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Trans-ethnic association study of blood pressure determinants in over 750,000 individuals

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Abstract

In this trans-ethnic multi-omic study, we reinterpret the genetic architecture of blood pressure to identify genes, tissues, phenomes and medication contexts of blood pressure homeostasis. We discovered 208 novel common blood pressure SNPs and 53 rare variants in genome-wide association studies of systolic, diastolic and pulse pressure in up to 776,078 participants from the Million Veteran Program (MVP) and collaborating studies, with analysis of the blood pressure clinical phenome in MVP. Our transcriptome-wide association study detected 4,043 blood pressure associations with genetically predicted gene expression of 840 genes in 45 tissues, and mouse renal single-cell RNA sequencing identified upregulated blood pressure genes in kidney tubule cells.

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Fig. 1: Study design schematic.
Fig. 2: Manhattan plots summarizing discovery and replication meta-analysis.
Fig. 3: Mapping blood pressure–associated genes to mouse kidney cell type clusters.

Data availability

Full summary statistics relating to the Million Veteran Program (MVP) are publicly available and may be accessed from dbGaP with the accession code phs001672.v1.p1. The UK Biobank data are available upon application to the UK Biobank (https://www.ukbiobank.ac.uk). Combined summary statistics for common and rare variant analysis (discovery and replication) for sentinel SNPs for each blood pressure trait are available in the supplementary tables. Statistically significant reports for S-PrediXcan results for all 45 tissues and PheWAS analyses for all blood pressure traits evaluated are also in the supplementary tables. Mouse single-cell sequencing data can be found at Gene Expression Omnibus (GSE107585).

Change history

  • 25 January 2019

    In the version of the article originally published, the accession code phs001672.v1.p1 in the ‘Data availability’ section was hyperlinked incorrectly. The error has been corrected in the HTML and PDF versions of the article.

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Acknowledgements

This work is a product of the effort, initiative and funds made available to several individuals by multiple funding organizations. Detailed acknowledgements and funding details are provided in the Supplementary Note. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the US National Heart, Lung and Blood Institute; the US National Institutes of Health; the US Department of Health and Human Services; the UK National Health Service; the European Commission (UK); the UK National Institute for Health Research; or the UK Department of Health and Social Care. This publication does not represent the views of the US Department of Veterans Affairs or the US government.

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A.G., J.N.H., J.M.K., E.S.T., C.P.K., Y.V.S., O.D.W., C.R.-C., C.L.R., C.P. Chung, K.A.B., H.R.W., C.P. Cabrera, E.E., J.M.M.H., M.J.C., P.E., M.E.M., E.E.S., J.M.G., J.C., P.W.F.W., P.S.T., D.R.V.E., C.J.O., A.M.H. and T.L.E. contributed to discovery analysis. H.R.W., E.E., C.P. Cabrera, L.V.W., M.J.C., P.E., B.M.P., M.S., P. Amouyel, S.D, M.L., M.B., L.J.S., E.Z., P.B.M., M.F., P. Sever, N.R.P., J.M.M.H., P. Surendran, J.L., J.-H.Z., S.M.W., R.A.S., C. Langenberg, N.J.W., D.C., S.T., C.N.A.P., N. Shah, C.O., J.A., D.I.C., P.M.R., O.M., P. Almgren, R.L.-G., D.M.-K., P.v.d.H., N.V., F.C., D.S., C.H. T.S.B., M.M. and T.D.S. (ICBP); P.B.M., E.E., E.Z., P. Surendran, D.I.C., I.N., C. Lindgren, M.-R.J., B.J.H., N.J.T., K.-H.H., N. Sepúlveda., T.G.R., G.D., E.F., J.P.C., A.K., S.K., N.L., J.M.M.H., C. Liu and C.N.-C. (BP-ICE); J.N.H., D.R.V.E. and T.L.E. (BioVU) contributed to the replication study. A.G., J.N.H., J.M.K., E.S.T., O.D.W., S.M.D., Y.W., Y.X., S.L.D., D.K., J.C.D., W.-Q.W., J.C.S., D.R.V.E., A.M.H. and T.L.E. performed central analysis. J.P., C.Q., R.S. and K.S. worked on human kidney and mouse model systems. A.G., J.N.H., J.M.K., C.P.K., Y.V.S., S.M.D., C.R.-C., B.S.M., E.A.A, M.E.M., P.W.F.W., P.S.T., D.R.V.E., C.J.O., A.M.H. and T.L.E. wrote the manuscript.

