Cited by 152Genetic Associations and EpidemiologyGenetic Mapping and Diversity in Plants and AnimalsDiabetes and associated disorders

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Transferability of Type 2 Diabetes Implicated Loci in Multi-Ethnic Cohorts from Southeast Asia

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Sim, Xueling, Ong, Rick Twee-Hee, Suo, Chen, Tay, Wan-Ting, Liu, Jianjun, Ng, Daniel Peng-Keat, Boehnke, Michael, Chia, Kee-Seng, Wong, Tien-Yin, Seielstad, Mark, Teo, Yik-Ying, Tai, E-Shyong·2011·PLoS Genetics

DOI 10.1371/journal.pgen.1001363PMC PMC3072366

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Abstract

Recent large genome-wide association studies (GWAS) have identified multiple loci which harbor genetic variants associated with type 2 diabetes mellitus (T2D), many of which encode proteins not previously suspected to be involved in the pathogenesis of T2D. Most GWAS for T2D have focused on populations of European descent, and GWAS conducted in other populations with different ancestry offer a unique opportunity to study the genetic architecture of T2D. We performed genome-wide association scans for T2D in 3,955 Chinese (2,010 cases, 1,945 controls), 2,034 Malays (794 cases, 1,240 controls), and 2,146 Asian Indians (977 cases, 1,169 controls). In addition to the search for novel variants implicated in T2D, these multi-ethnic cohorts serve to assess the transferability and relevance of the previous findings from European descent populations in the three major ethnic populations of Asia, comprising half of the world's population. Of the SNPs associated with T2D in previous GWAS, only variants at CDKAL1 and HHEX/IDE/KIF11 showed the strongest association with T2D in the meta-analysis including all three ethnic groups. However, consistent direction of effect was observed for many of the other SNPs in our study and in those carried out in European populations. Close examination of the associations at both the CDKAL1 and HHEX/IDE/KIF11 loci provided some evidence of locus and allelic heterogeneity in relation to the associations with T2D. We also detected variation in linkage disequilibrium between populations for most of these loci that have been previously identified. These factors, combined with limited statistical power, may contribute to the failure to detect associations across populations of diverse ethnicity. These findings highlight the value of surveying across diverse racial/ethnic groups towards the fine-mapping efforts for the casual variants and also of the search for variants, which may be population-specific.

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Figure 1 Bivariate plots comparing odds ratios observed in each of the ethnic groups with odd ratios established in populations of European ancestry. (A) Chinese, (B) Malays, (C) Indians, (D) Combined meta-analysis. Each SNP is plotted with a colour that indicates if the SNP was identified through candidate gene studies (black) or linkage studies (red) or candidate-pathway analysis (green) or T2D genome-wide scans (blue).CC BY · PubMed Central

Figure 2 Regional association plots of the index SNP in CDKAL1. For each ethnic group, the univariate analysis regional plot, A) Chinese B) Malays C) Indians, is shown together with analysis conditioned on established index SNP rs7754840, D) Chinese E) Malays F) Indians, in populations of Caucasian ancestry. In each panel, the index SNP is represented with a purple diamond and surrounding SNPs coloured based on their r2 with the index SNP. Estimated recombination rates reflect the local LD structure in the 500kb buffer around the index SNP and the proxies are plotted on Hapmap values from the Hapmap JPT+CHB. Data for gene annotations are obtained from the RefSeq track of the UCSC Gene Browser (See LocusZoom http://csg.sph.umich.edu/locuszoom/ for more details).CC BY · PubMed Central

Figure 3 Regional association plots of the index SNP in HHEXX/IDE/KIF11. For each ethnic group, the univariate analysis regional plot, A) Chinese B) Malays C) Indians, is shown together with analysis conditioned on the index SNP rs6583826, D) Chinese E) Malays, F) Indians, found in meta-analysis across three ethnic groups and established index SNP rs1111875, G) Chinese H) Malays I) Indians, in populations of European ancestry.CC BY · PubMed Central

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BackgroundInfluential
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“We selected SNPs from T2D risk loci with prior evidence of association (p < 5 × 10−8) in GWAS of European, African American and other ethnicities (Steinthorsdottir et al. 2007; Diabetes Genetics Initiative of Broad Institute of Harvard and MIT, Lund University, and Novartis Institutes of BioMedical Research et al. 200…”
Method
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Background
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Background
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“Twelve SNPs in CDKAL1, including the three SNPs examined in the present study, have been associated with T2D [rs4712523 (23); rs4712524 (13); rs10440833 (24); rs6931514 (25); rs9465871 (26); rs9295474 (27); rs9356744, rs7773318, rs9465994 (28)].”
