BMC Medical Genetics

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Genome-wide association to body mass index and waist
circumference: the Framingham Heart Study 100K project
Caroline S Fox*1, Nancy Heard-Costa2, L Adrienne Cupples2, Josée Dupuis2,
Ramachandran S Vasan2 and Larry D Atwood2
Address: 1The National Heart Lung and Blood Institute's Framingham Heart Study, Framingham, MA, USA and 2Boston University Schools of
Medicine and Public Health, Boston, MA, USA
Email: Caroline S Fox* - foxca@nhlbi.nih.gov; Nancy Heard-Costa - nheard@bu.edu; L Adrienne Cupples - adrienne@bu.edu;
Josée Dupuis - dupuis@bu.edu; Ramachandran S Vasan - vasan@bu.edu; Larry D Atwood - lda@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):S18

doi:10.1186/1471-2350-8-S1-S18

This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S18© 2007 Fox 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: Obesity is related to multiple cardiovascular disease (CVD) risk factors as well as
CVD and has a strong familial component. We tested for association between SNPs on the
Affymetrix 100K SNP GeneChip and measures of adiposity in the Framingham Heart Study.
Methods: A total of 1341 Framingham Heart Study participants in 310 families genotyped with the
Affymetrix 100K SNP GeneChip had adiposity traits measured over 30 years of follow up. Body
mass index (BMI), waist circumference (WC), weight change, height, and radiographic measures ofadiposity (subcutaneous adipose tissue, visceral adipose tissue, waist circumference, sagittal height)
were measured at multiple examination cycles. Multivariable-adjusted residuals, adjusting for age,
age-squared, sex, smoking, and menopausal status, were evaluated in association with the genotype
data using additive Generalized Estimating Equations (GEE) and Family Based Association Test
(FBAT) models. We prioritized mean BMI over offspring examinations (1–7) and cohort
examinations (10, 16, 18, 20, 22, 24, 26) and mean WC over offspring examinations (4–7) forpresentation. We evaluated associations with 70,987 SNPs on autosomes with minor allele
frequencies of at least 0.10, Hardy-Weinberg equilibrium p ≥ 0.001, and call rates of at least 80%.
Results: The top SNPs to be associated with mean BMI and mean WC by GEE were rs110683 (pvalue 1.22*10-7) and rs4471028 (p-values 1.96*10-7). Please see http://www.ncbi.nlm.nih.gov/
projects/gap/cgi-bin/study.cgi?id=phs000007 for the complete set of results. We were able tovalidate SNPs in known genes that have been related to BMI or other adiposity traits, including the
ESR1 Xba1 SNP, PPARG, and ADIPOQ.
Conclusion: Adiposity traits are associated with SNPs on the Affymetrix 100K SNP GeneChip.
Replication of these initial findings is necessary. These data will serve as a resource for replication
as more genes become identified with BMI and WC.

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Introduction
Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the United States, affecting
roughly twelve million people and accounting for nearly
one million deaths per year [1]. Although improvements
in cardiovascular risk factor profiles have contributed toreductions in CVD mortality, an increasing prevalence ofobesity may have slowed this rate of decline [2]. Obesity
increases the risk of all-cause mortality [3], vascular disease [4], and non-vascular causes of death including certain cancers [5]. Genetic and environmental factors havebeen linked to obesity [6]. We have previously reported
linkage to body mass index (BMI) on chromosomes 6q23
and 11q24 [7,8], waist circumference (WC) on chromosome 6q23 [9], and weight change on chromosome
20q13 [10] in the Framingham Heart Study. Additionally,
multiple quantitative trait loci and candidate genes havebeen mapped to adiposity-related traits, as recently
reviewed [11].
As part of the Framingham Heart Study 100K Project, wesought to test the relation of multiple adiposity-related
traits with the Affymetrix one hundred thousand single
nucleotide polymorphisms (SNP) chip. A broad range ofphenotypes were studied and include BMI, WC, height,
and radiographic quantification of subcutaneous (SAT)and visceral (VAT) fat. In this manuscript we focus onmean BMI and mean WC. We tested the relation of these
traits to 70,987 SNPs.

