Abstract
We assembled an ancestrally diverse collection of genome-wide association studies (GWAS) of type 2 diabetes (T2D) in 180,834 affected individuals and 1,159,055 controls (48.9% non-European descent) through the Diabetes Meta-Analysis of Trans-Ethnic association studies (DIAMANTE) Consortium. Multi-ancestry GWAS meta-analysis identified 237 loci attaining stringent genome-wide significance (P < 5 × 10−9), which were delineated to 338 distinct association signals. Fine-mapping of these signals was enhanced by the increased sample size and expanded population diversity of the multi-ancestry meta-analysis, which localized 54.4% of T2D associations to a single variant with >50% posterior probability. This improved fine-mapping enabled systematic assessment of candidate causal genes and molecular mechanisms through which T2D associations are mediated, laying the foundations for functional investigations. Multi-ancestry genetic risk scores enhanced transferability of T2D prediction across diverse populations. Our study provides a step toward more effective clinical translation of T2D GWAS to improve global health for all, irrespective of genetic background.
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Data availability
Association summary statistics from the multi-ancestry meta-analysis and annotation-informed fine-mapping are available through the AMP T2D Knowledge Portal (http://www.type2diabetesgenetics.org/) and the DIAGRAM Consortium data download website (http://diagram-consortium.org/downloads.html). Source data are provided with this paper.
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Acknowledgements
A complete list of acknowledgements and funding appears in the Supplementary Note. This research was funded in part by the Wellcome Trust (grant numbers 064890, 072960, 083948, 084723, 085475, 086113, 088158, 090367, 090532, 095101, 098017, 098051, 098381, 098395, 101033, 101630, 104085, 106130, 200186, 200837, 202922, 203141, 206194, 212259, 212284, 212946 and 220457). For the purpose of open access, the authors have applied a CC-BY public copyright licence to any author accepted manuscript version arising from this submission.
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DIAMANTE Consortium coordination, A. Mahajan, M.I.M., A.P.M.; manuscript preparation, A. Mahajan, C.N.S., W. Zhang, M.C.Y.N., L.E.P., H.K., G.Z.Y., S. Rüeger, L.S., A.L.G., M.B., J.I.R., M.I.M., A.P.M.; coordination of ancestry-specific GWAS collections, A. Mahajan, C.N.S., W. Zhang, M.C.Y.N., L.E.P., D.W.B., J.E.B., J.C.C., X.S., M.B.; central analysis group, A. Mahajan, C.N.S., W. Zhang, M.C.Y.N., L.E.P., H.K., Y.J.K., M. Horikoshi, J.M.M., D.T., S. Moon, S.-H.K., N.R.R., N.W.R., M. Loh, B.-J.K., J. Flanagan, J.B.M., K.L.M., J.E.B., J.C.C., X.S., M.B., J.I.R., M.I.M., A.P.M.; PROX1 functional analyses, G.Z.Y., F.A., J.M.T., A.L.G.; GRS analyses in FinnGen, S. Rüeger, P.d.B.P.; selection analyses, L.S., S.R.M.; single-cell chromatin accessibility data, J. Chiou, D.G., S.P., M. Sander, K.J.G.; islet promoter Hi-C data generation, I.M.