BMC Medical Genetics

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Open AccessA genome-wide association study for blood lipid phenotypes in the
Framingham Heart Study
Sekar Kathiresan1,2,3, Alisa K Manning1,4, Serkalem Demissie1,4,Ralph B D'Agostino1,5, Aarti Surti2, Candace Guiducci2, Lauren Gianniny2,
Nöel P Burtt2, Olle Melander6, Marju Orho-Melander7, Donna K Arnett8,
Gina M Peloso1,4, Jose M Ordovas9 and L Adrienne Cupples*1,4
Address: 1National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA, 2Broad Institute of Massachusetts Instituteof Technology and Harvard University, Cambridge, MA, USA, 3Cardiovascular Disease Prevention Center, Cardiology Division, Massachusetts
General Hospital, Harvard Medical School, Boston, MA, USA, 4School of Public Health, Boston University, Boston, MA, USA, 5Department ofMathematics and Statistics, Boston University, Boston, MA, USA, 6Department of Clinical Sciences, Hypertension and Cardiovascular Diseases,
University Hospital Malmö, Lund University, Malmö, Sweden, 7Diabetes and Endocrinology, University Hospital Malmö, Lund University,
Malmö, Sweden, 8Department of Epidemiology, University of Alabama at Birmingham, Birmingham, AL, USA and 9Nutrition and Genomics
Laboratory, Jean Mayer-USDA Human Nutrition Research Center at Tufts University, Boston, MA, USA
Email: Sekar Kathiresan - skathiresan@partners.org; Alisa K Manning - amanning@bu.du; Serkalem Demissie - demissie@bu.edu;
Ralph B D'Agostino - Ralph@bu.edu; Aarti Surti - asurti@broad.mit.edu; Candace Guiducci - candace@broad.mit.edu;
Lauren Gianniny - gianniny@broad.mit.edu; Nöel P Burtt - burtt@broad.mit.edu; Olle Melander - olle.melander@med.lu.se; Marju OrhoMelander - marju.orho@med.lu.se; Donna K Arnett - arnett@ms.soph.uab.edu; Gina M Peloso - gpeloso@bu.edu;
Jose M Ordovas - jose.ordovas@tufts.edu; L Adrienne Cupples* - adrienne@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):S17

doi:10.1186/1471-2350-8-S1-S17

This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S17© 2007 Kathiresan 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: Blood lipid levels including low-density lipoprotein cholesterol (LDL-C), high-density lipoproteincholesterol (HDL-C), and triglycerides (TG) are highly heritable. Genome-wide association is a promising approach tomap genetic loci related to these heritable phenotypes.
Methods: In 1087 Framingham Heart Study Offspring cohort participants (mean age 47 years, 52% women), we
conducted genome-wide analyses (Affymetrix 100K GeneChip) for fasting blood lipid traits. Total cholesterol, HDL-C,
and TG were measured by standard enzymatic methods and LDL-C was calculated using the Friedewald formula. The
long-term averages of up to seven measurements of LDL-C, HDL-C, and TG over a ~30 year span were the primaryphenotypes. We used generalized estimating equations (GEE), family-based association tests (FBAT) and variancecomponents linkage to investigate the relationships between SNPs (on autosomes, with minor allele frequency ≥10%,
genotypic call rate ≥80%, and Hardy-Weinberg equilibrium p ≥ 0.001) and multivariable-adjusted residuals. We pursued
a three-stage replication strategy of the GEE association results with 287 SNPs (P < 0.001 in Stage I) tested in Stage II (n~1450 individuals) and 40 SNPs (P < 0.001 in joint analysis of Stages I and II) tested in Stage III (n~6650 individuals).
Results: Long-term averages of LDL-C, HDL-C, and TG were highly heritable (h2 = 0.66, 0.69, 0.58, respectively; each
P < 0.0001). Of 70,987 tests for each of the phenotypes, two SNPs had p < 10-5 in GEE results for LDL-C, four for HDLC, and one for TG. For each multivariable-adjusted phenotype, the number of SNPs with association p < 10-4 ranged from13 to 18 and with p < 10-3, from 94 to 149. Some results confirmed previously reported associations with candidate
genes including variation in the lipoprotein lipase gene (LPL) and HDL-C and TG (rs7007797; P = 0.0005 for HDL-C and0.002 for TG). The full set of GEE, FBAT and linkage results are posted at the database of Genotype and Phenotype
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http://www.biomedcentral.com/1471-2350/8/S1/S17(dbGaP). After three stages of replication, there was no convincing statistical evidence for association (i.e., combined P< 10-5 across all three stages) between any of the tested SNPs and lipid phenotypes.
Conclusion: Using a 100K genome-wide scan, we have generated a set of putative associations for common sequencevariants and lipid phenotypes. Validation of selected hypotheses in additional samples did not identify any new lociunderlying variability in blood lipids. Lack of replication may be due to inadequate statistical power to detect modest
quantitative trait locus effects (i.e., <1% of trait variance explained) or reduced genomic coverage of the 100K array.
GWAS in FHS using a denser genome-wide genotyping platform and a better-powered replication strategy may identify
novel loci underlying blood lipids.