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Background
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“after first round of quality control [52] and removal of monomorphic SNPs.”
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Background
Dissection of type 2 diabetes: a genetic perspective
Bonnefond, A., Florez, J. C., Loos, R. J. F., & Froguel, P. (2025) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(24)00339-5 · Cited by 44
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“When the linkage disequilibrium structure in the region differs across populations this may result in decreased power and lack of replication [74–77].”
Background
Investigation on Cardiovascular Risk Prediction Using Genetic Information
Pu, L., Zhao, Z., & Zhang, Y. (2012) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2012.2205009 · Cited by 37
Does genetic heterogeneity account for the divergent risk of type 2 diabetes in South Asian and white European populations?
Sohani, Z. N., Deng, W. Q., Paré, G., Meyre, D., Gerstein, H. C., & Anand, S. S. (2014) · Diabetologia · doi:10.1007/s00125-014-3354-1 · Cited by 35
“Four GWAS have been conducted in South Asians [14–17], which collectively identified nine SNPs ( p<5×10).”
Influential
SgD-CNV, a database for common and rare copy number variants in three Asian populations
Xu, H., Poh, W.-T., Sim, X., Ong, R. T., Suo, C., Tay, W.-T., Khor, C. C., Seielstad, M. T., Liu, J., Aung, T., et al. (2011) · Human Mutation · doi:10.1002/humu.21601 · Cited by 34
Self-citation
Association Analysis of Genetic Variants with Type 2 Diabetes in a Mongolian Population in China
Bai, H., Liu, H., Suyalatu, S., Guo, X., Chu, S., Chen, Y., Lan, T., Borjigin, B., Orlov, Y. L., Posukh, O. L., et al. (2015) · Journal of Diabetes Research · doi:10.1155/2015/613236 · Cited by 33
“SNPs found to be associated with T2D in Asian populations, rs1048886 (C6orf57) [5], rs4402960 (IGF2BP2) [28], rs5015480 (HHEX, IDE), rs1359790 (SPRY2) [29], rs1552224 (CENTD2), rs3923113 (GRB14), rs5215 (KCNJ11), rs7903146 (TCF7L2) [6], rs10886471 (GRK5), and rs7403531 (RASGRP1) [30], were not replicated in our study.”
Background
Association of melatonin & MTNR1B variants with type 2 diabetes in Gujarat population
Patel, R., RATHWA, N. N., Palit, S. P., Ramachandran, A. V., & Begum, R. (2018) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2018.04.058 · Cited by 32
Trans-ethnic fine mapping identifies a novel independent locus at the 3′ end of CDKAL1 and novel variants of several susceptibility loci for type 2 diabetes in a Han Chinese population
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“05, which followed the αs used in an earlier trans-ethnic study of type 2 diabetes mellitus [11].”
BackgroundMethodResult
Genetic variants of tumor necrosis factor-α and its levels: A correlation with dyslipidemia and type 2 diabetes susceptibility
Patel, R., Palit, S. P., RATHWA, N. N., Ramachandran, A. V., & Begum, R. (2018) · Clinical Nutrition · doi:10.1016/j.clnu.2018.06.962 · Cited by 30
Genetic Risk Factors for Type 2 Diabetes: Insights from the Emerging Genomic Evidence
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A Common Susceptibility Gene for Type 2 Diabetes Is Associated with Drug Response to a DPP-4 Inhibitor: Pharmacogenomic Cohort in Okinawa Japan
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“Both of these pathways may be hampered in carriers of CDKAL1 risk variants, in which actually impaired insulin secretion is reported [18][19][8][20].”
