Abstract
We conducted a three-stage genetic study to identify susceptibility loci for type 2 diabetes (T2D) in east Asian populations. We followed our stage 1 meta-analysis of eight T2D genome-wide association studies (6,952 cases with T2D and 11,865 controls) with a stage 2 in silico replication analysis (5,843 cases and 4,574 controls) and a stage 3 de novo replication analysis (12,284 cases and 13,172 controls). The combined analysis identified eight new T2D loci reaching genome-wide significance, which mapped in or near GLIS3, PEPD, FITM2-R3HDML-HNF4A, KCNK16, MAEA, GCC1-PAX4, PSMD6 and ZFAND3. GLIS3, which is involved in pancreatic beta cell development and insulin gene expression1,2, is known for its association with fasting glucose levels3,4. The evidence of an association with T2D for PEPD5 and HNF4A6,7 has been shown in previous studies. KCNK16 may regulate glucose-dependent insulin secretion in the pancreas. These findings, derived from an east Asian population, provide new perspectives on the etiology of T2D.
This is a preview of subscription content, access via your institution
Access options
Subscribe to this journal
Receive 12 print issues and online access
206,07 € per year
only 17,17 € per issue
Buy this article
- Purchase on SpringerLink
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout


Similar content being viewed by others
References
Kang, H.S. et al. Transcription factor Glis3, a novel critical player in the regulation of pancreatic beta-cell development and insulin gene expression. Mol. Cell. Biol. 29, 6366–6379 (2009).
Yang, Y., Chang, B.H., Samson, S.L., Li, M.V. & Chan, L. The Kruppel-like zinc finger protein Glis3 directly and indirectly activates insulin gene transcription. Nucleic Acids Res. 37, 2529–2538 (2009).
Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42, 105–116 (2010).
Barker, A. et al. Association of genetic loci with glucose levels in childhood and adolescence: a meta-analysis of over 6,000 children. Diabetes 60, 1805–1812 (2011).
Takeuchi, F. et al. Confirmation of multiple risk loci and genetic impacts by a genome-wide association study of type 2 diabetes in the Japanese population. Diabetes 58, 1690–1699 (2009).
Barroso, I. et al. Population-specific risk of type 2 diabetes conferred by HNF4A P2 promoter variants: a lesson for replication studies. Diabetes 57, 3161–3165 (2008).
Silander, K. et al. Genetic variation near the hepatocyte nuclear factor-4 α gene predicts susceptibility to type 2 diabetes. Diabetes 53, 1141–1149 (2004).
Zimmet, P., Alberti, K.G. & Shaw, J. Global and societal implications of the diabetes epidemic. Nature 414, 782–787 (2001).
Tkác, I. Metabolic syndrome in relationship to type 2 diabetes and atherosclerosis. Diabetes Res. Clin. Pract. 68 (suppl. 1), S2–S9 (2005).
Prokopenko, I., McCarthy, M.I. & Lindgren, C.M. Type 2 diabetes: new genes, new understanding. Trends Genet. 24, 613–621 (2008).
Rung, J. et al. Genetic variant near IRS1 is associated with type 2 diabetes, insulin resistance and hyperinsulinemia. Nat. Genet. 41, 1110–1115 (2009).
Manolio, T.A. et al. Finding the missing heritability of complex diseases. Nature 461, 747–753 (2009).
Yasuda, K. et al. Variants in KCNQ1 are associated with susceptibility to type 2 diabetes mellitus. Nat. Genet. 40, 1092–1097 (2008).
Unoki, H. et al. SNPs in KCNQ1 are associated with susceptibility to type 2 diabetes in east Asian and European populations. Nat. Genet. 40, 1098–1102 (2008).
Yamauchi, T. et al. A genome-wide association study in the Japanese population identifies susceptibility loci for type 2 diabetes at UBE2E2 and C2CD4A-C2CD4B. Nat. Genet. 42, 864–868 (2010).
Tsai, F.J. et al. A genome-wide association study identifies susceptibility variants for type 2 diabetes in Han Chinese. PLoS Genet. 6, e1000847 (2010).
Shu, X.O. et al. Identification of new genetic risk variants for type 2 diabetes. PLoS Genet. 6, e1001127 (2010).
Stommel, M. & Schoenborn, C.A. Variations in BMI and prevalence of health risks in diverse racial and ethnic populations. Obesity (Silver Spring) 18, 1821–1826 (2010).
Barrett, J.C. et al. Genome-wide association study and meta-analysis find that over 40 loci affect risk of type 1 diabetes. Nat. Genet. 41, 703–707 (2009).
Kadereit, B. et al. Evolutionarily conserved gene family important for fat storage. Proc. Natl. Acad. Sci. USA 105, 94–99 (2008).
Nakajima, H. et al. Hepatocyte nuclear factor-4 α gene mutations in Japanese non-insulin dependent diabetes mellitus (NIDDM) patients. Res. Commun. Mol. Pathol. Pharmacol. 94, 327–330 (1996).
