Identification of Genetic Variants of Gestational Diabetes in South Indians

Abstract

Background:

This study examined the association in a South Indian population with gestational diabetes mellitus (GDM) of type 2 diabetes risk variants that have previously conferred susceptibility to GDM in other populations.

Subjects and Methods:

The study groups comprised 518 women with GDM and 910 pregnant women with normal glucose tolerance (NGT). Women with GDM were recruited from a tertiary diabetes center in Chennai, in south India, and NGT women were selected from antenatal clinics also in Chennai. Genomic DNA was isolated from whole blood using the phenol chloroform method. Twelve previously reported GDM-associated single nucleotide polymorphisms (SNPs) in or near nine loci were genotyped using the MassARRAY™ system (Sequenom, San Diego, CA).

Results:

Among the 12 SNPs genotyped, 11 SNPs were in Hardy–Weinberg equilibrium and had a call rate of >95%. Of the 11 SNPs previously associated with GDM in other populations, significant association was observed only with the rs7754840 and rs7756992 SNPs of the CDK5 regulatory subunit associated protein 1-like 1 (CDKAL1) gene in this population. The minor alleles of the SNPs rs7754840 and rs7756992 showed significant susceptibility to GDM with an odds ratio of 1.34 (95% confidence interval, 1.12–1.60; P=0.0013) and 1.45 (95% confidence interval, 1.21–1.72; P=0.00004), respectively.

Conclusions:

The rs7754840 and rs7756992 SNPs of the CDKAL1 gene were found to be associated with GDM in this south Indian population. This is the first study describing genetic susceptibility of GDM in Asian Indians.

Introduction

Gestational diabetes mellitus (GDM) is defined as abnormal glucose tolerance with onset during pregnancy or first detected at pregnancy.¹ The prevalence of GDM varies from 1% to 14% of all pregnancies, among different ethnic groups.^2
–4 Several studies have shown that GDM occurs in 12–21% of Indian women.⁵ In south India, the prevalence of GDM was found to be 17.8% in urban, 13.8% in semi-urban, and 9.9% in rural areas.^6,7 In our recent clinic-based study of young diabetes patients, the prevalence of GDM was found to be 4.5%.⁸

Evidence is also accumulating that susceptibility to GDM has a genetic component.⁹ Studies have shown clustering of type 2 diabetes mellitus (T2DM) and impaired glucose tolerance in families with GDM and evidence for higher prevalence of T2DM in mothers of women with GDM.¹⁰ Earlier studies have indicated that the main pathophysiological defects that occur in GDM may be the same as those observed with T2DM, such as marked insulin resistance, impairment of insulin secretion, and abnormal utilization of glucose.^9,11 Several studies, including genome-wide association studies (GWASs) and meta-analysis reports, have provided evidence that GDM and T2DM may share similar risk factors and genetic background.^12

–17

Recently, replication of T2DM-associated genes identified in various GWASs has been carried out in GDM patients in different Asian populations from Korea, China, and Malaysia,^4,18
–20 but there are no data for Asian Indians, to the best of our knowledge. Although the genetics of T2DM have been extensively studied in Asian Indians using different approaches such as candidate gene and GWAS,^{21

–29} GDM, which is probably one of the early indicators of future occurrence of T2DM in women, has not been well studied in India. We have previously reported that associations of certain genetic variants were different in Asian Indians compared with other ethnic groups.^{21
–23,27,30} The replication of GWASs in our south Indian population showed that out of 45 single nucleotide polymorphisms (SNPs) that showed an association in white T2DM populations, only six—namely, CDK5 regulatory subunit associated protein 1-like 1 (CDKAL1) (rs7754840, rs7756992, and rs6931514), HHEX (rs7923837), and BAZ1B (rs12056034)—showed association with T2DM in our population.²⁶ Moreover, a GWAS on south Asians with T2DM has shown the association of six novel genes (AP3S2 rs2028299, GRB14 rs3923113, HMG20A rs7178572, HNF4A rs4812829, ST6GAL1 rs16861329, and VPS26A rs1802295) that were not found in Europeans.²⁸ Therefore, it is likely that GDM might also show differences in genetic association in our population. Hence, it is relevant to study the role of genetic factors in GDM in Asian Indians.