Introduction
Blood lipid levels are a major contributor to atherosclerotic cardiovascular disease [1]. Current evidence suggeststhat blood lipids are complex genetic phenotypes, influenced by both environmental and genetic factors. Heritability estimates for blood lipids are high, including ~40–
60% for high-density lipoprotein cholesterol (HDL-C),~40–50% for low-density lipoprotein cholesterol (LDLC), and ~35–48% for triglycerides (TG) [2]. These estimates indicate that DNA sequence variation plays animportant role in explaining inter-individual variation inblood lipid levels. Indeed, sequence variants in individual
genes have been consistently related to blood lipid phenotypes, including APOE/PCSK9 with LDL-C [3-5], CETP/
LIPC/LPL with HDL-C [6-9], and APOA5/LPL with TG[10,11], among others. However, the extent to which
common genetic variants across the genome account for
total variation in blood lipid levels is unknown.
Recent advances in genomics enable a genome-wide association study (GWAS), an approach in which a substantial
fraction of common genetic variation is tested for a role indetermining phenotypic variation [12]. These advances
include a map of the correlation structure for approximately 4 million common genetic variants (minor allele
frequency >5%) and whole-genome genotyping technologies capable of assaying 100,000–500,000 single nucleotide polymorphisms (SNPs) in an individual [13].
Utilizing a fixed genotyping marker set such as theAffymetrix 100K GeneChip in an association study tests a
substantial fraction of the genome in whites, ~30–45% insome estimates [14]. GWAS has been successfully applied
to identify novel genetic loci related to several medical
phenotypes including age-related macular degeneration[15], inflammatory bowel disease [16], and electrocardiographic QT interval [17]. Identifying novel genetic variants related to blood lipid phenotypes may provide new
drug targets to alter blood lipid levels and may aid in theprediction of cardiovascular disease.
We hypothesized that common genetic variants explain aproportion of the inter-individual variability in LDL-C,
HDL-C, and TG. Accordingly, we conducted genome-wide

linkage and association studies for these three phenotypes
in Framingham Heart Study (FHS) participants.

Materials and methods
GWAS sample
Of the 1345 FHS participants who are part of the family
plate set (see Executive Summary), we focused our analyses on the 1087 participants from the Offspring cohort
who had Affymetrix 100K genotypes. Lipid phenotypes
were measured at various examinations as described inTable 1. Each study participant provided written informed
consent for genetic analyses and the study was approved
by Boston University's Institutional Review Board.
Phenotype definition and methods
Blood lipids were measured from fasting venous bloodcollected at each of seven clinical examination time points
extending from 1971 to 2001. Total cholesterol, HDL-C,
and TG were measured by standard enzymatic methods.
LDL-C was calculated using the Friedewald formula, witha missing value assigned for participants with a measured
TG > 400 mg/dL. Clinical covariates utilized in phenotypic regression modeling included age at the time ofblood lipid measurement, age2, body mass index (weightin kg divided by the height in m2), alcohol intake (drinks
per week), current cigarette smoking (yes, no), menopausal status (postmenopausal yes, no), and hormone
replacement therapy (yes, no).

Commonly-used lipid lowering therapies affect total cholesterol and TG. To account for treatment effect, we
imputed total cholesterol and TG values for those treatedwith lipid-lowering therapy. The imputation procedure
was modeled after prior work on imputing blood pressure
values for those on antihypertensive medication [18]. For
each treated individual, a correction factor was added tothe observed [treated] lipid value (total cholesterol orTG). This correction factor consisted of the difference
between an ''expected'' residual and the ''calculated'' residual. The ''calculated'' residual for each individual was generated in a sex-specific manner after adjustment for age,
age2, age3, and examination year (by decade). The''expected'' residual was generated within each sex and 10
year-age-group as the average of ''calculated'' residuals
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Table 1: Lipid Phenotypes Examined Using Affymetrix 100K GeneChip Scan
Phenotype

Total cholesterol

Low-density lipoproteincholesterol
High-density lipoproteincholesterol
Triglycerides
Mean total cholesterol
Mean low-density
lipoprotein cholesterol
Mean high-density
lipoprotein cholesterol
Mean triglycerides
Apolipoprotein A-I
Apolipoprotein B
Apolipoprotein C3
Apolipoprotein E
High-density lipoprotein 2cholesterol
High-density lipoprotein 3cholesterol
Large high-density
lipoprotein by NMRIntermediate high-density
lipoprotein by NMR
Small high-density
lipoprotein by NMR
High-density lipoprotein
particle size by NMRIntermediate-density
lipoprotein particle by
NMR
Large low-density
lipoprotein by NMR
Small low-density
lipoprotein by NMR
Low-density lipoprotein
particle size by NMR
Large very low-density
lipoprotein particle by
NMRIntermediate very lowdensity lipoprotein particle
by NMR
Small very low-density
lipoprotein particle by
NMR
Very low-density
lipoprotein particle size by
NMR
Triglyceride/high-density
lipoprotein cholesterol
ratio
Total cholesterol/highdensity lipoproteincholesterol ratio
Lipoprotein(a)Remnant lipoproteincholesterol
Remnant lipoprotein
triglycerides

AcronymPhenotypes

N

h2 *

Offspring Exam

Adjustment† Multivariable model

TC

7

1069

0.57‡

1,2,3,4,5,6,7

Age, age2, smoking, bodymass index, alcohol
consumption, menopausal
status, hormone replacement
therapy; Covariate-adjusted
residuals created separatelyby gender