Methods
Participants from the Framingham Heart Study Original
Cohort and Offspring Cohort underwent genotyping with
the Affymetrix 100K GeneChip; details about the selection
process and genotyping are provided in the Overview[12]. Participants (n = 1345) were genotyped for theAffymetrix GeneChip Human Mapping 100K SNP set. For
the current analysis, phenotype data were available in
1341 participants for mean BMI and 1079 participants formean WC. For this manuscript, we focused on mean BMI
over offspring examinations (1–7) and cohort examinations (10, 16, 18, 20, 22, 24, 26) and mean WC over offspring examinations (4–7). We evaluated associations
with 70,987 SNPs on autosomes with minor allele frequencies ≥ 0.10, Hardy-Weinberg equilibrium (HWE) pvalue ≥ 0.001, and call rates ≥80%.
Phenotype assessment
Body weight and height were measured at all 7 Offspringexamination cycles, from 1971 to 2001 and chronologically corresponding to 7 Original cohort examinations(10, 16, 18, 20, 22, 24, 26); WC was measured at the levelof the umbilicus at 4 Offspring examinations (4, 5, 6, and
7). BMI was calculated by taking the weight (in kilograms)
over the height (in meters-squared). Mean BMI across 7

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offspring examinations (1–7) and 7 cohort examinations(10, 16, 18, 20, 22, 24, 26) was obtained by taking the
average of all available measurements; mean WC across 4
examinations was obtained by taking the average of allavailable offspring measurements. Covariates were also
averaged over the exams at which the adiposity measures
were available.
Subcutaneous and visceral fat volumes (SAT and VAT,
respectively) were measured on a subset of individuals
who took part in the Framingham Offspring Multi-Detector Computed Tomography Study between 2002 and2005. Briefly, subjects underwent eight-slice multi-detector computed tomography imaging of the chest and abdomen in a supine position as previously described(LightSpeed Ultra, General Electric, Milwaukee, WI) [13].
SAT and VAT volumes were assessed (Aquarius 3D Workstation, TeraRecon Inc., San Mateo, CA) via manual tracing of the abdominal muscular wall that separates thevisceral from the subcutaneous compartment, with excellent inter-reader variability of 0.99 for VAT and SAT, aspreviously reported [13].
Genotyping
Genotyping was performed using the 100K Affymetrix
GeneChip. Please see the Overview [12] for details.
Statistical methods
In total, a maximum of 1341 genotyped participants withphenotype information were available for analysis. Residuals were created from multiple linear regression models
to adjust traits for covariates; these residuals were createdseparately in the Original Cohort and Offspring, and in
women and men separately. The standardized residuals
from these regression models were used to create ranked
normalized deviates, which were in turn used for genetic
analyses. Adiposity traits were age-adjusted (age and agesquared) and then multivariable adjusted; details of multivariable adjustment for each trait are presented in Table
1. Only multivariable-adjusted results are presented inthis manuscript. All association analyses were performed
using generalized estimating equations (GEE) and familybased association testing (FBAT); variance component
methods were used for linkage; details are provided in the
Overview [12]. To consider concordance of results amongcorrelated adiposity traits (see the third table in this article), we selected SNPs with significant association (p <0.01 in GEE or FBAT analyses) for at least 6 out of 8 following weight-related traits: BMI at Offspring exams 1–7
and chronologically corresponding Cohort exams 10, 16,
18, 20, 22, 24, 26 and mean BMI from these exams, andcomputed a geometric mean GEE p-values across all 8
traits for FBAT and GEE separately. We evaluated associations with 70,987 SNPs on autosomes with minor allele
frequencies of at least 0.10, HWE p ≥ 0.001, and call rates

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of at least 80%. Linkage analysis was performed using variance components methods on a subset of 100K markers
in linkage equilibrium and Marshfield short tandem
repeats; please see the Overview [12] for more details,
including power calculations.

