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Jonathan T. Tan, Daniel P. K. Ng, Siti Nurbaya, Sandra Ye, Xiu Li Lim, Helen Leong, Lin Tze Seet, Wei Fong Siew, Winston Kon, Tien Yin Wong, Seang Mei Saw, Tin Aung, Kee Seng Chia, Jeannette Lee, Suok Kai Chew, Mark Seielstad, E. Shyong Tai, Polymorphisms Identified through Genome-Wide Association Studies and Their Associations with Type 2 Diabetes in Chinese, Malays, and Asian-Indians in Singapore, The Journal of Clinical Endocrinology & Metabolism, Volume 95, Issue 1, 1 January 2010, Pages 390–397, https://doi.org/10.1210/jc.2009-0688
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Abstract
Context: Novel type 2 diabetes mellitus (T2DM) susceptibility loci, identified through genome-wide association studies (GWAS), have been replicated in many European and Japanese populations. However, the association in other East Asian populations is less well characterized.
Objective: To examine the effects of SNPs in CDKAL1, CDKN2A/B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761, and KCNQ1 on risk of T2DM in Chinese, Malays, and Asian-Indians in Singapore.
Design: We genotyped these candidate single-nucleotide polymorphisms (SNPs) in subjects from three major ethnic groups in Asia, namely, the Chinese (2196 controls and 1541 cases), Malays (2257 controls and 1076 cases), and Asian-Indians (364 controls and 246 cases). We also performed a metaanalysis of our results with published studies in East Asians.
Results: In Chinese, SNPs in CDKAL1 [odds ratio (OR) = 1.19; P = 2 × 10−4], HHEX (OR = 1.15; P = 0.013), and KCNQ1 (OR = 1.21; P = 3 × 10−4) were significantly associated with T2DM. Among Malays, SNPs in CDKN2A/B (OR = 1.22; P = 3.7 × 10−4), HHEX (OR = 1.12; P = 0.044), SLC30A8 (OR = 1.12; P = 0.037), and KCNQ1 (OR = 1.19–1.25; P = 0.003–2.5 × 10−4) showed significant association with T2DM. The combined analysis of the three ethnic groups revealed significant associations between SNPs in CDKAL1 (OR = 1.13; P = 3 × 10−4), CDKN2A/B (OR = 1.16; P = 9 × 10−5), HHEX (OR = 1.14; P = 6 × 10−4), and KCNQ1 (OR = 1.16–1.20; P = 3 × 10−4 to 3 × 10−6) with T2DM. SLC30A8 (OR = 1.06; P = 0.039) showed association only after adjustment for gender and body mass index. Metaanalysis with data from other East Asian populations showed similar effect sizes to those observed in populations of European ancestry.
Conclusions: SNPs at T2DM susceptibility loci identified through GWAS in populations of European ancestry show similar effects in Asian populations. Failure to detect these effects across different populations may be due to issues of power owing to limited sample size, lower minor allele frequency, or differences in genetic effect sizes.
Genome-wide association studies (GWAS) have identified several type 2 diabetes mellitus (T2DM) susceptibility loci including CDKAL1, CDKN2B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761 (1–5), and KCNQ1, which was recently identified by similar GWAS approach in two independent Japanese samples (6, 7). Although these associations have been well replicated in Japanese populations (8), the role of these loci in other East Asian populations remains less clear. For example, a study in China by Wu et al. (9) did not find significant associations between single-nucleotide polymorphisms (SNPs) in IGF2BP2 and SLC30A8 with T2DM, whereas an association between SNPs at the HHEX locus and T2DM was reported among Chinese living in Shanghai, but not among Chinese in Beijing. Another study in Hong Kong Chinese (10) also did not find an association with SNPs at the IGF2BP2 locus; however, they reported an association between T2DM with SNPs at the HHEX and SLC30A8 loci.
Singapore has a multiethnic population including Chinese, Malays, and Asian-Indians. These three ethnic groups represent a predominant portion of the population resident in Asia, where a doubling in the prevalence of T2DM is expected in the next 20 yr as a result of the rapid urbanization occurring in this region (11). The Malay ethnicity alone is the third largest ethnic group in Asia with a total population of over 200 million in Indonesia, Malaysia, Singapore, and other Southeast Asian countries. This ethnic group certainly represents a population with the propensity to develop T2DM and have a prevalence of T2DM of 8.5% in men and 10.1% in women in Singapore (12). However, to date, the effects of these T2DM susceptibility loci in Malays has not been examined.
The aims of this study are to 1) ascertain the association and contribution of nine SNPs in recently identified T2DM susceptibility loci (CDKAL1, CDKN2A/B, IGF2BP2, HHEX, SLC30A8, PKN2, LOC387761, and KCNQ1) with the risk of T2DM in Chinese, Malays, and Asian-Indians and 2) to perform a metaanalysis of similar studies in East Asians.
Subjects and Methods
We used a case-control approach using subjects from two cross-sectional studies, the 1998 Singapore National Health Survey (n = 4323), the Singapore Malay Eye Study (n = 2997), and a case-series study, the Singapore Diabetes Cohort Study (n = 1703).
1998 Singapore National Health Survey (NHS98)
NHS98 is a population-based, cross-sectional study comprising Chinese, Malays, and Asian-Indians aged between 18 and 69 yr. The survey methods have been described previously (13) and were based on the World Health Organization (WHO)-recommended model for field surveys of diabetes and other noncommunicable diseases, and the WHO MONICA protocol for population surveys. Fasting blood samples were drawn for measurement of serum glucose (Boehringer Manheim, Mannheim, Germany) and insulin (immunoassay using an Abbott AxSYM; Abbott Laboratories, Chicago, IL) in all subjects after a 10-h overnight fast. All participants who were not taking oral hypoglycemic agents or insulin were subjected to a 75-g oral glucose tolerance test. Subjects were considered to have T2DM if they gave a history of type 2 diabetes or if their fasting glucose was 7.0 mmol/liter or higher or if their 2-h postchallenge glucose (2HPG) was 11.1 mmol/liter or higher. Impaired fasting glucose (IFG) was defined as fasting glucose higher than 6.0 mmol/liter and lower than 7.0 mmol/liter and 2HPG lower than 7.8 mmol/liter, and impaired glucose tolerance (IGT) was defined as fasting glucose higher than 7.0 mmol/liter and 2HPG higher than 7.8 mmol/liter and less than 11.1 mmol/liter.
