Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis

Abstract

We investigated type 2 diabetes (T2D) genetic susceptibility via multi-ancestry meta-analysis of 228,499 cases and 1,178,783 controls in the Million Veteran Program (MVP), DIAMANTE, Biobank Japan and other studies. We report 568 associations, including 286 autosomal, 7 X-chromosomal and 25 identified in ancestry-specific analyses that were previously unreported. Transcriptome-wide association analysis detected 3,568 T2D associations with genetically predicted gene expression in 687 novel genes; of these, 54 are known to interact with FDA-approved drugs. A polygenic risk score (PRS) was strongly associated with increased risk of T2D-related retinopathy and modestly associated with chronic kidney disease (CKD), peripheral artery disease (PAD) and neuropathy. We investigated the genetic etiology of T2D-related vascular outcomes in the MVP and observed statistical SNP–T2D interactions at 13 variants, including coronary heart disease (CHD), CKD, PAD and neuropathy. These findings may help to identify potential therapeutic targets for T2D and genomic pathways that link T2D to vascular outcomes.

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Fig. 1: Trans-ancestry GWAS meta-analysis identifies 318 loci associated with T2D.
Fig. 2: T2D genome-wide polygenic risk score is mainly predictive of microvascular outcomes.

Data availability

The full summary-level association data from the trans-ancestry, European, African American, Hispanic and Asian meta-analysis from this report are available through dbGAP under accession number phs001672.v3.p1 (Veterans Administration Million Veteran Program Summary Results from Omics Studies). Source data are provided with this paper. More specifically, dbGaP accession number pha004943.1 refers to the African American–specific summary statistics, pha004944.1 to the Asian-specific summary statistics, pha004945.1 refers to the European-specific summary statistics, pha004946.1 refers to the Hispanic-specific summary statistics, and pha004947.1 refers to the trans-ancestry summary statistics.

Code availability

Imputation was performed using MiniMac4 and EAGLE v2. Association analysis was performed using PLINK2A and XWAS v3.0. Post-GWAS processing software include: PRSice-2, LD Hub v1.9.3, FlashPCA v2.0, METAL v2011-03-25, GCTA-COJO v1.93, S-PrediXcan v0.6.1, SUGEN v8.9, DEPICT v140721, SIDER v4.1, DGIdb v3.0 and KING v2.1.6, as outlined in the Methods. Clear code for analysis is available at the associated website of each software package. Additional analyses were performed in R-3.2.

References

  1. 1.

    International Diabetes Federation. IDF Diabetes Atlas 8th edn (International Diabetes Federation, 2017).

  2. 2.

    American Diabetes Association Standards of medical care in diabetes—2018. Diabetes Care 41, S1–S2 (2018).

    Google Scholar 

  3. 3.

    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).

    CAS  PubMed  PubMed Central  Google Scholar 

  4. 4.

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

    CAS  PubMed  Google Scholar 

  5. 5.

    Gaziano, J. M. et al. Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol. 70, 214–223 (2016).

    PubMed  Google Scholar 

  6. 6.

    Levin, M. G. et al. Genomic risk stratification predicts all-cause mortality after cardiac catheterization. Circ. Genom. Precis. Med. 11, e002352 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. 7.

    Saleheen, D. et al. Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity. Nature 544, 235–239 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. 8.

    Berglund, G., Elmstahl, S., Janzon, L. & Larsson, S. A. The Malmo Diet and Cancer Study. Design and feasibility. J. Intern. Med. 233, 45–51 (1993).

    CAS  PubMed  Google Scholar 

  9. 9.

    Reilly, M. P. et al. Identification of ADAMTS7 as a novel locus for coronary atherosclerosis and association of ABO with myocardial infarction in the presence of coronary atherosclerosis: two genome-wide association studies. Lancet 377, 383–392 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10.

    Bonas-Guarch, S. et al. Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes. Nat. Commun. 9, 321 (2018).

    PubMed  PubMed Central  Google Scholar 

  11. 11.

    Xue, A. et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9, 2941 (2018).

    PubMed  PubMed Central  Google Scholar 

  12. 12.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  13. 13.

