BMC Medical Genetics

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Framingham Heart Study 100K Project: genome-wide associations
for blood pressure and arterial stiffness
Daniel Levy*1,2,3, Martin G Larson1,4, Emelia J Benjamin1,3,5,
Christopher Newton-Cheh1,6, Thomas J Wang1,6, Shih-Jen Hwang1,2,
Ramachandran S Vasan1,3,5 and Gary F Mitchell1,7
Address: 1The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA, 2The National Heart, Lung, and Blood
Institute, Bethesda, MD, USA, 3Boston University School of Medicine, Boston MA, USA, 4Department of Mathematics, Boston University, Boston,
MA, USA, 5Division of Cardiology, Boston Medical Center, Boston, MA, USA, 6Division of Cardiology, Massachusetts General Hospital andHarvard Medical School, Boston, MA, USA and 7Cardiovascular Engineering Inc., Waltham, MA, USA
Email: Daniel Levy* - Levyd@nih.gov; Martin G Larson - mlarson@bu.edu; Emelia J Benjamin - emelia@bu.edu; Christopher NewtonCheh - cnewtoncheh@partners.org; Thomas J Wang - tjwang@partners.org; Shih-Jen Hwang - hwangs2@nhlbi.nih.gov;
Ramachandran S Vasan - vasan@bu.edu; Gary F Mitchell - garyfmitchell@mindspring.com* Corresponding author

Published: 19 September 2007
<supplement>
S Vasan</editor><title><note>Research</note><p>The Framingham <url>http://www.biomedcentral.com/content/pdf/1471-2350-8-S1-info.pdf</url>
Heart Study 100,000 single nucleotide polymorphisms resource</p> </title> <editor>Emelia</supplement>
J Benjamin, Caroline S Fox, Daniel J Gottlieb, Cashell E Jaquish, Daniel Levy, James B Meigs, Christopher Newton-Cheh, Christopher J O'Donnell and Ramachandran

BMC Medical Genetics 2007, 8(Suppl 1):S3

doi:10.1186/1471-2350-8-S1-S3This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S3© 2007 Levy et al; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract
Background: About one quarter of adults are hypertensive and high blood pressure carries increased risk for heartdisease, stroke, kidney disease and death. Increased arterial stiffness is a key factor in the pathogenesis of systolichypertension and cardiovascular disease. Substantial heritability of blood-pressure (BP) and arterial-stiffness suggests
important genetic contributions.
Methods: In Framingham Heart Study families, we analyzed genome-wide SNP (Affymetrix 100K GeneChip) associations
with systolic (SBP) and diastolic (DBP) BP at a single examination in 1971–1975 (n = 1260), at a recent examination in
1998–2001 (n = 1233), and long-term averaged SBP and DBP from 1971–2001 (n = 1327, mean age 52 years, 54%
women) and with arterial stiffness measured by arterial tonometry (carotid-femoral and carotid-brachial pulse wave
velocity, forward and reflected pressure wave amplitude, and mean arterial pressure; 1998–2001, n = 644). In primary
analyses we used generalized estimating equations in models for an additive genetic effect to test associations between
SNPs and phenotypes of interest using multivariable-adjusted residuals. A total of 70,987 autosomal SNPs with minor
allele frequency ≥ 0.10, genotype call rate ≥ 0.80, and Hardy-Weinberg equilibrium p ≥ 0.001 were analyzed. We also
tested for association of 69 SNPs in six renin-angiotensin-aldosterone pathway genes with BP and arterial stiffness
phenotypes as part of a candidate gene search.
Results: In the primary analyses, none of the associations attained genome-wide significance. For the six BP phenotypes,
seven SNPs yielded p values < 10-5. The lowest p-values for SBP and DBP respectively were rs10493340 (p = 1.7 ×
10-6) and rs1963982 (p = 3.3 × 10-6). For the five tonometry phenotypes, five SNPs had p values < 10-5; lowest p-values
were for reflected wave (rs6063312, p = 2.1 × 10-6) and carotid-brachial pulse wave velocity (rs770189, p = 2.5 × 10-6)
in MEF2C, a regulator of cardiac morphogenesis. We found only weak association of SNPs in the renin-angiotensinaldosterone pathway with BP or arterial stiffness.
Conclusion: These results of genome-wide association testing for blood pressure and arterial stiffness phenotypes inan unselected community-based sample of adults may aid in the identification of the genetic basis of hypertension andPage 1 of 11(page number not for citation purposes)

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arterial disease, help identify high risk individuals, and guide novel therapies for hypertension. Additional studies areneeded to replicate any associations identified in these analyses.

Background

Methods

Hypertension affects about one quarter of adults in industrialized countries [1] and carries a substantial burden ofrisk for cardiovascular disease (CVD), kidney disease, anddeath [2]. Increased arterial stiffness is a key factor in thepathogenesis of hypertension in older people and it contributes to the development of hypertensive target organ
damage, CVD, and death [3-5]. Substantial heritability ofblood pressure [6] and arterial stiffness [7]), as measured
by arterial tonometry, points to genetic contributions tothese cardiovascular phenotypes.

