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New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk

An Erratum to this article was published on 01 May 2010

This article has been updated

Abstract

Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes.

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Figure 1: Regional plots of ten newly discovered genome-wide significant associations.
Figure 2: Quantile-quantile plots.
Figure 3: Variation in levels of fasting glucose depending on the number of risk alleles at newly identified loci, weighted by effect size in an aggregate genotype score for the Framingham Heart Study.

Change history

  • 26 March 2010

    In the version of this article initially published, there were several errors in the author affiliations. These errors have been corrected in the HTML and PDF versions of the article.

References

  1. Genuth, S. et al. The Expert Committee on the Diagnosis and Classification of Diabetes Mellitus: Follow-up report on the diagnosis of diabetes mellitus. Diabetes Care 26, 3160–3167 (2003).

    Article  Google Scholar 

  2. Coutinho, M., Gerstein, H.C., Wang, Y. & Yusuf, S. The relationship between glucose and incident cardiovascular events. A metaregression analysis of published data from 20 studies of 95,783 individuals followed for 12.4 years. Diabetes Care 22, 233–240 (1999).

    Article  CAS  Google Scholar 

  3. Meigs, J.B., Nathan, D.M., D'Agostino, R.B. Sr. & Wilson, P.W. Fasting and postchallenge glycemia and cardiovascular disease risk: the Framingham Offspring Study. Diabetes Care 25, 1845–1850 (2002).

    Article  Google Scholar 

  4. UKPDS. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 352, 837–853 (1998).

  5. Patel, A. et al. Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes. N. Engl. J. Med. 358, 2560–2572 (2008).

    Article  CAS  Google Scholar 

  6. Holman, R.R., Paul, S.K., Bethel, M.A., Matthews, D.R. & Neil, H.A. 10-year follow-up of intensive glucose control in type 2 diabetes. N. Engl. J. Med. 359, 1577–1589 (2008).

    Article  CAS  Google Scholar 

  7. Ray, K.K. et al. Effect of intensive control of glucose on cardiovascular outcomes and death in patients with diabetes mellitus: a meta-analysis of randomised controlled trials. Lancet 373, 1765–1772 (2009).

    Article  CAS  Google Scholar 

  8. Prokopenko, I., McCarthy, M.I. & Lindgren, C.M. Type 2 diabetes: new genes, new understanding. Trends Genet. 24, 613–621 (2008).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  10. Weedon, M.N. et al. A common haplotype of the glucokinase gene alters fasting glucose and birth weight: association in six studies and population-genetics analyses. Am. J. Hum. Genet. 79, 991–1001 (2006).

    Article  CAS  Google Scholar 

  11. Sparsø, T. et al. The GCKR rs780094 polymorphism is associated with elevated fasting serum triacylglycerol, reduced fasting and OGTT-related insulinaemia, and reduced risk of type 2 diabetes. Diabetologia 51, 70–75 (2008).

    Article  Google Scholar 

  12. Orho-Melander, M. et al. A common missense variant in the glucokinase regulatory protein gene (GCKR) is associated with increased plasma triglyceride and C-reactive protein but lower fasting glucose concentrations. Diabetes 57, 3112–3121 (2008).

    Article  CAS  Google Scholar 

  13. Bouatia-Naji, N. et al. A polymorphism within the G6PC2 gene is associated with fasting plasma glucose levels. Science 320, 1085–1088 (2008).

    Article  CAS  Google Scholar 

  14. Chen, W.-M. et al. Association studies in Caucasians identify variants in the G6PC2/ABCB11 region regulating fasting glucose levels. J. Clin. Invest. 118, 2620–2628 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Prokopenko, I. et al. Variants in MTNR1B influence fasting glucose levels. Nat. Genet. 41, 77–81 (2009).

    Article  CAS  Google Scholar 

  16. Lyssenko, V. et al. Common variant in MTNR1B associated with increased risk of type 2 diabetes and impaired early insulin secretion. Nat. Genet. 41, 82–88 (2009).

    Article  CAS  Google Scholar 

  17. Bouatia-Naji, N. et al. A variant near MTNR1B is associated with increased fasting plasma glucose levels and type 2 diabetes risk. Nat. Genet. 41, 89–94 (2009).

