Introduction Type 2 diabetes (T2D) is a common complex disease that affects over a billion people worldwide [1]. Through genome-wide association studies (GWAS), at least 24 genetic susceptibility loci have been reported for T2D [1]–[9], including a SNP, rs7593730, at 2q24 near the RBMS1 and ITGB6 genes that was associated with diabetes risk in a recent report from the Nurses' Health Study/Health Professionals Follow-up Study (NHS/HPFS) [2]. However, most of the reported genetic variants have small to moderate effects and account for only a small proportion of the heritability of T2D, suggesting that the majority of inter-person genetic variation in this disease remains to be determined. Over the last two decades, China, like many other Asian countries, has experienced a dramatic increase in T2D incidence. Cumulative evidence suggests that Asians may be more susceptible to insulin resistance compared with populations of European ancestry [10]. However, among the previously reported T2D genetic markers, only three SNPs – including two reported very recently – have been identified in populations of Asian ancestry [8], [9]. SNP rs2283228 in the KCNQ1 gene was identified in a 3-stage study that included 194 diabetes patients and 1,558 controls and 268,068 SNPs in the first (discovery) stage [8]. A study conducted among Han Chinese in Taiwan recently identified two additional novel loci in the protein tyrosine phosphatase receptor type D (PTPRD; P = 8.54×10−10) and serine racemase (SRR; P = 3.06×10−9) genes [9]. Large genetic studies conducted in Asian populations will facilitate the identification of additional genetic markers for T2D, particularly for markers with a higher frequency in Asians than in other populations. We recently completed a GWAS of T2D in Shanghai. We report here our first effort, using a fast-track, multiple-stage study approach, to identify novel genetic markers for diabetes. Methods Ethics statement The study protocol was approved by the institutional review boards at Vanderbilt University Medical Center and at each of the collaborating institutes. Informed consent was obtained from all participants. Study design and population This study consisted of a discovery stage and two validation stages, i.e. an in silico and a de novo validation study. The overall study design is presented in Figure S1. The discovery stage included 1,019 T2D cases, 886 incident T2D cases from the Shanghai Women's Health Study (SWHS), an ongoing, population-based, prospective cohort study of women living in Shanghai, and 133 prevalent T2D cases identified among controls of the Shanghai Breast Cancer Study (SBCS), who were recruited in Shanghai during approximately the same period as the SWHS [11]. Controls for the discovery phase were 1,710 non-diabetic female controls from the SBCS (for further details, see Text S1, online). The biologic samples used for genotyping in this study were collected by the SWHS and SBCS. Genotyping and quality control procedures DNA samples were genotyped using the Affymetrix Genome-Wide Human SNP Array 6.0. Extensive quality control (QC) procedures were implemented in the study. In the SWHS/SBCS GWAS scan, three positive QC samples purchased from Coriell Cell Repositories and a negative QC sample were included in each of the 96-well plates of the Affymetrix SNP Array 6.0. SNP data obtained from positive quality control samples showed a very high concordance rate of called genotypes based on 79,764,872 comparisons (mean, 99.87%; median, 100%). Samples with genotyping call rates less than 95% were excluded. The sex of all study samples was confirmed to be female. The identity-by-descent analysis based on identity by state was performed to detect first-degree cryptic relationships using PLINK version 1.06 [12]. We excluded from the study 21 samples that had: 1) call rate 20% were chosen for a second in silico replication