BMC Medical Genetics

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Genome-wide association study for subclinical atherosclerosis in
major arterial territories in the NHLBI's Framingham Heart Study
Christopher J O'Donnell*1,5,9, L Adrienne Cupples1,3, Ralph B D'Agostino4,
Caroline S Fox1,7,9, Udo Hoffmann6, Shih-Jen Hwang1,9, Erik Ingellson1,
Chunyu Liu3, Joanne M Murabito1,2, Joseph F Polak8, Philip A Wolf1,2 andSerkalem Demissie3
Address: 1The National Heart, Lung, and Blood Institute's Framingham Heart Study, Framingham, MA, USA, 2School of Medicine, Boston
University, Boston, MA, USA, 3School of Public Health, Boston University, Boston, MA, USA, 4Department of Mathematics and Statistics, Boston
University, Boston, MA, USA, 5Cardiology Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA, 6Department ofRadiology, Massachusetts General Hospital, Boston, MA, USA, 7Endocrinology Division, Department of Medicine, Brigham and Women's
Hospital, Harvard Medical School, Harvard University, Boston, MA, USA, 8Department of Radiology, Tufts-New England Medical Center, Boston,
MA, USA and 9National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
Email: Christopher J O'Donnell* - odonnellc@nhlbi.nih.gov; L Adrienne Cupples - adrienne@bu.edu; Ralph B D'Agostino - ralph@bu.edu;
Caroline S Fox - foxca@nhlbi.nih.gov; Udo Hoffmann - uhoffman@partners.org; Shih-Jen Hwang - hwangs2@mail.nih.gov;
Erik Ingellson - erik.ingelsson@pubcare.uu.se; Chunyu Liu - liuchunyu2002@yahoo.com; Joanne M Murabito - murabito@bu.edu;
Joseph F Polak - JPolak@tufts-nemc.org; Philip A Wolf - pawolf@bu.edu; Serkalem Demissie - demissie@bu.edu* 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):S4

doi:10.1186/1471-2350-8-S1-S4This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S4© 2007 O'Donnell 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
Introduction: Subclinical atherosclerosis (SCA) measures in multiple arterial beds are heritable phenotypes that areassociated with increased incidence of cardiovascular disease. We conducted a genome-wide association study (GWAS)
for SCA measurements in the community-based Framingham Heart Study.
Methods: Over 100,000 single nucleotide polymorphisms (SNPs) were genotyped (Human 100K GeneChip, Affymetrix)in 1345 subjects from 310 families. We calculated sex-specific age-adjusted and multivariable-adjusted residuals insubjects tested for quantitative SCA phenotypes, including ankle-brachial index, coronary artery calcification andabdominal aortic calcification using multi-detector computed tomography, and carotid intimal medial thickness (IMT)
using carotid ultrasonography. We evaluated associations of these phenotypes with 70,987 autosomal SNPs with minor
allele frequency ≥ 0.10, call rate ≥ 80%, and Hardy-Weinberg p-value ≥ 0.001 in samples ranging from 673 to 984 subjects,
using linear regression with generalized estimating equations (GEE) methodology and family-based association testing(FBAT). Variance components LOD scores were also calculated.
Results: There was no association result meeting criteria for genome-wide significance, but our methods identified 11
SNPs with p < 10-5 by GEE and five SNPs with p < 10-5 by FBAT for multivariable-adjusted phenotypes. Among theassociated variants were SNPs in or near genes that may be considered candidates for further study, such as rs1376877(GEE p < 0.000001, located in ABI2) for maximum internal carotid artery IMT and rs4814615 (FBAT p = 0.000003,
located in PCSK2) for maximum common carotid artery IMT. Modest significant associations were noted with various
SCA phenotypes for variants in previously reported atherosclerosis candidate genes, including NOS3 and ESR1.
Associations were also noted of a region on chromosome 9p21 with CAC phenotypes that confirm associations withcoronary heart disease and CAC in two recently reported genome-wide association studies. In linkage analyses, several
regions of genome-wide linkage were noted, confirming previously reported linkage of internal carotid artery IMT on
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chromosome 12. All GEE, FBAT and linkage results are provided as an open-access results resource at http://
www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.
Conclusion: The results from this GWAS generate hypotheses regarding several SNPs that may be associated with SCA
phenotypes in multiple arterial beds. Given the number of tests conducted, subsequent independent replication in astaged approach is essential to identify genetic variants that may be implicated in atherosclerosis.

Background
Myocardial infarction, stroke and other atherosclerotic
cardiovascular diseases comprise the leading cause ofdeath for men and women in the U.S. [1], and will soon
become the leading cause of death worldwide [2]. Atherosclerosis in the arterial wall precedes the onset of mostcases of clinically apparent cardiovascular disease by decades, and subclinical atherosclerosis (SCA) is quite common in young and middle-aged persons [3-5]. SCA can bedetected and quantified in major arteries, such as the
carotid and coronary arteries, which provide essential
blood flow to major organs, using noninvasive, high resolution imaging modalities. Such measures include
peripheral arterial atherosclerosis detected by the anklebrachial index (ABI); internal and common carotid intimal medial thickness (IMT) detected by B-mode ultrasound; and coronary artery calcium (CAC) andabdominal aortic calcium (AAC) deposits by multidetector computed tomography (MDCT). We have previously
reported evidence for incomplete correlations between
carotid IMT, CAC and aortic calcium [6]. Both genetic andenvironmental factors underlie interindividual variability
in SCA, and significant heritability has been found formeasures of SCA in the peripheral vasculature [7], carotid
arteries [8], aorta [9], and coronary arteries [10]. Further,while there is evidence for partial correlation of major cardiovascular risk factors with atherosclerosis, multiple SCA
measures, including ABI, carotid IMT, CAC, and AAC,
have been shown to predict future risks for cardiovasculardisease independent of risk factors [11-14].
While SCA measures are heritable, relatively little is
known regarding the role of genetic variants in the interindividual variability in these quantitative measures ofatherosclerosis. To date, results from candidate gene association studies for clinically apparent cardiovascular disease or subclinical disease have been inconsistent, though
overviews of multiple studies seem to provide evidence
for modest associations for variants in a number of candidate genes, such as APOE and ACE [8,15]. Genome-wide
association studies (GWAS) using densely spaced single
nucleotide polymorphisms (SNPs) provide a more comprehensive approach unconstrained by existing knowledge to test common genetic variation across the genome
using high-throughput genotyping arrays. From GWAS,
several previously unrecognized genes have been identified that may contribute to disease, including CFH andage-related macular degeneration [16], INSIG2 and obesity [17], NOS1AP with QT interval variation [18], and several genes including TCF7L2 [19] as well as IGF2BP2 andCDKA1 with diabetes mellitus [20-22]. Additionally, twoother GWAS have identified an association with coronary
heart disease of SNPs in a region of chromosome 9p21
[23,24] that was also associated with diabetes mellitus inthree previous GWAS [20-22]. Of note, there was an association of this chromosome 9 region with the SCA measure of coronary artery calcium in one replication study[19,24].
With the availability of high resolution imaging of atherosclerosis phenotypes in specific arterial beds in community-based cohorts such as the Framingham Heart Study,
GWAS analysis is now possible for quantitative SCA phenotypes in major arterial beds. Recently, over 100,000 single nucleotide polymorphisms (SNPs) were genotyped(Human 100K GeneChip, Affymetrix) in 1,345 subjects
from 310 pedigrees in the Framingham Heart Study. The
primary objective of this report is to present a brief summary of the results of this GWAS for SCA detected in thelower extremity arteries, carotid arteries, aorta and coronary arteries, by conduct of genetic association analyses
and genetic linkage mapping. This report is one of a seriesof manuscripts from a collaborative project conducted by
Framingham Heart Study investigators; the overall
approach to this project's statistical genetic methods issummarized in an Overview manuscript that summarizes
the 100K genome-wide association study [25].

