Volume 8 Supplement 1

The Framingham Heart Study 100,000 single nucleotide polymorphisms resource

Open Access

Genome-wide association study for subclinical atherosclerosis in major arterial territories in the NHLBI's Framingham Heart Study

  • Christopher J O'Donnell1, 5, 9Email author,
  • 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 and
  • Serkalem Demissie3
BMC Medical Genetics20078(Suppl 1):S4

DOI: 10.1186/1471-2350-8-S1-S4

Published: 19 September 2007

Abstract

Introduction

Subclinical atherosclerosis (SCA) measures in multiple arterial beds are heritable phenotypes that are associated 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 in subjects tested for quantitative SCA phenotypes, including ankle-brachial index, coronary artery calcification and abdominal 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 the associated 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 with coronary 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 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 a staged 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 of death 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 most cases of clinically apparent cardiovascular disease by decades, and subclinical atherosclerosis (SCA) is quite common in young and middle-aged persons [35]. SCA can be detected 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 ankle-brachial index (ABI); internal and common carotid intimal medial thickness (IMT) detected by B-mode ultrasound; and coronary artery calcium (CAC) and abdominal 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 and environmental factors underlie interindividual variability in SCA, and significant heritability has been found for measures 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 cardiovascular disease independent of risk factors [1114].

While SCA measures are heritable, relatively little is known regarding the role of genetic variants in the interindividual variability in these quantitative measures of atherosclerosis. 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 and age-related macular degeneration [16], INSIG2 and obesity [17], NOS1AP with QT interval variation [18], and several genes including TCF7L2 [19] as well as IGF2BP2 and CDKA1 with diabetes mellitus [2022]. Additionally, two other 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 in three previous GWAS [2022]. 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 the lower extremity arteries, carotid arteries, aorta and coronary arteries, by conduct of genetic association analyses and genetic linkage mapping. This report is one of a series of manuscripts from a collaborative project conducted by Framingham Heart Study investigators; the overall approach to this project's statistical genetic methods is summarized 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 not undergo 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 were analyzed. Details regarding selection of participants and genotyping are provided in the Overview [25].

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 and weight, 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 the analysis were obtained from the earlier examination cycle.

Carotid ultrasonography for carotid IMT

Participants 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 a high 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 degree of 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 and internal carotid artery were defined as the mean of the wall 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 with an eight-slice multidetector computed tomography (Lightspeed Ultra, GE, Milwaukee, WI, USA) of the chest, as previously described [30], as well as the abdomen. Each subject 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 were acquired covering 150 mm above the level of S1. A calibration 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 two chest 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 midpoint of 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 and deflated at a rate of 2 mmHg per second until the systolic blood 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 blood pressure in the ankle divided by the average systolic blood pressure 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 for analysis.

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, systolic blood pressure and use of anti-hypertensive treatment were used as covariates. For carotid IMT, treatment-adjusted 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 the subject were not on treatment [31]. Ranked normalized deviates, created from the standardized residuals from the regression models, were used in the genetic analyses. For each sex, SCA phenotypes were age-adjusted and multivariable-adjusted; details of the covariates included in the multivariable adjustment for each phenotype are presented in Table 1.
Table 1

Phenotype distribution, examination cycle, and numbers of participants with subclinical atherosclerosis phenotypes

Subclinical atherosclerosis measure

Phenotype Variable name

N

Offspring, exam

Covariate adjustment

Ankle-brachial index

RANKLEBI6

984

Offspring, 6

Age, sex-specific

 

RANKLEBI6MV

984

Offspring, 6

Age, smoking, diabetes, hypertension, total cholesterol/HDL ratio, log triglyceride, sex-specific

 

RANKLEBI7

982

Offspring, 7

Age, sex-specific

 

RANKLEBI7MV

982

Offspring, 7

Age, smoking, diabetes, hypertension, total cholesterol/HDL ratio, log triglyceride, sex-specific

Maximum carotid artery bulb IMT

RNKCAROTBULBAS6

959

Offspring, 6

Age, sex-specific

 

RNKCAROTBULBMV6

951

Offspring, 6

Age, treatment-adjusted systolic blood pressure, BMI, cigarettes per day, HDL cholesterol, total cholesterol, diabetes, log triglyceride, and in women, menopausal status, hormone therapy, sex-specific

Maximum common carotid artery IMT

RNKCAROTCCAMAXAS6

978

Offspring, 6

Age, sex-specific

 

RNKCAROTCCAMAXMV6

969

Offspring, 6

Age, treatment-adjusted systolic blood pressure, BMI, cigarettes per day, HDL cholesterol, total cholesterol, diabetes, log triglyceride, and in women, menopausal status, hormone therapy, sex-specific

Mean common carotid artery IMT

RNKCAROTCCAMEANAS6

978

Offspring, 6

Age, sex-specific

 