Study sample
The Framingham Heart Study began in 1948 when 5209
men and women from Framingham, Mass, who were
between 28 and 62 years of age were recruited to participate in an observational study [12]. Subjects underwent a
medical history, physician-administered physical examination including blood pressure measurement, laboratory
tests, and electrocardiography. Examinations have been
repeated every 2 years. In 1971, 5124 offspring andspouses of offspring of original participants were recruited
into the Framingham Offspring Cohort [13]. The offspring cohort was reexamined approximately every 4
years, except for an 8 year interval between their initialand second visit. All subjects gave written informed consent before each clinic visit, and the examination protocol
was approved by the Institutional Review Board at Boston
Medical Center (Boston, Mass).

The search for genetic variants contributing to hypertension and arterial stiffness has focused on complementary
approaches: linkage applied to rare Mendelian bloodpressure disorders and to large family-based studies to
identify positional candidate genes, and the study of biologically plausible candidate genes selected by virtue of
their role in blood pressure regulation or vascular properties. A great deal is known about mutations responsible
for Mendelian blood pressure disorders [8], but neither
these rare variants nor more common variants in thesegenes account for substantial blood pressure variation in
the general population. Similarly, although numerous
linkage [9] and candidate gene association studies [10]have been conducted, there is a paucity of evidence that
common genetic variation contributes to alterations inblood pressure or arterial stiffness in the general population.
Genome-wide association offers the opportunity to conduct analysis of common genetic variants unconstrainedby prior knowledge of biological pathways in relation tophenotypes of interest. This approach succeeded in identifying the association of complement factor H with agerelated macular degeneration [11]. The Framingham
Heart Study, which enrolled participants without regard
to phenotype status, provides a setting for a genome-wide
association study in a community-based sample in which
selection bias is inherently low. In addition, because of
the familial structure of the study, it also provides anopportunity to use genome-wide SNP data for family
based association testing (FBAT) and linkage analyses.
In this report we provide results of a genome-wide association study of blood pressure and arterial stiffness, including results of generalized estimating equation (GEE)association testing, FBAT, and linkage, as well as a summary of associations of these phenotypes with candidate
genes in the renin-angiotensin-aldosterone pathways.

Blood pressure phenotypes
At each clinic visit, the examining physician measured thesystolic and diastolic BP in the left arm using a mercury
column sphygmomanometer. BP was measured twice ateach visit, with the exception of the first Offspring Cohort
clinic visit, when it was measured once in about half theparticipants. Systolic and diastolic pressures were determined by the first and fifth Korotkoff sounds, respectively,
and the two BP measurements were averaged to derive thesystolic and diastolic pressures for that examination.

Examination cycles for the two cohorts were overlaid temporally as follows [offspring cohort/original cohort (earliest - latest year)]: examination 1/examination 12 (1971–1975), examination 2/examination 16 (1979–1983),
examination 3/examination 18 (1983–1987), examination 4/examination 20 (1986–1991), examination 5/
examination 22 (1990–1995), examination 6/examination 24 (1995–1998) and examination 7/examination 26(1998–2001). Referring to offspring cycle numbers, thesix BP phenotypes analyzed for this investigation wereresiduals for SBP and DBP at Examination 1, at Examination 7, and average of residuals from available Examinations 1 to 7. BP was imputed for treated observations aspreviously described [6]. No adjustment was made for
untreated observations, which constituted the vast majority of BP values. Systolic and diastolic BP phenotypes wereanalyzed independently. Residuals were obtained fromcohort- and examination-specific regression models
accounting for sex, age and BMI; for DBP, age-squared wasadded. For inclusion in long-term BP analyses, each parPage 2 of 11(page number not for citation purposes)

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ticipant had to have BP measured on at least three examinations over a period of 12 years or more.
Arterial stiffness phenotypes
Arterial tonometry for assessment of arterial stiffness was
conducted on Offspring Cohort participants attending
their 7th clinic examination. Five primary tonometry phenotypes were analyzed: carotid-femoral and carotid-brachial pulse wave velocity, forward and reflected pressure
wave amplitude, and mean arterial pressure. Tonometry
was performed in the supine position after 5 minutes ofrest. Arterial tonometry with simultaneous ECG recording
was obtained from brachial, radial, femoral and carotid
arteries using a commercially available tonometer (SPT301, Millar Instruments, Houston, TX). Carotid-brachial,
carotid-radial and carotid-femoral PWV were calculated aspreviously described [14]. Mean arterial pressure was calculated from the planimetered brachial arterial tracing
after calibration to the brachial blood pressure, which was
obtained by an oscillometric device. Forward pressure
wave amplitude was defined as the difference between
pressure at the waveform foot and pressure at the firstsystolic inflection point or peak of the carotid pressure
waveform; reflected pressure wave amplitude was definedas the difference between the central systolic pressure andthe pressure at the forward wave peak. Sex-specific regressions were conducted for each tonometry phenotype withthe following covariates: age, age2, height, weight, to generate sex-specific residuals.
Genotyping methods
Details of the genotyping methods are available in the
Executive Summary [15]. Briefly, 112990 autosomal SNPs
on the Affymetrix 100K chip were genotyped in the Boston University School of Medicine Genetics Laboratory on
the Framingham Heart Study family plate set. SNPs were
excluded for the following reasons: minor allele frequency<10% (n = 38062); call rate <80% (n = 2346); Hardy
Weinberg equilibrium p value < 0.001 (n = 1595), leaving
70,987 SNPs available for analysis.
Statistical methods
Standardized multivariable-adjusted blood pressure andtonometry residuals were generated as described above.
Table 1 lists the covariates used for each phenotype. As
described in the Executive Summary [15], we conductedassociation testing using family based association testing(FBAT), and generalized estimating equations (GEE)applied to the additive genetic effects model. In secondary
analyses that used the GEE general genetic effects model,
which is more sensitive to recessive genetic effects, to be
more conservative, we limited analyses to two phenotypes: long-term SBP and long-term DBP, and we limited
eligible SNPs to those with a minor allele frequency >=0.20 and Hardy-Weinberg equilibrium p value >= 0.05.