Johansson, S. et al. Studies in 3,523 Norwegians and meta-analysis in 11,571 subjects indicate that variants in the hepatocyte nuclear factor 4 α (HNF4A) P2 region are associated with type 2 diabetes in Scandinavians. Diabetes 56, 3112–3117 (2007).
Girard, C. et al. Genomic and functional characteristics of novel human pancreatic 2P domain K+ channels. Biochem. Biophys. Res. Commun. 282, 249–256 (2001).
Ashcroft, F.M. ATP-sensitive potassium channelopathies: focus on insulin secretion. J. Clin. Invest. 115, 2047–2058 (2005).
Soni, S. et al. Absence of erythroblast macrophage protein (Emp) leads to failure of erythroblast nuclear extrusion. J. Biol. Chem. 281, 20181–20189 (2006).
Nam, D., Kim, J., Kim, S.Y. & Kim, S. GSA-SNP: a general approach for gene set analysis of polymorphisms. Nucleic Acids Res. 38, W749–W754 (2010).
Scott, L.J. et al. A genome-wide association study of type 2 diabetes in Finns detects multiple susceptibility variants. Science 316, 1341–1345 (2007).
Luke, M.R., Houghton, F., Perugini, M.A. & Gleeson, P.A. The trans-Golgi network GRIP-domain proteins form α-helical homodimers. Biochem. J. 388, 835–841 (2005).
Jo, W., Endo, M., Ishizu, K., Nakamura, A. & Tajima, T. A novel PAX4 mutation in a Japanese patient with maturity-onset diabetes of the young. Tohoku J. Exp. Med. 223, 113–118 (2011).
Wang, X. et al. Mass spectrometric characterization of the affinity-purified human 26S proteasome complex. Biochemistry 46, 3553–3565 (2007).
Voight, B.F. et al. Twelve type 2 diabetes susceptibility loci identified through large-scale association analysis. Nat. Genet. 42, 579–589 (2010).
Frayling, T.M. et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science 316, 889–894 (2007).
Raychaudhuri, S. et al. Identifying relationships among genomic disease regions: predicting genes at pathogenic SNP associations and rare deletions. PLoS Genet. 5, e1000534 (2009).
Hyndman, R.J. & Fan, Y. Sample quantiles in statistical packages. Am. Stat. 50, 361–365 (1996).
Devlin, B., Roeder, K. & Wasserman, L. Genomic control, a new approach to genetic-based association studies. Theor. Popul. Biol. 60, 155–166 (2001).
Nica, A.C. et al. The architecture of gene regulatory variation across multiple human tissues: the MuTHER study. PLoS Genet. 7, e1002003 (2011).
Acknowledgements
We thank all the participants and the staff of the BioBank Japan project. The project was supported by a grant from the Leading Project of Ministry of Education, Culture, Sports, Science and Technology Japan.
The Japan Cardiometabolic Genome Epidemiology (CAGE) Network Studies were supported by grants for the Program for Promotion of Fundamental Studies in Health Sciences, National Institute of Biomedical Innovation Organization (NIBIO); the Core Research for Evolutional Science and Technology (CREST) from the Japan Science Technology Agency; the Grant of National Center for Global Health and Medicine (NCGM).
We thank the Office of Population Studies Foundation research and data collection teams for the Cebu Longitudinal Health and Nutrition Survey. This work was supported by US National Institutes of Health grants DK078150, TW05596, HL085144 and TW008288 and pilot funds from grants RR20649, ES10126, and DK56350.
We acknowledge support from the Hong Kong Government Research Grants Council Central Allocation Scheme (CUHK 1/04C), Research Grants Council Earmarked Research Grant (CUHK4724/07M) and the Innovation and Technology Fund of the Government of the Hong Kong Special Administrative Region (ITS/487/09FP). We acknowledge the Chinese University of Hong Kong Information Technology Services Center for support of computing resources. We would also like to thank the dedicated medical and nursing staff at the Prince of Wales Hospital Diabetes and Endocrine Centre.
This work was supported by grants from Korea Centers for Disease Control and Prevention (4845-301, 4851-302, 4851-307) and an intramural grant from the Korea National Institute of Health (2011-N73005-00), the Republic of Korea.
The work by the National Taiwan University Hospital was supported in part by the grant (NSC99-3112-B-002-019) from the National Science Council of Taiwan. We would also like to acknowledge the National Genotyping Center of National Research Program for Genomic Medicine (NSC98-3112-B-001-037), Taiwan.
The work by the Shanghai Diabetes Genetic Study was supported in part by the US National Institutes of Health grants R01CA124558, R01CA64277, R01CA70867, R01CA90899, R01CA100374, R01CA118229, R01CA92585, UL1 RR024975, DK58845 and HG004399, the Department of Defense Idea Award BC050791, Vanderbilt Ingram professorship funds and the Allen Foundation Fund. We thank the dedicated investigators and staff members from research teams at Vanderbilt University, Shanghai Cancer Institute and the Shanghai Institute of Preventive Medicine, and especially, the study participants for their contributions in the studies.
The work of the Shanghai Diabetes Study was supported by grants from the National 973 Program (2011CB504001), the Project of National Natural Science Foundation of China (30800617) and the Shanghai Rising-Star Program (09QA1404400), China.
