Skip main navigation

Novel Blood Pressure Locus and Gene Discovery Using Genome-Wide Association Study and Expression Data Sets From Blood and the Kidney

Originally published 2017;70:e4–e19


Elevated blood pressure is a major risk factor for cardiovascular disease and has a substantial genetic contribution. Genetic variation influencing blood pressure has the potential to identify new pharmacological targets for the treatment of hypertension. To discover additional novel blood pressure loci, we used 1000 Genomes Project–based imputation in 150 134 European ancestry individuals and sought significant evidence for independent replication in a further 228 245 individuals. We report 6 new signals of association in or near HSPB7, TNXB, LRP12, LOC283335, SEPT9, and AKT2, and provide new replication evidence for a further 2 signals in EBF2 and NFKBIA. Combining large whole-blood gene expression resources totaling 12 607 individuals, we investigated all novel and previously reported signals and identified 48 genes with evidence for involvement in blood pressure regulation that are significant in multiple resources. Three novel kidney-specific signals were also detected. These robustly implicated genes may provide new leads for therapeutic innovation.


Genetic support for a drug target increases the likelihood of success in drug development,1 and there is clear unmet need for novel therapeutic strategies to treat individuals with hypertension.2 Several large studies have described blood pressure (BP) variant identification by genome-wide and targeted association approaches.319 Clinically, the most predictive BP traits for cardiovascular risk are systolic BP (SBP) and diastolic BP (DBP), reflecting roughly the peak and trough of the BP curve, and pulse pressure, the difference between SBP and DBP,20 reflecting arterial stiffness. Using these 3 traits, we undertook a meta-analysis of 150 134 individuals from 54 genome-wide association studies (GWAS) of European ancestry with imputation based on the 1000 Genomes Project Phase 1. To minimize reporting of false-positive associations, we sought stringent evidence for significant independent replication in a further 228 245 individuals. We further followed up novel and previously reported association signals in multiple large gene expression databases and the largest kidney tissue gene expression resource currently available. Finally, we searched for enrichment of associated genes in biological pathways and gene sets and identified whether any of the genes were known drug targets or had tool molecules.

Materials and Methods

Studies Stage 1

Results from 54 independent European-ancestry studies, totaling 150 134 individuals, were included in the stage 1 meta-analysis: AGES (n=3215), ARIC (n=9402), ASPS (n=828), B58C (n=6458), BHS (n=4492), CHS (n=3254), Cilento study (n=999), COLAUS (n=5404), COROGENE-CTRL (n=1878), CROATIA-Vis (n=945), CROATIA-Split (n=494), CROATIA-Korcula (n=867), EGCUT (n=6395), EGCUT2 (n=1844), EPIC (n=2100), ERF (n=2617), Fenland (n=1357), FHS (n=8096), FINRISK-ctrl (n=861), FINRISK CASE (n=839), FUSION (n=1045), GRAPHIC (n=1010), H2000-CTRL (n=1078), HealthABC (n=1661), HTO (n=1000), INGI-CARL (n=456), INGI-FVG (n=746), INGI-VB (n=1775), IPM (n=300), KORAS3 (n=1590), KORAS4 (n=3748), LBC1921 (n=376), LBC1936 (n=800), LOLIPOP-EW610 (n=927), MESA (n=2678), MICROS (n=1148), MIGEN (n=1214), NESDA (n=2336), NSPHS (n=1005), NTR (n=1490), PHASE (n=4535), PIVUS (n=945), PROCARDIS (n=1652), SHIP (n=4068), ULSAM (n=1114), WGHS (n=23 049), YFS (n=1987), ORCADES (n=1908), RS1 (n=5645), RS2 (n=2152), RS3 (n=3018), TRAILS (n=1262), TRAILS-CC (n=282), and TWINGENE (n=9789). Full study names and general study information is given in Table S1 in the online-only Data Supplement.

Study-Level Genotyping and Association Testing

Three quantitative BP traits were analyzed: SBP, DBP, and pulse pressure (difference between SBP and DBP). Within each study, individuals known to be taking antihypertensive medication had 15 mm Hg added to their raw SBP value and 10 mm Hg added to their raw DBP values.21 A summary of BP phenotypes in each study is given in Table S2. Association testing was undertaken according to a central analysis plan that specified the use of sex, age, age2, and body mass index as covariates and optional inclusion of additional covariates to account for population stratification (Table S3). Trait residuals were calculated for each trait using a normal linear regression of the medication-adjusted trait values (mm Hg) onto all covariates. The genotyping array, preimputation quality control filters, imputation software, and association testing software used by each study are listed in Table S4. Each participating study imputed genotypes based on the 1000 Genomes Project Phase 1 integrated release version 3 (March 2012) all ancestry reference panel.22 Imputed genotype dosages were used to take into account uncertainty in the imputation. Association testing was performed using linear regression of the trait residuals onto genotype dosages under an additive genetic model. Methods to account for relatedness within a study were used where appropriate (Table S3). Results for all variants (single nucleotide polymorphisms [SNPs] and insertion/deletion polymorphisms [INDELs]) were then returned to the central analysis group for further quality control checks and meta-analysis.

Stage 1 Meta-Analysis

Central quality control checks were undertaken across all results sets. This included checks to ensure allele frequency consistency (across studies and with reference populations), checks of effect size and standard error distributions (ie, to highlight phenotype issues), and generation of quantile–quantile plots and genomic inflation factor lambdas to check for over- or underinflation of test statistics. Genomic control was applied (if lambda >1) at study level. Variants with imputation quality <0.3 were excluded prior to meta-analysis. Inverse variance–weighted meta-analysis was undertaken. After meta-analysis, variants with a weighted minor allele frequency of <1% or N effective (product of study sample size and imputation quality summed across contributing studies) <60% were then excluded and meta-analysis genomic control lambda calculated and used to adjust the meta-analysis results.

Selection of Regions for Follow-Up

For each trait, regions of association were selected by ranking variants by P value, recording the variant with the lowest P value as a sentinel variant and then excluding all variants ±500 kb from the sentinel and reranking the remaining variants. This was undertaken iteratively until all sentinel variants representing 1 Mb regions containing associations with P<10–6 had been identified. To identify additional signals represented by secondary sentinel variants within 500 kb of each of the sentinel variants, GCTA (the Genome-wide Complex Trait Analysis software)23 was used to run conditional analyses (conditioned on the first sentinel variant) on each of the 1 Mb regions using GWAS summary statistics and linkage disequilibrium (LD) information from ARIC. This was done both for putatively novel regions and for regions that had previously been reported. A χ2 test of heterogeneity of effect sizes across the 54 studies was run for each sentinel variant, and those with P<0.05 for heterogeneity were excluded from further follow-up. Variants with P<10–6 after conditioning on the sentinel SNP (novel or known) in the region and for which any attenuation of the –log10 P value was <1.5 fold were also taken forward for replication.

