Volume 8 Supplement 1

The Framingham Heart Study 100,000 single nucleotide polymorphisms resource

Open Access

Genome-wide association with diabetes-related traits in the Framingham Heart Study

  • James B Meigs1Email author,
  • Alisa K Manning2,
  • Caroline S Fox3,
  • Jose C Florez4,
  • Chunyu Liu2,
  • L Adrienne Cupples2 and
  • Josée Dupuis2
BMC Medical Genetics20078(Suppl 1):S16

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

Published: 19 September 2007

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 with risk 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.

Background

Type 2 diabetes is a cause of poor health and early death that 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 of diabetes has been elusive [37].

Among type 2 diabetes susceptibility genes few, if any, individual loci are expected to carry alleles of major effect explaining 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 were common and the increase in risk were modest [6, 7]. Such genes 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], the E23K polymorphism in the gene encoding the islet ATP-dependent potassium channel Kir6.2 (ABCC8-KCNJ11) [810] and common variants in the gene encoding the transcription factor 7-like 2 gene (TCF7L2) [11, 12] were all found using well-powered association mapping, and all have been reproducibly associated with diabetes in diverse 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 across coding and conserved non-coding genomic regions using an association analysis approach, where power is superior to linkage analysis when seeking common variants of modest effect [6]. Resources are now becoming available to perform such genome-wide association (GWA) studies of type 2 diabetes [1518].

In this report we describe the Framingham Heart Study (FHS) Affymetrix 100K SNP genome-wide association (GWA) study resource for type 2 diabetes. This resource complements the several other large extant type 2 diabetes GWA studies in three major respects: it is population-based (not diabetes proband-based), studies two generations, and has decades of longitudinal, standardized, detailed follow-up. We describe results of a simple low p-value-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 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 the mean 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 are described 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. Diabetes-related 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 of the 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 most recent 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.
Table 1

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

Trait

Number of traits

Offspring Exam Cycle

Cohort Exam Cycle

Adjustment *

Number with Genotype and Trait Levels †

Fasting plasma glucose (FPG)

1

5, 7

-

age, age2 age, age2, BMI

1,027

Hemoglobin A1c (HbA1c)

1

5, 7

-

age, age2 age, age2, BMI

623

28 yr time averaged FPG (tFPG)

1

1–7

-

age, age2 age, age2, BMI

1,087

Fasting insulin

1

5, 7

-

age, age2 age, age2, BMI

982

Homeostasis model insulin resistance (HOMA-IR)

1

5

-

age, age2 age, age2, BMI

980

0–120 min insulin sensitivity (ISI_0-120)

1

5

-

age, age2 age, age2, BMI

935

Incident type 2 diabetes

1

1–7

-

age, age2 age, age2, BMI

91 with diabetes

1,083 without diabetes

Adiponectin

1

7

-

age, age2 age, age2, BMI

828

Resistin

1

7

-

age, age2 age, age2, BMI

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

In this presentation we focus on six (three glucose and three 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 informative subjects with respect to hyperglycemia. Subjects with diabetes 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 by insulin treatment [2022]. We also analyzed incident diabetes from first exam through last follow-up. We previously have described FHS laboratory methods for these diabetes-related quantitative traits [4, 2325]. In addition to glucose and insulin traits, levels of adiponectin and resistin 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 potentially associated with diabetes or diabetes related traits. In the first, we simply ordered SNPs from lowest to highest p-value 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 three glucose traits OR all three insulin-related traits OR (two glucose and two insulin traits). Among these we used extent of LD to select a non-redundant set of SNPs; when several 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 and FBAT 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 to measure the age-of-onset of type 2 diabetes; residuals were analyzed with FBAT [26]. Individuals with lower values of this 'martingale residual' trait developed diabetes at younger ages, and those with the highest values had been observed for the longest time without development of diabetes [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-age2-adjusted or age-age2-BMI-adjusted residuals from sex-specific models. In this analysis we only consider the age-age2-adjusted traits. Among these, the following were the primary 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. Additional diabetes-related quantitative traits not used in this 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 and 42 significant associations with age-of-onset in FBAT, representing 128 non-overlapping SNPs. The 25 SNPs with lowest p-values in GEE or FBAT models, and LOD scores > 2.0 in linkage analyses, are displayed in Table 2. After accounting for the overlap between sets of significant associations, 736 non-overlapping SNPs were identified by the p-value approach for SNP prioritization.
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

8

40603396

0.00000002

0.0047

ZMAT4

 