RNKCAROTCCAMEANMV6

969

Offspring, 6

Age, treatment-adjusted systolic blood pressure, BMI, cigarettes per day, HDL cholesterol, total cholesterol, diabetes, log triglyceride, and in women, menopausal status, hormone therapy, sex-specific

Maximum internal carotid artery IMT

RNKCAROTICAMAXAS6

888

Offspring, 6

Age, sex-specific

 

RNKCAROTICAMAXMV6

880

Offspring, 6

Age, treatment-adjusted systolic blood pressure, BMI, cigarettes per day, HDL cholesterol, total cholesterol, diabetes, log triglyceride, and in women, menopausal status, hormone therapy, sex-specific

Mean internal carotid artery IMT

RNKCAROTICAMEANAS6

888

Offspring, 6

Age, sex-specific

 

RNKCAROTICAMEANMV6

880

Offspring, 6

Age, treatment-adjusted systolic blood pressure, BMI, cigarettes per day, HDL cholesterol, total cholesterol, diabetes, log triglyceride, and in women, menopausal status, hormone therapy, sex-specific

Maximum carotid artery stenosis

RNKCAROTSTENAS6

977

Offspring, 6

Age, sex-specific

 

RNKCAROTSTENMV6

968

Offspring, 6

Age, treatment-adjusted systolic blood pressure, BMI, cigarettes per day, HDL cholesterol, total cholesterol, diabetes, log triglycerides, menopausal status, hormone therapy, sex-specific

Mean abdominal aortic calcification

RESMDCTAACAS7

675

Offspring, 7

Age, sex-specific

 

RESMDCTAACMV7

673

Offspring, 7

Age, BMI, current cigarette smoking status, diabetes, systolic blood pressure, anti-HTN therapy, total-to-HDL-C ratio, lipid therapy, sex-specific

Mean coronary artery calcification

RESMDCTCACAS7

680

Offspring, 7

Age, sex-specific

 

RESMDCTCACMV7

678

Offspring, 7

Age, BMI, current cigarette smoking status, Diabetes, systolic blood pressure, anti-HTN therapy, total-to-HDL cholesterol ratio, lipid therapy, sex-specific

Maximum coronary artery calcification

RESMDCTCACMAXAS7

680

Offspring, 7

Age, sex-specific

 

RESMDCTCACMAXMV7

678

Offspring, 7

Age, BMI, current cigarette smoking status, diabetes, systolic blood pressure, anti-HTN therapy, total-to-HDL cholesterol ratio, lipid therapy, sex-specific

Abbreviations: 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.

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 regarding these 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].
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

Phenotype

SNP

Chr

Physical location

GEE P-value

FBAT P-value

Gene position

Gene symbol

1

Internal carotid artery IMT

rs1376877

2

204,097,596

3.8 × 10-7

0.15

IN

ABI2

3

Coronary artery calcification

rs2390582

1

90,655,928

1.4 × 10-6

0.34

OUT

 

4

Abdominal aortic calcification

rs3849150

10

49,779,229

1.6 × 10-6

4.0 × 10-3

NEAR

LRRC18

6

Ankle brachial index

rs2896103

5

13,817,419

4.5 × 10-6

0.01

IN

DNAH5

7

Coronary artery calcification

rs10483853

14

72,826,052

6.1 × 10-6

1.7 × 10-3

IN

NUMB

8

Common carotid artery IMT

rs1400544

1

186,593,061

6.2 × 10-6

4.7 × 10-4

OUT

 

9

Ankle 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

11

Ankle brachial index

rs1320267

4

127,290,897

6.9 × 10-6

0.03

OUT

 

13

Ankle brachial index

rs1350445

11

91,591,802

8.5 × 10-6

0.07

OUT

 

14

Ankle brachial index

rs1502050

5

13,832,743

8.7 × 10-6

0.02

IN

DNAH5

15

Coronary artery calcification

rs10519394

4

137,962,214

1.1 × 10-5

0.03

OUT

 

16

Internal carotid artery IMT

rs683366

18

52,370,975

1.2 × 10-5

0.01

NEAR

TXNL1

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

 

22

Ankle brachial index

rs10501784

11

91,592,192

1.6 × 10-5

0.30

OUT

 

23

Ankle brachial index

rs10493529

1

74,335,469

1.9 × 10-5

0.098

NEAR

LRRC44, FPGT

24

Common carotid artery IMT

rs28207

5

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

C18orf20

26

Ankle brachial index

rs2949535

18

26,111,783

2.0 × 10-5

7.5 × 10-4

OUT

 