using GWAS scan data from a Korean T2D study, which included 1,042 cases and 2,943 controls genotyped with the Affymetrix Genome-Wide Human SNP Array 5.0 platform. In order to improve yield, only the top SNPs that are included in Affymetrix 5.0 (N = 56) or that are in high LD (r2>0.8) with at least one SNP on Affymetrix 5.0 (N = 9) were selected for replication (Table S1). Of the 65 SNPs, the top 8 SNPs replicated in the Korean T2D study were further investigated using GWAS data from a T2D study conducted among Singapore Chinese (2,010 cases and 1,945 controls) who were genotyped by using Illumina HumanHap 610 or Illumina Human1M (Table S2). Four of the 8 SNPs were not directly genotyped in the Singapore study, so instead, we selected SNPs that are in strong LD with these 4 SNPs (imputed SNP information became available recently and is presented in this report). Finally, the 5 top SNPs (rs2815429, rs10906115, rs1359790, rs10751301, and rs1436955) were selected for de novo genotyping in an independent sample set of 1,645 T2D cases and 1,649 controls identified from the SWHS and Shanghai Men's Health Study (SMHS). Four of these SNPs (rs10906115, rs1359790, rs10751301, and rs1436955) were selected for the final stage of de novo genotyping replication in two independent Chinese studies, the Wuhan Diabetes Study (WDS; 1,063 cases and 1,408 controls) and the Nutrition and Health of Aging Population in China (NHAPC) study (424 cases and 1,908 controls). Detailed descriptions of the study designs and populations for each of the participating studies are presented in Text S1 online. Genotyping for the 5 SNPs included in the SWHS and SMHS sample set was completed using the iPLEX Sequenom MassArray platform. Included in each 96-well plate as quality control samples were two negative controls, two blinded duplicates, and two samples included in the HapMap project. We also included 65 subjects who had been genotyped by the Affymetrix SNP Array 6.0 in the Sequenom genotyping. The consistency rate was 100% for all SNPs for the blinded duplicates, compared with the HapMap data and compared with data from the Affymetrix SNP Array 6.0. Genotyping for the final 4 SNPs in the WDS and NHAPC was completed using TaqMan assays at the two local institute laboratories using reagents provided by the Vanderbilt Molecular Epidemiology Laboratory. Both laboratories were asked to genotype a trial plate provided by the Vanderbilt Molecular Epidemiology Laboratory that contained DNA from 70 Chinese samples before the main study genotyping was conducted. The consistency rates for these trial samples were 100% compared with genotypes previously determined at Vanderbilt for all four SNPs in both local laboratories. In addition, replicate samples comparing 3–7% of all study samples were dispersed among genotyping plates for both studies. Imputation The imputation of un-genotyped SNPs in all participating GWASs was carried out after the completion of the current study using the programs MACH (http://www.sph.umich.edu/csg/abecasis/MACH/) or IMPUTE (https://mathgen.stats.ox.ac.uk/impute) with HapMap Asian data as the reference for Asians and CEU data as the reference for European-ancestry samples. Only data with high imputation quality (RSQR >0.3 for MACH) were included in the current analysis. Statistical analyses PLINK version 1.06 was used to analyze genome-wide data obtained in the SBCS/SWHS GWAS scan. Population structure was evaluated by principal component analysis using EIGENSTRAT (http://genepath.med.harvard.edu/~reich/Software.htm).A set of 12,533 SNPs with a MAF ≥10% in Chinese samples and a distance of ≥25 kb between two adjacent SNPs was selected to evaluate the population structure. The first two principal components were included in the logistic regression models for adjustment of population structures. The inflation factor λ was estimated to be 1.03, suggesting