Diabetologia

, Volume 56, Issue 6, pp 1291–1305

Genome-wide association study in a Chinese population identifies a susceptibility locus for type 2 diabetes at 7q32 near PAX4

  • R. C. W. Ma
  • C. Hu
  • C. H. Tam
  • R. Zhang
  • P. Kwan
  • T. F. Leung
  • G. N. Thomas
  • M. J. Go
  • K. Hara
  • X. Sim
  • J. S. K. Ho
  • C. Wang
  • H. Li
  • L. Lu
  • Y. Wang
  • J. W. Li
  • Y. Wang
  • V. K. L. Lam
  • J. Wang
  • W. Yu
  • Y. J. Kim
  • D. P. Ng
  • H. Fujita
  • K. Panoutsopoulou
  • A. G. Day-Williams
  • H. M. Lee
  • A. C. W. Ng
  • Y-J. Fang
  • A. P. S. Kong
  • F. Jiang
  • X. Ma
  • X. Hou
  • S. Tang
  • J. Lu
  • T. Yamauchi
  • S. K. W. Tsui
  • J. Woo
  • P. C. Leung
  • X. Zhang
  • N. L. S. Tang
  • H. Y. Sy
  • J. Liu
  • T. Y. Wong
  • J. Y. Lee
  • S. Maeda
  • G. Xu
  • S. S. Cherny
  • T. F. Chan
  • M. C. Y. Ng
  • K. Xiang
  • A. P. Morris
  • DIAGRAM Consortium
  • S. Keildson
  • The MuTHER Consortium
  • R. Hu
  • L. Ji
  • X. Lin
  • Y. S. Cho
  • T. Kadowaki
  • E. S. Tai
  • E. Zeggini
  • M. I. McCarthy
  • K. L. Hon
  • L. Baum
  • B. Tomlinson
  • W. Y. So
  • Y. Bao
  • J. C. N. Chan
  • W. Jia
Open Access
Article

DOI: 10.1007/s00125-013-2874-4

Cite this article as:
Ma, R.C.W., Hu, C., Tam, C.H. et al. Diabetologia (2013) 56: 1291. doi:10.1007/s00125-013-2874-4

Abstract

Aims/hypothesis

Most genetic variants identified for type 2 diabetes have been discovered in European populations. We performed genome-wide association studies (GWAS) in a Chinese population with the aim of identifying novel variants for type 2 diabetes in Asians.

Methods

We performed a meta-analysis of three GWAS comprising 684 patients with type 2 diabetes and 955 controls of Southern Han Chinese descent. We followed up the top signals in two independent Southern Han Chinese cohorts (totalling 10,383 cases and 6,974 controls), and performed in silico replication in multiple populations.

Results

We identified CDKN2A/B and four novel type 2 diabetes association signals with p < 1 × 10−5 from the meta-analysis. Thirteen variants within these four loci were followed up in two independent Chinese cohorts, and rs10229583 at 7q32 was found to be associated with type 2 diabetes in a combined analysis of 11,067 cases and 7,929 controls (pmeta = 2.6 × 10−8; OR [95% CI] 1.18 [1.11, 1.25]). In silico replication revealed consistent associations across multiethnic groups, including five East Asian populations (pmeta = 2.3 × 10−10) and a population of European descent (p = 8.6 × 10−3). The rs10229583 risk variant was associated with elevated fasting plasma glucose, impaired beta cell function in controls, and an earlier age at diagnosis for the cases. The novel variant lies within an islet-selective cluster of open regulatory elements. There was significant heterogeneity of effect between Han Chinese and individuals of European descent, Malaysians and Indians.

Conclusions/interpretation

Our study identifies rs10229583 near PAX4 as a novel locus for type 2 diabetes in Chinese and other populations and provides new insights into the pathogenesis of type 2 diabetes.

Keywords

Chinese Diabetes East Asians Genetics Genome-wide association study 

Abbreviations

AAD

Age at diagnosis

ALR

Alternating logistic regressions

DIAGRAM Consortium

Diabetes Genetics Replication And Meta-analysis Consortium

eQTL

cis-Expression quantitative trait loci

FPG

Fasting plasma glucose

GWAS

Genome-wide association studies

HOMA-B

HOMA of beta cell function

HWE

Hardy–Weinberg equilibrium

LCL

Lymphoblastoid cell lines

LD

Linkage disequilibrium

MAF

Minor allele frequency

MC

Monte Carlo

The MuTHER Consortium

The Multiple Tissue Human Expression Resource Consortium

SNP

Single-nucleotide polymorphisms

Introduction

Type 2 diabetes is a common complex disease characterised by deficient insulin secretion and decreased insulin sensitivity. In 2010, 285 million people worldwide were affected by type 2 diabetes [1], with 60% of them located in Asia [2, 3]. China now has the largest number of patients with diabetes in the world, with an estimated 92 million affected individuals, and an additional 150 million with impaired glucose tolerance [4].

To identify common type 2 diabetes susceptibility variants, large-scale genome-wide association studies (GWAS) have been conducted in white individuals, yielding more than 60 genetic loci to date [5, 6]. Although many of these regions have been successfully replicated in Asian populations [7, 8, 9, 10, 11], discrepancies in allelic frequencies and effect sizes have demonstrated that interethnic differences exist. GWAS conducted in Japanese individuals [12, 13], as well as meta-analyses of GWAS in South Asian [14] and East Asian [15] groups, have revealed additional variants not detected in GWAS with white individuals, with several signals, including KCNQ1, later replicated in many populations [12, 13]. Previous GWAS in Chinese suggested several loci but lacked large-scale replication [16, 17, 18].

We therefore conducted this study to identify new type 2 diabetes susceptibility loci in Southern Han Chinese individuals. We performed a meta-analysis of three GWAS comprising 684 patients with type 2 diabetes and 955 controls, and analysed 2.9 million (genotyped and imputed) single-nucleotide polymorphisms (SNPs) in an additive model. Putatively associated SNPs (p < 1 × 10−5) were genotyped de novo in two independent Southern Han Chinese cohorts (10,383 cases and 6,974 controls), and SNPs reaching a genome-wide significance of p < 5 × 10−8 were replicated in silico in five East Asian and three non-East Asian populations for a total of 31,541 cases and 60,344 controls.

Methods

Participants

In the first-stage discovery cohort (stage 1), we performed genome-wide scanning in three different case–control samples: 198 Hong Kong Chinese individuals (99 patients with type 2 diabetes and 99 healthy controls) in Hong Kong GWAS 1, 1,047 Hong Kong Chinese individuals (388 with type 2 diabetes and 659 controls) in Hong Kong GWAS 2 and 394 Shanghai Chinese (197 patients with type 2 diabetes and 197 normal controls) in the Shanghai GWAS. Individuals included in the stage 2 replication included 5,366 with type 2 diabetes and 2,474 controls from Hong Kong, and 4,035 cases and 3,964 controls from Shanghai. We also included 325 cases and 368 controls from 178 Hong Kong families, as well as 657 cases and 168 controls from 248 Shanghai families.

Case–control samples for in silico replication in stage 3 were taken from several published type 2 diabetes GWAS in East Asian individuals. These included the Korea Association Resource Study [19], the Singapore Chinese from the Singapore Diabetes Cohort Study and the Singapore Prospective Study Program [20], the BioBank Japan Study [13] and a Han Chinese Study [21]. For stage 4 in silico replication in other populations, Malaysian participants from the Singapore Malay Eye Study, Indian participants from the Singapore Indian Eye Study [20] and participants of European descent in the Diabetes Genetics Replication And Meta-analysis (DIAGRAM) Consortium [6] were included.