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Correspondence to Adriana M. Hung or Todd L. Edwards.

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P.S. received support from Pfizer Inc. N.P. has received financial support from several pharmaceutical companies which manufacture blood pressure-lowering agents, for consultancy fees (Servier), research projects and staff (Servier, Pfizer) and for arranging and speaking at educational meetings (AstraZeneca, Lri Therapharma, Napi, Servier and Pfizer). He holds no stocks and shares in any such companies. M.J.C. is Chief Scientist for Genomics England, a UK Government company. B.M.P. serves on the Data Safety Monitoring Board of a clinical trial funded by Zoll LifeCor and on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. D.M.-K. works as a part-time clinical research consultant for Metabolon, Inc. R.A.S. is an employee and shareholder in GlaxoSmithKline plc. 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 (US); the National Institutes of Health (US); National Health Service (UK); National Institute for Health Research (UK); The Department of Health and Social Care (UK); the EC; or the US Department of Health and Human Services. This publication does not represent the views of the Department of Veterans Affairs or the United States government.

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Supplementary Figure 1 Comparison of effect sizes for known and novel sentinel SNPs identified with SBP, DBP, and pulse pressure across whites, blacks, and Hispanics.

Sentinel SNPs from final meta-analysis for each blood pressure trait (left to right) were compared for consistency between UKB whites and MVP whites (row 1), MVP blacks and MVP whites (row 2), MVP Hispanics and MVP whites (row 3), and MVP Hispanics and MVP blacks (row 4). Blue dots denote sentinel SNPs from known loci, and red dots denote sentinel SNPs from novel loci.

Supplementary Figure 2 Juxtaposed mirror plot for S-PrediXcan (–log10 P) and GWAS (log10 P) for SBP.

–log10 P values for associations between genetically predicted gene expression (GPGE) analyses with SBP in 45 tissues are juxtaposed with log10 P values from GWAS analyses for SBP (maximum effective n = 760,226 biologically independent samples). All GWAS plots represent discovery + replication samples included. GPGE analysis with S-PrediXcan was also performed with the full discovery + replication summary statistics. Two-sided Wald test was performed to obtain z-scores and resulting P values. The upper red line denotes the Bonferroni significance threshold for S-PrediXcan (P < 2.5 × 10–7). The lower red line denotes the genome-wide significance threshold (P < 5 × 10–8). SBP, systolic blood pressure.

Supplementary Figure 3 Juxtaposed mirror plots for S-PrediXcan (–log10 P) and GWAS (log10 P) for DBP.

–log10 P values for associations between genetically predicted gene expression (GPGE) analyses with DBP in 45 tissues are juxtaposed with log10 P values from GWAS analyses for DBP (maximum effective n = 767,920 biologically independent samples). All GWAS plots represent discovery + replication samples included. GPGE analysis with S-PrediXcan was also performed with the full discovery + replication summary statistics. Two-sided Wald test was performed to obtain z-scores and resulting P values. The upper red line denotes the Bonferroni significance threshold for S-PrediXcan (P < 2.5 × 10–7). The lower red line denotes the genome-wide significance threshold (P < 5 × 10–8). DBP, diastolic blood pressure.

Supplementary Figure 4 Juxtaposed mirror plots for S-PrediXcan (–log10 P) and GWAS (log10 P) for pulse pressure.

–log10 P values for associations between genetically predicted gene expression (GPGE) analyses with pulse pressure in 45 tissues are juxtaposed with log10 P values from GWAS analyses for pulse pressure (maximum effective n = 759,768 biologically independent samples). All GWAS plots represent discovery + replication samples included. GPGE analysis with S-PrediXcan was also performed with the full discovery + replication summary statistics. Two-sided Wald test was performed to obtain z-scores and resulting P values. The upper red line denotes the Bonferroni significance threshold for S-PrediXcan (P < 2.5 × 10–7). The lower red line denotes the genome-wide significance threshold (P < 5 × 10–8).

Supplementary Figure 5 Comparison of effect sizes for significant PheWAS results identified with SBP, DBP, and pulse pressure across whites, blacks, and Hispanics.