DNA was isolated from blood samples using DNA blood Midi kits (QIAGEN, Hilden, Germany) following the manufacturer’s recommended protocol. At the time of this study, DNA samples from 2937 Chinese, 788 Malay, and 598 Asian-Indian subjects were available for analysis. Height, weight, and blood pressure were measured for all subjects. Body mass index (BMI) was calculated as weight (in kilograms) divided by the square of height (in meters).
Singapore Diabetes Cohort Study (SDCS)
SDCS comprises Chinese, Malay, and Asian-Indian individuals with T2DM (http://www.med.nus.edu.sg/cof/resch_sdcs.html). Since 2004, all individuals treated for T2DM at primary care facilities of the Singapore National Healthcare Group Polyclinics have been invited to participate in the SDCS (14). An excellent response rate of 91% was achieved, and this formed our SDCS case group. At the time of this study, DNA samples from 1317 Chinese, 256 Malay, and 130 Asian-Indian subjects were available for analysis. Blood specimens were obtained for DNA extraction and analysis at the Disease Genetics Laboratory, Department of Epidemiology and Public Health, National University of Singapore. BMI was measured in all subjects in the same way as in NHS98.
A total of 1881 Chinese subjects had previously served as the controls for the Singapore replication arm in the original report by Unoki et al. (6) identifying KCNQ1 as a diabetes susceptibility loci. In the original paper, only normal glucose-tolerant controls were derived from NHS98, whereas T2DM cases were from the SDCS. In the present study, all Chinese, Malay, and Asian-Indian subjects from the NHS98 study (inclusive subjects with normal glucose tolerance and T2DM) were included.
Singapore Malay Eye Study (SiMES)
SiMES is a population-based, cross-sectional epidemiological study of Malay adults residing in Singapore. Details of the study design, sampling plan, and methodology have been reported elsewhere (15–18). In brief, age-stratified random sampling of all Malay adults aged from 40–80 yr residing in 15 residential districts in the southwestern part of Singapore was performed. A 40-ml sample of nonfasting venous blood was collected, and levels of serum glucose, glycated hemoglobin (HbA1c),and lipids were measured on the same day using enzymatic methods implemented in the Advia 2400 Chemistry System (Siemens Medical Solutions Diagnostics, Deerfield, IL). T2DM was diagnosed if the subject reported a history of type 2 diabetes or if the nonfasting plasma glucose was 11.1 mmol/liter or higher. DNA was extracted from serum using an automated DNA extraction technique at the Singapore Tissue Network. DNA samples for 2997 subjects were available for analysis. BMI was measured in all subjects in the same way as in NHS98.
Selection of cases and controls
Controls from NHS98 included subjects with normal glucose tolerance based on the fasting glucose and the 2HPG (2196 Chinese, 472 Malays, and 364 Indians). Cases included subjects with the diagnosis of T2DM from NHS98 (224 Chinese, 113 Malays, and 116 Indians) as well as all subjects from the SDCS (1317 Chinese, 256 Malays, and 130 Indians). NHS98 Subjects with IFG/IGT (n = 838) were excluded from analysis. In SiMES, controls (n = 1785) were selected on the basis of having a nonfasting blood glucose level lower than 11.1 mmol/liter and HbA1c lower than 6.1% (2 sd above the mean for the nondiabetic population), whereas cases (n = 707) were as described in the preceding paragraph.
Genotyping
We genotyped SNPs in nine diabetes susceptibility loci identified by recent GWAS studies. These include rs7756992 in CDKAL1, rs10811661 in CDKN2A/B, rs4402960 in IGF2BP2, rs1111875 in HHEX, rs13266634 in SLC30A8, and rs2237897 and rs2237892 in KCNQ1. We also examined rs6698181 in PKN2 because it did show some association in the GWAS by Diabetes Genetics Initiative (DGI) (1) and Finland-United States Investigation of Non-insulin Diabetes Genetics (FUSION) (2) (P = 10−3–10−5) and rs7480010 in LOC387761, which showed an association in the GWAS by Sladek et al. (5) (P = 10−5). Because there have been fewer reports examining these loci, we felt it might be of interest to examine this in our multiethnic population. Although polymorphisms at the TCF7L2 locus have the largest effects on the risk of T2DM in most studies carried out in populations of European ancestry, we did not include these in this study because the low allele frequency in Asian populations limits the power of our study to detect any effects, even if they were present. Furthermore, strictly speaking, these polymorphisms were not identified through GWAS.
Genotyping of the SNPs (except rs2237892) was carried out using the Sequenom MassARRAY platform (Sequenom, San Diego, CA). Genotyping of rs2237892 was performed using the TaqMan SNP genotyping assay (Applied Biosystems, Foster City, CA). All SNPs passed the genotyping call rate threshold (>90%). Thirty samples were analyzed in duplicate; genotyping was 100% concordant for these samples. Genotyping details of the SNPs are listed in Supplemental Table 1 (published as supplemental data on The Endocrine Society’s Journals Online web site at http://jcem.endojournals.org). Minor allele frequency and deviation from Hardy-Weinberg Equilibrium (HWE) were estimated using Haploview (19).
Statistical analysis
A general inheritance model was fitted, and an additive model was used based on observed effects. Allele-specific odds ratios (ORs) were calculated under the assumption of an additive risk model by assigning subjects as 0, 1, or 2 according to the number of risk alleles (not necessarily the minor alleles at the polymorphic site). Logistic regression was performed to study the association between the SNPs with T2DM. We stratified the analysis by the three ethnic groups (with adjustment for study). The primary analysis considered only the genetic variants in the model. These analyses were subsequently adjusted for gender and BMI by adding these variables to the model.
