Genome-wide association study identifies eight loci associated with blood pressure

TLDR: In this paper, the association between systolic or diastolic blood pressure and common variants in eight regions near the CYP17A1 (P = 7 × 10(-24)), CYP1A2(P = 1 × 10-23), FGF5 (P=1 × 10 -21), SH2B3(P= 3 × 10−18), MTHFR(MTHFR), c10orf107(P), ZNF652(ZNF652), PLCD3 (P,P = 5 × 10 −9),

Abstract: Elevated blood pressure is a common, heritable cause of cardiovascular disease worldwide. To date, identification of common genetic variants influencing blood pressure has proven challenging. We tested 2.5 million genotyped and imputed SNPs for association with systolic and diastolic blood pressure in 34,433 subjects of European ancestry from the Global BPgen consortium and followed up findings with direct genotyping (N ≤ 71,225 European ancestry, N ≤ 12,889 Indian Asian ancestry) and in silico comparison (CHARGE consortium, N = 29,136). We identified association between systolic or diastolic blood pressure and common variants in eight regions near the CYP17A1 (P = 7 × 10(-24)), CYP1A2 (P = 1 × 10(-23)), FGF5 (P = 1 × 10(-21)), SH2B3 (P = 3 × 10(-18)), MTHFR (P = 2 × 10(-13)), c10orf107 (P = 1 × 10(-9)), ZNF652 (P = 5 × 10(-9)) and PLCD3 (P = 1 × 10(-8)) genes. All variants associated with continuous blood pressure were associated with dichotomous hypertension. These associations between common variants and blood pressure and hypertension offer mechanistic insights into the regulation of blood pressure and may point to novel targets for interventions to prevent cardiovascular disease.

Figures

Figure 2. Relationship of genome-wide significant loci to SBP, DBP and hypertension. Shown are the effects of each variant on continuous SBP and DBP and on the odds ratio for dichotomous hypertension compared to normotension (see Methods). For comparability, SBP and DBP effects are shown on the standard deviation scale (SBP SD = 16.6 mm Hg, DBP SD = 10.9 mm Hg). Alleles are coded as shown in Table 2.