LDL-C

7

1056

0.59‡

1,2,3,4,5,6,7

1062

0.52‡

1,2,3,4,5,6,7

HDL-C

7

TG
MeanTC
MeanLDL-C

7
1
1

1068
1087
1086

0.48‡
0.61
0.66

1,2,3,4,5,6,7
Avg of Exams 1,2,3,4,5,6,7
Avg of Exams 1,2,3,4,5,6,7

MeanHDL-C

1

1087

0.69

Avg of Exams 1,2,3,4,5,6,7

MeanTG
ApoA-I
ApoB
ApoC3
ApoE
HDL2-C

1
1
1
1
1
2

0.58
0.42
0.47
0.38
0.54
0.50 0.53

Avg of Exams 1,2,3,4,5,6,7
4
4
4
5
4,5

HDL3-C2

0.48 0.54

4,5

HDLNMRlg

1

1087
997
997
767
744
955 (exam 4) 958(exam 5)984 (exam 4) 1020
(exam 5)
851

0.53

4

HDLNMRint

1

851

0.22

4

HDLNMRsm

1

851

0.24

4

HDLNMRsz

1

851

0.50

4

IDLNMR

1

851

0.20

4

LDLNMRlg

1

851

0.39

4

LDLNMRsm

1

851

0.40

4

LDLNMRsz

1

851

0.40

4

VLDLNMRlg

1

851

0.34

4

VLDLNMRint

1

878

0.40

4

VLDLNMRsm

1

851

0.24

4

VLDLNMRsz

1

851

0.42

4

tghdl

7

1060

0.56‡

1,2,3,4,5,6,7cholhdl

7

1060

0.58‡

1,2,3,4,5,6,7

Lp(a)
RLP-C

1
1

763
746

0.90
0.34

3
4

RLP-TG

1

715

0.33

4

Note: TG, MeanTG, cholhdl, and tghdl were natural log transformed due to skewed distribution;*Heritability (h2) estimates presented are those after multivariable-adjustment; P < 0.0001 for all heritability estimates.
†Each phenotype had 2 adjustment schemes: age- and sex-adjusted and multivariable-adjusted. Both age- and sex-adjusted and multivariable-adjusted model results are web
posted.
‡Heritability estimates were derived from lipid phenotypes at a single time-point, that of FHS Examination 1.

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equal or greater than the treated individual's ''calculated''
residual.
Lipoprotein subclass profiles were measured by a commercially available proton NMR spectroscopic assay(LipoScience, Raleigh, NC) on plasma samples stored at 70°C as described previously [19]. The particle concentration of the following 9 lipoprotein species were determined: 3 VLDL subclasses [large, >60 nm (including
chylomicrons); intermediate, 35–60 nm; small, 27–35nm]; 3 LDL subclasses (IDL, 23–27 nm; large LDL 21.3–
23 nm; small LDL, 18.3–21.2 nm); and 3 HDL subclasses(large, 8.8–13 nm; intermediate, 8.2–8.8 nm; small, 7.3–
8.2 nm). The small LDL subclass comprises the sum ofsubclasses formerly labeled "intermediate" (19.8–21.2nm) and "small" (18.3–19.7 nm) [19], since concentrations of both have very similar relations to lipid levels.
Genotyping methods
All analyses were based on the Affymetrix 100K GeneChip
genotyping data generated in Framingham Heart Study
participants as described previously [20]. In order to minimize false positive associations due to genotyping artifact, we limited our analyses to SNPs with a genotyping
call rate ≥80% and a Hardy-Weinberg Equilibrium P ≥0.001. Given lower statistical power to detect associations
with rarer SNPs, we limited our results to SNPs with aminor allele frequency ≥10%.
Statistical analysis methods
TG levels were log-transformed to approximate a normal
distribution. For each blood lipid phenotype, the longterm average of 4 to 7 serial measurements was used as theprimary phenotype. Participants contributing fewer than
4 of 7 measures of a given phenotype were excluded fromthat analysis. MeanLDL-C, MeanHDL-C, and MeanTG
were adjusted for covariates in sex-specific linear regression models. Two sets of phenotypic models were created:
Model 1 (age, age2) and Model 2 (age, age2, body massindex, alcohol intake, cigarette smoking, menopausal status, and hormone replacement therapy). For quantitative
covariates (age, body mass index, and alcohol intake), themean value across examinations was used as a covariate.
For categorical covariates, the proportion of exams scoredas 'yes' was used. The residual MeanLDL-C, MeanHDL-C,
and MeanlogTG values from Model 1 and Model 2 served
as the primary phenotypes.

For genotype-phenotype association analyses, we
assumed an additive model of inheritance. We conducted
multivariable linear regression using GEE, family-based
association testing using FBAT, and linkage using Merlin
for computation of IBDs and SOLAR for variance component models as described in the Executive Summary.

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Heritability analyses
Heritability estimates for the lipid phenotypes wereobtained from extended families with at least two members by variance-components methods using the Sequential Oligogenic Linkage Analysis Routines (SOLAR)
package [21]. Using this approach, maximum-likelihood
estimation was applied to a mixed-effects model thatincorporated fixed covariate effects, additive genetic
effects, and residual error. The additive genetic effects andresidual errors were assumed to be normally distributed
and to be mutually independent. The analyses were performed using residuals from the multivariable models(Model 1 and Model 2) mentioned above. For phenotypes with kurtosis > 1, heritability estimates were computed on ranked normalized deviates.
Replication samples
Replication genotyping was attempted in three independent sample sets: a) the FHS unrelated plate set; b) Geneticsof Lipid Lowering Drugs and Diet Network (GOLDN);
and c) Malmö Diet and Cancer Study – Cardiovascular
Cohort (MDC-CC).

The second stage consisted of ~1450 biologically unrelated individuals from the FHS unrelated plate set. The
third stage consisted of ~1450 participants from GOLDN
and ~5200 participants from MDC-CC. GOLDN is a family-based sample recruited from two National Heart,
Lung, and Blood Institute's Family Heart Study field centers (Minneapolis, MN and Salt Lake City, UT). The Family
Heart Study is a multi-center, population-based cohort
designed to study the genetic and environmental determinations of cardiovascular disease.
The MDC study is a community-based prospective epidemiologic cohort of 28,098 persons recruited for a baseline
examination between 1991 and 1996. From this cohort,
6103 persons were randomly selected to participate in the
MDC-CC which sought to investigate risk factors for cardiovascular disease. Of the MDC-CC participants, 5466
had DNA and lipid phenotypes available. Individuals on
lipid lowering therapy and with outlier values of LDL-C,
HDL-C, or TG (top 0.5% of the distribution) were
excluded, leaving 5212 individuals available for the SNPlipid association analyses
Staged replication strategy
For follow-up into Stage II (the FHS unrelated plate set),
we selected all SNPs in the GWAS with an association P <0.001 for the MeanLDL-C, MeanHDL-C, or MeanTG phenotypes from the minimally-adjusted phenotypic model(Model 1, adjustment for age, age2 only). We next conducted a joint analysis of Stage I (GWAS 100K data) andStage II (FHS unrelated plate set). The joint analysis consisted of a weighted average of the beta estimates andPage 4 of 10(page number not for citation purposes)

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standard errors from Stages I and II and used the inverseof the variance in each stage as weights.
For follow-up into Stage III (GOLDN and MDC-CC), we
selected for genotyping all SNPs with a P < 0.001 in the
joint analysis of Stages I and II. For genotype-phenotype
association analyses in MDC-CC and GOLDN, we
assumed an additive model of inheritance. In MDC-CC,
we conducted multivariable linear regression analyses totest the null hypothesis that LDL-C, HDL-C, or TG residuals (sex-specific residuals adjusted for age and age2) did
not differ by increasing minor allele copy number. In
GOLDN, to account for correlated observations due tofamily relationships we used linear mixed-effects methods
in SOLAR.
To summarize the statistical evidence for association foreach SNP across all three stages, we reiterated theweighted average beta-estimates and standard errors as
described above.

