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. 2015 Apr 1;19(4):230–237. doi: 10.1089/omi.2014.0162

The Uyghur Population and Genetic Susceptibility to Type 2 Diabetes: Potential Role for Variants in CDKAL1, JAZF1, and IGF1 Genes

Manshu Song 1,,2,,§, Feifei Zhao 1,,2,,§, Longjin Ran 3,,§, Mamatyusupu Dolikun 4, Lijuan Wu 1,,2, Siqi Ge 1,,2, Hao Dong 1,,2, Qing Gao 1,,2, Yanchun Zhai 1,,2, Ling Zhang 1,,2, Yuxiang Yan 1,,2, Fen Liu 1,,2, Xinghua Yang 1,,2, Xiuhua Guo 1,,2, Youxin Wang 1,,2,,5,✉, Wei Wang 1,,2,,5,✉
PMCID: PMC4390191  PMID: 25785549

Abstract

Substantial evidence suggests that type 2 diabetes mellitus (T2DM) is a multi-factorial disease with a strong genetic component. A list of genetic susceptibility loci in populations of European and Asian ancestry has been established in the literature. Little is known on the inter-ethnic contribution of such established functional polymorphic variants. We performed a case-control study to explore the genetic susceptibility of 16 selected T2DM-related SNPs in a cohort of 102 Uyghur objects (51 cases and 51 controls). Three of the 16 SNPs showed significant association with T2DM in the Uyghur population. There were significant differences between the T2DM and control groups in frequencies of the risk allelic distributions of rs7754840 (CDKAL1) (p=0.014), rs864745 (JAZF1) (p=0.032), and rs35767 (IGF1) (p=0.044). Carriers of rs7754840-C, rs35767-A, and rs864745-C risk alleles had a 2.32-fold [OR (95% CI): 1.19–4.54], 2.06-fold [OR (95% CI): 1.02–4.17], 0.48-fold [OR (95% CI): 0.24–0.94] increased risk for T2DM, respectively. The cumulative risk allelic scores of these 16 SNPs differed significantly between the T2DM patients and the controls [17.1±8.1 vs. 15.4±7.3; OR (95%CI): 1.27(1.07–1.50), p=0.007]. This is the first study to evaluate genomic variation at 16 SNPs in respective T2DM candidate genes for the Uyghur population compared with other ethnic groups. The SNP rs7754840 in CDKAL1, rs864745 in JAZF1, and rs35767 in IGF1 might serve as potential susceptibility loci for T2DM in Uyghurs. We suggest a broader capture and study of the world populations, including who that are hitherto understudied, are essential for a comprehensive understanding of the genetic/genomic basis of T2DM.

Introduction

Type 2 diabetes mellitus (T2DM) is a complex disease characterized by insulin resistance in peripheral tissues and dysregulated insulin secretion by pancreatic beta cells (Banerjee et al., 2014). Substantial evidence suggests that T2DM is a multi-factorial disease with a strong genetic component. High concordance rates obtained in monozygotic twins (96%) support a substantial contribution of genetic factors to T2DM (Barnett et al., 1981; Newman et al., 1987). Furthermore, 40% of first-degree relatives of T2DM patients develop diabetes as compared to 6% in the general population (KoÈbberling and Tillil, 1982). The general estimates of heritability (h2) of T2DM is 0.49 and the relative recurrence risk for a sibling of an affected person (λs) to develop T2DM is 3.5 (Lander and Schork, 1994; Risch, 1990).

Multiple genetic loci have been discovered as risk factors for T2DM, most of which were detected from genome-wide association studies (GWAS) in populations of European and Asian ancestry (Ahlqvist et al., 2011; Saxena et al., 2007; Scott et al., 2007; Sladek et al., 2007; Zeggini et al., 2008), for example, loci near cyclin-dependent kinase 5 (CDK5), regulatory subunit-associated protein 1-like 1 (CDKAL1) (Saxena et al., 2007), peroxisome proliferator-activated receptor gamma (PPARG) (Saxena et al., 2007), and juxtaposed with another zinc finger 1 (JAZF1) ( Zeggini et al., 2008). Most of these loci are related to insulin secretion and beta-cell function, while a few are involved in insulin resistance (Ahlqvist et al., 2011; Billings and Florez, 2010). To date, more than 40 genetic loci have been reported and reproduced to be associated with T2DM or glycemic traits in Caucasian and Asian populations (Ahlqvist et al., 2011).

