BMC Medical Genetics

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Framingham Heart Study 100K project: genome-wide associations
for cardiovascular disease outcomes
Martin G Larson*1,2, Larry D Atwood1,3, Emelia J Benjamin1,3,4,
L Adrienne Cupples1,5, Ralph B D'Agostino Sr1,2, Caroline S Fox1,
Diddahally R Govindaraju1,3, Chao-Yu Guo1,3, Nancy L Heard-Costa1,3, ShihJen Hwang1, Joanne M Murabito1,6, Christopher Newton-Cheh1,7,8,
Christopher J O'Donnell1,7, Sudha Seshadri1,3, Ramachandran S Vasan1,3,4,
Thomas J Wang1,7, Philip A Wolf1,3 and Daniel Levy1
Address: 1The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA, 2Department of Mathematics andStatistics, Boston University, Boston, MA, USA, 3Boston University School of Medicine, Boston, MA, USA, 4Whitaker Cardiovascular Institute,
Boston University School of Medicine, Boston, MA, USA, 5Department of Biostatistics, Boston University School of Public Health, Boston, MA,
USA, 6Section of General Internal Medicine, Boston University School of Medicine, Boston, MA, USA, 7Cardiology Division, Massachusetts General
Hospital, Harvard Medical School, Boston, MA, USA and 8Broad Institute of Harvard and MIT, Cambridge, MA, USA
Email: Martin G Larson* - mlarson@bu.edu; Larry D Atwood - lda@bu.edu; Emelia J Benjamin - emelia@bu.edu;
L Adrienne Cupples - adrienne@bu.edu; Ralph B D'Agostino - ralph@bu.edu; Caroline S Fox - foxca@nhlbi.nih.gov;
Diddahally R Govindaraju - drgraju@bu.edu; Chao-Yu Guo - Chao-Yu.Guo@childrens.harvard.edu; Nancy L Heard-Costa - nheard@bu.edu;
Shih-Jen Hwang - hwangs2@nhlbi.nih.gov; Joanne M Murabito - murabito@bu.edu; Christopher Newton-Cheh - cnewtoncheh@partners.org;
Christopher J O'Donnell - odonnellc@nhlbi.nih.gov; Sudha Seshadri - suseshad@bu.edu; Ramachandran S Vasan - vasan@bu.edu;
Thomas J Wang - tjwang@partners.org; Philip A Wolf - pawolf@bu.edu; Daniel Levy - levyd@nhlbi.nih.gov* 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):S5

doi:10.1186/1471-2350-8-S1-S5This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S5© 2007 Larson 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: Cardiovascular disease (CVD) and its most common manifestations – including coronary heart disease(CHD), stroke, heart failure (HF), and atrial fibrillation (AF) – are major causes of morbidity and mortality. In manyindustrialized countries, cardiovascular disease (CVD) claims more lives each year than any other disease. Heart disease
and stroke are the first and third leading causes of death in the United States. Prior investigations have reported several
single gene variants associated with CHD, stroke, HF, and AF. We report a community-based genome-wide association
study of major CVD outcomes.
Methods: In 1345 Framingham Heart Study participants from the largest 310 pedigrees (54% women, mean age 33 years
at entry), we analyzed associations of 70,987 qualifying SNPs (Affymetrix 100K GeneChip) to four major CVD outcomes:
major atherosclerotic CVD (n = 142; myocardial infarction, stroke, CHD death), major CHD (n = 118; myocardial
infarction, CHD death), AF (n = 151), and HF (n = 73). Participants free of the condition at entry were included inproportional hazards models. We analyzed model-based deviance residuals using generalized estimating equations to test
associations between SNP genotypes and traits in additive genetic models restricted to autosomal SNPs with minor allele
frequency ≥0.10, genotype call rate ≥0.80, and Hardy-Weinberg equilibrium p-value ≥ 0.001.
Results: Six associations yielded p < 10-5. The lowest p-values for each CVD trait were as follows: major CVD, rs499818,
p = 6.6 × 10-6; major CHD, rs2549513, p = 9.7 × 10-6; AF, rs958546, p = 4.8 × 10-6; HF: rs740363, p = 8.8 × 10-6. Of
note, we found associations of a 13 Kb region on chromosome 9p21 with major CVD (p 1.7 – 1.9 × 10-5) and major
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CHD (p 2.5 – 3.5 × 10-4) that confirm associations with CHD in two recently reported genome-wide association studies.
Also, rs10501920 in CNTN5 was associated with AF (p = 9.4 × 10-6) and HF (p = 1.2 × 10-4). Complete results for these
phenotypes can be found at the dbgap website http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.
Conclusion: No association attained genome-wide significance, but several intriguing findings emerged. Notably, we
replicated associations of chromosome 9p21 with major CVD. Additional studies are needed to validate these results.
Finding genetic variants associated with CVD may point to novel disease pathways and identify potential targeted
preventive therapies.

