Genome-wide association analysis identifies novel blood
pressure loci and offers biological insights into cardiovascular
risk
A full list of authors and affiliations appears at the end of the article.
#
These authors contributed equally to this work.
Abstract
Elevated blood pressure is the leading heritable risk factor for cardiovascular disease worldwide.
We report genetic association of blood pressure (systolic, diastolic, pulse pressure) among UK
Biobank participants of European ancestry with independent replication in other cohorts, and
robust validation of 107 independent loci. We also identify new independent variants at 11
previously reported blood pressure loci. Combined with results from a range of
in silico
functional
analyses and wet bench experiments, our findings highlight new biological pathways for blood
pressure regulation enriched for genes expressed in vascular tissues and identify potential
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†
Corresponding authors: Paul Elliott (p.elliott@imperial.ac.uk) and Mark Caulfield (m.j.caulfield@qmul.ac.uk).
#
A full list of ICBP consortium members and affiliations can be found at the end of the paper.
URLs
UK Biobank:
https://www.ukbiobank.ac.uk/
Genotype imputation and genetic association studies using UK Biobank data: http://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=157020
UK Biobank Axion Array Content Summary: http://biobank.ctsu.ox.ac.uk/crystal/refer.cgi?id=146640
Exome chip design: http://genome.sph.umich.edu/wiki/Exome_Chip_Design
Genotype-Tissue Expression (GTEx) database: www.gtexportal.org
GREAT Enrichment: http://bejerano.stanford.edu/great
Ingenuity Pathway Analysis (IPA) software: www.qiagen.com/ingenuity
Chembl: www.ebi.ac.uk/chembl/
Drug Gene Interaction database: dgidb.genome.wustl.edu
FORGE (accessed 16 Aug 2016): http://browser.1000genomes.org/Homo_sapiens/UserData/Forge?db=core
Fantom5 data (accessed 16 Aug 2016): http://fantom.gsc.riken.jp/5/
ENCODE DNase I data (wgEncodeAwgDnaseMasterSites; accessed 20 Aug 2016 using Table browser)
ENCODE cell type data (accessed 20 Aug 2016), http://genome.ucsc.edu/ENCODE/cellTypes.html.
Servier Medical Art: www.servier.fr/servier-medical-art
Conflicts/Disclosures
MJC is Chief Scientist for Genomics England, a wholly owned UK government company. He leads the 100,000 Genomes Project
which includes syndromic forms of blood pressure.
Author Contributions
Central analysis: HRW, CPC, HG, MRB, MPSL, MR, IT, BM, IK, EE.
Writing of the paper: HRW*, MRB, EE, CPC, HG, IT, BM, MR, MJC*, PE* (*Writing group leads).
Working group membership: MJC*, HRW, EE, IT, PBM, LVW, NJS, MT, JMMH, MDT, IN, BK, HG, MRB, CPC, JSK, PE* (*Co-
Chairs).
Replication consortium contributor: [ICBP-1000G] GBE, LVW, DL, AC, MJC, MDT, POR, JK, HS; [CHD Exome+ Consortium ]
PSu, RC, DSa, JMMH [ExomeBP Consortium] JPC, FD, PBM [T2D-GENES Consortium and GoT2DGenes Consortium] CML;
[CHARGE] GBE, CL, AK, DL, CNC, DIC; [iGEN-BP] ML, JCC, NK, JH, EST, PE, JSK, PVDH.
Replication study contributor: [Lifelines] NV, PVDH, HS, AMS; [GS:SFHS] JM, CH, DP, SP; [EGCUT] TE, MA, RM, AM;
[PREVEND] PVDH, NV, RTG, SJLB; [ASCOT] HRW, MJC, PBM, PS, NP, AS, DS, ST; [BRIGHT] HRW, MJC, PBM, MB, MF, JC;
[Airwave] HG, EE, MPSL, IK, IT, PE.
All authors critically reviewed and approved the final version of the manuscript.
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. 2017 March ; 49(3): 403–415. doi:10.1038/ng.3768.
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therapeutic targets for hypertension. Results from genetic risk score models raise the possibility of
a precision medicine approach through early lifestyle intervention to offset the impact of blood
pressure raising genetic variants on future cardiovascular disease risk.