The study design, type 2 diabetes diagnostic criteria and clinical evaluation used in each study are described in the electronic supplementary material (ESM) Methods. The clinical characteristics of the study individuals are described in Table 1. Each study obtained approval from the appropriate institutional review boards of the respective institutions, and written informed consent was obtained from all participants. The overall study design is depicted in Fig. 1.
Table 1

Clinical characteristics of the participants

Study

Cohort

N (male %)

Age (years)

AAD (year)

Diabetes duration (years)

BMI (kg/m2)

FPG (mmol/l)

Stage 1 (genome scan)

HK1

Control

99 (36.4)

37.3 ± 10.2

20.8 ± 2.0

4.7 ± 0.4

T2D patient

99 (40.4)

40.6 ± 8.8

31.8 ± 7.7

8.0 ± 8.3

30.9 ± 4.4

HK2

Diseased control

659 (48.7)

37.1 ± 17.0

23.3 ± 3.7

T2D patient

388 (49.5)

60.6 ± 10.8

51.1 ± 12.1

9.5 ± 7.0

25.0 ± 3.8

SH

Control

197 (50.8)

66.4 ± 10.1

20.6 ± 1.7

4.8 ± 0.4

T2D patient

197 (57.9)

41.6 ± 10.4

34.5 ± 4.8

7.3 ± 8.5

23.8 ± 4.1

Stage 2 (de novo replication in Chinese)

HK1

Adolescent control

985 (44.2)

15.5 ± 1.9

22.7 ± 5.4

4.9 ± 0.4

Adults control

513 (47.0)

42.0 ± 10.4

19.9 ± 3.5

4.7 ± 0.3

Elderly control

976 (51.4)

72.3 ± 5.3

23.2 ± 3.2

T2D patient

5,366 (45.1)

56.7 ± 13.4

48.8 ± 14.9

6.6 ± 6.9

24.6 ± 5.3

SH1

Control

3,964 (37.6)

51.3 ± 13.5

23.6 ± 3.2

5.0 ± 0.5

T2D patient

4,035 (52.0)

61.2 ± 12.1

54.2 ± 11.3

7.2 ± 6.9

24.5 ± 3.5

HK Family 2

Control

368 (41.0)

37.0 ± 13.6

24.0 ± 4.1

4.9 ± 0.4

T2D patient

325 (40.6)

48.0 ± 14.4

41.7 ± 13.1

6.3 ± 7.6

25.9 ± 4.4

SH Family 2

Control

168 (51.2)

62.8 ± 11.2

23.7 ± 3.5

4.8 ± 0.6

T2D patient

657 (43.7)

54.6 ± 15.6

50.0 ± 14.2

4.9 ± 7.3

23.9 ± 3.5

Stage 3 (in silico replication in East Asians)

Japanese

Control

3,023 (54.5)

51.9 ± 15.2

22.4 ± 3.7

T2D patient

4,465 (68.0)

65.8 ± 10.0

56.5 ± 11.4

9.4 ± 8.4

24.1 ± 3.8

Korean 1

Control

2,943 (46.0)

51.1 ± 8.6

24.1 ± 3.0

4.5 + 0.4

T2D patient

1,042 (51.7)

56.4 ± 8.6

25.5 ± 3.3

7.0 ± 2.6

Korean 2

Control

1,305 (54.5)

65.2 ± 2.6

23.9 ± 3.0

5.0 ± 0.5

T2D patient

1,183 (46.5)

58.6 ± 7.1

25.2 ± 3.4

7.4 ± 2.7

Singapore Chinese 1

Control

1,006 (21.6)

47.7 ± 11.1

22.3 ± 3.7

4.7 ± 0.4

T2D patient

1,082 (37.2)

65.1 ± 9.7

55.7 ± 12.0

25.3 ± 3.9

Singapore Chinese 2

Control

939 (63.8)

46.7 ± 10.2

22.8 ± 3.4

4.7 ± 0.5

T2D patient

928 (64.9)

63.7 ± 10.8

52.2 ± 14.4

25.4 ± 3.8

Chinese

Control

1,839 (43.7)

54.1 ± 9.2

24.00 ± 3.18

5.04 ± 0.35

T2D patient

1,873 (46.0)

58.6 ± 8.4

25.00 ± 3.24

8.43 ± 2.90

Stage 4 (in silico replication in non-East Asians)

Singapore Malaysian

Control

1,240 (52.0)

56.9 ± 11.4

25.1 ± 4.8

T2D patient

794 (51.0)

62.3 ± 9.90

54.4 ± 11.2

27.8 ± 4.9

Singapore Indians

Control

1,169 (48.4)

55.7 ± 9.7

25.3 ± 4.4

T2D patient

977 (54.4)

60.7 ± 9.9

51.4 ± 10.6

27.1 ± 5.1

DIAGRAM+

Control

38,987 (–)

T2D patient

8,130 (–)

Data are shown as N, percentage or mean ± SD

T2D, type 2 diabetes

Fig. 1

Summary of study design. CHB, Han Chinese in Beijing, China; JPT, Japanese in Tokyo, Japan

Quality control on the samples for the GWAS

In our study, individuals were excluded from further analysis if: (1) duplicate samples existed; (2) the sex identified from the X chromosome was discordant with the sex obtained from the medical records; (3) the genotype call rate yield was <98%. We detected possible familial relationship using estimates of identity by descent derived from pair-wise analyses of independence (r2 ≈ 0) and quality SNPs. Individuals with evidence for relatedness were excluded (\( \widehat{{{p_1}}} > 0.05 \)). ESM Table 1 shows the quality control for the participants in stage 1.

To discriminate individuals from different geographical origins, we conducted multidimensional scaling analysis using the genotype data obtained from unrelated individuals in the present study and the other 11 populations studied by the HapMap project (ESM Fig. 1). Individuals were excluded from subsequent analyses if they lay between clusters.

Genotyping and quality control on the SNP data

Individuals for the stage 1, 3 and 4 analyses were genotyped using high-density SNP typing arrays that covered the entire genome. Only autosomal SNPs were included. Quality checks for SNPs were performed in the case and control samples separately, although the same criteria were applied to each. SNPs were excluded from further analysis if: (1) p < 1 × 10−4 for Hardy–Weinberg equilibrium (HWE); (2) minor allele frequency (MAF) was <1%; (3) call rate was <95%; in particular, SNPs with MAF ≥ 1% but ≤5% were excluded if their call rate was <99%; or (4) the SNPs showed a significant difference in MAF (p < 1 × 10−4) between the Hong Kong control cohorts with other conditions (450 with epilepsy, 110 with eczema and 99 non-hypertensive individuals). Only SNPs that passed the quality control criteria for both cases and controls were used for further analysis. ESM Table 2 shows the quality control of the genotyping results in stage 1. We imputed genotypes for autosomal SNPs according to the 1000 Genomes reference panel. See the ESM Methods for further details.

For de novo replication in stage 2, all selected SNPs were genotyped in the Hong Kong and Shanghai case–control samples by a primer extension of multiplex products with detection by Matrix-assisted laser desorption ionisation-time of flight mass spectroscopy using a MassARRAY platform (Sequenom; San Diego, CA, USA). Family samples were genotyped using TaqMan SNP Genotyping Assays (Applied Biosystems, Foster City, CA, USA) or by direct sequencing.