Background
Cardiovascular disease (CVD) and its most common
manifestations, coronary heart disease (CHD), stroke,heart failure (HF), and atrial fibrillation (AF) are major
causes of morbidity and mortality. In many industrializedcountries CVD claims more lives each year than any otherdisease. In the United States, for example, heart disease
and stroke are the first and third leading causes of death[1]. At age 40 the lifetime risk of developing CHD is one
in two for men and one in three for women [2], the lifetime risk for stroke is one in six for men and one in five forwomen [3], the lifetime risk for HF is one in five in men
and women [4] and the lifetime risk for AF is one in fourin both sexes [5].
Prior Framingham Heart Study research points to strong
familial patterns of CVD, HF, and AF [6-8] and such evidence is consistent with a genetic effect. Several single
gene variants associated with CHD and atherosclerotic
CVD have been reported [9-13]. A substantial body ofresearch has also identified a number of genetic variants
associated with HF and AF [14,15].
We report results of a genome-wide association study offour CVD outcomes in community-based Framingham
Heart Study participants who were enrolled without
regard to disease status. Analysis for each specific outcome
was restricted to those free of the condition at baseline.
We also provide association results for previously
reported candidate genes and candidate regions for theseCVD outcomes.

Methods
Study sample
In 1948, 5209 men and women from Framingham, Massachusetts, who were between 28 and 62 years of age, wererecruited to participate in the Framingham Heart Study[16]. Periodic clinic visits, performed every two years,
included a medical history, physical examination focusing on the cardiovascular system, laboratory tests, andelectrocardiogram. The offspring cohort of the Framingham Heart Study began in 1971, with the enrollment of5124 offspring and spouses of offspring of original participants [17]. Repeated examinations of the offspringcohort occurred approximately every 4 years, except for an

8 year interval between their initial and second visit. At
each clinic visit, participants gave written informed consent. The consent documents and the examination content were approved by the Institutional Review Board at
Boston University Medical Center (Boston, Massachusetts).
Phenotype definition & methods
All participants in both cohorts who were free of a specificcondition at enrollment were analyzed for onset of that
endpoint during follow up through the end of 2004. All
suspected CVD events were reviewed and adjudicated by apanel of three Framingham physician investigators after
review of all available Framingham Heart Study examination records, hospitalization records, and physician notes,
using previously published criteria [18].