Materials and methods
Study sample
Participants from the Offspring Cohort of the Framingham Heart Study who underwent one or more SCA measurements and genotyping with the Affymetrix 100K
GeneChip are included in the study sample; genotyping
was completed in a total of 1,345 subjects (1,084 Offspring cohort subjects). Original cohort subjects did notundergo the recently conducted MDCT or carotid IMT
testing and as such were not included in this study. Of the
1,084 Offspring cohort subjects with genotyping, up to
984 participants with SCA phenotype information wereanalyzed. Details regarding selection of participants andgenotyping are provided in the Overview [25].

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Phenotype definitions & methods
Determination of the clinical characteristics in Offspring
participants reported here was obtained at study entry(baseline) and at each follow-up examination, including
Offspring examinations 6 and 7. The methods of measurement of the covariates (blood pressure, body height andweight, lipids, diabetes mellitus, smoking and other clinical characteristics) used in these analyses have been previously described [26]. For imaging tests conducted
between two examination cycles, covariates used in theanalysis were obtained from the earlier examination cycle.
Carotid ultrasonography for carotid IMTParticipants underwent carotid ultrasonography according to a previously reported standardized protocol at Offspring examination 6 (1995 to 1998) [27]. Imaging was
conducted with a Toshiba SSH-140A imaging unit using ahigh resolution 7.5 MHz transducer for the common
carotid artery, and a 5.0 MHz transducer for the internal
carotid artery. Images were gated to an electrocardiogram;end-diastolic images were acquired. As previously
described, correlation coefficients for the mean and maximum internal carotid artery were 0.83 and 0.84, respectively, based upon 25 readings by two separate readers[28].

All studies were recorded on optical disk and read according to a standardized protocol [29]. To quantify the degreeof thickening of the carotid artery walls, the measures of
IMT were summarized into two variables: one for common carotid artery and one for internal carotid artery.
Mean and maximum wall thicknesses of the common andinternal carotid artery were defined as the mean of thewall thickness or the mean of the maximum wall thickness for the near and far wall on the left and right sides.
The number of available measurements for averaging
ranged from 1–4 for the common carotid artery, and 1–8
for the internal carotid artery.
MDCT for CAC and AAC
Measures of CAC and AAC were obtained between 2002
and 2005 in Offspring participants, between examinations 7 and 8. All eligible participants were imaged withan eight-slice multidetector computed tomography(Lightspeed Ultra, GE, Milwaukee, WI, USA) of the chest,
as previously described [30], as well as the abdomen. Eachsubject underwent two chest CT scans and one abdominal
scan that were performed using a sequential scan protocol
with a slice collimation of 8 mm × 2.5 mm (120 KVp,
320/400 mA for .220 lbs body weight, respectively) during a single end-inspiratory breath hold (typical duration
18 s). Image acquisition (330 ms) was prospectively initiated at 50% of the cardiac cycle. For the abdominal scan,
thirty contiguous 5 mm thick slices of the abdomen wereacquired covering 150 mm above the level of S1. A cali-

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bration phantom (Image Analysis, Lexington, KY, USA)
that contained rods of water and 75 and 150 mg/cm3 calcium hydroxyapatite, was placed underneath each subject.
Calcium measurements were performed on an offline
workstation (Acquarius, Terarecon, San Matteo, CA, USA)by a trained technician. Scoring of coronary calcification
has been previously described, and we reported excellent
intra and inter reader reproducibility for the CAC measurements [30]. A calcified lesion in either the coronary
arteries or in the aorta was defined as an area of at least
three connected pixels with CT attenuation >130 Hounsfield Units using 3D connectivity criteria. A score for AAC(for the abdominal scan) and CAC (for each of the twochest scans) was calculated by multiplying the area of a
calcified lesion with a weighted CT attenuation score
dependent on the maximal CT attenuation (Hounsfield
Units) within a lesion. In modification to the original
Agatston Score that was originally developed for electron
beam CT, we applied this algorithm to our MDCT scan
protocol to score for CAC, as used in numerous previous
studies [30], as well as for AAC.
Ankle brachial index (ABI) for peripheral arterial disease
Ankle-brachial systolic blood pressure measurements
were obtained at Offspring examinations 6 and 7 according to a standard protocol by trained technicians, as previously described [7]. Participants rested for a minimum of
five minutes in the supine position on the examining
table prior to blood pressure measurement. Blood pressure cuffs were applied to bare ankles with the midpointof the bladder over the posterior tibial artery approximately three centimeters above the medial malleolus.
Systolic blood pressure was measured using an 8 megahertz Doppler pen probe and an ultrasonic Doppler flow
detector (Parks Medical Electronics, Inc.). For each limb,(right and left arms, right and left ankles) the cuff was
inflated quickly to the maximal inflation level anddeflated at a rate of 2 mmHg per second until the systolicblood pressure became audible. All limb blood pressures
were repeated in reverse order. If the initial and repeat
blood pressures differed by more than 10 mmHg at any
one site, a third measurement was taken. Measurement
was taken from the dorsalis pedis artery only if the posterior tibial pulse could not be located by palpation or with
the Doppler probe [7].