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Genome-wide association study identifies eight loci associated
with blood pressure
Citation for published version:
Wellcome Trust Case Control Consortium, Newton-Cheh, C, Johnson, T, Gateva, V, Tobin, MD, Bochud, M,
Coin, L, Najjar, SS, Zhao, JH, Heath, SC, Eyheramendy, S, Papadakis, K, Voight, BF, Scott, LJ, Zhang, F,
Farrall, M, Tanaka, T, Wallace, C, Chambers, JC, Khaw, K-T, Nilsson, P, van der Harst, P, Polidoro, S,
Grobbee, DE, Onland-Moret, NC, Bots, ML, Wain, LV, Elliott, KS, Teumer, A, Luan, J, Lucas, G, Kuusisto,
J, Burton, PR, Hadley, D, McArdle, WL, Brown, M, Dominiczak, A, Newhouse, SJ, Samani, NJ, Webster, J,
Zeggini, E, Beckmann, JS, Bergmann, S, Lim, N, Song, K, Vollenweider, P, Waeber, G, Waterworth, DM,
Yuan, X, Groop, L & Orho-Melander, M 2009, 'Genome-wide association study identifies eight loci
associated with blood pressure', Nature Genetics, vol. 41, no. 6, pp. 666-76. https://doi.org/10.1038/ng.361
Digital Object Identifier (DOI):
10.1038/ng.361
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Eight blood pressure loci identified by genome-wide association
study of 34,433 people of European ancestry
Christopher Newton-Cheh
1,2,3,94
, Toby Johnson
4,5,6,94
, Vesela Gateva
7,94
, Martin D
Tobin
8,94
, Murielle Bochud
5
, Lachlan Coin
9
, Samer S Najjar
10
, Jing Hua Zhao
11,12
, Simon C
Heath
13
, Susana Eyheramendy
14,15
, Konstantinos Papadakis
16
, Benjamin F Voight
1,3
,
Laura J Scott
7
, Feng Zhang
17
, Martin Farrall
18,19
, Toshiko Tanaka
20,21
, Chris Wallace
22,23
,
John C Chambers
9
, Kay-Tee Khaw
12,24
, Peter Nilsson
25
, Pim van der Harst
26
, Silvia
Polidoro
27
, Diederick E Grobbee
28
, N Charlotte Onland-Moret
28,29
, Michiel L Bots
28
, Louise
V Wain
8
, Katherine S Elliott
19
, Alexander Teumer
30
, Jian’an Luan
11
, Gavin Lucas
31
,
Correspondence to: Gonçalo Abecasis, Center for Statistical Genetics, Department of Biostatistics, University of Michigan School of
Public Health, 1420 Washington Heights, Ann Arbor, MI 48109, Phone 734 763 4901, Fax 734 615 8322, Email:
goncalo@umich.edu, Mark Caulfield, Clinical Pharmacology, William Harvey Research Institute, Barts and The London,
Charterhouse Square, London, EC1M 6BQ, Tel: 02078823402, Fax: 02078823408, Email: m.j.caulfield@qmul.ac.uk, Patricia
Munroe, Clinical Pharmacology, William Harvey Research Institute, Barts and The London, Charterhouse Square, London, EC1M
6BQ, Tel: 02078823410, Fax: 02078823408, email: p.b.munroe@qmul.ac.uk, Christopher Newton-Cheh, Center for Genetic
Research, Cardiovascular Research Center, Massachusetts General Hospital, 185 Cambridge Street, CPZN 5.242, Boston, MA 02114,
Tel: +1 617 643 3615, Fax: 617 249 0127, Email: cnewtoncheh@chgr.mgh.harvard.edu.
94
These authors contributed equally.
95
These authors contributed equally.
Author contributions (alphabetical)
Project conception, design, management: ARYA: M.L.B., C.S.U.; BLSA: L.F.; B58C-T1DGC: D.P.S.; B58C-WTCCC: D.P.S.;
BRIGHT: M.Brown, M.C., J.M.C., A. Dominiczak, M.F., P.B.M., N.J.S., J.W.; CoLaus: J.S.B., S.Bergmann, M.Bochud, V.M. (PI),
P.Vollenweider (PI), G.W., D.M.W.; DGI: D.A., C.N.-C., L.G.; EPIC-Norfolk-GWAS: I.B., P.D., R.J.F.L., M.S.S., N.J.W., J.H.Z.
EPIC-Italy: S. Polidoro, P.Vineis. Fenland Study: R.J.F.L., N.G. F., N.J.W.; Finrisk97: L.P., V.S.; FUSION: R.N.B., M Boehnke,
F.S.C., K.L.M., L.J.S., T.V., J.T.; InCHIANTI: S. Bandinelli., L.F.; KORA: A. Döring, C.G., T.I., M.L., T.M., E.O., H.E.W. (PI);
LOLIPOP: J.C.C., P.E., J.S.K. (PI); MDC-CC: G.B., O.M.; MPP: G.B., O.M.; MIGen: D.A., R.E., S.K., J.M., O.M., C.J.O., V.S.,