Background
The combined effect of the T2DM susceptibility genes is an important risk factor for T2DM in non-obese Japanese: a population based case-control study
Yamakawa‐Kobayashi, K., Natsume, M., Aoki, S., Nakano, S., Inamori, T., Kasezawa, N., & Goda, T. (2012) · BMC Medical Genetics · doi:10.1186/1471-2350-13-11 · Cited by 27
“Furthermore, additional GWAS for Japanese and Chinese populations were performed and new susceptibility loci were detected [24-28], some of which have also been confirmed in Caucasians as well as Asian populations [24,25,29].”
Background
Association of Type 2 Diabetes Susceptibility Loci With One‐Year Weight Loss in the Look AHEAD Clinical Trial
Peter, I., McCaffery, J. M., Kelley-Hedgepeth, A., Hakonarson, H. H., Reís, S. E., Wagenknecht, L. E., Kopin, A. S., Huggins, G. S., & Study, F. T. G. S. O. T. L. A. (2012) · Obesity · doi:10.1038/oby.2012.11 · Cited by 27
Replication of newly identified type 2 diabetes susceptible loci in Northwest Indian population
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Associations with metabolites in Chinese suggest new metabolic roles in Alzheimer’s and Parkinson’s diseases
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Self-citation
Interactome-transcriptome analysis discovers signatures complementary to GWAS Loci of Type 2 Diabetes
Li, J., Lee, H. M., Wang, Y., Tong, A. H.-Y., Yip, K. Y.-L., Tsui, S. K., Lok, S., Ozaki, R., Luk, A. O. Y., Kong, A. P. S., et al. (2016) · Scientific Reports · doi:10.1038/srep35228 · Cited by 24
“Asian GWAS datasets, we noted that rs7636 , a synonymous SNP within Acetylcholinesterase ( AChE ), exhibited genome-wide significance for T2D in Asian but not Caucasian populations with an odd ratios of 1.85 (95% CI: 1.42–2.41, p : 5.00E-6) 39 .”
Influential
Abdominal obesity and incident cardio-metabolic disorders in Asian-Indians: A 10-years prospective cohort study
Jayant, S. S., Gupta, R., Rastogi, A., Agrawal, K., Sachdeva, N. K., Ram, S., Dutta, P. R., Bhadada, S. K., & Bhansali, A. K. (2022) · Diabetes & Metabolic Syndrome Clinical Research & Reviews · doi:10.1016/j.dsx.2022.102418 · Cited by 23
Common and Distinct Genetic Architecture of Age at Diagnosis of Diabetes in South Indian and European Populations
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Transferability and fine-mapping of glucose and insulin quantitative trait loci across populations: CARe, the Candidate Gene Association Resource
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Relevance of the dietary glycemic index, glycemic load and genetic predisposition for the glucose homeostasis of Chinese adults without diabetes
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Comparative Genome of GK and Wistar Rats Reveals Genetic Basis of Type 2 Diabetes
Liu, T., Li, H., Ding, G., Wang, Z., Chen, Y., Liu, L., Li, Y., & Li, Y. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141859 · Cited by 25
Transancestral fine-mapping of four type 2 diabetes susceptibility loci highlights potential causal regulatory mechanisms
Horikoshi, M., Pasquali, L., Wiltshire, S., Huyghe, J. R., Mahajan, A., Asimit, J. L., Ferreira, T. C., Locke, A. E., Robertson, N. R., Wang, X., et al. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw048 · Cited by 23
Self-citation
Genetic Approaches to the Study of Gene Variants and Their Impact on the Pathophysiology of Type 2 Diabetes
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Genetic Information and the Prediction of Incident Type 2 Diabetes in a High-Risk Multiethnic Population
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“Other investigators have evaluated T2DM genetic associations across multiple ethnicities with conflicting results, possibly attributable to limited statistical power, allelic heterogeneity, or linkage disequilibrium differences (20,21).”