As a supplementary analysis, we also assessed the joint effect of the SNPs using logistic regression to calculate the OR with respect to the number of risk alleles carried (under an additive model). We grouped individuals into categories based on the number of risk alleles, with each category treated as an independent variable in the logistic regression model. Adjacent categories were combined if they had a frequency of less than 5%.
For the metaanalysis, Cochran’s Q test and I2 were used to assess heterogeneity between the studies. Based on the range of I2 values observed (17–65%), metaanalysis was performed using a random effects model. A comparison between the estimates derived from a random-effects model vs. a fixed-effects model is listed in Supplemental Table 2. The statistical analyses were performed using STATA (version 9.2; College Station, TX).
Results
Table 1 shows the clinical characteristics of the three study populations. In NHS98, the prevalence of T2DM is observed to be highest among Asian-Indians (19.9%), followed by Malays (14.4%) and Chinese (7.6%). Correspondingly, the Chinese had lower levels for diabetes-related traits (i.e. obesity measures, blood glucose levels, and insulin resistance) compared with the Malays and Asian-Indians. Similarly, in SDCS, HbA1c levels were highest among Asian-Indians, followed by Malays and the Chinese (P < 0.001). Although SiMES participants were older compared with the NHS98 Malays, they had comparable levels of BMI (P = 0.303). SDCS participants also tended to be older compared with NHS98 participants; however, there was no significant difference in the mean BMI between SDCS participants and NHS98 participants with T2DM (P > 0.26). Allele frequencies for the nine genotyped SNPs, test for HWE deviation, and genotype call rates are listed in Supplemental Table 1. Significant deviation from HWE (P < 0.01) was observed for rs2237892 and rs2237897 in the Asian-Indians and for rs7480010 in the SiMES Malays; consequently, these were excluded from subsequent analysis.
. | NHS98 . | SDCS . | SiMES, Malays . | ||||
---|---|---|---|---|---|---|---|
Chinese . | Malays . | Asian-Indians . | Chinese . | Malays . | Asian-Indians . | . | |
n | 2937 | 788 | 598 | 1317 | 256 | 130 | 2997 |
Age (yr) | 37.9 ± 12.2 | 38.8 ± 12.7 | 40.5 ± 12 | 63.9 ± 9.7 | 60.5 ± 9.5 | 60 ± 10.5 | 58.7 ± 11 |
% male | 0.46 | 0.48 | 0.48 | 0.49 | 0.47 | 0.51 | 0.48 |
BMI (kg/m2) | 22.7 ± 3.7 | 25.6 ± 5 | 25.2 ± 4.8 | 25.3 ± 3.9 | 28.5 ± 5 | 26.9 ± 4.6 | 26.4 ± 5.1 |
Fasting glucose (mmol/liter) | 5.6 ± 1.3 | 6.1 ± 2.2 | 6.3 ± 2.2 | 6.8 ± 3.7a | |||
2-h glucose (mmol/liter) | 6.6 ± 2.8 | 7.4 ± 3.6 | 7.6 ± 4 | ||||
Fasting insulin (μU/ml) | 7.4 ± 11.1 | 9.0 ± 7.1 | 10.7 ± 8.2 | ||||
Glucose tolerance, n (%) | |||||||
Normal | 2196 (74.8) | 472 (59.9) | 364 (60.9) | 2290 (76.4)b | |||
IGT/IFG | 517 (17.6) | 203 (25.7) | 118 (19.7) | ||||
T2DM | 224 (7.6) | 113 (14.3) | 116 (19.4) | 1317 (100) | 256 (100) | 130 (100) | 707 (23.6) |
. | NHS98 . | SDCS . | SiMES, Malays . | ||||
---|---|---|---|---|---|---|---|
Chinese . | Malays . | Asian-Indians . | Chinese . | Malays . | Asian-Indians . | . | |
n | 2937 | 788 | 598 | 1317 | 256 | 130 | 2997 |
Age (yr) | 37.9 ± 12.2 | 38.8 ± 12.7 | 40.5 ± 12 | 63.9 ± 9.7 | 60.5 ± 9.5 | 60 ± 10.5 | 58.7 ± 11 |
% male | 0.46 | 0.48 | 0.48 | 0.49 | 0.47 | 0.51 | 0.48 |
BMI (kg/m2) | 22.7 ± 3.7 | 25.6 ± 5 | 25.2 ± 4.8 | 25.3 ± 3.9 | 28.5 ± 5 | 26.9 ± 4.6 | 26.4 ± 5.1 |
Fasting glucose (mmol/liter) | 5.6 ± 1.3 | 6.1 ± 2.2 | 6.3 ± 2.2 | 6.8 ± 3.7a | |||
2-h glucose (mmol/liter) | 6.6 ± 2.8 | 7.4 ± 3.6 | 7.6 ± 4 | ||||
Fasting insulin (μU/ml) | 7.4 ± 11.1 | 9.0 ± 7.1 | 10.7 ± 8.2 | ||||
Glucose tolerance, n (%) | |||||||
Normal | 2196 (74.8) | 472 (59.9) | 364 (60.9) | 2290 (76.4)b | |||
IGT/IFG | 517 (17.6) | 203 (25.7) | 118 (19.7) | ||||
T2DM | 224 (7.6) | 113 (14.3) | 116 (19.4) | 1317 (100) | 256 (100) | 130 (100) | 707 (23.6) |
Data are means ± sd unless indicated otherwise.
Nonfasting blood glucose in SiMES subjects.
SiMES subjects with HbA1c higher than 6.1% (n = 505) were not included as controls in case-control analysis.