There are differences in the contribution of known SNPs in T2DM susceptibility genes among various ethnic populations (Klimentidis et al., 2011). SNP rs7754840 in CDKAL1, identified as T2DM susceptibility locus in subjects from Finland and Sweden by GWAS (Saxena et al., 2007), was replicated in Koreans (Lee et al., 2008), Han Chinese (Hu et al., 2009), Pima Indians (Rong et al., 2009), and Lebanese Arabs (Nemr et al., 2012), but showed inconsistency in risk allele frequencies and odds ratios (ORs) among these ethnic populations. SNP rs864745 in JAZF1 was associated with T2DM in European populations (Zeggini et al., 2008), whereas it was not found to be associated with T2DM in Chinese (Hu et al., 2009). Therefore, studies in other ethnic populations could aid in determining population-specific risk variants for T2DM.

The Uyghur population, settled in Xinjiang Uyghur autonomous region, northwestern frontier area of China, accounts for 46% of the population in Xinjiang (Han Chinese accounts for 40% and Kazak accounts for 7%). It is a population presenting a typical admixture of Eastern and Western anthropometric traits (Black et al., 2006; Wang et al., 2003). Uyghurs are overwhelmingly Muslim, and have their own language, religious beliefs, and lifestyles that are very different from either Han Chinese or American/European populations. Taking dietary style as an example, the Uyghur has a high dairy intake level (over 200 g) with more flour and meat, and less bean, vegetable, and fishery products compared with Han Chinese living in the same area (Zhai et al., 2007).

The prevalence of diabetes in Uyghur was higher than other ethnic groups in Xinjiang. Tao et al. (2008) reported that the prevalence of T2DM was 8.16% in Uyghur, which was higher than 1.47% in Kazak population. Awuti et al. (2012) reported that the prevalence of T2DM for Uyghur adults was 9.5%. In addition, a cross-sectional study in Xinjiang found that 19.6% of Uyghur had diabetes, exceptionally higher than that in Han Chinese (9.1%) and Kazakh (7.3%) (Li et al., 2012). 6.23% of the Uyghur adults over 35 years old had diabetes, while 3.65% of Kazak adults had diabetes (Yang et al., 2012). For children under 17 years old, 0.77% of the Uyghur suffered from impaired fasting glucose (IFG) and diabetes, and that was 0.1% in Kazak (Zhang et al., 2012).

Up to date, there have been only two reports on association of genetics makers with T2DM in Uyghur. The G allele of adiponectin gene carriers with reduced plasma concentrations of adiponectin might be associated with insulin resistance in Uyghur (Li et al., 2007). In addition, peroxisome proliferator-activated receptor (PPAR)-gamma Pro12Ala polymorphism might affect susceptibility to diabetes in Uyghur (Li et al., 2008).

In the present case-control study, we investigated the associations between 16 SNPs susceptibility loci and T2DM, as well as the combined effects of the SNPs on the risk of T2DM in a Uyghur population.

Materials and Methods

Participants

A sample of 102 Uyghur participants was recruited from the Uyghur population at Hetian of Xinjiang, China, where the Uyghur population was less affected by the recent migration of Han Chinese. Fifty-one T2DM patients (25 men and 26 women, 54.1±7.9 years old) were recruited from the local hospital as the T2DM group. The control group without diabetes was comprised of 51 health check-up participants (23 men and 28 women, 55.9±9.9 years old).

