Article | Published:

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

Nature Geneticsvolume 51pages5162 (2019) | Download Citation

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.

Main

Decades of scientific evidence implicate elevated blood pressure in the etiology of cardiovascular disease, including coronary artery diseases, peripheral arterial disease and stroke, as well as renal and ocular damage. Elevated blood pressure accounts for at least 13% of annual deaths worldwide1,2. The risk of death from ischemic heart disease and stroke increases linearly with systolic blood pressure (SBP) and diastolic blood pressure (DBP) elevations greater than 115 mm Hg and 75 mm Hg, respectively3. Recent treatment guidelines emphasize the benefit of blood pressure–lowering strategies, including drug treatments, at lower thresholds of SBP or DBP4. These guidelines also identify a substantial population who are untreated or undertreated for elevated blood pressure, or do not have sufficient treatment response to antihypertensive drugs, and highlight the need to identify new gene targets for therapies5.

Large-scale genome-wide association studies (GWAS) have reported >250 loci associated with blood pressure traits, establishing that blood pressure traits are complex with many genetic determinants of modest effect6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27. Large blood pressure GWAS meta-analyses combine evidence from many cohorts and identify genetic determinants of SBP, DBP and pulse pressure levels. Regulatory effects may account for substantial heritability in GWAS, and GWAS sentinel SNPs are enriched for regulatory SNPs compared with the proportion of the genome containing regulatory elements28,29,30. Most blood-pressure SNPs are noncoding, are not in strong linkage disequilibrium (LD) with trait-associated coding variants and are found in regulatory elements12. Several methods have recently been developed to use multiple SNPs to perform gene-based tests of association between imputed gene expression levels and phenotypes, which are tissue specific and provide interpretable direction and magnitude of effects31,32,33,34.

In this trans-ethnic study, we performed a meta-analysis of data for 459,777 participants, 318,891 from the Million Veteran Program (MVP) and 140,886 from the UK Biobank (UKB)12. We subsequently performed independent replication in 316,301 participants from the International Consortium for Blood Pressure (ICBP)25 and Vanderbilt University’s BioVU cohort to study common variant associations with minor-allele frequency (MAF) >1% (Fig. 1). With 318,891 participants from the MVP as the discovery sample, we conducted two studies of rare variants, one focused on variants across the genome with independent replication in 445,360 participants from UKB and the other focused on exonic regions with replication in up to 420,704 participants from the Blood Pressure–International Consortium of Exome chip studies (BP-ICE). We report associations between blood pressure and common and rare SNPs, and blood pressure and genetically predicted gene expression (GPGE). We also evaluated gene–drug relationships and toxicities, conducted a phenome-wide association study (PheWAS) of blood pressure genetic risk scores, conducted pathway and tissue gene-set enrichment analyses and performed studies to identify mouse kidney cell types in which implicated genes were upregulated.

Fig. 1: Study design schematic.
Fig. 1

Flowchart depicting strategy for the three association analysis strategies (common, rare and exonic variants), as well as replication selection criteria and numbers of samples and SNPs by stage. Subsequent transcriptome-wide association study (TWAS) analysis with S-PrediXcan and PheWAS analyses using common variant summary statistics are also presented. PP, pulse pressure.

Results

MVP participants (n = 318,891), representing the majority of the discovery sample size, were predominantly male (91.5%) and were administratively identified as non-Hispanic whites (69.1%), and non-Hispanic blacks, Hispanics, non-Hispanic Asians and non-Hispanic Native Americans represented 18.8%, 6.7%, 0.77% and 0.85% of the population, respectively (Supplementary Table 1). Blacks were older on average (60.6 ± 11.4 years), followed by whites (58.9 ± 12.6 years), Native Americans (58.9 ± 12.6 years), Hispanics (52.7 ± 14.5 years) and Asians (48.6 ± 16.1 years) (mean ± s.d.). About half of the MVP participants were on antihypertensive medications, and a quarter had diabetes. Participants from the UKB interim release, originating from UKB application number 236, (n = 140,886), were also included in the discovery analysis; their characteristics have been reported elsewhere12.

Single-variant analyses

Common variants

We identified a total of 505 independent loci (201 novel loci, 304 previously reported) associated with one or more blood pressure traits: SBP, DBP and pulse pressure. Among the previously reported loci, 216 were associated with SBP, 76 with DBP and 208 with pulse pressure (Table 1; Fig. 2a–c; Supplementary Table 2a–c); none of these loci were evaluated for replication. Sentinel variants from loci that were deemed to be novel (by comparison with the GWAS catalog accessed March 2017 or literature report; P < 1 × 10−6 in the discovery phase; >500 kb from a known sentinel SNP; r2 ≤ 0.1 with known sentinel SNPs) and up to two proxies (n = 1,478) were carried forward for replication with the ICBP. Replicated variants had consistent directions of effect in the discovery and replication phases, had P < 0.05 in the replication stage and had meta-analysis (discovery and replication) P < 5 × 10−8 (details in Methods). These replicated SNPs represented 201 novel loci and included 124 loci for SBP, 4 loci for DBP and 123 loci for pulse pressure (Supplementary Table 3a–c). Comparison of mean effect estimates of blood pressure trait–increasing alleles showed that, on average, novel loci had smaller magnitudes of effect (0.24, 0.14 and 0.18 mm Hg per allele for SBP, DBP and pulse pressure, respectively) than known loci (0.32, 0.27 and 0.27 mm Hg per allele for SBP, DBP and pulse pressure, respectively; Table 1). Sentinel SNPs at all independent loci from meta-analysis of common variants explained 3.56%, 1.06% and 3.72% of the total variance for SBP, DBP and pulse pressure, respectively. Novel variants contributed to 0.80%, 0.24% and 0.72% of the total variance explained by all independent loci for SBP, DBP and pulse pressure, respectively.

Table 1 Summary of known and novel loci achieving statistical significance from analysis of common variants
Fig. 2: Manhattan plots summarizing discovery and replication meta-analysis.
Fig. 2

a–c, Manhattan plot of the discovery and replication meta-analysis for SPB (a), DBP (b) and pulse pressure (c). The y axis shows the –log10[P], and the x axis shows the chromosomal positions. The horizontal red line represents the threshold of P = 5 × 10−8 for genome-wide significance. SNPs in red are in previously identified loci (includes discovery only; neff-max = 459,670 for SBP, 459,093 for DBP and 459,305 for pulse pressure), whereas SNPs in orange are in novel loci (includes discovery and replication; neff-max = 760,226 for SBP, 767,920 for DBP and 759,768 for pulse pressure). All P values are computed for associations between genotyped or imputed SNPs and blood pressure traits as dependent variables in multivariable adjusted logistic regression models.

We detected an additional 37 conditionally independent SNPs from 29 loci by using discovery meta-analysis results (seven novel, three within the boundaries of both known and novel loci) as significant in one or more blood pressure traits (Supplementary Table 4).

Trans-ancestry comparisons

To update the observations reported by Franceschini et al. in 2013 (ref. 9), in which 29 SNP effects on blood pressure reported by Ehret et al. in 2011 (ref. 35) were consistent across European, African, East Asian and South Asian ancestries, we compared known and novel loci across racial and ethnic groups in MVP. We examined the correlations between effect sizes in white, black and Hispanic samples. The observed correlations of effect sizes between race and ethnic groups in MVP were weaker than those previously reported, although directions of effects were largely consistent (Supplementary Fig. 1; Supplementary Table 5).