Results
Clinical characteristics of the FHS sample of 1345 subjects
are presented in the Executive Summary. Table 1 displays
the variables that were studied in our analyses of lipid
phenotypes. Further information on these phenotypes
can be found at http://www.ncbi.nlm.nih.gov/projects/
gap/cgi-bin/study.cgi?id=phs000007. Since Original
cohort members were non-fasting at examination, ouranalyses considered only the 1087 Offspring Study participants with fasting lipid measurements and Affymetrix
100K SNP genotypes. For this paper we focus on longitudinal mean levels of serially measured values (minimumof 4, maximum of 7) of LDL-C, HDL-C, and TG (labeled
MeanLDL-C, MeanHDL-C, and MeanTG).
Heritability estimates for long-term average lipid phenotypes (Mean LDL-C, MeanHDL-C, and MeanTG) weregreater than those from single time-point measurements(Table 1). For example, the heritabilities of MeanLDL-C,
MeanHDL-C, and MeanTG were 0.66, 0.69, and 0.58,
respectively, whereas heritabilities for LDL-C, HDL-C, andTG measured at FHS Examination 1 (a single time-point)
were 0.59, 0.52, and 0.48, respectively. The highest heritability estimate for any available lipid phenotype was thatfor lipoprotein (a) at 0.90.
From the GEE analyses, the strongest associations for
MeanLDL-C, MeanHDL-C, and MeanTG were for SNPs
rs287474 (p = 6.3*10-9), rs524802 (p = 7.6*10-7), andrs7007075 (p = 7.7*10-6), respectively (Table 2a). From
the FBAT analyses, the strongest associations for MeanLDL-C, MeanHDL-C, and MeanTG were for SNPs
rs287474 (p = 1.4*10-8), rs10495594 (p = 5.1*10-5), andrs1449866 (p = 1.8*10-5), respectively (Table 2b). For

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each multivariable-adjusted phenotype, the number of
SNPs with a GEE association p < 10-4 ranged from 13 to 18
and with p < 10-3, from 94 to 149. The number of SNPs
with FBAT association p < 10-4 ranged from 2 to 5 andwith p < 10-3, from 74 to 79.
Linkage LOD scores > 2.0 are presented in Table 2c. The
best evidence for linkage was a peak LOD score of 3.3 onchromosome 7 for the MeanHDL-C phenotype.
Because the prior probability of any SNP relating to a phenotype is low and given the number of tests, the P value
distribution in a GWAS should approach a null distribution. Any strong departure from this expectation mightsuggest artifacts in genotyping or analysis. For the 70,987
SNPs that passed quality-control filters, the distribution of
association P values (generated by the GEE methodology)
approached a null distribution but with a slight excess of
low P values. For example, for the MeanLDL-C, whereas
one would expect 1% of SNPs to demonstrate a P < 0.01
by chance, we found that 1.34% of SNPs displayed a P <0.01. Similar results were seen for meanHDL-C andmeanTG (data not shown).
We evaluated the association results for a SNP and each ofa set of four correlated phenotypes – ApoA-I, LDLNMRsm,
MeanHDL-C, and MeanTG (Table 3). Several SNPs wereassociated with P < 0.01 for 3 of the 4 phenotypes.
Among the GEE association results, a SNP (rs7007797) inthe lipoprotein lipase (LPL) was associated with MeanHDL-C (p = 0.0005) and MeanTG (p = 0.002) (Table 4).
This SNP is a perfect proxy (r2 = 1) to the previously studied rs328 (also known as S447X) [22]. The minor allele of
rs328 has been consistently related to higher HDL-C andlower TG. The direction of effect for SNP rs7007797 in ourdataset was consistent with previous observations. Due toa lack of SNPs in the Affymetrix 100K GeneChip correlated with previously reported variants (at r2 > 0.5 threshold) in the APOE, PCSK9, CETP, LIPC, and APOA5 genes,
we were unable to confirm these other previously
reported associations (Table 4).
Replication is critical to distinguish true positives fromfalse ones in a GWAS. We pursued a three-stage replication strategy with 287 SNPs (P < 0.001 in Stage I) tested inStage II (n~1450 individuals) and 40 SNPs (P < 0.001 in
joint analysis of Stages I and II) tested in Stage III (n~6650
individuals). Results are displayed in Table 5. After threestages of replication, there was no convincing statisticalevidence for association (i.e. joint analysis stages I, II & III
P < 10-5) between any of the tested SNPs and lipid phenotypes.