Inclusion criteria for the T2DM group were: 1) ability to provide written informed consent, 2) aged more than 18 years, self-reported Uyghur ethnicity without intermarriage history with other ethnic groups within the latest three generations, 3) diagnosis of diabetes by physicians according to 1999 World Health Organization (WHO) Criteria (fasting plasma glucose greater than or equal to 7.0 mmol/L and/or 2-h plasma glucose greater than or equal to 11.1 mmol/L) (Alberti et al., 1998), and 4) diagnosis of T2DM from clinical records obtained from the patients' health care provider.

Inclusion criteria for the control group were: 1) ability to provide written informed consent, 2) aged more than 18 years, self-reported Uyghur ethnicity without intermarriage history with other ethnic groups within the latest three generations, 3) no documented clinical diagnosis of diabetes or other metabolic diseases, and 4) not taking glucose-lowering medications and had fasting and 2 h glucose values below the diagnostic thresholds for diabetes.

Anthropometric measurements, including weight, height, and waist measurements, were obtained using standardized techniques. The body mass index (BMI) was calculated by the formula weight (in kilograms)/height (in square meters). Systolic blood pressure (SBP) and diastolic blood pressure (DBP) were measured with a mercury sphygmomanometer. Peripheral blood samples of patients and controls were collected in EDTA-anti-coagulated tubes after an overnight fast. Plasma glucose concentrations were measured by the glucose oxidase-peroxidase method using commercial kits. Serum total cholesterol, serum triglycerides, high-density lipoprotein cholesterol (HDL), low-density lipoprotein cholesterol (LDL), and HbA1c were measured using standard methods (cholesterol oxidase-peroxidase-amidopyrine method, glycerol phosphate oxidase-peroxidase-amidopyrine method, enzymatic methods on a Hitachi 911 automated analyzer (Boehringer Mannheim, Mannheim, Germany), calculating by the Friedewald formula and determined by high-performance liquid chromatography, respectively).

The study was approved by the local community leaders and the ethics committee of the Capital Medical University, Beijing, China.

Selection of the candidate SNPs and genotyping

Sixteen SNPs (rs1801282 (PPARG), rs7754840 (CDKAL1), rs10811661 (CDKN2A/B), rs13266634 (SLC30A8), rs4402960 (IGF2BP2), rs7903146 (TCF7L2), rs10885122 (ADRA2A), rs174550 (FADS1), rs2191349 (DGKB), rs35767 (IGF1), rs12779790 (CDC123/CAMK1D), rs7961581 (TSPAN8), rs864745 (JAZF1), rs7578326 (IRS1), rs780094 (GCKR), and rs4607517 (GCK)) were selected for the following reasons. First, minor allele frequencies (MAF) of these selected SNPs were more than 0.05 in both HapMap CEU data and HapMap CHB data (http://hapmap.ncbi.nlm.nih.gov/).

These SNPs were reported to be associated with T2DM in several GWAS results among European ancestry (Dupuis et al., 2010; Saxena et al., 2007; Voight et al., 2010; Zeggini et al., 2008), while 13 of them (except for rs7578326 (IRS1), rs780094 (GCKR) and rs4607517 (GCK)) were also evaluated in Han Chinese population (Hu et al., 2009; 2010; Liu et al., 2011; Wen et al., 2010; Zeggini et al., 2008; Zhou et al., 2010). IRS1, GCKR, and GCK were selected based on their roles in insulin signaling pathway (Kyoto Encyclopedia of Genes and Genomes: http://www.genome.jp/kegg/kegg2.html).

Genomic DNA was extracted from whole-blood samples using QIAamp DNA Blood Mini Kit (Qiagen, Germany) according to the manual instructions. Genomic DNA samples were subsequently diluted to 25 ng/μL. SNPs were genotyped using Mass ARRAY system (Sequenom, Inc., SanDiego, CA).

