and proposed mechanisms , 2004, Nature Reviews Cancer.

[38]  Vittorio Krogh,et al.  -344C/T Variant in the promoter of the aldosterone synthase gene (CYP11B2) is associated with metabolic syndrome in men. , 2007, American journal of hypertension.

[39]  Jason H. Moore,et al.  The Ubiquitous Nature of Epistasis in Determining Susceptibility to Common Human Diseases , 2003, Human Heredity.

[40]  L. Peltonen,et al.  Use of Genome-Wide Expression Data to Mine the “Gray Zone” of GWA Studies Leads to Novel Candidate Obesity Genes , 2010, PLoS genetics.

[41]  T. Dawber,et al.  Epidemiological approaches to heart disease: the Framingham Study. , 1951, American journal of public health and the nation's health.

[42]  Hiroyuki Ogata,et al.  KEGG: Kyoto Encyclopedia of Genes and Genomes , 1999, Nucleic Acids Res..

[43]  J. Gilbert,et al.  Complement Factor H Variant Increases the Risk of Age-Related Macular Degeneration , 2005, Science.

[44]  Yasuko Hagiwara,et al.  Insulin resistance in skeletal muscles of caveolin-3-null mice. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[45]  Jason H. Moore,et al.  Why epistasis is important for tackling complex human disease genetics , 2014, Genome Medicine.

[46]  D. Reich,et al.  Principal components analysis corrects for stratification in genome-wide association studies , 2006, Nature Genetics.

[47]  David Botstein,et al.  Genetic variation in aldosterone synthase predicts plasma glucose levels , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[48]  T. Mackay.  Epistasis and quantitative traits: using model organisms to study gene–gene interactions , 2013, Nature Reviews Genetics.

[49]  Michael Schuler,et al.  PGC1alpha expression is controlled in skeletal muscles by PPARbeta, whose ablation results in fiber-type switching, obesity, and type 2 diabetes. , 2006, Cell metabolism.

[50]  James Strait,et al.  Genome-Wide Association Scan Shows Genetic Variants in the FTO Gene Are Associated with Obesity-Related Traits , 2007, PLoS genetics.

[51]  Nicholas A. Johnson,et al.  Genome-wide association study of body height in African Americans: the Women's Health Initiative SNP Health Association Resource (SHARe). , 2012, Human molecular genetics.

[52]  J. Ott,et al.  Complement Factor H Polymorphism in Age-Related Macular Degeneration , 2005, Science.

[53]  Jean Tichet,et al.  Associations of the -344 T>C and the 3097 G>A polymorphisms of CYP11B2 gene with hypertension, type 2 diabetes, and metabolic syndrome in a French population. , 2010, American journal of hypertension.

[54]  Casey S. Greene,et al.  IMP: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks , 2012, Nucleic Acids Res..

[55]  K. Flegal,et al.  Prevalence of Childhood and Adult Obesity in the United States, 2011–2012 , 2014 .

[56]  Jason H. Moore,et al.  Missing heritability and strategies for finding the underlying causes of complex disease , 2010, Nature Reviews Genetics.

[57]  A. Folsom,et al.  The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. The ARIC investigators. , 1989, American journal of epidemiology.

[58]  Víctor Quesada,et al.  Identification and Characterization of Human and Mouse Ovastacin , 2004, Journal of Biological Chemistry.

[59]  Joseph T. Glessner,et al.  Role of BMI‐Associated Loci Identified in GWAS Meta‐Analyses in the Context of Common Childhood Obesity in European Americans , 2011, Obesity.

[60]  Scott M. Williams,et al.  challenges for genome-wide association studies , 2010 .

[61]  Judy H. Cho,et al.  Finding the missing heritability of complex diseases , 2009, Nature.

[62]  M. Neale,et al.  Genetic and Environmental Factors in Relative Body Weight and Human Adiposity , 1997, Behavior genetics.

[63]  Olga G. Troyanskaya,et al.  IMP 2.0: a multi-species functional genomics portal for integration, visualization and prediction of protein functions and networks , 2015, Nucleic Acids Res..

[64]  A. Edwards,et al.  Complement Factor H Polymorphism and Age-Related Macular Degeneration , 2005, Science.

[65]  M. Jarvelin,et al.  A Common Variant in the FTO Gene Is Associated with Body Mass Index and Predisposes to Childhood and Adult Obesity , 2007, Science.

[66]  R. Kronmal,et al.  The Cardiovascular Health Study: design and rationale. , 1991, Annals of epidemiology.

[67]  S B Hulley,et al.  CARDIA: study design, recruitment, and some characteristics of the examined subjects. , 1988, Journal of clinical epidemiology.

[68]  Manuel A. R. Ferreira,et al.  PLINK: a tool set for whole-genome association and population-based linkage analyses. , 2007, American journal of human genetics.