Studies Stage 2

Data from 14 independent studies, totaling 87 360 individuals, and the first release of UK Biobank, totaling 140 886 individuals, were combined to replicate the findings from stage 1 (ie, totaling 228 245 individuals). Stage 2 study details, including full study names, are given in Table S6 and included 3C-Dijon (n=4061), Airwave (n=14 023), ASCOT-SC (n=2462), ASCOT-UK (n=3803), BRIGHT (n=1791), GAPP (n=1685), GoDARTs (n=7413), GS:SFHS (n=9749), HCS (n=2112), JUPITER (n=8718), LifeLines (n=13 376), NEO (n=5731), TwinsUK (n=4973), UK Biobank-CMC (n=140 886), and UKHLS (n=7462). Analysis was undertaken using the same methods as described for stage 1 studies. UK Biobank-CMC used a newer imputation reference panel than the other studies, and where a requested variant was not available, a proxy was used (next most significant P value with LD r2>0.6 with original top variant). Results from all stage 2 studies were meta-analyzed using inverse variance–weighted meta-analysis. Two of the variants, rs1048238 and chr1:243458005:INDEL, were not available in the largest study in stage 2 (UK Biobank-CMC), and so proxy variants were selected (based on P value and LD).

Stage 1+Stage 2 Meta-Analysis

After meta-analysis of stage 1 and stage 2 results, signals with a P>5×108 were excluded. Of the signals with a final P<5×108, support for independent replication within the stage 2 studies only was sought. Any signals that had P<5×108 and evidence for independent replication in stage 2 alone indicated by P<8.2×104 (Bonferroni correction for 61 tests) were reported as novel signals of association with BP. Any signals that were subsequently reported by other BP GWAS that were accepted for publication during the time this analysis was ongoing, or signals for which independence from another known signal could not be established, were removed from our list of novel signals at this stage (Table S5).

Genotype and Gene Expression

We searched for signals of association of genotype with gene expression for the 22 signals (including 8 novel) described in this study (Table S7) and all signals reported prior to our study (Table S10)316,18,24 in 3 whole-blood data sets, 1 kidney data set, and the GTEx (Genotype-Tissue Expression) multiple tissue data resource, which included whole blood.25 We selected cis signals of association, which were significant after controlling for 5% false discovery rate. The 3 whole-blood expression quantitative trait loci (eQTL) data sets were the National Heart, Lung, and Blood Institute SABRe (Systems Approach to Biomarker Research in Cardiovascular Disease) initiative whole-blood eQTL resource (microarray, n=5257), NESDA-NTR (microarray, n=4896), BIOS (RNAseq, n=2116). The whole-blood data from GTEx was based on data from 338 samples. The kidney data set comprised 236 donor kidney samples from 134 donors.26 Full details of each data set can be found in the online-only Data Supplement. The source transcriptomic renal data as described26 have been deposited in the GeneExpression Omnibus (NCBI) and are accessible online through GEO Series accession number GSE43974.

LD Lookup

The 1000 Genomes Project phase 3 release of variant calls was used (February 20, 2015) using 503 subjects of European ancestry.22r2 between the sentinel SNPs and all other biallelic SNPs within the corresponding 2 Mb area were calculated using the Tabix and PLINK software package (v1.07).27,28 Annotation was performed using the ANNOVAR software package.29

Gene-Based Pathway Analysis

All genes identified in 3 or 4 of the whole-blood eQTL resources above (Table 2) and genes containing a nonsynonymous variant with r2>0.5 with the sentinel variant (Table S14) were tested for enrichment of biological pathways and gene ontology (GO) terms using ConsensusPathDB30 using a false discovery rate <5% cutoff. Enriched pathways and GO terms containing genes only implicated by a single BP-associated variant were not reported.

Table 1. Novel Genome-Wide Significant Signals of Association

Variant ID (Noncoded/Coded Allele), Chr:Position, Nearest Gene(s) (Type*)CAFResults for Most Significant TraitStage 1+Stage 2 Meta-Analysis P Values for All Traits
Stage 1Stage 2Stage 1+Stage 2
Beta (SE)P ValueNeffBeta (SE)P ValueNeffBeta (SE)P ValueNeffSBPDBPPP
rs1048238 (C/T), 1:16341649, HSPB7 (3′UTR)0.5710.366 (0.074)8.09E-07140299NANANANANANANANANA
rs848309 (proxy) (T/C), 1:163084470.5670.347 (0.072)1.70E-061467550.347 (0.071)9.10E-071404620.347 (0.051)7.07E-122872177.07E-121.07E-105.48E-06
rs185819 (T/C), 6:32,050,067, TNXB (ns)0.5130.534 (0.073)1.93E-131423970.277 (0.053)1.49E-072217480.365 (0.043)1.04E-173641441.04E-172.24E-118.50E-15
rs6557876 (C/T), 8:25,900,675, EBF20.252−0.411 (0.084)8.50E-07143653−0.350 (0.060)5.66E-09225803−0.371 (0.049)2.85E-143694572.85E-142.50E-101.51E-08
rs35783704 (G/A), 8:105,966,258, LRP12/ZFPM20.109−0.609 (0.121)4.96E-07133924−0.310 (0.089)4.78E-04215528−0.414 (0.072)7.08E-093494527.08E-091.60E-062.92E-07
rs73099903 (C/T), 12:53,440,779, LOC2833350.0740.768 (0.143)8.05E-081360640.396 (0.098)5.32E-052072530.515 (0.081)1.95E-103433181.95E-104.53E-065.46E-08
rs8904 (G/A), 14:35,871,217, NFKBIA (3′UTR)0.3750.377 (0.076)6.76E-071404240.278 (0.054)2.31E-072247710.311 (0.044)1.31E-123651951.31E-121.13E-043.44E-12
rs57927100 (C/G), 17:75,317,300, SEPT90.258−0.489 (0.086)1.10E-08136624−0.220 (0.061)3.12E-04210563−0.310 (0.050)4.04E-103471884.04E-101.16E-101.81E-05
rs9710247 (A/G), 19:40,760,449, AKT20.4470.252 (0.051)8.11E-071096950.129 (0.032)5.76E-051983320.164 (0.027)1.61E-093080283.82E-021.61E-095.03E-01

Results from stage 1 and stage 2, and the meta-analysis of stage 1 and stage 2, for all novel genome-wide significant signals of association. P values of association for all 3 traits from a meta-analysis of stages 1 and 2 are also presented. Results from proxy SNPs are indicated by (proxy); rs848309 was a proxy SNP for rs1048238, and rs10926988 was a proxy SNP for chr1:243458005:INDEL. CAF indicates coded allele frequency; DBP, diastolic blood pressure; Neff, effective sample size; ns, nonsynonymous; PP, pulse pressure; s, synonymous; SBP, systolic blood pressure; and UTR, untranslated region.

*For intragenic variants, the nearest genes are listed; all other variants are intronic unless indicated otherwise.

Novel signal at previously reported locus.

Genome-wide significant P values (P<5×10−8).

Table 2. BP-Associated SNPs Associated With Expression of the Same Gene Across 4 or 3 Independent Whole-Blood eQTL Resources and the Kidney Resource

Sentinel SNPChrPositionGeneBlood Data SetsTop eQTLSignal in Other Tissue(s) in GTExSignal in KidneyeQTL Signal Previously Reported
Signal in 4 whole-blood eQTL resources
Signal in 3 (out of 4) whole-blood eQTL resources

Signals of association of SNP genotype and gene expression in other nonblood tissues in GTEx and in kidney are also indicated. Blood data set order: (1) SABRe, (2) NESDA-NTR, (3) BIOS, and (4) GTEx (whole-blood). Top eQTL: top GWAS SNP is top eQTL SNP (or in high LD, r2>0.9, with top eQTL SNP) in at least 1 data set. eQTL signal previously reported: Genes for which eQTL signals have been previously reported for that sentinel SNP.15,16,18 For full list, see Table S12 in the online-only Data Supplement. eQTL indicates expression quantitative trait loci; GWAS, genome-wide association studies; GTEx, genotype-tissue expression; and LD, linkage disequilibrium; and SABRe, Systems Approach to Biomarker Research in Cardiovascular Disease.

