BMC Medical Genetics

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Genome-wide association with diabetes-related traits in the
Framingham Heart StudyJames B Meigs*1, Alisa K Manning2, Caroline S Fox3, Jose C Florez4,
Chunyu Liu2, L Adrienne Cupples2 and Josée Dupuis2
Address: 1General Medicine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA,
2Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA, 3The Division of Endocrinology, Diabetes, andHypertension, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, and the National Heart, Lung, and Blood Institute's
Framingham Heart Study, Framingham, MA, USA and 4Diabetes Unit, Department of Medicine and Center for Human Genetic Research,
Massachusetts General Hospital and Harvard Medical School, Boston, MA, and Program in Medical and Population Genetics, Broad Institute ofHarvard and MIT, Cambridge, MA, USA
Email: James B Meigs* - jmeigs@partners.org; Alisa K Manning - amanning@bu.edu; Caroline S Fox - foxca@nhlbi.nih.gov;
Jose C Florez - jcflorez@partners.org; Chunyu Liu - liuc@bu.edu; L Adrienne Cupples - adrienne@bu.edu; Josée Dupuis - dupuis@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):S16

doi:10.1186/1471-2350-8-S1-S16

This article is available from: http://www.biomedcentral.com/1471-2350/8/S1/S16© 2007 Meigs 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: Susceptibility to type 2 diabetes may be conferred by genetic variants having modest
effects on risk. Genome-wide fixed marker arrays offer a novel approach to detect these variants.
Methods: We used the Affymetrix 100K SNP array in 1,087 Framingham Offspring Study family
members to examine genetic associations with three diabetes-related quantitative glucose traits(fasting plasma glucose (FPG), hemoglobin A1c, 28-yr time-averaged FPG (tFPG)), three insulin
traits (fasting insulin, HOMA-insulin resistance, and 0–120 min insulin sensitivity index); and withrisk for diabetes. We used additive generalized estimating equations (GEE) and family-based
association test (FBAT) models to test associations of SNP genotypes with sex-age-age2-adjusted
residual trait values, and Cox survival models to test incident diabetes.
Results: We found 415 SNPs associated (at p < 0.001) with at least one of the six quantitative
traits in GEE, 242 in FBAT (18 overlapped with GEE for 639 non-overlapping SNPs), and 128
associated with incident diabetes (31 overlapped with the 639) giving 736 non-overlapping SNPs.
Of these 736 SNPs, 439 were within 60 kb of a known gene. Additionally, 53 SNPs (of which 42
had r2 < 0.80 with each other) had p < 0.01 for incident diabetes AND (all 3 glucose traits OR all
3 insulin traits, OR 2 glucose traits and 2 insulin traits); of these, 36 overlapped with the 736 other
SNPs. Of 100K SNPs, one (rs7100927) was in moderate LD (r2 = 0.50) with TCF7L2 (rs7903146),
and was associated with risk of diabetes (Cox p-value 0.007, additive hazard ratio for diabetes =1.56) and with tFPG (GEE p-value 0.03). There were no common (MAF > 1%) 100K SNPs in LD(r2 > 0.05) with ABCC8 A1369S (rs757110), KCNJ11 E23K (rs5219), or SNPs in CAPN10 or HNFa.
PPARG P12A (rs1801282) was not significantly associated with diabetes or related traits.
Conclusion: Framingham 100K SNP data is a resource for association tests of known and novel
genes with diabetes and related traits posted at http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007. Framingham 100K data replicate the TCF7L2 association with diabetes.

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Background
Type 2 diabetes is a cause of poor health and early deaththat is spreading worldwide and exerting a fearsome
human and economic toll [1,2]. Prevention and control
of diabetes requires a better understanding of its basic
molecular causes. Type 2 diabetes is a heterogeneous disease arising from physiological dysfunction in the pancreas, skeletal muscle, liver, adipose and vascular tissue.
Much of the heterogeneity of type 2 diabetes has a genetic
basis. A full picture of the complex genetic architecture ofdiabetes has been elusive [3-7].
Among type 2 diabetes susceptibility genes few, if any,
individual loci are expected to carry alleles of major effectexplaining a substantial proportion of cases, although a
few genes could have a substantial population effect but
not give a strong genetic signal if the causal alleles werecommon and the increase in risk were modest [6,7]. Suchgenes have proven hard to detect using linkage-based
approaches, although recent rapid advances in genetic
association methodologies have led to some successes.
The P12A polymorphism in the gene encoding the peroxisome proliferator-activated receptor-g (PPARG) [7], theE23K polymorphism in the gene encoding the islet ATPdependent potassium channel Kir6.2 (ABCC8-KCNJ11)[8-10] and common variants in the gene encoding thetranscription factor 7-like 2 gene (TCF7L2) [11,12] were
all found using well-powered association mapping, andall have been reproducibly associated with diabetes indiverse samples at highly significant p-values.
Current gene discovery strategies have focused on coding
regions, but regulatory variants also influence disease[11,13,14]. A comprehensive picture of diabetes genetics
will require a wide and adequately dense search acrosscoding and conserved non-coding genomic regions using
an association analysis approach, where power is superior
to linkage analysis when seeking common variants ofmodest effect [6]. Resources are now becoming available
to perform such genome-wide association (GWA) studies
of type 2 diabetes [15-18].
In this report we describe the Framingham Heart Study(FHS) Affymetrix 100K SNP genome-wide association(GWA) study resource for type 2 diabetes. This resourcecomplements the several other large extant type 2 diabetes
GWA studies in three major respects: it is populationbased (not diabetes proband-based), studies two generations, and has decades of longitudinal, standardized,
detailed follow-up. We describe results of a simple low pvalue-based SNP selection strategy and an alternate novel
SNP selection strategy that takes advantage of the unique
FHS diabetes-related quantitative traits data. We use FHS
100K SNPs in an in silico replication analysis that tests the
hypothesis that SNPs in LD with published causal variants

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in PPARG, ABCC8, TCF7L2, CAPN10, and HNFa are associated with diabetes and related quantitative traits.