2

Incident DM

rs10497721

2

192739868

0.0000007

0.0346

TMEFF2

 

3

Fasting Insulin

rs2877832

14

26870017

0.000002

0.0770

  

4

HOMA-IR

rs2877832

14

26870017

0.000003

0.0918

  

5

tFPG

rs10510634

3

30321972

0.000005

0.0516

  

6

FPG

rs180730

4

122159395

0.000005

0.0374

PRDM5

 

7

tFPG

rs180730

4

122159395

0.000006

0.0252

PRDM5

 

8

tFPG

rs7731657

5

129971218

0.000007

0.0015

  

9

HbA1c

rs10486607

7

28957729

0.000008

0.0440

CPVL

 

10

ISI_0-120

rs2066219

13

68428665

0.000009

0.0245

  

11

FPG

rs2722425

8

40603396

0.000009

0.0998

ZMAT4

 

12

Incident DM

rs2195499

2

41778050

0.000011

0.3860

  

13

Incident DM

rs830604

3

71673037

0.000014

0.5914

FOXP1

 

14

FPG

rs2377689

2

106924358

0.000017

0.0015

ST6GAL2

 

15

Incident DM

rs931567

3

31410581

0.000018

0.0095

  

16

FPG

rs10494331

1

156395176

0.000019

0.0369

APCS

 

17

tFPG

rs7147624

14

64935378

0.000019

0.0201

FUT8

 

18

tFPG

rs931567

3

31410581

0.000019

0.0189

  

19

Incident DM

rs10511182

3

102255525

0.000020

0.0510

  

20

FPG

rs337112

5

122556671

0.000022

0.0148

  

21

ISI_0-120

rs9319109

13

84510270

0.000025

0.0048

  

22

HOMA-IR

rs1927384

13

101943751

0.000026

0.0059

  

23

ISI_0-120

rs7139897

13

107879625

0.000026

0.0033

  

24

HbA1c

rs721346

11

103242667

0.000027

0.5054

PDGFD

 

25

HOMA-IR

rs300703

2

229416

0.000027

0.1086

SH3YL1

 

2b. Ordered by FBAT p-value

1

HbA1c

rs7719971

5

119990475

0.0324

0.00002

  

2

HOMA-IR

rs10425253

19

36038375

0.0005

0.00002

  

3

HOMA-IR

rs10511886

9

31826555

0.0933

0.00002

  

4

Incident DM

rs256962

5

114970610

0.0030

0.00002

TICAM2

 

5

Fasting Insulin

rs10494321

1

154721517

0.0029

0.00002

KIRREL

 

6

Incident DM

rs1549415

8

120252290

0.0332

0.00002

  

7

FPG

rs6910169

6

112990680

0.0062

0.00003

  

8

HbA1c

rs2400207

5

145360290

0.0305

0.00003

SH3RF2

 

9

HbA1c

rs991672

5

120002649

0.0272

0.00003

  

10

ISI_0-120

rs633082

9

107992360

0.0068

0.00004

  

11

tFPG

rs10496802

2

139478604

0.0172

0.00004

  

12

FPG

rs7684538

4

96725483

0.0202

0.00005

UNC5C

 

13

Fasting Insulin

rs963328

1

209426056

0.2949

0.00005

FLVCR

 

14

Incident DM

rs2432961

8

120266196

0.0069

0.00005

  

15

Incident DM

rs2468168

8

120238819

0.0426

0.00006

COLEC10

 

16

ISI_0-120

rs6594987

5

116256211

0.1298

0.00006

  

17

HOMA-IR

rs10511885

9

31821043

0.1250

0.00007

  

18

tFPG

rs10487976

7

122429976

0.0076

0.00007

SLC13A1

 

19

tFPG

rs2204295

7

122432041

0.0060

0.00007

SLC13A1

 

20

HbA1c

rs9325002

5

145406441

0.0295

0.00007

SH3RF2

 

21

HbA1c

rs1365371

8

129018405

0.0298

0.00007

  

22

HOMA-IR

rs2020362

19

36033107

0.0016

0.00009

  

23

Incident DM

rs1489092

3

76204196

0.0535

0.00010

  

24

ISI_0-120

rs2942321

5

19365227

0.4753

0.00010

  

25

ISI_0-120

rs10501828

11

94883857

0.0067

0.00010

  

2c. LOD > 2, Ordered by Lod Score

No.

