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Implication of genetic variants near SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, FTO, TCF2, KCNQ1, and WFS1 in Type 2 Diabetes in a Chinese population
- Xueyao Han†1,
- Yingying Luo1,
- Qian Ren1,
- Xiuying Zhang1,
- Fang Wang1,
- Xiuqin Sun1,
- Xianghai Zhou1 and
- Linong Ji1Email author
© Han et al; licensee BioMed Central Ltd. 2010
Received: 16 August 2009
Accepted: 28 May 2010
Published: 28 May 2010
Recently, several genome-wide and candidate gene association studies have identified many novel genetic loci for type 2 diabetes (T2D); among these genes, CDKAL1, IGF2BP2, SLC30A8, CDKN2A/B, HHEX, FTO, TCF2, KCNQ1, and WFS1 are the most important. We aimed to determine the effects of these genetic loci associated with T2D in the Chinese Han population of China.
Single-nucleotide polymorphisms (SNPs) in or near CDKAL1, IGF2BP2, SLC30A8, CDKN2A/B, HHEX, FTO, TCF2, KCNQ1, and WFS1 genes were genotyped in a case-control Chinese Han sample living in Beijing, China involving 1024 patients with T2D and 1005 control subjects.
In Chinese Han, we replicated the associations between 7 genetic loci and T2D, with risk allele-specific odds ratios (ORs) as follows: 1.27 (95% CI, 1.11-1.45; p = 0.0008) for CDKAL1-rs10946398, 1.26 (95% CI, 1.08-1.47; p = 0.003) for IGF2BP2-rs4402960, 1.19 (95% CI, 1.04-1.37; p = 0.009) for SLC30A8-rs13266634, 1.22 (95% CI, 1.06-1.41; p = 0.005) for CDKN2A/B-rs10811661, 1.20 (95% CI, 1.01-1.42; p = 0.03) for HHEX-rs5015480, 1.37 (95% CI, 1.19-1.69; p = 1.0 × 10-4) for KCNQ1-rs2237892, and 1.24 (95% CI, 1.01-1.52; p = 0.046) for FTO-rs8050136 after adjustment for age, gender, and body mass index. Not only did an association between WFS1-rs6446482 and early-onset T2D exist in the subgroup analysis, but TCF2-rs7501939 and WFS1-rs6446482 were also confirmed to confer risk for T2D in this meta-analysis. Moreover, the relationship between FTO-rs8050136 and body mass index, together with the effect of CDKAL1-rs10946398 on beta cell function, was also observed in the control individuals.
Our findings support the important contribution of these genetic loci to susceptibility for T2D in the Chinese Han population in Beijing of China.
Genome-wide association studies (GWAS) have identified many novel susceptibility genes for type 2 diabetes (T2D) since 2007. In the early GWAS, common variants in IGF2BP2, CDKAL1, SLC30A8, HHEX, CDKN2A/B, FTO, and TCF2 loci were reported to increase the risk of T2D in Caucasians [1–6], and KCNQ1 was recently discovered as a new diabetogenic gene in Japanese samples . In addition, the Wolfram syndrome 1(WFS1) gene is one of the novel susceptibility genes for T2D identified by the candidate gene approach in Caucasians until recently . Compared with the susceptibility genes for T2D revealed in the following meta-analysis of GWAS , these 9 genes, which were discovered in relatively smaller samples, might represent the most diabetogenic genes and have larger effect sizes in the studied populations. Although these associations have been replicated in Caucasians, the roles of some loci remain less clear in Chinese. For example, in the Chinese Han population, the associations between single nucleotide polymorphisms (SNPs) at the HHEX, IGF2BP2, TCF2, and FTO loci with T2D were not consistently replicated [6, 10–15], and the findings in previous studies involving Caucasians for SNPs at the WFS1 locus have not been confirmed . Moreover, in contrast to the large-scale association studies in a European Caucasian population, such studies in China often have a relatively small sample size, and meta-analysis is becoming particularly useful to evaluate the effects of these genes.
Therefore, we hypothesized that the aforementioned genes were also the most important susceptibility genes for T2D in the Chinese Han population living in China. We aimed to test this hypothesis in a case-control study and perform a meta-analysis of all published data for these genetic loci in the Chinese Han population to fully evaluate their effects.
Clinical characteristics of the participants.
