Genome-wide analysis of epistasis in body mass index using multiple human populations

We surveyed gene–gene interactions (epistasis) in human body mass index (BMI) in four European populations (n<1200) via exhaustive pair-wise genome scans where interactions were computed as F ratios by testing a linear regression model fitting two single-nucleotide polymorphisms (SNPs) with interactions against the one without. Before the association tests, BMI was corrected for sex and age, normalised and adjusted for relatedness. Neither single SNPs nor SNP interactions were genome-wide significant in either cohort based on the consensus threshold (P=5.0E−08) and a Bonferroni corrected threshold (P=1.1E−12), respectively. Next we compared sub genome-wide significant SNP interactions (P<5.0E−08) across cohorts to identify common epistatic signals, where SNPs were annotated to genes to test for gene ontology (GO) enrichment. Among the epistatic genes contributing to the commonly enriched GO terms, 19 were shared across study cohorts of which 15 are previously published genome-wide association loci, including CDH13 (cadherin 13) associated with height and SORCS2 (sortilin-related VPS10 domain containing receptor 2) associated with circulating insulin-like growth factor 1 and binding protein 3. Interactions between the 19 shared epistatic genes and those involving BMI candidate loci (P<5.0E−08) were tested across cohorts and found eight replicated at the SNP level (P<0.05) in at least one cohort, which were further tested and showed limited replication in a separate European population (n>5000). We conclude that genome-wide analysis of epistasis in multiple populations is an effective approach to provide new insights into the genetic regulation of BMI but requires additional efforts to confirm the findings.

[1]  J Blangero,et al.  Large upward bias in estimation of locus-specific effects from genomewide scans. , 2001, American journal of human genetics.

[2]  W. Gauderman Sample size requirements for association studies of gene-gene interaction. , 2002, American journal of epidemiology.

[3]  Igor Rudan,et al.  3000 years of solitude: extreme differentiation in the island isolates of Dalmatia, Croatia , 2006, European Journal of Human Genetics.

[4]  Thomas Meitinger,et al.  The genetic study of three population microisolates in South Tyrol (MICROS): study design and epidemiological perspectives , 2007, BMC Medical Genetics.

[5]  Yurii S. Aulchenko,et al.  BIOINFORMATICS APPLICATIONS NOTE doi:10.1093/bioinformatics/btm108 Genetics and population analysis GenABEL: an R library for genome-wide association analysis , 2022 .

[6]  C. Haley,et al.  Genomewide Rapid Association Using Mixed Model and Regression: A Fast and Simple Method For Genomewide Pedigree-Based Quantitative Trait Loci Association Analysis , 2007, Genetics.

[7]  M. McCarthy,et al.  Genome-wide association studies for complex traits: consensus, uncertainty and challenges , 2008, Nature Reviews Genetics.

[8]  Igor Rudan,et al.  Runs of homozygosity in European populations. , 2008, American journal of human genetics.

[9]  M. LeBlanc,et al.  Increasing the power of identifying gene × gene interactions in genome‐wide association studies , 2008, Genetic epidemiology.

[10]  P. Phillips Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems , 2008, Nature Reviews Genetics.

[11]  Israel Steinfeld,et al.  BMC Bioinformatics BioMed Central , 2008 .

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

[13]  U. Manne,et al.  Immunohistochemical expression of rabphilin-3A-like (Noc2) in normal and tumor tissues of human endocrine pancreas , 2009, Biotechnic & histochemistry : official publication of the Biological Stain Commission.

[14]  Luigi Ferrucci,et al.  Genome-wide association analysis of total cholesterol and high-density lipoprotein cholesterol levels using the Framingham Heart Study data , 2010, BMC Medical Genetics.

[15]  Chris S Haley,et al.  A combined strategy for quantitative trait loci detection by genome-wide association , 2009, BMC proceedings.

[16]  I. Rudan,et al.  Genome-wide association study of anthropometric traits in Korcula Island, Croatia. , 2009, Croatian medical journal.