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Table 2: Overview of Top Association and Linkage Results for MeanLDL-C, MeanHDL-C, and MeanTG
2a. Top 25 SNPs for association with MeanLDL-C, MeanHDL-C, or MeanTG based on the lowest p values of the GEE tests
Phenotype

SNP rs ID*

Chr

Physical location (bp)

GEE P-value

FBAT P-value

MeanLDL-C
MeanLDL-C
MeanHDL-C
MeanHDL-C
MeanHDL-C
MeanTG
MeanHDL-C
MeanLDL-C
MeanTG
MeanLDL-C
MeanHDL-C
MeanTG
MeanHDL-C
MeanHDL-C
MeanTG
MeanHDL-C
MeanLDL-C
MeanHDL-C
MeanLDL-C
MeanHDL-C
MeanLDL-C
MeanHDL-C
MeanLDL-C
MeanHDL-C
MeanHDL-C

rs287474
rs287354
rs524802rs505717
rs544543
rs7007075
rs3734678
rs10488824
rs2142136
rs1555173
rs688933
rs314474
rs966376
rs4459845
rs220840
rs3850301
rs1317538
rs7021834
rs504527
rs10488779
rs2415621
rs10508518
rs1245058
rs10488780
rs541326

13
13
19
19
19
8
6
11
10
6
9
11
18
3
11
14
6
9
1
11
14
10
1
11
9

68173961
68137953
42138787
42122156
42122663
138168448
107639853
81678287
89753050
85121177
70902713
11482634
27086836
127400326
114826173
58150885
7370613271123639
34142701
79494759
40114277
17081479
69760085
79490000
70936505

6.3*10-9
3.4*10-8
7.6*10-7
1.2*10-6
2.5*10-6
7.7*10-6
8.2*10-6
1.4*10-5
1.5*10-5
1.7*10-5
1.8*10-5
1.8*10-5
2.1*10-5
2.3*10-5
2.5*10-5
2.8*10-5
3.0*10-5
3.0*10-5
3.2*10-5
3.6*10-5
3.8*10-5
3.9*10-5
4.1*10-5
4.4*10-5
4.5*10-5

1.4*10-8
6.8*10-8
0.04
0.07
0.07
0.004
0.009
0.03
0.06
0.09
0.38
0.09
0.07
0.005
0.09
8.6*10-4
0.02
0.003
0.007
0.07
0.005
0.09
0.004
0.06
0.39

gene (IN or NEAR)ZNF345C6orf210
BC005821| PTEN
TRPM3| AJ505026| AL136545
AB094146| IGSF4| BC047021
ALDH1L1| CR749807
AB094146| IGSF4| BC047021
BX161433| DACT1
BC050689| KCNQ5
AL136545
CSMD2

CUBN
LRRC7
TRPM3| AJ505026| AL136545

2b. Top 25 SNPs for association with MeanLDL-C, MeanHDL-C, or MeanTG based on the lowest p values of the FBAT tests
Phenotype

SNP rs ID*

Chr

Physical location (bp)

GEE P-value

FBAT P-value

MeanLDL-C
MeanLDL-C
MeanTG
MeanTG
MeanTG
MeanTG
MeanHDL-C
MeanHDL-C
MeanLDL-C
MeanLDL-C
MeanHDL-C
MeanTG
MeanLDL-C
MeanLDL-C
MeanLDL-C
MeanHDL-C
MeanLDL-C
MeanTG
MeanTG
MeanHDL-C
MeanHDL-C
MeanHDL-C
MeanTG
MeanTG
MeanTG

rs287474
rs287354
rs1449866
rs10506354
rs10494989
rs2371978
rs10495594
rs10495593
rs1411931
rs10516433
rs2182114
rs1835353
rs10516434
rs10507755
rs6853079
rs1508116
rs10518072
rs715260
rs7784056
rs6505623
rs1541296
rs6821328
rs1501572
rs4684343
rs225634

13
13
3
12
1
12
2
2
1
4
1
2
4
13
4
2
4
4
7
18
18
4
9
3
6

68173961
68137953
144359331
57759034
211576634
57759239
12280997
12279945
37451967
99800704
111496269
124880057
99802493
68355991
99800789
41437773
71271418
980441577762038
111505759173095
54058427
2894794
2366098
142518312

1.4*10-8
6.8*10-8
1.8*10-5
2.2*10-5
2.4*10-5
2.4*10-5
5.1*10-5
5.1*10-55.8*10-5
7.7*10-5
8.2*10-5
8.3*10-5
8.6*10-5
1.1*10-4
1.3*10-4
1.3*10-4
1.4*10-4
1.5*10-4
1.6*10-4
1.6*10-4
1.6*10-4
1.7*10-4
1.7*10-4
1.8*10-4
1.9*10-4

6.3*10-9
3.4*10-8
0.002
0.007
4.7*10-4
0.01
1.8*10-4
1.8*10-4
0.03
0.03
0.07
0.02
0.03
1.6*10-4
0.06
0.07
0.05
0.002
0.12
0.006
0.02
0.02
1.1*10-4
0.24
2.4*10-4

gene (IN or NEAR)CHST2| AK131346
AY552981| AK123409| KCNK2TSPAN5
CHI3L2
CNTNAP5| AK056528
TSPAN5TSPAN5
APIN| C4orf7| CSN3
WDR1
AB014605| MAGI2
FVT1
SCFD2
CNTN4
C6orf55

2c. Magnitude and Location of Peak LOD scores > 2.0 for MeanLDL-C, MeanHDL-C, and MeanTG
Phenotype

Exam

Chr

Physical location (Mb)

Maximum LOD

LOD-1.5 Interval

LOD+1.5 Interval

MeanHDL-C
MeanLDL-C
MeanTG
MeanLDL-C

1–7
1–7
1–7
1–7

7
9
1
3

33485983
94130181
153444389
196384998

3.30
2.83
2.73
2.12

27810820
88057135
151582274
187706181

36074331
98516033
155505440
199138789

Chr = chromosome

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BMC Medical Genetics 2007, 8(Suppl 1):S17

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Table 3: GEE results for 4 correlated phenotypes (ApoA-I, LDLNMRsm, MeanHDL-C, and MeanTG), ranked by proportion of GEE P <
0.01*
rs id

Chr

Physical Position

GEE P ApoA-I

GEE P LDLNMRsm

GEE P MeanHDL-C

GEE P MeanTG

rs505717
rs3734678
rs2256038
rs10486788
rs232885
rs4982795
rs524802rs544543
rs2823097
rs1394041
rs10499320
rs2889195
rs7007797
rs7597861
rs6063858
rs2425524
rs1509384
rs9317760
rs6804032
rs8069913

