OBJECTIVE

To test if knowledge of type 2 diabetes genetic variants improves disease prediction.

RESEARCH DESIGN AND METHODS

We tested 40 single nucleotide polymorphisms (SNPs) associated with diabetes in 3,471 Framingham Offspring Study subjects followed over 34 years using pooled logistic regression models stratified by age (<50 years, diabetes cases = 144; or ≥50 years, diabetes cases = 302). Models included clinical risk factors and a 40-SNP weighted genetic risk score.

RESULTS

In people <50 years of age, the clinical risk factors model C-statistic was 0.908; the 40-SNP score increased it to 0.911 (P = 0.3; net reclassification improvement (NRI): 10.2%, P = 0.001). In people ≥50 years of age, the C-statistics without and with the score were 0.883 and 0.884 (P = 0.2; NRI: 0.4%). The risk per risk allele was higher in people <50 than ≥50 years of age (24 vs. 11%; P value for age interaction = 0.02).

CONCLUSIONS

Knowledge of common genetic variation appropriately reclassifies younger people for type 2 diabetes risk beyond clinical risk factors but not older people.

A genetic risk score built with 18 type 2 diabetes genetic loci predicted new diabetes cases (1), though it did not add to common diabetes clinical risk factors that usually appear during adulthood (1,3). In recent years, the number of genetic loci convincingly associated with diabetes has doubled (4,,,,,10). Here, we test two hypotheses: an updated genetic risk score incorporating a larger number of common diabetes-associated single nucleotide polymorphisms (SNPs) improves ∼8-year risk prediction of diabetes beyond common clinical diabetes risk factors; and the predictive ability is better in younger subjects in whom early preventive strategies could delay diabetes onset (11).

We have previously described the methods (1). We pooled data of the Framingham Offspring Study (12) into four time periods (exams 1 and 2, 2 to 4, 4 to 6, and 6 to 8) (3), extending follow-up 6 years beyond our previous report (1). We generated 11,358 person-observations for 3,471 subjects with available genetic data. We excluded prevalent diabetes at the baseline of each period. Diabetes was defined as fasting plasma glucose >7.0 mmol/l (>125 mg/dl) or use of antidiabetic therapy.

We genotyped or imputed 40 autosomal diabetes-SNPs reported in European-origin populations (4,,,,,10), thus adding 23 new SNPs and excluding INS from our previous 18-SNP analysis (1). Genotypes were obtained from Affymetrix array data available in the Framingham Heart Study SNP Health Associate Resource dataset (13) or from de novo genotyping on the iPLEX (Sequenom) platform. Minimum call rates were 97% for Affymetrix and 96.9% for iPLEX SNPs. All SNPs were in Hardy-Weinberg equilibrium. Median variance ratio for the imputed SNPs was 0.94; only for rs725210 at HNF1B, the variance ratio was <0.3 (namely, 0.2).

We modeled the 40 SNPs by constructing a 40-SNP weighted genetic risk score based on the published β coefficients (8,10) (see footnote, Table 1) and alternatively by entering one term per SNP in an additive model using the expected or observed number of minor alleles plus terms for sex or clinical variables. A general nonadditive genetic model was also fit for each SNP, but inclusion of a nonadditive term did not improve the fit (P > 0.043 for all SNPs). We also performed bootstrap resampling with replacement to assess the degree of statistical overestimation.

Table 1

Odds ratios (ORs) and risk for incident type 2 diabetes associated with 40 individual SNPs, a weighted 40-SNP genetic risk score, and a weighted 17-SNP genetic risk score in the Framingham Offspring Study, stratified by age (<50 years and ≥50 years old), in the simple clinical variables–adjusted model

