High blood pressure is a highly heritable and modifiable risk factor for cardiovascular disease. We report the largest genetic association study of blood pressure traits (systolic, diastolic and pulse pressure) to date in over 1 million people of European ancestry. We identify 535 novel blood pressure loci that not only offer new biological insights into blood pressure regulation but also highlight shared genetic architecture between blood pressure and lifestyle exposures. Our findings identify new biological pathways for blood pressure regulation with potential for improved cardiovascular disease prevention in the future.
Subscribe to Journal
Get full journal access for 1 year
only $17.42 per issue
All prices are NET prices.
VAT will be added later in the checkout.
Rent or Buy article
Get time limited or full article access on ReadCube.
All prices are NET prices.
The genetic and phenotypic UKB data are available upon application to the UK Biobank (https://www.ukbiobank.ac.uk). ICBP summary data can be accessed through request to the ICBP steering committee. Contact the corresponding authors to apply for access to the data. The UKB + ICBP summary GWAS discovery data can be accessed by request to the corresponding authors and will be available via LDHub (http://ldsc.broadinstitute.org/ldhub/). All replication data generated during this study are included in the published article. For example, association results of look-up variants from our replication analyses and the subsequent combined meta-analyses are contained within the Supplementary Tables.
Forouzanfar, M. H. et al. Global burden of hypertension and systolic blood pressure of at least 110 to 115 mm Hg, 1990–2015. J. Am. Med. Assoc. 317, 165–182 (2017).
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).
Poulter, N. R., Prabhakaran, D. & Caulfield, M. Hypertension. Lancet 386, 801–812 (2015).
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).
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).
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).
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).
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).
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).
Warren, H. R. et al. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk. Nat. Genet. 49, 403–415 (2017).
Wain, L. V. et al. Novel blood pressure locus and gene discovery using genome-wide association study and expression data sets from blood and the kidney. Hypertension 70, e4–e19 (2017).
Ehret, G. B. et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature 478, 103–109 (2011).
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).
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).
Leitsalu, L. et al. Cohort profile: Estonian Biobank of the Estonian Genome Center, University of Tartu. Int. J. Epidemiol. 44, 1137–1147 (2015).
McCarthy, S. et al. A reference panel of 64,976 haplotypes for genotype imputation. Nat. Genet. 48, 1279–1283 (2016).
Loh, P. R. et al. Efficient Bayesian mixed-model analysis increases association power in large cohorts. Nat. Genet. 47, 284–290 (2015).
Bulik-Sullivan, B. K. et al. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat. Genet. 47, 291–295 (2015).
Ioannidis, J. P., Patsopoulos, N. A. & Evangelou, E. Heterogeneity in meta-analyses of genome-wide association investigations. PLoS One 2, e841 (2007).
Evangelou, E. & Ioannidis, J. P. Meta-analysis methods for genome-wide association studies and beyond. Nat. Rev. Genet. 14, 379–389 (2013).
Pulit, S. L., de With, S. A. & de Bakker, P. I. Resetting the bar: statistical significance in whole-genome sequencing-based association studies of global populations. Genet. Epidemiol. 41, 145–151 (2017).
Yang, J., Lee, S. H., Goddard, M. E. & Visscher, P. M. GCTA: a tool for genome-wide complex trait analysis. Am. J. Hum. Genet. 88, 76–82 (2011).
Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
Schmitt, A. D. et al. A compendium of chromatin contact maps reveals spatially active regions in the human genome. Cell Rep. 17, 2042–2059 (2016).
Dunham, I. K., 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).
MacArthur, J. et al. The new NHGRI-EBI Catalog of published genome-wide association studies (GWAS Catalog). Nucleic Acids Res. 45 D1, D896–D901 (2017).
Staley, J. R. et al. PhenoScanner: a database of human genotype-phenotype associations. Bioinformatics 32, 3207–3209 (2016).
Piñero, J. et al. DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes. Database (Oxford) 2015, bav028 (2015).
Piñero, J. et al. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res. 45(D1), D833–D839 (2017).
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).
Ehret, G. B. & Caulfield, M. J. Genes for blood pressure: an opportunity to understand hypertension. Eur. Heart J. 34, 951–961 (2013).
Blood Pressure Lowering Treatment Trialists’ Collaboration. Blood pressure-lowering treatment based on cardiovascular risk: a meta-analysis of individual patient data. Lancet 384, 591–598 (2014).
GBD 2015 Risk Factors Collaborators. Global, regional, and national comparative risk assessment of 79 behavioural, environmental and occupational, and metabolic risks or clusters of risks, 1990–2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet 388, 1659–1724 (2016).