Age at examination(years)
58 ± 9
56 ± 12
25.0 ± 3.3
25.0 ± 3.1
Age of diagnosis(years)
49 ± 11
Fasting glucose (mmol/l)
5.2 ± 0.4
7.8 ± 2.8
Glucose at 2 h (mmol/l)
5.7 ± 1.2
Fasting insulin (pmol/l)
Anthropometric and biological measurements
All of the patients with T2D and control subjects were examined in the morning after an overnight fast for at least 8 hours, with measurements of height, weight, and blood pressure. Blood samples were collected for biochemical measurements of fasting plasma glucose for all the participants, and 2-hour plasma glucose during a 75 g OGTT for the controls. The plasma glucose concentrations were measured by the glucose oxidase-peroxidase method. The fasting serum insulin concentrations were measured by an electrochemiluminescence immunoassay on a Roche Elecsys 2010 Chemistry Analyzer. The homeostasis model assessment of the insulin resistance index (HOMA-IR) and beta-cell function (HOMA-ί) were assessed, as described previously .
Nine SNPs representing nine genes identified in recent GWAS or large candidate gene studies [1–8] were selected, including SLC30A8-rs13266634, HHEX- rs5015480, CDKAL1-rs10946398, CDKN2A/B-rs10811661, IGF2BP2-rs4402960, FTO-rs8050136, TCF2-rs7501939, KCNQ1-rs2237892, and WFS1- rs6446482. Genotyping of these SNPs was performed with an ABI SNaPshot® Multiplex System (Applied Biosystems, Foster City, CA, USA) in the Chinese National Human Genome Center in Shanghai. Genotyping accuracy, as determined from the genotype concordance between duplicate samples (130 samples), was 100%. The genotyping success rate was >95% for the patient and control groups.
Data are given as the means ± SD for quantitative variables with a normal distribution, and as medians (interquartile range) for non-normally distributed data. Fasting serum insulin, HOMA-IR, and HOMA-ί were natural logarithm-transformed to normal distributions before statistical analysis.
Characteristics were compared and tested for significant differences between patients and controls using Student's t test for continuous variables, and using χ2 test for categorical variable. Chi-squared tests were used to determine whether or not individual polymorphisms were in Hardy-Weinberg equilibrium (p > 0.05). The differences in allele and genotype frequencies between the patients with T2D and controls were analyzed using Pearson's χ2 test. Logistic regression analysis was performed to calculate risk allele-specific odds ratios (ORs), 95% confidential intervals (CIs), and corresponding p -values after adjustment for gender, age, and BMI as covariates; under an additive model, 10,000 permutations were used to obtain a true estimate for statistical significance.
Linear regression was applied to test quantitative variables for differences between the genotype groups. SNPs that showed nominal evidence for an association between the age of diagnosis (AOD) for T2D, fasting plasma glucose, fasting serum insulin, HOMA-ί, and HOMA-IR were adjusted by age, gender and BMI. Additionally, BMI was adjusted for age and gender. All analyses were adjusted using linear regression on a priori genotypic models (dominant, additive. and recessive). Due to a lack of validity of the large sample χ2 test statistic, only the dominant model was considered for SNPs with ≤10 individuals homozygous for the minor allele (for WFS1 and FTO loci in the present study).
All statistical tests were performed by PLINK, version 1.06 http://pngu.mgh.harvard.edu/~purcell/plink or SPSS, version 11.5 for Windows (SPSS, Inc., Chicago, IL, USA). A p-value < 0.05 was considered statistically significant (two-tailed).
Power calculations were performed using Quanto software (available at http://hydra.usc.edu/gxe/), and the powers shown in Additional file 1, Table S1 were calculated using ORs from published studies, sample sizes and minor allele frequencies (MAF) in the present study, the prevalence of T2D in Chinese of mainland China , and the type 1 error rate (0.05).
Pairwise linkage disequilibrium (LD) coefficients (r2) between SNPs were calculated by Haploview software, version 4.0 (Daly Lab at the Broad Institute, Cambridge, MA, USA).
To identify studies eligible for meta-analysis, MEDLINE citations (January 2000-January 2010) were surveyed using the National Library of Medicine's PubMed on-line search engine with each susceptibility gene name (CDKAL1, IGF2BP2, SLC30A8, CDKN2A/B, HHEX, KCNQ1, FTO, TCF2, and WFS1) and Chinese as key words. The retrieved references were read to identify studies that examined the allelic associations between the SNPs within the above nine genetic regions and T2D in Chinese Han living in China. For meta-analysis of WFS1, FTO, and TCF2, the C allele of WFS1-rs6446482, the A allele of FTO-rs8050136, and the T allele of TCF2- rs7501939 were used as surrogates for the minor A allele of WFS1- rs10010131, the A allele of FTO-rs9939609, and the G allele of TCF2-rs4430796 because they are in strong LD (r2 = 1) in a HapMap Chinese Han population. In the meta-analysis, Cochran's x2-based Q-statistic test was performed to assess heterogeneity in all of the studies included, and a random-effect model was adopted when heterogeneity existed, otherwise a fixed-effects model was appropriate. Combined ORs were calculated using the Mantel-Haenszel (fixed-effects) and DerSimonian and Laird (random-effects) tests. The significant P value of overall ORs was determined using the Z-test. All calculations for the meta-analysis were performed on Revman42 (provided by Cochrane-Collaboration, available at http://ims.cochrane.org/revman/download/revman-4).