that population substructure, if present, should not have any appreciable effect on the results. Pooled and meta-analyses were carried out in SAS to derive combined odds ratios (OR) by using data from studies of all stages. We applied the weighted z-statistics method, where weights are proportional to the square root of the number of subjects in each study. Results from both random and fixed effect models are presented. ORs and 95% confidence intervals (CI) were estimated using logistic regression models with adjustment for age, BMI, population structure (for GWAS data), and gender, when appropriate. Analyses with additional adjustment for smoking were conducted by pooled analysis whenever possible and by meta-analysis when KARE data were included in order to examine the confounding and modification effects of these factors (Table S2). Genotype distributions for the top 4 SNPs included in the final de novo genotyping were consistent with HWE (P> 0.05) in each study. All P values presented are based on two-tailed tests, except where indicated otherwise. Results The general characteristics of the participating study populations are presented in Table 1. T2D cases had a higher BMI than controls across all studies. Except for the SWHS, SMHS, and Shanghai Nutrition Institute (SNI) validation studies, where cases and controls were matched on age, cases were older than controls in all other studies. A difference in gender distribution was also seen in several studies. These variables were adjusted for in subsequent analyses. 10.1371/journal.pgen.1001127.t001 Table 1 Characteristics of the study participants. Study population Cases Controls % Men Agea BMIa Study area N = 9,794 N = 14,615 Cases Controls Cases Controls Cases Controls Discovery Stage SBCS/SWHS Shanghaib 1019 1710 0.0 0.0 51.7±6.7 48.7±8.5 26.5±3.7 23.1±3.3 Replication Set I NHS/HPFS USA 2591 3052 43.4 42.5 54.2±7.6 53.9±7.5 27.6±4.6 24.1±3.6 KARE Korea 1042 2943 51.7 46.0 56.4±8.6 51.1±8.6 25.5±3.3 24.1±2.9 SDCS/SP2 Singaporec 2010 1945 50.0 42.0 64.4±10.2 47.2±10.7 25.3±3.9 22.6±3.6 Replication Set II SWHS/SMHS Shanghaib 1645 1649 44.6 44.5 58.9±8.8 58.9±8.7 26.3±3.5 24.2±3.4 NHAPC Shanghai/Beijing b 424 1908 48.8 41.5 59.7±5.7 58.4±6.0 25.4±3.5 23.8±3.4 WDS Wuhanb 1063 1408 57.6 58.7 50.7±10.4 42.8±10.0 24.8±3.7 23.0±3.1 a Age and BMI are presented as mean ± SD. b Conducted in China and included Chinese participants only. c Only Chinese participants were included. Table 2 presents the results of analyses of associations of T2D with previously reported, GWAS-identified genetic markers in our discovery samples [1]–[9]. Of the 24 SNPs reported by previous GWAS, 15 were directly genotyped by the Affymetrix SNP Array 6.0. One SNP (rs7578597) showed a MAF = 0 in HapMap CHB data and was not included on the Affymetrix 6.0 chip. The remaining 8 SNPs, including rs2943641, rs10010131, rs13266634, rs12779790, and rs4430796, as well as the newly identified markers rs391300 and rs17584499, were imputed. SNP rs4430796 showed low imputation quality (RSQR = 0.06) in the SBCS/SWHS GWAS and was excluded from the analysis. 10.1371/journal.pgen.1001127.t002 Table 2 Associations of previously-reported T2D related SNPs with disease risk in Chinese women. Risk allele frequency SNPa Region Allelesb Gene Genotypingc Cases Controls OR (95% CI)d P for trende Reported Effect Power to detect associationf rs10923931 [1] 1p12 T/G NOTCH2,ADAM30 Affy 6.0 0.03 0.03 1.02 (0.71–1.48) 0.454 1.13 (1.08–1.17) 0.19 rs2943641 [3] 2q36.3 C/T LOC64673/IRS1 Imputed 0.93 0.93 1.20 (0.94–1.54) 0.070 1.19 (1.13–1.25) 0.45 rs1801282 [1] 3p25.2 C/G PPARG Affy 6.0 0.94 0.95 0.99 (0.76–1.30) 0.523 1.14 (1.08–1.20) 0.25 rs4607103 [1] 3p14.1 C/T ADAMTS9 Affy 6.0 0.62 0.63 1.03 (0.91–1.17) 0.302 1.09 (1.06–1.12) 0.43 rs4402960 [6] 3q27.2 T/G IGF2BP2 Affy 6.0 0.28 0.24 1.28 (1.12–1.49) 2.4×10−4 1.14 (1.11–1.18) 0.65 rs10010131 [4] 4p16.1 