Subjects <50 years old (n = 144 diabetes cases)
Model without genetic informationModel using 40 individual SNPsModel using 40-SNP weighted risk scoreModel using prior 17-SNP weighted risk score
Men (vs. women) 0.45 (0.30–0.68) 0.43 (0.28–0.67) 0.46 (0.30–0.70) 0.46 (0.30–0.70) 
Family history of diabetes vs. not 2.26 (1.55–3.30) 2.22 (1.49–3.29) 2.20 (1.50–3.22) 2.18 (1.49–3.19) 
BMI (kg/m21.10 (1.06–1.14) 1.11 (1.07–1.15) 1.11 (1.07–1.15) 1.11 (1.08–1.15) 
Fasting plasma glucose (mg/dl) 1.14 (1.11–1.16) 1.13 (1.11–1.16) 1.13 (1.11–1.16) 1.13 (1.11–1.16) 
Systolic blood pressure (mmHg) 1.02 (1.01–1.03) 1.03 (1.01–1.04) 1.02 (1.01–1.03) 1.02 (1.01–1.03) 
HDL cholesterol (mg/dl) 0.96 (0.95–0.98) 0.96 (0.95–0.98) 0.96 (0.95–0.98) 0.96 (0.95–0.98) 
Fasting triglycerides (mg/dl) 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00–1.02) 1.00 (1.00–1.00) 
Genetic risk score   1.24 (1.13–1.36) 1.39 (1.22–1.59) 
C-statistic (95% CI) 0.908 (0.884–0.932) 0.920 (0.898–0.941) 0.911 (0.887–0.935) 0.909 (0.884–0.933) 
P value for difference in C-statistic  0.02 0.3 0.89 
Calibration χ2 (P value)  4.37 (0.8) 6.60 (0.6) 9.78 (0.28) 
NRI (%)  11.4 10.2 7.5 
P value  0.002 0.001 0.01 
Subjects <50 years old (n = 144 diabetes cases)
Model without genetic informationModel using 40 individual SNPsModel using 40-SNP weighted risk scoreModel using prior 17-SNP weighted risk score
Men (vs. women) 0.45 (0.30–0.68) 0.43 (0.28–0.67) 0.46 (0.30–0.70) 0.46 (0.30–0.70) 
Family history of diabetes vs. not 2.26 (1.55–3.30) 2.22 (1.49–3.29) 2.20 (1.50–3.22) 2.18 (1.49–3.19) 
BMI (kg/m21.10 (1.06–1.14) 1.11 (1.07–1.15) 1.11 (1.07–1.15) 1.11 (1.08–1.15) 
Fasting plasma glucose (mg/dl) 1.14 (1.11–1.16) 1.13 (1.11–1.16) 1.13 (1.11–1.16) 1.13 (1.11–1.16) 
Systolic blood pressure (mmHg) 1.02 (1.01–1.03) 1.03 (1.01–1.04) 1.02 (1.01–1.03) 1.02 (1.01–1.03) 
HDL cholesterol (mg/dl) 0.96 (0.95–0.98) 0.96 (0.95–0.98) 0.96 (0.95–0.98) 0.96 (0.95–0.98) 
Fasting triglycerides (mg/dl) 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00–1.02) 1.00 (1.00–1.00) 
Genetic risk score   1.24 (1.13–1.36) 1.39 (1.22–1.59) 
C-statistic (95% CI) 0.908 (0.884–0.932) 0.920 (0.898–0.941) 0.911 (0.887–0.935) 0.909 (0.884–0.933) 
P value for difference in C-statistic  0.02 0.3 0.89 
Calibration χ2 (P value)  4.37 (0.8) 6.60 (0.6) 9.78 (0.28) 
NRI (%)  11.4 10.2 7.5 
P value  0.002 0.001 0.01 
Subjects ≥50 years old (n = 302 diabetes cases)
Model without genetic informationModel using 40 individual SNPsModel using 40-SNP weighted risk scoreModel using prior 17-SNP weighted risk score
Men (vs. women) 1.03 (0.76–1.38) 1.04 (0.76–1.41) 1.05 (0.78–1.41) 1.05 (0.78–1.41) 
Family history of diabetes vs. not 2.09 (1.54–2.85) 2.18 (1.58–3.00) 2.11 (1.55–2.88) 2.12 (1.56–2.88) 
BMI (kg/m21.08 (1.05–1.11) 1.09 (1.06–1.12) 1.09 (1.06–1.12) 1.09 (1.06–1.12) 
Fasting plasma glucose (mg/dl) 1.14 (1.13–1.16) 1.14 (1.12–1.16) 1.14 (1.12–1.16) 1.14 (1.12–1.16) 
Systolic blood pressure (mmHg) 1.01 (1.00–1.02) 1.01 (1.00–1.02) 1.01 (1.01–1.02) 1.01 (1.01–1.02) 
HDL cholesterol (mg/dl) 0.98 (0.97–0.99) 0.98 (0.97–0.99) 0.98 (0.97–0.99) 0.98 (0.97–0.99) 
Fasting triglycerides (mg/dl) 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00–1.00) 
Genetic risk score   1.11 (1.03–1.19) 1.13 (1.02–1.25) 
C-statistic (95% CI) 0.883 (0.863–0.903) 0.888 (0.869–0.908) 0.884 (0.865–0.904) 0.884 (0.865–0.904) 
P value for difference in C-statistic  0.02 0.2 0.18 
Calibration χ2 (P value)  10.97 (0.2) 15.01 (0.06) 8.46 (0.39) 
NRI (%)  5.7 0.4 0.02% 
P value  0.001 0.7 0.98 
Subjects ≥50 years old (n = 302 diabetes cases)
Model without genetic informationModel using 40 individual SNPsModel using 40-SNP weighted risk scoreModel using prior 17-SNP weighted risk score
Men (vs. women) 1.03 (0.76–1.38) 1.04 (0.76–1.41) 1.05 (0.78–1.41) 1.05 (0.78–1.41) 
Family history of diabetes vs. not 2.09 (1.54–2.85) 2.18 (1.58–3.00) 2.11 (1.55–2.88) 2.12 (1.56–2.88) 
BMI (kg/m21.08 (1.05–1.11) 1.09 (1.06–1.12) 1.09 (1.06–1.12) 1.09 (1.06–1.12) 
Fasting plasma glucose (mg/dl) 1.14 (1.13–1.16) 1.14 (1.12–1.16) 1.14 (1.12–1.16) 1.14 (1.12–1.16) 
Systolic blood pressure (mmHg) 1.01 (1.00–1.02) 1.01 (1.00–1.02) 1.01 (1.01–1.02) 1.01 (1.01–1.02) 
HDL cholesterol (mg/dl) 0.98 (0.97–0.99) 0.98 (0.97–0.99) 0.98 (0.97–0.99) 0.98 (0.97–0.99) 
Fasting triglycerides (mg/dl) 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00–1.00) 1.00 (1.00–1.00) 
Genetic risk score   1.11 (1.03–1.19) 1.13 (1.02–1.25) 
C-statistic (95% CI) 0.883 (0.863–0.903) 0.888 (0.869–0.908) 0.884 (0.865–0.904) 0.884 (0.865–0.904) 
P value for difference in C-statistic  0.02 0.2 0.18 
Calibration χ2 (P value)  10.97 (0.2) 15.01 (0.06) 8.46 (0.39) 
NRI (%)  5.7 0.4 0.02% 
P value  0.001 0.7 0.98 