Association with type 2 diabetes in the Chinese Han population.
Genotypes distribution a
Odd ratio (95%CI)
Allelic Pnominal c
Allelic Ppermuted d
1.23 × 10-5
2.47 × 10-5
1.0 × 10-4
Meta-analysis for SNPs at nine genetic loci in the Chinese Han population living in China.
Case risk AF
Control risk AF
Pfor overall effect
Genotype-phenotype relationship analysis
Genotype-age of diagnosis (AOD) for diabetes For the patients with T2D, multiple linear regression was used to evaluate the effect of nine representative SNPs on AOD with adjustment for gender and BMI under a priori models. The analysis revealed that only WFS1-rs6446482 was negatively related to AOD under a dominant model (p = 0.02), and the patients with one or two risk alleles of WFS1- rs6446482 were diagnosed approximately 3 years earlier than those without risk alleles (49 ± 11 years versus 52 ± 11 years, p = 0.03). Prompted by this observation, we performed a subgroup analysis of 406 early-onset T2D patients (age of diagnosis ≦45 years) and 973 control subjects, showing that WFS1- rs6446482 was significantly associated with early-onset T2D (risk allele-specific OR, 1.96; 95% CI, 1.05-4.76; nominal p = 0.03; empiric p = 0.03) after adjustment for gender, age, and BMI.
Genotype-BMI For analysis of the genotype-BMI relationship, only the control subjects were examined because the treatment for T2D might distort the relationship. Only FTO-rs8050136 of nine genetic loci was found to be significantly associated with BMI (p = 0.04) after adjusting for gender and age as covariates. The individuals with one or two risk alleles had a higher BMI (25. ± 3.3 kg/m2, n = 205) than those without a risk allele (24.8 ± 3.4 kg/m2, n = 790; p = 0.04).
The genotype-phenotype relationship in control individuals at CDKAL1 and FTO genetic loci.
Fasting glucose (mmol/l)
Fasting insulin (pmol/l)
57 ± 8
25.1 ± 2.9
5.2 ± 0.4
57 ± 8
25.1 ± 3.2
5.3 ± 0.4
57 ± 9
25.9 ± 3.6
5.4 ± 0.4
57 ± 8
25.1 ± 3.2
5.3 ± 0.4
57 ± 8
25.8 ± 3.2
5.2 ± 0.4
Dominant p value
In the present study, we have replicated the associations between seven SNPs (CDKAL1-rs10946398, IGF2BP2-rs4402960, SLC30A8-rs13266634, CDKN2A/B-rs10811661, HHEX-rs5015480, FTO-rs8050136, and KCNQ1-rs2237892) with T2D in the Chinese Han population living in China, and an association between WFS1-rs6446482 with early-onset T2D was also identified in the subgroup analysis. For the genotype-phenotype analysis in control subjects, we found that the SNP at FTO loci showed a significant association with BMI, beta cell function, and insulin sensitivity. Moreover, we observed the influence of CDKAL1 on beta cell function, and our meta-analysis also indicated the SNPs at TCF2-rs7501939 and WFS1-rs6446482 were associated with T2D.
SLC30A8, HHEX, CDKAL1, CDKN2A/B, IGF2BP2, and FTO are among the first cohort of susceptibility genes discovered in relatively small samples by GWAS with ORs ranging from 1.14-1.20 [1–6]. In the present study, the OR values for the risk alleles range from 1.19-1.27, which are slightly higher than the Caucasian population [1–6], and slightly different from other Chinese studies. This is partly due to the features of our study (e.g., small sample sizes, enriched subjects with earlier onset T2D, and older subjects without a family history of T2D in the control group) or the difference in the studied population in genetic background as observed for the TCF7L2 and KCNQ1 genes in previous reports [7, 21, 22]. Based on our observation, we found that the minor allele frequencies of FTO, WFS1, TCF2, HHEX, and IGF2BP2 were much lower, while those of CDKAL1, CDKN2A/B, SLC30A8, and KCNQ1 were much higher than in the Caucasian population (Additional file 1, Table S1), indicating the different genetic background between Caucasians and the Chinese population. Our findings on CDKAL1, SLC30A8, CDKN2A/B, and KCNQ1 are further supported by several recent studies in the Chinese population [10–12, 23–27]. However, for IGF2BP2, no significant association was demonstrated with T2D [10, 11]. For HHEX, a relationship between the SNPs at the HHEX locus and T2D was reported among Chinese living in Shanghai, but not among Chinese in Beijing, even in the same study . We believe that the inconsistent results of these studies are due, at least in part, to the small sample size  and the different enrollment criteria. For example, the plasma glucose at 2 hours during the OGTT was not determined and some subjects with impaired glucose tolerance(IGT) or T2D might have been falsely classified into the control group , or some adolescents in the control group may develop T2D in the future . Therefore, the meta-analysis of the combined data from these published studies and our study is necessary for evaluating the association of these genetic loci with T2D and the effect of size in the Chinese population.