Network Analysis

To construct a functional association network, we combined 2 prioritized candidate gene sets into a single query gene set as (1) genes mapping to the nonsynonymous SNPs in high LD (r2>0.5) with the corresponding sentinel BP-associated SNP and (2) genes with eQTL evidence from 3 or 4 of the blood eQTL resources. Three sentinel SNPs (rs185819, rs926552, and rs805303) mapping to the HLA (human leukocyte antigen) region on chromosome 6 were excluded from downstream analyses. The single query gene set was then used as input for the functional network analysis.31 We used the Cytoscape32 software platform extended by the GeneMANIA33 plugin (Data Version: August 12, 2014).34 All the genes in the composite network, either from the query or the resulting gene sets, were then used for functional enrichment analysis against GO terms35 to identify the most relevant GO terms using the same plugin.34

DNase1 Hypersensitivity Overlap Enrichment Across Tissue and Cell Types

The functional element overlap analysis of the results of GWAS experiments (Forge tool v1.1)36 was used to test for enrichment of overlap of BP SNPs in tissues and cell lines from the Roadmap and ENCODE (Encyclopedia of DNA Elements) projects. All 164 SNPs were entered and 143 were included in the analysis. SNPs from 9 commonly used GWAS arrays were used to select background sets of SNPs for comparison, and 10 000 background repetitions were run. A Z score threshold of ≥3.39 (estimated false-positive rate of 0.5%) was used to declare significance.

Drug–Gene Interactions

Genes used for pathway and GO enrichment analyses were further investigated for potential druggable or drugged targets using DGIdb (drug gene interaction database).37 Known drug–gene interactions were interrogated across 15 source databases in DGIdb and include all types of interactions. The analysis performed for druggability prediction included all 9 databases exclusively inspecting expert curated data only. We also evaluate genes for known tool compounds using Chembl (; version 22.1).


The stage 1 discovery meta-analysis included 150 134 individuals (Tables S1 through S4 and Figures S1 and S2) and 7 994 604 variants with minor allele frequency >1% and an effective sample size of at least 60% of the total. We used the widely used 2-stage design38 and identified 61 signals in the discovery analysis that were candidates for novel BP signals (P<10–6 for any trait; Table S5). To ensure robustness of signals, we examined BP associations in an additional 228 245 individuals from 15 independent studies for replication, including 140 886 individuals from UK Biobank19 (Table S6). We used the most significant (sentinel) SNP and trait for each locus in replication (61 tests). Twenty-two putatively novel association signals were initially confirmed, showing significant evidence of replication in the independent stage-2 studies (P<8.2×104, Bonferroni correction for 61 tests) and genome-wide significance (P<5×108) in a meta-analysis across all 378 376 individuals (Table 1 and Table S7). Of these, 14 were subsequently published in 2 other studies17,19 which presented genome-wide significant associations with evidence of replication. A further 2 were highlighted as putative novel signals in one of those studies17 but had not been confirmed by replication. In our study, we report the 6 remaining novel signals, and the 2 previously unconfirmed signals (in EBF2 and in NFKBIA), as novel signals. The 8 novel signals included 7 signals at 7 independent loci (Figure S3) and 1 novel independent signal near a previously reported hit near TNXB (Table S8 and Figure S4). The novel signals show both significant evidence of replication in the independent stage-2 studies (P<8.2×104, Bonferroni correction for 61 tests) and genome-wide significance (P<5×108) in a meta-analysis across all 378 376 individuals. The sentinel variants at all 8 signals were common (minor allele frequency >5%), and the novel secondary signal at TNXB was in high linkage disequilibrium (r2>0.8) with a nonsynonymous SNP. With the exception of rs9710247, which was only significant for association with DBP, all signals were significantly associated (P<0.006, Bonferroni corrected for 8 tests) with all 3 traits (Table 1 and Table S9).

We next sought to identify which genes might have expression levels that were associated with genotypes of the BP-associated variants reported in this study and others. Strong evidence of an association with expression of a specific gene may provide clues as to which gene(s) might be functionally relevant to that signal. We took the 139 BP association signals reported prior to these studies17,19 and 22 novel signals of association identified and confirmed in this study and 2 contemporaneous studies319,24 (Table S10) and searched for evidence of association with gene expression in whole blood (4 studies, total n=12 607; supporting information in the online-only Data Supplement) and in kidney tissue (n=134, the largest kidney eQTL resource currently available). Although of unclear direct relevance to BP, whole blood was studied because of the availability of large data sets enabling a powerful assessment of expression patterns that are likely present across multiple cell and tissue types. Similarly, circulating blood cells have been used for ion transport experiments in the past, and altered ion transport levels in erythrocytes were linked to hypertension.39 Kidney was chosen because of the many renal pathways that regulate BP and outstanding questions about the relevance of kidney pathways to the genetic component of BP regulation in the general population.3,15 eQTL signals were filtered by false discovery rate (<5%), and we examined cis (within 1 Mb) associations only (supporting information in the online-only Data Supplement).

The 4 blood eQTL data sets were NESDA-NTR,40,41 SABRe,15 the BIOS resource,42 and GTEx25 (supporting information in the online-only Data Supplement). The BIOS resource (n=2116) has not previously been used in the analysis of BP associations, and findings from NESDA-NTR and SABRe have been reported for a subset of the previously published signals.16,18 For a total of 369 genes, gene expression was associated with the BP SNP in ≥1 of the 4 blood data sets at experiment-wide significance (Table S11). This included 14 genes for 6 of the 8 novel signals. For 110 genes, we found eQTL evidence in 2 out of 4 data sets (Figure), including 4 genes for 2 of the novel signals: EIF4B and TNS2 for rs73099903 and MAP3K10 and PLD3 for rs9710247. SNP rs73099903 was in strong LD (r2> 0.9), with the SNP most strongly associated with TNS2 expression in the BIOS resource. TNS2 encodes a tensin focal adhesion molecule and may have a role in renal function.43


Figure. Overlap of expression quantitative trait loci (eQTL) evidence from 4 whole-blood and 1 kidney resource. The figure indicates overlap of evidence for eQTLs from 4 whole-blood studies (SABRe, NESDA-NTR, BIOS, and GTEx) and from 1 kidney resource (TransplantLines). Every colored line indicates that this gene was analysis-wide significant in a given resource. Only genes identified by at least 2 resources are shown. The genes are sorted by genomic position on the y axis.

For 48 genes, we found evidence in 3 out of the 4 resources (Table 2), suggesting robustness of the SNP–gene expression correlation signal and highlighting those genes as potential candidates in genetic BP regulation. Of the 48 genes, 28 have not previously been described in eQTL analyses using BP-associated SNPs, and all were correlated with previously reported BP association signals.