Statistical analysis

All statistical analyses were performed using PLINK version 1.07 (http://pngu.mgh.harvard.edu/~purcell/plink/) [22], SAS version 9.1 (SAS Institute, Cary, NC, USA) or SPSS for Windows version 18 (SPSS, Chicago, IL, USA), unless specified otherwise. Haploview version 4.1 was used to generate pair-wise linkage disequilibrium (LD) measures (r2).

To test for an association with type 2 diabetes, we applied logistic regression under an additive genetic model using the MACH2DAT software (www.sph.umich.edu/csg/abecasis/MACH/download/) [23] adjusted for sex and age according to situations in the individual studies.

To combine the type 2 diabetes association results in stage 1, GWAMA software (www.well.ox.ac.uk/gwama/) [24] was used to calculate the combined estimates of the ORs (95% CIs) from multiple groups by weighting the natural log-transformed ORs of each study using the inverse of their variance under the random effect model [25]. By using the random effect model, we excluded SNPs with some degree of heterogeneity between studies, which helped to attenuate the number of false-positive findings in this study. Cochran’s Q statistic (p < 0.05) and I2 index were used to assess the heterogeneity of ORs between studies.

The most strongly associated SNPs were prioritised for follow-up in stage 2 based on the meta-analysis results from stage 1. SNPs located within a previously reported type 2 diabetes locus were excluded. We finally considered 13 top and proxy SNPs from four distinct loci available in all three GWAS with (1) a meta-analysis p < 1 × 10−5; (2) a heterogeneity test p > 0.05; (3) the same direction of risk allele across all three GWAS; (4) a common allele frequency (MAF ≥ 0.1). For SNPs imputed across all three studies, we selected the most significant SNP associated with type 2 diabetes. ESM Tables 3 and 4 describe the details of the selected SNPs and the quality control for the genotyping results in stage 2.

In the replication stage, genotype frequencies were compared between cases and controls using logistic regression under an additive genetic model. In the family studies, alternating logistic regressions (ALR) implemented in the SAS procedure GENMOD was used to test for the association between type 2 diabetes and SNPs under an additive genetic model adjusted for age and sex. ALR is one type of generalised estimating equation applicable to binary outcomes that can handle correlated data (e.g. familial correlation). ORs (95% CIs) are presented in both analyses. Meta-analyses and heterogeneity tests were conducted as described previously to combine estimates of the ORs (95% CIs) from multiple case–control and family groups under the fixed effect model. Multiple testing in the combined analysis of the case–control study were controlled by Bonferroni correction, and p < 4.5 × 10−3 (0.05 divided by 11 SNPs in the stage 2 replication studies) was used as the threshold for filtering SNPs genotyped in the family studies.

Continuous data are presented as mean ± SD or geometric mean (95% CI). Traits were loge-transformed due to skewed distributions. Associations between genotypes and quantitative traits were tested by linear regression (adjusted for sex, age and/or BMI) in each healthy control cohort, as were associations for age at diagnosis (AAD) among patients with type 2 diabetes (adjusted for sex, BMI and/or HbA1c). Meta-analyses implemented by GWAMA were applied to combine effect size (β ± SE) from multiple groups under the fixed effect model.

We performed bioinformatics and cis-expression quantitative trait loci (eQTL) analysis for functional implication of the identified SNP. See the ESM Methods for additional information on methods, including adjustment for genomic control and the gene network analysis.

Results

Meta-analysis of patients with Chinese ancestry

A summary of the study design and the clinical characteristics of the participants in all stages are shown in Fig. 1 and Table 1. In stage 1, we genotyped 684 patients with type 2 diabetes and 955 controls. We did not detect any population stratification between case and control individuals in multidimensional scaling analysis for all GWAS (ESM Fig. 2). Meta-analysis was implemented to combine the individual association results for 2,925,090 imputed and genotyped SNPs (under additive genetic models) available in all three GWAS using the inverse-variance approach for random effect models.

In the stage 1 meta-analysis of three Chinese GWAS, 44 SNPs within five loci were prioritised for follow-up (Fig. 2 and ESM Table 5). We did not observe a substantial change in the stage 1 results after adjusting either for λs (1.01–1.04 in individual cohorts) or the first principal component in the meta-analysis, reflecting that the results were not likely to be due to population stratification (ESM Fig. 3 and ESM Table 6).
Fig. 2

Manhattan plot of combined genome-wide association results from the Hong Kong 1, Hong Kong 2 and Shanghai studies based on the random effect models. The y-axis represents the −log10p value, and the x-axis represents the 2,925,090 analysed SNPs. The dashed horizontal line indicates the threshold of significance p < 1 × 10−5. There are 44 points with p < 1 × 10−5, and the arrow and labels localise the susceptibility loci to type 2 diabetes uncovered in the present study

Of the five loci identified in stage 1, CDKN2A/B has previously been reported to be strongly associated with type 2 diabetes. In line with our previous findings, two SNPs in CDKN2A/B showing strong signals for type 2 diabetes in the present study were in high LD (r2 ≈ 0.8) with rs10811661, which is well-replicated in most populations. After eliminating the signal of CDKN2A/B and redundant markers, we took forward 13 top and proxy SNPs among the remaining 42 SNPs in four regions to stage 2, de novo replication, in two independent Chinese case–control cohorts (ESM Table 3). We successfully obtained genotypes for 11 SNPs in Hong Kong replication 1 cohort with 5,366 cases and 2,474 controls, and Shanghai replication 1 cohort with 4,035 cases and 3,964 controls to proceed for subsequent analysis (ESM Table 4). Of these, rs10229583 and rs2737250, located on chromosomes 7 and 8, respectively, gave p ≤ 4.5 × 10−3 (threshold of significance after Bonferroni correction) with the same directions of association as the original signals (Table 2). These two SNPs were genotyped in 1,518 additional samples from 426 families of Han Chinese descent (325 cases and 368 controls from 178 Hong Kong families, and 657 cases and 168 controls from 248 Shanghai families). Although we did not detect a significant association in either family study using ALR, all were in the concordant direction for rs10229583 (ESM Table 7). Taken together, the overall observed association for type 2 diabetes with rs10229583 by combining all studies from Chinese ancestry in stages 1 and 2 yielded an OR (95% CI) of 1.18 (1.11, 1.25) with a corresponding p = 2.6 × 10−8 (Table 3). For another variant taken to genotyping in family samples, rs2737250, meta-analysis of GWAS and de novo genotyping in the Hong Kong and Shanghai case–control samples revealed OR 1.10 (1.05, 1.15) with a corresponding p = 7.05 × 10−5 using a fixed effect model (p for heterogeneity test = 0.0012, I2 = 0.852), with OR 1.16 (1.01, 1.33), p = 0.0299 by random effect model. However, genotyping of the variant in the Hong Kong and Shanghai family samples suggested an association in the opposite direction (ESM Table 7).
Table 2

Association results for type 2 diabetes (T2D) with 11 top and proxy SNPs in de novo replication stage in Chinese populations

     

Hong Kong replication 1 (5,366 T2D vs 2,474 controls)

Shanghai replication 1 (4,035 T2D vs 3,964 controls)

Combined

SNP

Chromosome

Nearest gene(s)