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The software package Merlin [16] was used to compute
exact identity by descent linkage probabilities for allele
sharing, and linkage analysis by variance component
method was carried out SOLAR using 11,200 SNPs and
STRs. Heritability was estimated using variance-components methods (SOLAR). For BP, 2155 study participants
were used for examination 1 SBP and DBP, 1479 forexamination 7, and 2009 for long-term average; 770 individuals were used in heritability analysis of arterial stiffness phenotypes.
Candidate gene analyses
GEE and FBAT additive genetic effect models were run for
SNPs in or near 6 genes in the renin-angiotensin-aldosterone pathways. These genes were selected a priori becauseof a substantial body of literature implicating them inhypertension and altered vascular properties. All SNPs
from 200 Kb proximal to the start and extending to 200 kb
of the terminus of each gene were included in analysis
providing the minor allele frequency was >= 0.1, the genotype call rate was 0.8, and the Hardy-Weinberg equilibrium p value was >= 0.001.

Results
The six primary BP phenotypes were examination 1 SBP
and DBP (n = 1260), examination 7 SBP and DBP (n =1233), and long-term averaged SBP and DBP (n = 1327).
The five primary arterial stiffness phenotypes werecarotid-femoral and carotid-brachial pulse wave velocity,
forward and reflected pressure wave amplitude, and meanarterial pressure (n = 644). The study sample available forBP phenotypes included up to 1327 individuals (meanage 52 years, 54% women for the long-term SBP and DBP
phenotypes). The complete list of blood pressure and arterial stiffness phenotypes analyzed and the covariates usedin generating sex-specific standardized residuals for eachphenotype are listed in Table 1. Full disclosure of all GEE
and FBAT associations for the traits listed in Table 1 can befound at the National Center for Biotechnology Information dbGaP website: http://web.ncbi.nlm.nih.gov/
projects/gap/framingham/cgi-bin/study.cgi?id=phs000007.
Results of primary GEE models for an additive genetic
effect for DBP, SBP, and arterial stiffness phenotypes arepresented in Table 2a. None of the association results
attained genome-wide significance. The lowest p values
for DBP, SBP, and arterial stiffness phenotypes, respectively, were rs1963982 (p = 3.31 × 10-6), rs10493340 (p =1.7 × 10-6), and rs6063312 (p = 2.1 × 10-6 for reflectedwave amplitude). For the same three phenotype groups
the number of associations with p values < 10-5 were 6, 1,
and 5, respectively.

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Table 1: Phenotype List
Exam cycle/s
N*

Offspring

CohortAdjustment

Covariates

Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable

Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI

Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable

Sex, age, age^2, height, weight
Sex, age, age^2, height, weight
Sex, age, age^2, height, weight
Sex, age, age^2, height, weight
Sex, age, age^2, height, weight

Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable

Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI
Cohort, sex, age, BMI

Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable
Age and sex, multivariable

Sex, age, age^2, height, weight
Sex, age, age^2, height, weight
Sex, age, age^2, height, weight
Sex, age, age^2, height, weight
Sex, age, age^2, height, weight
Sex, age, age^2, height, weight
Sex, age, age^2, height, weight
Sex, age, age^2, height, weight
Sex, age, age^2, height, weight
Sex, age, age^2, height, weightPrimary Phenotypes
Blood Pressure
SBP 1
SBP 7
SBP 1–7
DBP 1
DBP 7
DBP 1–7

2
2
2
2
2
2

1
7
Mean of exams 1–7
17
Mean of exams 1–7

1226
Mean of exams 12, 16, 18, 20, 22, 24, 26
12
26
Mean of exams 12, 16, 18, 20, 22, 24, 26
Tonometry

Carotid-femoral PWVCarotid-brachial PWVForward pressure wave
Reflected pressure wave
Mean arterial pressure

2
2
2
2
2

7
7
7
7
7

Not included
Not included
Not included
Not included
Not included
Secondary Phenotypes^
Blood Pressure

DBP 2
DBP 3
DBP 4
DBP 5
DBP 6
SBP 2
SBP 3
SBP4
SBP 5
SBP 6
PP 1
PP 2
PP 3
PP 4
PP 5
PP 6
PP 7
PP 1–7

2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2
2

2
3
4
5
6
2
3
4
5
6
1
2
3
4
5
6
7
Mean of exams 1–7

16
18
20
22
24
16
18
20
22
24
12
16
18
20
22
24
26
Mean of exams 12, 16, 18, 20, 22, 24, 26
Tonometry