Results
All traits (n = 157), including relevant examination cycles
and multivariable-adjustments, are presented in Table 1.
Table 2a presents the top 25 p-values obtained via GEE formean BMI and mean WC. The top SNPs to be associatedwith mean BMI and mean WC by GEE were rs110683 (pvalue 1.22*10-7) and rs4471028 (p-values 1.9*10-7);
Table 2b presents the top SNPs for the FBAT procedure.
Additional results can be found on the following website:
http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Table 2c presents all LOD scoresof at least 2.0. For mean BMI, we observed a peak LOD
score of 2.3 on chromosome 2p16, whereas for mean WC,
we observed a peak LOD score of 2.3 on chromosome
2q14.
Table 3 presents the top 25 SNPs for our multiple traits
analysis, summarizing concordance of results in related
BMI traits. The SNP with the lowest p-value was
rs1106683 (p-value 3.8*10-6). We also evaluated 4 wellreplicated genes in the obesity field (ADIPOQ [14], ESR1[15], LEP [16], and PPARG [17]), as well as the recently
identified INSIG2 gene [18]; Table 4a displays the associated validated SNPs from the literature that are either
present in the Affymetrix 100K or that are in linkage disequilibrium (LD) with these SNPs. We found significant
results for a SNP in LD with the ESR1 Xba1 SNP(rs3853250; FBAT p-value for mean BMI = 0.047). We
also confirmed the association between a SNP in the
INSIG2 gene (rs7566605; GEE p-value 0.001 for mean

BMI) previously identified in this sample using a different
analytic method [18]. We further explored associations
with all SNPs in the Affymetrix 100K either within thesegenes or within 200 kb of these genes (Table 4b); only
associations with p < 0.05 are presented. We identified 3
additional associated SNPs in the INSIG2 gene, 5 SNPs in
the PPARG gene, 1 SNP in the ADIPOQ gene, and 5 SNPs
in the ESR1 gene. Of the 4 SNPs present in the LEP gene,
there were no associations with a p-value < 0.05.
Additional Findings
We also identified several additional SNPs in genes inrelation to mean BMI or mean WC among our list of the
top 500 SNPs http://www.ncbi.nlm.nih.gov/projects/gap/
cgi-bin/study.cgi?id=phs000007. The LRP1B gene (SNP
rs3923350, GEE p-value 0.0005) was associated with bothmean BMI and mean WC. We also found association with
the VIP gene (SNP rs620598, GEE p = 0.001), the LEPR
gene (SNP rs2025804, GEE p-value = 0.003), the ADRB1
gene (SNP rs6585258, FBAT p-value 0.004), the NPY2R
gene (SNP rs2880411, p-value = 0.006), the HSD3B1 gene(rs 4659200, FBAT p-value 0.0007), the ADRA1B gene
(SNP rs952037, GEE p-value = 0.002), IL6R (SNP
rs4129267, FBAT p-value = 0.003), AGTR1 (SNP
rs275678, FBAT p-value = 0.006), and FSHR (SNP
rs1504155; GEE p-value = 0.0004).

Discussion
In our analysis of adiposity-related traits, we found strong
and significant results to SNPs on the Affymetrix 100K
GeneChip. Further, we have confirmed or replicated several well-validated genes that have been reported to berelated to adiposity.
One of the top SNPs that we identified via the GEE
method is located in the SSTR2 gene, the somatostatin

Table 1: Phenotype master trait table: exam cycle, and numbers of participants in family plates with phenotype
Exam cycle/s
Phenotype

Body Mass Index; men and women combined
and separate
Weight change
Weight; men and women combined andseparate
Height; men and women combined andseparate
Waist circumference; men and women
combined and separate
Subcutaneous fat by computed tomography
Visceral fat by computed tomography
Waist circumference by computed
tomography
Sagittal diameter by computed tomography

