Identification of type 2 diabetes loci in 433,540 East Asian individuals


Meta-analyses of genome-wide association studies (GWAS) have identified more than 240 loci that are associated with type 2 diabetes (T2D)1,2; however, most of these loci have been identified in analyses of individuals with European ancestry. Here, to examine T2D risk in East Asian individuals, we carried out a meta-analysis of GWAS data from 77,418 individuals with T2D and 356,122 healthy control individuals. In the main analysis, we identified 301 distinct association signals at 183 loci, and across T2D association models with and without consideration of body mass index and sex, we identified 61 loci that are newly implicated in predisposition to T2D. Common variants associated with T2D in both East Asian and European populations exhibited strongly correlated effect sizes. Previously undescribed associations include signals in or near GDAP1, PTF1A, SIX3, ALDH2, a microRNA cluster, and genes that affect the differentiation of muscle and adipose cells3. At another locus, expression quantitative trait loci at two overlapping T2D signals affect two genes—NKX6-3 and ANK1—in different tissues4,5,6. Association studies in diverse populations identify additional loci and elucidate disease-associated genes, biology, and pathways.

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Fig. 1: Two distinct T2D-association signals at the ANK1NKX6-3 locus are associated with expression levels of two transcripts in two tissues.
Fig. 2: Effect size comparison of lead variants identified in this East Asian T2D GWAS BMI-unadjusted meta-analysis and previous European T2D GWAS meta-analysis2.
Fig. 3: rs117624659 at the NKX6-1 locus exhibits allelic differences in transcriptional activity.

Data availability

Summary-level statistics are publicly available on the AGEN consortium website (, and the Accelerating Medicines Partnership T2D portal ( A complete list of web resources is available in the Supplementary Information.


  1. 1.

    Mahajan, A. et al. Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps. Nat. Genet. 50, 1505–1513 (2018).

  2. 2.

    Suzuki, K. et al. Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population. Nat. Genet. 51, 379–386 (2019).

  3. 3.

    Moon, Y. S. et al. Mice lacking paternally expressed Pref-1/Dlk1 display growth retardation and accelerated adiposity. Mol. Cell. Biol. 22, 5585–5592 (2002).

  4. 4.

    van de Bunt, M. et al. The miRNA profile of human pancreatic islets and beta-cells and relationship to type 2 diabetes pathogenesis. PLoS One 8, e55272 (2013).

  5. 5.

    Scott, L. J. et al. The genetic regulatory signature of type 2 diabetes in human skeletal muscle. Nat. Commun. 7, 11764 (2016).

  6. 6.

    Civelek, M. et al. Genetic regulation of adipose gene expression and cardio-metabolic traits. Am. J. Hum. Genet. 100, 428–443 (2017).

  7. 7.

    Stumvoll, M., Goldstein, B. J. & van Haeften, T. W. Type 2 diabetes: principles of pathogenesis and therapy. Lancet 365, 1333–1346 (2005).

  8. 8.

    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 (2011).

  9. 9.

    Huxley, R. et al. Ethnic comparisons of the cross-sectional relationships between measures of body size with diabetes and hypertension. Obes. Rev. 9 (Suppl. 1), 53–61 (2008).

  10. 10.

    Lassiter, D. G., Sjögren, R. J. O., Gabriel, B. M., Krook, A. & Zierath, J. R. AMPK activation negatively regulates GDAP1, which influences metabolic processes and circadian gene expression in skeletal muscle. Mol. Metab. 16, 12–23 (2018).

  11. 11.

    Hoang, C. Q. et al. Transcriptional maintenance of pancreatic acinar identity, differentiation, and homeostasis by PTF1A. Mol. Cell. Biol. 36, 3033–3047 (2016).

  12. 12.

    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).

  13. 13.

    Fuchsberger, C. et al. The genetic architecture of type 2 diabetes. Nature 536, 41–47 (2016).

  14. 14.

    Kwak, S. H. et al. Nonsynonymous variants in PAX4 and GLP1R are associated with type 2 diabetes in an East Asian population. Diabetes 67, 1892–1902 (2018).