The ABI is defined as the ratio of the average systolic bloodpressure in the ankle divided by the average systolic bloodpressure in the arm. The higher arm mean was used to calculate the ankle-brachial index for each leg. The lower of
the two ankle-brachial index measurements was used foranalysis.

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Genotyping methods
Details of the genotyping methods are available in the
Overview [25]. Briefly, 112,990 SNPs on the Affymetrix
100K chip were genotyped using DNA from family members of the Framingham Heart Study. For this report, SNPs
were excluded for the following reasons: minor allele frequency ≤10% (n = 38,062); genotypic call rate ≤80% (n =2346); Hardy Weinberg Equilibrium p-value ≤ 0.001(n =1,595), leaving 70,987 SNPs available for analysis [25].
Results for all SNPs are reported on the open-access results
website.
Statistical analysis methods
In total, of the 1,084 Offspring cohort subjects with genotyping, 673 to 984 participants with analyzable phenotype information were available for analysis. Residuals
were created from multiple linear regression models in all
subjects with the SCA measures, regardless of whether
they were genotyped or not, to adjust phenotypes for covariates; these residuals were created in women and men
separately. Covariate adjustment for blood pressure and/or hypertension was performed is several different ways.
For ABI, a dichotomous measure for hypertension (systolic blood pressure > 140 or diastolic blood pressure > 90
or on treatment) was used. For CAC and AAC, systolicblood pressure and use of anti-hypertensive treatment
were used as covariates. For carotid IMT, treatmentadjusted systolic blood pressure was used. As previously
described, systolic blood pressures for those on treatment
are imputed to estimate what these values would be if thesubject were not on treatment [31]. Ranked normalized
deviates, created from the standardized residuals from theregression models, were used in the genetic analyses. For
each sex, SCA phenotypes were age-adjusted and multivariable-adjusted; details of the covariates included in themultivariable adjustment for each phenotype are presented in Table 1.

All association analyses were performed using either generalized estimating equations (GEE) or family-based association testing (FBAT). We evaluated associations of the
SCA phenotypes using an additive genetic model with
70,987 SNPs meeting criteria above. Details regardingthese analytic methods are provided in the Overview [25].
Only results of multivariable-adjusted SCA phenotypes
are displayed in Table 2. Linkage analysis was performed
using variance components methods on a subset of the
100K chip SNPs in linkage equilibrium and Marshfield
STR markers that were previously obtained [25].
We further sought to identify SNPs with consistent associations with phenotypes within measurement groups
using GEE and FBAT analyses. SNPs were selected basedon associations with multiple correlated traits in both
population- and family-based tests. We evaluated the fol-

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lowing five phenotypic subgroups using sex-specific ageadjusted and multivariable-adjusted residuals (Table 1):
ABI, common carotid artery IMT, internal carotid artery
IMT, AAC, and CAC. For each SNP we calculated the proportion of phenotypes significantly associated with the
SNP with P < 0.01 in both GEE and FBAT. For instance, for
ABI, we evaluated GEE and FBAT results for both age-sexadjusted and multivariable-adjusted measures of anklebrachial index from both examination cycle 6 and 7 (inTable 1, these variables are named RANKLEBI6,RANKLEBI6MV, RANKLEBI7, and RANKLEBI7MV).
Results are displayed in Table 3 of the top 5 SNPs with thehighest proportions of significantly (P < 0.01) associatedphenotypes. For identical proportions of significant phenotypes, SNPs were additionally ranked by the logarithmof the mean of GEE p-values.

Results
Ten SCA atherosclerosis measures were studied (Table 1).
For each SCA measurement, sex-specific, age-adjusted
phenotypes as well as age- and multivariable-adjusted
phenotypes were evaluated. Measures for ABI and carotid
IMT phenotypes were available in 880–984 participants,
whereas measures for MDCT phenotypes were available in
673–680 participants.
Tables 2a, 2b and 2c provides a summary of the most significant findings in GEE, FBAT and linkage analyses,
respectively, across a number of selected multivariableadjusted phenotypes described in Table 1. Full-disclosureof all results for all associations can be found at http://
www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. The top 25 most statistically significant associated SNPs in GEE analyses are presented inTable 2a. There were 11 SNPs with p < 10-5 in GEE analyses. The top three associated SNPs are rs1376877 (p <1*10-6, located in ABI2) associated with maximum internal carotid IMT, rs2390582 (p = 1*10-6, not located neara known gene) associated with maximum CAC score, andrs3849150 (p = 2*10-6, located near LRRC18) associatedwith AAC score. The top 25 most significantly associated
SNPs in FBAT analyses are shown in Table 2b. There were
5 SNPs with p < 10-5 by FBAT. The top three associated
SNPs by FBAT are rs4814615 (p = 3*10-6, located in
PCSK2) associated with maximum common carotid IMT,
rs6053733 (p = 4*10-6, located near FLJ25067) associatedwith mean common carotid IMT, and rs10499903 (p =4*10-6, located near PFTK1) associated with ABI.
When we examined below the top 25 associations on ourlist of nominally significant GEE results for various SCA
phenotypes, there were significant associations with multivariable-adjusted CAC for several SNPs on chromosome
9, including three SNPs (rs10511701, rs1556516 andrs1537371; p-values for association 1.1 × 10-4, 8.8 × 10-5,

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Table 1: Phenotype distribution, examination cycle, and numbers of participants with subclinical atherosclerosis phenotypes
Subclinical atherosclerosis
measurePhenotype Variable nameN

Offspring, exam

Covariate adjustment

Ankle-brachial index

RANKLEBI6RANKLEBI6MV

984
984

Offspring, 6Offspring, 6

RANKLEBI7RANKLEBI7MV

982
982

Offspring, 7Offspring, 7

Maximum carotid artery bulb IMTRNKCAROTBULBAS6RNKCAROTBULBMV6

959
951

Offspring, 6Offspring, 6

Maximum common carotid arteryIMT

RNKCAROTCCAMAXAS6

978Offspring, 6Age, sex-specificAge, smoking, diabetes, hypertension, total cholesterol/HDL ratio, log
triglyceride, sex-specificAge, sex-specificAge, smoking, diabetes, hypertension, total cholesterol/HDL ratio, log
triglyceride, sex-specificAge, sex-specificAge, treatment-adjusted systolic blood pressure, BMI, cigarettes perday, HDL cholesterol, total cholesterol, diabetes, log triglyceride, andin women, menopausal status, hormone therapy, sex-specificAge, sex-specific