Numberof traits§

Sample Size

Offspring

CohortAdjustment

46

529–1341

1–7; mean 1–7; change from 1–7

10, 16, 18, 20, 22, 24, 26; mean
10, 16, 18, 20, 22, 24, 26

*

11
43

468–1115
498–1342

Change from 1–7
1–7

10, 16, 18, 20, 22, 24, 26

*
*

25

529–1341

1–7

10, 16, 18, 20, 22, 24, 26*

24

479–1252

4–7; mean 4–7; change from 4–7

20, 22, 23; change from 20–23;

**

2
2
2

654
653
664

7
7
7

-

*
*
*

2

665

7

-

*

§Refers to number of traits within each group actually analyzed*All models included age, age-squared, sex, smoking, menopause**Models additionally adjusted for body mass index

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Table 2: (a) Top association results for mean BMI and mean WC based on GEE p-value; (b) Top 25 association results for mean BMI
and mean WC based on FBAT p-value; (c) LOD scores of at least 2.0 with accompanying LOD score 1.5 support interval for mean BMI
and mean WC
Trait

SNP

Chromosome

Physical Position (Mb)

GEE p-value

FBAT p-value

Gene

2a. Top association results for mean BMI and mean WC based on GEE p-value
Mean BMI
Mean WC
Mean WC
Mean WC
Mean WC
Mean WC
Mean BMI
Mean BMI
Mean BMI
Mean BMI
Mean WC
Mean BMI
Mean BMI
Mean WC
Mean BMI
Mean BMI
Mean BMI
Mean WC
Mean BMI
Mean BMI
Mean BMI
Mean BMI
Mean BMI
Mean BMI
Mean BMI

rs1106683
rs4471028
rs4469448
rs6996971
rs10504576
rs1875517
rs1106684
rs1333026
rs2296465
rs1374489
rs1466113
rs10486301
rs2221880
rs1456873
rs10513097
rs2361128
rs2942329
rs4129319
rs10509361
rs2967001
rs464766
rs10487263
rs4922571
rs7013836
rs7202384

7
8
8
8
8
3
7
13
10
5
17
7
5
4
5
19
5
4
10
5
3
7
11
8
16

130910780
7545753075457665
75455047
75429234
118790257
130910920
65018785
3299647
19658900
68676913
18183021
19420500
62130929
11406101
62431059
19429660
88752564
77223192
19422000
162570294
123603987
31643025
5666540
13658225

1.2*10-07
2.0*10-07
2.6*10-07
4.9*10-07
7.1*10-07
1.5*10-06
1.6*10-06
8.1*10-06
1.5*10-05
1.6*10-05
2.7*10-05
2.7*10-05
3.0*10-05
3.1*10-05
4.4*10-05
4.4*10-05
4.5*10-05
4.7*10-05
5.2*10-05
6.0*10-05
6.1*10-05
7.0*10-05
7.0*10-05
8.2*10-05
8.2*10-05

3.2*10-04
3.2*10-04
0.003
0.002
0.005
0.002
0.002
0.209
0.262
0.348
0.004
0.016
0.072
0.005
0.022
0.004
0.089
0.003
0.044
0.074
0.072
0.036
0.062
0.103
0.002

GDAP1
GDAP1
GDAP1
GDAP1

CDH18
SSTR2

CTNND2ZNF264SPARCL1
C10orf11
ADMP
ELP4

2b. Top 25 association results for mean BMI and mean WC based on FBAT p-value
Trait

SNP

Mean WC
Mean WC
Mean WC
Mean WC
Mean WC
Mean WC
Mean BMI
Mean BMI
Mean WC
Mean WC
Mean WC
Mean WC
Mean WC
Mean WC
Mean BMI
Mean WC
Mean WC
Mean WC
Mean BMI
Mean WC
Mean WC
Mean BMI
Mean BMI
Mean BMI
Mean WC

rs10488165
rs2206682
rs2223662
rs953536
rs10517461
rs7941883
rs10503776
rs711702
rs4312989
rs10519381
rs10483872
rs315711
rs4715571
rs667463
rs7320523
rs3752591
rs1496389
rs1619682
rs10492197
rs10501467
SNP_A-1731932
rs10512326
rs7533902
rs2870950
rs2226351