G/A WFS1 Imputed 0.95 0.95 0.95 (0.71–1.28) 0.632 1.11 (1.05–1.16) 0.19 rs10946398 [6] 6p22.3 C/A CDKAL1 Affy 6.0 0.45 0.41 1.20 (1.06–1.36) 0.002 1.14 (1.11–1.17) 0.75 rs864745 [1] 7p15.1 T/C JAZF1 Affy 6.0 0.78 0.78 0.97 (0.84–1.13) 0.637 1.10 (1.07–1.13) 0.40 rs13266634 [7] 8q24.11 C/T SLC30A8 Imputed 0.61 0.58 1.19 (1.04–1.35) 0.004 1.15 (1.12–1.19) 0.79 rs10811661 [6] 9p21.3 T/C CDKN2A/B Affy 6.0 0.56 0.52 1.24 (1.10–1.40) 3.05×10−4 1.20 (1.14–1.25) 0.95 rs564398 [5] 9p21.3 T/C CDKN2B Affy 6.0 0.89 0.88 1.08 (0.89–1.31) 0.209 1.12 (1.07–1.17) 0.36 rs12779790 [1] 10p13 G/A CDC123,CAMK1D Imputed 0.17 0.17 1.04 (0.88–1.23) 0.309 1.11 (1.07–1.14) 0.42 rs5015480 [1] 10q23.33 C/T HHEX Affy 6.0 0.20 0.17 1.42 (1.20–1.66) 9.1×10−6 1.17 (1.11–1.24) 0.70 rs7901695 [7] 10q25.2 C/T TCF7L2 Affy 6.0 0.04 0.03 1.41 (1.03–1.93) 0.017 1.37 (1.31–1.43) 0.67 rs2283228 [8] 11p15.5 C/T KCNQ1 Affy 6.0 0.66 0.62 1.16 (1.03–1.30) 0.003 1,26 (1.18–1.34) 0.96 rs5215 [4] 11p15.1 C/T KCNJ11 Affy 6.0 0.42 0.38 1.21 (1.07–1.37) 0.001 1.14 (1.10–1.19) 0.74 rs1495377 [5] 12q15 G/C NR Affy 6.0 0.27 0.27 0.97 (0.84–1.11) 0.683 1.12 (1.06–1.18) 0.57 rs7961581 [1] 12q21.1 C/T TSPAN8,LGR5 Affy 6.0 0.20 0.21 0.96 (0.82–1.12) 0.708 1.09 (1.06–1.12) 0.36 rs8050136 [6] 16q12.2 A/C FTO Affy 6.0 0.13 0.12 0.98 (0.81–1.19) 0.574 1.17 (1.12–1.22) 0.60 rs391300 [9] 17p13.3 C/T SRR Imputed 0.71 0.72 0.96 (0.83–1.10) 0.739 1.28 (1.18–1.39) 0.99 rs17584499 [9] 9p24.1 T/C PTPRD Imputed 0.10 0.10 0.97 (0.71–1.32) 0.575 1.57 (1.13–1.83) 1.00 rs7593730 [2] 2q24.2 T/C RBMS1/ITGB6 Affy 6.0 0.82 0.84 0.96 (0.82–1.14) 0.331 1.11 (1.08–1.16) 0.40 a Two SNPs were not included in the current analysis because of poor imputation quality (RSQR = 0.06 for rs4430796) or a very low MAF in the Chinese population (rs7578597 MAF = 0 in HapMap CHB samples). b Risk allele/reference allele initially reported (based on forward strand). c For imputed SNPs, dosage data with imputation uncertainty taken into account were used to evaluate the association. d Results were derived from analysis of 1,019 cases and 1,710 controls with adjustment for age, BMI, and two major principal components. e One-tailed p-values. f The power was estimated under the additive model and given the reported effect size, 1,019 cases, 1,710 controls, and α = 0.05 (one-sided). We found that 8 of these SNPs showed an association consistent with initial reports at P 0.9 and for the Singapore studies using IMPUTE with PROPER_INFO >0.85. The imputed SNP rs12779790 was associated with a per allele OR of 1.10 (95% CI = 1.01–1.19, P = 0.035) in the analysis of pooled data from three studies. However, when both rs12779790 and rs10906115 were included in the same logistic model, the association with rs10906115 remained statistically significant (per allele OR = 1.09, 95% CI = 1.02–1.16, P = 0.007), while the association with rs12779790 was no longer statistically significant (per allele OR = 1.04 [95% CI = 0.96–1.12], P = 0.38; Table 4). These data provide strong evidence that rs10906115 is a new genetic variant at 10p13 independent of the previously-identified SNP rs12779790. 10.1371/journal.pgen.1001127.t004 Table 4 Association of SNPs rs10906115 and rs12779790 with type 2 diabetes. SNP Study OR (95% CI)a P rs10906115 Without adjustment for rs12779790 SBCS/SWHS 1.20 (1.05–1.36) 0.007 NHS/HPFS 1.10 (1.02–1.20) 0.020 Singapore 1.02 (0.90–1.16) 0.74 Combinedb 1.10 (1.04–1.17) 5.0×10−4 With adjustment for rs12779790 SBCS/SWHS 1.21 (1.05–1.38) 0.007 NHS/HPFS 1.07 (0.98–1.17) 0.16 Singapore 1.01 (0.88–1.16) 0.86 Combinedb 1.09 (1.02–1.16) 0.007 rs12779790 Without adjustment for rs10906115 SBCS/SWHS 1.05 (0.90–1.23) 0.54 NHS/HPFS 1.16 (1.04–1.28) 0.006 Singapore 1.04 (0.89–1.23) 0.63 Combinedb 1.10 (1.01–1.19) 0.035 With adjustment for rs10906115 SBCS/SWHS 0.97 (0.82–1.15) 0.73 NHS/HPFS 1.12 (1.01–1.25) 0.048 Singapore 1.04 (0.87–1.23) 0.69 Combinedb 