Statistical analyses

Analyses were conducted with SPSS for WINDOWS version 18.0 (SPSS, Chicago, IL, USA). Hardy-Weinberg equilibrium for genotype frequencies were tested by the Chi-square test. All continuous variables were expressed as the mean±standard deviation (SD). Continuous variables between T2DM and control group were compared by Student's t-test. The differences in the frequencies of various alleles and genotypes between T2DM patients and controls were performed by Chi-square test, and Fisher's exact test was applied to the loci with a small number of alleles or genotypes (equal to or less than 5). Associations between SNPs and T2DM risks were assessed using odds ratios (ORs) with 95% confidence intervals (95% CIs) and P value derived from logistic regression adjusted for age and body mass index (BMI).

In addition, to evaluate the combined effects of the SNPs, cumulative scores of risk alleles were counted. Risk allele was defined as OR >1 based on the results of association analysis of candidate SNPs for T2DM. We considered an additive genetic model for each SNP, and assigned a score of 0, 1, or 2 to genotypes at the 16 loci, depending on whether subjects carried the wild-type allele or were heterozygous or homozygous for the risk allele. The count method assumed that each risk allele contributes equally and independently to the risk for T2DM. Association of cumulative risk allelic scores and T2DM were also assessed using ORs with 95% CIs and p value derived from logistic regression. The significant level was set at p<0.05, and all the analysis was two-sided.

Results

Sample characteristics

Participants' characteristics are shown in Table 1. The BMI and HbAlc were significantly higher in T2DM patients than those in control (25.0±0.4 vs. 23.3±0.3, p=0.002; 7.3±2.1 vs. 5.1±0.7, p<0.001, respectively). The SBP, DBP, total cholesterol, triacylglycerol, and glucose were also higher in T2DM patients than in the control group (135.9±1.8 vs. 124.0±1.5, p<0.001; 87.8±1.4 vs. 81.8±1.1, p=0.001; 4.9±0.1 vs. 4.4±0.1, p=0.020; 2.2±0.1 vs. 1.7±0.1, p=0.006, 9.1±0.4 vs. 5.3±0.1, p<0.001, respectively).

Table 1.

Characteristics of the Study Subjects

  T2DM N=51 Control N=51 P value
Age (years) 54.1±7.9 55.9±9.9 0.243
Sex (Male/female) 25/26 23/28 0.843
BMI (kg/m2) 25.0±0.4 23.3±0.3 0.002
SBP (mmHg) 135.9±1.8 124.0±1.5 <0.001
DBP (mmHg) 87.8±1.4 81.8±1.1 0.001
Total cholesterol (mmol/L) 4.9±0.1 4.4±0.1 0.020
Triacylglycerol (mmol/L) 2.2±0.1 1.7±0.1 0.006
HDL (mmol/L) 1.1±0.04 1.2±0.04 0.629
LDL (mmol/L) 3.2±0.1 3.0±0.1 0.263
Glucose (mmol/L) 9.1±0.4 5.3±0.1 <0.001
HbAlc (%) 7.3±2.1 5.1±0.73 <0.001
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The p values with statistical significance are indicated in bold numbers.

Association analysis of candidate SNPs for T2DM

Sixteen SNPs from known T2DM susceptibility loci were genotyped in 102 Uyghur participants. The genotype call rate for each SNP was >95%. There are two major causes for the missing calls. One is due to poor quality of DNA samples, which often fails to be amplified and to generate strong enough intensity of fluorescence signals over the background. The other arises when an observation (i.e., a read out of fluorescence signals) cannot be assigned unequivocally to any of the clusters of genotype, and, therefore, is subject to 'no-call' procedure. In this report, the missing calls are mainly due to the poor quality of DNA samples. Allelic frequencies of these 16 SNPs are shown in Table 2. The distributions of allelic frequencies of the 15 SNPs were all in Hardy-Weinberg equilibrium (HWE) in both cases and controls (p>0.05), except that of the rs4402960 (p=0.05) in the T2DM cases. The minor allele frequencies (MAF) of these SNPs range from 0.10 to 0.46.

Table 2.

