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 association analysis of venous thromboembolism identifies new risk loci and genetic overlap with arterial vascular disease

Nature Genetics volume 51, pages 1574–1579 (2019)Cite this article

Subjects

Abstract

Venous thromboembolism is a significant cause of mortality1, yet its genetic determinants are incompletely defined. We performed a discovery genome-wide association study in the Million Veteran Program and UK Biobank, with testing of approximately 13 million DNA sequence variants for association with venous thromboembolism (26,066 cases and 624,053 controls) and meta-analyzed both studies, followed by independent replication with up to 17,672 venous thromboembolism cases and 167,295 controls. We identified 22 previously unknown loci, bringing the total number of venous thromboembolism-associated loci to 33, and subsequently fine-mapped these associations. We developed a genome-wide polygenic risk score for venous thromboembolism that identifies 5% of the population at an equivalent incident venous thromboembolism risk to carriers of the established factor V Leiden p.R506Q and prothrombin G20210A mutations. Our data provide mechanistic insights into the genetic epidemiology of venous thromboembolism and suggest a greater overlap among venous and arterial cardiovascular disease than previously thought.

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

Access options

Rent or buy this article

Prices vary by article type

from$1.95

to$39.95

Learn more

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Venous thromboembolic disease genetic discovery and replication study design.
Fig. 2: Blood lipids and VTE risk.
Fig. 3: Functional assessment of PAI-1 in murine models.
Fig. 4: Genome-wide PRS for VTE.
Fig. 5: Genome-wide PRS and incident VTE events.

Similar content being viewed by others

Data availability

The full summary-level association data from the MVP trans-ancestry VTE meta-analysis from this report are available on request through dbGAP, accession code no. phs001672.v2.p1. Data contributed by the CARDIoGRAMplusC4D investigators are available at http://www.CARDIOGRAMPLUSC4D.org/. Data on large artery stroke have been contributed by the MEGASTROKE investigators and are available at http://www.megastroke.org/. The genetic and phenotypic UK Biobank data are available on application to the UK Biobank.

References

  1. Heit, J. A. Epidemiology of venous thromboembolism. Nat. Rev. Cardiol. 12, 464–474 (2015).

    PubMed  PubMed Central  Google Scholar 

  2. Bertina, R. M. et al. Mutation in blood coagulation factor V associated with resistance to activated protein C. Nature 369, 64–67 (1994).

    CAS  PubMed  Google Scholar 

  3. Poort, S. R., Rosendaal, F. R., Reitsma, P. H. & Bertina, R. M. A common genetic variation in the 3′-untranslated region of the prothrombin gene is associated with elevated plasma prothrombin levels and an increase in venous thrombosis. Blood 88, 3698–3703 (1996).

    CAS  PubMed  Google Scholar 

  4. Klarin, D., Emdin, C. A., Natarajan, P., Conrad, M. F. & Kathiresan, S. Genetic analysis of venous thromboembolism in UK biobank identifies the ZFPM2 locus and implicates obesity as a causal risk factor. Circ. Cardiovasc. Genet. 10, e001643 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Hinds, D. A. et al. Genome-wide association analysis of self-reported events in 6135 individuals and 252 827 controls identifies 8 loci associated with thrombosis. Hum. Mol. Genet. 25, 1867–1874 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  6. Heit, J. A. et al. A genome-wide association study of venous thromboembolism identifies risk variants in chromosomes 1q24.2 and 9q. J. Thromb. Haemost. 10, 1521–1531 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Germain, M. et al. Meta-analysis of 65,734 individuals identifies TSPAN15 and SLC44A2 as two susceptibility loci for venous thromboembolism. Am. J. Hum. Genet. 96, 532–542 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Hernandez, W. et al. Novel genetic predictors of venous thromboembolism risk in African Americans. Blood 127, 1923–1929 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Tang, W. et al. A genome-wide association study for venous thromboembolism: the extended cohorts for heart and aging research in genomic epidemiology (CHARGE) consortium. Genet. Epidemiol. 37, 512–521 (2013).