The present study was designed to invesigate whether the SNPs that have previously shown association with GDM in other populations are also associated with GDM in this south Indian population. To our knowledge, this is the first major study of genetic susceptibility of GDM in Asian Indians.

Subjects and Methods

Study subjects

Pregnant women without previous diagnosis of glucose intolerance were selected for the present study. The study groups comprised 518 women with GDM and 910 pregnant women with normal glucose tolerance (NGT). Women with GDM were recruited from a tertiary diabetes center in Chennai. Diagnosis of GDM is done using International Association of Diabetes and Pregnancy Study Groups criteria: one or more of fasting plasma glucose level of ≥5.1 mmol/L, 1-h plasma glucose level of ≥10.0 mmol/L, and 2-h plasma glucose level of ≥8.5 mmol/L following a 75-g oral glucose tolerance test. Women with NGT (fasting plasma glucose level of <5.6 mmol/L [100 mg/dL] and 2-h postprandial blood glucose level of <7.8 mmol/L [140 mg/dL]) were selected from antenatal clinics in Chennai. Women with advanced maternal age (>40 years), prepregnant body mass index of ≥30 kg/m², previous history of still birth, a macrosomic baby, or a baby with congenital abnormality, pregnancy-induced or preexisting hypertension, and history of other complicated pregnancies were excluded from the study. The subjects included in the study were all unrelated individuals of south Indian origin and Dravidian ethnicity as per the self-reported information. The study has been approved by the Madras Diabetes Research Foundation Ethics Committee, and written informed consent was obtained from all study subjects included in the study.

SNP selection and genotyping

Genomic DNA was isolated from whole blood using the phenol chloroform method. The SNPs that were selected for the present study include 12 previously reported GDM-associated SNPs from nine loci: CDKAL1 (rs7754840 and rs7756992), glucokinase (hexokinase 4) (GCK) (rs1799884 and rs4607517), insulin-like growth factor 2 mRNA binding protein 2 (IGF2BP2) (rs4402960), potassium inwardly-rectifying channel, subfamily J, member 11 (KCNJ11) (rs5219), potassium voltage-gated channel, KQT-like subfamily Q, member 1 (KCNQ1) (rs2237892), melatonin receptor 1A (MTNR1A) (rs2119882), melatonin receptor 1B (MTNR1B) (rs1387153 and rs10830963), serine racemase (SRR) (rs391300), and transcription factor 7-like 2 (TCF7L2) (rs12255372). All the SNPs were genotyped using the MassARRAY™ system (Sequenom, San Diego, CA) following the manufacturer's instructions. SpectroTYPER™ software (Sequenom) automatically called genotypes, and only conservative and moderate calls were accepted for this study. Ten percent of the samples were replicated, and the discordance rate was less than 0.1%. The call rate required for each assay was ≥90%.

Statistical analysis

Hardy–Weinberg equilibrium was tested using Pearson χ² statistics with a threshold of P<0.05 in cases and controls separately for each SNP. Statistical analyses were performed in SPSS version 15.0 software (SPSS, Inc., Chicago, IL) and PLINK, an open-source tool (http://pngu.mgh.harvard.edu/∼purcell/plink/). Logistic regression analysis was performed to determine the association between SNPs. The odds ratios (ORs) with 95% confidence intervals (CIs) are presented for each SNP. Bonferroni's corrections for multiple comparisons were performed. Power was estimated using PS Power and Sample Size program (Vanderbilt University, Nashville, TN) calculations. Haplotype analysis of CDKAL1 gene SNPs was performed using PLINK, which uses an expectation-maximization algorithm, and the haplotype structure was visualized with HAPLOVIEW (www.broadinstitute.org/haploview/haploview).