Position (B36)

Minor/major allele

Case MAF

Control MAF

OR (95% CI)

padditive

Case MAF

Control MAF

OR (95% CI)

padditive

OR (95% CI)

pmeta (uncorrected)

phet

I2

rs10229583

7

PAX4

127034139

A/G

0.847

0.83

1.14 (1.03, 1.23)

0.0077

0.846

0.825

1.16 (1.08, 1.27)

3.7 × 10−4

1.15 (1.08, 1.22)

1.0 × 10−5

0.6406

0.000

rs2721960

8

TRPS1

116725904

T/C

0.657

0.644

1.05 (0.98, 1.14)

0.1566

0.655

0.638

1.08 (1.01, 1.15)

0.0277

1.06 (1.02, 1.12)

0.0095

0.7067

0.000

rs2737250

8

TRPS1

116731048

G/A

0.631

0.62

1.05 (0.98, 1.12)

0.1807

0.641

0.621

1.09 (1.02, 1.16)

0.0090

1.08 (1.02, 1.12)

0.0045

0.4582

0.000

rs3858158

10

COL13A1

71310056

C/T

0.516

0.521

0.98 (0.92, 1.05)

0.6000

0.569

0.561

1.03 (0.97, 1.10)

0.3211

1.01 (0.96, 1.05)

0.7408

0.3026

0.506

rs2395272

10

COL13A1

71310261

A/G

0.531

0.534

0.99 (0.93, 1.06)

0.7680

0.594

0.584

1.04 (0.97, 1.11)

0.2312

1.02 (0.97, 1.06)

0.4589

0.3027

0.364

rs57703465

10

COL13A1

71311074

T/C

0.654

0.662

0.96 (0.89, 1.04)

0.3463

0.667

0.656

1.05 (0.98, 1.12)

0.1502

1.01 (0.96, 1.06)

0.6467

0.0976

0.765

rs11065441

12

P2RX7

120045354

C/T

0.728

0.724

1.02 (0.94, 1.11)

0.6224

0.728

0.733

0.97 (0.91, 1.04)

0.4312

0.99 (0.94, 1.05)

0.7756

0.3748

0.000

rs684201

12

P2RX7

120054726

A/G

0.73

0.726

1.02 (0.94, 1.10)

0.5916

0.735

0.739

0.98 (0.91, 1.05)

0.5609

1.00 (0.94, 1.05)

0.9462

0.4332

0.000

rs11065450

12

P2RX7

120064040

A/C

0.682

0.688

0.97 (0.90, 1.05)

0.4995

0.702

0.707

0.98 (0.92, 1.05)

0.5520

0.98 (0.93, 1.03)

0.3699

0.9111

0.000

rs208290

12

P2RX7

120078439

T/C

0.612

0.609

1.01 (0.94, 1.09)

0.7086

0.643

0.645

0.99 (0.93, 1.06)

0.7950

1.00 (0.95, 1.05)

0.9605

0.6472

0.000

rs10849851

12

P2RX7

120081027

G/A

0.727

0.72

1.03 (0.95, 1.12)

0.4079

0.737

0.741

0.98 (0.91, 1.05)

0.5237

1.00 (0.95, 1.05)

0.9308

0.3002

0.068

Nearest Entrez genes within 250 kb

p, pmeta and phet represent p values from logistic regression without any adjustment under the additive genetic model, meta-analysis under a fixed effect model (uncorrected for multiple testing) and test of heterogeneity, respectively

ORs are reported with respect to the minor allele

Table 3

Association results for rs10229583 and type 2 diabetes (T2D)

   

N

Risk allele frequencies

Stage

Cohort

Adjustment

T2D

Control

T2D

Control

OR (95% CI)

padditive (uncorrected GC)

phet

I2

1. Discovery

Hong Kong GWAS 1

Sex and age

99

99

0.879

0.818

1.48 (0.85, 2.59)

0.1645

  

Hong Kong GWAS 2

Sex and age

388

659

0.857

0.820

1.56 (1.14, 2.13)

0.0055

Shanghai GWAS

None

197

197

0.873

0.777

1.92 (1.32, 2.79)

5.0 × 10−4

Meta-analysis of GWAS

684

955

1.66 (1.33, 2.07)

7.7 × 10−6

0.6455

0.000

2. De novo replications in Hong Kong and Shanghai

Hong Kong replication 1

None

5,366

2,474

0.847

0.831

1.13 (1.03, 1.24)

7.7 × 10−3

  

Shanghai replication 1

None

4,035

3,964

0.846

0.825

1.17 (1.07, 1.27)

3.7 × 10−4

Hong Kong family replication 2

Sex and age

325

368

0.872

0.856

1.22 (0.85, 1.74)

0.2817

Shanghai family replication 2

Sex and age

657

168

0.824

0.813

1.09 (0.80, 1.49)

0.5757

Replication in Chinese

10,383

6,974

1.15 (1.08, 1.22)

1.0 × 10−5

0.6406

0.000

Meta-analysis of Chinese

11,067

7,929

1.18 (1.11, 1.25)

2.6 × 10−8

0.0839

0.596

3. In silico replications in East Asians

Japanese replication

None

4,465

3,023

0.892

0.881

1.11 (1.01, 1.23)

0.0379

  

Korean replication 1

None

1,042

2,943

0.894

0.878

1.17 (0.99, 1.38)

0.0577

Korean replication 2

None

1,183

1,305

0.841

0.844

0.98 (0.84, 1.15)

0.8101

Singapore Chinese replication 1

None

1,082

1,006

0.832

0.819

1.07 (0.95, 1.20)

0.2728

Singapore Chinese replication 2

None

928

939

0.833

0.816

Chinese replication

First 2 PCs

1,873

1,839

0.8396

0.8167

1.17 (1.04, 1.32)

0.01091

Replication in other East Asian

10,573

11,055

1.10 (1.04, 1.17)

6.0 × 10−4

0.6767

0.000

Meta-analysis of East Asian

21,640

18,984

1.14 (1.09, 1.19)

2.3 × 10−10

0.5939

0.000

4. In silico replications in South Asians and Europeans

Singapore Malaysian replication

None

794

1,204

0.798

0.804

0.97 (0.83, 1.14)

0.7185

  

Singapore Indian replication

None

977

1,169

0.647

0.682

0.86 (0.76, 0.98)

0.0276

DIAGRAM

None

8,130

38,987

1.06 (1.02, 1.12)

8.6 × 10−3

Replication in non-East Asian

9,901

41,360

1.03 (0.99, 1.08)

0.1156

0.0042

0.878

ORs and 95% CIs were reported with respect to the T2D-related risk alleles (G)

phet refers to the p value obtained from the heterogeneity test

GC, genomic control; PC, principal components

Meta-analysis in East Asian and other populations

To further validate the association of rs10229583 with type 2 diabetes, we conducted in silico replication of rs10229583 in five East Asian GWAS (one Japanese, two Korean, one Singapore Chinese and one Han Chinese study), and three non-East Asian GWAS (Singapore Indian, Singapore Malaysian and the DIAGRAM Consortium). Meta-analysis for the East Asian populations (p = 2.3 × 10−10) gave an OR (95% CI) of 1.14 (1.09, 1.19). Among non-East Asian populations, we observed replication of the association in participants of European descent from the DIAGRAM Consortium (p = 8.6 × 10−3), with OR 1.06 and (95% CI 1.02, 1.12) (Table 3 and Figs 3 and 4).
Fig. 3