    PubMed  PubMed Central  Google Scholar 

  10. Trégouët, D. A. et al. Common susceptibility alleles are unlikely to contribute as strongly as the FV and ABO loci to VTE risk: results from a GWAS approach. Blood 113, 5298–5303 (2009).

    PubMed  Google Scholar 

  11. Collins, R. What makes UK Biobank special? Lancet 379, 1173–1174 (2012).

    PubMed  Google Scholar 

  12. 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 

  13. Lindstrom, S.et al. Genomic and transcriptomic association studies identify 16 novel susceptibility loci for venous thromboembolism. Blood https://doi.org/10.1182/blood.2019000435 (2019).

  14. Glynn, R. J. et al. A randomized trial of rosuvastatin in the prevention of venous thromboembolism. N. Engl. J. Med. 360, 1851–1861 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Liu, D. J. et al. Exome-wide association study of plasma lipids in >300,000 individuals. Nat. Genet. 49, 1758–1766 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44, 512–525 (2015).

    PubMed  PubMed Central  Google Scholar 

  17. Eitzman, D. T., Westrick, R. J., Nabel, E. G. & Ginsburg, D. Plasminogen activator inhibitor-1 and vitronectin promote vascular thrombosis in mice. Blood 95, 577–580 (2000).

    CAS  PubMed  Google Scholar 

  18. Liu, B., Gloudemans, M. J., Rao, A. S., Ingelsson, E. & Montgomery, S. B. Abundant associations with gene expression complicate GWAS follow-up. Nat. Genet. 51, 768–769 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  19. Sun, B. B. et al. Genomic atlas of the human plasma proteome. Nature 558, 73–79 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  Google Scholar 

  21. Hormozdiari, F. et al. Colocalization of GWAS and eQTL signals detects target genes. Am. J. Hum. Genet. 99, 1245–1260 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  22. Fogo, A. B. Renal fibrosis: not just PAI-1 in the sky. J. Clin. Invest. 112, 326–328 (2003).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. Pawlinski, R. & Mackman, N. Cellular sources of tissue factor in endotoxemia and sepsis. Thromb. Res. 125 (Suppl. 1), S70–S73 (2010).

    CAS  PubMed  Google Scholar 

  24. Henke, P. K. et al. Deep vein thrombosis resolution is modulated by monocyte CXCR2-mediated activity in a mouse model. Arterioscler. Thromb. Vasc. Biol. 24, 1130–1137 (2004).

    CAS  PubMed  Google Scholar 

  25. Obi, A. T. et al. Plasminogen activator-1 overexpression decreases experimental postthrombotic vein wall fibrosis by a non-vitronectin-dependent mechanism. J. Thromb. Haemost. 12, 1353–1363 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Wassel, C. L. et al. A genetic risk score comprising known venous thromboembolism loci is associated with chronic venous disease in a multi-ethnic cohort. Thromb. Res. 136, 966–973 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Ridker, P. M. et al. Rosuvastatin to prevent vascular events in men and women with elevated C-reactive protein. N. Engl. J. Med. 359, 2195–2207 (2008).

    CAS  PubMed  Google Scholar 

  28. Mihaylova, B. et al. The effects of lowering LDL cholesterol with statin therapy in people at low risk of vascular disease: meta-analysis of individual data from 27 randomised trials. Lancet 380, 581–590 (2012).

    CAS  PubMed  Google Scholar 

  29. Khera, A. V. et al. Genome-wide polygenic scores for common diseases identify individuals with risk equivalent to monogenic mutations. Nat. Genet. 50, 1219–1224 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  30. Bahl, V. et al. A validation study of a retrospective venous thromboembolism risk scoring method. Ann. Surg. 251, 344–350 (2010).

    PubMed  Google Scholar 

  31. Anderson, F. A. Jr. & Spencer, F. A. Risk factors for venous thromboembolism. Circulation 107, I9–I16 (2003).

    PubMed  Google Scholar 

  32. The Women’s Health Initiative Study Group. Design of the Women’s Health Initiative clinical trial and observational study. Control. Clin. Trials 19, 61–109 (1998).