Data are OR (95% CI) unless otherwise indicated. Data in bold represent statistical significance.

†The simple clinical variables–adjusted model included sex, family history of diabetes (self-report that one or both parents had diabetes), BMI, fasting glucose level, systolic blood pressure, HDL cholesterol, and fasting triglycerides levels (3). No age adjustment was done in the age-stratified models.

To evaluate the individual contribution of each SNP, we entered one term per SNP (total 40 terms plus terms for sex or clinical variables) in the logistic regression models.

We constructed a weighted genetic risk score using 40 SNPs currently associated with type 2 diabetes and a weighted genetic risk score using 17 SNPs that we used in our previous report (1). rs689 at INS on chromosome 11, previously included in our 18-SNP genetic risk score (1), was not replicated in posterior meta-analyses and is therefore not included in the current 17-SNP or 40-SNP analyses. Moreover, rs5945326 at DUSP9 on chromosome X (10) is not included in the analysis because there are no available genotyping or imputation data for this SNP in the Framingham Offspring Study.

For the construction of the weighted risk scores, we counted risk alleles (0, 1, 2) for each genotyped SNP—or its dosage when imputed—(actual distribution ranging from 28 to 53) and multiplied each SNP genotype by its published β coefficient for diabetes risk (10). We added up the product of that multiplication at each SNP, divided the sum by twice the sum of the β coefficients, and multiplied the result by the number of SNPs.

ORs, 95% CIs, and C-statistics for the 144 cases of diabetes in 6,763 person-observations in subjects <50 years old and for the 302 cases of diabetes in 4,595 person-observations in subjects ≥50 years old were calculated using pooled logistic regression with generalized estimating equations. Mean age at diabetes onset was 49.30 years for subjects <50 years old at baseline and 66.07 years for subjects ≥50 years old at baseline. We took 50 years as the age cutoff point because of the low incidence rate of diabetes in younger subjects when lower values were chosen. Sensitivity analyses using a cutoff age of 45 years (84 cases in 5,095 person-observations) showed a lower NRI in younger subjects (3.59%; P = 0.2), though this result should be taken with caution because of the low number of cases.

For NRI evaluation, we established three risk categories (low, intermediate, and high). The percentages of low, medium, and high risk of diabetes are based on the distribution of the cumulative incidence of diabetes across our population, in which cumulative incidence was low for a predicted risk <2%, intermediate for predicted risks ≥2% and ≤8%, and high when predicted risk was >8% (this assumption is an a priori requirement for the NRI calculation) (15). NRI is better if more people who develop diabetes are reclassified as higher risk when the genotype score is added to the model, and more people who remain free of diabetes are classified as lower risk when the score is added. The NRI is penalized for misreclassification; for instance, if many people who develop diabetes are classified as lower risk by adding the genetic risk score to the model.

Data for the sex-adjusted model in age-stratified analyses are shown in supplementary Table A3. Complete data for the population overall are shown in supplementary Table A4.

Association tests were done after age-stratification (<50 and ≥50 years) and in the sample overall. We compared the mean genetic risk score for persons who did develop diabetes with those who did not using mixed-effects linear models to account for family relatedness. Likewise, we used generalized estimating equations in pooled logistic-regression models (14) to test associations of the genetic risk scores with diabetes onset in sex- and simple clinical diabetes risk factors–adjusted models, which included sex, family history of diabetes (self-report that any parent had diabetes), BMI, fasting glucose and triglyceride levels, systolic blood pressure, and HDL cholesterol (3).

We evaluated model discrimination using C-statistics and net reclassification improvement (NRI) (15) (see footnote, Table 1). A two-tailed P value <0.05 indicated statistical significance. The institutional review board at Boston University approved the study, and all participants gave written informed consent.

Mean age was 36 ± 9 years at the first exam; nearly half the subjects were men, and BMI increased over follow-up (supplementary Table A1 in the online appendix available at http://care.diabetesjournals.org/cgi/content/full/dc10-1265/DC1). Over 11,358 person-observations we diagnosed 446 cases of diabetes. Few individual SNPs were significantly associated with diabetes in our sample, but for most SNPs the effects were in the same direction as in the original reports and of expected effect sizes (1.05–1.3) (supplementary Table A2). Individuals who developed diabetes had higher genetic risk scores than those who did not (20.4 vs. 19.7; P = 1.7 × 10−10).