The E3 ubiquitin ligase MARCH1 regulates glucose-tolerance and lipid storage in a sex-specific manner
Bhagwandin, C., Ashbeck, E. L., Whalen, M. B., Bandola-Simon, J. M., Roche, P. A., Szajman, A., Truong, S. M., Wertheim, B. C., Klimentidis, Y. C., Ishido, S., et al. (2018) · PLoS ONE · doi:10.1371/journal.pone.0204898 · Cited by 23
Efficiency of trans-ethnic genome-wide meta-analysis and fine-mapping
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Self-citation
Predictive utility of a genetic risk score of common variants associated with type 2 diabetes in a black South African population
Chikowore, T., van Zyl, T., Feskens, E. J. M., & Conradie, K. R. (2016) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2016.09.019 · Cited by 19
CDKAL1 and HHEX are associated with type 2 diabetes‐related traits among Y up'ik people (在尤皮克人群中CDKAL1和HHEX与2型糖尿病相关特征有关)
Klimentidis, Y. C., Lemas, D. J., Wiener, H. W., O’Brien, D. M., Havel, P. J., Stanhope, K. L., Hopkins, S. E., Tiwari, H. K., & Boyer, B. B. (2013) · Journal of Diabetes · doi:10.1111/1753-0407.12093 · Cited by 19
“Both CDKAL1 and HHEX variants were strongly associated with T2D and insulin secretion in several south-east Asian cohorts and among Pima Indians, respectively.(47,48) Our finding that the risk allele for the HHEX SNP as identified in GWAS corresponds to the “protective” allele in this sample is not uncommon in genetic…”
Background
Studying the Genetics of Complex Disease With Ancestry‐Specific Human Phenotype Networks: The Case of Type 2 Diabetes in East Asian Populations
Qiu, J., Moore, J. H., & Darabos, C. (2016) · Genetic Epidemiology · doi:10.1002/gepi.21964 · Cited by 18
“E-mail: JHMoore@upenn.edu developing countries and 20% in developed countries by 2030 [Shaw et al., 2010; Zhao et al., 2012].”
Background
Dissecting the Brain/Islet Axis in Metabesity
Fuente-Martín, E., Mellado‐Gil, J. M., Cobo‐Vuilleumier, N., Martín‐Montalvo, A., Romero‐Zerbo, S., Contreras, I. D., Hmadcha, A., Soria, B., Bermudo, F. M., Reyes, J. C., et al. (2019) · Genes · doi:10.3390/genes10050350 · Cited by 17
Hints of Unique Genetic Effects for Type 2 Diabetes in India
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A statistical method for region-based meta-analysis of genome-wide association studies in genetically diverse populations
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Self-citation
Ribosomal biogenesis regulator DIMT1 controls β-cell protein synthesis, mitochondrial function, and insulin secretion
Verma, G., Bowen, A., Gheibi, S., Hamilton, A., Muthukumar, S., Cataldo, L. R., Asplund, O., Esguerra, J. L. S., Karagiannopoulos, A., Lyons, C., et al. (2022) · Journal of Biological Chemistry · doi:10.1016/j.jbc.2022.101692 · Cited by 17
Genetic Determinants of Type 2 Diabetes in Asians
Qi, Q., Wang, X., Strizich, G., & Wang, T. (2015) · International Journal of Diabetology & Vascular Disease Research · doi:10.19070/2328-353x-si01001 · Cited by 19
“In addition, a GWAS of T2D in multi-ethnic cohorts from Southeast Asia (including Chinese, Malays and Asian Indians) observed consistent direction of effect for many T2D SNPs identified in European populations [85].”
Background
Joint Effects of Known Type 2 Diabetes Susceptibility Loci in Genome-Wide Association Study of Singapore Chinese: The Singapore Chinese Health Study
Chen, Z., Pereira, M. A., Seielstad, M. T., Koh, W., Tai, E. S., Teo, Y.-Y., Liu, J., Hsu, C., Wang, R., Odegaard, A., et al. (2014) · PLoS ONE · doi:10.1371/journal.pone.0087762 · Cited by 15
“Since 2007, genomewide association studies (GWAS) have reported at least 57 additional thoroughly replicated genetic susceptibility loci harboring common variants for T2D [38–57].”