    Google Scholar 

  33. Loh, P. R., Palamara, P. F. & Price, A. L. Fast and accurate long-range phasing in a UK Biobank cohort. Nat. Genet. 48, 811–816 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  34. Howie, B., Fuchsberger, C., Stephens, M., Marchini, J. & Abecasis, G. R. Fast and accurate genotype imputation in genome-wide association studies through pre-phasing. Nat. Genet. 44, 955–959 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Price, A. L. et al. Principal components analysis corrects for stratification in genome-wide association studies. Nat. Genet. 38, 904–909 (2006).

    CAS  PubMed  Google Scholar 

  36. 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 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Winkler, T. W. et al. Quality control and conduct of genome-wide association meta-analyses. Nat. Protoc. 9, 1192–1212 (2014).

    PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Bellenguez, C., Strange, A., Freeman, C., Donnelly, P. & Spencer, C. C. A robust clustering algorithm for identifying problematic samples in genome-wide association studies. Bioinformatics 28, 134–135 (2012).

    CAS  PubMed  Google Scholar 

  41. 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 

  42. Benner, C. et al. FINEMAP: efficient variable selection using summary data from genome-wide association studies. Bioinformatics 32, 1493–1501 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  43. Eitzman, D. T. et al. Bleomycin-induced pulmonary fibrosis in transgenic mice that either lack or overexpress the murine plasminogen activator inhibitor-1 gene. J. Clin. Invest. 97, 232–237 (1996).

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Baldwin, J. F. et al. The role of urokinase plasminogen activator and plasmin activator inhibitor-1 on vein wall remodeling in experimental deep vein thrombosis. J. Vasc. Surg. 56, 1089–1097 (2012).

    PubMed  PubMed Central  Google Scholar 

  45. Wojcik, B. M. et al. Interleukin-6: a potential target for post-thrombotic syndrome. Ann. Vasc. Surg. 25, 229–239 (2011).

    PubMed  Google Scholar 

  46. Diaz, J. A. et al. Critical review of mouse models of venous thrombosis. Arterioscler. Thromb. Vasc. Biol. 32, 556–562 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. Obi, A. T. et al. Endotoxaemia-augmented murine venous thrombosis is dependent on TLR-4 and ICAM-1, and potentiated by neutropenia. Thromb. Haemost. 117, 339–348 (2017).

    PubMed  Google Scholar 

  48. Henke, P. K. et al. Targeted deletion of CCR2 impairs deep vein thrombosis resolution in a mouse model. J. Immunol. 177, 3388–3397 (2006).

    CAS  PubMed  Google Scholar 

  49. Laser, A. et al. Deletion of cysteine-cysteine receptor 7 promotes fibrotic injury in experimental post-thrombotic vein wall remodeling. Arterioscler. Thromb. Vasc. Biol. 34, 377–385 (2014).

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

Funding was received from the Department of Veterans Affairs Office of Research and Development, Million Veteran Program (grant no. MVP000). This publication does not represent the views of the Department of Veterans Affairs or the US Government. This research was also supported by three additional Department of Veterans Affairs awards (no. I01-01BX03340 to K.C. and P.W.; no I01-BX003362 to P.T. and K.M.C.; and no. I01-CX001025 to P.W.) and used the resources and facilities at the VA Informatics and Computing Infrastructure (no. VA HSR RES 13-457). S.M.D. is supported by the Veterans Administration (no. IK2-CX001780). S.K. is supported by a Research Scholar award from the Massachusetts General Hospital, the Donovan Family Foundation and the National Institutes of Health (NIH) (no. R01HL127564). P.N. is supported by the NIH/National Heart, Lung, and Blood Institute (NHLBI) (nos. K08HL140203 and R01HL142711). D.T. was financially supported by the EPIDEMIOM-VTE Senior Chair from the Initiative of Excellence of the University of Bordeaux. C.K. is supported by the NIH (grant no. HL116854). Data on coronary artery disease have been contributed by the CARDIoGRAMplusC4D investigators. Data on large artery stroke have been contributed by the MEGASTROKE investigators. The MEGASTROKE project received funding from sources specified at http://www.megastroke.org/acknowledgements.html. The WHI program is funded by the NHLBI, NIH and the US Department of Health and Human Services (contract nos. HHSN268201600018C, HHSN268201600001C, HHSN268201600002C, HHSN268201600003C and HHSN268201600004C). For a list of all the investigators who have contributed to WHI science, see https://www.whi.org/researchers/Documents%20%20Write%20a%20Paper/WHI%20Investigator%20Long%20List.pdf. This research has been conducted using the UK Biobank resource, application no. 7089.

