Cited by 741Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsDiabetes and associated disorders

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

Open access
Vujkovic, Marijana, Keaton, Jacob M., Lynch, Julie A., Miller, Donald R., Zhou, Jin, Tcheandjieu, Catherine, Huffman, Jennifer E., Assimes, Themistocles L. +176 more·2020·Nature Genetics

Published version of a preprint

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1038/s41588-020-0637-y

Cites

13 of 13 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • JAKMIP1 is associated with type 2 diabetes and medications used to treat it.

    “associated with type 2 diabetes and medications used to treat it82–84.”

    From Genetic determinants of plasma protein levels in the Estonian population (2024) · cites this paper as [84]

    The Results section on gene–drug relationships discusses type 2 diabetes loci and drug interactions, but does not identify JAKMIP1. Searches for JAKMIP1 and its proposed association with diabetes medications found no passage in our copy addressing that gene. (LLM, conf=0.72)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 49

  1. [2]Standards of Medical Care in Diabetes, 2018. Diabetes Care 41, S1– S2 (2018). 29222369
  2. [3]Mahajan A 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). 30297969 PMC6287706
  3. [4]Suzuki K Identification of 28 new susceptibility loci for type 2 diabetes in the Japanese population. Nat. Genet 51, 379– 386 (2019). 30718926
  4. [5]Gaziano JM Million Veteran Program: a mega-biobank to study genetic influences on health and disease. J. Clin. Epidemiol 70, 214– 223 (2016). 26441289
  5. [6]Levin MG Genomic risk stratification predicts all-cause mortality after cardiac catheterization. Circ. Genom. Precis. Med 11, e002352 (2018). 30571185 PMC6310018
  6. [8]Berglund G, Elmstahl S, Janzon L & Larsson SA The Malmo Diet and Cancer Study. Design and feasibility. J. Intern. Med 233, 45– 51 (1993). 8429286
  7. [9]Reilly MP 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). 21239051 PMC3297116
  8. [10]Bonas-Guarch S Re-analysis of public genetic data reveals a rare X-chromosomal variant associated with type 2 diabetes. Nat. Commun 9, 321 (2018). 29358691 PMC5778074
  9. [11]Xue A Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun 9, 2941 (2018). 30054458 PMC6063971
  10. [14]Luo Y Estimating heritability of complex traits in admixed populations with summary statistics. bioRxiv, 503144 (2018).
  11. [15]Kuhn M, Letunic I, Jensen LJ & Bork P The SIDER database of drugs and side effects. Nucleic Acids Res. 44, D1075– D1079 (2016). 26481350 PMC4702794
  12. [16]Finucane HK Partitioning heritability by functional annotation using genome-wide association summary statistics. Nat. Genet 47, 1228– 1235 (2015). 26414678 PMC4626285
  13. [17]Schmidt EM GREGOR: evaluating global enrichment of trait-associated variants in epigenomic features using a systematic, data-driven approach. Bioinformatics 31, 2601– 2606 (2015). 25886982 PMC4612390
  14. [18]Ng MC 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). 25102180 PMC4125087
  15. [19]Chen J Genome-wide association study of type 2 diabetes in Africa. Diabetologia 62, 1204– 1211 (2019). 31049640 PMC6560001
  16. [20]Palmer ND A genome-wide association search for type 2 diabetes genes in African Americans. PLoS One 7, e29202 (2012). 22238593 PMC3251563
  17. [22]Mahajan A Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes. Nat. Genet 50, 559– 571 (2018). 29632382 PMC5898373
  18. [23]Flannick J Exome sequencing of 20,791 cases of type 2 diabetes and 24,440 controls. Nature 570, 71– 76 (2019). 31118516 PMC6699738
  19. [24]Carrano AC, Mulas F, Zeng C & Sander M Interrogating islets in health and disease with single-cell technologies. Mol. Metab 6, 991– 1001 (2017). 28951823 PMC5605723
  20. [25]Martin CK, Han H, Anton SD, Greenway FL & Smith SR 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). 18583434 PMC2753432
  21. [26]Buse M Expanding the phenotype of reciprocal 1q21.1 deletions and duplications: a case series. Ital. J. Pediatr 43, 61 (2017). 28724436 PMC5518118