19
6
21
7
1
14
19
19
21
3
7
1
8
2
20
20
2
13
3
17

42122156
107639853
15443047
15788581
58824078
23294875
42138787
42122663
15445015
148579545
2906624
65868751
19921250
100311705
50715959
40750358
22556488
68480686
116031161
13812167

7.8*10-4
4.7*10-4
0.11
4.7*10-4
0.86
0.001
0.001
0.001
0.10
0.02
0.001
6.6*10-4
0.01
0.08
0.04
0.01
8.5*10-4
0.003
0.002
0.03

0.003
0.18
0.002
0.005
0.002
1.9*10-7
0.003
0.004
0.004
0.003
0.001
0.003
0.02
0.004
3.0*10-4
0.05
0.01
0.05
0.02
0.009

1.3*10-6
8.2*10-6
8.8*10-4
8.5*10-4
9.6*10-4
0.001
7.6*10-7
2.5*10-4
0.002
0.002
8.8*10-4
0.003
5.0*10-4
1.0*10-4
0.002
3.6*10-4
0.007
6.6*10-4
0.009
0.001

0.04
0.006
6.4*10-50.01
5.7*10-4
0.11
0.02
0.03
8.0*10-5
5.6*10-5
0.03
0.02
0.002
0.006
0.003
0.008
0.03
0.002
8.7*10-4
6.7*10-4

Gene Symbol
ZNF568PDSS2

AB067502
ZNF568ZNF568*All SNPs in this table had GEE association P values < 0.01 for at least three of the four traits (ApoA-I, LDLNMRsm, MeanHDL-C, and MeanTG). No SNPs in our dataset had
GEE association P values < 0.01 for all four traits.

Discussion
We examined associations of Affymetrix 100K SNPs andlipid traits in FHS and identified putative associations
with lipid phenotypes. We studied the long-term averageof up to 7 measurements each of LDL-C, HDL-C, and TG
as the primary phenotypes and for one phenotype, the
MeanLDL-C, we observed a nominal P that exceeded
genome-wide significance [13]. However, validation of
selected hypotheses in additional samples did not identify
any new loci underlying variability in blood lipids.
GWAS offers the potential to identify novel genetic variants/loci that are associated with blood lipid variation,
unlimited by our current knowledge of lipoprotein biology. However, a central limitation of GWAS is that thetrue signals are mixed amidst a large number of false pos-

itive results. Validation in additional samples is required
to distinguish the true positives from the false ones.
Replication of initial GWAS findings using a staged design
has been suggested to minimize genotyping cost and maximize statistical power [23,24]. An important consideration in such a design is the proportion of markers taken
forward to a second stage. We estimated the statisticalpower for our three-stage GWAS strategy. Assuming amodest number of markers (all SNPs with P < 0.001 foreach phenotype, ~0.1% of markers) are taken forward toStage II, a second stage sample size of 1450, that SNPs
with P < 0.001 are taken forward from Stage II to Stage III,a stage III sample size of 6650, and that the final alpha(after Stages I, II, & III) is set at a conservative 5*10-8, weestimated that we had 89% power to detect a quantitative

Table 4: Comparison with the prior literature
Gene

Phenotype

Selected SNPs
previously associatedwith phenotype# SNPs in Affymetrix
100K within 60 kb ofgene# SNPs in Affymetrix 100K
within 60 kb of Gene Locus
and with r2 > 0.5 to previously
reported SNP

SNP in Affymetrix
100K (r2 to previously
reported SNP)

p for association in
FHS 100K

APOE

LDL-C

1

0

-

-

PCSK9
CETP
LIPC
LPL

LDL-C
HDL-C
HDL-C
HDL-C

rs429358
rs7412
rs11591147
rs1800775
rs1800588
rs328

7
2
18
8

0
0
0
2

LPL

TG

rs328

8

2

APOA5

TG

rs662799
rs3135506

40

rs10503669 (r2 = 1.0)rs7007797 (r2 = 1.0)
rs10503669 (r2 = 1.0)rs7007797 (r2 = 1.0)-

rs10503669 – 9.1*10-4
rs7007797 – 5.0*10-4
rs10503669 – 0.02
rs7007797 – 0.002-

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rs7231460
rs966376
rs744134
rs7233386
rs1428445
rs2304589
rs2278528
rs2142136
rs1555173
rs6053754
rs8032553
rs1741662
rs1741660
rs890945
rs10512297
rs541326
rs719829
rs10509384
rs688933
rs1122080
rs6743779
rs950828
rs1365032
rs287474
rs10507756
rs1507203
rs4941236
rs544543
rs3763188
rs717987
rs10508997
rs2395833
rs6677589
rs10513320
rs508969
rs7858099
rs4134425
rs7021834
rs10500770
rs10516430

Chr

18
18
13
18
5
2
2
10
6
20
15
6
6
5
9
9
5
10
9
5
2
4
8
13
13
13
18
19
6
6
10
7
1
3
18
9
14
9
11
4

Gene

L10374

LOC130502
LOC130502
PTEN
FLJ25067
KIF7
RFXDC1RFXDC1TRPM3KCNMA1TRPM3KLF7SERPINB8
REPS1
RUNX2
PRKAR2B
JUN
CETN1

AL136545
RAP1GDS1

Allele*

C
C
C
T
A
A
G
G
C
C
A
T
T
A
T
G
G
G
T
A
C
A
C
T
C
C
C
G
C
G
G
T
G
A
A
G
T
T
T
T

Trait

MeanHDL-C
MeanHDL-C
MeanLDL-C
MeanHDL-C
MeanHDL-C
MeanLDL-C
MeanLDL-C
MeanTG
MeanLDL-C
MeanLDL-C
MeanLDL-C
MeanTG
MeanTG
MeanHDL-C
MeanLDL-C
MeanHDL-C
MeanTG
MeanLDL-C
MeanHDL-C
MeanHDL-C
MeanLDL-C
MeanHDL-C
MeanLDL-C
MeanLDL-C
MeanLDL-C
MeanLDL-C
MeanLDL-C
MeanHDL-C
MeanTG
MeanHDL-C
MeanTG
MeanHDL-C
MeanTG
MeanTG
MeanLDL-C
MeanLDL-C
MeanLDL-C
MeanHDL-C
MeanLDL-C
MeanHDL-C