S.M.S., D.S.S.; METSIM: J.K., M.L.; NFBC1966: P.E., M.-R.J.; PREVEND: P.E.d.J. (PI), G.N., W.H.v.G.; PROCARDIS: R.C.,
M.F., A.H., J.F.P., U.S., G.T., H.W.(PI); PROSPECT-EPIC. N.C.O.-M., Y.T.v.d.S.; SardiNIA: E.G.L., D.S.; SHIP: M.D., S.B.F.,
G.H., R.L., T.R., R.R., U.V., H.V.; SUVIMAX: P.M.; TwinsUK: P.D., T.D.S. (PI); UHP: D.E.G., M.E.N.
Phenotype collection, data management: ARYA: M.L.B., C.S.U.; B58C-T1DGC: D.H., W.L.M., D.P.S.; B58C-WTCCC: D.H.,
K.P., D.P.S.; BRIGHT: M. Brown, M.C., J.M.C., A.Dominiczak, M.F., P.B.M., N.J.S., J.W.; CoLaus: G.W.; DGI: L.G., O.M.;
EPIC-Italy: P. Vineis (PI); Finrisk97: P.J., M.P., V.S.;FUSION: J.T., T.V.; KORA: A. Doring, C.G., T.I.; MDC-CC: O.M.; MPP:
O.M., P.N.; MIGen: D.A., R.E., S.K., J.M., O.M., C.J.O., S.M.S., D.S.S., V.S.; NFBC1966: A.-L.H., M.-R.J., A.P.; PREVEND:
P.E.d.J., G.N., P.v.d.H., W.H.v.G.; PROCARDIS: R.C., A.H., U.S., G.T.; PROSPECT-EPIC. N.C.O.-M., Y.T.v.d.S.; SardiNIA:
S.S.N., A.S.; SHIP: M.D., R.L., R.R., H.V.; SUVIMAX: P.G., S.H.; TwinsUK: F.M.W.; UHP: D.E.G., M.E.N.
Genome-wide, validation genotyping: B58C-T1DGC: W.L.M.; B58C-WTCCC: W.L.M.; DGI: D.A., O.M., M.O.-M.; EPIC-
Norfolk-GWAS: I.B., P.D., N.J.W., J.H.Z.; EPIC-Norfolk-replication: S.A.B., K.-T.K., R.J.F.L., R.N.L., N.J.W.; EPIC-Italy:
G.M.; EPIC-Italy: A.A., A.d.G., S.G., V.R.; Finrisk97: G.J.C., C.N.-C.; FUSION: L.L.B., M.A.M.; KORA: T.I., T.M., E.O., A.P.;
MDC-CC: O.M., M.O.-M.; MPP: O.M., M.O.-M.; NFBC1966: P.E., N.B.F., M.-R.J., M.I.M., L.P. ; PREVEND: G.N., P.v.d.H. ;
W.H.v.G.; PROCARDIS: S.C.H., G.M.L., A.-C.S.; SardiNIA: M.U.; SHIP: F.E., G.H., A.T., U.V.; SUVIMAX: I.G.G., S.C.H.,
G.M.L., D.Z.; TwinsUK: P.D.
Data analysis: BLSA: T.T.; B58C-T1DGC: D.H., S.H., D.P.S.; B58C-WTCCC: P.R.B., D.H., K.P., D.P.S, M.D.T.; B58C-T1DGC:
D.H., S.H., D.P.S.; BRIGHT: S.J.N., C.W., E.Z.; CoLaus: S. Bergmann, M. Bochud, T.J., N.L., K.S., X.Y., DGI: O.M., C.N.-C.,
M.O.-M., B.F.V.; EPIC-Norfolk-GWAS: R.J.F.L., J.H.Z.; EPIC-Norfolk-replication: S.A.B., K.-T.K., R.J.F.L., R.N.L., N.J.W.;
EPIC-Italy: S.G., G.M., S. Panico, S. Polidoro, F.R., C.S., P. Vineis; Fenland Study: J.L.; Finrisk97: C.N.-C.; FUSION: A.U.J.,
L.J.S., H.M.S., C.J.W.; InCHIANTI: T.T.; KORA: S.E., C.G., M.L., E.O.; LOLIPOP: J.C.C.; MDC-CC: O.M., M.O.-M.; MPP:
O.M., M.O.-M.; MIGen: R.E., G.L., I.S., B.F.V.; NFBC1966: L.C., P.F.O.; PREVEND: H.S., P.v.d.H.; PROCARDIS: M.F., A.G.,
J.F.P.; SardiNIA: V.G., S.S., P.S.; SHIP: F.E., G.H., A.T., U.V.; SUVIMAX: S.C.H., T.J., P.M.; TwinsUK: N.S., F.Z., G.Z.
Analysis group. G.R.A., M.C., V.G., T.J., P.B.M., C.N.-C., M.D.T., L.V.W.
Writing group: G.R.A., M.C., P.E., V.G., T.J., P.B.M., C.N.-C., M.D.T.
COMPETING INTERESTS STATEMENT
The following authors declare the following potential conflicts of interest: N.L., V.M., K.S., D.M.W. and X.Y. are all full-time
employees at GlaxoSmithKline. P. Vollenweider and G.W received financial support from GlaxoSmithKline to assemble the CoLaus
study. No other authors reported conflicts of interest.
NIH Public Access
Author Manuscript
Nat Genet
. Author manuscript; available in PMC 2010 September 21.
Published in final edited form as:
Nat Genet
. 2009 June ; 41(6): 666–676. doi:10.1038/ng.361.
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Johanna Kuusisto
32
, Paul R Burton
8
, David Hadley
16
, Wendy L McArdle
33
, Wellcome Trust
Case Control Consortium
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, Morris Brown
35
, Anna Dominiczak
36
, Stephen J Newhouse
22
,
Nilesh J Samani
37
, John Webster
38
