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.

Genome-wide trans-ancestry meta-analysis provides insight into the genetic architecture of type 2 diabetes susceptibility

Abstract

To further understanding of the genetic basis of type 2 diabetes (T2D) susceptibility, we aggregated published meta-analyses of genome-wide association studies (GWAS), including 26,488 cases and 83,964 controls of European, east Asian, south Asian and Mexican and Mexican American ancestry. We observed a significant excess in the directional consistency of T2D risk alleles across ancestry groups, even at SNPs demonstrating only weak evidence of association. By following up the strongest signals of association from the trans-ethnic meta-analysis in an additional 21,491 cases and 55,647 controls of European ancestry, we identified seven new T2D susceptibility loci. Furthermore, we observed considerable improvements in the fine-mapping resolution of common variant association signals at several T2D susceptibility loci. These observations highlight the benefits of trans-ethnic GWAS for the discovery and characterization of complex trait loci and emphasize an exciting opportunity to extend insight into the genetic architecture and pathogenesis of human diseases across populations of diverse ancestry.

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

Relevant articles

Open Access articles citing this article.

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Figure 1: Signal plots of the trans-ethnic discovery-stage GWAS meta-analysis for new T2D susceptibility loci.
Figure 2: Signal plots presenting 99% credible sets of SNPs at the JAZF1 and SLC30A8 loci.

References

  1. Zeggini, E. et al. Meta-analysis of genome-wide association data and large-scale replication identified additional susceptibility loci for type 2 diabetes. Nat. Genet. 40, 638–645 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  2. Kong, A. et al. Parental origin of sequence variants associated with complex diseases. Nature 462, 868–874 (2009).

    CAS  Article  PubMed  PubMed Central  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  PubMed  PubMed Central  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  PubMed  PubMed Central  Google Scholar 

  5. Morris, A.P. et al. Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes. Nat. Genet. 44, 981–990 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  6. Rosenberg, N.A. et al. Genome-wide association studies in diverse populations. Nat. Rev. Genet. 11, 356–366 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. Qi, L. et al. Genetic variants at 2q24 are associated with susceptibility to type 2 diabetes. Hum. Mol. Genet. 19, 2706–2715 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  8. Tsai, F.-J. et al. A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. PLoS Genet. 6, e1000847 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Shu, X.O. et al. Identification of new genetic risk variants for type 2 diabetes. PLoS Genet. 6, e1001127 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Yamauchi, T. et al. A genome-wide association study in the Japanese population identifies susceptibility loci for type 2 diabetes at UBE2E2 and C2CD4A–C2CD4B. Nat. Genet. 42, 864–868 (2010).

    CAS  Article  PubMed  Google Scholar 

  11. Cho, Y.S. et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in East Asians. Nat. Genet. 44, 67–72 (2012).

    CAS  Article  Google Scholar 

  12. Li, H. et al. A genome-wide association study identifies GRK5 and RASGRP1 as type 2 diabetes loci in Chinese Hans. Diabetes 62, 291–298 (2013).

    CAS  Article  PubMed  Google Scholar 

  13. Kooner, J.S. 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).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Tabassum, R. et al. Genome-wide association study for type 2 diabetes in Indians identifies a new susceptibility locus at 2q21. Diabetes 62, 977–986 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Parra, E.J. et al. Genome-wide association study of type 2 diabetes in a sample from Mexico City and a meta-analysis of a Mexican-American samples from Starr County, Texas. Diabetologia 54, 2038–2046 (2011).

    CAS  Article  PubMed  Google Scholar 

  16. Palmer, N.D. et al. A genome-wide association search for type 2 diabetes genes in African Americans. PLoS ONE 7, e29202 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Waters, K.M. et al. Consistent association of type 2 diabetes risk variants found in Europeans in diverse racial and ethnic groups. PLoS Genet. 6, e1001078 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Saxena, R. et al. Large-scale gene-centric meta-analysis across 39 studies identifies type 2 diabetes loci. Am. J. Hum. Genet. 90, 410–425 (2012); erratum 90, 753 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  19. Cooper, R.S., Tayo, B. & Zhu, X. Genome-wide association studies: implications for multi-ethnic samples. Hum. Mol. Genet. 17, R151–R155 (2008).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  20. Zaitlen, N., Pasaniuc, B., Gur, T., Ziv, E. & Halperin, E. Leveraging genetic variability across populations for the identification of causal variants. Am. J. Hum. Genet. 86, 23–33 (2010).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Franceschini, N. et al. Discovery and fine-mapping of serum protein loci through transethnic meta-analysis. Am. J. Hum. Genet. 91, 744–753 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  22. The International HapMap Consortium. A second generation human haplotype map of over 3.1 million SNPs. Nature 449, 851–861 (2007).

  23. The International HapMap Consortium. Integrating common and rare genetic variation in diverse human populations. Nature 467, 52–58 (2010).