In the kidney data set (TransplantLines),26 there was association of gene expression and genotype for 9 SNPs and 13 genes (Table 2 and Figure; Table S12). Nine of the SNP–gene expression associations were also observed in the whole-blood eQTL data sets, suggesting that those signals may not be unique to the kidney. We report 3 signals that were unique to the kidney and not previously reported (C4orf34, HIP2, and ASIC1) and confirm a previously reported kidney eQTL signal for an antisense RNA for PSMD5.15 The same SNP was also an eQTL for PSMD5 itself in both blood and kidney. ASIC1 encodes the acid sensing ion channel subunit 1, which may interact (and be coexpressed) with ENaC subunits, which mediate transepithelial Na transport in the distal nephron of the kidney.44 The comparatively small number of signals using kidney tissue (Table 2 and Figure) compared with whole blood could be because of the small sample size. Complete GTEx results are given in Table S13.

For genes implicated by eQTL information from whole blood, we tested for enrichment of biological pathways and GOs. We noted enrichment of the 48 genes implicated by 3 or 4 blood eQTL resources (Table 2) and a further 54 genes containing a nonsynonymous variant with r2>0.5 with the top SNP (Table S14) in pathways and ontology terms related to actin and striated muscle (Tables S15 and S16). Network analysis using the same genes highlighted further GO terms relating to muscle function, particularly cardiac muscle (Table S17). We tested the overlap of 161 non-HLA BP-associated variants with DNase hypersensitivity sites identified in the Roadmap and ENCODE cell lines and identified an overall enrichment in multiple cell and tissue types, including heart, kidney, and smooth muscle (Figure S5).

We next investigated these genes for potential suitability as drug targets (druggability), known tool compounds, and clinically approved drugs using DGIdb37 (Table S18). Twelve genes had known drugs, including 4 genes with known antihypertensive drugs. We noted that drugs modulating all but 1 of the 12 drugged targets had a reported influence on BP, either as a primary antihypertensive indication or as a reported side effect of raised BP. Twenty additional genes were predicted druggable, among these 7 genes have known small molecule tool modulators, based on a query of the Chembl database (; version 22.1).


Enhanced discovery of BP loci increases the potential targets for therapeutic advances. After major advances in the number of BP loci known over the last years and months, we report 8 novel signals that implicate 5 regions of the genome not previously connected to BP regulation.

Six of the 8 novel signals we report had not previously been reported. Two signals (in EBF2 and NFKBIA) have been suggested previously but without evidence for replication.17 For these 2 signals, we present, for the first time, stringent evidence of replication, confirming their relevance to BP genetics.

The path from signal to genes is the essential next step toward realizing the therapeutic potential of a genetic locus and understanding the mechanisms of BP regulation. We have used several large eQTL resources as a first step to realize this objective. As expected, we observed that even across eQTL studies of the same tissue, there is limited overlap in experiment-wide significant signals, suggesting either biological variability (differences in the characteristics of the samples or in the methods for extraction and processing of mRNA in each of the studies), technology-specific differences in coverage of genes (use of RNAseq data for the BIOS blood data set and microarray-based expression levels for the kidney and other blood data sets), or the possibility of false-positive results despite stringent within-experiment significance thresholds. We were unable to distinguish these scenarios using the data available to us, but by selecting genes that were significant in at least 3 resources, and therefore robust to these differences, we identified 48 genes as candidates for further study. These results are limited by the availability of large eQTL resources for whole blood only, which precludes well-powered comparisons across tissue types, particularly, as the origin of BP control is unlikely to be located in the blood. Enrichment and pathway analyses using these genes, and genes containing a correlated functional variant, highlight the potential relevance of muscular tissue and pathways, compatible with a vascular and cardiac origin of BP genetics, extending previous evidence.15 We identify several drugged targets in the pathways identified, including 4 existing hypertension targets. Other drugs identified are not suitable candidates for repositioning to hypertension because most were reported in adverse events to raise BP; however, the targets would be valid for investigation using a reverse mechanism, for example, agonism in place of inhibition. We also identified 7 genes with small molecule tool modulators (mainly inhibitory or binding). These molecules and targets might be suitable candidates for further investigation to build a target validation case to support clinical investigation in hypertension.

Among the genes implicated in our eQTL, analyses were several for which there is already some evidence that they are relevant to BP regulation. The intronic SNP rs10926988 was independently associated with expression of SDCCAG8 in all 4 whole-blood resources. Rare mutations in SDCCAG8 cause Bardet–Biedl syndrome, which features hypertension. Expression levels of MYBPC3 were correlated with rs710364815 in the 3 largest blood eQTL resources (ie, SABRe, NESDA-NTR, and BIOS). MYBPC3 encodes the cardiac isoform of myosin-binding protein C, which is expressed in heart muscle, and mutations in MYBPC3 are known to cause familial hypertrophic cardiomyopathy.45

This study has several limitations. Given the nature of statistical power for genome-wide association analyses, the sample size is limited, even though this is one of the largest efforts in BP GWAS undertaken to date. The study would clearly have benefited from the availability of larger eQTL resources on multiple tissues in sample sizes even larger than those available today. Our analyses were limited to cis signals, and future analyses, with larger sample sizes, might also consider trans signals.


Our study reports robust novel BP association signals and reports new candidate BP genes, contributing to the transition from variants to genes to explain BP variation. These genes now require further functional validation to establish their potential as drug targets. Our study additionally highlights the challenges of combining and interpreting data from multiple eQTL studies and emphasizes the need for harmonization of data and development of new eQTL resources for multiple tissue types.

In summary, our study reports novel BP association signals and reports new candidate BP genes, contributing to the transition from variants to genes to explain BP variation.


We thank all the study participants of this study for their contributions. Detailed acknowledgment of funding sources is provided in the Sources of Funding section.

Author Contributions

Secondary Analyses

Design of secondary analyses: L.V. Wain, G.B. Ehret, M.J. Caulfield, H. Snieder, M.D. Tobin, R. Joehanes, A. Vaez, R. Jansen, A.V. Smith, J. Knight, P.F. O’Reilly, A.P. Morris, and C.P. Cabrera. Computation of secondary analysis: L.V. Wain, G.B. Ehret, A.P. Morris, A.M. Erzurumluoglu, T. Blake, L. Lin, R. Joehanes, A. Vaez, P.J. van der Most, R. Jansen, and C.P. Cabrera.


WGHS: Study phenotyping, P.M. Ridker; Genotyping or analysis, D.I. Chasman and L.M. Rose; Study PI, D.I. Chasman and P.M. Ridker.

RS: Study phenotyping, G.C. Verwoert; Genotyping or analysis, G.C. Verwoert and A.G. Uitterlinden; Study PI, O.H. Franco, A. Hofman, and A.G. Uitterlinden.

NTR: Study phenotyping, E.J. de Geus and G. Willemsen; Genotyping or analysis: J.-J. Hottenga, E.J. de Geus, and G. Willemsen; Study PI, D.I. Boomsma and E.J. de Geus.

STR: Study phenotyping, E. Ingelsson; Genotyping or analysis, R.J. Strawbridge and M. Frånberg; Study PI, E. Ingelsson and A. Hamsten.

EGCUT: Genotyping or analysis, T. Esko; Study PI, A. Metspalu.

ARIC: Genotyping or analysis, D.E. Arking, A.C. Morrison, and P. Nandakumar; Study PI, A. Chakravarti.

