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 identifies novel blood pressure loci and offers biological insights into cardiovascular risk

A Corrigendum to this article was published on 01 October 2017

This article has been updated

Abstract

Elevated blood pressure is the leading heritable risk factor for cardiovascular disease worldwide. We report genetic association of blood pressure (systolic, diastolic, pulse pressure) among UK Biobank participants of European ancestry with independent replication in other cohorts, and robust validation of 107 independent loci. We also identify new independent variants at 11 previously reported blood pressure loci. In combination with results from a range of in silico functional analyses and wet bench experiments, our findings highlight new biological pathways for blood pressure regulation enriched for genes expressed in vascular tissues and identify potential therapeutic targets for hypertension. Results from genetic risk score models raise the possibility of a precision medicine approach through early lifestyle intervention to offset the impact of blood pressure–raising genetic variants on future cardiovascular disease risk.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Figure 1: Study design schematic for discovery and validation of loci.
Figure 2: Venn diagram of the 107 validated loci from our study.
Figure 3: Distribution of genetic risk score and its relationship with blood pressure, hypertension and cardiovascular disease outcomes.
Figure 4: Summary of cardiovascular gene expression from validated loci.

Change history

  • 20 February 2017

    In the version of this article initially published online, the name of Chiara Batini was misspelled as Chiara Battini in the list of collaborators affiliated with International Consortium of Blood Pressure (ICBP) 1000G Analyses. The error has been corrected in the print, PDF and HTML versions of this article.

References

  1. 1

    Muñoz, M. et al. Evaluating the contribution of genetics and familial shared environment to common disease using the UK Biobank. Nat. Genet. 48, 980–983 (2016).

    PubMed  PubMed Central  Google Scholar 

  2. 2

    Feinleib, M. et al. The NHLBI twin study of cardiovascular disease risk factors: methodology and summary of results. Am. J. Epidemiol. 106, 284–285 (1977).

    CAS  PubMed  Google Scholar 

  3. 3

    Poulter, N.R., Prabhakaran, D. & Caulfield, M. Hypertension. Lancet 386, 801–812 (2015).

    PubMed  Google Scholar 

  4. 4

    Mongeau, J.G., Biron, P. & Sing, C.F. The influence of genetics and household environment upon the variability of normal blood pressure: the Montreal Adoption Survey. Clin. Exp. Hypertens. A 8, 653–660 (1986).

    CAS  PubMed  Google Scholar 

  5. 5

    Forouzanfar, M.H. et al. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks in 188 countries, 1990–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet 386, 2287–2323 (2015).

    PubMed  Google Scholar 

  6. 6

    Sundström, J. et al. Blood pressure–lowering treatment based on cardiovascular risk: a meta-analysis of individual patient data. Lancet 384, 591–598 (2014).

    Google Scholar 

  7. 7

    Cabrera, C.P. et al. Exploring hypertension genome-wide association studies findings and impact on pathophysiology, pathways, and pharmacogenetics. Wiley Interdiscip. Rev. Syst. Biol. Med. 7, 73–90 (2015).

    CAS  PubMed  Google Scholar 

  8. 8

    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 

  9. 9

    Surendran, P. et al. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension. Nat. Genet. 48, 1151–1161 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  10. 10

    Liu, C. et al. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci. Nat. Genet. 48, 1162–1170 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  11. 11

    Kato, N. et al. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation. Nat. Genet. 47, 1282–1293 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  12. 12

    Elliott, P. & Peakman, T.C. The UK Biobank sample handling and storage protocol for the collection, processing and archiving of human blood and urine. Int. J. Epidemiol. 37, 234–244 (2008).

    PubMed  Google Scholar 

  13. 13

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

    Article  Google Scholar 

  14. 14

    Huang, J. et al. Improved imputation of low-frequency and rare variants using the UK10K haplotype reference panel. Nat. Commun. 6, 8111 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  15. 15

    Hoffmann, T.J. et al. Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation. Nat. Genet. 49, 54–64 (2017).