27

Coronary artery calcification

rs2270861

12

100,238,849

2.0 × 10-5

6.8 × 10-3

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

30

Common carotid artery IMT

rs1581413

3

158,532,867

2.2 × 10-5

1.8 × 10-3

IN

VEPH1

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

FBAT rank

Phenotype

SNP

Chr

Physical location

GEE P-value

FBAT P-value

Gene position

Gene symbol

1

Common carotid artery IMT

rs4814615

20

17,305,573

4.9 × 10-5

3.4 × 10-6

IN

PCSK2

2

Common carotid artery IMT

rs6053733

20

5,763,074

7.2 × 10-3

3.7 × 10-6

IN

C20orf196

3

Ankle brachial index

rs10499903

7

90,504,268

1.9 × 10-3

4.1 × 10-6

NEAR

PFTK1, FZD1

4

Ankle brachial index

rs9302997

17

71,723,439

4.8 × 10-3

5.1 × 10-6

IN

RNF157

5

Ankle brachial index

rs590183

4

23,733,535

0.01

8.6 × 10-6

OUT

 

7

Ankle brachial index

rs6832344

4

23,733,805

9.9 × 10-3

1.0 × 10-5

OUT

 

8

Abdominal 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

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

12

Coronary artery calcification

rs4461066

16

51,469,110

8.7 × 10-3

3.1 × 10-5

OUT

 

15

Coronary artery calcification

rs367421

20

15,202,689

2.5 × 10-3

3.6 × 10-5

IN

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

3

54,782,280

0.01

3.8 × 10-5

IN

CACNA2D3

19

Common carotid artery IMT

rs304409

18

1,096,342

1.8 × 10-4

4.6 × 10-5

OUT

 

21

Internal 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

ZMAT4

23

Abdominal aortic calcification

rs243030

2

60,518,222

3.1 × 10-5

5.6 × 10-5

OUT

 

24

Abdominal aortic calcification

rs321967

7

77,959,200

4.7 × 10-4

6.0 × 10-5

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

4

184,055,754

0.08

7.0 × 10-5

IN

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

Phenotype

Marker

Chr

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

rs959987

12

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_A-1679277

6

149,300,408

136,476,887

154,228,170

2.81

 

7

Ankle brachial index

rs394884

2

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

2

13,935,308

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 away from a protein-coding gene gene, and “NEAR” refer to a SNP within 60 kb of a protein-coding gene.

We further sought to identify SNPs with consistent associations with phenotypes within measurement groups using GEE and FBAT analyses. SNPs were selected based on associations with multiple correlated traits in both population- and family-based tests. We evaluated the following five phenotypic subgroups using sex-specific age-adjusted 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-sex-adjusted and multivariable-adjusted measures of ankle-brachial index from both examination cycle 6 and 7 (in Table 1, these variables are named RANKLEBI6, RANKLEBI6MV, RANKLEBI7, and RANKLEBI7MV). Results are displayed in Table 3 of the top 5 SNPs with the highest proportions of significantly (P < 0.01) associated phenotypes. For identical proportions of significant phenotypes, SNPs were additionally ranked by the logarithm of the mean of GEE p-values.
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

Physical location

%Phenotypes

P < 0.01 for GEE & FBAT

GEE P-value geometric mean

MAF

Gene position

Gene symbol

Ankle brachial index

1

rs7989017

13

25,164,878

100

4.7 × 10-3

0.22

IN

ATP8A2

 

2

rs7546903

1

6,870,538

75

1.7 × 10-3

0.27

IN

CAMTA1

 

3

rs6135095

20

1,422,405

75

3.0 × 10-3

0.14

NEAR

SIRPB2

 

4

rs1875517

3

118,790,257

75

3.4 × 10-3

0.44

OUT

 
 

5

rs6507763

18

43,310,669

75

3.5 × 10-3

0.14

OUT

 

Common carotid artery IMT

1

rs1039610

18

74,049,795

100

4.9 × 10-5

0.32

OUT

 
 

2

rs1587893

18

74,039,913

100

5.8 × 10-5

0.31

OUT

 
 

3

rs28207

5

13,267,852

100

8.1 × 10-5

0.37

OUT

 
 

4

rs4814615

20

17,305,573

100

1.3 × 10-4

0.16

IN

PCSK2

 

5

rs2470209

17

28,263,442

100

1.9 × 10-4

0.39

NEAR

MYO1D

Internal carotid artery IMT

1

rs933890

12

106,941,389

100

6.6 × 10-4

0.22

OUT

 
 

2

rs8075776

17

36,408,189

100

9.8 × 10-4

0.16

NEAR

KRTAP1-1, KRTAP1-3

 

3

rs252984

5

106,780,236

100

1.0 × 10-3

0.16

IN

EFNA5

 

4

rs10490889

3

6,257,028

100

1.6 × 10-3

0.36

OUT

 
 

5

rs10516308

4

16,780,451

100

3.3 × 10-3

0.41

OUT

 

Coronary artery calcification

1

rs10483853

14

72,826,052

100

9.8 × 10-6

0.19

IN

NUMB

 

2

rs10507130

12

100,256,422

100

1.3 × 10-5

0.22

IN

DRIM

 

3

rs9321354

6

132,946,880

100

3.4 × 10-5

0.12

NEAR

TAAR8, TAAR6, TAAR5

 

4

rs220457

17

27,126,748

100

3.8 × 10-4

0.25

OUT

 
 

5

rs10505182

8

113,337,274

100

5.2 × 10-4

0.23

IN

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 brachial index,” 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 is shown 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 protein-coding gene gene, and “NEAR” refer to a SNP within 60 kb of a protein-coding gene.

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 multivariable-adjusted phenotypes described in Table 1. Full-disclosure of 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 in Table 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 near a known gene) associated with maximum CAC score, and rs3849150 (p = 2*10-6, located near LRRC18) associated with 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) associated with 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 our list 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 and rs1537371; p-values for association 1.1 × 10-4, 8.8 × 10-5, 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 displayed in 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 score results) 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 the phenotypes, 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 near regions of 37 candidate genes previously reported to have been associated with SCA or overt coronary heart disease. 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 and FBAT analyses. Associations were noted with one or more SCA measure for: CCR5, FGB, ESR1, IL6, NOS3, TNFRSF11, ADM, CD44, and CCL2 (Table 4).
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

Phenotype

Lowest P-value

       

GEE

FBAT

3

CCR5

1/6

rs3762823

0.25

46,371,620

Common carotid artery IMT

0.02

0.04

      

Common carotid artery IMT

0.05

0.04

4

FGB

1/7

rs871540

0.22

155,766,635

Abdominal aortic calcification

0.11

3.0 × 10-3

6

ESR1

8/18

rs3866461

0.12

152,238,835

Coronary artery calcification

5.4 × 10-3

0.11

      

Coronary artery calcification

3.4 × 10-3

0.12

   

rs3853250

0.45

152,252,014

Ankle brachial index

0.05

0.04

   

rs3853251

0.34

152,252,870

Ankle brachial index

0.03

0.02

   

rs722208

0.27

152,414,999

Coronary artery calcification

0.54

5.7 × 10-3

      

Coronary artery calcification

0.57

5.0 × 10-3

   

rs3798573

0.11

152,481,476

Internal carotid artery IMT

6.4 × 10-3

0.78

   

rs3778099

0.10

152,510,689

Common carotid artery IMT

0.82

6.9 × 10-3

   

rs2813563

0.19

152,538,601

Abdominal aortic calcification

0.02

0.03

   

rs725467

0.19

152,539,833

Abdominal aortic calcification

0.02

0.03

7

IL6

2/9

rs1581498

0.45

22,681,483

Coronary artery calcification

0.16

7.7 × 10-3

      

Coronary artery calcification

0.04

3.3 × 10-3

   

rs10240716

0.27

22,765,225

Ankle brachial index

0.01

0.04

7

NOS3

2/3

rs768403

0.38

150,297,894

Abdominal aortic calcification

2.0 × 10-3

0.02

   

rs7812088

0.12

150,357,477

Internal carotid artery IMT

0.05

5.5 × 10-3

      

Internal carotid artery IMT

0.05

0.01

8

TNFRSF11

1/9

rs10505346

0.21

120,033,024

Ankle brachial index

0.04

0.05

11

ADM

3/5

rs10500724

0.44

10,258,592

Ankle brachial index

2.0 × 10-4

5.0 × 10-4

   

rs4444073

0.47

10,288,240

Ankle brachial index

3.3 × 10-3

5.0 × 10-4

   

rs4444073

  

Ankle brachial index

0.01

0.04

11

CD44

10/24

rs1365057

0.22

35,066,251

Coronary artery calcification

0.05

8.9 × 10-3

      

Coronary artery calcification

0.05

7.7 × 10-3

   

rs353589

0.21

35,077,802

Ankle brachial index

9.0 × 10-3

0.08

      

Abdominal aortic calcification

7.4 × 10-3

0.10

   

rs353639

0.21

35,140,940

Ankle brachial index

8.3 × 10-3

0.03

      

Ankle brachial index

3.1 × 10-3

0.04

   

rs353638

0.21

35,141,103

Ankle brachial index

5.6 × 10-3

0.05

      

Ankle brachial index

4.2 × 10-3

0.06

   

rs353636

0.21

35,141,306

Ankle brachial index

7.6 × 10-3

0.05

      

Ankle brachial index

4.2 × 10-3

0.06

   

rs353635

0.46

35,141,399

Ankle brachial index

0.09

4.6 × 10-3

   

rs353631

0.20

35,144,144

Ankle brachial index

4.8 × 10-3

0.06

      

Ankle brachial index

4.5 × 10-3

0.15

   

rs1467558

0.21

35,186,249

Coronary artery calcification

0.03

0.02

      

Coronary artery calcification

0.03

0.02

   

rs10488813

0.13

35,225,530

Abdominal aortic calcification

8.8 × 10-3

0.48

   

rs7115246

0.46

35,243,054

Ankle brachial index

7.1 × 10-3

9.4 × 10-3

17

CCL2

1/7

rs1024612

0.41

29,573,469

Abdominal aortic calcification

3.6 × 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 of any 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 or exons of the candidate gene or no greater than 60 kb away from the candidate gene were considered for Table 4 (denominator of Column 3).

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 threshold of genome-wide significance to be 5*10-8, and none of our association findings meet these criteria for genome-wide association., However, we did note a number of associations with p < 10-5 after rank-ordering SNP associations using GEE and/or FBAT analysis. While we expect that many of these genotype-phenotype associations may be false positives, the SNPs identified in this manner are nonetheless hypothesized to include SNPs that merit further follow-up. To our knowledge, this is the first report of a 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 a region 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 three other recent GWAS [2022]. When we examined our GEE results for various SCA phenotypes, there were nominally significant 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 heart disease. While these SNPs were not our top-ranked SNPs, their specific association with coronary artery atherosclerosis (CAC) phenotypes likely represent strong evidence of replication in light of the recent GWAS.

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 consistency of 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 to note that there were some SNPs for which there were nominally significant associations with more than one SCA measure. For example, rs10263213 is associated with both 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.

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 of larger 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 some previously studied candidate genes, we confirmed a number 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 prior hypotheses. 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 afforded by newer, more dense SNP arrays. Nevertheless, these data suggest that GWAS may be used to contribute substantial information to the literature on the presence and strength of previously reported "candidate gene" associations, including those identified from other GWAS, as well as for unbiased 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 for prior 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 particular study 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 and distribution 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 existing knowledge, 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 of subjects [23, 24] provides evidence that we were able to identify true associations. These findings provide evidence for the exciting promise of GWAS of SCA to identify novel genetic variants underlying atherosclerosis within specific arterial territories or across multiple arterial territories. In this manuscript and in the accompanying web-posted results http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007., we provide a full disclosure of 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 for an association of common variants with complex traits is a staged design, in which a modest number of results from the 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 completion of the planned dense GWAS (the NHLBI's SNP Health Association [SHARe] Study) in over 9,000 men and women from the Framingham Heart Study [37].

Abbreviations

AAC: 

abdominal aortic calcium

ABI: 

ankle-brachial index

CAC: 

coronary artery calcium

FBAT: 

family-based 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.

Declarations

Acknowledgements

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 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). 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.

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.

Authors’ Affiliations

(1)
The National Heart, Lung, and Blood Institute's Framingham Heart Study
(2)
School of Medicine, Boston University
(3)
School of Public Health, Boston University
(4)
Department of Mathematics and Statistics, Boston University
(5)
Cardiology Division, Department of Medicine, Massachusetts General Hospital
(6)
Department of Radiology, Massachusetts General Hospital
(7)
Endocrinology Division, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Harvard University
(8)
Department of Radiology, Tufts-New England Medical Center
(9)
National Heart, Lung, and Blood Institute, National Institutes of Health

References

  1. Thom T, Haase N, Rosamond W, Howard VJ, Rumsfeld J, Manolio T, Zheng ZJ, Flegal K, O'Donnell C, Kittner S, Lloyd-Jones D, Goff DC, Hong Y, Adams R, Friday G, Furie K, Gorelick P, Kissela B, Marler J, Meigs J, Roger V, Sidney S, Sorlie P, Steinberger J, Wasserthiel-Smoller S, Wilson M, Wolf P: Heart disease and stroke statistics – 2006 update: a report from the American Heart Association Statistics Committee and Stroke Statistics Subcommittee. Circulation. 2006, 113: e85-151. 10.1161/CIRCULATIONAHA.105.171600.View ArticlePubMedGoogle Scholar
  2. Murray CJ, Lopez AD: Evidence-based health policy – lessons from the Global Burden of Disease Study. Science. 1996, 274: 740-743. 10.1126/science.274.5288.740.View ArticlePubMedGoogle Scholar
  3. Pathobiological Determinants of Atherosclerosis in Youth (PDAY) Research Group: Natural history of aortic and coronary atherosclerotic lesions in youth. Findings from the PDAY Study. Arterioscler Thromb. 1993, 13: 1291-1298.View ArticleGoogle Scholar
  4. McNamara JJ, Molot MA, Stremple JF, Cutting RT: Coronary artery disease in combat casualties in Vietnam. JAMA. 1971, 216: 1185-1187. 10.1001/jama.216.7.1185.View ArticlePubMedGoogle Scholar
  5. Enos WF, Beyer JC, Holmes RH: Pathogenesis of coronary disease in American soldiers killed in Korea. JAMA. 1954, 158: 912-914.View ArticleGoogle Scholar
  6. Kathiresan S, Larson MG, Keyes MJ, Polak JF, Wolf PA, D'Agostino RB, Jaffer FA, Clouse ME, Levy D, Manning WJ, O'Donnell CJ: Assessment by cardiovascular magnetic resonance, electron beam computed tomography, and carotid ultrasonography of the distribution of subclinical atherosclerosis across Framingham risk strata. Am J Cardiol. 2007, 99: 310-314. 10.1016/j.amjcard.2006.08.028.View ArticlePubMedGoogle Scholar
  7. Murabito JM, Guo CY, Fox CS, D'Agostino RB: Heritability of the ankle-brachial index: the Framingham Offspring study. Am J Epidemiol. 2006, 164: 963-968. 10.1093/aje/kwj295.View ArticlePubMedGoogle Scholar
  8. Manolio TA, Boerwinkle E, O'Donnell CJ, Wilson AF: Genetics of ultrasonographic carotid atherosclerosis. Arterioscler Thromb Vasc Biol. 2004, 24: 1567-1577. 10.1161/01.ATV.0000138789.11433.c1.View ArticlePubMedGoogle Scholar
  9. O'Donnell CJ, Chazaro I, Wilson PW, Fox C, Hannan MT, Kiel DP, Cupples LA: Evidence for heritability of abdominal aortic calcific deposits in the Framingham Heart Study. Circulation. 2002, 106: 337-341. 10.1161/01.CIR.0000022663.26468.5B.View ArticlePubMedGoogle Scholar
  10. Peyser PA, Bielak LF, Chu JS, Turner ST, Ellsworth DL, Boerwinkle E, Sheedy PF: Heritability of coronary artery calcium quantity measured by electron beam computed tomography in asymptomatic adults. Circulation. 2002, 106: 304-308. 10.1161/01.CIR.0000022664.21832.5D.View ArticlePubMedGoogle Scholar
  11. Doobay AV, Anand SS: Sensitivity and specificity of the ankle-brachial index to predict future cardiovascular outcomes: a systematic review. Arterioscler Thromb Vasc Biol. 2005, 25: 1463-1469. 10.1161/01.ATV.0000168911.78624.b7.View ArticlePubMedGoogle Scholar
  12. O'Leary DH, Polak JF, Kronmal RA, Manolio TA, Burke GL, Wolfson SK: Carotid-artery intima and media thickness as a risk factor for myocardial infarction and stroke in older adults. Cardiovascular Health Study Collaborative Research Group. N Engl J Med. 1999, 340: 14-22. 10.1056/NEJM199901073400103.View ArticlePubMedGoogle Scholar
  13. Pletcher MJ, Tice JA, Pignone M, Browner WS: Using the coronary artery calcium score to predict coronary heart disease events: a systematic review and meta-analysis. Arch Intern Med. 2004, 164: 1285-1292. 10.1001/archinte.164.12.1285.View ArticlePubMedGoogle Scholar
  14. Wilson PW, Kauppila LI, O'Donnell CJ, Kiel DP, Hannan M, Polak JM, Cupples LA: Abdominal aortic calcific deposits are an important predictor of vascular morbidity and mortality. Circulation. 2001, 103: 1529-1534.View ArticlePubMedGoogle Scholar
  15. Ginsburg GS, Donahue MP, Newby LK: Prospects for personalized cardiovascular medicine: the impact of genomics. J Am Coll Cardiol. 2005, 46: 1615-1627. 10.1016/j.jacc.2005.06.075.View ArticlePubMedGoogle Scholar
  16. Klein RJ, Zeiss C, Chew EY, Tsai JY, Sackler RS, Haynes C, Henning AK, Sangiovanni JP, Mane SM, Mayne ST, Bracken MB, Ferris FL, Ott J, Barnstable C, Hoh J: Complement factor H polymorphism in age-related macular degeneration. Science. 2005, 308: 385-389. 10.1126/science.1109557.PubMed CentralView ArticlePubMedGoogle Scholar
  17. Herbert A, Gerry NP, McQueen MB, Heid IM, Pfeufer A, Illig T, Wichmann HE, Meitinger T, Hunter D, Hu FB, Colditz G, Hinney A, Hebebrand J, Koberwitz K, Zhu X, Cooper R, Ardlie K, Lyon H, Hirschhorn JN, Laird NM, Lenburg ME, Lange C, Christman MF: A common genetic variant is associated with adult and childhood obesity. Science. 2006, 312: 279-283. 10.1126/science.1124779.View ArticlePubMedGoogle Scholar
  18. Arking DE, Pfeufer A, Post W, Kao WH, Newton-Cheh C, Ikeda M, West K, Kashuk C, Akyol M, Perz S, Jalilzadeh S, Illig T, Gieger C, Guo CY, Larson MG, Wichmann HE, Marban E, O'Donnell CJ, Hirschhorn JN, Kaab S, Spooner PM, Meitinger T, Chakravarti A: A common genetic variant in the NOS1 regulator NOS1AP modulates cardiac repolarization. Nat Genet. 2006, 38: 644-651. 10.1038/ng1790.View ArticlePubMedGoogle Scholar
  19. Sladek R, Rocheleau G, Rung J, Dina C, Shen L, Serre D, Boutin P, Vincent D, Belisle A, Hadjadj S, Balkau B, Heude B, Charpentier G, Hudson TJ, Montpetit A, Pshezhetsky AV, Prentki M, Posner BI, Balding DJ, Meyre D, Polychronakos C, Froguel P: A genome-wide association study identifies novel risk loci for type 2 diabetes. Nature. 2007, 445: 881-885. 10.1038/nature05616.View ArticlePubMedGoogle Scholar
  20. Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix JJ, Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L, Altshuler D, Almgren P, Florez JC, Meyer J, Ardlie K, Bengtsson K, Isomaa B, Lettre G, Lindblad U, Lyon HN, Melander O, Newton-Cheh C, Nilsson P, Orho-Melander M, Rastam L, Speliotes EK, Taskinen MR, Tuomi T, Guiducci C, Berglund A, Carlson J, Gianniny L, Hackett R, Hall L, Holmkvist J, Laurila E, Sjogren M, Sterner M, Surti A, Svensson M, Svensson M, Tewhey R, Blumenstiel B, Parkin M, DeFelice M, Barry R, Brodeur W, Camarata J, Chia N, Fava M, Gibbons J, Handsaker B, Healy C, Nguyen K, Gates C, Sougnez C, Gage D, Nizzari M, Gabriel SB, Chirn GW, Ma Q, Parikh H, Richardson D, Ricke D, Purcell S: Genome-Wide Association Analysis Identifies Loci for Type 2 Diabetes and Triglyceride Levels. Science. 2007Google Scholar
  21. Scott LJ, Mohlke KL, Bonnycastle LL, Willer CJ, Li Y, Duren WL, Erdos MR, Stringham HM, Chines PS, Jackson AU, Prokunina-Olsson L, Ding CJ, Swift AJ, Narisu N, Hu T, Pruim R, Xiao R, Li XY, Conneely KN, Riebow NL, Sprau AG, Tong M, White PP, Hetrick KN, Barnhart MW, Bark CW, Goldstein JL, Watkins L, Xiang F, Saramies J, Buchanan TA, Watanabe RM, Valle TT, Kinnunen L, Abecasis GR, Pugh EW, Doheny KF, Bergman RN, Tuomilehto J, Collins FS, Boehnke M: A Genome-Wide Association Study of Type 2 Diabetes in Finns Detects Multiple Susceptibility Variants. Science. 2007Google Scholar
  22. Zeggini E, Weedon MN, Lindgren CM, Frayling TM, Elliott KS, Lango H, Timpson NJ, Perry JR, Rayner NW, Freathy RM, Barrett JC, Shields B, Morris AP, Ellard S, Groves CJ, Harries LW, Marchini JL, Owen KR, Knight B, Cardon LR, Walker M, Hitman GA, Morris AD, Doney AS, McCarthy MI, Hattersley AT: Replication of Genome-Wide Association Signals in U.K. Samples Reveals Risk Loci for Type 2 Diabetes. Science. 2007Google Scholar
  23. Helgadottir A, Thorleifsson G, Manolescu A, Gretarsdottir S, Blondal T, Jonasdottir A, Jonasdottir A, Sigurdsson A, Baker A, Palsson A, Masson G, Gudbjartsson D, Magnusson KP, Andersen K, Levey AI, Backman VM, Matthiasdottir S, Jonsdottir T, Palsson S, Einarsdottir H, Gunnarsdottir S, Gylfason A, Vaccarino V, Hooper WC, Reilly MP, Granger CB, Austin H, Rader DJ, Shah SH, Quyyumi AA, Gulcher JR, Thorgeirsson G, Thorsteinsdottir U, Kong A, Stefansson K: A Common Variant on Chromosome 9p21 Affects the Risk of Myocardial Infarction. Science. 2007Google Scholar
  24. McPherson R, Pertsemlidis A, Kavaslar N, Stewart A, Roberts R, Cox DR, Hinds DA, Pennacchio LA, Tybjaerg-Hansen A, Folsom AR, Boerwinkle E, Hobbs HH, Cohen JC: A Common Allele on Chromosome 9 Associated with Coronary Heart Disease. Science. 2007Google Scholar
  25. Cupples LA, Arruda HT, Benjamin EJ, D'Agostino RB, Demissie S, DeStefano AL, Dupuis J, Falls K, Fox CS, Gottlieb DJ, Govindaraju DR, Guo CY, Heard-Costa NL, Hwang SJ, Kathiresan S, Kiel DP, Laramie JM, Larson MG, Levy D, Liu CY, Lunetta KL, Mailman MD, Manning AK, Meigs JB, Murabito JM, Newton-Cheh C, O'Connor GT, O'Donnell CJ, Pandey MA, Seshadri S, Vasan RS, Wang ZY, Wilk JB, Wolf PA, Yang Q, Atwood LD: The Framingham Heart Study 100K SNP genome-wide association study resource: Overview of 17 phenotype working group reports. BMC Med Genet. 2007, 8 (Suppl 1): S1-PubMed CentralView ArticlePubMedGoogle Scholar