FHS: Study phenotyping, D. Levy; Genotyping or analysis, S.-J. Hwang; Study PI: D. Levy.

MESA: Study phenotyping, J.I. Rotter; Genotyping or analysis, W. Palmas, X. Guo, J.I. Rotter, J. Yao; Study PI, W. Palmas.

B58C: Study phenotyping, D.P. Strachan; Genotyping or analysis, D.P. Strachan; Study PI, D.P. Strachan.

COLAUS: Study phenotyping, P. Vollenweider; Genotyping or analysis, M. Bochud and Z. Kutalik; Study PI, P. Vollenweider.

PROSPER: Study phenotyping, J.W. Jukema and D.J. Stott; Genotyping or analysis, S. Trompet and J. Deelen; Study PI, J.W. Jukema.

BHS: Study phenotyping, A. James; Genotyping or analysis, N. Shrine, J. Hui, and J. Beilby.

SHIP: Study phenotyping, M. Dörr; Genotyping or analysis, A. Teumer, M. Dörr, and U. Völker; Study PI, R. Rettig.

KORA S4: Genotyping or analysis, J.S. Ried; Study PI, A. Peters.

CHS: Study phenotyping, B.M. Psaty; Genotyping or analysis, J.C. Bis, K. Rice, and K.D. Taylor; Study PI, B.M. Psaty.

AGES-Reykjavik: Genotyping or analysis, A.V. Smith; Study PI, V. Gudnason, T.B. Harris, and L.J. Launer.

ERF: Study phenotyping, C.M. van Duijn and B.A. Oostra; Genotyping or analysis, N. Amin; Study PI, C.M. van Duijn and B.A. Oostra.

NESDA: Study phenotyping, B.W.J.H. Penninx; Genotyping or analysis, I.M. Nolte and Y. Milaneschi; Study PI, H. Snieder and B.W.J.H. Penninx.

YFS: Study phenotyping, T. Lehtimäki, M. Kähönen, and O.T. Raitakari; Genotyping or analysis, T. Lehtimäki, L.-P. Lyytikäinen, M. Kähönen, and O.T. Raitakari; Study PI, T. Lehtimäki, M. Kähönen, and O.T. Raitakari.

EPIC: Genotyping or analysis, N.J. Wareham; Study PI, J.H. Zhao.

ASPS: Study phenotyping, R. Schmidt; Genotyping or analysis, H. Schmidt, E. Hofer, Y. Saba, and R. Schmidt; Study PI, H. Schmidt and R. Schmidt.

ORCADES: Study phenotyping, J.F. Wilson, H. Campbell, and S. Wild; Genotyping or analysis, J.F. Wilson, P.K. Joshi, and S. Wild; Study PI, J.F. Wilson.

FINRISK (COROGENE_CTRL): Study phenotyping, P. Jousilahti; Genotyping or analysis, K. Kristiansson and A.P. Sarin; Study PI, M. Perola and P. Jousilahti.

INGI-VB: Study phenotyping, C.F. Sala; Genotyping or analysis, M. Traglia, C.M. Barbieri, and C.F. Sala; Study PI, D. Toniolo.

FINRISK_PREDICT_CVD: Study phenotyping, V. Salomaa and A.S. Havulinna; Study PI, V. Salomaa, A. Palotie, and S. Ripatti.

TRAILS: Study phenotyping, H. Riese; Genotyping or analysis, P.J. van der Most; Study PI, C.A. Hartman and A.J. Oldehinkel.

PROCARDIS: Study phenotyping, A. Goel; Genotyping or analysis, A. Goel; Study PI, H. Watkins, and M. Farrall.

HABC: Study phenotyping, Y. Liu and T.B. Harris; Genotyping or analysis, M.A. Nalls; Study PI, Y. Liu and T.B. Harris.

KORA S3: Study phenotyping, C. Gieger; Genotyping or analysis, S. Sõber, C. Gieger, and E. Org. Study PI, M. Laan.

INGI-FVG: Genotyping or analysis, D. Vuckovic, M. Brumat, and M. Cocca; Study PI, P. Gasparini.

Fenland: Study phenotyping, R.A. Scott, J. Luan, C. Langenberg, and N.J. Wareham; Genotyping or analysis, R.A. Scott, J. Luan, C. Langenberg, and N.J. Wareham; Study PI, R.A. Scott, C. Langenberg, and N.J. Wareham.

MICROS: Genotyping or analysis, A.A. Hicks, F. Del Greco M., and A. Saint Pierre; Study PI, F. Del Greco M. and P.P. Pramstaller.

HTO: Study phenotyping, B.D. Keavney; Genotyping or analysis, B.D. Keavney, K.L. Ayers, and C. Mamasoula; Study PI, B.D. Keavney and H.J. Cordell.

MIGEN: Study phenotyping, R. Elosua, J. Marrugat, S. Kathiresan, and D. Siscovick; Genotyping or analysis, R. Elosua, S. Kathiresan, and D. Siscovick; Study PI, S. Kathiresan.

ULSAM: Study phenotyping, V. Giedraitis and E. Ingelsson; Genotyping or analysis, A.P. Morris and A. Mahajan; Study PI, A.P. Morris, V. Giedraitis, and E. Ingelsson.

Cilento study: Study phenotyping, R. Sorice; Genotyping or analysis, D. Ruggiero, and T. Nutile; Study PI, M. Ciullo.

LBC1936: Study phenotyping, I.J. Deary and A.J. Gow; Genotyping or analysis, L.M. Lopez, G. Davies, and A.J. Gow; Study PI, I.J. Deary.

H2000_CTRL: Study phenotyping, T. Niiranen; Study PI, P. Knekt, A. Jula, and S. Koskinen.

NSPHS: Genotyping or analysis, S. Enroth and Å. Johansson; Study PI, U. Gyllensten.

FUSION: Genotyping or analysis, A.U. Jackson; Study PI, J. Tuomilehto, M. Boehnke, and F. Collins.

GRAPHIC: Study phenotyping, N.J. Samani, P.S. Braund, and M.D. Tobin. Genotyping or analysis: C.P. Nelson, P.S. Braund, and M.D. Tobin; Study PI, N.J. Samani.

CROATIA_Vis: Study phenotyping, I. Rudan; Genotyping or analysis, V. Vitart and J.E. Huffman; Study PI, V. Vitart and I. Rudan.

PIVUS: Study phenotyping, L. Lind and J. Sundström; Genotyping or analysis, C.M. Lindgren and A. Mahajan; Study PI, C.M. Lindgren, L. Lind, and J. Sundström.

LOLIPOP: Study phenotyping, J.S. Kooner and J.C. Chambers; Genotyping or analysis, J.S. Kooner, W. Zhang, J.C. Chambers, and B. Lehne; Study PI, J.S. Kooner and J.C. Chambers.

CROATIA_Korcula: Genotyping or analysis, C. Hayward and J. Marten; Study PI, C. Hayward and A.F. Wright.

INGI-CARL: Study phenotyping, G. Girotto; Genotyping or analysis, I. Gandin, A. Morgan, and A. Robino.

LBC1921: Study phenotyping, J.M. Starr and A. Pattie; Genotyping or analysis, J.M. Starr, S.E. Harris, D.C.M. Liewald, and A. Pattie; Study PI, J.M. Starr.