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

    CAS  PubMed  Google Scholar 

  14. 14.

    Luo, Y. et al. Estimating heritability of complex traits in admixed populations with summary statistics. Preprint at bioRxiv https://doi.org/10.1101/503144 (2018).

  15. 15.

    Klarin, D. et al. Genetics of blood lipids among ~300,000 multi-ethnic participants of the Million Veteran Program. Nat. Genet. 50, 1514–1523 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Kuhn, M., Letunic, I., Jensen, L. J. & Bork, P. The SIDER database of drugs and side effects. Nucleic Acids Res. 44, D1075–D1079 (2016).

    CAS  PubMed  Google Scholar 

  17. 17.

    Finucane, H. K. et al. Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet. 47, 1228–1235 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. 18.

    Schmidt, E. M. et al. GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31, 2601–2606 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19.

    Ng, M. C. et al. Meta-analysis of genome-wide association studies in African Americans provides insights into the genetic architecture of type 2 diabetes. PLoS Genet. 10, e1004517 (2014).

    PubMed  PubMed Central  Google Scholar 

  20. 20.

    Chen, J. et al. Genome-wide association study of type 2 diabetes in Africa. Diabetologia 62, 1204–1211 (2019).

    PubMed  PubMed Central  Google Scholar 

  21. 21.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  22. 22.

    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).

    PubMed  PubMed Central  Google Scholar 

  23. 23.

    Mahajan, A. et al. Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet. 50, 559–571 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  24. 24.

    Flannick, J. et al. Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 570, 71–76 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Carrano, A. C., Mulas, F., Zeng, C. & Sander, M. Interrogating islets in health and disease with single-cell technologies. Mol. Metab. 6, 991–1001 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Martin, C. K., Han, H., Anton, S. D., Greenway, F. L. & Smith, S. R. Effect of valproic acid on body weight, food intake, physical activity and hormones: results of a randomized controlled trial. J. Psychopharmacol. 23, 814–825 (2009).

    CAS  PubMed  Google Scholar 

  27. 27.

    Buse, M. et al. Expanding the phenotype of reciprocal 1q21.1 deletions and duplications: a case series. Ital. J. Pediatr. 43, 61 (2017).

    PubMed  PubMed Central  Google Scholar 

  28. 28.

    Devi, R. R. & Vijayalakshmi, P. Novel mutations in GJA8 associated with autosomal dominant congenital cataract and microcornea. Mol. Vis. 12, 190–195 (2006).

    CAS  PubMed  Google Scholar 

  29. 29.

    Mackay, D. S., Bennett, T. M., Culican, S. M. & Shiels, A. Exome sequencing identifies novel and recurrent mutations in GJA8 and CRYGD associated with inherited cataract. Hum. Genomics 8, 19 (2014).

    PubMed  PubMed Central  Google Scholar 

  30. 30.

    Luo, J. et al. TCF7L2 variation and proliferative diabetic retinopathy. Diabetes 62, 2613–2617 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Eiden, L. E., Schafer, M. K., Weihe, E. & Schutz, B. The vesicular amine transporter family (SLC18): amine/proton antiporters required for vesicular accumulation and regulated exocytotic secretion of monoamines and acetylcholine. Pflugers Arch. 447, 636–640 (2004).

    CAS  PubMed  Google Scholar 

  32. 32.

    Sharma, P. & Sharma, R. Toxic optic neuropathy. Indian J. Ophthalmol 59, 137–141 (2011).

    PubMed  PubMed Central  Google Scholar 

  33. 33.

    Pattaro, C. et al. Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat. Commun. 7, 10023 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. 34.

    Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Nat. Genet. 50, 1593–1599 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Ehret, G. B. et al. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat. Genet. 48, 1171–1184 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Sung, Y. J. et al. A large-scale multi-ancestry genome-wide study accounting for smoking behavior identifies multiple significant loci for blood pressure. Am. J. Hum. Genet. 102, 375–400 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Maass, P. G. et al. PDE3A mutations cause autosomal dominant hypertension with brachydactyly. Nat. Genet. 47, 647–653 (2015).

