Impact of KCNQ1, CDKN2A/2B, CDKAL1, HHEX, MTNR1B, SLC30A8, TCF7L2, and UBE2E2 on risk of developing type 2 diabetes in Thai population

Impact of KCNQ1, CDKN2A/2B, CDKAL1, HHEX, MTNR1B, SLC30A8, TCF7L2, and UBE2E2 on risk of... Background: Several type 2 diabetes (T2D) susceptibility loci identified via genome-wide association studies were found to be replicated among various populations. However, the influence of these loci on T2D in Thai population is unknown. The aim of this study was to investigate the influence of eight single nucleotide polymorphisms (SNPs) reported in GWA studies on T2D and related quantitative traits in Thai population. Methods: Eight SNPs in or near the KCNQ1, CDKN2A/2B, SLC30A8, HHEX, CDKAL1, TCF7L2, MTNR1B, and UBE2E2 genes were genotyped. A case-control association study comprising 500 Thai patients with T2D and 500 ethnically- matched control subjects was conducted. Associations between SNPs and T2D were examined by logistic regression analysis. The impact of these SNPs on quantitative traits was examined by linear regression among case and control subjects. Results: Five SNPs in KCNQ1 (rs2237892), CDK2A/2B (rs108116610, SLC30A8 (rs13266634), TCF7L2 (rs7903146) and MTNR1B (rs1387153) were found to be marginally associated with risk of developing T2D, with odds ratios ranging from 1.43 to 2.02 (p = 0.047 to 3.0 × 10–4) with adjustments for age, sex, and body mass index. Interestingly, SNP rs13266634 of SLC30A8 gene reached statistical significance after correcting for multiple testing (p = 0.0003) (p <0. 006 after Bonferroni correction). However, no significant association was detected between HHEX (rs1111875), CDKAL1 (rs7756992), or UBE2E2 (rs7612463) and T2D. We also observed association between rs10811661 and both waist circumference and waist-hip ratio (p = 0.007 and p = 0.023, respectively). In addition, rs13266634 in SLC30A8 was associated with glycated hemoglobin (p = 0.018), and rs7903146 in TCF7L2 was associated with high-density lipoprotein cholesterol level (p = 0.023). Conclusion: Of the eight genes included in our analysis, significant association was observed between KCNQ1, CDKN2A/2B, SLC30A8, TCF7L2, and MTNR1B loci and T2D in our Thai study population. Of these, CDKN2A/2B, SLC30A8, and TCF7L2 genes were also significantly associated with anthropometric, glycemic and lipid characteristics. Larger cohort studies and meta-analyses are needed to further confirm the effect of these variants in Thai population. Keywords: Type 2 diabetes, Association study, Single nucleotide polymorphisms, Thai population, Genome-wide association study * Correspondence: watip.tan@mahidol.edu Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 2 of 9 Background and C2CD4A-C2CD4B were reported to be associated The prevalence of diabetes, especially type 2 diabetes with T2D by two relatively small East Asian GWA studies, (T2D), continues to increase worldwide. The Inter- while these loci did not reach genome-wide significance in national Diabetes Federation (IDF) has estimated that European studies [4, 5, 14]. Furthermore, there are rela- the prevalence of diabetes will increase from 451 million tively large frequency differences between population in 2017 to 693 million by 2045 [1]. The number of groups for rs7903146 within TCF7L2 [15]. Although nu- people with diabetes will increase as a result of changes merous T2D risk loci were identified from GWA studies in lifestyle caused by urbanization, high population in multi-ethnic populations, the functional impact of these growth, ageing population, and high economic growth. risk loci still needs to be elucidated. Most of these risk loci In 2017, the prevalence of diabetes among adults in likely affect insulin secretion and beta-cell function, with a Thailand was estimated to be 8.3% [1]. T2D is a complex few potentially involved in insulin action. Given this vari- and multifactorial disease that is characterized by im- ability among populations, it is important that we under- paired insulin secretion and insulin resistance [2, 3]. stand the association between genetic variations and T2D, T2D is associated with significant morbidity and mortal- and the roles of these T2D risk loci in Thai population. In ity, especially decreased life expectancy and reduced this study, we investigated single nucleotide polymor- quality of life, as well as high cost of treatment and re- phisms (SNPs) that were previously found to be asso- duced productivity. Although the etiology of T2D is not ciated with T2D in Asian populations. We focused on thoroughly understood, genetic and environmental fac- the following eight SNPs: 1) potassium voltage-gated tors are thought to be involved in the pathogenesis of channel, KQT-like subfamily, member 1 (KCNQ1; this disease. rs2237892); 2) a variant found near cyclin-dependent Prior to the development of currently available methods kinase inhibitor 2A (CDKN2A/2B; rs10811661); 3) a and technologies, linkage and candidate gene association zinc transporter member of solute carrier family 30 studies were conducted; however, these two methods (SLC30A8; rs13266634); 4) hematopoietically-expressed could identify only a few genes involved in the pathogen- homeobox (HHEX; rs1111875); 5) CDK5 regulatory esis of T2D (i.e., PPARG, KCNJ11, CAPN10,and TCF7L2). subunit-associated protein 1-like 1 (CDKAL1; rs7756992); Advances in genotyping technology and genetic informa- 6) melatonin receptor type 1B (MTNR1B; rs1387153); 7) tion have facilitated the application of genome-wide asso- transcription factor-7-like 2 (TCF7L2; rs7903146); and, 8) ciation (GWA) studies to identify T2D susceptibility Ubiquitin Conjugating Enzyme E2 E2 (UBE2E2; genes. Currently, over 120 common risk variants have rs7612463). Our specific aim was to investigate the influ- been successfully identified as being associated with T2D ence of these eight SNPs on T2D and related quantitative using this approach [4–12]. Several reproducible GWA traits in Thai population. studies confirmed these well-established susceptibility genes, and they were found to be replicated for association Methods with T2D and diabetes-related traits in or near SLC30A8, Study subjects KCNQ1, CDC123, HNF1B, KCNJ11, TCF7L2, CDKAL1, A total of 1000 individuals, including 500 Thai T2D CDKN2A/2B, PPARG, HHEX, IGF2BP2, GLIS3, JAZF1, patients and 500 ethnically-matched control subjects WFS1, and MTNR1B in Europeans and East Asians. Most were enrolled during the 2005 to 2015 study period. of the susceptibility risk loci identified were shared among T2D patients were recruited at the Siriraj Diabetes East Asians and populations with Europeans ancestry. Center, Department of Endocrinology and Metabolism, However, significant association signals at these shared Faculty of Medicine Siriraj Hospital, Mahidol University, loci seems to be independent among populations. This Bangkok, Thailand. Siriraj Hospital is Thailand’s largest suggests that the pathogenesis of the disease is common national tertiary referral center. Diabetes was diagnosed among populations, but that risk variants are often popu- based on American Diabetes Association (ADA) criteria lation specific. These differences in risk variants between [3]. Control subjects consisted of 500 individuals without Europeans and East Asians are likely due to differences in diabetes who were enrolled from the Health Check-up genetic background, risk allele frequencies, and character- Center of the Department of Preventive and Social istics, such as body shape, food and drink, culture, and Medicine, Faculty of Medicine Siriraj Hospital, Mahidol other lifestyle factors. For example, association of var- University. Healthy controls had to satisfy all of the iants within KCNQ1 [6, 13] was primarily reported in following inclusion criteria: age > 40 years, no family GWA studies in East Asians; however, these variants history of diabetes in any first-degree relatives, fasting were not identified in any of the larger GWA studies plasma glucose < 100 mg/dl (6.1 mmol/l), and HbA 1c in European populations, because there are different < 5.7%. Written informed consent was obtained from all frequencies of these variants between East Asian and participants after a full explanation of the study’s pur- European populations. Similarly, variants within UBE2E2 pose and protocol. The study protocol and informed Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 3 of 9 consent procedures were approved by the Siriraj Institu- at a rate of 25 acquisitions per one degree Celcius. Quality tional Review Board (SIRB), Faculty of Medicine Siriraj of SNP genotyping was checked by the reproducibility of Hospital, Mahidol University, Bangkok, Thailand (COA genotypes of control DNA samples, which included no. Si491/2014). This study was conducted in accord- homozygous major allele, heterozygous major and minor ance with the principles set forth in the Declaration of alleles, homozygous minor allele, and negative control. The Helsinki and all of its subsequent amendments. genotyping success rate was greater than 98% for all SNPs. Genotyping of SLC30A8 (rs13266634) was performed SNP selection and genotyping by PCR, followed by restriction enzyme digestion according Genomic DNA was extracted from peripheral blood to the manufacturer’s instructions (Fermentas, Vilnius, leukocytes using the standard phenol-chloroform Lithuania). All genotypes were confirmed by direct method. Eight common SNPs from eight loci were sequencing. Primer sequences and conditions of HRM and selected for analysis of association with T2D. First, we PCR-RFLP assays are presented in Additional file 1: selected loci that demonstrated strong association with Tables S2 and S3, respectively. T2D from prior GWA studies and meta-analyses in Caucasian and Asian populations (Chinese, Japanese, Statistical analysis and Korean). The following 8 loci were selected for Hardy-Weinberg equilibrium (HWE) was evaluated