Chromosome

Physical Location (Mb)

GEE p-value

FBAT p-value

7
6
6
9
4
11
8
3
6
5
14
9
6
9
13
22
5
7
12
11
1
9
1
12
21

132594899
56001938
56001756
111569442
37789743
123262095
25765786
22956280
55918441
113700141
75239061
111628006
55917006
111647315
67552774
40664016
113753570
133453958
66871874
79913437
24035982
103934100
97791249
66870973
25259810

0.011
0.011
0.013
0.007
2.3*10-04
0.084
0.009
0.143
0.075
0.01
0.018
0.008
0.03
0.004
0.193
0.001
0.018
0.14
0.038
0.037
0.152
0.063
0.029
0.034
1.8*10-04

2.6*10-06
4.2*10-06
5.1*10-06
8.2*10-06
2.9*10-05
3.0*10-05
3.8*10-05
4.0*10-05
4.2*10-05
4.4*10-05
4.5*10-05
5.4*10-05
5.9*10-05
7.0*10-05
7.4*10-05
8.5*10-05
9.8*10-05
1.0*10-04
1.0*10-04
1.1*10-04
1.1*10-04
1.2*10-04
1.2*10-04
1.2*10-04
1.3*10-04

Gene
SEC8L1
COL21A1
COL21A1
C9orf84
TBC1D1
OR8D4|OR4D5|OR6T1
EBF2

KCNN2
KIAA0998
C9orf84
C9orf84
C22orf18
KCNN2
SLC35B4
IFNG|IL26|IL22

SMC2L1DPYD
IFNG|IL26|IL22

2c. LOD scores of at least 2.0 with accompanying LOD score 1.5 support interval for mean BMI and mean WC
Trait

SNP

Mean BMI
Mean WC
Mean BMI

rs9309153
rs1992901
rs10518418

Chromosome

Physical Position (bp)

LOD

LOD1.5 Lower Bound (bp)

LOD1.5 Upper Bound (bp)22
148856798
121386899
89149134

2.31252.27212.005344048379
116579748
5172713264579948
132195205
99668198

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Table 3: Results informed by combination of GEE and FBAT based on p-value of ≤0.01 for GEE or FBAT for 6 out of 8 BMI traits

SNP
rs1106683
rs2296465
rs10513097
rs2361128
rs1374489
rs10486301
rs1333026
rs10509361
rs10504368
rs947599
rs2012187
rs6480902
rs336583
rs9290065
rs10494810
rs2012064
rs1869731
rs775748
rs10499068
rs10236525
rs9309770
rs1504294
rs7142517
rs2051545
rs910623

Chromosome

Physical Position (bp)

7
10
5
19
5
7
13
10
8
10
5
10
3
3
1
7
8
3
6
7
3
3
14
16
1

130910780
3299647
11406101
62431059
19658900
18183021
65018785
77223192
64947097
95256673
11375037
80157318
162564683
162259666
196868239
18220564
63972241
77679150
113189197
18184912
77647000
68831828
54376554
13680100
115336446

receptor 2 gene, which has been reported to suppress
growth hormone secretion. We also identified several
additional SNPs in genes in relation to mean BMI or mean
WC among our list of the top 500 SNPs. The LRP1B geneis a member of the LDL receptor gene family, and represents a potentially attractive candidate gene. The VIP gene
(SNP rs620598, GEE p = 0.001) is a member of the glucagon family that plays a role in multiple physiologic andmetabolic pathways, including myocardial contractility,smooth muscle relaxation, blood pressure lowering andvasodilation, and glycogenolysis. We also found significant associations with multiple genes that have been previously associated with adiposity-related traits [11].
Using our clustered traits analysis, we identified the
CTNND2 gene, a gene that is part of the catenin family
that may be involved in nutrient absorption in the intestine and signaling with nuclear receptors including PPAR[19]. We also identified the NR5A2 gene, a gene that ispart of the nuclear receptor subfamily, a family of orphan
receptors. NR5A2 is a key regulator of CYP7A expression
in the liver, and PPM1L (protein phosphatase 1), a genethat is a suppressor of the SAPK pathway and may be
involved in oxidative stress and apotosis.
Well-replicated candidate genes
We were able to confirm association (i.e. validate) with
the INSIG2 gene (SNP rs7566605, GEE p-value = 0.001).

