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A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance

Abstract

Recent genome-wide association studies have described many loci implicated in type 2 diabetes (T2D) pathophysiology and β-cell dysfunction but have contributed little to the understanding of the genetic basis of insulin resistance. We hypothesized that genes implicated in insulin resistance pathways might be uncovered by accounting for differences in body mass index (BMI) and potential interactions between BMI and genetic variants. We applied a joint meta-analysis approach to test associations with fasting insulin and glucose on a genome-wide scale. We present six previously unknown loci associated with fasting insulin at P < 5 × 10−8 in combined discovery and follow-up analyses of 52 studies comprising up to 96,496 non-diabetic individuals. Risk variants were associated with higher triglyceride and lower high-density lipoprotein (HDL) cholesterol levels, suggesting a role for these loci in insulin resistance pathways. The discovery of these loci will aid further characterization of the role of insulin resistance in T2D pathophysiology.

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Figure 1: Genome-wide association plots of the discovery JMA.
Figure 2: Regional plot of the COBLL1-GRB14 genomic locus.

References

  1. Billings, L.K. & Florez, J.C. The genetics of type 2 diabetes: what have we learned from GWAS? Ann. NY Acad. Sci. 1212, 59–77 (2010).

    CAS  Article  Google Scholar 

  2. Sladek, R. et al. A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature 445, 881–885 (2007).

    CAS  Article  Google Scholar 

  3. Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).

    CAS  Article  Google Scholar 

  4. Voight, B.F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010).

    CAS  Article  Google Scholar 

  5. Prudente, S., Morini, E. & Trischitta, V. Insulin signaling regulating genes: effect on T2DM and cardiovascular risk. Nat. Rev. Endocrinol. 5, 682–693 (2009).

    CAS  Article  Google Scholar 

  6. Kahn, S.E., Hull, R.L. & Utzschneider, K.M. Mechanisms linking obesity to insulin resistance and type 2 diabetes. Nature 444, 840–846 (2006).

    CAS  Article  Google Scholar 

  7. Wang, X. et al. Heritability of insulin sensitivity and lipid profile depend on BMI: evidence for gene-obesity interaction. Diabetologia 52, 2578–2584 (2009).

    CAS  Article  Google Scholar 

  8. Florez, J.C. et al. Effects of the type 2 diabetes–associated PPARG P12A polymorphism on progression to diabetes and response to troglitazone. J. Clin. Endocrinol. Metab. 92, 1502–1509 (2007).

    CAS  Article  Google Scholar 

  9. Ludovico, O. et al. Heterogeneous effect of peroxisome proliferator–activated receptor γ2 Ala12 variant on type 2 diabetes risk. Obesity (Silver Spring) 15, 1076–1081 (2007).

    CAS  Article  Google Scholar 

  10. Cauchi, S. et al. The genetic susceptibility to type 2 diabetes may be modulated by obesity status: implications for association studies. BMC Med. Genet. 9, 45 (2008).

    Article  Google Scholar 

  11. Trujillo, M.E. & Scherer, P.E. Adipose tissue–derived factors: impact on health and disease. Endocr. Rev. 27, 762–778 (2006).

    CAS  Article  Google Scholar 

  12. Shoelson, S.E., Lee, J. & Goldfine, A.B. Inflammation and insulin resistance. J. Clin. Invest. 116, 1793–1801 (2006).

    CAS  Article  Google Scholar 

  13. Kraft, P., Yen, Y.C., Stram, D.O., Morrison, J. & Gauderman, W.J. Exploiting gene-environment interaction to detect genetic associations. Hum. Hered. 63, 111–119 (2007).

    CAS  Article  Google Scholar 

  14. Manning, A.K. et al. Meta-analysis of gene-environment interaction: joint estimation of SNP and SNP × environment regression coefficients. Genet. Epidemiol. 35, 11–18 (2011).

    Article  Google Scholar 

  15. Matthews, D.R. et al. Homeostasis model assessment: insulin resistance and β-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 28, 412–419 (1985).