BackgroundSelf-citation
Type 2 Diabetes Risk Allele UBE2E2 Is Associated With Decreased Glucose-Stimulated Insulin Release in Elderly Chinese Han Individuals
Xu, K., Jiang, L., Zhang, M., Zheng, X., Gu, Y., Wang, Z., Cai, Y., Dai, H., Shi, Y., Zheng, S., et al. (2016) · Medicine · doi:10.1097/md.0000000000003604 · Cited by 15
Genome-wide meta-analysis associates GPSM1 with type 2 diabetes, a plausible gene involved in skeletal muscle function
Ding, Q., Tan, A. L. M., Parra, E. J., Cruz, M., Sim, X., Teo, Y., Long, J., AlSafar, H. S., Petretto, E. G., Tai, E. S., et al. (2020) · Journal of Human Genetics · doi:10.1038/s10038-019-0720-3 · Cited by 14
“…0.586 Mexico City 2 2016 Mexico Mexican 140 448 306 166 415 306 0.592 0.578 Scott et al. 2007 Finland European 41 808 311 36 854 283 0.616 0.605 Sim et al. 2011 China Asian 190 864 907 255 852 833 0.666 0.647 correlation with serum glucose and insulin level across obese mice samples (r= 0.77…”
MethodInfluentialSelf-citation
The New Perspectives on Genetic Studies of Type 2 Diabetes and Thyroid Diseases
Xu, M., Bi, Y., Cui, B., Hong, J., Wang, W., & Ning, G. (2013) · Current Genomics · doi:10.2174/138920213804999138 · Cited by 14
“Since the first GWAS, the number of susceptibility loci for T2D has grown up to more than 50 (Table 1) [22-45].”
Background
Common Variants Associated with Type 2 Diabetes in a Black South African Population of Setswana Descent: African Populations Diverge
Chikowore, T., Conradie, K. R., Towers, G. W., & van Zyl, T. (2015) · OMICS A Journal of Integrative Biology · doi:10.1089/omi.2015.0116 · Cited by 13
Improved detection of genetic loci in estimated glomerular filtration rate and type 2 diabetes using a pleiotropic cFDR method
Liu, H., He, J., Zhang, Q., Lv, W., Xia, X., Sun, C., Zhang, W., & Deng, H. (2017) · Molecular Genetics and Genomics · doi:10.1007/s00438-017-1381-6 · Cited by 13
“Another subset of 13 SNPs had not been reported to be associated with T2D but their annotated genes were mentioned as T2D associated in previous studies (details in the Supplementary Table S6) (Sim et al. 2011; Voight et al. 2011; Mahajan et al. 2014; Cook and Morris 2016).”
Admixture mapping and fine-mapping of type 2 diabetes susceptibility loci in African American women
Uribe‐Salazar, J. M., Palmer, J. R., Haddad, S. A., Rosenberg, L. A., & Ruiz-Narváez, E. (2018) · Journal of Human Genetics · doi:10.1038/s10038-018-0503-2 · Cited by 14
Genetic Associations of Type 2 Diabetes with Islet Amyloid Polypeptide Processing and Degrading Pathways in Asian Populations
Lam, V. K. L., Ronald, C. W., Lee, H. M., Hu, C., Park, K. S., Furuta, H., Wang, Y., Tam, C. H. T., Sim, X., Ng, D. P. K., et al. (2013) · PLoS ONE · doi:10.1371/journal.pone.0062378 · Cited by 11
“Notwithstanding some heterogeneity of effect sizes possibly due to differences in population-specific LD architecture, allele frequency [18] and factors such as lifestyle and environment [22], rs1583645 of CPE and rs6583813 of IDE showed consistent associations with T2D on meta-analysis of multiple East Asian cohorts.”