-E., J. Ferrer; study-level primary analyses, A. Mahajan, C.N.S., W. Zhang, M.C.Y.N., L.E.P., Y.J.K., M. Horikoshi, J.M.M., D.T., S. Moon, S.-H.K., K. Lin, F.B., M.H.P., F.T., J.N., X.G., A. Lamri, M.N., R.A.S., J.-J.L., A.H.-C., M. Graff, J.-F.C., E.J.P., J.Y., L.F.B., Y.T., Y.H., V.S., J.P.C., M.K., N.G., E.M.S., I.P., T.S., M.W., C. Sarnowski, C.G., D.N., S. Trompet, J. Long, M. Sun, L.T., W.-M.C., M. Ahmad, R.N., V.J.Y.L., C.H.T.T., Y.Y.J., C.-H.C., L.M.R., C. Lecoeur, B.P.P., A.N., L.R.Y., G.C., R.A.J., S. Tajuddin, E.K.K., P.A., A.H.X., H.S.C., B.E.C., J. Tan, X.S., A.P.M.; study-level phenotyping, genotyping and additional analyses, L.S.A., A.A., C.A.A.-S., M. Akiyama, S.S.A., A.B., Z.B., J.B.-J., I.B., J.A.B., C.M.B., T.A.B., M. Canouil, J.C.N.C., L.-C.C., M.-L.C., J. Chen, S.-H.C., Y.-T.C., Z.C., L.-M.C., M. Cushman, S.K.D., H.J.d.S., G.D., L.D., A.P.D., S.D., Q.D., K.-U.E., L.S.E., D.S.E., M.K.E., K.F., J.S.F., I.F., M.F., O.H.F., T.M.F., B.I.F., C.F., P.G., H.C.G., V.G., C.G.-V., M.E.G.-V., M.O.G., P.G.-L., M. Gross, Y.G., S. Hackinger, S. Han, A.T.H., C.H., A.-G.H., W. Hsueh, M. Huang, W. Huang, Y.-J.H., M.Y.H., C.-M.H., S.I., M.A.I., M. Ingelsson, M.T.I., M. Isono, H.-M.J., F.J., G.J., J.B.J., M.E.J., T.J., Y.K., F.R.K., A. Kasturiratne, T. Katsuya, V.K., T. Kawaguchi, J.M.K., A.N.K., C.-C.K., M.G.K., K.K., J. Kriebel, F.K., J. Kuusisto, K. Läll, L.A.L., M.-S.L., N.R.L., A. Leong, L. Li, Y. Li, R.L.-G., S. Ligthart, C.M.L., A. Linneberg, C.-T.L., J. Liu, A.E.L., T.L., J. Luan, A.O.L., X.L., J. Lv, V.L., V.M., K.R.M., T.M., A. Metspalu, A.D.M., G.N.N., J.L.N., M.A.N., U.N., S.S.N., I.N., Y.O., L.O., S.R.P., M.A. Pereira, A.P., F.J.P., B.P., G. Prasad, L.J.R.-T., A.P.R., M.R., R.R., K.R., C. Sabanayagam, K. Sandow, N.S., S.S., C. Schurmann, M. Shahriar, J.S., D.M.S., D. Shriner, J.A.S., W.Y.S., A.S., A.M.S., K. Strauch, K. Suzuki, A.T., K.D.T., B. Thorand, G.T., U.T., B. Tomlinson, F.-J.T., J. Tuomilehto, T.T.-L., M.S.U., A.V.-S., R.M.v.D., J.B.v.K., R.V., M.V., N.W.-R., E.W., E.A.W., A.R.W., K.W.v.D., D.R.W., C.S.Y., K. Yamamoto, T.Y., L.Y., K. Yoon, C.Y., J.-M.Y., S.Y., L.Z., W. Zheng; study-level principal investigator, L.J.R., M. Igase, E. Ipp, S. Redline, Y.S.C., L. Lind, M.A. Province, C.L.H., P.A.P., E. Ingelsson, A.B.Z., B.M.P., Y.-X.W., C.N.R., D.M.B., F.M., Y. Liu, E.Z., M.Y., S.S.R., C.K., J.S.P., J.C.E., Y.-D.I.C., P.F., J.G.W., W.H.H.S., S.L.R.K., J.-Y.W., M.G.H., R.C.W.M., T.-Y.W., L.G., D.O.M.-K., G.R.C., F.S.C., D.B., G. Paré, M.M.S., H.A., A.A.M., X.-O.S., K.-S.P., J.W.J., M. Cruz, R.M.-C., H.G., C.-Y.C., E.P.B., A.D., E.-S.T., J.D., N.K., M. Laakso, A. Köttgen, W.-P.K., C.N.A.P., S. Liu, G.A., J.S.K., R.J.F.L., K.E.N., C.A.H., J.C.F., D. Saleheen, T.H., O.P., R.M., C. Langenberg, N.J.W., S. Maeda, T. Kadowaki, J. Lee, I.Y.M., R.G.W., K. Stefansson, J.B.M., K.L.M., D.W.B., J.C.C., M.B., J.I.R., M.I.M., A.P.M.