RNKCAROTCCAMAXMV6969

Offspring, 6Mean common carotid artery IMTRNKCAROTCCAMEANAS6RNKCAROTCCAMEANMV6

978
969

Offspring, 6Offspring, 6

Maximum internal carotid artery IMTRNKCAROTICAMAXAS6RNKCAROTICAMAXMV6

888
880

Offspring, 6Offspring, 6Mean internal carotid artery IMTRNKCAROTICAMEANAS6RNKCAROTICAMEANMV6

888
880

Offspring, 6Offspring, 6

Maximum carotid artery stenosis

RNKCAROTSTENAS6RNKCAROTSTENMV6

977
968

Offspring, 6Offspring, 6Mean abdominal aortic calcification

RESMDCTAACAS7RESMDCTAACMV7

675
673

Offspring, 7Offspring, 7Mean coronary artery calcification

RESMDCTCACAS7RESMDCTCACMV7

680
678

Offspring, 7Offspring, 7

Maximum coronary artery
calcification

RESMDCTCACMAXAS7

680

Offspring, 7

RESMDCTCACMAXMV7

678

Offspring, 7Age, treatment-adjusted systolic blood pressure, BMI, cigarettes perday, HDL cholesterol, total cholesterol, diabetes, log triglyceride, andin women, menopausal status, hormone therapy, sex-specificAge, sex-specificAge, treatment-adjusted systolic blood pressure, BMI, cigarettes perday, HDL cholesterol, total cholesterol, diabetes, log triglyceride, andin women, menopausal status, hormone therapy, sex-specificAge, sex-specificAge, treatment-adjusted systolic blood pressure, BMI, cigarettes perday, HDL cholesterol, total cholesterol, diabetes, log triglyceride, andin women, menopausal status, hormone therapy, sex-specificAge, sex-specificAge, treatment-adjusted systolic blood pressure, BMI, cigarettes perday, HDL cholesterol, total cholesterol, diabetes, log triglyceride, andin women, menopausal status, hormone therapy, sex-specificAge, sex-specificAge, treatment-adjusted systolic blood pressure, BMI, cigarettes perday, HDL cholesterol, total cholesterol, diabetes, log triglycerides,
menopausal status, hormone therapy, sex-specificAge, sex-specificAge, BMI, current cigarette smoking status, diabetes, systolic bloodpressure, anti-HTN therapy, total-to-HDL-C ratio, lipid therapy, sexspecificAge, sex-specificAge, BMI, current cigarette smoking status, Diabetes, systolic bloodpressure, anti-HTN therapy, total-to-HDL cholesterol ratio, lipid
therapy, sex-specificAge, sex-specificAge, BMI, current cigarette smoking status, diabetes, systolic bloodpressure, anti-HTN therapy, total-to-HDL cholesterol ratio, lipid
therapy, sex-specificAbbreviations: IMT = intimal medial thickness; MDCT = multidetector Computed Tomography; AAC = abdominal aortic calcification; CAC = coronary artery calcification;
HDL = high density lipoprotein; BMI = body mass index; anti-HTN therapy = drug treatment for hypertension; lipid therapy = drug treatment for hyperlipidemia.

and 1.7 × 10-4, respectively) lying within a 15 kB region
implicated in recent GWAS's for coronary heart disease.
Similar associations were noted for age-and sex-adjusted
residuals for CAC.
Results for all multipoint LOD scores > 2.0 are displayedin Table 2c. There were four LOD score results exceeding
3.0 and ten additional LOD score results exceeding 2.0.
Results for linkage of internal carotid artery IMT phenotypes to chromosome 12 and 1 (the top three LOD scoreresults) and for chromosome 11 are consistent with findings from our previous report [32]. Upon further inspection of the list of associations with p < 0.01 for each of thephenotypes, SNPs in or near biologically plausible genes
were noted, including: fibroblast growth factor (FGF1) for
AAC, adrenergic, beta-2-, receptor (ADRB2) for CAC,
myocyte enhancer factor 2C (MEF2C) and thrombospondin 2 (THBS2) for common carotid artery IMT, and cAMP-

specific phosphodiesterase 4D (PDE4D) for ankle brachial index.
In Table 3, we provide a summary of the top five associated SNPs for each of four phenotypic categories – ABI,
common carotid artery IMT, internal carotid artery IMT,
and CAC. SNPs were rank ordered first according to percent of phenotypes with p < 0.01 for GEE and FBAT associations and second by geometric mean of GEE p-values.
A number of SNPs were common to the top 25 associated
SNPs by GEE or FBAT reported in Tables 2a and 2b,
respectively, including rs28207 and rs4814615 for common carotid IMT and rs10483853 and rs10507130 for
CAC.
We further examined associations with SNPs in or nearregions of 37 candidate genes previously reported to havebeen associated with SCA or overt coronary heart disease.

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Table 2: 25 Most significant results for multivariable-adjusted subclinical atherosclerosis measures in multiple arterial territories by
GEE (2a), FBAT (2b) and linkage (2c) analyses

2a. Most significant results for GEE analyses for multivariable-adjusted subclinical atherosclerosis measures