Author information

Author notes

  1. These authors contributed equally: Scott M. Damrauer, Pradeep Natarajan.

  2. A full list of authors appears in the Supplementary Note.

Authors and Affiliations

  1. Veterans Affairs Boston Healthcare System, Boston, MA, USA

    Derek Klarin, Christopher J. O’Donnell & Pradeep Natarajan

  2. Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Derek Klarin, Krishna Aragam, Sekar Kathiresan & Pradeep Natarajan

  3. Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA

    Derek Klarin, Krishna Aragam, Mark Chaffin, Mary Haas, Sekar Kathiresan & Pradeep Natarajan

  4. Division of Vascular Surgery and Endovascular Therapy, University of Florida School of Medicine, Gainesville, FL, USA

    Derek Klarin

  5. Cardiovascular Research Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    Emma Busenkell & Pradeep Natarajan

  6. Corporal Michael Crescenz Veterans Affairs Medical Center, Philadelphia, PA, USA

    Renae Judy, Michael Levin, Marijana Vujkovic, Danish Saleheen, Kyong-Mi Chang & Scott M. Damrauer

  7. Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Renae Judy, Michael Levin, Daniel J. Rader & Kyong-Mi Chang

  8. Veterans Affairs Informatics and Computing Infrastructure, Veterans Affairs Salt Lake City Health Care System, Salt Lake City, UT, USA

    Julie Lynch, Kyung Min Lee & Scott DuVall

  9. University of Massachusetts College of Nursing & Health Sciences, Boston, MA, USA

    Julie Lynch

  10. Division of Public Health Sciences, Fred Hutchinson Cancer Research Center, Seattle, WA, USA

    Jeffery Haessler, Sara Lindström, Alex P. Reiner & Charles Kooperberg

  11. Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA

    Sara Lindström & Nicholas L. Smith

  12. Veterans Affairs Palo Alto Health Care System, Palo Alto, CA, USA

    Themistocles L. Assimes & Philip S. Tsao

  13. Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA

    Themistocles L. Assimes & Philip S. Tsao

  14. Massachusetts Veterans Epidemiology Research and Information Center, Veterans Affairs Boston Healthcare System, Boston, MA, USA

    Jie Huang, Jennifer E. Huffman, William E. Boden, Saiju Pyarajan, J. Michael Gaziano, Kelly Cho & Christopher J. O’Donnell

  15. Center for Healthcare Organization and Implementation Research, Edith Nourse Rogers Memorial Veterans Hospital, Bedford, MA, USA

    Kyung Min Lee, Qing Shao & Donald R. Miller

  16. Boston University School of Public Health, Department of Health Law, Policy & Management, Boston, MA, USA

    Kyung Min Lee & Donald R. Miller

  17. Center for Vascular Emergencies, Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA

    Christopher Kabrhel

  18. Channing Division of Network Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Christopher Kabrhel

  19. Department of Epidemiology, Emory University Rollins School of Public Health, Department of Biomedical Informatics Emory University School of Medicine, Atlanta, GA, USA

    Yunfeng Huang & Yan V. Sun

  20. Atlanta Veterans Affairs Health Care System, Decatur, GA, USA

    Yunfeng Huang, Yan V. Sun & Peter W. F. Wilson

  21. Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, Philadelphia, PA, USA

    Marijana Vujkovic & Danish Saleheen

  22. Phoenix Veterans Affairs Health Care System, Phoenix, AZ, USA

    Peter Reaven

  23. Division of Epidemiology, Department of Internal Medicine, University of Utah School of Medicine, Salt Lake City, UT, USA