investigation in this study: KCNQ1, CDKN2A/2B, HHEX, using an exact test implemented in the SNPstats online SLC30A8, CDKAL1, UBE2E2, TCF7L2, and MTNR1B. program (http://bioinfo.iconcologia.net/snpstats/start.htm). Subsequently, one SNP was selected in each locus Analyses to determine associations between SNPs and T2D according to the following criteria: (a) the most repli- were performed under the additive, dominant, and reces- cated SNP in each locus with the lowest reported sive models using logistic regression analysis, with/without p-value was selected; and, (b) that SNP as a tagSNP adjustment for age, sex, and BMI as covariates. In the addi- within observed Linkage Disequilibrium (LD) based on tive model, homozygous for risk allele (1/1), heterozygotes HapMap project (CHB data) using Haploview 4.2, except (1/0), and homozygous for non-risk allele (0/0) were coded for SNP rs10811661 near CDKN2A/2B. SNP rs10811661 to a continuous variable for the genotype (2, 1, and 0), re- is out of LD mapping; however, this SNP is the spectively. The dominant model was defined as 1/1 + 1/0 most highly replicated T2D in Caucasian and Asian vs. 0/0, and the recessive model was defined as 1/1 vs. 1/0 populations. The following 8 SNPs were tested for + 0/0. Bonferroni test was used to correction for multiple association with T2D: KCNQ1 (rs2237892), CDKN2A/2B testing. A p < 0.006 (0.05 divided by 8, the total (rs10811661), CDKAL1 (rs7756992), HHEX (rs1111875), number of SNPs studied) was regarded as being MTNR1B (rs1387153), SLC30A8 (rs13266634), TCF7L2 statistically significant. Characteristics were compared (rs7903146), and UBE2E2 (rs7612463). Genotyping of and tested for significant differences between patients these SNPs was performed using high-resolution melting and controls using Student’s t-test or Mann-Whiney (HRM) analysis or polymerase chain reaction-restriction U test for continuous variables, and using χ2testfor fragment length polymorphism (PCR-RFLP) method categorical variables. (Additional file 1: Tables S2 and S3, respectively). For Data are reported as mean ± standard deviation for HRM assay, PCR was performed in 96-well plates with a quantitative variables with normally distributed data, and total volume of 10 ul in each, which consisted of 50 ng as median and interquartile range for non-normally dis- of DNA template, 10 uM of forward and reverse tributed data. Linear regression was applied to test quanti- primers, 1× buffer, 1.5 mM of MgCl , 0.5 U of Taq tative variables for differences between genotype groups DNA polymerase (Bioline, Inc., London, UK), and 1× by adjusting for age and sex for BMI; for age, sex, and ResoLight dye (Roche Diagnostics, Risch-Rotkreuz, BMI for weight, waist circumference, and waist-to-hip ra- Switzerland). HRM assays were performed using a tio; or, age, sex, and medication (as appropriate) for all LightCycler® 480 System with LightCycler® 480 Gene other traits. Other adjustments that were made included Scanning Software Version 1.5 (Roche Diagnostics). PCR anti-diabetic medications (e.g., sulphonylurea, metformin, program consisted of an initial denaturation step at 95 °C and DPP4 inhibitor) for fasting plasma glucose (FPG) and for 10 min, followed by a 50-cycle program consisting of glycated hemoglobin (HbA ); anti-hypertensive medica- 1c denaturation at 95 °C for 30 s, annealing at the condition tions (e.g., ACE inhibitors, calcium channel blocker, beta appropriate for each SNP (Additional file 1:Table S2) for blocker) for systolic and diastolic blood pressure; and, 30 s, and elongation at 72 °C for 30 s, with a single acquisi- anti-hyperlipidemic medications (e.g., statins, fibrate, and tion mode for fluorescence signals. The melting program ezetimibe) for total cholesterol (TC), triglycerides (TG), included denaturing at 95 °C for 30 s, annealing at 40 °C low-density lipoprotein (LDL-C), and high-density lipo- for 30 s, and subsequent melting that included a continu- protein (HDL-C). Values of FPG, HbA , TG, LDL-C, and 1c ous fluorescent reading of fluorescence from 60 °C to 99 °C HDL-C were natural logarithm-transformed to normal Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 4 of 9 distributions before statistical analysis. Data analysis was were in Hardy-Weinberg equilibrium in the control group performed using SPSS Statistics version 17.0 (SPSS, Inc., (all p > 0.05) (Additional file 1: Table S1). Logistic regression Chicago, IL, USA). analysis was used to assess association between each SNP The statistical power of this study for each SNP was esti- and T2D in 3 different genetic models (dominant, recessive, mated using Quanto (http://biostats.usc.edu/Quanto.html), and additive). Among the 8 evaluated SNPs, the following and powers were calculated using ORs from previously SNPs were found to be marginally associated with increased published studies, sample sizes, and minor allele frequen- risk of T2D after adjustment for age, sex, and BMI: KCNQ1 cies (MAF). In the present study, the prevalence of T2D in (rs2237892) [odds ratio (OR): 2.02, 95% CI: 1.08–3.79; p = Thailand was 8.3% [16], and the type 1 error rate was 0.006. 0.020 (additive model)] and [OR: 1.43, 95% CI: 1.06–1.92; p = 0.018 (recessive model)]; CDKN2A/2B (rs10811661) [OR: Results 1.65, 95% CI: 1.01–2.71; p = 0.044 (additive model)]; Clinical and biochemical characteristics SLC30A8 (rs13266634) [OR: 1.86, 95% CI: 1.19–2.90; p = The clinical characteristics of both study groups are pre- 0.006 (additive model)] and [OR: 1.81, 95% CI: 1.31–2.50; p sented in Table 1. There were significant differences be- = 0.0003 (recessive model)]; TCF7L2 (rs7903146) [OR: 1.70, tween patients and control group individuals relative to 95% CI: 1.06–2.72; p = 0.025 (dominant model)]; and, age, weight, waist circumference, waist-to-hip ratio MTNR1B (rs1387153) [OR: 1.44, 95% CI: 1.01–2.08; p = (WHR), body mass index (BMI), systolic blood pressure 0.047 (recessive model)]. Interestingly, after Bonferroni cor- (SBP), diastolic blood pressure (DBP), fasting plasma rection (p < 0.006 indicates statistical significance), SNP glucose (FPG), glycated hemoglobin (HbA ), total chol- rs13266634 of the SLC30A8 gene remains significantly asso- 1c esterol (TC), triglycerides (TG), low-density lipoprotein ciated with T2D susceptibility (OR: 1.81, 95% CI: 1.31–2.50; cholesterol (LDL-C), and high-density lipoprotein chol- p = 0.0003). No associations between CDKAL1 (rs7756992) esterol (HDL-C) (all p < 0.05). or UBE2E2 (rs7612463) and T2D were observed in any of the 3 genetic models (Table 2). Analysis of association between SNPs and T2D We genotyped eight SNPs in a case-control cohort of 1000 Analysis of association between SNPs and quantitative Thai subjects, including 500 T2D patients and 500 traits non-diabetic controls. The allele and genotype distributions The 5 SNPs that were found to be significantly associ- of SNPs are summarized in Table 2.All 8SNP genotypes ated with T2D were then evaluated for association with Table 1 Demographic, anthropometric, and clinical characteristics of the control and patient study groups Clinical characteristics Controls (n = 500) Patients (n = 500) p-value Gender (M, F) (%) 28.8, 71.2 32.8, 67.2 0.170164:336 Age (yrs) (years) 53.0 ± 8.4 57.2 ± 12.2 < 0.001 Age at diagnosis (yrs) – 49.4 ± 11.3 ND Weight (kg) 60.0 ± 10.0 67.7 ± 13.8 < 0.001 Waist circumference (cm) 83.3 ± 9.4 89.6 ± 11.4 < 0.001 Waist-to-hip ratio 0.8 ± 0.1 0.9 ± 0.1 < 0.001 2 a BMI (kg/m ) 24.1 ± 3.3 27.3 ± 5.0 < 0.001 SBP (mmHg) 116.7 ± 15.4 134.9 ± 19.4 < 0.001 DBP (mmHg) 71.7 ± 10.3 79.3 ± 10.4 < 0.001 FPG (mmol/l) 3.9 (3.4, 4.4) 8.4 (6.6, 12.1) < 0.001 HbA (mmol/mol) 36.6 (34.2, 37.7) 57.4 (48.0, 74.9) < 0.001 1c TC (mmol/l) 208.4 ± 37.9 200.2 ± 53.3 < 0.001 TG (mmol/l) 2.3 (2.0, 2.6) 1.5 (1.1, 2.1) < 0.001 LDL-C (mmol/l) 2.5 (1.8, 3.5) 2.1 (1.2, 3.6) < 0.001 HDL-C (mmol/l) 3.2 (2.6, 3.9) 1.6 (1.1, 2.2) < 0.001 Data presented as mean ± standard deviation or median and interquartile range A p-value< 0.05 indicates statistical significance Gender distribution between patients and controls was analyzed using Pearson’s χ2 test Comparison of quantitative variables between patients and controls was performed using Student’s t-test or Mann-Whitney U test a b p-values and p-values were determined using Student’s t-test and Mann-Whitney U test, respectively Abbreviations: M male, F female, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, HbA glycated 1c hemoglobin, TC total cholesterol, TG triglycerides, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, ND not determined Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 5 of 9 Table 2 Association between eight SNPs and T2D in Thai population a b Gene SNP Risk Risk Subject Genotype frequency, n Additive model Dominant model Recessive model (major/ allele allele (%) P P adjusted P P adjusted P P adjusted minor freq. freq. A/A A/B B/B unadjusted OR (95% unadjusted OR (95% unadjusted OR (95% alleles*) OR (95% CI) OR (95% CI) OR (95% CI) CI) CI) CI) KCNQ1 rs2237892 0.76 0.71 Patient 285 192 23 0.060 0.020 0.128 0.057 0.025 0.018 (C>T) (57.0) (38.4) (4.6) 1.74 (0.99– 2.02 (1.08– 1.53 (0.88– 1.83 (0.98– 1.36 (1.04– 1.43 (1.06– 3.07) 3.79) 2.66) 3.42) 1.78) 1.92) Control 254 205 41 (50.8) (41.0) (8.2) CDKN2A/ rs10811661 0.68 0.62 Patient 228 220 52 0.100 0.044 0.227 0.105 0.077 0.066 2B (T> C) (45.6) (44.0) (10.4) 1.45 (0.93– 1.65 (1.01– 1.29 (0.85– 1.46 (0.92– 1.28 (0.97– 1.33 (0.98– 2.27) 2.71) 1.96) 2.31) 1.69) 1.81) Control 185 248 67 (37.0) (49.6) (13.4) SLC30A8 rs13266634 0.59 0.54 Patient 172 242 86 0.040 0.006 0.309 0.191 0.008 0.0003 (C>T) (34.4) (48.4) (17.2) 1.51 (1.01– 1.86 (1.19– 1.20 (0.84– 1.29 (0.87– 1.48 (1.11– 1.81 (1.31– 2.21) 2.90) 1.71) 1.91) 1.98) 2.50) Control 137 268 95 (27.4) (53.6) (19.0) HHEX rs1111875 0.33 0.29 Patient 234 200 66 0.039 0.082 0.260 0.367 0.044 0.087 (A > G) (46.8) (40.0) (13.2) 1.61 (1.02– 1.55 (0.94– 1.16 (0.89– 1.14 (0.85– 