    CAS  PubMed  Google Scholar 

  38. 38.

    Shin, S. et al. CREB mediates the insulinotropic and anti-apoptotic effects of GLP-1 signaling in adult mouse β-cells. Mol. Metab 3, 803–812 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Omar, B., Banke, E., Ekelund, M., Frederiksen, S. & Degerman, E. Alterations in cyclic nucleotide phosphodiesterase activities in omental and subcutaneous adipose tissues in human obesity. Nutr. Diabetes 1, e13 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Manichaikul, A. et al. Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867–2873 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41.

    1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).

    Google Scholar 

  42. 42.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  43. 43.

    Fang, H. et al. Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am. J. Hum. Genet. 105, 763–772 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. 44.

    Tirschwell, D. L. & Longstreth, W. T. Jr. Validating administrative data in stroke research. Stroke 33, 2465–2470 (2002).

    PubMed  Google Scholar 

  45. 45.

    Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).

    PubMed  PubMed Central  Google Scholar 

  46. 46.

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

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Gao, F. et al. XWAS: a software toolset for genetic data analysis and association studies of the X chromosome. J. Hered. 106, 666–671 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    Ko, Y. A. et al. Genetic-variation-driven gene-expression changes highlight genes with important functions for kidney disease. Am. J. Hum. Genet. 100, 940–953 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  49. 49.

    Ackermann, A. M., Wang, Z., Schug, J., Naji, A. & Kaestner, K. H. Integration of ATAC-seq and RNA-seq identifies human alpha cell and beta cell signature genes. Mol. Metab. 5, 233–244 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  50. 50.

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

    Google Scholar 

  51. 51.

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

    Google Scholar 

  52. 52.

    Roadmap Epigenomics Consortium et al. Integrative analysis of 111 reference human epigenomes. Nature 518, 317–330 (2015).

    Google Scholar 

  53. 53.

    Andersson, R. et al. An atlas of active enhancers across human cell types and tissues. Nature 507, 455–461 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. 54.

    Finucane, H. K. et al. Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet. 50, 621–629 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Fehrmann, R. S. et al. Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet. 47, 115–125 (2015).

    CAS  PubMed  Google Scholar 

  56. 56.

    Cahoy, J. D. et al. A transcriptome database for astrocytes, neurons, and oligodendrocytes: a new resource for understanding brain development and function. J. Neurosci. 28, 264–278 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  57. 57.

    Heng, T. S. & Painter, M. W., Immunological Genome Project Consortium. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol. 9, 1091–1094 (2008).

    CAS  PubMed  Google Scholar 

  58. 58.

    Denny, J. C. et al. PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 26, 1205–1210 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Voorman, A., Lumley, T., McKnight, B. & Rice, K. Behavior of QQ-plots and genomic control in studies of gene-environment interaction. PLoS ONE 6, e19416 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Lin, D. Y. et al. Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos. Am. J. Hum. Genet. 95, 675–688 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47, 1236–1241 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

This research is based on data from the MVP, Office of Research and Development, Veterans Health Administration and was supported by award no. MVP000. This publication does not represent the views of the VA, the US Food and Drug Administration, or the US Government. This research was also supported by funding from: the VA award I01-BX003362 (P.S.T. and K.-M.C.) and the VA Informatics and Computing Infrastructure (VINCI) VA HSR RES 130457 (S.L.D.). B.F.V. acknowledges support for this work from the National Institutes of Health (NIH) National Institute of Diabetes and Digestive and Kidney Diseases (DK101478), the NIH National Human Genome Research Institute (HG010067) and a Linda Pechenik Montague Investigator award. K.-M.C., S.M.D., J.M.G., C.J.O., L.S.P., J.S.L., and P.S.T. are supported by the VA Cooperative Studies Program. S.M.D. is supported by the Veterans Administration [IK2-CX001780]. D.K. is supported by the National Heart, Lung, and Blood Institute of the NIH (T32 HL007734). K.H.K. is supported by NIH award UC4-DK-112217. K.S. is supported by NIH R01 DK087635. L.S.P. is supported in part by VA awards I01-CX001025, and I01CX001737, NIH awards R21DK099716, U01 DK091958, U01 DK098246, P30DK111024 and R03AI133172, and a Cystic Fibrosis Foundation award PHILLI12A0. We thank all study participants for their contribution. Data on T2D were contributed by investigators from the DIAMANTE Consortium, Biobank Japan, Malmö Diet and Cancer Study, PennCath, MedStar, Pakistan Genomic Resource, Penn Medicine Biobank, and Regeneron Genetics Center. Data on stroke were provided by MEGASTROKE investigators, and data on CKD were contributed by CKDgen investigators. Data on islet α- and β-cells were contributed by the HPAP Consortium (RRID:SCR_016202 and https://hpap.pmacs.upenn.edu/). Data on coronary artery disease were contributed by the CARDIoGRAMplusC4D investigators. We thank Josep Maria Mercader and Aaron Leong for careful review and comments.

