Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci

This article has been updated

Abstract

We carried out a genome-wide association study of type-2 diabetes (T2D) in individuals of South Asian ancestry. Our discovery set included 5,561 individuals with T2D (cases) and 14,458 controls drawn from studies in London, Pakistan and Singapore. We identified 20 independent SNPs associated with T2D at P < 10−4 for testing in a replication sample of 13,170 cases and 25,398 controls, also all of South Asian ancestry. In the combined analysis, we identified common genetic variants at six loci (GRB14, ST6GAL1, VPS26A, HMG20A, AP3S2 and HNF4A) newly associated with T2D (P = 4.1 × 10−8 to P = 1.9 × 10−11). SNPs at GRB14 were also associated with insulin sensitivity (P = 5.0 × 10−4), and SNPs at ST6GAL1 and HNF4A were also associated with pancreatic beta-cell function (P = 0.02 and P = 0.001, respectively). Our findings provide additional insight into mechanisms underlying T2D and show the potential for new discovery from genetic association studies in South Asians, a population with increased susceptibility to T2D.

This is a preview of subscription content, access via your institution

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Prices may be subject to local taxes which are calculated during checkout

Figure 1
Figure 2: Manhattan plot for the primary South Asian genome-wide association analysis of men and women using directly genotyped SNPs.
Figure 3: Regional plots for the six loci associated with type 2 diabetes in South Asians.

Similar content being viewed by others

Change history

  • 16 September 2011

    In the version of this article initially published online, Elin Grundberg’s name was misspelled as Elin Grunberg, and Xinzhong Li’s name was misspelled as Xinzhing Li. The error has been corrected for the print, PDF and HTML versions of the article.

References

  1. Chambers, J.C. et al. Plasma homocysteine concentrations and risk of coronary heart disease in UK Indian Asian and European men. Lancet 355, 523–527 (2000).

    Article  CAS  PubMed  Google Scholar 

  2. Ramachandran, A., Ma, R.C. & Snehalatha, C. Diabetes in Asia. Lancet 375, 408–418 (2010).

    Article  PubMed  Google Scholar 

  3. Shaw, J.E., Sicree, R.A. & Zimmet, P.Z. Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res. Clin. Pract. 87, 4–14 (2010).

    Article  CAS  PubMed  Google Scholar 

  4. McCarthy, M.I. Genomics, type 2 diabetes, and obesity. N. Engl. J. Med. 363, 2339–2350 (2010).

    Article  CAS  PubMed  Google Scholar 

  5. Jowett, J.B. et al. Genetic influences on type 2 diabetes and metabolic syndrome related quantitative traits in Mauritius. Twin Res. Hum. Genet. 12, 44–52 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Chambers, J.C. et al. Genetic variation in SCN10A influences cardiac conduction. Nat. Genet. 42, 149–152 (2010).

    Article  CAS  PubMed  Google Scholar 

  7. Saleheen, D. et al. The Pakistan Risk of Myocardial Infarction Study: a resource for the study of genetic, lifestyle and other determinants of myocardial infarction in South Asia. Eur. J. Epidemiol. 24, 329–338 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Lavanya, R. et al. Methodology of the Singapore Indian Chinese Cohort (SICC) eye study: quantifying ethnic variations in the epidemiology of eye diseases in Asians. Ophthalmic Epidemiol. 16, 325–336 (2009).

    Article  PubMed  Google Scholar 

  9. Price, A.L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  13. 1000 Genomes Project Consortium. et al. A map of human genome variation from population-scale sequencing. Nature 467, 1061–1073 (2010).

  14. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Chambers, J.C. et al. Common genetic variation near MC4R is associated with waist circumference and insulin resistance. Nat. Genet. 40, 716–718 (2008).

    Article  CAS  PubMed  Google Scholar 

  16. Kooner, J.S. et al. Genome-wide scan identifies variation in MLXIPL associated with plasma triglycerides. Nat. Genet. 40, 149–151 (2008).