1.56 (1.01– 1.51 (0.94– 2.55) 2.56) 1.53) 1.54) 2.42) 2.45) Control 254 201 45 (50.8) (40.2) (9.0) CDKAL1 rs7756992 0.43 0.40 Patient 156 257 87 0.249 0.107 0.355 0.169 0.340 0.182 (A > G) (31.2) (51.4) (17.4) 1.27 (0.84– 1.44 (0.92– 1.14 (0.85– 1.25 (0.90– 1.19 (0.83– 1.30 (0.88– 1.90) 2.24) 1.52) 1.72) 1.71) 1.94) Control 171 254 75 (34.2) (50.8) (15.0) TCF7L2 rs7903146 0.08 0.04 Patient 429 67 4 (0.0) NA NA 0.006 0.025 NA NA (C > T) (85.8) (13.4) 1.80 (1.18– 1.70 (1.06– 2.76) 2.72) Control 456 44 0 (0.0) (91.2) (8.8) MTNR1B rs1387153 0.44 0.42 Patient 164 232 104 0.113 0.120 0.618 0.757 0.058 0.047 (C > T) (32.7) (46.3) (21.0) 1.35 (0.93– 1.38 (0.91– 1.07 (0.80– 1.05 (0.76– 1.38 (0.98– 1.44 (1.01– 1.97) 2.09) 1.42) 1.43) 1.92) 2.08) Control 174 231 95 (34.8) (46.2) (19.0) UBE2E2 rs7612463 0.83 0.82 Patient 346 134 20 0.875 0.855 0.890 0.872 0.847 0.826 (C>A) (69.3) (26.6) (4.0) 0.94 (0.47– 0.93 (0.42– 0.95 (0.47– 0.93 (0.43– 0.97 (0.72– 0.96 (0.70– 1.89) 2.03) 1.90) 2.03) 1.29) 1.32) Control 339 141 20 (67.8) (28.2) (4.0) Abbreviations: SNP single nucleotide polymorphism, T2D type 2 diabetes, Freqfrequency, A/A homo major allele, A/B heterozygote allele, B/B homo minor allele, OR odds ratio, CI confidence interval, NA not applicable (analytical model not applicable due to low frequency) A p < 0.006 (0.05 divided by 8, the total number of SNPs studied) was regarded as being statistically significant P-values were calculated for the additive, dominant, and recessive genetic models using logistic regression with/without adjustment for age, gender, and body mass index. The OR and 95% CI of having the risk allele are shown *Alleles in bold are the risk alleles for T2D identified in previous studies, while underlined alleles indicate the risk alleles for T2D observed in this study a b c Risk allele frequency in patients; Risk allele frequency in controls; Statistically significant after Bonferroni correction (multiple-testing) with p-value < 0.006 quantitative traits, including body weight, waist circum- p = 0.052, respectively). No significant associations ference, WHR, BMI, FPG, HbA , TC, TG, LDL-C, and were observed between any SNPs and any quantitative 1c HDL-C using linear regression analysis after adjusting traits in control subjects (data not shown). for age, sex, BMI, and medication (as appropriate) in both cases and controls (Table 3). Our analysis revealed Discussion CDKN2A/2B (rs10811661) to be significantly associated The present study evaluated 8 SNPs in or near the with both waist circumference and WHR (p = 0.007 KCNQ1, CDKN2A/2B, SLC30A8, HHEX, CDKAL1, and p = 0.023, respectively). TCF7L2 (rs7903146) was TCF7L2, MTNR1B, and UBE2E2 genes for significant associated with HDL-C (p = 0.023), and SLC30A8 association with T2D. Of those, 5 SNPs [KCNQ1 (rs13266634) was associated with HbA (p = 0.018). (rs2237892), CDKN2A/2B (rs10811661), SLC30A8 1c KCNQ1 (rs2237892) was significantly associated at (rs13266634), TCF7L2 (rs7903146) and MTNR1B borderline with both FPG and HbA (p = 0.050 and (rs1387153)] were found to be associated with T2D in 1c Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 6 of 9 Table 3 Association between CDKN2A/2B (rs10811661), SLC30A8 (rs13266634), and TCF7L2 (rs7903146) genotypes and quantitative traits among Thai patients with T2D Clinical CDKN2A/2B (rs10811661) genotypes SLC30A8 (rs13266634) genotypes TCF7L2 (rs7903146) genotypes characteristics T/T T/C C/C P C/C C/T T/T P C/C C/T + TT P Number of 228 220 52 – 172 242 86 – 429 71 – patients Weight (kg) 68.99 ± 67.42 ± 65.01 ± 0.250 66.38 ± 66.38 ± 66.38 ± 0.867 67.58 ± 68.83 ± 0.172 13.96 13.73 14.28 14.78 14.78 14.78 13.60 15.45 Waist 89.34 ± 89.39 ± 92.59 ± 0.007 89.55 ± 89.79 ± 89.87 ± 0.506 89.66 ± 89.84 ± 0.861 circumference (cm) 11.32 11.69 11.04 11.58 10.82 12.75 11.30 12.27 Waist-to-hip ratio 0.89 ± 0.07 0.91 ± 0.08 0.92 ± 0.11 0.023 0.91 ± 0.07 0.90 ± 0.08 0.90 ± 0.08 0.263 0.90 ± 0.08 0.90 ± 0.080 0.334 BMI (kg/m ) 27.60 ± 5.06 27.07 ± 26.82 ± 0.442 26.65 ± 27.62 ± 27.59 ± 5.38 0.273 27.27 ± 27.14 ± 4.71 0.736 4.99 5.075 5.15 4.72 5.067 SBP (mmHg) 133.60 ± 134.21 ± 143.52 ± 0.746 134.94 ± 134.44 ± 136.47 ± 0.740 135.25 ± 132.98 ± 0.800 19.99 17.58 23.29 18.56 19.30 21.82 19.64 18.15 DBP (mmHg) 78.84 ± 79.42 ± 80.75 ± 0.228 79.95 ± 78.56 ± 80.18 ± 9.89 0.348 79.43 ± 78.87 ± 9.21 0.469 10.72 9.87 11.65 10.16 10.85 10.62 FPG (mmol/l) 9.27 (7.05, 8.16 (6.38, 7.79 (6.33, 0.244 8.33 (6.64, 8.49 (6.38, 8.77 (6.84, 0.164 8.49 (6.50, 8.55 (6.94, 0.721 12.49) 11.88) 10.68) 11.62) 12.93) 11.94) 12.25) 8.55) HbA (mmol/mol) 59.05 (49.70, 56.30 53.00 0.683 56.30 57.40 59.05 (48.32, 0.018 57.40 56.30 (47.50, 0.535 1c 78.92) (48.6, 71.6) (44.82, 77.3) (48.6, 71.6) (49.7, 76.5) 78.10) (48.60, 74.9) 56.30) TC (mmol/l) 202.96 ± 197.52 ± 195.80 ± 0.748 197.40 ± 199.65 ± 208.33 ± 0.853 200.65 ± 198.00 ± 0.592 53.19 51.12 60.28 55.56 51.20 55.57 54.08 49.65 TG (mmol/l) 1.46 (1.03, 1.50 (1.18, 1.64 (1.17, 0.197 1.43 (1.02, 1.51 (1.14, 1.51 (1.18, 0.664 1.52 (1.15, 1.32 (1.06, 0.600 2.06) 2.11) 2.06) 2.05) 2.08) 2.26) 2.10) 1.32) LDL-C (mmol/l) 2.22 (1.39, 2.15 (1.30, 1.47 (1.10, 0.943 1.89 (1.34, 2.38 (1.22, 2.22 (1.37, 0.854 2.17 (1.33, 1.71 (1.16, 0.741 3.85) 3.50) 2.87) 3.64) 3.78) 3.71) 3.68) 1.71) HDL-C (mmol/l) 1.58 (1.14, 1.63 (1.19, 1.78 (1.44, 0.965 1.64 (1.17, 1.61 (1.19, 1.55 (1.14, 0.464 1.60 (1.16, 1.81 (1.34, 0.023 2.21) 2.265) 2.32) 2.24) 2.17) 2.50) 2.18) 1.81) Data presented as mean ± standard deviation or median and interquartile range P-values were calculated using linear regression after adjusting for age, gender, BMI, and medication, as appropriate. FPG, HbA1c, TG, LDL-C, and HDL-C values were log-transformed to improve normality before regression analysis Abbreviations: T2D type 2 diabetes, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, HbA glycated 1c hemoglobin, TC, total cholesterol, TG, triglycerides, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol Thai population. We also found association between Asian populations (Han Chinese: 54.4%, Japanese: 52.4%), CDKN2A/2B (rs10811661), SLC30A8 (rs13266634), and but different from European (71.7%) population (source: TCF7L2 (rs7903146) and diabetes-related quantitative dbSNP database). We also found SLC30A8 (rs13266634) traits. to be associated with HbA (p =0.018). A similar finding 1c SLC30A8 (solute carrier family 30 member 8) encodes was observed in a Chinese study population [23]. Func- zinc transporter-8 (ZnT8), which is specifically expressed tional study found that zinc transporter 8 (ZnT8) is re- in pancreatic endocrine cells and may participate in quired for normal insulin crystallization and insulin insulin secretion. SLC30A8 is expressed at high levels processing and secretion [24]. In addition, subjects who only in the pancreas, and is highly expressed in had homozygous of the risk C allele of SLC30A8 showed insulin-secreting beta cells [17, 18]. Association between decreased peripheral insulin levels in the early phase of SNP rs13266634 in SLC30A8 and T2D risk was discovered intravenous glucose tolerance test (GTT) [22]. This sug- in European subjects [7, 9, 10]. The presence of risk C al- gests that SLC30A8 may influence the Zn transporter, lele of rs13266634 (SLC30A8) was found to be associated thereby affecting insulin stability and insulin secretion, but with increased risk of developing T2D in multiethnic the precise role of variants of this gene remains unclear. case-control studies, including Asians [19, 20] and Euro- TCF7L2 (Transcription Factor 7-Like 2) is a transcrip- peans [21, 22]. This SNP also showed strong association tion factor protein that is capable of binding beta ca- with T2D in our study with an OR of 1.78 [95% CI: 1.31– tenin, which is involved in Wnt receptor signaling. This − 4 2.50; p =3.0 × 10 (recessive model)], which is higher signaling pathway affects the expression of several genes, than the odds ratios reported in previous studies (OR including insulin and glucagon genes [25]. A strong as- range: 1.1–1.2). The risk C allele frequency of 59.0% in sociation was identified between TCF7L2 genetic vari- our population was similar to frequencies reported in ants and risk of T2D in a European population [8], Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 7 of 9 which was then replicated in East Asian populations, in- rs10811661 was associated with both waist circumfer- cluding Chinese and Japanese [15, 26]. In this study, we ence and WHR among T2D patients in this study (p = found significant association between SNP rs7903146 0.007 and p = 0.032, respectively). In a similar result, a and increased risk of T2D in the dominant model (OR: recent study from Vietnam found association between 1.80, 95% CI: 1.18–2.76; p = 0.006). In contrast to popu- SNP rs10811661 and WHR in prediabetes subjects [38]. lations with European and African ancestries, the risk T However, the mechanism by which the CDKN2A/2B allele of rs7903146 was rare in our Thai cohort, with a locus influences diabetes risk remains uncertain. Recent minor allele frequency (MAF) of 0.08. The TCF7L2 gene publication showed CDKN2A/2B locus SNPs may im- was reported to be associated with T2D, insulin sensitiv- pact T2D risk by modulating islet gene expression and ity, and insulin resistance [25]. In addition, the SNP beta cell proliferation [39]. rs7903146 was reported to be a risk factor for various Several susceptibility genes have been identified and metabolic disorders involving glucose and lipoprotein replicated for association with T2D and diabetes-related homeostasis [27, 28]. A previous study by our team traits in European and East Asians populations. Most of showed significant association between an SNP in TCF7L2 the susceptibility risk loci identified were shared among and age of onset of diabetes [29]. In that study, we found East Asians and populations of Europeans ancestry. that patients who carried a minor allele of an SNP were