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M.V., J.M.K., K.-M.C., D.S., B.F.V., P.S.T. and C.J.O. were responsible for the concept and design. The acquisition, analysis or interpretation of data were performed by M.V., J.M.K., K.-M.C., D.S., B.F.V., P.S.T., R.L.J., C.T., T.L.A., J.E.H., J.Z., J.H., K.L., X.Z., J.A.L., A.T.H., K.M.L, D.K., S.P., J.D., O.M., A.R., N.H.M., S.H., I.H.Q., M.N.A., U.M., A.J., S.A., X.S., L.G., K.H.K., K.S., Y.V.S., S.L.D., K.C., J.S.L., J.M.G., L.S.P., D.R.M., J.B.M., P.D.R., P.W.W., T.L.E., D.J.R., S.M.D. and C.J.O. The authors M.V. and D.S. drafted the manuscript. The critical revision of the manuscript for important intellectual content was carried out by M.V., J.M.K., K.-M.C., D.S., B.F.V., J.A.L., P.S.T., C.T., J.Z., J.H., X.Z., D.K., X.S., L.G., K.H.K., K.S., L.S.P., J.B.M., P.D.R., T.L.E., S.M.D. and C.J.O. Finally, K.-M.C., D.S., and B.F.V. provided administrative, technical or material support.

Corresponding authors

Correspondence to Benjamin F. Voight or Danish Saleheen.

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Competing interests

None of the sponsors of the following authors had a role in the design and conduct of the study, in the collection, management, analysis and interpretation of the data, or in the preparation, review or approval of the manuscript. D.S. has received support from the British Heart Foundation, Pfizer, Regeneron, Genentech and Eli Lilly pharmaceuticals. L.S.P. has served on Scientific Advisory Boards for Janssen, and received research support from Abbvie, Merck, Amylin, Eli Lilly, Novo Nordisk, Sanofi, PhaseBio, Roche, Abbvie, Vascular Pharmaceuticals, Janssen, Glaxo SmithKline, Pfizer, Kowa and the Cystic Fibrosis Foundation. L.S.P. is a cofounder, officer, board member and stockholder of the diabetes management-related software company Diasyst. S.L.D. has received research grant support from the following for-profit companies through the University of Utah or the Western Institute for Biomedical Research (an affiliated non-profit of VA Salt Lake City Health Care System): AbbVie, Anolinx, Astellas Pharma, AstraZeneca Pharmaceuticals, Boehringer Ingelheim International, Celgene Corporation, Eli Lilly and Company, Genentech, Genomic Health, Gilead Sciences, GlaxoSmithKline, Innocrin Pharmaceuticals, Janssen Pharmaceuticals, Kantar Health, Myriad Genetic Laboratories, Novartis International and PAREXEL International Corporation. P.D.R. has received research grant support from the following for-profit companies: Bristol Myers Squib and Lysulin, and has consulted with Intercept Pharmaceuticals and Boston Heart Diagnostics. S.M.D. receives research support to the University of Pennsylvania from RenalytixAI and consults for Calico Labs.