Elevated blood pressure (BP) is a strong, heritable1–4 and modifiable driver of risk for
stroke and coronary artery disease and a leading cause of global mortality and morbidity5,6.
At the time of analysis, genome-wide association study (GWAS) meta-analyses, and
analyses of bespoke or exome content, have identified and replicated genetic variants of
mostly modest or weak effect on blood pressure at over 120 loci7–11. Here, we report
association analyses between BP traits and genetic variants among ˜150,000 participants in
UK Biobank, a prospective cohort study of 500,000 men and women aged 40-69 years with
extensive baseline phenotypic measurements, stored biological samples12, and follow-up by
electronic health record linkage13. We undertake independent replication in large
international consortia and other cohorts, providing robust validation of our findings and
new biological insights into BP regulation.
Our study design is summarized in Fig. 1. Briefly, data are available for 152,249 UK
Biobank participants genotyped using a customised array (including GWAS and exome
content) and with genome-wide imputation based on 1000 Genomes and UK10K sequencing
data14. (Further details on the UK Biobank imputation are available at the UK Biobank
website.) After quality measures and exclusions (see Online Methods), we study 140,886
unrelated individuals of European ancestry with two seated clinic BP measurements using
the Omron HEM-7015IT device (Supplementary Table 1). We carry out GWAS analyses of
systolic (SBP), diastolic (DBP) and pulse pressure (PP) using single-variant linear regression
under an additive model, based on ˜9.8 million single nucleotide variants (SNVs) with minor
allele frequency (MAF) ≥1% and imputation quality score (INFO) >0.1. For SNVs with
P
<1x10
-6
, we take forward for replication the sentinel SNV (i.e. with lowest
P
-value) at each
locus, defined by linkage disequilibrium (LD) r
2
< 0.2, within a 1Mb interval. We similarly
analyze exome content for variants with MAF ≥0.01%, including rare variants, taking into
replication the sentinel SNV (
P
< 1x10
-5
) from loci that are non-overlapping (r
2
<0.2) with
the GWAS findings. Overall we took sentinel SNVs from 240 loci into replication: 218 from
GWAS and 22 from exome analysis (r
2
< 0.2 and >500kb from previously reported BP
SNVs at the time of analysis and not annotated to previously reported BP genes;
Supplementary Table 2).
The replication resources comprise individuals of European ancestry from a large BP meta-
analysis consortium (ICBP cohorts listed in Supplementary Note) and further cohorts with
1000 Genomes data for GWAS (Supplementary Table 3), and two large BP exome consortia.
We use
P
<5x10
-8
to denote genome-wide significance in the combined (discovery and
replication) meta-analyses, with
P
< 0.01 for support in the replication data alone and
concordant direction of effect. Additionally, we take forward for replication potential
secondary signals at 51 previously reported BP loci at the time of analysis (excluding the
HLA region).
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To better understand the functional consequences of our findings, we carry out a series of
in
silico
investigations and experimental analysis of gene expression in relevant vascular tissue
for selected putative functional SNVs (Supplementary Fig. 1).
Results
Genetic variants at novel and previously unvalidated loci
Of the 240 loci taken forward to replication, we validate 107 loci at
P
< 5x10
-8
, of which
102 derive from the GWAS analysis replicated and meta-analyzed in a total of 330,956
individuals (Tables 1-3; Supplementary Fig. 2a-c; Supplementary Fig. 3a), and a further five
from the exome analysis in a total of 422,604 individuals (Tables 1-3 and Supplementary
Fig. 3b; Supplementary Tables 4, 5 and 6). Thirty-two of these validated loci are novel
findings. Since the time of analysis, the remaining 75 loci have also been reported in another
study15, although at least 53 of these were previously unvalidated (Tables 1-3), hence we
now validate these loci for the first time. We therefore present results here for all 107
validated loci in our study. Most SNVs also show association with hypertension in the UK
Biobank data, for example 93 of the 107 validated sentinel SNVs are nominally significant
(
P
< 0.01) (Supplementary Table 7).