For these analyses, we considered four groups of events:
major CHD events included recognized myocardial infarction, coronary insufficiency, and death due to CHD;
major atherosclerotic CVD events included major CHD
plus atherothrombotic stroke; the remaining groups were
HF and AF. Myocardial infarction was diagnosed by thepresence of 2 out of 3 clinical criteria: new diagnostic Qwaves on ECG, prolonged ischemic chest discomfort, andelevation of serum biomarkers of myocardial necrosis.
CHD death was established upon review of all available
records, if the cause of death was probably CHD and noother cause could be ascribed.
Atherothrombotic brain infarction was defined as a nonembolic acute-onset focal neurological deficit of vascularetiology that persisted for more than 24 hours or anischemic infarct was documented at autopsy.
History of interim hospitalizations and symptoms of HF
were obtained at each clinic examination; outside medical
records were evaluated for participants who did not attend
an examination. Three physicians reviewed all suspectedinterim events using Framingham Heart Study clinic
notes, external physician reports and hospitalization
records. HF was diagnosed when at least two major criteria were present, or one major and two minor criteria.
Major criteria were paroxysmal nocturnal dyspnea, pulmonary rales, distended jugular veins, enlarging heart size
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on chest radiography, acute pulmonary edema, hepatojugular reflux, third heart sound, jugular venous pressureof 16 cm or greater, weight loss of 4.5 kg or greater in
response to diuresis, pulmonary edema, visceral congestion, or cardiomegaly on autopsy. Minor criteria counted
only if not attributed to another disease. Minor criteria
were bilateral ankle edema, nocturnal cough, shortness ofbreath on ordinary exertion, hepatomegaly, pleural effusion, vital capacity decreased by one third from previous
maximum, and heart rate ≥120 beats/min.
AF was diagnosed when, upon review by a study cardiologist, AF or atrial flutter was present on an ECG obtained
from a routine Framingham clinic examination or from a
hospital or physician record. HF was defined on the basisof review of medical records and the finding of concurrent
presence of two major or one major plus two minor criteria [19].
Genotyping methods
The accompanying Overview [20] provides details of the
genotyping methods used in this investigation. The
Affymetrix 100K chip with 112,990 autosomal SNPs wasused to genotype individual participant DNA on the
Framingham Heart Study family plate set. SNPs were
excluded for minor allele frequency < 0.1 (n = 38062); callrate < 0.8 (n = 2346); Hardy Weinberg equilibrium pvalue < 0.001 (n = 1595). After these exclusions, 70,987
SNPs were available for analysis.
Statistical methods
Proportional-hazards models were used to analyze timeto each endpoint, stratified by cohort, using covariate values obtained at enrollment. Models were adjusted for (i)sex and age, or (ii) sex, age and multiple covariates. For
CVD and CHD, covariates included smoking, diabetes,
systolic BP, anti-hypertensive treatment and total cholesterol; for HF, covariates were smoking, diabetes, systolic
BP, anti-hypertensive treatment and body mass index; forAF, covariates were diabetes, systolic BP, anti-hypertensive
therapy and valve disease. Deviance residuals estimatedfrom each model were standardized (mean 0, variance 1)
to form the phenotypes analyzed with genetic models. For

genotype-phenotype association analyses, we assumed an
additive-allele model of inheritance and we conductedassociation tests using regression models with generalized
estimating equations (GEE), as well as family-based association testing using FBAT. Due to relatively small numbers of outcome events and non-normality of thedeviance residuals, we decided a priori not to perform
linkage analysis on outcomes residuals. The distributionof observed p values for the four CVD outcomes was compared to that which would be expected under the null
hypothesis of no genetic associations with outcomes.
Candidate gene analyses
GEE and FBAT additive genetic effect models also wererun for SNPs in or near candidate genes for each of the
CVD outcomes. Candidate genes were selected after separate literature searches for each outcome. All SNPs acrossthe interval extending from 200 Kb proximal to the start
to 200 kb beyond the end of each gene were eligible if theminor allele frequency was ≥0.1, the genotype call rate
was ≥0.8, and the Hardy-Weinberg equilibrium p value
was ≥0.001.

Results
Four primary phenotypes were analyzed: major atherosclerotic CVD (n = 142), major CHD (n = 118), HF (n =73), and AF (n = 151). Covariates for each outcome arelisted in Table 1. In this sample, deviance residuals frommultivariable models generally had low heritability: HF,
0.023 (SE = 0.054); Major CVD, 0.036 (SE = 0.058), Major
CHD, 0.085 (SE = 0.061); and AF, 0.135 (SE = 0.058).
GEE additive genetic models yielded six associations with
p values < 10-5 and another 31 with p values < 10-4 (seeTable 2a for best 25). The lowest p-values for each CVD
phenotype were as follows: major CVD, rs499818, p = 6.6
× 10-6; major CHD, rs2549513, p = 9.7 × 10-6; AF,
rs958546, p = 4.8 × 10-6; HF: rs740363, p = 8.8 × 10-6. Of
note, rs10501920 in CNTN5 was associated with AF (p =9.4 × 10-6) and HF (p = 1.2 × 10-4). Three SNPs near
PHACTR1 were associated with major CVD (rs499818,
rs1512411, rs507369; lowest p = 6.6 × 10-6) and one ofthese was associated with major CHD (rs1512411;

Table 1: Phenotype definitions
PhenotypeDefinition

Number of individuals

Number with event

Adjustment*

Major CVD

Myocardial infarction, coronary
insufficiency, CHD death, oratherothrombotic stroke
Myocardial infarction, coronary
insufficiency, or CHD deathHeart failure, hospitalized or nonhospitalized
Atrial fibrillation or atrial flutter on ECG

1345

142

1345

118

Age, sex; Multivariable: Age, sex, smoking, diabetes,
systolic BP, anti-hypertensive therapy, totalcholesterol
Same as Major CVD