1/CF-PWVAI
CPP
CR-PWV
DBP-osc
PA-1
PA-2
PP-oscRWTT
SBP-osc

2
2
2
2
2
2
2
2
2
2

7
7
7
7
7
7
7
7
7
7

Not included
Not included
Not included
Not included
Not included
Not included
Not included
Not included
Not included
Not included*n = number of phenotypes analyzed^Association results for primary and secondary phenotypes are available on the worldwide web at: http://web.ncbi.nlm.nih.gov/projects/gap/
framingham/cgi-bin/study.cgi?id=phs000007
AI = augmentation index; CPP = central pulse pressure; CB-PWV = carotid brachial pulse wave velocity; CF-PWV = carotid-femoral pulse wave
velocity; CR-PWV = carotid-radial pulse wave velocity; DBP = diastolic blood pressure; DBP-osc = brachial DBP by oscillometric device; FW =forward wave amplitude; MAP = mean arterial pressure; PA-1 = apparent peripheral amplification; PA-2 = true peripheral amplification; PP = pulsepressure; PP-osc = brachial PP by oscillometric device; RW = reflected wave amplitude; RWTT = reflected wave transit time; SBP = systolic bloodpressure; SBP-osc = brachial SBP by oscillometric device; 1/CF-PWV = inverse of CF-PWV.

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FBAT models for an additive genetic effect are presented inTable 2b. Two SNPs for DBP and one for SBP yielded pvalues < 10-5. Of note, rs10520569 in ADAMTSL3 wasassociated with DBP (4.2 × 10-5) and SBP (1.4 × 10-4). For
arterial stiffness phenotypes there were 2 p values < 10-5,
including rs792833 in COL8A1.
Linkage analyses (Table 2c) yielded a LOD score of 3 forlong-term SBP on chromosome 15 at 100 Mb. Several
tonometry linkage peaks exceeded a LOD score of 3,
including a LOD of 5.0 for reflected wave (chromosome 8
at 19 Mb). Heritability estimates (Table 2d) were high forlong-term average DBP (h2 = 0.55) and SBP (h2 = 0.57),
and intermediate for the other BP phenotypes (h2 = 0.28–0.45). Among the arterial stiffness phenotypes, heritability was high for the reflected arterial waveform (h2 = 0.66),
low for carotid-brachial PWV (h2 = 0.02), and intermediate for the other phenotypes (h2 = 0.22–0.43). These heritability results are consistent with our prior findings[6,7].
Secondary analyses using the GEE general genetic effects
model (2 degrees of freedom; more sensitive in detecting
recessive effects) are presented in Table 3. The lowest pvalue for long-term DBP was in CCL20 (rs7591163, p =2.3 × 10-7) and for SBP was in CDH13 (rs3096277, p = 9.9
× 10-8). Of note, SNPs in CDH13, CCL20, and WDR69
were associated with DBP and SBP. GEE general effects
models for the tonometry phenotypes identified association of mean arterial pressure with TGFBR2 (rs3773643, p= 2 × 10-7).
Geometric means of GEE association results (additive
genetic effect model) for SBP and DBP considered jointly
are summarized in Table 4. The lowest p values werenoted for Examination 1 BP values (rs10493340, p = 1.5
× 10-5). Geometric means of association results for the 5
tonometry phenotypes considered concurrently yielded
its lowest p value (rs10518082, p = 0.002) for DCK.
SNPs in 6 renin-angiotensin-aldosterone pathway genes
were analyzed for association with the BP and tonometry
phenotypes (Table 5). A total of 69 SNPs qualified foranalysis (minor allele frequency >= 0.1, Hardy Weinberg
equilibrium p >= 0.001, call rate >= 0.8). For the primary
traits there were few associations from GEE models for an
additive genetic effect with p values < 0.05 and none withp < 0.001.

Discussion and conclusion
We provide results of genome-wide association study for
6 blood pressure and 5 arterial stiffness phenotypes in a
carefully characterized study sample. Association analyses
and linkage reveal a number of intriguing results. For the
GEE model of additive genetic effects (Table 2a) there

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were 7 SNPs with p values < 10-5 for blood pressure and 5
for arterial stiffness phenotypes. Among the GEE additiveeffect model results the most likely candidate genes were
MEF2C, SYNE1, and TNFSF11, which were associatedwith arterial stiffness. We have not yet attempted replication of our results. Follow-up genotyping of the top SBP
and DBP SNPs reported in our study sample in additional
Framingham participants is planned; additional replication attempts will be needed in independent samples toconfirm any of the association results we report.
FBAT (Table 2b) identified association of COL8A1 witharterial stiffness (p value 6 × 10-6 for rs792833). This genecodes for type VIII collagen, which is produced by aortic
endothelial cells [17], suggesting a biologically plausible
association.
Linkage yielded a LOD score of 3, approaching genomewide significance, for long-term SBP on chromosome 15.
A meta-analysis of blood pressure and hypertension linkage studies did not identify this as a region of interest [9].
The lower LOD scores for long-term SBP on chromosome
17 (~67 cM) in this investigation compared with our priorfindings [6] appears to be largely due to differences inphenotype definition of long-term SBP with the exclusionof early examination BP values in the original cohort participants and the inclusion of offspring cohort examination 7 blood pressures in this analysis. When linkage
analyses were repeated with the inclusion of the early original cohort exams using the prior phenotype definitions,the same linkage peak on chromosome 17 emerged (LOD
> 4).
For tonometry phenotypes, we found LOD scores forreflected wave amplitude of 5.0 (chromosome 8 at 19Mb) and 3.2 (chromosome 4, 169 Mb) near peaks for thisphenotype that we previously reported in a largely overlapping study sample [7]. Similarly, we once again identified a linkage peak for carotid-femoral pulse wave velocity(LOD 3.0; chromosome 2 at 74 mb).
Compared with the primary GEE model for additive
genetic effects (Table 2a), a different set of SNPs was identified in secondary GEE general effects models (Table 3)
for long-term DBP and SBP, including 2 SNPs with p values < 10-6. Differences in model results may be due to thegreater sensitivity of the general model to detect recessive
genotype effects. SNPs in CCL20, CDH13, and LPP wereassociated with both long-term SBP and DBP. GEE generalgenetic effects models for arterial stiffness phenotypes
yielded the lowest p value (p = 1.99 × 10-7) for rs3773643
in TGFBR2, which has been implicated in aortic aneurysm
and Loeys-Dietz syndrome [18,19]. Disruption of the aortic wall would be expected to affect arterial stiffness.