    CAS  Article  Google Scholar 

  16. Speliotes, E.K. et al. Association analyses of 249,796 individuals reveal 18 new loci associated with body mass index. Nat. Genet. 42, 937–948 (2010).

    CAS  Article  Google Scholar 

  17. Heid, I.M. et al. Meta-analysis identifies 13 new loci associated with waist-hip ratio and reveals sexual dimorphism in the genetic basis of fat distribution. Nat. Genet. 42, 949–960 (2010).

    CAS  Article  Google Scholar 

  18. Teslovich, T.M. et al. Biological, clinical and population relevance of 95 loci for blood lipids. Nature 466, 707–713 (2010).

    CAS  Article  Google Scholar 

  19. Rampersaud, E. et al. Identification of novel candidate genes for type 2 diabetes from a genome-wide association scan in the Old Order Amish: evidence for replication from diabetes-related quantitative traits and from independent populations. Diabetes 56, 3053–3062 (2007).

    CAS  Article  Google Scholar 

  20. Stolerman, E.S. et al. Haplotype structure of the ENPP1 gene and nominal association of the K121Q missense single nucleotide polymorphism with glycemic traits in the Framingham Heart Study. Diabetes 57, 1971–1977 (2008).

    CAS  Article  Google Scholar 

  21. Dehghan, A. et al. Meta-analysis of genome-wide association studies in &gt;80 000 subjects identifies multiple loci for C-reactive protein levels. Circulation 123, 731–738 (2011).

    CAS  Article  Google Scholar 

  22. Hivert, M.F. et al. Insulin resistance influences the association of adiponectin levels with diabetes incidence in two population-based cohorts: the Cooperative Health Research in the Region of Augsburg (KORA) S4/F4 study and the Framingham Offspring Study. Diabetologia 54, 1019–1024 (2011).

    CAS  Article  Google Scholar 

  23. Florez, J.C. Newly identified loci highlight β cell dysfunction as a key cause of type 2 diabetes: where are the insulin resistance genes? Diabetologia 51, 1100–1110 (2008).

    CAS  Article  Google Scholar 

  24. Timpson, N.J. et al. Adiposity-related heterogeneity in patterns of type 2 diabetes susceptibility observed in genome-wide association data. Diabetes 58, 505–510 (2009).

    CAS  Article  Google Scholar 

  25. Skol, A.D., Scott, L.J., Abecasis, G.R. & Boehnke, M. Optimal designs for two-stage genome-wide association studies. Genet. Epidemiol. 31, 776–788 (2007).

    Article  Google Scholar 

  26. Skol, A.D., Scott, L.J., Abecasis, G.R. & Boehnke, M. Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat. Genet. 38, 209–213 (2006).

    CAS  Article  Google Scholar 

  27. Saxena, R. et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat. Genet. 42, 142–148 (2010).

    CAS  Article  Google Scholar 

  28. Soranzo, N. et al. Common variants at 10 genomic loci influence hemoglobin A1C levels via glycemic and nonglycemic pathways. Diabetes 59, 3229–3239 (2010).

    CAS  Article  Google Scholar 

  29. Strawbridge, R.J. et al. Genome-wide association identifies nine common variants associated with fasting proinsulin levels and provides new insights into the pathophysiology of type 2 diabetes. Diabetes 60, 2624–2634 (2011).

    CAS  Article  Google Scholar 

  30. Lango Allen, H. et al. Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467, 832–838 (2010).

    CAS  Article  Google Scholar 

  31. Carroll, E.A. et al. Cordon-bleu is a conserved gene involved in neural tube formation. Dev. Biol. 262, 16–31 (2003).

    CAS  Article  Google Scholar 

  32. Depetris, R.S. et al. Structural basis for inhibition of the insulin receptor by the adaptor protein Grb14. Mol. Cell 20, 325–333 (2005).

    CAS  Article  Google Scholar 

  33. Ridker, P.M. et al. Polymorphism in the CETP gene region, HDL cholesterol, and risk of future myocardial infarction: genomewide analysis among 18 245 initially healthy women from the Women's Genome Health Study. Circ. Cardiovasc. Genet. 2, 26–33 (2009).