  22. [27]Devi RR & Vijayalakshmi P Novel mutations in GJA8 associated with autosomal dominant congenital cataract and microcornea. Mol. Vis 12, 190– 5 (2006). 16604058
  23. [28]Mackay DS, Bennett TM, Culican SM & Shiels A Exome sequencing identifies novel and recurrent mutations in GJA8 and CRYGD associated with inherited cataract. Hum. Genomics 8, 19 (2014). 25403472 PMC4240822
  24. [29]Luo J TCF7L2 variation and proliferative diabetic retinopathy. Diabetes 62, 2613– 2617 (2013). 23434931 PMC3712060
  25. [30]Eiden LE, Schafer MK, 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). 12827358
  26. [31]Sharma P & Sharma R Toxic optic neuropathy. Indian J. Ophthalmol 59, 137– 141 (2011). 21350283 PMC3116542
  27. [32]Pattaro C Genetic associations at 53 loci highlight cell types and biological pathways relevant for kidney function. Nat. Commun 7, 10023 (2016). 26831199 PMC4735748
  28. [33]Canela-Xandri O, Rawlik K & Tenesa A An atlas of genetic associations in UK Biobank. Nat. Genet 50, 1593– 1599 (2018). 30349118 PMC6707814
  29. [34]Ehret GB The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals. Nat. Genet 48, 1171– 1184 (2016). 27618452 PMC5042863
  30. [35]Sung YJ 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). 29455858 PMC5985266
  31. [36]Maass PG PDE3A mutations cause autosomal dominant hypertension with brachydactyly. Nat. Genet 47, 647– 653 (2015). 25961942
  32. [37]Shin S CREB mediates the insulinotropic and anti-apoptotic effects of GLP-1 signaling in adult mouse beta-cells. Mol. Metab 3, 803– 812 (2014). 25379405 PMC4216406
  33. [38]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). 23449489 PMC3302168
  34. [39]Manichaikul A Robust relationship inference in genome-wide association studies. Bioinformatics 26, 2867– 2873 (2010). 20926424 PMC3025716
  35. [41]Das S Next-generation genotype imputation service and methods. Nat. Genet 48, 1284– 1287 (2016). 27571263 PMC5157836
  36. [42]Fang H Harmonizing genetic ancestry and self-identified race/ethnicity in genome-wide association studies. Am. J. Hum. Genet 105, 763– 772 (2019). 31564439 PMC6817526
  37. [43]Tirschwell DL & Longstreth WT Jr. Validating administrative data in stroke research. Stroke 33, 2465– 2470 (2002). 12364739
  38. [44]Chang CC Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015). 25722852 PMC4342193
  39. [46]Gao F XWAS: a software toolset for genetic data analysis and association studies of the X chromosome. J. Hered 106, 666– 671 (2015). 26268243 PMC4567842
  40. [47]Ko YA Genetic-variation-driven gene-expression changes highlight genes with important functions for kidney disease. Am. J. Hum. Genet 100, 940– 953 (2017). 28575649 PMC5473735
  41. [48]Ackermann AM, Wang Z, Schug J, Naji A & Kaestner KH Integration of ATAC-seq and RNA-seq identifies human alpha cell and beta cell signature genes. Mol. Metab 5, 233– 244 (2016). 26977395 PMC4770267
  42. [50]ENCODE Project Consortium. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57– 74 (2012). 22955616 PMC3439153
  43. [53]Finucane HK Heritability enrichment of specifically expressed genes identifies disease-relevant tissues and cell types. Nat. Genet 50, 621– 629 (2018). 29632380 PMC5896795
  44. [54]Fehrmann RS Gene expression analysis identifies global gene dosage sensitivity in cancer. Nat. Genet 47, 1151– 25 (2015). 25581432
  45. [56]Heng TS, Painter MW & Immunological Genome Project Consortium. The Immunological Genome Project: networks of gene expression in immune cells. Nat. Immunol 9, 1091– 1094 (2008). 18800157
  46. [57]Denny JC PheWAS: demonstrating the feasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics 26, 1205– 1210 (2010). 20335276 PMC2859132
  47. [58]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). 21589913 PMC3093379
  48. [59]Lin DY Genetic association analysis under complex survey sampling: the Hispanic Community Health Study/Study of Latinos. Am. J. Hum. Genet 95, 675– 688 (2014). 25480034 PMC4259979
  49. [60]Bulik-Sullivan B An atlas of genetic correlations across human diseases and traits. Nat. Genet 47, 1236– 1241 (2015). 26414676 PMC4797329