BackgroundResultSelf-citation
CAPN10 SNP43 G>A gene polymorphism and type 2 diabetes mellitus in the Asian population: a meta-analysis of 9353 participants
Li, Y., Gong, G., Geng, H., Yang, Z., Zhou, C., Xu, J., Qian, Y., Wang, X., & Chen, A. (2014) · Endocrine Journal · doi:10.1507/endocrj.ej14-0297 · Cited by 12
Contiguous mutation syndrome in the era of high‐throughput sequencing
Langouët, M., Siquier-Pernet, K., Sanquer, S., Bôle‐Feysot, C., Nitschké, P., Boddaert, N., Münnich, A., Mancini, G. M. S., Barouki, R., Amiel, J., et al. (2015) · Molecular Genetics & Genomic Medicine · doi:10.1002/mgg3.134 · Cited by 12
“associated with obesity in Chinese population (Sell et al. 1998; Li et al. 2003; Sim et al. 2011; Huang et al. 2012; Sanghera and Blackett 2012; Zhu et al. 2012).”
Common and rare exonic MUC5B variants associated with type 2 diabetes in Han Chinese
Chen, G., Zhang, Z., Adebamowo, S. N., Liu, G., Adeyemo, A., Zhou, Y., Doumatey, A. P., Wang, C., Zhou, J., Yan, W., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0173784 · Cited by 11
Risk Prediction Using Genome-Wide Association Studies on Type 2 Diabetes
Choi, S., Bae, S., & Park, T. (2016) · Genomics & Informatics · doi:10.5808/gi.2016.14.4.138 · Cited by 12
“Some genes (AGR3, C2CD4B, C6orf57, CAMK1D, DNER, IGF2BP2, KCNJ11, KCNQ1, NXN, PLS1, and RGS7) were previously reported to be associated with T2D [44-53].”
Background
Genetic Architecture of Type 2 Diabetes
Prasad, R. B., & Groop, L. (2016) · Wiley eBooks · doi:10.1002/9781118924853.ch14 · Cited by 12
"Diabetes Associated Genes from the Dark Matter of the Human Proteome"
Narayanan, R. (2014) · MOJ Proteomics & Bioinformatics · doi:10.15406/mojpb.2014.01.00020 · Cited by 10
“The ORFs C6orf27 and C6orf47 were associated exclusively with type 1 diabetes [58-60], whereas ORF C6orf57 was associated with type 2 diabetes and CD40 ligand [61,62].”
Background
Direct‐to‐consumer genetic testing in China and its role in GWAS discovery and replication
Kang, K., Sun, X., Wang, L., Yao, X., Tang, S., Deng, J., Wu, X., Yang, C., & Chen, G. (2020) · Quantitative Biology · doi:10.1007/s40484-020-0209-2 · Cited by 11
Genome-Wide Association for HbA1c in Malay Identified Deletion on SLC4A1 that Influences HbA1c Independent of Glycemia
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Self-citation
GENOME-WIDE INTERACTION WITH SELECTED TYPE 2 DIABETES LOCI REVEALS NOVEL LOCI FOR TYPE 2 DIABETES IN AFRICAN AMERICANS
Keaton, J. M., Hellwege, J. N., Ng, M. C. Y., Palmer, N. D., Pankow, J. S., Fornage, M., Wilson, J. G., Correa, A., Rasmussen‐Torvik, L. J., Rotter, J. I., et al. (2016) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813207813_0024 · Cited by 10
Pharmacogenomics of Sulfonylureas Response in Relation to rs7754840 Polymorphisms in Cyclin-Dependent Kinase 5 Regulatory Subunit-associated Protein 1-like (CDKAL1) Gene in Iranian Type 2 Diabetes Patients
Soltani, G., Hatefi, Z., Salehi, A. R., Khosravi, S., Ghiasi, M. R., Teke, K., Aminorroaya, A., & Salehi, R. (2018) · Advanced Biomedical Research · doi:10.4103/abr.abr_144_17 · Cited by 10
“[19-22] Carrying risk variants of CDKAL1 may attenuate normal functioning of CDKAL1 resulting in impaired insulin secretion.[16,23] Many studies have shown that rs7754840 in CDKAL1 gene is associated with risk of type 2 diabetes.”