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A. Mahajan is now an employee of Genentech and a holder of Roche stock. R.A.S. is now an employee of GlaxoSmithKline. V.S. is an employee of deCODE Genetics–Amgen. L.S.E. is now an employee of Bristol Myers Squibb. J.S.F. has consulted for Shionogi. T.M.F. has consulted for Sanofi and Boerhinger Ingelheim and received funding from GSK. H.C.G. holds the McMaster–Sanofi Population Health Institute Chair in Diabetes Research and Care; reports research grants from Eli Lilly, AstraZeneca, Merck, Novo Nordisk and Sanofi; reports honoraria for speaking from AstraZeneca, Boehringer Ingelheim, Eli Lilly, Novo Nordisk, DKSH, Zuellig, Roche and Sanofi; and reports consulting fees from Abbott, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Merck, Novo Nordisk, Pfizer, Sanofi, Kowa and Hanmi. M. Ingelsson is a paid consultant for BioArctic. R.L.-G. is a part-time consultant for Metabolon. A.E.L. is now an employee of the Regeneron Genetics Center and holds shares in Regeneron Pharmaceuticals. M.A.N. currently serves on the scientific advisory board for Clover Therapeutics and is an advisor to Neuron23. S.R.P. has received grant funding from Bayer Pharmaceuticals, Philips Respironics and Respicardia. N.S. has consulted for or been on speaker bureaus for Abbott, Amgen, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Hanmi, Novartis, Novo Nordisk, Sanofi and Pfizer and has received grant funding from AstraZeneca, Boehringer Ingelheim, Novartis and Roche Diagnostics. A.M.S. receives funding from Seven Bridges Genomics to develop tools for the NHLBI BioData Catalyst consortium. G.T. is an employee of deCODE Genetics–Amgen. U.T. is an employee of deCODE Genetics–Amgen. E. Ingelsson is now an employee of GlaxoSmithKline. B.M.P. serves on the steering committee of the Yale Open Data Access Project funded by Johnson & Johnson. R.C.W.M. reports research funding from AstraZeneca, Bayer, Novo Nordisk, Pfizer, Tricida and Sanofi and has consulted for or received speakers fees from AstraZeneca, Bayer and Boehringer Ingelheim, all of which have been donated to the Chinese University of Hong Kong to support diabetes research. D.O.M.-K. is a part-time clinical research consultant for Metabolon. S. Liu reports consulting payments and honoraria or promises of the same for scientific presentations or reviews at numerous venues, including but not limited to Barilla, by-Health, Ausa Pharmed, the Fred Hutchinson Cancer Center, Harvard University, the University of Buffalo, Guangdong General Hospital and the Academy of Medical Sciences; is a consulting member for Novo Nordisk; is a member of the data safety and monitoring board for a trial of pulmonary hypertension in patients with diabetes at Massachusetts General Hospital; receives royalties from UpToDate; and receives an honorarium from the American Society for Nutrition for his duties as an associate editor. K. Stefansson is an employee of deCODE Genetics–Amgen. K.J.G. consults for Genentech and holds stock in Vertex Pharmaceuticals. A.L.G.’s spouse is an employee of Genentech and holds stock options in Roche. M.I.M. has served on advisory panels for Pfizer, Novo Nordisk and Zoe Global; has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly and research funding from AbbVie, AstraZeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, Novo Nordisk, Pfizer, Roche, Sanofi Aventis, Servier and Takeda; is now an employee of Genentech and a holder of Roche stock. The remaining authors declare no competing interests. The views expressed in this article are those of the authors and do not necessarily represent those of the NHS, the NIHR or the UK Department of Health; the National Heart, Lung, and Blood Institute, the National Institutes of Health or the US Department of Health and Human Services.
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Extended data
Extended Data Fig. 1 Study overview.
Summary of data resources and downstream analyses to identify candidate causal genes at T2D susceptibility loci.
Extended Data Fig. 2 Axes of genetic variation separating GWAS of T2D across diverse populations.
The first three axes of genetic variation (PC 1, PC 2 and PC 3) from multi-dimensional scaling of the Euclidean distance matrix between populations are sufficient to separate five ancestry groups: African (AFR), East Asian (EAS), European (EUR), Hispanic (HIS) and South Asian (SAS). GWAS acronyms are defined in Supplementary Table 1. The second axis of genetic variation (PC 2) separates African American and continental African GWAS. The third axis of genetic variation (PC 3) reveals finer-scale differences between GWAS within ancestry groups: Hispanic studies with a greater proportion of American ancestry (SIGMA (2), MC (1) and MC (2)) or African ancestry (WHI, MESA, HCHS/SOL and BIOME); East Asian studies of Chinese, Japanese and Korean ancestry from those of Malay and Filipino ancestry (SIMES and CLHNS); South Asian studies of Sri Lankan, Bangladeshi and South Indian ancestry (RHS, EPIDREAM, SINDI, GRCCDS and BPC) from those of North Indian and Pakistani ancestry; and Northern European ancestry studies from the study of Greek ancestry from Southern Europe (GOMAP). GWAS were aligned to ancestry groups based on self-report at the study level.
Extended Data Fig. 3 Manhattan plot of genome-wide T2D association from multi-ancestry meta-regression (MR-MEGA) of up to 180,834 cases and 1,159,055 controls.
Each point represents an SNV passing quality control in the multi-ancestry meta-regression, plotted with their association P-value (on a -log10 scale, truncated at 300) as a function of genomic position (NCBI build 37). Association signals attaining genome-wide significance are highlighted in pale blue (P < 5 × 10-9) and dark blue (P < 5 × 10−8). The names of novel loci names are highlighted with their association P-value from the multi-ancestry meta-regression.
Extended Data Fig. 4 Comparison of association P-values at lead SNVs at T2D loci between multi-ancestry meta-regression (MR-MEGA), fixed-effects meta-analysis and random-effects (RE2) meta-analysis of up to 180,834 cases and 1,159,055 controls.