GEE rank

1

Phenotype

SNP

Chr

Internal carotid artery IMT

rs1376877

2

Physical location

GEE P-value

FBAT P-value

204,097,596

3.8 × 10-7

0.15

Gene position

Gene symbol

IN

ABI2

3

Coronary artery calcification

rs2390582

1

90,655,928

1.4 × 10-6

0.34

OUT

4

Abdominal aortic calcification

rs3849150

1049,779,229

1.6 × 10-6

4.0 × 10-3

NEAR

LRRC18

6

Ankle brachial index

rs2896103

513,817,419

4.5 × 10-6

0.01

IN

DNAH5

72,826,052

6.1 × 10-6

1.7 × 10-3

IN

NUMB

4.7 × 10-4

7Coronary artery calcification

rs10483853

148

Common carotid artery IMT

rs1400544

1

186,593,061

6.2 × 10-6

9Ankle brachial index

rs7715811

5

13,822,974

6.4 × 10-6

0.02

IN

DNAH5

10

Coronary artery calcification

rs10507130

12

100,256,422

6.7 × 10-6

1.6 × 10-3

IN

DRIM

127,290,897

6.9 × 10-6

0.03

11

Ankle brachial index

rs1320267

4

OUT

OUT

13

Ankle brachial index

rs1350445

11

91,591,802

8.5 × 10-6

0.07

OUT

14

Ankle brachial index

rs1502050

513,832,743

8.7 × 10-6

0.02

IN

15

Coronary artery calcification

rs10519394

4

137,962,214

1.1 × 10-5

0.03

OUT

52,370,975

1.2 × 10-5

0.01

NEAR

TXNL1

16

Internal carotid artery IMT

rs683366

18

DNAH5

19

Abdominal aortic calcification

rs2850711

18

59,938,018

1.5 × 10-5

0.05

IN

C18orf20

20

Internal carotid artery IMT

rs601746

10

96,932,273

1.6 × 10-5

0.22

NEAR

C10orf129

21

Ankle brachial index

rs1905155

5

113,301,097

1.6 × 10-5

5.1 × 10-3

OUT

91,592,192

1.6 × 10-5

0.30

OUT

22

Ankle brachial index

rs10501784

11

23

Ankle brachial index

rs10493529

1

74,335,469

1.9 × 10-5

0.098

NEAR

24

Common carotid artery IMT

rs282075

13,267,852

1.9 × 10-5

2.1 × 10-4

OUT

25

Abdominal aortic calcification

rs8094641

18

59,873,880

2.0 × 10-5

0.04

NEAR

26,111,783

2.0 × 10-5

7.5 × 10-4

OUT

6.8 × 10-3

26

Ankle brachial index

rs2949535

18

LRRC44, FPGT

C18orf20

27

Coronary artery calcification

rs2270861

12

100,238,849

2.0 × 10-5

IN

DRIM

28

Common carotid artery IMT

rs853406

6

4,002,159

2.2 × 10-5

1.9 × 10-3

IN

PRPF4B

29

Common carotid artery IMT

rs853407

6

4,002,232

2.2 × 10-5

2.5 × 10-3

IN

PRPF4B

158,532,867

2.2 × 10-5

1.8 × 10-3

IN

VEPH1

Gene position

Gene symbol

30

Common carotid artery IMT

rs1581413

3

2b. Most significant results for FBAT analyses for multivariable-adjusted subclinical atherosclerosis measures

FBAT
rank

1

Phenotype

SNP

Chr

Common carotid artery IMT

rs4814615

20

Physical location

GEE P-value

FBAT P-value

17,305,573

4.9 × 10-5

3.4 × 10-6

IN

PCSK2

3.7 × 10-62

Common carotid artery IMT

rs6053733

20

5,763,074

7.2 × 10-3IN

C20orf196

3

Ankle brachial index

rs10499903

790,504,268

1.9 × 10-3

4.1 × 10-6

NEAR

PFTK1, FZD1

4

Ankle brachial index

rs9302997

1771,723,439

4.8 × 10-3

5.1 × 10-6

IN

RNF15723,733,535

0.01

8.6 × 10-6

OUT

1.0 × 10-5

OUT

5

Ankle brachial index

rs590183

4

7

Ankle brachial index

rs6832344

4

23,733,805

9.9 × 10-3

8Abdominal aortic calcification

rs1023568

2

191,340,948

0.09

1.2 × 10-5

IN

NAB1

9

Common carotid artery IMT

rs2214959

12

124,674,521

0.01

1.5 × 10-5

NEAR

TMEM132B

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Table 2: 25 Most significant results for multivariable-adjusted subclinical atherosclerosis measures in multiple arterial territories by
GEE (2a), FBAT (2b) and linkage (2c) analyses (Continued)
10

Ankle brachial index

rs9285151

13

42,712,236

0.06

1.7 × 10-5

IN

ENOX1

11

Ankle brachial index

rs1381632

4

88,926,163

0.36

1.8 × 10-5

NEAR

DMP1

3.1 × 10-5

OUT

12

Coronary artery calcification

rs4461066

16

51,469,110

8.7 × 10-3

15Coronary artery calcification

rs367421

20

15,202,689

2.5 × 10-3

3.6 × 10-5IN

C20orf133

16

Internal carotid artery IMT

rs9323431

14

62,638,241

0.24

3.6 × 10-5

IN

KCNH5

17

Common carotid artery IMT

rs1997463

354,782,280

0.01

3.8 × 10-5

IN

CACNA2D3

4.6 × 10-5

OUT

19

Common carotid artery IMT

rs304409

18

1,096,342

1.8 × 10-4

21Internal carotid artery IMT

rs1349008

3

76,804,103

0.23

4.9 × 10-5

OUT

22

Ankle brachial index

rs2251671

8

40,501,973

1.3 × 10-4

4.9 × 10-5

NEAR

23

Abdominal aortic calcification

rs2430302

60,518,222

3.1 × 10-5

5.6 × 10-5

OUT

6.0 × 10-5

ZMAT4

24

Abdominal aortic calcification

rs321967

7

77,959,200

4.7 × 10-4

IN

MAGI2

25

Ankle brachial index

rs6569792

6

132,736,444

0.56

6.4 × 10-5

IN

MOXD1

26

Abdominal aortic calcification

rs4985741

17

17,028,605

7.2 × 10-3

6.6 × 10-5

IN

MRIP

27

Ankle brachial index

rs7995026

13

42,751,550

6.9 × 10-4

6.9 × 10-5

IN

ENOX1

28

Coronary artery calcification

rs10520541

4184,055,754

0.08

7.0 × 10-5IN

AK001336

29

Internal carotid artery IMT

rs2113945

15

29,111,823

0.09

7.2 × 10-5

IN

TRPM1

30

Common carotid artery IMT

rs6944400

7

131,389,740

0.12

8.2 × 10-5

OUT

2c. Genomic regions with LOD > 2.0 for linkage analyses for multivariable-adjusted subclinical atherosclerosis measures