    Scott DuVall

  24. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Saiju Pyarajan, J. Michael Gaziano & Kelly Cho

  25. Bordeaux Population Health Research Center (INSERM UMR S 1219), University of Bordeaux, Bordeaux, France

    David-Alexandre Trégouët

  26. Section of Vascular Surgery, Department of Surgery, University of Michigan, Ann Arbor, MI, USA

    Peter Henke & Andrea Obi

  27. Clinical Epidemiology Research Center, Veterans Affairs Connecticut Healthcare System, West Haven, CT, USA

    John Concato

  28. Department of Medicine, Yale University School of Medicine, New Haven, CT, USA

    John Concato

  29. Emory Clinical Cardiovascular Research Institute, Atlanta, GA, USA

    Peter W. F. Wilson

  30. Seattle Epidemiologic Research and Information Center, Department of Veterans Affairs Office of Research and Development, Seattle, WA, USA

    Nicholas L. Smith

  31. Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA

    Nicholas L. Smith

  32. Cardiovascular Medicine Division, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Christopher J. O’Donnell

  33. Verve Therapeutics, Cambridge, MA, USA

    Sekar Kathiresan

  34. Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    Scott M. Damrauer

Authors
  1. Derek Klarin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Emma Busenkell
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Renae Judy
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Julie Lynch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Michael Levin
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jeffery Haessler
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Krishna Aragam
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Mark Chaffin
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Mary Haas
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Sara Lindström
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Themistocles L. Assimes
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Jie Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Kyung Min Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Qing Shao
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Jennifer E. Huffman
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Christopher Kabrhel
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Yunfeng Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Yan V. Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Marijana Vujkovic
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Danish Saleheen
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Donald R. Miller
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Peter Reaven
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Scott DuVall
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. William E. Boden
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Saiju Pyarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Alex P. Reiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. David-Alexandre Trégouët
    View author publications

    You can also search for this author in PubMed Google Scholar

  28. Peter Henke
    View author publications

    You can also search for this author in PubMed Google Scholar

  29. Charles Kooperberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  30. J. Michael Gaziano
    View author publications