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Table 2: Results of GEE and FBAT Additive Genetic Effects: Association, Linkage, and Heritability of Blood Pressure and Arterial
Stiffness Phenotypes
2a. Results of GEE Additive Genetic Effects Models
Phenotype

Exam

SNP

Chr.

Position

GEE P value

FBAT P value

7
1
7
7
1
1
7
7
1
7

rs1963982
rs935334
rs4370013
rs10491334
rs2121070
rs2509458
rs6950982
rs10510911
rs1816088
rs1519592

8
14
3
5
14
6
7
3
5
6

73,269,470
75,683,431
2,629,691
110,800,303
75,720,517
88,709,299
100,360,038
63,678,681
39,897,583
140,585,329

3.31 × 10-6
3.32 × 10-6
3.73 × 10-6
4.47 × 10-6
4.88 × 10-6
6.94 × 10-6
1.22 × 10-5
1.65 × 10-5
1.73 × 10-5
1.89 × 10-5

0.002
0.002
0.032
0.133
0.02
0.001
0.036
0.021
0.012
2.83 × 10-4

1
7
1
1–7
7
7
7
7
1
7

rs10493340
rs1841055
rs2035254
rs1408263
rs1408113
rs629448
rs10485320
rs10512889
rs1328925
rs9321764

1
4
3
6
9
9
6
5
4
6

63,303,150
70,039,785
107,292,420
18,515,722
113,822,387
26,263,322
47,884,860
6,921,922
159,547,895
140,532,157

1.69 × 10-6
2.07 × 10-5
2.20 × 10-5
2.43 × 10-5
2.54 × 10-5
3.14 × 10-5
3.28 × 10-5
4.17 × 10-5
4.32 × 10-5
4.39 × 10-5

0.13
0.003
0.046
0.121
0.034
0.011
0.012
0.008
0.118
4.76 × 10-4

rs6063312
rs770189
rs10514688
rs7042864
rs1322512
rs348384
rs10507514
rs3793427
rs10506928
rs4075701
rs11784583
rs10513957
rs10506440
rs1197850

20
5
3
9
6
19
13
8
12
2
8
18
12
13

46,776,466
88,124,195
34,937,673
107,951,862
153,040,067
6,503,386
42,132,814
17,188,201
85,003,844
116,146,020
103,154,213
65,039,417
60,993,853
34,828,744

2.09 × 10-6
2.53 × 10-6
5.66 × 10-6
6.13 × 10-6
7.76 × 10-6
1.16 × 10-5
1.28 × 10-5
1.43 × 10-5
1.62 × 10-5
1.63 × 10-5
3.83 × 10-5
4.15 × 10-5
4.18 × 10-5
4.57 × 10-5

0.063
0.005
0.027
0.077
0.038
0.058
0.066
0.059
0.021
0.025
0.036
0.019
0.064
0.042

Chr.

Position

GEE P value

FBAT P Value

Gene

Diastolic Blood Pressure
DBP
DBP
DBP
DBP
DBP
DBP
DBP
DBP
DBP
DBP

CNTN4
CAMK4
C14orf118
TRIM56, SERPINE1, AP1S1

Systolic Blood Pressure
SBP
SBP
SBP
SBP
SBP
SBP
SBP
SBP
SBP
SBP

UGT2A3
IBRDC2ZNF618
OPN5
TMEM144

Tonometry Phenotypes
RW
CB-PWV
CB-PWV
CB-PWV
MAP
FW
RW
FW
RW
FW
RW
RW
CF-PWV
RW

7
7
7
7
7
7
7
7
7
7
7
7
7
7

PREX1
MEF2C

SYNE1
TUBB4, TNFSF9, TNFSF7
TNFSF11
VPS37A

USP15

2b. Results of FBAT Additive Genetic Effects Models
Phenotype

Exam

SNP

DBP
DBP
DBP
DBP
DBP
DBP
DBP
DBP
DBP
DBP

1
1–7
1
7
1–7
7
1
7
7
1–7

rs1590919
rs636864
rs726698
rs1338657
rs10506595
rs9311171
rs10520569
rs4514016
rs2322509
rs10504389

13
6
2
6
12
3
15
8
8
8

104,000,000
150,000,000
35,366,992
103,000,000
69,191,621
37,971,481
82,520,393
120,000,000
27,052,291
66,718,741

0.079
4.49 × 10-4
0.02
0.001
0.133
0.025
0.577
3.70 × 10-5
0.172
0.1

1.42 × 10-6
1.55 × 10-6
1.15 × 10-5
2.57 × 10-5
3.40 × 10-5
4.03 × 10-5
4.24 × 10-5
4.52 × 10-5
4.91 × 10-5
5.53 × 10-5