  15. 15.

    Klok, M. D., Jakobsdottir, S. & Drent, M. L. The role of leptin and ghrelin in the regulation of food intake and body weight in humans: a review. Obes. Rev. 8, 21–34 (2007).

  16. 16.

    Rasmussen-Torvik, L. J. et al. Associations of body mass index and insulin resistance with leptin, adiponectin, and the leptin-to-adiponectin ratio across ethnic groups: the Multi-Ethnic Study of Atherosclerosis (MESA). Ann. Epidemiol. 22, 705–709 (2012).

  17. 17.

    Imamura, M. et al. Genome-wide association studies in the Japanese population identify seven novel loci for type 2 diabetes. Nat. Commun. 7, 10531 (2016).

  18. 18.

    van de Bunt, M. et al. Transcript expression data from human islets links regulatory signals from genome-wide association studies for type 2 diabetes and glycemic traits to their downstream effectors. PLoS Genet. 11, e1005694 (2015).

  19. 19.

    Varshney, A. et al. Genetic regulatory signatures underlying islet gene expression and type 2 diabetes. Proc. Natl Acad. Sci. USA 114, 2301–2306 (2017).

  20. 20.

    Thurner, M. et al. Integration of human pancreatic islet genomic data refines regulatory mechanisms at type 2 diabetes susceptibility loci. eLife 7, e31977 (2018).

  21. 21.

    Henseleit, K. D. et al. NKX6 transcription factor activity is required for alpha- and beta-cell development in the pancreas. Development 132, 3139–3149 (2005).

  22. 22.

    Yan, R. et al. A novel type 2 diabetes risk allele increases the promoter activity of the muscle-specific small ankyrin 1 gene. Sci. Rep. 6, 25105 (2016).

  23. 23.

    Wen, W. et al. Meta-analysis of genome-wide association studies in East Asian-ancestry populations identifies four new loci for body mass index. Hum. Mol. Genet. 23, 5492–5504 (2014).

  24. 24.

    Akiyama, M. et al. Genome-wide association study identifies 112 new loci for body mass index in the Japanese population. Nat. Genet. 49, 1458–1467 (2017).

  25. 25.

    Prokopenko, I. et al. A central role for GRB10 in regulation of islet function in man. PLoS Genet. 10, e1004235 (2014).

  26. 26.

    Hartiala, J. A. et al. Genome-wide association study and targeted metabolomics identifies sex-specific association of CPS1 with coronary artery disease. Nat. Commun. 7, 10558 (2016).

  27. 27.

    Okada, Y. et al. Deep whole-genome sequencing reveals recent selection signatures linked to evolution and disease risk of Japanese. Nat. Commun. 9, 1631 (2018).

  28. 28.

    Xu, F. et al. ALDH2 genetic polymorphism and the risk of type II diabetes mellitus in CAD patients. Hypertens. Res. 33, 49–55 (2010).

  29. 29.

    Kato, N. et al. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians. Nat. Genet. 43, 531–538 (2011).

  30. 30.

    Takeuchi, F. et al. Confirmation of ALDH2 as a major locus of drinking behavior and of its variants regulating multiple metabolic phenotypes in a Japanese population. Circ. J. 75, 911–918 (2011).

  31. 31.

    Schrieks, I. C., Heil, A. L., Hendriks, H. F., Mukamal, K. J. & Beulens, J. W. The effect of alcohol consumption on insulin sensitivity and glycemic status: a systematic review and meta-analysis of intervention studies. Diabetes Care 38, 723–732 (2015).

  32. 32.

    Lek, M. et al. Analysis of protein-coding genetic variation in 60,706 humans. Nature 536, 285–291 (2016).

  33. 33.

    Puig, M. et al. Functional impact and evolution of a novel human polymorphic inversion that disrupts a gene and creates a fusion transcript. PLoS Genet. 11, e1005495 (2015).

  34. 34.