1345

73

1341

151

Major CHDHeart failure
Atrial fibrillation

Same as Major CVD except BMI added, totalcholesterol removed
Age, sex; Multivariable: Age, sex, diabetes, systolic
BP, anti-hypertensive therapy, valve disease* Covariates in cohort-stratified proportional-hazards models for time to event

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Table 2: Additive Genetic Model – ordered by GEE (2a) and FBAT (2b) p-value Results

Phenotype

SNP

Chromosome

Position

GEE P value

FBAT P value

13
6
15
10
11
16
13
6
9
9
9
2
15
17
13
11
9
8
15
15
17
11
4
14
6

45,731,718
13,440,446
67,793,927
118,565,596
98,998,383
78,108,228
45,732,707
13,439,076
22,102,599
22,090,176
22,089,568
192,876,826
55,129,991
61,344,845
109,989,414
98,971,412
17,151,527
96,774,748
55,120,501
55,028,949
61,344,827
97,372,875
92,265,754
90,256,423
13,440,039

4.78E-06
6.64E-06
7.87E-06
8.82E-06
9.40E-06
9.65E-06
1.05E-05
1.55E-05
1.67E-05
1.86E-05
1.87E-05
1.98E-05
1.98E-05
2.28E-05
2.33E-05
2.40E-05
2.59E-05
2.63E-05
2.83E-05
3.09E-05
3.22E-05
4.03E-05
4.33E-05
5.23E-05
6.23E-05

0.104
0.17
0.042
0.065
0.448
0.106
0.02
0.366
0.132
0.071
0.068
0.046
0.279
0.547
0.026
0.093
0.044
0.003
0.218
0.278
0.613
0.248
0.858
0.138
0.137

12
7
3
12
5
14
14
6
4
7
7
1
14
10
4
11
1
11
15
10
2
5
4
11
3

22,539,123
116,548,736
113,538,529
88,264,967
163,444,804
67,762,403
67,769,347
120,497,469
31,119,019
116,560,255
116,599,175
98,357,234
67,747,245
60,686,639
77,062,193
121,794,555
187,190,664
121,794,482
31,705,234
132,315,529
63,665,926
163,444,569
158,199,764
33,698,233
110,400,929

0.058
0.138
0.003
0.018
0.029
0.138
0.126
0.117
0.086
0.219
0.594
0.801
0.275
0.384
0.018
0.074
0.294
0.073
0.003
0.165
0.003
0.015
0.282
0.251
0.002

3.06E-05
4.37E-05
4.45E-05
4.58E-05
4.72E-05
5.14E-05
5.53E-05
6.39E-05
6.55E-05
6.72E-05
6.95E-05
7.10E-05
7.40E-05
9.94E-05
9.98E-05
1.24E-04
1.29E-04
1.32E-04
1.35E-04
1.49E-04
1.51E-04
1.55E-04
1.72E-04
1.78E-04
1.78E-04

Gene

2a. Results ordered by GEE p-value results
AF
Major CVD
AF
HF
AF
Major CHD
AF
Major CVD
Major CVD
Major CVD
Major CVD
Major CHD
Major CHD
Major CHD
Major CVDAF
HF
Major CHD
Major CHD
Major CHD
Major CHD
AF
Major CHD
Major CVD
Major CVD

rs958546
rs499818
rs4776472
rs740363
rs10501920
rs2549513
rs10507539
rs1512411
rs10511701
rs1556516
rs1537371
rs10497726
rs2962994
rs997651
rs2148079
rs10501918
rs10511633
rs7836535
rs1820996
rs213168
rs997652
rs4590838
rs10516882
rs1742083
rs507369

KIAA1598
CNTN5

PHACTR1, TBC1D7

TMEFF2
TCF12
MGC33887
RAB20
CNTN5
C9orf39
TCF12
TCF12
MGC33887

TTC7B
PHACTR1, TBC1D7

2b. Results Ordered by FBAT
Major CHD
Major CVD
AF
AF
HF
AF
AF
AF
AF
Major CVD
Major CVD
Major CVD
AF
Major CVD
AF
Major CHD
AF
Major CHD
Major CHD
HF
AF
HF
AF
Major CHD
AF

rs10505879
rs39312
rs10511311
rs1427828
rs10515869
rs1751382
rs1314913
rs262467
rs412253
rs39317
rs9886209
rs10493900
rs1298340
rs2452503
rs324735
rs580069
rs1604355
rs559453
rs951442
rs1176486
rs2421954
rs9313999
rs7676376
rs10501127
rs1163397