Rare exonic variants

Rare exonic variants (MAF <1%) with suggestive evidence of association (P < 1 × 10−6) from the discovery sample were queried for replication in populations from BioVU (n = 17,277) and BP-ICE (nmax = 420,704). Eighteen variants were on the exome chip and available for final meta-analysis. Ten missense variants from seven genes were associated with blood pressure traits (P < 5 × 10−8; Table 2). Five variants were associated with SBP and/or DBP (rs141325069 PDE3A; p.Arg137Gln); rs139491786, (SLC9A3R2; p.Arg60Trp), rs61760904 (RRAS; p.Asp133Asn), rs73181210 (PHC3; p.Lys745Glu) and rs3025380 (DBH; p.Gly88Ala)) with consistent directions of effect for SBP and DBP. Three rare variantsfrom COL21A1 (rs115079907 (p.Gly882Arg), rs200999181 (p.Gly665Val) and rs2764043 (p.Leu277Pro)) and one variantfrom NOX4 (rs139341533; p.Leu73Phe) were significantly associated with pulse pressure but not with SBP or DBP and had opposite directions of effect for SBP and DBP. SNPs in RRAS, DBH and one of the three SNPs in COL21A1 (rs200999181) have been previously reported22,23,24. Average absolute values of effect estimates for SBP, DBP and pulse pressure in these variants were 1.52, 0.63 and 1.50 mm Hg per allele, respectively.

Table 2 Associations between missense variants identified in collaboration with consortia evaluating exonic variants and rare variants

We conditioned these SNPs on the sentinel common variant in each respective locus, where available, in the MVP discovery sample of white participants and compared effect estimates before and after conditioning. SNP rs139491786 in SLC9A3R2 showed a >50% reduction in effect size after conditioning on common variant rs140869992 (r2 = 0.35). Effect sizes for all other rare exonic variants were considered independent, as no substantial differences in effect estimates were observed after conditioning (Supplementary Table 6).

All rare variants

Discovery analysis in the MVP samples only (excluding UKB interim release data) identified 1,684 rare variants with suggestive evidence for association across the three blood pressure traits; 1,066 of these variants were available in UKB for meta-analysis. We observed statistically significant associations (P < 5 × 10−8) between 48 rare variants and one or more blood pressure traits. We identified 40 SNPs for pulse pressure, 8 SNPs for SBP and 2 SNPs for DBP (Supplementary Table 7). Average absolute values of effect estimates for SBP, DBP and pulse pressure were 9.67, 2.33 and 13.89 mm Hg per allele, respectively. The missense variants from NOX4 (rs139341533), SLC9A3R2 (rs139491786) and COL21A1 (rs200999181, rs2764043) were evaluated in the both the exonic and rare-variant analyses separately (Table 2; Supplementary Table 7).

Transcriptome-wide association analyses

Common variants from the final meta-analysis were used to evaluate the associations between blood pressure traits and GPGE levels across 44 Genotype-Tissue Expression Project (GTEx)36 tissues and the human kidney reference described by Ko et al.37 by using S-PrediXcan31. We identified statistically significant GPGE associations for 1,552 gene–tissue pairs with SBP, 521 with DBP and 1,970 with pulse pressure (Supplementary Table 8a–c; Supplementary Figs. 24). We identified 409 genes with this analysis that would not have been identified if SNPs were annotated using the nearest gene. MTHFR was the top result from SBP and showed decreasing SBP with increasing GPGE in skeletal muscle, aorta and several other tissues.

Mouse kidney single-cell sequencing analysis

Homologs of human genes identified as significant in the S-PrediXcan analysis of kidney tissue were investigated for kidney cell type–specific RNA expression by using single-cell sequencing in mouse kidney cells. Cells were clustered into 11 groups representing structural features and other cell types found in the kidney. Of the 28 genes, 16 were most expressed in one of the five tubule-related cell types: proximal tubules, loop of Henle, distal convoluted tubules, collecting duct principal cells or collecting duct intercalated cells (Fig. 3; Supplementary Table 9a–c). Cross-referencing protein expression levels in the Human Protein Atlas38 confirmed findings from mouse kidney, including higher expression of CDC16, SRR, SFXN2 and CLCN6 proteins in tubules than glomeruli (Supplementary Table 10).

Fig. 3: Mapping blood pressure–associated genes to mouse kidney cell type clusters.
Fig. 3

ac, Average expression level of genes identified by S-PrediXcan analyses in mouse kidney cell types for SBP (a), DBP (b) and pulse pressure (c). Expression levels were determined in 43,745 kidney cells derived from seven mice. Mean expression values of the genes were calculated in each cluster. Color scheme is based on z-score distribution obtained from two-sided Wald test. z-scores are not corrected for multiple comparisons. Each row represents one gene and each column is a single-cell type cluster (as defined by Park et al.83) on the heat map. Endo, endothelial, vascular, descending loop of Henle; Podo, podocyte, PT, proximal tubule; LOH, ascending loop of Henle; DCT, distal convoluted tubule; CD-PC, collecting duct principal cell; CD-IC, CD intercalated cell; Fib, fibroblast; Macro, macrophage; Neutro, neutrophil; NK, natural killer cell.

Assessment of gene–drug relationships

To better understand how genes identified in the study relate to medications, associations identified from the GPGE analyses were investigated for overlap with gene targets of known antihypertensive medications, non-antihypertensive medications, and medications with adverse drug events (ADEs) for hypertension or hypotension (Supplementary Tables 1114). A total of 2,177 tissue- and blood-pressure-trait-specific drug-gene relationships were identified in this analysis. Of these, there were 617 unique drug–gene relationships, with 175 (28.36%) with antagonistic effects.

The genes PDE3A, PSMB9 and SH2B3, which are targeted by the non-antihypertensive drugs theophylline, carfilzomib and pazopanib, respectively, have ADEs of either hypotension or hypertension and increased blood pressure with increasing GPGE. The most significant gene in S-PrediXcan targeted by an antihypertensive medication was CLCN6 (SBP β = −2.76, P = 8.14 × 10−45) in tibial artery tissue (Supplementary Table 11). The most significant gene in S-PrediXcan with a positive effect size and that was targeted by a non-antihypertensive medication was PRKAR2B (β = 1.38, P = 1.39 × 10−81) in aortic artery tissue identified from the pulse pressure analysis (Supplementary Table 12). The most significant gene in S-PrediXcan with a positive effect size and that was targeted by a drug with an ADE for hypertension was PSMB9 (pulse pressure β = 0.42, P = 7.57 × 10−9) in tibial artery tissue (Supplementary Table 13).

PheWAS with blood pressure genetic risk scores

To systematically evaluate pleiotropy between genetic predictors of blood pressure traits and diseases throughout the phenome, we performed PheWAS using blood pressure trait–weighted genetic risk scores (w-GRSs) separately in self-reported or administratively identified non-Hispanic white, non-Hispanic black and Hispanic individuals in the MVP. We used all known and novel common sentinel SNPs from the final meta-analysis and trait-specific weights from the UKB discovery sample to generate w-GRSs for each blood pressure trait and regressed PheWAS outcomes from MVP onto those scores, adjusted for the top ten genetic principal components. Of 1,813 phenotypes, 88 were significantly associated with any w-GRSs at a Bonferroni-corrected P threshold < 2.76 × 10−5 (Supplementary Table 15). Hypertension (smallest P < 1 × 10−305), essential hypertension (smallest P < 1 × 10−305) and hypertensive heart and/or renal disease (smallest P = 3.3 × 10−173) were the top associations for all nine w-GRSs. PheWAS associations were consistent across race and ethnic groups (Supplementary Fig. 5). Associations with phenotypes in the circulatory system (n = 52) accounted for >50% of the significant results in whites. The phenotype groups with the next most abundant associations were endocrine or metabolic (n = 28), genitourinary (n = 10) and hematopoietic (n = 6).