    Article  CAS  Google Scholar 

  18. 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).

    Article  CAS  Google Scholar 

  19. Pe'er, I., Yelensky, R., Altshuler, D. & Daly, M.J. Estimation of the multiple testing burden for genomewide association studies of nearly all common variants. Genet. Epidemiol. 32, 381–385 (2008).

    Article  Google Scholar 

  20. Brunzell, J.D. et al. Relationships between fasting plasma glucose levels and insulin secretion during intravenous glucose tolerance tests. J. Clin. Endocrinol. Metab. 42, 222–229 (1976).

    Article  CAS  Google Scholar 

  21. Weir, G.C. & Bonner-Weir, S. Five stages of evolving β-cell dysfunction during progression to diabetes. Diabetes 53, S16–S21 (2004).

    Article  CAS  Google Scholar 

  22. 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).

    Article  CAS  Google Scholar 

  23. Li, Y. & Mach Abecasis, G.R. 1.0: Rapid haplotype reconstruction and missing genotype inference. Am. J. Hum. Genet. S79, 2290 (2006).

    Google Scholar 

  24. Sabatti, C. et al. Genome-wide association analysis of metabolic traits in a birth cohort from a founder population. Nat. Genet. 41, 35–46 (2009).

    Article  CAS  Google Scholar 

  25. Diabetes Genetics Initiative of Broad Institute of Harvard and MIT. Lund University and Novartis Institutes for BioMedical Research. Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 316, 1331–1336 (2007).

  26. Ioannidis, J.P., Ntzani, E.E., Trikalinos, T.A. & Contopoulos-Ioannidis, D.G. Replication validity of genetic association studies. Nat. Genet. 29, 306–309 (2001).

    Article  CAS  Google Scholar 

  27. Nejentsev, S., Walker, N., Riches, D., Egholm, M. & Todd, J.A. Rare variants of IFIH1, a gene implicated in antiviral responses, protect against type 1 diabetes. Science 324, 387–389 (2009).

    Article  CAS  Google Scholar 

  28. Tirosh, A. et al. Normal fasting plasma glucose levels and type 2 diabetes in young men. N. Engl. J. Med. 353, 1454–1462 (2005).

    Article  CAS  Google Scholar 

  29. Saxena, R. et al. Genetic variation in GIPR influences the glucose and insulin responses to an oral glucose challenge. Nat. Genet. advance online publication, doi:10.1038/ng.521 (17 January 2010).

  30. Vaxillaire, M. et al. The common P446L polymorphism in GCKR inversely modulates fasting glucose and triglyceride levels and reduces type 2 diabetes risk in the DESIR prospective general French population. Diabetes 57, 2253–2257 (2008).

    Article  CAS  Google Scholar 

  31. Willer, C.J. et al. Six new loci associated with body mass index highlight a neuronal influence on body weight regulation. Nat. Genet. 41, 25–34 (2009).

    Article  CAS  Google Scholar 

  32. Newton-Cheh, C. et al. Eight blood pressure loci identified by genomewide association study of 34,433 people of European ancestry. Nat. Genet. 41, 666–676 (2009).

    Article  CAS  Google Scholar 

  33. Aulchenko, Y.S. et al. Loci influencing lipid levels and coronary heart disease risk in 16 European population cohorts. Nat. Genet. 41, 47–55 (2009).

    Article  CAS  Google Scholar 

  34. Kathiresan, S. et al. Common variants at 30 loci contribute to polygenic dyslipidemia. Nat. Genet. 41, 56–65 (2009).

    Article  CAS  Google Scholar 

  35. Sunyaev, S. et al. Prediction of deleterious human alleles. Hum. Mol. Genet. 10, 591–597 (2001).

    Article  CAS  Google Scholar 

  36. Thomas, P.D. et al. Applications for protein sequence-function evolution data: mRNA/protein expression analysis and coding SNP scoring tools. Nucleic Acids Res. 34, W645–W650 (2006).

