BMC Medical Genetics

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Genome-wide association and linkage analyses of hemostatic
factors and hematological phenotypes in the Framingham Heart
Study
Qiong Yang1,2, Sekar Kathiresan1,3,6, Jing-Ping Lin4, Geoffrey H Tofler5 andChristopher J O'Donnell*1,6
Address: 1The National Heart, Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA, 2Department of Biostatistics, Boston
University School of Public Health, Boston, MA, USA, 3Broad Institute of Harvard University and Massachusetts Institute of Technology,
Cambridge, MA, USA, 4Office of Biostatistics Research, NHLBI, National Institute of Health; Bethesda, MD, USA, 5Royal North Shore Hospital
Sydney, Australia and 6Cardiology Division, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
Email: Qiong Yang - qyang@bu.edu; Sekar Kathiresan - skathiresan1@partners.org; Jing-Ping Lin - linj@nhlbi.nih.gov;
Geoffrey H Tofler - gtofler@nsccahs.health.nsw.gov.au; Christopher J O'Donnell* - odonnellc@nhlbi.nih.gov* Corresponding author

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

BMC Medical Genetics 2007, 8(Suppl 1):S12

doi:10.1186/1471-2350-8-S1-S12

This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S12© 2007 Yang et al; licensee BioMed Central Ltd.
This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0),
which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract
Background: Increased circulating levels of hemostatic factors as well as anemia have been associated with increased
risk of cardiovascular disease (CVD). Known associations between hemostatic factors and sequence variants at genes
encoding these factors explain only a small proportion of total phenotypic variation. We sought to confirm known
putative loci and identify novel loci that may influence either trait in genome-wide association and linkage analyses using
the Affymetrix GeneChip 100K single nucleotide polymorphism (SNP) set.
Methods: Plasma levels of circulating hemostatic factors (fibrinogen, factor VII, plasminogen activator inhibitor-1, vonWillebrand factor, tissue plasminogen activator, D-dimer) and hematological phenotypes (platelet aggregation, viscosity,
hemoglobin, red blood cell count, mean corpuscular volume, mean corpuscular hemoglobin concentration) wereobtained in approximately 1000 Framingham Heart Study (FHS) participants from 310 families. Population-based
association analyses using the generalized estimating equations (GEE), family-based association test (FBAT), andmultipoint variance components linkage analyses were performed on the multivariable adjusted residuals of hemostaticand hematological phenotypes.
Results: In association analysis, the lowest GEE p-value for hemostatic factors was p = 4.5*10-16 for factor VII at SNP
rs561241, a variant located near the F7 gene and in complete linkage disequilibrium (LD) (r2 = 1) with the Arg353Gln F7
SNP previously shown to account for 9% of total phenotypic variance. The lowest GEE p-value for hematologicalphenotypes was 7*10-8 at SNP rs2412522 on chromosome 4 for mean corpuscular hemoglobin concentration. We
presented top 25 most significant GEE results with p-values in the range of 10-6 to 10-5 for hemostatic or hematologicalphenotypes. In relating 100K SNPs to known candidate genes, we identified two SNPs (rs1582055, rs4897475) inerythrocyte membrane protein band 4.1-like 2 (EPB41L2) associated with hematological phenotypes (GEE p < 10-3). In
linkage analyses, the highest linkage LOD score for hemostatic factors was 3.3 for factor VII on chromosome 10 around
15 Mb, and for hematological phenotypes, LOD 3.4 for hemoglobin on chromosome 4 around 55 Mb. All GEE and FBAT
association and variance components linkage results can be found at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007

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Conclusion: Using genome-wide association methodology, we have successfully identified a SNP in complete LD witha sequence variant previously shown to be strongly associated with factor VII, providing proof of principle for thisapproach. Further study of additional strongly associated SNPs and linked regions may identify novel variants that
influence the inter-individual variability in hemostatic factors and hematological phenotypes.

Background
The relationship of hemostasis and thrombosis withatherothrombotic cardiovascular disease has been extensively studied in the past decades. Elevated circulating levels of hemostatic factors, such as fibrinogen [1-3],
plasminogen activator inhibitor (PAI-1) [4,5], von Willebrand factor (vWF) [6], tissue plasminogen activator (tPA)[4,5,7], factor VII (FVII) [8], and D-dimer [9,10] are linked
to the development of atherothrombosis and are risk
markers for coronary heart disease (CHD), stroke andother cardiovascular disease (CVD) events.
In addition to coagulation proteins, the cellular and rheological components of circulating blood have been implicated in CHD, stroke and peripheral arterial disease,including hematological phenotypes such as hematocrit(HCT), hemoglobin (Hgb), red blood cell count (RBCC)
and size, mean corpuscular volume (MCV) and mean corpuscular hemoglobin (MCH) [11,12], as well as measuresof platelet aggregation (induced by adenosine 5'-diphosphate (ADP), epinephrine (Epi) and collagen respectively) [12,13], and viscosity [14,15].

The recent completion of a genome-wide scan using theAffymetrix GeneChip Human Mapping 100K single
nucleotide polymorphism (SNP) set on participants in
the Framingham Heart Study offered the opportunity to
conduct a genome-wide association study (GWAS) andlinkage scan for variants that influence hemostatic factors
and hematological phenotypes.

Methods
Study participants and genotyping methods
The Framingham Heart Study design and the genotypingof the Affymetrix GeneChip Human Mapping 100K SNP
set on Framingham Heart Study participants are detailed
in the overview of this project [25]. To avoid potential
bias due to genotyping artifacts, we limited the association analyses to 70987 SNPs on autosomes with minor
allele frequency (MAF) ≥ 10%, genotyping call rate ≥ 80%,and Hardy-Weinberg equilibrium test p-value ≥ 0.001.

Cis-acting sequence variants in the following genes –fibrinogen-β (FGB), fibrinogen-α (FGA), fibrinogen-γ(FGG), FVII (F7), and PAI-1 (SERPINE1) – have been
associated with corresponding levels of circulating hemostatic factor. By comprehensively characterizing common
genetic variation at each of these loci, we have recently
clarified that cis-acting variants, in sum, explain a modest
proportion of phenotypic variation, ranging from 1% –10% [16,17]. For hematological variables such as hematocrit and hemoglobin, sequence variation in the majorhemoglobin genes is well described to be associated withanemias, such as beta- and alpha-thalassemia, and sickle
cell anemia [18-20].

Measurements of hemostatic factors and hematologicalphenotypes
Venous blood samples of Framingham Heart Study Offspring Cohort taken at the first and second examination
cycles (1971–1975, and 1979–1983) were used to measure Hgb, RBCC, MCV and MCH, and samples taken at the
fifth examination cycle (1991–1995) were used to measure all the hemostatic factors, platelet aggregation, Ddimer, and viscosity. Fibrinogen was additionally measured at the sixth (1995–1998) and seventh (1998–2001)
examination cycles, and PAI-I antigen levels at the sixth
exam. Details of the assessment of hemostatic factor levels
have been described previously [17,26]. Plasma fibrinogen levels were measured using the Clauss method [27].
Plasma PAI-I antigen, tPA antigen, von Willebrand factor
and FVII antigen were assessed using enzyme-linked
immunosorbent assays.

Systematic searches for novel genes beyond the known
genetic determinants influencing these phenotypes havebeen carried out using genome-wide linkage analyses withmicrosatellite markers: Chromosome regions that may
harbor novel loci influencing fibrinogen, PAI-1 [21,22],
hematocrit, Hgb, RBCC, MCV and MCH [23,24], havebeen identified. However, linkage scans with microsatellite markers generally had low power to detect loci withsmall effects, and lacked precision in localizing the loci;thus, few novel loci have been identified.

The determination of hematological phenotypes has beendetailed previously. Platelet aggregation was performed
according to the method of Born [28]. The reagents usedwere epinephrine, ADP and collagen. The percent extent
of aggregation in duplicate to epinephrine and ADP wasdetermined in varying concentrations (0.01 to 15 mmol/L). For each subject, the aggregation response (yes/no)
was also tested to a fixed concentration of arachidonic
acid (5 mg/mL). The collagen lag time was measured in
response to 1.9 mmol/L collagen. Participants who were

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Table 1: Description of hemostatic factors, hematological phenotypes, and covariates adjustment
Phenotype

Number of adjusted
phenotypes

Offsrping cohort
exam cycles†

Sample size
(min-max)

Covariates adjusted

Hemostatic factors:
fibrinogen4

5, 6, 7 and average

986–1073

age (and its squared and cubic terms), sex, body massindex, prevalent cardiovascular disease, current cigarettesmoking, hypertension treatment, systolic bloodpressure, diastolic blood pressure, estrogen therapy(women only), alcohol intake, triglycerides, diabetes,
total cholesterol, and the ratio of total cholesterol tohigh-density lipoprotein cholesterol.

PAI-1
tPA
vWF
FVII
D-dimer

3
11
1
1

5, 6 and average
5555

788–1037
786
883
886
987

1

5

724

Hematological andrheological phenotypes
platelet aggregation (ADPinduced)
platelet aggregation (collageninduced)
platelet aggregation (Epi-induced)
Viscosity
Hgb

1

5

702

1
1
3

5
5
1, 2, and average719
832
903–1066

RBCC
MCH
Hematocrit*
MCV
WBC*

3
3
3
3
3

1, 2, and average
1, 2, and average
1, 2, and average
1, 2, and average
1, 2, and average903–1062
903–1062
903–1062
903–1062
903–1062

age, sex, height, weight, high-density lipoproteincholesterol, total serum protein, alcohol intake,triglycerides, and current cigarette smoking*Results for these two phenotypes are not reported in this manuscript. The complete results of all the phenotypes listed above can be found at http://
www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007

taking aspirin were excluded from the analyses for platelet
aggregation phenotypes as well as PAI-1 and tPA.
HCT was measured by the Wintrobe method [29]. Blood
was collected and spun at 5000 rpm for 20 minutes in abalanced oxalate tube. The percent of total blood volume
that was due to red blood cells was determined visually
against a calibrated scale. MCV is the average volume of anindividual's red blood cells determined as the ratio of
HCT to RBCC. MCH is the average amount of hemoglobin of an individual's red cell determined as the ratioof Hgb to RBCC.
Statistical methods
Standardized multivariable adjusted residuals of thehemostatic and hematological phenotypes were computed and used in all the linkage and association analyses.
Covariates used in the adjustments were determinedbased upon what has been reported in the literature as
potential risk factors for hemostatic factors or hematological phenotypes. Hardy-Weinberg equilibrium was examined using an exact chi-square test statistic [30].
Association between each SNP and each hemostatic or
hematological phenotype was examined using a population based association method via generalized estimating
equations (GEE) [31] and family-based association test
(FBAT) [32], assuming an additive genetic model. Vari-ance components linkage analyses were conducted using
a subset of SNPs with pairwise r2 < 0.5. Details of both
association and linkage methods are described in theoverview of this project [25].
In secondary analyses, we combined the GEE association
tests results across multiple phenotypes that may share thecommon pathway to reduce the type I error rates, and possibly detect SNPs of smaller effect sizes. We ranked SNPs
by the number of GEE test p-values less than 0.01, andthen by the geometric mean of the GEE test p-values. We
also examined the β coefficient from the GEE regressionthat is the change in the phenotype in one standardized
deviation unit with an increment of a copy of the alphabetically second allele (for example, allele G for a SNP
with alleles A and G). This analysis was conducted for aphenotype assessed using multiple measurement methods such as the platelet aggregation with ADP-, collagen-,and Epi-induced platelet aggregation; or for a phenotype
with serial measurements such as fibrinogen level measured at examination cycles 5, 6 and 7.
We attempted to identify association of 100K SNPs in orwithin 60 kilo base pairs (kbp) of selected candidate genes
previously reported to be associated with hemostatic factors or hematological phenotypes. For hemostatic factors
and platelet aggregation phenotypes, we included the fol-

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Table 2: The 25 SNPs with lowest GEE association test p-values with hemostatic factors measured at exam 5
Phenotype*

SNP

MAF

CHR

Physical Position (bp)†

GEE p-val (Rank)††

FBAT p-val†††Fibrinogen
Fibrinogen
Fibrinogen
Fibrinogen
FVII
FVII
FVII
FVII
FVII
FVII
FVII
FVII
FVII
FVII

rs6683832
rs4861952
rs10506540
rs974673
rs966321
rs1245072
rs2362932
rs10496112
rs1001254
rs4591494
rs10488360
rs727435
rs7938734
rs561241

0.41
0.11
0.12
0.15
0.47
0.17
0.24
0.30
0.13
0.19
0.30
0.13
0.31
0.12

1
4
12
14
1
1
1
2
3
3
7
9
11
13

62,988,925
182,696,179
65,699,364
33,550,265
4,225,577
69,827,777
242,240,325
64,535,384
7,909,333
7,957,827
4,184,450
80,809,022
49,834,857
112,808,035

5.6 × 10-5(4)3.5 × 10-5(24)2.2 × 10-5(16)3.1 × 10-5(19)7.6 × 10-6(6)1.8 × 10-5(13)2.4 × 10-5(17)3.3 × 10-5(23)2.0 × 10-5(14)9.2 × 10-6(7)7.2 × 10-6(5)9.6 × 10-6(8)3.2 × 10-5(21)4.5 × 10-16(1)3.4 × 10-2
8.2 × 10-3
3.3 × 10-2
1.3 × 10-3
4.5 × 10-2
7.5 × 10-3
1.5 × 10-2
7.4 × 10-3
2.0 × 10-2
1.2 × 10-4
9.3 × 10-3
2.8 × 10-2
7.0 × 10-2
3.4 × 10-4

PAI1
PAI1
tPA
tPA
tPA
tPA
vWF
vWF
vWF
vWF
vWF

rs10490733
rs4460176
rs10493485
rs10494076
rs2029637
rs953377
rs9295740
rs202906
rs708356
rs1954971
rs7319671

0.14
0.29
0.29
0.32
0.25
0.28
0.14
0.12
0.35
0.38
0.30

2
5
1
1
3
6
6
6
12
18
13

137,872,229
109,524,859
71,783,150
108,050,369
113,404,510
23,998,670
27,797,481
28,119,631
128,998,794
26,633,241
36,923,224

3.2 × 10-5(22)3.1 × 10-6(3)1.6 × 10-6(2)3.1 × 10-5(20)1.3 × 10-5(10)1.5 × 10-5(11)2.8 × 10-5(18)2.1 × 10-5(15)1.5 × 10-5(12)1.0 × 10-5(9)3.6 × 10-5(25)2.4 × 10-2
7.6 × 10-2
1.1 × 10-3
2.3 × 10-2
6.1 × 10-2
3.4 × 10-2
5.7 × 10-3
1.1 × 10-1
2.4 × 10-2
1.6 × 10-2
1.1 × 10-2

Genes within 60 kb
APG4C

LRRC7
SMYD3
HSPC159

MCF2L; AB116074; AK092739; AK123267;
AB002360; F7; CR603372; F10; PROZ

BC036771; NEGR1VAV3
SLC9A10ZNF165
AK127723* All the phenotypes reported here were multivariable adjusted residuals from the measurements obtained at exam cycle 5.
†Physical position is in base pair (bp) and based on the May 2004 human reference sequence (NCBI Build 35).
††P-value from GEE genotype association test and rank of the GEE p-values in ascending order.
†††P-value from family-based association test using the FBAT program.

lowing candidate genes in the search: F7, fibrinogen genecluster (FGB, FGA, FGG), SERPINE1, plasminogen activator-tissue (PLAT), vWF and integrin beta 3 (ITGB3). For
hematological phenotypes excluding platelet aggregation,
we included erythropoietin receptor (EPOR), erythropoietin (EPO), erythrocyte membrane protein band 4.1-like
2 (EPB41L2), Kruppel-like factor 1(KLF1), heme binding
protein 2 (HEBP2), the hemoglobin gene clusters on chromosome 11: hemoglobin-β chain complex (HBB), hemoglobin-δ (HBD), hemoglobin-γ A (HBG1), hemoglobin-γ
G (HBG2), hemoglobin-ε 1 (HBE1), and the hemoglobin
gene clusters on chromosome 16: hemoglobin-α 1(HBA1), hemoglobin-α 2 (HBA2), hemoglobin-μ (HBM).

Results
Table 1 displays the hemostatic and hematological phenotypes analyzed in this study, as well as the number ofindividuals, examination cycles, and covariates used in
multivariable models. The sample size ranged from 702 to
1073. Traits measured at multiple examinations were analyzed using multivariable adjusted residuals from eachexamination measure, and also the average of all the multivariable adjusted residuals from individual examination
cycles.
Among individuals who were included in the genotyping
and had at least one hemostatic factor or platelet aggrega-tion phenotype measured at examination cycle five, 52%
were women, mean age was 52 years, and 6% had prevalent CVD. Among individuals who were included in the
genotyping and had at least one hematological phenotype
measured at examination cycle one or two, 52% were
women, with a mean age over the two examinations of 36
years, and 2% had prevalent CVD.
Association between SNPs and hemostatic andhematological phenotypes
We report the 25 SNPs with lowest GEE association test pvalues in Table 2 for hemostatic factors, and in Table 3 forhematological phenotypes. The lowest GEE p-value(4.5*10-16) for hemostatic factors was obtained from thetest of association between circulating levels of FVII andrs561241; this SNP resides near the F7 gene on chromosome 13 and is in complete linkage disequilibrium (LD)(r2 = 1) with the Arg353Gln F7 SNP (rs6046) we previously reported to account for 9% of total phenotypic variance [16]. The lowest GEE p-value (6.9*10-8) forhematological phenotypes was obtained in the test of
association between MCH and rs1397048 on chromosome 11 near the olfactory receptors, olfactory receptor,
family 5, subfamily AP, member 2 (OR5AP2), olfactory
receptor, family 5, subfamily AR, member 1 (OR5AR1),
olfactory receptor, family 9, subfamily G, member
1(OR9G1) and olfactory receptor, family 9, subfamily G,

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Table 3: The 25 SNPs with lowest GEE association tests p-values with hematological phenotypes
Phenotype*

SNP

MAF

CHR

Physical Position (bp)†

Hgb
Hgb
Hgb
Hgb
Hgb
MCH
MCH
MCH
platelet aggregation (ADP-induced)
platelet aggregation (ADP-induced)
platelet aggregation (collagen-induced)
platelet aggregation (collagen-induced)
platelet aggregation (collagen-induced)
platelet aggregation (Epi-induced)
platelet aggregation (Epi-induced)
platelet aggregation (Epi-induced)
RBCC
RBCC
RBCC
RBCC
RBCC
Viscosity
Viscosity
Viscosity
Viscosity

rs4133289
rs1510107
rs1160297
rs2357013
rs7844723
rs1829883
rs1200821
rs1397048
rs10493895
rs10484128
rs848523
rs565229
rs10506458
rs6811964
rs1958208
rs10502583
rs9253
rs10489087
rs636864
rs727979
rs6108011
rs10490258
rs1359339
rs10485968
rs7159841

0.19
0.41
0.43
0.45
0.45
0.41
0.47
0.40
0.19
0.15
0.42
0.10
0.13
0.11
0.32
0.17
0.18
0.13
0.21
0.14
0.28
0.11
0.18
0.20
0.21

1
2
2
2
8
5
10
11
1
14
2
11
12
4
14
18
1
4
6
6
20
2
6
7
14

156,267,010
53,071,788
53,148,971
53,177,780
122,977,684
98,809,002
37,599,586
56,222,675
97,983,247
97,712,325
36,659,307
121,694,675
61,731,359
158,143,059
81,194,793
28,011,131
37,627,906
13,470,685
149,567,299
149,635,613
7,500,504
40,717,605
18,606,507
81,108,037
46,933,585

GEE Pval (Rank)††
1.6 × 10-7(2)1.0 × 10-5(17)1.0 × 10-6(4)6.1 × 10-6(14)2.1 × 10-6(5)5.8 × 10-6(11)5.9 × 10-6(13)6.9 × 10-8(1)1.6 × 10-5(23)5.8 × 10-6(12)2.1 × 10-5(24)3.7 × 10-6(6)4.5 × 10-6(9)1.3 × 10-5(20)1.1 × 10-5(18)1.1 × 10-5(19)4.2 × 10-6(7)4.5 × 10-6(8)6.3 × 10-6(15)7.5 × 10-6(16)5.8 × 10-6(10)1.5 × 10-5(21)2.2 × 10-5(25)1.6 × 10-5(22)2.1 × 10-7(3)FBAT Pval†††
6.0 × 10-4
2.2 × 10-3
8.3 × 10-4
1.3 × 10-3
2.6 × 10-3
1.4 × 10-2
1.0 × 10-1
1.0 × 10-2
4.5 × 10-2
1.3 × 10-3
4.3 × 10-1
2.6 × 10-2
1.1 × 10-4
5.5 × 10-2
7.0 × 10-1
1.1 × 10-2
1.1 × 10-5
9.6 × 10-4
1.3 × 10-4
2.6 × 10-5
7.0 × 10-2
2.9 × 10-2
2.3 × 10-2
3.0 × 10-3
2.0 × 10-1

Genes within 60 Kb
OR10J1; OR10J5ANKRD30A
OR5AP2; OR5AR1; OR9G1; OR9G4
BC064027; DPYD
CRIM1

PDGFC
RNF138; MEP1B
FLJ11730; BC016328

MAP3K7IP2IBRDC2
MAMDC1* For Hgb, MCH and RBCC reported here, multivariable adjusted residuals from average of measurements over exam cycles 1 and 2 were used; for allplatelet aggregation phenotypes, and viscosity reported here, multivariable adjusted residuals from measurements at examination cycle 5 were used.
†Physical position is in base pair (bp) and based on the May 2004 human reference sequence (NCBI Build 35).
††P-value from GEE genotype association test and rank of the GEE p-values in ascending order.
†††P-value from family-based association test using the FBAT program.

member 4 (OR9G4). The 25 SNPs with lowest FBAT association test p-values are presented in Additional file 1,
Table A1 and Table A2, respectively.
Linkage results
Maximum multipoint LOD scores greater than 2 and the
1.5-LOD support intervals around the maximum LOD
scores are presented in Table 4. The highest LOD score forhemostatic factors was 3.3 for factor VII at approximately
15 Mb on chromosome 10. The highest LOD for hematological phenotypes was 3.4 for Hgb at approximately 55
Mb on chromosome 4.
Combining association tests across multiple phenotypes
The top 10 SNPs with most number of p-values < 0.01 andlowest mean p-values are reported in Tables 5 and 6 forplatelet aggregation phenotypes and fibrinogen levels
respectively. The top ranked SNP for platelet aggregation
was rs10500631 on chromosome 11 located near an
olfactory gene cluster. The p-values of the GEE association
test for ADP-, collagen- and epinephrine-induced platelet
aggregation levels with this SNP were all less than 0.01,
with average p-value 0.007 over the three tests. The rangeof the regression coefficients was 0.19–0.24, indicating
the effect size was consistently estimated across the threephenotypes.

For fibrinogen, the top ranked SNP was rs4861952 on
chromosome 4, which was also listed in the Table 2 as oneof the 25 most significantly associated SNPs with hemostatic factors. This SNP was consistently associated withfibrinogen levels across three examination cycles witheffect size ranging from -0.28 to -0.17.
Association of SNPs in known candidate genes
100K SNPs residing in or near known candidate genes forhemostatic factors are presented in Table 7. Among thecandidate genes for hemostatic factors, no 100K SNP wasin or within 60 kb of PLAT. Only SNPs in or near the restof the candidate genes (F7, FGG, FGA, FGB, ITGB3,SERPINE1 and vWF) are presented. Among all these associations, three reached nominal significance (p-value< 0.05): rs561241 for factor VII, and rs6950982 andrs6956010 for PAI-1.

Among the candidate genes for hematological traits, no100K SNP was in or within 60 Kb of EPOR, EPO, KLF1,
HBA1, HBA2, HBM. For the rest of the candidate genes,
associations between hematological phenotypes and100K SNPs in/near EPB41L2, the beta hemoglobin genecluster on chromosome 11 (HBB, HBD, HBG1, HBG2,
HBE1), and HEBP2 are presented in Table 8. The most significant associations were SNP rs1582055 near EPB41L2

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Table 4: Maximum LOD scores (≥2) on each chromosomes for hemostatic factors and hematological phenotypes

Phenotype*

SNP

CHR

Physical Position (bp)†LOD

1.5 LOD Support Interval

Fibrinogen
FVII
FVII
PAI1
vWF
platelet aggregation (ADP-induced)
platelet aggregation (collagen-induced)
Hgb
Hgb
MCVavg12
MCVavg12
MCVavg12
RBCC
RBCC
RBCC
RBCC
RBCC
RBCC
RBCC
RBCC
RBCC
RBCC

rs1273819
rs1542535
rs2400107
143xd8rs10514670
rs4148751
rs17310225
rs2412522
rs10514838
rs38993
rs9299952
rs10522006
rs956275
rs2023048
rs10500769
rs10500770
rs2178692
rs10498633
GATA27A03
rs2271090
rs486633
rs20037

2
3
10
8
3
7
X
4
9
7
11
X
4
6
11
11
12
14
15
17
18
22

227,307,918
138,548,560
15,113,999
2,117,752
18,544,296
86,787,804
41,973,417
54,779,471
120,826,350
153,013,905
19,075,037
32,347,187
57,108,334
162,441,307
12,975,794
13,053,050
6,706,312
91,996,705
100,152,332
76,861,200
555,760
27,802,395

2.4
2.8
3.3
2.3
2.1
2.0
2.1
3.4
2.5
2.2
3.3
2.3
2.5
2.9
3.2
3.2
2.8
2.6
2.3
2.3
3.3
2.2

221,455,139
128,478,856
13,834,912
181,076
9,261,438
81,841,699
30,937,095
41,552,790
113,537,965
149,613,755
12,241,666
27,553,388
44,437,719
156,253,205
9,335,581
9,335,581
4,817,859
74,270,406
98,566,644
73,915,579
156,277
25,095,430

234,213,322
147,720,726
21,013,729
5,519,520
25,964,778
94,233,832
81,367,217
57,899,713
133,903,932
154,729,817
21,560,867
40,183,712
74,935,678
165,513,097
15,231,734
15,231,734
9,212,944
93,717,052
100,152,332
78,166,561
4,009,209
42,711,182* For Hgb, MCH and RBCC reported here, multivariable adjusted residuals from average of measurements over exam cycles 1 and 2 were used; forall platelet aggregation phenotypes, fibrinogen, FVII, PAI1, vWF reported here, multivariable adjusted residuals from measurements at examination
cycle 5 were used.
†Physical position is in base pair (bp) and based on the May 2004 human reference sequence (NCBI Build 35).

Table 5: Top 10 ranked SNPs in combining GEE association tests of ADP-induced, Collagen-induced and Epi-induced platelet
aggregation levels
GEE P-value of individual
phenotypes
SNP

MAF

CHR

PhysicalPosition (bp)*

Mean GEE
p-valuers10500631

0.13

11

4,882,639

6.6 × 10-3

0.19

rs930323
rs7033457
rs2974490
rs10517543

0.18
0.25
0.11
0.13

1
9
5
4

221,172,525
7,383,934
113,433,775
40,332,115

1.9 × 10-3
2.6 × 10-3
2.7 × 10-3
3.2 × 10-3

89,556,637
106,463,038
108,340,545

3.5 × 10-3

rs2916601
rs4350297
rs7956194

0.13
0.18
0.42

5
10
12

3.9 × 10-3
4.3 × 10-3

Range of Beta Coefficient† (min, max)

Number of
Phenotypes with
GEE P-value < 0.01

Gene
within 60
Kb

ADP

Collagen

Epi

0.24

3

9.7 × 10-3

7.8 × 10-3

3.8 × 10-3

-0.26
0.15
0.07
0.13

-0.16
0.24
0.28
0.27

2
2
2
2

OR51S1;
OR51T1;
OR51A7;
OR51G2;
OR51G1;
OR51A4;
OR51A2
CNIH3

3.8 × 10-2
2.5 × 10-3
4.2 × 10-1
5.8 × 10-3

1.6 × 10-3
3.7 × 10-2
8.7 × 10-5
1.0 × 10-1

1.2 × 10-4
2.0 × 10-4
5.2 × 10-4
5.2 × 10-5

0.11
-0.24
0.09

0.29
-0.10
0.20

2
2
2

9.7 × 10-2
3.5 × 10-4
3.6 × 10-3

3.2 × 10-4
1.0 × 10-1
1.1 × 10-1

1.4 × 10-3
1.7 × 10-3
2.0 × 10-4

AF262323;
FLJ20273
SORCS3
FLJ37587;
KCTD10;BC040062;
AK056912;
BC051266;
UBE3B* Physical position is in base pair (bp) and based on the May 2004 human reference sequence (NCBI Build 35).
†β coefficient is the change in the phenotype in one standardized deviation unit with an increment of a copy of the alphabetically higher allele.

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Table 6: Top 10 ranked SNPs in combining GEE association tests of fibrinogen levels measured at examination cycles 5, 6 and 7
SNP

rs4861952
rs1869733
rs9317390
rs1561478
rs277330
rs1649053
rs2175271
rs9323656
rs1423741
rs10497881

MAF

0.11
0.15
0.47
0.15
0.16
0.41
0.09
0.06
0.42
0.32

CHR

4
8
13
4
5
10
15
14
16
2

PhysicalPosition (bp)*182,696,179.00
63,970,954.00
63,625,531.00
61,317,252.00
53,489,351.00
59,991,493.00
34,097,057.00
77,394,323.00
59,874,592.00
205,818,470.00

mean
GEE
P-value

4.9 × 10-4
5.5 × 10-4
6.5 × 10-4
7.3 × 10-4
1.1 × 10-3
1.2 × 10-3
1.5 × 10-3
1.5 × 10-3
1.8 × 10-3
2.2 × 10-3

Range of β
Coefficients†(min, max)

-0.28
-0.23
0.13
0.15
-0.22
0.13
-0.26
-0.31
-0.17
0.14

-0.17
-0.16
0.17
0.22
-0.16
0.18
-0.20
-0.27
-0.11
0.17

Number of
Phenotypes with
GEE P-value < 0.01

3
3
3
3
3
3
3
3
3
3

Gene within 60
Kb

FLJ39630

ARFRP2

ADCK1; AK096919
ALS2CR19

GEE P-value of individual phenotypes

Exam 5

Exam 6

Exam 7

3.5 × 10-5
6.7 × 10-5
3.8 × 10-3
3.0 × 10-4
1.5 × 10-3
2.8 × 10-3
6.5 × 10-3
1.2 × 10-3
7.6 × 10-4
4.8 × 10-4

9.2 × 10-3
4.1 × 10-3
1.9 × 10-4
1.6 × 10-4
1.9 × 10-4
5.8 × 10-3
3.1 × 10-4
2.5 × 10-3
9.4 × 10-3
5.0 × 10-3

3.6 × 10-4
6.1 × 10-4
3.8 × 10-4
8.6 × 10-3
4.1 × 10-3
1.0 × 10-4
1.8 × 10-3
1.3 × 10-3
7.6 × 10-4
4.5 × 10-3

* Physical position is in base pair (bp) and based on the May 2004 human reference sequence (NCBI Build 35).
†β coefficient is the change in the phenotype in one standardized deviation unit with an increment of a copy of the alphabetically higher allele.

with hematocrit (p = 7.7 × 10-5), Hgb (p = 2.9 × 10-4), and
RBCC (p = 3.9 × 10-4); SNP rs4897475 with hematocrit (p= 1.6 × 10-4) and Hgb (p = 6.0 × 10-4).

Discussion
We conducted a GWAS and a genome-wide linkage analyses for hemostatic factors and hematological phenotypes
measured in Framingham Heart Study Offspring participants. We identified a highly significant association
between factor VII level and SNP rs561241 in complete
LD with the F7 SNP rs6046 (Arg353Gln) previously demonstrated to explain about 9% of total phenotypic variation. This association is significant after Bonferroni
correction for multiple testing (we used a conservative α =5 × 10-8), and confirms the strong association at this locus
that has previously been reported by us and others. This
SNP was also significant (p-value = 3.4 × 10-4) at a nominal α level 0.05 for FBAT and linkage test (LOD = 1.8, pvalue = 0.002), but not after Bonferroni correction. Thatmay be explained by the well known fact that FBAT andlinkage test are less powerful than population-based association
tests.
FBAT lacks power to detect variants that explain small proportion of variance for this study. It is difficult to distinguish true positives from false ones among FBAT results
because it was evident that few 100K SNPs explain a large
proportion of variance for hemostatic factors or hematological phenotypes. Given that there is no evidence formajor population substructure in FHS [33] and there isgreater power from use of GEE testing, we emphasize ourpopulation-based GEE analysis results in this report. Linkage analyses have the same problem of low power todetect small effects. However, a linkage peak can becaused by loci in linkage but not in LD with the SNPs, orby several loci of small effects in the region. Thus linkage
peaks deserve additional attention. For example, we identified a linkage peak on chromosome 10 for multivariate

adjusted factor VII. The SNP underneath the peak is
rs2400107. However, the GEE association p-value was
0.52. This could occur because rs2400107 was linked but
not in LD with the disease locus (loci) under the peak, or
because this linkage peak was caused by several loci ofsmall effects, or this peak was a false positive. Therefore, amore careful examination of the association results of
SNPs under the linkage peak along with potentially additional genotyping may be needed to confirm the linkage
results.
Among the SNPs with top GEE p-values in single phenotype or multiple phenotypes analyses, only a few resided
near genes that were known for a likely role in hemostasis
and thrombosis and hematological biology. For hemostatic factors, the cis-acting SNP rs561241 near F7 gene
was associated with factor. For hematological phenotypes,
we identified rs6811964 near PDGFC, platelet derived
growth factor-C. It has been shown that PDGFC highly
expressed in vascular smooth muscle cells, renal mesangial cells and platelets, and was likely involved in platelet
biology [34]. This SNP was found associated with Epiinduced platelet aggregation (P = 10-5, Table 3), with
ADP-induced platelet aggregation at nominal significance(P = 0.02), and with collagen-induced platelet aggregation
at borderline nominal significance (P = 0.08). Other associations were found with SNPs in genes not clearly related
to the phenotypes, or with SNPs that are not in known
genes. These associations, together with other findings
from this GWAS, must be viewed as hypotheses that warrant further testing in other cohorts.
Although we only summarized results for multivariableadjusted phenotype, we have also conducted linkage andassociation analyses for age-sex adjusted phenotypes. It is
possible that the effects of some loci may be mediatedthrough the covariates included in multivariable adjustment, and thus only associated with age and sex adjusted

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Table 7: Association between SNPs in/near known hemostatic candidate genes, and the corresponding phenotypes
Candidate Gene

SNP

F7
FGG
FGG
FGG
FGG
FGG
FGG
FGG
FGG
ITGB3
ITGB3
ITGB3
ITGB3
ITGB3
ITGB3
ITGB3
ITGB3
ITGB3
SERPINE1SERPINE1SERPINE1SERPINE1SERPINE1SERPINE1
VWF
VWF
VWF
VWF
VWF
VWF

rs561241
rs2066864
rs2066864
rs2066864
rs2066864
rs1074801
rs1074801
rs1074801
rs1074801
rs4525555
rs4525555
rs4525555
rs10514919
rs10514919
rs10514919
rs2015729
rs2015729
rs2015729
rs6950982
rs6950982
rs6950982
rs6956010
rs6956010
rs6956010
rs917858
rs917859
rs2239138
rs216901
rs216903
rs216904

CHR

Physical Position (bp)*

MAF

Position toGeneFunction

Phenotype†

13
4
4
4
4
4
4
4
4
17
17
17
17
17
17
17
17
17
7
7
7
7
7
7
12
12
12
12
12
12

112,808,035
155,883,300
155,883,300
155,883,300
155,883,300
155,933,889
155,933,889
155,933,889
155,933,889
42,692,948
42,692,948
42,692,948
42,697,128
42,697,128
42,697,128
42,709,492
42,709,492
42,709,492
100,360,038
100,360,038
100,360,038
100,360,470
100,360,470
100,360,470
5,952,199
5,952,374
5,952,819
5,975,129
5,975,760
5,976,279

0.12
0.19
0.19
0.19
0.19
0.49
0.49
0.49
0.49
0.31
0.31
0.31
0.24
0.24
0.24
0.36
0.36
0.36
0.18
0.18
0.18
0.19
0.19
0.19
0.32
0.32
0.32
0.48
0.48
0.34

NEAR
IN
IN
IN
IN
NEAR
NEAR
NEAR
NEAR
IN
IN
IN
IN
IN
IN
IN
IN
IN
NEAR
NEAR
NEAR
NEAR
NEAR
NEAR
IN
IN
IN
IN
IN
IN

unknown
intronintronintronintronunknown
unknown
unknown
unknown
intronintronintronintronintronintronintronintronintronunknown
unknown
unknown
unknown
unknown
unknown
intronintronintronintronintronintronFVII, exam 5
Fibrinogen, average
Fibrinogen, exam 5
Fibrinogen, exam 6
Fibrinogen, exam 7
Fibrinogen, average
Fibrinogen, exam 5
Fibrinogen, exam 6
Fibrinogen, exam 7
Platelet Aggregation, ADP, exam 5
Platelet Aggregation, Collagen, exam 5
Platelet Aggregation, epinephrine, exam 5
Platelet Aggregation, ADP, exam 5
Platelet Aggregation, Collagen, exam 5
Platelet Aggregation, epinephrine, exam 5
Platelet Aggregation, ADP, exam 5
Platelet Aggregation, Collagen, exam 5
Platelet Aggregation, epinephrine, exam 5
PAI1, average
PAI1, exam 5
PAI1, exam 6
PAI1, average
PAI1, exam 5
PAI1, exam 6
wVW, exam 5
wVW, exam 5
wVW, exam 5
wVW, exam 5
wVW, exam 5
wVW, exam 5

GEE
Association
Pvalue
4.5 × 10-16
0.239
0.711
0.095
0.586
0.667
0.898
0.521
0.608
0.231
0.375
0.292
0.495
0.331
0.302
0.326
0.919
0.963
0.187
0.035
0.688
0.218
0.053
0.740
0.398
0.413
0.487
0.297
0.316
0.584

* Physical position is in base pair (bp) and based on the May 2004 human reference sequence (NCBI Build 35).
†'NEAR' means within 60 kb to a gene; 'IN' means within a gene.
††Phenotypes are multivariate adjusted residuals.

phenotypes. Among the 52 SNPs that were associatedwith age and sex adjusted hemostatic factors or hematological phenotypes with a GEE p-value equal or less than
10-5, 28 SNPs had a GEE p-value greater than 10-5 withmultivariable adjusted phenotypes. However, no age andsex adjusted GEE p-value for the 28 SNPs reachedgenome-wide significance (p-value < 5 × 10-8), and no
new highly plausible candidate genes resided within 60
Kb of these SNPs. The full disclosure results of all analyses,
including the age-sex adjusted analyses, can be viewed at
http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.
There are some limitations to this study. The participants
are Caucasian and thus the results may not be generalizable to other racial groups. The study sample size was relatively small, and as such, we may have insufficient power
to detect small effects. To avoid worsening the multiple
testing problem, we performed only sex-pooled and not
sex-specific analyses. There may be some SNPs that areassociated with some phenotypes only in female or maleundetected in the current study. The advantages of thisstudy are that we had family data, which enabled us toalso apply family-based association tests that are robust to
population admixture, and linkage analyses that can
detect loci not in LD but in linkage with any 100K SNP.
The study subjects were recruited without regarding totheir phenotypic values, which makes the analyses of multiple phenotypes possible without the need to correct
ascertainment bias.
Finally, compared with studies focused only on SNPs
within candidate genes, GWAS approaches are unbiased
and as such they have the advantage of detecting novel
genes or confirming genes that are not well-known tohave an influence on a phenotype. However, since the current GWAS uses only a subset of all the SNPs in HapMap[35], it may miss some genes due to lack of coverage. For
the same reason, GWAS data usually are not enough tostudy a candidate gene comprehensively. To understandthe roles played by each SNP in a candidate gene, additional genotyping, and single-SNP and haplotype analyses
are needed. A large GWAS involving more than 550,000
SNPs in more than 9000 participants of FHS will be avail-

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Table 8: Association between hematological phenotypes and SNPs in/near known candidate genes
GEE Association Test P-values††
Candidate Gene

SNP

EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
EPB41L2
HBB, HBDHBB, HBD, HBG1, HBG2, HBE1
HBB, HBD, HBG1, HBG2, HBE1
HBB, HBD, HBG1, HBG2, HBE1
HBB, HBD, HBG1, HBG2, HBE1
HBG1, HBG2, HBE1
HEBP2
HEBP2
HEBP2
HEBP2
HEBP2

rs7741731
rs1582055
rs1413753
rs1413754
rs4075265
rs915171
rs9285468
rs6938586
rs3777442
rs4897472
rs4897475
rs1334689
rs7748468
rs2105250
rs9321263
rs1811949
rs9321265
rs7761311
rs1412540
rs6907809
rs951251
rs4128714
rs2105819
rs968856
rs10488676
rs10488675
rs3898916
rs10499199
rs10499200
rs10499201
rs6902919
rs6926204

CHR

Physical Position
(bp)*

MAF

Positionto Gene†Function

6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
6
11
11
11
11
11
11
6
6
6
6
6

131,152,063
131,186,056
131,192,197
131,192,319
131,208,150
131,226,607
131,233,823
131,251,097
131,251,481
131,254,831
131,274,643
131,275,701
131,297,742
131,325,875
131,340,606
131,340,872
131,345,926
131,352,801
131,368,085
131,374,355
131,432,905
5,151,240
5,216,303
5,217,152
5,225,373
5,254,606
5,284,996
138,811,539
138,813,645
138,818,107
138,819,479
138,820,053

0.17
0.3
0.34
0.3
0.33
0.26
0.33
0.18
0.48
0.49
0.28
0.31
0.3
0.28
0.33
0.32
0.14
0.37
0.24
0.21
0.15
0.25
0.46
0.47
0.48
0.27
0.38
0.16
0.16
0.16
0.21
0.16

NEAR
NEAR
NEAR
NEAR
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
IN
NEAR
NEAR
NEAR
NEAR
NEAR
NEAR
NEAR
NEAR
NEAR
NEAR
NEAR
NEAR

unknown
unknown
intronintronuntranslated
intronintronintronintronintronintronintronintronunknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
unknown
intronintronintronintronintronHematocrit