. | NHS98 . | SDCS . | SiMES, Malays . | ||||
---|---|---|---|---|---|---|---|
Chinese . | Malays . | Asian-Indians . | Chinese . | Malays . | Asian-Indians . | . | |
n | 2937 | 788 | 598 | 1317 | 256 | 130 | 2997 |
Age (yr) | 37.9 ± 12.2 | 38.8 ± 12.7 | 40.5 ± 12 | 63.9 ± 9.7 | 60.5 ± 9.5 | 60 ± 10.5 | 58.7 ± 11 |
% male | 0.46 | 0.48 | 0.48 | 0.49 | 0.47 | 0.51 | 0.48 |
BMI (kg/m2) | 22.7 ± 3.7 | 25.6 ± 5 | 25.2 ± 4.8 | 25.3 ± 3.9 | 28.5 ± 5 | 26.9 ± 4.6 | 26.4 ± 5.1 |
Fasting glucose (mmol/liter) | 5.6 ± 1.3 | 6.1 ± 2.2 | 6.3 ± 2.2 | 6.8 ± 3.7a | |||
2-h glucose (mmol/liter) | 6.6 ± 2.8 | 7.4 ± 3.6 | 7.6 ± 4 | ||||
Fasting insulin (μU/ml) | 7.4 ± 11.1 | 9.0 ± 7.1 | 10.7 ± 8.2 | ||||
Glucose tolerance, n (%) | |||||||
Normal | 2196 (74.8) | 472 (59.9) | 364 (60.9) | 2290 (76.4)b | |||
IGT/IFG | 517 (17.6) | 203 (25.7) | 118 (19.7) | ||||
T2DM | 224 (7.6) | 113 (14.3) | 116 (19.4) | 1317 (100) | 256 (100) | 130 (100) | 707 (23.6) |
. | NHS98 . | SDCS . | SiMES, Malays . | ||||
---|---|---|---|---|---|---|---|
Chinese . | Malays . | Asian-Indians . | Chinese . | Malays . | Asian-Indians . | . | |
n | 2937 | 788 | 598 | 1317 | 256 | 130 | 2997 |
Age (yr) | 37.9 ± 12.2 | 38.8 ± 12.7 | 40.5 ± 12 | 63.9 ± 9.7 | 60.5 ± 9.5 | 60 ± 10.5 | 58.7 ± 11 |
% male | 0.46 | 0.48 | 0.48 | 0.49 | 0.47 | 0.51 | 0.48 |
BMI (kg/m2) | 22.7 ± 3.7 | 25.6 ± 5 | 25.2 ± 4.8 | 25.3 ± 3.9 | 28.5 ± 5 | 26.9 ± 4.6 | 26.4 ± 5.1 |
Fasting glucose (mmol/liter) | 5.6 ± 1.3 | 6.1 ± 2.2 | 6.3 ± 2.2 | 6.8 ± 3.7a | |||
2-h glucose (mmol/liter) | 6.6 ± 2.8 | 7.4 ± 3.6 | 7.6 ± 4 | ||||
Fasting insulin (μU/ml) | 7.4 ± 11.1 | 9.0 ± 7.1 | 10.7 ± 8.2 | ||||
Glucose tolerance, n (%) | |||||||
Normal | 2196 (74.8) | 472 (59.9) | 364 (60.9) | 2290 (76.4)b | |||
IGT/IFG | 517 (17.6) | 203 (25.7) | 118 (19.7) | ||||
T2DM | 224 (7.6) | 113 (14.3) | 116 (19.4) | 1317 (100) | 256 (100) | 130 (100) | 707 (23.6) |
Data are means ± sd unless indicated otherwise.
Nonfasting blood glucose in SiMES subjects.
SiMES subjects with HbA1c higher than 6.1% (n = 505) were not included as controls in case-control analysis.
Table 2 shows the association between the SNPs at the nine loci with risk of T2DM. In Chinese, SNPs in CDKAL1 (OR = 1.19; P = 2 × 10−4), HHEX (OR = 1.15; P = 0.013), and KCNQ1 (OR = 1.21; P = 3 × 10−4) were significantly associated with T2DM. Among Malays, SNPs in CDKN2A/B (OR = 1.22; P = 3.7 × 10−4), HHEX (OR = 1.12; P = 0.044), SLC30A8 (OR = 1.12; P = 0.037), and KCNQ1 (OR = 1.19–1.25; P = 0.003–2.5 × 10−4) showed significant association with T2DM. In Asian-Indians, SNPs at the CDKAL1 locus (OR = 1.39; P = 0.015) and KCNQ1 locus (OR = 2.48–2.50; P = 0.012–0.022) were significantly associated with T2DM. However, the SNPs in KCNQ1 among Asian-Indians were not in HWE; together with their smaller sample size, the results for the Asian-Indians should be interpreted with caution. The combined analysis of the three ethnic groups revealed significant associations between SNPs in CDKAL1 (OR = 1.13; P = 3 × 10−4), CDKN2A/B (OR = 1.16; P = 9 × 10−5), HHEX (OR = 1.14; P = 6 × 10−4) and KCNQ1 (OR = 1.16–1.20; P = 3 × 10−4–3 × 10−6) with T2DM. Subsequent adjustment for gender and BMI largely recapitulated the findings of the unadjusted analysis, with the exception of SLC30A8 (OR = 1.06; P = 0.039), which showed association only after adjustment for gender and BMI in the combined analysis. No statistically significant associations were observed for SNPs at the PKN2 and the LOC387761 loci.