    Iype, T. et al. The transcriptional repressor Nkx6.1 also functions as a deoxyribonucleic acid context-dependent transcriptional activator during pancreatic beta-cell differentiation: evidence for feedback activation of the nkx6.1 gene by Nkx6.1. Mol. Endocrinol. 18, 1363–1375 (2004).

  35. 35.

    Taylor, B. L., Liu, F. F. & Sander, M. Nkx6.1 is essential for maintaining the functional state of pancreatic beta cells. Cell Rep. 4, 1262–1275 (2013).

  36. 36.

    Spracklen, C. N. et al. Identification and functional analysis of glycemic trait loci in the China Health and Nutrition Survey. PLoS Genet. 14, e1007275 (2018).

  37. 37.

    Accelerating Medicines Partnership. Type 2 Diabetes Knowledge Portal (2019).

  38. 38.

    Kanai, M. et al. Genetic analysis of quantitative traits in the Japanese population links cell types to complex human diseases. Nat. Genet. 50, 390–400 (2018).

  39. 39.

    Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

  40. 40.

    Kameswaran, V. et al. Epigenetic regulation of the DLK1-MEG3 microRNA cluster in human type 2 diabetic islets. Cell Metab. 19, 135–145 (2014).

  41. 41.

    You, L. et al. Downregulation of long noncoding RNA Meg3 affects insulin synthesis and secretion in mouse pancreatic beta cells. J. Cell. Physiol. 231, 852–862 (2016).

  42. 42.

    Wang, Y. et al. Overexpression of Pref-1 in pancreatic islet β-cells in mice causes hyperinsulinemia with increased islet mass and insulin secretion. Biochem. Biophys. Res. Commun. 461, 630–635 (2015).

  43. 43.

    Rhee, M. et al. Preadipocyte factor 1 induces pancreatic ductal cell differentiation into insulin-producing cells. Sci. Rep. 6, 23960 (2016).

  44. 44.

    Onengut-Gumuscu, S. et al. Fine mapping of type 1 diabetes susceptibility loci and evidence for colocalization of causal variants with lymphoid gene enhancers. Nat. Genet. 47, 381–386 (2015).

  45. 45.

    Chen, Y. et al. MicroRNA-17-92 cluster regulates pancreatic beta-cell proliferation and adaptation. Mol. Cell. Endocrinol. 437, 213–223 (2016).

  46. 46.

    Dou, L. et al. MiR-19a mediates gluconeogenesis by targeting PTEN in hepatocytes. Mol. Med. Rep. 17, 3967–3971 (2018).

  47. 47.

    Chen, Z. et al. Hepatocyte TRAF3 promotes insulin resistance and type 2 diabetes in mice with obesity. Mol. Metab. 4, 951–960 (2015).

  48. 48.

    Liu, F., Cheng, L., Xu, J., Guo, F. & Chen, W. miR-17-92 functions as an oncogene and modulates NF-κB signaling by targeting TRAF3 in MGC-803 human gastric cancer cells. Int. J. Oncol. 53, 2241–2257 (2018).

  49. 49.

    Ma, R. C. & Chan, J. C. Type 2 diabetes in East Asians: similarities and differences with populations in Europe and the United States. Ann. NY Acad. Sci. 1281, 64–91 (2013).

  50. 50.

    Zhu, Y. et al. Racial/ethnic disparities in the prevalence of diabetes and prediabetes by BMI: patient outcomes research to advance learning (PORTAL) multisite cohort of adults in the U.S. Diabetes Care 42, 2211–2219 (2019).

  51. 51.

    Kim, Y., Han, B. G. & the KoGES Group. Cohort profile: The Korean Genome and Epidemiology Study (KoGES) Consortium. Int. J. Epidemiol. 46, e20 (2017).

  52. 52.

    Moon, S. et al. The Korea Biobank Array: design and identification of coding variants associated with blood biochemical traits. Sci. Rep. 9, 1382 (2019).

  53. 53.

    Auton, A. et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

  54. 54.