Rank

PhenotypeMarkerChr

Physical location

LOD score 1.5 interval bounds

Maximum
LOD

1

Internal carotid artery IMT

ATA29A06

12

128,922,618

127,583,749

129,277,512

5.05

2

Internal carotid artery IMT

rs95998712

127,817,680

126,600,362

129,277,512

4.78

3

Internal carotid artery IMT

rs1180937

1

54,903,665

45,417,474

56,950,040

4.23

8

Abdominal aortic calcification

rs17030524

12

99,640,364

97,320,770

102,339,523

3.51

6

Coronary artery calcification

SNP_A1679277

6

149,300,408

136,476,887

154,228,170

2.81

7

Ankle brachial index

rs3948842

151,954,268

142,801,622

168,477,710

2.73

9

Internal carotid artery IMT

rs300574

4

124,681,343

118,755,997

131,657,093

2.49

10

Internal carotid artery IMT

rs714647

15

99,870,157

96,719,695

100,152,332

2.49

12

Coronary artery calcification

rs1106679

14

102,555,405

91,636,556

106,312,036

2.33

13

Abdominal aortic calcification

rs1454183

213,935,308

3,238,478

20,119,479

2.31

15

Coronary artery calcification

rs7000744

8

140,003,669

136,360,837

146,039,126

2.23

16

Ankle brachial index

rs10483084

21

42,268,393

40,113,290

45,025,009

2.12

17

Internal carotid artery IMT

rs1948685

5

21,064,843

10,288,046

31,560,322

2.11

19

Internal carotid artery IMT

rs10832008

11

13,232,905

6,384,682

19,637,706

2.09

Abbreviations: SNP = single nucleotide polymorphism; Chr = chromosome; GEE = generalized estimating equations; FBAT = family based association testing; LOD =logarithm of the odds; IMT = intimal medial thickness;. Gaps appear in the rank order for markers that appear more than once. In the column entitled “Phenotype”, 'internal
carotid artery IMT' denotes either mean or maximum internal carotid IMT, 'common carotid artery IMT' denotes either mean or maximum common carotid IMT, 'coronary
artery calcification' denotes either mean CAC or max CAC, and 'ankle brachial index' denotes and ankle brachial index from either examination cycle 6 or cycle 7. For
proximity to known genes (Table columns nine and ten), “IN” refers to a SNP within a protein-coding gene intron or exon, “OUT” refers to a SNP greater than 60 kb awayfrom a protein-coding gene gene, and “NEAR” refer to a SNP within 60 kb of a protein-coding gene.

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Table 3: Five most significant association results using FBAT and GEE within phenotype clusters for each of five multivariable-adjusted
subclinical atherosclerosis measures
Phenotype group

Rank GEE/FBAT

SNP

Chr

Physicallocation%Phenotypes
P < 0.01 for
GEE & FBATGEE P-value
geometric
mean

MAF

Gene position

Ankle brachial index

1
2
3
4
5

rs7989017
rs7546903
rs6135095
rs1875517
rs6507763

13
1
20
3
18

25,164,878
6,870,538
1,422,405
118,790,257
43,310,669

100
75
75
75
75

4.7 × 10-3
1.7 × 10-3
3.0 × 10-3
3.4 × 10-3
3.5 × 10-3

0.22
0.27
0.14
0.44
0.14

IN
IN
NEAR
OUT
OUT

Common carotid artery
IMT1

rs1039610

18

74,049,795

100

4.9 × 10-5

0.32

OUT

2
3
4
5

rs1587893
rs28207
rs4814615
rs2470209

18
5
20
17

74,039,913
13,267,852
17,305,573
28,263,442

100
100
100
100

5.8 × 10-5
8.1 × 10-5
1.3 × 10-4
1.9 × 10-4

0.31
0.37
0.16
0.39

OUT
OUT
IN
NEAR

1

rs933890

12

106,941,389

100

6.6 × 10-4

0.22

OUT

2
3
4
5

rs8075776
rs252984
rs10490889
rs10516308

17
5
3
4

36,408,189
106,780,236
6,257,028
16,780,451

100
100
100
100

9.8 × 10-4
1.0 × 10-3
1.6 × 10-3
3.3 × 10-3

0.16
0.16
0.36
0.41

NEAR
IN
OUT
OUT

1

rs10483853

14

72,826,052

100

9.8 × 10-6

0.19

IN

2
3
4
5

rs10507130
rs9321354
rs220457
rs10505182

12
6
17
8

100,256,422
132,946,880
27,126,748
113,337,274

100
100
100
100

1.3 × 10-5
3.4 × 10-5
3.8 × 10-4
5.2 × 10-4

0.22
0.12
0.25
0.23

IN
NEAR
OUT
IN

Internal carotid artery
IMT

Coronary artery
calcification

Gene symbol

ATP8A2
CAMTA1SIRPB2

PCSK2
MYO1D

KRTAP1-1, KRTAP1-3
EFNA5

NUMB
DRIM
TAAR8, TAAR6, TAAR5
CSMD3

Abbreviations: SNP = single nucleotide polymorphism; Chr = chromosome; GEE = generalized estimating equations; FBAT = family based association testing; IMT = intimal
medial thickness; MAF = minor allele frequency.
Column Definitions: For each Phenotype Group (Table column one), a cluster analysis is conducted for several phenotype variables defined in Table 1. For “Ankle brachialindex,” GEE and FBAT results were evaluated for RANKLEBI6, RANKLEBI6MV, RANKLEBI7, and RANKLEBI7MV; for “Common carotid artery IMT,”RNKCAROTCCAMAXAS6, RNKCAROTCCAMAXMV6, RNKCAROTCCAMEANAS6, and RNKCAROTCCAMEANMV6; for “Internal carotid artery IMT,”RNKCAROTICAMAXAS6, RNKCAROTICAMAXMV6, RNKCAROTICAMEANAS6, and RNKCAROTICAMEANMV6; and for “Coronary artery calcification,”
RESMDCTCACMAXMV7 and RESMDCTCACMV7. The percent of phenotypes in the cluster analysis for which there was a p < 0.01 by both GEE and FBAT analyses isshown in Table column five. The geometric mean p-value is shown in Table column six for GEE associations for all phenotypes in the cluster analyses. For proximity to known
genes (Gene position, Table column eight), “IN” refers to a SNP within a protein-coding gene intron or exon, “OUT” refers to a SNP greater than 60 kb away from a proteincoding gene gene, and “NEAR” refer to a SNP within 60 kb of a protein-coding gene.

Of these genes, there were 28 genes with SNPs on the
100K array that were in or nearby (within 60 kb of) the
gene. In Table 4, we report significant associations for
SNPs in or near nine of these 28 candidate genes with p <0.01 in GEE or FBAT analyses or p < 0.05 in both GEE andFBAT analyses. Associations were noted with one or more
SCA measure for: CCR5, FGB, ESR1, IL6, NOS3,TNFRSF11, ADM, CD44, and CCL2 (Table 4).