Regional plots for the identified variant rs10229583, including results for both genotyped and imputed SNPs in the Chinese population. The purple circle and diamond represent the sentinel SNP in meta-analysis of three GWAS in the stage 1 and the East Asian meta-analysis in stages 1 + 2 + 3, respectively. Other SNPs are coloured according to their level of LD, which is measured by r2, with the sentinel SNP. The recombination rates estimated from the 1000 Genomes project JPT + CHB data are shown. CHB, Han Chinese in Beijing, China; JPT, Japanese in Tokyo, Japan

Fig. 4

Forest plot for meta-analysis of the association between type 2 diabetes and rs10229583 for all populations in the present study. ORs and 95% CIs were reported with respect to the type 2 diabetes-related risk alleles (G)

Impact of rs10229583 on clinical traits and course of disease

We next investigated the associations of rs10229583 with the AAD of type 2 diabetes and quantitative metabolic traits related to type 2 diabetes. Among all the patients with type 2 diabetes, individuals who carried the common, type 2 diabetes risk allele (G) were concordantly and significantly younger at the time of diagnosis in both Hong Kong and Shanghai, and the meta-analysis showed that presence of the risk variant had a significant association with younger AAD (p = 2.3 × 10−4, βunadjusted ± SE =−0.90 ± 0.24), which remained unchanged following adjustment for sex and BMI (ESM Table 8). We also observed a nominal association of the G-alleles of rs10229583 with beta cell function as assessed by HOMA-B (βunadjusted ± SE =−0.06 ± 0.03, p = 0.0221) in healthy Hong Kong adolescents, a reduced Stumvoll Index (βunadjusted ± SE = 0.03 ± 0.01, p = 0.0303) and increased fasting plasma glucose (FPG) level (βunadjusted ± SE = 0.03 ± 0.01, p = 0.0460) in healthy Shanghai adults (Fig. 5).
Fig. 5

Associations of the risk variant (G allele) of rs10229583 with measures of insulin secretion in Chinese controls. (a) Association with reduced HOMA-B in a Hong Kong Chinese adolescent cohort (p = 0.0221). (b) Association with a reduced Stumvoll Index of beta cell function in healthy Shanghai controls (p = 0.0303). (c) Association of the risk variant with higher FPG in healthy Shanghai controls (p = 0.0460). Data are expressed as mean (for FPG) or geometric mean (for HOMA-B and Stumvoll Index). SDs or 95% CIs are expressed as error bars. The number of individuals analysed for each genotype is shown in parentheses under each column

Functional implication of the identified locus rs10229583

In order to evaluate the functional implication of our identified variant, we performed an extensive bioinformatics analysis. Consistent with its observed effect on pancreatic beta cell function, the gene region of our locus has been identified as one of the islet-selective clusters of open regulatory elements using a formaldehyde-assisted isolation of regulatory elements coupled with high-throughput sequencing in human pancreatic islets [26]. In addition, the variant and its tagging SNPs lie within an area near PAX4 and SND1, which is enriched with DNase I hypersensitive sites, histone H3 lysine modifications and CCCTC factor binding in human islets (ESM Fig. 4) [27].

We next investigated the relationship of rs10229583 with eQTLs in adipose tissue and other tissues in available datasets. The variant rs1440971, a proxy of our associated SNP (MAF ∼ 0.1, r2 = 0.8 and D' = 1, to rs10229583), was significantly associated with the level of expression of GRM8, ARF5 and PAX4 in lymphoblastoid cells in the GenCord Project, although this did not correlate with the eQTL peak (ESM Fig. 5 and ESM Table 9) [28]. Analysis of all eQTLs associated with rs10229583, or its close proxy, rs1440971, was performed using data from the Multiple Tissue Human Expression Resource (MuTHER) Consortium [29]. Of note, eQTL data were only available for PAX4 in adipose tissue, but not lymphoblastoid cell lines (LCLs) or skin, for which no expression data were available from MuTHER. There was a nominal association (p < 0.05) between the variant and expression of C7orf54 and ARF5 in LCLs, and C7orf68 in adipose tissue (ESM Table 10). The r2 between the GWAS SNP and the peak eQTL SNPs ranged between 0.56 and 1.

Complex diseases such as type 2 diabetes are caused by a combination of alterations, and each genomic perturbation or alteration can potentially impact on thousands of genes [30, 31]. Nevertheless, functionally important genes often organise into the same pathway of functional grouping. Therefore, we can overlay the alterations on a gene network that was built using highly confident gene–gene relationships (ESM Fig. 6) [32]. We identified interactions between these genes, with additional interaction with other key pancreatic transcription factors such as NEUGRO3 (ESM Fig. 6). Taken together, we speculate that ARF5, GCC1, SND1 and PAX4 may function together with NEUGRO3 in the same network for pancreatic islet development.

Heterogeneity of effect in Chinese vs other ethnic groups

To investigate why the novel loci identified in the present study had not been detected in previous GWAS performed in other populations, we examined the heterogeneity of effect between East Asians and Europeans. There was no evidence of heterogeneity of effect between Chinese, Korean and Japanese populations, but significant heterogeneity of effect was seen between Han Chinese and individuals of European descent in the DIAGRAM Consortium, as well as between Chinese, Malaysians and Indians (ESM Table 11).

To test for the variation of LD structure between Chinese and other populations, we implemented the targeted varLD approach to examine the pattern of r2 between every pair of SNPs within the 100 kb region centred on our index SNP rs10229583. (www.statgen.nus.edu.sg/~SGVP/software/varld.html) [33]. This region shows highly significant evidence of LD variation between Chinese, European (Monte Carlo [MC] p = 0.0018), and African (MC p = 0.0003) individuals, but nominal evidence of variations between Chinese and Japanese (MC p = 0.0107) (ESM Table 12 and ESM Figs 7 and 8). We also observed discrepancies in allele frequency of rs10229583 between East Asians, Europeans and Africans (ESM Table 12).

Discussion

This study reports a meta-analysis of GWAS for type 2 diabetes in a Chinese population, and has identified a novel diabetes-associated locus. Furthermore, we replicated the association in additional East Asian samples, and found an association in samples of European descent. In addition to the multiethnic samples used in our study, our study also benefits from a detailed phenotyping of the Chinese samples, which allowed additional analyses of the effect of the risk variant on clinical traits and the course of disease to be carried out.

Type 2 diabetes in Asians is characterised by an earlier AAD, strong family history and evidence of impaired beta cell function [2, 3]. In a recent nationwide study conducted in China, the prevalence of diabetes was 3.2% among persons aged 20–39 years, and 11.5% among adults aged 40–59 [4]. The risk variant we identified, rs10229583, was associated with earlier AAD in both the Hong Kong and Shanghai samples, highlighting its potential contribution to young-onset diabetes in the Chinese population. Healthy adults and adolescents who carry the risk variant were found to have elevated fasting glucose and impaired beta cell function, respectively.