KIAA0528
WNT2
CD200
DUSP6
RAD51L1
RAD51L1

WNT2, ASZ1
ASZ1
RAD51L1
FAM13C1FAM5C
RYR3LOC51057
PDGFC
CD59

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Table 3: Distribution of Observed and Expected P Values from GEE models

P value group0.10 ≤ p
0.01 ≤ p < 0.10
0.001 ≤ p < 0.01
0.0001 ≤ p < 0.001
0.00001 ≤ p < 0.0001
p < 0.00001

Frequency

Percent

Expected*

Ratio**

254,464
26,218
2,892
337
31
6

89.61649.23341.01850.11870.01090.002190.000%9.000%
0.900%
0.090%
0.009%
0.001%

1.00
1.03
1.13
1.32
1.21
2.11

*Expected under uniform distribution. **Ratio of observed to expected.

p = 6.3 × 10-5). Among GEE results for HF was rs939698(p = 3.6 × 10-4) in RYR2, which has been implicated inarrhythmogenic right ventricular dysplasia/cardiomyopathy [21], a rare familiar cardiomyopathy.
Results of FBAT are provided in Table 2b. The lowest p values for each phenotype were: major CVD, rs39312 in
WNT2, p = 4.4 × 10-5; major CHD, rs10505879, p = 3.1 ×
10-5; AF, rs10511311 in CD200, p = 4.5 × 10-5; and HF,
rs10515869, 4.72 × 10-5.
The distribution of observed GEE p values is presented inTable 3. Note that the ratio of observed to expected numbers is inflated only at very low p values.
Association results for 408 SNPs in 46 candidate genes(Table 4) revealed suggestive evidence for major CHD
events for ALOX5AP (23 SNPs, 7 with p < 0.05 by GEE or
FBAT), GJA4 (14 SNPs, 6 with p < 0.05), MEF2A (5 SNPs,
2 with p < 0.05), and PCSK9 (11 SNPs, 3 with p < 0.05).
For HF, 4 SNPs in PLN and 2 each in ADRB2 and TPM1
had p values < 0.05. There was little evidence of association of AF with SNPs in specified candidate genes. Overall,
538 candidate-SNP association tests were carried out
because there were 130 SNPs common to both major
CHD and major CVD. Results with GEE p < 0.05 wereobtained for 28 tests (5.2%) and p < 0.01 for 5 tests(0.9%), similar to the overall distribution in Table 3. Lackof consistency between GEE and FBAT results may be due
to lower power of FBAT compared with GEE tests.
Additionally, we examined all association results for
major CHD and major CVD in the region of chromosome
9 that was recently reported to be associated with MI and
CHD [22,23], We found that 7 SNPs in a 76 Kb region had
p < 10-5 for one or both outcomes.

Discussion
Cardiovascular disease is the leading cause of death inindustrialized countries and will soon be the leading
cause of death in the developing world [24]. Genomewide association studies provide an opportunity to extend
our understanding of CVD pathogenesis and improve
public health. The identification of novel genes and path-ways that play a causal role in CVD is an essential objective for the development of new therapies for theprevention and treatment of CVD. Finding genetic associations with CVD risk that are robust across multiple studies will aid in the personalization of medicine byidentifying high risk individuals who can be targeted forearly and aggressive preventive care.
We provide results of genome-wide association for 4 CVD
outcomes of great public health impact: major CVD,
major CHD, AF, and HF. No associations attained
genome-wide significance [4.4 × 10-8 = 0.05/(70,987 SNPs
× 4 major traits × 2 adjustment levels × 2 association models)] in our analyses using GEE or FBAT additive genetic
models. With dramatic declines in the cost of highthroughput genotyping, selective genotyping of SNPs withsuggestive evidence of association can be considered.
Two-stage approaches – genome-wide association followed by selective genotyping – have been adopted as apractical and efficient strategy for pursuing initial
genome-wide results [25,26].
Results of GEE and FBAT associations pointed to few candidate genes of obvious interest for any CVD outcomes.
One intriguing result was the association of RYR2
(rs939698, p = 3.6 × 10-4) with HF. The ryanodine receptor has been implicated in arrhythmogenic right ventricular dysplasia/cardiomyopathy [21,27], a rare familial
cardiomyopathy.
The lowest p values we identified may be purely bychance. The number of events (maximum of 142 formajor CVD) was small to detect association, but would be
sufficient to detect a SNP with high minor allele frequency
in linkage disequilibrium with a causal variant that contributed high risk. This was the case for a genome-wide
association study of age-related macular degeneration –only 96 cases and 50 controls were sufficient to identify
genome-wide association with complement factor H [28].
Sometimes multiple SNPs in the same chromosomal
region had low GEE p values for a trait; for example, Table
2a has SNP clusters on chromosomes 6, 9, 11, 13, 15 and
17. Linkage disequilibrium exists for those clustered SNPs
(typically, pair-wise r2 above 0.80) and it is uncertain