    Article  CAS  PubMed  Google Scholar 

  17. Coronary Artery Disease (C4D) Genetics Consortium. A genome-wide association study in Europeans and South Asians identifies five new loci for coronary artery disease. Nat. Genet. 43, 339–344 (2011).

  18. Dufresne, A.M. & Smith, R.J. The adapter protein GRB10 is an endogenous negative regulator of insulin-like growth factor signaling. Endocrinology 146, 4399–4409 (2005).

    Article  CAS  PubMed  Google Scholar 

  19. Depetris, R.S., Wu, J. & Hubbard, S.R. Structural and functional studies of the Ras-associating and pleckstrin-homology domains of Grb10 and Grb14. Nat. Struct. Mol. Biol. 16, 833–839 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Holt, L.J. et al. Dual ablation of Grb10 and Grb14 in mice reveals their combined role in regulation of insulin signaling and glucose homeostasis. Mol. Endocrinol. 23, 1406–1414 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Woodard-Grice, A.V., McBrayer, A.C., Wakefield, J.K., Zhuo, Y. & Bellis, S.L. Proteolytic shedding of ST6Gal-I by BACE1 regulates the glycosylation and function of α4β1 integrins. J. Biol. Chem. 283, 26364–26373 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Maeda, N. et al. Diet-induced insulin resistance in mice lacking adiponectin/ACRP30. Nat. Med. 8, 731–737 (2002).

    Article  CAS  PubMed  Google Scholar 

  23. Siitonen, N. et al. Association of ADIPOQ gene variants with body weight, type 2 diabetes and serum adiponectin concentrations: the Finnish Diabetes Prevention Study. BMC Med. Genet. 12, 5 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Seaman, M.N., Marcusson, E.G., Cereghino, J.L. & Emr, S.D. Endosome to Golgi retrieval of the vacuolar protein sorting receptor, Vps10p, requires the function of the VPS29, VPS30, and VPS35 gene products. J. Cell Biol. 137, 79–92 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Seaman, M.N., Harbour, M.E., Tattersall, D., Read, E. & Bright, N. Membrane recruitment of the cargo-selective retromer subcomplex is catalysed by the small GTPase Rab7 and inhibited by the Rab-GAP TBC1D5. J. Cell Sci. 122, 2371–2382 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Kim, E. et al. Identification of novel retromer complexes in the mouse testis. Biochem. Biophys. Res. Commun. 375, 16–21 (2008).

    Article  CAS  PubMed  Google Scholar 

  27. Dell'Angelica, E.C. et al. AP-3: an adaptor-like protein complex with ubiquitous expression. EMBO J. 16, 917–928 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Brasaemle, D.L. et al. Perilipin A increases triacylglycerol storage by decreasing the rate of triacylglycerol hydrolysis. J. Biol. Chem. 275, 38486–38493 (2000).

    Article  CAS  PubMed  Google Scholar 

  29. Qi, L. et al. Genetic variation at the perilipin (PLIN) locus is associated with obesity-related phenotypes in White women. Clin. Genet. 66, 299–310 (2004).

    Article  CAS  PubMed  Google Scholar 

  30. Beller, M. et al. PERILIPIN-dependent control of lipid droplet structure and fat storage in Drosophila. Cell Metab. 12, 521–532 (2010).

    Article  CAS  PubMed  Google Scholar 

  31. Sumoy, L. et al. HMG20A and HMG20B map to human chromosomes 15q24 and 19p13.3 and constitute a distinct class of HMG-box genes with ubiquitous expression. Cytogenet. Cell Genet. 88, 62–67 (2000).