Cited by

Sorted by citations

Authors
Vujkovic, M., Keaton, J. M., Lynch, J. A., Miller, D. R., Zhou, J., Tcheandjieu, C., Huffman, J. E., Assimes, T. L., Lorenz, K., Zhu, X., Hilliard, A. T., Judy, R. L., Huang, J., Lee, K. M., Klarin, D., Pyarajan, S., Danesh, J., Melander, O., Rasheed, A., Mallick, N. H., Hameed, S., Qureshi, I. H., Afzal, M. N., Malik, U., Jalal, A., Abbas, S., Sheng, X., Gao, L., Kaestner, K. H., Susztak, K., Sun, Y. V., DuVall, S. L., Cho, K., Lee, J. S., Gaziano, J. M., Phillips, L. S., Meigs, J. B., Reaven, P. D., Wilson, P. W., Edwards, T. L., Rader, D. J., Damrauer, S. M., O’Donnell, C. J., Tsao, P. S., The HPAP Consortium, Atkinson, M. A., Powers, A. C., Naji, A., Kaestner, K. H., Regeneron Genetics Center, Abecasis, G. R., Baras, A., Cantor, M. N., Coppola, G., Economides, A. N., Lotta, L. A., Overton, J. D., Reid, J. G., Shuldiner, A. R., Beechert, C., Forsythe, C., Fuller, E. D., Gu, Z., Lattari, M., Lopez, A. E., Schleicher, T. D., Padilla, M. S., Toledo, K., Widom, L., Wolf, S. E., Pradhan, M., Manoochehri, K., Ulloa, R. H., Bai, X., Balasubramanian, S., Barnard, L., Blumenfeld, A. L., Eom, G., Habegger, L., Hawes, A., Khalid, S., Maxwell, E. K., Salerno, W. J., Staples, J. C., Yadav, A., Jones, M. B., Mitnaul, L. J., Program, V. M. V., Aguayo, S. M., Ahuja, S. K., Ballas, Z. K., Bhushan, S., Boyko, E. J., Cohen, D. M., Concato, J., Constans, J. I., Dellitalia, L. J., Fayad, J. M., Fernando, R. S., Florez, H. J., Gaddy, M. A., Gappy, S. S., Gibson, G., Godschalk, M., Greco, J. A., Gupta, S., Gutierrez, S., Hammer, K. D., Hamner, M. B., Harley, J. B., Hung, A. M., Huq, M., Hurley, R. A., Iruvanti, P. R., Ivins, D. J., Jacono, F. J., Jhala, D. N., Kaminsky, L. S., Kinlay, S., Klein, J. B., Liangpunsakul, S., Lichy, J. H., Mastorides, S. M., Mathew, R. O., Mattocks, K. M., McArdle, R., Meyer, P. N., Meyer, L. J., Moorman, J. P., Morgan, T. R., Murdoch, M., Nguyen, X.-M. T., Okusaga, O. O., Oursler, K.-A. K., Ratcliffe, N. R., Rauchman, M. I., Robey, R. B., Ross, G. W., Servatius, R. J., Sharma, S. C., Sherman, S. E., Sonel, E., Sriram, P., Stapley, T., Striker, R. T., Tandon, N., Villareal, G., Wallbom, A. S., Wells, J. M., Whittle, J. C., Whooley, M. A., Xu, J., Yeh, S.-S., Aslan, M., Brewer, J. V., Brophy, M. T., Connor, T., Argyres, D. P., Do, N. V., Hauser, E. R., Humphries, D. E., Selva, L. E., Shayan, S., Stephens, B., Whitbourne, S. B., Zhao, H., Moser, J., Beckham, J. C., Breeling, J. L., Romero, J. P. C., Huang, G. D., Ramoni, R. B., Pyarajan, S., Sun, Y. V., Cho, K., Wilson, P. W., O’Donnell, C. J., Tsao, P. S., Chang, K.-M., Gaziano, J. M., Muralidhar, S., Chang, K.-M., Voight, B. F., & Saleheen, D.
Affiliations
  • VA Tennessee Valley Healthcare System (US)
  • University of Massachusetts Lowell (US)
  • University of Arizona (US)
  • VA Palo Alto Health Care System (US)
  • VA Boston Healthcare System (US)
  • Philadelphia VA Medical Center (US)
  • Peking University (CN)
  • VA Salt Lake City Healthcare System (US)
  • Broad Institute (US)
  • University of Cambridge (GB)
  • Lund University (SE)
  • Center for Non-Communicable Diseases (PK)
  • Punjab Institute of Cardiology (PK)
  • King Edward Medical University (PK)
  • University of Pennsylvania (US)
  • University of Utah (US)
  • Emory University (US)
  • Nashville VA Medical Center (US)
  • University of Florida Health (US)
  • Regeneron (United States) (US)
  • Phoenix VA Health Care System (US)
  • Iowa City VA Health Care System (US)
  • VA North Texas Health Care System (US)
  • VA Puget Sound Health Care System (US)
  • Portland VA Medical Center (US)
  • VA Connecticut Healthcare System (US)
  • Birmingham VA Medical Center (US)
  • VA Southern Nevada Healthcare System (US)