    CAS  Article  Google Scholar 

  34. White, M.F. The IRS-signalling system: a network of docking proteins that mediate insulin action. Mol. Cell. Biochem. 182, 3–11 (1998).

    CAS  Article  Google Scholar 

  35. Rung, J. et al. Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat. Genet. 41, 1110–1115 (2009).

    CAS  Article  Google Scholar 

  36. Samani, N.J. et al. Genomewide association analysis of coronary artery disease. N. Engl. J. Med. 357, 443–453 (2007).

    CAS  Article  Google Scholar 

  37. Waterworth, D.M. et al. Genetic variants influencing circulating lipid levels and risk of coronary artery disease. Arterioscler. Thromb. Vasc. Biol. 30, 2264–2276 (2010).

    CAS  Article  Google Scholar 

  38. Schadt, E.E. et al. Mapping the genetic architecture of gene expression in human liver. PLoS Biol. 6, e107 (2008).

    Article  Google Scholar 

  39. Smith, S., Giriat, I., Schmitt, A. & de Lange, T. Tankyrase, a poly(ADP-ribose) polymerase at human telomeres. Science 282, 1484–1487 (1998).

    CAS  Article  Google Scholar 

  40. Chi, N.W. & Lodish, H.F. Tankyrase is a golgi-associated mitogen-activated protein kinase substrate that interacts with IRAP in GLUT4 vesicles. J. Biol. Chem. 275, 38437–38444 (2000).

    CAS  Article  Google Scholar 

  41. Huang, S.M. et al. Tankyrase inhibition stabilizes axin and antagonizes Wnt signalling. Nature 461, 614–620 (2009).

    CAS  Article  Google Scholar 

  42. Royce, P.M. & Steinmann, B. Prolidase deficiency. Connective Tissue and Its Heritable Disorders 727–743 (Wiley-Liss, New York, 2002).

  43. Nica, A.C. et al. The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLoS Genet. 7, e1002003 (2011).

    CAS  Article  Google Scholar 

  44. The ARIC investigators. The Atherosclerosis Risk in Communities (ARIC) Study: design and objectives. Am. J. Epidemiol. 129, 687–702 (1989).

  45. Bostick, M. et al. UHRF1 plays a role in maintaining DNA methylation in mammalian cells. Science 317, 1760–1764 (2007).

    CAS  Article  Google Scholar 

  46. Ng, P.C. & Henikoff, S. Predicting deleterious amino acid substitutions. Genome Res. 11, 863–874 (2001).

    CAS  Article  Google Scholar 

  47. Evans, R.M., Barish, G.D. & Wang, Y.X. PPARs and the complex journey to obesity. Nat. Med. 10, 355–361 (2004).

    CAS  Article  Google Scholar 

  48. Reigstad, L.J. et al. Platelet-derived growth factor (PDGF)-C, a PDGF family member with a vascular endothelial growth factor–like structure. J. Biol. Chem. 278, 17114–17120 (2003).

    CAS  Article  Google Scholar 

  49. Handford, C.A. et al. The human glycine receptor β subunit: primary structure, functional characterisation and chromosomal localisation of the human and murine genes. Brain Res. Mol. Brain Res. 35, 211–219 (1996).

    CAS  Article  Google Scholar 

  50. den Hoed, M. et al. Genetic susceptibility to obesity and related traits in childhood and adolescence: influence of loci identified by genome-wide association studies. Diabetes 59, 2980–2988 (2010).

    CAS  Article  Google Scholar 

  51. Hotta, K. et al. Polymorphisms in NRXN3, TFAP2B, MSRA, LYPLAL1, FTO and MC4R and their effect on visceral fat area in the Japanese population. J. Hum. Genet. 55, 738–742 (2010).

    CAS  Article  Google Scholar 

  52. Lindgren, C.M. et al. Genome-wide association scan meta-analysis identifies three loci influencing adiposity and fat distribution. PLoS Genet. 5, e1000508 (2009).