Among significant associations, 45 were significant for all three w-GRSs, 10 were significant for both SBP and DBP, and 15 were significant for both SBP and pulse pressure, demonstrating substantial overlap between signals captured by genetically predicted blood pressure traits (Supplementary Fig. 6; Supplementary Table 15). Thirteen associations were significant only with the pulse pressure w-GRS, five of which were diabetes sequelae: ophthalmic manifestations, neurological manifestations, diabetic retinopathy, other abnormal glucose and polyneuropathy. Aortic and other aneurysms were associated only with the DBP w-GRS but not with other w-GRSs.

Enrichment and pathway analyses

We evaluated whether statistically significant genes from S-PrediXcan analyses were enriched in one or more tissues. Compared with other tissues, the aorta showed the greatest evidence for enrichment of significant genes across all three traits (SBP P = 3.7 × 10−3; DBP P = 5.7 × 10−3; and pulse pressure P = 1.2 × 10−9; Supplementary Table 16). We provided sentinel SNPs from known and novel loci for each blood pressure trait (Supplementary Tables 17a–c and 18a–c) to DEPICT39 and detected enrichment of 36 tissues across seven systems for pulse pressure (false discovery rate <5%). The greatest enrichment was seen in arteries (P = 3.43 × 10−10), and 11 of the 36 tissues are grouped in the cardiovascular system (Supplementary Table 17c). Gene-set enrichment of the pulse pressure GWAS results identified 574 enriched gene sets (false discovery rate <5%); abnormal vascular smooth muscle morphology (MP:0005592; P = 1.47 × 10−8) was the top gene set (Supplementary Table 18c).

We prioritized statistically significant results from the S-PrediXcan aorta tissue analyses for all three traits and investigated pathways by using Ingenuity Pathway Analysis (IPA) software (Supplementary Figs. 79). Cardiovascular disease (SBP P = 7.2 × 10−6; pulse pressure P = 9.53 × 10−4) and cardiovascular system development and function (SBP P = 7.7 × 10−5; pulse pressure P = 9.53 × 10−4) networks were among the top enriched networks. Notable features in the SBP IPA results included the TGF-β and Notch signaling pathways (Supplementary Fig. 7), whereas pulse pressure IPA results featured atherosclerosis genes including CDH13, TCF7L2, PHACTR1 and MTHFR (Supplementary Fig. 9).

Convergence of evidence

We collated evidence for genes that were associated in two or more types of investigations that inform relevant gene targets (rare coding variants, predicted gene expression, single-cell sequencing expression enrichment and drug query) and highlighted noteworthy genes (Table 3). We identified 46 known and 7 novel genes satisfying this criterion, including three Mendelian hypotension or hypertension genes and 15 genes targeted by antihypertensive medications. Nine genes were expressed in murine kidney tubule cell types and 19 genes were identified in at least one aorta IPA network.

Table 3 Converging evidence across analyses

Discussion

We present results from multi-omic analyses of a trans-ethnic GWAS consortium for blood pressure traits. By incorporating large sample sizes, bioinformatics and measures of gene expression, we re-interpret the genetic architecture of blood pressure and identify tissues and anatomical features where blood pressure genes exert effects. Interrogation of gene–drug relationships and toxicities for GPGE associations provides additional evidence for known and novel blood pressure genes, and suggests genes as potential leads for drug development and repurposing potential for existing drugs. We emphasize the utility of large-scale blood pressure GWAS as a requisite starting point for analyses providing insight into clinical factors, genetic etiology, pathophysiology and pharmacology of blood pressure homeostasis.

The MVP comprises US military veterans and has an overrepresentation of male and black participants compared with the US population. We had a larger number of Hispanic participants than that in the largest previous study of blood pressure traits in that population13, and almost twice the number of black participants as that in the largest previous study of populations of African ancestry10. Our comparison of SNP effects on blood pressure traits demonstrated a lower correlation between race or ethnic groups than did Franceschini et al.9; however, the effects that we compared were much more subtle than the first 29 blood pressure SNPs detected by Ehret et al6. We compared effects on clinical outcomes of genetically imputed blood pressure traits in a PheWAS and observed consistent effects between racial or ethnic groups in MVP. These results support the previous observation9 that genetic effects on blood pressure of common SNPs are consistent among populations and suggest that an increasing burden of blood pressure–increasing alleles has a similar effect on health across white, black and Hispanic populations.

Evidence from S-PrediXcan and DEPICT identified arteries as the tissue type with greatest evidence for gene enrichment. These findings are in agreement with results from Warren et al.12 showing arteries as the top tissue from a DEPICT analysis of blood pressure traits, and also with results from Gamazon et al.40 showing aorta as the top PrediXcan tissue with highly enriched gene signals for SBP. The lack of enrichment of genes in other relevant tissues such as the kidney may be explained by the smaller sample of kidney tissues available in prediction training sets or by the finding that tissue-specific gene expression is differentially enriched.

The SBP IPA analysis highlights genes linked to TGF-β and Notch signaling pathways including FURIN, GUCY1A3 (also known as GUCY1A1) and GUCY1B3 (Table 3; Supplementary Fig. 7). GPGE of FURIN in the aorta was positively associated with SBP. This effect is likely to be mediated by furin-induced activation of pro-TGF-β1 to TGF-β1, which works along with the RAS pathway to increase blood pressure41,42,43,44. Predicted expression of FES, a gene <1 kb upstream of FURIN, is inversely associated with SBP in the aorta, coronary artery, tibial artery and kidney (Supplementary Table 8a), thus suggesting the presence of two proximal blood pressure loci with different regulatory mechanisms. Naproxen, a nonsteroidal anti-inflammatory drug, has an inhibitory effect on FES (Supplementary Tables 12 and 13), and hypertension is one of its known side effects45. Findings highlight the importance of the role of soluble guanylyl cyclase expression via the Notch signaling pathway in the mouse aorta in hypertension46. GUCY1A3 and GUCY1B3 encode subunits of soluble guanylyl cyclase, a major nitric oxide receptor in the vascular wall46,47,48. As mediators of the vasodilatory effects of nitric oxide, increased expression of these genes predicted a decrease in SBP in aorta (Table 3 and Supplementary Table 8a).

To understand how genetically predicted blood pressure is associated with the clinical phenome, we calculated w-GRSs for each blood pressure trait and evaluated them with a PheWAS (Supplementary Table 15). The pulse pressure w-GRS was associated with diabetic complications, whereas the w-GRS for SBP or DBP had fewer diabetes-related associations. Pulse pressure is an independent predictor of cardiovascular disease and incident diabetes49,50. Elevated pulse pressure is a marker for arterial stiffness51, which is positively associated with diabetic retinopathy and neuropathy52. These findings are supported by the pulse pressure IPA results, in which the top cardiovascular gene network includes at least four genes that may directly or indirectly mediate arterial stiffness or atherosclerosis, including TCF7L2, CDH13, PHACTR1 and MTHFR (Supplementary Fig. 9)53,54,55,56,57,58,59. Our finding of a positive association between the DBP w-GRS and aortic and other aneurysms supports evidence from a previous study of 1.25 million individuals showing an association between DBP and aortic aneurysm60. Our study provides evidence for a genetic etiology of this observation.

Convergent evidence from multiple analyses identified several blood pressure genes with strong biologic importance, including PDE3A and novel genes RXFP2 and ADK. RXFP2 is a receptor for the hormone relaxin61, which causes vasodilation, increases cardiac output and renal perfusion, and has been evaluated in clinical trials as a treatment for acute heart failure62,63,64. RXFP2 is expressed in multiple tissues, and it probably underlies the multiple physiological effects of the relaxin hormone throughout the circulatory system65.