Methods
Study subjects
The study sample is described in the Overview Methods
section [19]. With respect to diabetes-related traits, Offspring subjects provided genotypes and diabetes-related
traits to the analyses, and Offspring parents from the Original FHS Cohort contributed genotypes for linkage analysis and FBAT statistics. Of 1,345 FHS subjects with 100K
SNP data, 1,087 were Offspring and of these 560 were
women, the mean age at exam 5 was 52 years, and themean age at last follow-up was 59 years. Every study subject provided written informed consent at every examination, including consent for genetic analyses, and the study
was approved by Boston University's Institutional Review
Board.
Genotyping and annotation
Affymetrix 100K SNP and Marshfield STR genotyping aredescribed in the Overview Methods section [19]. Genotype
annotation sources are described in the Overview Methods
section [19].
Diabetes phenotyping
Diabetes and related quantitative traits have been ascertained at every FHS exam for every generation. Diabetesrelated quantitative traits available in the FHS 100K
resource are displayed in Table 1. FPG data for the analyses came from all 7 Offspring exams, but the remainder ofthe data came from exam 5 (1991–94), when subjects
without diagnosed diabetes underwent a 75 gram oral
glucose tolerance test, or exam 7 (1998–2001), the mostrecent exam. We defined diabetes as chart-review-confirmed diabetes, new or ongoing hypoglycemic treatment
for diabetes at any exam, or a FPG > 125 mg/dl at two or
more of the seven exams. Diabetes age-of-onset was
defined as the subject's age at the exam at which diabetes
was first identified. Among Offspring with diabetes, >99%
have type 2 diabetes [4]. Of the 1,083 Offspring with
100K genotypes and known diabetes status, 91 had diabetes. The mean age of onset of was 58 yr; through exam 7,
9.3% of diabetic subjects had developed diabetes by age
40 yr, 33.0% by age 50, 68.1% by age 60, and 99.7% by
age 80.

In this presentation we focus on six (three glucose andthree insulin) primary Offspring diabetes-related quantitative traits. Glucose traits are fasting plasma glucose(FPG) and hemoglobin A1c (HbA1c) measured at exam 5,
and up to 28 yr time-averaged FPG (tFPG) level obtained
from the mean of up to seven serial exams. Glucose traits
included all subjects, including those with diabetes
regardless of treatment, as these were the most informa-

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Table 1: Type 2 diabetes-related quantitative traits in 1087 Framingham Offspring Study subjects with 100K genotype data
Trait

Number oftraits

Offspring Exam
Cycle

Cohort Exam
Cycle

Adjustment *Number with Genotype
and Trait Levels †Fasting plasma glucose (FPG)
Hemoglobin A1c (HbA1c)
28 yr time averaged FPG (tFPG)
Fasting insulin
Homeostasis model insulin resistance (HOMA-IR)
0–120 min insulin sensitivity (ISI_0-120)
Incident type 2 diabetes

1
1
1
1
1
1
1

5, 7
5, 7
1–7
5, 7
5
5
1–7

-

age, age2 age, age2, BMI
age, age2 age, age2, BMI
age, age2 age, age2, BMI
age, age2 age, age2, BMI
age, age2 age, age2, BMI
age, age2 age, age2, BMI
age, age2 age, age2, BMI

Adiponectin
Resistin

1
1

7
7

-

age, age2 age, age2, BMI
age, age2 age, age2, BMI

1,027
623
1,087
982
980
935
91 with diabetes
1,083 without diabetes
828
831* Traits were modeled as log(trait value) in sex-specific models. Residuals from these models were tested as quantitative traits associated with SNP genotype, and ranked
residuals were used in linkage analyses.
† For traits with data at both exams 5 and 7, numbers are given for subjects with data at exam 5

tive subjects with respect to hyperglycemia. Subjects withdiabetes had the highest glucose values when subjects
were ranked with respect to any glucose trait; those on
treatment had the highest values. The three insulin traits
are fasting insulin, homeostasis model-assessed insulin
resistance (HOMA-IR), and Gutt's 0–120 min insulin sensitivity index (ISI_0-120) measured at exam 5. Subjects
with insulin-treated diabetes were removed from all insulin trait analyses, as we had no information on insulin
dose and so measured insulin values were confounded byinsulin treatment [20-22]. We also analyzed incident diabetes from first exam through last follow-up. We previously have described FHS laboratory methods for thesediabetes-related quantitative traits [4,23-25]. In addition
to glucose and insulin traits, levels of adiponectin andresistin are available in the FHS dbGaP resource. Plasma
adiponectin and resistin concentrations were measured
using a commercial ELISA (R&D Systems, Minneapolis,
MN); inter- and intra-assays CVs were 5.3%–9.6% for adiponectin and 7.6%–10.5% for resistin.
SNP prioritization
We used two approaches to prioritize SNPs potentiallyassociated with diabetes or diabetes related traits. In thefirst, we simply ordered SNPs from lowest to highest pvalue for association with one or more of the six primary
glucose and insulin traits. We also ordered SNPs or Marshfield STRS by highest to lowest LOD score for linkage to
one or more of the six primary traits, and present LOD
scores > 2.0. In an alternative SNP prioritization strategy,
we selected SNPs associated with multiple-related traits.
In this approach, we selected SNPs with consistent nominal associations (p < 0.01 in GEE or FBAT) with all threeglucose traits OR all three insulin-related traits OR (twoglucose and two insulin traits). Among these we used
extent of LD to select a non-redundant set of SNPs; whenseveral were perfect proxies for each other (r2 ≥ 0.8) only
one SNP was selected, based on the highest genotyping
call rate.

Statistical analysis
The general statistical methods for linkage and GWA analyses are described in the Overview Methods [19]. For diabetes-related quantitative traits we used additive GEE andFBAT models, testing associations between SNP genotypes
and age-age2-sex-adjusted residual trait values. We kept
70,987 SNPs in the analyses that were on autosomes, had
genotypic call rates ≥ 80%, HWE p ≥ 0.001 and MAF≥ 10%.

We tested association of 100K SNPs with incident type 2
diabetes in two additional models using the same adjustment strategy. First, Martingale residuals were created tomeasure the age-of-onset of type 2 diabetes; residuals wereanalyzed with FBAT [26]. Individuals with lower values ofthis 'martingale residual' trait developed diabetes atyounger ages, and those with the highest values had been
observed for the longest time without development ofdiabetes [27]. Second, we used a Cox proportional hazard
survival analysis with robust covariance estimates in order
to find SNPs associated with development of diabetes
over all seven exams [28].