Background
Identification of susceptible genes for complex chronic diseases based on disease risk functional SNPs and interaction networks
Wan, L., Zhu, L., Huang, H., He, Y., Lv, J., Li, W., Chen, L., & He, W. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.09.006 · Cited by 10
The high mobility group protein HMG20A cooperates with the histone reader PHF14 to modulate TGFβ and Hippo pathways
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Characterizing the genetic risk for Type 2 diabetes in a Malaysian multi‐ethnic cohort
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Zhang, S., Xiao, J., Ren, Q., Han, X., Tang, Y. C., Yang, W., Zhou, X., & Ji, L. (2013) · Journal of Diabetes Investigation · doi:10.1111/jdi.12145 · Cited by 9

Authors: Sim, X., Ong, R. T.-H., Suo, C., Tay, W.-T., Liu, J., Ng, D. P.-K., Boehnke, M., Chia, K.-S., Wong, T.-Y., Seielstad, M., Teo, Y.-Y., & Tai, E.-S.
Affiliations: National University of Singapore (SG)
Agency for Science, Technology and Research (SG)
Singapore National Eye Center (SG)
University of Michigan (US)
The University of Melbourne (AU)
Corresponding author: Teo, Yik-Ying
Tai, E-Shyong
ORCID: Sim, Xueling
Ong, Rick Twee-Hee
Suo, Chen
Liu, Jianjun
Boehnke, Michael
Chia, Kee-Seng
Wong, Tien-Yin
Seielstad, Mark
Teo, Yik-Ying
Tai, E-Shyong
Venue: PLoS Genetics
Publisher: Public Library of Science (PLoS)
Published: 2011-04-07
Volume: 7
Issue: 4
Pages: e1001363
ISSN: 1553-7390 (ISSN-L)
1553-7404
DOI: 10.1371/journal.pgen.1001363
PMC: PMC3072366
OpenAlex: W2025086473
Cited by: 152
References: 67
Citations by year: 2026: 2 · 2025: 3 · 2024: 4 · 2023: 3 · 2022: 6 · 2021: 9 · 2020: 9 · 2019: 5 · 2018: 14 · 2017: 9 · 2016: 15 · 2015: 12 · 2014: 19 · 2013: 19 · 2012: 17
FWCI: 12.17
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
Locations: PLoS Genetics (publishedVersion, cc-by)
Europe PMC (PubMed Central) (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
National University of Singapore (submittedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
eScholarship (California Digital Library) (submittedVersion)
eScholarship (California Digital Library) (submittedVersion, cc-by)
Figshare (submittedVersion, cc-by)
Minerva Access (University of Melbourne) (submittedVersion, cc-by)
Funders: National Research Foundation (03/1/27/18/216)
National University of Singapore
National Research Foundation Singapore (NRF-RF-2010-05)
Genome Institute of Singapore
Medical Research Council
National Medical Research Council (09/1/35/19/616, NMRC/STaR/0003/2008, 03/1/27/18/216, 08/1/35/19/550, 0796/2003, STAR/0003/2008)
Biomedical Research Council (08/1/35/19/550, 03/1/27/18/216, 0796/2003, 09/1/35/19/616)
National Human Genome Research Institute (R56HG000376, R01HG000376)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Mapping and Diversity in Plants and Animals
Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
Keywords: type 2 diabetes, genetic susceptibility loci, multi-ethnic cohort, Chinese, Malay language, Asian Indians, CDKAL1, population-specific variants, allelic heterogeneity, linkage disequilibrium, fine mapping, genetic architecture
MeSH terms: Adult, Aged, Asia, Southeastern, Diabetes Mellitus, Type 2, Female, Humans, Male, Middle Aged, tRNA Methyltransferases, Linkage Disequilibrium, Case-Control Studies, Kinesins, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Cyclin-Dependent Kinase 5, Genome-Wide Association Study, Genetic Loci

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Transferability of Type 2 Diabetes Implicated Loci in Multi-Ethnic Cohorts from Southeast Asia

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