Results

The clinical and biochemical parameters of the groups of GDM and pregnant NGT women are presented in Table 1. The mean age, fasting plasma glucose, 2-h postload plasma glucose, and glycosylated hemoglobin were significantly higher in the GDM group than in controls (P<0.001).

Table 1.

Clinical and Biochemical Characteristics of the Study Groups

Parameter	GDM (n=518)	Pregnant women with NGT (n=910)	P value
Age at pregnancy (years)	28.1±5.0	26.3±5.5	<0.001^a
BMI (kg/m²)	27.5±2.4	23.3±4.7	<0.001^a
Blood pressure (mm Hg)
Systolic	121.0±15.4	116.0±18.7	<0.001^a
Diastolic	77.6±8.0	71.8±12.0	<0.001^a
Fasting plasma glucose (mmol/L)	5.9±1.5	4.3±0.4	<0.001^a
2-h postload glucose (mmol/L)	10.1±3.6	4.9±0.5	<0.001^a
Glycosylated hemoglobin (%)	5.7±0.5	4.5±1.8	<0.001^a

Data are mean±SD values.

Indicates a statistically significant value (P value<0.05).

BMI, body mass index; GDM, gestational diabetes mellitus; NGT, normal glucose tolerance.

In total, 12 SNPs (in or near nine loci) were genotyped in 518 women with GDM and 910 pregnant women with NGT, of which 11 SNPs were in Hardy–Weinberg equilibrium and had a call rate of >95%: rs7754840, 97.2%; rs7756992, 96.7%; rs1799884, 97.4%; rs4607517, 99.1%; rs4402960, 97.1%; rs5219, 97.8%; rs2119882, 97.4%; rs1387153, 97.4%; rs10830963, 97.9%; rs391300, 95.4%; and rs12255372, 97.3%. The KCNQ1 (rs2237892) SNP with a call rate of 98.2% was excluded from further analysis as it deviated from Hardy–Weinberg equilibrium. The P value of Hardy–Weinberg equilibrium for the failed SNP rs2237892 remained significant even after multiple corrections using Bonferroni's correction (P>0.05/12=0.004). The distributions of the allelic frequencies of all the GDM-associated SNPs were compared between the GDM and pregnant NGT women (Table 2). Of the 11 SNPs in or near eight loci (CDKAL1, GCK, IGF2BP2, KCNJ11, MTNR1A, MTNR1B, SRR, and TCF7L2) that were tested, significant association with GDM was seen only with two SNPs of the CDKAL1 gene: rs7754840 (P= 0.0013) and rs7756992 (P=0.00004). The association of rs7754840 and rs7756992 with GDM remained significant even after multiple corrections using Bonferroni's correction (P>0.05/11=0.0045). Association with GDM was not observed in the other nine SNPs. However, the SNP of the TCF7L2 gene (rs12255372) showed a moderately significant association (P=0.054) with GDM.

Table 2.

Distribution of the Allelic Frequencies of Gestational Diabetes Mellitus-Associated Single Nucleotide Polymorphisms Included in the Present Study

					MAF
Study number	SNP	Chromosome	Gene	Risk allele	GDM (n=518)	Pregnant women with NGT (n=910)	P value
1	rs7754840	6	CDKAL1	C	0.273	0.219	0.0013^a
2	rs7756992	6	CDKAL1	G	0.291	0.221	0.00004^a
3	rs1799884	7	GCK	T	0.118	0.116	0.9022
4	rs4607517	7	GCK	A	0.117	0.121	0.7717
5	rs4402960	3	IGF2BP2	T	0.507	0.468	0.0815
6	rs5219	11	KCNJ11	T	0.379	0.363	0.4527
7	rs2119882	4	MTNR1A	C	0.354	0.354	0.9891
8	rs10830963	11	MTNR1B	G	0.447	0.419	0.1961
9	rs1387153	11	MTNR1B	T	0.38	0.348	0.1416
10	rs391300	17	SRR	C	0.382	0.376	0.7714
11	rs12255372	10	TCF7L2	T	0.241	0.206	0.0540

Indicates a statistically significant value (P value<0.05). They are less than the significance threshold after Bonferroni's correction (0.05/11=0.0045).