SBP
SBP
SBP

1
1
7

rs1588260
rs726698
rs963328

5
2
1

121,000,000
35,366,992
209,000,000

0.001
0.023
0.036

3.43 × 10-6
2.70 × 10-5
3.01 × 10-5

Gene

PTPRB
CTDSPLADAMTSL3
SAMD12
ARMC1, MTFR1

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Table 2: Results of GEE and FBAT Additive Genetic Effects: Association, Linkage, and Heritability of Blood Pressure and Arterial
Stiffness Phenotypes (Continued)
SBP
SBP
SBP
SBP
SBP
SBP
SBP
FW
RW
MAP
CB-PWV
CB-PWVFW
CF-PWV
CB-PWV
CB-PWV
RW

7
7
1–7
1
1–7
1
7

rs729053
rs1434939
rs10498500
rs3853241
rs1590919
rs6763833
rs6940110

18
8
14
5
13
3
6

50,960,679
69,666,816
62,030,261
166,000,000
104,000,000
65,953,132
10,377,050

0.008
0.004
0.005
0.003
0.14
0.374
0.145

3.41 × 10-5
4.97 × 10-5
6.25 × 10-5
6.25 × 10-5
6.66 × 10-5
8.18 × 10-5
8.42 × 10-5

7
7
7
7
7
7
7
7
7
7

rs1539377
rs792833
rs10495191
rs10494786
rs2160595
rs28899
rs1349721
rs3001450
rs1389608
rs10499221

9
3
1
1
18
5
4
9
14
6

81,441,976
101,000,000
219,000,000
196,000,000
61,742,129
82,798,839
86,693,958
93,164,925
46,027,527
141,000,000

5.48 × 10-5
0.123
0.007
0.079
0.001
0.001
0.105
0.61
0.111
0.003

5.26 × 10-6
6.01 × 10-6
1.46 × 10-5
1.56 × 10-5
2.38 × 10-5
2.99 × 10-5
3.34 × 10-5
3.91 × 10-5
4.08 × 10-5
5.92 × 10-5

MAGI1

TLE1
COL8A1
TAF1A
CDH7
VCAN
ARHGAP24
WNK22c. Linkage Results
Phenotype

Exam

LOD

Chr.

Position

Lower bound*

Upper bound

DBP

1–7

2.03

17

12,245,760

9,173,838

16,450,642

SBP
SBP
SBP
SBP
SBP
SBP

1–7
7
7
1–7
1–7
7

3
2.55
2.39
2.18
2.07
2.06

15
15
3
5
3
12

100,152,332
79,161,506
129,657,137
41,710,612
107,844,505
101,785,625

97,636,843
75,509,164
105,768,506
36,665,015
99,203,989
94,922,502

100,152,332
85,958,968
141,888,352
67,696,396
144,119,612
107,253,596

7
7
7
7
7
7
7
7
7
7

5.02
3.35
3.17
3.04
2.68
2.47
2.47
2.43
2.29
2.17

8
9
4
2
18
3
15
15
1
4

19,102,897
10,499,434
169,091,021
74,021,676
40,229,747
60,298,724
100,152,332
99,551,603
12,153,078
11,998,283

17,257,073
6,759,229
162,723,480
49,795,460
38,788,852
24,621,158
94,749,239
92,469,518
4,266,833
7,901,357

21,506,898
10,671,522
170,955,956
103,043,940
43,206,229
62,757,508
100,152,332
100,152,332
17,528,974
25,777,055

RW
RW
RW
CF-PWV
CF-PWVFW
RW
CF-PWV
RW
CF-PWV2d. Heritability of Blood Pressure and Arterial Stiffness Phenotypes
Phenotype

Exam

Heritability

s.e.

DBP
DBP
DBP

1
7
1–7

0.3
0.35
0.55

0.04
0.06
0.05

SBP
SBP
SBP

1
7
1–7

0.28
0.45
0.57

0.04
0.06
0.04

7
7
7
7
7

0.02
0.43
0.22
0.32
0.66

0.09
0.1
0.09
0.1
0.1

CB-PWV
CF-PWVFW
MAP
RW

Association results based on minor allele frequency >= 0.1, HWE p value >= 0.001, call rate >= 0.8
CB-PWV = carotid-brachial pulse wave velocity; CF-PWV = carotid-femoral pulse wave velocity; DBP = diastolic blood pressure; FW = forward wave amplitude; MAP = meanarterial pressure; RW = reflected wave amplitude; SBP = systolic blood pressure*Lower and upper bounds for LOD-1.5 interval.

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Table 3: Results of GEE General Genetic Effects Model for Long-term Average Blood Pressure Phenotypes and Arterial Stiffness

Phenotype

SNP

Chr.