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Table 4: Association 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

Major CHD
Major CHD
Major CHD
Major CHD
Major CHD
Major CHD
Major CVD
Major CHD
Major CHD
Major CHD
Major CVD
Major CHD
Major CHD
Major CVD
Major CHD
Major CVD
Major CVD
Major CHD

0.011
0.014
0.015
0.016
0.018
0.028
0.043
0.049
0.004
0.004
0.009
0.011
0.016
0.025
0.520
0.538
0.547
1.000

0.373
0.455
0.266
0.568
0.257
0.660
0.878
0.967
0.169
0.145
0.464
0.051
0.135
0.237
0.030
0.044
0.019
0.031

Major CHD
Major CVD
Major CVD
Major CHD
Major CHD
Major CVD
Major CHD
Major CVD

0.013
0.004
0.006
0.016
0.062
0.028
0.032
0.042

0.080
0.006
0.006
0.013
0.021
0.506
0.610
0.079

Major CHD
Major CVD
Major CVD
Major CHD
Major CHD
Major CVD
Major CVD
Major CHD
Major CHD

0.010
0.026
0.076
0.077
0.478
0.584
0.192
0.255
0.443

0.075
0.057
0.002
0.002
0.029
0.026
0.035
0.032
0.048

0.041

0.204

0.140
0.136
0.745
0.119

0.041
0.046
0.047
0.018

0.065

0.040

Major CVD/Major CHD
ALOX5
ALOX5AP

5
23

GJA4

14

LGALS2
LTA
LTA4H
MEF2A

1
2
22
5

MMP3

17

SERPINE1
PCSK9

2
11

THBS2

7

THBS4
VAMP8

16
5

0
rs7983138
rs2985183
rs7984952
rs117395
rs4603405
rs10507391
rs10507391
rs7995384
rs618675
rs10489658
rs618675
rs10493062
rs768586
rs10489658
rs10489656
rs10489656
rs2093185
rs6686484
00rs10492225
rs2033546
rs2863274
rs2033546
rs2863274
rs2096767
rs2096767
rs5661250
rs2114580
rs2114580
rs2317951
rs2317951
rs2317948
rs2317948
rs911839
rs911839
rs264986
0
Atrial fibrillation

ACE
AGT
GJA5
KCNE2
KCNH2
KCNJ2
KCNQ1
KCNE1

3
13
13
14
6
23
5
20

0
rs758216
000rs10512574
rs10488674
rs7277304
rs9305551
Heart failure

ABCC9
ACTC

8
15

0
rs752876

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BMC Medical Genetics 2007, 8(Suppl 1):S5

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Table 4: Association Results for Pre-Specified Candidate Genes (Continued)

ADRB1
ADRB2

12
18

ADRBK1
ATP2A2CALML3
CTF1
DES
DSP
LDB3
LMNA
MYBPC3
MYH6
MYH7
MYL2
MYL3
PLN

0
3
2
0
2
15
0
5
4
1
2
3
1
16

SGCD
TNNC1
TNNI3
TNNT2
TPM1

37
4
1
9
7

TTN
VCL

13
3

0
rs40949
rs185021
000rs10484326
0
0
0
0
0
0
rs3951042
rs724868
rs9320660
rs10484286
0
rs1133415
0
rs832177
rs10519186
rs902027
rs10497521
0

0.545
0.947

0.025
0.040

0.671

0.029

0.025
0.055
0.063
0.074

0.083
0.039
0.034
0.043

0.040

0.131

0.015
0.011
0.152
0.705

0.164
0.085
0.011
0.030

*Includes all SNPs within 200 kb upstream of start to 200 kb downstream of end of gene, with genotype call rate ≥0.8; minor allele frequency ≥0.1;
HWE p ≥ 0.001.
Data are sorted by GEE additive genetic effects model with FBAT results provided alongside.