Association Analysis of Xandidate SNPs for T2DM in the Uyghur Participants

      T2DM Control Chi-square test Logistic regression analysis (adjusted for age and BMI)
Locus db SNP Minor allele Fminor PHWET Fminor PHWET P value OR (95%CI) P value
CDKAL1 rs7754840 C 0.46 0.92 0.24 0.89 0.005 2.32 (1.19–4.54) 0.014
JAZF1 rs864745 C 0.29 0.48 0.42 0.09 0.049 0.48 (0.24–0.94) 0.032
IGF1 rs35767 A 0.33 0.36 0.21 0.12 0.033 2.06 (1.02–4.17) 0.044
IGF2BP2 rs4402960 T 0.26 0.05 0.30 0.64 0.182 0.99 (0.52–1.90) 0.980
CDKN2A/B rs10811661 C 0.31 0.16 0.30 0.91 0.911 1.01 (0.51–2.01) 0.984
ADRA2A rs10885122 T 0.10 0.44 0.13 0.29 0.443 0.67 (0.23–1.93) 0.463
CDC123/CAMK1D rs12779790 G 0.16 0.43 0.12 0.34 0.357 1.61 (0.67–3.87) 0.299
SLC30A8 rs13266634 T 0.29 0.69 0.28 0.93 0.927 1.11 (0.57–2.15) 0.765
FADS1 rs174550 C 0.36 0.36 0.42 0.69 0.455 0.78 (0.41–1.50) 0.458
PPARG rs1801282 G 0.14 0.27 0.17 0.15 0.542 0.62 (0.24–1.64) 0.340
DGKB rs2191349 G 0.37 0.58 0.41 0.20 0.369 0.79 (0.42–1.49) 0.463
GCK rs4607517 A 0.23 0.74 0.16 0.06 0.283 1.34 (0.65–2.77) 0.419
IRS1 rs7578326 G 0.32 0.39 0.23 0.74 0.200 1.51 (0.76–3.01) 0.236
GCKR rs780094 T 0.40 0.89 0.38 0.13 0.492 0.95 (0.51–1.72) 0.873
TCF7L2 rs7903146 T 0.12 0.09 0.17 0.49 0.570 0.49 (0.12–1.22) 0.133
TSPAN8 rs7961581 C 0.35 0.41 0.29 0.34 0.621 1.70 (0.84–3.43) 0.147
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PHWET, p value of Hardy–Weinberg equilibrium test; Fminor, minor allele frequency.

ADRA2A: adrenergic α2A receptor; CDC123/CAMK1D: cell division cycle 123; CDKAL1: cyclin-dependent kinase 5 regulatory subunit associated protein 1–like 1; CDKN2A/B: cyclin-dependent kinase inhibitor-2A/B; DGKB: diacylglycerol kinase, beta 90kDa; FADS1: fatty acid desaturase 1; GCK: glucokinase; GCKR: glucokinase regulatory protein; IGF1: insulin-like growth factor 1; IGF2BP2: insulin-like growth factor 2 mRNA binding protein 2; IRS1: insulin receptor substrate 1; JAZF1: juxtaposed with another zinc finger 1; PPARG: peroxisome proliferator-activated receptor gamma; SLC30A8: solute carrier family 30 member 8; TCF7L2: transcription factor 7-like 2; TSPAN8: tetraspanin 8. The p values with statistical significance are indicated in bold numbers.