Position

P value* Gene

2
5
4
3
16
4
8
19
10
12

228,423,620
40,996,921
85,916,019
189,874,213
82,321,705
99,346,035
14,326,753
16,213,403
72,737,658
109,837,230

2.90 × 10-7
6.40 × 10-5
8.10 × 10-5
1.10 × 10-4
1.40 × 10-4
1.40 × 10-4
1.40 × 10-4
2.10 × 10-4
3.70 × 10-4
4.10 × 10-4

CCL20, WDR69
C7
CDS1
LPP
CDH13

rs3096277
rs1721359
rs225942
rs298988
rs10514096
rs10512245
rs294593
rs6085660
rs6796000
rs575121

16
2
14
4
5
9
5
20
3
12

82,321,705
228,460,118
29,595,139
119,867,850
76,700,940
95,771,366
163,000,000
6,639,069
190,000,000
117,000,000

9.90 × 10-8
1.00 × 10-55.30 × 10-5
7.80 × 10-5
1.10 × 10-4
1.40 × 10-4
1.80 × 10-4
1.90 × 10-4
2.20 × 10-4
2.20 × 10-4

CDH13
CCL20, WDR69
PRKD1
SEC24D
PDE8B

rs3773643
rs3793427
rs6492654
rs1367248
rs10521232
rs3766680
rs1371924
rs10488172
rs10507534
rs719856

3
8
13
2
17
1
3
7
13
6

30,685,247
17,188,201
92,688,671
124,734,834
13,480,529
173,563,0070
144,732,760
132,985,716
44,724,220
47,702,681

1.99 × 10-7
1.96 × 10-6
2.28 × 10-6
2.88 × 10-6
3.88 × 10-6
4.15 × 10-6
4.44 × 10-6
8.49 × 10-6
1.05 × 10-5
1.21 × 10-5

TGFBR2
VPS37AGPC6
CNTNAP5
HS3ST3A1TNR
SLC9A9EXOC4GTF2F2
CD2AP

Diastolic Blood Pressure (long-term average)
DBP
DBP
DBP
DBP
DBP
DBP
DBP
DBP
DBP
DBP

rs7591163
rs1901167
rs6829806
rs6796000
rs3096277
rs969049
rs10503497
rs2262138
rs10509333
rs933296

SGCZ
FAM32A, AP1M1
UNC5B, SLC29A3
MYL2

Systolic Blood Pressure (long-term average)
SBP
SBP
SBP
SBP
SBP
SBP
SBP
SBP
SBP
SBP

MAT2B
BMP2
LPP
TAOK3

Tonometry
MAP
FW
RW
CF-PWV
CF-PWV
FW
RW
RW
FW
FW

*P values from 2 degree of freedom test
CB-PWV = carotid-brachial pulse wave velocity; CF-PWV = carotid-femoral pulse wave velocity; DBP = diastolic blood pressure; FW = forward
wave amplitude; MAP = mean arterial pressure; RW = reflected wave amplitude; SBP = systolic blood pressure
Minor allele frequency >= 0.20, HWE P value >= 0.05, call rate >= 0.80

Due to high correlations of SBP and DBP (within examination r = 0.77; long-term r = 0.82), joint analyses of SBP
and DBP added little to what was identified in individual
phenotype analyses. In contrast, joint analyses of the five
tonometry phenotypes, which are less highly correlated,
identified LOXL2, SYNE1, and MEF2C as attractive candidates. LOXL2 is a member of the lysyl oxidase family of
enzymes that initiate cross-linking of collagens and elastin, and alter arterial elasticity [20]. Collagen and elastin
cross-links are critical to tensile strength of the extracellular matrix. Mice null for lysyl oxidase (LOX) die perinatally from aortic aneurysm [21]. MEF2C is involved in
cardiac morphogenesis and extracellular matrix remode-ling [22]. SYNE1 is involved in aortic vascular smoothmuscle differentiation [23]. To our knowledge, genetic
variation in these genes has not previously been shown to
be associated with alterations in arterial properties inhumans. Whether our results provide nominal evidenceof such association or merely chance findings remains to
be determined.
Since none of the primary associations attained genomewide significance, this investigation should be viewed as
hypothesis generating. Association analyses for SNPs in
six renin-angiotensin-aldosterone pathway genes showed
weak evidence of association. Negative results for these

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Table 4: Top Results for Geometric Means of SBP and DBP Considered Jointly (at examinations 1, 7 and in the long term), and arterial
stiffness phenotypes considered jointly.

SNP

Exam

Chr.

Position

P value Gene

1
7
7
1
1
1
7
7
7
7

1
6
5
14
4
10
2
4
6
8

63,303,150
140,532,157
110,800,303
75,720,517
159,547,895
122,473,101
12,149,816
70,039,785
47,884,860
73,270,276

1.49 × 10-5
2.89 × 10-5
3.74 × 10-5
3.96 × 10-5
4.22 × 10-5
7.27 × 10-5
7.52 × 10-5
7.63 × 10-5
7.75 × 10-5
8.28 × 10-5

7
7
7
7
7
7
7
7
7
7

4
6
3
8
5
11
17
2
2
5

72,282,885
153,040,067
120,557,547
23,250,536
82,518,340
112,708,233
64,731,363
116,146,020
123,501,987
88,124,195

0.002
0.005
0.007
0.008
0.008
0.009
0.010
0.011
0.011
0.011

DBP and SBP
rs10493340
rs9321764
rs10491334
rs2121070
rs1328925
rs10510079
rs7562854
rs1841055
rs10485320
rs9298203

CAMK4
C14orf118
TMEM144UGT2A3
OPN5

Arterial stiffness
rs10518082
rs1322512
rs10511389
rs883524
rs965674
rs10502173
rs1468512
rs4075701
rs10496604
rs770189

DCK
SYNE1
CDGAP
LOXL2
XRCC4
TTC12
ABCA10

MEF2C

Based on Additive genetic effects model using GEE and minor allele frequency of 0.1, call rate >= 0.80 and HWE p value >= 0.001

candidate genes may be due in part to incomplete linkage
disequilibrium coverage of these genes by the SNPs in thisgenome-wide scan. It is likely that the vast majority of low
p values from association analyses are due to chance. Replication studies in other populations, using a genomewide approach or selective genotyping is needed to establish if any of our results are indicative of true positive associations.

the interpretation of the findings we report in this manuscript.