    CAS  PubMed  Google Scholar 

  16. 16

    Staley, J.R. et al. PhenoScanner: a database of human genotype–phenotype associations. Bioinformatics 32, 3207–3209 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  17. 17

    Ettehad, D. et al. Blood pressure lowering for prevention of cardiovascular disease and death: a systematic review and meta-analysis. Lancet 387, 957–967 (2016).

    PubMed  Google Scholar 

  18. 18

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

    CAS  PubMed  PubMed Central  Google Scholar 

  19. 19

    Munroe, P.B., Barnes, M.R. & Caulfield, M.J. Advances in blood pressure genomics. Circ. Res. 112, 1365–1379 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20

    den Hoed, M. et al. Identification of heart rate–associated loci and their effects on cardiac conduction and rhythm disorders. Nat. Genet. 45, 621–631 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  21. 21

    Hamilton, C.A., Brosnan, M.J., McIntyre, M., Graham, D. & Dominiczak, A.F. Superoxide excess in hypertension and aging: a common cause of endothelial dysfunction. Hypertension 37, 529–534 (2001).

    CAS  PubMed  Google Scholar 

  22. 22

    Shin, S.Y. et al. An atlas of genetic influences on human blood metabolites. Nat. Genet. 46, 543–550 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  23. 23

    Raffler, J. et al. Genome-wide association study with targeted and non-targeted NMR metabolomics identifies 15 novel loci of urinary human metabolic individuality. PLoS Genet. 11, e1005487 (2015).

    PubMed  PubMed Central  Google Scholar 

  24. 24

    van Setten, J. et al. Genome-wide association study of coronary and aortic calcification implicates risk loci for coronary artery disease and myocardial infarction. Atherosclerosis 228, 400–405 (2013).

    CAS  PubMed  Google Scholar 

  25. 25

    McCarthy, J.J. et al. Large scale association analysis for identification of genes underlying premature coronary heart disease: cumulative perspective from analysis of 111 candidate genes. J. Med. Genet. 41, 334–341 (2004).

    CAS  PubMed  PubMed Central  Google Scholar 

  26. 26

    van Meurs, J.B. et al. Common genetic loci influencing plasma homocysteine concentrations and their effect on risk of coronary artery disease. Am. J. Clin. Nutr. 98, 668–676 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  27. 27

    Pu, X. et al. ADAMTS7 cleavage and vascular smooth muscle cell migration is affected by a coronary-artery-disease-associated variant. Am. J. Hum. Genet. 92, 366–374 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28

    Rizzoni, D. & Agabiti-Rosei, E. Structural abnormalities of small resistance arteries in essential hypertension. Intern. Emerg. Med. 7, 205–212 (2012).

    PubMed  Google Scholar 

  29. 29

    Ray, R. et al. Endothelial Nox4 NADPH oxidase enhances vasodilatation and reduces blood pressure in vivo. Arterioscler. Thromb. Vasc. Biol. 31, 1368–1376 (2011).

    CAS  PubMed  Google Scholar 

  30. 30

    Touyz, R.M. & Montezano, A.C. Vascular Nox4: a multifarious NADPH oxidase. Circ. Res. 110, 1159–1161 (2012).

    CAS  PubMed  Google Scholar 

  31. 31

    Steppan, J., Barodka, V., Berkowitz, D.E. & Nyhan, D. Vascular stiffness and increased pulse pressure in the aging cardiovascular system. Cardiol. Res. Pract. 2011, 263585 (2011).

    PubMed  PubMed Central  Google Scholar 

  32. 32

    Yan, F. et al. Nox4 and redox signaling mediate TGF-β-induced endothelial cell apoptosis and phenotypic switch. Cell Death Dis. 5, e1010 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. 33

    Chan, E.C. et al. Nox4 modulates collagen production stimulated by transforming growth factor β1 in vivo and in vitro. Biochem. Biophys. Res. Commun. 430, 918–925 (2013).

    CAS  PubMed  Google Scholar 

  34. 34

    Vasa-Nicotera, M. et al. miR-146a is modulated in human endothelial cell with aging. Atherosclerosis 217, 326–330 (2011).