    You can also search for this author in PubMed Google Scholar

  31. John Concato
    View author publications

    You can also search for this author in PubMed Google Scholar

  32. Daniel J. Rader
    View author publications

    You can also search for this author in PubMed Google Scholar

  33. Kelly Cho
    View author publications

    You can also search for this author in PubMed Google Scholar

  34. Kyong-Mi Chang
    View author publications

    You can also search for this author in PubMed Google Scholar

  35. Peter W. F. Wilson
    View author publications

    You can also search for this author in PubMed Google Scholar

  36. Nicholas L. Smith
    View author publications

    You can also search for this author in PubMed Google Scholar

  37. Christopher J. O’Donnell
    View author publications

    You can also search for this author in PubMed Google Scholar

  38. Philip S. Tsao
    View author publications

    You can also search for this author in PubMed Google Scholar

  39. Sekar Kathiresan
    View author publications

    You can also search for this author in PubMed Google Scholar

  40. Andrea Obi
    View author publications

    You can also search for this author in PubMed Google Scholar

  41. Scott M. Damrauer
    View author publications

    You can also search for this author in PubMed Google Scholar

  42. Pradeep Natarajan
    View author publications

    You can also search for this author in PubMed Google Scholar

Consortia

INVENT Consortium

Veterans Affairs’ Million Veteran Program

Contributions

D.K., E.B., R.J., J.L., M.L., J. Haessler, K.A., M.C., M.H., T.L.A., J. Huang, S.P., A.P.R., P.H., C. Kooperberg, J.M.G., J.C., D.J.R., K.C., K.-M.C., P.W.F.W., C.J.O., P.S.T., S.K., A.O., S.M.D. and P.N. conceived and designed the study. D.K., E.B., R.J., J.L., M.L., J. Haessler, K.A., M.C., M.H., S.L., T.L.A., J. Huang, K.M.L., Q.S., J.E.H., C. Kabrhel, Y.H., Y.V.S., M.V., D.S., D.R.M., P.R., S.D., W.E.B., S.P., A.P.R., D.A.T., P.H., C. Kooperberg, J.M.G., J.C., D.J.R., K.C., K.-M.C., P.W.F.W., N.L.S., C.J.O., P.S.T., S.K., A.O., S.M.D. and P.N. acquired, analyzed or interpreted the data. D.K., S.K., S.M.D. and P.N. drafted the manuscript. D.K., E.B., R.J., J.L., M.L., J. Haessler, K.A., M.C., M.H., S.L., T.L.A., J. Huang, K.M.L., Q.S., J.E.H., C. Kabrhel, Y.H., Y.V.S., M.V., D.S., D.R.M., P.R., S.D., W.E.B., S.P., A.P.R., D.A.T., P.H., C. Kooperberg, J.M.G., J.C., D.J.R., K.C., K.-M.C., P.W.F.W., N.L.S., C.J.O., P.S.T., S.K., A.O., S.M.D. and P.N. critically revised the manuscript for key intellectual content. D.K., Y.V.S, J.C., J.M.G., D.J.R., K.C., K.-M.C., C.J.O., P.W.F.W., S.K., P.S.T., S.M.D. and P.N. provided administrative, technical or material support.

Corresponding author

Correspondence to Pradeep Natarajan.

Ethics declarations

Competing interests

P.N. reports grant support from Amgen, Apple and Boston Scientific, and consulting income from Apple, all unrelated to the submitted work. S.K. reports grant support from Regeneron Pharmaceuticals and Bayer, grant support and personal fees from Aegerion Pharmaceuticals, personal fees from the Regeneron Genetics Center, Merck, Celera Corporation, Novartis, Bristol-Myers Squibb, Sanofi, AstraZeneca, Alnylam Pharmaceuticals, Eli Lilly and Company and Leerink Partners, personal fees and other support from Catabasis and other support from San Therapeutics outside the submitted work. He is also the chair of the scientific advisory board at Genomics plc and the Chief Executive Officer of Verve Therapeutics. S.D. reports grants to his institution in the last 3 years outside the submitted work: AbbVie Inc.; Anolinx LLC; Astellas Pharma Inc.; AstraZeneca Pharmaceuticals LP; Boehringer Ingelheim International GmbH; Celgene Corporation; Eli Lilly and Company; Genentech, Inc.; Genomic Health, Inc.; Gilead Sciences, Inc.; GlaxoSmithKline; Innocrin Pharmaceuticals Inc.; Janssen Pharmaceuticals; Kantar Health; Myriad Genetic Laboratories, Inc.; Novartis International AG; and Parexel International Corporation. C.K. reports grants to his institution from Janssen Pharmaceuticals, Diagnostica Stago and Siemens Healthcare Diagnostics for research related to VTE, but not related to the current work. J.C. is now with the US Food and Drug Administration.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Supplementary Fig. 1 Overall study design.

The primary analysis consisted of a genome-wide association study to identify novel VTE risk variants. Secondary analyses included: an analysis of VTE and atherosclerosis overlap, a fine-mapping analysis, colocalization analysis, and functional analysis of PAI-1 using trans-ethnic summary statistics, pQTL data, and murine models respectively, a closer examination of autosomal VTE risk variants through PheWAS, generation and analysis of a 297 variant VTE polygenic risk score, and a Mendelian randomization analysis of blood lipids and VTE. Abbreviations: PAI-1, Plasminogen Activator Inhibitor-1; BMI, Body-Mass Index; CAD, Coronary Artery Disease; GLGC, Global Lipids Genetics Consortium; GTEx, Genotype-Tissue Expression Project; LAS, Large Artery Stroke; MVP, Million Veteran Program; PAD, Peripheral Artery Disease; PheWAS, Phenome-wide Association Study; VTE, Venous Thromboembolism; WHI, Women’s Health Initiative.

Supplementary Fig. 2 Quantile-quantile plot for the discovery VTE GWAS in MVP (N = 11,844 VTE cases and 251,951 controls).