    Article  Google Scholar 

  37. Beer, N.L. et al. The P446L variant in GCKR associated with fasting plasma glucose and triglyceride levels exerts its effect through increased glucokinase activity in liver. Hum. Mol. Genet. 18, 4081–4088 (2009).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  Google Scholar 

  40. Myers, A.J. et al. A survey of genetic human cortical gene expression. Nat. Genet. 39, 1494–1499 (2007).

    Article  CAS  Google Scholar 

  41. Dixon, A.L. et al. A genome-wide association study of global gene expression. Nat. Genet. 39, 1202–1207 (2007).

    Article  CAS  Google Scholar 

  42. Gieger, C. et al. Genetics meets metabolomics: a genome-wide association study of metabolite profiles in human serum. PLoS Genet. 4, e1000282 (2008).

    Article  Google Scholar 

  43. Schaeffer, L. et al. Common genetic variants of the FADS1 FADS2 gene cluster and their reconstructed haplotypes are associated with the fatty acid composition in phospholipids. Hum. Mol. Genet. 15, 1745–1756 (2006).

    Article  CAS  Google Scholar 

  44. McCarroll, S.A. et al. Integrated detection and population-genetic analysis of SNPs and copy number variation. Nat. Genet. 40, 1166–1174 (2008).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  46. Doria, A., Patti, M.-E. & Kahn, C.R. The emerging genetic architecture of type 2 diabetes. Cell Metab. 8, 186–200 (2008).

    Article  CAS  Google Scholar 

  47. Bergman, R.N. et al. Minimal model-based insulin sensitivity has greater heritability and a different genetic basis than homeostasis model assessment or fasting insulin. Diabetes 52, 2168–2174 (2003).

    Article  CAS  Google Scholar 

  48. Higgins, J.P. & Thompson, S.G. Quantifying heterogeneity in a metaanalysis. Stat. Med. 21, 1539–1558 (2002).

    Article  Google Scholar 

  49. Peter-Riesch, B., Fathi, M., Schlegel, W. & Wollheim, C.B. Glucose and carbachol generate 1,2-diacylglycerols by different mechanisms in pancreatic islets. J. Clin. Invest. 81, 1154–1161 (1988).

    Article  CAS  Google Scholar 

  50. Prentki, M. & Matschinsky, F.M. Ca2+, cAMP, and phospholipid-derived messengers in coupling mechanisms of insulin secretion. Physiol. Rev. 67, 1185–1248 (1987).

    Article  CAS  Google Scholar 

  51. Drucker, D.J. The role of gut hormones in glucose homeostasis. J. Clin. Invest. 117, 24–32 (2007).

    Article  CAS  Google Scholar 

  52. Fukada, T. et al. The zinc transporter SLC39A13/ZIP13 is required for connective tissue development; its involvement in BMP/TGF-β signaling pathways. PLoS One 3, e3642 (2008).

    Article  Google Scholar 

  53. Mitro, N. et al. The nuclear receptor LXR is a glucose sensor. Nature 445, 219–223 (2007).

    Article  CAS  Google Scholar 

  54. Rorsman, P. et al. Activation by adrenaline of a low-conductance G protein-dependent K+ channel in mouse pancreatic β cells. Nature 349, 77–79 (1991).

    Article  CAS  Google Scholar 

  55. Keane, D. & Newsholme, P. Saturated and unsaturated (including arachidonic acid) non-esterified fatty acid modulation of insulin secretion from pancreatic β-cells. Biochem. Soc. Trans. 36, 955–958 (2008).

    Article  CAS  Google Scholar 

  56. Kume, K. et al. mCRY1 and mCRY2 are essential components of the negative limb of the circadian clock feedback loop. Cell 98, 193–205 (1999).

    Article  CAS  Google Scholar 

  57. Rudic, R.D. et al. BMAL1 and CLOCK, two essential components of the circadian clock, are involved in glucose homeostasis. PLoS Biol. 2, e377 (2004).

    Article  Google Scholar 

  58. Song, J.J. & Lee, Y.J. Cross-talk between JIP3 and JIP1 during glucose deprivation: SEK1–JNK2 and Akt1 act as mediators. J. Biol. Chem. 280, 26845–26855 (2005).

    Article  CAS  Google Scholar 

  59. Waeber, G. et al. The gene MAPK8IP1, encoding islet-brain-1, is a candidate for type 2 diabetes. Nat. Genet. 24, 291–295 (2000).