    CAS  PubMed  Google Scholar 

  35. 35

    Tian, X. et al. Phosphodiesterase 10A upregulation contributes to pulmonary vascular remodeling. PLoS One 6, e18136 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36

    Takimoto, E. et al. Chronic inhibition of cyclic GMP phosphodiesterase 5A prevents and reverses cardiac hypertrophy. Nat. Med. 11, 214–222 (2005).

    CAS  PubMed  Google Scholar 

  37. 37

    Pérez, N.G. et al. Phosphodiesterase 5A inhibition induces Na+/H+ exchanger blockade and protection against myocardial infarction. Hypertension 49, 1095–1103 (2007).

    PubMed  Google Scholar 

  38. 38

    Oliver, J.J., Melville, V.P. & Webb, D.J. Effect of regular phosphodiesterase type 5 inhibition in hypertension. Hypertension 48, 622–627 (2006).

    CAS  PubMed  Google Scholar 

  39. 39

    Levy, D. et al. Genome-wide association study of blood pressure and hypertension. Nat. Genet. 41, 677–687 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40

    Newton-Cheh, C. et al. Genome-wide association study identifies eight loci associated with blood pressure. Nat. Genet. 41, 666–676 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. 41

    DeStefano, A.L. et al. Autosomal dominant orthostatic hypotensive disorder maps to chromosome 18q. Am. J. Hum. Genet. 63, 1425–1430 (1998).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. 42

    Hong, X. et al. Genetic polymorphisms of the urea transporter gene are associated with antihypertensive response to nifedipine GITS. Methods Find. Exp. Clin. Pharmacol. 29, 3–10 (2007).

    CAS  PubMed  Google Scholar 

  43. 43

    Takimoto, E. et al. Sodium calcium exchanger plays a key role in alteration of cardiac function in response to pressure overload. FASEB J. 16, 373–378 (2002).

    CAS  PubMed  Google Scholar 

  44. 44

    Ronaldson, P.T. & Davis, T.P. Targeting transporters: promoting blood–brain barrier repair in response to oxidative stress injury. Brain Res. 1623, 39–52 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  45. 45

    Carta, L. et al. Fibrillins 1 and 2 perform partially overlapping functions during aortic development. J. Biol. Chem. 281, 8016–8023 (2006).

    CAS  PubMed  Google Scholar 

  46. 46

    Kazenwadel, J. et al. Loss-of-function germline GATA2 mutations in patients with MDS/AML or MonoMAC syndrome and primary lymphedema reveal a key role for GATA2 in the lymphatic vasculature. Blood 119, 1283–1291 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47

    Akashi, M., Higashi, T., Masuda, S., Komori, T. & Furuse, M. A coronary artery disease–associated gene product, JCAD/KIAA1462, is a novel component of endothelial cell–cell junctions. Biochem. Biophys. Res. Commun. 413, 224–229 (2011).

    CAS  PubMed  Google Scholar 

  48. 48

    Cakstina, I. et al. Primary culture of avian embryonic heart forming region cells to study the regulation of vertebrate early heart morphogenesis by vitamin A. BMC Dev. Biol. 14, 10 (2014).

    PubMed  PubMed Central  Google Scholar 

  49. 49

    Wang, J., Karra, R., Dickson, A.L. & Poss, K.D. Fibronectin is deposited by injury-activated epicardial cells and is necessary for zebrafish heart regeneration. Dev. Biol. 382, 427–435 (2013).

    CAS  PubMed  Google Scholar 

  50. 50

    Dietrich, T. et al. ED-B fibronectin (ED-B) can be targeted using a novel single chain antibody conjugate and is associated with macrophage accumulation in atherosclerotic lesions. Basic Res. Cardiol. 102, 298–307 (2007).

    CAS  PubMed  Google Scholar 

  51. 51

    Stoynev, N. et al. Gene expression in peripheral blood of patients with hypertension and patients with type 2 diabetes. J. Cardiovasc. Med. (Hagerstown) 15, 702–709 (2014).