Of the 107 validated loci, 24 are reported for association with SBP as the primary trait (most
significant from combined meta-analysis), 41 for DBP and 42 for PP, although many loci are
associated with more than one BP trait (Supplementary Fig. 4). For example, in the
combined meta-analysis, 24 validated loci are associated with both SBP and DBP, 11 with
SBP and PP, one locus with DBP and PP and four loci (
NADK-CPSF3L
,
GTF2B
,
METTL21A
-AC079767.3 and
PAX2
) with all three traits at genome-wide significance (Fig.
2).
After conditional analysis on the sentinel SNV we identify an independent validated
secondary SNV at five of the 107 loci (Supplementary Table 8a; Supplementary Table 9).
Compared with previously reported SNVs at the time of analysis, the contribution of our
validated loci increases the percentage trait variance explained by ˜1%, e.g. to 3.56% for
SBP.
We report signals at known hypertension drug targets, including the angiotensin converting
enzyme (
ACE
) locus (rs4308,
P
= 6.8 x 10
-14
, ACE-inhibitors),
CACNA2D2
(rs743757
, P
=
2.4 x 10
-10
, calcium channel blockers),
MME
(rs143112823 in the RP11-439C8.2 locus,
P
=
1.4 x 10
-14
, omapatrilat),
ADRA2B
(rs2579519 in the
GPAT2-FAHD2CP
locus,
P
= 4.8 x
10
-12
, beta blockers),
SLC14A2
(rs7236548,
P
= 2.0 x 10
-18
, nifedipine), and
phosphodiesterase 5A (
PDE5A
; rs66887589,
P
= 3.4 x 10
-15
, sildenafil).
Additionally, we evaluate our validated SNVs, where available, in cohorts of non-European
ancestry9–11, while recognizing that these analyses are likely underpowered
(Supplementary Table 10). We find concordance in direction of effect (
P
<0.05) for GWAS
SNVs for all three BP traits among individuals of East Asian ancestry and for DBP for South
Asian ancestry, also for exome SNVs among individuals of Hispanic ancestry, pointing to
cosmopolitan effects for many of the BP associated variants.
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A PhenoScanner16 search showed that 27 of our 107 validated sentinel SNVs (or proxies; r
2
≥ 0.8) exhibit genome-wide significant associations with other traits (Supplementary Fig. 5),
including coronary artery disease and myocardial infarction (where BP is likely on the
causal pathway17), cardiovascular risk factors (e.g. lipids, height, body mass index) and
non-cardiovascular traits (e.g. lung function, cancer, Alzheimer’s).
Variants at previously reported loci at time of analysis
In conditional analyses, we identify 22 secondary SNVs (17 common, one rare, four low-
frequency variants) that are conditionally independent of the BP associated SNVs at 16
previously reported loci at the time of analysis (Supplementary Table 8b; Supplementary
Tables 11 and 12). One rare variant (rs138582164, MAF=0.1%) in the
CDH17
locus
anticipated to act as an exonic stop/gain mutation at the
GEM
gene is associated with a
relatively large effect on PP (3.5 mm Hg per allele copy, Supplementary Table 8b). At three
previously reported loci (
EBF1
,
PDE3A
,
JAG1
) we identify multiple independent secondary
SNVs in addition to the previously reported SNVs (Supplementary Table 11).
The UK Biobank data show support (
P
< 0.01) for 119 of 122 previously reported BP loci at
the time of analysis (159 of 163 SNVs) for one or more BP traits (Supplementary Fig. 2 a-c;
Supplementary Table 13). We do not show support for one SNV (rs11066280,
RPL6-
ALDH1
) identified from a GWAS of East Asian ancestry18, which may indicate ancestry-
specific effects. We compare the MAF and effect sizes in UK Biobank with published results
of previously reported variants (Supplementary Fig. 6), indicating consistency of results
between the two sources of data.
We also examine findings for low-frequency and rare gene mutations previously reported to
be associated with monogenic hypertension disorders19 and included on the UK Biobank
gene array. Despite lack of power overall, the variant with the lowest
P
-value (rs387907156;
KLH3
; MAF=0.02%) has a seemingly large effect on BP: 8.2 mm Hg (SE=4.1,
P
= 0.046)
per allele for SBP; 5.6 mm Hg (SE=2.6,
P
= 0.048) for PP (Supplementary Table 14).