    Article  CAS  Google Scholar 

  60. Santer, R. et al. Mutations in GLUT2, the gene for the liver-type glucose transporter, in patients with Fanconi-Bickel syndrome. Nat. Genet. 17, 324–326 (1997).

    Article  CAS  Google Scholar 

  61. Guillam, M.T. et al. Early diabetes and abnormal postnatal pancreatic islet development in mice lacking Glut-2. Nat. Genet. 17, 327–330 (1997).

    Article  CAS  Google Scholar 

  62. Kim, Y.-S., Nakanishi, G., Lewandoski, M. & Jetten, A.M. GLIS3, a novel member of the GLIS subfamily of Kruppel-like zinc finger proteins with repressor and activation functions. Nucleic Acids Res. 31, 5513–5525 (2003).

    Article  CAS  Google Scholar 

  63. Senée, V. et al. Mutations in GLIS3 are responsible for a rare syndrome with neonatal diabetes mellitus and congenital hypothyroidism. Nat. Genet. 38, 682–687 (2006).

    Article  Google Scholar 

  64. Song, K.-H., Li, T. & Chiang, J.Y.L. A prospero-related homeodomain protein is a novel co-regulator of hepatocyte nuclear factor 4α that regulates the cholesterol 7α-hydroxylase gene. J. Biol. Chem. 281, 10081–10088 (2006).

    Article  CAS  Google Scholar 

  65. Yamagata, K. et al. Mutations in the hepatocyte nuclear factor-4α gene in maturity- onset diabetes of the young (MODY1). Nature 384, 458–460 (1996).

    Article  CAS  Google Scholar 

  66. Warton, K., Foster, N.C., Gold, W.A. & Stanley, K.K. A novel gene family induced by acute inflammation in endothelial cells. Gene 342, 85–95 (2004).

    Article  CAS  Google Scholar 

  67. Clemmons, D.R. Role of insulin-like growth factor in maintaining normal glucose homeostasis. Horm. Res. 62 (Suppl. 1), 77–82 (2004).

    CAS  PubMed  Google Scholar 

  68. Servin, B. & Stephens, M. Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet. 3, e114 (2007).

    Article  Google Scholar 

  69. Aulchenko, Y.S., Ripke, S., Isaacs, A. & van Duijn, C.M. GenABEL: an R library for genome-wide association analysis. Bioinformatics 23, 1294–1296 (2007).

    Article  CAS  Google Scholar 

  70. R Development Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2007).

  71. Petitti, D.B. Statistical methods in meta-analysis. in Meta-analysis, Decision Analysis, and Cost-effectiveness Analysis (ed. Petitti, D.B.) 94–118 (Oxford University Press, New York, 2000).

    Chapter  Google Scholar 

  72. Devlin, B. & Roeder, K. Genomic control for association studies. Biometrics 55, 997–1004 (1999).

    Article  CAS  Google Scholar 

  73. Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009).

    Article  Google Scholar 

  74. Lukowiak, B. et al. Identification and purification of functional human β-cells by a new specific zinc-fluorescent probe. J. Histochem. Cytochem. 49, 519–528 (2001).

    Article  CAS  Google Scholar 

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Correspondence to Michael Boehnke, Mark I McCarthy, Jose C Florez or Inês Barroso.

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J.B.M. currently has research grants from GlaxoSmithKline and Sanofi-Aventis, and serves on consultancy boards for Eli Lilly and Interleukin Genetics. J.C.F. has received consulting honoraria from Merck, Pfizer, bioStrategies, XOMA and Publicis Healthcare Communications Group, a global advertising agency engaged by Amylin Pharmaceuticals. deCODE authors are employees at deCODE genetics and own stock or stock options in the company. P.W.F. has received consulting honoraria from Unilever. P.V. and G.W. received financial support from GlaxoSmithKline to build the CoLaus study. V.M., K.S. and D.M.W. are all full-time employees at GlaxoSmithKline. I.B. and spouse own stock in GlaxoSmithKline and Incyte.

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Supplementary Table 1

Study characteristics for discovery (a) and replication (b) cohorts (XLS 162 kb)

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Dupuis, J., Langenberg, C., Prokopenko, I. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat Genet 42, 105–116 (2010). https://doi.org/10.1038/ng.520

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