The 40-SNP genetic risk score significantly reclassified subjects <50 years of age in the simple clinical variables model (NRI: 10.2%; P = 0.001), although it did not improve model discrimination (P = 0.3) (Table 1). In subjects ≥50 years, the 40-SNP score neither improved model discrimination (P = 0.2) nor risk reclassification (NRI: 0.4%; P = 0.7). The relative risk per risk allele was higher in subjects <50 years of age (24%) than in those ≥50 years of age (11%) (P = 0.02 for age-interaction effect). Results for the sex-adjusted model are shown in supplementary Table A3.

We also tested a weighted genetic risk score constructed with the originally modeled 17 SNPs (1), whereby fewer subjects were appropriately reclassified for diabetes risk (Table 1).

In the population overall, the 40-SNP genetic risk score marginally improved risk prediction (C-statistics: 0.903 and 0.906, without and with the score; P = 0.04), whereas the 17-SNP score did not (P = 0.11) (supplementary Table A4). In the whole population, NRI with the score was lower than in subjects <50 years of age (at most, 1.8%).

The individual incorporation of 40 SNPs improved model discrimination beyond the 40-SNP score (C-statistics: 0.908 and 0.920 without and with individual SNPs; P = 0.02), but after bootstrap resampling, median C-statistic values dropped to 0.905 and 0.907, respectively, thus lowering optimism about the effect of modeling individual SNPs.

We found that 40 SNPs selected based on the latest genetic association data improved diabetes risk reclassification after accounting for common diabetes clinical risk predictive factors.

The 40 SNPs contributing individually had the highest discrimination ability, but this model was probably overfit. The increased prediction performance of 40 as opposed to 17 SNPs appeared to be due to additional, more comprehensively modeled genetic information rather than to longer follow-up or greater number of diabetes cases as compared to our earlier report.

Limitations include that the Framingham Offspring Study subjects are mostly white and of European ancestry. Although we did not find sufficient evidence for departure from an additive model, we cannot definitely rule out that other nonadditive models are operating. We only analyzed common genetic variants; eventual incorporation of rare variants might enhance prediction. Lastly, criticism has been raised on the somewhat arbitrary assumptions needed to estimate NRI.

In summary, diabetes risk prediction improved with 40 diabetes-associated SNPs, especially in people <50 years of age. More subjects were appropriately reclassified for diabetes risk. Genetic prediction could be useful in younger people. Nonetheless, the clinical usefulness of common genetic variants for diabetes risk prediction should be further confirmed in other samples and in randomized controlled trials.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked “advertisement” in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

This study was supported by the by the National Heart, Lung, and Blood Institute's Framingham Heart Study (contract no. N01-HC- 25195), the National Institute for Diabetes and Digestive and Kidney Diseases (NIDDK) grants R01 DK078616 and K24 DK080140 (to J.B.M.), NIDDK Research Career Award K23 DK65978 (to J.C.F.), NIDDK Grant R21 DK084527 (to R.W.G.), “Bolsa de Ampliación de Estudios” from the “Instituto de Salud Carlos III”, Madrid, Spain (2009/90071) (to J.M.D.M.Y.), and the Boston University Linux Cluster for Genetic Analysis (LinGA) funded by the National Institutes of Health National Center for Research Resources Shared Instrumentation Grant (1S10RR163736-01A1).

J.B.M. has a consulting agreement with Interleukin Genetics, Inc. No other potential conflicts of interest relevant to this article were reported.

J.M.D.M.Y. researched data and wrote the manuscript. P.S. researched data and contributed to discussion. M.J.P., J.D., R.B.D., and L.A.C. researched data, contributed to discussion, and reviewed the manuscript. C.S.F. and A.K.M. researched data and reviewed the manuscript. R.W.G. and J.C.F. contributed to discussion and reviewed the manuscript. J.B.M. contributed to discussion and wrote the manuscript.

Parts of this study were presented in poster form at the 70th Scientific Sessions of the American Diabetes Association, Orlando, Florida, 25–29 June 2010.