The novel locus for type 2 diabetes we identified, rs10229583, is located downstream of the ARF5 and PAX4 genes in 7q32, and upstream of SND1. PAX4, which is a member of the paired box family of transcription factors, plays a critical role in pancreatic beta cell formation during fetal development [34, 35] and is therefore a very strong candidate for the implicated gene. The gene region lies within an area of islet-specific cluster of open chromatin sites and may therefore act in cis with local chromatin and regulatory changes [25]. PAX4 is expressed in early pancreatic endocrine cells, but expression is later restricted to beta cells and it is not expressed in mature pancreas [36]. In pancreatic endocrine cells, PAX4 represses ghrelin and glucagon expression, and can induce the expression of PDX1, a key transcription factor for islet development [37]. Targeted disruption of PAX4 in mice was found to lead to reduced beta cell mass at birth [37].

Several human studies have implicated PAX4 in the pathogenesis of diabetes [38, 39]. In one report, a missense mutation (R121W) was identified in six heterozygous patients and one homozygous patient out of 200 unrelated Japanese patients with type 2 diabetes [39]. For example, Japanese patients carrying PAX4 mutations have severe defects in first-phase insulin secretion [40]. Mutations in PAX4 may lead to rare monogenic forms of young-onset diabetes [41]. Common variants in several other MODY genes, namely, HNF4a, HNF1a and TCF2, have been identified as susceptibility loci for type 2 diabetes [42].

Our finding is consistent with other studies that have highlighted the important role of genes implicated in pancreatic development in the pathogenesis of type 2 diabetes. In a previous study, a risk variant at HNF4a has been found to be associated with increased risk of type 2 diabetes, and carriers of the risk allele have impaired beta cell function [43]. The MAF of the R121W PAX4 mutation was 1% in Asians, and the mutation is in low LD with rs10229583. It is possible that both rare mutations and common variation within the same gene confer risk towards type 2 diabetes independently. The common variant we identified, rs10229583, may be associated with altered gene expression, while the other rare non-synonymous mutations lead to impaired gene function. For example, while common non-coding variants in MTNR1B increase type 2 diabetes risk with a modest effect, large-scale resequencing has identified rare loss-of-function MTNR1B variants that significantly contribute towards type 2 diabetes risk [44]. Some regulatory elements harbouring type 2 diabetes-associated loci have recently been found to exhibit allele-specific differences in activity, providing evidence supporting the functional role of non-coding common variants identified through GWAS [26, 27].

The recent East Asian meta-analysis comprising eight type 2 diabetes GWAS identified a locus on chromosome 7 near GRIP and GCC1-PAX4 to be associated with type 2 diabetes. The protein encoded by GCC1 may play a role in transmembrane transport [45]. The variant identified from the East Asian study, rs6467136, appears to be independent of our signal, with r2 = 0.044 in our Chinese samples (ESM Fig. 8). Furthermore, we found no change in the effect size of rs10229583 after conditioning on rs6467136 (OR [95% CI] = 1.20 [1.11, 1.29], p = 4.6 × 10−6 vs OR [95% CI] = 1.19 [1.10, 1.29), p = 1.6 × 10−5, before and after the conditional analysis in 9,886 Chinese samples). Likewise, rs6467136 had little change in effect after conditioning on rs10229583 (OR [95% CI] = 1.09 [1.02, 1.17], p = 0.0125 before; OR [95% CI] = 1.07 [0.99, 1.15], p = 0.0729 after). In the recent analysis from the DIAGRAM Consortium, rs231362 near KCNQ1 was identified to be associated with type 2 diabetes. This signal is independent of the original signal identified in the Japanese population as revealed by conditional analysis. Consistent with the evidence observed for KCNQ1, our finding highlighted that multiple common genetic variations within the same gene region may independently contribute to disease risk [6, 12]. It will be worthwhile undertaking a further investigation of this region to search for population-specific and/or disease causal variants in different ethnic groups by fine-mapping as well as transethnic mapping.

The other genes in the region of our identified variant are also potential candidate genes for diabetes. ARF5 belongs to a family of guanine nucleotide-binding proteins that have been shown to play a role in vesicular trafficking and as activators of phospholipase D [46]. Islet expression of ARF5 was found to be induced threefold in rats receiving a high-carbohydrate diet [47]. The nearby SND1 gene, also known as the p100 transcription co-activator, is a member of the micronuclease family and plays a key role in transcription and splicing. The p100 transcriptional co-activator is present in endocrine cells and tissues, including the pancreas of cattle [48].

Among the type 2 diabetes loci first identified in non-European populations, other than KCNQ1, few have consistently been found to show a significant association in studies of individuals of European descent [6, 14, 15, 42]. The diabetes gene variant we identified, rs10229583, also showed a significant association in Europeans in the DIAGRAM Consortium, with a smaller effect size compared with East Asian individuals (p = 0.0024 by Cochran’s Q statistics, I2 = 0.8913). Interestingly, rare PAX4 mutations were first identified in Asian MODY probands [39, 41], but seldom found in those of European descent [49, 50]. This suggests that PAX4, like KCNQ1, may be particularly relevant for the pathogenesis of type 2 diabetes in East Asians individuals. Interestingly, rs10229583 is also in strong LD with a region spanning the neighbouring SND1 gene (Fig. 3). Further resequencing and transethnic mapping should help to identify the causal gene variant for type 2 diabetes within this region.

The novel locus we identified in Chinese individuals with type 2 diabetes has not been detected in previous GWAS performed in mainly individuals of European descent. We noted a highly significant LD variation between Chinese and European individuals in the region surrounding our identified variant. There is also significant variation in allele frequencies in Chinese compared with Europeans, as well as between Chinese and African individuals. This ethnic difference in LD pattern and risk allele frequency may lead to a differential impact in different populations and warrants further investigation by resequencing.

Our study has several limitations. The sample size of our GWAS was modest, resulting in limited power to identify genetic variants with small effect sizes. We have limited our discovery study to Southern Han Chinese, although the consistent replication seen in other East Asian population suggests that the findings may be applicable to other populations of Chinese descent.

In summary, we identify rs10229583 near PAX4 as a novel locus for type 2 diabetes in Chinese and other populations, providing new insights into the pathogenesis of type 2 diabetes.

Acknowledgements

We thank all medical and nursing staff of the Prince of Wales Hospital Diabetes Mellitus Education Centre, Hong Kong, for their commitment and professionalism. We would also like to thank the Genome Institution in Quebec for help with replication genotyping, and the Chinese University of Hong Kong Information Technology Services Centre for support with computing resources. Thanks go also to all the medical staff of the Shanghai Clinical Center for Diabetes, and to all participants and staff of the BioBank Japan Project. The Singapore BioBank and the Genome Institute of Singapore, Agency for Science, Technology and Research, Singapore provided services for tissue archival and genotyping, respectively.

Funding

This Hong Kong arm of the project was supported by the Hong Kong Foundation for Research and Development in Diabetes established under the auspices of the Chinese University of Hong Kong, the Hong Kong Governments Research Grant Committee Central Allocation Scheme (CUHK 1/04C), a Research Grants Council Earmarked Research Grant (CUHK4724/07M), the Innovation and Technology Fund (ITS/088/08 and ITS/487/09FP), National Institutes of Health Grant NIH-RFA DK-085545-01 (from the National Institute of Diabetes and Digestive and Kidney Diseases), a Chinese University Focused Investment Fund, a Chinese University Direct Grant, and support from the Research Fund of the Department of Medicine and Therapeutics and the Diabetes and Endocrine Research Fund of the Chinese University of Hong Kong. T. F. Leung. is supported by the Research Grants Council General Research Fund (469908 and 470909) and the CUHK Research Committee Group Research Scheme (3110034 and 3110060). P. Kwan and S. S. Cherny are supported by the Research Grants Council of the Hong Kong Special Administrative Region, China (HKU762308M and CUHK4466/06M). N. L. S. Tang. acknowledges support from the Sir Michael and Lady Kadoorie Funded Research Into Cancer Genetics and a CUHK direct grant. J. W. Li. and T. F. Chan. are supported by the RGC General Research Fund (461708). G. N. Thomas acknowledges support from the Research Grants Council Earmarked Research Fund (HKU7672/06M).