, Eleftheria Zeggini
19,39
, Jacques S Beckmann
4,40
, Sven
Bergmann
4,6
, Noha Lim
41
, Kijoung Song
41
, Peter Vollenweider
42
, Gerard Waeber
42
, Dawn
M Waterworth
41
, Xin Yuan
41
, Leif Groop
43,44
, Marju Orho-Melander
25
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27
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Alessandra Di Gregorio
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Jackson
7
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51
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7
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52
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7
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Richard N Bergman
53
, Mario A Morken
50
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15
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15
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Illig
15
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54,55
, Elin Org
56
, Arne Pfeufer
54
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15,57
, Sekar
Kathiresan
1,2,3
, Jaume Marrugat
31
, Christopher J O’Donnell
58,59
, Stephen M Schwartz
60,61
,
David S Siscovick
60,61
, Isaac Subirana
31,62
, Nelson B Freimer
63
, Anna-Liisa Hartikainen
64
,
Mark I McCarthy
19,65,66
, Paul F O’Reilly
9
, Leena Peltonen
39,49
, Anneli Pouta
64,67
, Paul E de
Jong
68
, Harold Snieder
69
, Wiek H van Gilst
26
, Robert Clarke
70
, Anuj Goel
18,19
, Anders
Hamsten
71
, John F Peden
18,19
, Udo Seedorf
72
, Ann-Christine Syvänen
73
, Giovanni
Tognoni
74
, Edward G Lakatta
10
, Serena Sanna
75
, Paul Scheet
76
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77
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Angelo Scuteri
78
, Marcus Dörr
79
, Florian Ernst
30
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79
, Georg Homuth
30
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Roberto Lorbeer
80
, Thorsten Reffelmann
79
, Rainer Rettig
81
, Uwe Völker
30
, Pilar Galan
82
,
Ivo G Gut
13
, Serge Hercberg
82
, G Mark Lathrop
13
, Diana Zeleneka
13
, Panos Deloukas
12,39
,
Nicole Soranzo
17,39
, Frances M Williams
17
, Guangju Zhai
17
, Veikko Salomaa
48
, Markku
Laakso
32
, Roberto Elosua
31,62
, Nita G Forouhi
11
, Henry Völzke
80
, Cuno S Uiterwaal
28
,
Yvonne T van der Schouw
28
, Mattijs E Numans
28
, Giuseppe Matullo
27,45
, Gerjan Navis
68
,
Göran Berglund
25
, Sheila A Bingham
12,83
, Jaspal S Kooner
84
, Andrew D Paterson
85
, John
M Connell
36
, Stefania Bandinelli
86
, Luigi Ferrucci
21
, Hugh Watkins
18,19
, Tim D Spector
17
,
Jaakko Tuomilehto
52,87,88
, David Altshuler
1,3,89,90
, David P Strachan
16
, Maris Laan
56
, Pierre
Meneton
91
, Nicholas J Wareham
11,12
, Manuela Uda
75
, Marjo-Riitta Jarvelin
9,67,92
, Vincent
Mooser
41
, Olle Melander
25
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11,12
, Paul Elliott
9,95
, Goncalo R Abecasis
93,95
,
Mark Caulfield
22,95
, and Patricia B Munroe
22,95
1
Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street,
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2
Cardiovascular Research Center, Massachusetts General Hospital,
Boston, Massachusetts 02114, USA
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Program in Medical and Population Genetics, Broad
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02142, USA
4
Department of Medical Genetics, University of Lausanne, 1005 Lausanne,
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Universitaire Vaudois (CHUV) and University of Lausanne, 1005 Lausanne, Switzerland
