Background  KCNJ11, ABCC8, PPARG, and HNF4A have been found to be associated with type 2 diabetes in populations with different genetic backgrounds. The aim of this study was to test, in a Chinese Han population from Beijing, whether the genetic variants in these four genes were associated with genetic predisposition to type 2 diabetes.
Methods  We studied the association of four representative SNPs in KCNJ11, ABCC8, PPARG, and HNF4A by genotyping them using ABI SNaPshot® Multiplex System in 400 unrelated type 2 diabetic patients and 400 unrelated normoglycaemic subjects.
Results  rs5219(E23K) in KCNJ11 was associated with genetic susceptibility to type 2 diabetes (OR=1.400 with 95% CI 1.117 1.755, P=0.004 under an additive model, OR=1.652 with 95% CI 1.086 2.513, P=0.019 under a recessive model, and OR=1.521 with 95% CI 1.089 2.123, P=0.014 under a dominant model) after adjusting for sex and body mass index (BMI). We did not find evidence of association for ABCC8 rs1799854, PPARG rs1801282 (Pro12Ala) and HNF4A rs2144908. Genotype-phenotype correlation analysis revealed that rs1799854 in ABCC8 was associated with 2-hour postprandial insulin secretion (P=0.005) after adjusting for sex, age and BMI. Although no interactions between the four variants on the risk of type 2 diabetes were detected, the multiplicative interaction between PPARG Pro12Ala and HNF4A rs2144908 was found to be associated with 2-hour postprandial insulin (P=0.004 under an additive model for rs2144908; and P=0.001 under a dominant model for rs2144908) after adjusting for age, sex and BMI, assuming a dominant model for PPARG Pro12Ala.
Conclusions  Our study replicated the association of rs5219 in KCNJ11 with type 2 diabetes in Chinese Han population in Beijing. And we also observed that ABCC8 as well as the interaction between PPARG and HNF4A may contribute to post-challenge insulin secretion.

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Type 2 diabetes is a chronic metabolic disorder characterized by impaired insulin secretion and insulin action, and it is believed to be caused by interaction between genetic and environmental factors. In the past two decades, many candidate genes have been implicated in the predisposition to type 2 diabetes, however, only a few have been replicated in diverse populations with different ethnic backgrounds. Among these, KCNJ11, PPARG, ABCC8 as well as HNF4A are the most promising genes.¹

KCNJ11 and ABCC8 are both located on 11p15.1.^2,3 adjacent to each other and encode the two subunits Kir6.2 and SUR1of ATP sensitive potassium channels which are expressed in pancreatic β cells and play a pivotal role in insulin secretion by coupling cell metabolism with membrane potential.³ It has been found that inactivating mutations of the two genes can cause persistent hyperinsulinemic hypoglycemia of infancy (PHHI),⁴ while activating mutations are common causes of neonatal diabetes.⁵ As candidate genes for common type 2 diabetes, several studies conducted in different ethnic groups including Asian populations have provided the evidence that polymorphisms in these two genes were associated with genetic susceptibility of common type 2 diabetes.^6,7 In addition, the association of KCNJ11 with type 2 diabetes was also repeatedly confirmed recently by more robust Genome Wide Association studies.^8-10

PPARG encodes a transcription factor which plays a key role in adipocyte differentiation and fuel metabolism. In 1998, it was first discovered that a mutation CCA(Pro)-GCA(Ala) in condon12 of PPARG2 specific exon B was correlated with lower body mass index (BMI) and improved insulin sensitivity.¹¹ Subsequently, A meta-analysis that included 16 studies and used a family based design to control stratification confirmed the association of the variant with type 2 diabetes, and identified the common allele Pro as a risk allele.¹² In addition, the association of PPARG with type 2 diabetes was also repeatedly confirmed recently by more robust Genome Wide Association studies.^8-10

HNF4A also encodes a transcription factor which regulates the expression of a series of genes involved in insulin secretion and glucose regulation. The gene is located on 20q13, where evidence from family based linkage analysis has been found to be linked with type 2 diabetes in several Caucasian and Asian populations.^13,14 The more severe form of mutation of HNF4A is the cause of maturity onset diabetes of the young type 1 (MODY 1), a dominantly inherited diabetes.¹⁵ As a candidate gene, mutations in the P2 promoter region have been found to be associated with common type 2 diabetes.^16,17

Genetic studies of these genes in different ethnic groups will be a key step not only for replication of these findings but also for assessing their effects in pathogenesis of type 2 diabetes in populations with different genetic background and environment. In the present study, we attempt to examine the association of four well replicated representative SNPs, rs5219 (E23K), rs1799854 (exon 16–3c/t variant), rs1801282(Pro12Ala), and rs2144908 that reside in KCNJ11, ABCC8, PPARG, and HNF4A, respectively, with the genetic susceptibility of type 2 diabetes in Beijing Chinese Han population in Beijing using a case-control study design involving 400 type 2 diabetic patients and 400 normoglycaemic subjects.