The work of the Shanghai Jiao Tong University Diabetes Study was supported by grants from the National 863 Program (2006AA02A409) and the major program of the Shanghai Municipality for Basic Research (08dj1400601), China.
The work of the Seoul National University Hospital was supported by grants from the Korea Health 21 R&D Project, Ministry of Health & Welfare (00-PJ3-PG6-GN07-001) and the World Class University project of the Ministry of Education, Science and Technology (MEST) and National Research Foundation (NRF) (R31-2008-000-10103-0), Korea. The Singapore Prospective Study Program (SP2) was funded through grants from the Biomedical Research Council of Singapore (BMRC05/1/36/19/413 and 03/1/27/18/216) and the National Medical Research Council of Singapore (NMRC/1174/2008). E.S.T. also receives additional support from the National Medical Research Council through a clinicians scientist award (NMRC/CSA/008/2009). The Singapore Malay Eye Study (SiMES) was funded by the National Medical Research Council (NMRC0796/2003 and NMRC/STaR/0003/2008) and Biomedical Research Council (BMRC, 09/1/35/19/616). Y.Y.T. acknowledges support from the Singapore National Research Foundation, NRF-RF-2010-05. The Genome Institute of Singapore carried out all the genotyping for the samples from Singapore also provided funding for the genotyping of the samples from SP2.
Author information
Authors and Affiliations
Consortia
Contributions
The study was supervised by E.S.T., B.-G.H., N.K., Y.S.C., Y.Y.T., W.Z., Q.C., X.O.S., Y.-T.C., J.-Y.W., L.S.A., K.L.M., T.K., C.H., W.J., L.-M.C., Y.M.C., K.S.P., J.-Y.L. and J.C.N.C. The experiments were conceived of and designed by Y.S.C., E.S.T., N.K., D.P.-K.N., J.J.-M.L., M.S., T.Y.W., Y.Y.T., W.Z., F.B.H., X.O.S., C.-H.C., F.-J.T., Y.-T.C., J.-Y.W., L.S.A., K.L.M., S.M., C.H., L.-M.C., K.S.P., M.J.G., M.I.M. and R.C.W.M. The experiments were performed by J.L., M.S., J.J.L., J.-Y.W., S.M., R.Z., K.Y., Y.-C.C., T.-J.C., L.-M.C. and S.H.K. Statistical analyses was performed by M.J.G., X.S., Y.J.K., R.T.H.O., W.T.T., Y.Y.T., F.T., J.L., C.-H.C., L.-C.C., Y.W., Y.L., K.H., C.H., Y.-C.C., S.H.K., A.P.M. and R.C.W.M. The data were analyzed by M.J.G., X.S., Y.J.K., R.T.H.O., W.T.T., Y.Y.T., J.L., C.-H.C., L.-C.C., Y.W., N.R.L., Y.L., L.S.A., K.L.M., T.Y., C.H., Y.-C.C., S.H.K., Y.S.C., S.K., Å.K.H. and R.C.W.M. The reagents, materials and analysis tools were contributed by E.S.T., B.-G.H., N.K., D.P.-K.N., J.J.-M.L., J.L., M.S., T.A., T.Y.W., E.N., M.Y., J.N., J.J.L., W.Z., Q.C., Y.G., W.L., F.B.H., X.O.S., F.-J.T., Y.-T.C., J.-Y.W., N.R.L., Y.L., K.O., H.I., R.T., C.W., Y.B., T.-J.C., L.-M.C., K.S.P., H.-L.K., N.H.C., J.-Y.L., W.Y.S. and J.C.N.C. The manuscript was written by Y.S.C., M.S. and E.S.T. All authors reviewed the manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing financial interests.
Additional information
A list of full members is provided in the Supplementary Note.
A list of full members is provided in the Supplementary Note.
Supplementary information
Supplementary Text and Figures
Supplementary Note, Supplementary Tables 1–10 and Supplementary Figures 1–4. (PDF 3723 kb)
Rights and permissions
About this article
Cite this article
Cho, Y., Chen, CH., Hu, C. et al. Meta-analysis of genome-wide association studies identifies eight new loci for type 2 diabetes in east Asians. Nat Genet 44, 67–72 (2012). https://doi.org/10.1038/ng.1019
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/ng.1019
This article is cited by
-
Early-onset diabetes involving three consecutive generations had different clinical features from age-matched type 2 diabetes without a family history in China
Endocrine (2022)
-
Long non-coding RNAs: a valuable biomarker for metabolic syndrome
Molecular Genetics and Genomics (2022)
-
Decreased GLUT2 and glucose uptake contribute to insulin secretion defects in MODY3/HNF1A hiPSC-derived mutant β cells
Nature Communications (2021)
-
VPS39-deficiency observed in type 2 diabetes impairs muscle stem cell differentiation via altered autophagy and epigenetics
Nature Communications (2021)
-
Early-life exposure to the Chinese famine, genetic susceptibility and the risk of type 2 diabetes in adulthood
Diabetologia (2021)