Allelic frequencies of three SNPs (rs7754840 (CDKAL1), rs864745 (JAZF1), and rs35767 (IGF1)) were significantly different between the T2DM and control group (p<0.05). For rs7754840 (CDKAL1), frequencies of the C and G alleles were 0.24 and 0.76, respectively, in the control group. Frequencies of the C allele were significantly higher in T2DM patients than that in control group (0.46 vs. 0.24, p=0.005). For rs864745 (JAZF1), frequencies of the C and T alleles were 0.42 and 0.58 in the control group. Frequency of the T allele was significantly higher in T2DM patients than that in control group (0.71 vs. 0.58, p=0.049). For rs35767 (IGF1), frequencies of the A and G alleles were 0.21 and 0.79 in the control group. Frequency of the G allele was significantly lower in T2DM patients than that in the control group (0.67 vs. 0.79, p=0.033). Logistic regression analysis (adjusted for age and BMI) revealed that participants with the C allele for rs7754840 (CDKAL1) had a 2.32-fold [OR (95%CI): 1.19–4.54, p=0.014] risk of T2DM compared with the G allele. SNP rs864745 (JAZF1) and SNP rs35767 (IGF1) were also found to be significantly associated with T2DM in logistic regression analysis [OR (95% CI): 0.48 (0.24–0.94), p=0.032 vs. OR (95% CI): 2.06 (1.02–4.17), p=0.044] (Table 2). Contrary to SNP rs35767/GG, SNPs rs7754840/CC and rs864745/TT were more frequent in the diabetes group compared to the control group (42% vs 66.7%; 21.6% vs 5.9%; 49% vs 39.2%, respectively; Table 3).

Table 3.

Frequencies of Genotypes of rs7754840 (CDKAL1), rs864745 (JAZF1), and rs35767 (IGF1) in T2DM Participants and Controls

            Logistic regression analysis (adjusted for age and BMI)c
    T2DM Control Chi-square test Additive Dominant Recessive
SNP Genotype No. (%) No. (%) Chi-square P P OR (95%CI) P OR (95%CI) P OR (95%CI)
rs7754840 (CDKAL1) GG 15 (29.4) 30 (58.8) 17.448 <0.001 0.014 2.32 (1.19–4.54) 0.035 1.63 (1.04–2.57) 0.057 1.99 (0.98–3.99)
  GC 25 (49.0) 18 (35.3)                
  CC 11 (21.6) 3 (5.9)                
rs864745a (JAZF1) TT 24 (49.0) 20 (39.2) 5.946 0.051 0.032 0.48 (0.24–0.94) 0.160 0.73 (0.46–1.14) 0.025 0.39 (0.17–0.89)
  TC 22 (44.9) 19 (37.3)                
  CC 3 (6.1) 12 (23.5)                
rs35767b (IGF1) GG 21 (42.0) 34 (66.7) 6.853 0.033 0.044 2.06 (1.02–4.17) 0.014 1.75 (1.12–2.75) 0.829 1.09 (0.49–2.44)
  GA 25 (50.0) 13 (25.5)                
  AA 4 (8.0) 4 (7.8)                
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a

The number of T2DM participants is 49, due to two samples failed to call out the genotypes; bThe number of T2DM participants is 50, due to one sample failed to call out the genotypes; cThe logistic regression model was used to obtain the odds ratios of the minor allele with the major allele as reference group.

The p values with statistical significance are indicated in bold numbers.

To evaluate the combined effects of these 16 SNPs, we calculated the cumulative risk allelic scores of these 16 risk alleles that each participant had. The average of cumulative risk allelic scores of T2DM patients and control group were 17.1±8.1 and 15.4±7.3 (p=0.002, t-test), respectively. The risk allelic scores were associated with T2DM in logistic regression analysis adjusted with age and BMI (OR: 1.27, 95% CI: 1.07–1.50, p=0.007).

Discussion

Variants in more than 40 loci were identified to be associated with T2DM in more than one population by GWAS results. Some variants were associated with T2DM in one ethnic group of subjects but not in others. SNPs in PPARG, CDKAL1, CDKN2A/B, SLC30A8, IGF2BP2, TCF7L2, ADRA2A, FADS1, DGKB, IGF1, CDC123/CAMK1D, TSPAN8, and JAZF1 were identified to be associated with T2DM in European populations by GWAS (Dupuis et al., 2010; Saxena et al.,2007; Zeggini et al., 2008). These SNPs have been confirmed by multiple studies in various populations, such as Danes, Han Chinese, and Japanese (Grarup et al., 2008; Hu et al., 2009; 2010; Liu et al., 2011; Ohshige et al., 2011; Wen et al., 2010; Zhou et al., 2010).