The work of the Shanghai Jiao Tong University Diabetes Study was supported from grants from the National 973 Program (2011CB504001), 863 Program (2006AA02A409, 2012AA02A509), National Science Foundation of China (30800617, 81170735, 81200582), National Top Young Talents Supporting Program, Excellent Young Medical Expert of Shanghai (XYQ2011041), Shanghai Rising Star Program (12QH1401700), Key Program of the Shanghai Municipality for Basic Research (11JC1409600), Shanghai Talent Development Fund (2012041) and Key Discipline of Public Health of Shanghai (12GWZX0104).

This work was also supported by grants from the Korea Centers for Disease Control and Prevention (4845-301, 4851-302, 4851-307) and an intramural grant from the Korea National Institute of Health (2010-N73002-00), Republic of Korea. Y. S. Cho was supported from Hallym University Research Fund 2012 (HRF-201203-008) and the National Research Foundation of Korea (NRF) grant funded by the Korean government (MEST) (2012R1A2A1A03006155).

The Japanese part of the project was supported by a grant from the Leading Project of Ministry of Education, Culture, Sports, Science and Technology Japan.

The Singapore Prospective Study Program was funded through grants from the Biomedical Research Council of Singapore (BMRC 05/1/36/19/413 and 03/1/27/18/216) and the National Medical Research Council of Singapore (NMRC/1174/2008). The Singapore Malay Eye Study was funded by the National Medical Research Council (NMRC 0796/2003, IRG07nov013, and NMRC/STaR/0003/2008) and Biomedical Research Council (BMRC, 09/1/35/19/616). The Singapore Indian Eye Study was funded by grants from the Biomedical Research Council of Singapore (BMRC 09/1/35/19/616 and BMRC 08/1/35/19/550) and National Medical Research Council of Singapore (NMRC/STaR/0003/2008). E. S. Tai also receives additional support from the National Medical Research Council through a clinician scientist award.

The Han Chinese GWAS was funded by the Knowledge Innovation Program of Chinese Academy of Sciences (KSCX2-EW-R-10), the Key Program of National Natural Science Foundation of China (30930081), the National High Technology Research and Development Program of China (863 Program) (2009AA022704).

At the Wellcome Trust Sanger Institute, E. Zeggini, K. Panoutsopoulou and A. G. Day-Williams are supported by the Wellcome Trust (098051).

M. I. McCarthy acknowledges support from the EU Framework 7 ENGAGE (HEALTH-F4-2007-201413), The Wellcome Trust (098381) and the Medical Research Council (MRC-G0601261).

Duality of interest

The authors declare that there is no duality of interest associated with this manuscript.

Contribution statement

The study was supervised by RCWM, CH, WYS, YB, JCNC and WJ. The experiments were conceived and designed by RCWM, CH, PK, TFL, GNT, MJG, KH, TW, J-YL, Y-JF, SM, SSC, MCYN, KX, RH, LJ, XL, YSC, TK, EST, EZ, MIM, KLH, LB, BT, WYS, YB, JCNC and WJ. Data were acquired by RCWM, CH, VKLL, JW, YW (Shanghai), YJK, HF, HML, FJ, XM, XH, ST, JL (Shanghai), XZ, GX, PK, TFL, GNT, KH, CW, JW, WY, DP-KN, Y-JF, ACWN, HF, APSK, TY, SK-WT, JW, PCL, NLST, JL (Singapore), T-YW, J-YL, SM, SSC, MCYN, KX, APM, SK, RH, LJ, XL, YSC, TK, EST, EZ, MIM, KLH, LB, BT, WYS, YB, JCNC and WJ. Statistical analysis was performed by CHT, RZ, XS, JSKH, YW (Hong Kong) and CH. The data were analysed by CHT, RZ, RCWM, CH, MJG, XS, KH, JSKH, CW, HL, LL, YW (Shanghai), KP, AGDW, JWL, TY, T-FC, SKWT, HYS, APM, SK, EZ, WYS, JCNC and WJ. The manuscript was written by RCWM, CH, CHT, RZ, JWL, TFL, WYS, JCNC and WJ. All authors contributed to the drafting or critical revision of the manuscript for important intellectual content. All authors reviewed and approved the final manuscript.

Supplementary material

125_2013_2874_MOESM1_ESM.pdf (166 kb)
ESM Methods(PDF 165 kb)
125_2013_2874_MOESM2_ESM.pdf (40 kb)
ESM Members of the DIAGRAM and MuTHER Consortia(PDF 40.2 kb)
125_2013_2874_MOESM3_ESM.pdf (255 kb)
ESM Fig. 1(PDF 254 kb)
125_2013_2874_MOESM4_ESM.pdf (216 kb)
ESM Fig. 2(PDF 216 kb)
125_2013_2874_MOESM5_ESM.pdf (112 kb)
ESM Fig. 3(PDF 111 kb)
125_2013_2874_MOESM6_ESM.pdf (655 kb)
ESM Fig. 4(PDF 654 kb)
125_2013_2874_MOESM7_ESM.pdf (102 kb)
ESM Fig. 5(PDF 101 kb)
125_2013_2874_MOESM8_ESM.pdf (164 kb)
ESM Fig. 6(PDF 163 kb)
125_2013_2874_MOESM9_ESM.pdf (75 kb)
ESM Fig. 7(PDF 74.9 kb)
125_2013_2874_MOESM10_ESM.pdf (284 kb)
ESM Fig. 8(PDF 283 kb)
125_2013_2874_MOESM11_ESM.pdf (52 kb)
ESM Table 1(PDF 52.3 kb)
125_2013_2874_MOESM12_ESM.pdf (71 kb)
ESM Table 2(PDF 70.9 kb)
125_2013_2874_MOESM13_ESM.pdf (63 kb)
ESM Table 3(PDF 62.5 kb)
125_2013_2874_MOESM14_ESM.pdf (68 kb)
ESM Table 4(PDF 68.4 kb)
125_2013_2874_MOESM15_ESM.pdf (150 kb)
ESM Table 5(PDF 150 kb)
125_2013_2874_MOESM16_ESM.pdf (80 kb)
ESM Table 6(PDF 79.5 kb)
125_2013_2874_MOESM17_ESM.pdf (96 kb)
ESM Table 7(PDF 96.2 kb)
125_2013_2874_MOESM18_ESM.pdf (85 kb)
ESM Table 8(PDF 84.9 kb)
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ESM Table 9(PDF 78.2 kb)
125_2013_2874_MOESM20_ESM.pdf (75 kb)
ESM Table 10(PDF 75.2 kb)
125_2013_2874_MOESM21_ESM.pdf (72 kb)
ESM Table 11(PDF 72.4 kb)
125_2013_2874_MOESM22_ESM.pdf (85 kb)
ESM Table 12(PDF 84.9 kb)

Copyright information

© The Author(s) 2013

Open Access This article is distributed under the terms of the Creative Commons Attribution Noncommercial License which permits any noncommercial use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.