    Das, S. et al. Next-generation genotype imputation service and methods. Nat. Genet. 48, 1284–1287 (2016).

  55. 55.

    Howie, B., Marchini, J. & Stephens, M. Genotype imputation with thousands of genomes. G3 (Bethesda) 1, 457–470 (2011).

  56. 56.

    Ma, C., Blackwell, T., Boehnke, M. & Scott, L. J. Recommended joint and meta-analysis strategies for case-control association testing of single low-count variants. Genet. Epidemiol. 37, 539–550 (2013).

  57. 57.

    Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).

  58. 58.

    Cook, J. P., Mahajan, A. & Morris, A. P. Guidance for the utility of linear models in meta-analysis of genetic association studies of binary phenotypes. Eur. J. Hum. Genet. 25, 240–245 (2017).

  59. 59.

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

  60. 60.

    Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).

  61. 61.

    Bulik-Sullivan, B. K. et al. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).

  62. 62.

    Magi, R., Lindgren, C. M. & Morris, A. P. Meta-analysis of sex-specific genome-wide association studies. Genet. Epidemiol. 34, 846–853 (2010).

  63. 63.

    Mägi, R. & Morris, A. P. GWAMA: software for genome-wide association meta-analysis. BMC Bioinformatics 11, 288 (2010).

  64. 64.

    Scott, R. A. et al. An expanded genome-wide association study of type 2 diabetes in Europeans. Diabetes 66, 2888–2902 (2017).

  65. 65.

    Schunkert, H. et al. Large-scale association analysis identifies 13 new susceptibility loci for coronary artery disease. Nat. Genet. 43, 333–338 (2011).

  66. 66.

    Shungin, D. et al. New genetic loci link adipose and insulin biology to body fat distribution. Nature 518, 187–196 (2015).

  67. 67.

    Yengo, L. et al. Meta-analysis of genome-wide association studies for height and body mass index in 700000 individuals of European ancestry. Hum. Mol. Genet. 27, 3641–3649 (2018).

  68. 68.

    Willer, C. J. et al. Discovery and refinement of loci associated with lipid levels. Nat. Genet. 45, 1274–1283 (2013).

  69. 69.

    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).

  70. 70.

    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).

  71. 71.

    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).

  72. 72.

    Manning, A. K. 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).

  73. 73.

    Wheeler, E. et al. Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: a transethnic genome-wide meta-analysis. PLoS Med. 14, e1002383 (2017).

  74. 74.

    Spracklen, C. N. et al. Association analyses of East Asian individuals and trans-ancestry analyses with European individuals reveal new loci associated with cholesterol and triglyceride levels. Hum. Mol. Genet. 27, 1122 (2018).

  75. 75.

    Sudlow, C. et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 12, e1001779 (2015).

  76. 76.

    Ehret, G. B. et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).

  77. 77.

    Gamazon, E. R. et al. Using an atlas of gene regulation across 44 human tissues to inform complex disease- and trait-associated variation. Nat. Genet. 50, 956–967 (2018).

  78. 78.

    Võsa, U. et al. Unraveling the polygenic architecture of complex traits using blood eQTL metaanalysis. Preprint at bioRxiv (2018).

  79. 79.

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

  80. 80.

    Ezzat, S. et al. The cancer-associated FGFR4-G388R polymorphism enhances pancreatic insulin secretion and modifies the risk of diabetes. Cell Metab. 17, 929–940 (2013).

  81. 81.

    Kundaje, A. et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

  82. 82.

    Miyazaki, J. et al. Establishment of a pancreatic beta cell line that retains glucose-inducible insulin secretion: special reference to expression of glucose transporter isoforms. Endocrinology 127, 126–132 (1990).

  83. 83.

    Fogarty, M. P., Cannon, M. E., Vadlamudi, S., Gaulton, K. J. & Mohlke, K. L. Identification of a regulatory variant that binds FOXA1 and FOXA2 at the CDC123/CAMK1D type 2 diabetes GWAS locus. PLoS Genet. 10, e1004633 (2014).