Results
Diabetes-related quantitative traits available in the FHS
100K SNP resource are listed in Table 1 and posted on the
NCBI web site [29]. Each trait is available as an age-age2adjusted or age-age2-BMI-adjusted residuals from sex-specific models. In this analysis we only consider the ageage2-adjusted traits. Among these, the following were theprimary traits used in this analysis: exam 5 fasting plasma
glucose (FPG; n with data = 1,027; mean, SD 99, 24.7 mg/dl); exam 5 HbA1c (n = 623; 5.28, 0.9%); 28-year time
averaged FPG (tFPG; n = 1,087; 98, 16.2 mg/dl); exam 5
fasting insulin (n = 982; 30.1, 16.4 uU/ml); exam 5
HOMA-IR (n = 980; 7.8, 7.3 units); and the 0–120 min
insulin sensitivity index (ISI_0-120; n = 935; 26.1, 7.6
mg·l2/mmol·mU·min). Among 1,087 Offspring with
100K SNP data there were 91 cases of type 2 diabetes.

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Additional diabetes-related quantitative traits not used inthis analysis but that are available in the FHS 100K SNP
dbGaP resource include, at exam 7: FPG (n = 987; 103, 26
mg/dl); fasting insulin (n = 999; 15.8, 12.8 uU/ml);
HOMA-IR (n = 969; 4.2, 4.1 units); HbA1c (n = 893; 5.59,
0.97%); resistin (n = 831; 14.5, 7.4 ng/dl); adiponectin (n= 828; 9.9, 6.2 ng/dl).
The six primary quantitative traits had significant associations with 415 SNPs in GEE models and 242 SNPs in
FBAT models, using p-value < 0.001, and only considering
SNPs with call rate ≥ 0.80, HWE p-value ≥ 0.001, and MAF≥ 10%. Additionally, there were 91 significant associations with incident diabetes in the survival analyses and42 significant associations with age-of-onset in FBAT, representing 128 non-overlapping SNPs. The 25 SNPs withlowest p-values in GEE or FBAT models, and LOD scores> 2.0 in linkage analyses, are displayed in Table 2. Afteraccounting for the overlap between sets of significant
associations, 736 non-overlapping SNPs were identified
by the p-value approach for SNP prioritization.
The FHS has multiple measures of diabetes-related quantitative traits. We used a multiple-related trait approach ina strategy different from prioritizing SNPs based solely onsmall p-values. This approach yielded 203 SNPs associated with multiple traits. Of these, 53 were also associatedwith incident diabetes (p < 0.01 by GEE or FBAT). We
defined redundant SNPs as those in LD with r2 >= 0.80 toselect 168 non-redundant SNPs associated with multiple
traits; 42 of these non-redundant SNPs also were associated with incident diabetes (Table 3). Examination of themultiple trait-based approach revealed 1) consistent associations of traits with SNPs that were in LD (providing
reassurance that the signal was due to an association of
traits with a particular genomic region rather than to technical error); 2) several putative associations of traits with
SNPs in the same gene but not in perfect LD (suggestingthat the association signal may be due to a functional roleof that gene rather than a statistical fluctuation); and 3)
associations of traits with SNPs in a variety of novel but
plausible biological candidate genes.
We used the UCSC Genome Browser (http://
genome.ucsc.edu/; accessed September 2006) to annotate
SNP details [30,31]. Of the 823 (736 + 203; 116 overlapped) SNPs identified by both prioritization methods
without removing SNPs in LD (r2 >= 0.80), 304 (36.9%)
were in genes, 173 (21%) were within 60 kb of a known
gene and 5 (0.61%) were coding. For comparison, of the70,987 SNPs included in this analysis, 25,916 (36.5%)
were in genes, 14,333 (20.2%) were within 60 kb of aknown gene and 421 (0.59%) were coding.

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Some SNPs had p-values < 0.001 overlapping more than
one analytical method. For instance, 18 SNPs were associated at p < 0.001 with at least one quantitative trait inboth the GEE and the FBAT analyses. For incident diabetes, 5 SNPs were associated with diabetes survival in the
Cox models and with age-of-onset in the FBAT analyses.
We used the FHS 100K array data to verify, in silico, replicated associations of reported diabetes candidate genes(Table 4). We found 7 SNPs in or near TCF7L2. One 100K
SNP (rs7100927) was in moderate LD (r2 = 0.5) with
TCF7L2-associated SNP rs7903146 and was nominallyassociated with a 56% increased relative risk of diabetes (p= 0.007) and with tFPG (GEE p = 0.03). We found 6 SNPs
in or near ABCC8, but no SNPs in strong LD with ABCC8
A1369S (rs757110) or KCNJ11 E23K (rs5219), and thus
could not replicate these associations. One 100K SNP(rs878208) ~25 kb upstream of ABCC8 showed nominal
association with risk of diabetes, but it was not in LD with
rs757110 in ABCC8 (r2 = 0.04). We found 15 SNPs in ornear PPARG, but none were associated with diabetes. Four
SNPs were associated (p < 0.05) with quantitative traits
but were not in LD (r2 < 0.03) with PPARG P12A
(rs1801282), the variant previously associated with type 2
diabetes [7]. We found no polymorphic (MAF > 1%)
100K SNPs in, near, or in LD with CAPN10 or HNFA.
We also assessed our approach for confirmation of 4 SNPs
associated with FPG reported on the Boston University
Department of Genetics and Genomics public site http://
gmed.bu.edu/about/index.html that displays selectedassociations with FHS 100K data. We found no association (all p-values > 0.6) of incident diabetes or levels of
FPG with SNPs rs10495355, rs9302082, rs10483948, or
rs1148509.

Discussion and conclusion
In this paper we describe the characteristics and initial
GWA results for type 2 diabetes and related quantitative
traits in the FHS 100K SNP resource. Over 1000 men andwomen from a community-based sample have detailed
linkage and association of diabetes-related phenotypes
and 100K dense array SNP results available on the web.
About 0.3%–0.6% of SNPs in the 100K array with MAF> 10% are associated at p < 0.001 with six diabetes-related
quantitative traits or with incident type 2 diabetes. A similar proportion of SNPs in the array (0.21%) are associated with multiple related diabetes traits. These several
hundred SNPs likely contain more false positive than true
positive associations with diabetes and related traits, however, they offer logical next targets for the follow-up replication studies in independent samples necessary toresolve true diabetes risk genes. The FHS 100K data replicate the otherwise widely-replicated TCF7L2 association
with diabetes [11,12,32-40] in an in silico analysis.

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Table 2: Twenty five lowest p-values from GEE and FBAT models and LOD scores > 2 for 100K SNPs and FHS diabetes-related
quantitative traits
No.