Authors and Affiliations

  • R. C. W. Ma
    • 1
    • 2
    • 3
  • C. Hu
    • 4
    • 5
  • C. H. Tam
    • 1
  • R. Zhang
    • 4
  • P. Kwan
    • 1
  • T. F. Leung
    • 6
  • G. N. Thomas
    • 7
  • M. J. Go
    • 8
  • K. Hara
    • 9
    • 10
  • X. Sim
    • 11
    • 12
  • J. S. K. Ho
    • 1
  • C. Wang
    • 4
  • H. Li
    • 13
  • L. Lu
    • 13
  • Y. Wang
    • 13
  • J. W. Li
    • 14
  • Y. Wang
    • 1
  • V. K. L. Lam
    • 1
  • J. Wang
    • 4
  • W. Yu
    • 4
  • Y. J. Kim
    • 8
  • D. P. Ng
    • 15
  • H. Fujita
    • 9
  • K. Panoutsopoulou
    • 16
  • A. G. Day-Williams
    • 16
  • H. M. Lee
    • 1
  • A. C. W. Ng
    • 1
  • Y-J. Fang
    • 17
  • A. P. S. Kong
    • 1
  • F. Jiang
    • 4
  • X. Ma
    • 4
  • X. Hou
    • 4
  • S. Tang
    • 4
  • J. Lu
    • 4
  • T. Yamauchi
    • 9
  • S. K. W. Tsui
    • 18
  • J. Woo
    • 1
  • P. C. Leung
    • 19
  • X. Zhang
    • 5
  • N. L. S. Tang
    • 20
  • H. Y. Sy
    • 6
  • J. Liu
    • 21
  • T. Y. Wong
    • 22
    • 23
    • 24
  • J. Y. Lee
    • 8
  • S. Maeda
    • 25
  • G. Xu
    • 1
  • S. S. Cherny
    • 26
  • T. F. Chan
    • 14
  • M. C. Y. Ng
    • 27
  • K. Xiang
    • 4
  • A. P. Morris
    • 28
  • DIAGRAM Consortium
  • S. Keildson
    • 28
  • The MuTHER Consortium
  • R. Hu
    • 29
  • L. Ji
    • 30
  • X. Lin
    • 13
  • Y. S. Cho
    • 31
  • T. Kadowaki
    • 9
  • E. S. Tai
    • 32
    • 33
  • E. Zeggini
    • 16
  • M. I. McCarthy
    • 28
    • 34
  • K. L. Hon
    • 6
  • L. Baum
    • 35
  • B. Tomlinson
    • 1
  • W. Y. So
    • 1
  • Y. Bao
    • 4
  • J. C. N. Chan
    • 1
    • 2
    • 3
  • W. Jia
    • 4
  1. 1.Department of Medicine and TherapeuticsChinese University of Hong Kong, Prince of Wales HospitalHong KongPeople’s Republic of China
  2. 2.Hong Kong Institute of Diabetes and ObesityChinese University of Hong KongHong KongPeople’s Republic of China
  3. 3.Li Ka Shing Institute of Life SciencesChinese University of Hong KongHong KongPeople’s Republic of China
  4. 4.Department of Endocrinology and Metabolism, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai Clinical Center for Diabetes, Shanghai Key Clinical Center for Metabolic DiseaseShanghai Jiao Tong University Affiliated Sixth People’s HospitalShanghaiPeople’s Republic of China
  5. 5.Shanghai Jiao Tong University Affiliated Sixth People’s Hospital South CampusShanghaiPeople’s Republic of China
  6. 6.Department of PaediatricsChinese University of Hong KongHong KongPeople’s Republic of China
  7. 7.Department of Public Health, Epidemiology and BiostatisticsUniversity of BirminghamBirminghamUK
  8. 8.Center for Genome Science, National Institute of Health, Osong Health Technology Administration ComplexCheongwon-gunRepublic of Korea
  9. 9.Department of Diabetes and Metabolic Diseases, Graduate School of MedicineUniversity of TokyoTokyoJapan
  10. 10.Department of Integrated Molecular Science on Metabolic DiseasesUniversity of Tokyo HospitalTokyoJapan
  11. 11.Centre for Molecular Epidemiology, Saw Swee Hock School of Public HealthNational University of SingaporeSingaporeRepublic of Singapore
  12. 12.Center for Statistical Genetics and Department of BiostatisticsUniversity of MichiganAnn ArborUSA
  13. 13.Key Laboratory of Nutrition and Metabolism, Institute for Nutritional Sciences, Shanghai Institutes for Biological SciencesChinese Academy of Sciences and Graduate School of the Chinese Academy of SciencesShanghaiPeople’s Republic of China
  14. 14.School of Life SciencesChinese University of Hong KongHong KongPeople’s Republic of China
  15. 15.Saw Swee Hock School of Public HealthNational University of SingaporeSingaporeRepublic of Singapore
  16. 16.Wellcome Trust Sanger Institute, Wellcome Trust Genome CampusCambridgeUK
  17. 17.Department of Colorectal Surgery, State Key Laboratory of Oncology in South ChinaSun Yat-sen University Cancer CenterGuangzhouPeople’s Republic of China
  18. 18.School of Biomedical SciencesChinese University of Hong KongHong KongPeople’s Republic of China
  19. 19.Department of OrthopaedicsChinese University of Hong KongHong KongPeople’s Republic of China
  20. 20.Department of Chemical PathologyChinese University of Hong KongHong KongPeople’s Republic of China
  21. 21.Genome Institute of Singapore, Agency for Science, Technology and ResearchSingaporeRepublic of Singapore
  22. 22.Singapore Eye Research Institute, Singapore National Eye CentreSingaporeRepublic of Singapore
  23. 23.Department of Ophthalmology, Yong Loo Lin School of MedicineNational University of SingaporeSingaporeRepublic of Singapore
  24. 24.Centre for Eye Research AustraliaUniversity of MelbourneEast MelbourneAustralia
  25. 25.Laboratory for Endocrinology and Metabolism, RIKEN Center for Genomic MedicineYokohamaJapan
  26. 26.Department of Psychiatry and State Key Laboratory of Brain and Cognitive SciencesUniversity of Hong KongHong KongPeople’s Republic of China
  27. 27.Center for Genomics and Personalized Medicine Research, Center for Diabetes Research, Wake Forest School of MedicineWinston-SalemUSA
  28. 28.Wellcome Trust Centre for Human GeneticsUniversity of OxfordOxfordUK
  29. 29.Institute of Endocrinology and Diabetology, Huashan Hospital, Shanghai Medical CollegeFudan UniversityShanghaiPeople’s Republic of China
  30. 30.Department of Endocrinology and MetabolismPeking University People’s HospitalBeijingPeople’s Republic of China
  31. 31.Department of Biomedical ScienceHallym UniversityChuncheonRepublic of Korea
  32. 32.Department of Medicine, Yong Loo Lin School of MedicineNational University of SingaporeSingaporeRepublic of Singapore
  33. 33.Graduate Medical SchoolDuke-National University of SingaporeSingaporeRepublic of Singapore
  34. 34.Oxford Centre for Diabetes, Endocrinology and MetabolismUniversity of Oxford, Churchill HospitalOxfordUK
  35. 35.School of PharmacyChinese University of Hong KongHong KongPeople’s Republic of China

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