GDM, gestational diabetes mellitus; MAF, minor allele frequency; NGT, normal glucose tolerance; SNP, single nucleotide polymorphism.

Logistic regression analysis was performed for the CDKAL1 gene SNPs rs7754840 and rs7756992 under multiple genetic models, and the findings are presented in Table 3. The minor alleles of the SNPs rs7754840 and rs7756992 showed significant susceptibility to GDM with ORs of 1.34 (95% CI, 1.12–1.60; P=0.0013) and 1.45 (95% CI, 1.21–1.72; P=0.00004) respectively. Under the dominant model, the SNP rs7756992 showed significant association with GDM (adjusted OR=1.37; 95% CI, 1.07–1.74; P=0.012), whereas SNP rs7754840 did not show any association (adjusted OR=1.20; 95% CI, 0.94–1.50; P=0.138). Adjusting this P value by the number of competing models (three) for two SNPs using Bonferroni's correction, we found that the association finding did not remain significant (Bonferroni threshold P=0.05/6=0.008); therefore the association of both of the SNPs did not follow the dominant mode of inheritance. Although the heterozygous variant genotype showed significant association with GDM in both the SNPs, the homozygous variant failed to show any association. This showed that the genotypic distribution of both the SNPs was not compatible under an additive model. However, the SNPs rs7754840 (adjusted OR=1.93; 95% CI, 1.20–3.00; P=0.005) and rs7756992 (adjusted OR=2.12; 95% CI, 1.35–3.30; P=0.001) of the CDKAL1 gene showed association with GDM under the recessive model. Therefore the association of the CDKAL1 gene SNPs with GDM fits well into a recessive model of inheritance.

Table 3.

Genotype Distribution Among the Study Subjects and Corresponding Odds Ratios for Gestational Diabetes Mellitus

	Genotype frequency ^a			Model^b
SNP, genotype	GDM	Pregnant women with NGT	P value	Additive	Dominant	Recessive
rs7754840 (CDKAL1)			0.0014^c
CC	49 (9.9)	46 (5.1)		1.08 (0.83–1.40); 0.583	1.20 (0.94–1.50); 0.138	1.93 (1.20–3.00); 0.005^c
GC	172 (34.7)	306 (33.6)		1.98 (1.24–3.15); 0.004^c
GG	274 (55.4)	558 (60.3)		Reference
rs7756992 (CDKAL1)			0.00004^c
GG	52 (10.6)	48 (5.3)		1.22 (0.95–1.60); 0.128	1.37 (1.07–1.74); 0.012^d	2.12 (1.35–3.30); 0.001^c
AG	182 (37)	306 (33.6)		2.29 (1.50–3.60); 0.0004^c
AA	258 (52.4)	556 (61.1)		Reference

Data are number of subjects (%).

Data are adjusted (for age) odds ratio (95% confidence interval); P value.

Indicates a statistically significant difference (P value<0.05). They are less than the significance threshold after Bonferroni's correction.

Indicates a statistically significant difference (P value<0.05).

GDM, gestational diabetes mellitus; NGT, normal glucose tolerance; SNP, single nucleotide polymorphism.