Trait

SNP

Chr

Physical position

GEE or Cox p-value

FBAT p-value

Known Genes

2a. Ordered by GEE p-value

1

tFPG

rs2722425

840603396

0.00000002

0.0047ZMAT4

2Incident DM

rs10497721

2

192739868

0.0000007

0.0346

TMEFF2

3

Fasting Insulin

rs2877832

14

26870017

0.000002

0.07704

HOMA-IR

rs2877832

14

26870017

0.000003

0.09185tFPG

rs10510634

330321972

0.000005

0.0516

6

FPG

rs180730

4122159395

0.000005

0.0374

PRDM5

7

tFPG

rs180730

4122159395

0.000006

0.0252

PRDM5

8

tFPG

rs7731657

5129971218

0.000007

0.00159

HbA1c

rs10486607

728957729

0.000008

0.044010

ISI_0-120

rs2066219

1368428665

0.000009

0.0245

11

FPG

rs2722425

840603396

0.000009

0.0998

12

Incident DM

rs2195499

241778050

0.000011

0.3860

CPVL

ZMAT4

13

Incident DM

rs830604

371673037

0.000014

0.5914

FOXP1

14

FPG

rs2377689

2106924358

0.000017

0.0015ST6GAL2

15

Incident DM

rs931567

331410581

0.000018

0.009516

FPG

rs10494331

1156395176

0.000019

0.0369

APCS

17

tFPG

rs7147624

1464935378

0.000019

0.0201

FUT8

18

tFPG

rs931567

331410581

0.000019

0.018919

Incident DM

rs10511182

3102255525

0.000020

0.051020

FPG

rs337112

5122556671

0.000022

0.014821

ISI_0-120

rs9319109

1384510270

0.000025

0.004822

HOMA-IR

rs1927384

13

101943751

0.000026

0.005923

ISI_0-120

rs7139897

13

107879625

0.000026

0.003324

HbA1c

rs721346

11103242667

0.000027

0.5054

PDGFD

25

HOMA-IRrs300703

22294160.000027

0.1086

SH3YL12b. Ordered by FBAT p-value

1

HbA1c

rs7719971

5119990475

0.03240.00002

2

HOMA-IR

rs10425253

1936038375

0.00050.00002

3

HOMA-IR

rs10511886

931826555

0.09330.00002

4

Incident DM

rs256962

5114970610

0.00300.00002

TICAM2

5

Fasting Insulin

rs10494321

1154721517

0.00290.00002

KIRREL

6

Incident DM

rs1549415

8120252290

0.03320.00002

7

FPG

rs6910169

6112990680

0.00620.000038

HbA1c

rs2400207

5145360290

0.03050.000039

HbA1c

rs991672

5120002649

0.02720.0000310

ISI_0-120

rs633082

9107992360

0.00680.00004

SH3RF2

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Table 2: Twenty five lowest p-values from GEE and FBAT models and LOD scores > 2 for 100K SNPs and FHS diabetes-related
quantitative traits (Continued)
11

tFPG

rs10496802

2139478604

0.01720.00004

12

FPG

rs7684538

496725483

0.02020.00005

UNC5C
FLVCR

13

Fasting Insulin

rs963328

1209426056

0.29490.00005

14

Incident DM

rs2432961

8120266196

0.00690.00005

15

Incident DM

rs2468168

8120238819

0.04260.0000616

ISI_0-120

rs6594987

5116256211

0.12980.00006

17

HOMA-IR

rs10511885

931821043

0.12500.00007

18

tFPG

rs10487976

7122429976

0.00760.00007

SLC13A1

19

tFPG

rs2204295

7122432041

0.00600.00007

SLC13A1

20

HbA1c

rs9325002

5145406441

0.02950.00007

SH3RF221

HbA1c

rs1365371

8129018405

0.02980.00007

22

HOMA-IR

rs2020362

19360331070.00160.00009

23

Incident DM

rs1489092

376204196

0.05350.0001024

ISI_0-120

rs2942321

519365227

0.47530.0001025

ISI_0-120

rs10501828

1194883857

0.00670.00010Chr

Physical position

Marshfield cM

Max LODCOLEC102c. LOD > 2, Ordered by Lod Score

No.

Trait

SNP or STR

Physical position

Lower boundwhere LOD = 1.5

Upper bound where
LOD = 1.5

1FPG

rs1890843

1207225242

230.9

3.64

205357346

209935673

2

HbA1c

rs1463697

3

195278503

217.5

3.16

191762568

197963623

3

HOMA-IR

rs10513843

3190998205

209.4

3.08

188644318

193634077

4

Fasting
insulinrs4803953

1951650847

70.1

2.98

43726908

56203682

5

HbA1c

rs10510060

10121853460

139.9

2.41

119524854

125827901

6

HOMA-IR

rs10500300

1953439815

73.3

2.36

42063572

57117898

7

tFPG

rs2837076

2139850406

38.7

2.36

34879124

41000937

8

HbA1c

rs10497392

2174176465

177.5

2.30

153549351

177629785

9

tFPG

rs876362

280327060

102.6

2.29

70585709

112151653

10

Fasting
insulinrs10513860

3

191902243

212.8

2.21

187447466

196384998

11

FPG

rs1882347

2164233821

167.3

2.20

146837297

171264176

12

FPG

rs10512296

9102073227

108.4

2.15

91062454

107815119

13

tFPG

ATA20G07

51804310.0

2.10

1804312855065

14

tFPG

rs2444962

1531214059

24.7

2.05

23732660

35462954

15

HbA1c

rs10494382

1159838765

175.2

2.03

157467831

200055293

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BMC Medical Genetics 2007, 8(Suppl 1):S16

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Table 3: Forty two (42) SNPs associated with (FPG, HbA1c, and tFPG) OR (fasting insulin, HOMA-IR, and ISI_0-120) OR (any two of
either) AND incident DM
No.