Discussion
In this manuscript, we report the principle findings for a
GWAS of SNPs from a 100K Affymetrix scan with SCA
phenotypes in multiple major arterial beds. A conservative Bonferroni correction using this number of tests(0.05/1,000,000) would yield an approximate thresholdof genome-wide significance to be 5*10-8, and none of
our association findings meet these criteria for genomewide association., However, we did note a number ofassociations with p < 10-5 after rank-ordering SNP associations using GEE and/or FBAT analysis. While we expectthat many of these genotype-phenotype associations may

be false positives, the SNPs identified in this manner arenonetheless hypothesized to include SNPs that merit further follow-up. To our knowledge, this is the first report ofa GWAS using SCA phenotypes.
The first replicated GWAS findings for clinically apparent
coronary heart disease were recently reported by two independent groups, identifying an association with SNPs in aregion of chromosome 9p21 [23,24]. Interestingly, there
was an association of this chromosome 9 region with CAC
in one replication study [24], and this same chromosome
9 region was associated with diabetes mellitus in threeother recent GWAS [20-22]. When we examined our GEE
results for various SCA phenotypes, there were nominallysignificant associations with multivariable-adjusted CAC
for several SNPs on chromosome 9 lying within a 15 kB
region implicated in the recent GWAS's for coronary heartdisease. While these SNPs were not our top-ranked SNPs,
their specific association with coronary artery atherosclerosis (CAC) phenotypes likely represent strong evidenceof replication in light of the recent GWAS.

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Table 4: SNPs in or near previously reported candidate genes for coronary heart disease or subclinical atherosclerosis for
multivariable-adjusted SCA phenotypes: SNPs with p < 0.01 in GEE or FBAT analyses, or p < 0.05 in both GEE and FBAT analyses.
Chr

Gene

N SNPs within 60 kb

SNP from Affy 100K Array

MAF

Physical Location

3

CCR5

1/6

rs3762823

0.25

46,371,620

4
6

FGB
ESR1

1/7
8/18

rs871540
rs3866461

0.22
0.12

155,766,635
152,238,835

rs3853250
rs3853251
rs722208

0.45
0.34
0.27

152,252,014
152,252,870
152,414,999

rs3798573
rs3778099
rs2813563
rs725467
rs1581498

0.11
0.10
0.19
0.19
0.45

152,481,476
152,510,689
152,538,601
152,539,833
22,681,483

rs10240716
rs768403
rs7812088

0.27
0.38
0.12

22,765,225
150,297,894
150,357,477

rs10505346
rs10500724
rs4444073
rs4444073
rs1365057

0.21
0.44
0.47

120,033,024
10,258,592
10,288,240

0.22

35,066,251

rs353589

0.21

35,077,802

rs353639

0.21

35,140,940

rs353638

0.21

35,141,103

rs353636

0.21

35,141,306

rs353635
rs353631

0.46
0.20

35,141,399
35,144,144

rs1467558

0.21

35,186,249

rs10488813
rs7115246
rs1024612

0.13
0.46
0.41

35,225,530
35,243,054
29,573,469

7

IL6

2/9

7

NOS3

2/3

8
11

TNFRSF11
ADM

1/9
3/5

11

CD44

10/24

17

CCL2

1/7

Phenotype

Lowest P-value

Common carotid artery IMTCommon carotid artery IMT
Abdominal aortic calcification
Coronary artery calcification
Coronary artery calcification
Ankle brachial index
Ankle brachial index
Coronary artery calcification
Coronary artery calcification
Internal carotid artery IMTCommon carotid artery IMT
Abdominal aortic calcification
Abdominal aortic calcification
Coronary artery calcification
Coronary artery calcification
Ankle brachial index
Abdominal aortic calcification
Internal carotid artery IMTInternal carotid artery IMT
Ankle brachial index
Ankle brachial index
Ankle brachial index
Ankle brachial index
Coronary artery calcification
Coronary artery calcification
Ankle brachial index
Abdominal aortic calcification
Ankle brachial index
Ankle brachial index
Ankle brachial index
Ankle brachial index
Ankle brachial index
Ankle brachial index
Ankle brachial index
Ankle brachial index
Ankle brachial index
Coronary artery calcification
Coronary artery calcification
Abdominal aortic calcification
Ankle brachial index
Abdominal aortic calcification

GEE

FBAT

0.02
0.05
0.11
5.4 × 10-3
3.4 × 10-3
0.05
0.03
0.54
0.57
6.4 × 10-3
0.82
0.02
0.02
0.16
0.04
0.01
2.0 × 10-3
0.05
0.05
0.04
2.0 × 10-4
3.3 × 10-3
0.01
0.05
0.05
9.0 × 10-3
7.4 × 10-3
8.3 × 10-3
3.1 × 10-3
5.6 × 10-3
4.2 × 10-3
7.6 × 10-3
4.2 × 10-3
0.09
4.8 × 10-3
4.5 × 10-3
0.03
0.03
8.8 × 10-3
7.1 × 10-3
3.6 × 10-3

0.04
0.04
3.0 × 10-3
0.11
0.12
0.04
0.02
5.7 × 10-3
5.0 × 10-3
0.78
6.9 × 10-3
0.03
0.03
7.7 × 10-3
3.3 × 10-3
0.04
0.02
5.5 × 10-3
0.01
0.05
5.0 × 10-4
5.0 × 10-4
0.04
8.9 × 10-3
7.7 × 10-3
0.08
0.10
0.03
0.04
0.05
0.06
0.05
0.06
4.6 × 10-3
0.06
0.15
0.02
0.02
0.48
9.4 × 10-3
0.22

Abbreviations: SNP = single nucleotide polymorphism; Chr = chromosome; GEE = generalized estimating equations; FBAT = family based association testing; IMT = intimal
medial thickness; MAF = minor allele frequency.
Of 37 candidate genes considered, there were 28 genes covered by the 100K array and meeting exclusion criteria, of which nine genes had a significantly associated SNP(s)with either p < 0.01 for GEE, p < 0.01 for FBAT, or p < 0.05 for both GEE and FBAT. Genes without significant associations meeting these criteria included: F5, MTHFR, REN,
APOB, CX3CR1, GATA2, EDN1, CTGF, VEGF, PON1, MMP3, SCARB1, ALOX5AP, CETP, ITGB3, NOS2A, APO3, and MMP9. However, it should be noted that 100K SNP coverage ofany given gene region may be insufficient to exclude real associations. Better coverage may be afforded by newer, more dense SNP arrays. Only SNPs within the introns orexons of the candidate gene or no greater than 60 kb away from the candidate gene were considered for Table 4 (denominator of Column 3).