Analysis of linkage disequilibrium found the SNPs of CDKAL1 (rs7754840 and rs7756992; D′=0.91), GCK (rs1799884 and rs4607517; D′=1.0), and MTNR1B (rs1387153 and rs10830963; D′=0.80) were in a strong linkage disequilibrium. Table 4 shows the comparision of diplotype frequencies of CDKAL1 (rs7754840 and rs7756992), GCK (rs1799884 and rs4607517), and MTNR1B (rs10830963 and rs1387153) gene variants in the study subjects. The CC/GG diplotype CDKAL1 gene with a frequency of 4.1% in pregnant women with NGT and 8.8% in GDM was significantly different, and this diplotype showed association with a 2.1 times increased risk of GDM (adjusted P=0.019; OR=2.08; 95% CI, 1.13–3.84).

Table 4.

Comparison of Diplotype Frequencies in Pregnant Women with Normal Glucose Tolerance and Gestational Diabetes Mellitus

Gene (SNPs), haplotype combinations (>1% frequency)	GDM (n=518)	Pregnant women with NGT (n=910)	Unadjusted OR (95% CI); P value
CDKAL1 (rs7754840G>C, rs7756992A>G)
GG/AA	0.507	0.576	0.76 (0.61–0.95), 0.014^a0.77 (0.58–1.03), 0.082^b
GG/GA	0.045	0.038	1.19 (0.69–2.06), 0.530
GC/AA	0.018	0.035	0.52 (0.25–1.11), 0.090
GC/GA	0.312	0.291	1.11 (0.87–1.40), 0.414
CC/GA	0.012	0.010	1.22 (0.43–3.45), 0.705
CC/GG	0.088	0.041	2.23 (1.42–3.51), 0.001^a2.08 (1.13–3.84), 0.019^a,b
GCK (rs1799884C>T, rs4607517G>A)
CC/GG	0.779	0.782	0.99 (0.73–1.33), 0.919
CT/GA	0.207	0.200	1.04 (0.76–1.42), 0.799
TT/AA	0.012	0.016	0.76 (0.26–2.20), 0.607
MTNR1B (rs10830963C>G, rs1387153C>T)
CC/CC	0.257	0.305	0.79 (0.60–1.04), 0.091
CC/CT	0.043	0.034	1.26 (0.66–2.43), 0.481
CG/CC	0.111	0.118	0.94 (0.64–1.39), 0.750
CG/CT	0.368	0.331	1.18 (0.91–1.53), 0.220
CG/TT	0.028	0.030	0.95 (0.45–1.98), 0.881
GG/CT	0.059	0.076	0.76 (0.46–1.25), 0.282

Indicates a statistically significant difference (P value<0.05).

Odds ratio (OR) adjusted for age.

CI, confidence interval; GDM, gestational diabetes mellitus; NGT, normal glucose tolerance; SNP, single nucleotide polymorphism.

Discussion

Our study is the first major genetic study of GDM from India. Recent studies have shown that the T2DM-associated gene variants were also found to be associated with GDM susceptibility.^4,16

–20 Among the genes that were investigated in the present study, CDKAL1 is the only gene that showed association with GDM in our south Indian population. Both SNPs of the CDKAL1 gene—rs7754840 and rs7756992—showed association with GDM even after Bonferroni's correction with a power of 61% and 82%, respectively.

The impact of CDKAL1 gene variants in our population can be noticed in that the minor alleles of rs7754840 and rs7756992 are shown to be the risk alleles and are significantly associated with GDM with an increased risk of 1.34 and 1.45 times, respectively. Logistic regression analysis performed for the CDKAL1 gene SNPs rs7754840 and rs7756992 showed that the association of the CDKAL1 gene SNPs with GDM fits well into a recessive model of inheritance.