Study findings

In this study, the associations of SNPs in 16 respective T2DM candidate genes were investigated in Uyghurs. The Uyghur participants were recruited from Uyghur population at Hetian of Xinjiang, China, where the Uyghur population is less affected by the recent migration of Han Chinese and keeps its traditional lifestyles for generations, with its own language, religious beliefs, and marriage patterns (Black et al., 2006). Therefore, the Uyghur would be an ideal population for the study of evaluating genetic susceptibility. To our knowledge, this was the first attempt that a cohort of SNPs in PPARG, CDKAL1, CDKN2A/B, SLC30A8, IGF2BP2, TCF7L2, ADRA2A, FADS1, DGKB, IGF1, CDC123/CAMK1D, TSPAN8, JAZF1, IRS1, GCKR, and GCK were genotyped in Uyghur participants for T2DM susceptibility.

CDKAL1 is one of the most significant diabetes susceptibility genes identified to date in various populations. Intronic CDKAL1 variant rs7754840 has been associated with T2DM, mainly due to impaired first-phase insulin release (Chistiakov et al., 2011; Stancakova et al., 2008). SNPs rs7756992, rs10946398, and rs9465871 in CDKAL1 were also found to be associated with T2DM among European populations, Pima Indian, Han Chinese, and Korean populations in previous studies (Dehwah et al., 2010; Hu et al., 2009; Lee et al., 2008; Rong et al., 2009; Steinthorsdottir et al., 2007). Our study revealed that the C allele of rs7754840 of CDKAL1 was significantly associated with T2DM in Uyghur participants (p=0.014, adjusted for age and BMI). The OR value of rs7754840 was 2.32, higher than that of Han Chinese (1.127) (Hu et al., 2009), Pima Indians (1.06) (Rong et al., 2009), and Lebanese Arabs (1.86) (Nemr et al., 2012). T2DM patients had a higher CC genotype and lower GG genotype of CDKAL1 when compared with the control group (Chi-square: 17.448, p<0.001, Table 3). This result was compatible with a study among a Korean population, in which the CC genotype was significant higher in T2DM patients than that in controls (Lee et al., 2008).

Among the other loci examined in this study, SNP rs864745 in JAZF1 also showed the most significant association with T2DM in Uyghur (p=0.032, adjusted for age and BMI). SNP rs864745 resides in intron-1 of JAZF1 gene, encoding a transcriptional repressor of the nuclear receptor subfamily-2, group C, member-2 (NR2C2) gene (Nakajima et al., 2004). The carriage of JAZF1 risk variants may lead to postnatal growth restriction mainly due to affecting pancreatic β-cell mass and function (Grarup et al., 2008). The association between rs864745 and T2DM varied among populations. SNP rs864745 was strongly associated with T2DM in European participants (p=5.0×10−14) (Zeggini et al., 2008), whereas no significant association was observed in Han Chinese (p>0.05) (Hu et al., 2009) and Japanese subjects (P>0.05) (Takeuchi et al., 2009). In our study, the OR value of rs864745 (0.48) was lower than that in Caucasians (1.50) (An et al., 2009), Han Chinese population (1.05–1.09) (Hu et al., 2009; Zhou et al., 2010), and Pakistani population (1.16) (Rees et al., 2011). Frequency of the CC genotype of rs864745 (JAZF1) was lower in T2DM patients than that in control group (Table 3, 6.1% vs. 23.5%), indicating its protective effect on T2DM in Uyghur participants.

The rs35767 polymorphism resides 1.2 kb upstream of IGF1, which is a biologically plausible fasting insulin-raising gene. A previous study reported that the effects of the rs35767 polymorphism near IGF1 on fasting insulin are mediated by reduced insulin sensitivity or impaired insulin clearance; and those carriers of the GG genotype have lower insulin sensitivity as compared with subjects carrying the A allele in white Europeans (Mannino et al., 2013). The finding that the G allele of rs35767 (IGF1) was associated with fasting insulin and HOMA-IR in European population (Dupuis et al., 2010) as well as in Han Chinese (Hu et al., 2010) is not new.