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Swiss
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Laboratory of Cardiovascular Science, Intramural
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MRC Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital,
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Dept of Twin Research & Genetic Epidemiology, King’s College London,
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Molecular Medicine, Dept. Medical Sciences, Uppsala University, SE-751 85 Uppsala, Sweden
74
Consorzio Mario Negri Sud, Via Nazionale, 66030 Santa Maria Imbaro (Chieti), Italy
75
Istituto
di Neurogenetica e Neurofarmacologia, CNR, Monserrato, 09042 Cagliari, Italy
76
Department of
Epidemiology, Univ. of Texas M. D. Anderson Cancer Center, Houston, TX 77030
77
Laboratory
of Genetics, Intramural Research Program, National Institute on Aging, National Institutes of
Health, Baltimore, Maryland, USA 21224
78
Unitá Operativa Geriatria, Istituto Nazionale Ricovero
e Cura per Anziani (INRCA) IRCCS, Rome, Italy
79
Department of Internal Medicine B, Ernst-
Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany
80
Institute for Community
Medicine, Ernst-Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany
81
Institute of
Physiology, Ernst-Moritz-Arndt-University Greifswald, 17487 Greifswald, Germany
82
U557 Institut
National de la Sante et de la Recherche Médicale, U1125 Institut National de la Recherche
Agronomique, Université Paris 13, 74 rue Marcel Cachin, 93017 Bobigny Cedex, France
83
MRC
Dunn Human Nutrition Unit, Wellcome Trust/MRC Building, Cambridge CB2 0XY, U.K
84
National
Heart and Lung Institute, Imperial College London SW7 2AZ
85
Program in Genetics and Genome
Biology, Hospital for Sick Children, Toronto, Canada, Dalla Lana School of Public Health,
University of Toronto, Toronto, Canada M5T 3M7
86
Geriatric Rehabilitation Unit, Azienda
Sanitaria Firenze (ASF), 50125, Florence, Italy
87
Department of Public Health, University of
Helsinki, 00014 Helsinki, Finland
88
South Ostrobothnia Central Hospital, 60220 Seinäjoki,
Finland
89
Department of Medicine and Department of Genetics, Harvard Medical School, Boston,
Massachusetts 02115, USA
90
Diabetes Unit, Massachusetts General Hospital, Boston,
Massachusetts 02114, USA
91
U872 Institut National de la Santét de la Recherche Médicale,
Faculté de Médecine Paris Descartes, 15 rue de l’Ecole de Medé0cine, 75270 Paris Cedex,
France
92
Institute of Health Sciences and Biocenter Oulu, Aapistie 1, FIN-90101, University of
Oulu, Finland
93
Center for Statistical Genetics, Department of Biostatistics, University of
Michigan, Ann Arbor, Michigan 48109 USA
Abstract
Elevated blood pressure is a common, heritable cause of cardiovascular disease worldwide. To
date, identification of common genetic variants influencing blood pressure has proven challenging.
We tested 2.5m genotyped and imputed SNPs for association with systolic and diastolic blood
pressure in 34,433 subjects of European ancestry from the Global BPgen consortium and followed
up findings with direct genotyping (N≤71,225 European ancestry, N=12,889 Indian Asian
ancestry) and
in silico
comparison (CHARGE consortium, N=29,136). We identified association
between systolic or diastolic blood pressure and common variants in 8 regions near the
CYP17A1
(
P
=7×10
−24
),
CYP1A2
(
P
=1×10
−23
),
FGF5
(
P
=1×10
−21
),
SH2B3
(
P
=3×10
−18
),
MTHFR
Newton-Cheh et al.
Page 4
Nat Genet
. Author manuscript; available in PMC 2010 September 21.
NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript

Frequently asked questions

Q1. What are the contributions mentioned in the paper "Eight blood pressure loci identified by genome-wide association study of 34,433 people of european ancestry" ?

The authors tested 2. 5m genotyped and imputed SNPs for association with systolic and diastolic blood pressure in 34,433 subjects of European ancestry from the Global BPgen consortium and followed up findings with direct genotyping ( N≤71,225 European ancestry, N=12,889 Indian Asian ancestry ) and in silico comparison ( CHARGE consortium, N=29,136 ). The authors therefore formed the Global Blood Pressure Genetics ( Global BPgen ) consortium and conducted meta-analysis of GWAS in 34,433 individuals of European ancestry with SBP and DBP measurements ( stage 1 ), followed by large-scale direct genotyping ( stage 2a ) and in silico ( stage 2b ) analyses ( Supplementary Figure 1 ). In their primary analysis ( stage 1 ), the authors examined individuals aged ≤70 years from 13 population-based studies and from control groups from 4 case-control studies ( Table 1 ). First, the authors selected 12 SNPs for follow-up genotyping in up to 71,225 individuals drawn from 13 cohorts of European ancestry and up to 12,889 individuals of Indian Asian ancestry from one cohort ( stage 2a, Table 1, Supplementary Figure 1, Supplementary Table 2 ). Second, the authors performed a reciprocal exchange of association results for 10 independent signals each for SBP and DBP ( stage 2b, Supplementary Figure 1, Supplementary Table 3 ) with colleagues from the Cohorts for Heart and Aging Research in Genome Epidemiology ( CHARGE ) blood pressure consortium who had recently meta-analyzed GWAS data for SBP and DBP in 29,136 individuals, independent of Global BPgen ( Table 1 ). A recent study found that the minor allele of Newton-Cheh et al. To explore this further, the authors looked up SNPs reported to be associated with T1D, celiac disease or myocardial infarction in the Global BPgen GWAS results and failed to find convincing association other than that for the SH2B3 missense SNP ( data not shown ). There was also no evidence of heterogeneity of effect across the samples examined for the eight SNPs ( Q-statistic P > 0. 05 ). While the authors describe here promising candidates at each locus identified, the causal gene could be any of the genes around the association signal in each locus ( Figure 1 ). Fine mapping and resequencing will be required to refine each association signal and to identify likely causal genetic variants which could be studied further in humans and in animal models. Some have advocated the study of pulse pressure ( SBP-DBP ), which increases with advancing age, and is correlated positively with SBP and negatively with DBP and also shows evidence of heritability. In their GWAS and follow up, the authors chose a priori to consider SBP and DBP as separate traits. A study designed to examine pulse pressure would be expected to show weaker ( if any ) association signals for the variants identified which all showed concordant effects on SBP and DBP. The authors did not perform a global GWAS of hypertension, which is expected to be underpowered to detect common variants of modest incremental effects on continuous blood pressure. For the eight SNPs that were genome-wide significant in continuous trait analysis, the authors examined the association with hypertension ( SBP ≥ 140 mm Hg or DBP ≥ 90 mm Hg or antihypertensive medication use ) compared to normotension ( SBP ≤ 120 mm Hg and DBP ≤ 85 mm Hg and no antihypertensive medication use ) in planned secondary analyses ( N range = 57,410 – 99,802 ). However, when the authors examined the hypertension association of each of the 8 SNPs genome-wide significantly associated with continuous SBP or DBP in just the stage 1 Global BPgen samples, 4 had 0. Thus, the study of continuous blood pressure allowed us to identify effects on risk of hypertension that would not have been readily discovered in a GWAS of hypertension drawn from these samples. Studies of familial aggregation suggest that there is also a substantial heritable component to blood pressure5. Detailed study of these genes has identified rare variants ( minor allele frequency [ MAF ] < 0. 1 % ) that impact blood pressure in the general population7 and evolving evidence suggests a potential role for common variation in some of the same genes8–10. However, meta-analysis of multiple studies with large total sample sizes has the potential to facilitate detection of variants with modest effects. The plots of test statistics against expectations under the null suggest an excess of extreme values ( cohort-specific and meta-analysis quantile-quantile plots are presented in Supplementary Figure 2 ). On meta-analysis of results from 34,433 individuals in stage 1, the authors observed 11 independent signals with P < 10−5 for SBP and 15 for DBP, with two results attaining P < 5×10−8, corresponding to genome-wide significance when adjusting for ~1m independent common variant tests estimated for samples of European ancestry ( Supplementary Figure 3 ) 15. A correlated SNP, rs762551 ( MAF = 0. 31, r2 = 0. 63, HapMap CEU ) in an intron of CYP1A2 has been found to influence caffeine metabolism and recently association has been suggested between myocardial infarction risk and the allele associated with slow caffeine metabolism30. The ARID3B gene is embryonic lethal when knocked out in mouse, with branchial arch and vascular developmental abnormalities31, but is potentially interesting because of the presence of ARID5B at the 10q21 locus described below. The authors observed no significant interaction between the eight genome-wide significant SNPs and gender ( P > 0. 01, Supplementary Table 5 ). The marked allele frequency differences between the European samples ( C allele frequency ~0. 35 ), the Indian Asian samples ( 0. 77 ) and HapMap YRI ( 1. 00 ) suggest distinct patterns of genetic variation at this locus across populations. A signal of positive selection has been suggested at the locus41 raising the potential functional importance of genetic variation in the region. 