1.
Meigs
JB
,
Shrader
P
,
Sullivan
LM
,
McAteer
JB
,
Fox
CS
,
Dupuis
J
,
Manning
AK
,
Florez
JC
,
Wilson
PW
,
D'Agostino
RB
 Sr
,
Cupples
LA
:
Genotype score in addition to common risk factors for prediction of type 2 diabetes
.
N Engl J Med
2008
;
359
:
2208
2219
2.
Lyssenko
V
,
Jonsson
A
,
Almgren
P
,
Pulizzi
N
,
Isomaa
B
,
Tuomi
T
,
Berglund
G
,
Altshuler
D
,
Nilsson
P
,
Groop
L
:
Clinical risk factors, DNA variants, and the development of type 2 diabetes
.
N Engl J Med
2008
;
359
:
2220
2232
3.
Wilson
PW
,
Meigs
JB
,
Sullivan
L
,
Fox
CS
,
Nathan
DM
,
D'Agostino
RB
 Sr
:
Prediction of incident diabetes mellitus in middle-aged adults: the Framingham Offspring Study
.
Arch Intern Med
2007
;
167
:
1068
1074
4.
Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research
,
Saxena
R
,
Voight
BF
,
Lyssenko
V
,
Burtt
NP
,
de Bakker
PI
,
Chen
H
,
Roix
JJ
,
Kathiresan
S
,
Hirschhorn
JN
,
Daly
MJ
,
Hughes
TE
,
Groop
L
,
Altshuler
D
,
Almgren
P
,
Florez
JC
,
Meyer
J
,
Ardlie
K
,
Bengtsson Boström
K
,
Isomaa
B
,
Lettre
G
,
Lindblad
U
,
Lyon
HN
,
Melander
O
,
Newton-Cheh
C
,
Nilsson
P
,
Orho-Melander
M
,
Råstam
L
,
Speliotes
EK
,
Taskinen
MR
,
Tuomi
T
,
Guiducci
C
,
Berglund
A
,
Carlson
J
,
Gianniny
L
,
Hackett
R
,
Hall
L
,
Holmkvist
J
,
Laurila
E
,
Sjögren
M
,
Sterner
M
,
Surti
A
,
Svensson
M
,
Svensson
M
,
Tewhey
R
,
Blumenstiel
B
,
Parkin
M
,
Defelice
M
,
Barry
R
,
Brodeur
W
,
Camarata
J
,
Chia
N
,
Fava
M
,
Gibbons
J
,
Handsaker
B
,
Healy
C
,
Nguyen
K
,
Gates
C
,
Sougnez
C
,
Gage
D
,
Nizzari
M
,
Gabriel
SB
,
Chirn
GW
,
Ma
Q
,
Parikh
H
,
Richardson
D
,
Ricke
D
,
Purcell
S
:
Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels
.
Science
2007
;
316
:
1331
1336
5.
Zeggini
E
,
Scott
LJ
,
Saxena
R
,
Voight
BF
,
Marchini
JL
,
Hu
T
,
de Bakker
PI
,
Abecasis
GR
,
Almgren
P
,
Andersen
G
,
Ardlie
K
,
Boström
KB
,
Bergman
RN
,
Bonnycastle
LL
,
Borch-Johnsen
K
,
Burtt
NP
,
Chen
H
,
Chines
PS
,
Daly
MJ
,
Deodhar
P
,
Ding
CJ
,
Doney
AS
,
Duren
WL
,
Elliott
KS
,
Erdos
MR
,
Frayling
TM
,
Freathy
RM
,
Gianniny
L
,
Grallert
H
,
Grarup
N
,
Groves
CJ
,
Guiducci
C
,
Hansen
T
,
Herder
C
,
Hitman
GA
,
Hughes
TE
,
Isomaa
B
,
Jackson
AU
,
Jørgensen
T
,
Kong
A
,
Kubalanza
K
,
Kuruvilla
FG
,
Kuusisto
J
,
Langenberg
C
,
Lango
H
,
Lauritzen
T
,
Li
Y
,
Lindgren
CM
,
Lyssenko
V
,
Marvelle
AF
,
Meisinger
C
,
Midthjell
K
,
Mohlke
KL
,
Morken
MA
,
Morris
AD
,
Narisu
N
,
Nilsson
P
,
Owen
KR
,
Palmer
CN
,
Payne
F
,
Perry
JR
,
Pettersen
E
,
Platou
C
,
Prokopenko
I
,
Qi
L
,
Qin
L
,
Rayner
NW
,
Rees
M
,
Roix
JJ
,
Sandbaek
A
,
Shields
B
,
Sjögren
M
,
Steinthorsdottir
V
,
Stringham
HM
,
Swift
AJ
,
Thorleifsson
G
,
Thorsteinsdottir
U
,
Timpson
NJ
,
Tuomi
T
,
Tuomilehto
J
,
Walker
M
,
Watanabe
RM
,
Weedon
MN
,
Willer
CJ
Wellcome Trust Case Control Consortium
Illig
T
,
Hveem
K
,
Hu
FB
,
Laakso
M
,
Stefansson
K
,
Pedersen
O
,
Wareham
NJ
,
Barroso
I
,
Hattersley
AT
,
Collins
FS
,
Groop
L
,
McCarthy
MI
,
Boehnke
M
,
Altshuler