In our study, there was a significant difference in the frequency of G allele for rs35767 (IGF1) between T2DM patients and controls (p=0.033). T2DM patients had a lower frequency of GG genotype (42.0% vs. 66.7%) compared to that of control group. The unique life style and marriage pattern might be responsible for such an observation. Most Uyghurs live as farmers, and have different dietary habits than Han Chinese. They have high carbohydrate diets with a higher salt (more than 20 g per day), more meat, and less unsaturated fatty acids compared with Han Chinese (Zhai et al., 2007). Then, the practice of endogamy in Uyghur population might also be a reason (Mamet et al., 2005; Wang et al., 2003).

The previous studies reported that accumulative number of risk alleles may be associated with T2DM, even though these alleles were not observed to be statistically significances individually (Fontaine-Bisson et al., 2010; Yamakawa-Kobayashi et al., 2012). We calculated the cumulative risk allelic scores of these 16 risk alleles to evaluate the combined effects of these 16 SNPs, and a significant association was observed between accumulative risk allelic scores and T2DM in the Uyghur population samples (p=0.007).

Study limitations

While this study provides valuable insight into the genetic difference of T2DM related loci in minority groups, it has some limitations. First, the ideal sample size should be about 300 pairs for cases and controls when we initiated this study. No multiple comparisons were attempted due to the relative small sample size that we originally designed in this pilot study. The statistical power of rs7754840 was calculated to 95% at the significance of 0.05, and those of other SNPs were less than 80% (SAS Proc Power), because the people of minority groups often refuse to participate in some investigation studies. Combined with less participant rate, the blood sampling is a very hard practice in Xinjiang, a very remote area of the northwestern frontier part of China. These are preliminary findings and further case-control studies with large samples and multiple comparisons are warranted to provide a more definite explanation about the relationships between those SNPs and T2DM based on our observation. Second, the existence of the interaction between environmental factors (e.g., life style, dietary, climates) and the T2DM susceptibility loci must be further validated in the Uyghur population. Moreover, important information on rare variants with stronger effects on T2DM could be revealed by the next-generation of whole genome sequencing when the technique routine could be available in not far future.

Conclusions and Future Outlook

This is the first study to evaluate genomic variation at 16 SNPs in respective T2DM candidate genes for the Uyghur population compared with other ethnic groups. The SNP rs7754840 in CDKAL1, rs864745 in JAZF1, and rs35767 in IGF1 might serve as potential susceptibility loci for T2DM in Uyghurs. We suggest that a broader capture and study of the world populations, including those that are hitherto understudied or overlooked, are necessary for a comprehensive understanding of the genetic/genomic basis of T2DM. Additionally, plant OMICS research, including those with traditional plants (Sahu et al., 2014), that offer potential novel therapeutics for T2DM could be usefully is combined with studies of T2DM genetics so as to inform future personalized medicine research, global health, and society.

Acknowledgments

This study was supported by research grants from the National Natural Science Foundation of China (81273170, 30901238, and 81370083), Mason Foundation National Medical Program-ANZ-Australia (CT21946), National 12th Five-Year Major Projects of China (2012BAI37B03), ECU-Industry Collaborative Scheme 2013 (G1001368), Australian National Health and Medical Research Council (NHMRC-APP1046711), Project for Academic Human Resources Development in Institutions of Higher Learning under the Jurisdiction of Beijing Municipality (PHR201008393), Natural Science Foundation of Xinjiang Uyghur Autonomous Region (2013211A016), and Natural Science Foundation of Capital Medical University, Beijing, China (2014ZR16). Wei Wang and Manshu Song were supported by the Importation and Development of High-Calibre Talents Project of Beijing Municipal Institutions (IDHT20130213, CIT&TCD201404185), and Youxin Wang was supported by Beijing Higher Education Young Elite Teacher Project (YETP1671) and Beijing Nova Program (Z141107001814058). The authors thank the Uyghur volunteers and community leaders for their support and participation.

Author Disclosure Statement

The authors declare that no conflicting financial interests exist.

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