Gene name

CTNND2ZNF264|AURKC
CDH18

C10orf11
C10orf3
CTNND2
ADMP
PPMIL
NR5A2
FLJ39630
ROBO2

ROBO2
FAM19A4
SAMD4|GCH1
TSPAN2

Mean GEE geometric p-value
3.8*10-6
5.9*10-5
6.8*10-5
7.9*10-5
1.1*10-4
1.3*10-4
1.3*10-4
1.7*10-4
2.7*10-4
3.6*10-4
3.6*10-4
3.9*10-4
3.9*10-4
4.1*10-4
4.3*10-4
4.5*10-4
4.7*10-4
4.8*10-4
4.9*10-4
5.5*10-4
5.5*10-4
5.5*10-4
5.5*10-4
5.6*10-4
5.8*10-4

This same SNP was previously identified in association
with BMI in this same sample using a different analytic
method [18]. We also had nominal significance with a
SNP in LD with the ESR1 Xba1 SNP, and multiple other
SNPs in well-replicated obesity genes, suggesting that theAffymetrix 100K GeneChip provides a valid tool for
uncovering candidate gene associations with adiposityrelated traits. Of note, some of our SNPs did overlap withresults reported for BMI using different analytic methods(Herbert et al, http://gmed.bu.edu/about/index.html[20]).
Comparison with prior linkage results
We have previously identified a locus for BMI on chromosome 1 (D1S1665, LOD score 1.85) [7]. This peak falls
within the 1.5 LOD score interval for our current finding
on chromosome 1. We also have previously identified a
LOD score of 2.0 for waist circumference on chromosome
2q14 [9], nearby to our current LOD score of 2.27 formean WC. Differences with previously reported results
may stem from our use of different phenotypes.
Strengths and limitations
Strengths of our study lie in our assessment of multiple
measures of BMI and WC in a sample unselected for these
traits, thus improving precision. We also have excellent
assessment of potential confounders that we are able toadjust for in our residual creation. Because the Framing-

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Table 4: (a) Comparison of mean BMI and mean WC Results with prior literature for SNPs that are either present in the 100K or in
LD with a SNP in the 100K; (b) Associations of mean BMI and mean WC with all SNPs in or near genes (up to 200 kb away) for 5 wellreplicated genes in the published literature (INSIG, PPARG, ADIPOQ, ESR1, LEP)* with a p-value < 0.05 in either FBAT or GEE**
4a. Comparison of mean BMI and mean WC Results with prior literature for SNPs that are either present in the 100K or in LD with a SNP in the 100K
Mean BMI p-value

Mean WC p-value

Gene

Candidate
SNP

100K SNP

Location
Candidate
SNP (bp)

Location
100K SNP(bp)

D. prime

r2

FBAT

GEE

FBAT

GEE

INSIG2
ESR1-Xba1

rs7566605
rs9340799
rs9340799
rs1349419
rs12535747
rs12535747
rs1801282

Rs7566605
rs3853250
rs3853251
rs10487506
rs10487505
rs10487506
rs1801282

118552255
152255495
152255495
127471164
127472286
127472286
12368125

118552255
152252014
152252870
127472106
127454114
127472106
12368125

1
1
1
1
0.83
1
1

1
0.62
0.96
0.69
0.34
0.48
1

0.449
0.047
0.309
0.422
0.188
0.422
0.556

0.001
0.350
0.963
0.583
0.286
0.583
0.178

0.975
0.980
0.333
0.788
0.420
0.788
0.290

0.480
0.105
0.336
0.848
0.975
0.848
0.406

LEP

PPARG

4b. Associations of mean BMI and mean WC with all SNPs in or near genes (up to 200 kb away) for 5 well-replicated genes in the published literature(INSIG, PPARG, ADIPOQ, ESR1, LEP)* with a p-value < 0.05 in either FBAT or GEE**
Mean BMI p-value