Stage I – FHS 100K

Stage II – FHS Unrelated

Joint P

Stage III – MDC

Stage III – GOLDN

Joint Stages I, II, & III

P

Beta†

SE

P

Beta

SE

Stages I & II

P

Beta

SE

P

Beta

SE

P

Beta

SE

4.3*10-4
2.5*10-4
5.4*10-4
2.4*10-4
3.0*10-4
6.4*10-4
6.7*10-4
8.1*10-6
1.0*10-5
3.1*10-4
3.0*10-5
7.2*10-5
7.8*10-5
5.4*10-4
2.1*10-4
3.6*10-5
1.3*10-4
7.7*10-4
3.1*10-6
3.4*10-4
2.4*10-5
9.1*10-4
1.4*10-4
8.1*10-6
6.0*10-5
1.9*10-4
4.7*10-4
4.8*10-6
5.2*10-4
8.0*10-5
4.1*10-4
6.2*10-4
1.9*10-4
1.3*10-4
6.8*10-4
6.9*10-4
6.6*10-4
4.3*10-5
3.9*10-4
7.2*10-4-0.145-0.1480.168
0.149-0.187
0.187
0.180
0.228
-0.243
0.166
0.171
0.198
0.197
-0.181-0.235-0.201-0.156-0.148-0.232-0.202-0.181-0.151-0.161
0.209
0.202
0.172
0.151
-0.185-0.191
0.250-0.165-0.145-0.145-0.190-0.144-0.140-0.156-0.174-0.157-0.139

0.041
0.040
0.048
0.041
0.052
0.055
0.053
0.051
0.055
0.046
0.041
0.050
0.050
0.052
0.064
0.049
0.041
0.044
0.050
0.056
0.043
0.046
0.042
0.047
0.050
0.046
0.043
0.040
0.055
0.063
0.047
0.042
0.039
0.050
0.042
0.041
0.046
0.043
0.044
0.041

2.4*10-4
4.8*10-4
4.7*10-4
0.005
0.01
0.006
0.007
0.14
0.10
0.02
0.08
0.07
0.07
0.03
0.05
0.28
0.11
0.04
0.80
0.07
0.24
0.06
0.12
0.16
0.22
0.15
0.09
0.61
0.12
0.26
0.15
0.12
0.27
0.31
0.18
0.15
0.13
0.65
0.17
0.20

-0.131-0.1250.124
0.100-0.113
0.124
0.122
0.077
-0.083
0.090
0.061
0.083
0.084
-0.099-0.108-0.061-0.062-0.076-0.015-0.084-0.043-0.080-0.053
0.056
0.052
0.057
0.059
-0.019-0.0760.063-0.062-0.060-0.042-0.046-0.053-0.051-0.055-0.021-0.049-0.052

0.036
0.036
0.036
0.036
0.044
0.045
0.045
0.052
0.050
0.037
0.034
0.046
0.046
0.044
0.054
0.056
0.039
0.038
0.059
0.047
0.036
0.043
0.034
0.040
0.043
0.039
0.034
0.038
0.048
0.057
0.043
0.038
0.037
0.045
0.040
0.035
0.036
0.045
0.036
0.040

3.7*10-7
4.5*10-7
1.1*10-6
6.3*10-6
1.8*10-5
1.8*10-5
2.0*10-5
2.4*10-5
2.7*10-5
3.4*10-5
5.5*10-5
5.9*10-5
6.1*10-5
8.0*10-5
8.6*10-5
1.3*10-4
1.4*10-4
1.9*10-4
2.0*10-4
2.5*10-4
2.8*10-4
2.9*10-4
3.2*10-4
3.4*10-4
4.2*10-4
4.3*10-4
4.3*10-4
4.7*10-4
5.1*10-4
5.4*10-4
5.6*10-4
5.6*10-4
7.1*10-4
9.0*10-4
9.5*10-4
9.6*10-4
9.8*10-4
9.9*10-4
0.001
0.001

0.50
0.43
0.67
0.32
0.24
0.47
0.51
0.38
0.31
0.46
0.09
0.15
Failed‡
0.18
0.71
0.10
0.66
0.02
0.17
0.44
0.69
0.58
0.01
0.97
0.24
0.23
0.83
0.95
0.13
0.06
0.10
0.99
0.18
0.24
0.31
0.53
0.14
0.05
0.41
0.64

0.013
0.016
0.009
-0.020-0.028-0.020-0.018-0.026-0.0270.017
0.033-0.037
Failed-0.033-0.011
0.048
0.009
0.050
0.041
-0.0200.009
0.012-0.056
0.001
0.032
0.027
-0.004-0.001
0.042
0.058
0.039
0.000
0.028
0.028
0.023
-0.013
0.032
0.045
0.017
-0.010

0.020
0.020
0.020
0.020
0.024
0.027
0.027
0.029
0.027
0.022
0.020
0.026
Failed
0.024
0.030
0.029
0.022
0.021
0.030
0.025
0.022
0.022
0.020
0.020
0.028
0.023
0.020
0.021
0.028
0.031
0.023
0.021
0.021
0.024
0.023
0.021
0.021
0.023
0.021
0.022

0.60
0.56
0.20
0.61
0.77
0.09
0.12
0.85
0.03
0.99
Failed0.03
Failed
0.79
0.44
0.02
0.58
Failed0.01
0.47
FailedFailed
0.11
0.62
0.89
0.32
0.18
0.78
0.02
0.87
Failed
0.06
0.84
0.21
0.64
0.31
0.16
1.00
0.90
Failed