D
:
Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes
.
Nat Genet
2008
;
40
:
638
645
6.
Yasuda
K
,
Miyake
K
,
Horikawa
Y
,
Hara
K
,
Osawa
H
,
Furuta
H
,
Hirota
Y
,
Mori
H
,
Jonsson
A
,
Sato
Y
,
Yamagata
K
,
Hinokio
Y
,
Wang
HY
,
Tanahashi
T
,
Nakamura
N
,
Oka
Y
,
Iwasaki
N
,
Iwamoto
Y
,
Yamada
Y
,
Seino
Y
,
Maegawa
H
,
Kashiwagi
A
,
Takeda
J
,
Maeda
E
,
Shin
HD
,
Cho
YM
,
Park
KS
,
Lee
HK
,
Ng
MC
,
Ma
RC
,
So
WY
,
Chan
JC
,
Lyssenko
V
,
Tuomi
T
,
Nilsson
P
,
Groop
L
,
Kamatani
N
,
Sekine
A
,
Nakamura
Y
,
Yamamoto
K
,
Yoshida
T
,
Tokunaga
K
,
Itakura
M
,
Makino
H
,
Nanjo
K
,
Kadowaki
T
,
Kasuga
M
:
Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus
.
Nat Genet
2008
;
40
:
1092
1097
7.
Kong
A
,
Steinthorsdottir
V
,
Masson
G
,
Thorleifsson
G
,
Sulem
P
,
Besenbacher
S
,
Jonasdottir
A
,
Sigurdsson
A
,
Kristinsson
KT
,
Jonasdottir
A
,
Frigge
ML
,
Gylfason
A
,
Olason
PI
,
Gudjonsson
SA
,
Sverrisson
S
,
Stacey
SN
,
Sigurgeirsson
B
,
Benediktsdottir
KR
,
Sigurdsson
H
,
Jonsson
T
,
Benediktsson
R
,
Olafsson
JH
,
Johannsson
OT
,
Hreidarsson
AB
,
Sigurdsson
G
DIAGRAM Consortium
Ferguson-Smith
AC
,
Gudbjartsson
DF
,
Thorsteinsdottir
U
,
Stefansson
K
:
Parental origin of sequence variants associated with complex diseases
.
Nature
2009
;
462
:
868
874
8.
Dupuis
J
,
Langenberg
C
,
Prokopenko
I
,
Saxena
R
,
Soranzo
N
,
Jackson
AU
,
Wheeler
E
,
Glazer
NL
,
Bouatia-Naji
N
,
Gloyn
AL
,
Lindgren
CM
,
Mägi
R
,
Morris
AP
,
Randall
J
,
Johnson
T
,
Elliott
P
,
Rybin
D
,
Thorleifsson
G
,
Steinthorsdottir
V
,
Henneman
P
,
Grallert
H
,
Dehghan
A
,
Hottenga
JJ
,
Franklin
CS
,
Navarro
P
,
Song
K
,
Goel
A
,
Perry
JR
,
Egan
JM
,
Lajunen
T
,
Grarup
N
,
Sparsø
T
,
Doney
A
,
Voight
BF
,
Stringham
HM
,
Li
M
,
Kanoni
S
,
Shrader
P
,
Cavalcanti-Proença
C
,
Kumari
M
,
Qi
L
,
Timpson
NJ
,
Gieger
C
,
Zabena
C
,
Rocheleau
G
,
Ingelsson
E
,
An
P
,
O'Connell
J
,
Luan
J
,
Elliott
A
,
McCarroll
SA
,
Payne
F
,
Roccasecca
RM
,
Pattou
F
,
Sethupathy
P
,
Ardlie
K
,
Ariyurek
Y
,
Balkau
B
,
Barter
P
,
Beilby
JP
,
Ben-Shlomo
Y
,
Benediktsson
R
,
Bennett
AJ
,
Bergmann
S
,
Bochud
M
,
Boerwinkle
E
,
Bonnefond
A
,
Bonnycastle
LL
,
Borch-Johnsen
K
,
Böttcher
Y
,
Brunner
E
,
Bumpstead
SJ
,
Charpentier
G
,
Chen
YD
,
Chines
P
,
Clarke
R
,
Coin
LJ
,
Cooper
MN
,
Cornelis
M
,
Crawford
G
,
Crisponi
L
,
Day
IN
,
de Geus
EJ
,
Delplanque
J
,
Dina
C
,
Erdos
MR
,
Fedson
AC
,
Fischer-Rosinsky
A
,
Forouhi
NG
,
Fox
CS
,
Frants
R
,
Franzosi
MG
,
Galan
P
,
Goodarzi
MO
,
Graessler
J
,
Groves
CJ
,
Grundy
S
,
Gwilliam
R
,
Gyllensten
U
,
Hadjadj
S
,
Hallmans
G
,
Hammond
N
,
Han
X
,
Hartikainen
AL
,
Hassanali
N
,
Hayward
C
,
Heath
SC
,
Hercberg
S
,
Herder
C
,
Hicks
AA
,
Hillman
DR
,
Hingorani
AD
,
Hofman
A
,
Hui
J
,
Hung
J
,
Isomaa
B
,
Johnson
PR
,
Jørgensen
T
,
Jula
A
,
Kaakinen
M
,
Kaprio
J
,
Kesaniemi
YA
,
Kivimaki
M
,
Knight
B
,
Koskinen
S
,
Kovacs
P
,
Kyvik
KO
,
Lathrop
GM
,
Lawlor
DA
,
Le Bacquer
O
,
Lecoeur
C
,
Li
Y
,
Lyssenko
V
,