Trait

SNP or STR

Chr

Physical position

Marshfield cM

Max LOD

Physical position

       

Lower bound where LOD = 1.5

Upper bound where LOD = 1.5

1

FPG

rs1890843

1

207225242

230.9

3.64

205357346

209935673

2

HbA1c

rs1463697

3

195278503

217.5

3.16

191762568

197963623

3

HOMA-IR

rs10513843

3

190998205

209.4

3.08

188644318

193634077

4

Fasting insulin

rs4803953

19

51650847

70.1

2.98

43726908

56203682

5

HbA1c

rs10510060

10

121853460

139.9

2.41

119524854

125827901

6

HOMA-IR

rs10500300

19

53439815

73.3

2.36

42063572

57117898

7

tFPG

rs2837076

21

39850406

38.7

2.36

34879124

41000937

8

HbA1c

rs10497392

2

174176465

177.5

2.30

153549351

177629785

9

tFPG

rs876362

2

80327060

102.6

2.29

70585709

112151653

10

Fasting insulin

rs10513860

3

191902243

212.8

2.21

187447466

196384998

11

FPG

rs1882347

2

164233821

167.3

2.20

146837297

171264176

12

FPG

rs10512296

9

102073227

108.4

2.15

91062454

107815119

13

tFPG

ATA20G07

5

180431

0.0

2.10

180431

2855065

14

tFPG

rs2444962

15

31214059

24.7

2.05

23732660

35462954

15

HbA1c

rs10494382

1

159838765

175.2

2.03

157467831

200055293

The FHS has multiple measures of diabetes-related quantitative traits. We used a multiple-related trait approach in a strategy different from prioritizing SNPs based solely on small p-values. This approach yielded 203 SNPs associated with multiple traits. Of these, 53 were also associated with incident diabetes (p < 0.01 by GEE or FBAT). We defined redundant SNPs as those in LD with r2 >= 0.80 to select 168 non-redundant SNPs associated with multiple traits; 42 of these non-redundant SNPs also were associated with incident diabetes (Table 3). Examination of the multiple 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 (suggesting that the association signal may be due to a functional role of that gene rather than a statistical fluctuation); and 3) associations of traits with SNPs in a variety of novel but plausible biological candidate genes.
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.

Chr

SNP

N other SNPs with r2 > 0.8

Minor Allele A/G/T/C

MAF

Gene *

Gene Position

GEE Mean p-value

FBAT Mean p-value

Cox p-value

Minor Allele Cox HR for DM

FBAT DM Incidence p-value

        

3 Glucose Traits

3 Insulin Traits

3 Glucose Traits

3 Insulin Traits

   

1

12

rs1368254

135

G

48.3%

LOC387882

Near

0.02

0.001

0.01

0.007

0.007

0.67

0.0008

2

12

rs10506806

76

T

29.7%

 

Out

0.003

0.03

0.01

0.01

0.02

0.65

0.004

3

2

rs10496417

74

A

34.5%

SLC5A7

Near

0.01

0.003

0.04

0.02

0.007

1.58

0.11

4

8

rs10503835

8

C

21.9%

HMBOX1

In

0.004

0.03

0.03

0.008

0.005

0.59

0.03

5

5

rs459743

83

C

16.9%

 

Out

0.009

0.009

0.03

0.02

0.002

0.42

0.012

6

10

rs1879316

55

A

13.5%

RASGEF1A

Near

0.004

0.001

0.18

0.07

0.009

0.45

0.48

7

13

rs2066219

79

G

23.0%

 

Out

0.005

0.0009

0.22

0.08

0.009

0.59

0.22

8

7

rs10487974

11

A

36.9%

SLC13A1

Near

0.02

0.08

0.002

0.02

0.001

0.58

0.001

9

3

rs1878175

54

G

11.2%

 

Out

0.003

0.003

0.39

0.02

0.003

0.36

0.12

10

3

rs697957

32

T

25.2%

CD47

Near

0.005

0.03

0.03

0.03

0.001

1.65

0.02

11

1

rs952635

9

G

31.3%

PDE4B

In

0.0007

0.009

0.06

0.41

0.001

0.56

0.16

12

3

rs10512839

77

C

25.9%

CPNE4

In

0.02

0.02

0.05

0.009

0.000

1.78

0.03

13

12

rs4767161

84

A

13.3%

RBM19

In

0.19

0.05

0.01

0.002

0.15

0.63

0.0014

14

2

rs1073893

27

A

17.7%

FLJ32745

In

0.008

0.16

0.003

0.06

0.18

0.75

0.0008

15

16

rs10500547

133

G

16.3%

AB051533

In

0.07

0.06

0.03

0.003

0.21

1.29

0.002

16

3

rs1489100

29

G

41.0%

 