Nakao, E. et al. Elevated plasma transforming growth factor β1 levels predict the development of hypertension in normotensives: the 14-year follow-up study. Am. J. Hypertens. 30, 808–814 (2017).
Feng, W., Dell’Italia, L. J. & Sanders, P. W. Novel paradigms of salt and hypertension. J. Am. Soc. Nephrol. 28, 1362–1369 (2017).
Lane, K. B. et al. Heterozygous germline mutations in BMPR2, encoding a TGF-beta receptor, cause familial primary pulmonary hypertension. Nat. Genet. 26, 81–84 (2000).
Voight, B. F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010).
Douma, S. et al. Prevalence of primary hyperaldosteronism in resistant hypertension: a retrospective observational study. Lancet 371, 1921–1926 (2008).
Rossi, G. P. et al. A prospective study of the prevalence of primary aldosteronism in 1,125 hypertensive patients. J. Am. Coll. Cardiol. 48, 2293–2300 (2006).
Calhoun, D. A., Nishizaka, M. K., Zaman, M. A., Thakkar, R. B. & Weissmann, P. Hyperaldosteronism among black and white subjects with resistant hypertension. Hypertension 40, 892–896 (2002).
Drelon, C., Berthon, A., Mathieu, M., Martinez, A. & Val, P. Adrenal cortex tissue homeostasis and zonation: a WNT perspective. Mol. Cell. Endocrinol. 408, 156–164 (2015).
El Wakil, A. & Lalli, E. The Wnt/beta-catenin pathway in adrenocortical development and cancer. Mol. Cell. Endocrinol. 332, 32–37 (2011).
Teo, A. E. et al. Pregnancy, primary aldosteronism, and adrenal CTNNB1 mutations. N. Engl. J. Med. 373, 1429–1436 (2015).
Tissier, F. et al. Mutations of beta-catenin in adrenocortical tumors: activation of the Wnt signaling pathway is a frequent event in both benign and malignant adrenocortical tumors. Cancer Res. 65, 7622–7627 (2005).
Oliveira-Paula, G. H. et al. Polymorphisms in VEGFA gene affect the antihypertensive responses to enalapril. Eur. J. Clin. Pharmacol. 71, 949–957 (2015).
Yang, R. et al. Hypertension and endothelial dysfunction in apolipoprotein E knockout mice. Arterioscler. Thromb. Vasc. Biol. 19, 2762–2768 (1999).
Sofat, R. et al. Circulating apolipoprotein E concentration and cardiovascular disease risk: meta-analysis of results from three studies. PLoS Med. 13, e1002146 (2016).
Conrad, K. P. Unveiling the vasodilatory actions and mechanisms of relaxin. Hypertension 56, 2–9 (2010).
Sun, H. J. et al. Relaxin in paraventricular nucleus contributes to sympathetic overdrive and hypertension via PI3K-Akt pathway. Neuropharmacology 103, 247–256 (2016).
Miyamoto, Y. et al. Phosphatidylinositol 3-kinase inhibition induces vasodilator effect of sevoflurane via reduction of Rho kinase activity. Life Sci. 177, 20–26 (2017).
Pawlak, J. B., Wetzel-Strong, S. E., Dunn, M. K. & Caron, K. M. Cardiovascular effects of exogenous adrenomedullin and CGRP in Ramp and Calcrl deficient mice. Peptides 88, 1–7 (2017).
Ohtsu, H. et al. Signal-crosstalk between Rho/ROCK and c-Jun NH2-terminal kinase mediates migration of vascular smooth muscle cells stimulated by angiotensin II. Arterioscler. Thromb. Vasc. Biol. 25, 1831–1836 (2005).
Tzoulaki, I., Elliott, P., Kontis, V. & Ezzati, M. Worldwide exposures to cardiovascular risk factors and associated health effects: current knowledge and data gaps. Circulation 133, 2314–2333 (2016).
Munafò, M. R., Tilling, K., Taylor, A. E., Evans, D. M. & Davey Smith, G. Collider scope: when selection bias can substantially influence observed associations. Int. J. Epidemiol. 47, 226–235 (2018).
Pazoki, R. et al. Genetic predisposition to high blood pressure and lifestyle factors: associations with midlife blood pressure levels and cardiovascular events. Circulation 137, 653–661 (2018).
Boyle, E. A., Li, Y. I. & Pritchard, J. K. An expanded view of complex traits: from polygenic to omnigenic. Cell 169, 1177–1186 (2017).
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).
Bycroft, C.F. et al. Genome-wide genetic data on 500,000 UK Biobank participants. Preprint at bioRxiv https://doi.org/10.1101/166298 (2017).
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).
Marouli, E. et al. Rare and low-frequency coding variants alter human adult height. Nature 542, 186–190 (2017).
Wain, L. V. et al. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure. Nat. Genet. 43, 1005–1011 (2011).
1000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74 (2015).
Willer, C. J., Li, Y. & Abecasis, G. R. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics 26, 2190–2191 (2010).
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).
Zhou, J. & Troyanskaya, O. G. Predicting effects of noncoding variants with deep learning-based sequence model. Nat. Methods 12, 931–934 (2015).
Pers, T. H. et al. Biological interpretation of genome-wide association studies using predicted gene functions. Nat. Commun. 6, 5890 (2015).
Wang, J., Vasaikar, S., Shi, Z., Greer, M. & Zhang, B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 45, W130–W137 (2017).
Canela-Xandri, O., Rawlik, K. & Tenesa, A. An atlas of genetic associations in UK Biobank. Preprint at bioRxiv https://doi.org/10.1101/176834 (2017).
H.R.W. was funded by the National Institute for Health Research (NIHR) as part of the portfolio of translational research of the NIHR Biomedical Research Centre at Barts and The London School of Medicine and Dentistry. D.M.-A. is supported by the Medical Research Council (grant number MR/L01632X.1). B.M. holds an MRC eMedLab Medical Bioinformatics Career Development Fellowship, funded from award MR/L016311/1. H.G. was funded by the NIHR Imperial College Health Care NHS Trust and Imperial College London Biomedical Research Centre. C.P.C. was funded by the National Institute for Health Research (NIHR) as part of the portfolio of translational research of the NIHR Biomedical Research Center at Barts and The London School of Medicine and Dentistry. S. Thériault was supported by Canadian Institutes of Health Research; Université Laval (Quebec City, Canada). G.P. was supported by Canada Research Chair in Genetic and Molecular Epidemiology and CISCO Professorship in Integrated Health Biosystems. I. Karaman was supported by the EU PhenoMeNal project (Horizon 2020, 654241). C.P.K. is supported by grant U01DK102163 from the NIH-NIDDK and by resources from the Memphis VA Medical Center. S.D. was supported for this work by grants from the European Research Council (ERC), the EU Joint Programme – Neurodegenerative Disease Research (JPND) and the Agence Nationale de la Recherche (ANR). T. Boutin, J. Marten, V.V., A.F.W. and C.H. were supported by a core MRC grant to the MRCHGU QTL in Health and Disease research programme. M. Boehnke is supported by NIH grant R01-DK062370. H.W. and A. Goel acknowledge support of the Tripartite Immunometabolism Consortium (TrIC), Novo Nordisk Foundation (grant NNF15CC0018486). N.V. was supported by a Marie Sklodowska-Curie GF grant (661395) and ICIN-NHI. C. Menni is funded by the MRC AimHy (MR/M016560/1) project grant. M.A.N.’s participation is supported by a consulting contract between Data Tecnica International and the National Institute on Aging, NIH. M. Brumat, M. Cocca, I.G., P.G., G.G., A. Morgan, A.R., D.V., C.M.B., C.F.S., M. Traglia and D.T. were supported by Italian Ministry of Health grant RF2010 to P.G. and RC2008 to P.G. D.I.B. is supported by the Royal Netherlands Academy of Science Professor Award (PAH/6635). J.C.C. is supported by the Singapore Ministry of Health’s National Medical Research Council under its Singapore Translational Research Investigator (STaR) Award (NMRC/STaR/0028/2017). C.P.C., P.B.M. and M.R.B. were funded by the National Institutes for Health Research (NIHR) as part of the portfolio of translational research of the NIHR Biomedical Research Centre at Barts. T.F. is supported by the NIHR Biomedical Research Centre, Oxford. M.R. is the recipient of an award from China Scholarship Council (No. 2011632047). C.L. was supported by the Medical Research Council UK (G1000143, MC_UU_12015/1, MC_PC_13048 and MC_U106179471), Cancer Research UK (C864/A14136) and EU FP6 programme (LSHM_CT_2006_037197). G.B.E. is supported by the Swiss National Foundation SPUM project FN 33CM30-124087, Geneva University, and the Fondation pour Recherches Médicales, Genève. C.M.L.is supported by the Li Ka Shing Foundation; WT-SSI/John Fell funds; the NIHR Biomedical Research Centre, Oxford; Widenlife; and NIH (CRR00070 CR00.01). R.J.F.L. is supported by the NIH (R01DK110113, U01HG007417, R01DK101855 and R01DK107786). D.O.M.-K. is supported by the Dutch Science Organization (ZonMW-VENI Grant 916.14.023). M.M. was supported by the National Institute for Health Research (NIHR) BioResource Clinical Research Facility and Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London. H.W. and M.F. acknowledge the support of the Wellcome Trust core award (090532/Z/09/Z) and the BHF Centre of Research Excellence (RE/13/1/30181). A. Goel and H.W. acknowledge the European Union Seventh Framework Programme FP7/2007-2013 under grant agreement no. HEALTH-F2-2013-601456 (CVGenes@Target) and A. Goel the Wellcome Trust Institutional strategic support fund. L.R. was supported by Forschungs- und Förder-Stiftung INOVA, Liechtenstein. M. Tomaszewski is supported by British Heart Foundation (PG/17/35/33001). P. Sever is recipient of an NIHR Senior Investigator Award and is supported by the Biomedical Research Centre Award to Imperial College Healthcare NHS Trust. P.v.d.H. was supported by the ICIN-NHI and Marie Sklodowska-Curie GF (call: H2020-MSCA-IF-2014, Project ID: 661395). N.J.W. was supported by the Medical Research Council UK (G1000143, MC_UU_12015/1, MC_PC_13048 and MC_U106179471), Cancer Research UK (C864/A14136) and EU FP6 programme (LSHM_CT_2006_037197). E.Z. was supported by the Wellcome Trust (WT098051). J.N.H. was supported by the Vanderbilt Molecular and Genetic Epidemiology of Cancer (MAGEC) training program, funded by T32CA160056 (PI: X.-O. Shu) and by VA grant 1I01CX000982. A. Giri was supported by VA grant 1I01CX000982. T.L.E. and D.R.V.E. were supported by grant R21HL121429 from NHLBI, NIH. A.M.H. was supported by VA Award #I01BX003360. C.J.O. was supported by VA Boston Healthcare, Section of Cardiology and Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School. The MRC/BHF Cardiovascular Epidemiology Unit is supported by the UK Medical Research Council (MR/L003120/1), British Heart Foundation (RG/13/13/30194) and UK National Institute for Health Research Cambridge Biomedical Research Centre. J. Danesh is a British Heart Foundation Professor and NIHR Senior Investigator. L.V.W. holds a GlaxoSmithKline/British Lung Foundation Chair in Respiratory Research. P.E. acknowledges support from the NIHR Biomedical Research Centre at Imperial College Healthcare NHS Trust and Imperial College London, the NIHR Health Protection Research Unit in Health Impact of Environmental Hazards (HPRU-2012-10141), and the Medical Research Council (MRC) and Public Health England (PHE) Centre for Environment and Health (MR/L01341X/1). P.E. is a UK Dementia Research Institute (DRI) professor at Imperial College London, funded by the MRC, Alzheimer’s Society and Alzheimer’s Research UK. He is also associate director of Health Data Research–UK London, funded by a consortium led by the Medical Research Council. M.J.C. was funded by the National Institute for Health Research (NIHR) as part of the portfolio of translational research of the NIHR Biomedical Research Center at Barts and The London School of Medicine and Dentistry. M.J.C. is a National Institute for Health Research (NIHR) senior investigator, and this work is funded by the MRC eMedLab award to M.J.C. and M.R.B. and by the NIHR Biomedical Research Centre at Barts.
This research has been conducted using the UK Biobank Resource under application numbers 236 and 10035. This research was supported by the British Heart Foundation (grant SP/13/2/30111). Large-scale comprehensive genotyping of UK Biobank for cardiometabolic traits and diseases: UK CardioMetabolic Consortium (UKCMC).
Computing: This work was enabled using the computing resources of (i) the UK Medical Bioinformatics aggregation, integration, visualisation and analysis of large, complex data (UK Med-Bio), which is supported by the Medical Research Council (grant number MR/L01632X/1), and (ii) the MRC eMedLab Medical Bioinformatics Infrastructure, supported by the Medical Research Council (grant number MR/L016311/1). The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Institutes of Health; or the US Department of Health and Human Services. C.P.K. is an employee of the US Department of Veterans Affairs. Opinions expressed in this paper are those of the authors and do not necessarily represent the opinion of the Department of Veterans Affairs or the United States Government.