However, a significant association signals at these shared diagnosed with T2D at an earlier age. The present study loci seems to be independent among populations. This also found association between SNP rs7903146 and suggests that pathogenesis of the disease is common HDL-C among T2D patients. The specific mechanism of among populations, but that some of the risk variants TCF7L2 (rs7903146) that resides in a noncoding region may be population specific. Differences in genetic back- and that drives the development of T2D remains unclear; ground, risk allele frequencies, and clinical characteris- however, the possible effect of the TCF7L2 risk allele is via tics, such as BMI cut-point for metabolic syndrome, a defect in insulin secretion [25]. diet, culture, and other lifestyle factors, may explain dif- KCNQ1 (potassium voltage gated channel, KQT like ferences in risk variants between European and East subfamily, member 1) plays a key role in potassium Asians. It is, therefore, important to have information channels that control insulin secretion [30]. Common about the association of genetic variations and T2D, and variants (SNP rs2237892, rs2237895, and rs2237897) in to understand the roles of these T2D risk loci in differ- KCNQ1 were identified in East Asian population [6, 13]. ent Asian populations, including Thai population. For The association of these SNPs with T2D was replicated example, rare PAX4 mutations were first identified in in European, Mexican, and Asian populations [6, 13, 31, Thai MODY probands (designated MODY9) [40], but 32]. Of the three aforementioned SNPs, SNP rs2237892 they are seldom found in those of European descent. A appeared to have the strongest association with T2D GWA study identified a susceptibility locus for T2D at [13]. In the present study, we also found SNP rs2237892 7q32 near PAX4 [41], and exome-chip association ana- to be significantly associated with T2D [OR: 2.02, 95% lysis revealed an Asian-specific missense variant in PAX4 CI: 1.08–3.79; p = 0.020 (additive model)] and [OR: 1.43, associated with T2D in Chinese population [42]. Only 95% CI: 1.06–1.92; p = 0.018 (recessive model)]. Other one variant reached genome-wide significance (PAX-Ar- studies found variants in KCNQ1 to be associated g192His, rs2233580). This association is exclusive to with impaired fasting plasma glucose, β-cell function, East Asian individuals, in whom the 192His allele is and metabolic traits [6, 13, 33]. However, we did not common (MAF = 10%) with a substantial effect size, with find evidence of association between KCNQ1 and 192His observed to be virtually absent in individuals diabetic-related quantitative traits. The potential mechan- from other ancestries [43]. This suggests that PAX4 or ism of KCNQ1 and the pathogenesis of T2D needs to be T2D loci may be particularly relevant in the pathogen- further explored. esis of T2D in both East Asians and in Thais. SNP rs10811661 is located 125 k-bases upstream from With regard to the remaining 3 loci, HHEX (rs1111875), the cyclin-dependent kinase inhibitor 2A/2B (CDKN2A/ CDKAL1 (rs7756992), and UBE2E2 (rs7612463) showed 2B) gene on chromosome 9p21. SNP rs10811661 was no significant association with T2D in our cohort. This re- found to be associated with risk of T2D in multiple large sult may be explained by different environmental risk pro- GWA studies [4, 9, 10, 34]. In this study, we also found files between Europeans and Asians, different body this SNP to be significantly associated with increased composition and genetic backgrounds, different linkage risk of T2D [OR: 1.65, 95% CI: 1.01–2.71; p = 0.044 disequilibrium patterns, or by the fact that we have insuffi- (additive model)]. The risk T allele was found to be more cient statistical power with the current sample size to rep- common in T2D patients (MAF: 0.68), which is similar licate some of these previously reported T2D risk loci. to several previous reports in Caucasian and Asian pop- The sample size in this study yielded power to identify sig- ulations [19, 35–37]. In addition, the risk allele of SNP nificant association between T2D and variants in the Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 8 of 9 KCNQ1, CDKN2A/2B, CDKAL1, HHEX, MTNR1B, thank Dr. Sirikul Kulanuwat for her helpful advice and guidance, and Mr. Choochai Nattuwakul for technical assistance. TCF7L2, and UBE2E2 genes ranging from 2.13 to 37.41%. A sample size ranging from 1094 to 12,600 would be Funding needed to achieve an 80% power threshold to detect sta- This study was supported by a Siriraj Grant for Research Development and Medical Education, Mahidol University to NP, a grant from the Thailand tistically significant results at alpha = 0.006. The sample Research Fund to PY (IRG5980006), a grant from the Thailand Research Fund size included in this study for SLC30A8 (n = 500) had 96% Grant for New Researcher to WT (TRG5280002, TRG5780113), and a grant power to detect significant association with an OR of 1.81 from the Faculty of Medicine Siriraj Hospital, Mahidol University to CC and PY. The funding body played no role in the design of the study, data assuming a minor allele frequency of 0.41. This study has collection, analysis, interpretation or writing of the manuscript. some mentionable limitations. First, the relatively small sample size and the commensurately limited statistical Availability of data and materials The data used and identified in this study are included within the article and power that accompanied it may have limited our ability to in the provided supplementary data file. identify all significant differences and associations. Second, several risk loci have been proposed as being associated Authors’ contributions with T2D in Caucasian and Asian populations. However, NP, CC, NC, PY, MH and WT contributed to the conceptualization of the study and obtained funding. NP and PY designed the study and interpreted we limited our investigation to 8 SNPs from 8 candidate data. CC and NJ performed the analyses. WT curated the data and oversaw loci that were found to be common from studies in Cau- laboratory work. All authors contributed to editing the manuscript, review casian and several different Asian populations. Thus, fur- and provided intellectual input to the final manuscript. All authors read and approved the final manuscript. ther studies that include the T2D risk loci not include in this study should be further investigated. Ethics approval and consent to participate Informed written consent was obtained from all the patients involved in this study. This study was approved by the Siriraj Institutional Review Board (SIRB), Conclusion Faculty of Medicine Siriraj Hospital, Mahidol University (COA no. Si491/2014). Of the eight genes included in our analysis, association was observed between KCNQ1, CDKN2A/2B, SLC30A8, Consent for publication The patients participating in this study signed their consent for the TCF7L2,and MTNR1B loci and T2D in our Thai study publication of the results obtained. This statement was approved by the population. Of these, CDKN2A/2B, SLC30A8, and Siriraj Institutional Review Board (SIRB), Faculty of Medicine Siriraj Hospital, TCF7L2 genes were also associated with anthropometric, Mahidol University. glycemic and lipid characteristics. Larger cohort studies Competing interests and meta-analyses are needed to further confirm the ef- The authors declare that they have no competing interests. fect of these variants in Thai population. Publisher’sNote Additional file Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Additional file 1: Table S1 Summary of the single nucleotide Author details polymorphisms (SNPs) investigated in this study. Table S2 Primer Division of Endocrinology and Metabolism, Department of Medicine, Faculty sequence, PCR product size, and annealing temperature for HRM assay of of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand. Research designated SNPs. Table S3 Primer sequences of SLC30A8 (rs13266634) Division, Department of Research and Development, Faculty of Medicine used for PCR-RFLP method. (DOCX 27 kb) Siriraj Hospital, Mahidol University, Bangkok, Thailand. Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Abbreviations Bangkok, Thailand. Siriraj Center of Research Excellence for Molecular ADA: American Diabetes Association; BMI: Body Mass index; bp: Base pair; Medicine, Department of Research and Development, Faculty of Medicine CDKAL1: CDK5 regulatory subunit-associated protein 1-like 1; CDKN2A/ Siriraj Hospital Mahidol University, Bangkok, Thailand. Department of 2B: cyclin-dependent kinase inhibitor 2A; CI: Confidence interval; Preventive and Social Medicine, Faculty of Medicine Siriraj Hospital, Mahidol DBP: diastolic blood pressure; FPG: fasting plasma glucose; GWA: genome- University, Bangkok, Thailand. wide association; HbA : glycated hemoglobin; HDL: High density 1c lipoprotein;; HDL-C: high-density lipoprotein; HHEX: hematopoietically- Received: 7 August 2017 Accepted: 23 May 2018 expressed homeobox; HWE: Hardy-Weinberg equilibrium; IDF: International Diabetes Federation; KCNQ1: potassium voltage-gated channel, KQT-like sub- family, member 1; LD: Linkage Disequilibrium; LDL: Low density lipoprotein; References LDL-C: low-density lipoprotein; MAF: minor allele frequencies; 1. Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge MTNR1B: melatonin receptor type 1B; ND: Non-diabetic; OR: Odd ratio; RFLP- AW, et al. 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Impact of KCNQ1, CDKN2A/2B, CDKAL1, HHEX, MTNR1B, SLC30A8, TCF7L2, and UBE2E2 on risk of developing type 2 diabetes in Thai population

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/lp/springer_journal/impact-of-kcnq1-cdkn2a-2b-cdkal1-hhex-mtnr1b-slc30a8-tcf7l2-and-ube2e2-Wd2zg2lfy3
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BioMed Central
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Copyright © 2018 by The Author(s).