The study of these CDKAL1 gene SNPs with GDM in the Malaysian population showed its strongest evidence of association under the additive model.⁴ Analysis of different genetic models showed that the association of these SNPs with T2DM in this south Indian population best fits the additive and dominant models.²⁶ The minor alleles of both the associated CDKAL1 gene variants were shown to be the risk alleles. Previous studies among Asian populations have reported the association between SNPs rs7754840 and rs7756992 and GDM risk.^15,16,19 In the Korean population, the minor alleles of the SNPs rs7754840 and rs7756992 showed significant susceptibility to GDM with ORs of 1.55 (95% CI, 1.34–1.79; P=4.17×10^–9) and 1.39 (95% CI, 1.20–1.61; P=9.14×10^–9), respectively.¹⁹ However, no significant association was found in the Chinese population.²⁰ The meta-analysis among Asian populations indicated that the C allele of rs7754840 was significantly associated with risk of GDM with a pooled OR of 1.40 (95% CI, 1.13–1.72; P=0.002).¹⁷ In the present study, haplotype analysis of the CDKAL1 gene SNPs showed that both SNPs of CDKAL1 (rs7754840 and rs7756992) were in a strong linkage disequilibrium, and the risk allele haplotype CG was found to be significantly associated with GDM (P=0.0216). The risk of rs7754840 and rs7756992 SNPs is strengthened by the diplotypes containing the risk alleles of both these SNPs. The CC/GG diplotype of the CDKAL1 gene showed a stronger association with GDM (OR=2.1) compared with the single risk allele.

Although the function of the CDKAL1 gene is still unclear, earlier studies have shown that this gene may play a role in β-cell function by inhibiting activity of cyclin-dependent kinase 5, which prevents a decrease in insulin secretion due to glucotoxicity.^31,32 The CDKAL1 protein has been reported to function as a tRNA modification enzyme, and CDKAL1 risk variants affect the protein translation, thereby impairing the conversion of proinsulin to insulin in pancreatic β-cells.^33,34

The CDKAL1 gene has been previously reported to be associated with T2DM in this south Indian population.²⁶ The association of the SNP rs7756992 with GDM risk has been reported in the Malaysian population.⁴ The rs7754840 SNP has shown association with GDM in earlier studies, mostly among Asian populations.^4,15,19,20 The directionality of the association of the CDKAL1 gene SNPs between cases and controls was found to be similar to that observed in other studies in GDM women.^4,15,19,20 The frequency of the C allele of the rs7754840 variant of the CDKAL1 gene in our study is lower when compared with that among other Asian ethnic groups like Korean (approximately 56%), Chinese (approximately 47%), and Malaysian (approximately 38%).^4,15,19,20 Similarly, the frequency of the G allele of the rs7756992 variant of the CDKAL1 gene is also lower compared with the Malaysian study (approximately 44%).⁴

In the present study, we observed that most of the common T2DM-associated SNPs that showed susceptibility to GDM in other populations did not show any association with GDM in this south Indian population. Earlier studies have reported the significant association of the SNPs GCK (rs1799884 and rs4607517), IGF2BP2 (rs4402960), KCNJ11 (rs5219), MTNR1A (rs2119882), MTNR1B (rs1387153 and rs10830963), SRR (rs391300), and TCF7L2 (rs12255372) with GDM in whites and Asians, mainly Koreans, Chinese, and Malayasians.^4,16

–20 The TCF7L2 gene SNP rs12255372 showed only a moderate significant association (P=0.054) with GDM in the present study, although this SNP has previously shown association with T2DM in Asian Indians.³⁰ The association of rs12255372 with GDM has been reported in other populations like Mexican Americans, whites, and Koreans.^14,19,35,36 The frequency of the T allele was found to be lower (approximately 24%) in our GDM group compared with other populations such as Mexican Americans (approximately 39%),¹⁴ whites (approximately 31%),^35,36 and Asians (Koreans; approximately 40%).¹⁹ Analysis of the present study was limited to candidate SNPs that have been validated in multiple large studies mainly with the concern of multiple testing. This is the limitation of our study.

The most significant finding of the present study is the identification of SNPs of the CDKAL1 gene, which is found to be strongly associated with GDM in this south Indian population. Testing these SNPs with a larger sample size and in a prospective study would help to validate our findings and also help in evaluating these SNPs for their ability to predict development of future diabetes mellitus in this population.