The product of ADK, adenosine kinase, catalyzes the transfer of γ-phosphate from ATP to adenosine to form AMP and has widespread effects on multiple systems including the cardiovascular, nervous and respiratory systems66. Adenosine terminates supraventricular tachycardia involving the atrioventricular node and has been attributed to cardiac bradyarrhythmias67,68. Intravenous adenosine injection in humans induces vasodilation and systemic hypotension69, and is the primary drug used in the treatment of stable narrow-complex supraventricular tachycardia70. It reduces blood pressure and blood pressure variability in rats, and its actions are mediated through adenosine receptors71. We show a positive association among GPGE of ADK and SBP and pulse pressure in aortic tissue (Supplementary Table 8a,c), a result consistent with the known directional effects of adenosine on blood pressure.

PDE3A is targeted by a wide variety of inhibitors for indications including congestive heart failure, hypertension and heart disease. The PDE3A inhibitor theophylline, which is used to treat chronic obstructive pulmonary disease, has a hypotension ADE, in agreement with the effects of increased gene expression in our analysis (Supplementary Tables 12 and 13). The autosomal dominant Mendelian condition hypertension and brachydactyly syndrome (HTNB; MIM 112410) is caused by at least six distinct rare PDE3A mutations72,73. HTNB features include brachydactyly type E, severe salt-independent but age-dependent hypertension, increased fibroblast growth rate, neurovascular contact at the rostral-ventrolateral medulla, altered baroreflex blood pressure regulation and death from stroke before age 50 when untreated74,75. Associations between common variants in the PDE3A locus and blood pressure have been reported previously25,26.

A novel aspect of this study is the incorporation of single-cell gene expression data from cells derived from murine kidneys. We show that most blood pressure genes identified by S-PrediXcan analyses in human kidneys are enriched in tubule cell types derived from mouse kidneys. This observation suggests that genes detected through GPGE associations and expressed in tubules, including SRR, ACHE, SFXN2 and CLCN6, may have a role in blood pressure regulation. Several lines of evidence implicate the SRR gene, whereas the nearest gene annotation strategy identifies SMG6 as associated with blood pressure at this locus. A SNP in SMG6, rs216172, has been associated with coronary artery disease76; however, this SNP is an expression quantitative trait locus for SRR and not for SMG6 (GTEx portal). SNPs near SRR have been associated with type 2 diabetes and type 2 diabetes–mediated arterial stiffness53. ACHE terminates signal transduction at the neuromuscular junction by rapid hydrolysis of acetylcholine released into the synaptic cleft77. Inhibition of acetylcholinesterase is an effective treatment for orthostatic hypotension, especially in people with supine hypertension78. Dimetacrine, a tricyclic antidepressant, and decamethonium, a muscle relaxant, have inhibitory effects on ACHE and are not currently prescribed as antihypertension medications.

This work helps to clarify the complex MTHFR locus by providing unique tissue-specific evidence for several genes in the region in relation to blood pressure79,80. In addition to MTHFR, our study provides evidence for the role of NPPA (novel missense variant; Table 2), NPPB (GPGE association with SBP and pulse pressure in the left ventricle; Supplementary Table 8a and 8c) and CLCN6 (inverse association in the kidney S-PrediXcan and enrichment in mouse kidney; Supplementary Tables 8a–c and 9a–c) in blood pressure. NPPA and NPPB are exclusively expressed in the heart, have biological functions including natriuresis, diuresis, vasorelaxation, inhibition of renin and aldosterone secretion, and have a key role in cardiovascular homeostasis81. GPGE of CLCN6, a putative chloride antiporter82, was consistently associated with SBP, DBP and pulse pressure. CLCN6 is targeted by the antihypertensive medication chlorthalidone, and NPPB is targeted by the antihypertensive medication carvedilol (Supplementary Table 11). Findings for this locus show that effects of multiple associated genes from the same locus may vary by tissue type, and several nearby genes with very different biological functions may jointly contribute to the trait of interest.

In conclusion, we applied multiple post-GWAS analyses to identify genes with effects on blood pressure regulation. We report hundreds of novel SNPs and genes, including several with strong biological plausibility, and tissue-specific gene associations with directions of effect. We provide insight into the natural experiments of gene regulation and direct perturbation of proteins by mutation with regard to effects on blood pressure, enhancing the biological understanding of blood pressure traits. Our study identifies a refined set of genes that often have coordinated expression across multiple tissues with potential relevance to blood pressure traits, and these gene-tissue pairs are prime candidates for causal investigation.

URLs

Affymetrix Power Tools 2.10.0, https://www.thermofisher.com/us/en/home/life-science/microarray-analysis/affymetrix.html; BIDD TTD database, https://db.idrblab.org/ttd/; Corporate Data Warehouse, https://www.hsrd.research.va.gov/for_researchers/vinci/cdw.cfm; DEPICT, https://data.broadinstitute.org/mpg/depict/; DGIdb, http://www.dgidb.org/; EAGLE v2, https://data.broadinstitute.org/alkesgroup/Eagle/; Ensembl BioMart, http://www.ensembl.org/biomart/martview; FlashPCA2: https://github.com/gabraham/flashpca; GCTA v1.91.4beta, http://cnsgenomics.com/software/gcta/#Overview; GTEx portal, https://www.gtexportal.org/home/; GWAS catalog, https://www.ebi.ac.uk/gwas/; Human Protein Atlas, https://www.proteinatlas.org/; Ingenuity Pathway Analysis, https://www.qiagenbioinformatics.com/products/ingenuity-pathway-analysis/; KING software, http://people.virginia.edu/~wc9c/KING/; Kmeans package, http://stat.ethz.ch/R-manual/R-devel/library/stats/html/kmeans.html; LDSC v1.0.0, https://github.com/bulik/ldsc; METAL software, http://csg.sph.umich.edu/abecasis/metal/; Minimac3, https://genome.sph.umich.edu/wiki/Minimac3; Observational Medical Outcomes Partnership, https://fnih.org/what-we-do/major-completed-programs/omop; PheWAS package, https://github.com/PheWAS/PheWAS; R statistical software, https://www.r-project.org/; SIDER, http://sideeffects.embl.de/; SNPDOC, https://wakegen.phs.wakehealth.edu/public/snpdoc3/index.cfm; SNPTEST-v2.5.4-beta, https://mathgen.stats.ox.ac.uk/genetics_software/snptest/old/snptest_v2.3.0.html; S-PrediXcan, https://github.com/hakyimlab/MetaXcan

Methods

We conducted a multi-stage GWAS of common and rare variants in >750,000 participants. We then performed additional bioinformatics analyses of GPGE for blood pressure traits, evaluated cell types in which associated genes are expressed, performed a phenome-wide association study of genetic risk scores for blood pressure traits from the electronic health records of MVP participants and screened known drugs to evaluate potential for repurposing and validate observed associations. A flow chart for analyses is presented in Fig. 1.

Discovery cohorts

The Million Veteran Program

The MVP is a large cohort of fully consented participants who were recruited from the patient populations of 63 Department of Veterans Affairs (VA) medical facilities. The MVP is recruited at VA hospitals from men and women who are veterans of the US armed forces. It is enriched with African-American and Hispanic participants compared with the general US population, and males are overrepresented. Across race groups, the average age ranged between 49 for Asian and 61 for black participants (Supplementary Table 1). Average body mass index (BMI) ranged between 27.8 for Asians and 30.9 for Native Americans. The proportion of males ranged between 87% for Native Americans and 93% for whites. Average SBP ranged between 132 mm Hg for Asians and 140 mm Hg for blacks, average DBP ranged between 81 mm Hg for Asians and 85 mm Hg for Blacks, and average pulse pressure ranged between 51 mm Hg for Asians and 57 mm Hg for whites. The proportion of participants on an antihypertensive drug at the time of blood pressure measure ranged between 31% for Asians and 53% for blacks.