  24. Dickson, S.P., Wang, K., Krantz, I., Hakonarson, H. & Goldstein, D.B. Rare variants create synthetic genome-wide associations. PLoS Biol. 8, e1000294 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

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

  26. Voight, B.F. et al. The Metabochip, a custom genotyping array for genetic studies of metabolic, cardiovascular, and anthropometric traits. PLoS Genet. 8, e1002793 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Bradfield, J.P. et al. A genome-wide meta-analysis of six type 1 diabetes cohorts identifies multiple associated loci. PLoS Genet. 7, e1002293 (2011).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Cervin, C. et al. Genetic similarities between latent autoimmune diabetes in adults, type 1 diabetes, and type 2 diabetes. Diabetes 57, 1433–1437 (2008).

    CAS  Article  PubMed  Google Scholar 

  29. Grant, S.F., Hakonarson, H. & Schwartz, S. Can the genetics of type 1 and type 2 diabetes shed light on the genetics of latent autoimmune diabetes in adults? Endocr. Rev. 31, 183–193 (2010).

    CAS  Article  PubMed  Google Scholar 

  30. Scott, R.A. et al. Large-scale association analyses identify new loci influencing glycemic traits and provide insight into the underlying biological pathways. Nat. Genet. 44, 991–1005 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  31. Richards, J.B. et al. A genome-wide association study reveals variants in ARL15 that influence adiponectin levels. PLoS Genet. 5, e1000768 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  32. 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  PubMed  PubMed Central  Google Scholar 

  33. Heid, I.M. et al. Meta-analysis identifies 12 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  PubMed  PubMed Central  Google Scholar 

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

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  35. Ng, P.C. & Henikoff, S. SIFT: predicting amino acid changes that affect protein function. Nucleic Acids Res. 31, 3812–3814 (2003).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  36. The ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

  37. Lee, E.K. et al. RNA-binding protein HuD controls insulin translation. Mol. Cell 45, 826–835 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  38. Trynka, G. et al. Chromatin marks identify critical cell types for fine-mapping complex trait variants. Nat. Genet. 45, 124–130 (2013).

    CAS  Article  PubMed  Google Scholar 

  39. Morris, A.P. Transethnic meta-analysis of genomewide association studies. Genet. Epidemiol. 35, 809–822 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  40. Wang, X. et al. Comparing methods for performing trans-ethnic meta-analysis of genome-wide association studies. Hum. Mol. Genet. 22, 2303–2311 (2013).

    CAS  Article  PubMed  Google Scholar 

  41. Maller, J.B. et al. Bayesian refinement of association signals for 14 loci in 3 common diseases. Nat. Genet. 44, 1294–1301 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  42. Yang, J. et al. Conditional and joint multiple-SNP analysis of GWAS summary statistics identifies additional variants influencing complex traits. Nat. Genet. 44, 369–375 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  43. Fogarty, M.P., Panhuis, T.M., Vadlamudi, S., Buchkovich, M.L. & Mohlke, K.L. Allele-specific transcriptional activity at type 2 diabetes-associated single nucleotide polymorphisms in regions of pancreatic islet open chromatin at the JAZF1 locus. Diabetes 62, 1756–1762 (2013).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  44. Nicolson, T.J. et al. Insulin storage and glucose homeostasis in mice null for the granule zinc transporter ZnT8 and studies of the type 2 diabetes–associated variants. Diabetes 58, 2070–2083 (2009).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  45. The 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

  46. Sung, Y.J., Wang, L., Rankinen, T., Bouchard, C. & Rao, D.C. Performance of genotype imputations using data from the 1000 Genomes Project. Hum. Hered. 73, 18–25 (2012).

    Article  PubMed  Google Scholar 

  47. Zheng, H.F., Ladouceur, M., Greenwood, C.M. & Richards, J.B. Effect of genome-wide genotyping and reference panels on rare variant imputation. J. Genet. Genomics 39, 545–550 (2012).

    CAS  Article  PubMed  Google Scholar 

  48. Nelson, S.C. et al. Imputation-based genomic coverage assessments of current human genotyping arrays. G3 (Bethesda) 3, 1795–1080 (2013).

    Article  Google Scholar 

  49. Stahl, E.A. et al. Bayesian inference analyses of the polygenic architecture of rheumatoid arthritis. Nat. Genet. 44, 483–489 (2012).

    CAS  Article  PubMed  PubMed Central  Google Scholar 

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

    CAS  Article  PubMed  Google Scholar 

  51. Ioannidis, J.P. et al. Heterogeneity in meta-analyses of genome-wide association investigations. PLoS ONE 2, e841 (2007).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

Funding for the research undertaken in this study has been received from the following: the Canadian Institutes of Health Research; the European Commission (ENGAGE FP7 HEALTH-F4-2007- 201413); the Medical Research Council UK (G0601261); the Mexico Convocatoria (SSA/IMMS/ISSSTE-CONACYT 2012-2, clave 150352, IMSS R-2011-785-018 and CONACYT Salud-2007-C01-71068); the US National Institutes of Health (DK062370, HG000376, DK085584, DK085545, DK073541 and DK085501); and the Wellcome Trust (WT098017, WT090532, WT090367, WT098381, WT081682 and WT085475). We acknowledge the many colleagues who contributed to collection and phenotypic characterization of the clinical samples and the genotyping and analysis of the GWAS data, full details of which are provided in the contributing consortia papers5,11,13,15. We also thank those individuals who agreed to participate in this study.

Author information

Authors and Affiliations

Authors