The studies in other ethnic groups have made clear that the association between FTO and T2D is mediated through its effect on body weight [2, 3, 28, 29]. In the present study, we have confirmed the association between FTO and BMI and T2D, and the association with T2D remained of borderline significance (p = 0.046), even after adjustment for BMI. The independent effect of FTO on T2D is consistent with a recent study . It is worth noticing that the study results on FTO in a Chinese population have been conflicting. The study in Chinese living in Taiwan has reported this association with BMI, but not T2D . However, another study in a Chinese population living in Shanghai did not show an association between FTO-rs8050136 and BMI or T2D . The discrepancy is partly due to population-specific bias, insufficient power, and the effects of duration and treatment for T2D, which may have distorted the real relationships among BMI, genotypes, and T2D. Europeans and Asians are different in their environmental risk profiles, body composition, and genetic background. In particular, Asians are at risk for T2D at a lower level of obesity, partly due to their increased predisposition to visceral adiposity  and reduced pancreatic β-cell function . Our pooled analysis demonstrated FTO really contributed to T2D in Chinese. Unfortunately, the individual BMI from all published studies are not available for meta-analysis and so it is difficult to determine whether or not the association is really independent of BMI. As observed in the present study, FTO-rs8050136 may directly or indirectly affect HOMA-ί and HOMA-IR, and a reasonable interpretation is that the control subjects who carry the risk allele of FTO are prone to insulin resistance, and are able to increase insulin secretion to completely compensate for low insulin sensitivity to keep glucose homeostasis. Thus, it is necessary to clarify the pathogenesis of the contribution of FTO to T2D.
It has been shown that common SNPs at the WFS1 locus are clearly associated with T2D in a Caucasian population [8, 33], but not in a Chinese population in a recent study with insufficient power . However, we observed an association between WFS1-rs6446482 with T2D in our meta-analysis, and early-onset T2D in our own sample. Wolfram syndrome, characterized by early-onset diabetes, optic atrophy, deafness, and diabetes insipidus is due to mutations of the WFS1 gene , which regulates ί cell function in mice  and glucagon-like peptide 1-induced insulin secretion in humans , suggesting that impaired beta cell function in subjects with the risk allele of WFS1 may promote the onset of T2D as seen in other monogenetic diabetes [37, 38]. Thus, it is necessary to replicate this study in a larger sample and sequence this gene for real causative variants.
The C allele of rs7501939 and the A allele of rs4430796 in the TCF2 gene were shown by GWAS to confer risk for prostate cancer and are protective for T2D . However, that study did not show a significant association between TCF2-rs7501939 with T2D in a Chinese sample , consistent with the results of our study. The negative finding should be attributed to the insufficient power of this study (Additional file 1, Table S1). Nevertheless, in another Chinese study involving TCF2-rs4430796 , the association was replicated. A meta-analysis of combined data from both the previous study  and our study in Chinese confirmed the findings.
There are now as many as 17 genetic loci associated with T2D. In our previous Chinese Han studies [22, 39, 40], we have assessed the effects of PPARG, KCNJ11, and TCF7L2, and successfully replicated the associations between the KCNJ11 and TCF7L2 loci with T2D. However, the remaining six genetic loci were identified by meta-analysis of GWAS, including JAZF1, TSPAN8-LGR5, THADA, ADAMTS9, NOTCH2-ADAM30, and MTNR1B, of which only MTNR1B in one study was shown to increase the risk for T2D and an increased fasting plasma glucose concentration in the Chinese Han [12, 41]. Therefore, further investigation with a large sample and meta-analysis is needed to confirm the roles in the Chinese Han population.
In summary, the present study has confirmed an association between genetic variations at CDKAL1, IGF2BP2, SLC30A8, CDKN2A/B, HHEX, KCNQ1, and FTO loci with T2D in the Chinese Han population living in China. Our meta-analysis has provided the evidence that the variants at TCF2 and WFS1 genetic loci also contribute to the genetic susceptibility for T2D in Chinese.
We would like express our appreciation to all patients and control subjects who were involved in this study. We would also like to give our thanks to the Chinese National Human Genome Center (Shanghai) for genotyping services. This study was supported by the National Natural Science Foundation of China (30570873, 30771031), National Basic Research Program of China (973 program, 2006CB503900), 985 Program of Peking University (PUDC2007-2), National High Technology Research and Development Program (863 program, 2006AA02A409) and Development Fund of Peking University People's Hospital (RDC2007-27).
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