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42

Chr

12
12
2
8
5
10
13
7
3
3
1
3
12
2
16
3
2
4
20
5
7
8
4
17
4
7
10
16
16
8
18
14
10
5
1
7
5
3
9
17
3
10

SNP

rs1368254
rs10506806
rs10496417
rs10503835
rs459743
rs1879316
rs2066219
rs10487974
rs1878175
rs697957
rs952635
rs10512839
rs4767161
rs1073893
rs10500547
rs1489100
rs2367204
rs10489088
rs6093416
rs871853
rs1355037
rs4418368
rs10516471
rs2322969
rs1395114
rs711517
rs332148
rs2042389
rs7186570
rs9297181
rs540128
rs1954673
rs10509923
rs861085
rs7531174
rs6949530
rs2967017
rs729511
rs1060586
rs2190706
rs509208
rs7089102

N other
SNPs
with r2 >
0.8

135
76
74
8
83
55
79
11
54
32
9
77
84
27
133
29
73
71
86
14
156
82
26
158
28
88
80
136
90
36
85
78
154
35
33
57
137
159
155
157
31
87

Minor
Allele
A/G/T/
C

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

MAF

48.3%
29.7%
34.5%
21.9%
16.9%
13.5%
23.0%
36.9%
11.2%
25.2%
31.3%
25.9%
13.3%
17.7%
16.3%
41.0%
47.4%
12.7%
14.0%
41.5%
23.4%
36.1%
43.6%
46.6%
19.3%
18.4%
18.5%
32.9%
17.2%
19.4%
36.2%
26.4%
33.0%
30.6%
20.6%
18.8%
45.1%
43.8%
49.3%
34.2%
16.8%
47.2%

Gene *

LOC387882
SLC5A7
HMBOX1RASGEF1A
SLC13A1
CD47
PDE4B
CPNE4
RBM19
FLJ32745
AB051533
IMMT
TOP1
CPLX2
ZPBP
DLGAP2
PPP3CA
BX537758
WAC
A2BP1
PHLPP
CSPG6
NUDT12
SLC44A3TAS2R16
SLC9A9
RBM18

Gene
Position

Near
Out
Near
In
Out
Near
Out
Near
Out
Near
In
In
In
In
In
Out
In
Out
Near
In
In
In
In
Out
In
Out
In
Out
In
Out
In
Out
Near
Near
In
Near
Out
In
Near
Out
Out
Out

GEE Mean p-value

FBAT Mean pvalue

3
GlucoseTraits3
InsulinTraits3
GlucoseTraits3
InsulinTraits

0.02
0.003
0.01
0.004
0.009
0.004
0.005
0.02
0.003
0.005
0.0007
0.02
0.19
0.008
0.07
0.01
0.007
0.24
0.01
0.10
0.29
0.08
0.006
0.18
0.004
0.02
0.02
0.10
0.34
0.18
0.38
0.005
0.18
0.008
0.001
0.67
0.65
0.45
0.16
0.48
0.002
0.04

0.001
0.03
0.003
0.03
0.009
0.001
0.0009
0.08
0.003
0.03
0.009
0.02
0.05
0.16
0.06
0.06
0.006
0.22
0.003
0.68
0.07
0.08
0.12
0.006
0.17
0.008
0.01
0.17
0.12
0.08
0.13
0.008
0.002
0.48
0.21
0.83
0.15
0.003
0.0008
0.04
0.06
0.006

0.01
0.01
0.04
0.03
0.03
0.18
0.22
0.002
0.39
0.03
0.06
0.05
0.01
0.003
0.03
0.004
0.27
0.01
0.22
0.001
0.05
0.03
0.02
0.08
0.01
0.26
0.18
0.07
0.01
0.004
0.02
0.35
0.42
0.01
0.09
0.007
0.06
0.14
0.67
0.20
0.64
0.71

0.007
0.01
0.02
0.008
0.02
0.07
0.08
0.02
0.02
0.03
0.41
0.009
0.002
0.06
0.003
0.16
0.05
0.001
0.12
0.02
0.001
0.008
0.10
0.02
0.22
0.05
0.09
0.002
0.008
0.08
0.005
0.43
0.03
0.28
0.68
0.005
0.003
0.13
0.28
0.007
0.53
0.49

Cox pvalue

Minor
AlleleCox
HR forDM

FBATDM
Incidence
p-value

0.007
0.02
0.007
0.005
0.002
0.009
0.009
0.001
0.003
0.001
0.001
0.000
0.15
0.18
0.21
0.09
0.001
0.35
0.00
0.36
0.32
0.27
0.04
0.04
0.001
0.000
0.004
0.003
0.91
0.25
0.83
0.005
0.008
0.001
0.001
0.96
0.82
0.03
0.00
0.00
0.01
0.01

0.67
0.65
1.58
0.59
0.42
0.45
0.59
0.58
0.36
1.65
0.56
1.78
0.63
0.75
1.29
0.75
1.58
1.22
0.35
1.15
1.18
1.18
0.70
1.34
1.79
1.86
0.46
1.54
1.03
0.79
0.97
0.56
0.64
0.52
1.72
0.99
1.04
0.70
1.60
1.55
0.51
1.48

0.0008
0.004
0.11
0.03
0.012
0.48
0.22
0.001
0.12
0.02
0.16
0.03
0.00140.00080.002
0.0015
0.03
0.005
0.02
0.0003
0.006
0.004
0.007
0.003
0.02
0.17
0.03
0.05
0.005
0.003
0.004
0.63
0.20
0.02
0.09
0.009
0.007
0.008
0.99
0.03
0.63
0.86

* Gene symbol and position from UCSC Genome Browser (http://genome.ucsc.edu/; accessed September 2006); SNPs within 60 kb of a known gene are considered 'Near'.

The FHS 100K SNP data resource has potential value todetect and replicate novel type 2 diabetes susceptibility
genes. The 100K SNP array is limited by relatively sparse
coverage in some regions, accounting on average for just
30%–40% of the human genome in whites [17,41]. Association with the risk SNP in TCF7L2 is detectable at p <0.05, but there are no SNPs in adequate LD with ABCC8
or PPARG to assess replication of causal SNPs in these
accepted diabetes susceptibility genes. Thin coverage will
be remedied to a large degree by the incipient availability
in FHS of Affymetrix 500 k SNP array data as part of theplanned FHS SHARe Study. (http://www.nhlbi.nih.gov/
meetings/nhlbac/sept06sum.htm; accessed September2006) Our analysis also demonstrates that true positive
diabetes susceptibility gene signals are likely to be associated with modest p-values and will remain challenging todetect at the stringent p-values required for GWA studies.
The enormous datasets generated by GWA scans have thepotential to greatly advance understanding, or conversely
to overwhelm the field with false leads. SNP prioritization
strategies that leverage the complexity of the diabetes phenotype may offer some advantages over strictly p-value

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BMC Medical Genetics 2007, 8(Suppl 1):S16

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Table 4: FHS 100K SNP Test of Association with SNPs in Established Candidate Genes for Type 2 Diabetes
Candidate
Gene