Recruitment began in 2011 and is conducted in person, initiated by an invitation letter and completed by participants answering baseline and lifestyle questionnaires, providing a blood sample, providing access to medical records and giving permission for re-contact. Consent to participate is provided after counseling by research staff and mailing of informational materials. All documents and protocols are approved by the VA Central Institutional Review Board. Blood samples are collected by phlebotomists and banked at the VA Central Biorepository in Boston. Genotyping was conducted using a customized Affymetrix Axiom Biobank Array chip with content added to provide coverage of African and Hispanic haplotypes, as well as markers for common diseases in the VA population. Researchers are provided with de-identified data, and do not have the ability or authorization to link these details with a participant’s identity.

MVP genotype quality control

Blood samples drawn from consenting MVP participants were shipped to the Central Biorepository in Boston, where DNA was extracted and shipped to two external centers for genotyping on an Affymetrix Axiom Biobank array designed specifically for the MVP. The MVP genomics working group applied standard quality control and genotype calling algorithms to the data in batches using the Affymetrix Power Tools Suite (v1.18). Standard quality control pipelines were used to exclude duplicate samples, samples with more heterozygosity than expected, samples with an excess (>2.5%) of missing genotype calls and samples with discordance of genetically inferred sex versus self-report. We excluded related individuals (halfway between second- and third-degree relatives or closer) as measured by the KING software84. Before imputation, variants that were poorly called or that deviated from their expected allele frequency based on reference data from the 1000 Genomes Project85 were excluded. After prephasing using EAGLE v2 (ref. 86), genotypes from the 1000 Genomes Project85 phase 3, version 5 reference panel were imputed into MVP participants via Minimac3 software87. Principal component analysis was performed using FlashPCA88 to generate the top ten genetic principal components explaining the greatest variability.

Race and ethnicity

Information on race (whites, blacks, Asians and Native Americans) and ethnicity (Hispanic, yes or no) was obtained based on self-report through centralized VA data collection methods using standardized survey forms, or through the use of information from corporate data warehouse or Observational Medical Outcomes Partnership data, when information from self-report survey was missing. Race and ethnicity categories were then merged to form the following race or ethnicity variables: non-Hispanic whites (whites), non-Hispanic blacks (blacks), non-Hispanic Asians (Asians), non-Hispanic Native Americans (Native Americans) and Hispanics. Individuals for whom race and ethnicity could not be assigned due to conflicting records or missing data were categorized as unknown. Before analysis quality control, there were 15,710 veterans with unknown status for race or ethnicity. For these individuals, we used a K-means clustering approach in R (McQueen algorithm) with the top ten genetic principal components as input. To obtain the most reliable cluster designations for the missing data, the K-means approach was applied to the maximum available samples: the 1000 Genomes reference populations and all individuals for whom principal components were available regardless of whether race or ethnicity designations were unknown. K-clusters were optimized by testing values K = 2 through K = 10. K = 4 was chosen as the optimal value, as visual examination of these clusters most closely corresponded to whites (n = 5,265), blacks (n = 4,671), Asians (n = 3,936) and Hispanics (n = 1,838).

MVP blood pressure phenotypes

We selected adults (age ≥ 18), used the median eligible non-Emergency Department outpatient–measured SBP in the entire available electronic health record (EHR) and used the corresponding DBP from this measure. In individuals for which the median value was observed at multiple clinical encounters on distinct dates, we used the earliest of those measures to identify the DBP, age, BMI and antihypertensive treatment status of the individual at that time. Measures were ineligible if they occurred at or after an International Classification of Diseases, 9th Revision (ICD-9) code from the groups 585 (chronic kidney disease), 405 (secondary hypertension) or 428 (heart failure). If pain scores were available, blood pressure measures taken during encounters when a pain score was ≥5 was recorded were also ineligible, because severe pain can elevate blood pressure89,90. For measures taken while a subject was on an antihypertensive medication we added 15 mm Hg to SBP and 10 mm Hg to DBP8,91.

MVP analysis

For the MVP GWAS, we performed linear regression association tests with additive models for untransformed blood pressure traits, after adjusting for medication use. We adjusted linear regression models analyzing SNP associations for age at blood pressure measure, age2, sex, BMI measured within 1 year of blood pressure measure and the top 10 genetic principal components in analyses. All primary analyses for the MVP were conducted by either strata of administratively assigned race or ethnicity or by their empirically designated clusters. All regression-based analyses were conducted in SNPTEST-v2.5.4-beta92. Analyses were limited to genotyped and imputed variants with SNPTEST Info scores of ≥0.4, with Hardy-Weinberg equilibrium P > 5 × 10−8 for common variant analysis (minor allele frequency > 0.1). Analyses of rare variants, SNPs with MAF ≤ 1%, were restricted to variants with an effective minor allele count (SNPTEST Info score multiplied by minor allele count) of ≥10 in each analysis subcohort.

The UK Biobank

Summary statistics from the analysis of the interim data from the UKB were used in our meta-analysis. These results have been reported by Warren et al12. Briefly, following central and study-specific quality control protocols, 140,886 empirically classified white individuals were analyzed for SBP, DBP and pulse pressure traits. Blood pressure measures were averaged over two measures and adjusted for medication use by adding 15 and 10 mm Hg to SBP and DBP, respectively. Linear models were adjusted for the top ten principal components of ancestry, age, age-squared, sex, an indicator for genotyping platform and BMI.

Meta-analysis of discovery data sets

Inverse-variance weighted fixed-effects meta-analysis of common variants across MVP subsets and summary statistics from UKB was performed by using METAL software. Genomic inflation factor were calculated, and λGC values for SBP, DBP and pulse pressure were 1.195, 1.149 and 1.171, respectively for the discovery from MVP, 1.303, 1.315 and 1.270 for the discovery from UKB, and 1.275, 1.140 and 1.244 in the overall discovery analysis (Supplementary Fig. 10). Subsequently, we used the LD score regression approach93 to ascertain whether inflation was due to residual population stratification or polygenicity. Calculation of the intercept in the MVP discovery analysis data set of white participants was 1.05 (s.e.m. = 0.01), 1.03 (s.e.m. = 0.01) and 1.04 (s.e.m. = 0.01) for SBP, DBP and pulse pressure, respectively, suggesting that little of the observed inflation in the λGC is due to population stratification.

Selection of SNPs for replication

Common variants

For common variants, we considered for follow-up SNPs in loci that did not overlap with previously reported loci according to both an LD threshold of r2 ≤ 0.1 and a 1 Mb interval. We obtained a list of these SNPs with P < 1 × 10−6 for any of the three blood pressure traits, a MAF ≥1% and concordant directions of effect between UKB and MVP.

In silico replication summary statistics were provided for 942 SNPs by the ICBP25 after meta-analysis of 77 individual participating cohorts for a total maximum of 299,024 individuals who were genotyped and analyzed according to study-specific protocols. Additional replication results were provided from Vanderbilt University’s BioVU EHR-linked biorepository, including genotypes from the MEGA array and phenotype data from 17,277 participants. Discovery and replication data were combined using fixed-effects inverse-variance weighted meta-analysis implemented in METAL94.