. | Risk allele frequency . | 95% CI . | P value . | P valuea . | |
---|---|---|---|---|---|
Control . | Case . | ||||
CDKAL1 (rs7756992 A→G) | |||||
Chinese | 0.45 | 0.50 | 1.19 (1.09–1.31) | 2 × 10−4 | 8 × 10−5 |
Malay | 0.44 | 0.44 | 1.03 (0.93–1.14) | 0.612 | 0.24 |
Indian | 0.22 | 0.28 | 1.39 (1.06–1.81) | 0.015 | 0.011 |
Combinedb | 1.13 (1.06–1.21) | 3 × 10−4 | 1 × 10−5 | ||
CDKN2A/B (rs10811661 T→C) | |||||
Chinese | 0.58 | 0.61 | 1.10 (0.99–1.23) | 0.074 | 0.304 |
Malay | 0.60 | 0.64 | 1.22 (1.09–1.36) | 3.7 × 10−4 | 4.4 × 10−5 |
Indian | 0.81 | 0.83 | 1.19 (0.87–1.63) | 0.282 | 0.482 |
Combinedb | 1.16 (1.08–1.25) | 9 × 10−5 | 3 × 10−5 | ||
HHEX (rs1111875 T→C) | |||||
Chinese | 0.29 | 0.33 | 1.15 (1.03–1.29) | 0.013 | 0.028 |
Malay | 0.31 | 0.33 | 1.12 (0.99–1.26) | 0.044 | 0.013 |
Indian | 0.33 | 0.37 | 1.20 (0.93–1.57) | 0.167 | 0.248 |
Combinedb | 1.14 (1.06–1.24) | 6 × 10−4 | 2 × 10−4 | ||
IGF2BP2 (rs4402960 G→T) | |||||
Chinese | 0.23 | 0.25 | 1.11 (0.98–1.26) | 0.103 | 0.263 |
Malay | 0.31 | 0.31 | 1.00 (0.89–1.12) | 0.97 | 0.741 |
Indian | 0.44 | 0.45 | 1.02 (0.81–1.3) | 0.849 | 0.941 |
Combinedb | 1.05 (0.97–1.13) | 0.26 | 0.226 | ||
SLC30A8 (rs13266634 C→T) | |||||
Chinese | 0.53 | 0.53 | 0.98 (0.88–1.09) | 0.734 | 0.512 |
Malay | 0.57 | 0.59 | 1.12 (1.01–1.25) | 0.037 | 0.023 |
Indian | 0.74 | 0.77 | 1.18 (0.88–1.58) | 0.269 | 0.316 |
Combinedb | 1.06 (0.98–1.13) | 0.145 | 0.039 | ||
KCNQ1 (rs2237982 C→T) | |||||
Chinese | 0.67 | 0.69 | 1.08 (0.96–1.22) | 0.183 | 0.075 |
Malay | 0.68 | 0.72 | 1.25 (1.11–1.42) | 2.5 × 10−4 | 3.7 × 10−5 |
Indianc | 0.94 | 0.98 | 2.50 (1.14–5.46) | 0.022 | 0.023 |
Combinedb | 1.16 (1.07–1.27) | 4.1 × 10−4 | 2 × 10−5 | ||
KCNQ1 (rs2237987 C→T) | |||||
Chinese | 0.65 | 0.69 | 1.21 (1.09–1.34) | 3 × 10−4 | 2 × 10−4 |
Malay | 0.68 | 0.71 | 1.19 (1.06–1.34) | 0.003 | 3.9 × 10−4 |
Indianc | 0.94 | 0.98 | 2.48 (1.22–5.04) | 0.012 | 0.019 |
Combinedb | 1.20 (1.11–1.30) | 3 × 10−6 | 1 × 10−7 | ||
PKN2 (rs6698181 C→T) | |||||
Chinese | 0.34 | 0.35 | 1.06 (0.96–1.17) | 0.221 | 0.387 |
Malay | 0.21 | 0.22 | 1.04 (0.92–1.18) | 0.552 | 0.900 |
Indian | 0.18 | 0.18 | 1.02 (0.76–1.38) | 0.873 | 0.815 |
Combinedb | 1.05 (0.98–1.13) | 0.183 | 0.535 | ||
LOC387761 (rs7480010 A→G) | |||||
Chinese | 0.22 | 0.21 | 0.94 (0.84–1.05) | 0.266 | 0.224 |
Malay | 0.28 | 0.27 | 0.98 (0.88–1.10) | 0.763 | 0.455 |
Indian | 0.44 | 0.45 | 1.04 (0.83–1.30) | 0.734 | 0.995 |
Combinedb | 0.97 (0.90–1.04) | 0.405 | 0.186 |
. | Risk allele frequency . | 95% CI . | P value . | P valuea . | |
---|---|---|---|---|---|
Control . | Case . | ||||
CDKAL1 (rs7756992 A→G) | |||||
Chinese | 0.45 | 0.50 | 1.19 (1.09–1.31) | 2 × 10−4 | 8 × 10−5 |
Malay | 0.44 | 0.44 | 1.03 (0.93–1.14) | 0.612 | 0.24 |
Indian | 0.22 | 0.28 | 1.39 (1.06–1.81) | 0.015 | 0.011 |
Combinedb | 1.13 (1.06–1.21) | 3 × 10−4 | 1 × 10−5 | ||
CDKN2A/B (rs10811661 T→C) | |||||
Chinese | 0.58 | 0.61 | 1.10 (0.99–1.23) | 0.074 | 0.304 |
Malay | 0.60 | 0.64 | 1.22 (1.09–1.36) | 3.7 × 10−4 | 4.4 × 10−5 |
Indian | 0.81 | 0.83 | 1.19 (0.87–1.63) | 0.282 | 0.482 |
Combinedb | 1.16 (1.08–1.25) | 9 × 10−5 | 3 × 10−5 | ||
HHEX (rs1111875 T→C) | |||||
Chinese | 0.29 | 0.33 | 1.15 (1.03–1.29) | 0.013 | 0.028 |
Malay | 0.31 | 0.33 | 1.12 (0.99–1.26) | 0.044 | 0.013 |
Indian | 0.33 | 0.37 | 1.20 (0.93–1.57) | 0.167 | 0.248 |
Combinedb | 1.14 (1.06–1.24) | 6 × 10−4 | 2 × 10−4 | ||
IGF2BP2 (rs4402960 G→T) | |||||
Chinese | 0.23 | 0.25 | 1.11 (0.98–1.26) | 0.103 | 0.263 |