  • VA Loma Linda Healthcare System (US)
  • VA Eastern Kansas Health Care System (US)
  • John D. Dingell VA Medical Center (US)
  • Hunter Holmes McGuire VA Medical Center (US)
  • VA San Diego Healthcare System (US)
  • Edward Hines, Jr. VA Hospital (US)
  • Ralph H. Johnson VA Medical Center (US)
  • W. G. (Bill) Hefner VA Medical Center (US)
  • Hampton VA Medical Center (US)
  • VA Northeast Ohio Healthcare System (US)
  • New York/New Jersey VA Health Care Network (US)
  • Richard L. Roudebush VA Medical Center (US)
  • James A. Haley Veterans' Hospital (US)
  • Columbia VA Health Care System (US)
  • Bay Pines VA Healthcare System (US)
  • James H. Quillen VA Medical Center (US)
  • Minneapolis VA Health Care System (US)
  • Michael E. DeBakey VA Medical Center (US)
  • Manchester VA Medical Center (US)
  • White River Junction VA Medical Center (US)
  • VA Pacific Islands Health Care System (US)
  • Providence VA Medical Center (US)
  • VA NY Harbor Healthcare System (US)
  • North Florida/South Georgia Veterans Health System (US)
  • William S. Middleton Memorial Veterans Hospital (US)
  • Overton Brooks VA Medical Center (US)
  • New Mexico VA Health Care System (US)
  • Edith Nourse Rogers Memorial Veterans Hospital (US)
  • VA Western New York Healthcare System (US)
  • Raymond G. Murphy VA Medical Center (US)
  • Durham VA Medical Center (US)
  • Harvard University (US)
  • South Texas Veterans Health Care System (US)
  • Columbia University Irving Medical Center (US)
Corresponding author
  • Voight, Benjamin F.
  • Saleheen, Danish
ORCID
Venue
Nature Genetics
Published
2020-06-15
Volume
52
Issue
7
Pages
680-691
ISSN
  • 1061-4036 (ISSN-L)
  • 1546-1718
OpenAlex
W3035152464
Cited by
741
References
13
Citations by year
2026: 94 · 2025: 111 · 2024: 147 · 2023: 129 · 2022: 150 · 2021: 94 · 2020: 16
FWCI
14.06
Type
journal article
Language
en
Open access
Locations
  • Nature Genetics (publishedVersion)
  • medRxiv (acceptedVersion, cc-by)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • Lund University Publications (Lund University) (submittedVersion)
Funders
  • U.S. Department of Veterans Affairs (I01-CX001737, IK2-CX001780, I01-BX004821, I01 CX001025, I01-BX003362, MVP000, U01 DK098246)
  • Office of Research and Development (MVP#000, I01-CX001025)
  • University of Pennsylvania
  • Regeneron Pharmaceuticals
  • University of Pennsylvania Health System
  • National Institutes of Health (5t32hl007734-30, 1uc4dk112217-01, HL0-07734, DK101478, T32-HL007734)
  • National Heart, Lung, and Blood Institute (T-32HL007734, HL007734)
  • National Human Genome Research Institute (HG010067, R01HG010067)
  • National Institute of Allergy and Infectious Diseases (R03AI133172)
  • National Institute of Diabetes and Digestive and Kidney Diseases (DK101478, UC4-DK-112217, R01DK087635, U01 DK091958, R56DK101478, R21DK099716, R01DK101478, U34DK091958, U01DK098246, UM1DK078616, P30DK111024, P30DK123704)
  • National Institute of Environmental Health Sciences (P30ES006694)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
Keywords
type 2 diabetes, genetic susceptibility, genetic susceptibility loci, vascular outcomes, diabetic retinopathy, chronic kidney disease, peripheral arterial disease, diabetic neuropathy, coronary artery disease, polygenic risk score, transcriptome-wide association study, drug-gene interactions, Million Veteran Program
MeSH terms
Black or African American, Diabetes Mellitus, Type 2, Diabetic Angiopathies, Europe, Female, Humans, Hypoglycemic Agents, Male, Risk Assessment, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Chromosomes, Human, X, Diabetes Complications, Genetic Association Studies

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review