Footnotes

Acknowledgments

This study was supported by funding from the MDRF Innovative Research Fund of the Madras Diabetes Research Foundation, Chennai, India.

Author Disclosure Statement

No competing financial interests exist.

References

Buchanan

, Xiang

: Gestational diabetes mellitus. J Clin Invest, 2005; 115:485–491.

Shaat

, Groop

: Genetics of gestational diabetes mellitus. Curr Med Chem, 2007; 14:569–583.

Jang

: Gestational diabetes in Korea: incidence and risk factors of diabetes in women with previous gestational diabetes. Diabetes Metab J, 2011; 35:1–7.

Aris

NKM

, Ismail

NAM

, Mahdy

, et al.: An analysis of targeted single nucleotide polymorphisms for the risk prediction of gestational diabetes mellitus in a cohort of Malaysian patients. Asia Pac J Mol Med, 2011; 1:1–8.

Kalra

, Malik

, John

: Gestational diabetes mellitus: a window of opportunity. Indian J Endocr Metab, 2011; 15:149–151.

Seshiah

, Balaji

, et al.: Prevalence of GDM in South India (Tamil Nadu)—a community based study. J Assoc Physicians India, 2008; 56:329–333.

Seshiah

, Balaji

, et al.: Pregnancy and diabetes scenario around the world: India. Int J Gynaecol Obstet, 2009; 104(Suppl 1):S35–S38.

Amutha

, Datta

, Unnikrishnan

, et al.: Clinical profile of diabetes in the young seen between 1992 and 2009 at a specialist diabetes centre in south India. Prim Care Diabetes, 2011; 5:223–229.

Robitaille

, Grant

: The genetics of gestational diabetes mellitus: evidence for relationship with type 2 diabetes mellitus. Genet Med, 2008; 10:240–250.

10.

McLellan

, Barrow

, Levy

, et al.: Prevalence of diabetes mellitus and impaired glucose tolerance in parents of women with gestational diabetes. Diabetologia, 1995; 38:693–698.

11.

Lauenborg

, Grarup

, Damm

, et al.: Common type 2 diabetes risk gene variants associate with gestational diabetes. J Clin Endocrinol Metab, 2009; 94:145–150.

12.

Watanabe

, Allayee

, Xiang

, et al.: Transcription factor 7-like 2 (TCF7L2) is associated with gestational diabetes mellitus and interacts with adiposity to alter insulin secretion in Mexican Americans. Diabetes, 2007; 56:1481–1485.

13.

Kim

, Cheong

, Park

, et al.: Melatonin receptor 1 B polymorphisms associated with the risk of gestational diabetes mellitus. BMC Med Genet, 2011; 12:82.

14.

Pappa

, Gazouli

, Economou

, et al.: Gestational diabetes mellitus shares polymorphisms of genes associated with insulin resistance and type 2 diabetes in the Greek population. Gynecol Endocrinol, 2011; 27:267–272.

15.

Kwak

, Kim

, Cho

, et al.: A genome-wide association study of gestational diabetes mellitus in Korean women. Diabetes, 2012; 61:531–541.

16.

Mao

, Li

, Gao

: Meta-analysis of the relationship between common type 2 diabetes risk gene variants with gestational diabetes mellitus. PloS One, 2012; 7:e45882.

17.

Zhang

, Bao

, Rong

, et al.: Genetic variants and the risk of gestational diabetes mellitus: a systematic review. Hum Reprod Update, 2013; 19:376–390.

18.

Low

, Mohd Tohit

, Chong

, et al.: Adiponectin SNP45TG is associated with gestational diabetes mellitus. Arch Gynecol Obstet, 2010; 283:1255–1260.

19.

Cho

, Kim

, Lim

, et al.: Type 2 diabetes-associated genetic variants discovered in the recent genome-wide association studies are related to gestational diabetes mellitus in the Korean population. Diabetologia, 2009; 52:253–261.

20.