Genetic risk scores (GRS) weighted for SBP, DBP and pulse pressure were regressed onto the clinical phenome in whites (maximum n = 188,008 biologically independent samples), blacks (maximum n = 52,530 biologically independent samples) and Hispanics (maximum n = 16,735 biologically independent samples) separately. Effect estimates for phenotypes that were significant in whites, blacks or Hispanics were compared across three ethnicities. Comparison of effect estimates are presented in the following order: blacks and whites (row 1), Hispanics and whites (row 2) and Hispanics and blacks (row 3) for SBP, DBP and pulse pressure (left to right). R2 denotes correlation between effect estimates calculated from a linear regression model. The blue line represents the regression line, and the shaded area represents the 95% confidence interval.

Supplementary Figure 6 Venn diagram of associations from PheWAS for blood pressure trait-specific GRS.

The diagram shows overlap of associations between SBP, DBP and pulse pressure w-GRS. PheWAS analysis was conducted in self-reported/administratively assigned white MVP participants only. W-GRS were constructed using statistically significant SNPs using weights from the UK Biobank data set.

Supplementary Figure 7 Subcellular layout of the top network from IPA analysis of significant SBP genes in aorta.

Genes significant (P < 2.5 × 10–7) in S-PrediXcan analysis of SBP GWAS loci in aorta were provided as input for IPA. Biological networks ranked by the number of overlapping loci were generated, and the top network is presented here. Twenty of 45 molecules are represented by genes significant in S-PrediXcan analyses, as indicated by node coloring. Arrows indicate the direction of the relationship, while solid lines indicate direct interaction (for example, phosphorylation) and broken lines indicate indirect relationships (for example, activation). Interactions without direction (for example, protein–protein) do not have an arrow. Nodes outlined in purple indicate overlay of cardiovascular disease (enrichment P = 7.16 × 10–6) and cardiovascular system and development (enrichment P = 7.73 × 10–5) pathways. Right-tailed Fisher’s exact test was performed to obtain enrichment P values without correction for multiple testing.

Supplementary Figure 8 Subcellular layout of the top network from IPA analysis of significant DBP genes in aorta.

Genes significant (P < 2.5 × 10–7) in S-PrediXcan analysis of DBP GWAS loci in aorta were provided as input for IPA. Biological networks ranked by the number of overlapping loci were generated, and the top network is presented here. Eleven of 27 molecules are represented by genes significant in S-PrediXcan analyses, as indicated by node coloring. Arrows indicate the direction of the relationship, while solid lines indicate direct interaction (for example, phosphorylation) and broken lines indicate indirect relationships (for example, activation). Interactions without direction (for example, protein–protein) do not have an arrow. Nodes outlined in purple indicate overlay of hematopoiesis (enrichment P = 6.57 × 10–7) and hematological system and development (enrichment P = 6.57 × 10–7) pathways. Right-tailed Fisher’s exact test was performed to obtain enrichment P values without correction for multiple testing.

Supplementary Figure 9 Subcellular layout of the top network from IPA analysis of significant pulse pressure genes in aorta.

Genes significant (P < 2.5 × 10–7) in S-PrediXcan analysis of pulse pressure GWAS loci in aorta were provided as input for IPA. Biological networks ranked by the number of overlapping loci were generated, and the top network is presented here. Eighteen of 36 molecules are represented by genes significant in S-PrediXcan analyses, as indicated by node coloring. Arrows indicate the direction of the relationship, while solid lines indicate direct interaction (for example, phosphorylation) and broken lines indicate indirect relationships (for example, activation). Interactions without direction (for example, protein–protein) do not have an arrow. Nodes outlined in purple indicate overlay of cardiovascular disease (enrichment P = 9.53 × 10–4) and cardiovascular system and development (enrichment P = 9.53 × 10–4) pathways. Right-tailed Fisher’s exact test was performed to obtain enrichment P values without correction for multiple testing.

Supplementary Figure 10 Quantile–quantile (QQ) plots for discovery meta-analysis GWAS of BP traits.

Shown are QQ plots for SBP (a; maximum n = 459,777 biologically independent samples), DBP (b; maximum n = 459,377 biologically independent samples) and pulse pressure (c; maximum n = 459,374 biologically independent samples). The genomic inflation statistic lambda is presented for discovery meta-analysis for each blood pressure trait. The red line represents the expected distribution. The blue dots represent the 95% confidence interval about the expected distribution.

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Giri, A., Hellwege, J.N., Keaton, J.M. et al. Trans-ethnic association study of blood pressure determinants in over 750,000 individuals. Nat Genet 51, 51–62 (2019). https://doi.org/10.1038/s41588-018-0303-9

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