Subject
Biomedicine; Human Genetics; Cytogenetics; Gene Function
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1471-2350
D.O.I.
10.1186/s12881-018-0614-9
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Abstract

Background: Several type 2 diabetes (T2D) susceptibility loci identified via genome-wide association studies were found to be replicated among various populations. However, the influence of these loci on T2D in Thai population is unknown. The aim of this study was to investigate the influence of eight single nucleotide polymorphisms (SNPs) reported in GWA studies on T2D and related quantitative traits in Thai population. Methods: Eight SNPs in or near the KCNQ1, CDKN2A/2B, SLC30A8, HHEX, CDKAL1, TCF7L2, MTNR1B, and UBE2E2 genes were genotyped. A case-control association study comprising 500 Thai patients with T2D and 500 ethnically- matched control subjects was conducted. Associations between SNPs and T2D were examined by logistic regression analysis. The impact of these SNPs on quantitative traits was examined by linear regression among case and control subjects. Results: Five SNPs in KCNQ1 (rs2237892), CDK2A/2B (rs108116610, SLC30A8 (rs13266634), TCF7L2 (rs7903146) and MTNR1B (rs1387153) were found to be marginally associated with risk of developing T2D, with odds ratios ranging from 1.43 to 2.02 (p = 0.047 to 3.0 × 10–4) with adjustments for age, sex, and body mass index. Interestingly, SNP rs13266634 of SLC30A8 gene reached statistical significance after correcting for multiple testing (p = 0.0003) (p <0. 006 after Bonferroni correction). However, no significant association was detected between HHEX (rs1111875), CDKAL1 (rs7756992), or UBE2E2 (rs7612463) and T2D. We also observed association between rs10811661 and both waist circumference and waist-hip ratio (p = 0.007 and p = 0.023, respectively). In addition, rs13266634 in SLC30A8 was associated with glycated hemoglobin (p = 0.018), and rs7903146 in TCF7L2 was associated with high-density lipoprotein cholesterol level (p = 0.023). Conclusion: Of the eight genes included in our analysis, significant association was observed between KCNQ1, CDKN2A/2B, SLC30A8, TCF7L2, and MTNR1B loci and T2D in our Thai study population. Of these, CDKN2A/2B, SLC30A8, and TCF7L2 genes were also significantly associated with anthropometric, glycemic and lipid characteristics. Larger cohort studies and meta-analyses are needed to further confirm the effect of these variants in Thai population. Keywords: Type 2 diabetes, Association study, Single nucleotide polymorphisms, Thai population, Genome-wide association study * Correspondence: watip.tan@mahidol.edu Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand Full list of author information is available at the end of the article © The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 2 of 9 Background and C2CD4A-C2CD4B were reported to be associated The prevalence of diabetes, especially type 2 diabetes with T2D by two relatively small East Asian GWA studies, (T2D), continues to increase worldwide. The Inter- while these loci did not reach genome-wide significance in national Diabetes Federation (IDF) has estimated that European studies [4, 5, 14]. Furthermore, there are rela- the prevalence of diabetes will increase from 451 million tively large frequency differences between population in 2017 to 693 million by 2045 [1]. The number of groups for rs7903146 within TCF7L2 [15]. Although nu- people with diabetes will increase as a result of changes merous T2D risk loci were identified from GWA studies in lifestyle caused by urbanization, high population in multi-ethnic populations, the functional impact of these growth, ageing population, and high economic growth. risk loci still needs to be elucidated. Most of these risk loci In 2017, the prevalence of diabetes among adults in likely affect insulin secretion and beta-cell function, with a Thailand was estimated to be 8.3% [1]. T2D is a complex few potentially involved in insulin action. Given this vari- and multifactorial disease that is characterized by im- ability among populations, it is important that we under- paired insulin secretion and insulin resistance [2, 3]. stand the association between genetic variations and T2D, T2D is associated with significant morbidity and mortal- and the roles of these T2D risk loci in Thai population. In ity, especially decreased life expectancy and reduced this study, we investigated single nucleotide polymor- quality of life, as well as high cost of treatment and re- phisms (SNPs) that were previously found to be asso- duced productivity. Although the etiology of T2D is not ciated with T2D in Asian populations. We focused on thoroughly understood, genetic and environmental fac- the following eight SNPs: 1) potassium voltage-gated tors are thought to be involved in the pathogenesis of channel, KQT-like subfamily, member 1 (KCNQ1; this disease. rs2237892); 2) a variant found near cyclin-dependent Prior to the development of currently available methods kinase inhibitor 2A (CDKN2A/2B; rs10811661); 3) a and technologies, linkage and candidate gene association zinc transporter member of solute carrier family 30 studies were conducted; however, these two methods (SLC30A8; rs13266634); 4) hematopoietically-expressed could identify only a few genes involved in the pathogen- homeobox (HHEX; rs1111875); 5) CDK5 regulatory esis of T2D (i.e., PPARG, KCNJ11, CAPN10,and TCF7L2). subunit-associated protein 1-like 1 (CDKAL1; rs7756992); Advances in genotyping technology and genetic informa- 6) melatonin receptor type 1B (MTNR1B; rs1387153); 7) tion have facilitated the application of genome-wide asso- transcription factor-7-like 2 (TCF7L2; rs7903146); and, 8) ciation (GWA) studies to identify T2D susceptibility Ubiquitin Conjugating Enzyme E2 E2 (UBE2E2; genes. Currently, over 120 common risk variants have rs7612463). Our specific aim was to investigate the influ- been successfully identified as being associated with T2D ence of these eight SNPs on T2D and related quantitative using this approach [4–12]. Several reproducible GWA traits in Thai population. studies confirmed these well-established susceptibility genes, and they were found to be replicated for association Methods with T2D and diabetes-related traits in or near SLC30A8, Study subjects KCNQ1, CDC123, HNF1B, KCNJ11, TCF7L2, CDKAL1, A total of 1000 individuals, including 500 Thai T2D CDKN2A/2B, PPARG, HHEX, IGF2BP2, GLIS3, JAZF1, patients and 500 ethnically-matched control subjects WFS1, and MTNR1B in Europeans and East Asians. Most were enrolled during the 2005 to 2015 study period. of the susceptibility risk loci identified were shared among T2D patients were recruited at the Siriraj Diabetes East Asians and populations with Europeans ancestry. Center, Department of Endocrinology and Metabolism, However, significant association signals at these shared Faculty of Medicine Siriraj Hospital, Mahidol University, loci seems to be independent among populations. This Bangkok, Thailand. Siriraj Hospital is Thailand’s largest suggests that the pathogenesis of the disease is common national tertiary referral center. Diabetes was diagnosed among populations, but that risk variants are often popu- based on American Diabetes Association (ADA) criteria lation specific. These differences in risk variants between [3]. Control subjects consisted of 500 individuals without Europeans and East Asians are likely due to differences in diabetes who were enrolled from the Health Check-up genetic background, risk allele frequencies, and character- Center of the Department of Preventive and Social istics, such as body shape, food and drink, culture, and Medicine, Faculty of Medicine Siriraj Hospital, Mahidol other lifestyle factors. For example, association of var- University. Healthy controls had to satisfy all of the iants within KCNQ1 [6, 13] was primarily reported in following inclusion criteria: age > 40 years, no family GWA studies in East Asians; however, these variants history of diabetes in any first-degree relatives, fasting were not identified in any of the larger GWA studies plasma glucose < 100 mg/dl (6.1 mmol/l), and HbA 1c in European populations, because there are different < 5.7%. Written informed consent was obtained from all frequencies of these variants between East Asian and participants after a full explanation of the study’s pur- European populations. Similarly, variants within UBE2E2 pose and protocol. The study protocol and informed Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 3 of 9 consent procedures were approved by the Siriraj Institu- at a rate of 25 acquisitions per one degree Celcius. Quality tional Review Board (SIRB), Faculty of Medicine Siriraj of SNP genotyping was checked by the reproducibility of Hospital, Mahidol University, Bangkok, Thailand (COA genotypes of control DNA samples, which included no. Si491/2014). This study was conducted in accord- homozygous major allele, heterozygous major and minor ance with the principles set forth in the Declaration of alleles, homozygous minor allele, and negative control. The Helsinki and all of its subsequent amendments. genotyping success rate was greater than 98% for all SNPs. Genotyping of SLC30A8 (rs13266634) was performed SNP selection and genotyping by PCR, followed by restriction enzyme digestion according Genomic DNA was extracted from peripheral blood to the manufacturer’s instructions (Fermentas, Vilnius, leukocytes using the standard phenol-chloroform Lithuania). All genotypes were confirmed by direct method. Eight common SNPs from eight loci were sequencing. Primer sequences and conditions of HRM and selected for analysis of association with T2D. First, we PCR-RFLP assays are presented in Additional file 1: selected loci that demonstrated strong association with Tables S2 and S3, respectively. T2D from prior GWA studies and meta-analyses in Caucasian and Asian populations (Chinese, Japanese, Statistical analysis and Korean). The following 8 loci were selected for Hardy-Weinberg equilibrium (HWE) was evaluated investigation in this study: KCNQ1, CDKN2A/2B, HHEX, using an exact test implemented in the SNPstats online SLC30A8, CDKAL1, UBE2E2, TCF7L2, and MTNR1B. program (http://bioinfo.iconcologia.net/snpstats/start.htm). Subsequently, one SNP was selected in each locus Analyses to determine associations between SNPs and T2D according to the following criteria: (a) the most repli- were performed under the additive, dominant, and reces- cated SNP in each locus with the lowest reported sive models using logistic regression analysis, with/without p-value was selected; and, (b) that SNP as a tagSNP adjustment for age, sex, and BMI as covariates. In the addi- within observed Linkage Disequilibrium (LD) based on tive model, homozygous for risk allele (1/1), heterozygotes HapMap project (CHB data) using Haploview 4.2, except (1/0), and homozygous for non-risk allele (0/0) were coded for SNP rs10811661 near CDKN2A/2B. SNP rs10811661 to a continuous variable for the genotype (2, 1, and 0), re- is out of LD mapping; however, this SNP is the spectively. The dominant model was defined as 1/1 + 1/0 most highly replicated T2D in Caucasian and Asian vs. 0/0, and the recessive model was defined as 1/1 vs. 1/0 populations. The following 8 SNPs were tested for + 0/0. Bonferroni test was used to correction for multiple association with T2D: KCNQ1 (rs2237892), CDKN2A/2B testing. A p < 0.006 (0.05 divided by 8, the total (rs10811661), CDKAL1 (rs7756992), HHEX (rs1111875), number of SNPs studied) was regarded as being MTNR1B (rs1387153), SLC30A8 (rs13266634), TCF7L2 statistically significant. Characteristics were compared (rs7903146), and UBE2E2 (rs7612463). Genotyping of and tested for significant differences between patients these SNPs was performed using high-resolution melting and controls using Student’s t-test or Mann-Whiney (HRM) analysis or polymerase chain reaction-restriction U test for continuous variables, and using χ2testfor fragment length polymorphism (PCR-RFLP) method categorical variables. (Additional file 1: Tables S2 and S3, respectively). For Data are reported as mean ± standard deviation for HRM assay, PCR was performed in 96-well plates with a quantitative variables with normally distributed data, and total volume of 10 ul in each, which consisted of 50 ng as median and interquartile range for non-normally dis- of DNA template, 10 uM of forward and reverse tributed data. Linear regression was applied to test quanti- primers, 1× buffer, 1.5 mM of MgCl , 0.5 U of Taq tative variables for differences between genotype groups DNA polymerase (Bioline, Inc., London, UK), and 1× by adjusting for age and sex for BMI; for age, sex, and ResoLight dye (Roche Diagnostics, Risch-Rotkreuz, BMI for weight, waist circumference, and waist-to-hip ra- Switzerland). HRM assays were performed using a tio; or, age, sex, and medication (as appropriate) for all LightCycler® 480 System with LightCycler® 480 Gene other traits. Other adjustments that were made included Scanning Software Version 1.5 (Roche Diagnostics). PCR anti-diabetic medications (e.g., sulphonylurea, metformin, program consisted of an initial denaturation step at 95 °C and DPP4 inhibitor) for fasting plasma glucose (FPG) and for 10 min, followed by a 50-cycle program consisting of glycated hemoglobin (HbA ); anti-hypertensive medica- 1c denaturation at 95 °C for 30 s, annealing at the condition tions (e.g., ACE inhibitors, calcium channel blocker, beta appropriate for each SNP (Additional file 1:Table S2) for blocker) for systolic and diastolic blood pressure; and, 30 s, and elongation at 72 °C for 30 s, with a single acquisi- anti-hyperlipidemic medications (e.g., statins, fibrate, and tion mode for fluorescence signals. The melting program ezetimibe) for total cholesterol (TC), triglycerides (TG), included denaturing at 95 °C for 30 s, annealing at 40 °C low-density lipoprotein (LDL-C), and high-density lipo- for 30 s, and subsequent melting that included a continu- protein (HDL-C). Values of FPG, HbA , TG, LDL-C, and 1c ous fluorescent reading of fluorescence from 60 °C to 99 °C HDL-C were natural logarithm-transformed to normal Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 4 of 9 distributions before statistical analysis. Data analysis was were in Hardy-Weinberg equilibrium in the control group performed using SPSS Statistics version 17.0 (SPSS, Inc., (all p > 0.05) (Additional file 1: Table S1). Logistic regression Chicago, IL, USA). analysis was used to assess association between each SNP The statistical power of this study for each SNP was esti- and T2D in 3 different genetic models (dominant, recessive, mated using Quanto (http://biostats.usc.edu/Quanto.html), and additive). Among the 8 evaluated SNPs, the following and powers were calculated using ORs from previously SNPs were found to be marginally associated with increased published studies, sample sizes, and minor allele frequen- risk of T2D after adjustment for age, sex, and BMI: KCNQ1 cies (MAF). In the present study, the prevalence of T2D in (rs2237892) [odds ratio (OR): 2.02, 95% CI: 1.08–3.79; p = Thailand was 8.3% [16], and the type 1 error rate was 0.006. 0.020 (additive model)] and [OR: 1.43, 95% CI: 1.06–1.92; p = 0.018 (recessive model)]; CDKN2A/2B (rs10811661) [OR: Results 1.65, 95% CI: 1.01–2.71; p = 0.044 (additive model)]; Clinical and biochemical characteristics SLC30A8 (rs13266634) [OR: 1.86, 95% CI: 1.19–2.90; p = The clinical characteristics of both study groups are pre- 0.006 (additive model)] and [OR: 1.81, 95% CI: 1.31–2.50; p sented in Table 1. There were significant differences be- = 0.0003 (recessive model)]; TCF7L2 (rs7903146) [OR: 1.70, tween patients and control group individuals relative to 95% CI: 1.06–2.72; p = 0.025 (dominant model)]; and, age, weight, waist circumference, waist-to-hip ratio MTNR1B (rs1387153) [OR: 1.44, 95% CI: 1.01–2.08; p = (WHR), body mass index (BMI), systolic blood pressure 0.047 (recessive model)]. Interestingly, after Bonferroni cor- (SBP), diastolic blood pressure (DBP), fasting plasma rection (p < 0.006 indicates statistical significance), SNP glucose (FPG), glycated hemoglobin (HbA ), total chol- rs13266634 of the SLC30A8 gene remains significantly asso- 1c esterol (TC), triglycerides (TG), low-density lipoprotein ciated with T2D susceptibility (OR: 1.81, 95% CI: 1.31–2.50; cholesterol (LDL-C), and high-density lipoprotein chol- p = 0.0003). No associations between CDKAL1 (rs7756992) esterol (HDL-C) (all p < 0.05). or UBE2E2 (rs7612463) and T2D were observed in any of the 3 genetic models (Table 2). Analysis of association between SNPs and T2D We genotyped eight SNPs in a case-control cohort of 1000 Analysis of association between SNPs and quantitative Thai subjects, including 500 T2D patients and 500 traits non-diabetic controls. The allele and genotype distributions The 5 SNPs that were found to be significantly associ- of SNPs are summarized in Table 2.All 8SNP genotypes ated with T2D were then evaluated for association with Table 1 Demographic, anthropometric, and clinical characteristics of the control and patient study groups Clinical characteristics Controls (n = 500) Patients (n = 500) p-value Gender (M, F) (%) 28.8, 71.2 32.8, 67.2 0.170164:336 Age (yrs) (years) 53.0 ± 8.4 57.2 ± 12.2 < 0.001 Age at diagnosis (yrs) – 49.4 ± 11.3 ND Weight (kg) 60.0 ± 10.0 67.7 ± 13.8 < 0.001 Waist circumference (cm) 83.3 ± 9.4 89.6 ± 11.4 < 0.001 Waist-to-hip ratio 0.8 ± 0.1 0.9 ± 0.1 < 0.001 2 a BMI (kg/m ) 24.1 ± 3.3 27.3 ± 5.0 < 0.001 SBP (mmHg) 116.7 ± 15.4 134.9 ± 19.4 < 0.001 DBP (mmHg) 71.7 ± 10.3 79.3 ± 10.4 < 0.001 FPG (mmol/l) 3.9 (3.4, 4.4) 8.4 (6.6, 12.1) < 0.001 HbA (mmol/mol) 36.6 (34.2, 37.7) 57.4 (48.0, 74.9) < 0.001 1c TC (mmol/l) 208.4 ± 37.9 200.2 ± 53.3 < 0.001 TG (mmol/l) 2.3 (2.0, 2.6) 1.5 (1.1, 2.1) < 0.001 LDL-C (mmol/l) 2.5 (1.8, 3.5) 2.1 (1.2, 3.6) < 0.001 HDL-C (mmol/l) 3.2 (2.6, 3.9) 1.6 (1.1, 2.2) < 0.001 Data presented as mean ± standard deviation or median and interquartile range A p-value< 0.05 indicates statistical significance Gender distribution between patients and controls was analyzed using Pearson’s χ2 test Comparison of quantitative variables between patients and controls was performed using Student’s t-test or Mann-Whitney U test a b p-values and p-values were determined using Student’s t-test and Mann-Whitney U test, respectively Abbreviations: M male, F female, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, HbA glycated 1c hemoglobin, TC total cholesterol, TG triglycerides, LDL-C low-density lipoprotein cholesterol, HDL-C high-density lipoprotein cholesterol, ND not determined Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 5 of 9 Table 2 Association between eight SNPs and T2D in Thai population a b Gene SNP Risk Risk Subject Genotype frequency, n Additive model Dominant model Recessive model (major/ allele allele (%) P P adjusted P P adjusted P P adjusted minor freq. freq. A/A A/B B/B unadjusted OR (95% unadjusted OR (95% unadjusted OR (95% alleles*) OR (95% CI) OR (95% CI) OR (95% CI) CI) CI) CI) KCNQ1 rs2237892 0.76 0.71 Patient 285 192 23 0.060 0.020 0.128 0.057 0.025 0.018 (C>T) (57.0) (38.4) (4.6) 1.74 (0.99– 2.02 (1.08– 1.53 (0.88– 1.83 (0.98– 1.36 (1.04– 1.43 (1.06– 3.07) 3.79) 2.66) 3.42) 1.78) 1.92) Control 254 205 41 (50.8) (41.0) (8.2) CDKN2A/ rs10811661 0.68 0.62 Patient 228 220 52 0.100 0.044 0.227 0.105 0.077 0.066 2B (T> C) (45.6) (44.0) (10.4) 1.45 (0.93– 1.65 (1.01– 1.29 (0.85– 1.46 (0.92– 1.28 (0.97– 1.33 (0.98– 2.27) 2.71) 1.96) 2.31) 1.69) 1.81) Control 185 248 67 (37.0) (49.6) (13.4) SLC30A8 rs13266634 0.59 0.54 Patient 172 242 86 0.040 0.006 0.309 0.191 0.008 0.0003 (C>T) (34.4) (48.4) (17.2) 1.51 (1.01– 1.86 (1.19– 1.20 (0.84– 1.29 (0.87– 1.48 (1.11– 1.81 (1.31– 2.21) 2.90) 1.71) 1.91) 1.98) 2.50) Control 137 268 