Malay | 0.31 | 0.31 | 1.00 (0.89–1.12) | 0.97 | 0.741 |
Indian | 0.44 | 0.45 | 1.02 (0.81–1.3) | 0.849 | 0.941 |
Combinedb | 1.05 (0.97–1.13) | 0.26 | 0.226 | ||
SLC30A8 (rs13266634 C→T) | |||||
Chinese | 0.53 | 0.53 | 0.98 (0.88–1.09) | 0.734 | 0.512 |
Malay | 0.57 | 0.59 | 1.12 (1.01–1.25) | 0.037 | 0.023 |
Indian | 0.74 | 0.77 | 1.18 (0.88–1.58) | 0.269 | 0.316 |
Combinedb | 1.06 (0.98–1.13) | 0.145 | 0.039 | ||
KCNQ1 (rs2237982 C→T) | |||||
Chinese | 0.67 | 0.69 | 1.08 (0.96–1.22) | 0.183 | 0.075 |
Malay | 0.68 | 0.72 | 1.25 (1.11–1.42) | 2.5 × 10−4 | 3.7 × 10−5 |
Indianc | 0.94 | 0.98 | 2.50 (1.14–5.46) | 0.022 | 0.023 |
Combinedb | 1.16 (1.07–1.27) | 4.1 × 10−4 | 2 × 10−5 | ||
KCNQ1 (rs2237987 C→T) | |||||
Chinese | 0.65 | 0.69 | 1.21 (1.09–1.34) | 3 × 10−4 | 2 × 10−4 |
Malay | 0.68 | 0.71 | 1.19 (1.06–1.34) | 0.003 | 3.9 × 10−4 |
Indianc | 0.94 | 0.98 | 2.48 (1.22–5.04) | 0.012 | 0.019 |
Combinedb | 1.20 (1.11–1.30) | 3 × 10−6 | 1 × 10−7 | ||
PKN2 (rs6698181 C→T) | |||||
Chinese | 0.34 | 0.35 | 1.06 (0.96–1.17) | 0.221 | 0.387 |
Malay | 0.21 | 0.22 | 1.04 (0.92–1.18) | 0.552 | 0.900 |
Indian | 0.18 | 0.18 | 1.02 (0.76–1.38) | 0.873 | 0.815 |
Combinedb | 1.05 (0.98–1.13) | 0.183 | 0.535 | ||
LOC387761 (rs7480010 A→G) | |||||
Chinese | 0.22 | 0.21 | 0.94 (0.84–1.05) | 0.266 | 0.224 |
Malay | 0.28 | 0.27 | 0.98 (0.88–1.10) | 0.763 | 0.455 |
Indian | 0.44 | 0.45 | 1.04 (0.83–1.30) | 0.734 | 0.995 |
Combinedb | 0.97 (0.90–1.04) | 0.405 | 0.186 |
Number of subjects (control/case) were as follows: Chinese (2196/1541), Malay (2257/1076), and Asian-Indian (364/246). CI, Confidence interval.
Adjusted for gender and BMI.
Combined odds-ratio adjusted for ethnicity.
SNP not in HWE (P < 0.01), excluded from combined and metaanalysis.
. | Risk allele frequency . | 95% CI . | P value . | P valuea . | |
---|---|---|---|---|---|
Control . | Case . | ||||
CDKAL1 (rs7756992 A→G) | |||||
Chinese | 0.45 | 0.50 | 1.19 (1.09–1.31) | 2 × 10−4 | 8 × 10−5 |
Malay | 0.44 | 0.44 | 1.03 (0.93–1.14) | 0.612 | 0.24 |
Indian | 0.22 | 0.28 | 1.39 (1.06–1.81) | 0.015 | 0.011 |
Combinedb | 1.13 (1.06–1.21) | 3 × 10−4 | 1 × 10−5 | ||
CDKN2A/B (rs10811661 T→C) | |||||
Chinese | 0.58 | 0.61 | 1.10 (0.99–1.23) | 0.074 | 0.304 |
Malay | 0.60 | 0.64 | 1.22 (1.09–1.36) | 3.7 × 10−4 | 4.4 × 10−5 |
Indian | 0.81 | 0.83 | 1.19 (0.87–1.63) | 0.282 | 0.482 |
Combinedb | 1.16 (1.08–1.25) | 9 × 10−5 | 3 × 10−5 | ||
HHEX (rs1111875 T→C) | |||||
Chinese | 0.29 | 0.33 | 1.15 (1.03–1.29) | 0.013 | 0.028 |
Malay | 0.31 | 0.33 | 1.12 (0.99–1.26) | 0.044 | 0.013 |
Indian | 0.33 | 0.37 | 1.20 (0.93–1.57) | 0.167 | 0.248 |
Combinedb | 1.14 (1.06–1.24) | 6 × 10−4 | 2 × 10−4 | ||
IGF2BP2 (rs4402960 G→T) | |||||
Chinese | 0.23 | 0.25 | 1.11 (0.98–1.26) | 0.103 | 0.263 |
Malay | 0.31 | 0.31 | 1.00 (0.89–1.12) | 0.97 | 0.741 |
Indian | 0.44 | 0.45 | 1.02 (0.81–1.3) | 0.849 | 0.941 |
Combinedb | 1.05 (0.97–1.13) | 0.26 | 0.226 | ||
SLC30A8 (rs13266634 C→T) | |||||
Chinese | 0.53 | 0.53 | 0.98 (0.88–1.09) | 0.734 | 0.512 |
Malay | 0.57 | 0.59 | 1.12 (1.01–1.25) | 0.037 | 0.023 |
Indian | 0.74 | 0.77 | 1.18 (0.88–1.58) | 0.269 | 0.316 |
Combinedb | 1.06 (0.98–1.13) | 0.145 | 0.039 | ||
KCNQ1 (rs2237982 C→T) | |||||
Chinese | 0.67 | 0.69 | 1.08 (0.96–1.22) | 0.183 | 0.075 |
Malay | 0.68 | 0.72 | 1.25 (1.11–1.42) | 2.5 × 10−4 | 3.7 × 10−5 |
Indianc | 0.94 | 0.98 | 2.50 (1.14–5.46) | 0.022 | 0.023 |
Combinedb | 1.16 (1.07–1.27) | 4.1 × 10−4 | 2 × 10−5 | ||