    Article  CAS  PubMed  Google Scholar 

  32. Artegiani, B. et al. The interaction with HMG20a/b proteins suggests a potential role for β-dystrobrevin in neuronal differentiation. J. Biol. Chem. 285, 24740–24750 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Battle, M.A. et al. Hepatocyte nuclear factor 4α orchestrates expression of cell adhesion proteins during the epithelial transformation of the developing liver. Proc. Natl. Acad. Sci. USA 103, 8419–8424 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Harries, L.W., Brown, J.E. & Gloyn, A.L. Species-specific differences in the expression of the HNF1A, HNF1B and HNF4A genes. PLoS ONE 4, e7855 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  35. 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  PubMed  Google Scholar 

  36. Teo, Y.Y. et al. Singapore Genome Variation Project: a haplotype map of three Southeast Asian populations. Genome Res. 19, 2154–2162 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Chidambaram, M., Radha, V. & Mohan, V. Replication of recently described type 2 diabetes gene variants in a South Indian population. Metabolism 59, 1760–1766 (2010).

    Article  CAS  PubMed  Google Scholar 

  38. Jafar, T.H. et al. Community-based interventions to promote blood pressure control in a developing country: a cluster randomized trial. Ann. Intern. Med. 151, 593–601 (2009).

    Article  PubMed  Google Scholar 

  39. Bellary, S. et al. Enhanced diabetes care to patients of South Asian ethnic origin (the United Kingdom Asian Diabetes Study): a cluster randomised controlled trial. Lancet 371, 1769–1776 (2008).

    Article  CAS  PubMed  Google Scholar 

  40. Rees, S.D. et al. An FTO variant is associated with type 2 diabetes in South Asian populations after accounting for body mass index and waist circumference. Diabet. Med. 28, 673–680 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Söderberg, S. et al. High incidence of type 2 diabetes and increasing conversion rates from impaired fasting glucose and impaired glucose tolerance to diabetes in Mauritius. J. Intern. Med. 256, 37–47 (2004).

    Article  PubMed  Google Scholar 

  42. Takeuchi, F. et al. Common variants at the GCK, GCKR, G6PC2-ABCB11 and MTNR1B loci are associated with fasting glucose in two Asian populations. Diabetologia 53, 299–308 (2010).

    Article  CAS  PubMed  Google Scholar 

  43. Sanghera, D.K. et al. The Khatri Sikh Diabetes Study (SDS): study design, methodology, sample collection, and initial results. Hum. Biol. 78, 43–63 (2006).

    Article  PubMed  Google Scholar 

  44. Katulanda, P. et al. Prevalence and projections of diabetes and pre-diabetes in adults in Sri Lanka–Sri Lanka Diabetes, Cardiovascular Study (SLDCS). Diabet. Med. 25, 1062–1069 (2008).

    Article  CAS  PubMed  Google Scholar 

  45. 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  PubMed  Google Scholar 

  46. Reich, D., Thangaraj, K., Patterson, N., Price, A.L. & Singh, L. Reconstructing Indian population history. Nature 461, 489–494 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Bacanu, S.A., Devlin, B. & Roeder, K. The power of genomic control. Am. J. Hum. Genet. 66, 1933–1944 (2000).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Li, R. et al. SNP detection for massively parallel whole-genome resequencing. Genome Res. 19, 1124–1132 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Ong, R.T. & Teo, Y.Y. varLD: a program for quantifying variation in linkage disequilibrium patterns between populations. Bioinformatics 26, 1269–1270 (2010).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  52. Webster, J.A. et al. Genetic control of human brain transcript expression in Alzheimer disease. Am. J. Hum. Genet. 84, 445–458 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  54. Ge, B. et al. Global patterns of cis variation in human cells revealed by high-density allelic expression analysis. Nat. Genet. 41, 1216–1222 (2009).