Study population
All subjects were of Northern Han Chinese ancestry residing in Beijing metropolitan area. Four hundred type 2 diabetic patients with type 2 diabetes family history were recruited from outpatient clinics of Endocrinology and Metabolism at Peking University People′s Hospital in Beijing, China (age 47 years±11 years, 225 men, 175 women). Diabetes mellitus was diagnosed in accordance with 1999 World Health Organization (WHO) criteria.¹⁸ All patients were unrelated and diagnosed with type 2 diabetes after 30 years of age. The cases with high glutamic acid decarboxylase (GAD) antibody levels (>99th percentile of normal population) were excluded from this study. Known subtypes of diabetes (e.g., MODY, maturity onset diabetes of the young) were excluded by clinical criteria or genetic testing.^19,20 Four hundred unrelated non-diabetic control subjects (age 57 years±8 years, 153 men, 247 women) were recruited from either spouses of diabetic patients receiving diabetes care at People′s Hospital, or individuals attended annual health examination at community hospital according to the following criteria: Han Chinese, ≥45 years old, no history of diabetes, with normal oral glucose tolerance test (OGTT), and HbA1c<6%. All subjects gave written informed consent before participation, and this study was approved by the Ethics Committee of Peking University People′s Hospital.

SNP selection and genotyping
Referring to published data, we selected one representative SNP (Table 1) for each gene that was most consistently replicated, particularly in Asian populations.^6,7,17 Genotyping was conducted by Chinese National Human Genome Center in Shanghai. A method adapted to the ABI SNaPshot^® multiplex system was used to genotype the four polymorphisms. This protocol relies on the extension with fluorescent dideoxyNTPs of a primer that ends one nucleotide 5' of a given SNP (minisequencing). Over 96.8% (96.8%–99.0%) of the samples were successfully genotyped for each SNP in both the case and control groups. And the concordance rate between duplicate genotyping was 100% in randomly selected samples (30 samples).

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Table 1. Genotype distribution and association of the 4 SNPs with type 2 diabetes

Data analysis
Statistical analysis
Data are given as means ± standard deviation (SD) for variables with normal distribution, and otherwise as medians (inter-quartile range). Variables not normally distributed were logarithmically transformed before statistical analysis. Chi-squared tests were used to determine whether individual polymorphisms were in Hardy–Weinberg equilibrium (P >0.05). The differences of allele and genotype frequencies between the diabetic and the control individuals were analysed using Pearson's χ² test. Logistic regression analysis was used to calculate odds ratios (ORs), 95% confidential intervals (CIs), and corresponding P values, after adjusting for sex and BMI as covariates. Comparison of variables between different groups was performed using Students′ t test or analysis of variance (ANOVA). Multiple linear stepwise regression analysis was used to analyze the genotype-phenotype correlation under an additive genetic model. All statistical tests were performed by SPSS program version 11.5 for windows (SPSS, Chicago, Illinois, USA). A P value of < 0.05 was considered statistically significant (two tailed). Homeostasis model assessment (HOMA) of insulin resistance (HOMA-IR) and pancreatic β-cell function (HOMA-β) were calculated as described previously.²¹

Gene-gene interaction analysis
Analysis of gene-gene interaction on the risk of type 2 diabetes was carried out by Logistic regression model as assessed by likelihood ratio test as well as multifactor dimensionality reduction (MDR) method (version 1.1.0; http://www.epistasis.org) as described previously.^22,23 Interactions between variants in relation to diabetes-related quantitative traits were tested by univariate analysis under the general linear model adjusted for age, sex and BMI.

Power calculation
Power calculation was performed by Quanto software version 1.2.3 (University of Southern California, Los Angeles, USA). According to type 2 diabetes prevalence of 5.5% in China²⁴ and using additive genetic model, for any SNP with minor allele frequency (MAF) of at least 30%, the sample size in our study had an 80% power at P value of 0.05 to detect an effect size of 1.35.