KCNQ1 (rs2237987 C→T) | |||||
Chinese | 0.65 | 0.69 | 1.21 (1.09–1.34) | 3 × 10−4 | 2 × 10−4 |
Malay | 0.68 | 0.71 | 1.19 (1.06–1.34) | 0.003 | 3.9 × 10−4 |
Indianc | 0.94 | 0.98 | 2.48 (1.22–5.04) | 0.012 | 0.019 |
Combinedb | 1.20 (1.11–1.30) | 3 × 10−6 | 1 × 10−7 | ||
PKN2 (rs6698181 C→T) | |||||
Chinese | 0.34 | 0.35 | 1.06 (0.96–1.17) | 0.221 | 0.387 |
Malay | 0.21 | 0.22 | 1.04 (0.92–1.18) | 0.552 | 0.900 |
Indian | 0.18 | 0.18 | 1.02 (0.76–1.38) | 0.873 | 0.815 |
Combinedb | 1.05 (0.98–1.13) | 0.183 | 0.535 | ||
LOC387761 (rs7480010 A→G) | |||||
Chinese | 0.22 | 0.21 | 0.94 (0.84–1.05) | 0.266 | 0.224 |
Malay | 0.28 | 0.27 | 0.98 (0.88–1.10) | 0.763 | 0.455 |
Indian | 0.44 | 0.45 | 1.04 (0.83–1.30) | 0.734 | 0.995 |
Combinedb | 0.97 (0.90–1.04) | 0.405 | 0.186 |
. | Risk allele frequency . | 95% CI . | P value . | P valuea . | |
---|---|---|---|---|---|
Control . | Case . | ||||
CDKAL1 (rs7756992 A→G) | |||||
Chinese | 0.45 | 0.50 | 1.19 (1.09–1.31) | 2 × 10−4 | 8 × 10−5 |
Malay | 0.44 | 0.44 | 1.03 (0.93–1.14) | 0.612 | 0.24 |
Indian | 0.22 | 0.28 | 1.39 (1.06–1.81) | 0.015 | 0.011 |
Combinedb | 1.13 (1.06–1.21) | 3 × 10−4 | 1 × 10−5 | ||
CDKN2A/B (rs10811661 T→C) | |||||
Chinese | 0.58 | 0.61 | 1.10 (0.99–1.23) | 0.074 | 0.304 |
Malay | 0.60 | 0.64 | 1.22 (1.09–1.36) | 3.7 × 10−4 | 4.4 × 10−5 |
Indian | 0.81 | 0.83 | 1.19 (0.87–1.63) | 0.282 | 0.482 |
Combinedb | 1.16 (1.08–1.25) | 9 × 10−5 | 3 × 10−5 | ||
HHEX (rs1111875 T→C) | |||||
Chinese | 0.29 | 0.33 | 1.15 (1.03–1.29) | 0.013 | 0.028 |
Malay | 0.31 | 0.33 | 1.12 (0.99–1.26) | 0.044 | 0.013 |
Indian | 0.33 | 0.37 | 1.20 (0.93–1.57) | 0.167 | 0.248 |
Combinedb | 1.14 (1.06–1.24) | 6 × 10−4 | 2 × 10−4 | ||
IGF2BP2 (rs4402960 G→T) | |||||
Chinese | 0.23 | 0.25 | 1.11 (0.98–1.26) | 0.103 | 0.263 |
Malay | 0.31 | 0.31 | 1.00 (0.89–1.12) | 0.97 | 0.741 |
Indian | 0.44 | 0.45 | 1.02 (0.81–1.3) | 0.849 | 0.941 |
Combinedb | 1.05 (0.97–1.13) | 0.26 | 0.226 | ||
SLC30A8 (rs13266634 C→T) | |||||
Chinese | 0.53 | 0.53 | 0.98 (0.88–1.09) | 0.734 | 0.512 |
Malay | 0.57 | 0.59 | 1.12 (1.01–1.25) | 0.037 | 0.023 |
Indian | 0.74 | 0.77 | 1.18 (0.88–1.58) | 0.269 | 0.316 |
Combinedb | 1.06 (0.98–1.13) | 0.145 | 0.039 | ||
KCNQ1 (rs2237982 C→T) | |||||
Chinese | 0.67 | 0.69 | 1.08 (0.96–1.22) | 0.183 | 0.075 |
Malay | 0.68 | 0.72 | 1.25 (1.11–1.42) | 2.5 × 10−4 | 3.7 × 10−5 |
Indianc | 0.94 | 0.98 | 2.50 (1.14–5.46) | 0.022 | 0.023 |
Combinedb | 1.16 (1.07–1.27) | 4.1 × 10−4 | 2 × 10−5 | ||
KCNQ1 (rs2237987 C→T) | |||||
Chinese | 0.65 | 0.69 | 1.21 (1.09–1.34) | 3 × 10−4 | 2 × 10−4 |
Malay | 0.68 | 0.71 | 1.19 (1.06–1.34) | 0.003 | 3.9 × 10−4 |
Indianc | 0.94 | 0.98 | 2.48 (1.22–5.04) | 0.012 | 0.019 |
Combinedb | 1.20 (1.11–1.30) | 3 × 10−6 | 1 × 10−7 | ||
PKN2 (rs6698181 C→T) | |||||
Chinese | 0.34 | 0.35 | 1.06 (0.96–1.17) | 0.221 | 0.387 |
Malay | 0.21 | 0.22 | 1.04 (0.92–1.18) | 0.552 | 0.900 |
Indian | 0.18 | 0.18 | 1.02 (0.76–1.38) | 0.873 | 0.815 |
Combinedb | 1.05 (0.98–1.13) | 0.183 | 0.535 | ||
LOC387761 (rs7480010 A→G) | |||||
Chinese | 0.22 | 0.21 | 0.94 (0.84–1.05) | 0.266 | 0.224 |
Malay | 0.28 | 0.27 | 0.98 (0.88–1.10) | 0.763 | 0.455 |
Indian | 0.44 | 0.45 | 1.04 (0.83–1.30) | 0.734 | 0.995 |
Combinedb | 0.97 (0.90–1.04) | 0.405 | 0.186 |
Number of subjects (control/case) were as follows: Chinese (2196/1541), Malay (2257/1076), and Asian-Indian (364/246). CI, Confidence interval.
Adjusted for gender and BMI.