Rare variants

We conducted an in silico replication analysis of 18 rare exonic SNPs from our discovery analysis in 417,143 participants from BP-ICE. SNPs were chosen for replication if they had a discovery P < 1 × 10−6 and a MAF <1%.

BP-ICE used the exome array and did not have genome-wide coverage of rare variants. Therefore, we also pursued replication using the full release of the UKB data to capture non-exonic rare variation. Because of the inclusion of UKB data in the discovery set, for the second analysis we sought replication from variants suggestive (P <  1 × 10−6) only in MVP cohorts following meta-analysis as described above. Some 1,066 rare variants with P < 1 × 10−6 for any of the three phenotypes were selected for replication in 458,577 participants from UKB. Additional replication was provided by BioVU MEGA, and all data were meta-analyzed by using fixed-effects meta-analysis in METAL94.

Classifying results by evidence for association

For results that reached statistical significance of P ≤ 5 × 10−8 after final meta-analysis, and that had consistent direction of effect between discovery and replication stages, we established three tiers of evidence, which are annotated in results tables (Supplementary Table 3a–c):

(i) Genome-wide significance in the discovery stage, Bonferroni-corrected significance in replication and consistent trait-specific direction of effect across stages.

(ii) Genome-wide significance in the discovery stage, P ≤ 0.05 in the replication stage and consistent trait-specific direction of effect across stages.

(iii) Variants had P < 1 × 10−6 and P > 5 × 10−8 in the discovery stage, had P < 0.05 in the replication stage, had consistent trait-specific direction of effect across stages and were genome-wide significant after final analysis.

Conditional analysis

For conditional analysis of common variants we used two parallel approaches implemented in Genome-wide Complex Traits Analysis (GCTA) software95. Details are described in the  Supplementary Note.

Proportion of variance explained

We approximated the proportion of blood pressure trait–specific variance explained in the trans-ethnic meta-analysis by all independent sentinel SNPs (novel and known) and novel SNPs separately. Variance explained by each SNP was first estimated by the following equation:

$$r^2 = \frac{{{\mathrm{\chi }}^2}}{{\mathrm{n}}}$$

The sum of the variances of the independent sentinel SNPs for common variants provided estimates of the proportion of variance explained for all SNPs and novel SNPs for each of the blood pressure traits. The transformation of the relationship between t-statistic and r2 to χ2 statistic to r2 is described in the  Supplementary Note.

Genetic risk score construction

We constructed a genetic risk score (GRS) for each blood pressure trait by calculating a linear combination of weights derived from the 140,886 participants from the UKB common variant analysis and sentinel SNPs at each statistically significant locus observed in the MVP. w-GRSs were constructed for self-reported or administratively assigned non-Hispanic whites, blacks and Hispanics in the MVP.

Phenome-wide association study analysis

We performed a PheWAS96 of GRS for each blood pressure trait in MVP whites (nmax = 188,088), blacks (nmax = 52,530) and Hispanics (nmax = 16,735), leveraging the diverse nature of MVP as well as the full catalog of ICD-9 diagnosis codes. We used logistic regression to separately model up to 1,813 PheWAS traits as a function of the three GRSs, adjusted for age, age-squared, sex, BMI and ten principal components. We report the results from these analyses as odds ratios, in which the estimate is the average change in odds of the PheWAS trait per weighted blood pressure–increasing allele. Interpretation of results was limited to phenotypes with 25 or more cases. Multiple testing thresholds for statistical significance were set to P ≤ 2.75 × 10−5 (0.05/1,813). All PheWAS analyses were conducted by using the R PheWAS package97. Effect estimates based on significant PheWAS results from any one or more of the analyses were then compared among whites, blacks and Hispanics to report Pearson’s correlations (r2) for each pair per trait (Supplementary Fig. 5).

S-PrediXcan analysis

Genetically predicted gene expression was evaluated for the common variant subset with S-PrediXcan31, a gene-level approach that estimates the genetically determined component of gene expression in a given tissue and tests it for association with SNP-level summary statistics. We used all three blood pressure meta-analysis results for common variants and 44 tissues from GTEx36 for this analysis, as well as the collection of kidney reference data recently described by Ko et al.37, incorporating covariance matrices developed for European populations (1000 Genomes), as the majority of samples were European in origin.

Evaluation of kidney loci in mouse kidneys

For the genes implicated by the S-PrediXcan analysis as having associated expression in the kidney, we evaluated homologous genes in the single-cell atlas of the mouse kidney, in which expression levels are measured by single-cell RNA sequencing across 57,979 total mouse kidney cells from seven healthy mice83. This enabled us to observe what cell types in the mammalian kidney express the genes where there was evidence for association between expression and blood pressure traits. After quality filtering steps, a single cell-gene matrix containing the unique molecular identifier counts for 43,745 cells and 16,273 transcripts was generated from seven normal mouse kidneys by using 10x Chromium Single Cell solution83. Mouse homologs of the target human genes identified by the S-PrediXcan analysis were found by using Ensembl BioMart. Genes that expressed <5% of the cell clusters were excluded from analysis. To calculate the average expression level for each cluster, a z-score of unique molecular identifier count was first obtained for every single cell. Then we calculated the mean z-scores for individual cells in the same cluster, thus resulting in a z-score for each gene and each cell cluster.

Evaluation of drug classes for genes with associations with gene expression

To better understand the performance of the S-PrediXcan method, identify genes that could be leads for drug development and identify drug-gene pairs that might be leads for repurposing, we made three comparisons among significant associations from S-PrediXcan analyses: (i) we identified all S-PrediXcan genes targeted by an antihypertensive drug to validate associations and identify the most credible genes in regions with many associations; (ii) we listed candidate genes that might be leads for novel inhibitory antihypertensive therapies by considering genes with GPGE effects on blood pressure that are also targeted by a non-antihypertensive drug; and (iii) for genes that might be leads for developing novel treatments and drugs with repurposing potential, we reported gene–drug pairs for significant S-PrediXcan genes that are not targeted by an antihypertensive drug but are targeted by a drug with a toxicity that involves hypertension or hypotension.

A list of medications with a primary indication for hypertension and a list of medications with ADEs of hypertension or hypotension were created by using SIDER98 and the DEB2 database99. Gene targets for antihypertension medications, medications targeting genes significant in S-PrediXcan analyses with positive effect sizes and medications targeting genes mapped from significant GWAS signals were queried by using DGIdb100. Primary indications for medications targeting genes significant in S-PrediXcan analyses with positive effect sizes were compiled by using the BIDD TTD database101.

To identify genes that are attractive leads for novel inhibitory drugs, we report significant S-PrediXcan genes with a positive effect size (i.e., for which increasing gene expression is associated with an increase in one or more blood pressure traits) that are targeted by a non-antihypertensive medication without an indication of ADEs for hypertension or hypotension. This list thereby represents a set of genes that both are likely to be involved in blood pressure regulation in one or more tissues and can be targeted by drugs. Known targets for antihypertensive drugs found to be significant by S-PrediXcan and a summary of the most significant S-PrediXcan results across tissues and blood pressure traits are presented in Supplementary Table 11. Significant S-PrediXcan genes that are associated in any tissue and that are targeted by a nonhypertension drug with no ADE for hypertension or hypotension are presented in Supplementary Table 12.

We report another group of genes that might be attractive for treatment development on the basis of being both associated with blood pressure traits and targeted by non-antihypertensive drugs that feature an ADE of either hypotension or hypertension. These gene-drug pairs may be leads for modification of drug molecules, modification of dosing or delivery strategies, or potential gene targeting by new treatments. Genes that were found to be significant by S-PrediXcan, that are targeted by a drug, and that have an ADE involving hypertension or hypotension are presented in Supplementary Table 13. Gene–drug relationships for all genes mapped from significant association signals are presented in Supplementary Table 14.