Candidate
SNP

PhysicalPosition

FHS 100K
SNP

PhysicalPositionr2

GEE lowest
p-value

GEE Trait

FBAT Lowest
p-value

FBAT
Trait

Cox p-value

Cox HRfor DM

ABCC8

rs75711017375053

rs878208

17478662

0.04

0.05

0.12

1.96

17429722
17397049

0.05
0.02

0.11
0.18

0.70
0.47

1.09
0.84

rs2283257
rs2299641

17446021
17397566

0.03
0.01

0.23
0.38

0.05
0.21

Fasting
insulin
HbA1c
Fasting
insulin
ISI_0-120
ISI_0-120

0.02

rs722341
rs916829

0.98
0.19

0.99
1.24

rs2190454

17490211

0.01

0.35

0.25

ISI_0-120

0.48

0.90

rs10510422
rs3856808
rs10510421
rs2938392

12505413
12505184
12502242
12409608

0.00
0.00
0.00
0.03

0.003
0.005
0.006
0.007

0.13
0.17
0.14
0.14

3.86
0.25
0.26
1.13

12526881
12363563

0.00
0.04

0.05
0.07

ISI_0-120
ISI_0-120
ISI_0-120
Fasting
insulin
ISI_0-120
ISI_0-120

0.13
0.11
0.12
0.36

rs709157
rs10510418

0.68
0.46

0.93
0.89

rs1801282
rs1899951
rs4135268
rs10510417
rs2292101

12368125
12369840
12525199
12352294
12409901

1.00
1.00
0.01
0.31
0.00

0.07
0.11
0.11
0.17
0.19

Fasting
insulintFPG
Fasting
insulin
ISI_0-120
Fasting
insulinFasting
insulin
tFPG
tFPG
tFPG
Fasting
insulin
ISI_0-120
Fasting
insulin
ISI_0-120
ISI_0-120
ISI_0-120
ISI_0-120
tFPG

0.89
0.86
0.80
0.62
0.08

0.96
0.96
0.92
0.91
1.62

rs10510419
rs10510410

12401936
12321738

0.01
0.31

0.26
0.38

tFPG
FPG

0.35
0.36

0.24
0.88

1.31
0.97

rs10510411

12321849

0.31

0.40

FPG

0.39

0.94

0.99

rs10510412

12321962

0.31

0.44

FPG

0.38

rs10509967
rs7100927

0.00
0.50

0.04
0.03

HbA1c
tFPG

rs10509966
rs10509969

114685922
11478603
8
114666170
114903549

0.00
0.08

0.04
0.14

rs290483

114905204

0.10

0.17

rs7917983
rs10509970

114722872
114904903

0.09
0.05

0.27
0.32

HbA1c
Fasting
insulinFasting
insulin
tFPG
tFPG

PPARG

TCF7L2rs1801282

rs12255372*
rs7903146*

12368125

114798892
114748339

0.009
0.02

0.10
0.10
0.20
0.25
0.20
0.45
0.22

0.84

1.03

0.12
0.13

HbA1c
HbA1c
tFPG
ISI_0-120
Fasting
insulin
ISI_0-120
Fasting
insulinFasting
insulinFasting
insulin
ISI_0-120
tFPG

0.82
0.007

0.96
1.56

0.07
0.08

ISI_0-120
FPG

0.64
0.60

1.09
0.89

0.34

tFPG

0.93

0.99

0.29
0.43

HbA1c
tFPG

0.17
0.51

0.82
1.14

* LD betweaen rs12255372 and rs7903146 in HapMap CEU: r2 = 0.78; Bold = r2 >= 0.5 or p-value < 0.05

driven approaches. Replication, fine mapping, and functional studies are required to determine which approaches
are most efficient and which SNPs are true positive diabetes risk factors. Integration with other GWA scans in similar cohorts will allow in silico replication of significant
findings, increase power and reveal generalizability.
This report details the FHS contribution to publicly available diabetes-related genetic data. An important key toefficiently and economically achieving adequate power todetect association will be to integrate information fromseveral GWA scans. While several cohorts have beenassembled to perform GWA scans in type 2 diabetes, few
possess the wealth of longitudinal, multigenerational
phenotypic data available in Framingham. The FHS complements extant type 2 diabetes GWA studies. This report
guides the way to harness the power of the FHS 100K SNP
GWA resource to identify type 2 diabetes susceptibility
genes.

Abbreviations
FPG = fasting plasma glucose; FBAT = family-based association test; FHS = Framingham Heart Study; GEE = generalized estimating equations; GWA = Genome-wide
association; HbA1c = hemoglobin A1c; HOMA-IR =homeostasis model insulin resistance; HWE = Hardy
Weinberg equilibrium; IBD = Identity-by-descent; ISI_0120 = 0–120 min insulin sensitivity index; LD = Linkage
disequilibrium; LOD = Log odds score; MAF = Minor
allele frequency; SNP = Single nucleotide polymorphism;TFPG = 28-yr time-averaged FPG.

Authors' contributions
All authors participated in the design and conduct of thestudy and edited and approved the final manuscript. JM
drafted the manuscript and coordinated the study. JM and
CF contributed to FHS diabetes-related phenotyping. JD,
AM, and and LAC coordinated the data management andconducted the statistical analyses. CL prepared traits forPage 8 of 10(page number not for citation purposes)

BMC Medical Genetics 2007, 8(Suppl 1):S16

analyses. JF contributed the multiple-related traits
method for SNP selection and the literature review for
Table 4.

http://www.biomedcentral.com/1471-2350/8/S1/S16

12.

Acknowledgements and Disclosures
Supported by the by the National Heart, Lung, and Blood Institute's Framingham Heart Study (Contract No. N01-HC-25195), the Boston University
Linux Cluster for Genetic Analysis (LinGA) funded by the NIH NCRR
Shared Instrumentation grant (1S10RR163736-01A1), National Center for
Research Resources (NCRR) General Clinical Research Center (GCRC)
M01-RR-01066, and by an American Diabetes Association Career Development Award to Dr. Meigs. Dr. Meigs currently has research grants from
GlaxoSmithKline, Wyeth and Sanofi-aventis, and serves on safety or advisory boards for GlaxoSmithKline, Merck, and Lilly. Dr. Florez is supportedby the NIH Research Career Award K23 DK65978-03. The funding bodies
had no role in the research design and conduct or the decision to publish
this study.
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.

References
1.
2.
3.

4.
5.
6.
7.

8.

9.

10.

11.