whether the concordant results represent statistically correlated chance findings or indicate regions of heightenedinterest.
Candidate gene results for the 4 CVD outcomes provided
suggestive confirmation of prior associations reported for
ALOX5AP (23 SNPs, 7 with p < 0.05 by GEE or FBAT),GJA4 (14 SNPs, 6 with p < 0.05), MEF2A (5 SNPs, 2 withp < 0.05), and PCSK9 (11 SNPs, 3 with p < 0.05) in relation to CHD risk. In contrast, candidate gene results for
HF and AF provided little evidence of replication of previously reported associations. Null results of these associations may be due in part to poor coverage of thecandidates by the SNPs on the 100K chip and the modest
number of events available for analysis. Our results can becompared with other genome-wide associations of similar
phenotypes. We observed strong association of major
CVD with 3 SNPs in the region of chromosome 9 that wasrecently reported to be associated with MI and CHD in
multiple samples [22,23]. This provides convincing evidence that, despite modest numbers of events, we wereable to identify true associations.
This investigation has several limitations. This study used
CVD cases that were identified through careful surveil-

lance of a community-based sample with multigenerational participation. Recruitment of original and offspringcohort participants began long before DNA collection,
which occurred in recent years. Thus, most CVD cases
were prevalent at the time of DNA collection. For CVD
outcomes (such as these) with substantial mortality risk, asurvival bias may have been introduced by this study
design; individuals with early CVD events had to survive
and attend a later clinic examination at which DNA wascollected. Another limitation is the modest number of
events included in analyses, in particular for HF, where
only 73 events were available for analysis. For continuous
traits, we had 78% power to detect a SNP with QTL heritability of 1% at significance level 10-3, and at significancelevel 10-6 we had 84% power for QTL heritability 2% [20].
In the setting of a limited number of outcome events,
those are large effect sizes. The negative results of candidate gene analyses may underestimate associations forgenes that are incompletely covered by the SNPs used inthis investigation. Lastly, a large proportion of the results
are likely to be due to chance. Replication studies areneeded to determine which, if any, of the results we report
are indicative of true associations of causal variants withdisease outcomes.

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BMC Medical Genetics 2007, 8(Suppl 1):S5

These association results for major CVD outcomes extend
experience with genome-wide association studies. Replication studies are needed and will be used to guide future
genotyping and resequencing efforts. Finding genetic variants associated with CVD may facilitate the identificationof high risk patients and aid in identifying targeted future
approaches to prevention and treatment of CVD.

http://www.biomedcentral.com/1471-2350/8/S1/S5

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 are available online at http://www.biomedcentral.com/1471-2350/8?issue=S1.

References1.

Abbreviations
AF = atrial fibrillation; CHD = coronary heart disease; HF= heart failure; CVD = cardiovascular disease; FBAT = family based association test; GEE = generalized estimating
equation.

2.
3.

Competing interests
The authors declare that they have no competing interests.

Authors' contributions
MGL participated in study design, data collection, statistical analysis, interpretation of results, and manuscript
preparation. LDA contributed to the design and analysis.
EJB contributed to the design of analyses, acquisition andinterpretation of data, provided critical manuscript revisions. LAC contributed to the design and analysis. RBD
contributed to the design and analysis. CSF contributed todata acquisition and approved the final version of themanuscript. DRG contributed to project design and dataacquisition. CYG participated in statistical analysis. NLHC
contributed to the design and analysis. SJH participated instatistical analysis and manuscript preparation. JMM participated in acquisition of data, interpretation, revising &approval of final manuscript. CNC participated in theanalysis and interpretation of data and critical review ofthe manuscript. CJOD participated in the analysis andinterpretation of data and critical review of the manuscript. SS participated in data collection, definition of phenotypes and review of the manuscript. RSV participated indata collection, interpretation of analyses and review ofthe manuscript. TJW contributed to data acquisition data,interpretation of data analysis, and revision of the manuscript for important intellectual content. PAW participated in data collection and project conception anddesign. DL contributed to project conception and design,interpretation of results, and drafting the manuscript.

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12.

All authors approved the final manuscript.
13.

Acknowledgements
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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).

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