    Article  Google Scholar 

  53. Seve, M., Chimienti, F., Devergnas, S. & Favier, A. In silico identification and expression of SLC30 family genes: an expressed sequence tag data mining strategy for the characterization of zinc transporters' tissue expression. BMC Genomics 5, 32 (2004).

    Article  Google Scholar 

  54. Ohagi, S. et al. Human prohormone convertase 3 gene: exon-intron organization and molecular scanning for mutations in Japanese subjects with NIDDM. Diabetes 45, 897–901 (1996).

    Article  Google Scholar 

  55. Benzinou, M. et al. Common nonsynonymous variants in PCSK1 confer risk of obesity. Nat. Genet. 40, 943–945 (2008).

    CAS  Article  Google Scholar 

  56. Miura, K. et al. ARAP1: a point of convergence for Arf and Rho signaling. Mol. Cell 9, 109–119 (2002).

    CAS  Article  Google Scholar 

  57. Clément, S. et al. The lipid phosphatase SHIP2 controls insulin sensitivity. Nature 409, 92–97 (2001).

    Article  Google Scholar 

  58. Kent, W.J. et al. The human genome browser at UCSC. Genome Res. 12, 996–1006 (2002).

    CAS  Article  Google Scholar 

  59. Silva, J.P. et al. Regulation of adaptive behaviour during fasting by hypothalamic Foxa2. Nature 462, 646–650 (2009).

    CAS  Article  Google Scholar 

  60. Xing, C., Cohen, J.C. & Boerwinkle, E. A weighted false discovery rate control procedure reveals alleles at FOXA2 that influence fasting glucose levels. Am. J. Hum. Genet. 86, 440–446 (2010).

    CAS  Article  Google Scholar 

  61. Liu, F. & Roth, R.A. Grb-IR: a SH2-domain-containing protein that binds to the insulin receptor and inhibits its function. Proc. Natl. Acad. Sci. USA 92, 10287–10291 (1995).

    CAS  Article  Google Scholar 

  62. Giovannone, B. et al. Two novel proteins that are linked to insulin-like growth factor (IGF-I) receptors by the Grb10 adapter and modulate IGF-I signaling. J. Biol. Chem. 278, 31564–31573 (2003).

    CAS  Article  Google Scholar 

  63. Murray, M.V., Kobayashi, R. & Krainer, A.R. The type 2C Ser/Thr phosphatase PP2Cγ is a pre-mRNA splicing factor. Genes Dev. 13, 87–97 (1999).

    CAS  Article  Google Scholar 

  64. Schwitzgebel, V.M. et al. Agenesis of human pancreas due to decreased half-life of insulin promoter factor 1. J. Clin. Endocrinol. Metab. 88, 4398–4406 (2003).

    CAS  Article  Google Scholar 

  65. Stoffers, D.A., Ferrer, J., Clarke, W.L. & Habener, J.F. Early-onset type-II diabetes mellitus (MODY4) linked to IPF1. Nat. Genet. 17, 138–139 (1997).

    CAS  Article  Google Scholar 

  66. Cockburn, B.N. et al. Insulin promoter factor–1 mutations and diabetes in Trinidad: identification of a novel diabetes-associated mutation (E224K) in an Indo-Trinidadian family. J. Clin. Endocrinol. Metab. 89, 971–978 (2004).

    CAS  Article  Google Scholar 

  67. Rasmussen-Torvik, L.J. et al. Impact of repeated measures and sample selection on genome-wide association studies of fasting glucose. Genet. Epidemiol. 34, 665–673 (2010).

    Article  Google Scholar 

  68. Li, Y., Willer, C., Sanna, S. & Abecasis, G. Genotype imputation. Annu. Rev. Genomics Hum. Genet. 10, 387–406 (2009).

    CAS  Article  Google Scholar 

  69. Li, Y., Willer, C.J., Ding, J., Scheet, P. & Abecasis, G.R. MaCH: using sequence and genotype data to estimate haplotypes and unobserved genotypes. Genet. Epidemiol. 34, 816–834 (2010).