Mahley
R
,
Mangino
M
,
Manning
AK
,
Martínez-Larrad
MT
,
McAteer
JB
,
McCulloch
LJ
,
McPherson
R
,
Meisinger
C
,
Melzer
D
,
Meyre
D
,
Mitchell
BD
,
Morken
MA
,
Mukherjee
S
,
Naitza
S
,
Narisu
N
,
Neville
MJ
,
Oostra
BA
,
Orrù
M
,
Pakyz
R
,
Palmer
CN
,
Paolisso
G
,
Pattaro
C
,
Pearson
D
,
Peden
JF
,
Pedersen
NL
,
Perola
M
,
Pfeiffer
AF
,
Pichler
I
,
Polasek
O
,
Posthuma
D
,
Potter
SC
,
Pouta
A
,
Province
MA
,
Psaty
BM
,
Rathmann
W
,
Rayner
NW
,
Rice
K
,
Ripatti
S
,
Rivadeneira
F
,
Roden
M
,
Rolandsson
O
,
Sandbaek
A
,
Sandhu
M
,
Sanna
S
,
Sayer
AA
,
Scheet
P
,
Scott
LJ
,
Seedorf
U
,
Sharp
SJ
,
Shields
B
,
Sigurethsson
G
,
Sijbrands
EJ
,
Silveira
A
,
Simpson
L
,
Singleton
A
,
Smith
NL
,
Sovio
U
,
Swift
A
,
Syddall
H
,
Syvänen
AC
,
Tanaka
T
,
Thorand
B
,
Tichet
J
,
Tönjes
A
,
Tuomi
T
,
Uitterlinden
AG
,
van Dijk
KW
,
van Hoek
M
,
Varma
D
,
Visvikis-Siest
S
,
Vitart
V
,
Vogelzangs
N
,
Waeber
G
,
Wagner
PJ
,
Walley
A
,
Walters
GB
,
Ward
KL
,
Watkins
H
,
Weedon
MN
,
Wild
SH
,
Willemsen
G
,
Witteman
JC
,
Yarnell
JW
,
Zeggini
E
,
Zelenika
D
,
Zethelius
B
,
Zhai
G
,
Zhao
JH
,
Zillikens
MC
DIAGRAM Consortium, GIANT Consortium, Global BPgen Consortium
Borecki
IB
,
Loos
RJ
,
Meneton
P
,
Magnusson
PK
,
Nathan
DM
,
Williams
GH
,
Hattersley
AT
,
Silander
K
,
Salomaa
V
,
Smith
GD
,
Bornstein
SR
,
Schwarz
P
,
Spranger
J
,
Karpe
F
,
Shuldiner
AR
,
Cooper
C
,
Dedoussis
GV
,
Serrano-Ríos
M
,
Morris
AD
,
Lind
L
,
Palmer
LJ
,
Hu
FB
,
Franks
PW
,
Ebrahim
S
,
Marmot
M
,
Kao
WH
,
Pankow
JS
,
Sampson
MJ
,
Kuusisto
J
,
Laakso
M
,
Hansen
T
,
Pedersen
O
,
Pramstaller
PP
,
Wichmann
HE
,
Illig
T
,
Rudan
I
,
Wright
AF
,
Stumvoll
M
,
Campbell
H
,
Wilson
JF
Anders Hamsten on behalf of Procardis Consortium, MAGIC investigators
Bergman
RN
,
Buchanan
TA
,
Collins
FS
,
Mohlke
KL
,
Tuomilehto
J
,
Valle
TT
,
Altshuler
D
,
Rotter
JI
,
Siscovick
DS
,
Penninx
BW
,
Boomsma
DI
,
Deloukas
P
,
Spector
TD
,
Frayling
TM
,
Ferrucci
L
,
Kong
A
,
Thorsteinsdottir
U
,
Stefansson
K
,
van Duijn
CM
,
Aulchenko
YS
,
Cao
A
,
Scuteri
A
,
Schlessinger
D
,
Uda
M
,
Ruokonen
A
,
Jarvelin
MR
,
Waterworth
DM
,
Vollenweider
P
,
Peltonen
L
,
Mooser
V
,
Abecasis
GR
,
Wareham
NJ
,
Sladek
R
,
Froguel
P
,
Watanabe
RM
,
Meigs
JB
,
Groop
L
,
Boehnke
M
,
McCarthy
MI
,
Florez
JC
,
Barroso
I
:
New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk
.
Nat Genet
2010
;
42
:
105
116
9.
Qi
L
,
Cornelis
MC
,
Kraft
P
,
Stanya
KJ
,
Linda Kao
WH
,
Pankow
JS
,
Dupuis
J
,
Florez
JC
,
Fox
CS
,
Paré
G
,
Sun
Q
,
Girman
CJ
,
Laurie
CC
,
Mirel
DB
,
Manolio
TA
,
Chasman
DI
,
Boerwinkle
E
,
Ridker
PM
,
Hunter
DJ
,
Meigs
JB
,
Lee
CH
,
Hu
FB
,
van Dam
RM
Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC), Diabetes Genetics Replication and Meta-analysis (DIAGRAM) Consortium
.
Genetic variants at 2q24 are associated with susceptibility to type 2 diabetes
.
Hum Mol Genet
2010
;
19
:
2706
2715
10.
Voight
BF
,
Scott
LJ
,
Steinthorsdottir
V
,
Morris
AP
,
Dina
C
,
Welch