    CAS  Google Scholar 

  52. 52

    Erdos, B., Backes, I., McCowan, M.L., Hayward, L.F. & Scheuer, D.A. Brain-derived neurotrophic factor modulates angiotensin signaling in the hypothalamus to increase blood pressure in rats. Am. J. Physiol. Heart Circ. Physiol. 308, H612–H622 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  53. 53

    Chan, S.H., Wu, C.W., Chang, A.Y., Hsu, K.S. & Chan, J.Y. Transcriptional upregulation of brain-derived neurotrophic factor in rostral ventrolateral medulla by angiotensin II: significance in superoxide homeostasis and neural regulation of arterial pressure. Circ. Res. 107, 1127–1139 (2010).

    CAS  PubMed  Google Scholar 

  54. 54

    Crespo, K., Ménard, A. & Deng, A.Y. Retinoblastoma-associated protein 140 as a candidate for a novel etiological gene to hypertension. Clin. Exp. Hypertens. 38, 533–540 (2016).

    CAS  PubMed  Google Scholar 

  55. 55

    Watanabe, Y. et al. Accumulation of common polymorphisms is associated with development of hypertension: a 12-year follow-up from the Ohasama study. Hypertens. Res. 33, 129–134 (2010).

    CAS  PubMed  Google Scholar 

  56. 56

    Gale, D.P., Harten, S.K., Reid, C.D., Tuddenham, E.G. & Maxwell, P.H. Autosomal dominant erythrocytosis and pulmonary arterial hypertension associated with an activating HIF2α mutation. Blood 112, 919–921 (2008).

    CAS  PubMed  Google Scholar 

  57. 57

    Yndestad, A. et al. Elevated levels of activin A in clinical and experimental pulmonary hypertension. J. Appl. Physiol. 106, 1356–1364 (2009).

    CAS  PubMed  Google Scholar 

  58. 58

    Sacks, F.M. et al. Effects on blood pressure of reduced dietary sodium and the Dietary Approaches to Stop Hypertension (DASH) diet. N. Engl. J. Med. 344, 3–10 (2001).

    CAS  PubMed  Google Scholar 

  59. 59

    Intersalt Cooperative Research Group. Intersalt: an international study of electrolyte excretion and blood pressure. Results for 24 hour urinary sodium and potassium excretion. Br. Med. J. 297, 319–328 (1988).

  60. 60

    Whelton, P.K. et al. Primary prevention of hypertension: clinical and public health advisory from The National High Blood Pressure Education Program. J. Am. Med. Assoc. 288, 1882–1888 (2002).

    Google Scholar 

  61. 61

    Chan, Q. et al. An update on nutrients and blood pressure. J. Atheroscler. Thromb. 23, 276–289 (2016).

    PubMed  Google Scholar 

  62. 62

    Khera, A.V. et al. Genetic risk, adherence to a healthy lifestyle, and coronary disease. N. Engl. J. Med. 375, 2349–2358 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  63. 63

    Wain, L.V. et al. Novel insights into the genetics of smoking behaviour, lung function, and chronic obstructive pulmonary disease (UK BiLEVE): a genetic association study in UK Biobank. Lancet Respir. Med. 3, 769–781 (2015).

    PubMed  PubMed Central  Google Scholar 

  64. 64

    Tobin, M.D., Sheehan, N.A., Scurrah, K.J. & Burton, P.R. Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure. Stat. Med. 24, 2911–2935 (2005).

    PubMed  PubMed Central  Google Scholar 

  65. 65

    Marchini, J., Howie, B., Myers, S., McVean, G. & Donnelly, P. A new multipoint method for genome-wide association studies by imputation of genotypes. Nat. Genet. 39, 906–913 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  66. 66

    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 

  67. 67

    Wang, K., Li, M. & Hakonarson, H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 38, e164 (2010).

    PubMed  PubMed Central  Google Scholar 

  68. 68

    Barnes, M.R. Exploring the landscape of the genome. Methods Mol. Biol. 628, 21–38 (2010).