King H, Aubert RE, Herman WH: Global burden of diabetes,
1995–2025: prevalence, numerical estimates, and projections. Diabetes Care 1998, 21(9):1414-1431.
Ritz E, Rychlik I, Locatelli F, Halimi S: End-stage renal failure intype 2 diabetes: A medical catastrophe of worldwide dimensions. American Journal of Kidney Diseases 1999, 34(5):795-808.
Poulsen P, Kyvik KO, Vaag A, Beck-Nielsen H: Heritability of type
II (non-insulin-dependent) diabetes mellitus and abnormal
glucose tolerance – a population-based twin study. Diabetologia 1999, 42(2):139-145.
Meigs JB, Cupples LA, Wilson PWF: Parental transmission of type2 diabetes mellitus: the Framingham Offspring Study. Diabetes 2000, 49:2201-2207.
Florez JC, Hirschhorn J, Altshuler D: The inherited basis of diabetes mellitus: implications for the genetic analysis of complex
traits. Annu Rev Genomics Hum Genet 2003, 4:257-291.
Risch N, Merikangas K: The future of genetic studies of complex
human diseases. Science 1996, 273(5281):1516-1517.
Altshuler D, Hirschhorn JN, Klannemark M, Lindgren CM, Vohl MC,
Nemesh J, Lane CR, Schaffner SF, Bolk S, Brewer C, Tuomi T, Gaudet
D, Hudson TJ, Daly M, Groop L, Lander ES: The common PPARgamma Pro12Ala polymorphism is associated withdecreased risk of type 2 diabetes. Nat Genet 2000, 26(1):76-80.
Gloyn AL, Weedon MN, Owen KR, Turner MJ, Knight BA, Hitman G,
Walker M, Levy JC, Sampson M, Halford S, McCarthy MI, Hattersley
AT, Frayling TM: Large-scale association studies of variants ingenes encoding the pancreatic b-cell KATP channel subunits
Kir6.2 (KCNJ11) and SUR1 (ABCC8) confirm that the
KCNJ11 E23K variant is associated with type 2 diabetes. Diabetes 2003, 52(2):568-572.
Florez JC, Burtt N, de Bakker PIW, Almgren P, Tuomi T, Holmkvist J,
Gaudet D, Hudson TJ, Schaffner SF, Daly MJ, Hirschhorn JN, Groop
L, Altshuler D: Haplotype structure and genotype-phenotype
correlations of the sulfonylurea receptor and the islet ATPsensitive potassium channel gene region. Diabetes 2004,
53(5):1360-1368.
Barroso I, Luan J, Middelberg RPS, Harding A-H, Franks PW, Jakes
RW, Clayton D, Schafer AJ, O'Rahilly S, Wareham NJ: Candidate
gene association study in type 2 diabetes indicates a role forgenes involved in β-cell function as well as insulin action. PLoS
Biology 2003, 1(1):41-55.
Grant SF, Thorleifsson G, Reynisdottir I, Benediktsson R, Manolescu
A, Sainz J, Helgason A, Stefansson H, Emilsson V, Helgadottir A, Styrkarsdottir U, Magnusson KP, Walters GB, Palsdottir E, Jonsdottir T,
Gudmundsdottir T, Gylfason A, Saemundsdottir J, Wilensky RL, Reilly
MP, Rader DJ, Bagger Y, Christiansen C, Gudnason V, Sigurdsson G,

13.

14.

15.
16.

17.
18.

19.

20.

21.

22.

23.

24.

25.

26.
27.

Thorsteinsdottir U, Gulcher JR, Kong A, Stefansson K: Variant oftranscription factor 7-like 2 (TCF7L2) gene confers risk oftype 2 diabetes. Nat Genet 2006, 38(3):320-323.
Florez JC, Jablonski KA, Bayley N, Pollin TI, de Bakker PIW, Shuldiner
AR, Knowler WC, Nathan DM, Altshuler D, Group tDPPR: TCF7L2
polymorphisms and progression to diabetes in the Diabetes
Prevention Program. N Engl J Med 2006, 355(3):241-250.
Beysen D, Raes J, Leroy BP, Lucassen A, Yates JRW, Clayton-Smith J,
Ilyina H, Brooks SS, Christin-Maitre S, Fellous M, Fryns JP, Kim JR,
Lapunzina P, Lemyre E, Meire F, Messiaen LM, Oley C, Splitt M, Thomson J, Peer YVd, Veitia RA, De Paepe A, De Baere E: Deletions
involving long-range conserved nongenic sequences
upstream and downstream of FOXL2 as a novel diseasecausing mechanism in blepharophimosis syndrome. Am J
Hum Genet 2005, 77(2):205-218.
Drake JA, Bird C, Nemesh J, Thomas DJ, Newton-Cheh C, Reymond
A, Excoffier L, Attar H, Antonarakis SE, Dermitzakis ET, Hirschhorn
JN: Conserved noncoding sequences are selectively constrained and not mutation cold spots. 2006, 38(2):223-227.
The_International_HapMap_Consortium: A haplotype map of thehuman genome. Nature 2005, 437:1299-1320.
Gabriel SB, Schaffner SF, Nguyen H, Moore JM, Roy J, Blumenstiel B,
Higgins J, DeFelice M, Lochner A, Faggart M, Liu-Cordero SN, Rotimi
C, Adeyemo A, Cooper R, Ward R, Lander ES, Daly MJ, Altshuler D:
The structure of haplotype blocks in the human genome. Science 2002, 296(5576):2225-2229.
Pe'er I, de Bakker PIW, Maller J, Yelensky R, Altshuler D, Daly MJ:
Evaluating and improving power in whole-genome association studies using fixed marker sets. 2006, 38(6):663-667.
Skol AD, Scott LJ, Abecasis GR, Boehnke M: Joint analysis is more
efficient than replication-based analysis for two-stage
genome-wide association studies.
Nat Genet 2006,
38(2):209-213.
Cupples LA, Arruda H, Benjamin EJ, D'Agostino SRB, Demissie S,
DeStefano AL, Dupuis J, Falls K, CS F, Gottlieb DJ, Govindaraju DR,
Guo CY, Heard-Costa N, Hwang SJ, Kathiresan S, Kiel DP, Laramie
JM, Larson MG, Levy D, Liu CY, Lunetta KL, Mailman MD, Manning
AK, Meigs JB, Murabito JM, Newton-Cheh C, O'Connor GT, O'Donnell CJ, Pandey MA, Seshadri S, Vasan RS, Wang ZY, Wilk JB, Wolf
PA, Yang Q, Atwood LD: The Framingham Heart Study 100K
SNP genome-wide association study resource: Overview of
17 phenotype working group reports. BMC Med Genet 2007,
8(Suppl 1):S1.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF,
Turner RC: Homeostasis model assessment: insulin resistanceand beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985, 28(7):412-419.
Gutt M, Davis CL, Spitzer SB, Llabre MM, Kumar M, Czarnecki EM,
Schneiderman N, Skyler JS, Marks JB: Validation of the insulin sensitivity index (ISI(0,120)): comparison with other measures.
Diabetes Res Clin Pract 2000, 47(3):177-184.
Hanley AJ, Williams K, Gonzalez C, D'Agostino RB Jr, Wagenknecht
LE, Stern MP, Haffner SM: Prediction of type 2 diabetes using
simple measures of insulin resistance: combined resultsfrom the San Antonio Heart Study, the Mexico City Diabetes Study, and the Insulin Resistance Atherosclerosis Study.
Diabetes 2003, 52(2):463-469.
Meigs JB, Nathan DM, Wilson PWF, Cupples LA, Singer DE: Metabolic risk factors worsen continuously across the spectrumof nondiabetic glucose tolerance: the Framingham Offspring
Study. Annals of Internal Medicine 1998, 128:524-533.
Meigs JB, D'Agostino RB Sr, Nathan DM, Rifai N, Wilson PW: Longitudinal association of glycemia and microalbuminuria: theFramingham Offspring Study.
Diabetes Care 2002,
25(6):977-983.
Rutter MK, Meigs JB, Sullivan LM, D'Agostino RB Sr, Wilson PW:
Insulin Resistance, the Metabolic Syndrome, and Incident
Cardiovascular Events in The Framingham Offspring Study.
Diabetes 2005, 54:3252-3257.
Horvath S, Wei E, Xu X, Palmer LJ, Baur M: Family-based association test method: age of onset traits and covariates. Genet Epidemiol 2001, 21(Suppl 1):S403-408.
Hanson RL, Ehm MG, Pettitt DJ, Prochazka M, Thompson DB, Timberlake D, Foroud T, Kobes S, Baier L, Burns DK, Almasy L, Blangero
J, Garvey WT, Bennett PH, Knowler WC: An autosomal genomic
scan for loci linked to type II diabetes mellitus and body-