We provide results of genome-wide association testing forblood pressure and arterial stiffness phenotypes obtainedin a carefully described community-based sample ofadults who were recruited without regard to disease status.
Additional studies are needed to validate these results.
Finding genetic variants associated with hypertension oraltered arterial properties may aid in the identification ofhigh risk individuals and in the development of new targeted therapies for hypertension. Our report is one of theearlier genome-wide association studies of blood pressure. Several additional studies, some with larger sample
size and others with more dense genome-wide coverage ofcommon variation will follow. In that regard, a 550 k SNP
genome-wide association study in approximately 9400
Framingham Heart Study participants across three generations is underway and results from that study will help in

Competing interests

Abbreviations
DBP = diastolic blood pressure; FBAT = family based association test; GEE = generalized estimating equation; LOD= log of the odds; SBP = systolic blood pressure; SNP = single nucleotide polymorphism.

GFM is owner of Cardiovascular Engineering, Inc, a company that designs and manufactures devices that measure
vascular stiffness. All other authors declare that they haveno competing interests.

Authors' contributions
EJB secured funding for tonometry measurements,
assisted in planning the analyses, and critically revised themanuscript. CNC contributed to design, analysis, and critical review of the manuscript. SJH generated the phenotype data, participated in the analysis and interpretationof results. MGL assisted to secure funding for tonometry
measurements, generated phenotype data, assisted inplanning analyses, and critically revised the manuscript.
DL conceived of the FHS tonometry project and assisted
in securing funding, planned the analyses, interpreted thePage 9 of 11(page number not for citation purposes)

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Table 5: Results for Pre-Specified Candidate Genes

Candidate gene

Total number of SNPs*

SNPs with p value < 0.05 Phenotype

GEE p value

FBAT p value

0.021

0.186

0.008

0.399

0.003

0.275

0.010
0.024

0.323
0.598

0.022
0.024
0.037
0.046
0.025
0.046
0.003
0.009
0.011
0.014
0.018
0.021
0.027
0.042
0.049
0.012
0.022

0.397
0.688
0.217
0.663
0.023
0.963
0.308
0.383
0.741
0.409
0.175
0.649
0.268
0.293
0.529
0.115
0.207

Diastolic blood pressure
ACE
AGT
AGTR1
CYP11B2
NR3C2
REN

3
13
17
1
26
9

0
rs2478518
00rs6845733
0
Systolic blood pressure

ACE
AGT
AGTR1
CYP11B2
NR3C2

3
13
17
1
26

REN

9

0
rs2478518
00rs6845733
rs3916013
0
Arterial stiffness

ACE
AGT

3
13

AGTR1

17

CYP11B2
NR3C2

1
26

REN

9

0
rs731824
rs2478518
rs2478516
rs2478516
rs1059502
rs427832
rs2717594
rs3910046
rs9307847
rs3910046
rs10519959
rs3846317
rs4835136
rs3846318
rs10519958
rs16776
rs3911890

MAP
FW
FW
RW
MAP
FW
CF-PWV
CF-PWV
CB-PWV
CB-PWV
CB-PWVRW
CB-PWV
RW
RW
FW
FW*Includes all SNPs within 200 kb of start to 200 kb beyond end of gene, with genotype call rate >= 0.8; minor allele frequency >= 0.1; HWE p >=0.001
CB-PWV = carotid-brachial pulse wave velocity; CF-PWV = carotid-femoral pulse wave velocity; DBP = diastolic blood pressure; FW = forward
wave amplitude; MAP = mean arterial pressure; RW = reflected wave amplitude; SBP = systolic blood pressure

results, and drafted the manuscript. GFM conceived of the
FHS tonometry project and assisted in securing funding,planned the analyses, and critically revised the manuscript. RSV provided critical input in conceiving the
project, securing the funding, planning the analyses andcritically revising the manuscript. TJW contributed todesign, analysis, and critical review of the manuscript.

Acknowledgements
We thank the Framingham Study participants and acknowledge support
from N01-HC 25195. Arterial tonometry was supported by the Donald W.
Reynolds Foundation and NIH R01-HL70100 and R01-HL60040 and K24-

HL04334. A portion of the research was conducted using the Boston University Linux Cluster for Genetic Analysis (LinGA) funded by the NIH
NCRR (National Center for Research Resources) Shared Instrumentation
grant (1S10RR163736-01A1).
We also wish to acknowledge the contributions of Dr. Christopher J.
O'Donnell to this project.
This article has been published as part of BMC Medical Genetics Volume 8
Supplement 1, 2007: The Framingham Heart Study 100,000 single nucleotide polymorphisms resource. The full contents of the supplement areavailable online at http://www.biomedcentral.com/1471-2350/8?issue=S1.

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BMC Medical Genetics 2007, 8(Suppl 1):S3

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