Out

0.01

0.06

0.004

0.16

0.09

0.75

0.0015

17

2

rs2367204

73

G

47.4%

IMMT

In

0.007

0.006

0.27

0.05

0.001

1.58

0.03

18

4

rs10489088

71

C

12.7%

 

Out

0.24

0.22

0.01

0.001

0.35

1.22

0.005

19

20

rs6093416

86

A

14.0%

TOP1

Near

0.01

0.003

0.22

0.12

0.00

0.35

0.02

20

5

rs871853

14

G

41.5%

CPLX2

In

0.10

0.68

0.001

0.02

0.36

1.15

0.0003

21

7

rs1355037

156

C

23.4%

ZPBP

In

0.29

0.07

0.05

0.001

0.32

1.18

0.006

22

8

rs4418368

82

T

36.1%

DLGAP2

In

0.08

0.08

0.03

0.008

0.27

1.18

0.004

23

4

rs10516471

26

G

43.6%

PPP3CA

In

0.006

0.12

0.02

0.10

0.04

0.70

0.007

24

17

rs2322969

158

C

46.6%

 

Out

0.18

0.006

0.08

0.02

0.04

1.34

0.003

25

4

rs1395114

28

A

19.3%

BX537758

In

0.004

0.17

0.01

0.22

0.001

1.79

0.02

26

7

rs711517

88

G

18.4%

 

Out

0.02

0.008

0.26

0.05

0.000

1.86

0.17

27

10

rs332148

80

T

18.5%

WAC

In

0.02

0.01

0.18

0.09

0.004

0.46

0.03

28

16

rs2042389

136

T

32.9%

 

Out

0.10

0.17

0.07

0.002

0.003

1.54

0.05

29

16

rs7186570

90

G

17.2%

A2BP1

In

0.34

0.12

0.01

0.008

0.91

1.03

0.005

30

8

rs9297181

36

A

19.4%

 

Out

0.18

0.08

0.004

0.08

0.25

0.79

0.003

31

18

rs540128

85

A

36.2%

PHLPP

In

0.38

0.13

0.02

0.005

0.83

0.97

0.004

32

14

rs1954673

78

A

26.4%

 

Out

0.005

0.008

0.35

0.43

0.005

0.56

0.63

33

10

rs10509923

154

C

33.0%

CSPG6

Near

0.18

0.002

0.42

0.03

0.008

0.64

0.20

34

5

rs861085

35

T

30.6%

NUDT12

Near

0.008

0.48

0.01

0.28

0.001

0.52

0.02

35

1

rs7531174

33

C

20.6%

SLC44A3

In

0.001

0.21

0.09

0.68

0.001

1.72

0.09

36

7

rs6949530

57

T

18.8%

TAS2R16

Near

0.67

0.83

0.007

0.005

0.96

0.99

0.009

37

5

rs2967017

137

T

45.1%

 

Out

0.65

0.15

0.06

0.003

0.82

1.04

0.007

38

3

rs729511

159

A

43.8%

SLC9A9

In

0.45

0.003

0.14

0.13

0.03

0.70

0.008

39

9

rs1060586

155

T

49.3%

RBM18

Near

0.16

0.0008

0.67

0.28

0.00

1.60

0.99

40

17

rs2190706

157

T

34.2%

 

Out

0.48

0.04

0.20

0.007

0.00

1.55

0.03

41

3

rs509208

31

C

16.8%

 

Out

0.002

0.06

0.64

0.53

0.01

0.51

0.63

42

10

rs7089102

87

G

47.2%

 

Out

0.04

0.006

0.71

0.49

0.01

1.48

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'.

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 the 70,987 SNPs included in this analysis, 25,916 (36.5%) were in genes, 14,333 (20.2%) were within 60 kb of a known gene and 421 (0.59%) were coding.