Page 9 of 10(page number not for citation purposes)

BMC Medical Genetics 2007, 8(Suppl 1):S16

28.
29.
30.

31.
32.

33.

34.

35.

36.

37.

38.

39.
40.

41.

mass index in Pima Indians.
Am J Hum Genet 1998,
63(4):1130-1138.
Therneau TM, Grambsch PM: Modeling Survival Data: Extending
the Cox Model. New York: Springer; 2001.
National Center for Biotechnology Information dbGaP website[http://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?id=phs000007]
Karolchik D, Baertsch R, Diekhans M, Furey TS, Hinrichs A, Lu YT,
Roskin KM, Schwartz M, Sugnet CW, Thomas DJ, Weber RJ, Haussler
D, Kent WJ: The UCSC Genome Browser Database. Nucleic
Acids Res 2003, 31(1):51-54.
Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM,
Haussler D: The human genome browser at UCSC. Genome
Res 2002, 12(6):996-1006.
Groves CJ, Zeggini E, Minton J, Frayling TM, Weedon MN, Rayner
NW, Hitman GA, Walker M, Wiltshire S, Hattersley AT, McCarthy
MI: Association analysis of 6,736 U.K. subjects provides replication and confirms TCF7L2 as a type 2 diabetes susceptibility gene with a substantial effect on individual risk. Diabetes
2006, 55(9):2640-2644.
Zhang C, Qi L, Hunter DJ, Meigs JB, Manson JE, van Dam RM, Hu FB:
Variant of Transcription Factor 7-Like 2 (TCF7L2) Gene andthe Risk of Type 2 Diabetes in Large Cohorts of U.S. Womenand Men. Diabetes 2006, 55(9):2645-2648.
Scott LJ, Bonnycastle LL, Willer CJ, Sprau AG, Jackson AU, Narisu N,
Duren WL, Chines PS, Stringham HM, Erdos MR, Valle TT, Tuomilehto J, Bergman RN, Mohlke KL, Collins FS, Boehnke M: Associationof transcription factor 7-like 2 (TCF7L2) variants with type 2
diabetes in a Finnish sample. Diabetes 2006, 55(9):2649-2653.
Damcott CM, Pollin TI, Reinhart LJ, Ott SH, Shen H, Silver KD, Mitchell BD, Shuldiner AR: Polymorphisms in the transcription factor 7-like 2 (TCF7L2) gene are associated with type 2
diabetes in the Amish: Replication and evidence for a role inboth insulin secretion and insulin resistance. Diabetes 2006,
55(9):2654-2659.
Saxena R, Gianniny L, Burtt NP, Lyssenko V, Guiducci C, Sjögren M,
Florez JC, Almgren P, Isomaa B, Orho-Melander M, Lindblad U, Daly
MJ, Tuomi T, Hirschhorn JN, Groop L, Altshuler D: Common SNPs
in TCF7L2 are reproducibly associated with type 2 diabetes
and reduce the insulin response to glucose in non-diabetic
individuals. Diabetes 2006 in press.
Cauchi S, Meyre D, Dina C, Choquet H, Samson C, Gallina S, Balkau
B, Charpentier G, Pattou F, Stetsyuk V, Scharfmann R, Staels B, Fruhbeck G, Froguel P: Transcription factor TCF7L2 genetic study
in the French population: expression in human beta-cells andadipose tissue and strong association with type 2 diabetes.
Diabetes 2006, 55(10):2903-2908.
van Vliet-Ostaptchouk JV, Shiri-Sverdlov R, Zhernakova A, Strengman
E, van Haeften TW, Hofker MH, Wijmenga C: Association of variants of transcription factor 7-like 2 (TCF7L2) with susceptibility to type 2 diabetes in the Dutch Breda cohort.
Diabetologia 2006.
Field SF, Howson JM, Smyth DJ, Walker NM, Dunger DB, Todd JA:
Analysis of the type 2 diabetes gene, TCF7L2, in 13,795 type1 diabetes cases and control subjects. Diabetologia 2006.
Humphries SE, Gable D, Cooper JA, Ireland H, Stephens JW, Hurel
SJ, Li KW, Palmen J, Miller MA, Cappuccio FP, Elkeles R, Godsland I,
Miller GJ, Talmud PJ: Common variants in the TCF7L2 geneand predisposition to type 2 diabetes in UK European
Whites, Indian Asians and Afro-Caribbean men and women.
J Mol Med 2006.
Nicolae DL, Wen X, Voight BF, Cox NJ: Coverage and characteristics of the Affymetrix GeneChip Human Mapping 100K
SNP set. PLoS Genet 2006, 2(5):e67.

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