Q2. What are the genes responsible for drug and xenobiotic chemical metabolism in the liver?

Cytochrome P450 enzymes are responsible for drug and xenobiotic chemical metabolism in the liver and cellular metabolism of arachidonic acid derivatives29, some of which influence renal function, peripheral vascular tone and blood pressure. 

Q3. What could be the reason for the higher association of each SNP with one trait or the other?

The observation that each SNP shows stronger association with one trait or the other (typically by 1–2 orders of magnitude) could reflect sampling variation, small effect sizes or true differences in the underlying biologic basis of one trait or the other. 

Q4. What is the role of SBP in cardiovascular disease?

Increases in systolic and diastolic blood pressure (SBP, DBP), even within the normal range, have a continuous and graded impact on cardiovascular disease risk and are major contributors in half of all cardiovascular deaths 2,3. 

Q5. What is the role of sex in the biosynthesis of glucocorticoids?

The first enzymatic action is a key step in the biosynthesis of mineralocorticoids and glucocorticoids that affect sodium handling in the kidney and the second is involved in sex-steroid biosynthesis. 

Q6. What is the correlated SNP in the CYP1A2 gene?

A correlated SNP, rs762551 (MAF = 0.31, r2 = 0.63, HapMap CEU) in an intron of CYP1A2 has been found to influence caffeine metabolism and recently association has been suggested between myocardial infarction risk and the allele associated with slow caffeine metabolism30. 

Q7. Why was it retained as the tenth locus for DBP?

Because a SNP at the 3q26 locus (MDS1) was selected in an interim analysis for direct genotyping, it was retained as the tenth locus for DBP even though its significance was reduced in the final stage 1 DBP GWAS analysis. 

Q8. How many samples were collected from the CHARGE consortium?

The authors obtained results based on the analysis of the Cohorts for Heart and Aging Research in Genome Epidemiology (CHARGE) consortium, which comprises 29,136 samples from five population-based cohorts. 

Q9. How many SNPs were selected for in silico analysis?

In stage 2b, the authors selected 20 SNPs (10 SBP, 10 DBP) for in silico analysis in 29,136 individuals of European descent from the CHARGE consortium (stage 2b, see Supplementary Figure 1). 

Q10. How many individuals of European descent were selected for genotyping?

In stage 2a, the authors selected 12 SNPs for genotyping in up to 71,225 individuals of European descent from 13 studies and up to 12,889 individuals of Indian Asian ancestry from one study. 

Q11. What was the p-value of the SNP in the alternate blood pressure trait?

If a SNP in one top 10 list was also among the top 10 for the alternate blood pressure trait, the authors kept the locus with the lower p-value and went to the next locus on the list for the alternate blood pressure trait. 

Q12. How many individuals were excluded from the analysis?

The authors excluded individuals >70 years of age and individuals ascertained on case status for type 1 or 2 diabetes (DGI, FUSION), coronary artery disease (MIgen, PROCARDIS) or hypertension (BRIGHT), leaving 34,433 individuals for analysis (Table 1).