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This work was supported by subawards (to X.S. and Y.S.C.) from NIDDK U01DK105554 (J. C. Florez). The authors thank all investigators, staff members and study participants for their contributions to all participating studies, including but not limited to: CAGE (T. Ogihara, Y. Yamori, A. Fujioka, C. Makibayashi, S. Katsuya, K. Sugimoto, K. Kamide and R. Morishita); SBCS (R. Courtney, H. Cai, B. Zhang and J. He); and SMC (D.-H. Kim). A full list of funding, and individual and study acknowledgements are available in the Supplementary Information.

Author information




Project coordination (including supervision of all experiments and analyses): K.L.M. and X.S. Manuscript writing: C.N.S., E.-S.T., M.B., M.H., Y.J.K., K.L., K.L.M., X.S. Core and follow-up analyses including NKX6-1 functional work: C.N.S., M.H., Y.J.K., K.L., A.K.I., H.J.P., S.M.B., K.L.M., X.S. eQTL lookups for ANK1/NKX6-3: M.v.d.B., A.L.G. Core DIAMANTE group: J.E.B. (Hispanic), M.B. (European), D.W.B. (African–American), J.C.C. (South Asian), A.M. (European), M.I.M. (European), M.C.Y.N. (African–American), L.E.P. (Hispanic), W. Zhang (South Asian), A.P.M. (trans-ethnic), K.L.M., X.S. Study-level data analyses: Y.T. (AASC, NAGAHAMA), M.H. (BBJ), K.S. (BBJ), X.S. (BES, DC/SP2, SCHS), F.T. (CAGE-Amagasaki, CAGE-GWAS), M.N. (CAGE-KING), C.N.S. (CHNS, CLHNS), K.L. (CKB), F.B. (CKB), Y.J.K. (KARE, KBA), S. Moon (KBA), C.H.T.T. (HKDR), J.Y. (MESA), X.G. (MESA), J. Long (SBC/SWHS), J.F.C. (SCES), V.J.Y.L. (SiMES), S.-H.K. (SNUH), H.S.C. (SMC), C.-H.C. (TWT2D). Individual cohort study design and/or principal investigators for the individual cohorts: M. Igase (AASC), M.A. (BBJ), S. Maeda (BBJ), T. Kadowaki (BBJ), Y.-X.W. (BES), N.K. (CAGE-Amagasaki, CAGE-GWAS), M.Y. (CAGE-KING), K.L.M. (CHNS, CLHNS), R.G.W. (CKB), E.-S.T. (DC/SP2), J. Lee (KBA), B.-J.K. (KARE, KBA), R.C.W.M. (HKDR), J.I.R. (MESA), F.M. (NAGAHAMA), X.-O.S. (SBC/SWHS), C.-Y.C. (SCES), W.-P.K. (SCHS), T.-Y.W. (SiMES), K.-S.P. (SNUH), Y.S.C. (SMC), W.H.H.S. (TaiChi-G), J.Y.W. (TWT2D) Genotyping and/or phenotyping of the studies: K.K. (AASC), A.T. (BBJ), Y.K. (BBJ), T.Y. (BBJ), Y.O. (BBJ), J.B.J. (BES), T. Katsuya. (CAGE-Amagasaki), M. Isono (CAGE-GWAS), S.I. (CAGE-KING), K. Yamamoto (CAGE-KING), A.-G.H. (CHNS), S.D. (CHNS), W.H. (CHNS), J.S. (CHNS), P.G.-L. (CHNS), C.Y. (CKB), Y.G. (CKB), Z.B. (CKB), J. Lv (CKB), L.L. (CKB), Z.C. (CKB), N.R.L. (CLHNS), L.S.A. (CLHNS), J. Liu (DC/SP2), R.M.v.D. (DC/SP2), S.H. (KBA), K. Yoon (KBA), H.-M.J. (KBA), D.M.S. (KBA), G.J. (HKDR), A.O.L. (HKDR), B.T. (HKDR), W.Y.S. (HKDR), J.C.N.C. (HKDR), M.Y.H. (KARE, KBA), Y.-D.I.C. (MESA), T. Kawaguchi (NAGAHAMA), Y.-B.X. (SBC/SWHS), W. Zheng (SBC/SWHS), L.Z. (SCES), C.-C.K. (SCES), M.A.P. (SCHS), M.G. (SCHS), J.-M.Y. (SCHS), C.S. (SiMES), M.-L.C. (SiMES), M.-S.L. (SMC), C.-M.H. (TaiChi-G), L.-M.C. (TaiChi-G), Y.-J.H. (TaiChi-G), L.-C.C. (TWT2D), Y.-T.C. (TWT2D), F.-J.T. (TWT2D), J.I.R. (MESA).