1.04 (0.96–1.12) 0.38 a Adjusted for age, gender, BMI, and two major principal components. b Derived from meta-analysis. SNP rs10906115 is located 22.4 kb downstream of the cell division-cycle 123 homolog (S. cerevisiae) (CDC123) gene and 76.6 kb upstream of the calcium/calmodulin-dependent protein kinase ID (CAMK1D) gene (Figure 2). The CDC123 gene encodes a protein involved in cell cycle regulation and nutritional control of gene transcription [18]. The CAMK1D gene encodes a member of the Ca2+/calmodulin-dependent protein kinase 1 subfamily of serine/threonine kinases. The encoded protein may be involved in the regulation of granulocyte function through the chemokine signal transduction pathway [19]. The role of the CDC123 and CAMK1D genes in the etiology of T2D is unclear. SNP rs1436955, located on chromosome 15q22.2 (Figure 2), is 51.4 kb downstream of a C2 calcium-dependent domain containing the 4B gene (C2CD4B; also known as NLF2 or FAM148B). C2CD4B is up-regulated by pro-inflammatory cytokines and may play a role in regulating genes that control cellular architecture [20]. The role of inflammation in the pathophsyiology of T2D has been suggested previously [21]–[25]. C2CD4B and SPRY2 are both highly expressed in human pancreatic tissue [26]. Intriguingly, a very recent report from the Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) found that a SNP (rs11071657) near the C2CD4B gene was associated with fasting glucose (P = 3.6×10−8) and T2D (P = 2.9×10−3) [27]. SNPs rs11071657 and rs1436955, however, are not in LD (r2 = 0.04) in Asians, although they are weakly related (r2 = 0.25) in Europeans, according to HapMap data. SNP rs11071657 is not included in the Affymetrix SNP 6.0 array. Imputed data from the SBCS/SWHS GWAS showed that this SNP was not significantly associated with T2D risk (per A allele OR = 1.06, 95% CI = 0.94–1.19), although the direction of the association was consistent with that reported by the MAGIC consortium [27]. Adjusting for rs11071657 did not alter the association of T2D risk with rs1436955 (per allele OR = 1.21, 95% CI = 1.06–1.39, P = 0.006). Again, these data strongly imply that rs1436955 may be a new genetic risk variant for T2D at 15q22.2 independent of the recently reported SNP rs11071657. In summary, in this first GWAS of T2D conducted in a Chinese population, we identified a novel genetic susceptibility locus for T2D, rs1359790, at 13q31.1. Furthermore, we revealed two new genetic variants (rs10906115 at 10p13 and rs1436955 at15q22.2) near T2D susceptibility loci previously reported by GWAS of T2D conducted in European-ancestry populations. Our study demonstrates the value of conducting GWAS in non-European populations for the identification of novel genetic susceptibility markers for T2D. Supporting Information Table S1 Association of 65 SNPs included in Replication Set I with T2D risk. (0.13 MB DOC) Click here for additional data file. Table S2 Association of the top 4 SNPs with T2D risk with additional adjustments. (0.07 MB DOC) Click here for additional data file. Table S3 Association of SNPs that are in LD with established SNPs in European-ancestry populations (for SNPs discovered in European ancestry populations) or with the reported SNPs in Asians (for SNPs discovered in Asians). (0.48 MB DOC) Click here for additional data file. Table S4 Results for the four SNPs showing promising associations with T2D stratified by smoking, BMI, family history of type 2 diabetes, and age of diagnosis in the combined data. (0.07 MB DOC) Click here for additional data file. Figure S1 Study design. (0.09 MB TIF) Click here for additional data file. Figure S2 MDS analyses to confirm all subjects were Asians. (1.55 MB TIF) Click here for additional data file. Figure S3 QQ Plot. (9.73 MB TIF) Click here for additional data file. Text S1 Supplementary Methods. (0.13 MB DOC) Click here for additional data file.