95 (27.4) (53.6) (19.0) HHEX rs1111875 0.33 0.29 Patient 234 200 66 0.039 0.082 0.260 0.367 0.044 0.087 (A > G) (46.8) (40.0) (13.2) 1.61 (1.02– 1.55 (0.94– 1.16 (0.89– 1.14 (0.85– 1.56 (1.01– 1.51 (0.94– 2.55) 2.56) 1.53) 1.54) 2.42) 2.45) Control 254 201 45 (50.8) (40.2) (9.0) CDKAL1 rs7756992 0.43 0.40 Patient 156 257 87 0.249 0.107 0.355 0.169 0.340 0.182 (A > G) (31.2) (51.4) (17.4) 1.27 (0.84– 1.44 (0.92– 1.14 (0.85– 1.25 (0.90– 1.19 (0.83– 1.30 (0.88– 1.90) 2.24) 1.52) 1.72) 1.71) 1.94) Control 171 254 75 (34.2) (50.8) (15.0) TCF7L2 rs7903146 0.08 0.04 Patient 429 67 4 (0.0) NA NA 0.006 0.025 NA NA (C > T) (85.8) (13.4) 1.80 (1.18– 1.70 (1.06– 2.76) 2.72) Control 456 44 0 (0.0) (91.2) (8.8) MTNR1B rs1387153 0.44 0.42 Patient 164 232 104 0.113 0.120 0.618 0.757 0.058 0.047 (C > T) (32.7) (46.3) (21.0) 1.35 (0.93– 1.38 (0.91– 1.07 (0.80– 1.05 (0.76– 1.38 (0.98– 1.44 (1.01– 1.97) 2.09) 1.42) 1.43) 1.92) 2.08) Control 174 231 95 (34.8) (46.2) (19.0) UBE2E2 rs7612463 0.83 0.82 Patient 346 134 20 0.875 0.855 0.890 0.872 0.847 0.826 (C>A) (69.3) (26.6) (4.0) 0.94 (0.47– 0.93 (0.42– 0.95 (0.47– 0.93 (0.43– 0.97 (0.72– 0.96 (0.70– 1.89) 2.03) 1.90) 2.03) 1.29) 1.32) Control 339 141 20 (67.8) (28.2) (4.0) Abbreviations: SNP single nucleotide polymorphism, T2D type 2 diabetes, Freqfrequency, A/A homo major allele, A/B heterozygote allele, B/B homo minor allele, OR odds ratio, CI confidence interval, NA not applicable (analytical model not applicable due to low frequency) A p < 0.006 (0.05 divided by 8, the total number of SNPs studied) was regarded as being statistically significant P-values were calculated for the additive, dominant, and recessive genetic models using logistic regression with/without adjustment for age, gender, and body mass index. The OR and 95% CI of having the risk allele are shown *Alleles in bold are the risk alleles for T2D identified in previous studies, while underlined alleles indicate the risk alleles for T2D observed in this study a b c Risk allele frequency in patients; Risk allele frequency in controls; Statistically significant after Bonferroni correction (multiple-testing) with p-value < 0.006 quantitative traits, including body weight, waist circum- p = 0.052, respectively). No significant associations ference, WHR, BMI, FPG, HbA , TC, TG, LDL-C, and were observed between any SNPs and any quantitative 1c HDL-C using linear regression analysis after adjusting traits in control subjects (data not shown). for age, sex, BMI, and medication (as appropriate) in both cases and controls (Table 3). Our analysis revealed Discussion CDKN2A/2B (rs10811661) to be significantly associated The present study evaluated 8 SNPs in or near the with both waist circumference and WHR (p = 0.007 KCNQ1, CDKN2A/2B, SLC30A8, HHEX, CDKAL1, and p = 0.023, respectively). TCF7L2 (rs7903146) was TCF7L2, MTNR1B, and UBE2E2 genes for significant associated with HDL-C (p = 0.023), and SLC30A8 association with T2D. Of those, 5 SNPs [KCNQ1 (rs13266634) was associated with HbA (p = 0.018). (rs2237892), CDKN2A/2B (rs10811661), SLC30A8 1c KCNQ1 (rs2237892) was significantly associated at (rs13266634), TCF7L2 (rs7903146) and MTNR1B borderline with both FPG and HbA (p = 0.050 and (rs1387153)] were found to be associated with T2D in 1c Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 6 of 9 Table 3 Association between CDKN2A/2B (rs10811661), SLC30A8 (rs13266634), and TCF7L2 (rs7903146) genotypes and quantitative traits among Thai patients with T2D Clinical CDKN2A/2B (rs10811661) genotypes SLC30A8 (rs13266634) genotypes TCF7L2 (rs7903146) genotypes characteristics T/T T/C C/C P C/C C/T T/T P C/C C/T + TT P Number of 228 220 52 – 172 242 86 – 429 71 – patients Weight (kg) 68.99 ± 67.42 ± 65.01 ± 0.250 66.38 ± 66.38 ± 66.38 ± 0.867 67.58 ± 68.83 ± 0.172 13.96 13.73 14.28 14.78 14.78 14.78 13.60 15.45 Waist 89.34 ± 89.39 ± 92.59 ± 0.007 89.55 ± 89.79 ± 89.87 ± 0.506 89.66 ± 89.84 ± 0.861 circumference (cm) 11.32 11.69 11.04 11.58 10.82 12.75 11.30 12.27 Waist-to-hip ratio 0.89 ± 0.07 0.91 ± 0.08 0.92 ± 0.11 0.023 0.91 ± 0.07 0.90 ± 0.08 0.90 ± 0.08 0.263 0.90 ± 0.08 0.90 ± 0.080 0.334 BMI (kg/m ) 27.60 ± 5.06 27.07 ± 26.82 ± 0.442 26.65 ± 27.62 ± 27.59 ± 5.38 0.273 27.27 ± 27.14 ± 4.71 0.736 4.99 5.075 5.15 4.72 5.067 SBP (mmHg) 133.60 ± 134.21 ± 143.52 ± 0.746 134.94 ± 134.44 ± 136.47 ± 0.740 135.25 ± 132.98 ± 0.800 19.99 17.58 23.29 18.56 19.30 21.82 19.64 18.15 DBP (mmHg) 78.84 ± 79.42 ± 80.75 ± 0.228 79.95 ± 78.56 ± 80.18 ± 9.89 0.348 79.43 ± 78.87 ± 9.21 0.469 10.72 9.87 11.65 10.16 10.85 10.62 FPG (mmol/l) 9.27 (7.05, 8.16 (6.38, 7.79 (6.33, 0.244 8.33 (6.64, 8.49 (6.38, 8.77 (6.84, 0.164 8.49 (6.50, 8.55 (6.94, 0.721 12.49) 11.88) 10.68) 11.62) 12.93) 11.94) 12.25) 8.55) HbA (mmol/mol) 59.05 (49.70, 56.30 53.00 0.683 56.30 57.40 59.05 (48.32, 0.018 57.40 56.30 (47.50, 0.535 1c 78.92) (48.6, 71.6) (44.82, 77.3) (48.6, 71.6) (49.7, 76.5) 78.10) (48.60, 74.9) 56.30) TC (mmol/l) 202.96 ± 197.52 ± 195.80 ± 0.748 197.40 ± 199.65 ± 208.33 ± 0.853 200.65 ± 198.00 ± 0.592 53.19 51.12 60.28 55.56 51.20 55.57 54.08 49.65 TG (mmol/l) 1.46 (1.03, 1.50 (1.18, 1.64 (1.17, 0.197 1.43 (1.02, 1.51 (1.14, 1.51 (1.18, 0.664 1.52 (1.15, 1.32 (1.06, 0.600 2.06) 2.11) 2.06) 2.05) 2.08) 2.26) 2.10) 1.32) LDL-C (mmol/l) 2.22 (1.39, 2.15 (1.30, 1.47 (1.10, 0.943 1.89 (1.34, 2.38 (1.22, 2.22 (1.37, 0.854 2.17 (1.33, 1.71 (1.16, 0.741 3.85) 3.50) 2.87) 3.64) 3.78) 3.71) 3.68) 1.71) HDL-C (mmol/l) 1.58 (1.14, 1.63 (1.19, 1.78 (1.44, 0.965 1.64 (1.17, 1.61 (1.19, 1.55 (1.14, 0.464 1.60 (1.16, 1.81 (1.34, 0.023 2.21) 2.265) 2.32) 2.24) 2.17) 2.50) 2.18) 1.81) Data presented as mean ± standard deviation or median and interquartile range P-values were calculated using linear regression after adjusting for age, gender, BMI, and medication, as appropriate. FPG, HbA1c, TG, LDL-C, and HDL-C values were log-transformed to improve normality before regression analysis Abbreviations: T2D type 2 diabetes, BMI body mass index, SBP systolic blood pressure, DBP diastolic blood pressure, FPG fasting plasma glucose, HbA glycated 1c hemoglobin, TC, total cholesterol, TG, triglycerides, HDL-C high-density lipoprotein cholesterol, LDL-C low-density lipoprotein cholesterol Thai population. We also found association between Asian populations (Han Chinese: 54.4%, Japanese: 52.4%), CDKN2A/2B (rs10811661), SLC30A8 (rs13266634), and but different from European (71.7%) population (source: TCF7L2 (rs7903146) and diabetes-related quantitative dbSNP database). We also found SLC30A8 (rs13266634) traits. to be associated with HbA (p =0.018). A similar finding 1c SLC30A8 (solute carrier family 30 member 8) encodes was observed in a Chinese study population [23]. Func- zinc transporter-8 (ZnT8), which is specifically expressed tional study found that zinc transporter 8 (ZnT8) is re- in pancreatic endocrine cells and may participate in quired for normal insulin crystallization and insulin insulin secretion. SLC30A8 is expressed at high levels processing and secretion [24]. In addition, subjects who only in the pancreas, and is highly expressed in had homozygous of the risk C allele of SLC30A8 showed insulin-secreting beta cells [17, 18]. Association between decreased peripheral insulin levels in the early phase of SNP rs13266634 in SLC30A8 and T2D risk was discovered intravenous glucose tolerance test (GTT) [22]. This sug- in European subjects [7, 9, 10]. The presence of risk C al- gests that SLC30A8 may influence the Zn transporter, lele of rs13266634 (SLC30A8) was found to be associated thereby affecting insulin stability and insulin secretion, but with increased risk of developing T2D in multiethnic the precise role of variants of this gene remains unclear. case-control studies, including Asians [19, 20] and Euro- TCF7L2 (Transcription Factor 7-Like 2) is a transcrip- peans [21, 22]. This SNP also showed strong association tion factor protein that is capable of binding beta ca- with T2D in our study with an OR of 1.78 [95% CI: 1.31– tenin, which is involved in Wnt receptor signaling. This − 4 2.50; p =3.0 × 10 (recessive model)], which is higher signaling pathway affects the expression of several genes, than the odds ratios reported in previous studies (OR including insulin and glucagon genes [25]. A strong as- range: 1.1–1.2). The risk C allele frequency of 59.0% in sociation was identified between TCF7L2 genetic vari- our population was similar to frequencies reported in ants and risk of T2D in a European population [8], Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 7 of 9 which was then replicated in East Asian populations, in- rs10811661 was associated with both waist circumfer- cluding Chinese and Japanese [15, 26]. In this study, we ence and WHR among T2D patients in this study (p = found significant association between SNP rs7903146 0.007 and p = 0.032, respectively). In a similar result, a and increased risk of T2D in the dominant model (OR: recent study from Vietnam found association between 1.80, 95% CI: 1.18–2.76; p = 0.006). In contrast to popu- SNP rs10811661 and WHR in prediabetes subjects [38]. lations with European and African ancestries, the risk T However, the mechanism by which the CDKN2A/2B allele of rs7903146 was rare in our Thai cohort, with a locus influences diabetes risk remains uncertain. Recent minor allele frequency (MAF) of 0.08. The TCF7L2 gene publication showed CDKN2A/2B locus SNPs may im- was reported to be associated with T2D, insulin sensitiv- pact T2D risk by modulating islet gene expression and ity, and insulin resistance [25]. In addition, the SNP beta cell proliferation [39]. rs7903146 was reported to be a risk factor for various Several susceptibility genes have been identified and metabolic disorders involving glucose and lipoprotein replicated for association with T2D and diabetes-related homeostasis [27, 28]. A previous study by our team traits in European and East Asians populations. Most of showed