CROATIA_SPLIT: Study phenotyping, O. Polasek and I. Kolcic; Genotyping or analysis, O. Polasek and T. Zemunik; Study PI, O. Polasek.

BioMe (formerly IPM): Genotyping or analysis, Y. Lu; Study PI, R.J.F. Loos and E.P. Bottinger.


UKB-BP: Genotyping or analysis, H.R. Warren, M.R. Barnes, C.P. Cabrera, E. Evangelou, H. Gao, B. Mifsud, M. Ren, and I. Tzoulaki; Study PI, P. Elliott and M.J. Caulfield.

GoDARTS: Study phenotyping, C.N.A. Palmer and A.S.F. Doney; Genotyping or analysis, C.N.A. Palmer and N. Shah; Study PI, C.N.A. Palmer and A.D. Morris.

Lifelines: Study phenotyping: M.H. de Borst; Genotyping or analysis, M. Swertz; Study PI, P. van der Harst.

TwinsUK: Study phenotyping, C. Menni; Genotyping or analysis, M. Mangino and C. Menni; Study PI, T.D. Spector.

Airwave Health Monitoring Study: Genotyping or analysis, A.C. Vergnaud, E. Evangelou, H. Gao, and I. Tzoulaki; Study PI, E. Evangelou.

The UK Household Longitudinal Study (UKHLS): Genotyping or analysis, B.P. Prins; Study PI, E. Zeggini.

Generation Scotland (GS:SFHS): Study phenotyping, S. Padmanabhan; Genotyping or analysis, C. Hayward and A. Campbell.

JUPITER: Study phenotyping, P.M. Ridker; Genotyping or analysis, D.I. Chasman, L.M. Rose, F. Giulianini, and P.M. Ridker; Study PI, D.I. Chasman and P.M. Ridker.

NEO: Study phenotyping, R. de Mutsert; Genotyping or analysis, D.O. Mook-Kanamori and R. Li-Gao; Study PI, R. de Mutsert.

Three City-Dijon: Study phenotyping, S. Debette and C. Tzourio; Genotyping or analysis, G. Chauhan; Study PI, S. Debette and C. Tzourio.

ASCOT-UK: Study phenotyping: P. Sever and N. Poulter; Genotyping or analysis, P.B. Munroe and H.R. Warren; Study PI, P.B. Munroe, P. Sever, N. Poulter, and M.J. Caulfield.

ASCOT-SC: Study phenotyping, S. Thom and M.J. Caulfield; Genotyping or analysis, D.C. Shields, A. Stanton, H.R. Warren, and P.B. Munroe; Study PI, S. Thom, M.J. Caulfield, and P.B. Munroe.

Hunter Community Study: Study phenotyping, R. Scott; Genotyping or analysis, C. Oldmeadow and E.G. Holliday; Study PI, J. Attia.

GAPP: Study phenotyping, D. Conen; Genotyping or analysis, D. Conen, S. Thériault, and G. Paré; Study PI, D. Conen.

BRIGHT: Study phenotyping, M. Brown and J. Connell; Genotyping or analysis, M. Farrall, P.B. Munroe, and H.R. Warren; Study PI, M. Brown, J. Connell, M. Farrall, P.B. Munroe, and M.J. Caulfield.

Resources for Secondary Analyses

eQTL NESDA NTR: Design of secondary analysis, R. Jansen; Computation of secondary analysis, D.I. Boomsma, R. Jansen, and B.W.J.H. Penninx; Study PI, D.I. Boomsma and B.W.J.H. Penninx.

eQTL kidney: Study phenotyping, J.J. Damman and M.A. Seelen; Genotyping or analysis, P.J. van der Most; Study PI, H. Snieder.

eQTL BIOS: Design of secondary analysis, R. Jansen; Computation of secondary analysis, R. Jansen; Study PI, R. Jansen.

SABRe: Study phenotyping, Y. Demirkale, P.J. Munson, and Q.T. Nguyen; Genotyping or analysis, R. Joehanes; Design of secondary analysis, D. Levy; Study PI, D. Levy.

ICBP-Steering Committee

G. Abecasis, M.J. Caulfield, A. Chakravarti, D.I. Chasman, G.B. Ehret, P. Elliott, T. Ferreira, M.-R. Jarvelin, A.D. Johnson, M. Larson, D. Levy, A.P. Morris, P.B. Munroe, C. Newton-Cheh, P.F. O’Reilly, W. Palmas, B.M. Psaty, K. Rice, A.V. Smith, H. Snieder, M.D. Tobin, C.M. van Duijn, L.V. Wain, H.R. Warren.


*A list of contributing authors is given in the online-only Data Supplement.

This article was sent to Theodore A. Kotchen, Guest Editor, for review by expert referees, editorial decision, and final disposition.

†These authors contributed equally to this work.

The online-only Data Supplement is available with this article at

Correspondence to Georg B. Ehret, Cardiology, Department of Specialties of Medicine, Geneva University Hospital, 1205 Genève, Switzerland, E-mail or Louise V. Wain, Department of Health Sciences, University of Leicester, Leicester LE1 7RH, United Kingdom, E-mail