The expected logistic regression association P values versus the observed distribution of P values for VTE association (Wald statistic) are displayed. Quantile-quantile plots were inspected for ancestry-specific analyses in MVP (European/African/Hispanic) and genomic control values were < 1.05 for each racial group (data not shown). No systemic inflation was observed (λGC = 1.04). All P values were two-sided. Abbreviations: GWAS, Genome-wide Association Study; MVP, Million Veteran Program; VTE, Venous Thromboembolism.

Supplementary Fig. 3 Quantile-quantile plot for the discovery VTE GWAS in UK Biobank (N = 14,222 VTE cases and 372,102 controls).

The expected logistic regression association P versus the observed distribution of P values for VTE association (Wald statistic) are displayed. No systemic inflation was observed (λGC = 1.08). All P values were two-sided. Abbreviations: GWAS, Genome-wide Association Study; VTE, Venous Thromboembolism.

Supplementary Fig. 4 Quantile-quantile plot for the trans-ethnic VTE GWAS meta-analysis in MVP and UK Biobank (N = 26,066 VTE cases and 624,053 controls).

The expected logistic regression association P values versus the observed distribution of P values for VTE association (Wald statistic) are displayed. No systemic inflation was observed (λGC = 1.06). All P values were two-sided. In a linkage disequlibrium (LD) score regression analysis restricted to Europeans (N = 23,151 VTE cases and 553,439 controls), the LD score intercept was observed to equal 1.02, suggesting nearly all the inflation in test statistics is due to genuine polygenicity in VTE as a trait. Abbreviations: GWAS, Genome-wide Association Study; VTE, Venous Thromboembolism.

Supplementary Fig. 5 Manhattan plot for the trans-ethnic VTE GWAS (N = 26,066 VTE cases and 624,053 controls).

Plot of -log10(P) for association (logistic regression Wald statistic) of genotyped and imputed variants by chromosomal position (alternating blue and yellow) for all autosomal polymorphisms analyzed in the UK Biobank and MVP VTE GWAS meta-analysis. Logistic regression two-sided P values are displayed. Abbreviations: GWAS, Genome-Wide Association Study; VTE, Venous Thromboembolism.

Supplementary Fig. 6 LocusCompare visualization of colocalization between ZFPM2 VTE GWAS and PAI-1 pQTL signals.

Colocalization between the ZFPM2 locus in the VTE GWAS (N = 23,151 VTE cases, 553,439 controls) and PAI-1 human plasma pQTL (N = 3,301) signals. The pQTL p-values were derived from the plasma samples of 3,301 participants of the INTERVAL19 study based on a linear regression model. The GWAS p-values were derived from a logistic regression model (Wald statistic) and meta-analysis from the current study. Two-sided values of P are displayed.

Supplementary Fig. 7 Genetic correlation of VTE with atherosclerosis in different arterial beds.

Using linkage disequilibrium score regression, a stronger positive correlation between VTE [N= 14,222 cases; 372,102 controls] and PAD [N = 24,009 cases; 150,983 controls] (rg = 0.47, P = 2.0x10−15) than for VTE and CAD [N= 60,801 cases; 123,504 controls] (rg = 0.27, P = 1.2x10−7) or VTE and LAS [N = 6,688 cases; 454,450 controls] (rg = 0.35, P = 0.02) was observed across the genome. All values of P are two-sided; genetic correlations with associated standard errors are displayed. Abbreviations: VTE, Venous Thromboembolism; CAD, Coronary Artery Disease; LAS, Large Artery Stroke; PAD, Peripheral Artery Disease.

Supplementary information

Supplementary Information

Supplementary Figs. 1–7, Note and Tables 1 and 2

Reporting Summary

Supplementary Tables

Supplementary Tables 3–23

Rights and permissions

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Klarin, D., Busenkell, E., Judy, R. et al. Genome-wide association analysis of venous thromboembolism identifies new risk loci and genetic overlap with arterial vascular disease. Nat Genet 51, 1574–1579 (2019). https://doi.org/10.1038/s41588-019-0519-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-019-0519-3

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research