Because there may be distinct genetic determination for
atherosclerosis occurring in individual vascular beds (e.g.,
carotid arteries versus coronary arteries versus aorta versus
peripheral arteries), we further examined for consistencyof association of specific SNPs within five trait groups –ABI, common carotid artery IMT, internal carotid artery
IMT, AAC and CAC. By this method of ranking associations, we identified distinct sets of SNPs, for which there
was consistency of overlap with top SNP results by both
GEE and FBAT (Table 3). There is evidence for partial correlation between SCA measures [6], and it is interesting tonote that there were some SNPs for which there were
nominally significant associations with more than one
SCA measure. For example, rs10263213 is associated withboth multivariable adjusted CAC (p = 0.001) and multivariable adjusted wall thickness of the carotid bulb (p =0.013). Further research is warranted to determine
whether SNPs showing association with two or more SCA
measures are more likely to replicate in follow-up studies.
Again, because no results from these approaches met criteria for genome-wide association, further validation
studies will be required to confirm such associations.

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An extensive literature exists for prior studies of candidate
gene variation associated with clinical atherosclerotic cardiovascular disease, in particular coronary heart disease[15], as well as SCA, particularly carotid IMT [8]. While
many associations are not consistently noted from study
to study, a review of the available studies reveals a modest
overall association of clinical cardiovascular disease or
SCA with variants in candidate genes, such as APOE, ACE,
and NOS3 [15]. More recently, a number of screens oflarger numbers of variants have revealed new candidate
gene hypotheses, such as ALOX5AP [33,34] and LGALS2[35]. When we examined for associations between SCA
and SNPs on the 100K chip that reside in or near somepreviously studied candidate genes, we confirmed anumber of modestly significant SNP associations with
SCA phenotypes (Table 4). These and other associations
between SNPs and various SCA measures may be viewed
as supportive though not strongly confirmatory of priorhypotheses. However, it should be noted that 100K SNP
coverage of any given gene region may be insufficient to
exclude real associations. Better coverage may be affordedby newer, more dense SNP arrays. Nevertheless, these datasuggest that GWAS may be used to contribute substantial
information to the literature on the presence and strengthof previously reported "candidate gene" associations,
including those identified from other GWAS, as well as forunbiased searches for novel variants.
Our study was conducted in a moderate-sized, well-characterized community-based sample, and the conduct of
SCA imaging was conducted without ascertainment forprior cardiovascular disease. Strengths and limitations of
the GWAS in this sample using a 100K screen are discussed in the Overview [25]. A strength of this particularstudy is the substantial heritability of SCA phenotypes
determined by high resolution imaging conducted in a
reproducible manner. While these SCA phenotypes represent state of the art, non-invasive imaging in populations,
it must be acknowledged that the available modalities
allow a focus on only fixed anatomic components (e.g.,
calcific plaque or IMT) rather than dynamic or metabolically active components. Moreover, the occurrence anddistribution of these modalities may differ by race or environmental background. Thus, our results may not extrapolate to non-white populations.
In conclusion, using a GWAS unconstrained by existingknowledge, we have identified new candidate SNP association hypotheses and further confirm some existing candidate gene and candidate SNP hypotheses. In particular,
the evidence for association of several SNPs with CAC in
the region of chromosome 9 that was recently reported to
be associated with CHD or CAC in tens of thousands ofsubjects [23,24] provides evidence that we were able toidentify true associations. These findings provide evidence

http://www.biomedcentral.com/1471-2350/8/S1/S4

for the exciting promise of GWAS of SCA to identify novel
genetic variants underlying atherosclerosis within specificarterial territories or across multiple arterial territories. In
this manuscript and in the accompanying web-posted
results
http://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?id=phs000007., we provide a full disclosureof the totality of our results, including all "non-significant" associations. In light of the multiple association
tests that result from a GWAS, a powerful and efficient follow-up approach to increase the statistical confidence foran association of common variants with complex traits isa staged design, in which a modest number of results fromthe first stage are then tested in a second independent
sample and the combined statistical evidence considered[36]. A further approach may be to seek evidence of in silico replication of our initial findings in other GWAS studies. The latter approach will be feasible with completionof the planned dense GWAS (the NHLBI's SNP Health
Association [SHARe] Study) in over 9,000 men andwomen from the Framingham Heart Study [37].

Abbreviations
AAC = abdominal aortic calcium; ABI = ankle-brachialindex; CAC = coronary artery calcium; FBAT = familybased association test; GEE = generalized estimating equations; GWAS = genome-wide association study; IMT = intimal-media thickness; LOD = logarithm of the odds;
MDCT = multidetector computed tomography; SCA =subclinical atherosclerosis; SNP = single-nucleotide polymorphism.

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

Authors' contributions
CJO conceived of the MDCT project, planned the analyses, and drafted and critically revised the manuscript. LAC
assisted in planning andconducting the analyses, andinwriting and critically revising the manuscript. RBD
assisted in planning the carotid measurements and contributed to critically revised the manuscript. CSF assisted
in planning andconducting the analyses, and inwriting
and critically revising the manuscript. UH planned the
MDCT project and assisted in writing and critically revising the manuscript. SJH planned and conducted the analyses. EI critically revised the manuscript. CYL assisted in
the analysis and critically revised the manuscript. JMM
conceived of the ABI measurement project and contributed to drafting and critically revising the manuscript. JFP
contributed to conceiving the carotid measurement
project and contributed to critically revising the manuscript. PAW contributed to conceiving the carotid measurement project and contributed to critically revising themanuscript. SD planned and conducted the analyses, anddrafted and critically revised the manuscript.

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BMC Medical Genetics 2007, 8(Suppl 1):S4

Acknowledgements

http://www.biomedcentral.com/1471-2350/8/S1/S4

14.

The authors would like to thank the following collaborators: Martin G. Larson, ScD, Daniel Levy, MD, and Thomas J. Wang, MD. Supported by the by
the National Heart, Lung, and Blood Institute's Framingham Heart Study(Contract No. N01-HC-25195). A portion of the research was conductedusing the Boston University Linux Cluster for Genetic Analysis (LinGA)
funded by the NIH NCRR (National Center for Research Resources)
Shared Instrumentation grant (1S10RR163736-01A1). The funding agencies
had no role in the design, conduct or analysis of the research. The National
Heart Lung and Blood Institute reviewed the manuscript prior to submission.

16.

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.

18.

15.

17.

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