  • 1. Nelson MR, Tipney H, Painter JL, Shen J, Nicoletti P, Shen Y, Floratos A, Sham PC, Li MJ, Wang J, Cardon LR, Whittaker JC, Sanseau P. The support of human genetic evidence for approved drug indications.Nat Genet. 2015; 47:856–860. doi: 10.1038/ng.3314.CrossrefMedlineGoogle Scholar
  • 2. Mancia G, Fagard R, Narkiewicz K, et al. 2013 ESH/ESC guidelines for the management of arterial hypertension: the Task Force for the Management of Arterial Hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC).Eur Heart J. 2013; 34:2159–2219. doi: 10.1093/eurheartj/eht151.CrossrefMedlineGoogle Scholar
  • 3. Ehret GB, Munroe PB, Rice KM, et al. Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk.Nature. 2011; 478:103–109.CrossrefMedlineGoogle Scholar
  • 4. Ganesh SK, Chasman DI, Larson MG, et al; Global Blood Pressure Genetics Consortium. Effects of long-term averaging of quantitative blood pressure traits on the detection of genetic associations.Am J Hum Genet. 2014; 95:49–65. doi: 10.1016/j.ajhg.2014.06.002.CrossrefMedlineGoogle Scholar
  • 5. Johnson AD, Newton-Cheh C, Chasman DI, et al; Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium; Global BPgen Consortium; Women’s Genome Health Study. Association of hypertension drug target genes with blood pressure and hypertension in 86,588 individuals.Hypertension. 2011; 57:903–910. doi: 10.1161/HYPERTENSIONAHA.110.158667.LinkGoogle Scholar
  • 6. Johnson T, Gaunt TR, Newhouse SJ, et al; Cardiogenics Consortium; Global BPgen Consortium. Blood pressure loci identified with a gene-centric array.Am J Hum Genet. 2011; 89:688–700. doi: 10.1016/j.ajhg.2011.10.013.CrossrefMedlineGoogle Scholar
  • 7. Kato N, Takeuchi F, Tabara Y, et al. Meta-analysis of genome-wide association studies identifies common variants associated with blood pressure variation in east Asians.Nat Genet. 2011; 43:531–538. doi: 10.1038/ng.834.CrossrefMedlineGoogle Scholar
  • 8. Levy D, Ehret GB, Rice K, et al. Genome-wide association study of blood pressure and hypertension.Nat Genet. 2009; 41:677–687. doi: 10.1038/ng.384.CrossrefMedlineGoogle Scholar
  • 9. Newton-Cheh C, Johnson T, Gateva V, et al; Wellcome Trust Case Control Consortium. Genome-wide association study identifies eight loci associated with blood pressure.Nat Genet. 2009; 41:666–676. doi: 10.1038/ng.361.CrossrefMedlineGoogle Scholar
  • 10. Newton-Cheh C, Larson MG, Vasan RS, et al. Association of common variants in NPPA and NPPB with circulating natriuretic peptides and blood pressure.Nat Genet. 2009; 41:348–353. doi: 10.1038/ng.328.CrossrefMedlineGoogle Scholar
  • 11. Padmanabhan S, Melander O, Johnson T, et al; Global BPgen Consortium. Genome-wide association study of blood pressure extremes identifies variant near UMOD associated with hypertension.PLoS Genet. 2010; 6:e1001177. doi: 10.1371/journal.pgen.1001177.CrossrefMedlineGoogle Scholar
  • 12. Simino J, Shi G, Bis JC, et al; LifeLines Cohort Study. Gene-age interactions in blood pressure regulation: a large-scale investigation with the CHARGE, Global BPgen, and ICBP Consortia.Am J Hum Genet. 2014; 95:24–38. doi: 10.1016/j.ajhg.2014.05.010.CrossrefMedlineGoogle Scholar
  • 13. Tragante V, Barnes MR, Ganesh SK, et al. Gene-centric meta-analysis in 87,736 individuals of European ancestry identifies multiple blood-pressure-related loci.Am J Hum Genet. 2014; 94:349–360. doi: 10.1016/j.ajhg.2013.12.016.CrossrefMedlineGoogle Scholar
  • 14. Wain LV, Verwoert GC, O’Reilly PF, et al; LifeLines Cohort Study; EchoGen consortium; AortaGen Consortium; CHARGE Consortium Heart Failure Working Group; KidneyGen consortium; CKDGen consortium; Cardiogenics consortium; CardioGram. Genome-wide association study identifies six new loci influencing pulse pressure and mean arterial pressure.Nat Genet. 2011; 43:1005–1011. doi: 10.1038/ng.922.CrossrefMedlineGoogle Scholar
  • 15. Ehret GB, Ferreira T, Chasman DI, et al; CHARGE-EchoGen Consortium; CHARGE-HF Consortium; Wellcome Trust Case Control Consortium. The genetics of blood pressure regulation and its target organs from association studies in 342,415 individuals.Nat Genet. 2016; 48:1171–1184. doi: 10.1038/ng.3667.CrossrefMedlineGoogle Scholar
  • 16. Liu C, Kraja AT, Smith JA, et al; CHD Exome+ Consortium; ExomeBP Consortium; GoT2DGenes Consortium; T2D-GENES Consortium; Myocardial Infarction Genetics and CARDIoGRAM Exome Consortia; CKDGen Consortium. Meta-analysis identifies common and rare variants influencing blood pressure and overlapping with metabolic trait loci.Nat Genet. 2016; 48:1162–1170. doi: 10.1038/ng.3660.CrossrefMedlineGoogle Scholar
  • 17. Hoffmann TJ, Ehret GB, Nandakumar P, Ranatunga D, Schaefer C, Kwok PY, Iribarren C, Chakravarti A, Risch N. Genome-wide association analyses using electronic health records identify new loci influencing blood pressure variation.Nat Genet. 2017; 49:54–64. doi: 10.1038/ng.3715.CrossrefMedlineGoogle Scholar
  • 18. Surendran P, Drenos F, Young R, et al; CHARGE-Heart Failure Consortium; EchoGen Consortium; METASTROKE Consortium; GIANT Consortium; EPIC-InterAct Consortium; Lifelines Cohort Study; Wellcome Trust Case Control Consortium; Understanding Society Scientific Group; EPIC-CVD Consortium; CHARGE+ Exome Chip Blood Pressure Consortium; T2D-GENES Consortium; GoT2DGenes Consortium; ExomeBP Consortium; CHD Exome+ Consortium. Trans-ancestry meta-analyses identify rare and common variants associated with blood pressure and hypertension.Nat Genet. 2016; 48:1151–1161. doi: 10.1038/ng.3654.CrossrefMedlineGoogle Scholar
  • 19. Warren HR, Evangelou E, Cabrera CP, et al; International Consortium of Blood Pressure (ICBP) 1000G Analyses; BIOS Consortium; Lifelines Cohort Study; Understanding Society Scientific group; CHD Exome+ Consortium; ExomeBP Consortium; T2D-GENES Consortium; GoT2DGenes Consortium; Cohorts for Heart and Ageing Research in Genome Epidemiology (CHARGE) BP Exome Consortium; International Genomics of Blood Pressure (iGEN-BP) Consortium; UK Biobank CardioMetabolic Consortium BP working group. Genome-wide association analysis identifies novel blood pressure loci and offers biological insights into cardiovascular risk.Nat Genet. 2017; 49:403–415. doi: 10.1038/ng.3768.CrossrefMedlineGoogle Scholar
  • 20. Safar ME, Nilsson PM, Blacher J, Mimran A. Pulse pressure, arterial stiffness, and end-organ damage.Curr Hypertens Rep. 2012; 14:339–344. doi: 10.1007/s11906-012-0272-9.CrossrefMedlineGoogle Scholar
  • 21. Tobin MD, Sheehan NA, Scurrah KJ, Burton PR. Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure.Stat Med. 2005; 24:2911–2935. doi: 10.1002/sim.2165.CrossrefMedlineGoogle Scholar
  • 22. Abecasis GR, Auton A, Brooks LD, DePristo MA, Durbin RM, Handsaker RE, Kang HM, Marth GT, McVean GA; 1000 Genomes Project Consortium. An integrated map of genetic variation from 1,092 human genomes.Nature. 2012; 491:56–65. doi: 10.1038/nature11632.CrossrefMedlineGoogle Scholar