Enrichment and pathway analyses

We investigated whether one or more of the 45 tissues evaluated with S-PrediXcan were enriched. We also performed enrichment analyses in DEPICT39 by using trait-specific GWAS significant sentinel SNPs as input. We evaluated significant genes from the top enriched S-PrediXcan tissue (aorta) for each trait with Ingenuity Pathway Analysis (IPA) software (Qiagen) (Supplementary Figs. 79 and Supplementary Note).

Ethics statement

The central Veterans Affairs Institutional Review Board (IRB) and site-specific IRBs approved the MVP study. The Vanderbilt University Medical Center IRB approved the use of BioVU data for this study. All cohorts within ICBP and BP-ICE have ethical approval from their local institutions. All relevant ethical regulations were followed.

Reporting Summary

Further information on research design is available in the Nature Research Reporting Summary linked to this article.

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).

Additional information

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

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.

Author information

Author notes

  1. These authors contributed equally: A. Giri, J. N. Hellwege, J. M. Keaton, J. Park.

  2. These authors jointly supervised this work: K. Susztak, C. J. O’Donnell, A. M. Hung, T. L. Edwards.

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

Affiliations

  1. Division of Quantitative Sciences, Department of Obstetrics & Gynecology, Vanderbilt Genetics Institute, Vanderbilt Epidemiology Center, Institute for Medicine and Public Health, Vanderbilt University Medical Center, Nashville, TN, USA

    • Ayush Giri
    •  & Digna R. Velez Edwards
  2. Biomedical Laboratory Research and Development, Tennessee Valley Healthcare System (626)/Vanderbilt University, Nashville, TN, USA

    • Ayush Giri
    • , Jacklyn N. Hellwege
    • , Jacob M. Keaton
    • , Eric S. Torstenson
    • , Otis D. Wilson
    • , Digna R. Velez Edwards
    • , Adriana M. Hung
    •  & Todd L. Edwards
  3. Division of Epidemiology, Department of Medicine, Institute for Medicine and Public Health, Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Vanderbilt University, Nashville, TN, USA

    • Jacklyn N. Hellwege
    • , Jacob M. Keaton
    • , Eric S. Torstenson
    • , Brian S. Mautz
    •  & Todd L. Edwards
  4. Department of Medicine, Renal Electrolyte and Hypertension Division, University of Pennsylvania, Philadelphia, PA, USA

    • Jihwan Park
    • , Chengxiang Qiu
    • , Rojesh Shrestha
    •  & Katalin Susztak
  5. William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, UK

    • Helen R. Warren
    • , Claudia P. Cabrera
    • , Ioanna Ntalla
    • , Patricia B. Munroe
    •  & Mark J. Caulfield
  6. National Institute for Health Research Barts Cardiovascular Biomedical Research Centre, Queen Mary University of London, London, UK

    • Helen R. Warren
    • , Claudia P. Cabrera
    • , Patricia B. Munroe
    •  & Mark J. Caulfield
  7. Nephrology Section, Memphis VA Medical Center, Memphis, TN, USA

    • Csaba P. Kovesdy
  8. Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA, USA

    • Yan V. Sun
  9. Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA, USA

    • Yan V. Sun
  10. Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    • Otis D. Wilson
    • , Cassianne Robinson-Cohen
    • , Kelly A. Birdwell
    • , Elvis A. Akwo
    • , Edward E. Siew
    •  & Adriana M. Hung
  11. Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    • Christianne L. Roumie
    •  & Michael E. Matheny
  12. Geriatrics Research Education and Clinical Center, Tennessee Valley Health System, Veteran’s Health Administration, Nashville, TN, USA

    • Christianne L. Roumie
    • , Michael E. Matheny
    •  & Edward E. Siew
  13. Division of Rheumatology and Clinical Pharmacology, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA

    • Cecilia P. Chung
  14. Division of Nephrology, Department of Medicine, Nashville Veteran Affairs Hospital, Nashville, TN, USA

    • Kelly A. Birdwell
  15. Department of Surgery, Corporal Michael Crescenz VA Medical Center, Philadelphia, PA, USA

    • Scott M. Damrauer
  16. Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA

    • Scott M. Damrauer
  17. VA Salt Lake City Health Care System, Salt Lake City, UT, USA

    • Scott L. DuVall
  18. University of Utah School of Medicine, Salt Lake City, UT, USA

    • Scott L. DuVall
  19. VA Boston Health Care System, Boston, MA, USA

    • Derek Klarin
  20. Center for Genomic Medicine, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    • Derek Klarin
  21. Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA

    • Derek Klarin
    • , Najim Lahrouchi
    •  & Cecilia Lindgren
  22. Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

    • Derek Klarin
  23. Massachusetts Veterans Epidemiology Research and Information Center (MAVERIC), VA Boston Healthcare System, Boston, MA, USA

    • Kelly Cho
    •  & J. Michael Gaziano
  24. Division of Aging, Department of Medicine, Brigham and Women’s Hospital, Boston, MA, USA

    • Kelly Cho
  25. Department of Medicine, Harvard Medical School, Boston, MA, USA

    • Kelly Cho
  26. Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, USA

    • Yu Wang
    • , Yaomin Xu
    •  & Michael E. Matheny
  27. Department of Epidemiology and Biostatistics, Imperial College London, London, UK

    • Evangelos Evangelou
  28. Department of Hygiene and Epidemiology, University of Ioannina Medical School, Ioannina, Greece

    • Evangelos Evangelou
  29. Department of Health Sciences, University of Leicester, Leicester, UK

    • Louise V. Wain
  30. National Institute for Health Research, Leicester Biomedical Research Centre, Glenfield Hospital, Leicester, UK

    • Louise V. Wain
  31. University of Bordeaux, Bordeaux Population Health Research Center, INSERM UMR 1219, Bordeaux, France

    • Muralidharan Sargurupremraj
    •  & Stéphanie Debette
  32. Department of Neurology, Bordeaux University Hospital, Bordeaux, France

    • Stéphanie Debette
  33. Department of Biostatistics and Center for Statistical Genetics, University of Michigan, Ann Arbor, MI, USA

    • Michael Boehnke
    •  & Laura J. Scott
  34. MRC Epidemiology Unit, University of Cambridge School of Clinical Medicine, Cambridge, UK

    • Jian’an Luan
    • , Jing-Hua Zhao
    • , Sara M. Willems
    • , Robert A. Scott
    • , Claudia Langenberg
    •  & Nicholas J. Wareham
  35. Department of Pathology and Molecular Medicine, McMaster University, Hamilton, Ontario, Canada

    • Sébastien Thériault
  36. Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University, Quebec City, Quebec, Canada

    • Sébastien Thériault
  37. Division of Molecular and Clinical Medicine, Pat Macpherson Centre for Pharmacogenetics and Pharmacogenomics, Ninewells Hospital and Medical School, University of Dundee, Dundee, UK

    • Nabi Shah
    •  & Colin Neil Alexander Palmer
  38. Department of Pharmacy, COMSATS University Islamabad, Abbottabad, Pakistan

    • Nabi Shah
  39. Hunter Medical Research Institute, Newcastle, New South Wales, Australia

    • Christopher Oldmeadow
    •  & John Attia
  40. Department of Clinical Sciences, Lund University, Malmö, Sweden

    • Peter Almgren
    •  & Olle Melander
  41. Leiden University Medical Center, Leiden, the Netherlands