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 in both 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 nominally associated 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 or near 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.
Table 4

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

Candidate Gene

Candidate SNP

Physical Position

FHS 100K SNP

Physical Position

r2

GEE lowest p-value

GEE Trait

FBAT Lowest p-value

FBAT Trait

Cox p-value

Cox HR for DM

ABCC8

rs757110

17375053

rs878208

17478662

0.04

0.05

Fasting insulin

0.12

Fasting insulin

0.02

1.96

   

rs722341

17429722

0.05

0.11

tFPG

0.009

HbA1c

0.70

1.09

   

rs916829

17397049

0.02

0.18

Fasting insulin

0.02

Fasting insulin

0.47

0.84

   

rs2283257

17446021

0.03

0.23

ISI_0-120

0.05

ISI_0-120

0.98

0.99

   

rs2299641

17397566

0.01

0.38

Fasting insulin

0.21

ISI_0-120

0.19

1.24

   

rs2190454

17490211

0.01

0.35

Fasting insulin

0.25

ISI_0-120

0.48

0.90

PPARG

rs1801282

12368125

rs10510422

12505413

0.00

0.003

tFPG

0.13

ISI_0-120

0.13

3.86

   

rs3856808

12505184

0.00

0.005

tFPG

0.17

ISI_0-120

0.11

0.25

   

rs10510421

12502242

0.00

0.006

tFPG

0.14

ISI_0-120

0.12

0.26

   

rs2938392

12409608

0.03

0.007

Fasting insulin

0.14

Fasting insulin

0.36

1.13

   

rs709157

12526881

0.00

0.05

ISI_0-120

0.10

ISI_0-120

0.68

0.93

   

rs10510418

12363563

0.04

0.07

Fasting insulin

0.10

ISI_0-120

0.46

0.89

   

rs1801282

12368125

1.00

0.07

ISI_0-120

0.20

HbA1c

0.89

0.96

   

rs1899951

12369840

1.00

0.11

ISI_0-120

0.25

HbA1c

0.86

0.96

   

rs4135268

12525199

0.01

0.11

ISI_0-120

0.20

tFPG

0.80

0.92

   

rs10510417

12352294

0.31

0.17

ISI_0-120

0.45

ISI_0-120

0.62

0.91

   

rs2292101

12409901

0.00

0.19

tFPG

0.22

Fasting insulin

0.08

1.62

   

rs10510419

12401936

0.01

0.26

tFPG

0.35

ISI_0-120

0.24

1.31

   

rs10510410

12321738

0.31

0.38

FPG

0.36

Fasting insulin

0.88

0.97

   

rs10510411

12321849

0.31

0.40

FPG

0.39

Fasting insulin

0.94

0.99

   

rs10510412

12321962

0.31

0.44

FPG

0.38

Fasting insulin

0.84

1.03

 

rs12255372*

114798892

rs10509967

114685922

0.00

0.04

HbA1c

0.12

ISI_0-120

0.82

0.96

TCF7L2

rs7903146*

114748339

rs7100927

114786038

0.50

0.03

tFPG

0.13

tFPG

0.007

1.56

   

rs10509966

114666170

0.00

0.04

HbA1c

0.07

ISI_0-120

0.64

1.09

   

rs10509969

114903549

0.08

0.14

Fasting insulin

0.08

FPG

0.60

0.89

   

rs290483

114905204

0.10

0.17

Fasting insulin

0.34

tFPG

0.93

0.99

   

rs7917983

114722872

0.09

0.27

tFPG

0.29

HbA1c

0.17

0.82

   

rs10509970

114904903

0.05

0.32

tFPG

0.43

tFPG

0.51

1.14

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

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 selected associations 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 and women 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 to resolve true diabetes risk genes. The FHS 100K data replicate the otherwise widely-replicated TCF7L2 association with diabetes [11, 12, 3240] in an in silico analysis.

The FHS 100K SNP data resource has potential value to detect 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 the planned FHS SHARe Study. (http://www.nhlbi.nih.gov/meetings/nhlbac/sept06sum.htm; accessed September 2006) Our analysis also demonstrates that true positive diabetes susceptibility gene signals are likely to be associated with modest p-values and will remain challenging to detect at the stringent p-values required for GWA studies. The enormous datasets generated by GWA scans have the potential 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 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 to efficiently and economically achieving adequate power to detect association will be to integrate information from several GWA scans. While several cohorts have been assembled 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_0-120: 

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.

Declarations

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 supported by 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 are available online at http://www.biomedcentral.com/1471-2350/8?issue=S1.

Authors’ Affiliations

(1)
General Medicine Division, Department of Medicine, Massachusetts General Hospital and Harvard Medical School
(2)
Department of Biostatistics, Boston University School of Public Health
(3)
The Division of Endocrinology, Diabetes, and Hypertension, Brigham and Women's Hospital, Harvard Medical School
(4)
Diabetes Unit, Department of Medicine and Center for Human Genetic Research, Massachusetts General Hospital and Harvard Medical School
(5)
the National Heart, Lung, and Blood Institute's Framingham Heart Study
(6)
Program in Medical and Population Genetics, Broad Institute of Harvard and MIT

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