    CAS  PubMed  Google Scholar 

  69. 69

    Gong, J. et al. Genome-wide identification of SNPs in microRNA genes and the SNP effects on microRNA target binding and biogenesis. Hum. Mutat. 33, 254–263 (2012).

    CAS  PubMed  Google Scholar 

  70. 70

    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 

  71. 71

    Dunham, I., Kulesha, E., Iotchkova, V., Morganella, S. & Birney, E. FORGE: a tool to discover cell specific enrichments of GWAS associated SNPs in regulatory regions. F1000Res. 4, 18 (2015).

    Google Scholar 

  72. 72

    Dozmorov, M.G., Cara, L.R., Giles, C.B. & Wren, J.D. GenomeRunner: automating genome exploration. Bioinformatics 28, 419–420 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  73. 73

    McLean, C.Y. et al. GREAT improves functional interpretation of cis-regulatory regions. Nat. Biotechnol. 28, 495–501 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74

    Elliott, P. et al. The Airwave Health Monitoring Study of police officers and staff in Great Britain: rationale, design and methods. Environ. Res. 134, 280–285 (2014).

    CAS  PubMed  Google Scholar 

  75. 75

    Petersen, M. et al. Quantification of lipoprotein subclasses by proton nuclear magnetic resonance–based partial least-squares regression models. Clin. Chem. 51, 1457–1461 (2005).

    CAS  PubMed  Google Scholar 

  76. 76

    Chadeau-Hyam, M. et al. Metabolic profiling and the metabolome-wide association study: significance level for biomarker identification. J. Proteome Res. 9, 4620–4627 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

H.R.W., C.P.C., M.R., M.R.B., P.B.M., M.B. and M.J.C. were funded by the National Institute for Health Research (NIHR) as part of the portfolio of translational research of the NIHR Biomedical Research Unit at Barts and The London School of Medicine and Dentistry. H.G. was funded by the NIHR Imperial College Health Care NHS Trust and Imperial College London Biomedical Research Centre. M.R. was a recipient of a grant from the China Scholarship Council (2011632047). B.M. holds an MRC eMedLab Medical Bioinformatics Career Development Fellowship, funded from award MR/L016311/1. J.M.M.H. was funded by the UK Medical Research Council (G0800270), British Heart Foundation (SP/09/002), UK National Institute for Health Research Cambridge Biomedical Research Centre, European Research Council (268834) and European Commission Framework Programme 7 (HEALTH-F2-2012-279233). B.K. holds a British Heart Foundation Personal Chair (CH/13/2/30154). N.J.S. holds a chair funded by the British Heart Foundation and is an NIHR Senior Investigator. F.D. was funded by the MRC Unit at the University of Bristol (MC_UU_12013/1-9). P. Surendran was funded by the UK Medical Research Council (G0800270). C.L. and A.K. were funded by NHLBI intramural funding. C.N.-C. was funded by the National Institutes of Health (HL113933, HL124262). P.v.d.H. was funded by ZonMw grant 90.700.441, Marie Sklodowska-Curie GF (call, H2020-MSCA-IF-2014; project ID, 661395). N.V. was supported by a Marie Sklodowska-Curie GF grant (661395) and ICIN-NHI. N.P. received funding from the UK National Institute for Health Research Biomedical Research Centre at Imperial College Healthcare NHS Trust and Imperial College London and also from his Senior Investigator Award. P. Sever was supported by the NIHR Biomedical Research Centre at Imperial College Healthcare NHS Trust and Imperial College London. S.T. was supported by the NIHR Biomedical Research Centre at Imperial College Healthcare NHS Trust and Imperial College London. P.F.O'R. received funding from the UK Medical Research Council (MR/N015746/1) and the Wellcome Trust (109863/Z/15/Z). I.K. was supported by the EU PhenoMeNal project (Horizon 2020, 654241). A.C. was funded by the National Institutes of Health (HL128782, HL086694). M.F. was supported by a Wellcome Trust core award (090532/Z/09/Z) and the BHF Centre of Research Excellence, Oxford (RE/13/1/30181). C.H. was funded by an MRC core grant for QTL in Health and Disease programme. Some of this work used the ALICE and SPECTRE High-Performance Computing Facilities at the University of Leicester. M.J.C. is a National Institute for Health Research (NIHR) senior investigator. P.E. is a National Institute for Health Research (NIHR) senior investigator and acknowledges support from the NIHR Biomedical Research Centre at Imperial College Healthcare NHS Trust and Imperial College London, and the NIHR Health Protection Research Unit in Health Impact of Environmental Hazards (HPRU-2012-10141). As director of the MRC-PHE Centre for Environment and Health, P.E. acknowledges support from the Medical Research Council and Public Health England (MR/L01341X/1). This work used the computing resources of the UK Medical Bioinformatics partnership–aggregation, integration, visualisation and analysis of large, complex data (UK MED-BIO), which is supported by the Medical Research Council (MR/L01632X/1). This research was supported by the British Heart Foundation (grant SP/13/2/30111). Project title: Large-Scale Comprehensive Genotyping of UK Biobank for Cardiometabolic Traits and Diseases: UK CardioMetabolic Consortium (UKCMC). This research has been conducted using the UK Biobank Resource under application number 236.