    Article  CAS  PubMed  Google Scholar 

  55. Dimas, A.S. et al. Common regulatory variation impacts gene expression in a cell type-dependent manner. Science 325, 1246–1250 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We would like to thank the many colleagues who contributed to collection and phenotypic characterization of the clinical samples as well as genotyping and analysis of the GWAS data. We would also like to acknowledge those individuals who agreed to participate in these studies. Major funding for the work described in this paper comes from Wellcome Trust awards (070854/Z/03/Z, 080747/Z/06/Z, 083270/Z/07/Z, 084723/Z/08/Z); Chennai Wellingdon Corporate Foundation; Diabetes UK (07/0003512); National Institute for Health Research Comprehensive Biomedical Research Centre at Imperial College Healthcare NHS Trust; British Heart Foundation (SP/04/002); Medical Research Council (G0700931); National Institute for Health Research (RP-PG-0407-10371); US National Institutes of Health (DK-25446); KAKENHI (Grant-in-Aid for Scientific Research from the Ministry of Education, Culture, Sports, Science and Technology of Japan; National Center for Global Health and Medicine; US National Institutes of Health (KO1TW006087); National Institute of Diabetes and Digestive and Kidney Diseases (R01DK082766); A*STAR Biomedical Research Council (05/1/21/19/425); Biomedical Research Council Singapore (09/1/35/19/616, 08/1/35/19/550); National Medical Research Council Singapore (NMRC/STaR/0003/2008, 1174/2008); National Science Foundation of Sri Lanka; and Oxford NIHR Biomedical Research Centre. A full list of acknowledgments is provided in the Supplementary Note.

Author information

Authors and Affiliations

Authors

Consortia

Contributions

Manuscript preparation: J.S.K., D.S., X.S., J. Sehmi, W.Z., P.E., Y.Y.T., M.I.M., J.D., E.S.T. and J.C.C. wrote the manuscript. All authors read and provided critical comment on the manuscript. Data collection and analysis in the participating studies: COBRA study: T.J., M.I. and T.M.F. Chennai Urban Rural Epidemiology Study: V.R., M. Chidambaram, S.L. and V.M. Diabetes Genetics in Pakistan and UK Asian Diabetes Studies: S.D.R., A.B., Z.I.H., A.S.S., A.H.B. and M.A.K. London Life Sciences Population Study: J.S.K., W.Z., J. Sehmi, X.L., D.D., G.R.A., J. Scott, M. Caulfield, P. Froguel, P.E., M.I.M. and J.C.C. Mauritius study: J.B.M.J., S.K., M.M.K. and P.Z.Z. Pakistan Risk of Myocardial Infarction Study: D.S., P. Frossard, R.Y., A.R., M. Samuel, N.S., P.D. and J.D. Ragama Health Study: N.K., F.T., A.R.W. and J.M.P. Singapore Consortium of Cohort Studies: K.-S.C., W.-Y.L., C.-C.K., J. Liu and E.S.T. Sikh Diabetes Study: L.F.B. and D.K.S. Singapore Indian Eye Study: X.S., C.S., T.A., T.Y.W., M. Seielstad, Y.Y.T. and E.S.T. Sri Lankan Diabetes Study: N.H., I.P., D.R.M., P.K. and M.I.M. Sequencing of T2D loci: M.Y., F.Z., J. Liang, X.L., J.S.K. and J.C.C. Association results among Europeans in DIAGRAM: A.P.M. and M.I.M. eQTL analyses in MuTHER: A.S.D., E.G., Å.K.H., A.C.N., K.S.S. and M.I.M.

Corresponding authors

Correspondence to Jaspal S Kooner or John C Chambers.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Additional information

A list of members is provided in the Supplementary Note.

A list of members is provided in the Supplementary Note.

Supplementary information

Supplementary Text and Figures

Supplementary Note, Supplementary Tables 1–15 and Supplementary Figures 1–8. (PDF 2904 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Kooner, J., Saleheen, D., Sim, X. et al. Genome-wide association study in individuals of South Asian ancestry identifies six new type 2 diabetes susceptibility loci. Nat Genet 43, 984–989 (2011). https://doi.org/10.1038/ng.921

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ng.921

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing