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Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits

A Publisher Correction to this article was published on 14 November 2018

This article has been updated

Abstract

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.

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Fig. 1: Study design schematic for discovery and validation of loci.
Fig. 2: Manhattan plot showing the minimum P-value for the association across all blood pressure traits in the discovery stage excluding known and previously reported variants.
Fig. 3: Venn diagrams of novel locus results.
Fig. 4: Association of blood pressure loci with lifestyle traits.
Fig. 5: Association of blood pressure loci with other traits and diseases.
Fig. 6: Association of blood pressure loci with other traits and diseases.
Fig. 7: Relationship of deciles of the genetic risk score (GRS) based on all 901 loci with blood pressure, risk of hypertension and cardiovascular disease in UKB.
Fig. 8: Known and novel blood pressure associations in the TGFβ signaling pathway.

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Data availability

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.

Change history

  • 14 November 2018

    In the version of this article originally published, the name of author Martin H. de Borst was coded incorrectly in the XML. The error has now been corrected in the HTML version of the paper.

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Acknowledgements

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.

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Central analysis. E.E., H.R.W., D.M.-A., B.M., R.P., H.G., G.N., N.D., C.P.C., I. Karaman, F.L.N., M.E., K.W., E.T., L.V.W.

Writing of the manuscript. E.E., H.R.W., D.M.-A., B.M., R.P., H.G., I.T., M.R.B., L.V.W., P.E., M.J.C. (with group leads E.E., H.R.W., L.V.W., P.E., M.J.C.). All authors critically reviewed and approved the final version of the manuscript.

ICBP-Discovery contributor. (3C-Dijon) S.D., M.S., P. Amouyel, G.C., C.T.; (AGES-Reykjavik) V. Gudnason, L.J.L., A.V.S., T.B.H.; (ARIC) D.E.A., E.B., A. Chakravarti, A.C.M., P.N.; (ASCOT) N.R.P., D.C.S., A.S., S. Thom, P.B.M., P. Sever, M.J.C., H.R.W.; (ASPS) E.H., Y.S., R. Schmidt, H. Schmidt; (B58C) D.P.S., (BHS) A. James, N. Shrine; (BioMe (formerly IPM)) E.P.B., Y. Lu, R.J.F.L.; (BRIGHT) J.C., M.F., M.J.B., P.B.M., M.J.C., H.R.W.; (CHS) J.C.B., K.R., K.D.T., B.M.P.; (Cilento study) M. Ciullo, T. Nutile, D.R., R. Sorice; (COLAUS) M. Bochud, Z.K., P.V.; (CROATIA_Korcula) J. Marten, A.F.W.; (CROATIA_SPLIT) I. Kolcic, O.P., T.Z.; (CROATIA_Vis) J.E.H., I.R., V.V.; (EPIC) K.-T.K., R.J.F.L., N.J.W.; (EPIC-CVD) W.-Y.L., P. Surendran, A.S.B., J. Danesh, J.M.M.H.; (EPIC-Norfolk, Fenland-OMICS, Fenland-GWAS) J.-H.Z.; (EPIC-Norfolk, Fenland-OMICS, Fenland-GWAS, InterAct-GWAS) J.L., C.L., R.A.S., N.J.W.; (ERF) N.A., B.A.O., C.M.v.D.; (Fenland-Exome, EPIC-Norfolk-Exome) S.M.W., FHS, S.-J.H., D.L.; (FINRISK (COROGENE_CTRL)) P.J., K.K., M.P., A.-P.S.; (FINRISK_PREDICT_CVD) A.S.H., A. Palotie, S.R., V.S.; (FUSION) A.U.J., M. Boehnke, F. Collins, J.T., (GAPP) S. Thériault, G.P., D.C., L.R.; (Generation Scotland (GS:SFHS)) T. Boutin, C.H., A. Campbell, S.P.; (GoDARTs) N. Shah, A.S.F.D., A.D.M., C.N.A.P.; (GRAPHIC) P.S.B., C.P.N., N.J.S., M.D.T.; (H2000_CTRL) A. Jula, P.K., S. Koskinen, T. Niiranen; (HABC) Y. Liu, M.A.N., T.B.H.; (HCS) J.R.A., E.G.H., C.O., R.J. Scott; (HTO) K.L.A., H.J.C., B.D.K., M. Tomaszewski, C. Mamasoula; (ICBP-SC) G.A., T.F., M.-R.J., A.D.J., M. Larson, C.N.-C.; (INGI-CARL) I.G., G.G., A. Morgan, A.R.; (INGI-FVG) M. Brumat, M. Cocca, P.G., D.V.; (INGI-VB) C.M.B., C.F.S., D.T., M. Traglia; (JUPITER) F.G., L.M.R., P.M.R., D.I.C.; (KORA S3) C.G., M. Laan, E.O., S.S.; (KORA S4) A. Peters, J.S.R.; (LBC1921) S.E.H., D.C.M.L., A. Pattie, J.M.S.; (LBC1936) G.D., I.J.D., A.J.G., L.M.L.; (Lifelines) N.V., M.H.d.B., M.A.S., P.v.d.H.; (LOLIPOP) J.C.C., J.S.K., B.L., W.Z.; (MDC) P. Almgren, O.M.; (MESA) X.G., W.P., J.I.R., J.Y.; (METSIM) A.U.J., M. Laakso; (MICROS) F.D.G.M., A.A.H., P.P.P.; (MIGEN) R.E., S. Kathiresan, J. Marrugat, D.S.; (ΝΕΟ) R.L.-G., R.d.M., R.N., D.O.M.-K.; (NESDA) Y.M., I.M.N., B.W.J.H.P., H. Snieder; (NSPHS) S.E., U.G., Å. Johansson; (NTR) D.I.B., E.J.d.G., J.-J.H., G.W.; (ORCADES) H.C., P.K.J., S.H.W., J.F.W.; (PIVUS) L. Lin, C.M.L., J.S., A. Mahajan; (Prevend) N.V., P.v.d.H.; (PROCARDIS) M.F., A. Goel, H.W.; (PROSPER) J. Deelen, J.W.J., D.J.S., S. Trompet; (RS) O.H.F., A. Hofman, A.G.U., G.C.V.; (SardiNIA) J. Ding, Y.Q., F. Cucca, E.G.L.; (SHIP) M.D., R.R., A.T., U.V.; (STR) M. Frånberg, A. Hamsten, R.J. Strawbridge, E.I.; (TRAILS) C.A.H., A.J.O., H.R., P.J.v.d.M.; (TwinsUK) M.M., C. Menni, T.D.S.; (UKHLS) B.P.P., E.Z.; (ULSAM) V. Giedraitis, A.P.M., A. Mahajan, E.I.; (WGHS) F.G., L.M.R., P.M.R., D.I.C.; (YFS) M.K., T.L., L.-P.L., O.T.R.

ICBP analysis. T. Blake, C.Y.D., G.B.E, J.K., L. Lin, P.F.O., P.J.M., Q.T.N., R. Jansen, R. Joehanes, A.M.E., A.V.

Replication study contributor. (MVP) J.N.H., A. Giri, D.R.V.E., Y.V.S., K.C., J.M.G., P.W.F.W., P.S.T., C.P.K., A.M.H., C.J.O., T.L.E.; (EGCUT) T.E., R.M., L.M., A. Metspalu.

Airwave Health Monitoring Study. E.E., H.G., A.-C.V., R.P., I. Karaman, I.T., P.E.

Corresponding authors

Correspondence to Paul Elliott or Mark J. Caulfield.

Ethics declarations

Competing interests

K.W. is a commercial partnerships manager for Genomics England, a UK Government company. M.A.N. consults for Illumina Inc, the Michael J. Fox Foundation and University of California Healthcare, among others. A.S.B. has received grants outside of this work from Merck, Pfizer, Novartis, AstraZeneca, Biogen and Bioverativ and personal fees from Novartis. J. Danesh has the following competing interests: Pfizer Population Research Advisory Panel (grant), AstraZeneca (grant), Wellcome Trust (grant), UK Medical Research Council (grant), Pfizer (grant), Novartis (grant), NHS Blood and Transplant (grant), UK Medical Research Council (grant), British Heart Foundation (grant), UK National Institute of Health Research (grant), European Commission (grant), Merck Sharp and Dohme UK Atherosclerosis (personal fees), Novartis Cardiovascular and Metabolic Advisory Board (personal fees), British Heart Foundation (grant), European Research Council (grant), Merck (grant). B.M.P. serves on the DSMB of a clinical trial funded by Zoll LifeCor and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. M.J.C. is Chief Scientist for Genomics England, a UK Government company.

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Integrated supplementary information

Supplementary Figure 1 GWAS discovery Manhattan plots.

(a-c) Manhattan plots for systolic blood pressure (SBP) (a), diastolic blood pressure (DBP) (b), and pulse pressure (PP) (c). P-value results from the GWAS discovery meta-analysis (n = 757,601), were derived using inverse variance fixed effects meta-analysis and they are plotted on a –log10 scale for all SNPs with minor allele frequency (MAF) ≥ 1%. SNPs within the 274 known loci (± 500 kb; linkage disequilibrium r2 ≥ 0.1) are highlighted in green.

Supplementary Figure 2 Effect sizes of all blood pressure–associated loci.

(a) Plot shows strong correlation between the published effect size estimates (x-axis) from literature vs. the effect sizes from our discovery meta-analysis (y-axis), for known SNPs, color-coded according to the published primary trait from the first published report. From the 357 validated SNPs listed in Supplementary Table 4 from the 274 published loci, 327 are available within the MAF ≥ 1% HRC-imputed data. For comparison of effect sizes, we only consider 299 such SNPs that have been identified from main-effect genetic association studies within Europeans (that is excluding any SNPs from interaction/stratified/multi-phenotype analysis, or from studies of other ancestries). For reliable comparison of effect sizes, we further restrict to the 284 known SNPs that reach genome-wide significance within the discovery meta-analysis for at least one blood pressure trait. The r2 value is presented to show the correlation between published and observed effect sizes. (b-d) Trait-specific plots for SBP (b), DBP (c) and PP (d) (SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure). Across all plots, the 284 “known” SNPs (black squares) from a are compared against the 325 novel sentinel SNPs from the 2-stage analysis (red circles), the 210 novel sentinel SNPs from the 1-stage analysis (green triangles), and the 92 SNPs (blue diamonds) replicated for the first time from Hoffman et al.9. Each SNP is only plotted in one of the trait-specific plots, according to the published primary trait for the known SNPs, or the primary trait for the novel/replicated SNPs. For all SNPs, we show the relationship between MAF on the x-axis and the effect size (mmHg) on the y-axis, where results are taken from the UKB+ICBP discovery meta-analysis. All meta-analysis results were computed using inverse variance fixed effects models. The different symbols and colors distinguish the “known” vs “novel-2stage” vs “novel-1stage” vs “replicated-Hoffman” SNPs, and show that, in general, the novel SNPs have smaller effect sizes than known SNPs, and that there is no significant difference (P = 0.447) between the effect sizes of the 1-stage (n = 757,601) and 2-stage (n = 1,006,863) novel SNPs. (UKB, UK Biobank; ICBP, International Consortium of Blood Pressure).

Supplementary Figure 3 Venn diagram of novel locus results.

For all 535 novel loci, we show the blood pressure traits associated with each locus. We present the 2-stage loci first, followed by the 1-stage loci. The locus names provided in alphabetical order correspond to the nearest annotated gene. SNPs, single nucleotide polymorphisms; SBP, systolic blood pressure; DBP, diastolic blood pressure; PP, pulse pressure; UKB, UK Biobank; ICBP, International Consortium of Blood Pressure.

Supplementary Figure 4 Overview of functional annotation and prioritization of genome-wide associated variants and genes.

SNPs, single nucleotide polymorphisms; LD, linkage disequilibrium; eQTL, expression quantitative trait loci; UCSC, University of California Santa Cruz (UCSC) genome browser; IPA, Ingenuity Pathway Analysis (IPA) software (IPA®,QIAGEN Redwood City,www.qiagen.com/ingenuity); DEPICT, Data-driven Expression Prioritized Integration for Complex Traits; GREAT, Genomic Regions Enrichment of Annotations Tool.

Supplementary Figure 5 DEPICT enrichment analysis.

DEPICT software was used to investigate enrichment of a range of biological properties. In each case, we compared known sentinel SNPs (n = 357) to all known and novel SNPs with P < 1 x 10−12 (n = 227). The gene set enrichment analysis algorithm is described in Pers et al.66. Enrichment –log P-value is reported for both groups; we also present delta –log P-value as a measure of novelty introduced by novel associations reported. Enrichment categories are as follows. (a) Enrichment of tissues and cell types. (b) GO annotation. (c) Protein-protein interaction subnetwork annotation. (d) Mammalian phenotype annotation.

Supplementary Figure 6 Enrichments of eQTLs.

535 novel blood pressure associated SNPs and the SNPs in LD r2 > 0.8 were annotated for their effect on gene expression using the GTEx portal. The number of eGenes associated with blood pressure SNPs in a given tissue/cell type was normalized with the total number of eGenes in that tissue, and Z-score was calculated using the trimmed mean and standard deviation of the normalized scores. Tissues of the same tissue group were colored the same.

Supplementary Figure 7 FORGE DNase I–sensitive region enrichment in known sentinel SNPs, compared to known and novel sentinel SNP associations for blood pressure.

Sentinel SNPs were investigated for enrichment in ENCODE DNase I regulatory regions using FORGE. The background probability of overlap is determined from the 1,000 background set overlap counts and the probability of the observed test result under a binomial distribution is calculated. The P-value thresholds of 0.05 and 0.01 are corrected for multiple testing by division by the number of tissue groupings tested, and the corrected threshold is used. Strongest enrichment in known SNPs was seen in vasculature (Human Aortic artery fibroblast (AoAF) and also Human Villous Mesenchymal Fibroblasts (HVMF) found in placenta). Enrichment in all known and novel SNPs was increased across vasculature (AoAF; HMVEC, Human microvascular endothelial cells) and highly vascularized tissues. Tissues in red are significant after correction for false discovery.

Supplementary Figure 8 Ingenuity pathway analysis of blood pressure genes.

Ingenuity pathway analysis for genes mapped to 357 sentinel SNPs at 274 known loci and genes mapped to all 901 loci. Sentinel gene mapping is compared to genes identified by extended LD (r2 > 0.8). Pathway enrichment is represented as –log P-value. (a) Canonical pathway enrichment. (b) Upstream regulator enrichment. (c) Disease and Biofunction enrichment.

Supplementary Figure 9 Exploring known and novel drug mechanisms in blood pressure.

The figure summarizes known and novel target opportunities highlighted by blood pressure genetics. Ingenuity pathway analysis was used to create a network of 6,562 genes showing direct interaction with 145 known blood pressure target genes. This network was compared with all genes that are either directly associated with blood pressure or linked by LD (r2 > 0.8). Overlap between genetic associated genes and the BP drug interactome demonstrates genetic support for known drug mechanisms. Drugged or druggable genes showing genetic association with blood pressure, but no interaction with the known BP drug interactome, represent potentially new mechanisms in blood pressure drug development and repositioning. Number of known and novel drugged/druggable gene associations are shown in parentheses.

Supplementary Figure 10 Comparison of β effect sizes between individuals of European (n = 757,601), African (n = 7,782) and South Asian (n = 10,323) ancestry.

(a-f) Scatterplots showing the direction of the standardized regression coefficient (beta) of novel (red) and known (grey) blood pressure variants between Europeans and Africans (a-c) and South Asians (d-f), on the three studied blood pressure phenotypes.

Supplementary Figure 11 Correlation and distribution of minor allele frequencies of blood pressure variants in individuals of European (n = 757,601), African (n = 7,782) and South Asian (n = 10,323) ancestry.

(a,b) Scatterplots showing the correlation and the distribution of MAF of novel (red) and known (grey) blood pressure variants between Europeans and Africans (a) and Europeans and South Asians (b). ρ is the Pearson correlation coefficient.

Supplementary Figure 12 Ethnicity clustering performed using PCA.

PC1 is plotted against PC2 for all n = 486,683 UK Biobank participants post-QC, color-coded according to the five ethnic clusters created from our K-means PCA clustering, from which only “White” Caucasians are selected for analysis of individuals of European ancestry. (a) Plot showing the clustering for all subjects. (b) Plots showing the subsets of individuals selected for race-stratified analysis, after combining information together from both the PCA clustering and the self-reported ethnicity. PCA, principal component analysis; QC, quality control; PCs, principal components.

Supplementary Figure 13 Quantile–quantile plots.

(a-c) QQ plots of results for systolic blood pressure (SBP) (a), diastolic blood pressure (DBP) (b), and pulse pressure (PP) (c) from GWAS discovery (n = 757,601). The black curves are based on all SNPs in the corresponding analysis, with MAF ≥ 1%. The green curves are results after excluding SNPs within the 274 known loci (± 500 kb; linkage disequilibrium r2 ≥ 0.1). The P-values have been derived from inverse variance fixed effects meta-analysis.

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Supplementary Tables 1–25

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Evangelou, E., Warren, H.R., Mosen-Ansorena, D. et al. Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits. Nat Genet 50, 1412–1425 (2018). https://doi.org/10.1038/s41588-018-0205-x

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