    Article  Google Scholar 

  70. Howie, B.N., Donnelly, P. & Marchini, J. A flexible and accurate genotype imputation method for the next generation of genome-wide association studies. PLoS Genet. 5, e1000529 (2009).

    Article  Google Scholar 

  71. Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    CAS  Article  Google Scholar 

  72. Voorman, A., Lumley, T., McKnight, B. & Rice, K. Behavior of QQ-plots and genomic control in studies of gene-environment interaction. PLoS ONE 6, e19416 (2011).

    CAS  Article  Google Scholar 

  73. Aulchenko, Y.S., Struchalin, M.V. & van Duijn, C.M. ProbABEL package for genome-wide association analysis of imputed data. BMC Bioinformatics 11, 134 (2010).

    Article  Google Scholar 

  74. Petitti, D.B. Statistical methods in meta-analysis. in Meta-analysis, Decision Analysis, and Cost-effectiveness Analysis 90–114 (Oxford University Press, New York, 1994).

  75. Higgins, J.P. & Thompson, S.G. Quantifying heterogeneity in a meta-analysis. Stat. Med. 21, 1539–1558 (2002).

    Article  Google Scholar 

  76. The 1000 Genomes Project Consortium. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  77. Riva, A. & Kohane, I.S. A SNP-centric database for the investigation of the human genome. BMC Bioinformatics 5, 33 (2004).

    Article  Google Scholar 

  78. The Wellcome Trust Case Control Consortium. Genome-wide association study of CNVs in 16,000 cases of eight common diseases and 3,000 shared controls. Nature 464, 713–720 (2010).

  79. McCarroll, S.A. et al. Common deletion polymorphisms in the human genome. Nat. Genet. 38, 86–92 (2006).

    CAS  Article  Google Scholar 

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Acknowledgements

A full list of acknowledgments is provided in the Supplementary Note.

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Contributions

A.K.M., J.D. and J.B.M. conceived of the study, A.K.M. and R.A.S. performed the analysis, A.K.M., M.F.H. and R.A.S. wrote the manuscript, J.B.M. and C. Langenberg directed the work, and J.L.G., N.B.-N., H.C., D.R., C.-T.L., L.F.B., I.P., R.M.W., J.C.F., J.D., J.B.M. and C. Langenberg provided analytical advice and revised the manuscript.

Corresponding authors

Correspondence to James B Meigs or Claudia Langenberg.

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Competing interests

J.C.F. has received consulting fees from Novartis, Eli Lilly and Pfizer. I.B. and spouse own stock in GlaxoSmithKline and Incyte Ltd. A.R.S. is a consultant for Merck.

Additional information

A full list of members is provided in the Supplementary Note.

A full list of members is provided in the Supplementary Note.

Supplementary information

Supplementary Text and Figures

Supplementary Note and Supplementary Figures 1–3 (PDF 1606 kb)

Supplementary Table 1

Study descriptives of samples included in meta-analysis (XLS 106 kb)

Supplementary Table 2

Meta-analysis results of loci with known associations with glycemic traits (XLS 71 kb)

Supplementary Table 3

Meta-analysis results of fasting insulin and fasting glucose loci taken to the follow-up stage of analysis (XLS 225 kb)

Supplementary Table 4

Association results of index SNPs with other traits (HOMA-IR, HOMA-B, 2hr glucose, and HbA1c) in MAGIC (XLS 33 kb)

Supplementary Table 5

eQTL SNPs in linkage disequilibrium with fasting insulin index SNPs in gene expression database from liver tissue (XLS 41 kb)

Supplementary Table 6

Fasting insulin index SNPs in gene expression eQTL database from subcutaneous adipose tissue (MuTHER). (XLS 46 kb)

Supplementary Table 7

Details of coding SNPs (cSNPs) near index SNPs. (XLS 56 kb)

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Manning, A., Hivert, MF., Scott, R. et al. A genome-wide approach accounting for body mass index identifies genetic variants influencing fasting glycemic traits and insulin resistance. Nat Genet 44, 659–669 (2012). https://doi.org/10.1038/ng.2274

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