Corresponding authors

Correspondence to Takashi Kadowaki or Robin G. Walters or Bong-Jo Kim or Karen L. Mohlke or Xueling Sim.

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Competing interests

M.v.d.B. was supported by a Novo Nordisk postdoctoral fellowship run in partnership with the University of Oxford, and currently a full-time employee of Novo Nordisk. M.I.M and A.M. are currently employees of Genentech. The other authors declare no competing interests.

Additional information

Peer review information Nature thanks Timothy Frayling, Stephen Rich and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 Flow chart of study design.

The flow chart shows the different data analyses performed and corresponding summary of association results.

Extended Data Fig. 2 Manhattan plot for East Asian T2D meta-analysis association results in model unadjusted for BMI.

−log10P values from two-sided fixed-effect inverse-variance genome-wide meta-analysis association results for each variant (y axis; maximal Neff = 211,793) was plotted against the genomic position (hg19; x axis). Known T2D loci achieving genome-wide significance (P < 5.0 × 10−8) in our meta-analysis are shown in blue. Loci achieving genome-wide significance that were previously unreported for T2D association are shown in red.

Extended Data Fig. 3 The relationship between effect size and minor allele frequency.

Odds ratios (y axis) and minor allele frequencies (x axis) for 189 primary association signals from the T2D BMI-unadjusted models. Odds ratios are from two-sided fixed-effect inverse-variance meta-analysis on a maximal effective sample size of 211,793.

Extended Data Fig. 4 Regional association plots at three T2D-associated loci with the strongest association P values and more than five distinct association signals in East Asian individuals.

a, INS/IGF2/KCNQ1. b, CDKN2A/B. c, PAX4/LEP. −log10P values were from the two-sided fixed-effect inverse-variance meta-analysis. Distinct signals (P < 1.0 × 10−5 from GCTA conditional analyses) were plotted; Neff values for each distinct signal are reported in Supplementary Table 4. Variants are coloured based on East Asian 1000G Phase 3 LD with the lead variants for each association signal shown as diamonds.

Extended Data Fig. 5 Effect size comparison of lead variants in sex-combined models unadjusted and adjusted for BMI.

At 189 lead variants identified in the East Asian BMI-unadjusted sex-combined T2D meta-analysis, per-allele effect sizes (β) from the BMI-adjusted sex-combined model (T2DadjBMI) were plotted against the BMI-unadjusted sex-combined model (T2DunadjBMI). Both sex-combined models were from two-sided fixed-effect inverse-variance meta-analyses and included the same set of studies for comparable sample size. Each point denotes the per-allele effect size; grey lines, s.e. Effect sizes between the two models are highly correlated with a Pearson correlation coefficient r = 0.99 (Supplementary Table 4).

Extended Data Fig. 6 Regional plots of male-specific T2D-associated locus, ALDH2.

For each plot, −log10P values from association results from two-sided fixed-effect inverse-variance meta-analyses for each variant (y axis) were plotted against the genomic position (hg19; x axis). The lead variant rs12231737 plotted is the lead variant from the BMI-unadjusted male-specific meta-analysis (Neff = 65,202) and also the sex-combined meta-analysis (Neff = 138,947) from the same subset of individuals included in the sex-stratified analyses (female-specific Neff = 70,051). This lead variant rs12231737 is in high LD with rs77768175, identified from the larger BMI-unadjusted sex-combined meta-analysis (East Asian r2 = 0.80). Top, males only; middle, sex-combined; bottom, females only. Variants are shaded according to East Asian 1000G Phase 3 LD with the lead variant shown as a purple diamond.