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Extended data

Extended Data Fig. 1 Trans-ethnic and ancestry-specific GWAS Manhattan plots.

a–d, Each graph represents a Manhattan plot. The y-axis corresponds to –log10 (P) for association with T2D in the respective ancestral group (a, Europeans (148,726 T2D cases, 965,732 controls, λ = 1.21); b, African American (24,646 T2D cases, 31,446 controls, λ = 1.08); c, Hispanics (8,616 T2D cases, 11,829 controls, λ = 1.03); d, Asians (46,511 T2D cases, 169,776 controls, λ = 1.15)). The x-axis represents chromosomal position on the autosomal genome. The y-axis truncated at 1 × 10−300. Points that are color-coded blue correspond to a P-value between 5.0 × 10−8 and 1.0 × 10−6. Points color-coded red indicate genome-wide significance (P = 5.0 × 10−8).

Extended Data Fig. 2 Trans-ethnic and ancestry-specific chromosome X Manhattan plots.

a–d, Each graph represents a Manhattan plot. The y-axis corresponds to –log10 (P) for association with T2D in the respective ancestral group (a, Europeans (69,869 T2D cases, 127,197 controls); b, African American (23,305 T2D cases, 30,140 controls); c, Hispanics (8,616 T2D cases, 11,829 controls); d, Asians (893 T2D cases, 1,560 controls)). The x-axis represents chromosomal position on chromosome X. The blue line corresponds with a significance threshold of P = 5.0 × 10−8. The red line corresponds with genome-wide significance (P = 5.0 × 10−8).

Extended Data Fig. 3 Results from PrediXcan analysis using GTEX data.

This graph represents an inverted Manhattan plot based on the output from the European T2D GWAS (148,726 T2D cases, 965,732 controls). The y-axis corresponds to –log10 (P) for association with genetically predicted gene expression in the respective tissue type (color coding shown on the right). Data were analyzed using S-PrediXcan software. The x-axis represents chromosomal position on the autosomal genome. Source Data

Extended Data Fig. 4 Manhattan plots for T2D-related complications using interaction analysis in individuals of European ancestry.

a–f, Each graph represents a Manhattan plot. The y-axis corresponds to –log10 (P) for association of SNP×T2D on T2D-related vascular outcome (a, coronary heart disease (56,285 cases, 140,945 controls, λ = 1.06); b, chronic kidney disease (67,403 cases, 129,827 controls, λ = 1.02); c, neuropathy (40,475 cases, 110,331 controls, λ = 1.03); d, peripheral artery disease (5,882 cases, 161,348 controls, λ = 1.02); e, retinopathy (13,881 cases, 123,538 controls, λ = 1.02); f, acute ischemic stroke (11,796 cases, 178,481 controls, λ = 1.00)). The x-axis represents chromosomal position on the autosomal genome. Points that are color-coded blue correspond to a P-value between 5.0 × 10−8 and 1.0 × 10−6. Points color-coded red indicate genome-wide significance (P = 5.0 × 10−8).

Extended Data Fig. 5 T2D PRS and the risk of T2D.

A shape plot representing the risk of a T2D genome-wide PRS (gPRS) on the odds ratio of T2D in MVP participants of European ancestry (69,869 T2D cases, 127,197 controls). The weights for the PRS have been obtained from an external reference dataset, namely the DIAMANTE Consortium. The gPRS has been divided into 10 deciles based on gPRS values in MVP white participants without T2D. The reference group is the lowest decile (0-10%). Odds ratios are shown as red dots, with their respective 95th percent confidence intervals displayed as red vertical lines. Source Data

Supplementary information

Supplementary Information

Supplementary Note

Reporting Summary

Supplementary Tables 1–26

Supplementary Tables 1–26

Source data

Source Data Fig. 2

Raw odds ratios for T2D-related outcomes shape plots

Source Data Extended Data Fig. 3

Raw effect estimates and P values for inverted Manhattan plot depicting genetically predicted gene expression using S-PrediXcan

Source Data Extended Data Fig. 5

Raw odds ratios for T2D shape plot

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Vujkovic, M., Keaton, J.M., Lynch, J.A. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat Genet 52, 680–691 (2020). https://doi.org/10.1038/s41588-020-0637-y

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