Combined odds-ratio adjusted for ethnicity.
SNP not in HWE (P < 0.01), excluded from combined and metaanalysis.
The joint-effect analysis of the six SNPs showed a significant increase in the risk of T2DM with an increase in the number of risk alleles for the combined sample of Chinese, Malay, and Asian-Indian subjects from Singapore. Compared with subjects carrying zero to three risk alleles (8.3% of study population), each additional risk allele on average conferred a 14% increase in the odds of T2DM (supplemental Fig. 1).
Figure 1 illustrates the metaanalysis of risk estimates for six of the loci (CDKAL1, CDKN2A/B, HHEX, IGF2BP2, SLC30A8, and KCNQ1), using data from published studies in East Asia, including Chinese populations from China (9, 20–23) and Hong Kong (10) as well as Korean (7, 10, 24) and Japanese (6, 7, 25, 26) populations. In essence, the metaanalysis showed that these six diabetes susceptibility loci identified through GWAS are associated with T2DM in populations across Asia.
Discussion
This study reports on the contribution and importance of diabetes-susceptibility loci, identified through GWAS, in the three major ethnic groups in Asia and is the first study among ethnic Malays. We had previously reported that variants in the FTO gene were associated with BMI and contributed toward risk of T2DM in the Singapore population (18). To expand on this, in the present study, we have examined nine diabetes-susceptibility loci in Chinese, Malays, and Asian-Indians living in Singapore. In the combined analysis of the three ethnic groups, we found statistically significant associations with SNPs at five loci (CDKAL1, CDKN2A/B, HHEX, SLC30A8, and KCNQ1), demonstrating that these SNPs are also relevant in Asian populations, despite their allele frequencies differing from those observed in populations of European ancestry. In addition, the effects of these diabetes susceptibility loci appear additive, with subjects who carry nine or more risk alleles having 2.45 times the risk of T2DM compared with subjects with zero to three risk alleles (supplemental Fig. 1).
We also found that the effect estimates for IGF2BP2 were in the same direction as previously reported in other populations (1, 2, 4, 10). Although the association did not reach statistical significance in the present study, when metaanalysis was performed with other East Asian populations, the effect was as observed in populations of European ancestry and statistically significant. The failure to replicate the association with T2DM for all the susceptibility SNPs examined seems to be commonplace when replication studies are carried out in different populations and ethnic groups. It has been suggested that differences in the patterns of linkage disequilibrium between these SNPs and functional variants at these loci could underlie these disparate findings. Alternatively, gene-environment interactions may operate in the pathogenesis of T2DM and that differences in the level of environmental risk factors in different populations may alter the impact of susceptibility loci on the risk of T2DM. For example, Andreasen et al. (27) reported that physical activity attenuated the effects of the FTO variants on obesity. However, when we combined our data with those in other Asian populations, adding 4817 controls and 2863 cases to the existing literature, our metaanalysis suggests that the effects of the polymorphisms are similar to those in populations of European descent, in line with findings from a recent Japanese metaanalysis (8). These findings demonstrate that the importance of these polymorphisms is similar in Asians as it is in populations of European descent and that limitation in statistical power, to detect variants which confer a modest risk for T2DM, may underlie previous failure of replication studies. This is not to say that heterogeneity of effect does not exist between populations. In fact, significant heterogeneity of effect was observed between populations with I2 of up to 76%. However, our findings suggest that this heterogeneity, whether due to differences in the pattern of linkage disequilibrium or to gene-environment interactions, may be subtle compared with the main effects of these loci that have been observed thus far.
One caveat of this study is the smaller sample sizes for the Asian-Indians, which certainly decreased study power. Based on a risk allele frequency of 0.3, power calculations estimate that the Asian-Indian samples provided only 35–45% power to detect an OR of 1.2. In contrast, the larger sample sizes available in the Chinese and Malays provided 85–90% power. However, despite the Chinese and Malay samples being sufficiently powered to detect the effect estimates previously reported (1, 2, 5), we did not detect significant association between SNPs at PKN2 and LOC387761 with T2DM. With the exception of some of the initial GWAS, the association with these SNPs have largely been negative (4, 28, 29); taken together with our findings, this could suggest that the effects at these loci may be population specific or possibly false positives. Another limitation of this study is that we were not able to examine and correct for population stratification. One way to determine the presence of population stratification is to examine for differences between cases and controls through principal-components analyses of a large number of SNPs neutral to the disease of interest as is typically performed in GWAS. Unfortunately these data are not available for the samples in this paper. Nevertheless, we suggest that the consistency between our findings and other studies argues against population stratification as a source of confounding.
In conclusion, by studying populations of Chinese, Malays, and Asian-Indians in Singapore and performing a metaanalysis with other East Asian populations, we have found that common variants associated with T2DM, identified in populations of European ancestry, are also associated in East Asians. Thus, failure to detect these effects across different populations may be due to issues of power owing to limited sample size, lower minor allele frequency, or differences in genetic effect sizes. Several groups worldwide are currently undertaking resequencing studies to identify causative variants at these loci. Our findings suggest that examination of multiple ethnic groups may allow us to exploit differences in the patterns of linkage disequilibrium between ethnic groups to refine the genomic region of interest and aid in this effort.
Acknowledgments
Financial support for this work was provided by the Singapore National Medical Research Council (NMRC/1115/2007) and the Singapore Biomedical Research Council (05/1/36/19/413).
Disclosure Summary: All authors have nothing to declare.
M.S. and E.S.T. contributed equally to this study.
Abbreviations:
- BMI
Body mass index
- GWAS
genome-wide association studies
- HbA1c
glycated hemoglobin
- 2HPG
2-h postchallenge glucose
- HWE
Hardy-Weinberg equilibrium
- IFG
Impaired fasting glucose
- IGT
impaired glucose tolerance
- NHS98
1998 Singapore National Health Survey
- OR
odds ratio
- SDCS
Singapore Diabetes Cohort Study
- SiMES
Singapore Malay Eye Study
- SNP
single-nucleotide polymorphism
- T2DM
type 2 diabetes mellitus