Wang

, Nie

, Li

, et al.: Association of six single nucleotide polymorphisms with gestational diabetes mellitus in a Chinese population. PLoS One, 2011; 6:e26953.

21.

Abate

, Chandalia

, Satija

, et al.: ENPP1/Pc-1 K121Q polymorphism and genetic susceptibility to type 2 diabetes. Diabetes, 2005; 54:1207–1213.

22.

Radha

, Vimaleswaran

, Babu

HNS

, et al.: Role of genetic polymorphism peroxisome proliferator–activated receptor–2 Pro 12Ala on ethnic susceptibility to diabetes in south Asian and Caucasian subjects. Diabetes Care, 2006; 29:1046–1051.

23.

Vimaleswaran

, Radha

, Ghosh

, et al.: Peroxisome proliferator-activated receptor-γ co-activator-1α (PGC-1α) gene polymorphisms and their relationship to Type 2 diabetes in Asian Indians. Diabet Med, 2005; 22:1516-1521.

24.

Sanghera

, Been

, Ortega

, et al.: Testing the association of novel meta-analysis-derived diabetes risk genes with type II diabetes and related metabolic traits in Asian Indian Sikhs. J Hum Genet, 2009; 54:162–168.

25.

Sanghera

, Ortega

, Han

, et al.: Impact of nine common type 2 diabetes risk polymorphisms in Asian Indian Sikhs: PPARG2 (Pro12Ala), IGF2BP2, TCF7L2 and FTO variants confer a significant risk. BMC Med Genet, 2008; 9:59.

26.

Chidambaram

, Radha

, Mohan

: Replication of recently described type 2 gene variants in a South Indian population. Metabolism, 2010; 59:1760–1766.

27.

Bodhini

, Radha

, Ghosh

, et al.: Association of Calpain 10 gene polymorphisms with type 2 diabetes in Southern Indians. Metabolism, 2011; 60:681–688.

28.

Kooner

, Saleheen

, Sim

, et al.: Genome-wide association study in people of South Asian ancestry identifies six novel susceptibility loci for type 2 diabetes. Nat Genet, 2011; 43:984–989.

29.

Tabassum

, Chauhan

, Dwivedi

, et al.: Genome wide association study for type 2 diabetes in Indians identifies a new susceptibility locus at 2q21. Diabetes, 2013; 62:977–986.

30.

Bodhini

, Radha

, Dhar

, et al.: The rs12255372(G/T) and rs7903146(C/T) polymorphisms of the TCF7L2 gene are associated with type 2 diabetes mellitus in Asian Indians. Metabolism, 2007; 56:1174–1178.

31.

Dehwah

MAS

, Wang

, Huang

: CDKAL1 and type 2 diabetes: a global meta-analysis. Genet Mol Res, 2010; 9:1109–1120.

32.

Ohara-Imaizumi

, Yoshida

, Aoyagi

, et al.: Deletion of CDKAL1 affects mitochondrial ATP generation and first-phase insulin exocytosis. PLoS One, 2010; 5:e15553.

33.

Wei

, Tomizawa

: Functional loss of Cdkal1, a novel tRNA modification enzyme, causes the development of type 2 diabetes. Endocr J, 2011; 58:819–825.

34.

Chang

, Cai

, Huang

, et al.: Adaptive human CDKAL1 variants underlie hormonal response variations at the enteroinsular axis. PLoS One, 2014; 9:e105410.

35.

Papadopoulou

, Lynch

, Shaat

, et al.: Gestational diabetes mellitus is associated with TCF7L2 gene polymorphisms independent of HLA-DQB1*0602 genotypes and islet cell autoantibodies. Diabet Med, 2011; 28:1018–1027.

36.

Vcelak

, Vejrazkova

, Vankova

, et al.: T2D risk haplotypes of the TCF7L2 gene in the Czech population sample: the association with FFAs composition. Physiol Res, 2012; 61:229–240.