significant association between an SNP in TCF7L2 the susceptibility risk loci identified were shared among and age of onset of diabetes [29]. In that study, we found East Asians and populations of Europeans ancestry. that patients who carried a minor allele of an SNP were However, a significant association signals at these shared diagnosed with T2D at an earlier age. The present study loci seems to be independent among populations. This also found association between SNP rs7903146 and suggests that pathogenesis of the disease is common HDL-C among T2D patients. The specific mechanism of among populations, but that some of the risk variants TCF7L2 (rs7903146) that resides in a noncoding region may be population specific. Differences in genetic back- and that drives the development of T2D remains unclear; ground, risk allele frequencies, and clinical characteris- however, the possible effect of the TCF7L2 risk allele is via tics, such as BMI cut-point for metabolic syndrome, a defect in insulin secretion [25]. diet, culture, and other lifestyle factors, may explain dif- KCNQ1 (potassium voltage gated channel, KQT like ferences in risk variants between European and East subfamily, member 1) plays a key role in potassium Asians. It is, therefore, important to have information channels that control insulin secretion [30]. Common about the association of genetic variations and T2D, and variants (SNP rs2237892, rs2237895, and rs2237897) in to understand the roles of these T2D risk loci in differ- KCNQ1 were identified in East Asian population [6, 13]. ent Asian populations, including Thai population. For The association of these SNPs with T2D was replicated example, rare PAX4 mutations were first identified in in European, Mexican, and Asian populations [6, 13, 31, Thai MODY probands (designated MODY9) [40], but 32]. Of the three aforementioned SNPs, SNP rs2237892 they are seldom found in those of European descent. A appeared to have the strongest association with T2D GWA study identified a susceptibility locus for T2D at [13]. In the present study, we also found SNP rs2237892 7q32 near PAX4 [41], and exome-chip association ana- to be significantly associated with T2D [OR: 2.02, 95% lysis revealed an Asian-specific missense variant in PAX4 CI: 1.08–3.79; p = 0.020 (additive model)] and [OR: 1.43, associated with T2D in Chinese population [42]. Only 95% CI: 1.06–1.92; p = 0.018 (recessive model)]. Other one variant reached genome-wide significance (PAX-Ar- studies found variants in KCNQ1 to be associated g192His, rs2233580). This association is exclusive to with impaired fasting plasma glucose, β-cell function, East Asian individuals, in whom the 192His allele is and metabolic traits [6, 13, 33]. However, we did not common (MAF = 10%) with a substantial effect size, with find evidence of association between KCNQ1 and 192His observed to be virtually absent in individuals diabetic-related quantitative traits. The potential mechan- from other ancestries [43]. This suggests that PAX4 or ism of KCNQ1 and the pathogenesis of T2D needs to be T2D loci may be particularly relevant in the pathogen- further explored. esis of T2D in both East Asians and in Thais. SNP rs10811661 is located 125 k-bases upstream from With regard to the remaining 3 loci, HHEX (rs1111875), the cyclin-dependent kinase inhibitor 2A/2B (CDKN2A/ CDKAL1 (rs7756992), and UBE2E2 (rs7612463) showed 2B) gene on chromosome 9p21. SNP rs10811661 was no significant association with T2D in our cohort. This re- found to be associated with risk of T2D in multiple large sult may be explained by different environmental risk pro- GWA studies [4, 9, 10, 34]. In this study, we also found files between Europeans and Asians, different body this SNP to be significantly associated with increased composition and genetic backgrounds, different linkage risk of T2D [OR: 1.65, 95% CI: 1.01–2.71; p = 0.044 disequilibrium patterns, or by the fact that we have insuffi- (additive model)]. The risk T allele was found to be more cient statistical power with the current sample size to rep- common in T2D patients (MAF: 0.68), which is similar licate some of these previously reported T2D risk loci. to several previous reports in Caucasian and Asian pop- The sample size in this study yielded power to identify sig- ulations [19, 35–37]. In addition, the risk allele of SNP nificant association between T2D and variants in the Plengvidhya et al. BMC Medical Genetics (2018) 19:93 Page 8 of 9 KCNQ1, CDKN2A/2B, CDKAL1, HHEX, MTNR1B, thank Dr. Sirikul Kulanuwat for her helpful advice and guidance, and Mr. Choochai Nattuwakul for technical assistance. TCF7L2, and UBE2E2 genes ranging from 2.13 to 37.41%. A sample size ranging from 1094 to 12,600 would be Funding needed to achieve an 80% power threshold to detect sta- This study was supported by a Siriraj Grant for Research Development and Medical Education, Mahidol University to NP, a grant from the Thailand tistically significant results at alpha = 0.006. The sample Research Fund to PY (IRG5980006), a grant from the Thailand Research Fund size included in this study for SLC30A8 (n = 500) had 96% Grant for New Researcher to WT (TRG5280002, TRG5780113), and a grant power to detect significant association with an OR of 1.81 from the Faculty of Medicine Siriraj Hospital, Mahidol University to CC and PY. The funding body played no role in the design of the study, data assuming a minor allele frequency of 0.41. This study has collection, analysis, interpretation or writing of the manuscript. some mentionable limitations. First, the relatively small sample size and the commensurately limited statistical Availability of data and materials The data used and identified in this study are included within the article and power that accompanied it may have limited our ability to in the provided supplementary data file. identify all significant differences and associations. Second, several risk loci have been proposed as being associated Authors’ contributions with T2D in Caucasian and Asian populations. However, NP, CC, NC, PY, MH and WT contributed to the conceptualization of the study and obtained funding. NP and PY designed the study and interpreted we limited our investigation to 8 SNPs from 8 candidate data. CC and NJ performed the analyses. WT curated the data and oversaw loci that were found to be common from studies in Cau- laboratory work. All authors contributed to editing the manuscript, review casian and several different Asian populations. Thus, fur- and provided intellectual input to the final manuscript. All authors read and approved the final manuscript. ther studies that include the T2D risk loci not include in this study should be further investigated. Ethics approval and consent to participate Informed written consent was obtained from all the patients involved in this study. This study was approved by the Siriraj Institutional Review Board (SIRB), Conclusion Faculty of Medicine Siriraj Hospital, Mahidol University (COA no. Si491/2014). Of the eight genes included in our analysis, association was observed between KCNQ1, CDKN2A/2B, SLC30A8, Consent for publication The patients participating in this study signed their consent for the TCF7L2,and MTNR1B loci and T2D in our Thai study publication of the results obtained. This statement was approved by the population. Of these, CDKN2A/2B, SLC30A8, and Siriraj Institutional Review Board (SIRB), Faculty of Medicine Siriraj Hospital, TCF7L2 genes were also associated with anthropometric, Mahidol University. glycemic and lipid characteristics. Larger cohort studies Competing interests and meta-analyses are needed to further confirm the ef- The authors declare that they have no competing interests. fect of these variants in Thai population. Publisher’sNote Additional file Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Additional file 1: Table S1 Summary of the single nucleotide Author details polymorphisms (SNPs) investigated in this study. Table S2 Primer Division of Endocrinology and Metabolism, Department of Medicine, Faculty sequence, PCR product size, and annealing temperature for HRM assay of of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand. Research designated SNPs. Table S3 Primer sequences of SLC30A8 (rs13266634) Division, Department of Research and Development, Faculty of Medicine used for PCR-RFLP method. (DOCX 27 kb) Siriraj Hospital, Mahidol University, Bangkok, Thailand. Department of Immunology, Faculty of Medicine Siriraj Hospital, Mahidol University, Abbreviations Bangkok, Thailand. Siriraj Center of Research Excellence for Molecular ADA: American Diabetes Association; BMI: Body Mass index; bp: Base pair; Medicine, Department of Research and Development, Faculty of Medicine CDKAL1: CDK5 regulatory subunit-associated protein 1-like 1; CDKN2A/ Siriraj Hospital Mahidol University, Bangkok, Thailand. Department of 2B: cyclin-dependent kinase inhibitor 2A; CI: Confidence interval; Preventive and Social Medicine, Faculty of Medicine Siriraj Hospital, Mahidol DBP: diastolic blood pressure; FPG: fasting plasma glucose; GWA: genome- University, Bangkok, Thailand. wide association; HbA : glycated hemoglobin; HDL: High density 1c lipoprotein;; HDL-C: high-density lipoprotein; HHEX: hematopoietically- Received: 7 August 2017 Accepted: 23 May 2018 expressed homeobox; HWE: Hardy-Weinberg equilibrium; IDF: International Diabetes Federation; KCNQ1: potassium voltage-gated channel, KQT-like sub- family, member 1; LD: Linkage Disequilibrium; LDL: Low density lipoprotein; References LDL-C: low-density lipoprotein; MAF: minor allele frequencies; 1. Cho NH, Shaw JE, Karuranga S, Huang Y, da Rocha Fernandes JD, Ohlrogge MTNR1B: melatonin receptor type 1B; ND: Non-diabetic; OR: Odd ratio; RFLP- AW, et al. 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BMC Medical GeneticsSpringer Journals

Published: Jun 5, 2018

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