0.023
0.026
0.062
-0.0230.016-0.101-0.0920.011
0.143-0.001
Failed
-0.131
Failed
0.014
0.056-0.1590.028
Failed-0.172-0.041
FailedFailed0.077-0.023-0.008-0.052-0.065-0.013-0.1530.011
Failed-0.090-0.0100.068
0.025-0.049-0.0680.000
0.006
Failed

0.044
0.044
0.048
0.046
0.054
0.060
0.060
0.061
0.066
0.050
Failed
0.061
Failed
0.053
0.072
0.069
0.050
Failed0.069
0.058
FailedFailed
0.048
0.046
0.060
0.052
0.048
0.047
0.063
0.067
Failed
0.049
0.048
0.054
0.054
0.048
0.048
0.057
0.050
Failed

0.03
0.04
0.0007
0.11
0.002
0.18
0.14
0.06
0.01
0.003
0.0002
0.58
6E-05
0.002
0.03
0.08
0.08
0.79
0.05
0.005
0.05
0.10
0.0002
0.05
0.003
0.01
0.02
0.03
0.09
0.0007
0.51
0.01
0.30
0.61
0.29
0.006
0.24
0.72
0.22
0.02

-0.033-0.0310.053
0.024-0.058
0.027
0.029
0.040
-0.051
0.049
0.060
0.011
0.136
-0.057-0.051-0.038-0.028-0.005-0.044-0.055-0.033-0.030-0.055
0.030
0.059
0.044
0.022
-0.033-0.0350.079-0.012-0.041-0.016-0.010-0.018-0.043-0.019-0.006-0.019-0.041

0.015
0.015
0.016
0.015
0.018
0.020
0.020
0.021
0.021
0.017
0.016
0.020
0.034
0.018
0.023
0.022
0.016
0.017
0.022
0.019
0.017
0.018
0.015
0.016
0.020
0.017
0.015
0.016
0.021
0.023
0.019
0.016
0.016
0.018
0.017
0.016
0.016
0.018
0.016
0.017

*The allele on the positive strand of the reference genome was modeled in all analyses.
†Beta refers to the proportion of 1 standard deviation unit change in phenotype (phenotype is sex-specific residual adjusted for age and age2) per copy of the allele modeled.
‡"Failed" refers to SNP genotype failure in the sample.

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Table 5: Association Results for 40 SNPs Attempted for Replication in Three Stages

BMC Medical Genetics 2007, 8(Suppl 1):S17

trait locus explaining 2% of phenotypic variance, 48%
power to detect a locus explaining 1% of the variance, and
13% power to detect a locus explaining 0.5% of the variance.
With our replication effort, we failed to identify any novelloci related to blood lipids. At least two potential explanations are possible. First, our study design had limited statistical power to detect common SNPs that explain ≤1% oftrait variance. In the Diabetes Genetics Initiative genomewide association study for blood lipid traits, we recently
showed that for lipid traits, there are few common variants that explain >2% of the variance and most SNPs
explain <1% of trait variance [25]. To have adequate statistical power to detect these effects given an initial GWAS
sample size of ~1000, many more markers (i.e., hundredsof SNPs) will need to be taken to the second and third
stages. Second, the limited genomic coverage of theAffymetrix 100K array may have limited our ability to replicate previously reported loci and discover novel loci. For
example, using the Affymetrix 500 K array, we recently
identified glucokinase regulatory protein (GCKR) as anovel locus associated with TG [25]. Of any SNP on the
500 K array, an intronic GCKR SNP (rs780094) explained
the greatest proportion of blood TG variance in the Diabetes Genetics Initiative study. However, on the Affymetrix
100K array, there are no SNPs within the 60 kb spanning
GCKR.

Strengths and limitations
This study is distinguished by the availability of serial
lipid phenotypes over a 30-year time span, the community-based nature of the collection, and the routine ascertainment of covariates in a standardized clinicalexamination. We acknowledge several limitations. These
include the lack of validation for the imputation methodology used to address lipid lowering therapy, limited statistical power due to sample size, and confinement to asingle ancestral group – whites of European ancestry.

http://www.biomedcentral.com/1471-2350/8/S1/S17

Authors' contributions
SK, AM, SD, GP, JMO, and LAC participated in the designof the study and the interpretation of the data. AM, GP,
and SD conducted the statistical analyses. SK drafted themanuscript. AS, CG, LG, and NPB generated replication
genotype data and analyses. OM and MOM provided replication samples and conducted association analyses in
the Malmo Diet and Cancer Study. DKA and JMO provided replication samples and led the generation of lipid
phenotypes in the GOLDN study. SD, SK, RD, JMO, and
LAC revised the manuscript critically for important intellectual content. All authors read and approved the above
manuscript.

Acknowledgements
We thank the Framingham Heart Study participants for their long-term voluntary commitment to this study. The Framingham Heart Study is supported by a contract from the National Heart, Lung and Blood Institute(contract No. N01-HC-25195). We acknowledge Dr. Michael Christman,
Dr. Alan Herbert and colleagues at Boston University who conducted theAffymetrix 100K genotyping and have made these data publicly available. 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). Dr. Ordovas is supported by contracts 53-K06-510 and 58-1950-9-001 from the US Department of Agriculture ResearchService. Dr. Kathiresan is funded by the Doris Duke Charitable Foundation
Clinical Scientist Development Award, the Fannie E. Rippel Foundation, andNIH K23 HL083102.
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.

References
1.

2.
3.

Conclusions & future directions
Using a 100K genome-wide scan, we present associationand linkage results for a rich set of lipid phenotypes in
FHS. This resource may be useful for comparisons withother GWAS currently in progress. GWAS in FHS using a
denser genome-wide genotyping platform and a betterpowered replication strategy may identify novel lociunderlying blood lipids.

4.

5.
6.

Abbreviations
FBAT = family-based association test; GEE = generalized
estimating equations.

7.

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

8.

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