Characteristics of study subjects
As presented in Table 2, diabetic patients had significantly higher fasting plasma glucose, OGTT 2-hour glucose, fasting insulin, HbA1c, and triglycerides (TG), while they exhibited lower total cholesterol (T-CHO), low density lipoprotein cholesterol (LDL-C), high density lipoprotein cholesterol (HDL-C) and systolic blood pressure compared with the control subjects. As we selected subjects aged ≥45 years as our control group to ensure that they were less predisposed to type 2 diabetes, the age of subjects in the control group were significantly higher than that in the diabetic group.

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Table 2. Clinical characteristics of study subjects

Association study
The distribution of genotype and allele frequencies of the four SNPs were in accordance with Hardy-Weinberg equilibrium in both the case group and the control group, as well as the total samples as assessed by χ² test. For SNP rs5219 (E23K) in KCNJ11, the frequency of the risk allele K was significantly higher in diabetic subjects than that in normal controls (45.8% vs 37.2%, P=0.001), and this SNP showed strong association with type 2 diabetes (OR=1.400 with 95% CI 1.117–1.755, P=0.004 under an additive model, OR=1.652 with 95% CI 1.086–2.513, P=0.019 under a recessive model, and OR=1.521 with 95% CI 1.089–2.123, P=0.014 under a dominant model), after adjusting for sex and BMI. Association was not found for the other three SNPs rs1799854 (OR=1.014 with 95% CI 0.806–1.275, P=0.905 under an additive model), rs1801282 (Pro12Ala) (OR =0.929 with 95% CI 0.611–1.411, P= 0.729 under an additive model) and rs2144908 (OR=1.055 with 95% CI 0.839–1.326, P=0.647 under an additive model). In this study, a very weak linkage disequilibrium between rs5219 and rs1799854 (D′=0.116, r²=0.007) was found.

Genotype-phenotype correlation analysis
Genotype-phenotype correlation analysis was only conducted in the control group because treatment for diabetes in patients may distort the relationship. We examined the effect of the four SNPs on the diabetes-related quantitative traits including BMI, WHR, fasting and postprandial glucose, HbA1c, fasting and postprandial insulin, T-CHO, TG, LDL-C, HDL-C as well as β-cell function (HOMA-β) and insulin resistance (HOMA-IR). Rs1799854 in ABCC8 showed association with 2-hour postprandial insulin level under an additive model (P=0.005) adjusted for sex, age and BMI, the T/T genotype carriers had significantly higher post challenge insulin level compared with the subjects homozygous for C/C genotype, and there was a dose dependent trend between post-challenge insulin level and T allele numbers (Table 3). No significant genotype-phenotype correlations were found for rs5219, rs1801282, and rs2144908 (data not shown).

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Table 3. Association of rs1799854 with diabetes-related quantitative traits

Gene-gene interaction analysis
We explored interactions between the four variants on the risk of type 2 diabetes using both the Logistic regression model and MDR method. We did not find any interaction between these SNPs (P >0.05 for all models). We also examined the effects of interactions between the variants on diabetes-related quantitative traits in the control group. Although neither HNF4A rs2144908 nor PPARG Pro12Ala showed significant association with diabetic traits individually, the multiplicative interaction between the two loci was significantly associated with 2-hour post-challenge insulin level (P=0.004 under an additive model for rs2144908 and P=0.001 under a dominant model for rs2144908), after adjusting for sex, age and BMI, and assuming a dominant model for PPARG Pro12Ala (Table 4).

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Table 4. Phenotype association with interaction between PPARG and HNF4A

In this study, we explored the effects of four well-documented genes for diabetes, KCNJ11, ABCC8, PPARG, and HNF4A, on the genetic susceptibility to type 2 diabetes in Chinese Han population in Beijing using a case-control study design. In order to strengthen the power to detect association signals, we selected cases with a relatively younger age of onset and a strong family history as we expected that they carried more risk loci, while we enrolled controls aged ≥45 to ensure that they were less predisposed to type 2 diabetes. The male/female ratio in the case and control groups were not balanced, however as the genes we studied are all located on autosomal chromosomes and sex was adjusted as a covariate in data analysis, influence on results was expected to be minimal.

We confirmed the association of rs5219 (E23K) in KCNJ11 with type 2 diabetes was also existing in Chinese Han population. Since this SNP is located in exon, it might impair the function of the gene product. And an in vitro experiment found that the variant E23K was associated with reduced ATP sensitivity of the kir6.2/SUR1 channel complex—and thus inhibited insulin secretion,^24,25 which suggested that individuals carrying the risk allele of this SNP may be predisposed to the development of diabetes due to impaired insulin secretion. In a study carried out in glucose-tolerant subjects,²⁶ the association between the variant E23K and a reduction in glucose-induced serum insulin level was found. But we observed no association of this polymorphism with β-cell function as assessed by fasting and post-challenge insulin secretion as well as HOMA-β in the control group, perhaps due to a relatively small sample size.