Mean WC p-value

Gene

SNP

PhysicalPosition
(bp)

FBAT

GEE

FBAT

GEE

INSIG

rs9284719
rs3771942
rs10490628
rs7566605
rs2938392
rs709157
rs10510422
rs10510423
rs2454431
rs963163
rs1042464
rs851982
rs10484922
rs3853250
rs3778099
rs9322361

118395025
118425080
118446520
118552255
12409608
12437024
12505413
12526881
12558068
12632067
187878274
152117099
152224431
152252014
152510689
152551257

0.148
0.766
0.464
0.449
0.158
0.602
0.806
0.986
0.806
0.997
0.962
0.880
0.318
0.047
0.033
0.020

0.035
0.005
0.021
0.001
0.003
0.106
0.546
0.753
0.034
0.047
0.231
0.538
0.012
0.350
0.367
0.096

0.510
0.812
0.765
0.975
0.644
0.091
0.044
0.038
0.268
0.749
0.024
0.119
0.067
0.980
0.513
0.122

0.925
0.984
0.238
0.480
0.244
0.023
0.720
0.787
0.882
0.036
0.722
0.043
0.528
0.105
0.689
0.522

PPARG

ADIPOQ
ESR1*No SNPs in the LEP gene had a p-value < 0.05**The following number of SNPs were evaluated in the INSIG, PPARG, ADIPOQ, ESR1, and LEP genes: 19, 28, 16, 37, 4

ham Heart Study has measured multiple traits, we are able
to examine trait clustering, which may be more likely toidentify SNPs in coding regions. Limitations exist as well.
Our sample is neither ethnically diverse nor nationally
representative, and it is uncertain how our results would
apply to other ethnic groups. However, in genetics studies,sample homogeneity is beneficial in order to reduce population stratification. Further, none of these results
reached genome-wide significance; please see the Overview [12] for details regarding this threshold. These results
should be considered preliminary, and are likely to contain false negatives and false positives. Therefore, replication in independent samples is critical. For limitations

pertaining to our genotyping or statistical methods,
including multiple testing, please see the Overview [12].

Conclusion
Adiposity-related traits are associated with SNPs on the
Affymetrix 100K SNP GeneChip. Further work to replicate
some of these SNPs in other samples is necessary. These
data will serve as a resource for replication as more genes
become identified with BMI and WC.

Abbreviations
BMI = body mass index; CVD = cardiovascular disease;
FBAT = Family Based Association Test; GEE = Generalized

Page 6 of 7
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BMC Medical Genetics 2007, 8(Suppl 1):S18

Estimating Equations; HWE = Hardy-Weinberg equilibrium; LD = linkage disequilibrium; SAT = subcutaneousfat; SNP = single nucleotide polymorphisms; VAT = visceral fat; WC = waist circumference.

http://www.biomedcentral.com/1471-2350/8/S1/S18

10.

11.

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

12.

Authors' contributions
CF drafted the manuscript and interpreted the data. JD
assisted in the design of the study and in performing thestatistical analyses. LAC contributed to the analyticaldesign, the interpretation of these results and to edits ofthe manuscript. NHC contributed to the analytical design
and the phenotype creation. RV contributed to the phenotype acquisition, the interpretation of the results, andedits to the manuscript. LA contributed to the design,
analysis, and interpretation of the findings. All authors
gave approval to the final version of the manuscript.

Acknowledgements
We gratefully acknowledge the continued commitment and dedication of
the Framingham Study participants. This work was supported by the
National Heart, Lung and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195) and by Atwood R01 DK066241. A portion of
the research 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).
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.

13.

14.

15.

16.

17.

18.

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