Author information

Affiliations

Authors

Consortia

Contributions

Central analysis: H.R.W., C.P.C., H.G., M.R.B., M.P.S.L., M.R., I.T., B.M., I.K., E.E. Writing of the manuscript: H.R.W., M.R.B., E.E., C.P.C., H.G., I.T., B.M., M.R., M.J.C., P.E. (with group leads, M.J.C., P.E.). Working group membership: M.J.C., H.R.W., E.E., I.T., P.B.M., L.V.W., N.J.S., M.T., J.M.M.H., M.D.T., I.N., B.K., H.G., M.R.B., C.P.C., J.S.K., P.E. (with co-chairs M.J.C., P.E.). Replication consortium contributor: (ICBP-1000G) G.B.E., L.V.W., D.L., A.C., M.J.C., M.D.T., P.F.O'R., J.K., H.S.; (CHD Exome+ Consortium) P. Surendran, R.C., D.S., J.M.M.H.; (ExomeBP Consortium) J.P.C., F.D., P.B.M.; (T2D-GENES Consortium and GoT2DGenes Consortium) C.M.L.; (CHARGE) G.B.E., C.L., A.T.K., D.L., C.N.-C., D.I.C.; (iGEN-BP) M.L., J.C.C., N.K., J.H., E.S.T., P.E., J.S.K., P.v.d.H. Replication study contributor: (Lifelines) N.V., P.v.d.H., H.S., M.A.S.; (GS:SFHS) J.M., C.H., D.P., S.P.; (EGCUT) T.E., M.A., R.M., A.M.; (PREVEND) P.v.d.H., N.V., R.T.G., S.J.L.B.; (ASCOT) H.R.W., M.J.C., P.B.M., P.S., N.P., A.S., D.S., S.T.; (BRIGHT) H.R.W., M.J.C., P.B.M., M.B., M.F., J.C.; (Airwave) H.G., E.E., M.P.S.L., I.K., I.T., P.E. All authors critically reviewed and approved the final version of the manuscript.

Corresponding authors

Correspondence to Mark J Caulfield or Paul Elliott.

Ethics declarations

Competing interests

M.J.C. is Chief Scientist for Genomics England, a wholly owned UK government company. He leads the 100,000 Genomes Project, which includes syndromic forms of blood pressure.

Additional information

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

A full list of members and affiliations appears in the Supplementary Note.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–16 and Supplementary Note (PDF 10186 kb)

Supplementary Tables 1–31

Supplementary Tables 1–31 (XLSX 3211 kb)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Warren, H., Evangelou, E., Cabrera, C. et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat Genet 49, 403–415 (2017). https://doi.org/10.1038/ng.3768

Download citation

Further reading

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing