BMC Medical Genetics

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Genome-wide association with select biomarker traits in the
Framingham Heart Study
Emelia J Benjamin*1,2,3,4, Josée Dupuis1,4, Martin G Larson1,5,Kathryn L Lunetta1,4, Sarah L Booth6, Diddahally R Govindaraju1,2,
Sekar Kathiresan7,8, John F Keaney Jr2,3, Michelle J Keyes1,5, Jing-Ping Lin10,
James B Meigs9, Sander J Robins1,2, Jian Rong1,4, Renate Schnabel1,2,
Joseph A Vita2,3, Thomas J Wang8, Peter WF Wilson11, Philip A Wolf1,2 andRamachandran S Vasan1,2,3
Address: 1The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA, 2School of Medicine, Boston
University, Boston, MA, USA, 3Whitaker Cardiovascular Institute, Boston University, Boston, MA, USA, 4School of Public Health, Boston
University, Boston, MA, USA, 5Department of Mathematics and Statistics, Boston, MA, USA, 6Jean Mayer USDA Human Nutrition Research Centeron Aging, Tufts University, Boston, MA, USA, 7Broad Institute of Massachusetts Institute of Technology, Cambridge, MA, USA, 8Cardiology
Division, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA, 9Department of Medicine,
Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA, 10Office of Biostatistics Research, National Heart,
Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA and 11Emory School of Medicine, Atlanta, GA, USA
Email: Emelia J Benjamin* - emelia@bu.edu; Josée Dupuis - dupuis@bu.edu; Martin G Larson - mlarson@bu.edu;
Kathryn L Lunetta - klunetta@bu.edu; Sarah L Booth - sarah.booth@tufts.edu; Diddahally R Govindaraju - drgraju@bu.edu;
Sekar Kathiresan - SKATHIRESAN1@PARTNERS.ORG; John F Keaney - jkeaney@bu.edu; Michelle J Keyes - mjkeyes@bu.edu; JingPing Lin - linj@nhlbi.nih.gov; James B Meigs - jmeigs@partners.org; Sander J Robins - sjrobins@bu.edu; Jian Rong - jrong@bu.edu;
Renate Schnabel - schnabel@bu.edu; Joseph A Vita - jvita@bu.edu; Thomas J Wang - tjwang@partners.org;
Peter WF Wilson - pwwilso@emory.edu; Philip A Wolf - pawolf@bu.edu; Ramachandran S Vasan - vasan@bu.edu* Corresponding author

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

BMC Medical Genetics 2007, 8(Suppl 1):S11

doi:10.1186/1471-2350-8-S1-S11

This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S11© 2007 Benjamin 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: Systemic biomarkers provide insights into disease pathogenesis, diagnosis, and risk stratification. Manysystemic biomarker concentrations are heritable phenotypes. Genome-wide association studies (GWAS) provide
mechanisms to investigate the genetic contributions to biomarker variability unconstrained by current knowledge ofphysiological relations.
Methods: We examined the association of Affymetrix 100K GeneChip single nucleotide polymorphisms (SNPs) to 22
systemic biomarker concentrations in 4 biological domains: inflammation/oxidative stress; natriuretic peptides; liverfunction; and vitamins. Related members of the Framingham Offspring cohort (n = 1012; mean age 59 ± 10 years, 51%
women) had both phenotype and genotype data (minimum-maximum per phenotype n = 507–1008). We used
Generalized Estimating Equations (GEE), Family Based Association Tests (FBAT) and variance components linkage torelate SNPs to multivariable-adjusted biomarker residuals. Autosomal SNPs (n = 70,987) meeting the following criteria
were studied: minor allele frequency ≥ 10%, call rate ≥ 80% and Hardy-Weinberg equilibrium p ≥ 0.001.
Results: With GEE, 58 SNPs had p < 10-6: the top SNPs were rs2494250 (p = 1.00*10-14) and rs4128725(p = 3.68*10-12) for monocyte chemoattractant protein-1 (MCP1), and rs2794520 (p = 2.83*10-8) and rs2808629 (p =3.19*10-8) for C-reactive protein (CRP) averaged from 3 examinations (over about 20 years). With FBAT, 11 SNPs had

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p < 10-6: the top SNPs were the same for MCP1 (rs4128725, p = 3.28*10-8, and rs2494250, p = 3.55*10-8), and also
included B-type natriuretic peptide (rs437021, p = 1.01*10-6) and Vitamin K percent undercarboxylated osteocalcin(rs2052028, p = 1.07*10-6). The peak LOD (logarithm of the odds) scores were for MCP1 (4.38, chromosome 1) andCRP (3.28, chromosome 1; previously described) concentrations; of note the 1.5 support interval included the MCP1
and CRP SNPs reported above (GEE model). Previous candidate SNP associations with circulating CRP concentrations
were replicated at p < 0.05; the SNPs rs2794520 and rs2808629 are in linkage disequilibrium with previously reported
SNPs. GEE, FBAT and linkage results are posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.
Conclusion: The Framingham GWAS represents a resource to describe potentially novel genetic influences on systemic
biomarker variability. The newly described associations will need to be replicated in other studies.

Background
There is intense clinical and research interest in blood andurinary biomarkers to diagnose disease, to risk stratify
individuals for prognosis and potential intervention, andto provide insights into disease pathogenesis [1]. Hence,
it has been proposed that biomarkers may prove useful inthe goal of developing what has been referred to as "predictive, preemptive, personalized medicine" [2].
In the present analysis, we examined biomarkers involving four biological systems: inflammation, natriuretic
peptides, hepatic function, and vitamins. Circulating
inflammatory, natriuretic peptides [3-5], hepatic function[6,7] and vitamin [8] biomarker concentrations have been
linked to increased risk of cardiovascular disease and mortality. For instance, the inflammatory marker C-reactive
protein (CRP) predicts incident stroke [9], coronary heartdisease [10-12], and all-cause mortality [13].
Because of their prognostic importance, there has beeninterest in understanding the environmental and genetic
factors contributing to interindividual variability in systemic biomarker concentrations. Prior reports support theheritability of systemic biomarker concentrations reflecting inflammatory processes [14,15], natriuretic peptides
activation [16], hepatic function [17,18], and vitamin
metabolism [19]. The majority of prior studies examining
the genetic contribution to biomarker concentrations
have examined genetic linkage or variation in selectedcandidate genes. Although there have been some successes with both approaches [20], the specific genes contributing to variability of most circulating biomarkers areincompletely understood. We examined the relation of
single nucleotide polymorphisms (SNPs) on the Affymetrix 100K chip to variation in systemic biomarker concentrations. The GWAS approach has the advantage that it is
not constrained by known physiologic associations.

Materials and methods
Study sample
The biomarkers were assessed in the Framingham Offspring sample, which is described in the Framingham

100K Overview [21]. Briefly, the Framingham Offspringwere recruited in 1971–1974 from the children (and children's spouses) of the Framingham Original Cohort [22].
The examinations and the number of participants inwhich the biomarkers were assessed vary by analyte, as
noted in Table 1.
Phenotype definitions and methods
Biomarkers were measured on morning specimens afteran overnight fast (typically 10 hours) between 7:30 and
9:00 am. EDTA and citrated blood collection tubes arecentrifuged in a refrigerated centrifuge immediately after
venipuncture. Serum blood collection tubes sit for 30
minutes after venipuncture to allow for complete clotting.
Specimens are processed immediately after centrifugation. Blood samples were centrifuged and frozen at -20°
(examination 2 through 4) and -80° (examinations 5
through 7). The measurement of the inflammatory markers is detailed in the inflammatory marker manual at the
National Center for Biotechnology Information http://
www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007.

Inflammatory biomarkers (except CRP) were measured
in duplicate with commercially available ELISA kits: R&D
Systems (intercellular adhesion molecule-1, interleukin6, monocyte chemoattractant-1 [MCP1], P-selectin,
tumor necrosis factor receptor 2, high sensitivity tumor
necrosis factor-α), Bender MedSystems (CD40 ligand),Oxis (myeloperoxidase), and BIOMEDICA (osteoprotegerin). High-sensitivity CRP was measured in 2002 and2004 on examination cycle 2, 6 and 7 specimens with aDade Behring nephelometer; the less sensitive Hemagen
assay was used in 1998 for examination cycle 5 specimens. Natriuretic peptides were measured by Shionogi
using a noncompetitive high sensitivity immunoradiometric assay [23]. Liver function tests were measured at
examination cycle 2 by Quest Diagnostics (previously
METPATH) with a variety of methods: γ-glutamyl aminotransferase was measured with spectrophotometry [7],bilirubin was measured by the colorimetric method (Dow
Bilirubin Kit) [24,25]; alkaline phosphatase was measPage 2 of 12(page number not for citation purposes)

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Table 1: Types of traits phenotype master trait table, exam cycle, numbers of participants in family plates with phenotype
Phenotype

Acronym

Trait N = 27*

Subject N

Offspring Exam

Adjustment* Multivariable model

7

Age, sex, smoking, systolic anddiastolic blood pressure, hypertension
treatment, body mass index, waist
circumference, Total/HDL cholesterol,
triglyceride, lipid lowering medication,
glucose, diabetes, aspirin, hormone
replacement therapy and prevalentcardiovascular disease

Inflammation/Oxidative Stress
CD40 Ligand, serum & plasma

CD40L

2

998

C-reactive protein
Intercellular adhesion molecule-1
Interleukin-6
Urinary isoprostanes/creatinine
Monocyte chemoattractant protein-1
Myeloperoxidase
Osteoprotegerin
P-selectin
Tumor necrosis factor alphaTumor necrosis factor receptor-2

CRP
ICAM1
IL6
IsoCrUrine
MCP1
MPO
OPG
Pselectin
TNFA
TNFRII

5
1
1
1
1
1
1
1
1
1

980–1008
1006
1006
828
989
974
1005
1007
753
980

2, 5, 6, 7; Average: 2, 6, 7
7

Natriuretic Peptides
N-terminal pro-atrial natriuretic peptide

ANP

1

938

B-type natriuretic peptide

BNP

1

938

6

Age, sex, BMI, SBP, HTN Rx, LDL
Total/HDL, diabetes, LV mass, LA size,
CVD

2Age, sex, BMI, HDL, HTN, diabetes,
serum total protein, alcohol intake,TG, & smoking

6/7†Age, sex, SBP, DBP, BMI, waist, total/
HDL, smoking, glucose, TG, diabetes,
HTN Rx, lipid lowering Rx, hormone
replacement Rx, asthma Rx, alcoholuse, prevalent CVD

Liver Function
Bilirubin

Bili

1

910

Aspartate aminotransferase **Alanine aminotransferase
Alkaline phosphatase
Gamma-glutamyl transferase

AST
ALT
AlkPhos
GGT

1
1
1
1

904
904
904
896

Vitamins
Vitamin K plasma phylloquinone

VitKPhylloq

1518

Vitamin K percentage of undercarboxylated osteocalcin
Vitamin D plasma 25(OH)-D

VitKPucOC
VitD25OH

1
1

504
517

*Each trait had 2 adjustment schemes web posted: age- and sex-adjusted, and multivariable-adjusted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. GEE and FBAT traits are web displayed pha001115 through pha001218; Linkage traits are located from pha002301 through pha002352. In thepresent manuscript we examine the multivariable-adjusted trait, which we count as 1 trait. Note: biomarkers were natural log transformed due to skewed
distribution; **normalized deviates. †Vitamin measurements straddled exams 6 & 7, covariates from same exam biomarker assayed. SBP, DBP = systolic and diastolic bloodpressure, HTN Rx = hypertension treatment, BMI = body mass index, TC/HDL = total/high density lipoprotein cholesterol; TG = triglyceride, HRT = hormone replacement
therapy, Rx = medication therapy, CVD = cardiovascular disease; LDL = low density lipoprotein; LV mass = left ventricular mass; LA size = left atrial size; Atrial natriuretic
peptide = N-terminal pro-atrial natriuretic peptide.

ured with the kinetic method [26,27]; aspartate aminotransferase and alanine aminotransferase were
measured using the kinetic method with Beckman LiquidStat Reagent Kit [28]. Vitamin K status was measured asphylloquinone concentrations with reverse phase highperformance liquid chromatography [29], and percentage
of undercarboxylated osteocalcin was measured by radioimmunoassay [30,31], Vitamin D status was measured as

25(OH)D concentrations by using RIA (DiaSorin,
Stillwater MN).
Plasma samples were used for natriuretic peptides, vitamin K phylloquinone, vitamin D, and some inflammatory markers including CD40 ligand, osteoprotegerin, Pselectin, tumor necrosis factor receptor 2, and tumor
necrosis factor-α. Serum samples were analyzed for liver

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function, vitamin K, % undercarboxylated osteocalcin,
and other inflammatory markers including CRP, interleukin-6, soluble intracellular adhesion molecule-1,
MCP1, and myeloperoxidase concentrations. The reproducibility of the biomarkers was good; the intra-assay
coefficients of variation were CD40 ligand 4.4%, interleukin-6 3.1%, intercellular adhesion molecule-1 3.1%,
MCP1 4.1%, myeloperoxidase 3.0%, osteoprotegerin
3.7%, P-selectin 3.0%, tumor necrosis factor-α 8.8%, andtumor necrosis factor receptor-2 2.3%; the inter-assay
coefficients of variation were brain natriuretic peptide
12.2%, n-terminal-atrial natriuretic peptide 12.7%. The
Kappa statistic for 146 CRP samples run in duplicate was
0.95 [32]. Coefficients of variation for aspartate aminotransferase and alanine aminotransferase, respectively,
were 10.7 and 8.3%. The coefficients of variation for low
and high Vitamin K plasma phylloquinone concentrations were 15.2 and 10.9% respectively on control specimens. For low, medium and high osteocalcin
concentrations used to determine Vitamin K percentage ofundercarboxylated osteocalcin, the coefficients of variation were 22.3, 12.8, and 7.8%, respectively. For VitaminD, the coefficients of variation were 8.5% and 13.2%,
respectively.
Genotyping methods
Details of the genotyping methods are available in the
Framingham Heart Study 100K Overview [21]. Framingham staff extracted genomic DNA with a Qiagen Blood
and Cell Culture Maxi Kit from immortalized lymphoblasts. Briefly, SNPs on the Affymetrix 100K chip were genotyped (n = 112,990 autosomal SNPs) in a sample offamily members of the Original and Offspring cohorts of
the Framingham Heart Study [33]. SNPs were excluded forthe following reasons: minor allele frequency <10% n =38062; call rate <80% n = 2346; Hardy-Weinberg equilibrium p-value < 0.001 n = 1595, leaving 70,987 SNPs available for analysis.
Statistical analysis methods
We created standardized multivariable-adjusted naturallog transformed biomarker residuals adjusted for the covariates listed in Table 1. The CRP average residuals wereconstructed as follows: (1) create age- and sex-adjusted or
multivariable-adjusted residual at each of exams 2, 6 and
7; (2) take average of the residuals across exams; (3) the
residual was excluded if there were not at least 2 exams for
its calculation. In some instances we performed additional transformation (e.g. Winsorized models). Tobit
models were used to generate residuals for the natriuretic
peptides, because 2% of N-ANP levels and 30% of BNPlevels were below the respective assay detection limits.
Association and linkage results examining age- and sexadjusted residuals are posted at the web site. As describedin the Overview [21], we examined generalized estimating

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equations (GEE) and family based association testing(FBAT), assuming an additive genetic effect, to account forcorrelation among related individuals within nuclear families. We also used Merlin software [34] (splitting the largest families) to compute exact identity by descent linkage,with variance component analysis in SOLAR using 11,200
SNPs and short tandem repeats [35]. Traits with extreme
values, as defined by 4 standard deviations away from themean, were Winsorized at 4.0 in secondary linkage analyses to determine the sensitivity of the logarithm of theodds (LOD) score to the presence of outlier values.

Results
Twenty-two biomarker traits (plus 4 additional CRP
traits) were analyzed in 1012 Offspring participants, on
log-transformed multivariable-adjusted residuals as outlined in Table 1 (minimum-maximum per phenotype n =507–1008). The phenotypes were collected at various
Framingham Offspring examinations from cycles 2 to 7.
At examination cycles 2 and 7 the mean age of the participants with both phenotype and genotype data was 41 ±10 and 59 ± 10 years, and 51.2% and 51.1% were women,
respectively. For details of biomarker phenotype-genotype
association refer to http://www.ncbi.nlm.nih.gov/
projects/gap/cgi-bin/study.cgi?id=phs000007.
There were 58 SNPs associated with biomarker concentrations with a p < 10-6 by GEE. The 25 most statistically significant GEE associations sorted by p-value, listed with
their corresponding FBAT p-value are shown in Table 2a.
MCP1 concentrations were associated with rs2494250 (p= 1*10-14) and rs4128725 (p = 3.68*10-12), both on chromosome 1, near the FCER1A and the OR10J1 genes,
respectively. CRP concentrations averaged over 3 examinations (about 20 years) were associated with rs2794520(p = 2.83*10-8) and rs2808629 (p = 3.19*10-8).
We estimated the amount of variability in biomarker concentrations explained by the 4 most statistically significant SNPs in the GEE model using a pseudo measure of R2
based on log-likelihood estimates [36]. The two most statistically significant GEE SNPs explained about 7% and
4% of the variability in MCP1 concentrations (R2 = 0.070
for rs2494250 and R2 = 0.043 for rs4128725); for CRP
concentrations averaged over examinations 2, 6, and 7 thetwo most statistically significant GEE SNPs explained
2.3% of the variability [R2 = 0.023 for rs2794520 and
rs2808629) [36]. We also examined the linkage disequilibrium between the most statistically significant GEE
SNPs: rs2494250 and rs4128725 had a D' = 0.724 and an
r2 = 0.196, whereas rs2794520 and rs2808629 served as
perfect proxies for each other (D' = 1; r2 = 1).
With FBAT, 11 SNPs were associated with biomarker concentrations with a p < 10-6. The two most statistically sig-

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Table 2: Top genetic associations with biomarkers based on the lowest p value for GEE test (2a), FBAT (2b), and Linkage (2c)2a. Top 25 associations with biomarkers based on the lowest p value of the GEE test

Trait

SNP rs ID*

Chr

Physical location (bp)

GEE P-value

FBAT P-value

IN/NEAR gene

Monocyte chemoattractant protein-1

rs2494250

1156091324

1.0*10-14

3.5*10-8

FCER1A, OR10J3

3.3*10-8

OR10J1

Monocyte chemoattractant protein-1

rs4128725

1156219032

3.7*10-12

C-reactive protein average exams 2,6,7

rs2794520

1156491889

2.8*10-8

4.3*10-5

CRP

156489869

3.2*10-8

4.8*10-5

CRP

3.9*10-4

CRP
CRP

C-reactive protein average exams 2,6,7

rs2808629

1

C-reactive protein exam 6

rs2794520

1156491889

1.3*10-7

C-reactive protein exam 6

rs2808629

1

156489869

1.4*10-7

4.3*10-4
0.63

Tumor necrosis factor alphars7552393

1

83966572

5.1*10-7

C-reactive protein exam 6

rs746961

1935791730

7.5*10-7

0.03

Bilirubinrs17532515

4141745043

1.0*10-6

9.2*10-6
0.005

Alanine aminotransferase

rs1998303

982644535

1.1*10-6

Monocyte chemoattractant protein-1

rs10489849

1156009838

1.1*10-6

0.10

Alkaline phosphatasers10518765

1552467924

1.1*10-6

1.7*10-4
0.02

Vitamin K plasma phylloquinone

rs2387326

10129823446

1.1*10-6

C-reactive protein average exams 2,6,7

rs1119582

5125270919

1.2*10-6

4.2*10-4

Vitamin D plasma 25(OH)-D

rs10485165

689169536

1.4*10-6

0.003
0.009

Atrial natriuretic peptide exam 6

rs1417352

6107005919

1.8*10-6

C-reactive protein exam 2

rs583012

1054964880

1.9*10-6

0.09

Atrial natriuretic peptide exam 6

rs1486139

746048968

2.0*10-6

0.04
0.06

Atrial natriuretic peptide exam 6

rs1486140

746048877

2.2*10-6

Alanine aminotransferase exam 2

rs10492681

13

39705483

2.2*10-6

9.9*10-5

Vitamin D plasma 25(OH)-D

rs10507577

1352866092

2.6*10-6

0.004

ZNF536
CLGN, ELMOD2

IGSF4B

PTPRE, MKI67

Atrial natriuretic peptide exam 6

rs1079596

11112801829

2.6*10-6

0.03

DRD2

Monocyte chemoattractant protein-1

rs1474747

1155961586

2.8*10-6

8.7*10-4

IGSF4B

CD40 Ligand serumrs7778619

79923216

3.0*10-60.1976473583

3.5*10-6

0.01CD40 Ligand serum

rs8005745

14

2b. Top 25 associations with biomarkers based on the lowest p value of the FBAT test

Trait

SNP rs ID*

Chr

Physical location (bp)

GEE P-value

FBAT P-value

Monocyte chemoattractant protein-1

rs4128725

1

Monocyte chemoattractant protein-1

rs2494250

1

IN/NEAR gene

156219032

3.7*10-12

3.3*10-8

OR10J1156091324

1.0*10-14

3.5*10-8

FCER1A, OR10J3

1.0*10-6

NFIA

B-type natriuretic peptide

rs437021

161450291

1.5*10-4

Vitamin K % undercarboxylated
osteocalcin

rs2052028

7

15789103

5.2*10-6

1.1*10-6

CD40 Ligand plasma

rs2372184

365673194

0.003

2.5*10-6

MAGI1

Urinary isoprostanes/creatinine

rs717145

20

15826091

0.003

5.0*10-6

C20orf133

CD40 Ligand serum

rs4664604

2153398916

0.01

8.4*10-6

ARL6IP6
FMNL2, ARL6IP6

CD40 Ligand serum

rs9288125

2153348619

0.01

9.1*10-6

C-reactive protein exam 7

rs1363258

5103297593

0.02

9.2*10-6

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Table 2: Top genetic associations with biomarkers based on the lowest p value for GEE test (2a), FBAT (2b), and Linkage (2c)Bilirubinrs17532515

4141745043

1.0*10-6

9.2*10-6

CLGN, ELMOD2

Osteoprotegerin

rs496269

679457094

0.03

9.4*10-6

C-reactive protein average 2,6,7

rs1363258

5103297593

0.009

1.3*10-5

CD40 Ligand serum

rs303939

1371269472

0.008

1.3*10-5

DACH1

1.4*10-5

TRPC6

Myeloperoxidase

rs10501981

11

100880825

1.1*10-5

Urinary isoprostanes/creatinine

rs1461549

1424782140

0.26

1.5*10-5

Tumor necrosis factor alphars2353803

711060282

0.03

1.5*10-5
1.5*10-5

C9orf72

Intercellular adhesion molecule-1

rs3849944

927550594

5.3*10-6

CD40 Ligand serum

rs1986743

2153412407

0.01

1.6*10-5

ARL6IP6

Gamma-glutamyl transferase

rs962976

12

67006894

0.002

1.6*10-5

MDM1

C-reactive protein average 2,6,7

rs2421608

2117013763

0.02

1.8*10-5

C-reactive protein exam 2

rs642245

1186067184

0.03

1.9*10-5ME3

Tumor necrosis factor receptor-2

rs248328

5179309691

0.59

1.9*10-5

TBC1D9B,
RNF130C-reactive protein exam 7

rs2390582

190655928

0.07

2.0*10-5

Osteoprotegerin

rs9352609

6794421880.04

2.0*10-5

39166736

3.2*10-4

2.1*10-5

Intercellular adhesion molecule-1

rs744511

14

2c. Magnitude and Location of Peak LOD scores > 2.5 for regions in the Biomarker Phenotype Group

Trait

Exam

Chr

Physical location (bp)Maximum
LOD

LOD-1.5
Interval

LOD+1.5
Interval

Maximum LOD
WIN*

Monocyte chemoattractant protein-1

71159093573

4.96

154908901

159751221

4.38

Monocyte chemoattractant protein-1

710129553148

4.03

128294406

130084334

3.23

C-reactive protein

51154745847

3.53

153213133

156567571

3.28

Monocyte chemoattractant protein-1

71713630703

3.33

10874193

16776778

2.54Intercellular adhesion molecule-1

71203535232

2.95

202207846

215367881

2.93

Monocyte chemoattractant protein-1

7792544810

2.9488727093

105546050

2.01

Tumor necrosis factor receptor 2

7154001041

2.92

43070922

60590679

2.95

Gamma-glutamyl transferase2

326424584

2.89

24621158

27418642

2.96

B-type natriuretic peptide

61241405742.771320458137669

No outliers

Gamma-glutamyl transferase

210129553148

2.67

120112006

132560638

2.79

Vitamin D plasma 25(OH)-D

6/7

8140624328

2.67

138952328

146039126

2.68

B-type natriuretic peptide

61934016706

2.5913425865

43186344

No outliers

Myeloperoxidase

71911295505

2.563026853

16489850

2.56

Alkaline phosphatase26170538204

WIN

162441307

170788550

2.55

Osteoprotegerin

7

1375274475

2.52

71928655

81228082

2.95

bp = base pair; Chr = chromosome; WIN = Winsorized.
dbSNP positions are from NCBI Build 35 (hg17);
LD between rs2494250 and rs4128725 (top MCP1 SNPs): D' = 0.724 and r squared = 0.196.
LD between rs2794520 and rs2808629 (top CRP SNPs): D' = 1.0 and r squared = 1.0.
*Winsorized LOD scores were run for this manuscript, and are not displayed on the web.

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Table 3: Combined phenotypes
Trait

SNP rs ID

Chr

Physical location(bp)

GEE P-value

FBAT P-value

IN/NEAR gene3a. SNPs significant for 3 correlated phenotypes at exam 7 by either GEE or FBAT at p < 0.01
Interleukin-6, C-reactive protein and Fibrinogen

rs10511884
rs1887027
rs2831617
rs2831620
rs2831618
rs2044401
rs1457590
rs6848323
rs3110134
rs2016740
rs719006
rs877936
rs1436136
rs1436336
rs698270
rs847428
rs2359763
rs7969455
rs10503717
rs4899940

9
10
21
21
21
9
3
4
8
4
15
4
4
3
3
7
3
12
8
14

31668988
615378828481515
28481869
28481749
31659518
21530978113286305
60260538
113238018
59210481
113238472
113421130
106156256
137592210
16803192
23424931
775740222634817
87623621

5.7*10-5
2.6*10-4
6.2*10-4
6.4*10-4
6.4*10-4
6.6*10-4
0.00190.00220.00250.00390.00440.00550.00620.00670.0086
0.019
0.024
0.059
0.06
0.11

0.00650.19
0.00270.00220.0020
0.12
0.16
0.14
0.12
0.17
0.76
0.31
0.039
0.00400.020
0.025
0.00250.00150.00280.0019

1
1
13
14
13
1
20
3
4

156489869
156491889
35380415
72413330
84918646
17143820
32103708
5986639
180293700

6.9*10-5
6.1*10-5
7.3*10-4
5.1*10-3
0.002
0.002
0.01
0.01
0.02

4.7*10-4
4.85*10-4
0.30
0.17
0.09
0.01
8.4*10-6
9.1*10-6
9.2*10-6

IL2RA, RBM17

ZNF659

RORA

STAG1

DPPA3

3b. Combined phenotypes within a specific biological domain
C-reactive protein: exams 2, 5, 6, 7

Liver function: Alkaline phosphatase; AST; ALT; GGTVitamin D, Vitamin K phylloquinone & Vitamin K %undercarboxylated osteocalcin

rs2808629
rs2794520
rs6563212
rs11626844
rs9319160
rs910232
rs4911146
rs953402
rs1376544

NFIA, CRP
FCER1A, CRP
DCAMKL1
OR10J1, DPF3
MAGI1, PADI2
ARL6IP6, RALY
FMNL2

Chr = chromosome;For a given SNP, all of the phenotypes either FBAT or GEE significant if FBAT < 0.01 for particular SNP;
P-values = the geometric mean of the p-value for all traits within the biomarker cluster

nificant SNPs for FBAT were the same two SNPs observedwith GEE: MCP1 concentrations were significantly associated with rs4128725, p = 3.28*10-8, and rs2494250, p =3.55*10-8 (Table 2b). In addition, B-type natriuretic peptide (rs437021, p = 1.01*10-6) and Vitamin K% undercarboxylated osteocalcin (rs2052028, p = 1.07*10-6) also
were nominally statistically significantly associated.
In Table 2c we list the magnitude and location of LOD
scores > 2.5 observed for the circulating biomarker traits.
Because we were concerned that some of the LOD scores
might be inflated by individuals with extreme marker concentrations, we reanalyzed the LOD scores on Winsorized
residuals. The peak Winsorized LOD scores observed were
for the biomarkers MCP1 (4.38, chromosome 1), and
CRP (3.23, chromosome 10; 3.28, chromosome 1). Of
note the 1.5 LOD support intervals for the linkage peaks
on chromosome 1 included the SNPs significantly associated with MCP1 and CRP reported above (GEE model).

In an effort to potentially uncover genetic pleiotropy wedisplay in Table 3 two ways to synthesize findings acrossphenotypes. We examined 3 correlated inflammatory
biomarker phenotypes, interleukin-6, CRP and fibrinogen, and report SNPs that were significantly associatedwith all 3 phenotypes by GEE or FBAT at p < 0.01 (Table3a). We also examined phenotypes within a specificbiomarker category including CRP over multiple examinations, liver function tests and vitamin concentrations(nutrients involved in bone health [37,38]), and display
in Table 3b SNPs significant by either FBAT or GEE at a p< 0.01 for all of the phenotypes in a given phenotype cluster.
In Table 4 we compared our data with previously reported
phenotype-genotype associations in the published literature on systemic biomarker concentrations: bilirubin concentrations (TA repeat in UGT1A1) [39,40]; CRP (CRP)[20,32,41-50],
intercellularadhesion molecule-1(ICAM1) [51-54], interleukin-6 (IL6) [55-62], and MCP1

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BMC Medical Genetics 2007, 8(Suppl 1):S11(CCL2 = MCP1 gene [63,64]). Unfortunately, there wereno SNPs within 60 KB of the ICAM1 gene on the Affymetrix 100K chip. There was no association between
bilirubin concentrations and 1 SNP within 30 kb(rs741159) + 2 more SNPs within 50 kb (rs726017 andrs6752792) of a previously reported TA repeat in
UGT1A1. Additionally, there was no association between
interleukin-6 concentrations and SNPs in the IL6 region
despite one SNP in high LD (linkage disequilibrium; r2 =0.819) with the previously reported rs1800795 (-174G/C)
SNP. Similarly, we did not observe an association between
MCP1 concentrations and SNPs in the CCL2 region,
despite one SNP with a high r2 (0.956) with the SNP previously reported in the literature. For CRP concentrations,
we had 2 SNPs in perfect LD with rs1205, and we
observed strong evidence for replication. However, it
should be noted that this association has been previously
reported by Framingham investigators in unrelated participants [32]. Similarly, rs431568, which is in high LD (r2 =0.83) with 2 previously associated SNPs (rs3116653 andrs1417938), was highly associated with many of the CRP
phenotypes.

Discussion
In collaboration with NCBI we have web-posted our unfiltered biomarker-genotype associations and linkage results
to provide a resource to investigators seeking to understand and replicate their biomarker-genotype associations. We submit that the findings of highest priority forfollow-up are associations that were detected by several
statistical approaches. MCP1 was associated with 2 SNPs
on chromosome 1 (rs4128725 and rs2494250) with pvalues in the 10-8 by FBAT, ≤ 10-12 by GEE. Acknowledging
that linkage is less powerful and accurate, we note that the
1.5 support interval for the MCP1 linkage peak (Winsorized maximum LOD 4.38) on chromosome 1 supports
the GEE and FBAT analyses. Findings for CRP (chromosome 1), brain natriuretic peptide (chromosome 1) andVitamin K % undercarboxylated osteocalcin (Chromosome 7) are also of potential priority for follow-up. We
acknowledge that the ultimate validation of our findings
will require replication in other cohorts and functional
studies.
A fundamental challenge of GWAS tests is sorting throughassociations and prioritizing SNPs for follow-up. In theabsence of external replication, one approach to synthesizing findings is to examine associations across similar
biological domains, which may capture pleiotropy. We
presented the exploratory analyses in Tables 3a and 3b,
but reiterate that the findings will need to be examined inother cohorts.

http://www.biomedcentral.com/1471-2350/8/S1/S11

Do the findings represent true positive genetic
associations?
It is notable that some of the associations with the strongest statistical support were for associations between a geneand its protein product (e.g. CRP gene and CRP concentration). Cis-acting regulatory variants have been shown to
influence mRNA and protein levels for many genes [65].
Studies involving additional biomarker phenotypes andvariants (e.g. Affymetrix 500 K Chip) should clarify
whether cis- or trans-acting regulatory variants explain thegreatest proportion of phenotypic variation.

With GWAS, which typically test for the association of
1000s of SNPs with multiple traits, it is difficult for any
specific association to achieve genome wide significance.
For instance, a strict Bonferroni correction for the 30 traits
tested in the present study with both age/sex- and multivariable-adjusted models and 2 statistical methods (0.05/(70,987*30*2*2) would require a p = 5.9 × 10-9. We submit that the most significant association in the selectedbiomarker group, the FCER1A rs2494250 SNP with MCP1
concentrations achieved genome-wide significance with a
GEE p = 1.0*10-14 and a FBAT p = 3.5*10-8. It should be
noted that rs2494250 and rs4128725 are in modest linkage disequilibrium (D' = 0.724 and r squared = 0.196)
and hence, may be serving as proxies for the same causal
SNP.
Several human and experimental studies suggest that theassociation between FCER1A and MCP1 concentrations isbiologically plausible. FCER1A codes for the high affinity
Fc receptor fragment for IgE. In vitro experiments with rat
mast cells demonstrated that if aggregated the high affinity receptor for IgE (FcεRI) increased gene transcription
and secretion of MCP1 [66]. Similarly, in mice mast cells
if the FcεRI was occupied by small amounts IgE/antigen,
MCP1 mRNA increased significantly [67]. In humans IgE
and MCP1 concentrations are both increased in occupational asthma [68,69]. Similar to the animal data, humanmast cells exposed to anti-IgE antibody or to IgE released
MCP1 [70-72].
Comparison with prior literature
Our efforts to compare our findings with associations previously reported in the literature underscore some of thechallenges in genetic association studies. The ICAM1 gene
did not have any markers within 60 kb on the Affymetrix
100K chip. Of the 4 genes that did have SNPs in themarker genomic region coding, only the CRP association
was replicated in our cohort; however as noted above we[32], as well as others [20], have previously reported thisassociation. For bilirubin concentrations we previously
reported significant linkage to chromosome 2q telomere[39] and a significant association to a TA repeat in
UGT1A1, under this linkage peak [40] in Framingham

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Table 4: Comparison with the prior literatureGene

rs numberprevious reports

IL6
MCP1

rs1800795= -174G/C
rs1024611

CRP average 2,6,7

rs120537

rs1417938

rs3093077

rs3116653# Affy SNPs
within 60 kb

rs ID Affy
SNPs

Chr

D'

r2

Distance
Associated SNP

MAF

FBAT pvalue

GEE p-value

7

rs6461667

7

1

0.82

30098

0.36

0.09

0.66

13

rs10491109
rs1080327
rs1860181
rs1860182
rs3815341
rs1446959
rs1891187
rs2808629
rs2794520
rs4131568
rs1446959
rs1891187
rs2808629
rs2794520
rs4131568
rs1446959
rs1891187
rs2808629
rs2794520
rs4131568
rs1446959
rs1891187
rs2808629
rs2794520
rs4131568

17

1
1
0.92
0.92
1
0.56
0.39
1
1
0.63
0.5
0.29
1
1
1
0.66
0.47
1
1
1
0.51
0.31
1
1
1

0.04
0.96
0.27
0.27
0.02
0.12
0.05
1
1
0.12
0.16
0.07
0.25
0.25
0.83
0.03
0.02
0.03
0.03
0.03
0.17
0.08
0.25
0.25
0.83

30762
11878
37799
37649
34637
75429
53180
5437
3417
39823
77382
55133
7390
5370
37870
72832
50583
2840
820
42420
90106
67857
20114
18094
25146

0.15
0.25
0.45
0.45
0.05
0.39
0.33
0.34
0.34
0.30
0.39
0.33
0.34
0.34
0.30
0.39
0.33
0.34
0.34
0.30
0.39
0.33
0.34
0.34
0.30

0.048
0.78
0.04
0.046
0.001
0.86
0.30
4.8*105
4.3*10-5
0.004
0.86
0.30
4.8*10-5
4.3*10-5
0.004
0.86
0.30
4.8*10-5
4.3*10-5
0.004
0.86
0.30
4.8*10-5
4.3*10-5
0.004

0.13
0.35
0.11
0.11
0.50
0.002
0.02
3.2*10-8
2.8*10-8
0.001
0.002
0.02
3.2*10-8
2.8*10-8
0.001
0.002
0.02
3.2*10-8
2.8*10-8
0.001
0.002
0.02
3.2*10-8
2.8*10-8
0.001

1Displayed are SNPs that are either in the highest LD (r2) with previously reported SNPs or that have an FBAT or GEE p-value < 0.05.
For bilirubin concentrations in Framingham study unrelated participants we previously reported significant linkage to chromosome 2q telomere[39] and a significant association to a TA repeat UGT1A1, there were no association between bilirubin concentrations and 1 SNP within 30 kb(rs741159) + 2 more SNPs within 50 kb (rs726017 and rs6752792). The previously reported UGT1A1 variant is not a SNP and therefore not in
HapMap; we have no LD information and cannot assess whether the association previous reported is also present in the current sample. ICAM1 on
chromosome 19 has 3 reported SNPs in literature (rs1799969, rs5491, rs5498), but there were no Affymetrix SNPs within 60 KB of the gene.
CCL2 [Other associated SNPs: rs2857654, rs1024610, rs2857657 are NOT in HapMap, so no LD information was available.
CRP 2 SNPs are in perfect LD with rs1205. The previously reported triallelic SNP rs3091244 is not in HapMap. CRP association was previously
reported in Framingham unrelated participants [32].