Hgb

MCH

MCV

RBCC

0.488
7.7 × 10-5
0.003
0.242
0.001
0.712
0.003
0.878
0.017
0.002
1.6 × 10-4
0.001
0.002
0.013
0.461
0.511
0.985
0.986
0.006
0.739
0.851
0.557
0.007
0.013
0.051
0.050
0.089
0.503
0.706
0.633
0.789
0.887

0.326
2.9 × 10-4
0.007
0.158
0.004
0.486
0.005
0.511
0.124
0.022
6.0 × 10-4
0.005
0.005
0.002
0.137
0.210
0.909
0.553
0.006
0.928
0.789
0.402
0.039
0.049
0.134
0.180
0.165
0.585
0.742
0.725
0.977
0.979

0.847
0.427
0.691
0.561
0.470
0.355
0.437
0.578
0.834
0.972
0.633
0.703
0.616
0.050
0.188
0.176
0.734
0.202
0.356
0.153
0.419
0.345
0.128
0.202
0.174
0.798
0.353
0.557
0.535
0.487
0.225
0.429

0.549
0.762
0.730
0.039
0.506
0.232
0.921
0.661
0.568
0.551
0.485
0.639
0.956
0.696
0.095
0.119
0.941
0.066
0.725
0.049
0.009
0.175
0.027
0.016
0.047
0.077
0.260
0.412
0.382
0.414
0.148
0.381

0.163
3.9 × 10-4
0.007
0.016
0.002
0.273
0.022
0.951
0.044
0.009
0.011
0.020
0.009
0.076
0.056
0.076
0.896
0.143
0.008
0.281
0.075
0.431
0.463
0.576
0.735
0.920
0.367
0.972
0.793
0.897
0.541
0.660

* Physical position is in base pair (bp) and based on the May 2004 human reference sequence (NCBI Build 35).
†'NEAR' means within 60 kb to a gene; 'IN' means within a gene.
†† The p-values are from the GEE association test on multivariable adjusted phenotypes listed below.

able for analysis later in 2007, providing increased powerfor detection of smaller effects for the hemostatic andhematological phenotypes.

Conclusion
In summary, we have tested for association and linkage
using the Affymetrix 100K SNPs and a set of hemostatic
factor and hematological phenotypes. We have confirmeda previously reported association, providing proof of principle (a "positive control") for the GWAS approach. Our
results provide a set of hypotheses that warrant testing in
additional studies.

Abbreviations
ADP = adenosine 5'-diphosphate; bp = base pair(s); CHD= coronary heart disease; CVD = cardiovascular disease;
CHR = chromosome; EPB41L2 = erythrocyte membraneprotein band 4.1-like 2; EPO = erythropoietin; EPOR =erythropoietin receptor; FBAT = family-based association
test; FGB = fibrinogen-β; FGA = fibrinogen-α; FGG =fibrinogen-γ; FHS = Framingham Heart Study; FVII = fac-

tor VII; GAW = Genetic Analysis Workshop; GEE = generalized estimating equations; GWAS = genome-wide
association study; HBA1 = hemoglobin-α 1; HBA2 =hemoglobin-α 2; HBB = hemoglobin-β chain complex;
HBD = hemoglobin-δ; HBE1 = hemoglobin-ε 1; HBG1 =hemoglobin-γ A; HBG2 = hemoglobin-γ G; HBM = hemoglobin-μ; HCT = hematocrit; HEBP2 = heme binding protein 2; Hgb = hemoglobin; ITGB3 = integrin, beta 3(platelet glycoprotein IIIa, antigen CD61); kb = kilo basepairs (=1,000 bp); KLF1 = Kruppel-like factor 1; LD = linkage disequilibrium; MAF = minor allele frequency; MCV =mean corpuscular volume; MCH = mean corpuscularhemoglobin; Mb = mega base pairs (=1,000,000 bp);
OR5AP2 = olfactory receptor, family 5, subfamily AP,
member 2; OR5AR1 = olfactory receptor, family 5, subfamily AR, member 1; OR9G1 = olfactory receptor, family
9, subfamily G, member 1; OR9G4 = olfactory receptor,
family 9, subfamily G, member 4; PAI-1 = plasminogenactivator inhibitor; PDGFC = platelet derived growth factor-C; RBCC = red blood cell count; SERPINE1 = serpin
peptidase inhibitor, clade E, member 1; SNP = single

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BMC Medical Genetics 2007, 8(Suppl 1):S12

http://www.biomedcentral.com/1471-2350/8/S1/S12

nucleotide polymorphism; tPA = tissue plasminogen activator; vWF = von Willebrand factor; WBC = white blood
cell.

6.

Competing interests

7.

The authors declare that they have no competing interests.

Authors' contributions

8.

QY took a leading role in study design, results summarization and interpretation, and manuscript drafting. SK
contributed to the study design, summarization and interpretation of the results, and the manuscript revision. JPL
contributed to the study of hematological phenotypes.
GHT contributed to the measurement and study of thehemostatic traits. COD contributed to study design,
results summarization and manuscript drafting and revision. All authors read and approved the final manuscript.

11.

Additional material

12.

Additional file 1

13.

The 25 SNPs with lowest FBAT association test p-values are presented in
Additional data file 1, Table A1 and Table A2, respectively.
Click here for file
[http://www.biomedcentral.com/content/supplementary/14712350-8-S1-S12-S1.doc]

9.
10.

14.

15.
16.

Acknowledgements
This work is supported by National Institute of Health/National Heart,
Lung & Blood Institute (NHLBI) Contract N01-HC-25195. A portion of theresearch was conducted using the BU Linux Cluster for Genetic Analysis(LinGA) funded by the NIH NCRR (National Center for Research
Resources) Shared Instrumentation grant (1S10RR163736-01A1). We
express out gratitude to the Framinghan Heart Study participants, and helpful input from the collaborators: Emelia Benjamin and Martin G. Larson.
This article has been published as part of BMC Medical Genetics Volume 8
Supplement 1, 2007: The Framingham Heart Study 100,000 single nucleotide polymorphisms resource. The full contents of the supplement areavailable online at http://www.biomedcentral.com/1471-2350/8?issue=S1.

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