Extended Data Fig. 7 Effect size comparison of common lead variants (MAF ≥ 5%) identified in this East Asian meta-analysis and a previously published European T2D GWAS meta-analysis.

For 278 unique lead variants with MAF ≥ 5% in both the East Asian and European BMI-unadjusted meta-analyses, per-allele effect sizes (β) from the European T2D GWAS meta-analysis2 (y axis) were plotted against per-allele effect sizes from our East Asian meta-analysis (x axis). Effect sizes from both meta-analyses were from two-sided fixed-effect inverse-variance meta-analyses (maximal Neff = 211,793 for East Asian and 231,436 for European meta-analyses). Each point denotes the per-allele effect size; grey lines, s.e. Variants are coloured purple if they were significant in the East Asian meta-analysis only, green if they were significant in the European meta-analysis only, and blue if they were significant in both the East Asian and European meta-analyses (see Methods and Supplementary Table 7).

Extended Data Fig. 8 Effect size comparison of lead variants identified in East Asian BMI-unadjusted meta-analysis and previously published European T2D GWAS meta-analysis.

For 332 lead variants identified from the two BMI-unadjusted meta-analyses, per-allele effect sizes (β) from a European meta-analysis2 (y axis) were plotted against per-allele effect sizes from our East Asian meta-analysis (x axis). Effect sizes from both meta-analyses were from two-sided fixed-effect inverse-variance meta-analysis (maximal Neff = 211,793 for East Asian and 231,436 for European meta-analyses). Each point denotes the per-allele effect size; grey lines, s.e. a, The 152 lead variants that were significant in the East Asian meta-analysis (purple) or both the East Asian and European meta-analyses (blue). b, The 192 lead variants that were significant in the European meta-analysis (green) or both the East Asian and European meta-analyses (blue). These plots include only one variant per locus, in contrast to Fig. 2 and Extended Data Fig. 7.

Extended Data Fig. 9 Forest plots of BMI-unadjusted meta-analysis association results at the SIX3–SIX2 locus.

Odds ratios (black boxes) and 95% confidence intervals (horizontal lines) for T2D associations at the lead East Asian variant (rs12712928) across ancestries of African-American (AFR), East Asian (EAS), European (EUR)2, Hispanic (HIS), and South Asian (SAS) individuals (a); within four major East Asian populations (b; Chinese, Japanese, Korean, and Malay–Filipino, combined due to small sample sizes); and from each contributing cohort (c). Effect sizes from the East Asian study, ancestry, population, and combined meta-analysis were from two-sided fixed-effect inverse-variance meta-analysis. The size of each box is proportional to the sample size of each contributing study, ancestry or population (Supplementary Table 8). Our East Asian study had >90% power to detect the observed association with a MAF = 0.40, OR = 1.06, and 77,418 T2D cases. Given the number of T2D cases and frequency of rs12712928-C within the other data sets, at 80% power, we can reasonably exclude association OR > 1.07 in EUR and OR > 1.15 in AFR, HIS, and SAS between rs12782928 and T2D. Full study names can be found in Supplementary Table 1 and corresponding sample sizes can be found in Supplementary Table 2.

Extended Data Table 1 Novel lead variants associated with type 2 diabetes in East Asian individuals

Supplementary information

Supplementary Information

This file contains Study Descriptions, Supplementary Table legends, a list of Web Resources, Funding and Acknowledgments and Supplementary References.

Reporting Summary

Supplementary Tables

This file contains Supplementary Tables 1-13.

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Spracklen, C.N., Horikoshi, M., Kim, Y.J. et al. Identification of type 2 diabetes loci in 433,540 East Asian individuals. Nature (2020).

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