RP
,
Zeggini
E
,
Huth
C
,
Aulchenko
YS
,
Thorleifsson
G
,
McCulloch
LJ
,
Ferreira
T
,
Grallert
H
,
Amin
N
,
Wu
G
,
Willer
CJ
,
Raychaudhuri
S
,
McCarroll
SA
,
Langenberg
C
,
Hofmann
OM
,
Dupuis
J
,
Qi
L
,
Segrè
AV
,
van Hoek
M
,
Navarro
P
,
Ardlie
K
,
Balkau
B
,
Benediktsson
R
,
Bennett
AJ
,
Blagieva
R
,
Boerwinkle
E
,
Bonnycastle
LL
,
Bengtsson Boström
K
,
Bravenboer
B
,
Bumpstead
S
,
Burtt
NP
,
Charpentier
G
,
Chines
PS
,
Cornelis
M
,
Couper
DJ
,
Crawford
G
,
Doney
AS
,
Elliott
KS
,
Elliott
AL
,
Erdos
MR
,
Fox
CS
,
Franklin
CS
,
Ganser
M
,
Gieger
C
,
Grarup
N
,
Green
T
,
Griffin
S
,
Groves
CJ
,
Guiducci
C
,
Hadjadj
S
,
Hassanali
N
,
Herder
C
,
Isomaa
B
,
Jackson
AU
,
Johnson
PR
,
Jørgensen
T
,
Kao
WH
,
Klopp
N
,
Kong
A
,
Kraft
P
,
Kuusisto
J
,
Lauritzen
T
,
Li
M
,
Lieverse
A
,
Lindgren
CM
,
Lyssenko
V
,
Marre
M
,
Meitinger
T
,
Midthjell
K
,
Morken
MA
,
Narisu
N
,
Nilsson
P
,
Owen
KR
,
Payne
F
,
Perry
JR
,
Petersen
AK
,
Platou
C
,
Proença
C
,
Prokopenko
I
,
Rathmann
W
,
Rayner
NW
,
Robertson
NR
,
Rocheleau
G
,
Roden
M
,
Sampson
MJ
,
Saxena
R
,
Shields
BM
,
Shrader
P
,
Sigurdsson
G
,
Sparsø
T
,
Strassburger
K
,
Stringham
HM
,
Sun
Q
,
Swift
AJ
,
Thorand
B
,
Tichet
J
,
Tuomi
T
,
van Dam
RM
,
van Haeften
TW
,
van Herpt
T
,
van Vliet-Ostaptchouk
JV
,
Walters
GB
,
Weedon
MN
,
Wijmenga
C
,
Witteman
J
,
Bergman
RN
,
Cauchi
S
,
Collins
FS
,
Gloyn
AL
,
Gyllensten
U
,
Hansen
T
,
Hide
WA
,
Hitman
GA
,
Hofman
A
,
Hunter
DJ
,
Hveem
K
,
Laakso
M
,
Mohlke
KL
,
Morris
AD
,
Palmer
CN
,
Pramstaller
PP
,
Rudan
I
,
Sijbrands
E
,
Stein
LD
,
Tuomilehto
J
,
Uitterlinden
A
,
Walker
M
,
Wareham
NJ
,
Watanabe
RM
,
Abecasis
GR
,
Boehm
BO
,
Campbell
H
,
Daly
MJ
,
Hattersley
AT
,
Hu
FB
,
Meigs
JB
,
Pankow
JS
,
Pedersen
O
,
Wichmann
HE
,
Barroso
I
,
Florez
JC
,
Frayling
TM
,
Groop
L
,
Sladek
R
,
Thorsteinsdottir
U
,
Wilson
JF
,
Illig
T
,
Froguel
P
,
van Duijn
CM
,
Stefansson
K
,
Altshuler
D
,
Boehnke
M
,
McCarthy
MI
MAGIC investigators, GIANT Consortium
.
Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis
.
Nat Genet
2010
;
42
:
579
589
11.
Grant
RW
,
Moore
AF
,
Florez
JC
:
Genetic architecture of type 2 diabetes: recent progress and clinical implications
.
Diabetes Care
2009
;
32
:
1107
1114
12.
Kannel
WB
,
Feinleib
M
,
McNamara
PM
,
Garrison
RJ
,
Castelli
WP
:
An investigation of coronary heart disease in families: the Framingham Offspring Study
.
Am J Epidemiol
1979
;
110
:
281
290
13.
Framingham SNP Health Association Resource [Internet]
.
National Center for Biotechnology Information
. . Accessed 8 May 2010
14.
D'Agostino
RB
,
Lee
ML
,
Belanger
AJ
,
Cupples
LA
,
Anderson
K
,
Kannel
WB
:
Relation of pooled logistic regression to time dependent Cox regression analysis: the Framingham Heart Study
.
Stat Med
1990
;
9
:
1501
1515
15.
Pencina
MJ
,
D'Agostino
RB
 Sr
,
D'Agostino
RB
 Jr
,
Vasan
RS
:
Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond
.
Stat Med
2008
;
27
:
157
172

Supplementary data