unrelated participants. However, there was no association
between bilirubin concentrations and the 3 SNP within
60 kb of UGT1A1. The previously reported interleukin-6IL6 and the MCP1-CCL2 associations were not replicated.
Of note, our group previously reported that rs1024611 [inCCL2] was associated with MCP1 concentrations in unrelated participants [63]; the association was nowhere close
to significant in the present report (FBAT p = 0.78; GEE p= 0.35) Possible explanations of the failure to confirm thepreviously reported Framingham study MCP1-CCL2 association may stem from the current report having a smaller
sample size (n = 989), using different genetic markers, andbeing conducted with an additive genetic model in relatedparticipants, as opposed to the prior study using unrelatedparticipants (n = 1602) with recessive and dominantmodels. In a recent meta-analysis of phenotype-genotype
association studies, only about one third (8 of 25) of theassociations examined were replicated [73]. There aremany plausible explanations why we did not replicate
previously reported phenotype-genotype associations.
Previous reports could represent false positive findings, orthe present and prior study cohorts may differ on key fac-

tors, which may modify the phenotype-genotype associations, or our lack of replication may represent a false
negative report because of inadequate statistical power[73,74].
Strengths and limitations
The strengths of the present study include a comprehensively characterized community-based cohort, withbiomarker phenotypes routinely assessed with carefulattention to quality control. However, the cohort waslargely middle-aged to elderly, and white of European
descent, so the findings may not be generalizable to individuals who are younger or of other ethnicity/racial
descent. DNA was collected at the 5th and 6th examinations, which may have introduced a survival bias. In addition, our study was susceptible to false negative findings
because of the moderate size of the cohort; we lacked
power to detect modest associations. Conversely, similar
to most GWAS, the reported associations and linkage may
represent false positive findings from multiple statistical
testing.

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BMC Medical Genetics 2007, 8(Suppl 1):S11

Conclusions and future directions
The Framingham GWAS and the web posting of the unfiltered results represent a unique resource to discoverpotentially novel genetic influences on systemic biomarker variability. We acknowledge that the newly describedassociations will need to be replicated in other studies.

Abbreviations
bp, base pair; Chr, chromosome;CRP, C-reactive protein;
FBAT, Family Based Association Tests; GEE, Generalized
Estimating Equations; GWAS, Genome-wide association
studies; LD, linkage disequilibrium; LOD, logarithm of
the odds (base 10); MCP-1, monocyte chemoattractant
protein-1; SNPs, single nucleotide polymorphisms.

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

Authors' contributions
EJB conceived of the FHS inflammation project, secured
funding, planned the analyses, drafted and criticallyrevised the manuscript. JD assisted in planning and conducting the analyses, and in writing and critically revising
the manuscript. MGL planned the FHS inflammation
project including assisting in securing funding, andplanned and conducted analyses. KLL assisted in planningand conducting the analyses. SLB measured the vitamin
data, assisted in planning the analyses and critically revising the manuscript. DRG participated in the study design
and reviewed the manuscript. SK contributed to analysesof C-reactive protein and osteoprotegerin, and reviewed
the manuscript. JFK assisted in securing the funding,supervised and organized the performance of the assays
and reviewed the manuscript. MJK contributed to collecting the data base and revising the manuscript. JPL provided insights into the liver function test analyses andreviewed and approved the manuscript. JBM secured
funding for and oversaw measurement of high-sensitivity
TNFα concentrations and reviewed and approved themanuscript. SJR contributed to acquisition of the inflammation data, reviewing, revising and giving final approvalto the manuscript. JR provided critical assistance in organizing the inflammatory marker data set, conducted quality
control analyses and reviewed and gave final approval tothe manuscript. RS was involved in revising the manuscript critically for important intellectual content and gave
final approval of the version to be published. JAV assisted
in securing funding for the inflammation project andrevising the manuscript. TJW contributed to the analysis
and interpretation of the data, and revision of the manuscript for important intellectual content. PWFW contributed to data acquisition, revision of the manuscript andfinal approval of the version submitted. PAW participatedin 100K study design and reviewed and approved themanuscript. RSV provided critical input in conceiving the

http://www.biomedcentral.com/1471-2350/8/S1/S11

project, securing the funding, planning the analyses andcritically revising the manuscript.

Acknowledgements
The investigators would like to express their gratitude to the Framingham
Heart Study participants and key collaborators: Fox CS, Jacques PF, Lee DS,
Lipinska I, Massaro JM, Murabito JM, O'Donnell CJ, Seshadri S, Yang Q. The
core examinations were funded by N01-HC25195. A portion of theresearch 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 (1S10RR16373601A1). Inflammatory markers were measured via HL064753, HL076784,
AG028321 (EJB), HL71039 (RSV) and 2 K24HL04334 (RSV); osteoprotegerin work was supported by HL064753, HL076784, AG028321 (EJB) andthe Doris Duke Charitable Foundation and NIH 1K23 HL083102 (SK).
TNF-alpha concentrations were measured via American Diabetes Association Career Development Award and NCRR GCRC M01-RR-01066 (JBM);
Natriuretic peptides were measured by Shionogi & Co., Ltd. with an unrestricted research grant; Liver function tests were funded by the core contract; Vitamins were measured by federal funds from the U.S. Departmentof Agriculture, Agricultural Research Service under Cooperative Agreement No. 58-1950-001 and No. 58-1950-4-401, National Institute of Aging(AG14759).
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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