Each point corresponds to an SNV, plotted according to P-values (on a -log10 scale) from MR-MEGA on the x-axis and fixed- or random-effects meta-analysis on the y-axis. SNVs below the y = x line demonstrate stronger association with MR-MEGA. The lead SNV at the TCF7L2 locus has been removed to improve clarity of presentation.
Extended Data Fig. 5 Comparison of loci identified at genome-wide significance (P < 5 × 10-8) in multi-ancestry meta-regression (180,834 cases and 1,159,055 controls), and East Asian and European ancestry-specific meta-analyses (56,268 cases and 227,155 controls, and 80,154 cases and 853,816 controls, respectively).
a, Association P-values at loci identified in East Asian and European ancestry-specific meta-analyses. Each point corresponds to a locus, plotted according to the P-value (on a -log10 scale) for the lead SNP in the multi-ancestry meta-regression on the x-axis and the lead SNP in the ancestry-specific meta-analysis on the y-axis. The TCF7L2 locus has been removed to improve clarity of presentation. Loci plotted below the y = x line show stronger evidence for association in the multi-ancestry meta-regression. b, Overlap of loci identified in multi-ancestry meta-regression and ancestry-specific meta-analyses.
Extended Data Fig. 6 Summary statistics from joint fGWAS model of enriched functional and regulatory annotations across distinct T2D association signals from multi-ancestry meta-regression (MR-MEGA) of up to 180,834 cases and 1,159,055 controls.
Each point corresponds to an annotation, plotted for the log-enrichment for T2D association on the x-axis, with bars representing the corresponding 95% confidence interval (CI).
Extended Data Fig. 7 Comparison of number of SNVs in 99% credible set for distinct association signals for T2D obtained from the multi-ancestry meta-regression of 180,834 cases and 1,159,055 controls under uniform and annotation-informed prior models of causality.
Each point corresponds to a distinct association signal, plotted according to the log10 credible set size under the uniform prior on the x-axis and the log10 credible set size under the annotation-informed prior on the y-axis. The 144 (42.6%) signals below the y = x line were more precisely fine-mapped under the annotation-informed prior.
Extended Data Fig. 8 Differences in LD structure between ancestry groups at the PROX1 locus for distinct association signals from multi-ancestry meta-regression (MR-MEGA) of up to 180,840 cases and 1,159,185 controls.
Each point represents an SNV passing quality control in the multi-ancestry meta-regression (after conditional analysis), plotted with their association P-value (on a log10 scale) as a function of genomic position (NCBI build 37). The index SNV is represented by the purple symbol. The color coding of all other SNVs indicates LD with the index variant in the ancestry-matched reference haplotypes from the 1000 Genomes Project panel: red, r2 ≥ 0.8; gold, 0.6 ≤ r2 < 0.8; green, 0.4 ≤ r2 < 0.6; cyan, 0.2 ≤ r2 < 0.4; blue, r2 < 0.2; grey, r2 unknown. Recombination rates are estimated from Phase II HapMap and gene annotations are taken from the University of California Santa Cruz genome browser.
Extended Data Fig. 9 Power of multi-ancestry GRS to predict T2D status in 129,230 individuals of Finnish ancestry from FinnGen.
a, Age under receiver operating characteristic curve (AUROC) after adding BMI and GRS to a baseline model adjusting for age and sex. b, Prevalence of T2D across GRS deciles. c, Boxplot of the distribution of age at T2D diagnosis across GRS deciles: box defines upper quartile, median and lower quartile, bars define maximum and minimum values within 1.5 x interquartile range of the upper and lower quartiles, other points are outliers.
Extended Data Fig. 10 Evidence for selection from Relate in African ancestry populations of subsets of T2D risk variants (effect aligned to derived allele) that are associated with other traits available in the UK Biobank.
Nominal evidence for selection (P < 0 .05) is indicated by the dashed line. The color of each point indicates the evidence for selection of subsets of T2D risk variants that are not associated with the other trait: P < 0.05 (pink) and P ≥ 0.05 (black). Population abbreviations: ESN, Esan in Nigeria; GWD, Gambian in Western Divisions in the Gambia; LWK, Luhya in Webuye, Kenya; MSL, Mende in Sierra Leone; YRI, Yoruba in Ibadan, Nigeria.
Supplementary information
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Supplementary Note and Figs. 1–9
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Supplementary Tables 1–22.
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Source data for Fig. 3c.
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Mahajan, A., Spracklen, C.N., Zhang, W. et al. Multi-ancestry genetic study of type 2 diabetes highlights the power of diverse populations for discovery and translation. Nat Genet 54, 560–572 (2022). https://doi.org/10.1038/s41588-022-01058-3
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DOI: https://doi.org/10.1038/s41588-022-01058-3
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