Functional analyses
We annotate the 107 validated loci to 212 genes (based on LD r
2
≥0.8) and seek putative
function from
in silico
analyses and gene expression experiments. Candidate genes with the
strongest supporting evidence are indicated in the last column of Supplementary Table 4
with an indication of the supporting data source. All genome-wide significant variants in LD
(r
2
>0.8) with the variants reported here, ranked by supporting evidence, are annotated in
Supplementary Table 15. Of the 107 validated sentinel SNVs three are Indels; all other
variants are single nucleotide polymorphisms (SNPs). We identify non-synonymous SNVs at
13 of the 107 validated loci (Supplementary Table 16), three of which are predicted to be
damaging (ANNOVAR) in
TFAP2D
(rs78648104),
NOX4
(rs56061986) and
CCDC141
(rs17362588, reported to be associated with heart rate20) (Supplementary Fig. 5a). Beyond
the coding regions we identify 29 SNVs in 3’UTRs which are predicted to significantly
weaken or cause loss of miRNA regulation by altering the recognition motif in seven genes,
and strengthen or create target sites for miRNA binding in 13 genes (based on miRNASNP
db, Supplementary Table 16).
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From our expression Quantitative Trait locus (eQTL) analysis (GTEx), 59 of the 107
validated loci contain variants with eQTLs in at least one tissue (Supplementary Table 17);
arterial tissue has the largest number of loci with eQTLs (Supplementary Fig. 7), with
targeted
in silico
analysis showing six loci with eQTLs in arterial tissue (Supplementary
Table 16). For example, the GTEx tibial artery eQTL in
SF3A3
(rs4360494) shows strong
in
silico
supporting evidence, including an arterial DNase I site within which the major C allele
removes a predicted AP-2 binding site (Supplementary Fig. 8). Hence we prioritized this
gene for
in vitro
functional analysis (see below).
By considering all loci reported here (our 107 validated loci, and previously reported loci at
the time of analysis), our DEPICT analysis identifies enrichment of expression across 31
tissues and cells (Supplementary Fig. 9; Supplementary Table 18), with greatest enrichment
in the arteries (
P
= 1.9 x 10
-6
, false discovery rate (FDR) < 1%). We use FORGE to
investigate and identify significant (FDR,
P
<0.05) cell type specific enrichment within
DNase I hypersensitive sites in a range of tissues including dermal and lung microvascular
endothelial cell types, and cardiac fibroblasts (Supplementary Fig. 10). For a set of curated
candidate regulatory SNVs from our 107 validated loci (see Supplementary Note),
widespread enrichment is found in microvascular endothelium, aortic smooth muscle, aortic
fibroblasts, vascular epithelium, heart and skin (Supplementary Fig. 10). In addition, we
identify significant enrichment of histone marks in a wide range of cell types, including
strong enrichment seen for H3K4Me3 (an activating modification found near promoters)
marks in umbilical vein endothelial cells (HUVEC) (Supplementary Fig. 11). To explore
expression at the level of cardiovascular cell types specifically, we use Fantom5 reference
transcript expression data (see Online Methods) to cluster the 212 genes annotated to our
107 validated loci according to tissue specificity (Supplementary Fig. 12), with the
significantly clustered genes forming four tissue-specific clusters, including a vascular
smooth muscle cell (VSMC) and fibroblast cluster, an endothelial cell cluster (including
probable endothelial cells in highly vascularized tissues), and a combined vascular cell
cluster.
Additionally, Ingenuity pathway analysis and upstream transcriptional analysis show
enrichment of canonical pathways implicated in cardiovascular disease, including those
targeted by antihypertensive drugs, such as the alpha-adrenergic, CXCR4, endothelin
signalling and angiotensin receptor pathways (Supplementary Table 19). In keeping with
vascular mediation of genetic influence we identify diphenyleneiodonium, an inhibitor of
flavin-containing oxidases, including NAD(P)H oxidase (NOX), which is reported to reverse
endothelial dysfunction (and hypertension) in a rat model21.
To identify long range target genes of non-coding variants, we use chromatin interaction
(Hi-C) data from HUVEC, as enhancers and silencers often form chromatin loops with their
target promoter. In most loci the strongest promoter interaction involves a gene in high LD
with the SNV, but for 21 loci we find a distal potential target gene (Supplementary Table
16). Pathway analysis of the distal genes shows greatest enrichment in regulators of cardiac
hypertrophy.
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