    • Ruifang Li-Gao
    •  & Dennis Owen Mook-Kanamori
  42. Department of Cardiology, University Medical Center Groningen, University of Groningen, Groningen, the Netherlands

    • Niek Verweij
    •  & Pim van der Harst
  43. Medical Research Council Human Genetics Unit, Institute of Genetics and Molecular Medicine, University of Edinburgh, Edinburgh, UK

    • Thibaud S. Boutin
    •  & Caroline Hayward
  44. Department of Twin Research and Genetic Epidemiology, Kings College London, London, UK

    • Massimo Mangino
    •  & Tim D. Spector
  45. NIHR Biomedical Research Centre at Guy’s and St Thomas’ Foundation Trust, London, UK

    • Massimo Mangino
  46. Human Genetics Center, The University of Texas Health Science Center at Houston, Houston, TX, USA

    • Elena Feofanova
  47. BHF Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK

    • Praveen Surendran
    • , Savita Karthikeyan
    •  & Joanna M. M. Howson
  48. Department of Biostatistics, University of Liverpool, Liverpool, UK

    • James P. Cook
  49. Cardiovascular Research Center, Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA

    • Najim Lahrouchi
    •  & Christopher Newton-Cheh
  50. Heart Center, Department of Clinical and Experimental Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam UMC, University of Amsterdam, Amsterdam, the Netherlands

    • Najim Lahrouchi
  51. Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA

    • Chunyu Liu
  52. Immunology and Infection Department, London School of Hygiene & Tropical Medicine, London, UK

    • Nuno Sepúlveda
  53. MRC Integrative Epidemiology Unit (IEU), Bristol Medical School (Population Health Sciences), University of Bristol, Oakfield House, Oakfield Grove, Bristol, UK

    • Tom G. Richardson
    •  & Nicholas J. Timpson
  54. Department of Genetics, Washington University School of Medicine, St. Louis, MO, USA

    • Aldi Kraja
  55. Center for Genome Sciences and Systems Biology, Washington University School of Medicine, St. Louis, MO, USA

    • Aldi Kraja
  56. Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA

    • Aldi Kraja
  57. Risk Factors and Molecular Determinants of Aging-Related Diseases (RID-AGE), University of Lille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167, Lille, France

    • Philippe Amouyel
  58. Department of Cardiovascular Medicine, The Wellcome Trust Centre for Human Genetics, Oxford, UK

    • Martin Farrall
  59. International Centre for Circulatory Health, Imperial College London, London, UK

    • Neil R. Poulter
  60. University of Eastern Finland, School of Medicine, Kuopio, Finland

    • Markku Laakso
  61. Wellcome Trust Sanger Institute, Hinxton, UK

    • Eleftheria Zeggini
  62. National Heart and Lung Institute, Imperial College London, Hammersmith Campus, London, UK

    • Peter Sever
  63. Population Health Research Institute, McMaster University, Hamilton, Ontario, Canada

    • David Conen
  64. Faculty of Health, University of Newcastle, Newcastle, New South Wales, Australia

    • John Attia
  65. Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    • Daniel I. Chasman
    • , Paul M. Ridker
    • , J. Michael Gaziano
    •  & Christopher J. O’Donnell
  66. Istituto di Ricerca Genetica e Biomedica, Consiglio Nazionale delle Ricerche, Monserrato, Cagliari, Italy

    • Francesco Cucca
  67. Dipartimento di Scienze Biomediche, Università degli Studi di Sassari, Sassari, Italy

    • Francesco Cucca
  68. Laboratory of Genetics and Genomics, National Institute on Aging, NIH, Baltimore, MD, USA

    • David Schlessinger
  69. MRC-PHE Centre for Environment & Health, Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

    • Marjo-Riitta Jarvelin
    •  & Paul Elliott
  70. Center for Life Course Health Research, Faculty of Medicine, University of Oulu, Oulu, Finland

    • Marjo-Riitta Jarvelin
  71. Biocenter Oulu, University of Oulu, Oulu, Finland

    • Marjo-Riitta Jarvelin
  72. Unit of Primary Health Care, Oulu University Hospital, OYS, Oulu, Finland

    • Marjo-Riitta Jarvelin
  73. Department of Life Sciences, College of Health and Life Sciences, Brunel University London, Uxbridge, Middlesex, UK

    • Marjo-Riitta Jarvelin
  74. Wellcome Trust, London, UK

    • Branwen J. Hennig
  75. MRC Unit The Gambia, Atlantic Boulevard, Fajara, Banjul, The Gambia

    • Branwen J. Hennig
  76. London School of Hygiene & Tropical Medicine, London, UK

    • Branwen J. Hennig
  77. Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA

    • Wei-Qi Wei
    • , Joshua C. Smith
    • , Yaomin Xu
    • , Michael E. Matheny
    • , Joshua C. Denny
    •  & Digna R. Velez Edwards
  78. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK

    • Cecilia Lindgren
  79. Big Data Institute at the Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Oxford, UK

    • Cecilia Lindgren
  80. Institute of Biomedicine, Biocenter of Oulu, Medical Research Center, Oulu University and Oulu University Hospital, Oulu, Finland

    • Karl-Heinz Herzig
  81. Department of Gastroenterology and Metabolism, Poznan University of Medical Sciences, Poznan, Poland

    • Karl-Heinz Herzig
  82. Department of Nutrition and Dietetics, School of Health Science and Education, Harokopio University, Athens, Greece

    • George Dedoussis
  83. Departments of Medicine, University of Washington, Seattle, WA, USA

    • Bruce M. Psaty
  84. Departments of Epidemiology, University of Washington, Seattle, WA, USA

    • Bruce M. Psaty
  85. Departments of Health Services, University of Washington, Seattle, WA, USA

    • Bruce M. Psaty
  86. Kaiser Permanente Washington Health Research Institute, Seattle, WA, USA

    • Bruce M. Psaty
  87. National Institute for Health Research Imperial Biomedical Research Centre, Imperial College Healthcare NHS Trust, Imperial College London, London, UK

    • Paul Elliott
  88. UK Dementia Research Institute at Imperial College London, London, UK

    • Paul Elliott
  89. Clinical Epidemiology Research Center (CERC), VA Cooperative Studies Program, VA Connecticut Healthcare System, West Haven, CT, USA

    • John Concato
  90. Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA

    • John Concato
  91. Atlanta VA Medical Center, Atlanta, GA, USA

    • Peter W. F. Wilson
  92. Emory Clinical Cardiovascular Research Institute, Atlanta, GA, USA

    • Peter W. F. Wilson
  93. VA Palo Alto Health Care System, Palo Alto, CA, USA

    • Philip S. Tsao
  94. Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA, USA

    • Philip S. Tsao
  95. Department of Genetics, University of Pennsylvania, Perelman School of Medicine, Philadelphia, PA, USA

    • Katalin Susztak
  96. VA Boston Healthcare, Section of Cardiology and Department of Medicine, Boston, MA, USA

    • Christopher J. O’Donnell

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Consortia

  1. Understanding Society Scientific Group

    1. International Consortium for Blood Pressure

      1. Blood Pressure-International Consortium of Exome Chip Studies

        1. Million Veteran Program

          Contributions

          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.

          Competing interests

          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.

          Corresponding authors

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

          Integrated supplementary information

          1. 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.

          2. 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.

          3. 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.

          4. 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).

          5. 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.

          6. 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.

          7. 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.

          8. 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.

          9. 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.

          10. 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.

          Supplementary information

          1. Supplementary Text and Figures

            Supplementary Figures 1–10 and Supplementary Note

          2. Reporting Summary

          3. Supplementary Tables

            Supplementary Tables 1–18

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          DOI

          https://doi.org/10.1038/s41588-018-0303-9

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