  • 23. Yang J, Lee SH, Goddard ME, Visscher PM. GCTA: a tool for genome-wide complex trait analysis.Am J Hum Genet. 2011; 88:76–82. doi: 10.1016/j.ajhg.2010.11.011.CrossrefMedlineGoogle Scholar
  • 24. Kato N, Loh M, Takeuchi F, et al; BIOS-consortium; CARDIo GRAMplusCD; LifeLines Cohort Study; InterAct Consortium. Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation.Nat Genet. 2015; 47:1282–1293. doi: 10.1038/ng.3405.CrossrefMedlineGoogle Scholar
  • 25. GTEx Consortium. Human genomics. The genotype-tissue expression (gtex) pilot analysis: Multitissue gene regulation in humans.Science. 2015; 348:648–660. doi: 10.1126/science.1262110.CrossrefMedlineGoogle Scholar
  • 26. Damman J, Bloks VW, Daha MR, van der Most PJ, Sanjabi B, van der Vlies P, Snieder H, Ploeg RJ, Krikke C, Leuvenink HG, Seelen MA. Hypoxia and complement-and-coagulation pathways in the deceased organ donor as the major target for intervention to improve renal allograft outcome.Transplantation. 2015; 99:1293–1300. doi: 10.1097/TP.0000000000000500.CrossrefMedlineGoogle Scholar
  • 27. Li H. Tabix: fast retrieval of sequence features from generic TAB-delimited files.Bioinformatics. 2011; 27:718–719. doi: 10.1093/bioinformatics/btq671.CrossrefMedlineGoogle Scholar
  • 28. Purcell S, Neale B, Todd-Brown K, Thomas L, Ferreira MA, Bender D, Maller J, Sklar P, de Bakker PI, Daly MJ, Sham PC. PLINK: a tool set for whole-genome association and population-based linkage analyses.Am J Hum Genet. 2007; 81:559–575. doi: 10.1086/519795.CrossrefMedlineGoogle Scholar
  • 29. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data.Nucleic Acids Res. 2010; 38:e164. doi: 10.1093/nar/gkq603.CrossrefMedlineGoogle Scholar
  • 30. Kamburov A, Stelzl U, Lehrach H, Herwig R. The ConsensusPathDB interaction database: 2013 update.Nucleic Acids Res. 2013; 41(database issue):D793–D800. doi: 10.1093/nar/gks1055.CrossrefMedlineGoogle Scholar
  • 31. Vaez A, Jansen R, Prins BP, Hottenga JJ, de Geus EJ, Boomsma DI, Penninx BW, Nolte IM, Snieder H, Alizadeh BZ. In silico post genome-wide association studies analysis of C-reactive protein loci suggests an important role for interferons.Circ Cardiovasc Genet. 2015; 8:487–497. doi: 10.1161/CIRCGENETICS.114.000714.LinkGoogle Scholar
  • 32. Saito R, Smoot ME, Ono K, Ruscheinski J, Wang PL, Lotia S, Pico AR, Bader GD, Ideker T. A travel guide to Cytoscape plugins.Nat Methods. 2012; 9:1069–1076. doi: 10.1038/nmeth.2212.CrossrefMedlineGoogle Scholar
  • 33. Mostafavi S, Ray D, Warde-Farley D, Grouios C, Morris Q. GeneMANIA: a real-time multiple association network integration algorithm for predicting gene function.Genome Biol. 2008; 9(suppl 1):S4. doi: 10.1186/gb-2008-9-s1-s4.CrossrefMedlineGoogle Scholar
  • 34. Montojo J, Zuberi K, Rodriguez H, Kazi F, Wright G, Donaldson SL, Morris Q, Bader GD. GeneMANIA Cytoscape plugin: fast gene function predictions on the desktop.Bioinformatics. 2010; 26:2927–2928. doi: 10.1093/bioinformatics/btq562.CrossrefMedlineGoogle Scholar
  • 35. Ashburner M, Ball CA, Blake JA, et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium.Nat Genet. 2000; 25:25–29. doi: 10.1038/75556.CrossrefMedlineGoogle Scholar
  • 36. Dunham I, Kulesha E, Iotchkova V, Morganella S, Birney E. Forge: A tool to discover cell specific enrichments of gwas associated snps in regulatory regions.BioRxiv. 2014;10.1101/013045.Google Scholar
  • 37. Wagner AH, Coffman AC, Ainscough BJ, Spies NC, Skidmore ZL, Campbell KM, Krysiak K, Pan D, McMichael JF, Eldred JM, Walker JR, Wilson RK, Mardis ER, Griffith M, Griffith OL. DGIdb 2.0: mining clinically relevant drug-gene interactions.Nucleic Acids Res. 2016; 44(D1):D1036–D1044. doi: 10.1093/nar/gkv1165.CrossrefMedlineGoogle Scholar
  • 38. Skol AD, Scott LJ, Abecasis GR, Boehnke M. Optimal designs for two-stage genome-wide association studies.Genet Epidemiol. 2007; 31:776–788. doi: 10.1002/gepi.20240.CrossrefMedlineGoogle Scholar
  • 39. Trevisan M, Ostrow D, Cooper R, Liu K, Sparks S, Okonek A, Stevens E, Marquardt J, Stamler J. Abnormal red blood cell ion transport and hypertension. The People’s Gas Company study.Hypertension. 1983; 5:363–367.LinkGoogle Scholar
  • 40. Wright FA, Sullivan PF, Brooks AI, et al. Heritability and genomics of gene expression in peripheral blood.Nat Genet. 2014; 46:430–437. doi: 10.1038/ng.2951.CrossrefMedlineGoogle Scholar
  • 41. Jansen R, Batista S, Brooks AI, et al. Sex differences in the human peripheral blood transcriptome.BMC Genomics. 2014; 15:33. doi: 10.1186/1471-2164-15-33.CrossrefMedlineGoogle Scholar
  • 42. Zhernakova DV, Deelen P, Vermaat M, et al. Identification of context-dependent expression quantitative trait loci in whole blood.Nat Genet. 2017; 49:139–145. doi: 10.1038/ng.3737.CrossrefMedlineGoogle Scholar
  • 43. Marusugi K, Nakano K, Sasaki H, Kimura J, Yanobu-Takanashi R, Okamura T, Sasaki N. Functional validation of tensin2 SH2-PTB domain by CRISPR/Cas9-mediated genome editing.J Vet Med Sci. 2016; 78:1413–1420. doi: 10.1292/jvms.16-0205.CrossrefMedlineGoogle Scholar
  • 44. Jeggle P, Smith ES, Stewart AP, Haerteis S, Korbmacher C, Edwardson JM. Atomic force microscopy imaging reveals the formation of ASIC/ENaC cross-clade ion channels.Biochem Biophys Res Commun. 2015; 464:38–44. doi: 10.1016/j.bbrc.2015.05.091.CrossrefMedlineGoogle Scholar
  • 45. Carrier L, Mearini G, Stathopoulou K, Cuello F. Cardiac myosin-binding protein C (MYBPC3) in cardiac pathophysiology.Gene. 2015; 573:188–197. doi: 10.1016/j.gene.2015.09.008.CrossrefMedlineGoogle Scholar

Novelty and Significance

What Is New?

  • The root origin of hypertension and, hence, blood pressure (BP) variability in the population remains unclear.

  • This study adds data to explain the genetic basis of BP variability and identifies genes likely active in BP-regulating pathways.

What Is Relevant?

  • The results are of relevance for scientists, clinicians, and pharmacologists interested in hypertension.

  • The BP loci and the BP genes identified constitute new leads for the understanding of BP pathogenesis and possibly therapeutic innovation.


Using 1000 Genomes Project–based imputation in 150 134 European ancestry and independent replication in a further 228 245 individuals, we contribute 8 replicated BP loci to the collection of loci currently known. Using these and previous data, 48 BP genes are identified for priority follow-up.


eLetters should relate to an article recently published in the journal and are not a forum for providing unpublished data. Comments are reviewed for appropriate use of tone and language. Comments are not peer-reviewed. Acceptable comments are posted to the journal website only. Comments are not published in an issue and are not indexed in PubMed. Comments should be no longer than 500 words and will only be posted online. References are limited to 10. Authors of the article cited in the comment will be invited to reply, as appropriate.

Comments and feedback on AHA/ASA Scientific Statements and Guidelines should be directed to the AHA/ASA Manuscript Oversight Committee via its Correspondence page.