  26. Cupples LA, D'Agostino RB: Some risk factors related to the annual incidence of cardiovascular disease and death using pooled repeated biennial measurements: Framingham Study, 30-year follow-up. The Framingham Heart Study: An Epidemiological Investigation of Cardiovascular Disease. Edited by: Kannel WB, Polf PA, Garrison RJ. 1987, Washington, DC: Government Printing OfficeGoogle Scholar
  27. O'Leary DH, Polak JF, Kronmal RA, Manolio TA, Burke GL, Wolfson SK: Carotid-artery intima and media thickness as a risk factor for myocardial infarction and stroke in older adults. N Engl J Med. 1999, Cardiovascular Health Study Collaborative Research Group, 340: 14-22. 10.1056/NEJM199901073400103.Google Scholar
  28. Fox CS, Polak JF, Chazaro I, Cupples A, Wolf PA, D'Agostino RA, O'Donnell CJ: Genetic and environmental contributions to atherosclerosis phenotypes in men and women: heritability of carotid intima-media thickness in the Framingham Heart Study. Stroke. 2003, 34: 397-401. 10.1161/01.STR.0000048214.56981.6F.View ArticlePubMedGoogle Scholar
  29. O'Leary DH, Polak JF, Kronmal RA, Savage PJ, Borhani NO, Kittner SJ, Tracy R, Gardin JM, Price TR, Furberg CD: Thickening of the carotid wall. A marker for atherosclerosis in the elderly?. Stroke. 1996, Cardiovascular Health Study Collaborative Research Group, 27: 224-231.Google Scholar
  30. Hoffmann U, Siebert U, Bull-Stewart A, Achenbach S, Ferencik M, Moselewski F, Brady TJ, Massaro JM, O'Donnell CJ: Evidence for lower variability of coronary artery calcium mineral mass measurements by multi-detector computed tomography in a community-based cohort – consequences for progression studies. Eur J Radiol. 2006, 57: 396-402. 10.1016/j.ejrad.2005.12.027.View ArticlePubMedGoogle Scholar
  31. Levy D, DeStefano AL, Larson MG, O'Donnell CJ, Lifton RP, Gavras H, Cupples LA, Myers RH: Evidence for a gene influencing blood pressure on chromosome 17. Genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the Framingham Heart Study. Hypertension. 2000, 36: 477-483.View ArticlePubMedGoogle Scholar
  32. Fox CS, Cupples LA, Chazaro I, Polak JF, Wolf PA, D'Agostino RB, Ordovas JM, O'Donnell CJ: Genomewide linkage analysis for internal carotid artery intimal medial thickness: evidence for linkage to chromosome 12. Am J Hum Genet. 2004, 74: 253-61. 10.1086/381559.PubMed CentralView ArticlePubMedGoogle Scholar
  33. Helgadottir A, Manolescu A, Thorleifsson G, Gretarsdottir S, Jonsdottir H, Thorsteinsdottir U, Samani NJ, Gudmundsson G, Grant SF, Thorgeirsson G, Sveinbjornsdottir S, Valdimarsson EM, Matthiasson SE, Johannsson H, Gudmundsdottir O, Gurney ME, Sainz J, Thorhallsdottir M, Andresdottir M, Frigge ML, Topol EJ, Kong A, Gudnason V, Hakonarson H, Gulcher JR, Stefansson K: The gene encoding 5-lipoxygenase activating protein confers risk of myocardial infarction and stroke. Nat Genet. 2004, 36: 233-239. 10.1038/ng1311.View ArticlePubMedGoogle Scholar
  34. Helgadottir A, Gretarsdottir S, St Clair D, Manolescu A, Cheung J, Thorleifsson G, Pasdar A, Grant SF, Whalley LJ, Hakonarson H, Thorsteinsdottir U, Kong A, Gulcher J, Stefansson K, MacLeod MJ: Association between the gene encoding 5-lipoxygenase-activating protein and stroke replicated in a Scottish population. Am J Hum Genet. 2005, 76: 505-509. 10.1086/428066.PubMed CentralView ArticlePubMedGoogle Scholar
  35. Ozaki K, Inoue K, Sato H, Iida A, Ohnishi Y, Sekine A, Sato H, Odashiro K, Nobuyoshi M, Hori M, Nakamura Y, Tanaka T: Functional variation in LGALS2 confers risk of myocardial infarction and regulates lymphotoxin-alpha secretion in vitro. Nature. 2004, 429: 72-75. 10.1038/nature02502.View ArticlePubMedGoogle Scholar
  36. Skol AD, Scott LJ, Abecasis GR, Boehnke M: Joint analysis is more efficient than replication-based analysis for two-stage genome-wide association studies. Nat Genet. 2006, 38: 209-213. 10.1038/ng1706.View ArticlePubMedGoogle Scholar
  37. Kaiser J: Genomic databases. NIH goes after whole genome in search of disease genes. Science. 2006, 311: 933-10.1126/science.311.5763.933a.View ArticlePubMedGoogle Scholar

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