The initial finding that the association of rs1799854 (exon 16-3c/t variant) in the gene ABCC8 with type 2 diabetes²⁷ was replicated in a large case-control study conducted in a Japanese population including 1590 type 2 diabetic patients and 1244 controls.⁶ In that study, among 33 variants of 12 genes involved in pancreatic β-cell function, rs1799854 showed the strongest association with type 2 diabetes (P=0.0073), with an OR for the T/T genotype of 1.27 (95% CI 1.07–1.50; C/C+C/T vs T/T). According to the power calculation, if rs1799854 had an effect in a Chinese population similar to that observed in Japanese, our study was well powered to evaluate it. We did not find the association between rs1799854 and diabetes. Therefore, it is unlikely that the variant of ABCC8 contributes to the genetic susceptibility of type 2 diabetes in Chinese. In the genotype and phenotype correlation analysis, we have found a dose dependent correlation between 2 hours post-challenge insulin level and the number of T allele of rs1799854. However, due to the relatively small sample size in this study, whether this association is real needs to be confirmed by further studies.

PPARG has been perceived as one of the well replicated genes associated with type 2 diabetes in Europeans.^8-10 In our study, only 5 (1.3%) in cases and 4 (1.0%) in controls carried the A/A genotype (Table 1). And the frequency of the Ala allele was 7.3% in cases and 7.2% in controls, higher than that reported in Japanese (4%),²⁸ but still substantially lower than that in Caucasians (14%).²⁹ Assuming the same effect size of Pro12Ala polymorphism in Chinese population as was observed in Europe (OR=1.14)^8-10 and, based on observed MAF of this polymorphism in this study, power calculation using an additive genetic model showed that a case sample of 7190 would be necessary to achieve an 80% power for evaluating Pro12Ala polymorphism, indicating that the sample size in this study was under power in evaluating PPARG. This may explain why in Asian populations, with low frequency of Pro12Ala polymorphism, convincing evidence supporting association between PPARG and type 2 diabetes is lacking.³⁰

HNF4A encodes a transcription factor that is found in the liver and pancreas. It is located on 20q13, where evidence from family based studies were found for linkage with type 2 diabetes in Caucasians and Asian populations.^13,14 A genome-wide scan carried out in Chinese using non-parametric linkage analysis also yielded suggestive evidence of linkage with type 2 diabetes on 20q13.¹⁴ Even though no mutation in the exons and promoter region of HNF4A was found to be associated with type 2 diabetes,³¹ rs2144908 and rs1884614 which were located adjacent to the β-cell specific promoter P2 of HNF4A were found to be associated with type 2 diabetes.^16,32 In this study, no association with diabetes was found for rs2144908 in Chinese. The relationship between HNF4A and diabetes is somewhat elusive in terms of the inconsistency among the studies. In several studies, no associations were found between diabetes and individual SNPs including rs2144908, but a significant association was found between diabetes and haplotypes containing rs2144908.¹⁷ In the future, genotyping more SNPs in the vicinity of HNF4A may be needed to fully evaluate its role in the genetic predisposition to type 2 diabetes in Chinese.

It is worth noticing that in contrast to KCNJ11 and PPARG, of which the association with type 2 diabetes were repeatedly confirmed by the recently published genome wide association (GWA) studies,^8-10 signals from the HNF4A and ABCC8 loci showed up in none of these GWAs.

Type 2 diabetes is a complex multifactorial disease that is likely the result of interactions between multiple genetic and environmental factors. Previously, interactions between HNF4A and KCNJ11,²² and between UCP2 and PPARG²³ on the risk of type 2 diabetes were reported. Although we did not find interactions between the four genes on the risk of diabetes in our study population, we observed that interaction between PPARG Pro12Ala and HNF4A rs2144908 was associated with 2-hour post-challenge insulin secretion, similar to a recently published study conducted in Mexican Americans which reported that interaction between the two variants contributed to variation in insulin sensitivity and 2-hour insulin while neither PPARG Pro12Ala nor HNF4A rs2144908 was independently associated with type 2 diabetes related quantitative traits.

In summary, in this case-control study, we have confirmed that the association of KCNJ11 with type 2 diabetes also exists in Chinese Han population in Beijing. HNF4A and ABCC8 are unlikely associated with type 2 diabetes. Further study or meta-analysis with sufficient power will be needed to evaluate the impact of PPARG on genetic predisposition to type 2 diabetes in Chinese population.

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