[17]  H. Cordell Detecting gene–gene interactions that underlie human diseases , 2009, Nature Reviews Genetics.

[18]  C. Hoggart,et al.  Genome-wide association analysis of metabolic traits in a birth cohort from a founder population , 2008, Nature Genetics.

[19]  F. Collins,et al.  Potential etiologic and functional implications of genome-wide association loci for human diseases and traits , 2009, Proceedings of the National Academy of Sciences.

[20]  J. Hein,et al.  Using biological networks to search for interacting loci in genome-wide association studies , 2009, European Journal of Human Genetics.

[21]  Christian Gieger,et al.  Six new loci associated with body mass index highlight a neuronal influence on body weight regulation , 2009, Nature Genetics.

[22]  Matti Pirinen,et al.  A genome-wide association study identifies new psoriasis susceptibility loci and an interaction between HLA-C and ERAP1 , 2010, Nature Genetics.

[23]  Ioannis Xenarios,et al.  FastEpistasis: a high performance computing solution for quantitative trait epistasis , 2010, Bioinform..

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

[25]  C. Haley,et al.  Controlling false positives in the mapping of epistatic QTL , 2010, Heredity.

[26]  Y. Kamatani,et al.  A genome-wide association study in 19 633 Japanese subjects identified LHX3-QSOX2 and IGF1 as adult height loci. , 2010, Human Molecular Genetics.

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

[28]  P. Visscher,et al.  Comparing apples and oranges: equating the power of case‐control and quantitative trait association studies , 2009, Genetic epidemiology.

[29]  A. Kimmel,et al.  Adoption of PERILIPIN as a unifying nomenclature for the mammalian PAT-family of intracellular lipid storage droplet proteins , 2010, Journal of Lipid Research.

[30]  R. Luben,et al.  Cumulative effects and predictive value of common obesity-susceptibility variants identified by genome-wide association studies. , 2010, The American journal of clinical nutrition.

[31]  Thomas Meitinger,et al.  Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution , 2010, Nature Genetics.

[32]  K. S. Vimaleswaran,et al.  Progress in the genetics of common obesity and type 2 diabetes , 2010, Expert Reviews in Molecular Medicine.

[33]  P. Rudan,et al.  Modeling of Environmental Effects in Genome-Wide Association Studies Identifies SLC2A2 and HP as Novel Loci Influencing Serum Cholesterol Levels , 2010, PLoS genetics.

[34]  Qiong Yang,et al.  Analyze multivariate phenotypes in genetic association studies by combining univariate association tests , 2010, Genetic epidemiology.

[35]  N. Wareham,et al.  Life course variations in the associations between FTO and MC4R gene variants and body size , 2009, Human molecular genetics.

[36]  Paul Weston,et al.  Interaction between ERAP1 and HLA-B27 in ankylosing spondylitis implicates peptide handling in the mechanism for HLA-B27 in disease susceptibility , 2011, Nature Genetics.

[37]  J. Ordovás,et al.  Gene–gene and gene–environment interactions defining lipid-related traits , 2011, Current opinion in lipidology.

[38]  Yang Liu,et al.  Genome-Wide Interaction-Based Association Analysis Identified Multiple New Susceptibility Loci for Common Diseases , 2011, PLoS genetics.

[39]  Johnny S. H. Kwan,et al.  GATES: a rapid and powerful gene-based association test using extended Simes procedure. , 2011, American journal of human genetics.

[40]  Y. J. Kim,et al.  A Network-Based Approach to Prioritize Results from Genome-Wide Association Studies , 2011, PloS one.

[41]  Chris S. Haley,et al.  Characterisation of Genome-Wide Association Epistasis Signals for Serum Uric Acid in Human Population Isolates , 2011, PloS one.

[42]  C. Gieger,et al.  A genome-wide association study identifies novel loci associated with circulating IGF-I and IGFBP-3. , 2011, Human molecular genetics.